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Assignment 2
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Assignment-2.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Building Corpus and Dictionary**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Лев Толстой - Война и мир Том 1 до том 4**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download from: https://all-the-books.ru/authors/tolstoy-lev-nikolaevich/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:862: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
" warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
]
}
],
"source": [
"from nltk.tokenize import word_tokenize\n",
"from nltk.corpus import stopwords\n",
"from itertools import *\n",
"from pylab import *\n",
"from collections import Counter\n",
"import seaborn as sns\n",
"import multiprocessing\n",
"import gensim.models as w2v\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"stopwords.ensure_loaded\n",
"stopwords_dict = {lang:stopwords.words(lang) for lang in stopwords.__dict__.get('_fileids')}\n",
"rus_stopwords = stopwords_dict['russian']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['и',\n",
" 'в',\n",
" 'во',\n",
" 'не',\n",
" 'что',\n",
" 'он',\n",
" 'на',\n",
" 'я',\n",
" 'с',\n",
" 'со',\n",
" 'как',\n",
" 'а',\n",
" 'то',\n",
" 'все',\n",
" 'она',\n",
" 'так',\n",
" 'его',\n",
" 'но',\n",
" 'да',\n",
" 'ты',\n",
" 'к',\n",
" 'у',\n",
" 'же',\n",
" 'вы',\n",
" 'за',\n",
" 'бы',\n",
" 'по',\n",
" 'только',\n",
" 'ее',\n",
" 'мне',\n",
" 'было',\n",
" 'вот',\n",
" 'от',\n",
" 'меня',\n",
" 'еще',\n",
" 'нет',\n",
" 'о',\n",
" 'из',\n",
" 'ему',\n",
" 'теперь',\n",
" 'когда',\n",
" 'даже',\n",
" 'ну',\n",
" 'вдруг',\n",
" 'ли',\n",
" 'если',\n",
" 'уже',\n",
" 'или',\n",
" 'ни',\n",
" 'быть',\n",
" 'был',\n",
" 'него',\n",
" 'до',\n",
" 'вас',\n",
" 'нибудь',\n",
" 'опять',\n",
" 'уж',\n",
" 'вам',\n",
" 'ведь',\n",
" 'там',\n",
" 'потом',\n",
" 'себя',\n",
" 'ничего',\n",
" 'ей',\n",
" 'может',\n",
" 'они',\n",
" 'тут',\n",
" 'где',\n",
" 'есть',\n",
" 'надо',\n",
" 'ней',\n",
" 'для',\n",
" 'мы',\n",
" 'тебя',\n",
" 'их',\n",
" 'чем',\n",
" 'была',\n",
" 'сам',\n",
" 'чтоб',\n",
" 'без',\n",
" 'будто',\n",
" 'чего',\n",
" 'раз',\n",
" 'тоже',\n",
" 'себе',\n",
" 'под',\n",
" 'будет',\n",
" 'ж',\n",
" 'тогда',\n",
" 'кто',\n",
" 'этот',\n",
" 'того',\n",
" 'потому',\n",
" 'этого',\n",
" 'какой',\n",
" 'совсем',\n",
" 'ним',\n",
" 'здесь',\n",
" 'этом',\n",
" 'один',\n",
" 'почти',\n",
" 'мой',\n",
" 'тем',\n",
" 'чтобы',\n",
" 'нее',\n",
" 'сейчас',\n",
" 'были',\n",
" 'куда',\n",
" 'зачем',\n",
" 'всех',\n",
" 'никогда',\n",
" 'можно',\n",
" 'при',\n",
" 'наконец',\n",
" 'два',\n",
" 'об',\n",
" 'другой',\n",
" 'хоть',\n",
" 'после',\n",
" 'над',\n",
" 'больше',\n",
" 'тот',\n",
" 'через',\n",
" 'эти',\n",
" 'нас',\n",
" 'про',\n",
" 'всего',\n",
" 'них',\n",
" 'какая',\n",
" 'много',\n",
" 'разве',\n",
" 'три',\n",
" 'эту',\n",
" 'моя',\n",
" 'впрочем',\n",
" 'хорошо',\n",
" 'свою',\n",
" 'этой',\n",
" 'перед',\n",
" 'иногда',\n",
" 'лучше',\n",
" 'чуть',\n",
" 'том',\n",
" 'нельзя',\n",
" 'такой',\n",
" 'им',\n",
" 'более',\n",
" 'всегда',\n",
" 'конечно',\n",
" 'всю',\n",
" 'между']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rus_stopwords"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"D:\\Computer_Science_Project\\corpus\\voyna-i-mir-tom-1.txt\n",
"D:\\Computer_Science_Project\\corpus\\voyna-i-mir-tom-2.txt\n",
"D:\\Computer_Science_Project\\corpus\\voyna-i-mir-tom-3.txt\n",
"D:\\Computer_Science_Project\\corpus\\voyna-i-mir-tom-4.txt\n"
]
}
],
"source": [
"from glob import glob\n",
"path = 'D:\\\\Computer_Science_Project\\\\corpus\\\\*.txt'\n",
"corpus = \"\"\n",
"\n",
"for filename in glob(path):\n",
" print(filename)\n",
" with open(filename,'r', encoding=\"cp1251\") as f: \n",
" #corpus += \" \" + f.read().strip()\n",
" flist = f.readlines() \n",
" corpus+= ''.join(s.rstrip('\\n') for s in flist)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove punctuation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import string\n",
"translator = str.maketrans('', '', string.punctuation)\n",
"corpus = corpus.translate(translator)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import regex\n",
"pattern = r'\\w+'\n",
"token = regex.findall(pattern, corpus)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Лев',\n",
" 'Николаевич',\n",
" 'Толстой',\n",
" 'Война',\n",
" 'и',\n",
" 'мир',\n",
" 'Том',\n",
" '1',\n",
" 'Лев',\n",
" 'Николаевич',\n",
" 'ТолстойВОЙНА',\n",
" 'И',\n",
" 'МИР',\n",
" 'Том',\n",
" '1',\n",
" 'ЧАСТЬ',\n",
" 'ПЕРВАЯ',\n",
" 'I',\n",
" 'Так',\n",
" 'говорила',\n",
" 'в',\n",
" 'июле',\n",
" '1805',\n",
" 'года',\n",
" 'известная',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'Шерер',\n",
" 'фрейлина',\n",
" 'и',\n",
" 'приближенная',\n",
" 'императрицы',\n",
" 'Марии',\n",
" 'Феодоровны',\n",
" 'встречая',\n",
" 'важного',\n",
" 'и',\n",
" 'чиновного',\n",
" 'князя',\n",
" 'Василия',\n",
" 'первого',\n",
" 'приехавшего',\n",
" 'на',\n",
" 'ее',\n",
" 'вечер',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'кашляла',\n",
" 'несколько',\n",
" 'дней',\n",
" 'у',\n",
" 'нее',\n",
" 'был',\n",
" 'грипп',\n",
" 'как',\n",
" 'она',\n",
" 'говорила',\n",
" 'грипп',\n",
" 'был',\n",
" 'тогда',\n",
" 'новое',\n",
" 'слово',\n",
" 'употреблявшееся',\n",
" 'только',\n",
" 'редкими',\n",
" 'В',\n",
" 'записочках',\n",
" 'разосланных',\n",
" 'утром',\n",
" 'с',\n",
" 'красным',\n",
" 'лакеем',\n",
" 'было',\n",
" 'написано',\n",
" 'без',\n",
" 'различия',\n",
" 'во',\n",
" 'всех',\n",
" 'Si',\n",
" 'vous',\n",
" 'navez',\n",
" 'rien',\n",
" 'de',\n",
" 'mieux',\n",
" 'a',\n",
" 'faire',\n",
" 'M',\n",
" 'le',\n",
" 'comte',\n",
" 'или',\n",
" 'mon',\n",
" 'prince',\n",
" 'et',\n",
" 'si',\n",
" 'la',\n",
" 'perspective',\n",
" 'de',\n",
" 'passer',\n",
" 'la',\n",
" 'soiree',\n",
" 'chez',\n",
" 'une',\n",
" 'pauvre',\n",
" 'malade',\n",
" 'ne',\n",
" 'vous',\n",
" 'effraye',\n",
" 'pas',\n",
" 'trop',\n",
" 'je',\n",
" 'serai',\n",
" 'charmee',\n",
" 'de',\n",
" 'vous',\n",
" 'voir',\n",
" 'chez',\n",
" 'moi',\n",
" 'entre',\n",
" '7',\n",
" 'et',\n",
" '10',\n",
" 'heures',\n",
" 'Annette',\n",
" 'Scherer',\n",
" 'Если',\n",
" 'y',\n",
" 'вас',\n",
" 'граф',\n",
" 'или',\n",
" 'князь',\n",
" 'нет',\n",
" 'в',\n",
" 'виду',\n",
" 'ничего',\n",
" 'лучшего',\n",
" 'и',\n",
" 'если',\n",
" 'перспектива',\n",
" 'вечера',\n",
" 'у',\n",
" 'бедной',\n",
" 'больной',\n",
" 'не',\n",
" 'слишком',\n",
" 'вас',\n",
" 'пугает',\n",
" 'то',\n",
" 'я',\n",
" 'буду',\n",
" 'очень',\n",
" 'рада',\n",
" 'видеть',\n",
" 'вас',\n",
" 'нынче',\n",
" 'у',\n",
" 'себя',\n",
" 'между',\n",
" 'семью',\n",
" 'и',\n",
" 'десятью',\n",
" 'часами',\n",
" 'Анна',\n",
" 'Шерер',\n",
" 'Dieu',\n",
" 'quelle',\n",
" 'virulente',\n",
" 'sortie',\n",
" 'О',\n",
" 'какое',\n",
" 'жестокое',\n",
" 'нападение',\n",
" 'отвечал',\n",
" 'нисколько',\n",
" 'не',\n",
" 'смутясь',\n",
" 'такою',\n",
" 'встречей',\n",
" 'вошедший',\n",
" 'князь',\n",
" 'в',\n",
" 'придворном',\n",
" 'шитом',\n",
" 'мундире',\n",
" 'в',\n",
" 'чулках',\n",
" 'башмаках',\n",
" 'при',\n",
" 'звездах',\n",
" 'с',\n",
" 'светлым',\n",
" 'выражением',\n",
" 'плоского',\n",
" 'лица',\n",
" 'Он',\n",
" 'говорил',\n",
" 'на',\n",
" 'том',\n",
" 'изысканном',\n",
" 'французском',\n",
" 'языке',\n",
" 'на',\n",
" 'котором',\n",
" 'не',\n",
" 'только',\n",
" 'говорили',\n",
" 'но',\n",
" 'и',\n",
" 'думали',\n",
" 'наши',\n",
" 'деды',\n",
" 'и',\n",
" 'с',\n",
" 'теми',\n",
" 'тихими',\n",
" 'покровительственными',\n",
" 'интонациями',\n",
" 'которые',\n",
" 'свойственны',\n",
" 'состаревшемуся',\n",
" 'в',\n",
" 'свете',\n",
" 'и',\n",
" 'при',\n",
" 'дворе',\n",
" 'значительному',\n",
" 'человеку',\n",
" 'Он',\n",
" 'подошел',\n",
" 'к',\n",
" 'Анне',\n",
" 'Павловне',\n",
" 'поцеловал',\n",
" 'ее',\n",
" 'руку',\n",
" 'подставив',\n",
" 'ей',\n",
" 'свою',\n",
" 'надушенную',\n",
" 'и',\n",
" 'сияющую',\n",
" 'лысину',\n",
" 'и',\n",
" 'покойно',\n",
" 'уселся',\n",
" 'на',\n",
" 'диване',\n",
" 'Avant',\n",
" 'tout',\n",
" 'dites',\n",
" 'moi',\n",
" 'comment',\n",
" 'vous',\n",
" 'allez',\n",
" 'chere',\n",
" 'amie',\n",
" 'Прежде',\n",
" 'всего',\n",
" 'скажите',\n",
" 'как',\n",
" 'ваше',\n",
" 'здоровье',\n",
" 'Успокойте',\n",
" 'друга',\n",
" 'сказал',\n",
" 'он',\n",
" 'не',\n",
" 'изменяя',\n",
" 'голоса',\n",
" 'и',\n",
" 'тоном',\n",
" 'в',\n",
" 'котором',\n",
" 'изза',\n",
" 'приличия',\n",
" 'и',\n",
" 'участия',\n",
" 'просвечивало',\n",
" 'равнодушие',\n",
" 'и',\n",
" 'даже',\n",
" 'насмешка',\n",
" 'Как',\n",
" 'можно',\n",
" 'быть',\n",
" 'здоровой',\n",
" 'когда',\n",
" 'нравственно',\n",
" 'страдаешь',\n",
" 'Разве',\n",
" 'можно',\n",
" 'оставаться',\n",
" 'спокойною',\n",
" 'в',\n",
" 'наше',\n",
" 'время',\n",
" 'когда',\n",
" 'есть',\n",
" 'у',\n",
" 'человека',\n",
" 'чувство',\n",
" 'сказала',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'Вы',\n",
" 'весь',\n",
" 'вечер',\n",
" 'у',\n",
" 'меня',\n",
" 'надеюсь',\n",
" 'А',\n",
" 'праздник',\n",
" 'английского',\n",
" 'посланника',\n",
" 'Нынче',\n",
" 'середа',\n",
" 'Мне',\n",
" 'надо',\n",
" 'показаться',\n",
" 'там',\n",
" 'сказал',\n",
" 'князь',\n",
" 'Дочь',\n",
" 'заедет',\n",
" 'за',\n",
" 'мной',\n",
" 'и',\n",
" 'повезет',\n",
" 'меня',\n",
" 'Я',\n",
" 'думала',\n",
" 'что',\n",
" 'нынешний',\n",
" 'праздник',\n",
" 'отменен',\n",
" 'Je',\n",
" 'vous',\n",
" 'avoue',\n",
" 'que',\n",
" 'toutes',\n",
" 'ces',\n",
" 'fetes',\n",
" 'et',\n",
" 'tous',\n",
" 'ces',\n",
" 'feux',\n",
" 'dartifice',\n",
" 'commencent',\n",
" 'a',\n",
" 'devenir',\n",
" 'insipides',\n",
" 'Признаюсь',\n",
" 'все',\n",
" 'эти',\n",
" 'праздники',\n",
" 'и',\n",
" 'фейерверки',\n",
" 'становятся',\n",
" 'несносны',\n",
" 'Ежели',\n",
" 'бы',\n",
" 'знали',\n",
" 'что',\n",
" 'вы',\n",
" 'этого',\n",
" 'хотите',\n",
" 'праздник',\n",
" 'бы',\n",
" 'отменили',\n",
" 'сказал',\n",
" 'князь',\n",
" 'по',\n",
" 'привычке',\n",
" 'как',\n",
" 'заведенные',\n",
" 'часы',\n",
" 'говоря',\n",
" 'вещи',\n",
" 'которым',\n",
" 'он',\n",
" 'и',\n",
" 'не',\n",
" 'хотел',\n",
" 'чтобы',\n",
" 'верили',\n",
" 'Ne',\n",
" 'me',\n",
" 'tourmentez',\n",
" 'pas',\n",
" 'Eh',\n",
" 'bien',\n",
" 'quaton',\n",
" 'decide',\n",
" 'par',\n",
" 'rapport',\n",
" 'a',\n",
" 'la',\n",
" 'depeche',\n",
" 'de',\n",
" 'Novosiizoff',\n",
" 'Vous',\n",
" 'savez',\n",
" 'tout',\n",
" 'Не',\n",
" 'мучьте',\n",
" 'меня',\n",
" 'Ну',\n",
" 'что',\n",
" 'же',\n",
" 'решили',\n",
" 'по',\n",
" 'случаю',\n",
" 'депеши',\n",
" 'Новосильцова',\n",
" 'Вы',\n",
" 'все',\n",
" 'знаете',\n",
" 'Как',\n",
" 'вам',\n",
" 'сказать',\n",
" 'сказал',\n",
" 'князь',\n",
" 'холодным',\n",
" 'скучающим',\n",
" 'тоном',\n",
" 'Quaton',\n",
" 'decide',\n",
" 'On',\n",
" 'a',\n",
" 'decide',\n",
" 'que',\n",
" 'Buonaparte',\n",
" 'a',\n",
" 'brule',\n",
" 'ses',\n",
" 'vaisseaux',\n",
" 'et',\n",
" 'je',\n",
" 'crois',\n",
" 'que',\n",
" 'nous',\n",
" 'sommes',\n",
" 'en',\n",
" 'train',\n",
" 'de',\n",
" 'bruler',\n",
" 'les',\n",
" 'notres',\n",
" 'Что',\n",
" 'решили',\n",
" 'Решили',\n",
" 'что',\n",
" 'Бонапарте',\n",
" 'сжег',\n",
" 'свои',\n",
" 'корабли',\n",
" 'и',\n",
" 'мы',\n",
" 'тоже',\n",
" 'кажется',\n",
" 'готовы',\n",
" 'сжечь',\n",
" 'наши',\n",
" 'Князь',\n",
" 'Василий',\n",
" 'говорил',\n",
" 'всегда',\n",
" 'лениво',\n",
" 'как',\n",
" 'актер',\n",
" 'говорит',\n",
" 'роль',\n",
" 'старой',\n",
" 'пиесы',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'Шерер',\n",
" 'напротив',\n",
" 'несмотря',\n",
" 'на',\n",
" 'свои',\n",
" 'сорок',\n",
" 'лет',\n",
" 'была',\n",
" 'преисполнена',\n",
" 'оживления',\n",
" 'и',\n",
" 'порывов',\n",
" 'Быть',\n",
" 'энтузиасткой',\n",
" 'сделалось',\n",
" 'ее',\n",
" 'общественным',\n",
" 'положением',\n",
" 'и',\n",
" 'иногда',\n",
" 'когда',\n",
" 'ей',\n",
" 'даже',\n",
" 'того',\n",
" 'не',\n",
" 'хотелось',\n",
" 'она',\n",
" 'чтобы',\n",
" 'не',\n",
" 'обмануть',\n",
" 'ожиданий',\n",
" 'людей',\n",
" 'знавших',\n",
" 'ее',\n",
" 'делалась',\n",
" 'энтузиасткой',\n",
" 'Сдержанная',\n",
" 'улыбка',\n",
" 'игравшая',\n",
" 'постоянно',\n",
" 'на',\n",
" 'лице',\n",
" 'Анны',\n",
" 'Павловны',\n",
" 'хотя',\n",
" 'и',\n",
" 'не',\n",
" 'шла',\n",
" 'к',\n",
" 'ее',\n",
" 'отжившим',\n",
" 'чертам',\n",
" 'выражала',\n",
" 'как',\n",
" 'у',\n",
" 'избалованных',\n",
" 'детей',\n",
" 'постоянное',\n",
" 'сознание',\n",
" 'своего',\n",
" 'милого',\n",
" 'недостатка',\n",
" 'от',\n",
" 'которого',\n",
" 'она',\n",
" 'не',\n",
" 'хочет',\n",
" 'не',\n",
" 'может',\n",
" 'и',\n",
" 'не',\n",
" 'находит',\n",
" 'нужным',\n",
" 'исправляться',\n",
" 'В',\n",
" 'середине',\n",
" 'разговора',\n",
" 'про',\n",
" 'политические',\n",
" 'действия',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'разгорячилась',\n",
" 'Ах',\n",
" 'не',\n",
" 'говорите',\n",
" 'мне',\n",
" 'про',\n",
" 'Австрию',\n",
" 'Я',\n",
" 'ничего',\n",
" 'не',\n",
" 'понимаю',\n",
" 'может',\n",
" 'быть',\n",
" 'но',\n",
" 'Австрия',\n",
" 'никогда',\n",
" 'не',\n",
" 'хотела',\n",
" 'и',\n",
" 'не',\n",
" 'хочет',\n",
" 'войны',\n",
" 'Она',\n",
" 'предает',\n",
" 'нас',\n",
" 'Россия',\n",
" 'одна',\n",
" 'должна',\n",
" 'быть',\n",
" 'спасительницей',\n",
" 'Европы',\n",
" 'Наш',\n",
" 'благодетель',\n",
" 'знает',\n",
" 'свое',\n",
" 'высокое',\n",
" 'призвание',\n",
" 'и',\n",
" 'будет',\n",
" 'верен',\n",
" 'ему',\n",
" 'Вот',\n",
" 'одно',\n",
" 'во',\n",
" 'что',\n",
" 'я',\n",
" 'верю',\n",
" 'Нашему',\n",
" 'доброму',\n",
" 'и',\n",
" 'чудному',\n",
" 'государю',\n",
" 'предстоит',\n",
" 'величайшая',\n",
" 'роль',\n",
" 'в',\n",
" 'мире',\n",
" 'и',\n",
" 'он',\n",
" 'так',\n",
" 'добродетелен',\n",
" 'и',\n",
" 'хорош',\n",
" 'что',\n",
" 'Бог',\n",
" 'не',\n",
" 'оставит',\n",
" 'его',\n",
" 'и',\n",
" 'он',\n",
" 'исполнит',\n",
" 'свое',\n",
" 'призвание',\n",
" 'задавить',\n",
" 'гидру',\n",
" 'революции',\n",
" 'которая',\n",
" 'теперь',\n",
" 'еще',\n",
" 'ужаснее',\n",
" 'в',\n",
" 'лице',\n",
" 'этого',\n",
" 'убийцы',\n",
" 'и',\n",
" 'злодея',\n",
" 'Мы',\n",
" 'одни',\n",
" 'должны',\n",
" 'искупить',\n",
" 'кровь',\n",
" 'праведника',\n",
" 'На',\n",
" 'кого',\n",
" 'нам',\n",
" 'надеяться',\n",
" 'я',\n",
" 'вас',\n",
" 'спрашиваю',\n",
" 'Англия',\n",
" 'с',\n",
" 'своим',\n",
" 'коммерческим',\n",
" 'духом',\n",
" 'не',\n",
" 'поймет',\n",
" 'и',\n",
" 'не',\n",
" 'может',\n",
" 'понять',\n",
" 'всю',\n",
" 'высоту',\n",
" 'души',\n",
" 'императора',\n",
" 'Александра',\n",
" 'Она',\n",
" 'отказалась',\n",
" 'очистить',\n",
" 'Мальту',\n",
" 'Она',\n",
" 'хочет',\n",
" 'видеть',\n",
" 'ищет',\n",
" 'заднюю',\n",
" 'мысль',\n",
" 'наших',\n",
" 'действий',\n",
" 'Что',\n",
" 'они',\n",
" 'сказали',\n",
" 'Новосильцову',\n",
" 'Ничего',\n",
" 'Они',\n",
" 'не',\n",
" 'поняли',\n",
" 'они',\n",
" 'не',\n",
" 'могут',\n",
" 'понять',\n",
" 'самоотвержения',\n",
" 'нашего',\n",
" 'императора',\n",
" 'который',\n",
" 'ничего',\n",
" 'не',\n",
" 'хочет',\n",
" 'для',\n",
" 'себя',\n",
" 'и',\n",
" 'всё',\n",
" 'хочет',\n",
" 'для',\n",
" 'блага',\n",
" 'мира',\n",
" 'И',\n",
" 'что',\n",
" 'они',\n",
" 'обещали',\n",
" 'Ничего',\n",
" 'И',\n",
" 'что',\n",
" 'обещали',\n",
" 'и',\n",
" 'того',\n",
" 'не',\n",
" 'будет',\n",
" 'Пруссия',\n",
" 'уж',\n",
" 'объявила',\n",
" 'что',\n",
" 'Бонапарте',\n",
" 'непобедим',\n",
" 'и',\n",
" 'что',\n",
" 'вся',\n",
" 'Европа',\n",
" 'ничего',\n",
" 'не',\n",
" 'может',\n",
" 'против',\n",
" 'него',\n",
" 'И',\n",
" 'я',\n",
" 'не',\n",
" 'верю',\n",
" 'ни',\n",
" 'в',\n",
" 'одном',\n",
" 'слове',\n",
" 'ни',\n",
" 'Гарденбергу',\n",
" 'ни',\n",
" 'Гаугвицу',\n",
" 'Cette',\n",
" 'fameuse',\n",
" 'neutralite',\n",
" 'prussienne',\n",
" 'ce',\n",
" 'nest',\n",
" 'quun',\n",
" 'piege',\n",
" 'Этот',\n",
" 'пресловутый',\n",
" 'нейтралитет',\n",
" 'Пруссии',\n",
" 'только',\n",
" 'западня',\n",
" 'Я',\n",
" 'верю',\n",
" 'в',\n",
" 'одного',\n",
" 'Бога',\n",
" 'и',\n",
" 'в',\n",
" 'высокую',\n",
" 'судьбу',\n",
" 'нашего',\n",
" 'милого',\n",
" 'императора',\n",
" 'Он',\n",
" 'спасет',\n",
" 'Европу',\n",
" 'Она',\n",
" 'вдруг',\n",
" 'остановилась',\n",
" 'с',\n",
" 'улыбкою',\n",
" 'насмешки',\n",
" 'над',\n",
" 'своею',\n",
" 'горячностью',\n",
" 'Я',\n",
" 'думаю',\n",
" 'сказал',\n",
" 'князь',\n",
" 'улыбаясь',\n",
" 'что',\n",
" 'ежели',\n",
" 'бы',\n",
" 'вас',\n",
" 'послали',\n",
" 'вместо',\n",
" 'нашего',\n",
" 'милого',\n",
" 'Винценгероде',\n",
" 'вы',\n",
" 'бы',\n",
" 'взяли',\n",
" 'приступом',\n",
" 'согласие',\n",
" 'прусского',\n",
" 'короля',\n",
" 'Вы',\n",
" 'так',\n",
" 'красноречивы',\n",
" 'Вы',\n",
" 'дадите',\n",
" 'мне',\n",
" 'чаю',\n",
" 'Сейчас',\n",
" 'A',\n",
" 'propos',\n",
" 'прибавила',\n",
" 'она',\n",
" 'опять',\n",
" 'успокоиваясь',\n",
" 'нынче',\n",
" 'у',\n",
" 'меня',\n",
" 'два',\n",
" 'очень',\n",
" 'интересные',\n",
" 'человека',\n",
" 'le',\n",
" 'vicomte',\n",
" 'de',\n",
" 'MorteMariet',\n",
" 'il',\n",
" 'est',\n",
" 'allie',\n",
" 'aux',\n",
" 'Montmorency',\n",
" 'par',\n",
" 'les',\n",
" 'Rohans',\n",
" 'Кстати',\n",
" 'виконт',\n",
" 'Мортемар',\n",
" 'он',\n",
" 'в',\n",
" 'родстве',\n",
" 'с',\n",
" 'Монморанси',\n",
" 'чрез',\n",
" 'Роганов',\n",
" 'одна',\n",
" 'из',\n",
" 'лучших',\n",
" 'фамилий',\n",
" 'Франции',\n",
" 'Это',\n",
" 'один',\n",
" 'из',\n",
" 'хороших',\n",
" 'эмигрантов',\n",
" 'из',\n",
" 'настоящих',\n",
" 'И',\n",
" 'потом',\n",
" 'labbe',\n",
" 'Morio',\n",
" 'аббат',\n",
" 'Морио',\n",
" 'вы',\n",
" 'знаете',\n",
" 'этот',\n",
" 'глубокий',\n",
" 'ум',\n",
" 'Он',\n",
" 'был',\n",
" 'принят',\n",
" 'государем',\n",
" 'Вы',\n",
" 'знаете',\n",
" 'А',\n",
" 'Я',\n",
" 'очень',\n",
" 'рад',\n",
" 'буду',\n",
" 'сказал',\n",
" 'князь',\n",
" 'Скажите',\n",
" 'прибавил',\n",
" 'он',\n",
" 'как',\n",
" 'будто',\n",
" 'только',\n",
" 'что',\n",
" 'вспомнив',\n",
" 'чтото',\n",
" 'и',\n",
" 'особеннонебрежно',\n",
" 'тогда',\n",
" 'как',\n",
" 'то',\n",
" 'о',\n",
" 'чем',\n",
" 'он',\n",
" 'спрашивал',\n",
" 'было',\n",
" 'главною',\n",
" 'целью',\n",
" 'его',\n",
" 'посещения',\n",
" 'правда',\n",
" 'что',\n",
" 'limperatricemere',\n",
" 'императрицамать',\n",
" 'желает',\n",
" 'назначения',\n",
" 'барона',\n",
" 'Функе',\n",
" 'первым',\n",
" 'секретарем',\n",
" 'в',\n",
" 'Вену',\n",
" 'Cest',\n",
" 'un',\n",
" 'pauvre',\n",
" 'sire',\n",
" 'ce',\n",
" 'baron',\n",
" 'a',\n",
" 'ce',\n",
" 'quil',\n",
" 'parait',\n",
" 'Этот',\n",
" 'барон',\n",
" 'кажется',\n",
" 'ничтожная',\n",
" 'личность',\n",
" 'Князь',\n",
" 'Василий',\n",
" 'желал',\n",
" 'определить',\n",
" 'сына',\n",
" 'на',\n",
" 'это',\n",
" 'место',\n",
" 'которое',\n",
" 'через',\n",
" 'императрицу',\n",
" 'Марию',\n",
" 'Феодоровну',\n",
" 'старались',\n",
" 'доставить',\n",
" 'барону',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'почти',\n",
" 'закрыла',\n",
" 'глаза',\n",
" 'в',\n",
" 'знак',\n",
" 'того',\n",
" 'что',\n",
" 'ни',\n",
" 'она',\n",
" 'ни',\n",
" 'кто',\n",
" 'другой',\n",
" 'не',\n",
" 'могут',\n",
" 'судить',\n",
" 'про',\n",
" 'то',\n",
" 'что',\n",
" 'угодно',\n",
" 'или',\n",
" 'нравится',\n",
" 'императрице',\n",
" 'Monsieur',\n",
" 'le',\n",
" 'baron',\n",
" 'de',\n",
" 'Funke',\n",
" 'a',\n",
" 'ete',\n",
" 'recommande',\n",
" 'a',\n",
" 'limperatricemere',\n",
" 'par',\n",
" 'sa',\n",
" 'soeur',\n",
" 'Барон',\n",
" 'Функе',\n",
" 'рекомендован',\n",
" 'императрицематери',\n",
" 'ее',\n",
" 'сестрою',\n",
" 'только',\n",
" 'сказала',\n",
" 'она',\n",
" ...]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"token"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove stopwords"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"corpus_remove_stopword = [w for w in token if w not in rus_stopwords]\n",
"words = set(corpus_remove_stopword) #unique words"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"length of texts: 302713\n",
"number of unique words: 56728\n",
"a sample below\n"
]
}
],
"source": [
"print('length of texts: ', len(corpus_remove_stopword))\n",
"print('number of unique words:', len(words))\n",
"print('a sample below')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Лев',\n",
" 'Николаевич',\n",
" 'Толстой',\n",
" 'Война',\n",
" 'мир',\n",
" 'Том',\n",
" '1',\n",
" 'Лев',\n",
" 'Николаевич',\n",
" 'ТолстойВОЙНА',\n",
" 'И',\n",
" 'МИР',\n",
" 'Том',\n",
" '1',\n",
" 'ЧАСТЬ',\n",
" 'ПЕРВАЯ',\n",
" 'I',\n",
" 'Так',\n",
" 'говорила',\n",
" 'июле',\n",
" '1805',\n",
" 'года',\n",
" 'известная',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'Шерер',\n",
" 'фрейлина',\n",
" 'приближенная',\n",
" 'императрицы',\n",
" 'Марии',\n",
" 'Феодоровны',\n",
" 'встречая',\n",
" 'важного',\n",
" 'чиновного',\n",
" 'князя',\n",
" 'Василия',\n",
" 'первого',\n",
" 'приехавшего',\n",
" 'вечер',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'кашляла',\n",
" 'несколько',\n",
" 'дней',\n",
" 'грипп',\n",
" 'говорила',\n",
" 'грипп',\n",
" 'новое',\n",
" 'слово',\n",
" 'употреблявшееся',\n",
" 'редкими',\n",
" 'В',\n",
" 'записочках',\n",
" 'разосланных',\n",
" 'утром',\n",
" 'красным',\n",
" 'лакеем',\n",
" 'написано',\n",
" 'различия',\n",
" 'Si',\n",
" 'vous',\n",
" 'navez',\n",
" 'rien',\n",
" 'de',\n",
" 'mieux',\n",
" 'a',\n",
" 'faire',\n",
" 'M',\n",
" 'le',\n",
" 'comte',\n",
" 'mon',\n",
" 'prince',\n",
" 'et',\n",
" 'si',\n",
" 'la',\n",
" 'perspective',\n",
" 'de',\n",
" 'passer',\n",
" 'la',\n",
" 'soiree',\n",
" 'chez',\n",
" 'une',\n",
" 'pauvre',\n",
" 'malade',\n",
" 'ne',\n",
" 'vous',\n",
" 'effraye',\n",
" 'pas',\n",
" 'trop',\n",
" 'je',\n",
" 'serai',\n",
" 'charmee',\n",
" 'de',\n",
" 'vous',\n",
" 'voir',\n",
" 'chez',\n",
" 'moi',\n",
" 'entre',\n",
" '7',\n",
" 'et',\n",
" '10',\n",
" 'heures',\n",
" 'Annette',\n",
" 'Scherer',\n",
" 'Если',\n",
" 'y',\n",
" 'граф',\n",
" 'князь',\n",
" 'виду',\n",
" 'лучшего',\n",
" 'перспектива',\n",
" 'вечера',\n",
" 'бедной',\n",
" 'больной',\n",
" 'слишком',\n",
" 'пугает',\n",
" 'буду',\n",
" 'очень',\n",
" 'рада',\n",
" 'видеть',\n",
" 'нынче',\n",
" 'семью',\n",
" 'десятью',\n",
" 'часами',\n",
" 'Анна',\n",
" 'Шерер',\n",
" 'Dieu',\n",
" 'quelle',\n",
" 'virulente',\n",
" 'sortie',\n",
" 'О',\n",
" 'какое',\n",
" 'жестокое',\n",
" 'нападение',\n",
" 'отвечал',\n",
" 'нисколько',\n",
" 'смутясь',\n",
" 'такою',\n",
" 'встречей',\n",
" 'вошедший',\n",
" 'князь',\n",
" 'придворном',\n",
" 'шитом',\n",
" 'мундире',\n",
" 'чулках',\n",
" 'башмаках',\n",
" 'звездах',\n",
" 'светлым',\n",
" 'выражением',\n",
" 'плоского',\n",
" 'лица',\n",
" 'Он',\n",
" 'говорил',\n",
" 'изысканном',\n",
" 'французском',\n",
" 'языке',\n",
" 'котором',\n",
" 'говорили',\n",
" 'думали',\n",
" 'наши',\n",
" 'деды',\n",
" 'теми',\n",
" 'тихими',\n",
" 'покровительственными',\n",
" 'интонациями',\n",
" 'которые',\n",
" 'свойственны',\n",
" 'состаревшемуся',\n",
" 'свете',\n",
" 'дворе',\n",
" 'значительному',\n",
" 'человеку',\n",
" 'Он',\n",
" 'подошел',\n",
" 'Анне',\n",
" 'Павловне',\n",
" 'поцеловал',\n",
" 'руку',\n",
" 'подставив',\n",
" 'надушенную',\n",
" 'сияющую',\n",
" 'лысину',\n",
" 'покойно',\n",
" 'уселся',\n",
" 'диване',\n",
" 'Avant',\n",
" 'tout',\n",
" 'dites',\n",
" 'moi',\n",
" 'comment',\n",
" 'vous',\n",
" 'allez',\n",
" 'chere',\n",
" 'amie',\n",
" 'Прежде',\n",
" 'скажите',\n",
" 'ваше',\n",
" 'здоровье',\n",
" 'Успокойте',\n",
" 'друга',\n",
" 'сказал',\n",
" 'изменяя',\n",
" 'голоса',\n",
" 'тоном',\n",
" 'котором',\n",
" 'изза',\n",
" 'приличия',\n",
" 'участия',\n",
" 'просвечивало',\n",
" 'равнодушие',\n",
" 'насмешка',\n",
" 'Как',\n",
" 'здоровой',\n",
" 'нравственно',\n",
" 'страдаешь',\n",
" 'Разве',\n",
" 'оставаться',\n",
" 'спокойною',\n",
" 'наше',\n",
" 'время',\n",
" 'человека',\n",
" 'чувство',\n",
" 'сказала',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'Вы',\n",
" 'весь',\n",
" 'вечер',\n",
" 'надеюсь',\n",
" 'А',\n",
" 'праздник',\n",
" 'английского',\n",
" 'посланника',\n",
" 'Нынче',\n",
" 'середа',\n",
" 'Мне',\n",
" 'показаться',\n",
" 'сказал',\n",
" 'князь',\n",
