glove.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/divyansh/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package reuters to /home/divyansh/nltk_data...\n",
      "[nltk_data]   Package reuters is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/divyansh/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "import nltk\n",
    "import re\n",
    "import numpy as np\n",
    "\n",
    "nltk.download('stopwords')\n",
    "nltk.download('reuters')\n",
    "nltk.download('punkt')\n",
    "\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.corpus import reuters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(content):\n",
    "    \n",
    "    words = word_tokenize(content)\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    filtered_words = [lemmatizer.lemmatize(word.lower()) for word in words if word.lower() not in stop_words]\n",
    "    # print(len(filtered_words))\n",
    "    content = ' '.join(filtered_words)\n",
    "    content = re.sub(r\"[^a-zA-Z0-9. ]\", \"\", content)\n",
    "    content = re.sub(r'\\s+', ' ', content)\n",
    "    content = content.lower().strip()     \n",
    "    return content\n",
    "\n",
    "def euclidean(vec1,vec2):\n",
    "    return np.linalg.norm(vec1 - vec2)\n",
    "\n",
    "with open('GloVe/text8', 'r') as f:\n",
    "    content = f.read().lower()\n",
    "\n",
    "content=content[:1000000]\n",
    "# print(content[:1000])\n",
    "sentences=[content.split(\" \")]\n",
    "\n",
    "# print(sentences[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Word2Vec(sentences, min_count=1, vector_size=100, window=5, sg=1, workers=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9399\n",
      "{('old', 'new'): 0.535303, ('hard', 'difficult'): 0.5018999, ('hard', 'easy'): 0.3000028, ('fast', 'rapid'): 0.27536714, ('short', 'long'): 0.75090796, ('stupid', 'dumb'): 0.086510584, ('weird', 'strange'): 0.07788487, ('wide', 'narrow'): 0.4515856, ('bad', 'awful'): 0.21611969, ('easy', 'difficult'): 0.787063, ('bad', 'terrible'): 0.23389582, ('hard', 'simple'): 0.08970692, ('happy', 'mad'): 0.091338806, ('large', 'huge'): 0.64270514, ('hard', 'tough'): 0.3134427, ('new', 'fresh'): 1.1474775, ('quick', 'rapid'): 0.078163475, ('strange', 'odd'): 0.08385966, ('happy', 'angry'): 0.08072137, ('narrow', 'broad'): 0.21340607, ('simple', 'easy'): 0.33509842, ('old', 'fresh'): 0.6208373, ('apparent', 'obvious'): 0.19743408, ('nice', 'generous'): 0.22519782, ('weird', 'normal'): 1.3986548, ('weird', 'odd'): 0.0808176, ('bad', 'immoral'): 0.2286999, ('dumb', 'dense'): 0.08056731, ('large', 'big'): 0.15163706, ('big', 'broad'): 0.32518822, ('strong', 'proud'): 0.6311309, ('unnecessary', 'necessary'): 0.091039374, ('restless', 'young'): 0.07290964, ('dumb', 'intelligent'): 0.08369085, ('bad', 'great'): 0.5013457, ('difficult', 'simple'): 0.46468836, ('necessary', 'important'): 0.8152753, ('bad', 'terrific'): 0.22871426, ('honest', 'guilty'): 0.071277596, ('easy', 'tough'): 0.07344028, ('certain', 'sure'): 1.024115, ('essential', 'necessary'): 0.2438214, ('different', 'normal'): 0.22630714, ('crucial', 'important'): 0.734199, ('friendly', 'generous'): 0.08728878, ('long', 