Word2Vec for Sentiment Analysis in Russian.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word2Vec for Sentiment Analysis in Russian\n",
    "Word2vec [1] is a computationally-efficient predictive model for learning low-dimensional word embeddings from raw textual data. \n",
    "## 1. Loding sentiment data\n",
    "The corpus of short texts in Russian based on Twitter messages is available at http://study.mokoron.com/ (and also described in [2]). The entire database of unlabeled tweets was used in order to train Wor2Vec model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    text = text.lower().replace(\"ё\", \"е\")\n",
    "    text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))','URL', text)\n",
    "    text = re.sub('@[^\\s]+','USER', text)\n",
    "    text = re.sub('[^a-zA-Zа-яА-Я1-9]+', ' ', text)\n",
    "    text = re.sub(' +',' ', text)\n",
    "    return text.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python it is easier to work with SQLite databases rather then with SQL databases, so the original SQL file was converted into SQLite using <a href=\"https://github.com/dumblob/mysql2sqlite\">mysql2sqlite</a> script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "\n",
    "conn = sqlite3.connect('mysqlite3.db')\n",
    "c = conn.cursor()\n",
    "\n",
    "with open('tweets.txt', 'w', encoding='utf-8') as f:\n",
    "    for row in c.execute('SELECT ttext FROM sentiment'):\n",
    "        if row[0]:\n",
    "            tweet = preprocess_text(row[0])\n",
    "            print(tweet, file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets = open('tweets.txt').read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "preprocessed_data = pd.Series(tweets).apply(lambda x: len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c337e10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.figure(figsize=(16, 9)) \n",
    "# facecolor='g'\n",
    "n, bins, patches = plt.hist(preprocessed_data.values, 50, density=True)\n",
    "plt.xlabel('Number of words in a text')\n",
    "plt.ylabel('Share of texts')\n",
    "plt.axis([0, 40, 0, 0.12])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.94599 for sentence_length=20\n",
      "0.96172 for sentence_length=21\n",
      "0.97419 for sentence_length=22\n",
      "0.98353 for sentence_length=23\n",
      "0.99021 for sentence_length=24\n",
      "0.99457 for sentence_length=25\n",
      "0.99712 for sentence_length=26\n",
      "0.99849 for sentence_length=27\n",
      "0.99924 for sentence_length=28\n",
      "0.99959 for sentence_length=29\n",
      "0.99975 for sentence_length=30\n",
      "0.99984 for sentence_length=31\n",
      "0.99988 for sentence_length=32\n",
      "0.99990 for sentence_length=33\n",
      "0.99992 for sentence_length=34\n"
     ]
    }
   ],
   "source": [
    "for i in range(20, 35, 1):\n",
    "    preprocessed_data>i\n",
    "    print(\"%0.5f for sentence_length=%r\" % (1-(preprocessed_data>i).sum()/preprocessed_data.shape[0], i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Training the model\n",
    "Gensim [3] was used for obtaining vector representations of Russian words. The model was trained on the entire dataset, which was collected and pre-processed at the previous steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:04,512 : INFO : collecting all words and their counts\n",
      "2018-06-26 12:28:04,549 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2018-06-26 12:28:04,836 : INFO : PROGRESS: at sentence #10000, processed 99826 words, keeping 27469 word types\n",
      "2018-06-26 12:28:04,981 : INFO : PROGRESS: at sentence #20000, processed 197656 words, keeping 44504 word types\n",
      "2018-06-26 12:28:05,077 : INFO : PROGRESS: at sentence #30000, processed 295168 words, keeping 58483 word types\n",
      "2018-06-26 12:28:05,163 : INFO : PROGRESS: at sentence #40000, processed 392292 words, keeping 70694 word types\n",
      "2018-06-26 12:28:05,252 : INFO : PROGRESS: at sentence #50000, processed 490293 words, keeping 81650 word types\n",
      "2018-06-26 12:28:05,380 : INFO : PROGRESS: at sentence #60000, processed 587227 words, keeping 92228 word types\n",
      "2018-06-26 12:28:05,461 : INFO : PROGRESS: at sentence #70000, processed 683497 words, keeping 102462 word types\n",
      "2018-06-26 12:28:05,539 : INFO : PROGRESS: at sentence #80000, processed 779552 words, keeping 111844 word types\n",
      "2018-06-26 12:28:05,619 : INFO : PROGRESS: at sentence #90000, processed 877290 words, keeping 120604 word types\n",
      "2018-06-26 12:28:05,688 : INFO : PROGRESS: at sentence #100000, processed 976534 words, keeping 129075 word types\n",
      "2018-06-26 12:28:05,766 : INFO : PROGRESS: at sentence #110000, processed 1074610 words, keeping 136949 word types\n",
      "2018-06-26 12:28:05,851 : INFO : PROGRESS: at sentence #120000, processed 1172356 words, keeping 144679 word types\n",
      "2018-06-26 12:28:05,923 : INFO : PROGRESS: at sentence #130000, processed 1271467 words, keeping 152459 word types\n",
      "2018-06-26 12:28:05,998 : INFO : PROGRESS: at sentence #140000, processed 1371083 words, keeping 159416 word types\n",
      "2018-06-26 12:28:06,083 : INFO : PROGRESS: at sentence #150000, processed 1471443 words, keeping 165971 word types\n",
      "2018-06-26 12:28:06,157 : INFO : PROGRESS: at sentence #160000, processed 1571318 words, keeping 172411 word types\n",
      "2018-06-26 12:28:06,259 : INFO : PROGRESS: at sentence #170000, processed 1671600 words, keeping 178305 word types\n",
      "2018-06-26 12:28:06,346 : INFO : PROGRESS: at sentence #180000, processed 1773215 words, keeping 184221 word types\n",
      "2018-06-26 12:28:06,424 : INFO : PROGRESS: at sentence #190000, processed 1868347 words, keeping 190290 word types\n",
      "2018-06-26 12:28:06,501 : INFO : PROGRESS: at sentence #200000, processed 1957651 words, keeping 195853 word types\n",
      "2018-06-26 12:28:06,580 : INFO : PROGRESS: at sentence #210000, processed 2047865 words, keeping 201082 word types\n",
      "2018-06-26 12:28:06,643 : INFO : PROGRESS: at sentence #220000, processed 2138844 words, keeping 206410 word types\n",
      "2018-06-26 12:28:06,710 : INFO : PROGRESS: at sentence #230000, processed 2230087 words, keeping 211832 word types\n",
      "2018-06-26 12:28:06,780 : INFO : PROGRESS: at sentence #240000, processed 2321059 words, keeping 217110 word types\n",
      "2018-06-26 12:28:06,842 : INFO : PROGRESS: at sentence #250000, processed 2410821 words, keeping 222236 word types\n",
      "2018-06-26 12:28:06,912 : INFO : PROGRESS: at sentence #260000, processed 2503708 words, keeping 227316 word types\n",
      "2018-06-26 12:28:06,987 : INFO : PROGRESS: at sentence #270000, processed 2597382 words, keeping 232576 word types\n",
      "2018-06-26 12:28:07,057 : INFO : PROGRESS: at sentence #280000, processed 2691199 words, keeping 237720 word types\n",
      "2018-06-26 12:28:07,130 : INFO : PROGRESS: at sentence #290000, processed 2785301 words, keeping 242973 word types\n",
      "2018-06-26 12:28:07,207 : INFO : PROGRESS: at sentence #300000, processed 2879503 words, keeping 248063 word types\n",
      "2018-06-26 12:28:07,277 : INFO : PROGRESS: at sentence #310000, processed 2973734 words, keeping 253027 word types\n",
      "2018-06-26 12:28:07,346 : INFO : PROGRESS: at sentence #320000, processed 3067658 words, keeping 257831 word types\n",
      "2018-06-26 12:28:07,412 : INFO : PROGRESS: at sentence #330000, processed 3162260 words, keeping 262633 word types\n",
      "2018-06-26 12:28:07,483 : INFO : PROGRESS: at sentence #340000, processed 3257419 words, keeping 267397 word types\n",
      "2018-06-26 12:28:07,562 : INFO : PROGRESS: at sentence #350000, processed 3353271 words, keeping 272167 word types\n",
      "2018-06-26 12:28:07,626 : INFO : PROGRESS: at sentence #360000, processed 3448714 words, keeping 276684 word types\n",
      "2018-06-26 12:28:07,699 : INFO : PROGRESS: at sentence #370000, processed 3544371 words, keeping 281115 word types\n",
      "2018-06-26 12:28:07,770 : INFO : PROGRESS: at sentence #380000, processed 3641520 words, keeping 285738 word types\n",
      "2018-06-26 12:28:07,839 : INFO : PROGRESS: at sentence #390000, processed 3736782 words, keeping 290097 word types\n",
      "2018-06-26 12:28:07,913 : INFO : PROGRESS: at sentence #400000, processed 3834633 words, keeping 294485 word types\n",
      "2018-06-26 12:28:07,982 : INFO : PROGRESS: at sentence #410000, processed 3932725 words, keeping 298769 word types\n",
      "2018-06-26 12:28:08,053 : INFO : PROGRESS: at sentence #420000, processed 4030188 words, keeping 303180 word types\n",
      "2018-06-26 12:28:08,124 : INFO : PROGRESS: at sentence #430000, processed 4128208 words, keeping 307460 word types\n",
      "2018-06-26 12:28:08,196 : INFO : PROGRESS: at sentence #440000, processed 4224966 words, keeping 311690 word types\n",
      "2018-06-26 12:28:08,267 : INFO : PROGRESS: at sentence #450000, processed 4323561 words, keeping 315776 word types\n",
      "2018-06-26 12:28:08,341 : INFO : PROGRESS: at sentence #460000, processed 4422318 words, keeping 319829 word types\n",
      "2018-06-26 12:28:08,411 : INFO : PROGRESS: at sentence #470000, processed 4519444 words, keeping 323938 word types\n",
      "2018-06-26 12:28:08,476 : INFO : PROGRESS: at sentence #480000, processed 4616936 words, keeping 327916 word types\n",
      "2018-06-26 12:28:08,544 : INFO : PROGRESS: at sentence #490000, processed 4714592 words, keeping 331887 word types\n",
