data-preprocessing and text classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "label the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 2)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12311_10</td>\n",
       "      <td>Naturally in a film who's main themes are of m...</td>\n",
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       "      <th>1</th>\n",
       "      <td>8348_2</td>\n",
       "      <td>This movie is a disaster within a disaster fil...</td>\n",
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       "      <th>2</th>\n",
       "      <td>5828_4</td>\n",
       "      <td>All in all, this is a movie for kids. We saw i...</td>\n",
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       "      <th>3</th>\n",
       "      <td>7186_2</td>\n",
       "      <td>Afraid of the Dark left me with the impression...</td>\n",
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      ],
      "text/plain": [
       "         id                                             review\n",
       "0  12311_10  Naturally in a film who's main themes are of m...\n",
       "1    8348_2  This movie is a disaster within a disaster fil...\n",
       "2    5828_4  All in all, this is a movie for kids. We saw i...\n",
       "3    7186_2  Afraid of the Dark left me with the impression...\n",
       "4   12128_7  A very accurate depiction of small time mob li..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_csv('./data/testData.tsv', sep='\\t')\n",
    "print(data_test.shape)\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = data_test.id.str.split('_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_label = []\n",
    "for i in range(25000):\n",
    "    test_label.append(1 if int(temp.loc[i][1])>5 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   sentiment\n",
       "0          1\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label = pd.DataFrame(test_label)\n",
    "test_label.columns = ['sentiment']\n",
    "test_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = data_test.join(test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12311_10</td>\n",
       "      <td>Naturally in a film who's main themes are of m...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8348_2</td>\n",
       "      <td>This movie is a disaster within a disaster fil...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5828_4</td>\n",
       "      <td>All in all, this is a movie for kids. We saw i...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7186_2</td>\n",
       "      <td>Afraid of the Dark left me with the impression...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12128_7</td>\n",
       "      <td>A very accurate depiction of small time mob li...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                             review  sentiment\n",
       "0  12311_10  Naturally in a film who's main themes are of m...          1\n",
       "1    8348_2  This movie is a disaster within a disaster fil...          0\n",
       "2    5828_4  All in all, this is a movie for kids. We saw i...          0\n",
       "3    7186_2  Afraid of the Dark left me with the impression...          0\n",
       "4   12128_7  A very accurate depiction of small time mob li...          1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.to_csv('./data/labeledTestData.csv',index=None,encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeledTestData = pd.read_csv('./data/labeledTestData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25000 entries, 0 to 24999\n",
      "Data columns (total 3 columns):\n",
      "id           25000 non-null object\n",
      "review       25000 non-null object\n",
      "sentiment    25000 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 586.0+ KB\n"
     ]
    }
   ],
   "source": [
    "labeledTestData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>sentiment</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>8348_2</td>\n",
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      "text/plain": [
       "         id                                             review  sentiment\n",
       "0  12311_10  Naturally in a film who's main themes are of m...          1\n",
       "1    8348_2  This movie is a disaster within a disaster fil...          0\n",
       "2    5828_4  All in all, this is a movie for kids. We saw i...          0\n",
       "3    7186_2  Afraid of the Dark left me with the impression...          0\n",
       "4   12128_7  A very accurate depiction of small time mob li...          1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeledTestData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "preprocess the train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv( './data/labeledTrainData.tsv', header=0, delimiter='\\t', quoting=3)\n",