'narrow'): 1.030244, ('big', 'heavy'): 0.5200488, ('rough', 'frigid'): 0.07411345, ('illegal', 'immoral'): 0.08193404, ('bad', 'guilty'): 0.24054267, ('modern', 'ancient'): 0.7784371, ('new', 'ancient'): 0.93581414, ('happy', 'young'): 0.07831587, ('easy', 'big'): 0.51073056, ('great', 'awful'): 0.6995345, ('tiny', 'huge'): 0.086027935, ('noticeable', 'obvious'): 0.30315545, ('afraid', 'anxious'): 0.3867885, ('formal', 'proper'): 0.12031648, ('unhappy', 'mad'): 0.07950234, ('sad', 'terrible'): 0.0779974, ('sick', 'crazy'): 0.07999265, ('violent', 'angry'): 0.91609806, ('laden', 'heavy'): 0.07899173, ('hard', 'dense'): 0.3198357, ('recent', 'new'): 0.24497263, ('bold', 'proud'): 0.08611169, ('sly', 'strange'): 0.08162414, ('strange', 'sly'): 0.08162414, ('dumb', 'rare'): 0.38440946, ('sly', 'tough'): 0.07643115, ('terrific', 'mad'): 0.07963676, ('fresh', 'wide'): 0.46152765, ('huge', 'dumb'): 0.081539705, ('wife', 'husband'): 0.07812124, ('book', 'text'): 0.49809113, ('night', 'day'): 0.9674686, ('south', 'north'): 0.22431353, ('plane', 'airport'): 0.07780939, ('uncle', 'aunt'): 0.08464336, ('bottom', 'top'): 0.074536234, ('student', 'pupil'): 1.2054459, ('world', 'globe'): 1.2693111, ('leg', 'arm'): 0.08529591, ('plane', 'jet'): 0.08521172, ('woman', 'man'): 1.0100842, ('actress', 'actor'): 0.08592151, ('teacher', 'instructor'): 0.4744074, ('movie', 'film'): 0.7241749, ('bird', 'hawk'): 0.08525044, ('word', 'dictionary'): 0.9018753, ('dog', 'cat'): 0.08877361, ('area', 'region'): 0.20764418, ('navy', 'army'): 0.47719288, ('book', 'literature'): 0.44405186, ('child', 'adult'): 1.2715436, ('cow', 'cattle'): 0.08460581, ('book', 'story'): 0.8288843, ('winter', 'summer'): 0.075532116, ('taxi', 'cab'): 0.075641125, ('boat', 'anchor'): 0.07976596, ('priest', 'monk'): 0.07387767, ('toe', 'finger'): 0.4713177, ('river', 'stream'): 0.08339398, ('date', 'calendar'): 0.08236394, ('sea', 'ocean'): 0.0775863, ('second', 'minute'): 0.30399364, ('hand', 'thumb'): 0.81136835, ('wood', 'log'): 0.08132593, ('way', 'manner'): 0.96730894, ('mouse', 'cat'): 0.07691883, ('death', 'burial'): 0.08121665, ('music', 'melody'): 0.6513506, ('mouth', 'lip'): 0.077828094, ('storm', 'hurricane'): 0.08233977, ('tax', 'income'): 0.08108055, ('boat', 'deck'): 0.088562824, ('car', 'horn'): 0.08323223, ('friend', 'guest'): 0.23811056, ('hand', 'wrist'): 0.8083032, ('ball', 'cannon'): 0.1702544, ('victory', 'triumph'): 0.42808765, ('telephone', 'booth'): 0.07314993, ('clothes', 'cloth'): 0.07914946, ('band', 'orchestra'): 0.507838, ('book', 'bible'): 0.6585241, ('business', 'industry'): 0.21090434, ('winter', 'season'): 0.08151468, ('decade', 'century'): 0.7091695, ('hat', 'coat'): 0.084897816, ('arm', 'wrist'): 0.07821963, ('house', 'apartment'): 0.08049508, ('glass', 'crystal'): 0.07710173, ('arm', 'muscle'): 0.24971707, ('death', 'tragedy'): 0.08082254, ('absence', 'presence'): 0.3516524, ('gun', 'cannon'): 0.10469698, ('ball', 'basket'): 0.081963, ('boy', 'kid'): 0.08289173, ('church', 'choir'): 0.39003703, ('tower', 'bell'): 0.073031105, ('school', 'grade'): 0.97418725, ('car', 'highway'): 0.082777545, ('dictionary', 'definition'): 