" 'Дочь',\n",
" 'заедет',\n",
" 'мной',\n",
" 'повезет',\n",
" 'Я',\n",
" 'думала',\n",
" 'нынешний',\n",
" 'праздник',\n",
" 'отменен',\n",
" 'Je',\n",
" 'vous',\n",
" 'avoue',\n",
" 'que',\n",
" 'toutes',\n",
" 'ces',\n",
" 'fetes',\n",
" 'et',\n",
" 'tous',\n",
" 'ces',\n",
" 'feux',\n",
" 'dartifice',\n",
" 'commencent',\n",
" 'a',\n",
" 'devenir',\n",
" 'insipides',\n",
" 'Признаюсь',\n",
" 'праздники',\n",
" 'фейерверки',\n",
" 'становятся',\n",
" 'несносны',\n",
" 'Ежели',\n",
" 'знали',\n",
" 'хотите',\n",
" 'праздник',\n",
" 'отменили',\n",
" 'сказал',\n",
" 'князь',\n",
" 'привычке',\n",
" 'заведенные',\n",
" 'часы',\n",
" 'говоря',\n",
" 'вещи',\n",
" 'которым',\n",
" 'хотел',\n",
" 'верили',\n",
" 'Ne',\n",
" 'me',\n",
" 'tourmentez',\n",
" 'pas',\n",
" 'Eh',\n",
" 'bien',\n",
" 'quaton',\n",
" 'decide',\n",
" 'par',\n",
" 'rapport',\n",
" 'a',\n",
" 'la',\n",
" 'depeche',\n",
" 'de',\n",
" 'Novosiizoff',\n",
" 'Vous',\n",
" 'savez',\n",
" 'tout',\n",
" 'Не',\n",
" 'мучьте',\n",
" 'Ну',\n",
" 'решили',\n",
" 'случаю',\n",
" 'депеши',\n",
" 'Новосильцова',\n",
" 'Вы',\n",
" 'знаете',\n",
" 'Как',\n",
" 'сказать',\n",
" 'сказал',\n",
" 'князь',\n",
" 'холодным',\n",
" 'скучающим',\n",
" 'тоном',\n",
" 'Quaton',\n",
" 'decide',\n",
" 'On',\n",
" 'a',\n",
" 'decide',\n",
" 'que',\n",
" 'Buonaparte',\n",
" 'a',\n",
" 'brule',\n",
" 'ses',\n",
" 'vaisseaux',\n",
" 'et',\n",
" 'je',\n",
" 'crois',\n",
" 'que',\n",
" 'nous',\n",
" 'sommes',\n",
" 'en',\n",
" 'train',\n",
" 'de',\n",
" 'bruler',\n",
" 'les',\n",
" 'notres',\n",
" 'Что',\n",
" 'решили',\n",
" 'Решили',\n",
" 'Бонапарте',\n",
" 'сжег',\n",
" 'свои',\n",
" 'корабли',\n",
" 'кажется',\n",
" 'готовы',\n",
" 'сжечь',\n",
" 'наши',\n",
" 'Князь',\n",
" 'Василий',\n",
" 'говорил',\n",
" 'лениво',\n",
" 'актер',\n",
" 'говорит',\n",
" 'роль',\n",
" 'старой',\n",
" 'пиесы',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'Шерер',\n",
" 'напротив',\n",
" 'несмотря',\n",
" 'свои',\n",
" 'сорок',\n",
" 'лет',\n",
" 'преисполнена',\n",
" 'оживления',\n",
" 'порывов',\n",
" 'Быть',\n",
" 'энтузиасткой',\n",
" 'сделалось',\n",
" 'общественным',\n",
" 'положением',\n",
" 'хотелось',\n",
" 'обмануть',\n",
" 'ожиданий',\n",
" 'людей',\n",
" 'знавших',\n",
" 'делалась',\n",
" 'энтузиасткой',\n",
" 'Сдержанная',\n",
" 'улыбка',\n",
" 'игравшая',\n",
" 'постоянно',\n",
" 'лице',\n",
" 'Анны',\n",
" 'Павловны',\n",
" 'хотя',\n",
" 'шла',\n",
" 'отжившим',\n",
" 'чертам',\n",
" 'выражала',\n",
" 'избалованных',\n",
" 'детей',\n",
" 'постоянное',\n",
" 'сознание',\n",
" 'своего',\n",
" 'милого',\n",
" 'недостатка',\n",
" 'которого',\n",
" 'хочет',\n",
" 'находит',\n",
" 'нужным',\n",
" 'исправляться',\n",
" 'В',\n",
" 'середине',\n",
" 'разговора',\n",
" 'политические',\n",
" 'действия',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'разгорячилась',\n",
" 'Ах',\n",
" 'говорите',\n",
" 'Австрию',\n",
" 'Я',\n",
" 'понимаю',\n",
" 'Австрия',\n",
" 'хотела',\n",
" 'хочет',\n",
" 'войны',\n",
" 'Она',\n",
" 'предает',\n",
" 'Россия',\n",
" 'одна',\n",
" 'должна',\n",
" 'спасительницей',\n",
" 'Европы',\n",
" 'Наш',\n",
" 'благодетель',\n",
" 'знает',\n",
" 'свое',\n",
" 'высокое',\n",
" 'призвание',\n",
" 'верен',\n",
" 'Вот',\n",
" 'одно',\n",
" 'верю',\n",
" 'Нашему',\n",
" 'доброму',\n",
" 'чудному',\n",
" 'государю',\n",
" 'предстоит',\n",
" 'величайшая',\n",
" 'роль',\n",
" 'мире',\n",
" 'добродетелен',\n",
" 'хорош',\n",
" 'Бог',\n",
" 'оставит',\n",
" 'исполнит',\n",
" 'свое',\n",
" 'призвание',\n",
" 'задавить',\n",
" 'гидру',\n",
" 'революции',\n",
" 'которая',\n",
" 'ужаснее',\n",
" 'лице',\n",
" 'убийцы',\n",
" 'злодея',\n",
" 'Мы',\n",
" 'одни',\n",
" 'должны',\n",
" 'искупить',\n",
" 'кровь',\n",
" 'праведника',\n",
" 'На',\n",
" 'кого',\n",
" 'нам',\n",
" 'надеяться',\n",
" 'спрашиваю',\n",
" 'Англия',\n",
" 'своим',\n",
" 'коммерческим',\n",
" 'духом',\n",
" 'поймет',\n",
" 'понять',\n",
" 'высоту',\n",
" 'души',\n",
" 'императора',\n",
" 'Александра',\n",
" 'Она',\n",
" 'отказалась',\n",
" 'очистить',\n",
" 'Мальту',\n",
" 'Она',\n",
" 'хочет',\n",
" 'видеть',\n",
" 'ищет',\n",
" 'заднюю',\n",
" 'мысль',\n",
" 'наших',\n",
" 'действий',\n",
" 'Что',\n",
" 'сказали',\n",
" 'Новосильцову',\n",
" 'Ничего',\n",
" 'Они',\n",
" 'поняли',\n",
" 'могут',\n",
" 'понять',\n",
" 'самоотвержения',\n",
" 'нашего',\n",
" 'императора',\n",
" 'который',\n",
" 'хочет',\n",
" 'всё',\n",
" 'хочет',\n",
" 'блага',\n",
" 'мира',\n",
" 'И',\n",
" 'обещали',\n",
" 'Ничего',\n",
" 'И',\n",
" 'обещали',\n",
" 'Пруссия',\n",
" 'объявила',\n",
" 'Бонапарте',\n",
" 'непобедим',\n",
" 'вся',\n",
" 'Европа',\n",
" 'против',\n",
" 'И',\n",
" 'верю',\n",
" 'одном',\n",
" 'слове',\n",
" 'Гарденбергу',\n",
" 'Гаугвицу',\n",
" 'Cette',\n",
" 'fameuse',\n",
" 'neutralite',\n",
" 'prussienne',\n",
" 'ce',\n",
" 'nest',\n",
" 'quun',\n",
" 'piege',\n",
" 'Этот',\n",
" 'пресловутый',\n",
" 'нейтралитет',\n",
" 'Пруссии',\n",
" 'западня',\n",
" 'Я',\n",
" 'верю',\n",
" 'одного',\n",
" 'Бога',\n",
" 'высокую',\n",
" 'судьбу',\n",
" 'нашего',\n",
" 'милого',\n",
" 'императора',\n",
" 'Он',\n",
" 'спасет',\n",
" 'Европу',\n",
" 'Она',\n",
" 'остановилась',\n",
" 'улыбкою',\n",
" 'насмешки',\n",
" 'своею',\n",
" 'горячностью',\n",
" 'Я',\n",
" 'думаю',\n",
" 'сказал',\n",
" 'князь',\n",
" 'улыбаясь',\n",
" 'ежели',\n",
" 'послали',\n",
" 'вместо',\n",
" 'нашего',\n",
" 'милого',\n",
" 'Винценгероде',\n",
" 'взяли',\n",
" 'приступом',\n",
" 'согласие',\n",
" 'прусского',\n",
" 'короля',\n",
" 'Вы',\n",
" 'красноречивы',\n",
" 'Вы',\n",
" 'дадите',\n",
" 'чаю',\n",
" 'Сейчас',\n",
" 'A',\n",
" 'propos',\n",
" 'прибавила',\n",
" 'успокоиваясь',\n",
" 'нынче',\n",
" 'очень',\n",
" 'интересные',\n",
" 'человека',\n",
" 'le',\n",
" 'vicomte',\n",
" 'de',\n",
" 'MorteMariet',\n",
" 'il',\n",
" 'est',\n",
" 'allie',\n",
" 'aux',\n",
" 'Montmorency',\n",
" 'par',\n",
" 'les',\n",
" 'Rohans',\n",
" 'Кстати',\n",
" 'виконт',\n",
" 'Мортемар',\n",
" 'родстве',\n",
" 'Монморанси',\n",
" 'чрез',\n",
" 'Роганов',\n",
" 'одна',\n",
" 'лучших',\n",
" 'фамилий',\n",
" 'Франции',\n",
" 'Это',\n",
" 'хороших',\n",
" 'эмигрантов',\n",
" 'настоящих',\n",
" 'И',\n",
" 'labbe',\n",
" 'Morio',\n",
" 'аббат',\n",
" 'Морио',\n",
" 'знаете',\n",
" 'глубокий',\n",
" 'ум',\n",
" 'Он',\n",
" 'принят',\n",
" 'государем',\n",
" 'Вы',\n",
" 'знаете',\n",
" 'А',\n",
" 'Я',\n",
" 'очень',\n",
" 'рад',\n",
" 'буду',\n",
" 'сказал',\n",
" 'князь',\n",
" 'Скажите',\n",
" 'прибавил',\n",
" 'вспомнив',\n",
" 'чтото',\n",
" 'особеннонебрежно',\n",
" 'спрашивал',\n",
" 'главною',\n",
" 'целью',\n",
" 'посещения',\n",
" 'правда',\n",
" 'limperatricemere',\n",
" 'императрицамать',\n",
" 'желает',\n",
" 'назначения',\n",
" 'барона',\n",
" 'Функе',\n",
" 'первым',\n",
" 'секретарем',\n",
" 'Вену',\n",
" 'Cest',\n",
" 'un',\n",
" 'pauvre',\n",
" 'sire',\n",
" 'ce',\n",
" 'baron',\n",
" 'a',\n",
" 'ce',\n",
" 'quil',\n",
" 'parait',\n",
" 'Этот',\n",
" 'барон',\n",
" 'кажется',\n",
" 'ничтожная',\n",
" 'личность',\n",
" 'Князь',\n",
" 'Василий',\n",
" 'желал',\n",
" 'определить',\n",
" 'сына',\n",
" 'это',\n",
" 'место',\n",
" 'которое',\n",
" 'императрицу',\n",
" 'Марию',\n",
" 'Феодоровну',\n",
" 'старались',\n",
" 'доставить',\n",
" 'барону',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'закрыла',\n",
" 'глаза',\n",
" 'знак',\n",
" 'могут',\n",
" 'судить',\n",
" 'угодно',\n",
" 'нравится',\n",
" 'императрице',\n",
" 'Monsieur',\n",
" 'le',\n",
" 'baron',\n",
" 'de',\n",
" 'Funke',\n",
" 'a',\n",
" 'ete',\n",
" 'recommande',\n",
" 'a',\n",
" 'limperatricemere',\n",
" 'par',\n",
" 'sa',\n",
" 'soeur',\n",
" 'Барон',\n",
" 'Функе',\n",
" 'рекомендован',\n",
" 'императрицематери',\n",
" 'сестрою',\n",
" 'сказала',\n",
" 'грустным',\n",
" 'сухим',\n",
" 'тоном',\n",
" 'В',\n",
" 'время',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'назвала',\n",
" 'императрицу',\n",
" 'лицо',\n",
" 'представило',\n",
" 'глубокое',\n",
" 'искреннее',\n",
" 'выражение',\n",
" 'преданности',\n",
" 'уважения',\n",
" 'соединенное',\n",
" 'грустью',\n",
" 'бывало',\n",
" 'каждый',\n",
" 'разговоре',\n",
" 'упоминала',\n",
" 'своей',\n",
" 'высокой',\n",
" 'покровительнице',\n",
" 'Она',\n",
" 'сказала',\n",
" 'величество',\n",
" 'изволила',\n",
" 'оказать',\n",
" 'барону',\n",
" 'Функе',\n",
" 'beaucoup',\n",
" 'destime',\n",
" 'уважения',\n",
" 'взгляд',\n",
" 'подернулся',\n",
" 'грустью',\n",
" 'Князь',\n",
" 'равнодушно',\n",
" 'замолк',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'свойственною',\n",
" 'придворною',\n",
" 'женскою',\n",
" 'ловкостью',\n",
" 'быстротою',\n",
" 'такта',\n",
" 'захотела',\n",
" 'щелконуть',\n",
" 'князя',\n",
" 'дерзнул',\n",
" 'отозваться',\n",
" 'лице',\n",
" 'рекомендованном',\n",
" 'императрице',\n",
" 'время',\n",
" 'утешить',\n",
" 'Mais',\n",
" 'a',\n",
" 'propos',\n",
" 'de',\n",
" 'votre',\n",
" 'famille',\n",
" 'Кстати',\n",
" 'вашей',\n",
" 'семье',\n",
" 'сказала',\n",
" 'знаете',\n",
" 'ваша',\n",
" 'дочь',\n",
" 'тех',\n",
" 'пор',\n",
" 'выезжает',\n",
" 'fait',\n",
" 'les',\n",
" 'delices',\n",
" 'de',\n",
" 'tout',\n",
" 'le',\n",
" 'monde',\n",
" 'On',\n",
" 'la',\n",
" 'trouve',\n",
" 'belle',\n",
" 'comme',\n",
" 'le',\n",
" 'jour',\n",
" 'составляет',\n",
" 'восторг',\n",
" 'общества',\n",
" 'Ее',\n",
" 'находят',\n",
" 'прекрасною',\n",
" 'день',\n",
" 'Князь',\n",
" 'наклонился',\n",
" 'знак',\n",
" 'уважения',\n",
" 'признательности',\n",
" 'Я',\n",
" 'часто',\n",
" 'думаю',\n",
" 'продолжала',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'минутного',\n",
" 'молчания',\n",
" 'подвигаясь',\n",
" 'князю',\n",
" 'ласково',\n",
" 'улыбаясь',\n",
" 'выказывая',\n",
" 'этим',\n",
" 'политические',\n",
" 'светские',\n",
" 'разговоры',\n",
" 'кончены',\n",
" 'начинается',\n",
" 'задушевный',\n",
" 'часто',\n",
" 'думаю',\n",
" 'несправедливо',\n",
" 'распределяется',\n",
" 'счастие',\n",
" 'жизни',\n",
" 'За',\n",
" 'судьба',\n",
" 'дала',\n",
" 'таких',\n",
" 'двух',\n",
" 'славных',\n",
" 'детей',\n",
" 'исключая',\n",
" 'Анатоля',\n",
" 'вашего',\n",
" 'меньшого',\n",
" 'люблю',\n",
" 'вставила',\n",
" 'безапелляционно',\n",
" 'приподняв',\n",
" 'брови',\n",
" 'таких',\n",
" 'прелестных',\n",
" 'детей',\n",
" 'А',\n",
" 'право',\n",
" 'менее',\n",
" 'цените',\n",
" 'стоите',\n",
" 'И',\n",
" 'улыбнулась',\n",
" 'своею',\n",
" 'восторженною',\n",
" 'улыбкой',\n",
" 'Que',\n",
" 'voulezvous',\n",
" 'Lafater',\n",
" 'aurait',\n",
" 'dit',\n",
" 'que',\n",
" 'je',\n",
" 'nai',\n",
" 'pas',\n",
" 'la',\n",
" 'bosse',\n",
" 'de',\n",
" 'la',\n",
" 'paterienite',\n",
" 'Чего',\n",
" 'хотите',\n",
" 'Лафатер',\n",
" 'сказал',\n",
" 'шишки',\n",
" 'родительской',\n",
" 'любви',\n",
" 'сказал',\n",
" 'князь',\n",
" 'Перестаньте',\n",
" 'шутить',\n",
" 'Я',\n",
" 'хотела',\n",
" 'серьезно',\n",
" 'поговорить',\n",
" 'вами',\n",
" 'Знаете',\n",
" 'недовольна',\n",
" 'вашим',\n",
" 'меньшим',\n",
" 'сыном',\n",
" 'Между',\n",
" 'нами',\n",
" 'будь',\n",
" 'сказано',\n",
" 'лицо',\n",
" 'приняло',\n",
" 'грустное',\n",
" 'выражение',\n",
" 'нем',\n",
" 'говорили',\n",
" 'величества',\n",
" 'жалеют',\n",
" 'Князь',\n",
" 'отвечал',\n",
" 'молча',\n",
" 'значительно',\n",
" 'глядя',\n",
" 'ждала',\n",
" 'ответа',\n",
" 'Князь',\n",
" 'Василий',\n",
" 'поморщился',\n",
" 'Что',\n",
" 'хотите',\n",
" 'делал',\n",
" 'сказал',\n",
" 'Вы',\n",
" 'знаете',\n",
" 'сделал',\n",
" 'воспитания',\n",
" 'отец',\n",
" 'оба',\n",
" 'вышли',\n",
" 'des',\n",
" 'imbeciles',\n",
" 'дураки',\n",
" 'Ипполит',\n",
" 'крайней',\n",
" 'мере',\n",
" 'покойный',\n",
" 'дурак',\n",
" 'Анатоль',\n",
" 'беспокойный',\n",
" 'Вот',\n",
" 'одно',\n",
" 'различие',\n",
" 'сказал',\n",
" 'улыбаясь',\n",
" 'неестественно',\n",
" 'одушевленно',\n",
" 'обыкновенно',\n",
" 'особенно',\n",
" 'резко',\n",
" 'выказывая',\n",
" 'сложившихся',\n",
" 'около',\n",
" 'рта',\n",
" 'морщинах',\n",
" 'чтото',\n",
" 'неожиданногрубое',\n",
" 'неприятное',\n",
" 'И',\n",
" 'родятся',\n",
" 'дети',\n",
" 'таких',\n",
" 'людей',\n",
" 'Ежели',\n",
" 'отец',\n",
" 'могла',\n",
" 'упрекнуть',\n",
" 'сказала',\n",
" 'Анна',\n",
" 'Павловна',\n",
" 'задумчиво',\n",
" 'поднимая',\n",
" 'глаза',\n",
" 'Je',\n",
" 'suis',\n",
" 'votre',\n",
" 'Я',\n",
" 'ваш',\n",
" 'верный',\n",
" 'раб',\n",
" 'et',\n",
" 'a',\n",
" 'vous',\n",
" 'seule',\n",
" 'je',\n",
" 'puis',\n",
" 'lavouer',\n",
" 'Мои',\n",
" ...]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corpus_remove_stopword"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Show the most frequencies words**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nltk import FreqDist\n",
"import matplotlib.pyplot as plt\n",
"dist = FreqDist(corpus_remove_stopword)\n",
"dist.most_common(30)\n",
"plt.figure(figsize=[15,8], )\n",
"plt.title(\"Most 30 frequencies words\")\n",
"dist.plot(30)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Frequency with respects to word count"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"occurr_dist = FreqDist([w[1] for w in dist.most_common()])\n",
"plt.figure(figsize=[12,8])\n",
"occurr_dist.plot(50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Most words in the vocabulary occurrs less than 4 times.**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"words occurred <= 5 times in corpus: 0.005323649696798759\n"
]
}
],
"source": [
"percent = sum([w[1] for w in occurr_dist.most_common() if w[1]<=4])/sum([w[1] for w in occurr_dist.most_common()])\n",
"print('words occurred <= 5 times in corpus:',percent)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"len(more_than_five) 8173\n",
"len(le_than_five) 48555\n"
]
}
],
"source": [
"word_freq = dist.most_common()\n",
"more_than_five = dict([(w[0],i) for i, w in enumerate(word_freq) if w[1]>5])\n",
"print('len(more_than_five)', len(more_than_five))\n",
"n = len(more_than_five)\n",
"le_than_five = dict([(w[0],n) for w in word_freq if w[1]<=5])\n",
"print('len(le_than_five)', len(le_than_five))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Dictionary of words to id and id to words**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"word_2_int = dict([(w[0],i) for i, w in enumerate(word_freq)])\n",
"int_2_word = dict([(v,k) for k,v in word_2_int.items()])\n",
"# int_2_word[n] = '**unknown**'"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'это': 0,\n",
" 'сказал': 1,\n",
" 'Он': 2,\n",
" 'Пьер': 3,\n",
" 'И': 4,\n",
" 'Я': 5,\n",
" 'князь': 6,\n",
" 'В': 7,\n",
" 'Наташа': 8,\n",
" 'всё': 9,\n",
" 'Но': 10,\n",
" 'Андрей': 11,\n",
" 'время': 12,\n",
" 'сказала': 13,\n",
" 'которые': 14,\n",
" 'Она': 15,\n",
" 'говорил': 16,\n",
" 'который': 17,\n",
" 'de': 18,\n",
" 'А': 19,\n",
" 'Да': 20,\n",
" 'своей': 21,\n",
" 'Ростов': 22,\n",
" 'Марья': 23,\n",
" 'Что': 24,\n",
" 'очень': 25,\n",
" 'человек': 26,\n",
" 'Пьера': 27,\n",
" 'Ну': 28,\n",
" 'людей': 29,\n",
" 'князя': 30,\n",
" 'чтото': 31,\n",
" 'Как': 32,\n",
" 'Это': 33,\n",
" 'лицо': 34,\n",
" 'мог': 35,\n",
" 'своего': 36,\n",
" 'Князь': 37,\n",
" 'глаза': 38,\n",
" 'Николай': 39,\n",
" 'дело': 40,\n",
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" 'радости': 687,\n",
" 'последние': 688,\n",
" 'самым': 689,\n",
" 'другими': 690,\n",
" 'горе': 691,\n",
" 'мост': 692,\n",
" 'мира': 693,\n",
" 'остановилась': 694,\n",
" 'изредка': 695,\n",
" 'дал': 696,\n",
" 'Александр': 697,\n",
" 'желание': 698,\n",
" 'кабинет': 699,\n",
" 'старик': 700,\n",
" 'раза': 701,\n",
" 'старался': 702,\n",
" 'отношении': 703,\n",
" 'новое': 704,\n",
" 'Разве': 705,\n",
" 'вместо': 706,\n",
" 'дочь': 707,\n",
" 'оба': 708,\n",
" 'смотреть': 709,\n",
" 'Очень': 710,\n",
" 'гостей': 711,\n",
" 'стола': 712,\n",
" 'тело': 713,\n",
" 'громко': 714,\n",
" 'всему': 715,\n",
" 'домой': 716,\n",
" 'послышались': 717,\n",
" 'Наташей': 718,\n",
" 'известие': 719,\n",
" 'уехал': 720,\n",
" 'толпа': 721,\n",
" 'Vous': 722,\n",
" 'Бонапарте': 723,\n",
" 'страх': 724,\n",
" 'чемто': 725,\n",
" 'француз': 726,\n",
" 'перебил': 727,\n",
" 'ногами': 728,\n",
" 'пофранцузски': 729,\n",
" 'доктор': 730,\n",
" 'мое': 731,\n",
" 'новые': 732,\n",
" 'ногу': 733,\n",
" 'Анны': 734,\n",
" 'Александра': 735,\n",
" 'таких': 736,\n",
" 'взяв': 737,\n",
" 'речи': 738,\n",
" 'словами': 739,\n",
" 'Во': 740,\n",
" 'трудно': 741,\n",
" 'Боже': 742,\n",
" 'показалось': 743,\n",
" 'любить': 744,\n",
" 'слышались': 745,\n",
" 'большую': 746,\n",
" 'Илья': 747,\n",
" 'лежал': 748,\n",
" 'поля': 749,\n",
" 'какое': 750,\n",
" 'хотите': 751,\n",
" 'деревне': 752,\n",
" 'руке': 753,\n",
" 'Кроме': 754,\n",
" 'деньги': 755,\n",
" 'французские': 756,\n",
" 'сильнее': 757,\n",
" 'другую': 758,\n",
" 'рядом': 759,\n",
" 'ходить': 760,\n",
" 'Несвицкий': 761,\n",
" 'французами': 762,\n",
" 'десять': 763,\n",
" 'Балашев': 764,\n",
" 'человеку': 765,\n",
" 'одни': 766,\n",
" 'Болконского': 767,\n",
" 'моей': 768,\n",
" 'счастье': 769,\n",
" 'сделали': 770,\n",
" 'человеком': 771,\n",
" 'рассказывал': 772,\n",
" 'самому': 773,\n",
" 'положения': 774,\n",
" 'виноват': 775,\n",
" 'поцеловал': 776,\n",
" 'chere': 777,\n",
" 'se': 778,\n",
" 'плечами': 779,\n",
" 'внимания': 780,\n",
" 'лицами': 781,\n",
" 'От': 782,\n",
" 'офицеры': 783,\n",
" 'которыми': 784,\n",
" 'четыре': 785,\n",
" 'какойто': 786,\n",
" 'чегото': 787,\n",
" 'кампании': 788,\n",
" 'Старый': 789,\n",
" 'мир': 790,\n",
" 'постоянно': 791,\n",
" 'разговора': 792,\n",
" 'Cest': 793,\n",
" 'движением': 794,\n",
" 'смерть': 795,\n",
" 'война': 796,\n",
" 'действие': 797,\n",
" 'вниз': 798,\n",
" 'слегка': 799,\n",
" 'везде': 800,\n",
" 'вернулся': 801,\n",
" 'силу': 802,\n",
" 'мгновение': 803,\n",
" 'вечером': 804,\n",
" 'крики': 805,\n",
" 'мальчик': 806,\n",
" 'жалко': 807,\n",
" 'поднял': 808,\n",
" 'одинаково': 809,\n",
" 'нашел': 810,\n",
" 'приказал': 811,\n",
" 'войско': 812,\n",
" 'французского': 813,\n",
" 'д': 814,\n",
" 'дней': 815,\n",
" 'утром': 816,\n",
" 'здоровье': 817,\n",
" 'испуганно': 818,\n",
" 'план': 819,\n",
" 'рассказал': 820,\n",
" 'пошла': 821,\n",
" 'Пьером': 822,\n",
" 'народов': 823,\n",
" 'степени': 824,\n",
" 'выше': 825,\n",
" 'Il': 826,\n",
" 'хочешь': 827,\n",
" 'тобой': 828,\n",
" 'доктора': 829,\n",
" 'полку': 830,\n",
" 'шагов': 831,\n",
" 'гусар': 832,\n",
" 'понимать': 833,\n",
" 'понимая': 834,\n",
" 'Зачем': 835,\n",
" 'гору': 836,\n",
" 'приказание': 837,\n",
" 'событие': 838,\n",
" 'год': 839,\n",
" 'пять': 840,\n",
" 'сначала': 841,\n",
" 'Le': 842,\n",
" 'au': 843,\n",
" 'ею': 844,\n",
" 'такая': 845,\n",
" 'хуже': 846,\n",
" 'прошу': 847,\n",
" 'голосов': 848,\n",
" 'делалось': 849,\n",
" 'ходил': 850,\n",
" 'делает': 851,\n",
" 'трех': 852,\n",
" 'числа': 853,\n",
" 'Ничего': 854,\n",
" 'quil': 855,\n",
" 'продолжала': 856,\n",
" 'людям': 857,\n",
" 'головы': 858,\n",
" 'принял': 859,\n",
" 'мыслей': 860,\n",
" 'генералу': 861,\n",
" 'звук': 862,\n",
" 'столу': 863,\n",
" 'отвечать': 864,\n",
" 'французский': 865,\n",
" 'Хорошо': 866,\n",
" 'значит': 867,\n",
" 'крик': 868,\n",
" 'окна': 869,\n",
" 'последний': 870,\n",
" 'пожалуйста': 871,\n",
" 'позиции': 872,\n",
" 'Ваше': 873,\n",
" 'войне': 874,\n",
" 'подъехал': 875,\n",
" 'лицах': 876,\n",
" 'Билибин': 877,\n",
" 'бог': 878,\n",
" 'первого': 879,\n",
" 'дворе': 880,\n",
" 'сознание': 881,\n",
" 'общества': 882,\n",
" 'нами': 883,\n",
" 'проговорила': 884,\n",
" 'вошла': 885,\n",
" 'мнение': 886,\n",
" 'сквозь': 887,\n",
" 'приятно': 888,\n",
" 'этими': 889,\n",
" 'страха': 890,\n",
" 'огонь': 891,\n",
" 'всётаки': 892,\n",
" 'господа': 893,\n",
" 'полковник': 894,\n",
" 'Сони': 895,\n",
" 'Одно': 896,\n",
" 'первое': 897,\n",
" 'найти': 898,\n",
" 'необходимо': 899,\n",
" 'нечего': 900,\n",
" 'навстречу': 901,\n",
" 'деятельности': 902,\n",
" 'духа': 903,\n",
" 'твердо': 904,\n",
" 'орудия': 905,\n",
" 'стороне': 906,\n",
" 'всетаки': 907,\n",
" 'нашего': 908,\n",
" 'целью': 909,\n",
" 'моего': 910,\n",
" 'силах': 911,\n",
" 'стол': 912,\n",
" 'боялся': 913,\n",
" 'мои': 914,\n",
" 'любит': 915,\n",
" 'Вдруг': 916,\n",
" 'заметив': 917,\n",
" 'другу': 918,\n",
" 'никак': 919,\n",
" 'другая': 920,\n",
" 'возможность': 921,\n",
" 'происходило': 922,\n",
" 'улыбнулась': 923,\n",
" 'мере': 924,\n",
" 'женщина': 925,\n",
" 'маленькой': 926,\n",
" 'общество': 927,\n",
" 'прелесть': 928,\n",
" 'mais': 929,\n",
" 'меньше': 930,\n",
" 'принять': 931,\n",
" 'шопотом': 932,\n",
" 'рода': 933,\n",
" 'денег': 934,\n",
" 'Марью': 935,\n",
" 'адъютанта': 936,\n",
" 'При': 937,\n",
" 'взять': 938,\n",
" 'Солдаты': 939,\n",
" 'русской': 940,\n",
" 'землю': 941,\n",
" 'вышли': 942,\n",
" 'обыкновенно': 943,\n",
" 'села': 944,\n",
" 'вопросительно': 945,\n",
" 'смеясь': 946,\n",
" 'жене': 947,\n",
" 'Надо': 948,\n",
" 'ктото': 949,\n",
" 'сил': 950,\n",
" 'жил': 951,\n",
" 'оттуда': 952,\n",
" 'участие': 953,\n",
" 'графине': 954,\n",
" 'любила': 955,\n",
" 'двор': 956,\n",
" 'просил': 957,\n",
" 'верхом': 958,\n",
" 'частью': 959,\n",
" 'дел': 960,\n",
" 'французской': 961,\n",
" 'часу': 962,\n",
" 'возможности': 963,\n",
" 'si': 964,\n",
" 'тоном': 965,\n",
" 'детей': 966,\n",
" 'говорите': 967,\n",
" 'своею': 968,\n",
" 'свет': 969,\n",
" 'большие': 970,\n",
" 'груди': 971,\n",
" 'заговорил': 972,\n",
" 'Два': 973,\n",
" 'решительно': 974,\n",
" 'отношения': 975,\n",
" 'читал': 976,\n",
" 'значения': 977,\n",
" 'батюшка': 978,\n",
" 'командира': 979,\n",
" 'позади': 980,\n",
" 'слушая': 981,\n",
" 'Где': 982,\n",
" 'воображении': 983,\n",
" 'Соне': 984,\n",
" 'человечества': 985,\n",
" 'Балашева': 986,\n",
" 'наше': 987,\n",
" 'вещи': 988,\n",
" 'votre': 989,\n",
" 'ваш': 990,\n",
" 'ума': 991,\n",
" 'вижу': 992,\n",
" 'отцу': 993,\n",
" 'какие': 994,\n",
" 'просить': 995,\n",
" 'рука': 996,\n",
" 'состоит': 997,\n",
" 'казался': 998,\n",
" 'слез': 999,\n",
" ...}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"word_2_int"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zipf's Law"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zipf’s Law states that a small number of words are used all the time, while the vast majority are used very rarely. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"words_with_count = Counter(corpus_remove_stopword)\n",
"counts = list(words_with_count.values())\n",
"words = words_with_count.keys()\n",
"\n",
"# Plot rank X frequency \n",
"ranks = arange(1, len(counts)+1)\n",
"indices = argsort(-np.array(counts))\n",
"frequencies = np.array(counts)[indices]\n",
"loglog(ranks, frequencies, marker=\".\")\n",
"title(\"Zipf's Law for words\")\n",
"xlabel(\"Frequency rank of word\")\n",
"ylabel(\"Absolute frequency of word\")\n",
"grid(True)\n",
"for n in list(logspace(-0.5, log10(len(counts)), 20).astype(int)):\n",
" try:\n",
" dummy = text(ranks[n], \n",
" frequencies[n], \n",
" \" \" + list(words)[indices[n]], \n",
" verticalalignment=\"bottom\",\n",
" horizontalalignment=\"left\")\n",
" except:\n",
" pass\n",
"\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using Gensim to Visualization text "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"class Dictionary(object):\n",
" def __init__(self):\n",
" self.word2idx = {}\n",
" self.idx2word = []\n",
" \n",
" \n",
" def add_word(self, word):\n",
" if word not in self.word2idx:\n",
" self.idx2word.append(word)\n",
" self.word2idx[word] = len(self.idx2word) - 1\n",
"\n",
" return self.word2idx[word]\n",
"\n",
" def __len__(self):\n",
" return len(self.idx2word)\n",
"\n",
"\n",
"class Corpus(object):\n",
" def __init__(self, path):\n",
" self.dictionary = Dictionary()\n",
" self.sentences = []\n",
" # We will ingest only these characters: \n",
" self.whitelist = [' ', '!', '\"', '#', '$', '%', '&', \"'\", '(', ')', '*', '+', ',', '-', '.', '/', \n",
" '0', '1','2','3','4','5','6','7','8','9',':',';','<','=','>','?','@','Й','Ц','У',\n",
" 'К','Е','Н','Г','Ш','Щ','З','Х','Ъ','Э','Ж','Д','Л','О','Р','П','А','В','Ы','Ф','Я',\n",
" 'Ч','С','М','И','Т','Ь','Б','Ю','[','\\\\',']','^','_','`','й','ц','у','к','е','н','г',\n",
" 'ш','щ','з','х','ъ','э','ж','д','л','о','р','п','а','в','ы','ф','я','ч','с','м','и','т',\n",
" 'ь','б','ю', '{','|','}','~']\n",
