Doc2Vec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- coding: utf-8 -*-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING Failed to load 're2'.  Falling back to 're' for regular expression parsing. See https://github.com/blockspeiser/Sefaria-Project/wiki/Regular-Expression-Engines\n",
      "WARNING Failed to load 're2'.  Falling back to 're' for regular expression parsing. See https://github.com/Sefaria/Sefaria-Project/wiki/Regular-Expression-Engines\n"
     ]
    }
   ],
   "source": [
    "import warnings; warnings.simplefilter('ignore')\n",
    "import gensim as gen\n",
    "import json\n",
    "import codecs\n",
    "import regex as re\n",
    "import os.path\n",
    "from scipy import spatial\n",
    "\n",
    "import local_settings\n",
    "import django\n",
    "django.setup()\n",
    "\n",
    "from sefaria.model import *\n",
    "from sefaria.system.exceptions import InputError, PartialRefInputError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hebrew_spellcheck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_expander = hebrew_spellcheck.word_expander"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = codecs.open('./hebrew_stopwords.txt', encoding='utf8').read().strip().split('\\n')\n",
    "stopwords_regex = u\"(?:\\s|^)({})(?=\\s|$)\".format(u\"|\".join(stopwords))\n",
    "stopwords_regex = re.compile(stopwords_regex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "phrases = codecs.open('./select_phrases.txt', encoding='utf8').read().strip().split('\\n')\n",
    "phrases_regex = u\"(\\s|^)({})(?=\\s|$)\".format(u\"|\".join(phrases))\n",
    "phrases_regex = re.compile(phrases_regex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_talmud_topic_ranged():\n",
    "    \"\"\"\n",
    "    Reads every file in the sugyot directory.  \n",
    "    Each file in this directory contains semantically meaningful ranged refs for Talmud\n",
    "    :return: List of all Talmudic Ranged Refs\n",
    "    \"\"\"\n",
    "    all_gemara_topic_ranges = []\n",
    "    for filename in os.listdir('./sugyot/'):\n",
    "        with codecs.open('./sugyot/'+filename, 'rb', encoding='utf8') as csvfile:\n",
    "            reader = csv.reader(csvfile)\n",
    "            reader.next()\n",
    "            all_gemara_topic_ranges += [row[0] for row in reader]\n",
    "    return all_gemara_topic_ranges\n",
    "\n",
    "\n",
    "def create_list_off_talmud_books():\n",
    "    \"\"\"\n",
    "    Uses Sefaria Project to create a list of titles of every Talmud Tractate\n",
    "    :return: Set of all Talmud book titles\n",
    "    \"\"\"\n",
    "    indexes = library.all_index_records()\n",
    "    talmud_books = set(library.get_indexes_in_category(\"Bavli\"))\n",
    "    talmud_books_modified = {u'Avodah Zarah',  u'Bava Batra', u'Bava Kamma',\n",
    "     u'Bava Metzia', u'Beitzah', u'Berakhot', u'Chagigah', u'Eruvin', u'Gittin', u'Horayot',\n",
    "     u'Ketubot', u'Kiddushin', u'Makkot', u'Megillah', u'Menachot', u'Moed Katan',\n",
    "     u'Nazir', u'Nedarim', u'Pesachim', u'Rosh Hashanah', u'Sanhedrin', u'Shabbat', u'Shevuot',\n",
    "     u'Sotah', u'Sukkah', u'Taanit',  u'Yevamot', u'Yoma', u'Zevachim'}\n",
    "    return talmud_books_modified\n",
    "\n",
    "\n",
    "def create_list_off_tanakh_books():\n",
    "    \"\"\"\n",
    "    Uses Sefaria Project to create a list of titles of every book in Tanakh \n",
    "    :return: Set of all Tanakh book titles\n",
    "    \"\"\"\n",
    "    indexes = library.all_index_records()\n",
    "    tanakh_books = set(library.get_indexes_in_category(\"Tanakh\"))\n",
    "    tanakh_books_modified = set([u'Judges', u'Deuteronomy', u'Genesis', u'Exodus',\n",
    "                             u'Leviticus', u'II Kings', u'Joshua',\n",
    "                             u'I Samuel', u'Numbers', u'I Kings', u'II Samuel'])\n",
    "    return tanakh_books_modified\n",
    "    \n",
    "\n",
    "\n",
    "def get_tanakh_topic_ranges():\n",
    "    \"\"\"\n",
    "    Creates semantically meaningful ranged refs for Tanakh using Herzog's breakdown\n",
    "    :return: List of all Tanakh Ranged Refs\n",
    "    \"\"\"\n",
    "    with codecs.open('level_3_wo_overlaps.json', 'r', encoding='utf8' ) as the_file:\n",
    "        segs = json.load(the_file, encoding='utf8')\n",
    "    tanakh_topic_ranged_refs = []\n",
    "\n",
    "    for seg in segs:\n",
    "        b_ref = seg['b_ref']\n",
    "        e_ref = seg['e_ref']\n",
    "        book = b_ref.rsplit(' ', 1)[0]\n",
    "        b_chapter = b_ref.rsplit(' ', 1)[1].split(':')[0]\n",
    "        b_verse = b_ref.rsplit(' ', 1)[1].split(':')[1]\n",
    "        e_chapter = e_ref.rsplit(' ', 1)[1].split(':')[0]\n",
    "        e_verse = e_ref.rsplit(' ', 1)[1].split(':')[1]\n",
    "\n",
    "        verses = \"{}:{}-{}\".format(b_chapter, b_verse, e_verse) if b_chapter == e_chapter else \"{}:{}-{}:{}\".format(b_chapter, b_verse, e_chapter, e_verse)\n",
    "        tanakh_topic_ranged_refs.append(\"{} {}\".format(book, verses))\n",
    "    \n",
    "    return tanakh_topic_ranged_refs\n",
    "    \n",
    "\n",
    "def segment_range_dicts(topic_ranged_refs):\n",
    "    \"\"\"\n",
    "    Creates two dictionaries.  These are mappings between ranged refs and segments refs.  \n",
    "    This receives a list of ranged refs.  It iterates over every ranged ref and creates a mapping between the ranged refs and the segment refs.\n",
    "    :param topic_ranged_refs: list of ranged refs\n",
    "    :return: tuple of dicts. The first is dict mapping ranged refs to all segment refs.  The second is a dict mapping each segment ref to its corresponding ranged ref.\n",
    "    \"\"\"\n",
    "    ranged_to_segment = {}\n",
    "    segment_to_ranged = {}\n",
    "\n",
    "    for topic_ranged_ref in topic_ranged_refs:\n",
    "        ranged_to_segment[topic_ranged_ref] = {}\n",
    "        topic_seg_refs = Ref(topic_ranged_ref).range_list()\n",
    "        for seg_ref in topic_seg_refs:\n",
    "            ranged_to_segment[topic_ranged_ref][seg_ref.normal()] = \"\"\n",
    "            segment_to_ranged[seg_ref.normal()] = topic_ranged_ref\n",
    "            \n",
    "    return ranged_to_segment, segment_to_ranged\n",
    "\n",
    "def is_from_category(ref, books_in_category):\n",
    "    \"\"\"\n",
    "    Checks to see if a tref belongs to a list of book within a particular category\n",
    "    For Example:  Is Genesis 12:3 within Tanakh\n",
    "    :param ref: tref to be examined\n",
    "    :param books_in_category: List of books in category\n",
    "    :return: Boolean Value determining if Tref is within category\n",
    "    \"\"\"\n",
    "    try:\n",
    "        if Ref(ref).index.title in books_in_category:\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "    except InputError:\n",
    "        return False\n",
    "    \n",
    "\n",
    "def pull_out_suffix(string):\n",
    "    \"\"\"\n",
    "    Receives a string of Hebrew Text.  Iterates over every hebrew word and splits each\n",
    "    word into its root and suffix.  \n",
    "    :param string: String of Hebrew Text\n",
    "    :return: String with Hebrew compound words split into Root and Suffix\n",
    "    \"\"\"\n",
    "    string = string.split()\n",
    "    string = ' '.join([word_expander.get(word, word) for word in string])\n",
    "    return string\n",
    "\n",
    "def remove_stopwords(string):\n",
    "    \"\"\"\n",
    "    Replaces Stopwords with a space character  \n",
    "    :param string: String of Hebrew Text\n",
    "    :return: String without stopwords\n",
    "    \"\"\"\n",
    "    return re.sub(stopwords_regex, u' ', string)\n",
    "\n",
    "def remove_dicta_prefix(string):\n",
    "    \"\"\"\n",
    "    Removes prefixes that were detected by Dicta\n",
    "    :param string: String of Hebrew Text\n",
    "    :return: String without prefixes\n",
    "    \"\"\"\n",
    "    return re.sub(ur'[\\u05d0-\\u05ea]+┉', u'', string)\n",
    "\n",
    "def remove_punctuation(data):\n",
    "    \"\"\"\n",
    "    Removes various punctation from Hebrew text.\n",
    "    :param data: String of Hebrew Text\n",
    "    :return: String without punctuation\n",
    "    \"\"\"\n",
    "    data = re.sub(ur'־', u' ', data)\n",
    "    data = re.sub(ur'\\([^)]+\\)', u' ', data)\n",
    "    data = re.sub(ur'<[^>]+>', u' ', data)\n",
    "    data = re.sub(ur'\\[[^\\]]+\\]', u' ', data)\n",
    "    data = re.sub(ur'[^ \\u05d0-\\u05ea\"\\'״׳]', u' ', data)\n",
    "    data = re.sub(ur'(^|\\s)[\"\\'״׳]+', u' ', data)\n",
    "    data = re.sub(ur'[\"\\'״׳]+(\\s|$)', u' ', data)\n",