    "test = pd.read_csv('./data/labeledTestData.csv')\n",
    "unlabeled_train = pd.read_csv( './data/unlabeledTrainData.tsv', header=0, delimiter='\\t', quoting=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_all = pd.concat([train,test])\n",
    "data_all.to_csv('./data/data_all.csv',index=None,encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_all = pd.read_csv('./data/data_all.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 3 columns):\n",
      "id           50000 non-null object\n",
      "review       50000 non-null object\n",
      "sentiment    50000 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "def clean_str(string):\n",
    "    \"\"\"\n",
    "    Tokenization/string cleaning for all datasets \n",
    "    Every dataset is lower cased\n",
    "    \"\"\"\n",
    "    string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)     \n",
    "    string = re.sub(r\"\\'s\", \" \\'s\", string) \n",
    "    string = re.sub(r\"\\'ve\", \" \\'ve\", string) \n",
    "    string = re.sub(r\"n\\'t\", \" n\\'t\", string) \n",
    "    string = re.sub(r\"\\'re\", \" \\'re\", string) \n",
    "    string = re.sub(r\"\\'d\", \" \\'d\", string) \n",
    "    string = re.sub(r\"\\'ll\", \" \\'ll\", string) \n",
    "    string = re.sub(r\",\", \" , \", string) \n",
    "    string = re.sub(r\"!\", \" ! \", string) \n",
    "    string = re.sub(r\"\\(\", \" \\( \", string) \n",
    "    string = re.sub(r\"\\)\", \" \\) \", string) \n",
    "    string = re.sub(r\"\\?\", \" \\? \", string) \n",
    "    string = re.sub(r\"\\s{2,}\", \" \", string)    \n",
    "    return string.strip().lower()\n",
    "\n",
    "stops = set(stopwords.words(\"english\"))\n",
    "def review_to_words(raw_review):\n",
    "    review_text = BeautifulSoup(raw_review, \"lxml\").get_text()  \n",
    "    words = clean_str(review_text)\n",
    "    meaningful_words = [w for w in words.split(' ') if not w in stops]\n",
    "    return(\" \".join(meaningful_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = []\n",
    "texts_with_stopwords = []\n",
    "labels = []\n",
    "for idx in range(data_all.review.shape[0]):\n",
    "    text = review_to_words(data_all.review[idx])\n",
    "    texts.append(text)\n",
    "    texts_with_stopwords.append(clean_str(BeautifulSoup(data_all.review[idx], \"lxml\").get_text()))\n",
    "    labels.append(data_all.sentiment[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.91146\n",
      "clf__C: 15\n",
      "vect__min_df: 3\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "text_clf = Pipeline([('vect', TfidfVectorizer(max_df=0.95, min_df=3, ngram_range=(1, 2))),\n",
    "                     ('clf', LogisticRegression(C=3)),\n",
    "])\n",
    "\n",
    "parameters = {'vect__min_df': (3,5),\n",
    "              'clf__C': (10,15),\n",
    "}\n",
    "\n",
    "gs_clf = GridSearchCV(text_clf, parameters, n_jobs=4)\n",
    "gs_clf = gs_clf.fit(texts_with_stopwords, labels)\n",
    "print(gs_clf.best_score_)\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, gs_clf.best_params_[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90168"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=3, ngram_range=(1, 2))\n",
    "tfidf_features_with_stopwords = tfidf_vectorizer.fit_transform(texts_with_stopwords)\n",
    "lr = LogisticRegression(C=15)\n",
    "lr.fit(tfidf_features_with_stopwords[:25000],labels[:25000]).score(tfidf_features_with_stopwords[25000:], labels[25000:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90124"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=3, ngram_range=(1, 2))\n",
    "tfidf_features_with_stopwords = tfidf_vectorizer.fit_transform(texts_with_stopwords[:25000])\n",
    "tfidf_features_with_stopwords_test = tfidf_vectorizer.transform(texts_with_stopwords[25000:])\n",
    "lr = LogisticRegression(C=15)\n",
    "lr.fit(tfidf_features_with_stopwords,labels[:25000]).score(tfidf_features_with_stopwords_test, labels[25000:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90328"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=3, ngram_range=(1, 3))\n",
    "tfidf_features_with_stopwords = tfidf_vectorizer.fit_transform(texts_with_stopwords[:25000])\n",
    "tfidf_features_with_stopwords_test = tfidf_vectorizer.transform(texts_with_stopwords[25000:])\n",
    "lr = LogisticRegression(C=15)\n",
    "lr.fit(tfidf_features_with_stopwords,labels[:25000]).score(tfidf_features_with_stopwords_test, labels[25000:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use character features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from scipy.sparse import hstack\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=3, ngram_range=(1, 2))\n",
    "tfidf_vectorizer_char = TfidfVectorizer(max_df=0.95, min_df=5, analyzer='char', ngram_range=(1, 4))\n",