" \n",
" #self.train = self.tokenize(os.path.join(path, 'voyna-i-mir-tom-1.txt'))\n",
" self.train = self.tokenize(path)\n",
" # self.valid = self.tokenize(os.path.join(path, 'valid.txt'))\n",
"\n",
" def tokenize(self, path): \n",
" \"\"\"Tokenizes a text file.\"\"\"\n",
" #assert os.path.exists(path)\n",
" # Add words to the dictionary \n",
" for root, dirs, files in os.walk(path):\n",
" for file in filter(lambda file: file.endswith('.txt'), files): \n",
" file_path = os.path.join(path, file)\n",
" print(file_path)\n",
" with open(file_path, 'r', encoding=\"cp1251\") as f:\n",
" tokens = 0\n",
" for line in f:\n",
" self.sentences.append(line.strip())\n",
" line = ''.join([c for c in line if c in self.whitelist])\n",
" words = line.split() \n",
" tokens += len(words)\n",
" for word in words:\n",
" self.dictionary.add_word(word)\n",
"\n",
" # Tokenize file content\n",
" with open(file_path, 'r', encoding=\"cp1251\") as f:\n",
" #ids = torch.LongTensor(tokens)\n",
" ids = np.zeros([tokens])\n",
" token = 0\n",
" for line in f:\n",
" line = ''.join([c for c in line if c in self.whitelist])\n",
" words = line.split()\n",
" for word in words:\n",
" ids[token] = self.dictionary.word2idx[word]\n",
" token += 1\n",
"\n",
" return ids"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Peace_and_War/voyna-i-mir-tom-1.txt\n",
"Peace_and_War/voyna-i-mir-tom-2.txt\n",
"Peace_and_War/voyna-i-mir-tom-3.txt\n",
"Peace_and_War/voyna-i-mir-tom-4.txt\n"
]
}
],
"source": [
"corpus = Corpus('Peace_and_War/')"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
"import regex\n",
"sentences = []\n",
"pattern = r'\\w+'\n",
"#token = regex.findall(pattern, corpus)\n",
"for line in corpus.sentences:\n",
" token = regex.findall(pattern, line)\n",
" sentences.append(token)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"num_features = 300\n",
"# Minimum word count threshold.\n",
"min_word_count = 3\n",
"\n",
"# Number of threads to run in parallel.\n",
"#more workers, faster we train\n",
"num_workers = multiprocessing.cpu_count()\n",
"\n",
"# Context window length.\n",
"context_size = 7\n",
"\n",
"# Downsample setting for frequent words.\n",
"#0 - 1e-5 is good for this\n",
"downsampling = 1e-3\n",
"\n",
"# Seed for the RNG, to make the results reproducible.\n",
"#random number generator\n",
"#deterministic, good for debugging\n",
"seed = 1"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"pw2vec = w2v.Word2Vec(\n",
" sg=1,\n",
" seed=seed,\n",
" workers=num_workers,\n",
" size=num_features,\n",
" min_count=min_word_count,\n",
" window=context_size,\n",
" sample=downsampling)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"pw2vec.build_vocab(sentences)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10272,\n",
" 5,\n",
" {'Лев': <gensim.models.keyedvectors.Vocab at 0x2557992f668>,\n",
" 'Николаевич': <gensim.models.keyedvectors.Vocab at 0x2557992f9b0>,\n",
" 'Толстой': <gensim.models.keyedvectors.Vocab at 0x2557992f748>,\n",
" 'Война': <gensim.models.keyedvectors.Vocab at 0x2557992f198>,\n",
" 'и': <gensim.models.keyedvectors.Vocab at 0x2557992f080>,\n",
" 'мир': <gensim.models.keyedvectors.Vocab at 0x2557992f780>,\n",
" 'Том': <gensim.models.keyedvectors.Vocab at 0x255000c7240>,\n",
" '1': <gensim.models.keyedvectors.Vocab at 0x255000c7828>,\n",
" 'И': <gensim.models.keyedvectors.Vocab at 0x255000c78d0>,\n",
" 'ЧАСТЬ': <gensim.models.keyedvectors.Vocab at 0x255799de438>,\n",
" 'ПЕРВАЯ': <gensim.models.keyedvectors.Vocab at 0x255799de710>,\n",
" 'I': <gensim.models.keyedvectors.Vocab at 0x25577470278>,\n",
" 'Так': <gensim.models.keyedvectors.Vocab at 0x25579a36438>,\n",
" 'говорила': <gensim.models.keyedvectors.Vocab at 0x2557610ef98>,\n",
" 'в': <gensim.models.keyedvectors.Vocab at 0x25579962358>,\n",
" '1805': <gensim.models.keyedvectors.Vocab at 0x25550513908>,\n",
" 'года': <gensim.models.keyedvectors.Vocab at 0x255513bf240>,\n",
" 'известная': <gensim.models.keyedvectors.Vocab at 0x255026e87f0>,\n",
" 'Анна': <gensim.models.keyedvectors.Vocab at 0x255026e8828>,\n",
" 'Павловна': <gensim.models.keyedvectors.Vocab at 0x255026e8860>,\n",
" 'Шерер': <gensim.models.keyedvectors.Vocab at 0x255026e8898>,\n",
" 'фрейлина': <gensim.models.keyedvectors.Vocab at 0x255026e88d0>,\n",
" 'императрицы': <gensim.models.keyedvectors.Vocab at 0x255026e8908>,\n",
" 'Марии': <gensim.models.keyedvectors.Vocab at 0x255026e8940>,\n",
" 'встречая': <gensim.models.keyedvectors.Vocab at 0x255026e8978>,\n",
" 'важного': <gensim.models.keyedvectors.Vocab at 0x255026e89b0>,\n",
" 'князя': <gensim.models.keyedvectors.Vocab at 0x255026e89e8>,\n",
" 'Василия': <gensim.models.keyedvectors.Vocab at 0x255026e8a20>,\n",
" 'первого': <gensim.models.keyedvectors.Vocab at 0x255026e8a58>,\n",
" 'приехавшего': <gensim.models.keyedvectors.Vocab at 0x255026e8a90>,\n",
" 'на': <gensim.models.keyedvectors.Vocab at 0x255026e8ac8>,\n",
" 'ее': <gensim.models.keyedvectors.Vocab at 0x255026e8b00>,\n",
" 'вечер': <gensim.models.keyedvectors.Vocab at 0x255026e8b38>,\n",
" 'кашляла': <gensim.models.keyedvectors.Vocab at 0x255026e8b70>,\n",
" 'несколько': <gensim.models.keyedvectors.Vocab at 0x255026e8ba8>,\n",
" 'дней': <gensim.models.keyedvectors.Vocab at 0x255026e8be0>,\n",
" 'у': <gensim.models.keyedvectors.Vocab at 0x255026e8c18>,\n",
" 'нее': <gensim.models.keyedvectors.Vocab at 0x255026e8c50>,\n",
" 'был': <gensim.models.keyedvectors.Vocab at 0x255026e8c88>,\n",
" 'как': <gensim.models.keyedvectors.Vocab at 0x255026e8cc0>,\n",
" 'она': <gensim.models.keyedvectors.Vocab at 0x255026e8cf8>,\n",
" 'тогда': <gensim.models.keyedvectors.Vocab at 0x255026e8d30>,\n",
" 'новое': <gensim.models.keyedvectors.Vocab at 0x255026e8d68>,\n",
" 'слово': <gensim.models.keyedvectors.Vocab at 0x255026e8da0>,\n",
" 'только': <gensim.models.keyedvectors.Vocab at 0x255026e8dd8>,\n",
" 'редкими': <gensim.models.keyedvectors.Vocab at 0x255026e8e10>,\n",
" 'В': <gensim.models.keyedvectors.Vocab at 0x255026e8e48>,\n",
" 'утром': <gensim.models.keyedvectors.Vocab at 0x255026e8e80>,\n",
" 'с': <gensim.models.keyedvectors.Vocab at 0x255026e8eb8>,\n",
" 'красным': <gensim.models.keyedvectors.Vocab at 0x255026e8ef0>,\n",
" 'лакеем': <gensim.models.keyedvectors.Vocab at 0x255026e8f28>,\n",
" 'было': <gensim.models.keyedvectors.Vocab at 0x255026e8f60>,\n",
" 'написано': <gensim.models.keyedvectors.Vocab at 0x255026e8f98>,\n",
" 'без': <gensim.models.keyedvectors.Vocab at 0x255026e8fd0>,\n",
" 'различия': <gensim.models.keyedvectors.Vocab at 0x255027ea048>,\n",
" 'во': <gensim.models.keyedvectors.Vocab at 0x255027ea080>,\n",
" 'всех': <gensim.models.keyedvectors.Vocab at 0x255027ea0b8>,\n",
" 'Si': <gensim.models.keyedvectors.Vocab at 0x255027ea0f0>,\n",
" 'vous': <gensim.models.keyedvectors.Vocab at 0x255027ea128>,\n",
" 'n': <gensim.models.keyedvectors.Vocab at 0x255027ea160>,\n",
" 'avez': <gensim.models.keyedvectors.Vocab at 0x255027ea198>,\n",
" 'rien': <gensim.models.keyedvectors.Vocab at 0x255027ea1d0>,\n",
" 'de': <gensim.models.keyedvectors.Vocab at 0x255027ea208>,\n",
" 'mieux': <gensim.models.keyedvectors.Vocab at 0x255027ea240>,\n",
" 'a': <gensim.models.keyedvectors.Vocab at 0x255027ea278>,\n",
" 'faire': <gensim.models.keyedvectors.Vocab at 0x255027ea2b0>,\n",
" 'M': <gensim.models.keyedvectors.Vocab at 0x255027ea2e8>,\n",
" 'le': <gensim.models.keyedvectors.Vocab at 0x255027ea320>,\n",
" 'comte': <gensim.models.keyedvectors.Vocab at 0x255027ea358>,\n",
" 'или': <gensim.models.keyedvectors.Vocab at 0x255027ea390>,\n",
" 'mon': <gensim.models.keyedvectors.Vocab at 0x255027ea3c8>,\n",
" 'prince': <gensim.models.keyedvectors.Vocab at 0x255027ea400>,\n",
" 'et': <gensim.models.keyedvectors.Vocab at 0x255027ea438>,\n",
" 'si': <gensim.models.keyedvectors.Vocab at 0x255027ea470>,\n",
" 'la': <gensim.models.keyedvectors.Vocab at 0x255027ea4a8>,\n",
" 'soiree': <gensim.models.keyedvectors.Vocab at 0x255027ea4e0>,\n",
" 'chez': <gensim.models.keyedvectors.Vocab at 0x255027ea518>,\n",
" 'une': <gensim.models.keyedvectors.Vocab at 0x255027ea550>,\n",
" 'pauvre': <gensim.models.keyedvectors.Vocab at 0x255027ea588>,\n",
" 'malade': <gensim.models.keyedvectors.Vocab at 0x255027ea5c0>,\n",
" 'ne': <gensim.models.keyedvectors.Vocab at 0x255027ea5f8>,\n",
" 'pas': <gensim.models.keyedvectors.Vocab at 0x255027ea630>,\n",
" 'trop': <gensim.models.keyedvectors.Vocab at 0x255027ea668>,\n",
" 'je': <gensim.models.keyedvectors.Vocab at 0x255027ea6a0>,\n",
" 'serai': <gensim.models.keyedvectors.Vocab at 0x255027ea6d8>,\n",
" 'voir': <gensim.models.keyedvectors.Vocab at 0x255027ea710>,\n",
" 'moi': <gensim.models.keyedvectors.Vocab at 0x255027ea748>,\n",
" 'entre': <gensim.models.keyedvectors.Vocab at 0x255027ea780>,\n",
" '7': <gensim.models.keyedvectors.Vocab at 0x255027ea7b8>,\n",
" '10': <gensim.models.keyedvectors.Vocab at 0x255027ea7f0>,\n",
" 'heures': <gensim.models.keyedvectors.Vocab at 0x255027ea828>,\n",
" 'Annette': <gensim.models.keyedvectors.Vocab at 0x255027ea860>,\n",
" 'Если': <gensim.models.keyedvectors.Vocab at 0x255027ea898>,\n",
" 'y': <gensim.models.keyedvectors.Vocab at 0x255027ea8d0>,\n",
" 'вас': <gensim.models.keyedvectors.Vocab at 0x255027ea908>,\n",
" 'граф': <gensim.models.keyedvectors.Vocab at 0x255027ea940>,\n",
" 'князь': <gensim.models.keyedvectors.Vocab at 0x255027ea978>,\n",
" 'нет': <gensim.models.keyedvectors.Vocab at 0x255027ea9b0>,\n",
" 'виду': <gensim.models.keyedvectors.Vocab at 0x255027ea9e8>,\n",
" 'ничего': <gensim.models.keyedvectors.Vocab at 0x255027eaa20>,\n",
" 'лучшего': <gensim.models.keyedvectors.Vocab at 0x255027eaa58>,\n",
" 'если': <gensim.models.keyedvectors.Vocab at 0x255027eaa90>,\n",
" 'вечера': <gensim.models.keyedvectors.Vocab at 0x255027eaac8>,\n",
" 'бедной': <gensim.models.keyedvectors.Vocab at 0x255027eab00>,\n",
" 'больной': <gensim.models.keyedvectors.Vocab at 0x255027eab38>,\n",
" 'не': <gensim.models.keyedvectors.Vocab at 0x255027eab70>,\n",
" 'слишком': <gensim.models.keyedvectors.Vocab at 0x255027eaba8>,\n",
" 'то': <gensim.models.keyedvectors.Vocab at 0x255027eabe0>,\n",
" 'я': <gensim.models.keyedvectors.Vocab at 0x255027eac18>,\n",
" 'буду': <gensim.models.keyedvectors.Vocab at 0x255027eac50>,\n",
" 'очень': <gensim.models.keyedvectors.Vocab at 0x255027eac88>,\n",
" 'рада': <gensim.models.keyedvectors.Vocab at 0x255027eacc0>,\n",
" 'видеть': <gensim.models.keyedvectors.Vocab at 0x255027eacf8>,\n",
" 'нынче': <gensim.models.keyedvectors.Vocab at 0x255027ead30>,\n",
" 'себя': <gensim.models.keyedvectors.Vocab at 0x255027ead68>,\n",
" 'между': <gensim.models.keyedvectors.Vocab at 0x255027eada0>,\n",
" 'семью': <gensim.models.keyedvectors.Vocab at 0x255027eadd8>,\n",
" 'десятью': <gensim.models.keyedvectors.Vocab at 0x255027eae10>,\n",
" 'часами': <gensim.models.keyedvectors.Vocab at 0x255027eae48>,\n",
" 'Dieu': <gensim.models.keyedvectors.Vocab at 0x255027eae80>,\n",
" 'quelle': <gensim.models.keyedvectors.Vocab at 0x255027eaeb8>,\n",
" 'О': <gensim.models.keyedvectors.Vocab at 0x255027eaef0>,\n",
" 'какое': <gensim.models.keyedvectors.Vocab at 0x255027eaf28>,\n",
" 'нападение': <gensim.models.keyedvectors.Vocab at 0x255027eaf60>,\n",
" 'отвечал': <gensim.models.keyedvectors.Vocab at 0x255027eaf98>,\n",
" 'нисколько': <gensim.models.keyedvectors.Vocab at 0x255027eafd0>,\n",
" 'такою': <gensim.models.keyedvectors.Vocab at 0x255027eb048>,\n",
" 'вошедший': <gensim.models.keyedvectors.Vocab at 0x255027eb080>,\n",
" 'придворном': <gensim.models.keyedvectors.Vocab at 0x255027eb0b8>,\n",
" 'шитом': <gensim.models.keyedvectors.Vocab at 0x255027eb0f0>,\n",
" 'мундире': <gensim.models.keyedvectors.Vocab at 0x255027eb128>,\n",
" 'чулках': <gensim.models.keyedvectors.Vocab at 0x255027eb160>,\n",
" 'башмаках': <gensim.models.keyedvectors.Vocab at 0x255027eb198>,\n",
" 'при': <gensim.models.keyedvectors.Vocab at 0x255027eb1d0>,\n",
" 'звездах': <gensim.models.keyedvectors.Vocab at 0x255027eb208>,\n",
" 'светлым': <gensim.models.keyedvectors.Vocab at 0x255027eb240>,\n",
" 'выражением': <gensim.models.keyedvectors.Vocab at 0x255027eb278>,\n",
" 'лица': <gensim.models.keyedvectors.Vocab at 0x255027eb2b0>,\n",
" 'Он': <gensim.models.keyedvectors.Vocab at 0x255027eb2e8>,\n",
" 'говорил': <gensim.models.keyedvectors.Vocab at 0x255027eb320>,\n",
" 'том': <gensim.models.keyedvectors.Vocab at 0x255027eb358>,\n",
" 'французском': <gensim.models.keyedvectors.Vocab at 0x255027eb390>,\n",
" 'языке': <gensim.models.keyedvectors.Vocab at 0x255027eb3c8>,\n",
" 'котором': <gensim.models.keyedvectors.Vocab at 0x255027eb400>,\n",
" 'говорили': <gensim.models.keyedvectors.Vocab at 0x255027eb438>,\n",
" 'но': <gensim.models.keyedvectors.Vocab at 0x255027eb470>,\n",
" 'думали': <gensim.models.keyedvectors.Vocab at 0x255027eb4a8>,\n",
" 'наши': <gensim.models.keyedvectors.Vocab at 0x255027eb4e0>,\n",
" 'теми': <gensim.models.keyedvectors.Vocab at 0x255027eb518>,\n",
" 'тихими': <gensim.models.keyedvectors.Vocab at 0x255027eb550>,\n",
" 'интонациями': <gensim.models.keyedvectors.Vocab at 0x255027eb588>,\n",
" 'которые': <gensim.models.keyedvectors.Vocab at 0x255027eb5c0>,\n",
" 'свете': <gensim.models.keyedvectors.Vocab at 0x255027eb5f8>,\n",
" 'дворе': <gensim.models.keyedvectors.Vocab at 0x255027eb630>,\n",
" 'человеку': <gensim.models.keyedvectors.Vocab at 0x255027eb668>,\n",
" 'подошел': <gensim.models.keyedvectors.Vocab at 0x255027eb6a0>,\n",
" 'к': <gensim.models.keyedvectors.Vocab at 0x255027eb6d8>,\n",
" 'Анне': <gensim.models.keyedvectors.Vocab at 0x255027eb710>,\n",
" 'Павловне': <gensim.models.keyedvectors.Vocab at 0x255027eb748>,\n",
" 'поцеловал': <gensim.models.keyedvectors.Vocab at 0x255027eb780>,\n",
" 'руку': <gensim.models.keyedvectors.Vocab at 0x255027eb7b8>,\n",
" 'подставив': <gensim.models.keyedvectors.Vocab at 0x255027eb7f0>,\n",
" 'ей': <gensim.models.keyedvectors.Vocab at 0x255027eb828>,\n",
" 'свою': <gensim.models.keyedvectors.Vocab at 0x255027eb860>,\n",
" 'лысину': <gensim.models.keyedvectors.Vocab at 0x255027eb898>,\n",
" 'покойно': <gensim.models.keyedvectors.Vocab at 0x255027eb8d0>,\n",
" 'уселся': <gensim.models.keyedvectors.Vocab at 0x255027eb908>,\n",
" 'диване': <gensim.models.keyedvectors.Vocab at 0x255027eb940>,\n",
" 'tout': <gensim.models.keyedvectors.Vocab at 0x255027eb978>,\n",
" 'dites': <gensim.models.keyedvectors.Vocab at 0x255027eb9b0>,\n",
" 'comment': <gensim.models.keyedvectors.Vocab at 0x255027eb9e8>,\n",
" 'allez': <gensim.models.keyedvectors.Vocab at 0x255027eba20>,\n",
" 'chere': <gensim.models.keyedvectors.Vocab at 0x255027eba58>,\n",
" 'amie': <gensim.models.keyedvectors.Vocab at 0x255027eba90>,\n",
" 'Прежде': <gensim.models.keyedvectors.Vocab at 0x255027ebac8>,\n",
" 'всего': <gensim.models.keyedvectors.Vocab at 0x255027ebb00>,\n",
" 'скажите': <gensim.models.keyedvectors.Vocab at 0x255027ebb38>,\n",
" 'ваше': <gensim.models.keyedvectors.Vocab at 0x255027ebb70>,\n",
" 'здоровье': <gensim.models.keyedvectors.Vocab at 0x255027ebba8>,\n",
" 'друга': <gensim.models.keyedvectors.Vocab at 0x255027ebbe0>,\n",
" 'сказал': <gensim.models.keyedvectors.Vocab at 0x255027ebc18>,\n",
" 'он': <gensim.models.keyedvectors.Vocab at 0x255027ebc50>,\n",
" 'изменяя': <gensim.models.keyedvectors.Vocab at 0x255027ebc88>,\n",
" 'голоса': <gensim.models.keyedvectors.Vocab at 0x255027ebcc0>,\n",
" 'тоном': <gensim.models.keyedvectors.Vocab at 0x255027ebcf8>,\n",
" 'из': <gensim.models.keyedvectors.Vocab at 0x255027ebd30>,\n",
" 'за': <gensim.models.keyedvectors.Vocab at 0x255027ebd68>,\n",
" 'приличия': <gensim.models.keyedvectors.Vocab at 0x255027ebda0>,\n",
" 'участия': <gensim.models.keyedvectors.Vocab at 0x255027ebdd8>,\n",
" 'равнодушие': <gensim.models.keyedvectors.Vocab at 0x255027ebe10>,\n",
" 'даже': <gensim.models.keyedvectors.Vocab at 0x255027ebe48>,\n",
" 'насмешка': <gensim.models.keyedvectors.Vocab at 0x255027ebe80>,\n",
" 'Как': <gensim.models.keyedvectors.Vocab at 0x255027ebeb8>,\n",
" 'можно': <gensim.models.keyedvectors.Vocab at 0x255027ebef0>,\n",
" 'быть': <gensim.models.keyedvectors.Vocab at 0x255027ebf28>,\n",
" 'когда': <gensim.models.keyedvectors.Vocab at 0x255027ebf60>,\n",
" 'нравственно': <gensim.models.keyedvectors.Vocab at 0x255027ebf98>,\n",
" 'Разве': <gensim.models.keyedvectors.Vocab at 0x255027ebfd0>,\n",
" 'оставаться': <gensim.models.keyedvectors.Vocab at 0x255027ed048>,\n",
" 'наше': <gensim.models.keyedvectors.Vocab at 0x255027ed080>,\n",
" 'время': <gensim.models.keyedvectors.Vocab at 0x255027ed0b8>,\n",
" 'есть': <gensim.models.keyedvectors.Vocab at 0x255027ed0f0>,\n",
" 'человека': <gensim.models.keyedvectors.Vocab at 0x255027ed128>,\n",
" 'чувство': <gensim.models.keyedvectors.Vocab at 0x255027ed160>,\n",
" 'сказала': <gensim.models.keyedvectors.Vocab at 0x255027ed198>,\n",
" 'Вы': <gensim.models.keyedvectors.Vocab at 0x255027ed1d0>,\n",
" 'весь': <gensim.models.keyedvectors.Vocab at 0x255027ed208>,\n",
" 'меня': <gensim.models.keyedvectors.Vocab at 0x255027ed240>,\n",
" 'надеюсь': <gensim.models.keyedvectors.Vocab at 0x255027ed278>,\n",
" 'А': <gensim.models.keyedvectors.Vocab at 0x255027ed2b0>,\n",
" 'праздник': <gensim.models.keyedvectors.Vocab at 0x255027ed2e8>,\n",
" 'английского': <gensim.models.keyedvectors.Vocab at 0x255027ed320>,\n",
" 'посланника': <gensim.models.keyedvectors.Vocab at 0x255027ed358>,\n",
" 'Нынче': <gensim.models.keyedvectors.Vocab at 0x255027ed390>,\n",
" 'Мне': <gensim.models.keyedvectors.Vocab at 0x255027ed3c8>,\n",
" 'надо': <gensim.models.keyedvectors.Vocab at 0x255027ed400>,\n",
" 'показаться': <gensim.models.keyedvectors.Vocab at 0x255027ed438>,\n",
" 'там': <gensim.models.keyedvectors.Vocab at 0x255027ed470>,\n",
" 'Дочь': <gensim.models.keyedvectors.Vocab at 0x255027ed4a8>,\n",
" 'мной': <gensim.models.keyedvectors.Vocab at 0x255027ed4e0>,\n",
" 'Я': <gensim.models.keyedvectors.Vocab at 0x255027ed518>,\n",
" 'думала': <gensim.models.keyedvectors.Vocab at 0x255027ed550>,\n",
" 'что': <gensim.models.keyedvectors.Vocab at 0x255027ed588>,\n",
" 'нынешний': <gensim.models.keyedvectors.Vocab at 0x255027ed5c0>,\n",
" 'Je': <gensim.models.keyedvectors.Vocab at 0x255027ed5f8>,\n",
" 'avoue': <gensim.models.keyedvectors.Vocab at 0x255027ed630>,\n",
" 'que': <gensim.models.keyedvectors.Vocab at 0x255027ed668>,\n",
" 'toutes': <gensim.models.keyedvectors.Vocab at 0x255027ed6a0>,\n",
" 'ces': <gensim.models.keyedvectors.Vocab at 0x255027ed6d8>,\n",
" 'tous': <gensim.models.keyedvectors.Vocab at 0x255027ed710>,\n",
" 'd': <gensim.models.keyedvectors.Vocab at 0x255027ed748>,\n",
" 'devenir': <gensim.models.keyedvectors.Vocab at 0x255027ed780>,\n",
" 'Признаюсь': <gensim.models.keyedvectors.Vocab at 0x255027ed7b8>,\n",
" 'все': <gensim.models.keyedvectors.Vocab at 0x255027ed7f0>,\n",
" 'эти': <gensim.models.keyedvectors.Vocab at 0x255027ed828>,\n",
" 'становятся': <gensim.models.keyedvectors.Vocab at 0x255027ed860>,\n",
" 'Ежели': <gensim.models.keyedvectors.Vocab at 0x255027ed898>,\n",
" 'бы': <gensim.models.keyedvectors.Vocab at 0x255027ed8d0>,\n",
" 'знали': <gensim.models.keyedvectors.Vocab at 0x255027ed908>,\n",
" 'вы': <gensim.models.keyedvectors.Vocab at 0x255027ed940>,\n",
" 'этого': <gensim.models.keyedvectors.Vocab at 0x255027ed978>,\n",
" 'хотите': <gensim.models.keyedvectors.Vocab at 0x255027ed9b0>,\n",
" 'по': <gensim.models.keyedvectors.Vocab at 0x255027ed9e8>,\n",
" 'привычке': <gensim.models.keyedvectors.Vocab at 0x255027eda20>,\n",
" 'часы': <gensim.models.keyedvectors.Vocab at 0x255027eda58>,\n",
" 'говоря': <gensim.models.keyedvectors.Vocab at 0x255027eda90>,\n",
" 'вещи': <gensim.models.keyedvectors.Vocab at 0x255027edac8>,\n",
" 'которым': <gensim.models.keyedvectors.Vocab at 0x255027edb00>,\n",
" 'хотел': <gensim.models.keyedvectors.Vocab at 0x255027edb38>,\n",
" 'чтобы': <gensim.models.keyedvectors.Vocab at 0x255027edb70>,\n",
" 'верили': <gensim.models.keyedvectors.Vocab at 0x255027edba8>,\n",
" 'Ne': <gensim.models.keyedvectors.Vocab at 0x255027edbe0>,\n",
" 'me': <gensim.models.keyedvectors.Vocab at 0x255027edc18>,\n",
" 'Eh': <gensim.models.keyedvectors.Vocab at 0x255027edc50>,\n",
" 'bien': <gensim.models.keyedvectors.Vocab at 0x255027edc88>,\n",
" 'qu': <gensim.models.keyedvectors.Vocab at 0x255027edcc0>,\n",
" 't': <gensim.models.keyedvectors.Vocab at 0x255027edcf8>,\n",
" 'on': <gensim.models.keyedvectors.Vocab at 0x255027edd30>,\n",
" 'decide': <gensim.models.keyedvectors.Vocab at 0x255027edd68>,\n",
" 'par': <gensim.models.keyedvectors.Vocab at 0x255027edda0>,\n",
" 'rapport': <gensim.models.keyedvectors.Vocab at 0x255027eddd8>,\n",
" 'Vous': <gensim.models.keyedvectors.Vocab at 0x255027ede10>,\n",
" 'savez': <gensim.models.keyedvectors.Vocab at 0x255027ede48>,\n",
" 'Не': <gensim.models.keyedvectors.Vocab at 0x255027ede80>,\n",
" 'Ну': <gensim.models.keyedvectors.Vocab at 0x255027edeb8>,\n",
" 'же': <gensim.models.keyedvectors.Vocab at 0x255027edef0>,\n",
" 'решили': <gensim.models.keyedvectors.Vocab at 0x255027edf28>,\n",
" 'случаю': <gensim.models.keyedvectors.Vocab at 0x255027edf60>,\n",
" 'депеши': <gensim.models.keyedvectors.Vocab at 0x255027edf98>,\n",
" 'знаете': <gensim.models.keyedvectors.Vocab at 0x255027edfd0>,\n",
" 'вам': <gensim.models.keyedvectors.Vocab at 0x255027ef048>,\n",
" 'сказать': <gensim.models.keyedvectors.Vocab at 0x255027ef080>,\n",
" 'холодным': <gensim.models.keyedvectors.Vocab at 0x255027ef0b8>,\n",
" 'Qu': <gensim.models.keyedvectors.Vocab at 0x255027ef0f0>,\n",
" 'On': <gensim.models.keyedvectors.Vocab at 0x255027ef128>,\n",
" 'Buonaparte': <gensim.models.keyedvectors.Vocab at 0x255027ef160>,\n",
" 'ses': <gensim.models.keyedvectors.Vocab at 0x255027ef198>,\n",
" 'crois': <gensim.models.keyedvectors.Vocab at 0x255027ef1d0>,\n",
" 'nous': <gensim.models.keyedvectors.Vocab at 0x255027ef208>,\n",
" 'sommes': <gensim.models.keyedvectors.Vocab at 0x255027ef240>,\n",
" 'en': <gensim.models.keyedvectors.Vocab at 0x255027ef278>,\n",
" 'les': <gensim.models.keyedvectors.Vocab at 0x255027ef2b0>,\n",
" 'Что': <gensim.models.keyedvectors.Vocab at 0x255027ef2e8>,\n",
" 'Бонапарте': <gensim.models.keyedvectors.Vocab at 0x255027ef320>,\n",
" 'сжег': <gensim.models.keyedvectors.Vocab at 0x255027ef358>,\n",
" 'свои': <gensim.models.keyedvectors.Vocab at 0x255027ef390>,\n",
" 'мы': <gensim.models.keyedvectors.Vocab at 0x255027ef3c8>,\n",
" 'тоже': <gensim.models.keyedvectors.Vocab at 0x255027ef400>,\n",
" 'кажется': <gensim.models.keyedvectors.Vocab at 0x255027ef438>,\n",
" 'готовы': <gensim.models.keyedvectors.Vocab at 0x255027ef470>,\n",
" 'сжечь': <gensim.models.keyedvectors.Vocab at 0x255027ef4a8>,\n",
" 'Князь': <gensim.models.keyedvectors.Vocab at 0x255027ef4e0>,\n",
" 'Василий': <gensim.models.keyedvectors.Vocab at 0x255027ef518>,\n",
" 'всегда': <gensim.models.keyedvectors.Vocab at 0x255027ef550>,\n",
" 'лениво': <gensim.models.keyedvectors.Vocab at 0x255027ef588>,\n",
" 'говорит': <gensim.models.keyedvectors.Vocab at 0x255027ef5c0>,\n",
" 'роль': <gensim.models.keyedvectors.Vocab at 0x255027ef5f8>,\n",
" 'старой': <gensim.models.keyedvectors.Vocab at 0x255027ef630>,\n",
" 'напротив': <gensim.models.keyedvectors.Vocab at 0x255027ef668>,\n",
" 'несмотря': <gensim.models.keyedvectors.Vocab at 0x255027ef6a0>,\n",
" 'сорок': <gensim.models.keyedvectors.Vocab at 0x255027ef6d8>,\n",
" 'лет': <gensim.models.keyedvectors.Vocab at 0x255027ef710>,\n",
" 'была': <gensim.models.keyedvectors.Vocab at 0x255027ef748>,\n",
" 'оживления': <gensim.models.keyedvectors.Vocab at 0x255027ef780>,\n",
" 'Быть': <gensim.models.keyedvectors.Vocab at 0x255027ef7b8>,\n",
" 'сделалось': <gensim.models.keyedvectors.Vocab at 0x255027ef7f0>,\n",
" 'положением': <gensim.models.keyedvectors.Vocab at 0x255027ef828>,\n",
" 'иногда': <gensim.models.keyedvectors.Vocab at 0x255027ef860>,\n",
" 'того': <gensim.models.keyedvectors.Vocab at 0x255027ef898>,\n",
" 'хотелось': <gensim.models.keyedvectors.Vocab at 0x255027ef8d0>,\n",
" 'обмануть': <gensim.models.keyedvectors.Vocab at 0x255027ef908>,\n",
" 'людей': <gensim.models.keyedvectors.Vocab at 0x255027ef940>,\n",
" 'знавших': <gensim.models.keyedvectors.Vocab at 0x255027ef978>,\n",