    "    return data\n",
    "\n",
    "def strip_stopwords_and_remove_punctuation(data):\n",
    "    \"\"\"\n",
    "    This method takes a string of hebrew text and does all necessary cleaning for Word2Vec.\n",
    "    :param data: String of Hebrew Text\n",
    "    :return: String ready for Word2Vec model\n",
    "    \"\"\"\n",
    "    data = data.strip().split(u'~~')[1]\n",
    "    data = remove_dicta_prefix(data)\n",
    "    data = remove_punctuation(data)\n",
    "    data = pull_out_suffix(data)\n",
    "    data = remove_stopwords(data)\n",
    "    data = u' '.join(data.split())\n",
    "    return data\n",
    "\n",
    "\n",
    "\n",
    "def create_multiple_word_phrases(data):\n",
    "    \"\"\"\n",
    "    Combines selected multiple word phrases with underscore. \n",
    "    For example:  New York ----> New_York\n",
    "    This allows Word2Vec to handle multiple word phrases\n",
    "    :param data: String of Hebrew Text\n",
    "    :return: String with connected multiple word phrases\n",
    "    \"\"\"\n",
    "    def connect_with_underscore(matchobj):\n",
    "        return matchobj.group(1) + matchobj.group(2).replace(u\" \", u\"_\")\n",
    "    return re.sub(phrases_regex, connect_with_underscore, data)\n",
    "\n",
    "\n",
    "def this_is_a_bad_line(data):\n",
    "    \"\"\"\n",
    "    Checks to see if a line from the file should be included in the Word2Vec model.\n",
    "    There are some lines within the Dicta Prefix file that we cannot or do not want to include.\n",
    "    :param data: line of hebrew text from Dicta File\n",
    "    :return: Boolean Value determining if this line valid\n",
    "    \"\"\"\n",
    "    if u'~~' not in data:\n",
    "        return True\n",
    "    if data.strip().split(u'~~')[1].strip().startswith(u\"<br><br><big><strong>הדרן עלך\"):\n",
    "        return True\n",
    "    \n",
    "    return False\n",
    "    \n",
    "\n",
    "def get_segments(filename):\n",
    "    \"\"\"\n",
    "    Combs through the entire Sefaris Hebrew Library and cleans the text for Doc2Vec.\n",
    "    Creates a dict:\n",
    "        Key:  Ref\n",
    "        Value:  The text of that ref cleaned and ready for Doc2Vec\n",
    "    :param filename: Dicta Prefix Filename\n",
    "    :return: Dict for Doc2Vec\n",
    "    \"\"\"\n",
    "    \n",
    "    all_tanakh_books = create_list_off_tanakh_books()\n",
    "    all_talmud_books = create_list_off_talmud_books()\n",
    "    \n",
    "    all_data = {}\n",
    "    \n",
    "    \n",
    "    tanakh_topic_ranged_refs = get_tanakh_topic_ranges()\n",
    "    talmud_topic_ranged_refs = get_talmud_topic_ranged()\n",
    "    \n",
    "    tanakh_ranged_to_segment, tanakh_segment_to_ranged = segment_range_dicts(tanakh_topic_ranged_refs)\n",
    "    talmud_ranged_to_segment, talmud_segment_to_ranged = segment_range_dicts(talmud_topic_ranged_refs)\n",
    "\n",
    "\n",
    "    for index, data in enumerate(codecs.open(filename, encoding='utf8')):\n",
    "        \n",
    "        if this_is_a_bad_line(data):\n",
    "            continue\n",
    "            \n",
    "        ref = data.split(u'~')[0]\n",
    "        data = strip_stopwords_and_remove_punctuation(data)\n",
    "        data = create_multiple_word_phrases(data)\n",
    "        \n",
    "          \n",
    "        if index%100000 == 0:\n",
    "            print index\n",
    "            \n",
    "        if is_from_category(ref, all_tanakh_books):\n",
    "            this_ref_ranged_reg = tanakh_segment_to_ranged[ref]        \n",
    "            tanakh_ranged_to_segment[this_ref_ranged_reg][ref] = data\n",
    "        elif is_from_category(ref, all_talmud_books):\n",
    "            this_ref_ranged_reg = talmud_segment_to_ranged[ref]\n",
    "            talmud_ranged_to_segment[this_ref_ranged_reg][ref] = data\n",
    "        else:\n",
    "            all_data[ref] = data\n",
    "            \n",
    "    for text_ranged_ref in tanakh_topic_ranged_refs:\n",
    "        object_ref = Ref(text_ranged_ref)\n",
    "        all_text_subrefs = [seg_ref.normal() for seg_ref in object_ref.range_list()]\n",
    "        all_verses = [tanakh_ranged_to_segment[text_ranged_ref][seg_ref] for seg_ref in all_text_subrefs]\n",
    "        all_data[text_ranged_ref] = u' '.join(all_verses)\n",
    "        \n",
    "    for text_ranged_ref in talmud_topic_ranged_refs:\n",
    "        object_ref = Ref(text_ranged_ref)\n",
    "        all_text_subrefs = [seg_ref.normal() for seg_ref in object_ref.range_list()]\n",
    "        all_verses = [talmud_ranged_to_segment[text_ranged_ref][seg_ref] for seg_ref in all_text_subrefs]\n",
    "        all_data[text_ranged_ref] = u' '.join(all_verses)\n",
    "    \n",
    "    return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n",
      "1300000\n",
      "1400000\n",
      "1500000\n",
      "1600000\n"
     ]
    }
   ],
   "source": [
    "segments = get_segments('./sefaria-export_prefix_refs.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_clean_docs_filename = 'cleaned_docs_for_doc2vec.txt'\n",
    "with codecs.open(all_clean_docs_filename, 'wb', encoding='utf8') as the_file:\n",
    "    for k, v in segments.items():\n",
    "        the_file.write(u\"\"+k+u\"||||\"+v+u\"\\n\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initiate a SegmentGenerator object to pass to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SegmentGenerator(object):\n",
    "    def __init__(self, segments_filename):\n",
    "        self.segments_filename = segments_filename\n",
    "\n",
    "    def __iter__(self):\n",
    "        with codecs.open(self.segments_filename, 'rb', encoding='utf8') as the_file:\n",
    "            for line in the_file:\n",
    "                ref = line.split(u\"||||\")[0]\n",
    "                data = line.split(u\"||||\")[1]\n",
    "                yield gen.models.doc2vec.TaggedDocument(gen.utils.simple_preprocess(data), [ref])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "segments_generator = SegmentGenerator(all_clean_docs_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model intiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gen.models.doc2vec.Doc2Vec(vector_size=100, min_count=2, epochs=40, dm=0, dbow_words=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have initiated the Word2Vec model. The next step is to call the **build_vocab** method for the preliminary scan of the text. Call the method and print how many words are in the vocabulary of our text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO collecting all words and their counts\n",
      "INFO PROGRESS: at example #0, processed 0 words (0/s), 0 word types, 0 tags\n",
      "INFO PROGRESS: at example #10000, processed 480183 words (493516/s), 29343 word types, 10000 tags\n",
      "INFO PROGRESS: at example #20000, processed 951957 words (444592/s), 39961 word types, 20000 tags\n",
      "INFO PROGRESS: at example #30000, processed 1425416 words (464963/s), 48038 word types, 30000 tags\n",
      "INFO PROGRESS: at example #40000, processed 1894717 words (512670/s), 54567 word types, 40000 tags\n",
      "INFO PROGRESS: at example #50000, processed 2365377 words (501974/s), 60171 word types, 50000 tags\n",
      "INFO PROGRESS: at example #60000, processed 2823417 words (497557/s), 64944 word types, 60000 tags\n",
      "INFO PROGRESS: at example #70000, processed 3297189 words (506217/s), 69025 word types, 70000 tags\n",
      "INFO PROGRESS: at example #80000, processed 3766242 words (410361/s), 73037 word types, 80000 tags\n",
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      "INFO PROGRESS: at example #100000, processed 4707206 words (486392/s), 80225 word types, 100000 tags\n",
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      "INFO PROGRESS: at example #330000, processed 15564554 words (495161/s), 133663 word types, 330000 tags\n",
      "INFO PROGRESS: at example #340000, processed 16047131 words (509074/s), 135426 word types, 340000 tags\n",
      "INFO PROGRESS: at example #350000, processed 16528317 words (487438/s), 137102 word types, 350000 tags\n",
      "INFO PROGRESS: at example #360000, processed 16992728 words (385678/s), 138716 word types, 360000 tags\n",
      "INFO PROGRESS: at example #370000, processed 17459314 words (496587/s), 140194 word types, 370000 tags\n",
      "INFO PROGRESS: at example #380000, processed 17941815 words (497308/s), 141976 word types, 380000 tags\n",
      "INFO PROGRESS: at example #390000, processed 18425494 words (511493/s), 143586 word types, 390000 tags\n",