0.55443007, ('chair', 'bench'): 0.07365958, ('boy', 'son'): 0.08529188, ('water', 'ice'): 0.083655916, ('attorney', 'lawyer'): 0.082130425, ('area', 'zone'): 0.2148809, ('business', 'company'): 0.08727121, ('clothes', 'fabric'): 0.08880139, ('money', 'capital'): 0.25134265, ('hand', 'foot'): 0.82194287, ('alcohol', 'cocktail'): 0.2825827, ('molecule', 'atom'): 0.08930264, ('god', 'devil'): 0.77907276, ('loop', 'belt'): 0.07871672, ('motor', 'engine'): 0.472864, ('car', 'cab'): 0.086785056, ('cat', 'lion'): 0.0832565, ('size', 'magnitude'): 0.27362978, ('reality', 'fantasy'): 0.34153274, ('cat', 'pet'): 0.08688318, ('cereal', 'wheat'): 0.08319335, ('house', 'key'): 0.7094789, ('blood', 'flesh'): 0.07938065, ('god', 'spirit'): 0.38046756, ('abundance', 'plenty'): 0.19285707, ('wall', 'brick'): 0.08154589, ('organ', 'liver'): 0.08687225, ('strength', 'might'): 1.2596812, ('phrase', 'word'): 0.33693296, ('cloud', 'storm'): 0.07800446, ('joy', 'pride'): 0.23063302, ('man', 'uncle'): 1.0063885, ('intelligence', 'logic'): 0.082892925, ('communication', 'language'): 0.37265316, ('shore', 'coast'): 0.07350265, ('wire', 'cord'): 0.09161911, ('bird', 'turkey'): 0.08598706, ('bed', 'crib'): 0.0858989, ('water', 'salt'): 0.077969536, ('situation', 'condition'): 0.09939245, ('crime', 'theft'): 0.15217075, ('style', 'fashion'): 0.22353761, ('boundary', 'border'): 0.07425069, ('arm', 'body'): 1.3322128, ('boat', 'car'): 0.08757388, ('insect', 'bee'): 0.082913436, ('crib', 'cradle'): 0.08779235, ('animal', 'person'): 1.8469404, ('marijuana', 'herb'): 0.08566141, ('bed', 'hospital'): 0.27994707, ('cheek', 'tongue'): 0.32976723, ('school', 'law'): 0.27167246, ('foot', 'head'): 0.08517813, ('mother', 'guardian'): 0.32739964, ('alcohol', 'whiskey'): 0.08372742, ('plate', 'bowl'): 0.08701267, ('meat', 'bacon'): 0.083698496, ('body', 'chest'): 1.341011, ('son', 'father'): 0.07895093, ('heart', 'surgery'): 0.07628224, ('woman', 'secretary'): 0.08148786, ('man', 'father'): 1.010923, ('beach', 'island'): 0.08081445, ('story', 'topic'): 0.28281257, ('game', 'fun'): 0.08073446, ('couple', 'pair'): 0.08958511, ('woman', 'wife'): 0.07276228, ('sheep', 'cattle'): 0.07889061, ('bean', 'coffee'): 0.08188737, ('wood', 'paper'): 0.45854735, ('top', 'side'): 0.07662863, ('crime', 'fraud'): 0.08142037, ('pain', 'harm'): 0.232829, ('lover', 'companion'): 0.081816666, ('father', 'daughter'): 0.07469745, ('wine', 'liquor'): 0.07741144, ('cow', 'goat'): 0.09027984, ('belief', 'opinion'): 0.629031, ('reality', 'illusion'): 0.3495631, ('pact', 'agreement'): 0.08154303, ('wealth', 'poverty'): 0.33808148, ('accident', 'emergency'): 0.085418314, ('battle', 'conquest'): 0.16569704, ('friend', 'teacher'): 0.57285404, ('brother', 'son'): 0.08330841, ('aunt', 'nephew'): 0.08583006, ('worker', 'mechanic'): 0.24524584, ('bee', 'queen'): 0.074668325, ('august', 'month'): 0.13891299, ('cloud', 'weather'): 0.08534597, ('physician', 'doctor'): 0.08018407, ('river', 'valley'): 0.08087772, ('sun', 'sky'): 0.08256068, ('target', 'arrow'): 0.07390638, ('circumstance', 'situation'): 0.6838334, ('opinion', 'choice'): 0.26084384, ('gut', 'nerve'): 0.08897254, ('day', 'dawn'): 0.9642404, ('doctor', 