" 'улыбка': <gensim.models.keyedvectors.Vocab at 0x255027ef9b0>,\n",
" 'постоянно': <gensim.models.keyedvectors.Vocab at 0x255027ef9e8>,\n",
" 'лице': <gensim.models.keyedvectors.Vocab at 0x255027efa20>,\n",
" 'Анны': <gensim.models.keyedvectors.Vocab at 0x255027efa58>,\n",
" 'Павловны': <gensim.models.keyedvectors.Vocab at 0x255027efa90>,\n",
" 'хотя': <gensim.models.keyedvectors.Vocab at 0x255027efac8>,\n",
" 'шла': <gensim.models.keyedvectors.Vocab at 0x255027efb00>,\n",
" 'выражала': <gensim.models.keyedvectors.Vocab at 0x255027efb38>,\n",
" 'детей': <gensim.models.keyedvectors.Vocab at 0x255027efb70>,\n",
" 'сознание': <gensim.models.keyedvectors.Vocab at 0x255027efba8>,\n",
" 'своего': <gensim.models.keyedvectors.Vocab at 0x255027efbe0>,\n",
" 'милого': <gensim.models.keyedvectors.Vocab at 0x255027efc18>,\n",
" 'недостатка': <gensim.models.keyedvectors.Vocab at 0x255027efc50>,\n",
" 'от': <gensim.models.keyedvectors.Vocab at 0x255027efc88>,\n",
" 'которого': <gensim.models.keyedvectors.Vocab at 0x255027efcc0>,\n",
" 'хочет': <gensim.models.keyedvectors.Vocab at 0x255027efcf8>,\n",
" 'может': <gensim.models.keyedvectors.Vocab at 0x255027efd30>,\n",
" 'находит': <gensim.models.keyedvectors.Vocab at 0x255027efd68>,\n",
" 'нужным': <gensim.models.keyedvectors.Vocab at 0x255027efda0>,\n",
" 'середине': <gensim.models.keyedvectors.Vocab at 0x255027efdd8>,\n",
" 'разговора': <gensim.models.keyedvectors.Vocab at 0x255027efe10>,\n",
" 'про': <gensim.models.keyedvectors.Vocab at 0x255027efe48>,\n",
" 'политические': <gensim.models.keyedvectors.Vocab at 0x255027efe80>,\n",
" 'действия': <gensim.models.keyedvectors.Vocab at 0x255027efeb8>,\n",
" 'Ах': <gensim.models.keyedvectors.Vocab at 0x255027efef0>,\n",
" 'говорите': <gensim.models.keyedvectors.Vocab at 0x255027eff28>,\n",
" 'мне': <gensim.models.keyedvectors.Vocab at 0x255027eff60>,\n",
" 'Австрию': <gensim.models.keyedvectors.Vocab at 0x255027eff98>,\n",
" 'понимаю': <gensim.models.keyedvectors.Vocab at 0x255027effd0>,\n",
" 'Австрия': <gensim.models.keyedvectors.Vocab at 0x255027f2048>,\n",
" 'никогда': <gensim.models.keyedvectors.Vocab at 0x255027f2080>,\n",
" 'хотела': <gensim.models.keyedvectors.Vocab at 0x255027f20b8>,\n",
" 'войны': <gensim.models.keyedvectors.Vocab at 0x255027f20f0>,\n",
" 'Она': <gensim.models.keyedvectors.Vocab at 0x255027f2128>,\n",
" 'нас': <gensim.models.keyedvectors.Vocab at 0x255027f2160>,\n",
" 'Россия': <gensim.models.keyedvectors.Vocab at 0x255027f2198>,\n",
" 'одна': <gensim.models.keyedvectors.Vocab at 0x255027f21d0>,\n",
" 'должна': <gensim.models.keyedvectors.Vocab at 0x255027f2208>,\n",
" 'Европы': <gensim.models.keyedvectors.Vocab at 0x255027f2240>,\n",
" 'Наш': <gensim.models.keyedvectors.Vocab at 0x255027f2278>,\n",
" 'благодетель': <gensim.models.keyedvectors.Vocab at 0x255027f22b0>,\n",
" 'знает': <gensim.models.keyedvectors.Vocab at 0x255027f22e8>,\n",
" 'свое': <gensim.models.keyedvectors.Vocab at 0x255027f2320>,\n",
" 'высокое': <gensim.models.keyedvectors.Vocab at 0x255027f2358>,\n",
" 'призвание': <gensim.models.keyedvectors.Vocab at 0x255027f2390>,\n",
" 'будет': <gensim.models.keyedvectors.Vocab at 0x255027f23c8>,\n",
" 'верен': <gensim.models.keyedvectors.Vocab at 0x255027f2400>,\n",
" 'ему': <gensim.models.keyedvectors.Vocab at 0x255027f2438>,\n",
" 'Вот': <gensim.models.keyedvectors.Vocab at 0x255027f2470>,\n",
" 'одно': <gensim.models.keyedvectors.Vocab at 0x255027f24a8>,\n",
" 'верю': <gensim.models.keyedvectors.Vocab at 0x255027f24e0>,\n",
" 'государю': <gensim.models.keyedvectors.Vocab at 0x255027f2518>,\n",
" 'предстоит': <gensim.models.keyedvectors.Vocab at 0x255027f2550>,\n",
" 'величайшая': <gensim.models.keyedvectors.Vocab at 0x255027f2588>,\n",
" 'мире': <gensim.models.keyedvectors.Vocab at 0x255027f25c0>,\n",
" 'так': <gensim.models.keyedvectors.Vocab at 0x255027f25f8>,\n",
" 'хорош': <gensim.models.keyedvectors.Vocab at 0x255027f2630>,\n",
" 'Бог': <gensim.models.keyedvectors.Vocab at 0x255027f2668>,\n",
" 'оставит': <gensim.models.keyedvectors.Vocab at 0x255027f26a0>,\n",
" 'его': <gensim.models.keyedvectors.Vocab at 0x255027f26d8>,\n",
" 'исполнит': <gensim.models.keyedvectors.Vocab at 0x255027f2710>,\n",
" 'революции': <gensim.models.keyedvectors.Vocab at 0x255027f2748>,\n",
" 'которая': <gensim.models.keyedvectors.Vocab at 0x255027f2780>,\n",
" 'теперь': <gensim.models.keyedvectors.Vocab at 0x255027f27b8>,\n",
" 'еще': <gensim.models.keyedvectors.Vocab at 0x255027f27f0>,\n",
" 'злодея': <gensim.models.keyedvectors.Vocab at 0x255027f2828>,\n",
" 'Мы': <gensim.models.keyedvectors.Vocab at 0x255027f2860>,\n",
" 'одни': <gensim.models.keyedvectors.Vocab at 0x255027f2898>,\n",
" 'должны': <gensim.models.keyedvectors.Vocab at 0x255027f28d0>,\n",
" 'кровь': <gensim.models.keyedvectors.Vocab at 0x255027f2908>,\n",
" 'На': <gensim.models.keyedvectors.Vocab at 0x255027f2940>,\n",
" 'кого': <gensim.models.keyedvectors.Vocab at 0x255027f2978>,\n",
" 'нам': <gensim.models.keyedvectors.Vocab at 0x255027f29b0>,\n",
" 'надеяться': <gensim.models.keyedvectors.Vocab at 0x255027f29e8>,\n",
" 'спрашиваю': <gensim.models.keyedvectors.Vocab at 0x255027f2a20>,\n",
" 'своим': <gensim.models.keyedvectors.Vocab at 0x255027f2a58>,\n",
" 'духом': <gensim.models.keyedvectors.Vocab at 0x255027f2a90>,\n",
" 'поймет': <gensim.models.keyedvectors.Vocab at 0x255027f2ac8>,\n",
" 'понять': <gensim.models.keyedvectors.Vocab at 0x255027f2b00>,\n",
" 'всю': <gensim.models.keyedvectors.Vocab at 0x255027f2b38>,\n",
" 'высоту': <gensim.models.keyedvectors.Vocab at 0x255027f2b70>,\n",
" 'души': <gensim.models.keyedvectors.Vocab at 0x255027f2ba8>,\n",
" 'императора': <gensim.models.keyedvectors.Vocab at 0x255027f2be0>,\n",
" 'Александра': <gensim.models.keyedvectors.Vocab at 0x255027f2c18>,\n",
" 'отказалась': <gensim.models.keyedvectors.Vocab at 0x255027f2c50>,\n",
" 'очистить': <gensim.models.keyedvectors.Vocab at 0x255027f2c88>,\n",
" 'ищет': <gensim.models.keyedvectors.Vocab at 0x255027f2cc0>,\n",
" 'заднюю': <gensim.models.keyedvectors.Vocab at 0x255027f2cf8>,\n",
" 'мысль': <gensim.models.keyedvectors.Vocab at 0x255027f2d30>,\n",
" 'наших': <gensim.models.keyedvectors.Vocab at 0x255027f2d68>,\n",
" 'действий': <gensim.models.keyedvectors.Vocab at 0x255027f2da0>,\n",
" 'они': <gensim.models.keyedvectors.Vocab at 0x255027f2dd8>,\n",
" 'сказали': <gensim.models.keyedvectors.Vocab at 0x255027f2e10>,\n",
" 'Ничего': <gensim.models.keyedvectors.Vocab at 0x255027f2e48>,\n",
" 'Они': <gensim.models.keyedvectors.Vocab at 0x255027f2e80>,\n",
" 'поняли': <gensim.models.keyedvectors.Vocab at 0x255027f2eb8>,\n",
" 'могут': <gensim.models.keyedvectors.Vocab at 0x255027f2ef0>,\n",
" 'самоотвержения': <gensim.models.keyedvectors.Vocab at 0x255027f2f28>,\n",
" 'нашего': <gensim.models.keyedvectors.Vocab at 0x255027f2f60>,\n",
" 'который': <gensim.models.keyedvectors.Vocab at 0x255027f2f98>,\n",
" 'для': <gensim.models.keyedvectors.Vocab at 0x255027f2fd0>,\n",
" 'всё': <gensim.models.keyedvectors.Vocab at 0x255027f5048>,\n",
" 'блага': <gensim.models.keyedvectors.Vocab at 0x255027f5080>,\n",
" 'мира': <gensim.models.keyedvectors.Vocab at 0x255027f50b8>,\n",
" 'обещали': <gensim.models.keyedvectors.Vocab at 0x255027f50f0>,\n",
" 'Пруссия': <gensim.models.keyedvectors.Vocab at 0x255027f5128>,\n",
" 'уж': <gensim.models.keyedvectors.Vocab at 0x255027f5160>,\n",
" 'объявила': <gensim.models.keyedvectors.Vocab at 0x255027f5198>,\n",
" 'вся': <gensim.models.keyedvectors.Vocab at 0x255027f51d0>,\n",
" 'Европа': <gensim.models.keyedvectors.Vocab at 0x255027f5208>,\n",
" 'против': <gensim.models.keyedvectors.Vocab at 0x255027f5240>,\n",
" 'него': <gensim.models.keyedvectors.Vocab at 0x255027f5278>,\n",
" 'ни': <gensim.models.keyedvectors.Vocab at 0x255027f52b0>,\n",
" 'одном': <gensim.models.keyedvectors.Vocab at 0x255027f52e8>,\n",
" 'слове': <gensim.models.keyedvectors.Vocab at 0x255027f5320>,\n",
" 'Cette': <gensim.models.keyedvectors.Vocab at 0x255027f5358>,\n",
" 'ce': <gensim.models.keyedvectors.Vocab at 0x255027f5390>,\n",
" 'est': <gensim.models.keyedvectors.Vocab at 0x255027f53c8>,\n",
" 'un': <gensim.models.keyedvectors.Vocab at 0x255027f5400>,\n",
" 'Этот': <gensim.models.keyedvectors.Vocab at 0x255027f5438>,\n",
" 'Пруссии': <gensim.models.keyedvectors.Vocab at 0x255027f5470>,\n",
" 'одного': <gensim.models.keyedvectors.Vocab at 0x255027f54a8>,\n",
" 'Бога': <gensim.models.keyedvectors.Vocab at 0x255027f54e0>,\n",
" 'высокую': <gensim.models.keyedvectors.Vocab at 0x255027f5518>,\n",
" 'судьбу': <gensim.models.keyedvectors.Vocab at 0x255027f5550>,\n",
" 'спасет': <gensim.models.keyedvectors.Vocab at 0x255027f5588>,\n",
" 'Европу': <gensim.models.keyedvectors.Vocab at 0x255027f55c0>,\n",
" 'вдруг': <gensim.models.keyedvectors.Vocab at 0x255027f55f8>,\n",
" 'остановилась': <gensim.models.keyedvectors.Vocab at 0x255027f5630>,\n",
" 'улыбкою': <gensim.models.keyedvectors.Vocab at 0x255027f5668>,\n",
" 'насмешки': <gensim.models.keyedvectors.Vocab at 0x255027f56a0>,\n",
" 'над': <gensim.models.keyedvectors.Vocab at 0x255027f56d8>,\n",
" 'своею': <gensim.models.keyedvectors.Vocab at 0x255027f5710>,\n",
" 'горячностью': <gensim.models.keyedvectors.Vocab at 0x255027f5748>,\n",
" 'думаю': <gensim.models.keyedvectors.Vocab at 0x255027f5780>,\n",
" 'улыбаясь': <gensim.models.keyedvectors.Vocab at 0x255027f57b8>,\n",
" 'ежели': <gensim.models.keyedvectors.Vocab at 0x255027f57f0>,\n",
" 'послали': <gensim.models.keyedvectors.Vocab at 0x255027f5828>,\n",
" 'вместо': <gensim.models.keyedvectors.Vocab at 0x255027f5860>,\n",
" 'Винценгероде': <gensim.models.keyedvectors.Vocab at 0x255027f5898>,\n",
" 'взяли': <gensim.models.keyedvectors.Vocab at 0x255027f58d0>,\n",
" 'согласие': <gensim.models.keyedvectors.Vocab at 0x255027f5908>,\n",
" 'прусского': <gensim.models.keyedvectors.Vocab at 0x255027f5940>,\n",
" 'короля': <gensim.models.keyedvectors.Vocab at 0x255027f5978>,\n",
" 'чаю': <gensim.models.keyedvectors.Vocab at 0x255027f59b0>,\n",
" 'Сейчас': <gensim.models.keyedvectors.Vocab at 0x255027f59e8>,\n",
" 'A': <gensim.models.keyedvectors.Vocab at 0x255027f5a20>,\n",
" 'propos': <gensim.models.keyedvectors.Vocab at 0x255027f5a58>,\n",
" 'прибавила': <gensim.models.keyedvectors.Vocab at 0x255027f5a90>,\n",
" 'опять': <gensim.models.keyedvectors.Vocab at 0x255027f5ac8>,\n",
" 'успокоиваясь': <gensim.models.keyedvectors.Vocab at 0x255027f5b00>,\n",
" 'два': <gensim.models.keyedvectors.Vocab at 0x255027f5b38>,\n",
" 'vicomte': <gensim.models.keyedvectors.Vocab at 0x255027f5b70>,\n",
" 'MorteMariet': <gensim.models.keyedvectors.Vocab at 0x255027f5ba8>,\n",
" 'il': <gensim.models.keyedvectors.Vocab at 0x255027f5be0>,\n",
" 'aux': <gensim.models.keyedvectors.Vocab at 0x255027f5c18>,\n",
" 'Кстати': <gensim.models.keyedvectors.Vocab at 0x255027f5c50>,\n",
" 'виконт': <gensim.models.keyedvectors.Vocab at 0x255027f5c88>,\n",
" 'Мортемар': <gensim.models.keyedvectors.Vocab at 0x255027f5cc0>,\n",
" 'чрез': <gensim.models.keyedvectors.Vocab at 0x255027f5cf8>,\n",
" 'лучших': <gensim.models.keyedvectors.Vocab at 0x255027f5d30>,\n",
" 'Франции': <gensim.models.keyedvectors.Vocab at 0x255027f5d68>,\n",
" 'Это': <gensim.models.keyedvectors.Vocab at 0x255027f5da0>,\n",
" 'один': <gensim.models.keyedvectors.Vocab at 0x255027f5dd8>,\n",
" 'хороших': <gensim.models.keyedvectors.Vocab at 0x255027f5e10>,\n",
" 'настоящих': <gensim.models.keyedvectors.Vocab at 0x255027f5e48>,\n",
" 'потом': <gensim.models.keyedvectors.Vocab at 0x255027f5e80>,\n",
" 'l': <gensim.models.keyedvectors.Vocab at 0x255027f5eb8>,\n",
" 'аббат': <gensim.models.keyedvectors.Vocab at 0x255027f5ef0>,\n",
" 'Морио': <gensim.models.keyedvectors.Vocab at 0x255027f5f28>,\n",
" 'этот': <gensim.models.keyedvectors.Vocab at 0x255027f5f60>,\n",
" 'глубокий': <gensim.models.keyedvectors.Vocab at 0x255027f5f98>,\n",
" 'ум': <gensim.models.keyedvectors.Vocab at 0x255027f5fd0>,\n",
" 'принят': <gensim.models.keyedvectors.Vocab at 0x255027f8048>,\n",
" 'государем': <gensim.models.keyedvectors.Vocab at 0x255027f8080>,\n",
" 'рад': <gensim.models.keyedvectors.Vocab at 0x255027f80b8>,\n",
" 'Скажите': <gensim.models.keyedvectors.Vocab at 0x255027f80f0>,\n",
" 'прибавил': <gensim.models.keyedvectors.Vocab at 0x255027f8128>,\n",
" 'будто': <gensim.models.keyedvectors.Vocab at 0x255027f8160>,\n",
" 'вспомнив': <gensim.models.keyedvectors.Vocab at 0x255027f8198>,\n",
" 'особенно': <gensim.models.keyedvectors.Vocab at 0x255027f81d0>,\n",
" 'небрежно': <gensim.models.keyedvectors.Vocab at 0x255027f8208>,\n",
" 'о': <gensim.models.keyedvectors.Vocab at 0x255027f8240>,\n",
" 'чем': <gensim.models.keyedvectors.Vocab at 0x255027f8278>,\n",
" 'спрашивал': <gensim.models.keyedvectors.Vocab at 0x255027f82b0>,\n",
" 'целью': <gensim.models.keyedvectors.Vocab at 0x255027f82e8>,\n",
" 'посещения': <gensim.models.keyedvectors.Vocab at 0x255027f8320>,\n",
" 'правда': <gensim.models.keyedvectors.Vocab at 0x255027f8358>,\n",
" 'imperatrice': <gensim.models.keyedvectors.Vocab at 0x255027f8390>,\n",
" 'mere': <gensim.models.keyedvectors.Vocab at 0x255027f83c8>,\n",
" 'императрица': <gensim.models.keyedvectors.Vocab at 0x255027f8400>,\n",
" 'мать': <gensim.models.keyedvectors.Vocab at 0x255027f8438>,\n",
" 'желает': <gensim.models.keyedvectors.Vocab at 0x255027f8470>,\n",
" 'назначения': <gensim.models.keyedvectors.Vocab at 0x255027f84a8>,\n",
" 'Функе': <gensim.models.keyedvectors.Vocab at 0x255027f84e0>,\n",
" 'первым': <gensim.models.keyedvectors.Vocab at 0x255027f8518>,\n",
" 'секретарем': <gensim.models.keyedvectors.Vocab at 0x255027f8550>,\n",
" 'Вену': <gensim.models.keyedvectors.Vocab at 0x255027f8588>,\n",
" 'C': <gensim.models.keyedvectors.Vocab at 0x255027f85c0>,\n",
" 'sire': <gensim.models.keyedvectors.Vocab at 0x255027f85f8>,\n",
" 'parait': <gensim.models.keyedvectors.Vocab at 0x255027f8630>,\n",
" 'ничтожная': <gensim.models.keyedvectors.Vocab at 0x255027f8668>,\n",
" 'личность': <gensim.models.keyedvectors.Vocab at 0x255027f86a0>,\n",
" 'желал': <gensim.models.keyedvectors.Vocab at 0x255027f86d8>,\n",
" 'определить': <gensim.models.keyedvectors.Vocab at 0x255027f8710>,\n",
" 'сына': <gensim.models.keyedvectors.Vocab at 0x255027f8748>,\n",
" 'это': <gensim.models.keyedvectors.Vocab at 0x255027f8780>,\n",
" 'место': <gensim.models.keyedvectors.Vocab at 0x255027f87b8>,\n",
" 'которое': <gensim.models.keyedvectors.Vocab at 0x255027f87f0>,\n",
" 'через': <gensim.models.keyedvectors.Vocab at 0x255027f8828>,\n",
" 'императрицу': <gensim.models.keyedvectors.Vocab at 0x255027f8860>,\n",
" 'старались': <gensim.models.keyedvectors.Vocab at 0x255027f8898>,\n",
" 'доставить': <gensim.models.keyedvectors.Vocab at 0x255027f88d0>,\n",
" 'барону': <gensim.models.keyedvectors.Vocab at 0x255027f8908>,\n",
" 'почти': <gensim.models.keyedvectors.Vocab at 0x255027f8940>,\n",
" 'закрыла': <gensim.models.keyedvectors.Vocab at 0x255027f8978>,\n",
" 'глаза': <gensim.models.keyedvectors.Vocab at 0x255027f89b0>,\n",
" 'знак': <gensim.models.keyedvectors.Vocab at 0x255027f89e8>,\n",
" 'кто': <gensim.models.keyedvectors.Vocab at 0x255027f8a20>,\n",
" 'другой': <gensim.models.keyedvectors.Vocab at 0x255027f8a58>,\n",
" 'судить': <gensim.models.keyedvectors.Vocab at 0x255027f8a90>,\n",
" 'угодно': <gensim.models.keyedvectors.Vocab at 0x255027f8ac8>,\n",
" 'нравится': <gensim.models.keyedvectors.Vocab at 0x255027f8b00>,\n",
" 'императрице': <gensim.models.keyedvectors.Vocab at 0x255027f8b38>,\n",
" 'Monsieur': <gensim.models.keyedvectors.Vocab at 0x255027f8b70>,\n",
" 'ete': <gensim.models.keyedvectors.Vocab at 0x255027f8ba8>,\n",
" 'sa': <gensim.models.keyedvectors.Vocab at 0x255027f8be0>,\n",
" 'матери': <gensim.models.keyedvectors.Vocab at 0x255027f8c18>,\n",
" 'сестрою': <gensim.models.keyedvectors.Vocab at 0x255027f8c50>,\n",
" 'грустным': <gensim.models.keyedvectors.Vocab at 0x255027f8c88>,\n",
" 'сухим': <gensim.models.keyedvectors.Vocab at 0x255027f8cc0>,\n",
" 'назвала': <gensim.models.keyedvectors.Vocab at 0x255027f8cf8>,\n",
" 'лицо': <gensim.models.keyedvectors.Vocab at 0x255027f8d30>,\n",
" 'представило': <gensim.models.keyedvectors.Vocab at 0x255027f8d68>,\n",
" 'глубокое': <gensim.models.keyedvectors.Vocab at 0x255027f8da0>,\n",
" 'выражение': <gensim.models.keyedvectors.Vocab at 0x255027f8dd8>,\n",
" 'преданности': <gensim.models.keyedvectors.Vocab at 0x255027f8e10>,\n",
" 'уважения': <gensim.models.keyedvectors.Vocab at 0x255027f8e48>,\n",
" 'грустью': <gensim.models.keyedvectors.Vocab at 0x255027f8e80>,\n",
" 'ней': <gensim.models.keyedvectors.Vocab at 0x255027f8eb8>,\n",
" 'бывало': <gensim.models.keyedvectors.Vocab at 0x255027f8ef0>,\n",
" 'каждый': <gensim.models.keyedvectors.Vocab at 0x255027f8f28>,\n",
" 'раз': <gensim.models.keyedvectors.Vocab at 0x255027f8f60>,\n",
" 'разговоре': <gensim.models.keyedvectors.Vocab at 0x255027f8f98>,\n",
" 'своей': <gensim.models.keyedvectors.Vocab at 0x255027f8fd0>,\n",
" 'высокой': <gensim.models.keyedvectors.Vocab at 0x255027fd048>,\n",
" 'величество': <gensim.models.keyedvectors.Vocab at 0x255027fd080>,\n",
" 'изволила': <gensim.models.keyedvectors.Vocab at 0x255027fd0b8>,\n",
" 'оказать': <gensim.models.keyedvectors.Vocab at 0x255027fd0f0>,\n",
" 'beaucoup': <gensim.models.keyedvectors.Vocab at 0x255027fd128>,\n",
" 'estime': <gensim.models.keyedvectors.Vocab at 0x255027fd160>,\n",
" 'много': <gensim.models.keyedvectors.Vocab at 0x255027fd198>,\n",
" 'взгляд': <gensim.models.keyedvectors.Vocab at 0x255027fd1d0>,\n",
" 'равнодушно': <gensim.models.keyedvectors.Vocab at 0x255027fd208>,\n",
" 'замолк': <gensim.models.keyedvectors.Vocab at 0x255027fd240>,\n",
" 'свойственною': <gensim.models.keyedvectors.Vocab at 0x255027fd278>,\n",
" 'ловкостью': <gensim.models.keyedvectors.Vocab at 0x255027fd2b0>,\n",
" 'быстротою': <gensim.models.keyedvectors.Vocab at 0x255027fd2e8>,\n",
" 'такта': <gensim.models.keyedvectors.Vocab at 0x255027fd320>,\n",
" 'утешить': <gensim.models.keyedvectors.Vocab at 0x255027fd358>,\n",
" 'Mais': <gensim.models.keyedvectors.Vocab at 0x255027fd390>,\n",
" 'votre': <gensim.models.keyedvectors.Vocab at 0x255027fd3c8>,\n",
" 'famille': <gensim.models.keyedvectors.Vocab at 0x255027fd400>,\n",
" 'вашей': <gensim.models.keyedvectors.Vocab at 0x255027fd438>,\n",
" 'семье': <gensim.models.keyedvectors.Vocab at 0x255027fd470>,\n",
" 'ли': <gensim.models.keyedvectors.Vocab at 0x255027fd4a8>,\n",
" 'ваша': <gensim.models.keyedvectors.Vocab at 0x255027fd4e0>,\n",
" 'дочь': <gensim.models.keyedvectors.Vocab at 0x255027fd518>,\n",
" 'тех': <gensim.models.keyedvectors.Vocab at 0x255027fd550>,\n",
" 'пор': <gensim.models.keyedvectors.Vocab at 0x255027fd588>,\n",
" 'выезжает': <gensim.models.keyedvectors.Vocab at 0x255027fd5c0>,\n",
" 'fait': <gensim.models.keyedvectors.Vocab at 0x255027fd5f8>,\n",
" 'monde': <gensim.models.keyedvectors.Vocab at 0x255027fd630>,\n",
" 'trouve': <gensim.models.keyedvectors.Vocab at 0x255027fd668>,\n",
" 'belle': <gensim.models.keyedvectors.Vocab at 0x255027fd6a0>,\n",
" 'comme': <gensim.models.keyedvectors.Vocab at 0x255027fd6d8>,\n",
" 'jour': <gensim.models.keyedvectors.Vocab at 0x255027fd710>,\n",
" 'составляет': <gensim.models.keyedvectors.Vocab at 0x255027fd748>,\n",
" 'восторг': <gensim.models.keyedvectors.Vocab at 0x255027fd780>,\n",
" 'общества': <gensim.models.keyedvectors.Vocab at 0x255027fd7b8>,\n",
" 'Ее': <gensim.models.keyedvectors.Vocab at 0x255027fd7f0>,\n",
" 'день': <gensim.models.keyedvectors.Vocab at 0x255027fd828>,\n",
" 'наклонился': <gensim.models.keyedvectors.Vocab at 0x255027fd860>,\n",
" 'часто': <gensim.models.keyedvectors.Vocab at 0x255027fd898>,\n",
" 'продолжала': <gensim.models.keyedvectors.Vocab at 0x255027fd8d0>,\n",
" 'после': <gensim.models.keyedvectors.Vocab at 0x255027fd908>,\n",
" 'минутного': <gensim.models.keyedvectors.Vocab at 0x255027fd940>,\n",
" 'молчания': <gensim.models.keyedvectors.Vocab at 0x255027fd978>,\n",
" 'подвигаясь': <gensim.models.keyedvectors.Vocab at 0x255027fd9b0>,\n",
" 'князю': <gensim.models.keyedvectors.Vocab at 0x255027fd9e8>,\n",
" 'ласково': <gensim.models.keyedvectors.Vocab at 0x255027fda20>,\n",
" 'выказывая': <gensim.models.keyedvectors.Vocab at 0x255027fda58>,\n",
" 'этим': <gensim.models.keyedvectors.Vocab at 0x255027fda90>,\n",
" 'светские': <gensim.models.keyedvectors.Vocab at 0x255027fdac8>,\n",
" 'разговоры': <gensim.models.keyedvectors.Vocab at 0x255027fdb00>,\n",
" 'кончены': <gensim.models.keyedvectors.Vocab at 0x255027fdb38>,\n",
" 'начинается': <gensim.models.keyedvectors.Vocab at 0x255027fdb70>,\n",
" 'несправедливо': <gensim.models.keyedvectors.Vocab at 0x255027fdba8>,\n",
" 'счастие': <gensim.models.keyedvectors.Vocab at 0x255027fdbe0>,\n",
" 'жизни': <gensim.models.keyedvectors.Vocab at 0x255027fdc18>,\n",
" 'За': <gensim.models.keyedvectors.Vocab at 0x255027fdc50>,\n",
" 'судьба': <gensim.models.keyedvectors.Vocab at 0x255027fdc88>,\n",
" 'дала': <gensim.models.keyedvectors.Vocab at 0x255027fdcc0>,\n",
" 'таких': <gensim.models.keyedvectors.Vocab at 0x255027fdcf8>,\n",
" 'двух': <gensim.models.keyedvectors.Vocab at 0x255027fdd30>,\n",
" 'исключая': <gensim.models.keyedvectors.Vocab at 0x255027fdd68>,\n",
" 'Анатоля': <gensim.models.keyedvectors.Vocab at 0x255027fdda0>,\n",
" 'вашего': <gensim.models.keyedvectors.Vocab at 0x255027fddd8>,\n",
" 'меньшого': <gensim.models.keyedvectors.Vocab at 0x255027fde10>,\n",
" 'люблю': <gensim.models.keyedvectors.Vocab at 0x255027fde48>,\n",
" 'приподняв': <gensim.models.keyedvectors.Vocab at 0x255027fde80>,\n",
" 'брови': <gensim.models.keyedvectors.Vocab at 0x255027fdeb8>,\n",
" 'право': <gensim.models.keyedvectors.Vocab at 0x255027fdef0>,\n",
" 'менее': <gensim.models.keyedvectors.Vocab at 0x255027fdf28>,\n",
" 'их': <gensim.models.keyedvectors.Vocab at 0x255027fdf60>,\n",
" 'потому': <gensim.models.keyedvectors.Vocab at 0x255027fdf98>,\n",
" 'стоите': <gensim.models.keyedvectors.Vocab at 0x255027fdfd0>,\n",
" 'улыбнулась': <gensim.models.keyedvectors.Vocab at 0x25502800048>,\n",
" 'восторженною': <gensim.models.keyedvectors.Vocab at 0x25502800080>,\n",
" 'улыбкой': <gensim.models.keyedvectors.Vocab at 0x255028000b8>,\n",
" 'Que': <gensim.models.keyedvectors.Vocab at 0x255028000f0>,\n",
" 'aurait': <gensim.models.keyedvectors.Vocab at 0x25502800128>,\n",
" 'dit': <gensim.models.keyedvectors.Vocab at 0x25502800160>,\n",
" 'ai': <gensim.models.keyedvectors.Vocab at 0x25502800198>,\n",
" 'Чего': <gensim.models.keyedvectors.Vocab at 0x255028001d0>,\n",
" 'любви': <gensim.models.keyedvectors.Vocab at 0x25502800208>,\n",
" 'Перестаньте': <gensim.models.keyedvectors.Vocab at 0x25502800240>,\n",
" 'шутить': <gensim.models.keyedvectors.Vocab at 0x25502800278>,\n",
" 'серьезно': <gensim.models.keyedvectors.Vocab at 0x255028002b0>,\n",
" 'поговорить': <gensim.models.keyedvectors.Vocab at 0x255028002e8>,\n",
" 'вами': <gensim.models.keyedvectors.Vocab at 0x25502800320>,\n",
" 'Знаете': <gensim.models.keyedvectors.Vocab at 0x25502800358>,\n",
" 'недовольна': <gensim.models.keyedvectors.Vocab at 0x25502800390>,\n",
" 'вашим': <gensim.models.keyedvectors.Vocab at 0x255028003c8>,\n",
" 'сыном': <gensim.models.keyedvectors.Vocab at 0x25502800400>,\n",
" 'Между': <gensim.models.keyedvectors.Vocab at 0x25502800438>,\n",
" 'нами': <gensim.models.keyedvectors.Vocab at 0x25502800470>,\n",
" 'будь': <gensim.models.keyedvectors.Vocab at 0x255028004a8>,\n",
" 'сказано': <gensim.models.keyedvectors.Vocab at 0x255028004e0>,\n",
" 'приняло': <gensim.models.keyedvectors.Vocab at 0x25502800518>,\n",
" 'грустное': <gensim.models.keyedvectors.Vocab at 0x25502800550>,\n",
" 'нем': <gensim.models.keyedvectors.Vocab at 0x25502800588>,\n",
" 'величества': <gensim.models.keyedvectors.Vocab at 0x255028005c0>,\n",
" 'жалеют': <gensim.models.keyedvectors.Vocab at 0x255028005f8>,\n",
" 'молча': <gensim.models.keyedvectors.Vocab at 0x25502800630>,\n",
" 'значительно': <gensim.models.keyedvectors.Vocab at 0x25502800668>,\n",
" 'глядя': <gensim.models.keyedvectors.Vocab at 0x255028006a0>,\n",
" 'ждала': <gensim.models.keyedvectors.Vocab at 0x255028006d8>,\n",