      "INFO PROGRESS: at example #400000, processed 18879190 words (492661/s), 145130 word types, 400000 tags\n",
      "INFO PROGRESS: at example #410000, processed 19354118 words (503893/s), 146581 word types, 410000 tags\n",
      "INFO PROGRESS: at example #420000, processed 19827661 words (501680/s), 148164 word types, 420000 tags\n",
      "INFO PROGRESS: at example #430000, processed 20287581 words (493520/s), 149604 word types, 430000 tags\n",
      "INFO PROGRESS: at example #440000, processed 20731012 words (490291/s), 151023 word types, 440000 tags\n",
      "INFO PROGRESS: at example #450000, processed 21190904 words (498795/s), 152321 word types, 450000 tags\n",
      "INFO PROGRESS: at example #460000, processed 21676039 words (487313/s), 153989 word types, 460000 tags\n",
      "INFO PROGRESS: at example #470000, processed 22143892 words (500792/s), 155431 word types, 470000 tags\n",
      "INFO PROGRESS: at example #480000, processed 22595090 words (493761/s), 156769 word types, 480000 tags\n",
      "INFO PROGRESS: at example #490000, processed 23069809 words (497009/s), 158075 word types, 490000 tags\n",
      "INFO PROGRESS: at example #500000, processed 23541175 words (500073/s), 159463 word types, 500000 tags\n",
      "INFO PROGRESS: at example #510000, processed 23999573 words (474654/s), 160802 word types, 510000 tags\n",
      "INFO PROGRESS: at example #520000, processed 24504548 words (414534/s), 162198 word types, 520000 tags\n",
      "INFO PROGRESS: at example #530000, processed 24944456 words (442983/s), 163499 word types, 530000 tags\n",
      "INFO PROGRESS: at example #540000, processed 25402045 words (494520/s), 164807 word types, 540000 tags\n",
      "INFO PROGRESS: at example #550000, processed 25867080 words (450814/s), 166110 word types, 550000 tags\n",
      "INFO PROGRESS: at example #560000, processed 26345904 words (316561/s), 167317 word types, 560000 tags\n",
      "INFO PROGRESS: at example #570000, processed 26831921 words (449378/s), 168489 word types, 570000 tags\n",
      "INFO PROGRESS: at example #580000, processed 27287310 words (499445/s), 169658 word types, 580000 tags\n",
      "INFO PROGRESS: at example #590000, processed 27763317 words (436195/s), 170903 word types, 590000 tags\n",
      "INFO PROGRESS: at example #600000, processed 28248368 words (415449/s), 172304 word types, 600000 tags\n",
      "INFO PROGRESS: at example #610000, processed 28705378 words (388206/s), 173443 word types, 610000 tags\n",
      "INFO PROGRESS: at example #620000, processed 29140286 words (392120/s), 174605 word types, 620000 tags\n",
      "INFO PROGRESS: at example #630000, processed 29611834 words (425413/s), 175853 word types, 630000 tags\n",
      "INFO PROGRESS: at example #640000, processed 30099393 words (462707/s), 176973 word types, 640000 tags\n",
      "INFO PROGRESS: at example #650000, processed 30560224 words (343668/s), 178096 word types, 650000 tags\n",
      "INFO PROGRESS: at example #660000, processed 31032335 words (435902/s), 179330 word types, 660000 tags\n",
      "INFO PROGRESS: at example #670000, processed 31522748 words (403802/s), 180520 word types, 670000 tags\n",
      "INFO PROGRESS: at example #680000, processed 31986186 words (393097/s), 181623 word types, 680000 tags\n",
      "INFO PROGRESS: at example #690000, processed 32451329 words (477947/s), 182709 word types, 690000 tags\n",
      "INFO PROGRESS: at example #700000, processed 32926561 words (466986/s), 183848 word types, 700000 tags\n",
      "INFO PROGRESS: at example #710000, processed 33400653 words (479733/s), 184929 word types, 710000 tags\n",
      "INFO PROGRESS: at example #720000, processed 33886419 words (514915/s), 185957 word types, 720000 tags\n",
      "INFO PROGRESS: at example #730000, processed 34349188 words (493491/s), 187128 word types, 730000 tags\n",
      "INFO PROGRESS: at example #740000, processed 34817063 words (494190/s), 188190 word types, 740000 tags\n",
      "INFO PROGRESS: at example #750000, processed 35303212 words (458654/s), 189462 word types, 750000 tags\n",
      "INFO PROGRESS: at example #760000, processed 35758175 words (496467/s), 190695 word types, 760000 tags\n",
      "INFO PROGRESS: at example #770000, processed 36217788 words (507878/s), 191803 word types, 770000 tags\n",
      "INFO PROGRESS: at example #780000, processed 36670573 words (502227/s), 192843 word types, 780000 tags\n",
      "INFO PROGRESS: at example #790000, processed 37128139 words (388388/s), 193790 word types, 790000 tags\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO PROGRESS: at example #800000, processed 37611106 words (377172/s), 194861 word types, 800000 tags\n",
      "INFO PROGRESS: at example #810000, processed 38059767 words (388978/s), 195819 word types, 810000 tags\n",
      "INFO PROGRESS: at example #820000, processed 38515852 words (259422/s), 196805 word types, 820000 tags\n",
      "INFO PROGRESS: at example #830000, processed 38986308 words (354775/s), 197946 word types, 830000 tags\n",
      "INFO PROGRESS: at example #840000, processed 39459622 words (365770/s), 198984 word types, 840000 tags\n",
      "INFO PROGRESS: at example #850000, processed 39914003 words (392453/s), 199844 word types, 850000 tags\n",
      "INFO PROGRESS: at example #860000, processed 40391398 words (438661/s), 200825 word types, 860000 tags\n",
      "INFO PROGRESS: at example #870000, processed 40861805 words (459527/s), 201860 word types, 870000 tags\n",
      "INFO PROGRESS: at example #880000, processed 41321675 words (402163/s), 203070 word types, 880000 tags\n",
      "INFO PROGRESS: at example #890000, processed 41820841 words (486125/s), 204106 word types, 890000 tags\n",
      "INFO PROGRESS: at example #900000, processed 42287064 words (489752/s), 205031 word types, 900000 tags\n",
      "INFO PROGRESS: at example #910000, processed 42747493 words (377988/s), 205862 word types, 910000 tags\n",
      "INFO PROGRESS: at example #920000, processed 43191639 words (486907/s), 206732 word types, 920000 tags\n",
      "INFO PROGRESS: at example #930000, processed 43650966 words (494004/s), 207658 word types, 930000 tags\n",
      "INFO PROGRESS: at example #940000, processed 44116824 words (469940/s), 208718 word types, 940000 tags\n",
      "INFO PROGRESS: at example #950000, processed 44593782 words (477424/s), 209608 word types, 950000 tags\n",
      "INFO PROGRESS: at example #960000, processed 45068294 words (461353/s), 210547 word types, 960000 tags\n",
      "INFO PROGRESS: at example #970000, processed 45520131 words (485705/s), 211452 word types, 970000 tags\n",
      "INFO PROGRESS: at example #980000, processed 45971563 words (498317/s), 212325 word types, 980000 tags\n",
      "INFO PROGRESS: at example #990000, processed 46454885 words (495309/s), 213283 word types, 990000 tags\n",
      "INFO PROGRESS: at example #1000000, processed 46916453 words (495003/s), 214154 word types, 1000000 tags\n",
      "INFO PROGRESS: at example #1010000, processed 47371592 words (483597/s), 215034 word types, 1010000 tags\n",
      "INFO PROGRESS: at example #1020000, processed 47832857 words (486425/s), 215979 word types, 1020000 tags\n",
      "INFO PROGRESS: at example #1030000, processed 48294612 words (495680/s), 216921 word types, 1030000 tags\n",
      "INFO PROGRESS: at example #1040000, processed 48784908 words (503335/s), 217950 word types, 1040000 tags\n",
      "INFO PROGRESS: at example #1050000, processed 49219248 words (485683/s), 218670 word types, 1050000 tags\n",
      "INFO PROGRESS: at example #1060000, processed 49687739 words (486480/s), 219467 word types, 1060000 tags\n",
      "INFO PROGRESS: at example #1070000, processed 50168120 words (484467/s), 220266 word types, 1070000 tags\n",
      "INFO PROGRESS: at example #1080000, processed 50662117 words (490665/s), 221175 word types, 1080000 tags\n",
      "INFO PROGRESS: at example #1090000, processed 51135088 words (419273/s), 222031 word types, 1090000 tags\n",
      "INFO PROGRESS: at example #1100000, processed 51612820 words (469217/s), 222945 word types, 1100000 tags\n",
      "INFO PROGRESS: at example #1110000, processed 52092419 words (443567/s), 223850 word types, 1110000 tags\n",
      "INFO PROGRESS: at example #1120000, processed 52547235 words (380628/s), 224728 word types, 1120000 tags\n",
      "INFO PROGRESS: at example #1130000, processed 53014371 words (333196/s), 225778 word types, 1130000 tags\n",
      "INFO PROGRESS: at example #1140000, processed 53493442 words (494933/s), 226685 word types, 1140000 tags\n",