      "2018-06-26 12:28:08,613 : INFO : PROGRESS: at sentence #500000, processed 4813281 words, keeping 335628 word types\n",
      "2018-06-26 12:28:08,679 : INFO : PROGRESS: at sentence #510000, processed 4911368 words, keeping 339423 word types\n",
      "2018-06-26 12:28:08,751 : INFO : PROGRESS: at sentence #520000, processed 5009193 words, keeping 343120 word types\n",
      "2018-06-26 12:28:08,830 : INFO : PROGRESS: at sentence #530000, processed 5106160 words, keeping 346616 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:10,134 : INFO : PROGRESS: at sentence #710000, processed 6857332 words, keeping 407175 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:15,413 : INFO : PROGRESS: at sentence #1420000, processed 13804000 words, keeping 628780 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:20,608 : INFO : PROGRESS: at sentence #2130000, processed 20843915 words, keeping 808976 word types\n",
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      "2018-06-26 12:28:25,211 : INFO : PROGRESS: at sentence #2700000, processed 26538703 words, keeping 937360 word types\n",
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      "2018-06-26 12:28:25,352 : INFO : PROGRESS: at sentence #2720000, processed 26733814 words, keeping 941702 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:26,332 : INFO : PROGRESS: at sentence #2840000, processed 27937550 words, keeping 967426 word types\n",
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      "2018-06-26 12:28:26,785 : INFO : PROGRESS: at sentence #2900000, processed 28531263 words, keeping 979340 word types\n",
      "2018-06-26 12:28:26,858 : INFO : PROGRESS: at sentence #2910000, processed 28630193 words, keeping 981329 word types\n",
      "2018-06-26 12:28:26,942 : INFO : PROGRESS: at sentence #2920000, processed 28729227 words, keeping 983289 word types\n",
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      "2018-06-26 12:28:27,477 : INFO : PROGRESS: at sentence #2980000, processed 29320412 words, keeping 995039 word types\n",
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      "2018-06-26 12:28:31,916 : INFO : PROGRESS: at sentence #3530000, processed 34738006 words, keeping 1098951 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:32,009 : INFO : PROGRESS: at sentence #3540000, processed 34836608 words, keeping 1100709 word types\n",
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     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:37,427 : INFO : PROGRESS: at sentence #4240000, processed 41868044 words, keeping 1221620 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:42,876 : INFO : PROGRESS: at sentence #4940000, processed 49054020 words, keeping 1338899 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:48,736 : INFO : PROGRESS: at sentence #5640000, processed 56149007 words, keeping 1442585 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:53,885 : INFO : PROGRESS: at sentence #6340000, processed 62957366 words, keeping 1544539 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:28:58,900 : INFO : PROGRESS: at sentence #7040000, processed 69908105 words, keeping 1636189 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:08,888 : INFO : PROGRESS: at sentence #8440000, processed 83901662 words, keeping 1809087 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:13,560 : INFO : PROGRESS: at sentence #9140000, processed 90556662 words, keeping 1893362 word types\n",
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    {
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "2018-06-26 12:29:23,463 : INFO : PROGRESS: at sentence #10530000, processed 104164202 words, keeping 2056460 word types\n",
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     ]
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     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:28,072 : INFO : PROGRESS: at sentence #11220000, processed 110848208 words, keeping 2132735 word types\n",
      "2018-06-26 12:29:28,152 : INFO : PROGRESS: at sentence #11230000, processed 110944579 words, keeping 2133996 word types\n",
      "2018-06-26 12:29:28,228 : INFO : PROGRESS: at sentence #11240000, processed 111041316 words, keeping 2135157 word types\n",
      "2018-06-26 12:29:28,303 : INFO : PROGRESS: at sentence #11250000, processed 111138280 words, keeping 2136348 word types\n",
      "2018-06-26 12:29:28,373 : INFO : PROGRESS: at sentence #11260000, processed 111236871 words, keeping 2137562 word types\n",
      "2018-06-26 12:29:28,439 : INFO : PROGRESS: at sentence #11270000, processed 111334042 words, keeping 2138733 word types\n",
      "2018-06-26 12:29:28,508 : INFO : PROGRESS: at sentence #11280000, processed 111431558 words, keeping 2139901 word types\n",
      "2018-06-26 12:29:28,580 : INFO : PROGRESS: at sentence #11290000, processed 111526800 words, keeping 2141083 word types\n",
      "2018-06-26 12:29:28,653 : INFO : PROGRESS: at sentence #11300000, processed 111621322 words, keeping 2142259 word types\n",
      "2018-06-26 12:29:28,730 : INFO : PROGRESS: at sentence #11310000, processed 111716931 words, keeping 2143437 word types\n",
      "2018-06-26 12:29:28,800 : INFO : PROGRESS: at sentence #11320000, processed 111812920 words, keeping 2144596 word types\n",
      "2018-06-26 12:29:28,872 : INFO : PROGRESS: at sentence #11330000, processed 111909033 words, keeping 2145783 word types\n",
      "2018-06-26 12:29:28,970 : INFO : PROGRESS: at sentence #11340000, processed 112004533 words, keeping 2146943 word types\n",
      "2018-06-26 12:29:29,083 : INFO : PROGRESS: at sentence #11350000, processed 112098809 words, keeping 2148068 word types\n",
      "2018-06-26 12:29:29,152 : INFO : PROGRESS: at sentence #11360000, processed 112194980 words, keeping 2149227 word types\n",
      "2018-06-26 12:29:29,225 : INFO : PROGRESS: at sentence #11370000, processed 112296709 words, keeping 2150348 word types\n",
      "2018-06-26 12:29:29,294 : INFO : PROGRESS: at sentence #11380000, processed 112398118 words, keeping 2151480 word types\n",
      "2018-06-26 12:29:29,364 : INFO : PROGRESS: at sentence #11390000, processed 112500010 words, keeping 2152535 word types\n",
      "2018-06-26 12:29:29,439 : INFO : PROGRESS: at sentence #11400000, processed 112599883 words, keeping 2153602 word types\n",
      "2018-06-26 12:29:29,521 : INFO : PROGRESS: at sentence #11410000, processed 112700946 words, keeping 2154610 word types\n",
      "2018-06-26 12:29:29,597 : INFO : PROGRESS: at sentence #11420000, processed 112799526 words, keeping 2155567 word types\n",
      "2018-06-26 12:29:29,670 : INFO : PROGRESS: at sentence #11430000, processed 112894296 words, keeping 2156487 word types\n",
      "2018-06-26 12:29:29,738 : INFO : PROGRESS: at sentence #11440000, processed 112989878 words, keeping 2157542 word types\n",
      "2018-06-26 12:29:29,807 : INFO : PROGRESS: at sentence #11450000, processed 113084182 words, keeping 2158536 word types\n",
      "2018-06-26 12:29:29,881 : INFO : PROGRESS: at sentence #11460000, processed 113179061 words, keeping 2159581 word types\n",
      "2018-06-26 12:29:29,955 : INFO : PROGRESS: at sentence #11470000, processed 113273750 words, keeping 2160611 word types\n",
      "2018-06-26 12:29:30,025 : INFO : PROGRESS: at sentence #11480000, processed 113370687 words, keeping 2161616 word types\n",
      "2018-06-26 12:29:30,089 : INFO : PROGRESS: at sentence #11490000, processed 113466520 words, keeping 2162558 word types\n",
      "2018-06-26 12:29:30,160 : INFO : PROGRESS: at sentence #11500000, processed 113562714 words, keeping 2163753 word types\n",
      "2018-06-26 12:29:30,236 : INFO : PROGRESS: at sentence #11510000, processed 113658140 words, keeping 2165005 word types\n",
      "2018-06-26 12:29:30,309 : INFO : PROGRESS: at sentence #11520000, processed 113754901 words, keeping 2166272 word types\n",
      "2018-06-26 12:29:30,375 : INFO : PROGRESS: at sentence #11530000, processed 113850748 words, keeping 2167520 word types\n",
      "2018-06-26 12:29:30,453 : INFO : PROGRESS: at sentence #11540000, processed 113947015 words, keeping 2168717 word types\n",
      "2018-06-26 12:29:30,527 : INFO : PROGRESS: at sentence #11550000, processed 114042917 words, keeping 2169919 word types\n",
      "2018-06-26 12:29:30,597 : INFO : PROGRESS: at sentence #11560000, processed 114138783 words, keeping 2171066 word types\n",
      "2018-06-26 12:29:30,670 : INFO : PROGRESS: at sentence #11570000, processed 114240051 words, keeping 2172211 word types\n",
      "2018-06-26 12:29:30,743 : INFO : PROGRESS: at sentence #11580000, processed 114339811 words, keeping 2173308 word types\n",
      "2018-06-26 12:29:30,816 : INFO : PROGRESS: at sentence #11590000, processed 114442043 words, keeping 2174327 word types\n",
      "2018-06-26 12:29:30,889 : INFO : PROGRESS: at sentence #11600000, processed 114542000 words, keeping 2175417 word types\n",
      "2018-06-26 12:29:30,970 : INFO : PROGRESS: at sentence #11610000, processed 114643707 words, keeping 2176467 word types\n",
      "2018-06-26 12:29:31,042 : INFO : PROGRESS: at sentence #11620000, processed 114744250 words, keeping 2177573 word types\n",
      "2018-06-26 12:29:31,110 : INFO : PROGRESS: at sentence #11630000, processed 114848037 words, keeping 2178615 word types\n",
      "2018-06-26 12:29:31,179 : INFO : PROGRESS: at sentence #11640000, processed 114949596 words, keeping 2179761 word types\n",
      "2018-06-26 12:29:31,256 : INFO : PROGRESS: at sentence #11650000, processed 115050907 words, keeping 2180951 word types\n",