" 'ответа': <gensim.models.keyedvectors.Vocab at 0x25502800710>,\n",
" 'поморщился': <gensim.models.keyedvectors.Vocab at 0x25502800748>,\n",
" 'чтоб': <gensim.models.keyedvectors.Vocab at 0x25502800780>,\n",
" 'делал': <gensim.models.keyedvectors.Vocab at 0x255028007b8>,\n",
" 'наконец': <gensim.models.keyedvectors.Vocab at 0x255028007f0>,\n",
" 'сделал': <gensim.models.keyedvectors.Vocab at 0x25502800828>,\n",
" 'воспитания': <gensim.models.keyedvectors.Vocab at 0x25502800860>,\n",
" 'отец': <gensim.models.keyedvectors.Vocab at 0x25502800898>,\n",
" 'оба': <gensim.models.keyedvectors.Vocab at 0x255028008d0>,\n",
" 'вышли': <gensim.models.keyedvectors.Vocab at 0x25502800908>,\n",
" 'des': <gensim.models.keyedvectors.Vocab at 0x25502800940>,\n",
" 'дураки': <gensim.models.keyedvectors.Vocab at 0x25502800978>,\n",
" 'Ипполит': <gensim.models.keyedvectors.Vocab at 0x255028009b0>,\n",
" 'крайней': <gensim.models.keyedvectors.Vocab at 0x255028009e8>,\n",
" 'мере': <gensim.models.keyedvectors.Vocab at 0x25502800a20>,\n",
" 'дурак': <gensim.models.keyedvectors.Vocab at 0x25502800a58>,\n",
" 'а': <gensim.models.keyedvectors.Vocab at 0x25502800a90>,\n",
" 'Анатоль': <gensim.models.keyedvectors.Vocab at 0x25502800ac8>,\n",
" 'беспокойный': <gensim.models.keyedvectors.Vocab at 0x25502800b00>,\n",
" 'различие': <gensim.models.keyedvectors.Vocab at 0x25502800b38>,\n",
" 'более': <gensim.models.keyedvectors.Vocab at 0x25502800b70>,\n",
" 'неестественно': <gensim.models.keyedvectors.Vocab at 0x25502800ba8>,\n",
" 'обыкновенно': <gensim.models.keyedvectors.Vocab at 0x25502800be0>,\n",
" 'этом': <gensim.models.keyedvectors.Vocab at 0x25502800c18>,\n",
" 'резко': <gensim.models.keyedvectors.Vocab at 0x25502800c50>,\n",
" 'около': <gensim.models.keyedvectors.Vocab at 0x25502800c88>,\n",
" 'рта': <gensim.models.keyedvectors.Vocab at 0x25502800cc0>,\n",
" 'неожиданно': <gensim.models.keyedvectors.Vocab at 0x25502800cf8>,\n",
" 'неприятное': <gensim.models.keyedvectors.Vocab at 0x25502800d30>,\n",
" 'зачем': <gensim.models.keyedvectors.Vocab at 0x25502800d68>,\n",
" 'дети': <gensim.models.keyedvectors.Vocab at 0x25502800da0>,\n",
" 'были': <gensim.models.keyedvectors.Vocab at 0x25502800dd8>,\n",
" 'могла': <gensim.models.keyedvectors.Vocab at 0x25502800e10>,\n",
" 'упрекнуть': <gensim.models.keyedvectors.Vocab at 0x25502800e48>,\n",
" 'задумчиво': <gensim.models.keyedvectors.Vocab at 0x25502800e80>,\n",
" 'поднимая': <gensim.models.keyedvectors.Vocab at 0x25502800eb8>,\n",
" 'suis': <gensim.models.keyedvectors.Vocab at 0x25502800ef0>,\n",
" 'ваш': <gensim.models.keyedvectors.Vocab at 0x25502800f28>,\n",
" 'верный': <gensim.models.keyedvectors.Vocab at 0x25502800f60>,\n",
" 'раб': <gensim.models.keyedvectors.Vocab at 0x25502800f98>,\n",
" 'seule': <gensim.models.keyedvectors.Vocab at 0x25502800fd0>,\n",
" 'puis': <gensim.models.keyedvectors.Vocab at 0x25502803048>,\n",
" 'Мои': <gensim.models.keyedvectors.Vocab at 0x25502803080>,\n",
" 'sont': <gensim.models.keyedvectors.Vocab at 0x255028030b8>,\n",
" 'existence': <gensim.models.keyedvectors.Vocab at 0x255028030f0>,\n",
" 'одним': <gensim.models.keyedvectors.Vocab at 0x25502803128>,\n",
" 'могу': <gensim.models.keyedvectors.Vocab at 0x25502803160>,\n",
" 'признаться': <gensim.models.keyedvectors.Vocab at 0x25502803198>,\n",
" 'моего': <gensim.models.keyedvectors.Vocab at 0x255028031d0>,\n",
" 'существования': <gensim.models.keyedvectors.Vocab at 0x25502803208>,\n",
" 'помолчал': <gensim.models.keyedvectors.Vocab at 0x25502803240>,\n",
" 'выражая': <gensim.models.keyedvectors.Vocab at 0x25502803278>,\n",
" 'жестом': <gensim.models.keyedvectors.Vocab at 0x255028032b0>,\n",
" 'покорность': <gensim.models.keyedvectors.Vocab at 0x255028032e8>,\n",
" 'жестокой': <gensim.models.keyedvectors.Vocab at 0x25502803320>,\n",
" 'судьбе': <gensim.models.keyedvectors.Vocab at 0x25502803358>,\n",
" 'задумалась': <gensim.models.keyedvectors.Vocab at 0x25502803390>,\n",
" 'женить': <gensim.models.keyedvectors.Vocab at 0x255028033c8>,\n",
" 'Говорят': <gensim.models.keyedvectors.Vocab at 0x25502803400>,\n",
" 'старые': <gensim.models.keyedvectors.Vocab at 0x25502803438>,\n",
" 'девицы': <gensim.models.keyedvectors.Vocab at 0x25502803470>,\n",
" 'ont': <gensim.models.keyedvectors.Vocab at 0x255028034a8>,\n",
" 'Marieiages': <gensim.models.keyedvectors.Vocab at 0x255028034e0>,\n",
" 'имеют': <gensim.models.keyedvectors.Vocab at 0x25502803518>,\n",
" 'чувствую': <gensim.models.keyedvectors.Vocab at 0x25502803550>,\n",
" 'собою': <gensim.models.keyedvectors.Vocab at 0x25502803588>,\n",
" 'этой': <gensim.models.keyedvectors.Vocab at 0x255028035c0>,\n",
" 'слабости': <gensim.models.keyedvectors.Vocab at 0x255028035f8>,\n",
" 'petite': <gensim.models.keyedvectors.Vocab at 0x25502803630>,\n",
" 'personne': <gensim.models.keyedvectors.Vocab at 0x25502803668>,\n",
" 'маленькая': <gensim.models.keyedvectors.Vocab at 0x255028036a0>,\n",
" 'особа': <gensim.models.keyedvectors.Vocab at 0x255028036d8>,\n",
" 'несчастлива': <gensim.models.keyedvectors.Vocab at 0x25502803710>,\n",
" 'отцом': <gensim.models.keyedvectors.Vocab at 0x25502803748>,\n",
" 'princesse': <gensim.models.keyedvectors.Vocab at 0x25502803780>,\n",
" 'наша': <gensim.models.keyedvectors.Vocab at 0x255028037b8>,\n",
" 'родственница': <gensim.models.keyedvectors.Vocab at 0x255028037f0>,\n",
" 'княжна': <gensim.models.keyedvectors.Vocab at 0x25502803828>,\n",
" 'Болконская': <gensim.models.keyedvectors.Vocab at 0x25502803860>,\n",
" 'светским': <gensim.models.keyedvectors.Vocab at 0x25502803898>,\n",
" 'людям': <gensim.models.keyedvectors.Vocab at 0x255028038d0>,\n",
" 'быстротой': <gensim.models.keyedvectors.Vocab at 0x25502803908>,\n",
" 'соображения': <gensim.models.keyedvectors.Vocab at 0x25502803940>,\n",
" 'памяти': <gensim.models.keyedvectors.Vocab at 0x25502803978>,\n",
" 'показал': <gensim.models.keyedvectors.Vocab at 0x255028039b0>,\n",
" 'движением': <gensim.models.keyedvectors.Vocab at 0x255028039e8>,\n",
" 'головы': <gensim.models.keyedvectors.Vocab at 0x25502803a20>,\n",
" 'принял': <gensim.models.keyedvectors.Vocab at 0x25502803a58>,\n",
" 'сведения': <gensim.models.keyedvectors.Vocab at 0x25502803a90>,\n",
" 'Нет': <gensim.models.keyedvectors.Vocab at 0x25502803ac8>,\n",
" 'стоит': <gensim.models.keyedvectors.Vocab at 0x25502803b00>,\n",
" '40': <gensim.models.keyedvectors.Vocab at 0x25502803b38>,\n",
" '000': <gensim.models.keyedvectors.Vocab at 0x25502803b70>,\n",
" 'год': <gensim.models.keyedvectors.Vocab at 0x25502803ba8>,\n",
" 'видимо': <gensim.models.keyedvectors.Vocab at 0x25502803be0>,\n",
" 'силах': <gensim.models.keyedvectors.Vocab at 0x25502803c18>,\n",
" 'удерживать': <gensim.models.keyedvectors.Vocab at 0x25502803c50>,\n",
" 'печальный': <gensim.models.keyedvectors.Vocab at 0x25502803c88>,\n",
" 'ход': <gensim.models.keyedvectors.Vocab at 0x25502803cc0>,\n",
" 'своих': <gensim.models.keyedvectors.Vocab at 0x25502803cf8>,\n",
" 'мыслей': <gensim.models.keyedvectors.Vocab at 0x25502803d30>,\n",
" 'пять': <gensim.models.keyedvectors.Vocab at 0x25502803d68>,\n",
" 'пойдет': <gensim.models.keyedvectors.Vocab at 0x25502803da0>,\n",
" 'Voila': <gensim.models.keyedvectors.Vocab at 0x25502803dd8>,\n",
" 'etre': <gensim.models.keyedvectors.Vocab at 0x25502803e10>,\n",
" 'pere': <gensim.models.keyedvectors.Vocab at 0x25502803e48>,\n",
" 'богата': <gensim.models.keyedvectors.Vocab at 0x25502803e80>,\n",
" 'Отец': <gensim.models.keyedvectors.Vocab at 0x25502803eb8>,\n",
" 'богат': <gensim.models.keyedvectors.Vocab at 0x25502803ef0>,\n",
" 'живет': <gensim.models.keyedvectors.Vocab at 0x25502803f28>,\n",
" 'деревне': <gensim.models.keyedvectors.Vocab at 0x25502803f60>,\n",
" 'известный': <gensim.models.keyedvectors.Vocab at 0x25502803f98>,\n",
" 'Болконский': <gensim.models.keyedvectors.Vocab at 0x25502803fd0>,\n",
" 'покойном': <gensim.models.keyedvectors.Vocab at 0x25502807048>,\n",
" 'императоре': <gensim.models.keyedvectors.Vocab at 0x25502807080>,\n",
" 'королем': <gensim.models.keyedvectors.Vocab at 0x255028070b8>,\n",
" 'умный': <gensim.models.keyedvectors.Vocab at 0x255028070f0>,\n",
" 'человек': <gensim.models.keyedvectors.Vocab at 0x25502807128>,\n",
" 'со': <gensim.models.keyedvectors.Vocab at 0x25502807160>,\n",
" 'тяжелый': <gensim.models.keyedvectors.Vocab at 0x25502807198>,\n",
" 'La': <gensim.models.keyedvectors.Vocab at 0x255028071d0>,\n",
" 'malheureuse': <gensim.models.keyedvectors.Vocab at 0x25502807208>,\n",
" 'У': <gensim.models.keyedvectors.Vocab at 0x25502807240>,\n",
" 'брат': <gensim.models.keyedvectors.Vocab at 0x25502807278>,\n",
" 'вот': <gensim.models.keyedvectors.Vocab at 0x255028072b0>,\n",
" 'недавно': <gensim.models.keyedvectors.Vocab at 0x255028072e8>,\n",
" 'женился': <gensim.models.keyedvectors.Vocab at 0x25502807320>,\n",
" 'Lise': <gensim.models.keyedvectors.Vocab at 0x25502807358>,\n",
" 'адъютант': <gensim.models.keyedvectors.Vocab at 0x25502807390>,\n",
" 'Кутузова': <gensim.models.keyedvectors.Vocab at 0x255028073c8>,\n",
" 'Послушайте': <gensim.models.keyedvectors.Vocab at 0x25502807400>,\n",
" 'милая': <gensim.models.keyedvectors.Vocab at 0x25502807438>,\n",
" 'взяв': <gensim.models.keyedvectors.Vocab at 0x25502807470>,\n",
" 'собеседницу': <gensim.models.keyedvectors.Vocab at 0x255028074a8>,\n",
" 'пригибая': <gensim.models.keyedvectors.Vocab at 0x255028074e0>,\n",
" 'почему': <gensim.models.keyedvectors.Vocab at 0x25502807518>,\n",
" 'книзу': <gensim.models.keyedvectors.Vocab at 0x25502807550>,\n",
" 'cette': <gensim.models.keyedvectors.Vocab at 0x25502807588>,\n",
" 'affaire': <gensim.models.keyedvectors.Vocab at 0x255028075c0>,\n",
" 'дело': <gensim.models.keyedvectors.Vocab at 0x255028075f8>,\n",
" 'навсегда': <gensim.models.keyedvectors.Vocab at 0x25502807630>,\n",
" 'jamais': <gensim.models.keyedvectors.Vocab at 0x25502807668>,\n",
" 'староста': <gensim.models.keyedvectors.Vocab at 0x255028076a0>,\n",
" 'm': <gensim.models.keyedvectors.Vocab at 0x255028076d8>,\n",
" 'ecrit': <gensim.models.keyedvectors.Vocab at 0x25502807710>,\n",
" 'пишет': <gensim.models.keyedvectors.Vocab at 0x25502807748>,\n",
" 'мой': <gensim.models.keyedvectors.Vocab at 0x25502807780>,\n",
" 'п': <gensim.models.keyedvectors.Vocab at 0x255028077b8>,\n",
" 'фамилии': <gensim.models.keyedvectors.Vocab at 0x255028077f0>,\n",
" 'Всё': <gensim.models.keyedvectors.Vocab at 0x25502807828>,\n",
" 'нужно': <gensim.models.keyedvectors.Vocab at 0x25502807860>,\n",
" 'свободными': <gensim.models.keyedvectors.Vocab at 0x25502807898>,\n",
" 'движениями': <gensim.models.keyedvectors.Vocab at 0x255028078d0>,\n",
" 'взял': <gensim.models.keyedvectors.Vocab at 0x25502807908>,\n",
" 'поцеловав': <gensim.models.keyedvectors.Vocab at 0x25502807940>,\n",
" 'помахал': <gensim.models.keyedvectors.Vocab at 0x25502807978>,\n",
" 'рукой': <gensim.models.keyedvectors.Vocab at 0x255028079b0>,\n",
" 'сторону': <gensim.models.keyedvectors.Vocab at 0x255028079e8>,\n",
" 'Attendez': <gensim.models.keyedvectors.Vocab at 0x25502807a20>,\n",
" 'Подождите': <gensim.models.keyedvectors.Vocab at 0x25502807a58>,\n",
" 'соображая': <gensim.models.keyedvectors.Vocab at 0x25502807a90>,\n",
" 'поговорю': <gensim.models.keyedvectors.Vocab at 0x25502807ac8>,\n",
" 'femme': <gensim.models.keyedvectors.Vocab at 0x25502807b00>,\n",
" 'du': <gensim.models.keyedvectors.Vocab at 0x25502807b38>,\n",
" 'jeune': <gensim.models.keyedvectors.Vocab at 0x25502807b70>,\n",
" 'Лизой': <gensim.models.keyedvectors.Vocab at 0x25502807ba8>,\n",
" 'женой': <gensim.models.keyedvectors.Vocab at 0x25502807be0>,\n",
" 'молодого': <gensim.models.keyedvectors.Vocab at 0x25502807c18>,\n",
" 'Болконского': <gensim.models.keyedvectors.Vocab at 0x25502807c50>,\n",
" 'Ce': <gensim.models.keyedvectors.Vocab at 0x25502807c88>,\n",
" 'sera': <gensim.models.keyedvectors.Vocab at 0x25502807cc0>,\n",
" 'dans': <gensim.models.keyedvectors.Vocab at 0x25502807cf8>,\n",
" 'ferai': <gensim.models.keyedvectors.Vocab at 0x25502807d30>,\n",
" 'fille': <gensim.models.keyedvectors.Vocab at 0x25502807d68>,\n",
" 'вашем': <gensim.models.keyedvectors.Vocab at 0x25502807da0>,\n",
" 'семействе': <gensim.models.keyedvectors.Vocab at 0x25502807dd8>,\n",
" 'девки': <gensim.models.keyedvectors.Vocab at 0x25502807e10>,\n",
" 'II': <gensim.models.keyedvectors.Vocab at 0x25502807e48>,\n",
" 'начала': <gensim.models.keyedvectors.Vocab at 0x25502807e80>,\n",
" 'понемногу': <gensim.models.keyedvectors.Vocab at 0x25502807eb8>,\n",
" 'высшая': <gensim.models.keyedvectors.Vocab at 0x25502807ef0>,\n",
" 'знать': <gensim.models.keyedvectors.Vocab at 0x25502807f28>,\n",
" 'Петербурга': <gensim.models.keyedvectors.Vocab at 0x25502807f60>,\n",
" 'люди': <gensim.models.keyedvectors.Vocab at 0x25502807f98>,\n",
" 'самые': <gensim.models.keyedvectors.Vocab at 0x25502807fd0>,\n",
" 'обществу': <gensim.models.keyedvectors.Vocab at 0x2550280a048>,\n",
" 'каком': <gensim.models.keyedvectors.Vocab at 0x2550280a080>,\n",
" 'жили': <gensim.models.keyedvectors.Vocab at 0x2550280a0b8>,\n",
" 'приехала': <gensim.models.keyedvectors.Vocab at 0x2550280a0f0>,\n",
" 'красавица': <gensim.models.keyedvectors.Vocab at 0x2550280a128>,\n",
" 'Элен': <gensim.models.keyedvectors.Vocab at 0x2550280a160>,\n",
" 'ним': <gensim.models.keyedvectors.Vocab at 0x2550280a198>,\n",
" 'вместе': <gensim.models.keyedvectors.Vocab at 0x2550280a1d0>,\n",
" 'ехать': <gensim.models.keyedvectors.Vocab at 0x2550280a208>,\n",
" 'платье': <gensim.models.keyedvectors.Vocab at 0x2550280a240>,\n",
" 'plus': <gensim.models.keyedvectors.Vocab at 0x2550280a278>,\n",
" 'Petersbourg': <gensim.models.keyedvectors.Vocab at 0x2550280a2b0>,\n",
" 'самая': <gensim.models.keyedvectors.Vocab at 0x2550280a2e8>,\n",
" 'обворожительная': <gensim.models.keyedvectors.Vocab at 0x2550280a320>,\n",
" 'женщина': <gensim.models.keyedvectors.Vocab at 0x2550280a358>,\n",
" 'Петербурге': <gensim.models.keyedvectors.Vocab at 0x2550280a390>,\n",
" 'молодая': <gensim.models.keyedvectors.Vocab at 0x2550280a3c8>,\n",
" 'княгиня': <gensim.models.keyedvectors.Vocab at 0x2550280a400>,\n",
" 'прошлую': <gensim.models.keyedvectors.Vocab at 0x2550280a438>,\n",
" 'зиму': <gensim.models.keyedvectors.Vocab at 0x2550280a470>,\n",
" 'вышедшая': <gensim.models.keyedvectors.Vocab at 0x2550280a4a8>,\n",
" 'замуж': <gensim.models.keyedvectors.Vocab at 0x2550280a4e0>,\n",
" 'большой': <gensim.models.keyedvectors.Vocab at 0x2550280a518>,\n",
" 'свет': <gensim.models.keyedvectors.Vocab at 0x2550280a550>,\n",
" 'причине': <gensim.models.keyedvectors.Vocab at 0x2550280a588>,\n",
" 'беременности': <gensim.models.keyedvectors.Vocab at 0x2550280a5c0>,\n",
" 'небольшие': <gensim.models.keyedvectors.Vocab at 0x2550280a5f8>,\n",
" 'Приехал': <gensim.models.keyedvectors.Vocab at 0x2550280a630>,\n",
" 'сын': <gensim.models.keyedvectors.Vocab at 0x2550280a668>,\n",
" 'представил': <gensim.models.keyedvectors.Vocab at 0x2550280a6a0>,\n",
" 'приехал': <gensim.models.keyedvectors.Vocab at 0x2550280a6d8>,\n",
" 'многие': <gensim.models.keyedvectors.Vocab at 0x2550280a710>,\n",
" 'другие': <gensim.models.keyedvectors.Vocab at 0x2550280a748>,\n",
" 'видали': <gensim.models.keyedvectors.Vocab at 0x2550280a780>,\n",
" 'знакомы': <gensim.models.keyedvectors.Vocab at 0x2550280a7b8>,\n",
" 'ma': <gensim.models.keyedvectors.Vocab at 0x2550280a7f0>,\n",
" 'tante': <gensim.models.keyedvectors.Vocab at 0x2550280a828>,\n",
" 'моей': <gensim.models.keyedvectors.Vocab at 0x2550280a860>,\n",
" 'гостям': <gensim.models.keyedvectors.Vocab at 0x2550280a898>,\n",
" 'весьма': <gensim.models.keyedvectors.Vocab at 0x2550280a8d0>,\n",
" 'подводила': <gensim.models.keyedvectors.Vocab at 0x2550280a908>,\n",
" 'маленькой': <gensim.models.keyedvectors.Vocab at 0x2550280a940>,\n",
" 'высоких': <gensim.models.keyedvectors.Vocab at 0x2550280a978>,\n",
" 'комнаты': <gensim.models.keyedvectors.Vocab at 0x2550280a9b0>,\n",
" 'скоро': <gensim.models.keyedvectors.Vocab at 0x2550280a9e8>,\n",
" 'стали': <gensim.models.keyedvectors.Vocab at 0x2550280aa20>,\n",
" 'приезжать': <gensim.models.keyedvectors.Vocab at 0x2550280aa58>,\n",
" 'гости': <gensim.models.keyedvectors.Vocab at 0x2550280aa90>,\n",
" 'называла': <gensim.models.keyedvectors.Vocab at 0x2550280aac8>,\n",
" 'имени': <gensim.models.keyedvectors.Vocab at 0x2550280ab00>,\n",
" 'медленно': <gensim.models.keyedvectors.Vocab at 0x2550280ab38>,\n",
" 'переводя': <gensim.models.keyedvectors.Vocab at 0x2550280ab70>,\n",
" 'гостя': <gensim.models.keyedvectors.Vocab at 0x2550280aba8>,\n",
" 'отходила': <gensim.models.keyedvectors.Vocab at 0x2550280abe0>,\n",
" 'Все': <gensim.models.keyedvectors.Vocab at 0x2550280ac18>,\n",
" 'обряд': <gensim.models.keyedvectors.Vocab at 0x2550280ac50>,\n",
" 'никому': <gensim.models.keyedvectors.Vocab at 0x2550280ac88>,\n",
" 'неизвестной': <gensim.models.keyedvectors.Vocab at 0x2550280acc0>,\n",
" 'тетушки': <gensim.models.keyedvectors.Vocab at 0x2550280acf8>,\n",
" 'торжественным': <gensim.models.keyedvectors.Vocab at 0x2550280ad30>,\n",
" 'участием': <gensim.models.keyedvectors.Vocab at 0x2550280ad68>,\n",
" 'следила': <gensim.models.keyedvectors.Vocab at 0x2550280ada0>,\n",
" 'молчаливо': <gensim.models.keyedvectors.Vocab at 0x2550280add8>,\n",
" 'одобряя': <gensim.models.keyedvectors.Vocab at 0x2550280ae10>,\n",
" 'Ma': <gensim.models.keyedvectors.Vocab at 0x2550280ae48>,\n",
" 'каждому': <gensim.models.keyedvectors.Vocab at 0x2550280ae80>,\n",
" 'одних': <gensim.models.keyedvectors.Vocab at 0x2550280aeb8>,\n",
" 'выражениях': <gensim.models.keyedvectors.Vocab at 0x2550280aef0>,\n",
" 'своем': <gensim.models.keyedvectors.Vocab at 0x2550280af28>,\n",
" 'слава': <gensim.models.keyedvectors.Vocab at 0x2550280af60>,\n",
" 'Богу': <gensim.models.keyedvectors.Vocab at 0x2550280af98>,\n",
" 'лучше': <gensim.models.keyedvectors.Vocab at 0x2550280afd0>,\n",
" 'подходившие': <gensim.models.keyedvectors.Vocab at 0x2550280e048>,\n",
" 'поспешности': <gensim.models.keyedvectors.Vocab at 0x2550280e080>,\n",
" 'чувством': <gensim.models.keyedvectors.Vocab at 0x2550280e0b8>,\n",
" 'облегчения': <gensim.models.keyedvectors.Vocab at 0x2550280e0f0>,\n",
" 'тяжелой': <gensim.models.keyedvectors.Vocab at 0x2550280e128>,\n",
" 'обязанности': <gensim.models.keyedvectors.Vocab at 0x2550280e160>,\n",
" 'отходили': <gensim.models.keyedvectors.Vocab at 0x2550280e198>,\n",
" 'старушки': <gensim.models.keyedvectors.Vocab at 0x2550280e1d0>,\n",
" 'разу': <gensim.models.keyedvectors.Vocab at 0x2550280e208>,\n",
" 'подойти': <gensim.models.keyedvectors.Vocab at 0x2550280e240>,\n",
" 'работой': <gensim.models.keyedvectors.Vocab at 0x2550280e278>,\n",
" 'золотом': <gensim.models.keyedvectors.Vocab at 0x2550280e2b0>,\n",
" 'бархатном': <gensim.models.keyedvectors.Vocab at 0x2550280e2e8>,\n",
" 'хорошенькая': <gensim.models.keyedvectors.Vocab at 0x2550280e320>,\n",
" 'чуть': <gensim.models.keyedvectors.Vocab at 0x2550280e358>,\n",
" 'усиками': <gensim.models.keyedvectors.Vocab at 0x2550280e390>,\n",
" 'верхняя': <gensim.models.keyedvectors.Vocab at 0x2550280e3c8>,\n",
" 'губка': <gensim.models.keyedvectors.Vocab at 0x2550280e400>,\n",
" 'тем': <gensim.models.keyedvectors.Vocab at 0x2550280e438>,\n",
" 'открывалась': <gensim.models.keyedvectors.Vocab at 0x2550280e470>,\n",
" 'опускалась': <gensim.models.keyedvectors.Vocab at 0x2550280e4a8>,\n",
" 'нижнюю': <gensim.models.keyedvectors.Vocab at 0x2550280e4e0>,\n",
" 'бывает': <gensim.models.keyedvectors.Vocab at 0x2550280e518>,\n",
" 'вполне': <gensim.models.keyedvectors.Vocab at 0x2550280e550>,\n",
" 'женщин': <gensim.models.keyedvectors.Vocab at 0x2550280e588>,\n",
" 'недостаток': <gensim.models.keyedvectors.Vocab at 0x2550280e5c0>,\n",
" 'губы': <gensim.models.keyedvectors.Vocab at 0x2550280e5f8>,\n",
" 'рот': <gensim.models.keyedvectors.Vocab at 0x2550280e630>,\n",
" 'казались': <gensim.models.keyedvectors.Vocab at 0x2550280e668>,\n",
" 'особенною': <gensim.models.keyedvectors.Vocab at 0x2550280e6a0>,\n",
" 'собственно': <gensim.models.keyedvectors.Vocab at 0x2550280e6d8>,\n",
" 'красотой': <gensim.models.keyedvectors.Vocab at 0x2550280e710>,\n",
" 'Всем': <gensim.models.keyedvectors.Vocab at 0x2550280e748>,\n",
" 'весело': <gensim.models.keyedvectors.Vocab at 0x2550280e780>,\n",
" 'смотреть': <gensim.models.keyedvectors.Vocab at 0x2550280e7b8>,\n",
" 'эту': <gensim.models.keyedvectors.Vocab at 0x2550280e7f0>,\n",
" 'полную': <gensim.models.keyedvectors.Vocab at 0x2550280e828>,\n",
" 'здоровья': <gensim.models.keyedvectors.Vocab at 0x2550280e860>,\n",
" 'хорошенькую': <gensim.models.keyedvectors.Vocab at 0x2550280e898>,\n",
" 'будущую': <gensim.models.keyedvectors.Vocab at 0x2550280e8d0>,\n",
" 'легко': <gensim.models.keyedvectors.Vocab at 0x2550280e908>,\n",
" 'положение': <gensim.models.keyedvectors.Vocab at 0x2550280e940>,\n",
" 'мрачным': <gensim.models.keyedvectors.Vocab at 0x2550280e978>,\n",
" 'молодым': <gensim.models.keyedvectors.Vocab at 0x2550280e9b0>,\n",
" 'смотревшим': <gensim.models.keyedvectors.Vocab at 0x2550280e9e8>,\n",
" 'казалось': <gensim.models.keyedvectors.Vocab at 0x2550280ea20>,\n",
" 'сами': <gensim.models.keyedvectors.Vocab at 0x2550280ea58>,\n",
" 'делаются': <gensim.models.keyedvectors.Vocab at 0x2550280ea90>,\n",
" 'похожи': <gensim.models.keyedvectors.Vocab at 0x2550280eac8>,\n",
" 'поговорив': <gensim.models.keyedvectors.Vocab at 0x2550280eb00>,\n",
" 'времени': <gensim.models.keyedvectors.Vocab at 0x2550280eb38>,\n",
" 'Кто': <gensim.models.keyedvectors.Vocab at 0x2550280eb70>,\n",
" 'видел': <gensim.models.keyedvectors.Vocab at 0x2550280eba8>,\n",
" 'каждом': <gensim.models.keyedvectors.Vocab at 0x2550280ebe0>,\n",
" 'светлую': <gensim.models.keyedvectors.Vocab at 0x2550280ec18>,\n",
" 'блестящие': <gensim.models.keyedvectors.Vocab at 0x2550280ec50>,\n",
" 'белые': <gensim.models.keyedvectors.Vocab at 0x2550280ec88>,\n",
" 'зубы': <gensim.models.keyedvectors.Vocab at 0x2550280ecc0>,\n",
" 'виднелись': <gensim.models.keyedvectors.Vocab at 0x2550280ecf8>,\n",
" 'беспрестанно': <gensim.models.keyedvectors.Vocab at 0x2550280ed30>,\n",
" 'тот': <gensim.models.keyedvectors.Vocab at 0x2550280ed68>,\n",
" 'думал': <gensim.models.keyedvectors.Vocab at 0x2550280eda0>,\n",
" 'любезен': <gensim.models.keyedvectors.Vocab at 0x2550280edd8>,\n",
" 'Маленькая': <gensim.models.keyedvectors.Vocab at 0x2550280ee10>,\n",
" 'переваливаясь': <gensim.models.keyedvectors.Vocab at 0x2550280ee48>,\n",
" 'маленькими': <gensim.models.keyedvectors.Vocab at 0x2550280ee80>,\n",
" 'быстрыми': <gensim.models.keyedvectors.Vocab at 0x2550280eeb8>,\n",
" 'стол': <gensim.models.keyedvectors.Vocab at 0x2550280eef0>,\n",
" 'руке': <gensim.models.keyedvectors.Vocab at 0x2550280ef28>,\n",
" 'оправляя': <gensim.models.keyedvectors.Vocab at 0x2550280ef60>,\n",
" 'села': <gensim.models.keyedvectors.Vocab at 0x2550280ef98>,\n",
" 'диван': <gensim.models.keyedvectors.Vocab at 0x2550280efd0>,\n",
" 'самовара': <gensim.models.keyedvectors.Vocab at 0x25502811048>,\n",
" 'делала': <gensim.models.keyedvectors.Vocab at 0x25502811080>,\n",
" 'part': <gensim.models.keyedvectors.Vocab at 0x255028110b8>,\n",
" 'plaisir': <gensim.models.keyedvectors.Vocab at 0x255028110f0>,\n",
" 'окружавших': <gensim.models.keyedvectors.Vocab at 0x25502811128>,\n",
" 'J': <gensim.models.keyedvectors.Vocab at 0x25502811160>,\n",
" 'apporte': <gensim.models.keyedvectors.Vocab at 0x25502811198>,\n",
" 'ouvrage': <gensim.models.keyedvectors.Vocab at 0x255028111d0>,\n",
" 'захватила': <gensim.models.keyedvectors.Vocab at 0x25502811208>,\n",
" 'работу': <gensim.models.keyedvectors.Vocab at 0x25502811240>,\n",
" ...})"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pw2vec.corpus_count, pw2vec.iter, pw2vec.wv.vocab"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3510277"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pw2vec.train(sentences, \n",
" total_examples=pw2vec.corpus_count,\n",
" epochs = 10)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.manifold import TSNE"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"tsne = TSNE(n_components=2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"all_word_vectors_matrix = pw2vec.wv.syn0\n",