'professor'): 0.51357436, ('room', 'bath'): 0.3352914, ('corporation', 'business'): 0.20378257, ('fun', 'football'): 0.08734887, ('bone', 'ankle'): 0.076883264, ('leader', 'manager'): 0.124803625, ('bee', 'ant'): 0.0820899, ('basketball', 'baseball'): 0.08312826, ('rice', 'bean'): 0.08238316, ('bed', 'furniture'): 0.082381226, ('emotion', 'passion'): 0.32880017, ('anarchy', 'chaos'): 1.0745915, ('crime', 'violation'): 0.08457212, ('machine', 'engine'): 0.07210162, ('beach', 'sea'): 0.08095376, ('strength', 'capability'): 0.08643062, ('opinion', 'idea'): 0.46430418, ('north', 'west'): 0.22592078, ('mother', 'wife'): 0.32536858, ('captain', 'sailor'): 0.07537969, ('motor', 'boat'): 0.4779922, ('card', 'bridge'): 0.08031441, ('science', 'psychology'): 0.2803224, ('destruction', 'construction'): 0.49425614, ('crowd', 'bunch'): 0.261837, ('beach', 'reef'): 0.08281769, ('man', 'child'): 0.79535735, ('bread', 'cheese'): 0.18105339, ('champion', 'winner'): 0.07015922, ('celebration', 'ceremony'): 0.08202265, ('king', 'princess'): 0.40303117, ('wealth', 'prestige'): 0.34845382, ('endurance', 'strength'): 0.08189414, ('danger', 'threat'): 0.075204, ('god', 'priest'): 0.77510935, ('body', 'stomach'): 1.318105, ('tongue', 'throat'): 0.08359477, ('house', 'carpet'): 0.08101066, ('intelligence', 'skill'): 0.07607889, ('journey', 'conquest'): 0.17890343, ('god', 'prey'): 0.7759861, ('brother', 'soul'): 0.08833759, ('monster', 'demon'): 0.094310015, ('day', 'morning'): 0.95819205, ('man', 'victor'): 0.61678195, ('friend', 'guy'): 0.22773995, ('song', 'story'): 0.5042616, ('apartment', 'furniture'): 0.09121201, ('driver', 'pilot'): 0.07330262, ('sugar', 'honey'): 0.0792759, ('idea', 'image'): 0.70581704, ('father', 'brother'): 0.08202363, ('moon', 'planet'): 0.08635418, ('ball', 'costume'): 0.08393065, ('rail', 'fence'): 0.07065403, ('room', 'bed'): 0.0776293, ('flower', 'bush'): 0.08658677, ('bottom', 'side'): 0.07290374, ('cat', 'rabbit'): 0.08530338, ('belief', 'concept'): 0.6617591, ('intelligence', 'insight'): 0.07801991, ('attention', 'interest'): 0.27734914, ('attitude', 'confidence'): 0.07736602, ('right', 'justice'): 0.6772342, ('argument', 'agreement'): 0.085144304, ('depth', 'magnitude'): 0.27694857, ('medium', 'news'): 0.08598117, ('winner', 'candidate'): 0.077635534, ('birthday', 'date'): 0.20184967, ('bible', 'hymn'): 0.19361529, ('floor', 'deck'): 0.08065459, ('door', 'floor'): 0.075892165, ('car', 'carriage'): 0.08270998, ('wealth', 'fame'): 0.3475107, ('administration', 'management'): 0.3468249, ('communication', 'conversation'): 1.2110231, ('pollution', 'atmosphere'): 0.08672069, ('anatomy', 'biology'): 0.08273267, ('college', 'profession'): 0.33079892, ('book', 'topic'): 0.562422, ('formula', 'equation'): 0.076987475, ('book', 'information'): 0.12815475, ('boy', 'partner'): 0.09103695, ('sky', 'universe'): 0.086390674, ('population', 'people'): 2.8222616, ('college', 'class'): 1.2629373, ('chief', 'mayor'): 0.31356186, ('lawyer', 'banker'): 0.08271573, ('camp', 'cabin'): 0.079722546, ('girl', 'child'): 1.4591943, ('adult', 'baby'): 0.5407348, ('log', 'timber'): 0.08628706, ('body', 'nerve'): 1.3327807, ('man', 