    "tfidf_features_with_stopwords = tfidf_vectorizer.fit_transform(texts_with_stopwords)\n",
    "tfidf_features_with_stopwords_char = tfidf_vectorizer_char.fit_transform(texts_with_stopwords)\n",
    "\n",
    "tfidf_matrix_word_char =  hstack((tfidf_features_with_stopwords, tfidf_features_with_stopwords_char))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9018"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(C=10)\n",
    "lr.fit(tfidf_features_with_stopwords[:25000],labels[:25000]).score(tfidf_features_with_stopwords[25000:], labels[25000:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_unlabeled_train = pd.concat([train,test,unlabeled_train]).fillna(0)\n",
    "data_with_unlabeled_train.to_csv('./data/data_with_unlabeled_train.csv',index=None,encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_unlabeled_train = pd.read_csv('./data/data_with_unlabeled_train.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"5814_8\"</td>\n",
       "      <td>\"With all this stuff going down at the moment ...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"2381_9\"</td>\n",
       "      <td>\"\\\"The Classic War of the Worlds\\\" by Timothy ...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                             review  sentiment\n",
       "0  \"5814_8\"  \"With all this stuff going down at the moment ...        1.0\n",
       "1  \"2381_9\"  \"\\\"The Classic War of the Worlds\\\" by Timothy ...        1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_unlabeled_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>\"40155_0\"</td>\n",
       "      <td>\"Not a bad story, but the low budget rears its...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>\"35270_0\"</td>\n",
       "      <td>\"This not-very-good mummy-alien flick does fea...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                             review  sentiment\n",
       "99998  \"40155_0\"  \"Not a bad story, but the low budget rears its...        0.0\n",
       "99999  \"35270_0\"  \"This not-very-good mummy-alien flick does fea...        0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_unlabeled_train.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 3 columns):\n",
      "id           100000 non-null object\n",
      "review       100000 non-null object\n",
      "sentiment    100000 non-null float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_with_unlabeled_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts_with_unlabeled_train = []\n",
    "for idx in range(data_with_unlabeled_train.review.shape[0]):\n",
    "    texts_with_stopwords.append(clean_str(BeautifulSoup(data_with_unlabeled_train.review[idx], \"lxml\").get_text()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "from nltk.corpus import stopwords\n",
    "import nltk.data\n",
    "tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "stop_words = set(stopwords.words(\"english\"))\n",
    "\n",
    "def clean_str(string):\n",
    "        \"\"\"\n",
    "        Tokenization/string cleaning for all datasets \n",
    "        Every dataset is lower cased\n",
    "        \"\"\"\n",
    "        string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)     \n",
    "        string = re.sub(r\"\\'s\", \" \\'s\", string) \n",
    "        string = re.sub(r\"\\'ve\", \" \\'ve\", string) \n",
    "        string = re.sub(r\"n\\'t\", \" n\\'t\", string) \n",
    "        string = re.sub(r\"\\'re\", \" \\'re\", string) \n",
    "        string = re.sub(r\"\\'d\", \" \\'d\", string) \n",
    "        string = re.sub(r\"\\'ll\", \" \\'ll\", string) \n",
    "        string = re.sub(r\",\", \" , \", string) \n",
    "        string = re.sub(r\"!\", \" ! \", string) \n",
    "        string = re.sub(r\"\\(\", \" \\( \", string) \n",
    "        string = re.sub(r\"\\)\", \" \\) \", string) \n",
    "        string = re.sub(r\"\\?\", \" \\? \", string) \n",
    "        string = re.sub(r\"\\s{2,}\", \" \", string)    \n",
    "        return string.strip().lower().split()\n",
    "\n",
    "def review_to_words(raw_review, remove_stopwords=False):\n",
    "        review_text = BeautifulSoup(raw_review, \"lxml\").get_text()  \n",
    "        words = clean_str(review_text)\n",
    "        if remove_stopwords:\n",
    "            words = [w for w in words if not w in stop_words]\n",
    "        return words\n",
    "    \n",
    "def review_to_sentences(review, remove_stopwords=False):\n",
    "        raw_sentences = tokenizer.tokenize(review.strip())\n",
    "        sentences = []\n",
    "        for raw_sentence in raw_sentences:\n",
    "            if len(raw_sentence) > 0:\n",
    "                sentences.append(review_to_words(raw_sentence, remove_stopwords))\n",
    "        return sentences\n",
    "\n",
    "\n",
    "class MySentences(object):\n",