"all_word_vectors_matrix_2d = tsne.fit_transform(all_word_vectors_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"points = pd.DataFrame(\n",
" [\n",
" (word, coords[0], coords[1])\n",
" for word, coords in [\n",
" (word, all_word_vectors_matrix_2d[pw2vec.wv.vocab[word].index])\n",
" for word in pw2vec.wv.vocab\n",
" ]\n",
" ],\n",
" columns=[\"word\", \"x\", \"y\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Лев': <gensim.models.keyedvectors.Vocab at 0x2557992f668>,\n",
" 'Николаевич': <gensim.models.keyedvectors.Vocab at 0x2557992f9b0>,\n",
" 'Толстой': <gensim.models.keyedvectors.Vocab at 0x2557992f748>,\n",
" 'Война': <gensim.models.keyedvectors.Vocab at 0x2557992f198>,\n",
" 'и': <gensim.models.keyedvectors.Vocab at 0x2557992f080>,\n",
" 'мир': <gensim.models.keyedvectors.Vocab at 0x2557992f780>,\n",
" 'Том': <gensim.models.keyedvectors.Vocab at 0x255000c7240>,\n",
" '1': <gensim.models.keyedvectors.Vocab at 0x255000c7828>,\n",
" 'И': <gensim.models.keyedvectors.Vocab at 0x255000c78d0>,\n",
" 'ЧАСТЬ': <gensim.models.keyedvectors.Vocab at 0x255799de438>,\n",
" 'ПЕРВАЯ': <gensim.models.keyedvectors.Vocab at 0x255799de710>,\n",
" 'I': <gensim.models.keyedvectors.Vocab at 0x25577470278>,\n",
" 'Так': <gensim.models.keyedvectors.Vocab at 0x25579a36438>,\n",
" 'говорила': <gensim.models.keyedvectors.Vocab at 0x2557610ef98>,\n",
" 'в': <gensim.models.keyedvectors.Vocab at 0x25579962358>,\n",
" '1805': <gensim.models.keyedvectors.Vocab at 0x25550513908>,\n",
" 'года': <gensim.models.keyedvectors.Vocab at 0x255513bf240>,\n",
" 'известная': <gensim.models.keyedvectors.Vocab at 0x255026e87f0>,\n",
" 'Анна': <gensim.models.keyedvectors.Vocab at 0x255026e8828>,\n",
" 'Павловна': <gensim.models.keyedvectors.Vocab at 0x255026e8860>,\n",
" 'Шерер': <gensim.models.keyedvectors.Vocab at 0x255026e8898>,\n",
" 'фрейлина': <gensim.models.keyedvectors.Vocab at 0x255026e88d0>,\n",
" 'императрицы': <gensim.models.keyedvectors.Vocab at 0x255026e8908>,\n",
" 'Марии': <gensim.models.keyedvectors.Vocab at 0x255026e8940>,\n",
" 'встречая': <gensim.models.keyedvectors.Vocab at 0x255026e8978>,\n",
" 'важного': <gensim.models.keyedvectors.Vocab at 0x255026e89b0>,\n",
" 'князя': <gensim.models.keyedvectors.Vocab at 0x255026e89e8>,\n",
" 'Василия': <gensim.models.keyedvectors.Vocab at 0x255026e8a20>,\n",
" 'первого': <gensim.models.keyedvectors.Vocab at 0x255026e8a58>,\n",
" 'приехавшего': <gensim.models.keyedvectors.Vocab at 0x255026e8a90>,\n",
" 'на': <gensim.models.keyedvectors.Vocab at 0x255026e8ac8>,\n",
" 'ее': <gensim.models.keyedvectors.Vocab at 0x255026e8b00>,\n",
" 'вечер': <gensim.models.keyedvectors.Vocab at 0x255026e8b38>,\n",
" 'кашляла': <gensim.models.keyedvectors.Vocab at 0x255026e8b70>,\n",
" 'несколько': <gensim.models.keyedvectors.Vocab at 0x255026e8ba8>,\n",
" 'дней': <gensim.models.keyedvectors.Vocab at 0x255026e8be0>,\n",
" 'у': <gensim.models.keyedvectors.Vocab at 0x255026e8c18>,\n",
" 'нее': <gensim.models.keyedvectors.Vocab at 0x255026e8c50>,\n",
" 'был': <gensim.models.keyedvectors.Vocab at 0x255026e8c88>,\n",
" 'как': <gensim.models.keyedvectors.Vocab at 0x255026e8cc0>,\n",
" 'она': <gensim.models.keyedvectors.Vocab at 0x255026e8cf8>,\n",
" 'тогда': <gensim.models.keyedvectors.Vocab at 0x255026e8d30>,\n",
" 'новое': <gensim.models.keyedvectors.Vocab at 0x255026e8d68>,\n",
" 'слово': <gensim.models.keyedvectors.Vocab at 0x255026e8da0>,\n",
" 'только': <gensim.models.keyedvectors.Vocab at 0x255026e8dd8>,\n",
" 'редкими': <gensim.models.keyedvectors.Vocab at 0x255026e8e10>,\n",
" 'В': <gensim.models.keyedvectors.Vocab at 0x255026e8e48>,\n",
" 'утром': <gensim.models.keyedvectors.Vocab at 0x255026e8e80>,\n",
" 'с': <gensim.models.keyedvectors.Vocab at 0x255026e8eb8>,\n",
" 'красным': <gensim.models.keyedvectors.Vocab at 0x255026e8ef0>,\n",
" 'лакеем': <gensim.models.keyedvectors.Vocab at 0x255026e8f28>,\n",
" 'было': <gensim.models.keyedvectors.Vocab at 0x255026e8f60>,\n",
" 'написано': <gensim.models.keyedvectors.Vocab at 0x255026e8f98>,\n",
" 'без': <gensim.models.keyedvectors.Vocab at 0x255026e8fd0>,\n",
" 'различия': <gensim.models.keyedvectors.Vocab at 0x255027ea048>,\n",
" 'во': <gensim.models.keyedvectors.Vocab at 0x255027ea080>,\n",
" 'всех': <gensim.models.keyedvectors.Vocab at 0x255027ea0b8>,\n",
" 'Si': <gensim.models.keyedvectors.Vocab at 0x255027ea0f0>,\n",
" 'vous': <gensim.models.keyedvectors.Vocab at 0x255027ea128>,\n",
" 'n': <gensim.models.keyedvectors.Vocab at 0x255027ea160>,\n",
" 'avez': <gensim.models.keyedvectors.Vocab at 0x255027ea198>,\n",
" 'rien': <gensim.models.keyedvectors.Vocab at 0x255027ea1d0>,\n",
" 'de': <gensim.models.keyedvectors.Vocab at 0x255027ea208>,\n",
" 'mieux': <gensim.models.keyedvectors.Vocab at 0x255027ea240>,\n",
" 'a': <gensim.models.keyedvectors.Vocab at 0x255027ea278>,\n",
" 'faire': <gensim.models.keyedvectors.Vocab at 0x255027ea2b0>,\n",
" 'M': <gensim.models.keyedvectors.Vocab at 0x255027ea2e8>,\n",
" 'le': <gensim.models.keyedvectors.Vocab at 0x255027ea320>,\n",
" 'comte': <gensim.models.keyedvectors.Vocab at 0x255027ea358>,\n",
" 'или': <gensim.models.keyedvectors.Vocab at 0x255027ea390>,\n",
" 'mon': <gensim.models.keyedvectors.Vocab at 0x255027ea3c8>,\n",
" 'prince': <gensim.models.keyedvectors.Vocab at 0x255027ea400>,\n",
" 'et': <gensim.models.keyedvectors.Vocab at 0x255027ea438>,\n",
" 'si': <gensim.models.keyedvectors.Vocab at 0x255027ea470>,\n",
" 'la': <gensim.models.keyedvectors.Vocab at 0x255027ea4a8>,\n",
" 'soiree': <gensim.models.keyedvectors.Vocab at 0x255027ea4e0>,\n",
" 'chez': <gensim.models.keyedvectors.Vocab at 0x255027ea518>,\n",
" 'une': <gensim.models.keyedvectors.Vocab at 0x255027ea550>,\n",
" 'pauvre': <gensim.models.keyedvectors.Vocab at 0x255027ea588>,\n",
" 'malade': <gensim.models.keyedvectors.Vocab at 0x255027ea5c0>,\n",
" 'ne': <gensim.models.keyedvectors.Vocab at 0x255027ea5f8>,\n",
" 'pas': <gensim.models.keyedvectors.Vocab at 0x255027ea630>,\n",
" 'trop': <gensim.models.keyedvectors.Vocab at 0x255027ea668>,\n",
" 'je': <gensim.models.keyedvectors.Vocab at 0x255027ea6a0>,\n",
" 'serai': <gensim.models.keyedvectors.Vocab at 0x255027ea6d8>,\n",
" 'voir': <gensim.models.keyedvectors.Vocab at 0x255027ea710>,\n",
" 'moi': <gensim.models.keyedvectors.Vocab at 0x255027ea748>,\n",
" 'entre': <gensim.models.keyedvectors.Vocab at 0x255027ea780>,\n",
" '7': <gensim.models.keyedvectors.Vocab at 0x255027ea7b8>,\n",
" '10': <gensim.models.keyedvectors.Vocab at 0x255027ea7f0>,\n",
" 'heures': <gensim.models.keyedvectors.Vocab at 0x255027ea828>,\n",
" 'Annette': <gensim.models.keyedvectors.Vocab at 0x255027ea860>,\n",
" 'Если': <gensim.models.keyedvectors.Vocab at 0x255027ea898>,\n",
" 'y': <gensim.models.keyedvectors.Vocab at 0x255027ea8d0>,\n",
" 'вас': <gensim.models.keyedvectors.Vocab at 0x255027ea908>,\n",
" 'граф': <gensim.models.keyedvectors.Vocab at 0x255027ea940>,\n",
" 'князь': <gensim.models.keyedvectors.Vocab at 0x255027ea978>,\n",
" 'нет': <gensim.models.keyedvectors.Vocab at 0x255027ea9b0>,\n",
" 'виду': <gensim.models.keyedvectors.Vocab at 0x255027ea9e8>,\n",
" 'ничего': <gensim.models.keyedvectors.Vocab at 0x255027eaa20>,\n",
" 'лучшего': <gensim.models.keyedvectors.Vocab at 0x255027eaa58>,\n",
" 'если': <gensim.models.keyedvectors.Vocab at 0x255027eaa90>,\n",
" 'вечера': <gensim.models.keyedvectors.Vocab at 0x255027eaac8>,\n",
" 'бедной': <gensim.models.keyedvectors.Vocab at 0x255027eab00>,\n",
" 'больной': <gensim.models.keyedvectors.Vocab at 0x255027eab38>,\n",
" 'не': <gensim.models.keyedvectors.Vocab at 0x255027eab70>,\n",
" 'слишком': <gensim.models.keyedvectors.Vocab at 0x255027eaba8>,\n",
" 'то': <gensim.models.keyedvectors.Vocab at 0x255027eabe0>,\n",
" 'я': <gensim.models.keyedvectors.Vocab at 0x255027eac18>,\n",
" 'буду': <gensim.models.keyedvectors.Vocab at 0x255027eac50>,\n",
" 'очень': <gensim.models.keyedvectors.Vocab at 0x255027eac88>,\n",
" 'рада': <gensim.models.keyedvectors.Vocab at 0x255027eacc0>,\n",
" 'видеть': <gensim.models.keyedvectors.Vocab at 0x255027eacf8>,\n",
" 'нынче': <gensim.models.keyedvectors.Vocab at 0x255027ead30>,\n",
" 'себя': <gensim.models.keyedvectors.Vocab at 0x255027ead68>,\n",
" 'между': <gensim.models.keyedvectors.Vocab at 0x255027eada0>,\n",
" 'семью': <gensim.models.keyedvectors.Vocab at 0x255027eadd8>,\n",
" 'десятью': <gensim.models.keyedvectors.Vocab at 0x255027eae10>,\n",
" 'часами': <gensim.models.keyedvectors.Vocab at 0x255027eae48>,\n",
" 'Dieu': <gensim.models.keyedvectors.Vocab at 0x255027eae80>,\n",
" 'quelle': <gensim.models.keyedvectors.Vocab at 0x255027eaeb8>,\n",
" 'О': <gensim.models.keyedvectors.Vocab at 0x255027eaef0>,\n",
" 'какое': <gensim.models.keyedvectors.Vocab at 0x255027eaf28>,\n",
" 'нападение': <gensim.models.keyedvectors.Vocab at 0x255027eaf60>,\n",
" 'отвечал': <gensim.models.keyedvectors.Vocab at 0x255027eaf98>,\n",
" 'нисколько': <gensim.models.keyedvectors.Vocab at 0x255027eafd0>,\n",
" 'такою': <gensim.models.keyedvectors.Vocab at 0x255027eb048>,\n",
" 'вошедший': <gensim.models.keyedvectors.Vocab at 0x255027eb080>,\n",
" 'придворном': <gensim.models.keyedvectors.Vocab at 0x255027eb0b8>,\n",
" 'шитом': <gensim.models.keyedvectors.Vocab at 0x255027eb0f0>,\n",
" 'мундире': <gensim.models.keyedvectors.Vocab at 0x255027eb128>,\n",
" 'чулках': <gensim.models.keyedvectors.Vocab at 0x255027eb160>,\n",
" 'башмаках': <gensim.models.keyedvectors.Vocab at 0x255027eb198>,\n",
" 'при': <gensim.models.keyedvectors.Vocab at 0x255027eb1d0>,\n",
" 'звездах': <gensim.models.keyedvectors.Vocab at 0x255027eb208>,\n",
" 'светлым': <gensim.models.keyedvectors.Vocab at 0x255027eb240>,\n",
" 'выражением': <gensim.models.keyedvectors.Vocab at 0x255027eb278>,\n",
" 'лица': <gensim.models.keyedvectors.Vocab at 0x255027eb2b0>,\n",
" 'Он': <gensim.models.keyedvectors.Vocab at 0x255027eb2e8>,\n",
" 'говорил': <gensim.models.keyedvectors.Vocab at 0x255027eb320>,\n",
" 'том': <gensim.models.keyedvectors.Vocab at 0x255027eb358>,\n",
" 'французском': <gensim.models.keyedvectors.Vocab at 0x255027eb390>,\n",
" 'языке': <gensim.models.keyedvectors.Vocab at 0x255027eb3c8>,\n",
" 'котором': <gensim.models.keyedvectors.Vocab at 0x255027eb400>,\n",
" 'говорили': <gensim.models.keyedvectors.Vocab at 0x255027eb438>,\n",
" 'но': <gensim.models.keyedvectors.Vocab at 0x255027eb470>,\n",
" 'думали': <gensim.models.keyedvectors.Vocab at 0x255027eb4a8>,\n",
" 'наши': <gensim.models.keyedvectors.Vocab at 0x255027eb4e0>,\n",
" 'теми': <gensim.models.keyedvectors.Vocab at 0x255027eb518>,\n",
" 'тихими': <gensim.models.keyedvectors.Vocab at 0x255027eb550>,\n",
" 'интонациями': <gensim.models.keyedvectors.Vocab at 0x255027eb588>,\n",
" 'которые': <gensim.models.keyedvectors.Vocab at 0x255027eb5c0>,\n",
" 'свете': <gensim.models.keyedvectors.Vocab at 0x255027eb5f8>,\n",
" 'дворе': <gensim.models.keyedvectors.Vocab at 0x255027eb630>,\n",
" 'человеку': <gensim.models.keyedvectors.Vocab at 0x255027eb668>,\n",
" 'подошел': <gensim.models.keyedvectors.Vocab at 0x255027eb6a0>,\n",
" 'к': <gensim.models.keyedvectors.Vocab at 0x255027eb6d8>,\n",
" 'Анне': <gensim.models.keyedvectors.Vocab at 0x255027eb710>,\n",
" 'Павловне': <gensim.models.keyedvectors.Vocab at 0x255027eb748>,\n",
" 'поцеловал': <gensim.models.keyedvectors.Vocab at 0x255027eb780>,\n",
" 'руку': <gensim.models.keyedvectors.Vocab at 0x255027eb7b8>,\n",
" 'подставив': <gensim.models.keyedvectors.Vocab at 0x255027eb7f0>,\n",
" 'ей': <gensim.models.keyedvectors.Vocab at 0x255027eb828>,\n",
" 'свою': <gensim.models.keyedvectors.Vocab at 0x255027eb860>,\n",
" 'лысину': <gensim.models.keyedvectors.Vocab at 0x255027eb898>,\n",
" 'покойно': <gensim.models.keyedvectors.Vocab at 0x255027eb8d0>,\n",
" 'уселся': <gensim.models.keyedvectors.Vocab at 0x255027eb908>,\n",
" 'диване': <gensim.models.keyedvectors.Vocab at 0x255027eb940>,\n",
" 'tout': <gensim.models.keyedvectors.Vocab at 0x255027eb978>,\n",
" 'dites': <gensim.models.keyedvectors.Vocab at 0x255027eb9b0>,\n",
" 'comment': <gensim.models.keyedvectors.Vocab at 0x255027eb9e8>,\n",
" 'allez': <gensim.models.keyedvectors.Vocab at 0x255027eba20>,\n",
" 'chere': <gensim.models.keyedvectors.Vocab at 0x255027eba58>,\n",
" 'amie': <gensim.models.keyedvectors.Vocab at 0x255027eba90>,\n",
" 'Прежде': <gensim.models.keyedvectors.Vocab at 0x255027ebac8>,\n",
" 'всего': <gensim.models.keyedvectors.Vocab at 0x255027ebb00>,\n",
" 'скажите': <gensim.models.keyedvectors.Vocab at 0x255027ebb38>,\n",
" 'ваше': <gensim.models.keyedvectors.Vocab at 0x255027ebb70>,\n",
" 'здоровье': <gensim.models.keyedvectors.Vocab at 0x255027ebba8>,\n",
" 'друга': <gensim.models.keyedvectors.Vocab at 0x255027ebbe0>,\n",
" 'сказал': <gensim.models.keyedvectors.Vocab at 0x255027ebc18>,\n",
" 'он': <gensim.models.keyedvectors.Vocab at 0x255027ebc50>,\n",
" 'изменяя': <gensim.models.keyedvectors.Vocab at 0x255027ebc88>,\n",
" 'голоса': <gensim.models.keyedvectors.Vocab at 0x255027ebcc0>,\n",
" 'тоном': <gensim.models.keyedvectors.Vocab at 0x255027ebcf8>,\n",
" 'из': <gensim.models.keyedvectors.Vocab at 0x255027ebd30>,\n",
" 'за': <gensim.models.keyedvectors.Vocab at 0x255027ebd68>,\n",
" 'приличия': <gensim.models.keyedvectors.Vocab at 0x255027ebda0>,\n",
" 'участия': <gensim.models.keyedvectors.Vocab at 0x255027ebdd8>,\n",
" 'равнодушие': <gensim.models.keyedvectors.Vocab at 0x255027ebe10>,\n",
" 'даже': <gensim.models.keyedvectors.Vocab at 0x255027ebe48>,\n",
" 'насмешка': <gensim.models.keyedvectors.Vocab at 0x255027ebe80>,\n",
" 'Как': <gensim.models.keyedvectors.Vocab at 0x255027ebeb8>,\n",
" 'можно': <gensim.models.keyedvectors.Vocab at 0x255027ebef0>,\n",
" 'быть': <gensim.models.keyedvectors.Vocab at 0x255027ebf28>,\n",
" 'когда': <gensim.models.keyedvectors.Vocab at 0x255027ebf60>,\n",
" 'нравственно': <gensim.models.keyedvectors.Vocab at 0x255027ebf98>,\n",
" 'Разве': <gensim.models.keyedvectors.Vocab at 0x255027ebfd0>,\n",
" 'оставаться': <gensim.models.keyedvectors.Vocab at 0x255027ed048>,\n",
" 'наше': <gensim.models.keyedvectors.Vocab at 0x255027ed080>,\n",
" 'время': <gensim.models.keyedvectors.Vocab at 0x255027ed0b8>,\n",
" 'есть': <gensim.models.keyedvectors.Vocab at 0x255027ed0f0>,\n",
" 'человека': <gensim.models.keyedvectors.Vocab at 0x255027ed128>,\n",
" 'чувство': <gensim.models.keyedvectors.Vocab at 0x255027ed160>,\n",
" 'сказала': <gensim.models.keyedvectors.Vocab at 0x255027ed198>,\n",
" 'Вы': <gensim.models.keyedvectors.Vocab at 0x255027ed1d0>,\n",
" 'весь': <gensim.models.keyedvectors.Vocab at 0x255027ed208>,\n",
" 'меня': <gensim.models.keyedvectors.Vocab at 0x255027ed240>,\n",
" 'надеюсь': <gensim.models.keyedvectors.Vocab at 0x255027ed278>,\n",
" 'А': <gensim.models.keyedvectors.Vocab at 0x255027ed2b0>,\n",
" 'праздник': <gensim.models.keyedvectors.Vocab at 0x255027ed2e8>,\n",
" 'английского': <gensim.models.keyedvectors.Vocab at 0x255027ed320>,\n",
" 'посланника': <gensim.models.keyedvectors.Vocab at 0x255027ed358>,\n",
" 'Нынче': <gensim.models.keyedvectors.Vocab at 0x255027ed390>,\n",
" 'Мне': <gensim.models.keyedvectors.Vocab at 0x255027ed3c8>,\n",
" 'надо': <gensim.models.keyedvectors.Vocab at 0x255027ed400>,\n",
" 'показаться': <gensim.models.keyedvectors.Vocab at 0x255027ed438>,\n",
" 'там': <gensim.models.keyedvectors.Vocab at 0x255027ed470>,\n",
" 'Дочь': <gensim.models.keyedvectors.Vocab at 0x255027ed4a8>,\n",
" 'мной': <gensim.models.keyedvectors.Vocab at 0x255027ed4e0>,\n",
" 'Я': <gensim.models.keyedvectors.Vocab at 0x255027ed518>,\n",
" 'думала': <gensim.models.keyedvectors.Vocab at 0x255027ed550>,\n",
" 'что': <gensim.models.keyedvectors.Vocab at 0x255027ed588>,\n",
" 'нынешний': <gensim.models.keyedvectors.Vocab at 0x255027ed5c0>,\n",
" 'Je': <gensim.models.keyedvectors.Vocab at 0x255027ed5f8>,\n",
" 'avoue': <gensim.models.keyedvectors.Vocab at 0x255027ed630>,\n",
" 'que': <gensim.models.keyedvectors.Vocab at 0x255027ed668>,\n",
" 'toutes': <gensim.models.keyedvectors.Vocab at 0x255027ed6a0>,\n",
" 'ces': <gensim.models.keyedvectors.Vocab at 0x255027ed6d8>,\n",
" 'tous': <gensim.models.keyedvectors.Vocab at 0x255027ed710>,\n",
" 'd': <gensim.models.keyedvectors.Vocab at 0x255027ed748>,\n",
" 'devenir': <gensim.models.keyedvectors.Vocab at 0x255027ed780>,\n",
" 'Признаюсь': <gensim.models.keyedvectors.Vocab at 0x255027ed7b8>,\n",
" 'все': <gensim.models.keyedvectors.Vocab at 0x255027ed7f0>,\n",
" 'эти': <gensim.models.keyedvectors.Vocab at 0x255027ed828>,\n",
" 'становятся': <gensim.models.keyedvectors.Vocab at 0x255027ed860>,\n",
" 'Ежели': <gensim.models.keyedvectors.Vocab at 0x255027ed898>,\n",
" 'бы': <gensim.models.keyedvectors.Vocab at 0x255027ed8d0>,\n",
" 'знали': <gensim.models.keyedvectors.Vocab at 0x255027ed908>,\n",
" 'вы': <gensim.models.keyedvectors.Vocab at 0x255027ed940>,\n",
" 'этого': <gensim.models.keyedvectors.Vocab at 0x255027ed978>,\n",
" 'хотите': <gensim.models.keyedvectors.Vocab at 0x255027ed9b0>,\n",
" 'по': <gensim.models.keyedvectors.Vocab at 0x255027ed9e8>,\n",
" 'привычке': <gensim.models.keyedvectors.Vocab at 0x255027eda20>,\n",
" 'часы': <gensim.models.keyedvectors.Vocab at 0x255027eda58>,\n",
" 'говоря': <gensim.models.keyedvectors.Vocab at 0x255027eda90>,\n",
" 'вещи': <gensim.models.keyedvectors.Vocab at 0x255027edac8>,\n",
" 'которым': <gensim.models.keyedvectors.Vocab at 0x255027edb00>,\n",
" 'хотел': <gensim.models.keyedvectors.Vocab at 0x255027edb38>,\n",
" 'чтобы': <gensim.models.keyedvectors.Vocab at 0x255027edb70>,\n",
" 'верили': <gensim.models.keyedvectors.Vocab at 0x255027edba8>,\n",
" 'Ne': <gensim.models.keyedvectors.Vocab at 0x255027edbe0>,\n",
" 'me': <gensim.models.keyedvectors.Vocab at 0x255027edc18>,\n",
" 'Eh': <gensim.models.keyedvectors.Vocab at 0x255027edc50>,\n",
" 'bien': <gensim.models.keyedvectors.Vocab at 0x255027edc88>,\n",
" 'qu': <gensim.models.keyedvectors.Vocab at 0x255027edcc0>,\n",
" 't': <gensim.models.keyedvectors.Vocab at 0x255027edcf8>,\n",
" 'on': <gensim.models.keyedvectors.Vocab at 0x255027edd30>,\n",
" 'decide': <gensim.models.keyedvectors.Vocab at 0x255027edd68>,\n",
" 'par': <gensim.models.keyedvectors.Vocab at 0x255027edda0>,\n",
" 'rapport': <gensim.models.keyedvectors.Vocab at 0x255027eddd8>,\n",
" 'Vous': <gensim.models.keyedvectors.Vocab at 0x255027ede10>,\n",
" 'savez': <gensim.models.keyedvectors.Vocab at 0x255027ede48>,\n",
" 'Не': <gensim.models.keyedvectors.Vocab at 0x255027ede80>,\n",
" 'Ну': <gensim.models.keyedvectors.Vocab at 0x255027edeb8>,\n",
" 'же': <gensim.models.keyedvectors.Vocab at 0x255027edef0>,\n",
" 'решили': <gensim.models.keyedvectors.Vocab at 0x255027edf28>,\n",
" 'случаю': <gensim.models.keyedvectors.Vocab at 0x255027edf60>,\n",
" 'депеши': <gensim.models.keyedvectors.Vocab at 0x255027edf98>,\n",
" 'знаете': <gensim.models.keyedvectors.Vocab at 0x255027edfd0>,\n",
" 'вам': <gensim.models.keyedvectors.Vocab at 0x255027ef048>,\n",
" 'сказать': <gensim.models.keyedvectors.Vocab at 0x255027ef080>,\n",
" 'холодным': <gensim.models.keyedvectors.Vocab at 0x255027ef0b8>,\n",
" 'Qu': <gensim.models.keyedvectors.Vocab at 0x255027ef0f0>,\n",
" 'On': <gensim.models.keyedvectors.Vocab at 0x255027ef128>,\n",
" 'Buonaparte': <gensim.models.keyedvectors.Vocab at 0x255027ef160>,\n",
" 'ses': <gensim.models.keyedvectors.Vocab at 0x255027ef198>,\n",
" 'crois': <gensim.models.keyedvectors.Vocab at 0x255027ef1d0>,\n",
" 'nous': <gensim.models.keyedvectors.Vocab at 0x255027ef208>,\n",
" 'sommes': <gensim.models.keyedvectors.Vocab at 0x255027ef240>,\n",
" 'en': <gensim.models.keyedvectors.Vocab at 0x255027ef278>,\n",
" 'les': <gensim.models.keyedvectors.Vocab at 0x255027ef2b0>,\n",
" 'Что': <gensim.models.keyedvectors.Vocab at 0x255027ef2e8>,\n",
" 'Бонапарте': <gensim.models.keyedvectors.Vocab at 0x255027ef320>,\n",
" 'сжег': <gensim.models.keyedvectors.Vocab at 0x255027ef358>,\n",
" 'свои': <gensim.models.keyedvectors.Vocab at 0x255027ef390>,\n",
" 'мы': <gensim.models.keyedvectors.Vocab at 0x255027ef3c8>,\n",
" 'тоже': <gensim.models.keyedvectors.Vocab at 0x255027ef400>,\n",
" 'кажется': <gensim.models.keyedvectors.Vocab at 0x255027ef438>,\n",
" 'готовы': <gensim.models.keyedvectors.Vocab at 0x255027ef470>,\n",
" 'сжечь': <gensim.models.keyedvectors.Vocab at 0x255027ef4a8>,\n",
" 'Князь': <gensim.models.keyedvectors.Vocab at 0x255027ef4e0>,\n",
" 'Василий': <gensim.models.keyedvectors.Vocab at 0x255027ef518>,\n",
" 'всегда': <gensim.models.keyedvectors.Vocab at 0x255027ef550>,\n",
" 'лениво': <gensim.models.keyedvectors.Vocab at 0x255027ef588>,\n",
" 'говорит': <gensim.models.keyedvectors.Vocab at 0x255027ef5c0>,\n",
" 'роль': <gensim.models.keyedvectors.Vocab at 0x255027ef5f8>,\n",
" 'старой': <gensim.models.keyedvectors.Vocab at 0x255027ef630>,\n",
" 'напротив': <gensim.models.keyedvectors.Vocab at 0x255027ef668>,\n",
" 'несмотря': <gensim.models.keyedvectors.Vocab at 0x255027ef6a0>,\n",
" 'сорок': <gensim.models.keyedvectors.Vocab at 0x255027ef6d8>,\n",
" 'лет': <gensim.models.keyedvectors.Vocab at 0x255027ef710>,\n",
" 'была': <gensim.models.keyedvectors.Vocab at 0x255027ef748>,\n",
" 'оживления': <gensim.models.keyedvectors.Vocab at 0x255027ef780>,\n",
" 'Быть': <gensim.models.keyedvectors.Vocab at 0x255027ef7b8>,\n",
" 'сделалось': <gensim.models.keyedvectors.Vocab at 0x255027ef7f0>,\n",
" 'положением': <gensim.models.keyedvectors.Vocab at 0x255027ef828>,\n",
" 'иногда': <gensim.models.keyedvectors.Vocab at 0x255027ef860>,\n",
" 'того': <gensim.models.keyedvectors.Vocab at 0x255027ef898>,\n",
" 'хотелось': <gensim.models.keyedvectors.Vocab at 0x255027ef8d0>,\n",
" 'обмануть': <gensim.models.keyedvectors.Vocab at 0x255027ef908>,\n",
" 'людей': <gensim.models.keyedvectors.Vocab at 0x255027ef940>,\n",
" 'знавших': <gensim.models.keyedvectors.Vocab at 0x255027ef978>,\n",
" 'улыбка': <gensim.models.keyedvectors.Vocab at 0x255027ef9b0>,\n",
" 'постоянно': <gensim.models.keyedvectors.Vocab at 0x255027ef9e8>,\n",
" 'лице': <gensim.models.keyedvectors.Vocab at 0x255027efa20>,\n",
" 'Анны': <gensim.models.keyedvectors.Vocab at 0x255027efa58>,\n",
" 'Павловны': <gensim.models.keyedvectors.Vocab at 0x255027efa90>,\n",
" 'хотя': <gensim.models.keyedvectors.Vocab at 0x255027efac8>,\n",
" 'шла': <gensim.models.keyedvectors.Vocab at 0x255027efb00>,\n",
" 'выражала': <gensim.models.keyedvectors.Vocab at 0x255027efb38>,\n",
" 'детей': <gensim.models.keyedvectors.Vocab at 0x255027efb70>,\n",
" 'сознание': <gensim.models.keyedvectors.Vocab at 0x255027efba8>,\n",
" 'своего': <gensim.models.keyedvectors.Vocab at 0x255027efbe0>,\n",
" 'милого': <gensim.models.keyedvectors.Vocab at 0x255027efc18>,\n",
" 'недостатка': <gensim.models.keyedvectors.Vocab at 0x255027efc50>,\n",
" 'от': <gensim.models.keyedvectors.Vocab at 0x255027efc88>,\n",
" 'которого': <gensim.models.keyedvectors.Vocab at 0x255027efcc0>,\n",
" 'хочет': <gensim.models.keyedvectors.Vocab at 0x255027efcf8>,\n",
" 'может': <gensim.models.keyedvectors.Vocab at 0x255027efd30>,\n",
" 'находит': <gensim.models.keyedvectors.Vocab at 0x255027efd68>,\n",
" 'нужным': <gensim.models.keyedvectors.Vocab at 0x255027efda0>,\n",
" 'середине': <gensim.models.keyedvectors.Vocab at 0x255027efdd8>,\n",
" 'разговора': <gensim.models.keyedvectors.Vocab at 0x255027efe10>,\n",
" 'про': <gensim.models.keyedvectors.Vocab at 0x255027efe48>,\n",
" 'политические': <gensim.models.keyedvectors.Vocab at 0x255027efe80>,\n",
" 'действия': <gensim.models.keyedvectors.Vocab at 0x255027efeb8>,\n",
" 'Ах': <gensim.models.keyedvectors.Vocab at 0x255027efef0>,\n",
" 'говорите': <gensim.models.keyedvectors.Vocab at 0x255027eff28>,\n",
" 'мне': <gensim.models.keyedvectors.Vocab at 0x255027eff60>,\n",
" 'Австрию': <gensim.models.keyedvectors.Vocab at 0x255027eff98>,\n",
" 'понимаю': <gensim.models.keyedvectors.Vocab at 0x255027effd0>,\n",
" 'Австрия': <gensim.models.keyedvectors.Vocab at 0x255027f2048>,\n",
" 'никогда': <gensim.models.keyedvectors.Vocab at 0x255027f2080>,\n",
" 'хотела': <gensim.models.keyedvectors.Vocab at 0x255027f20b8>,\n",
" 'войны': <gensim.models.keyedvectors.Vocab at 0x255027f20f0>,\n",
" 'Она': <gensim.models.keyedvectors.Vocab at 0x255027f2128>,\n",