      "INFO PROGRESS: at example #1150000, processed 53966555 words (497125/s), 227472 word types, 1150000 tags\n",
      "INFO PROGRESS: at example #1160000, processed 54429962 words (485710/s), 228257 word types, 1160000 tags\n",
      "INFO PROGRESS: at example #1170000, processed 54898449 words (461863/s), 229102 word types, 1170000 tags\n",
      "INFO PROGRESS: at example #1180000, processed 55373291 words (472354/s), 229924 word types, 1180000 tags\n",
      "INFO PROGRESS: at example #1190000, processed 55852459 words (360845/s), 230745 word types, 1190000 tags\n",
      "INFO PROGRESS: at example #1200000, processed 56309943 words (381654/s), 231545 word types, 1200000 tags\n",
      "INFO PROGRESS: at example #1210000, processed 56793838 words (455807/s), 232429 word types, 1210000 tags\n",
      "INFO PROGRESS: at example #1220000, processed 57256097 words (424626/s), 233204 word types, 1220000 tags\n",
      "INFO PROGRESS: at example #1230000, processed 57747229 words (473773/s), 234040 word types, 1230000 tags\n",
      "INFO PROGRESS: at example #1240000, processed 58218349 words (499709/s), 234834 word types, 1240000 tags\n",
      "INFO PROGRESS: at example #1250000, processed 58677874 words (454044/s), 235611 word types, 1250000 tags\n",
      "INFO PROGRESS: at example #1260000, processed 59171763 words (414441/s), 236486 word types, 1260000 tags\n",
      "INFO PROGRESS: at example #1270000, processed 59655576 words (477829/s), 237276 word types, 1270000 tags\n",
      "INFO PROGRESS: at example #1280000, processed 60139135 words (393145/s), 238226 word types, 1280000 tags\n",
      "INFO PROGRESS: at example #1290000, processed 60594963 words (471462/s), 238977 word types, 1290000 tags\n",
      "INFO PROGRESS: at example #1300000, processed 61072313 words (463800/s), 239768 word types, 1300000 tags\n",
      "INFO PROGRESS: at example #1310000, processed 61546605 words (498854/s), 240615 word types, 1310000 tags\n",
      "INFO PROGRESS: at example #1320000, processed 62017047 words (504826/s), 241488 word types, 1320000 tags\n",
      "INFO PROGRESS: at example #1330000, processed 62498644 words (416928/s), 242309 word types, 1330000 tags\n",
      "INFO PROGRESS: at example #1340000, processed 62965296 words (490839/s), 243058 word types, 1340000 tags\n",
      "INFO PROGRESS: at example #1350000, processed 63442731 words (467168/s), 243916 word types, 1350000 tags\n",
      "INFO PROGRESS: at example #1360000, processed 63910408 words (481576/s), 244642 word types, 1360000 tags\n",
      "INFO PROGRESS: at example #1370000, processed 64361016 words (487169/s), 245334 word types, 1370000 tags\n",
      "INFO PROGRESS: at example #1380000, processed 64830483 words (456731/s), 246102 word types, 1380000 tags\n",
      "INFO PROGRESS: at example #1390000, processed 65318991 words (433514/s), 246857 word types, 1390000 tags\n",
      "INFO PROGRESS: at example #1400000, processed 65797489 words (410417/s), 247710 word types, 1400000 tags\n",
      "INFO PROGRESS: at example #1410000, processed 66285677 words (425977/s), 248463 word types, 1410000 tags\n",
      "INFO PROGRESS: at example #1420000, processed 66743720 words (358776/s), 249214 word types, 1420000 tags\n",
      "INFO PROGRESS: at example #1430000, processed 67226862 words (435795/s), 249953 word types, 1430000 tags\n",
      "INFO PROGRESS: at example #1440000, processed 67716891 words (461024/s), 250703 word types, 1440000 tags\n",
      "INFO PROGRESS: at example #1450000, processed 68200847 words (489491/s), 251555 word types, 1450000 tags\n",
      "INFO PROGRESS: at example #1460000, processed 68662086 words (485924/s), 252249 word types, 1460000 tags\n",
      "INFO PROGRESS: at example #1470000, processed 69137928 words (485530/s), 253020 word types, 1470000 tags\n",
      "INFO PROGRESS: at example #1480000, processed 69605075 words (476669/s), 253758 word types, 1480000 tags\n",
      "INFO PROGRESS: at example #1490000, processed 70076169 words (490202/s), 254478 word types, 1490000 tags\n",
      "INFO PROGRESS: at example #1500000, processed 70547096 words (455984/s), 255221 word types, 1500000 tags\n",
      "INFO PROGRESS: at example #1510000, processed 71023435 words (306855/s), 255949 word types, 1510000 tags\n",
      "INFO PROGRESS: at example #1520000, processed 71492092 words (456152/s), 256771 word types, 1520000 tags\n",
      "INFO PROGRESS: at example #1530000, processed 71989392 words (483385/s), 257515 word types, 1530000 tags\n",
      "INFO PROGRESS: at example #1540000, processed 72471610 words (468699/s), 258259 word types, 1540000 tags\n",
      "INFO PROGRESS: at example #1550000, processed 72946418 words (489503/s), 259014 word types, 1550000 tags\n",
      "INFO collected 259021 word types and 1550133 unique tags from a corpus of 1550133 examples and 72952683 words\n",
      "INFO Loading a fresh vocabulary\n",
      "INFO effective_min_count=2 retains 151713 unique words (58% of original 259021, drops 107308)\n",
      "INFO effective_min_count=2 leaves 72845375 word corpus (99% of original 72952683, drops 107308)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO deleting the raw counts dictionary of 259021 items\n",
      "INFO sample=0.001 downsamples 24 most-common words\n",
      "INFO downsampling leaves estimated 71459864 word corpus (98.1% of prior 72845375)\n",
      "INFO estimated required memory for 151713 words and 100 dimensions: 1127306700 bytes\n",
      "INFO resetting layer weights\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of words in vocabulary: 151713\n"
     ]
    }
   ],
   "source": [
    "model.build_vocab(segments_generator)\n",
    "print(\"Number of words in vocabulary: {}\".format(len(model.wv.vocab)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous step we created the vocabulary for our model, it is now time to train! Don't forget to add the following parameters:\n",
    "1. total_examples=model.corpus_count \n",
    "2. epochs=model.epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO training model with 3 workers on 151713 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=5\n",
      "INFO EPOCH 1 - PROGRESS: at 0.13% examples, 94930 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 0.30% examples, 104231 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 0.49% examples, 115582 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 0.70% examples, 120362 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 0.90% examples, 123805 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 1.11% examples, 126287 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 1.31% examples, 127989 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 1.50% examples, 128699 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 1.71% examples, 130050 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 1.91% examples, 131641 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 2.09% examples, 132014 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 2.30% examples, 132531 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 2.48% examples, 132942 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 2.68% examples, 133214 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 2.89% examples, 133936 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 3.09% examples, 134075 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 3.29% examples, 134405 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 3.50% examples, 134748 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 3.72% examples, 135239 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 3.93% examples, 135221 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 4.13% examples, 135669 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 4.31% examples, 135749 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 4.50% examples, 135860 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 4.72% examples, 136056 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 4.91% examples, 136119 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 5.12% examples, 136244 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 5.32% examples, 136230 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 5.48% examples, 135466 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 5.63% examples, 134802 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 5.81% examples, 134429 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 5.97% examples, 134149 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 6.17% examples, 134212 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 6.39% examples, 134410 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 