    "    def __init__(self, myfile):\n",
    "        self.myfile = myfile\n",
    "        \n",
    "    def __iter__(self):\n",
    "        for review in self.myfile:\n",
    "            raw_sentences = tokenizer.tokenize(review.strip())\n",
    "            for raw_sentence in raw_sentences:\n",
    "                if len(raw_sentence) > 0:\n",
    "                    yield review_to_words(raw_sentence, remove_stopwords=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = MySentences(data_with_unlabeled_train.review)  # a memory-friendly iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['with', 'all', 'this', 'stuff', 'going', 'down', 'at', 'the', 'moment', 'with', 'mj', 'i', \"'ve\", 'started', 'listening', 'to', 'his', 'music', ',', 'watching', 'the', 'odd', 'documentary', 'here', 'and', 'there', ',', 'watched', 'the', 'wiz', 'and', 'watched', 'moonwalker', 'again']\n"
     ]
    }
   ],
   "source": [
    "for sen1 in sentences:\n",
    "    print(sen1)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['with', 'all', 'this', 'stuff', 'going', 'down', 'at', 'the', 'moment', 'with', 'mj', 'i', \"'ve\", 'started', 'listening', 'to', 'his', 'music', ',', 'watching', 'the', 'odd', 'documentary', 'here', 'and', 'there', ',', 'watched', 'the', 'wiz', 'and', 'watched', 'moonwalker', 'again']\n",
      "['maybe', 'i', 'just', 'want', 'to', 'get', 'a', 'certain', 'insight', 'into', 'this', 'guy', 'who', 'i', 'thought', 'was', 'really', 'cool', 'in', 'the', 'eighties', 'just', 'to', 'maybe', 'make', 'up', 'my', 'mind', 'whether', 'he', 'is', 'guilty', 'or', 'innocent']\n",
      "['moonwalker', 'is', 'part', 'biography', ',', 'part', 'feature', 'film', 'which', 'i', 'remember', 'going', 'to', 'see', 'at', 'the', 'cinema', 'when', 'it', 'was', 'originally', 'released']\n",
      "['some', 'of', 'it', 'has', 'subtle', 'messages', 'about', 'mj', \"'s\", 'feeling', 'towards', 'the', 'press', 'and', 'also', 'the', 'obvious', 'message', 'of', 'drugs', 'are', 'bad', \"m'kay\", 'visually', 'impressive', 'but', 'of', 'course', 'this', 'is', 'all', 'about', 'michael', 'jackson', 'so', 'unless', 'you', 'remotely', 'like', 'mj', 'in', 'anyway', 'then', 'you', 'are', 'going', 'to', 'hate', 'this', 'and', 'find', 'it', 'boring']\n",
      "['some', 'may', 'call', 'mj', 'an', 'egotist', 'for', 'consenting', 'to', 'the', 'making', 'of', 'this', 'movie', 'but', 'mj', 'and', 'most', 'of', 'his', 'fans', 'would', 'say', 'that', 'he', 'made', 'it', 'for', 'the', 'fans', 'which', 'if', 'true', 'is', 'really', 'nice', 'of', 'him', 'the', 'actual', 'feature', 'film', 'bit', 'when', 'it', 'finally', 'starts', 'is', 'only', 'on', 'for', '20', 'minutes', 'or', 'so', 'excluding', 'the', 'smooth', 'criminal', 'sequence', 'and', 'joe', 'pesci', 'is', 'convincing', 'as', 'a', 'psychopathic', 'all', 'powerful', 'drug', 'lord']\n",
      "['why', 'he', 'wants', 'mj', 'dead', 'so', 'bad', 'is', 'beyond', 'me']\n",
      "['because', 'mj', 'overheard', 'his', 'plans', '\\\\?']\n",
      "['nah', ',', 'joe', 'pesci', \"'s\", 'character', 'ranted', 'that', 'he', 'wanted', 'people', 'to', 'know', 'it', 'is', 'he', 'who', 'is', 'supplying', 'drugs', 'etc', 'so', 'i', 'dunno', ',', 'maybe', 'he', 'just', 'hates', 'mj', \"'s\", 'music', 'lots', 'of', 'cool', 'things', 'in', 'this', 'like', 'mj', 'turning', 'into', 'a', 'car', 'and', 'a', 'robot', 'and', 'the', 'whole', 'speed', 'demon', 'sequence']\n",
      "['also', ',', 'the', 'director', 'must', 'have', 'had', 'the', 'patience', 'of', 'a', 'saint', 'when', 'it', 'came', 'to', 'filming', 'the', 'kiddy', 'bad', 'sequence', 'as', 'usually', 'directors', 'hate', 'working', 'with', 'one', 'kid', 'let', 'alone', 'a', 'whole', 'bunch', 'of', 'them', 'performing', 'a', 'complex', 'dance', 'scene', 'bottom', 'line', ',', 'this', 'movie', 'is', 'for', 'people', 'who', 'like', 'mj', 'on', 'one', 'level', 'or', 'another', '\\\\(', 'which', 'i', 'think', 'is', 'most', 'people', '\\\\)']\n",
      "['if', 'not', ',', 'then', 'stay', 'away']\n",
      "['it', 'does', 'try', 'and', 'give', 'off', 'a', 'wholesome', 'message', 'and', 'ironically', 'mj', \"'s\", 'bestest', 'buddy', 'in', 'this', 'movie', 'is', 'a', 'girl', '!']\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for sen1 in sentences:\n",
    "    i+=1\n",
    "    print(sen1)\n",
    "    if i > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.happierabroad.com\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'..'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'....................................................................'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.archive.org/details/LovefromaStranger\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.loosechangeguide.com/LooseChangeGuide.html\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'... ...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'....'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.msnbc.msn.com/id/4972055/site/newsweek/\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.youtube.com/watch?v=a0KSqelmgN8\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'.. .'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://jake-weird.blogspot.com/2007/08/beneath.html\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n"