      "2018-06-26 12:29:31,324 : INFO : PROGRESS: at sentence #11660000, processed 115152278 words, keeping 2182137 word types\n",
      "2018-06-26 12:29:31,387 : INFO : PROGRESS: at sentence #11670000, processed 115252306 words, keeping 2183332 word types\n",
      "2018-06-26 12:29:31,454 : INFO : PROGRESS: at sentence #11680000, processed 115351364 words, keeping 2184574 word types\n",
      "2018-06-26 12:29:31,524 : INFO : PROGRESS: at sentence #11690000, processed 115449867 words, keeping 2185788 word types\n",
      "2018-06-26 12:29:31,595 : INFO : PROGRESS: at sentence #11700000, processed 115547407 words, keeping 2186921 word types\n",
      "2018-06-26 12:29:31,667 : INFO : PROGRESS: at sentence #11710000, processed 115646384 words, keeping 2187979 word types\n",
      "2018-06-26 12:29:31,739 : INFO : PROGRESS: at sentence #11720000, processed 115744866 words, keeping 2189060 word types\n",
      "2018-06-26 12:29:31,810 : INFO : PROGRESS: at sentence #11730000, processed 115843920 words, keeping 2190147 word types\n",
      "2018-06-26 12:29:31,882 : INFO : PROGRESS: at sentence #11740000, processed 115942954 words, keeping 2191291 word types\n",
      "2018-06-26 12:29:31,966 : INFO : PROGRESS: at sentence #11750000, processed 116043712 words, keeping 2192397 word types\n",
      "2018-06-26 12:29:32,046 : INFO : PROGRESS: at sentence #11760000, processed 116141488 words, keeping 2193431 word types\n",
      "2018-06-26 12:29:32,120 : INFO : PROGRESS: at sentence #11770000, processed 116239563 words, keeping 2194547 word types\n",
      "2018-06-26 12:29:32,196 : INFO : PROGRESS: at sentence #11780000, processed 116341412 words, keeping 2195627 word types\n",
      "2018-06-26 12:29:32,271 : INFO : PROGRESS: at sentence #11790000, processed 116442232 words, keeping 2196670 word types\n",
      "2018-06-26 12:29:32,344 : INFO : PROGRESS: at sentence #11800000, processed 116543385 words, keeping 2197800 word types\n",
      "2018-06-26 12:29:32,417 : INFO : PROGRESS: at sentence #11810000, processed 116643013 words, keeping 2198754 word types\n",
      "2018-06-26 12:29:32,482 : INFO : PROGRESS: at sentence #11820000, processed 116742573 words, keeping 2199806 word types\n",
      "2018-06-26 12:29:32,567 : INFO : PROGRESS: at sentence #11830000, processed 116843231 words, keeping 2200921 word types\n",
      "2018-06-26 12:29:32,683 : INFO : PROGRESS: at sentence #11840000, processed 116942781 words, keeping 2202054 word types\n",
      "2018-06-26 12:29:32,753 : INFO : PROGRESS: at sentence #11850000, processed 117043747 words, keeping 2203133 word types\n",
      "2018-06-26 12:29:32,824 : INFO : PROGRESS: at sentence #11860000, processed 117143000 words, keeping 2204296 word types\n",
      "2018-06-26 12:29:32,899 : INFO : PROGRESS: at sentence #11870000, processed 117244521 words, keeping 2205447 word types\n",
      "2018-06-26 12:29:32,978 : INFO : PROGRESS: at sentence #11880000, processed 117344637 words, keeping 2206524 word types\n",
      "2018-06-26 12:29:33,053 : INFO : PROGRESS: at sentence #11890000, processed 117444223 words, keeping 2207668 word types\n",
      "2018-06-26 12:29:33,142 : INFO : PROGRESS: at sentence #11900000, processed 117546900 words, keeping 2209013 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:33,244 : INFO : PROGRESS: at sentence #11910000, processed 117650213 words, keeping 2210347 word types\n",
      "2018-06-26 12:29:33,329 : INFO : PROGRESS: at sentence #11920000, processed 117753094 words, keeping 2211632 word types\n",
      "2018-06-26 12:29:33,409 : INFO : PROGRESS: at sentence #11930000, processed 117854670 words, keeping 2212937 word types\n",
      "2018-06-26 12:29:33,490 : INFO : PROGRESS: at sentence #11940000, processed 117957238 words, keeping 2214237 word types\n",
      "2018-06-26 12:29:33,569 : INFO : PROGRESS: at sentence #11950000, processed 118060288 words, keeping 2215484 word types\n",
      "2018-06-26 12:29:33,665 : INFO : PROGRESS: at sentence #11960000, processed 118163640 words, keeping 2216837 word types\n",
      "2018-06-26 12:29:33,803 : INFO : PROGRESS: at sentence #11970000, processed 118265342 words, keeping 2218129 word types\n",
      "2018-06-26 12:29:33,892 : INFO : PROGRESS: at sentence #11980000, processed 118368429 words, keeping 2219369 word types\n",
      "2018-06-26 12:29:34,018 : INFO : PROGRESS: at sentence #11990000, processed 118468254 words, keeping 2220488 word types\n",
      "2018-06-26 12:29:34,104 : INFO : PROGRESS: at sentence #12000000, processed 118567677 words, keeping 2221511 word types\n",
      "2018-06-26 12:29:34,186 : INFO : PROGRESS: at sentence #12010000, processed 118665714 words, keeping 2222541 word types\n",
      "2018-06-26 12:29:34,276 : INFO : PROGRESS: at sentence #12020000, processed 118765926 words, keeping 2223624 word types\n",
      "2018-06-26 12:29:34,367 : INFO : PROGRESS: at sentence #12030000, processed 118865840 words, keeping 2224508 word types\n",
      "2018-06-26 12:29:34,455 : INFO : PROGRESS: at sentence #12040000, processed 118967193 words, keeping 2225559 word types\n",
      "2018-06-26 12:29:34,535 : INFO : PROGRESS: at sentence #12050000, processed 119067141 words, keeping 2226532 word types\n",
      "2018-06-26 12:29:34,615 : INFO : PROGRESS: at sentence #12060000, processed 119167855 words, keeping 2227517 word types\n",
      "2018-06-26 12:29:34,697 : INFO : PROGRESS: at sentence #12070000, processed 119268040 words, keeping 2228533 word types\n",
      "2018-06-26 12:29:34,777 : INFO : PROGRESS: at sentence #12080000, processed 119367958 words, keeping 2229549 word types\n",
      "2018-06-26 12:29:34,857 : INFO : PROGRESS: at sentence #12090000, processed 119468327 words, keeping 2230606 word types\n",
      "2018-06-26 12:29:34,945 : INFO : PROGRESS: at sentence #12100000, processed 119568705 words, keeping 2231658 word types\n",
      "2018-06-26 12:29:35,026 : INFO : PROGRESS: at sentence #12110000, processed 119669502 words, keeping 2232754 word types\n",
      "2018-06-26 12:29:35,111 : INFO : PROGRESS: at sentence #12120000, processed 119769395 words, keeping 2233964 word types\n",
      "2018-06-26 12:29:35,195 : INFO : PROGRESS: at sentence #12130000, processed 119868203 words, keeping 2235135 word types\n",
      "2018-06-26 12:29:35,277 : INFO : PROGRESS: at sentence #12140000, processed 119963632 words, keeping 2236215 word types\n",
      "2018-06-26 12:29:35,345 : INFO : PROGRESS: at sentence #12150000, processed 120059931 words, keeping 2237291 word types\n",
      "2018-06-26 12:29:35,423 : INFO : PROGRESS: at sentence #12160000, processed 120156933 words, keeping 2238390 word types\n",
      "2018-06-26 12:29:35,495 : INFO : PROGRESS: at sentence #12170000, processed 120253754 words, keeping 2239489 word types\n",
      "2018-06-26 12:29:35,571 : INFO : PROGRESS: at sentence #12180000, processed 120352761 words, keeping 2240596 word types\n",
      "2018-06-26 12:29:35,645 : INFO : PROGRESS: at sentence #12190000, processed 120451228 words, keeping 2241682 word types\n",
      "2018-06-26 12:29:35,719 : INFO : PROGRESS: at sentence #12200000, processed 120551029 words, keeping 2242794 word types\n",
      "2018-06-26 12:29:35,789 : INFO : PROGRESS: at sentence #12210000, processed 120649408 words, keeping 2243957 word types\n",
      "2018-06-26 12:29:35,853 : INFO : PROGRESS: at sentence #12220000, processed 120746651 words, keeping 2245058 word types\n",
      "2018-06-26 12:29:35,922 : INFO : PROGRESS: at sentence #12230000, processed 120845161 words, keeping 2246182 word types\n",
      "2018-06-26 12:29:36,004 : INFO : PROGRESS: at sentence #12240000, processed 120944553 words, keeping 2247350 word types\n",
      "2018-06-26 12:29:36,080 : INFO : PROGRESS: at sentence #12250000, processed 121043771 words, keeping 2248454 word types\n",
      "2018-06-26 12:29:36,157 : INFO : PROGRESS: at sentence #12260000, processed 121141261 words, keeping 2249606 word types\n",
      "2018-06-26 12:29:36,234 : INFO : PROGRESS: at sentence #12270000, processed 121238175 words, keeping 2250728 word types\n",
      "2018-06-26 12:29:36,307 : INFO : PROGRESS: at sentence #12280000, processed 121336032 words, keeping 2251800 word types\n",
      "2018-06-26 12:29:36,378 : INFO : PROGRESS: at sentence #12290000, processed 121434076 words, keeping 2252911 word types\n",
      "2018-06-26 12:29:36,447 : INFO : PROGRESS: at sentence #12300000, processed 121531626 words, keeping 2254006 word types\n",
      "2018-06-26 12:29:36,516 : INFO : PROGRESS: at sentence #12310000, processed 121629718 words, keeping 2255163 word types\n",
      "2018-06-26 12:29:36,590 : INFO : PROGRESS: at sentence #12320000, processed 121729889 words, keeping 2256299 word types\n",
      "2018-06-26 12:29:36,668 : INFO : PROGRESS: at sentence #12330000, processed 121828459 words, keeping 2257445 word types\n",
      "2018-06-26 12:29:36,756 : INFO : PROGRESS: at sentence #12340000, processed 121930555 words, keeping 2258499 word types\n",
      "2018-06-26 12:29:36,838 : INFO : PROGRESS: at sentence #12350000, processed 122032607 words, keeping 2259579 word types\n",
      "2018-06-26 12:29:36,922 : INFO : PROGRESS: at sentence #12360000, processed 122134814 words, keeping 2260685 word types\n",
      "2018-06-26 12:29:37,025 : INFO : PROGRESS: at sentence #12370000, processed 122235755 words, keeping 2261739 word types\n",
      "2018-06-26 12:29:37,111 : INFO : PROGRESS: at sentence #12380000, processed 122338419 words, keeping 2262798 word types\n",