6.59% examples, 134569 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 6.80% examples, 134595 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 7.01% examples, 134603 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 7.22% examples, 134776 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 7.42% examples, 134886 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 7.61% examples, 134693 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 7.79% examples, 134875 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 7.99% examples, 134814 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 8.19% examples, 135034 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 8.38% examples, 135083 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 8.54% examples, 134601 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 8.70% examples, 134194 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 8.86% examples, 133746 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 9.00% examples, 133254 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 9.18% examples, 133001 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 9.36% examples, 133069 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 9.56% examples, 133050 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 9.75% examples, 133131 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 9.91% examples, 133038 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 10.11% examples, 132972 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 10.29% examples, 132997 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 10.49% examples, 133006 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 10.70% examples, 133160 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 10.91% examples, 133156 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 11.13% examples, 133338 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 11.33% examples, 133238 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 11.49% examples, 133369 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 11.65% examples, 133080 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 11.82% examples, 132848 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 12.00% examples, 132815 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 12.19% examples, 132869 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 12.36% examples, 132769 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 12.54% examples, 132798 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 12.74% examples, 132697 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 1 - PROGRESS: at 57.13% examples, 135872 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 1 - PROGRESS: at 76.36% examples, 136216 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 1 - PROGRESS: at 90.71% examples, 137248 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 90.90% examples, 137254 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 91.12% examples, 137268 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 1 - PROGRESS: at 93.11% examples, 137404 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 1 - PROGRESS: at 95.40% examples, 137584 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 95.61% examples, 137596 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 95.80% examples, 137605 words/s, in_qsize 6, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 1 - PROGRESS: at 95.99% examples, 137620 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 96.21% examples, 137637 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 96.42% examples, 137649 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 1 - PROGRESS: at 97.83% examples, 137731 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 98.05% examples, 137746 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 98.25% examples, 137758 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 98.46% examples, 137758 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 98.63% examples, 137770 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 98.81% examples, 137784 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 99.02% examples, 137800 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 99.22% examples, 137819 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 1 - PROGRESS: at 99.61% examples, 137844 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 1 - PROGRESS: at 99.81% examples, 137859 words/s, in_qsize 5, out_qsize 0\n",
      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 1 : training on 72952683 raw words (72993472 effective words) took 529.3s, 137914 effective words/s\n",
      "INFO EPOCH 2 - PROGRESS: at 0.16% examples, 114586 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 0.36% examples, 128096 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 2.21% examples, 142650 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 3.03% examples, 143257 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 7.17% examples, 143442 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 14.48% examples, 140738 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 14.67% examples, 140720 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 14.87% examples, 140578 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 2 - PROGRESS: at 15.07% examples, 140606 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 15.27% examples, 140555 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 34.17% examples, 139406 words/s, in_qsize 6, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 2 - PROGRESS: at 34.31% examples, 139143 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 2 - PROGRESS: at 53.35% examples, 138047 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 66.23% examples, 137732 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 66.61% examples, 137722 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 66.80% examples, 137718 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 66.95% examples, 137688 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 67.16% examples, 137702 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 67.38% examples, 137697 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 67.60% examples, 137696 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 67.80% examples, 137711 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 68.00% examples, 137709 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 68.19% examples, 137697 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 68.40% examples, 137700 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 68.59% examples, 137715 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 68.78% examples, 137711 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 68.98% examples, 137729 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 69.17% examples, 137724 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 69.35% examples, 137734 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 69.54% examples, 137731 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 69.74% examples, 137727 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 69.93% examples, 137724 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 70.13% examples, 137743 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 70.33% examples, 137748 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 70.50% examples, 137765 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 70.70% examples, 137751 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 70.89% examples, 137748 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 71.11% examples, 137754 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 71.30% examples, 137767 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 71.49% examples, 137764 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 71.69% examples, 137763 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 71.91% examples, 137762 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 72.11% examples, 137772 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 72.32% examples, 137773 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 2 - PROGRESS: at 72.53% examples, 137772 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 72.73% examples, 137789 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 72.94% examples, 137763 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 73.13% examples, 137776 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 73.32% examples, 137779 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 73.53% examples, 137780 