     ]
    }
   ],
   "source": [
    "sentences = []  # Initialize an empty list of sentences\n",
    "\n",
    "for review in data_with_unlabeled_train[\"review\"]:\n",
    "    sentences += review_to_sentences(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1057275\n",
      "['with', 'all', 'this', 'stuff', 'going', 'down', 'at', 'the', 'moment', 'with', 'mj', 'i', \"'ve\", 'started', 'listening', 'to', 'his', 'music', ',', 'watching', 'the', 'odd', 'documentary', 'here', 'and', 'there', ',', 'watched', 'the', 'wiz', 'and', 'watched', 'moonwalker', 'again']\n"
     ]
    }
   ],
   "source": [
    "print(len(sentences))\n",
    "print(sentences[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:13:00,159 : INFO : collecting all words and their counts\n",
      "2018-10-19 19:13:00,163 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 19:13:02,690 : INFO : PROGRESS: at sentence #10000, processed 241944 words, keeping 18449 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.happierabroad.com\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 19:13:05,277 : INFO : PROGRESS: at sentence #20000, processed 483720 words, keeping 26155 word types\n",
      "2018-10-19 19:13:07,874 : INFO : PROGRESS: at sentence #30000, processed 718909 words, keeping 31608 word types\n",
      "2018-10-19 19:13:10,438 : INFO : PROGRESS: at sentence #40000, processed 961259 words, keeping 36357 word types\n",
      "2018-10-19 19:13:12,912 : INFO : PROGRESS: at sentence #50000, processed 1196249 words, keeping 40156 word types\n",
      "2018-10-19 19:13:15,407 : INFO : PROGRESS: at sentence #60000, processed 1433089 words, keeping 43426 word types\n",
      "2018-10-19 19:13:17,859 : INFO : PROGRESS: at sentence #70000, processed 1671705 words, keeping 46385 word types\n",
      "2018-10-19 19:13:20,385 : INFO : PROGRESS: at sentence #80000, processed 1906221 words, keeping 49064 word types\n",
      "2018-10-19 19:13:22,900 : INFO : PROGRESS: at sentence #90000, processed 2146491 words, keeping 51812 word types\n",
      "2018-10-19 19:13:25,341 : INFO : PROGRESS: at sentence #100000, processed 2384047 words, keeping 54189 word types\n",
      "2018-10-19 19:13:27,783 : INFO : PROGRESS: at sentence #110000, processed 2619479 words, keeping 56320 word types\n",
      "2018-10-19 19:13:30,229 : INFO : PROGRESS: at sentence #120000, processed 2857001 words, keeping 58637 word types\n",
      "2018-10-19 19:13:32,687 : INFO : PROGRESS: at sentence #130000, processed 3099128 words, keeping 60668 word types\n",
      "2018-10-19 19:13:35,109 : INFO : PROGRESS: at sentence #140000, processed 3326972 words, keeping 62397 word types\n",
      "2018-10-19 19:13:37,561 : INFO : PROGRESS: at sentence #150000, processed 3568211 words, keeping 64402 word types\n",
      "2018-10-19 19:13:40,003 : INFO : PROGRESS: at sentence #160000, processed 3806344 words, keeping 66233 word types\n",
      "2018-10-19 19:13:42,461 : INFO : PROGRESS: at sentence #170000, processed 4045170 words, keeping 67946 word types\n",
      "2018-10-19 19:13:44,914 : INFO : PROGRESS: at sentence #180000, processed 4280999 words, keeping 69624 word types\n",
      "2018-10-19 19:13:47,374 : INFO : PROGRESS: at sentence #190000, processed 4522051 words, keeping 71164 word types\n",
      "2018-10-19 19:13:49,828 : INFO : PROGRESS: at sentence #200000, processed 4762236 words, keeping 72681 word types\n",
      "2018-10-19 19:13:52,290 : INFO : PROGRESS: at sentence #210000, processed 4999023 words, keeping 74193 word types\n",
      "2018-10-19 19:13:54,749 : INFO : PROGRESS: at sentence #220000, processed 5239521 words, keeping 75757 word types\n",
      "2018-10-19 19:13:57,196 : INFO : PROGRESS: at sentence #230000, processed 5477612 words, keeping 77242 word types\n",
      "2018-10-19 19:13:59,658 : INFO : PROGRESS: at sentence #240000, processed 5720948 words, keeping 78750 word types\n",
      "2018-10-19 19:14:02,098 : INFO : PROGRESS: at sentence #250000, processed 5950179 words, keeping 80157 word types\n",
      "2018-10-19 19:14:04,546 : INFO : PROGRESS: at sentence #260000, processed 6186197 words, keeping 81506 word types\n",
      "2018-10-19 19:14:06,991 : INFO : PROGRESS: at sentence #270000, processed 6419502 words, keeping 83037 word types\n",
      "2018-10-19 19:14:09,578 : INFO : PROGRESS: at sentence #280000, processed 6662919 words, keeping 84850 word types\n",