      "2018-06-26 12:29:37,194 : INFO : PROGRESS: at sentence #12390000, processed 122439596 words, keeping 2263854 word types\n",
      "2018-06-26 12:29:37,272 : INFO : PROGRESS: at sentence #12400000, processed 122539542 words, keeping 2264918 word types\n",
      "2018-06-26 12:29:37,345 : INFO : PROGRESS: at sentence #12410000, processed 122639672 words, keeping 2266097 word types\n",
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      "2018-06-26 12:29:37,496 : INFO : PROGRESS: at sentence #12430000, processed 122841356 words, keeping 2268555 word types\n",
      "2018-06-26 12:29:37,575 : INFO : PROGRESS: at sentence #12440000, processed 122942368 words, keeping 2269786 word types\n",
      "2018-06-26 12:29:37,655 : INFO : PROGRESS: at sentence #12450000, processed 123043310 words, keeping 2271043 word types\n",
      "2018-06-26 12:29:37,722 : INFO : PROGRESS: at sentence #12460000, processed 123144167 words, keeping 2272230 word types\n",
      "2018-06-26 12:29:37,791 : INFO : PROGRESS: at sentence #12470000, processed 123245523 words, keeping 2273447 word types\n",
      "2018-06-26 12:29:37,861 : INFO : PROGRESS: at sentence #12480000, processed 123345332 words, keeping 2274597 word types\n",
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      "2018-06-26 12:29:38,001 : INFO : PROGRESS: at sentence #12500000, processed 123544955 words, keeping 2277002 word types\n",
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      "2018-06-26 12:29:38,144 : INFO : PROGRESS: at sentence #12520000, processed 123745395 words, keeping 2279388 word types\n",
      "2018-06-26 12:29:38,222 : INFO : PROGRESS: at sentence #12530000, processed 123844231 words, keeping 2280659 word types\n",
      "2018-06-26 12:29:38,288 : INFO : PROGRESS: at sentence #12540000, processed 123941800 words, keeping 2281898 word types\n",
      "2018-06-26 12:29:38,352 : INFO : PROGRESS: at sentence #12550000, processed 124037619 words, keeping 2283086 word types\n",
      "2018-06-26 12:29:38,416 : INFO : PROGRESS: at sentence #12560000, processed 124133214 words, keeping 2284257 word types\n",
      "2018-06-26 12:29:38,485 : INFO : PROGRESS: at sentence #12570000, processed 124230324 words, keeping 2285329 word types\n",
      "2018-06-26 12:29:38,559 : INFO : PROGRESS: at sentence #12580000, processed 124326518 words, keeping 2286405 word types\n",
      "2018-06-26 12:29:38,636 : INFO : PROGRESS: at sentence #12590000, processed 124424814 words, keeping 2287519 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:38,713 : INFO : PROGRESS: at sentence #12600000, processed 124526893 words, keeping 2288576 word types\n",
      "2018-06-26 12:29:38,788 : INFO : PROGRESS: at sentence #12610000, processed 124628568 words, keeping 2289639 word types\n",
      "2018-06-26 12:29:38,861 : INFO : PROGRESS: at sentence #12620000, processed 124732467 words, keeping 2290644 word types\n",
      "2018-06-26 12:29:38,925 : INFO : PROGRESS: at sentence #12630000, processed 124835746 words, keeping 2291647 word types\n",
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      "2018-06-26 12:29:40,039 : INFO : PROGRESS: at sentence #12790000, processed 126434956 words, keeping 2310311 word types\n",
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      "2018-06-26 12:29:41,320 : INFO : PROGRESS: at sentence #12960000, processed 128102842 words, keeping 2330743 word types\n",
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      "2018-06-26 12:29:42,745 : INFO : PROGRESS: at sentence #13140000, processed 129910208 words, keeping 2351000 word types\n",
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      "2018-06-26 12:29:42,903 : INFO : PROGRESS: at sentence #13160000, processed 130107339 words, keeping 2353098 word types\n",
      "2018-06-26 12:29:42,989 : INFO : PROGRESS: at sentence #13170000, processed 130205949 words, keeping 2354184 word types\n",
      "2018-06-26 12:29:43,070 : INFO : PROGRESS: at sentence #13180000, processed 130305635 words, keeping 2355312 word types\n",
      "2018-06-26 12:29:43,153 : INFO : PROGRESS: at sentence #13190000, processed 130405117 words, keeping 2356427 word types\n",
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      "2018-06-26 12:29:43,867 : INFO : PROGRESS: at sentence #13270000, processed 131189852 words, keeping 2365265 word types\n",
      "2018-06-26 12:29:43,958 : INFO : PROGRESS: at sentence #13280000, processed 131288311 words, keeping 2366339 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:44,043 : INFO : PROGRESS: at sentence #13290000, processed 131386378 words, keeping 2367382 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:49,180 : INFO : PROGRESS: at sentence #13980000, processed 138247812 words, keeping 2438926 word types\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:29:54,720 : INFO : PROGRESS: at sentence #14670000, processed 145272802 words, keeping 2515885 word types\n",
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      "2018-06-26 12:29:54,936 : INFO : PROGRESS: at sentence #14700000, processed 145589610 words, keeping 2519231 word types\n",
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      "2018-06-26 12:29:58,762 : INFO : PROGRESS: at sentence #15200000, processed 150723821 words, keeping 2570255 word types\n",
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      "2018-06-26 12:29:58,923 : INFO : PROGRESS: at sentence #15220000, processed 150936577 words, keeping 2572205 word types\n",
      "2018-06-26 12:29:59,018 : INFO : PROGRESS: at sentence #15230000, processed 151038333 words, keeping 2573229 word types\n",
      "2018-06-26 12:29:59,089 : INFO : PROGRESS: at sentence #15240000, processed 151143204 words, keeping 2574230 word types\n",
      "2018-06-26 12:29:59,158 : INFO : PROGRESS: at sentence #15250000, processed 151245776 words, keeping 2575250 word types\n",
      "2018-06-26 12:29:59,239 : INFO : PROGRESS: at sentence #15260000, processed 151351194 words, keeping 2576161 word types\n",
      "2018-06-26 12:29:59,313 : INFO : PROGRESS: at sentence #15270000, processed 151453291 words, keeping 2577154 word types\n",
      "2018-06-26 12:29:59,382 : INFO : PROGRESS: at sentence #15280000, processed 151556720 words, keeping 2578148 word types\n",
      "2018-06-26 12:29:59,458 : INFO : PROGRESS: at sentence #15290000, processed 151660712 words, keeping 2579238 word types\n",
      "2018-06-26 12:29:59,536 : INFO : PROGRESS: at sentence #15300000, processed 151766894 words, keeping 2580244 word types\n",
      "2018-06-26 12:29:59,616 : INFO : PROGRESS: at sentence #15310000, processed 151871810 words, keeping 2581274 word types\n",
      "2018-06-26 12:29:59,687 : INFO : PROGRESS: at sentence #15320000, processed 151978623 words, keeping 2582339 word types\n",
      "2018-06-26 12:29:59,762 : INFO : PROGRESS: at sentence #15330000, processed 152085417 words, keeping 2583362 word types\n",
      "2018-06-26 12:29:59,842 : INFO : PROGRESS: at sentence #15340000, processed 152193091 words, keeping 2584344 word types\n",
      "2018-06-26 12:29:59,921 : INFO : PROGRESS: at sentence #15350000, processed 152300319 words, keeping 2585297 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:30:00,008 : INFO : PROGRESS: at sentence #15360000, processed 152408938 words, keeping 2586347 word types\n",
      "2018-06-26 12:30:00,098 : INFO : PROGRESS: at sentence #15370000, processed 152516584 words, keeping 2587311 word types\n",
      "2018-06-26 12:30:00,176 : INFO : PROGRESS: at sentence #15380000, processed 152624257 words, keeping 2588240 word types\n",
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      "2018-06-26 12:30:00,315 : INFO : PROGRESS: at sentence #15400000, processed 152837314 words, keeping 2590204 word types\n",
      "2018-06-26 12:30:00,387 : INFO : PROGRESS: at sentence #15410000, processed 152947170 words, keeping 2591033 word types\n",
      "2018-06-26 12:30:00,455 : INFO : PROGRESS: at sentence #15420000, processed 153054720 words, keeping 2592002 word types\n",
      "2018-06-26 12:30:00,523 : INFO : PROGRESS: at sentence #15430000, processed 153160407 words, keeping 2593071 word types\n",
      "2018-06-26 12:30:00,593 : INFO : PROGRESS: at sentence #15440000, processed 153264066 words, keeping 2594064 word types\n",
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      "2018-06-26 12:30:00,736 : INFO : PROGRESS: at sentence #15460000, processed 153477364 words, keeping 2595989 word types\n",
      "2018-06-26 12:30:00,800 : INFO : PROGRESS: at sentence #15470000, processed 153583820 words, keeping 2596979 word types\n",
      "2018-06-26 12:30:00,865 : INFO : PROGRESS: at sentence #15480000, processed 153690034 words, keeping 2597924 word types\n",
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      "2018-06-26 12:30:00,994 : INFO : PROGRESS: at sentence #15500000, processed 153897037 words, keeping 2599954 word types\n",
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      "2018-06-26 12:30:01,688 : INFO : PROGRESS: at sentence #15600000, processed 154926361 words, keeping 2610002 word types\n",
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      "2018-06-26 12:30:01,883 : INFO : PROGRESS: at sentence #15630000, processed 155230091 words, keeping 2612938 word types\n",
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      "2018-06-26 12:30:04,454 : INFO : PROGRESS: at sentence #16000000, processed 159139807 words, keeping 2649301 word types\n",