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 73.73% examples, 137782 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 73.93% examples, 137809 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 74.12% examples, 137794 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 74.33% examples, 137787 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 74.53% examples, 137796 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 74.75% examples, 137788 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 74.94% examples, 137799 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 75.14% examples, 137796 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 75.33% examples, 137798 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 75.53% examples, 137797 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 75.74% examples, 137811 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 75.93% examples, 137816 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 76.14% examples, 137831 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 76.34% examples, 137812 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 76.53% examples, 137827 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 76.73% examples, 137833 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 76.93% examples, 137839 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 77.14% examples, 137836 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 77.34% examples, 137831 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 77.54% examples, 137833 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 77.74% examples, 137850 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 77.92% examples, 137856 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 78.13% examples, 137853 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 78.35% examples, 137848 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 78.54% examples, 137862 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 78.74% examples, 137857 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 78.94% examples, 137876 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 79.11% examples, 137853 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 79.29% examples, 137864 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 79.49% examples, 137869 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 79.70% examples, 137881 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 79.89% examples, 137878 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 80.08% examples, 137853 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 80.25% examples, 137811 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 80.47% examples, 137817 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 80.67% examples, 137821 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 80.86% examples, 137834 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 81.07% examples, 137820 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 81.26% examples, 137811 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 81.47% examples, 137820 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 81.63% examples, 137643 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 81.81% examples, 137621 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 81.98% examples, 137600 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 82.17% examples, 137586 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 82.35% examples, 137594 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 82.56% examples, 137573 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 82.77% examples, 137564 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 82.98% examples, 137560 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 83.19% examples, 137564 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 83.37% examples, 137564 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 83.56% examples, 137577 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 83.77% examples, 137584 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 83.96% examples, 137576 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 84.16% examples, 137581 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 84.55% examples, 137593 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 84.96% examples, 137598 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 85.15% examples, 137589 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 85.36% examples, 137603 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 85.55% examples, 137608 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 85.74% examples, 137605 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 85.95% examples, 137616 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 86.15% examples, 137612 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 86.36% examples, 137615 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 86.57% examples, 137625 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 86.78% examples, 137624 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 86.98% examples, 137637 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 87.16% examples, 137647 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 87.55% examples, 137647 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 87.75% examples, 137638 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 87.93% examples, 137606 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 88.15% examples, 137620 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 88.36% examples, 137613 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 88.56% examples, 137610 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 88.77% examples, 137625 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 88.96% examples, 137624 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 89.15% examples, 137598 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 89.33% examples, 137615 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 89.50% examples, 137595 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 89.70% examples, 137616 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 89.89% examples, 137620 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 90.10% examples, 137622 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 90.30% examples, 137634 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 90.47% examples, 137625 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 90.67% examples, 137636 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 90.86% examples, 137630 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 91.06% examples, 137624 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 91.27% examples, 137630 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 91.49% examples, 137639 words/s, in_qsize 6, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 2 - PROGRESS: at 91.67% examples, 137641 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 91.86% examples, 137635 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 92.06% examples, 137641 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 92.26% examples, 137646 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 92.44% examples, 137646 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 92.64% examples, 137656 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 92.85% examples, 137666 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 93.25% examples, 137667 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 93.44% examples, 137676 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 93.65% examples, 137689 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 93.84% examples, 137680 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 94.05% examples, 137689 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 94.23% examples, 137689 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 94.44% examples, 137692 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 94.63% examples, 137700 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 94.83% examples, 137690 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 95.03% examples, 137689 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 95.25% examples, 137699 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 95.44% examples, 137698 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 95.64% examples, 137700 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 95.83% examples, 137707 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 96.02% examples, 137709 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 96.24% examples, 137716 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 96.44% examples, 137716 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 97.04% examples, 137712 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 97.24% examples, 137723 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 97.42% examples, 137724 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 97.62% examples, 137726 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 98.01% examples, 137728 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 98.20% examples, 137729 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 98.40% examples, 137731 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 98.59% examples, 137744 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 98.76% examples, 137749 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 2 - PROGRESS: at 99.35% examples, 137766 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 99.55% examples, 137760 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 99.73% examples, 137772 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 2 - PROGRESS: at 99.97% examples, 137797 words/s, in_qsize 3, out_qsize 0\n",