      "2018-10-19 19:14:12,029 : INFO : PROGRESS: at sentence #290000, processed 6897354 words, keeping 86549 word types\n",
      "2018-10-19 19:14:14,495 : INFO : PROGRESS: at sentence #300000, processed 7134237 words, keeping 88213 word types\n",
      "2018-10-19 19:14:16,957 : INFO : PROGRESS: at sentence #310000, processed 7368457 words, keeping 89590 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'..'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 19:14:19,425 : INFO : PROGRESS: at sentence #320000, processed 7606474 words, keeping 91017 word types\n",
      "2018-10-19 19:14:21,895 : INFO : PROGRESS: at sentence #330000, processed 7846961 words, keeping 92546 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'....................................................................'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 19:14:24,371 : INFO : PROGRESS: at sentence #340000, processed 8082966 words, keeping 93885 word types\n",
      "2018-10-19 19:14:26,835 : INFO : PROGRESS: at sentence #350000, processed 8313720 words, keeping 95109 word types\n",
      "2018-10-19 19:14:29,324 : INFO : PROGRESS: at sentence #360000, processed 8551469 words, keeping 96462 word types\n",
      "2018-10-19 19:14:31,809 : INFO : PROGRESS: at sentence #370000, processed 8781534 words, keeping 97632 word types\n",
      "2018-10-19 19:14:34,290 : INFO : PROGRESS: at sentence #380000, processed 9018037 words, keeping 98708 word types\n",
      "2018-10-19 19:14:36,769 : INFO : PROGRESS: at sentence #390000, processed 9258408 words, keeping 99886 word types\n",
      "2018-10-19 19:14:39,232 : INFO : PROGRESS: at sentence #400000, processed 9494786 words, keeping 101123 word types\n",
      "2018-10-19 19:14:41,692 : INFO : PROGRESS: at sentence #410000, processed 9731245 words, keeping 102230 word types\n",
      "2018-10-19 19:14:44,151 : INFO : PROGRESS: at sentence #420000, processed 9970063 words, keeping 103433 word types\n",
      "2018-10-19 19:14:46,623 : INFO : PROGRESS: at sentence #430000, processed 10209043 words, keeping 104632 word types\n",
      "2018-10-19 19:14:49,091 : INFO : PROGRESS: at sentence #440000, processed 10444358 words, keeping 105734 word types\n",
      "2018-10-19 19:14:51,542 : INFO : PROGRESS: at sentence #450000, processed 10675921 words, keeping 106737 word types\n",
      "2018-10-19 19:14:53,995 : INFO : PROGRESS: at sentence #460000, processed 10914086 words, keeping 107868 word types\n",
      "2018-10-19 19:14:56,451 : INFO : PROGRESS: at sentence #470000, processed 11151508 words, keeping 108936 word types\n",
      "2018-10-19 19:14:58,891 : INFO : PROGRESS: at sentence #480000, processed 11382150 words, keeping 109977 word types\n",
      "2018-10-19 19:15:01,347 : INFO : PROGRESS: at sentence #490000, processed 11620335 words, keeping 111008 word types\n",
      "2018-10-19 19:15:03,808 : INFO : PROGRESS: at sentence #500000, processed 11857067 words, keeping 111995 word types\n",
      "2018-10-19 19:15:06,261 : INFO : PROGRESS: at sentence #510000, processed 12094353 words, keeping 112995 word types\n",
      "2018-10-19 19:15:08,707 : INFO : PROGRESS: at sentence #520000, processed 12329373 words, keeping 113983 word types\n",
      "2018-10-19 19:15:11,176 : INFO : PROGRESS: at sentence #530000, processed 12572975 words, keeping 114981 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.archive.org/details/LovefromaStranger\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 19:15:13,642 : INFO : PROGRESS: at sentence #540000, processed 12814200 words, keeping 116268 word types\n",
      "2018-10-19 19:15:16,103 : INFO : PROGRESS: at sentence #550000, processed 13053410 words, keeping 117505 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.loosechangeguide.com/LooseChangeGuide.html\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:15:18,570 : INFO : PROGRESS: at sentence #560000, processed 13296240 words, keeping 118743 word types\n",
      "2018-10-19 19:15:21,043 : INFO : PROGRESS: at sentence #570000, processed 13536737 words, keeping 119869 word types\n",
      "2018-10-19 19:15:23,511 : INFO : PROGRESS: at sentence #580000, processed 13776414 words, keeping 120994 word types\n",
      "2018-10-19 19:15:25,972 : INFO : PROGRESS: at sentence #590000, processed 14013310 words, keeping 122095 word types\n",
      "2018-10-19 19:15:28,440 : INFO : PROGRESS: at sentence #600000, processed 14259336 words, keeping 123261 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'... ...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 19:15:30,899 : INFO : PROGRESS: at sentence #610000, processed 14499025 words, keeping 124344 word types\n",
      "2018-10-19 19:15:33,350 : INFO : PROGRESS: at sentence #620000, processed 14735172 words, keeping 125389 word types\n",