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      "2018-06-26 12:30:04,713 : INFO : PROGRESS: at sentence #16040000, processed 159549799 words, keeping 2652409 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:30:04,783 : INFO : PROGRESS: at sentence #16050000, processed 159648856 words, keeping 2653081 word types\n",
      "2018-06-26 12:30:04,848 : INFO : PROGRESS: at sentence #16060000, processed 159750404 words, keeping 2653732 word types\n",
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      "2018-06-26 12:30:07,442 : INFO : PROGRESS: at sentence #16440000, processed 163686779 words, keeping 2688593 word types\n",
      "2018-06-26 12:30:07,510 : INFO : PROGRESS: at sentence #16450000, processed 163794175 words, keeping 2689494 word types\n",
      "2018-06-26 12:30:07,576 : INFO : PROGRESS: at sentence #16460000, processed 163900437 words, keeping 2690490 word types\n",
      "2018-06-26 12:30:07,643 : INFO : PROGRESS: at sentence #16470000, processed 164003587 words, keeping 2691424 word types\n",
      "2018-06-26 12:30:07,711 : INFO : PROGRESS: at sentence #16480000, processed 164107189 words, keeping 2692293 word types\n",
      "2018-06-26 12:30:07,778 : INFO : PROGRESS: at sentence #16490000, processed 164208858 words, keeping 2693288 word types\n",
      "2018-06-26 12:30:07,848 : INFO : PROGRESS: at sentence #16500000, processed 164314495 words, keeping 2694225 word types\n",
      "2018-06-26 12:30:07,915 : INFO : PROGRESS: at sentence #16510000, processed 164418735 words, keeping 2695175 word types\n",
      "2018-06-26 12:30:07,986 : INFO : PROGRESS: at sentence #16520000, processed 164524139 words, keeping 2696090 word types\n",
      "2018-06-26 12:30:08,056 : INFO : PROGRESS: at sentence #16530000, processed 164626680 words, keeping 2697076 word types\n",
      "2018-06-26 12:30:08,126 : INFO : PROGRESS: at sentence #16540000, processed 164727824 words, keeping 2698056 word types\n",
      "2018-06-26 12:30:08,211 : INFO : PROGRESS: at sentence #16550000, processed 164834082 words, keeping 2698919 word types\n",
      "2018-06-26 12:30:08,299 : INFO : PROGRESS: at sentence #16560000, processed 164937593 words, keeping 2699938 word types\n",
      "2018-06-26 12:30:08,381 : INFO : PROGRESS: at sentence #16570000, processed 165045240 words, keeping 2700848 word types\n",
      "2018-06-26 12:30:08,459 : INFO : PROGRESS: at sentence #16580000, processed 165147594 words, keeping 2701752 word types\n",
      "2018-06-26 12:30:08,534 : INFO : PROGRESS: at sentence #16590000, processed 165251518 words, keeping 2702633 word types\n",
      "2018-06-26 12:30:08,603 : INFO : PROGRESS: at sentence #16600000, processed 165355464 words, keeping 2703626 word types\n",
      "2018-06-26 12:30:08,669 : INFO : PROGRESS: at sentence #16610000, processed 165457595 words, keeping 2704568 word types\n",
      "2018-06-26 12:30:08,737 : INFO : PROGRESS: at sentence #16620000, processed 165557541 words, keeping 2705593 word types\n",
      "2018-06-26 12:30:08,803 : INFO : PROGRESS: at sentence #16630000, processed 165660169 words, keeping 2706550 word types\n",
      "2018-06-26 12:30:08,871 : INFO : PROGRESS: at sentence #16640000, processed 165769641 words, keeping 2707377 word types\n",
      "2018-06-26 12:30:08,942 : INFO : PROGRESS: at sentence #16650000, processed 165875336 words, keeping 2708272 word types\n",
      "2018-06-26 12:30:09,021 : INFO : PROGRESS: at sentence #16660000, processed 165980241 words, keeping 2709202 word types\n",
      "2018-06-26 12:30:09,087 : INFO : PROGRESS: at sentence #16670000, processed 166083882 words, keeping 2710078 word types\n",
      "2018-06-26 12:30:09,155 : INFO : PROGRESS: at sentence #16680000, processed 166185314 words, keeping 2710939 word types\n",
      "2018-06-26 12:30:09,223 : INFO : PROGRESS: at sentence #16690000, processed 166292748 words, keeping 2711758 word types\n",
      "2018-06-26 12:30:09,299 : INFO : PROGRESS: at sentence #16700000, processed 166396093 words, keeping 2712664 word types\n",
      "2018-06-26 12:30:09,371 : INFO : PROGRESS: at sentence #16710000, processed 166503484 words, keeping 2713481 word types\n",
      "2018-06-26 12:30:09,437 : INFO : PROGRESS: at sentence #16720000, processed 166605439 words, keeping 2714439 word types\n",
      "2018-06-26 12:30:09,507 : INFO : PROGRESS: at sentence #16730000, processed 166713002 words, keeping 2715395 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:30:09,577 : INFO : PROGRESS: at sentence #16740000, processed 166825235 words, keeping 2716249 word types\n",
      "2018-06-26 12:30:09,649 : INFO : PROGRESS: at sentence #16750000, processed 166933150 words, keeping 2717154 word types\n",
      "2018-06-26 12:30:09,721 : INFO : PROGRESS: at sentence #16760000, processed 167048833 words, keeping 2717980 word types\n",
      "2018-06-26 12:30:09,792 : INFO : PROGRESS: at sentence #16770000, processed 167158412 words, keeping 2718927 word types\n",
      "2018-06-26 12:30:09,863 : INFO : PROGRESS: at sentence #16780000, processed 167272420 words, keeping 2719799 word types\n",
      "2018-06-26 12:30:09,936 : INFO : PROGRESS: at sentence #16790000, processed 167386006 words, keeping 2720729 word types\n",
      "2018-06-26 12:30:10,011 : INFO : PROGRESS: at sentence #16800000, processed 167499637 words, keeping 2721693 word types\n",
      "2018-06-26 12:30:10,082 : INFO : PROGRESS: at sentence #16810000, processed 167616053 words, keeping 2722613 word types\n",
      "2018-06-26 12:30:10,151 : INFO : PROGRESS: at sentence #16820000, processed 167727925 words, keeping 2723515 word types\n",
      "2018-06-26 12:30:10,223 : INFO : PROGRESS: at sentence #16830000, processed 167846265 words, keeping 2724349 word types\n",
      "2018-06-26 12:30:10,294 : INFO : PROGRESS: at sentence #16840000, processed 167955015 words, keeping 2725239 word types\n",
      "2018-06-26 12:30:10,359 : INFO : PROGRESS: at sentence #16850000, processed 168061233 words, keeping 2726217 word types\n",
      "2018-06-26 12:30:10,425 : INFO : PROGRESS: at sentence #16860000, processed 168168087 words, keeping 2727206 word types\n",
      "2018-06-26 12:30:10,496 : INFO : PROGRESS: at sentence #16870000, processed 168273404 words, keeping 2728181 word types\n",
      "2018-06-26 12:30:10,564 : INFO : PROGRESS: at sentence #16880000, processed 168379905 words, keeping 2729077 word types\n",
      "2018-06-26 12:30:10,632 : INFO : PROGRESS: at sentence #16890000, processed 168484716 words, keeping 2730131 word types\n",
      "2018-06-26 12:30:10,700 : INFO : PROGRESS: at sentence #16900000, processed 168589916 words, keeping 2731031 word types\n",
      "2018-06-26 12:30:10,766 : INFO : PROGRESS: at sentence #16910000, processed 168693475 words, keeping 2731941 word types\n",
      "2018-06-26 12:30:10,828 : INFO : PROGRESS: at sentence #16920000, processed 168793788 words, keeping 2732910 word types\n",
      "2018-06-26 12:30:10,896 : INFO : PROGRESS: at sentence #16930000, processed 168902098 words, keeping 2733777 word types\n",
      "2018-06-26 12:30:10,970 : INFO : PROGRESS: at sentence #16940000, processed 169005405 words, keeping 2734783 word types\n",
      "2018-06-26 12:30:11,042 : INFO : PROGRESS: at sentence #16950000, processed 169109183 words, keeping 2735692 word types\n",
      "2018-06-26 12:30:11,106 : INFO : PROGRESS: at sentence #16960000, processed 169210943 words, keeping 2736685 word types\n",
      "2018-06-26 12:30:11,175 : INFO : PROGRESS: at sentence #16970000, processed 169311647 words, keeping 2737624 word types\n",
      "2018-06-26 12:30:11,240 : INFO : PROGRESS: at sentence #16980000, processed 169413149 words, keeping 2738451 word types\n",
      "2018-06-26 12:30:11,308 : INFO : PROGRESS: at sentence #16990000, processed 169517459 words, keeping 2739401 word types\n",
      "2018-06-26 12:30:11,372 : INFO : PROGRESS: at sentence #17000000, processed 169620614 words, keeping 2740431 word types\n",
      "2018-06-26 12:30:11,435 : INFO : PROGRESS: at sentence #17010000, processed 169722532 words, keeping 2741266 word types\n",
      "2018-06-26 12:30:11,500 : INFO : PROGRESS: at sentence #17020000, processed 169826613 words, keeping 2742120 word types\n",
      "2018-06-26 12:30:11,563 : INFO : PROGRESS: at sentence #17030000, processed 169932687 words, keeping 2742979 word types\n",
      "2018-06-26 12:30:11,627 : INFO : PROGRESS: at sentence #17040000, processed 170037993 words, keeping 2743887 word types\n",
      "2018-06-26 12:30:11,694 : INFO : PROGRESS: at sentence #17050000, processed 170144047 words, keeping 2744823 word types\n",
      "2018-06-26 12:30:11,764 : INFO : PROGRESS: at sentence #17060000, processed 170251817 words, keeping 2745729 word types\n",
      "2018-06-26 12:30:11,833 : INFO : PROGRESS: at sentence #17070000, processed 170355923 words, keeping 2746817 word types\n",
      "2018-06-26 12:30:11,903 : INFO : PROGRESS: at sentence #17080000, processed 170460155 words, keeping 2747851 word types\n",
      "2018-06-26 12:30:11,976 : INFO : PROGRESS: at sentence #17090000, processed 170565676 words, keeping 2748992 word types\n",
      "2018-06-26 12:30:12,052 : INFO : PROGRESS: at sentence #17100000, processed 170670832 words, keeping 2750027 word types\n",
      "2018-06-26 12:30:12,120 : INFO : PROGRESS: at sentence #17110000, processed 170777912 words, keeping 2751003 word types\n",
      "2018-06-26 12:30:12,189 : INFO : PROGRESS: at sentence #17120000, processed 170885551 words, keeping 2751995 word types\n",