" 'нас': <gensim.models.keyedvectors.Vocab at 0x255027f2160>,\n",
" 'Россия': <gensim.models.keyedvectors.Vocab at 0x255027f2198>,\n",
" 'одна': <gensim.models.keyedvectors.Vocab at 0x255027f21d0>,\n",
" 'должна': <gensim.models.keyedvectors.Vocab at 0x255027f2208>,\n",
" 'Европы': <gensim.models.keyedvectors.Vocab at 0x255027f2240>,\n",
" 'Наш': <gensim.models.keyedvectors.Vocab at 0x255027f2278>,\n",
" 'благодетель': <gensim.models.keyedvectors.Vocab at 0x255027f22b0>,\n",
" 'знает': <gensim.models.keyedvectors.Vocab at 0x255027f22e8>,\n",
" 'свое': <gensim.models.keyedvectors.Vocab at 0x255027f2320>,\n",
" 'высокое': <gensim.models.keyedvectors.Vocab at 0x255027f2358>,\n",
" 'призвание': <gensim.models.keyedvectors.Vocab at 0x255027f2390>,\n",
" 'будет': <gensim.models.keyedvectors.Vocab at 0x255027f23c8>,\n",
" 'верен': <gensim.models.keyedvectors.Vocab at 0x255027f2400>,\n",
" 'ему': <gensim.models.keyedvectors.Vocab at 0x255027f2438>,\n",
" 'Вот': <gensim.models.keyedvectors.Vocab at 0x255027f2470>,\n",
" 'одно': <gensim.models.keyedvectors.Vocab at 0x255027f24a8>,\n",
" 'верю': <gensim.models.keyedvectors.Vocab at 0x255027f24e0>,\n",
" 'государю': <gensim.models.keyedvectors.Vocab at 0x255027f2518>,\n",
" 'предстоит': <gensim.models.keyedvectors.Vocab at 0x255027f2550>,\n",
" 'величайшая': <gensim.models.keyedvectors.Vocab at 0x255027f2588>,\n",
" 'мире': <gensim.models.keyedvectors.Vocab at 0x255027f25c0>,\n",
" 'так': <gensim.models.keyedvectors.Vocab at 0x255027f25f8>,\n",
" 'хорош': <gensim.models.keyedvectors.Vocab at 0x255027f2630>,\n",
" 'Бог': <gensim.models.keyedvectors.Vocab at 0x255027f2668>,\n",
" 'оставит': <gensim.models.keyedvectors.Vocab at 0x255027f26a0>,\n",
" 'его': <gensim.models.keyedvectors.Vocab at 0x255027f26d8>,\n",
" 'исполнит': <gensim.models.keyedvectors.Vocab at 0x255027f2710>,\n",
" 'революции': <gensim.models.keyedvectors.Vocab at 0x255027f2748>,\n",
" 'которая': <gensim.models.keyedvectors.Vocab at 0x255027f2780>,\n",
" 'теперь': <gensim.models.keyedvectors.Vocab at 0x255027f27b8>,\n",
" 'еще': <gensim.models.keyedvectors.Vocab at 0x255027f27f0>,\n",
" 'злодея': <gensim.models.keyedvectors.Vocab at 0x255027f2828>,\n",
" 'Мы': <gensim.models.keyedvectors.Vocab at 0x255027f2860>,\n",
" 'одни': <gensim.models.keyedvectors.Vocab at 0x255027f2898>,\n",
" 'должны': <gensim.models.keyedvectors.Vocab at 0x255027f28d0>,\n",
" 'кровь': <gensim.models.keyedvectors.Vocab at 0x255027f2908>,\n",
" 'На': <gensim.models.keyedvectors.Vocab at 0x255027f2940>,\n",
" 'кого': <gensim.models.keyedvectors.Vocab at 0x255027f2978>,\n",
" 'нам': <gensim.models.keyedvectors.Vocab at 0x255027f29b0>,\n",
" 'надеяться': <gensim.models.keyedvectors.Vocab at 0x255027f29e8>,\n",
" 'спрашиваю': <gensim.models.keyedvectors.Vocab at 0x255027f2a20>,\n",
" 'своим': <gensim.models.keyedvectors.Vocab at 0x255027f2a58>,\n",
" 'духом': <gensim.models.keyedvectors.Vocab at 0x255027f2a90>,\n",
" 'поймет': <gensim.models.keyedvectors.Vocab at 0x255027f2ac8>,\n",
" 'понять': <gensim.models.keyedvectors.Vocab at 0x255027f2b00>,\n",
" 'всю': <gensim.models.keyedvectors.Vocab at 0x255027f2b38>,\n",
" 'высоту': <gensim.models.keyedvectors.Vocab at 0x255027f2b70>,\n",
" 'души': <gensim.models.keyedvectors.Vocab at 0x255027f2ba8>,\n",
" 'императора': <gensim.models.keyedvectors.Vocab at 0x255027f2be0>,\n",
" 'Александра': <gensim.models.keyedvectors.Vocab at 0x255027f2c18>,\n",
" 'отказалась': <gensim.models.keyedvectors.Vocab at 0x255027f2c50>,\n",
" 'очистить': <gensim.models.keyedvectors.Vocab at 0x255027f2c88>,\n",
" 'ищет': <gensim.models.keyedvectors.Vocab at 0x255027f2cc0>,\n",
" 'заднюю': <gensim.models.keyedvectors.Vocab at 0x255027f2cf8>,\n",
" 'мысль': <gensim.models.keyedvectors.Vocab at 0x255027f2d30>,\n",
" 'наших': <gensim.models.keyedvectors.Vocab at 0x255027f2d68>,\n",
" 'действий': <gensim.models.keyedvectors.Vocab at 0x255027f2da0>,\n",
" 'они': <gensim.models.keyedvectors.Vocab at 0x255027f2dd8>,\n",
" 'сказали': <gensim.models.keyedvectors.Vocab at 0x255027f2e10>,\n",
" 'Ничего': <gensim.models.keyedvectors.Vocab at 0x255027f2e48>,\n",
" 'Они': <gensim.models.keyedvectors.Vocab at 0x255027f2e80>,\n",
" 'поняли': <gensim.models.keyedvectors.Vocab at 0x255027f2eb8>,\n",
" 'могут': <gensim.models.keyedvectors.Vocab at 0x255027f2ef0>,\n",
" 'самоотвержения': <gensim.models.keyedvectors.Vocab at 0x255027f2f28>,\n",
" 'нашего': <gensim.models.keyedvectors.Vocab at 0x255027f2f60>,\n",
" 'который': <gensim.models.keyedvectors.Vocab at 0x255027f2f98>,\n",
" 'для': <gensim.models.keyedvectors.Vocab at 0x255027f2fd0>,\n",
" 'всё': <gensim.models.keyedvectors.Vocab at 0x255027f5048>,\n",
" 'блага': <gensim.models.keyedvectors.Vocab at 0x255027f5080>,\n",
" 'мира': <gensim.models.keyedvectors.Vocab at 0x255027f50b8>,\n",
" 'обещали': <gensim.models.keyedvectors.Vocab at 0x255027f50f0>,\n",
" 'Пруссия': <gensim.models.keyedvectors.Vocab at 0x255027f5128>,\n",
" 'уж': <gensim.models.keyedvectors.Vocab at 0x255027f5160>,\n",
" 'объявила': <gensim.models.keyedvectors.Vocab at 0x255027f5198>,\n",
" 'вся': <gensim.models.keyedvectors.Vocab at 0x255027f51d0>,\n",
" 'Европа': <gensim.models.keyedvectors.Vocab at 0x255027f5208>,\n",
" 'против': <gensim.models.keyedvectors.Vocab at 0x255027f5240>,\n",
" 'него': <gensim.models.keyedvectors.Vocab at 0x255027f5278>,\n",
" 'ни': <gensim.models.keyedvectors.Vocab at 0x255027f52b0>,\n",
" 'одном': <gensim.models.keyedvectors.Vocab at 0x255027f52e8>,\n",
" 'слове': <gensim.models.keyedvectors.Vocab at 0x255027f5320>,\n",
" 'Cette': <gensim.models.keyedvectors.Vocab at 0x255027f5358>,\n",
" 'ce': <gensim.models.keyedvectors.Vocab at 0x255027f5390>,\n",
" 'est': <gensim.models.keyedvectors.Vocab at 0x255027f53c8>,\n",
" 'un': <gensim.models.keyedvectors.Vocab at 0x255027f5400>,\n",
" 'Этот': <gensim.models.keyedvectors.Vocab at 0x255027f5438>,\n",
" 'Пруссии': <gensim.models.keyedvectors.Vocab at 0x255027f5470>,\n",
" 'одного': <gensim.models.keyedvectors.Vocab at 0x255027f54a8>,\n",
" 'Бога': <gensim.models.keyedvectors.Vocab at 0x255027f54e0>,\n",
" 'высокую': <gensim.models.keyedvectors.Vocab at 0x255027f5518>,\n",
" 'судьбу': <gensim.models.keyedvectors.Vocab at 0x255027f5550>,\n",
" 'спасет': <gensim.models.keyedvectors.Vocab at 0x255027f5588>,\n",
" 'Европу': <gensim.models.keyedvectors.Vocab at 0x255027f55c0>,\n",
" 'вдруг': <gensim.models.keyedvectors.Vocab at 0x255027f55f8>,\n",
" 'остановилась': <gensim.models.keyedvectors.Vocab at 0x255027f5630>,\n",
" 'улыбкою': <gensim.models.keyedvectors.Vocab at 0x255027f5668>,\n",
" 'насмешки': <gensim.models.keyedvectors.Vocab at 0x255027f56a0>,\n",
" 'над': <gensim.models.keyedvectors.Vocab at 0x255027f56d8>,\n",
" 'своею': <gensim.models.keyedvectors.Vocab at 0x255027f5710>,\n",
" 'горячностью': <gensim.models.keyedvectors.Vocab at 0x255027f5748>,\n",
" 'думаю': <gensim.models.keyedvectors.Vocab at 0x255027f5780>,\n",
" 'улыбаясь': <gensim.models.keyedvectors.Vocab at 0x255027f57b8>,\n",
" 'ежели': <gensim.models.keyedvectors.Vocab at 0x255027f57f0>,\n",
" 'послали': <gensim.models.keyedvectors.Vocab at 0x255027f5828>,\n",
" 'вместо': <gensim.models.keyedvectors.Vocab at 0x255027f5860>,\n",
" 'Винценгероде': <gensim.models.keyedvectors.Vocab at 0x255027f5898>,\n",
" 'взяли': <gensim.models.keyedvectors.Vocab at 0x255027f58d0>,\n",
" 'согласие': <gensim.models.keyedvectors.Vocab at 0x255027f5908>,\n",
" 'прусского': <gensim.models.keyedvectors.Vocab at 0x255027f5940>,\n",
" 'короля': <gensim.models.keyedvectors.Vocab at 0x255027f5978>,\n",
" 'чаю': <gensim.models.keyedvectors.Vocab at 0x255027f59b0>,\n",
" 'Сейчас': <gensim.models.keyedvectors.Vocab at 0x255027f59e8>,\n",
" 'A': <gensim.models.keyedvectors.Vocab at 0x255027f5a20>,\n",
" 'propos': <gensim.models.keyedvectors.Vocab at 0x255027f5a58>,\n",
" 'прибавила': <gensim.models.keyedvectors.Vocab at 0x255027f5a90>,\n",
" 'опять': <gensim.models.keyedvectors.Vocab at 0x255027f5ac8>,\n",
" 'успокоиваясь': <gensim.models.keyedvectors.Vocab at 0x255027f5b00>,\n",
" 'два': <gensim.models.keyedvectors.Vocab at 0x255027f5b38>,\n",
" 'vicomte': <gensim.models.keyedvectors.Vocab at 0x255027f5b70>,\n",
" 'MorteMariet': <gensim.models.keyedvectors.Vocab at 0x255027f5ba8>,\n",
" 'il': <gensim.models.keyedvectors.Vocab at 0x255027f5be0>,\n",
" 'aux': <gensim.models.keyedvectors.Vocab at 0x255027f5c18>,\n",
" 'Кстати': <gensim.models.keyedvectors.Vocab at 0x255027f5c50>,\n",
" 'виконт': <gensim.models.keyedvectors.Vocab at 0x255027f5c88>,\n",
" 'Мортемар': <gensim.models.keyedvectors.Vocab at 0x255027f5cc0>,\n",
" 'чрез': <gensim.models.keyedvectors.Vocab at 0x255027f5cf8>,\n",
" 'лучших': <gensim.models.keyedvectors.Vocab at 0x255027f5d30>,\n",
" 'Франции': <gensim.models.keyedvectors.Vocab at 0x255027f5d68>,\n",
" 'Это': <gensim.models.keyedvectors.Vocab at 0x255027f5da0>,\n",
" 'один': <gensim.models.keyedvectors.Vocab at 0x255027f5dd8>,\n",
" 'хороших': <gensim.models.keyedvectors.Vocab at 0x255027f5e10>,\n",
" 'настоящих': <gensim.models.keyedvectors.Vocab at 0x255027f5e48>,\n",
" 'потом': <gensim.models.keyedvectors.Vocab at 0x255027f5e80>,\n",
" 'l': <gensim.models.keyedvectors.Vocab at 0x255027f5eb8>,\n",
" 'аббат': <gensim.models.keyedvectors.Vocab at 0x255027f5ef0>,\n",
" 'Морио': <gensim.models.keyedvectors.Vocab at 0x255027f5f28>,\n",
" 'этот': <gensim.models.keyedvectors.Vocab at 0x255027f5f60>,\n",
" 'глубокий': <gensim.models.keyedvectors.Vocab at 0x255027f5f98>,\n",
" 'ум': <gensim.models.keyedvectors.Vocab at 0x255027f5fd0>,\n",
" 'принят': <gensim.models.keyedvectors.Vocab at 0x255027f8048>,\n",
" 'государем': <gensim.models.keyedvectors.Vocab at 0x255027f8080>,\n",
" 'рад': <gensim.models.keyedvectors.Vocab at 0x255027f80b8>,\n",
" 'Скажите': <gensim.models.keyedvectors.Vocab at 0x255027f80f0>,\n",
" 'прибавил': <gensim.models.keyedvectors.Vocab at 0x255027f8128>,\n",
" 'будто': <gensim.models.keyedvectors.Vocab at 0x255027f8160>,\n",
" 'вспомнив': <gensim.models.keyedvectors.Vocab at 0x255027f8198>,\n",
" 'особенно': <gensim.models.keyedvectors.Vocab at 0x255027f81d0>,\n",
" 'небрежно': <gensim.models.keyedvectors.Vocab at 0x255027f8208>,\n",
" 'о': <gensim.models.keyedvectors.Vocab at 0x255027f8240>,\n",
" 'чем': <gensim.models.keyedvectors.Vocab at 0x255027f8278>,\n",
" 'спрашивал': <gensim.models.keyedvectors.Vocab at 0x255027f82b0>,\n",
" 'целью': <gensim.models.keyedvectors.Vocab at 0x255027f82e8>,\n",
" 'посещения': <gensim.models.keyedvectors.Vocab at 0x255027f8320>,\n",
" 'правда': <gensim.models.keyedvectors.Vocab at 0x255027f8358>,\n",
" 'imperatrice': <gensim.models.keyedvectors.Vocab at 0x255027f8390>,\n",
" 'mere': <gensim.models.keyedvectors.Vocab at 0x255027f83c8>,\n",
" 'императрица': <gensim.models.keyedvectors.Vocab at 0x255027f8400>,\n",
" 'мать': <gensim.models.keyedvectors.Vocab at 0x255027f8438>,\n",
" 'желает': <gensim.models.keyedvectors.Vocab at 0x255027f8470>,\n",
" 'назначения': <gensim.models.keyedvectors.Vocab at 0x255027f84a8>,\n",
" 'Функе': <gensim.models.keyedvectors.Vocab at 0x255027f84e0>,\n",
" 'первым': <gensim.models.keyedvectors.Vocab at 0x255027f8518>,\n",
" 'секретарем': <gensim.models.keyedvectors.Vocab at 0x255027f8550>,\n",
" 'Вену': <gensim.models.keyedvectors.Vocab at 0x255027f8588>,\n",
" 'C': <gensim.models.keyedvectors.Vocab at 0x255027f85c0>,\n",
" 'sire': <gensim.models.keyedvectors.Vocab at 0x255027f85f8>,\n",
" 'parait': <gensim.models.keyedvectors.Vocab at 0x255027f8630>,\n",
" 'ничтожная': <gensim.models.keyedvectors.Vocab at 0x255027f8668>,\n",
" 'личность': <gensim.models.keyedvectors.Vocab at 0x255027f86a0>,\n",
" 'желал': <gensim.models.keyedvectors.Vocab at 0x255027f86d8>,\n",
" 'определить': <gensim.models.keyedvectors.Vocab at 0x255027f8710>,\n",
" 'сына': <gensim.models.keyedvectors.Vocab at 0x255027f8748>,\n",
" 'это': <gensim.models.keyedvectors.Vocab at 0x255027f8780>,\n",
" 'место': <gensim.models.keyedvectors.Vocab at 0x255027f87b8>,\n",
" 'которое': <gensim.models.keyedvectors.Vocab at 0x255027f87f0>,\n",
" 'через': <gensim.models.keyedvectors.Vocab at 0x255027f8828>,\n",
" 'императрицу': <gensim.models.keyedvectors.Vocab at 0x255027f8860>,\n",
" 'старались': <gensim.models.keyedvectors.Vocab at 0x255027f8898>,\n",
" 'доставить': <gensim.models.keyedvectors.Vocab at 0x255027f88d0>,\n",
" 'барону': <gensim.models.keyedvectors.Vocab at 0x255027f8908>,\n",
" 'почти': <gensim.models.keyedvectors.Vocab at 0x255027f8940>,\n",
" 'закрыла': <gensim.models.keyedvectors.Vocab at 0x255027f8978>,\n",
" 'глаза': <gensim.models.keyedvectors.Vocab at 0x255027f89b0>,\n",
" 'знак': <gensim.models.keyedvectors.Vocab at 0x255027f89e8>,\n",
" 'кто': <gensim.models.keyedvectors.Vocab at 0x255027f8a20>,\n",
" 'другой': <gensim.models.keyedvectors.Vocab at 0x255027f8a58>,\n",
" 'судить': <gensim.models.keyedvectors.Vocab at 0x255027f8a90>,\n",
" 'угодно': <gensim.models.keyedvectors.Vocab at 0x255027f8ac8>,\n",
" 'нравится': <gensim.models.keyedvectors.Vocab at 0x255027f8b00>,\n",
" 'императрице': <gensim.models.keyedvectors.Vocab at 0x255027f8b38>,\n",
" 'Monsieur': <gensim.models.keyedvectors.Vocab at 0x255027f8b70>,\n",
" 'ete': <gensim.models.keyedvectors.Vocab at 0x255027f8ba8>,\n",
" 'sa': <gensim.models.keyedvectors.Vocab at 0x255027f8be0>,\n",
" 'матери': <gensim.models.keyedvectors.Vocab at 0x255027f8c18>,\n",
" 'сестрою': <gensim.models.keyedvectors.Vocab at 0x255027f8c50>,\n",
" 'грустным': <gensim.models.keyedvectors.Vocab at 0x255027f8c88>,\n",
" 'сухим': <gensim.models.keyedvectors.Vocab at 0x255027f8cc0>,\n",
" 'назвала': <gensim.models.keyedvectors.Vocab at 0x255027f8cf8>,\n",
" 'лицо': <gensim.models.keyedvectors.Vocab at 0x255027f8d30>,\n",
" 'представило': <gensim.models.keyedvectors.Vocab at 0x255027f8d68>,\n",
" 'глубокое': <gensim.models.keyedvectors.Vocab at 0x255027f8da0>,\n",
" 'выражение': <gensim.models.keyedvectors.Vocab at 0x255027f8dd8>,\n",
" 'преданности': <gensim.models.keyedvectors.Vocab at 0x255027f8e10>,\n",
" 'уважения': <gensim.models.keyedvectors.Vocab at 0x255027f8e48>,\n",
" 'грустью': <gensim.models.keyedvectors.Vocab at 0x255027f8e80>,\n",
" 'ней': <gensim.models.keyedvectors.Vocab at 0x255027f8eb8>,\n",
" 'бывало': <gensim.models.keyedvectors.Vocab at 0x255027f8ef0>,\n",
" 'каждый': <gensim.models.keyedvectors.Vocab at 0x255027f8f28>,\n",
" 'раз': <gensim.models.keyedvectors.Vocab at 0x255027f8f60>,\n",
" 'разговоре': <gensim.models.keyedvectors.Vocab at 0x255027f8f98>,\n",
" 'своей': <gensim.models.keyedvectors.Vocab at 0x255027f8fd0>,\n",
" 'высокой': <gensim.models.keyedvectors.Vocab at 0x255027fd048>,\n",
" 'величество': <gensim.models.keyedvectors.Vocab at 0x255027fd080>,\n",
" 'изволила': <gensim.models.keyedvectors.Vocab at 0x255027fd0b8>,\n",
" 'оказать': <gensim.models.keyedvectors.Vocab at 0x255027fd0f0>,\n",
" 'beaucoup': <gensim.models.keyedvectors.Vocab at 0x255027fd128>,\n",
" 'estime': <gensim.models.keyedvectors.Vocab at 0x255027fd160>,\n",
" 'много': <gensim.models.keyedvectors.Vocab at 0x255027fd198>,\n",
" 'взгляд': <gensim.models.keyedvectors.Vocab at 0x255027fd1d0>,\n",
" 'равнодушно': <gensim.models.keyedvectors.Vocab at 0x255027fd208>,\n",
" 'замолк': <gensim.models.keyedvectors.Vocab at 0x255027fd240>,\n",
" 'свойственною': <gensim.models.keyedvectors.Vocab at 0x255027fd278>,\n",
" 'ловкостью': <gensim.models.keyedvectors.Vocab at 0x255027fd2b0>,\n",
" 'быстротою': <gensim.models.keyedvectors.Vocab at 0x255027fd2e8>,\n",
" 'такта': <gensim.models.keyedvectors.Vocab at 0x255027fd320>,\n",
" 'утешить': <gensim.models.keyedvectors.Vocab at 0x255027fd358>,\n",
" 'Mais': <gensim.models.keyedvectors.Vocab at 0x255027fd390>,\n",
" 'votre': <gensim.models.keyedvectors.Vocab at 0x255027fd3c8>,\n",
" 'famille': <gensim.models.keyedvectors.Vocab at 0x255027fd400>,\n",
" 'вашей': <gensim.models.keyedvectors.Vocab at 0x255027fd438>,\n",
" 'семье': <gensim.models.keyedvectors.Vocab at 0x255027fd470>,\n",
" 'ли': <gensim.models.keyedvectors.Vocab at 0x255027fd4a8>,\n",
" 'ваша': <gensim.models.keyedvectors.Vocab at 0x255027fd4e0>,\n",
" 'дочь': <gensim.models.keyedvectors.Vocab at 0x255027fd518>,\n",
" 'тех': <gensim.models.keyedvectors.Vocab at 0x255027fd550>,\n",
" 'пор': <gensim.models.keyedvectors.Vocab at 0x255027fd588>,\n",
" 'выезжает': <gensim.models.keyedvectors.Vocab at 0x255027fd5c0>,\n",
" 'fait': <gensim.models.keyedvectors.Vocab at 0x255027fd5f8>,\n",
" 'monde': <gensim.models.keyedvectors.Vocab at 0x255027fd630>,\n",
" 'trouve': <gensim.models.keyedvectors.Vocab at 0x255027fd668>,\n",
" 'belle': <gensim.models.keyedvectors.Vocab at 0x255027fd6a0>,\n",
" 'comme': <gensim.models.keyedvectors.Vocab at 0x255027fd6d8>,\n",
" 'jour': <gensim.models.keyedvectors.Vocab at 0x255027fd710>,\n",
" 'составляет': <gensim.models.keyedvectors.Vocab at 0x255027fd748>,\n",
" 'восторг': <gensim.models.keyedvectors.Vocab at 0x255027fd780>,\n",
" 'общества': <gensim.models.keyedvectors.Vocab at 0x255027fd7b8>,\n",
" 'Ее': <gensim.models.keyedvectors.Vocab at 0x255027fd7f0>,\n",
" 'день': <gensim.models.keyedvectors.Vocab at 0x255027fd828>,\n",
" 'наклонился': <gensim.models.keyedvectors.Vocab at 0x255027fd860>,\n",
" 'часто': <gensim.models.keyedvectors.Vocab at 0x255027fd898>,\n",
" 'продолжала': <gensim.models.keyedvectors.Vocab at 0x255027fd8d0>,\n",
" 'после': <gensim.models.keyedvectors.Vocab at 0x255027fd908>,\n",
" 'минутного': <gensim.models.keyedvectors.Vocab at 0x255027fd940>,\n",
" 'молчания': <gensim.models.keyedvectors.Vocab at 0x255027fd978>,\n",
" 'подвигаясь': <gensim.models.keyedvectors.Vocab at 0x255027fd9b0>,\n",
" 'князю': <gensim.models.keyedvectors.Vocab at 0x255027fd9e8>,\n",
" 'ласково': <gensim.models.keyedvectors.Vocab at 0x255027fda20>,\n",
" 'выказывая': <gensim.models.keyedvectors.Vocab at 0x255027fda58>,\n",
" 'этим': <gensim.models.keyedvectors.Vocab at 0x255027fda90>,\n",
" 'светские': <gensim.models.keyedvectors.Vocab at 0x255027fdac8>,\n",
" 'разговоры': <gensim.models.keyedvectors.Vocab at 0x255027fdb00>,\n",
" 'кончены': <gensim.models.keyedvectors.Vocab at 0x255027fdb38>,\n",
" 'начинается': <gensim.models.keyedvectors.Vocab at 0x255027fdb70>,\n",
" 'несправедливо': <gensim.models.keyedvectors.Vocab at 0x255027fdba8>,\n",
" 'счастие': <gensim.models.keyedvectors.Vocab at 0x255027fdbe0>,\n",
" 'жизни': <gensim.models.keyedvectors.Vocab at 0x255027fdc18>,\n",
" 'За': <gensim.models.keyedvectors.Vocab at 0x255027fdc50>,\n",
" 'судьба': <gensim.models.keyedvectors.Vocab at 0x255027fdc88>,\n",
" 'дала': <gensim.models.keyedvectors.Vocab at 0x255027fdcc0>,\n",
" 'таких': <gensim.models.keyedvectors.Vocab at 0x255027fdcf8>,\n",
" 'двух': <gensim.models.keyedvectors.Vocab at 0x255027fdd30>,\n",
" 'исключая': <gensim.models.keyedvectors.Vocab at 0x255027fdd68>,\n",
" 'Анатоля': <gensim.models.keyedvectors.Vocab at 0x255027fdda0>,\n",
" 'вашего': <gensim.models.keyedvectors.Vocab at 0x255027fddd8>,\n",
" 'меньшого': <gensim.models.keyedvectors.Vocab at 0x255027fde10>,\n",
" 'люблю': <gensim.models.keyedvectors.Vocab at 0x255027fde48>,\n",
" 'приподняв': <gensim.models.keyedvectors.Vocab at 0x255027fde80>,\n",
" 'брови': <gensim.models.keyedvectors.Vocab at 0x255027fdeb8>,\n",
" 'право': <gensim.models.keyedvectors.Vocab at 0x255027fdef0>,\n",
" 'менее': <gensim.models.keyedvectors.Vocab at 0x255027fdf28>,\n",
" 'их': <gensim.models.keyedvectors.Vocab at 0x255027fdf60>,\n",
" 'потому': <gensim.models.keyedvectors.Vocab at 0x255027fdf98>,\n",
" 'стоите': <gensim.models.keyedvectors.Vocab at 0x255027fdfd0>,\n",
" 'улыбнулась': <gensim.models.keyedvectors.Vocab at 0x25502800048>,\n",
" 'восторженною': <gensim.models.keyedvectors.Vocab at 0x25502800080>,\n",
" 'улыбкой': <gensim.models.keyedvectors.Vocab at 0x255028000b8>,\n",
" 'Que': <gensim.models.keyedvectors.Vocab at 0x255028000f0>,\n",
" 'aurait': <gensim.models.keyedvectors.Vocab at 0x25502800128>,\n",
" 'dit': <gensim.models.keyedvectors.Vocab at 0x25502800160>,\n",
" 'ai': <gensim.models.keyedvectors.Vocab at 0x25502800198>,\n",
" 'Чего': <gensim.models.keyedvectors.Vocab at 0x255028001d0>,\n",
" 'любви': <gensim.models.keyedvectors.Vocab at 0x25502800208>,\n",
" 'Перестаньте': <gensim.models.keyedvectors.Vocab at 0x25502800240>,\n",
" 'шутить': <gensim.models.keyedvectors.Vocab at 0x25502800278>,\n",
" 'серьезно': <gensim.models.keyedvectors.Vocab at 0x255028002b0>,\n",
" 'поговорить': <gensim.models.keyedvectors.Vocab at 0x255028002e8>,\n",
" 'вами': <gensim.models.keyedvectors.Vocab at 0x25502800320>,\n",
" 'Знаете': <gensim.models.keyedvectors.Vocab at 0x25502800358>,\n",
" 'недовольна': <gensim.models.keyedvectors.Vocab at 0x25502800390>,\n",
" 'вашим': <gensim.models.keyedvectors.Vocab at 0x255028003c8>,\n",
" 'сыном': <gensim.models.keyedvectors.Vocab at 0x25502800400>,\n",
" 'Между': <gensim.models.keyedvectors.Vocab at 0x25502800438>,\n",
" 'нами': <gensim.models.keyedvectors.Vocab at 0x25502800470>,\n",
" 'будь': <gensim.models.keyedvectors.Vocab at 0x255028004a8>,\n",
" 'сказано': <gensim.models.keyedvectors.Vocab at 0x255028004e0>,\n",
" 'приняло': <gensim.models.keyedvectors.Vocab at 0x25502800518>,\n",
" 'грустное': <gensim.models.keyedvectors.Vocab at 0x25502800550>,\n",
" 'нем': <gensim.models.keyedvectors.Vocab at 0x25502800588>,\n",
" 'величества': <gensim.models.keyedvectors.Vocab at 0x255028005c0>,\n",
" 'жалеют': <gensim.models.keyedvectors.Vocab at 0x255028005f8>,\n",
" 'молча': <gensim.models.keyedvectors.Vocab at 0x25502800630>,\n",
" 'значительно': <gensim.models.keyedvectors.Vocab at 0x25502800668>,\n",
" 'глядя': <gensim.models.keyedvectors.Vocab at 0x255028006a0>,\n",
" 'ждала': <gensim.models.keyedvectors.Vocab at 0x255028006d8>,\n",
" 'ответа': <gensim.models.keyedvectors.Vocab at 0x25502800710>,\n",
" 'поморщился': <gensim.models.keyedvectors.Vocab at 0x25502800748>,\n",
" 'чтоб': <gensim.models.keyedvectors.Vocab at 0x25502800780>,\n",
" 'делал': <gensim.models.keyedvectors.Vocab at 0x255028007b8>,\n",
" 'наконец': <gensim.models.keyedvectors.Vocab at 0x255028007f0>,\n",
" 'сделал': <gensim.models.keyedvectors.Vocab at 0x25502800828>,\n",
" 'воспитания': <gensim.models.keyedvectors.Vocab at 0x25502800860>,\n",
" 'отец': <gensim.models.keyedvectors.Vocab at 0x25502800898>,\n",
" 'оба': <gensim.models.keyedvectors.Vocab at 0x255028008d0>,\n",
" 'вышли': <gensim.models.keyedvectors.Vocab at 0x25502800908>,\n",
" 'des': <gensim.models.keyedvectors.Vocab at 0x25502800940>,\n",
" 'дураки': <gensim.models.keyedvectors.Vocab at 0x25502800978>,\n",
" 'Ипполит': <gensim.models.keyedvectors.Vocab at 0x255028009b0>,\n",
" 'крайней': <gensim.models.keyedvectors.Vocab at 0x255028009e8>,\n",
" 'мере': <gensim.models.keyedvectors.Vocab at 0x25502800a20>,\n",
" 'дурак': <gensim.models.keyedvectors.Vocab at 0x25502800a58>,\n",
" 'а': <gensim.models.keyedvectors.Vocab at 0x25502800a90>,\n",
" 'Анатоль': <gensim.models.keyedvectors.Vocab at 0x25502800ac8>,\n",
" 'беспокойный': <gensim.models.keyedvectors.Vocab at 0x25502800b00>,\n",
" 'различие': <gensim.models.keyedvectors.Vocab at 0x25502800b38>,\n",
" 'более': <gensim.models.keyedvectors.Vocab at 0x25502800b70>,\n",
" 'неестественно': <gensim.models.keyedvectors.Vocab at 0x25502800ba8>,\n",
" 'обыкновенно': <gensim.models.keyedvectors.Vocab at 0x25502800be0>,\n",
" 'этом': <gensim.models.keyedvectors.Vocab at 0x25502800c18>,\n",
" 'резко': <gensim.models.keyedvectors.Vocab at 0x25502800c50>,\n",
" 'около': <gensim.models.keyedvectors.Vocab at 0x25502800c88>,\n",
" 'рта': <gensim.models.keyedvectors.Vocab at 0x25502800cc0>,\n",
" 'неожиданно': <gensim.models.keyedvectors.Vocab at 0x25502800cf8>,\n",
" 'неприятное': <gensim.models.keyedvectors.Vocab at 0x25502800d30>,\n",
" 'зачем': <gensim.models.keyedvectors.Vocab at 0x25502800d68>,\n",
" 'дети': <gensim.models.keyedvectors.Vocab at 0x25502800da0>,\n",
" 'были': <gensim.models.keyedvectors.Vocab at 0x25502800dd8>,\n",
" 'могла': <gensim.models.keyedvectors.Vocab at 0x25502800e10>,\n",
" 'упрекнуть': <gensim.models.keyedvectors.Vocab at 0x25502800e48>,\n",