'husband'): 1.0154767, ('bone', 'neck'): 0.0788663, ('frustration', 'anger'): 0.20927787, ('river', 'sea'): 0.0810559, ('task', 'job'): 0.24128877, ('club', 'society'): 1.6138991, ('reflection', 'image'): 0.084979534, ('prince', 'king'): 0.38971525, ('snow', 'weather'): 0.081553936, ('people', 'party'): 2.461546, ('boy', 'brother'): 0.08242076, ('root', 'grass'): 0.08270938, ('money', 'pearl'): 0.22131784, ('money', 'diamond'): 0.07924182, ('house', 'barn'): 0.08720796, ('finger', 'palm'): 0.49389714, ('car', 'bridge'): 0.08152832, ('effort', 'difficulty'): 0.8238693, ('fact', 'insight'): 0.8000504, ('job', 'management'): 0.13829078, ('cancer', 'sickness'): 0.083085515, ('word', 'newspaper'): 0.89159685, ('composer', 'writer'): 0.07618056, ('actor', 'singer'): 0.086932406, ('value', 'belief'): 0.49797985, ('wisdom', 'intelligence'): 0.08748795, ('ignorance', 'intelligence'): 0.084981285, ('idea', 'scheme'): 0.70697045, ('happiness', 'peace'): 0.08308465, ('logic', 'arithmetic'): 0.0843086, ('argument', 'criticism'): 0.07696411, ('aggression', 'hostility'): 0.48611876, ('chemistry', 'theory'): 0.98208225, ('trial', 'verdict'): 0.084830776, ('comfort', 'safety'): 0.22273412, ('confidence', 'self'): 1.6562967, ('vision', 'perception'): 0.4798805, ('era', 'decade'): 0.16151792, ('biography', 'fiction'): 0.19241926, ('discussion', 'argument'): 0.61319274, ('code', 'symbol'): 0.081003614, ('danger', 'disease'): 0.81706154, ('journey', 'trip'): 0.08377259, ('activity', 'movement'): 0.9316587, ('father', 'god'): 0.7749462, ('action', 'course'): 1.1666987, ('aviation', 'flight'): 0.07490293, ('game', 'action'): 1.1649874, ('molecule', 'air'): 0.08043016, ('home', 'state'): 1.1631255, ('word', 'literature'): 0.5085579, ('adult', 'guardian'): 0.5326079, ('newspaper', 'information'): 0.9174024, ('communication', 'television'): 1.7789308, ('cousin', 'uncle'): 0.08337243, ('author', 'reader'): 0.55559194, ('guy', 'partner'): 0.079661965, ('area', 'corner'): 0.2211432, ('wall', 'decoration'): 0.08122908, ('word', 'page'): 0.1531368, ('nurse', 'scientist'): 0.22082527, ('politician', 'president'): 0.081336066, ('president', 'mayor'): 0.08467237, ('book', 'essay'): 0.8224516, ('man', 'warrior'): 1.0208709, ('article', 'journal'): 0.37477207, ('hip', 'lip'): 0.2746343, ('book', 'article'): 0.24216983, ('room', 'cell'): 0.08257448, ('box', 'booth'): 0.08483086, ('daughter', 'kid'): 0.08119454, ('mountain', 'ledge'): 0.090776026, ('clothes', 'coat'): 0.081962936, ('tobacco', 'cigarette'): 0.08570791, ('student', 'professor'): 0.7092997, ('baby', 'daughter'): 0.08218152, ('apartment', 'hotel'): 0.07630703, ('cup', 'cone'): 0.081294246, ('horse', 'ox'): 0.07787966, ('bone', 'teeth'): 0.083987415, ('bacon', 'bean'): 0.08191078, ('proof', 'fact'): 0.8055553, ('birthday', 'year'): 0.59549814, ('author', 'creator'): 0.79077625, ('atom', 'carbon'): 0.082214914, ('muscle', 'bone'): 0.25980857, ('muscle', 'tongue'): 0.25763455, ('boy', 'soldier'): 0.08663607, ('guy', 'girl'): 0.3538791, ('bed', 'chair'): 0.08285982, ('clothes', 'jacket'): 0.07347862, ('gun', 'knife'): 0.24892788, ('meat', 'bread'): 0.17871332, ('arm', 'bone'): 0.07968567, ('agony', 