      "2018-06-26 12:30:12,257 : INFO : PROGRESS: at sentence #17130000, processed 170992964 words, keeping 2752888 word types\n",
      "2018-06-26 12:30:12,322 : INFO : PROGRESS: at sentence #17140000, processed 171100799 words, keeping 2753827 word types\n",
      "2018-06-26 12:30:12,387 : INFO : PROGRESS: at sentence #17150000, processed 171203174 words, keeping 2754669 word types\n",
      "2018-06-26 12:30:12,452 : INFO : PROGRESS: at sentence #17160000, processed 171302877 words, keeping 2755405 word types\n",
      "2018-06-26 12:30:12,521 : INFO : PROGRESS: at sentence #17170000, processed 171400994 words, keeping 2756262 word types\n",
      "2018-06-26 12:30:12,594 : INFO : PROGRESS: at sentence #17180000, processed 171506161 words, keeping 2757251 word types\n",
      "2018-06-26 12:30:12,672 : INFO : PROGRESS: at sentence #17190000, processed 171612334 words, keeping 2758263 word types\n",
      "2018-06-26 12:30:12,749 : INFO : PROGRESS: at sentence #17200000, processed 171720942 words, keeping 2759210 word types\n",
      "2018-06-26 12:30:12,837 : INFO : PROGRESS: at sentence #17210000, processed 171828020 words, keeping 2760223 word types\n",
      "2018-06-26 12:30:12,918 : INFO : PROGRESS: at sentence #17220000, processed 171938819 words, keeping 2761127 word types\n",
      "2018-06-26 12:30:12,998 : INFO : PROGRESS: at sentence #17230000, processed 172046161 words, keeping 2762043 word types\n",
      "2018-06-26 12:30:13,084 : INFO : PROGRESS: at sentence #17240000, processed 172154167 words, keeping 2763008 word types\n",
      "2018-06-26 12:30:13,158 : INFO : PROGRESS: at sentence #17250000, processed 172263474 words, keeping 2763956 word types\n",
      "2018-06-26 12:30:13,235 : INFO : PROGRESS: at sentence #17260000, processed 172371551 words, keeping 2764939 word types\n",
      "2018-06-26 12:30:13,307 : INFO : PROGRESS: at sentence #17270000, processed 172480987 words, keeping 2765931 word types\n",
      "2018-06-26 12:30:13,378 : INFO : PROGRESS: at sentence #17280000, processed 172589085 words, keeping 2766875 word types\n",
      "2018-06-26 12:30:13,450 : INFO : PROGRESS: at sentence #17290000, processed 172694239 words, keeping 2767909 word types\n",
      "2018-06-26 12:30:13,519 : INFO : PROGRESS: at sentence #17300000, processed 172800266 words, keeping 2768862 word types\n",
      "2018-06-26 12:30:13,596 : INFO : PROGRESS: at sentence #17310000, processed 172905260 words, keeping 2769879 word types\n",
      "2018-06-26 12:30:13,681 : INFO : PROGRESS: at sentence #17320000, processed 173013318 words, keeping 2770804 word types\n",
      "2018-06-26 12:30:13,753 : INFO : PROGRESS: at sentence #17330000, processed 173116899 words, keeping 2771735 word types\n",
      "2018-06-26 12:30:13,820 : INFO : PROGRESS: at sentence #17340000, processed 173223679 words, keeping 2772626 word types\n",
      "2018-06-26 12:30:13,884 : INFO : PROGRESS: at sentence #17350000, processed 173328959 words, keeping 2773620 word types\n",
      "2018-06-26 12:30:13,953 : INFO : PROGRESS: at sentence #17360000, processed 173436780 words, keeping 2774481 word types\n",
      "2018-06-26 12:30:14,021 : INFO : PROGRESS: at sentence #17370000, processed 173542694 words, keeping 2775365 word types\n",
      "2018-06-26 12:30:14,095 : INFO : PROGRESS: at sentence #17380000, processed 173648092 words, keeping 2776263 word types\n",
      "2018-06-26 12:30:14,171 : INFO : PROGRESS: at sentence #17390000, processed 173753654 words, keeping 2777209 word types\n",
      "2018-06-26 12:30:14,238 : INFO : PROGRESS: at sentence #17400000, processed 173857228 words, keeping 2778148 word types\n",
      "2018-06-26 12:30:14,303 : INFO : PROGRESS: at sentence #17410000, processed 173960275 words, keeping 2779030 word types\n",
      "2018-06-26 12:30:14,371 : INFO : PROGRESS: at sentence #17420000, processed 174066304 words, keeping 2779927 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:30:14,445 : INFO : PROGRESS: at sentence #17430000, processed 174170658 words, keeping 2780837 word types\n",
      "2018-06-26 12:30:14,524 : INFO : PROGRESS: at sentence #17440000, processed 174273052 words, keeping 2781654 word types\n",
      "2018-06-26 12:30:14,611 : INFO : PROGRESS: at sentence #17450000, processed 174378969 words, keeping 2782593 word types\n",
      "2018-06-26 12:30:14,695 : INFO : PROGRESS: at sentence #17460000, processed 174484558 words, keeping 2783416 word types\n",
      "2018-06-26 12:30:14,772 : INFO : PROGRESS: at sentence #17470000, processed 174589270 words, keeping 2784185 word types\n",
      "2018-06-26 12:30:14,843 : INFO : PROGRESS: at sentence #17480000, processed 174696330 words, keeping 2785010 word types\n",
      "2018-06-26 12:30:14,915 : INFO : PROGRESS: at sentence #17490000, processed 174804520 words, keeping 2785796 word types\n",
      "2018-06-26 12:30:14,988 : INFO : PROGRESS: at sentence #17500000, processed 174910616 words, keeping 2786553 word types\n",
      "2018-06-26 12:30:15,055 : INFO : PROGRESS: at sentence #17510000, processed 175016476 words, keeping 2787351 word types\n",
      "2018-06-26 12:30:15,131 : INFO : PROGRESS: at sentence #17520000, processed 175119472 words, keeping 2788330 word types\n",
      "2018-06-26 12:30:15,203 : INFO : PROGRESS: at sentence #17530000, processed 175224791 words, keeping 2789258 word types\n",
      "2018-06-26 12:30:15,277 : INFO : PROGRESS: at sentence #17540000, processed 175327415 words, keeping 2790226 word types\n",
      "2018-06-26 12:30:15,350 : INFO : PROGRESS: at sentence #17550000, processed 175433077 words, keeping 2791120 word types\n",
      "2018-06-26 12:30:15,427 : INFO : PROGRESS: at sentence #17560000, processed 175535872 words, keeping 2792049 word types\n",
      "2018-06-26 12:30:15,503 : INFO : PROGRESS: at sentence #17570000, processed 175635409 words, keeping 2793018 word types\n",
      "2018-06-26 12:30:15,577 : INFO : PROGRESS: at sentence #17580000, processed 175737929 words, keeping 2793975 word types\n",
      "2018-06-26 12:30:15,653 : INFO : PROGRESS: at sentence #17590000, processed 175842270 words, keeping 2794937 word types\n",
      "2018-06-26 12:30:15,730 : INFO : PROGRESS: at sentence #17600000, processed 175941969 words, keeping 2795854 word types\n",
      "2018-06-26 12:30:16,049 : INFO : PROGRESS: at sentence #17610000, processed 176044274 words, keeping 2796707 word types\n",
      "2018-06-26 12:30:16,124 : INFO : PROGRESS: at sentence #17620000, processed 176143293 words, keeping 2797606 word types\n",
      "2018-06-26 12:30:16,201 : INFO : PROGRESS: at sentence #17630000, processed 176240884 words, keeping 2798475 word types\n",
      "2018-06-26 12:30:16,226 : INFO : collected 2798726 word types from a corpus of 176270198 raw words and 17632768 sentences\n",
      "2018-06-26 12:30:16,228 : INFO : Loading a fresh vocabulary\n",
      "2018-06-26 12:30:21,615 : INFO : min_count=3 retains 1027561 unique words (36% of original 2798726, drops 1771165)\n",
      "2018-06-26 12:30:21,616 : INFO : min_count=3 leaves 174131806 word corpus (98% of original 176270198, drops 2138392)\n",
      "2018-06-26 12:30:24,519 : INFO : deleting the raw counts dictionary of 2798726 items\n",
      "2018-06-26 12:30:24,604 : INFO : sample=0.001 downsamples 29 most-common words\n",
      "2018-06-26 12:30:24,605 : INFO : downsampling leaves estimated 149601601 word corpus (85.9% of prior 174131806)\n",
      "2018-06-26 12:30:28,461 : INFO : estimated required memory for 1027561 words and 200 dimensions: 2157878100 bytes\n",
      "2018-06-26 12:30:28,462 : INFO : resetting layer weights\n",
      "2018-06-26 12:30:39,934 : INFO : training model with 8 workers on 1027561 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
      "2018-06-26 12:30:40,955 : INFO : EPOCH 1 - PROGRESS: at 0.25% examples, 357267 words/s, in_qsize 12, out_qsize 1\n",
      "2018-06-26 12:30:41,986 : INFO : EPOCH 1 - PROGRESS: at 0.64% examples, 458101 words/s, in_qsize 13, out_qsize 0\n",
      "2018-06-26 12:30:42,999 : INFO : EPOCH 1 - PROGRESS: at 1.03% examples, 493869 words/s, in_qsize 16, out_qsize 0\n",
      "2018-06-26 12:30:44,053 : INFO : EPOCH 1 - PROGRESS: at 1.43% examples, 500278 words/s, in_qsize 16, out_qsize 1\n",
      "2018-06-26 12:30:45,054 : INFO : EPOCH 1 - PROGRESS: at 1.75% examples, 489626 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:30:46,092 : INFO : EPOCH 1 - PROGRESS: at 2.13% examples, 493597 words/s, in_qsize 12, out_qsize 3\n",
      "2018-06-26 12:30:47,106 : INFO : EPOCH 1 - PROGRESS: at 2.44% examples, 486357 words/s, in_qsize 15, out_qsize 1\n",
      "2018-06-26 12:30:48,114 : INFO : EPOCH 1 - PROGRESS: at 2.86% examples, 500969 words/s, in_qsize 10, out_qsize 1\n",
      "2018-06-26 12:30:49,118 : INFO : EPOCH 1 - PROGRESS: at 3.22% examples, 502832 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:30:50,145 : INFO : EPOCH 1 - PROGRESS: at 3.64% examples, 509654 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:30:51,154 : INFO : EPOCH 1 - PROGRESS: at 4.03% examples, 515304 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:30:52,165 : INFO : EPOCH 1 - PROGRESS: at 4.43% examples, 522723 words/s, in_qsize 10, out_qsize 1\n",
      "2018-06-26 12:30:53,171 : INFO : EPOCH 1 - PROGRESS: at 4.71% examples, 512398 words/s, in_qsize 14, out_qsize 1\n",
      "2018-06-26 12:30:54,188 : INFO : EPOCH 1 - PROGRESS: at 5.13% examples, 517305 words/s, in_qsize 9, out_qsize 1\n",
      "2018-06-26 12:30:55,213 : INFO : EPOCH 1 - PROGRESS: at 5.49% examples, 514700 words/s, in_qsize 12, out_qsize 3\n",