      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 2 : training on 72952683 raw words (72993317 effective words) took 529.7s, 137812 effective words/s\n",
      "INFO EPOCH 3 - PROGRESS: at 0.15% examples, 106003 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 0.35% examples, 124719 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 0.54% examples, 128469 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 0.74% examples, 131536 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 0.93% examples, 132313 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 1.14% examples, 133946 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 1.32% examples, 135485 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 1.51% examples, 135181 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 1.72% examples, 135859 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 1.93% examples, 136459 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 2.12% examples, 137098 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 3 - PROGRESS: at 2.51% examples, 137061 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 2.71% examples, 136924 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 2.92% examples, 137540 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 3.12% examples, 137643 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 3.30% examples, 137407 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 3.50% examples, 137344 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 3.72% examples, 137652 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 3.93% examples, 137708 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 3 - PROGRESS: at 10.23% examples, 137619 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 3 - PROGRESS: at 46.30% examples, 130486 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 3 - PROGRESS: at 63.50% examples, 127933 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 3 - PROGRESS: at 81.36% examples, 128127 words/s, in_qsize 6, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 3 - PROGRESS: at 81.55% examples, 128115 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 3 - PROGRESS: at 81.70% examples, 128087 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 3 - PROGRESS: at 83.18% examples, 128099 words/s, in_qsize 5, out_qsize 1\n",
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      "INFO EPOCH 3 - PROGRESS: at 98.79% examples, 127657 words/s, in_qsize 6, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 3 - PROGRESS: at 98.98% examples, 127672 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 3 : training on 72952683 raw words (72992918 effective words) took 571.1s, 127815 effective words/s\n",
      "INFO EPOCH 4 - PROGRESS: at 0.15% examples, 106490 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 4 - PROGRESS: at 16.28% examples, 126397 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 4 - PROGRESS: at 34.69% examples, 129610 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "INFO EPOCH 4 - PROGRESS: at 87.33% examples, 127275 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 4 - PROGRESS: at 87.50% examples, 127271 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 4 - PROGRESS: at 87.65% examples, 127207 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 4 : training on 72952683 raw words (72995886 effective words) took 573.9s, 127196 effective words/s\n",
      "INFO EPOCH 5 - PROGRESS: at 0.09% examples, 65485 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 5 - PROGRESS: at 4.79% examples, 128930 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 5 - PROGRESS: at 23.88% examples, 135276 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "INFO EPOCH 5 - PROGRESS: at 99.75% examples, 136067 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO EPOCH 5 - PROGRESS: at 100.00% examples, 136125 words/s, in_qsize 0, out_qsize 1\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 5 : training on 72952683 raw words (72992332 effective words) took 536.2s, 136124 effective words/s\n",
      "INFO EPOCH 6 - PROGRESS: at 0.15% examples, 105143 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 6 - PROGRESS: at 18.88% examples, 141930 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 6 - PROGRESS: at 19.07% examples, 141974 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 6 - PROGRESS: at 19.26% examples, 141937 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "INFO EPOCH 6 - PROGRESS: at 78.54% examples, 142911 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 6 - PROGRESS: at 78.74% examples, 142903 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 6 - PROGRESS: at 78.95% examples, 142915 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 6 - PROGRESS: at 97.64% examples, 142019 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 6 - PROGRESS: at 97.82% examples, 142007 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 6 - PROGRESS: at 99.97% examples, 141958 words/s, in_qsize 3, out_qsize 0\n",
      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 6 : training on 72952683 raw words (72994592 effective words) took 514.2s, 141963 effective words/s\n",
      "INFO EPOCH 7 - PROGRESS: at 0.15% examples, 105334 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 7 - PROGRESS: at 0.35% examples, 122986 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 7 - PROGRESS: at 16.32% examples, 136815 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 7 - PROGRESS: at 35.49% examples, 136910 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 7 - PROGRESS: at 54.85% examples, 136909 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 7 - PROGRESS: at 74.33% examples, 137259 words/s, in_qsize 5, out_qsize 0\n",
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      "INFO EPOCH 7 - PROGRESS: at 92.65% examples, 136736 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 7 - PROGRESS: at 92.85% examples, 136744 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 7 - PROGRESS: at 93.06% examples, 136744 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 7 - PROGRESS: at 99.81% examples, 136611 words/s, in_qsize 6, out_qsize 0\n",
      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 7 : training on 72952683 raw words (72991720 effective words) took 534.1s, 136664 effective words/s\n",
      "INFO EPOCH 8 - PROGRESS: at 0.15% examples, 105407 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 8 - PROGRESS: at 11.19% examples, 137312 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 8 - PROGRESS: at 11.37% examples, 137221 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 8 - PROGRESS: at 30.55% examples, 137566 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "INFO EPOCH 8 - PROGRESS: at 99.91% examples, 134574 words/s, in_qsize 5, out_qsize 0\n",
      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 8 : training on 72952683 raw words (72991422 effective words) took 542.3s, 134606 effective words/s\n",
      "INFO EPOCH 9 - PROGRESS: at 0.05% examples, 33787 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO EPOCH 9 - PROGRESS: at 4.24% examples, 133655 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 9 - PROGRESS: at 4.45% examples, 133897 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 9 - PROGRESS: at 23.56% examples, 136853 words/s, in_qsize 6, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "INFO EPOCH 9 - PROGRESS: at 81.05% examples, 137292 words/s, in_qsize 5, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 9 - PROGRESS: at 81.25% examples, 137311 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 9 - PROGRESS: at 81.44% examples, 137310 words/s, in_qsize 6, out_qsize 0\n",