'grief'): 0.08277474, ('assignment', 'task'): 0.07210314, ('night', 'dawn'): 0.077277265, ('snow', 'storm'): 0.08526007, ('dog', 'horse'): 0.087237924, ('arm', 'neck'): 0.08711613, ('nerve', 'brain'): 0.585936, ('beast', 'animal'): 0.082180254, ('child', 'boy'): 1.8022878, ('alcohol', 'wine'): 0.0854149, ('street', 'car'): 0.086239174, ('bell', 'door'): 0.084864706, ('box', 'hat'): 0.08356455, ('belief', 'impression'): 0.88191783, ('bias', 'opinion'): 0.2569778, ('attention', 'awareness'): 0.14993505, ('beauty', 'age'): 1.2588761, ('book', 'theme'): 0.8132633, ('friend', 'mother'): 0.13609271, ('vitamin', 'iron'): 0.07317633, ('car', 'factory'): 0.24852897, ('pact', 'condition'): 0.7502336, ('chapter', 'choice'): 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('vocab.txt',\"r\") as f:\n",
    "    unique_words=f.readlines()\n",
    "    \n",
    "vocab=[]\n",
    "\n",
    "for word in unique_words:\n",
    "    vocab.append(word.strip())\n",
    "\n",
    "vocab_size=len(vocab)+1\n",
    "print(vocab_size)\n",
    "\n",
    "filename = 'SimLex-999/SimLex-999.txt'\n",
    "\n",
    "\n",
    "# print(vocab)\n",
    "\n",
    "with open(filename,\"r\") as f:\n",
    "    content = f.readlines()\n",
    "content = content[1:]\n",
    "\n",
    "found=0\n",
    "notfound=0\n",
    "dicts = {}\n",
    "for line in content:\n",
    "    word1 = line.split()[0]\n",
    "    word2 = line.split()[1]\n",
    "\n",
    "\n",
    "    try:\n",
    "        found+=1\n",
    "        # print(\"Word found !\",end=\" \")\n",
    "        # print(word1,word2,euclidean(model.wv[word1],model.wv[word2]))\n",
    "        dicts[(word1,word2)] = euclidean(model.wv[word1],model.wv[word2])\n",
    "    except KeyError:\n",
    "        notfound+=1\n",
    "        \n",
    "#sort dict based on euclidean distance\n",
    "sorted_distances = sorted(dicts.items(), key=lambda x: x[1])\n",
    "print(dicts)\n",
    "\n",
    "##plot 5 highest and 5 lowest distances\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "top_5_highest = sorted_distances[-5:]\n",
    "top_word_pairs = [pair[0] for pair in top_5_highest]\n",
    "top_distances = [pair[1] for pair in top_5_highest]\n",
    "\n",
    "# Extract top 5 lowest distances and corresponding word pairs\n",
    "bottom_5_lowest = sorted_distances[:5]\n",
    "bottom_word_pairs = [pair[0] for pair in bottom_5_lowest]\n",
    "bottom_distances = [pair[1] for pair in bottom_5_lowest]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.barh(range(len(bottom_word_pairs)), bottom_distances, color='blue')\n",
    "plt.yticks(range(len(bottom_word_pairs)), [' '.join(pair) for pair in bottom_word_pairs])\n",
    "plt.xlabel('Distance')\n",
    "plt.title('Top 5 Lowest Distances')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(range(len(top_word_pairs)), top_distances, color='red')\n",
    "plt.yticks(range(len(top_word_pairs)), [' '.join(pair) for pair in top_word_pairs])\n",
    "plt.xlabel('Distance')\n",
    "plt.title('Top 5 Highest Distances')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# print(found,notfound)"
   ]
  }
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