      "2018-06-26 12:30:56,223 : INFO : EPOCH 1 - PROGRESS: at 5.87% examples, 517014 words/s, in_qsize 14, out_qsize 0\n",
      "2018-06-26 12:30:57,270 : INFO : EPOCH 1 - PROGRESS: at 6.22% examples, 516542 words/s, in_qsize 14, out_qsize 0\n",
      "2018-06-26 12:30:58,289 : INFO : EPOCH 1 - PROGRESS: at 6.61% examples, 518710 words/s, in_qsize 12, out_qsize 0\n",
      "2018-06-26 12:30:59,334 : INFO : EPOCH 1 - PROGRESS: at 6.99% examples, 520048 words/s, in_qsize 8, out_qsize 2\n",
      "2018-06-26 12:31:00,358 : INFO : EPOCH 1 - PROGRESS: at 7.37% examples, 521347 words/s, in_qsize 7, out_qsize 0\n",
      "2018-06-26 12:31:01,361 : INFO : EPOCH 1 - PROGRESS: at 7.72% examples, 521424 words/s, in_qsize 4, out_qsize 1\n",
      "2018-06-26 12:31:02,395 : INFO : EPOCH 1 - PROGRESS: at 7.98% examples, 514663 words/s, in_qsize 14, out_qsize 1\n",
      "2018-06-26 12:31:03,425 : INFO : EPOCH 1 - PROGRESS: at 8.38% examples, 517267 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:31:04,456 : INFO : EPOCH 1 - PROGRESS: at 8.75% examples, 518044 words/s, in_qsize 15, out_qsize 3\n",
      "2018-06-26 12:31:05,482 : INFO : EPOCH 1 - PROGRESS: at 9.15% examples, 520151 words/s, in_qsize 14, out_qsize 1\n",
      "2018-06-26 12:31:06,484 : INFO : EPOCH 1 - PROGRESS: at 9.58% examples, 524679 words/s, in_qsize 9, out_qsize 0\n",
      "2018-06-26 12:31:07,512 : INFO : EPOCH 1 - PROGRESS: at 9.93% examples, 523836 words/s, in_qsize 13, out_qsize 0\n",
      "2018-06-26 12:31:08,526 : INFO : EPOCH 1 - PROGRESS: at 10.29% examples, 523849 words/s, in_qsize 14, out_qsize 0\n",
      "2018-06-26 12:31:09,548 : INFO : EPOCH 1 - PROGRESS: at 10.67% examples, 525129 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:31:10,743 : INFO : EPOCH 1 - PROGRESS: at 11.02% examples, 521461 words/s, in_qsize 10, out_qsize 5\n",
      "2018-06-26 12:31:11,750 : INFO : EPOCH 1 - PROGRESS: at 11.39% examples, 522233 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:31:12,764 : INFO : EPOCH 1 - PROGRESS: at 11.78% examples, 523376 words/s, in_qsize 15, out_qsize 1\n",
      "2018-06-26 12:31:13,791 : INFO : EPOCH 1 - PROGRESS: at 12.18% examples, 524719 words/s, in_qsize 11, out_qsize 2\n",
      "2018-06-26 12:31:14,800 : INFO : EPOCH 1 - PROGRESS: at 12.55% examples, 525499 words/s, in_qsize 6, out_qsize 0\n",
      "2018-06-26 12:31:15,809 : INFO : EPOCH 1 - PROGRESS: at 12.89% examples, 525157 words/s, in_qsize 16, out_qsize 0\n",
      "2018-06-26 12:31:16,815 : INFO : EPOCH 1 - PROGRESS: at 13.24% examples, 524862 words/s, in_qsize 12, out_qsize 3\n",
      "2018-06-26 12:31:17,824 : INFO : EPOCH 1 - PROGRESS: at 13.63% examples, 526340 words/s, in_qsize 12, out_qsize 2\n",
      "2018-06-26 12:31:18,851 : INFO : EPOCH 1 - PROGRESS: at 14.03% examples, 527427 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:31:19,852 : INFO : EPOCH 1 - PROGRESS: at 14.27% examples, 523550 words/s, in_qsize 7, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:31:23,956 : INFO : EPOCH 1 - PROGRESS: at 15.61% examples, 519715 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "2018-06-26 12:33:47,449 : INFO : EPOCH 1 - PROGRESS: at 63.18% examples, 497847 words/s, in_qsize 14, out_qsize 1\n",
      "2018-06-26 12:33:48,454 : INFO : EPOCH 1 - PROGRESS: at 63.58% examples, 498287 words/s, in_qsize 5, out_qsize 0\n",
      "2018-06-26 12:33:49,471 : INFO : EPOCH 1 - PROGRESS: at 63.86% examples, 497757 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:33:50,503 : INFO : EPOCH 1 - PROGRESS: at 64.17% examples, 497369 words/s, in_qsize 15, out_qsize 1\n",
      "2018-06-26 12:33:51,505 : INFO : EPOCH 1 - PROGRESS: at 64.54% examples, 497549 words/s, in_qsize 13, out_qsize 3\n",
      "2018-06-26 12:33:52,559 : INFO : EPOCH 1 - PROGRESS: at 64.99% examples, 498297 words/s, in_qsize 15, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:33:53,574 : INFO : EPOCH 1 - PROGRESS: at 65.38% examples, 498532 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:33:54,603 : INFO : EPOCH 1 - PROGRESS: at 65.75% examples, 498681 words/s, in_qsize 16, out_qsize 3\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:35:08,246 : INFO : EPOCH 1 - PROGRESS: at 90.86% examples, 503875 words/s, in_qsize 15, out_qsize 0\n",
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      "2018-06-26 12:35:33,773 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2018-06-26 12:35:33,798 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
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      "2018-06-26 12:35:33,834 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-06-26 12:35:33,835 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-06-26 12:35:33,836 : INFO : EPOCH - 1 : training on 176270198 raw words (149595533 effective words) took 293.9s, 509012 effective words/s\n",
      "2018-06-26 12:35:34,844 : INFO : EPOCH 2 - PROGRESS: at 0.37% examples, 536250 words/s, in_qsize 0, out_qsize 2\n",
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      "2018-06-26 12:36:13,719 : INFO : EPOCH 2 - PROGRESS: at 14.44% examples, 530317 words/s, in_qsize 16, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:36:14,751 : INFO : EPOCH 2 - PROGRESS: at 14.80% examples, 530043 words/s, in_qsize 15, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:37:29,464 : INFO : EPOCH 2 - PROGRESS: at 39.43% examples, 505975 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-06-26 12:38:34,332 : INFO : EPOCH 2 - PROGRESS: at 60.93% examples, 499224 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:38:35,345 : INFO : EPOCH 2 - PROGRESS: at 61.18% examples, 498390 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:38:36,368 : INFO : EPOCH 2 - PROGRESS: at 61.49% examples, 498106 words/s, in_qsize 1, out_qsize 0\n",
      "2018-06-26 12:38:37,373 : INFO : EPOCH 2 - PROGRESS: at 61.82% examples, 497963 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:38:38,385 : INFO : EPOCH 2 - PROGRESS: at 62.19% examples, 498032 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:38:39,394 : INFO : EPOCH 2 - PROGRESS: at 62.56% examples, 498250 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:38:40,415 : INFO : EPOCH 2 - PROGRESS: at 62.88% examples, 498063 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:38:41,429 : INFO : EPOCH 2 - PROGRESS: at 63.12% examples, 497176 words/s, in_qsize 0, out_qsize 0\n",
      "2018-06-26 12:38:42,431 : INFO : EPOCH 2 - PROGRESS: at 63.42% examples, 496866 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:38:43,436 : INFO : EPOCH 2 - PROGRESS: at 63.62% examples, 495751 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-06-26 12:39:10,817 : INFO : EPOCH 2 - PROGRESS: at 71.17% examples, 484882 words/s, in_qsize 7, out_qsize 2\n",
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      "2018-06-26 12:39:34,368 : INFO : EPOCH 2 - PROGRESS: at 78.16% examples, 480742 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-06-26 12:39:56,702 : INFO : EPOCH 2 - PROGRESS: at 84.25% examples, 474917 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:39:57,709 : INFO : EPOCH 2 - PROGRESS: at 84.58% examples, 474975 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-06-26 12:40:50,264 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2018-06-26 12:40:50,269 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2018-06-26 12:40:50,277 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
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      "2018-06-26 12:40:50,304 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-06-26 12:40:50,306 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-06-26 12:40:50,306 : INFO : EPOCH - 2 : training on 176270198 raw words (149599285 effective words) took 316.5s, 472718 effective words/s\n",
      "2018-06-26 12:40:51,319 : INFO : EPOCH 3 - PROGRESS: at 0.34% examples, 483980 words/s, in_qsize 4, out_qsize 1\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:43:34,238 : INFO : EPOCH 3 - PROGRESS: at 58.46% examples, 527783 words/s, in_qsize 15, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:44:47,674 : INFO : EPOCH 3 - PROGRESS: at 85.17% examples, 531871 words/s, in_qsize 16, out_qsize 0\n",
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      "2018-06-26 12:45:30,692 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2018-06-26 12:45:30,695 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2018-06-26 12:45:30,696 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
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      "2018-06-26 12:45:30,724 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-06-26 12:45:30,724 : INFO : EPOCH - 3 : training on 176270198 raw words (149606218 effective words) took 280.4s, 533521 effective words/s\n",
      "2018-06-26 12:45:31,768 : INFO : EPOCH 4 - PROGRESS: at 0.34% examples, 468818 words/s, in_qsize 15, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:45:54,231 : INFO : EPOCH 4 - PROGRESS: at 8.80% examples, 543607 words/s, in_qsize 13, out_qsize 2\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "2018-06-26 12:48:10,728 : INFO : EPOCH 4 - PROGRESS: at 59.11% examples, 546660 words/s, in_qsize 14, out_qsize 0\n",
      "2018-06-26 12:48:11,746 : INFO : EPOCH 4 - PROGRESS: at 59.48% examples, 546571 words/s, in_qsize 13, out_qsize 2\n",
      "2018-06-26 12:48:12,762 : INFO : EPOCH 4 - PROGRESS: at 59.88% examples, 546691 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:48:13,777 : INFO : EPOCH 4 - PROGRESS: at 60.26% examples, 546701 words/s, in_qsize 14, out_qsize 1\n",