" 'задумчиво': <gensim.models.keyedvectors.Vocab at 0x25502800e80>,\n",
" 'поднимая': <gensim.models.keyedvectors.Vocab at 0x25502800eb8>,\n",
" 'suis': <gensim.models.keyedvectors.Vocab at 0x25502800ef0>,\n",
" 'ваш': <gensim.models.keyedvectors.Vocab at 0x25502800f28>,\n",
" 'верный': <gensim.models.keyedvectors.Vocab at 0x25502800f60>,\n",
" 'раб': <gensim.models.keyedvectors.Vocab at 0x25502800f98>,\n",
" 'seule': <gensim.models.keyedvectors.Vocab at 0x25502800fd0>,\n",
" 'puis': <gensim.models.keyedvectors.Vocab at 0x25502803048>,\n",
" 'Мои': <gensim.models.keyedvectors.Vocab at 0x25502803080>,\n",
" 'sont': <gensim.models.keyedvectors.Vocab at 0x255028030b8>,\n",
" 'existence': <gensim.models.keyedvectors.Vocab at 0x255028030f0>,\n",
" 'одним': <gensim.models.keyedvectors.Vocab at 0x25502803128>,\n",
" 'могу': <gensim.models.keyedvectors.Vocab at 0x25502803160>,\n",
" 'признаться': <gensim.models.keyedvectors.Vocab at 0x25502803198>,\n",
" 'моего': <gensim.models.keyedvectors.Vocab at 0x255028031d0>,\n",
" 'существования': <gensim.models.keyedvectors.Vocab at 0x25502803208>,\n",
" 'помолчал': <gensim.models.keyedvectors.Vocab at 0x25502803240>,\n",
" 'выражая': <gensim.models.keyedvectors.Vocab at 0x25502803278>,\n",
" 'жестом': <gensim.models.keyedvectors.Vocab at 0x255028032b0>,\n",
" 'покорность': <gensim.models.keyedvectors.Vocab at 0x255028032e8>,\n",
" 'жестокой': <gensim.models.keyedvectors.Vocab at 0x25502803320>,\n",
" 'судьбе': <gensim.models.keyedvectors.Vocab at 0x25502803358>,\n",
" 'задумалась': <gensim.models.keyedvectors.Vocab at 0x25502803390>,\n",
" 'женить': <gensim.models.keyedvectors.Vocab at 0x255028033c8>,\n",
" 'Говорят': <gensim.models.keyedvectors.Vocab at 0x25502803400>,\n",
" 'старые': <gensim.models.keyedvectors.Vocab at 0x25502803438>,\n",
" 'девицы': <gensim.models.keyedvectors.Vocab at 0x25502803470>,\n",
" 'ont': <gensim.models.keyedvectors.Vocab at 0x255028034a8>,\n",
" 'Marieiages': <gensim.models.keyedvectors.Vocab at 0x255028034e0>,\n",
" 'имеют': <gensim.models.keyedvectors.Vocab at 0x25502803518>,\n",
" 'чувствую': <gensim.models.keyedvectors.Vocab at 0x25502803550>,\n",
" 'собою': <gensim.models.keyedvectors.Vocab at 0x25502803588>,\n",
" 'этой': <gensim.models.keyedvectors.Vocab at 0x255028035c0>,\n",
" 'слабости': <gensim.models.keyedvectors.Vocab at 0x255028035f8>,\n",
" 'petite': <gensim.models.keyedvectors.Vocab at 0x25502803630>,\n",
" 'personne': <gensim.models.keyedvectors.Vocab at 0x25502803668>,\n",
" 'маленькая': <gensim.models.keyedvectors.Vocab at 0x255028036a0>,\n",
" 'особа': <gensim.models.keyedvectors.Vocab at 0x255028036d8>,\n",
" 'несчастлива': <gensim.models.keyedvectors.Vocab at 0x25502803710>,\n",
" 'отцом': <gensim.models.keyedvectors.Vocab at 0x25502803748>,\n",
" 'princesse': <gensim.models.keyedvectors.Vocab at 0x25502803780>,\n",
" 'наша': <gensim.models.keyedvectors.Vocab at 0x255028037b8>,\n",
" 'родственница': <gensim.models.keyedvectors.Vocab at 0x255028037f0>,\n",
" 'княжна': <gensim.models.keyedvectors.Vocab at 0x25502803828>,\n",
" 'Болконская': <gensim.models.keyedvectors.Vocab at 0x25502803860>,\n",
" 'светским': <gensim.models.keyedvectors.Vocab at 0x25502803898>,\n",
" 'людям': <gensim.models.keyedvectors.Vocab at 0x255028038d0>,\n",
" 'быстротой': <gensim.models.keyedvectors.Vocab at 0x25502803908>,\n",
" 'соображения': <gensim.models.keyedvectors.Vocab at 0x25502803940>,\n",
" 'памяти': <gensim.models.keyedvectors.Vocab at 0x25502803978>,\n",
" 'показал': <gensim.models.keyedvectors.Vocab at 0x255028039b0>,\n",
" 'движением': <gensim.models.keyedvectors.Vocab at 0x255028039e8>,\n",
" 'головы': <gensim.models.keyedvectors.Vocab at 0x25502803a20>,\n",
" 'принял': <gensim.models.keyedvectors.Vocab at 0x25502803a58>,\n",
" 'сведения': <gensim.models.keyedvectors.Vocab at 0x25502803a90>,\n",
" 'Нет': <gensim.models.keyedvectors.Vocab at 0x25502803ac8>,\n",
" 'стоит': <gensim.models.keyedvectors.Vocab at 0x25502803b00>,\n",
" '40': <gensim.models.keyedvectors.Vocab at 0x25502803b38>,\n",
" '000': <gensim.models.keyedvectors.Vocab at 0x25502803b70>,\n",
" 'год': <gensim.models.keyedvectors.Vocab at 0x25502803ba8>,\n",
" 'видимо': <gensim.models.keyedvectors.Vocab at 0x25502803be0>,\n",
" 'силах': <gensim.models.keyedvectors.Vocab at 0x25502803c18>,\n",
" 'удерживать': <gensim.models.keyedvectors.Vocab at 0x25502803c50>,\n",
" 'печальный': <gensim.models.keyedvectors.Vocab at 0x25502803c88>,\n",
" 'ход': <gensim.models.keyedvectors.Vocab at 0x25502803cc0>,\n",
" 'своих': <gensim.models.keyedvectors.Vocab at 0x25502803cf8>,\n",
" 'мыслей': <gensim.models.keyedvectors.Vocab at 0x25502803d30>,\n",
" 'пять': <gensim.models.keyedvectors.Vocab at 0x25502803d68>,\n",
" 'пойдет': <gensim.models.keyedvectors.Vocab at 0x25502803da0>,\n",
" 'Voila': <gensim.models.keyedvectors.Vocab at 0x25502803dd8>,\n",
" 'etre': <gensim.models.keyedvectors.Vocab at 0x25502803e10>,\n",
" 'pere': <gensim.models.keyedvectors.Vocab at 0x25502803e48>,\n",
" 'богата': <gensim.models.keyedvectors.Vocab at 0x25502803e80>,\n",
" 'Отец': <gensim.models.keyedvectors.Vocab at 0x25502803eb8>,\n",
" 'богат': <gensim.models.keyedvectors.Vocab at 0x25502803ef0>,\n",
" 'живет': <gensim.models.keyedvectors.Vocab at 0x25502803f28>,\n",
" 'деревне': <gensim.models.keyedvectors.Vocab at 0x25502803f60>,\n",
" 'известный': <gensim.models.keyedvectors.Vocab at 0x25502803f98>,\n",
" 'Болконский': <gensim.models.keyedvectors.Vocab at 0x25502803fd0>,\n",
" 'покойном': <gensim.models.keyedvectors.Vocab at 0x25502807048>,\n",
" 'императоре': <gensim.models.keyedvectors.Vocab at 0x25502807080>,\n",
" 'королем': <gensim.models.keyedvectors.Vocab at 0x255028070b8>,\n",
" 'умный': <gensim.models.keyedvectors.Vocab at 0x255028070f0>,\n",
" 'человек': <gensim.models.keyedvectors.Vocab at 0x25502807128>,\n",
" 'со': <gensim.models.keyedvectors.Vocab at 0x25502807160>,\n",
" 'тяжелый': <gensim.models.keyedvectors.Vocab at 0x25502807198>,\n",
" 'La': <gensim.models.keyedvectors.Vocab at 0x255028071d0>,\n",
" 'malheureuse': <gensim.models.keyedvectors.Vocab at 0x25502807208>,\n",
" 'У': <gensim.models.keyedvectors.Vocab at 0x25502807240>,\n",
" 'брат': <gensim.models.keyedvectors.Vocab at 0x25502807278>,\n",
" 'вот': <gensim.models.keyedvectors.Vocab at 0x255028072b0>,\n",
" 'недавно': <gensim.models.keyedvectors.Vocab at 0x255028072e8>,\n",
" 'женился': <gensim.models.keyedvectors.Vocab at 0x25502807320>,\n",
" 'Lise': <gensim.models.keyedvectors.Vocab at 0x25502807358>,\n",
" 'адъютант': <gensim.models.keyedvectors.Vocab at 0x25502807390>,\n",
" 'Кутузова': <gensim.models.keyedvectors.Vocab at 0x255028073c8>,\n",
" 'Послушайте': <gensim.models.keyedvectors.Vocab at 0x25502807400>,\n",
" 'милая': <gensim.models.keyedvectors.Vocab at 0x25502807438>,\n",
" 'взяв': <gensim.models.keyedvectors.Vocab at 0x25502807470>,\n",
" 'собеседницу': <gensim.models.keyedvectors.Vocab at 0x255028074a8>,\n",
" 'пригибая': <gensim.models.keyedvectors.Vocab at 0x255028074e0>,\n",
" 'почему': <gensim.models.keyedvectors.Vocab at 0x25502807518>,\n",
" 'книзу': <gensim.models.keyedvectors.Vocab at 0x25502807550>,\n",
" 'cette': <gensim.models.keyedvectors.Vocab at 0x25502807588>,\n",
" 'affaire': <gensim.models.keyedvectors.Vocab at 0x255028075c0>,\n",
" 'дело': <gensim.models.keyedvectors.Vocab at 0x255028075f8>,\n",
" 'навсегда': <gensim.models.keyedvectors.Vocab at 0x25502807630>,\n",
" 'jamais': <gensim.models.keyedvectors.Vocab at 0x25502807668>,\n",
" 'староста': <gensim.models.keyedvectors.Vocab at 0x255028076a0>,\n",
" 'm': <gensim.models.keyedvectors.Vocab at 0x255028076d8>,\n",
" 'ecrit': <gensim.models.keyedvectors.Vocab at 0x25502807710>,\n",
" 'пишет': <gensim.models.keyedvectors.Vocab at 0x25502807748>,\n",
" 'мой': <gensim.models.keyedvectors.Vocab at 0x25502807780>,\n",
" 'п': <gensim.models.keyedvectors.Vocab at 0x255028077b8>,\n",
" 'фамилии': <gensim.models.keyedvectors.Vocab at 0x255028077f0>,\n",
" 'Всё': <gensim.models.keyedvectors.Vocab at 0x25502807828>,\n",
" 'нужно': <gensim.models.keyedvectors.Vocab at 0x25502807860>,\n",
" 'свободными': <gensim.models.keyedvectors.Vocab at 0x25502807898>,\n",
" 'движениями': <gensim.models.keyedvectors.Vocab at 0x255028078d0>,\n",
" 'взял': <gensim.models.keyedvectors.Vocab at 0x25502807908>,\n",
" 'поцеловав': <gensim.models.keyedvectors.Vocab at 0x25502807940>,\n",
" 'помахал': <gensim.models.keyedvectors.Vocab at 0x25502807978>,\n",
" 'рукой': <gensim.models.keyedvectors.Vocab at 0x255028079b0>,\n",
" 'сторону': <gensim.models.keyedvectors.Vocab at 0x255028079e8>,\n",
" 'Attendez': <gensim.models.keyedvectors.Vocab at 0x25502807a20>,\n",
" 'Подождите': <gensim.models.keyedvectors.Vocab at 0x25502807a58>,\n",
" 'соображая': <gensim.models.keyedvectors.Vocab at 0x25502807a90>,\n",
" 'поговорю': <gensim.models.keyedvectors.Vocab at 0x25502807ac8>,\n",
" 'femme': <gensim.models.keyedvectors.Vocab at 0x25502807b00>,\n",
" 'du': <gensim.models.keyedvectors.Vocab at 0x25502807b38>,\n",
" 'jeune': <gensim.models.keyedvectors.Vocab at 0x25502807b70>,\n",
" 'Лизой': <gensim.models.keyedvectors.Vocab at 0x25502807ba8>,\n",
" 'женой': <gensim.models.keyedvectors.Vocab at 0x25502807be0>,\n",
" 'молодого': <gensim.models.keyedvectors.Vocab at 0x25502807c18>,\n",
" 'Болконского': <gensim.models.keyedvectors.Vocab at 0x25502807c50>,\n",
" 'Ce': <gensim.models.keyedvectors.Vocab at 0x25502807c88>,\n",
" 'sera': <gensim.models.keyedvectors.Vocab at 0x25502807cc0>,\n",
" 'dans': <gensim.models.keyedvectors.Vocab at 0x25502807cf8>,\n",
" 'ferai': <gensim.models.keyedvectors.Vocab at 0x25502807d30>,\n",
" 'fille': <gensim.models.keyedvectors.Vocab at 0x25502807d68>,\n",
" 'вашем': <gensim.models.keyedvectors.Vocab at 0x25502807da0>,\n",
" 'семействе': <gensim.models.keyedvectors.Vocab at 0x25502807dd8>,\n",
" 'девки': <gensim.models.keyedvectors.Vocab at 0x25502807e10>,\n",
" 'II': <gensim.models.keyedvectors.Vocab at 0x25502807e48>,\n",
" 'начала': <gensim.models.keyedvectors.Vocab at 0x25502807e80>,\n",
" 'понемногу': <gensim.models.keyedvectors.Vocab at 0x25502807eb8>,\n",
" 'высшая': <gensim.models.keyedvectors.Vocab at 0x25502807ef0>,\n",
" 'знать': <gensim.models.keyedvectors.Vocab at 0x25502807f28>,\n",
" 'Петербурга': <gensim.models.keyedvectors.Vocab at 0x25502807f60>,\n",
" 'люди': <gensim.models.keyedvectors.Vocab at 0x25502807f98>,\n",
" 'самые': <gensim.models.keyedvectors.Vocab at 0x25502807fd0>,\n",
" 'обществу': <gensim.models.keyedvectors.Vocab at 0x2550280a048>,\n",
" 'каком': <gensim.models.keyedvectors.Vocab at 0x2550280a080>,\n",
" 'жили': <gensim.models.keyedvectors.Vocab at 0x2550280a0b8>,\n",
" 'приехала': <gensim.models.keyedvectors.Vocab at 0x2550280a0f0>,\n",
" 'красавица': <gensim.models.keyedvectors.Vocab at 0x2550280a128>,\n",
" 'Элен': <gensim.models.keyedvectors.Vocab at 0x2550280a160>,\n",
" 'ним': <gensim.models.keyedvectors.Vocab at 0x2550280a198>,\n",
" 'вместе': <gensim.models.keyedvectors.Vocab at 0x2550280a1d0>,\n",
" 'ехать': <gensim.models.keyedvectors.Vocab at 0x2550280a208>,\n",
" 'платье': <gensim.models.keyedvectors.Vocab at 0x2550280a240>,\n",
" 'plus': <gensim.models.keyedvectors.Vocab at 0x2550280a278>,\n",
" 'Petersbourg': <gensim.models.keyedvectors.Vocab at 0x2550280a2b0>,\n",
" 'самая': <gensim.models.keyedvectors.Vocab at 0x2550280a2e8>,\n",
" 'обворожительная': <gensim.models.keyedvectors.Vocab at 0x2550280a320>,\n",
" 'женщина': <gensim.models.keyedvectors.Vocab at 0x2550280a358>,\n",
" 'Петербурге': <gensim.models.keyedvectors.Vocab at 0x2550280a390>,\n",
" 'молодая': <gensim.models.keyedvectors.Vocab at 0x2550280a3c8>,\n",
" 'княгиня': <gensim.models.keyedvectors.Vocab at 0x2550280a400>,\n",
" 'прошлую': <gensim.models.keyedvectors.Vocab at 0x2550280a438>,\n",
" 'зиму': <gensim.models.keyedvectors.Vocab at 0x2550280a470>,\n",
" 'вышедшая': <gensim.models.keyedvectors.Vocab at 0x2550280a4a8>,\n",
" 'замуж': <gensim.models.keyedvectors.Vocab at 0x2550280a4e0>,\n",
" 'большой': <gensim.models.keyedvectors.Vocab at 0x2550280a518>,\n",
" 'свет': <gensim.models.keyedvectors.Vocab at 0x2550280a550>,\n",
" 'причине': <gensim.models.keyedvectors.Vocab at 0x2550280a588>,\n",
" 'беременности': <gensim.models.keyedvectors.Vocab at 0x2550280a5c0>,\n",
" 'небольшие': <gensim.models.keyedvectors.Vocab at 0x2550280a5f8>,\n",
" 'Приехал': <gensim.models.keyedvectors.Vocab at 0x2550280a630>,\n",
" 'сын': <gensim.models.keyedvectors.Vocab at 0x2550280a668>,\n",
" 'представил': <gensim.models.keyedvectors.Vocab at 0x2550280a6a0>,\n",
" 'приехал': <gensim.models.keyedvectors.Vocab at 0x2550280a6d8>,\n",
" 'многие': <gensim.models.keyedvectors.Vocab at 0x2550280a710>,\n",
" 'другие': <gensim.models.keyedvectors.Vocab at 0x2550280a748>,\n",
" 'видали': <gensim.models.keyedvectors.Vocab at 0x2550280a780>,\n",
" 'знакомы': <gensim.models.keyedvectors.Vocab at 0x2550280a7b8>,\n",
" 'ma': <gensim.models.keyedvectors.Vocab at 0x2550280a7f0>,\n",
" 'tante': <gensim.models.keyedvectors.Vocab at 0x2550280a828>,\n",
" 'моей': <gensim.models.keyedvectors.Vocab at 0x2550280a860>,\n",
" 'гостям': <gensim.models.keyedvectors.Vocab at 0x2550280a898>,\n",
" 'весьма': <gensim.models.keyedvectors.Vocab at 0x2550280a8d0>,\n",
" 'подводила': <gensim.models.keyedvectors.Vocab at 0x2550280a908>,\n",
" 'маленькой': <gensim.models.keyedvectors.Vocab at 0x2550280a940>,\n",
" 'высоких': <gensim.models.keyedvectors.Vocab at 0x2550280a978>,\n",
" 'комнаты': <gensim.models.keyedvectors.Vocab at 0x2550280a9b0>,\n",
" 'скоро': <gensim.models.keyedvectors.Vocab at 0x2550280a9e8>,\n",
" 'стали': <gensim.models.keyedvectors.Vocab at 0x2550280aa20>,\n",
" 'приезжать': <gensim.models.keyedvectors.Vocab at 0x2550280aa58>,\n",
" 'гости': <gensim.models.keyedvectors.Vocab at 0x2550280aa90>,\n",
" 'называла': <gensim.models.keyedvectors.Vocab at 0x2550280aac8>,\n",
" 'имени': <gensim.models.keyedvectors.Vocab at 0x2550280ab00>,\n",
" 'медленно': <gensim.models.keyedvectors.Vocab at 0x2550280ab38>,\n",
" 'переводя': <gensim.models.keyedvectors.Vocab at 0x2550280ab70>,\n",
" 'гостя': <gensim.models.keyedvectors.Vocab at 0x2550280aba8>,\n",
" 'отходила': <gensim.models.keyedvectors.Vocab at 0x2550280abe0>,\n",
" 'Все': <gensim.models.keyedvectors.Vocab at 0x2550280ac18>,\n",
" 'обряд': <gensim.models.keyedvectors.Vocab at 0x2550280ac50>,\n",
" 'никому': <gensim.models.keyedvectors.Vocab at 0x2550280ac88>,\n",
" 'неизвестной': <gensim.models.keyedvectors.Vocab at 0x2550280acc0>,\n",
" 'тетушки': <gensim.models.keyedvectors.Vocab at 0x2550280acf8>,\n",
" 'торжественным': <gensim.models.keyedvectors.Vocab at 0x2550280ad30>,\n",
" 'участием': <gensim.models.keyedvectors.Vocab at 0x2550280ad68>,\n",
" 'следила': <gensim.models.keyedvectors.Vocab at 0x2550280ada0>,\n",
" 'молчаливо': <gensim.models.keyedvectors.Vocab at 0x2550280add8>,\n",
" 'одобряя': <gensim.models.keyedvectors.Vocab at 0x2550280ae10>,\n",
" 'Ma': <gensim.models.keyedvectors.Vocab at 0x2550280ae48>,\n",
" 'каждому': <gensim.models.keyedvectors.Vocab at 0x2550280ae80>,\n",
" 'одних': <gensim.models.keyedvectors.Vocab at 0x2550280aeb8>,\n",
" 'выражениях': <gensim.models.keyedvectors.Vocab at 0x2550280aef0>,\n",
" 'своем': <gensim.models.keyedvectors.Vocab at 0x2550280af28>,\n",
" 'слава': <gensim.models.keyedvectors.Vocab at 0x2550280af60>,\n",
" 'Богу': <gensim.models.keyedvectors.Vocab at 0x2550280af98>,\n",
" 'лучше': <gensim.models.keyedvectors.Vocab at 0x2550280afd0>,\n",
" 'подходившие': <gensim.models.keyedvectors.Vocab at 0x2550280e048>,\n",
" 'поспешности': <gensim.models.keyedvectors.Vocab at 0x2550280e080>,\n",
" 'чувством': <gensim.models.keyedvectors.Vocab at 0x2550280e0b8>,\n",
" 'облегчения': <gensim.models.keyedvectors.Vocab at 0x2550280e0f0>,\n",
" 'тяжелой': <gensim.models.keyedvectors.Vocab at 0x2550280e128>,\n",
" 'обязанности': <gensim.models.keyedvectors.Vocab at 0x2550280e160>,\n",
" 'отходили': <gensim.models.keyedvectors.Vocab at 0x2550280e198>,\n",
" 'старушки': <gensim.models.keyedvectors.Vocab at 0x2550280e1d0>,\n",
" 'разу': <gensim.models.keyedvectors.Vocab at 0x2550280e208>,\n",
" 'подойти': <gensim.models.keyedvectors.Vocab at 0x2550280e240>,\n",
" 'работой': <gensim.models.keyedvectors.Vocab at 0x2550280e278>,\n",
" 'золотом': <gensim.models.keyedvectors.Vocab at 0x2550280e2b0>,\n",
" 'бархатном': <gensim.models.keyedvectors.Vocab at 0x2550280e2e8>,\n",
" 'хорошенькая': <gensim.models.keyedvectors.Vocab at 0x2550280e320>,\n",
" 'чуть': <gensim.models.keyedvectors.Vocab at 0x2550280e358>,\n",
" 'усиками': <gensim.models.keyedvectors.Vocab at 0x2550280e390>,\n",
" 'верхняя': <gensim.models.keyedvectors.Vocab at 0x2550280e3c8>,\n",
" 'губка': <gensim.models.keyedvectors.Vocab at 0x2550280e400>,\n",
" 'тем': <gensim.models.keyedvectors.Vocab at 0x2550280e438>,\n",
" 'открывалась': <gensim.models.keyedvectors.Vocab at 0x2550280e470>,\n",
" 'опускалась': <gensim.models.keyedvectors.Vocab at 0x2550280e4a8>,\n",
" 'нижнюю': <gensim.models.keyedvectors.Vocab at 0x2550280e4e0>,\n",
" 'бывает': <gensim.models.keyedvectors.Vocab at 0x2550280e518>,\n",
" 'вполне': <gensim.models.keyedvectors.Vocab at 0x2550280e550>,\n",
" 'женщин': <gensim.models.keyedvectors.Vocab at 0x2550280e588>,\n",
" 'недостаток': <gensim.models.keyedvectors.Vocab at 0x2550280e5c0>,\n",
" 'губы': <gensim.models.keyedvectors.Vocab at 0x2550280e5f8>,\n",
" 'рот': <gensim.models.keyedvectors.Vocab at 0x2550280e630>,\n",
" 'казались': <gensim.models.keyedvectors.Vocab at 0x2550280e668>,\n",
" 'особенною': <gensim.models.keyedvectors.Vocab at 0x2550280e6a0>,\n",
" 'собственно': <gensim.models.keyedvectors.Vocab at 0x2550280e6d8>,\n",
" 'красотой': <gensim.models.keyedvectors.Vocab at 0x2550280e710>,\n",
" 'Всем': <gensim.models.keyedvectors.Vocab at 0x2550280e748>,\n",
" 'весело': <gensim.models.keyedvectors.Vocab at 0x2550280e780>,\n",
" 'смотреть': <gensim.models.keyedvectors.Vocab at 0x2550280e7b8>,\n",
" 'эту': <gensim.models.keyedvectors.Vocab at 0x2550280e7f0>,\n",
" 'полную': <gensim.models.keyedvectors.Vocab at 0x2550280e828>,\n",
" 'здоровья': <gensim.models.keyedvectors.Vocab at 0x2550280e860>,\n",
" 'хорошенькую': <gensim.models.keyedvectors.Vocab at 0x2550280e898>,\n",
" 'будущую': <gensim.models.keyedvectors.Vocab at 0x2550280e8d0>,\n",
" 'легко': <gensim.models.keyedvectors.Vocab at 0x2550280e908>,\n",
" 'положение': <gensim.models.keyedvectors.Vocab at 0x2550280e940>,\n",
" 'мрачным': <gensim.models.keyedvectors.Vocab at 0x2550280e978>,\n",
" 'молодым': <gensim.models.keyedvectors.Vocab at 0x2550280e9b0>,\n",
" 'смотревшим': <gensim.models.keyedvectors.Vocab at 0x2550280e9e8>,\n",
" 'казалось': <gensim.models.keyedvectors.Vocab at 0x2550280ea20>,\n",
" 'сами': <gensim.models.keyedvectors.Vocab at 0x2550280ea58>,\n",
" 'делаются': <gensim.models.keyedvectors.Vocab at 0x2550280ea90>,\n",
" 'похожи': <gensim.models.keyedvectors.Vocab at 0x2550280eac8>,\n",
" 'поговорив': <gensim.models.keyedvectors.Vocab at 0x2550280eb00>,\n",
" 'времени': <gensim.models.keyedvectors.Vocab at 0x2550280eb38>,\n",
" 'Кто': <gensim.models.keyedvectors.Vocab at 0x2550280eb70>,\n",
" 'видел': <gensim.models.keyedvectors.Vocab at 0x2550280eba8>,\n",
" 'каждом': <gensim.models.keyedvectors.Vocab at 0x2550280ebe0>,\n",
" 'светлую': <gensim.models.keyedvectors.Vocab at 0x2550280ec18>,\n",
" 'блестящие': <gensim.models.keyedvectors.Vocab at 0x2550280ec50>,\n",
" 'белые': <gensim.models.keyedvectors.Vocab at 0x2550280ec88>,\n",
" 'зубы': <gensim.models.keyedvectors.Vocab at 0x2550280ecc0>,\n",
" 'виднелись': <gensim.models.keyedvectors.Vocab at 0x2550280ecf8>,\n",
" 'беспрестанно': <gensim.models.keyedvectors.Vocab at 0x2550280ed30>,\n",
" 'тот': <gensim.models.keyedvectors.Vocab at 0x2550280ed68>,\n",
" 'думал': <gensim.models.keyedvectors.Vocab at 0x2550280eda0>,\n",
" 'любезен': <gensim.models.keyedvectors.Vocab at 0x2550280edd8>,\n",
" 'Маленькая': <gensim.models.keyedvectors.Vocab at 0x2550280ee10>,\n",
" 'переваливаясь': <gensim.models.keyedvectors.Vocab at 0x2550280ee48>,\n",
" 'маленькими': <gensim.models.keyedvectors.Vocab at 0x2550280ee80>,\n",
" 'быстрыми': <gensim.models.keyedvectors.Vocab at 0x2550280eeb8>,\n",
" 'стол': <gensim.models.keyedvectors.Vocab at 0x2550280eef0>,\n",
" 'руке': <gensim.models.keyedvectors.Vocab at 0x2550280ef28>,\n",
" 'оправляя': <gensim.models.keyedvectors.Vocab at 0x2550280ef60>,\n",
" 'села': <gensim.models.keyedvectors.Vocab at 0x2550280ef98>,\n",
" 'диван': <gensim.models.keyedvectors.Vocab at 0x2550280efd0>,\n",
" 'самовара': <gensim.models.keyedvectors.Vocab at 0x25502811048>,\n",
" 'делала': <gensim.models.keyedvectors.Vocab at 0x25502811080>,\n",
" 'part': <gensim.models.keyedvectors.Vocab at 0x255028110b8>,\n",
" 'plaisir': <gensim.models.keyedvectors.Vocab at 0x255028110f0>,\n",
" 'окружавших': <gensim.models.keyedvectors.Vocab at 0x25502811128>,\n",
" 'J': <gensim.models.keyedvectors.Vocab at 0x25502811160>,\n",
" 'apporte': <gensim.models.keyedvectors.Vocab at 0x25502811198>,\n",
" 'ouvrage': <gensim.models.keyedvectors.Vocab at 0x255028111d0>,\n",
" 'захватила': <gensim.models.keyedvectors.Vocab at 0x25502811208>,\n",
" 'работу': <gensim.models.keyedvectors.Vocab at 0x25502811240>,\n",
" ...}"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pw2vec.wv.vocab"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>word</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Лев</td>\n",
" <td>24.076664</td>\n",
" <td>-38.877079</td>\n",
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" <tr>\n",
" <th>1</th>\n",
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" <td>-20.156313</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <td>-38.390636</td>\n",
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" <td>и</td>\n",
" <td>-2.755297</td>\n",
" <td>31.812513</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>мир</td>\n",
" <td>-52.140125</td>\n",
" <td>-12.651149</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>Том</td>\n",
" <td>-64.165199</td>\n",
" <td>-2.887758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>-64.814507</td>\n",
" <td>-3.310599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>И</td>\n",
" <td>72.502350</td>\n",
" <td>-0.264474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ЧАСТЬ</td>\n",
" <td>49.408356</td>\n",
" <td>-31.671860</td>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" word x y\n",
"0 Лев 24.076664 -38.877079\n",
"1 Николаевич 4.448712 -20.156313\n",
"2 Толстой 24.381519 -38.390636\n",
"3 Война -64.219261 -2.914668\n",
"4 и -2.755297 31.812513\n",
"5 мир -52.140125 -12.651149\n",
"6 Том -64.165199 -2.887758\n",
"7 1 -64.814507 -3.310599\n",
"8 И 72.502350 -0.264474\n",
"9 ЧАСТЬ 49.408356 -31.671860"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"points.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [],
"source": [
"sns.set_context(\"poster\")"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x255056337b8>"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x864 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"points.plot.scatter(\"x\", \"y\", s=10, figsize=(20, 12))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Didn't expect that I got a beautiful visualization from Лев Толстой (A Leaf or Umbrella or an apple upside down)**"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"def plot_region(x_bounds, y_bounds):\n",
" slice = points[\n",
" (x_bounds[0] <= points.x) &\n",
" (points.x <= x_bounds[1]) & \n",
" (y_bounds[0] <= points.y) &\n",
" (points.y <= y_bounds[1])\n",
" ]\n",
" \n",
" ax = slice.plot.scatter(\"x\", \"y\", s=35, figsize=(15, 10))\n",
" for i, point in slice.iterrows():\n",
" ax.text(point.x + 0.005, point.y + 0.005, point.word, fontsize=11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check a part of graph above to see how close the words together!"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_region(x_bounds=(20.0, 25.0), y_bounds=(0.0, 5,0))"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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