      "2018-10-19 19:15:35,945 : INFO : PROGRESS: at sentence #630000, processed 14977916 words, keeping 126383 word types\n",
      "2018-10-19 19:15:38,394 : INFO : PROGRESS: at sentence #640000, processed 15217915 words, keeping 127427 word types\n",
      "2018-10-19 19:15:40,870 : INFO : PROGRESS: at sentence #650000, processed 15463760 words, keeping 128437 word types\n",
      "2018-10-19 19:15:43,332 : INFO : PROGRESS: at sentence #660000, processed 15703944 words, keeping 129417 word types\n",
      "2018-10-19 19:15:45,793 : INFO : PROGRESS: at sentence #670000, processed 15938096 words, keeping 130337 word types\n",
      "2018-10-19 19:15:48,261 : INFO : PROGRESS: at sentence #680000, processed 16177011 words, keeping 131253 word types\n",
      "2018-10-19 19:15:50,732 : INFO : PROGRESS: at sentence #690000, processed 16418855 words, keeping 132214 word types\n",
      "2018-10-19 19:15:53,208 : INFO : PROGRESS: at sentence #700000, processed 16661740 words, keeping 133201 word types\n",
      "2018-10-19 19:15:55,678 : INFO : PROGRESS: at sentence #710000, processed 16902469 words, keeping 134253 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'....'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 19:15:58,175 : INFO : PROGRESS: at sentence #720000, processed 17149826 words, keeping 135291 word types\n",
      "2018-10-19 19:16:00,650 : INFO : PROGRESS: at sentence #730000, processed 17392794 words, keeping 136121 word types\n",
      "2018-10-19 19:16:03,139 : INFO : PROGRESS: at sentence #740000, processed 17634670 words, keeping 137070 word types\n",
      "2018-10-19 19:16:05,646 : INFO : PROGRESS: at sentence #750000, processed 17873593 words, keeping 138098 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.msnbc.msn.com/id/4972055/site/newsweek/\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 19:16:08,149 : INFO : PROGRESS: at sentence #760000, processed 18111463 words, keeping 138932 word types\n",
      "2018-10-19 19:16:10,641 : INFO : PROGRESS: at sentence #770000, processed 18351797 words, keeping 139906 word types\n",
      "2018-10-19 19:16:13,128 : INFO : PROGRESS: at sentence #780000, processed 18592424 words, keeping 140858 word types\n",
      "2018-10-19 19:16:15,613 : INFO : PROGRESS: at sentence #790000, processed 18834700 words, keeping 141726 word types\n",
      "2018-10-19 19:16:18,083 : INFO : PROGRESS: at sentence #800000, processed 19071544 words, keeping 142662 word types\n",
      "2018-10-19 19:16:20,551 : INFO : PROGRESS: at sentence #810000, processed 19310448 words, keeping 143576 word types\n",
      "2018-10-19 19:16:23,026 : INFO : PROGRESS: at sentence #820000, processed 19550803 words, keeping 144444 word types\n",
      "2018-10-19 19:16:25,504 : INFO : PROGRESS: at sentence #830000, processed 19793367 words, keeping 145258 word types\n",
      "2018-10-19 19:16:27,970 : INFO : PROGRESS: at sentence #840000, processed 20034333 words, keeping 146131 word types\n",
      "2018-10-19 19:16:30,467 : INFO : PROGRESS: at sentence #850000, processed 20272773 words, keeping 146980 word types\n",
      "2018-10-19 19:16:32,940 : INFO : PROGRESS: at sentence #860000, processed 20512067 words, keeping 147754 word types\n",
      "2018-10-19 19:16:35,404 : INFO : PROGRESS: at sentence #870000, processed 20749689 words, keeping 148674 word types\n",
      "2018-10-19 19:16:37,870 : INFO : PROGRESS: at sentence #880000, processed 20991083 words, keeping 149467 word types\n",
      "2018-10-19 19:16:40,328 : INFO : PROGRESS: at sentence #890000, processed 21230330 words, keeping 150250 word types\n",
      "2018-10-19 19:16:42,782 : INFO : PROGRESS: at sentence #900000, processed 21465215 words, keeping 151121 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.youtube.com/watch?v=a0KSqelmgN8\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 19:16:45,249 : INFO : PROGRESS: at sentence #910000, processed 21708735 words, keeping 151943 word types\n",
      "2018-10-19 19:16:47,710 : INFO : PROGRESS: at sentence #920000, processed 21945702 words, keeping 152728 word types\n",
      "2018-10-19 19:16:50,169 : INFO : PROGRESS: at sentence #930000, processed 22185870 words, keeping 153492 word types\n",
      "2018-10-19 19:16:52,695 : INFO : PROGRESS: at sentence #940000, processed 22428839 words, keeping 154248 word types\n",
      "2018-10-19 19:16:55,153 : INFO : PROGRESS: at sentence #950000, processed 22664717 words, keeping 154975 word types\n",
      "2018-10-19 19:16:57,619 : INFO : PROGRESS: at sentence #960000, processed 22908377 words, keeping 155854 word types\n",
      "2018-10-19 19:17:00,071 : INFO : PROGRESS: at sentence #970000, processed 23147777 words, keeping 156565 word types\n",
      "2018-10-19 19:17:02,533 : INFO : PROGRESS: at sentence #980000, processed 23391163 words, keeping 157226 word types\n",