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      "2018-06-26 12:48:15,821 : INFO : EPOCH 4 - PROGRESS: at 61.13% examples, 547614 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:48:16,851 : INFO : EPOCH 4 - PROGRESS: at 61.56% examples, 547891 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:48:17,855 : INFO : EPOCH 4 - PROGRESS: at 61.97% examples, 548096 words/s, in_qsize 11, out_qsize 4\n",
      "2018-06-26 12:48:18,869 : INFO : EPOCH 4 - PROGRESS: at 62.29% examples, 547565 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:48:19,872 : INFO : EPOCH 4 - PROGRESS: at 62.66% examples, 547526 words/s, in_qsize 15, out_qsize 0\n",
      "2018-06-26 12:48:20,879 : INFO : EPOCH 4 - PROGRESS: at 63.07% examples, 547767 words/s, in_qsize 12, out_qsize 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:48:21,885 : INFO : EPOCH 4 - PROGRESS: at 63.51% examples, 548257 words/s, in_qsize 14, out_qsize 0\n",
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      "2018-06-26 12:48:49,288 : INFO : EPOCH 4 - PROGRESS: at 73.36% examples, 546199 words/s, in_qsize 15, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:49:35,284 : INFO : EPOCH 4 - PROGRESS: at 90.16% examples, 548242 words/s, in_qsize 7, out_qsize 1\n",
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      "2018-06-26 12:50:03,337 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2018-06-26 12:50:03,353 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2018-06-26 12:50:03,354 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
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      "2018-06-26 12:50:03,381 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-06-26 12:50:03,382 : INFO : EPOCH - 4 : training on 176270198 raw words (149603698 effective words) took 272.7s, 548694 effective words/s\n",
      "2018-06-26 12:50:04,407 : INFO : EPOCH 5 - PROGRESS: at 0.35% examples, 493981 words/s, in_qsize 16, out_qsize 1\n",
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      "2018-06-26 12:50:26,848 : INFO : EPOCH 5 - PROGRESS: at 8.79% examples, 544167 words/s, in_qsize 15, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
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      "2018-06-26 12:52:56,913 : INFO : EPOCH 5 - PROGRESS: at 61.92% examples, 527536 words/s, in_qsize 12, out_qsize 1\n",
      "2018-06-26 12:52:57,942 : INFO : EPOCH 5 - PROGRESS: at 62.29% examples, 527436 words/s, in_qsize 13, out_qsize 2\n",
      "2018-06-26 12:52:58,952 : INFO : EPOCH 5 - PROGRESS: at 62.67% examples, 527544 words/s, in_qsize 14, out_qsize 1\n",
      "2018-06-26 12:52:59,954 : INFO : EPOCH 5 - PROGRESS: at 63.01% examples, 527381 words/s, in_qsize 14, out_qsize 2\n",
      "2018-06-26 12:53:00,987 : INFO : EPOCH 5 - PROGRESS: at 63.39% examples, 527408 words/s, in_qsize 13, out_qsize 2\n",
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      "2018-06-26 12:53:03,065 : INFO : EPOCH 5 - PROGRESS: at 64.07% examples, 526767 words/s, in_qsize 14, out_qsize 3\n",
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      "2018-06-26 12:53:05,095 : INFO : EPOCH 5 - PROGRESS: at 64.83% examples, 526984 words/s, in_qsize 15, out_qsize 0\n",
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      "2018-06-26 12:53:07,110 : INFO : EPOCH 5 - PROGRESS: at 65.59% examples, 527158 words/s, in_qsize 13, out_qsize 1\n",
      "2018-06-26 12:53:08,134 : INFO : EPOCH 5 - PROGRESS: at 65.96% examples, 527216 words/s, in_qsize 15, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:53:09,135 : INFO : EPOCH 5 - PROGRESS: at 66.32% examples, 527290 words/s, in_qsize 15, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 12:54:22,885 : INFO : EPOCH 5 - PROGRESS: at 92.03% examples, 527934 words/s, in_qsize 12, out_qsize 0\n",
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      "2018-06-26 12:54:45,947 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2018-06-26 12:54:45,963 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2018-06-26 12:54:45,976 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
      "2018-06-26 12:54:45,979 : INFO : worker thread finished; awaiting finish of 4 more threads\n",
      "2018-06-26 12:54:45,980 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-06-26 12:54:45,990 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-06-26 12:54:45,992 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-06-26 12:54:46,002 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-06-26 12:54:46,003 : INFO : EPOCH - 5 : training on 176270198 raw words (149606006 effective words) took 282.6s, 529359 effective words/s\n",
      "2018-06-26 12:54:46,003 : INFO : training on a 881350990 raw words (748010740 effective words) took 1446.1s, 517272 effective words/s\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import multiprocessing\n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "\n",
    "data = gensim.models.word2vec.LineSentence('tweets.txt')\n",
    "model = Word2Vec(data, size=200, window=5, min_count=3, workers=multiprocessing.cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-06-26 13:09:14,817 : INFO : saving Word2Vec object under w2v/tweets_model.w2v, separately None\n",
      "2018-06-26 13:09:14,818 : INFO : storing np array 'vectors' to w2v/tweets_model.w2v.wv.vectors.npy\n",
      "2018-06-26 13:09:16,552 : INFO : not storing attribute vectors_norm\n",
      "2018-06-26 13:09:16,554 : INFO : storing np array 'syn1neg' to w2v/tweets_model.w2v.trainables.syn1neg.npy\n",
      "2018-06-26 13:09:22,268 : INFO : saved w2v/tweets_model.w2v\n"
     ]
    }
   ],
   "source": [
    "model.save(\"w2v/tweets_model.w2v\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Visualization\n",
    "\n",
    "t-SNE was used to plot a subset of similar words from trained Word2Vec model. Firstly, similar words were found and appended each of the similar words embedding vector to the matrix. Secondly, t-SNE was applied to the matrix in order to project each word to a 2D space (i.e. dimension reduction). Finally, the 2D position of each word with a label was plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "model = Word2Vec.load('w2v/tweets_model.w2v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "% matplotlib inline\n",
    "\n",
    "\n",
    "def tsne_plot(labels, tokens, classes, clusters):\n",
    "    tsne_model = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=33)\n",
    "    new_values = tsne_model.fit_transform(tokens)\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "\n",
    "    for value in new_values:\n",
    "        x.append(value[0])\n",
    "        y.append(value[1])\n",
    "\n",
    "    colors = cm.rainbow(np.linspace(0, 1, clusters))\n",
    "    plt.figure(figsize=(16, 9))\n",
    "    for i in range(len(x)):\n",
    "        plt.scatter(x[i], y[i], c=colors[classes[i]], alpha=0.75)\n",
    "        plt.annotate(labels[i], alpha=0.75, xy=(x[i], y[i]), xytext=(5, 2), \n",
    "                     textcoords='offset points', ha='right', va='bottom', size=10)\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = []\n",
    "tokens = []\n",
    "classes = []\n",
    "\n",
    "samples = 15\n",
    "for i, word in enumerate(['программирование', 'инструкция', 'рубашка', 'аэропорт', 'фильм', 'iphone', 'обидел', 'красивый', 'нравится', 'домашка']):#random.sample(list(model.wv.vocab), samples)):#['ссора', 'iphone', 'обидел', 'красивый', 'нравится', 'домашка']):#random.sample(list(model.wv.vocab), 10):\n",
    "    tokens.append(model.wv[word])\n",
    "    labels.append(word)\n",
    "    classes.append(i)\n",
    "    for similar_word, similarity in model.wv.most_similar(word, topn=20):\n",
    "        tokens.append(model.wv[similar_word])\n",
    "        labels.append(similar_word)\n",
    "        classes.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aade97e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne_plot(labels, tokens, classes, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "1. Mikolov T. et al. Distributed representations of words and phrases and their compositionality //Advances in neural information processing systems. – 2013. – С. 3111-3119.\n",
    "2. Rubtsova U. Constructing a corpus for sentiment classification training //Software and Systems. – 2015. – №. 109. – С. 72-78.\n",
    "3. Rehurek R., Sojka P. “Software framework for topic modelling with large corpora”, Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 46-50, 2010."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--bibtex\n",
    "\n",
    "@Article{PER-GRA:2007,\n",
    "  Author    = {P\\'erez, Fernando and Granger, Brian E.},\n",
    "  Title     = {{IP}ython: a System for Interactive Scientific Computing},\n",
    "  Journal   = {Computing in Science and Engineering},\n",
    "  Volume    = {9},\n",
    "  Number    = {3},\n",
    "  Pages     = {21--29},\n",
    "  month     = may,\n",
    "  year      = 2007,\n",
    "  url       = \"http://ipython.org\",\n",
    "  ISSN      = \"1521-9615\",\n",
    "  doi       = {10.1109/MCSE.2007.53},\n",
    "  publisher = {IEEE Computer Society},\n",
    "}\n",
    "\n",
    "@article{Papa2007,\n",
    "  author = {Papa, David A. and Markov, Igor L.},\n",
    "  journal = {Approximation algorithms and metaheuristics},\n",
    "  pages = {1--38},\n",
    "  title = {{Hypergraph partitioning and clustering}},\n",
    "  url = {http://www.podload.org/pubs/book/part\\_survey.pdf},\n",
    "  year = {2007}\n",
    "}\n",
    "\n",
    "-->"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}