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      "INFO worker thread finished; awaiting finish of 2 more threads\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 9 : training on 72952683 raw words (72993772 effective words) took 531.2s, 137415 effective words/s\n",
      "INFO EPOCH 10 - PROGRESS: at 0.15% examples, 106556 words/s, in_qsize 5, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "INFO EPOCH 10 - PROGRESS: at 89.29% examples, 135978 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 89.48% examples, 135974 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 89.67% examples, 135982 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 89.84% examples, 135985 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 90.06% examples, 135990 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 90.25% examples, 135990 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 90.44% examples, 136007 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 90.64% examples, 136007 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 90.84% examples, 136011 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 91.05% examples, 136016 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 91.26% examples, 136031 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 91.46% examples, 136033 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 91.65% examples, 136033 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 91.82% examples, 136037 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 92.01% examples, 136049 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 92.20% examples, 136047 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 92.40% examples, 136060 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 92.58% examples, 136063 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 92.78% examples, 136072 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 92.98% examples, 136082 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 93.17% examples, 136082 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 93.36% examples, 136094 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 93.55% examples, 136087 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 93.76% examples, 136093 words/s, in_qsize 6, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO EPOCH 10 - PROGRESS: at 93.97% examples, 136098 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 94.16% examples, 136093 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 94.34% examples, 136090 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 94.53% examples, 136080 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 94.73% examples, 136085 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 94.94% examples, 136098 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 95.13% examples, 136091 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 95.34% examples, 136109 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 95.54% examples, 136111 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 95.72% examples, 136107 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 95.90% examples, 136096 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 96.12% examples, 136092 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 96.32% examples, 136101 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 96.53% examples, 136107 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 96.72% examples, 136107 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 96.95% examples, 136118 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 97.14% examples, 136134 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 97.32% examples, 136132 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 97.51% examples, 136132 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 97.71% examples, 136149 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 97.89% examples, 136145 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 98.08% examples, 136143 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 98.29% examples, 136148 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 98.49% examples, 136140 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 98.67% examples, 136155 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 98.85% examples, 136167 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 99.08% examples, 136160 words/s, in_qsize 6, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 99.27% examples, 136173 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 99.47% examples, 136185 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 99.65% examples, 136183 words/s, in_qsize 5, out_qsize 0\n",
      "INFO EPOCH 10 - PROGRESS: at 99.86% examples, 136192 words/s, in_qsize 6, out_qsize 0\n",
      "INFO worker thread finished; awaiting finish of 2 more threads\n",
      "INFO worker thread finished; awaiting finish of 1 more threads\n",
      "INFO worker thread finished; awaiting finish of 0 more threads\n",
      "INFO EPOCH - 10 : training on 72952683 raw words (72993034 effective words) took 535.8s, 136228 effective words/s\n",
      "INFO training on a 729526830 raw words (729932465 effective words) took 5397.7s, 135230 effective words/s\n"
     ]
    }
   ],
   "source": [
    "model.train(segments_generator, total_examples=model.corpus_count, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_topics = [u\"פסח\", u\"תפילה\", u\"משה\", u\"אברהם\", u\"צדקה\", u\"שבת\", u\"רות\", u\"אסתר\", u\"תשובה\", u\"חטא\", u\"חגים\", u\"קהילה\", u\"ישראל\", u\"צדק\", u\"התגלות\", u\"אהבה\", u\"מין\", u\"תפילין\", u\"סביבה\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6645\n",
      "6100\n",
      "5354\n",
      "7285\n",
      "6111\n",
      "6109\n",
      "7745\n",
      "7344\n",
      "3993\n",
      "5056\n",
      "6052\n",
      "3521\n",
      "5853\n",
      "8124\n",
      "4154\n",
      "5105\n",
      "5900\n",
      "6217\n",
      "7724\n"
     ]
    }
   ],
   "source": [
    "def get_ref_score(ref):\n",
    "    score = 0\n",
    "    try:\n",
    "        score = 1-spatial.distance.cosine(model.docvecs[ref], model[u'פסח']) \n",
    "    except KeyError:\n",
    "        pass\n",
    "    return score\n",
    "\n",
    "\n",
    "test_topics_dict = {}\n",
    "for topic in test_topics:\n",
    "    test_topics_dict[topic] = {}\n",
    "    topic_sources = model.docvecs.most_similar([model[topic]], topn=3000)\n",
    "    topic_sources = [x[0] for x in topic_sources]\n",
    "    set_of_related = set(topic_sources)\n",
    "    for tref in topic_sources:\n",
    "        try:\n",
    "            oref = Ref(tref)\n",
    "        except PartialRefInputError:\n",
    "            continue\n",
    "        for l in oref.linkset():\n",
    "            for other_ref in l.refs:\n",
    "                if other_ref != tref:\n",
    "                    set_of_related.add(other_ref)\n",
    "    print len(set_of_related)\n",
    "    final_scores = {}\n",
    "    for tref in set_of_related:\n",
    "        try:\n",
    "            if not Ref(tref).is_segment_level():\n",
    "                continue\n",
    "        except InputError:\n",
    "            continue\n",
    "\n",
    "        final_scores[tref] = get_ref_score(tref)\n",
    "        try:\n",
    "            oref = Ref(tref)\n",
    "        except InputError:\n",
    "            print tref\n",
    "            continue\n",
    "        for l in oref.linkset():\n",
    "            for other_ref in l.refs:\n",
    "                if other_ref != tref:\n",
    "                    try:\n",
    "                        other_oref = Ref(other_ref)\n",
    "                    except InputError:\n",
    "                        continue\n",
    "                    if other_oref.is_segment_level():\n",
    "                        final_scores[tref] += get_ref_score(other_ref)\n",
    "    final_scores = sorted(final_scores.items(), key=lambda x: x[1], reverse=True)\n",
    "    for top_source in final_scores[:100]:\n",
    "        test_topics_dict[topic][top_source[0]] = top_source[1]   \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "with codecs.open('test_topics.json', 'w', encoding='utf8') as the_file:\n",
    "    json.dump(test_topics_dict, the_file, indent=2, ensure_ascii=False)   "
   ]
  }
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