      "2018-10-19 19:17:05,115 : INFO : PROGRESS: at sentence #990000, processed 23628855 words, keeping 157993 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'.. .'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 19:17:07,575 : INFO : PROGRESS: at sentence #1000000, processed 23866627 words, keeping 158724 word types\n",
      "2018-10-19 19:17:10,036 : INFO : PROGRESS: at sentence #1010000, processed 24103110 words, keeping 159410 word types\n",
      "2018-10-19 19:17:12,489 : INFO : PROGRESS: at sentence #1020000, processed 24337454 words, keeping 160132 word types\n",
      "2018-10-19 19:17:14,957 : INFO : PROGRESS: at sentence #1030000, processed 24579174 words, keeping 160915 word types\n",
      "2018-10-19 19:17:17,428 : INFO : PROGRESS: at sentence #1040000, processed 24824563 words, keeping 161719 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://jake-weird.blogspot.com/2007/08/beneath.html\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 19:17:19,914 : INFO : PROGRESS: at sentence #1050000, processed 25068033 words, keeping 162429 word types\n",
      "2018-10-19 19:17:21,720 : INFO : collected 163009 word types from a corpus of 25242693 raw words and 1057275 sentences\n",
      "2018-10-19 19:17:21,721 : INFO : Loading a fresh vocabulary\n",
      "2018-10-19 19:17:21,817 : INFO : min_count=5 retains 55895 unique words (34% of original 163009, drops 107114)\n",
      "2018-10-19 19:17:21,817 : INFO : min_count=5 leaves 25075676 word corpus (99% of original 25242693, drops 167017)\n",
      "2018-10-19 19:17:21,944 : INFO : deleting the raw counts dictionary of 163009 items\n",
      "2018-10-19 19:17:21,949 : INFO : sample=0.001 downsamples 51 most-common words\n",
      "2018-10-19 19:17:21,949 : INFO : downsampling leaves estimated 18336060 word corpus (73.1% of prior 25075676)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:17:25,354 : INFO : estimated required memory for 55895 words, 244023 buckets and 300 dimensions: 467819764 bytes\n",
      "2018-10-19 19:17:25,363 : INFO : resetting layer weights\n",
      "2018-10-19 19:17:27,694 : INFO : Total number of ngrams is 244023\n",
      "2018-10-19 19:17:30,234 : INFO : training model with 4 workers on 55895 vocabulary and 300 features, using sg=1 hs=0 sample=0.001 negative=5 window=10\n",
      "2018-10-19 19:17:31,292 : INFO : EPOCH 1 - PROGRESS: at 0.28% examples, 48519 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:32,298 : INFO : EPOCH 1 - PROGRESS: at 0.59% examples, 52956 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:33,303 : INFO : EPOCH 1 - PROGRESS: at 0.90% examples, 54583 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:34,318 : INFO : EPOCH 1 - PROGRESS: at 1.21% examples, 55195 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:17:37,493 : INFO : EPOCH 1 - PROGRESS: at 2.16% examples, 55033 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:38,516 : INFO : EPOCH 1 - PROGRESS: at 2.48% examples, 55230 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:17:40,550 : INFO : EPOCH 1 - PROGRESS: at 3.12% examples, 55570 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:41,668 : INFO : EPOCH 1 - PROGRESS: at 3.46% examples, 55845 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:42,674 : INFO : EPOCH 1 - PROGRESS: at 3.77% examples, 55984 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:43,688 : INFO : EPOCH 1 - PROGRESS: at 4.08% examples, 56089 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:17:46,772 : INFO : EPOCH 1 - PROGRESS: at 5.05% examples, 56125 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:17:49,869 : INFO : EPOCH 1 - PROGRESS: at 6.02% examples, 56129 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:50,891 : INFO : EPOCH 1 - PROGRESS: at 6.33% examples, 56168 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:51,901 : INFO : EPOCH 1 - PROGRESS: at 6.64% examples, 56226 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:52,947 : INFO : EPOCH 1 - PROGRESS: at 6.96% examples, 56196 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:53,969 : INFO : EPOCH 1 - PROGRESS: at 7.28% examples, 56227 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:55,004 : INFO : EPOCH 1 - PROGRESS: at 7.60% examples, 56217 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:56,130 : INFO : EPOCH 1 - PROGRESS: at 7.95% examples, 56288 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:17:57,154 : INFO : EPOCH 1 - PROGRESS: at 8.27% examples, 56304 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:18:00,254 : INFO : EPOCH 1 - PROGRESS: at 9.23% examples, 56294 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:18:08,553 : INFO : EPOCH 1 - PROGRESS: at 11.76% examples, 56209 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:18:23,083 : INFO : EPOCH 1 - PROGRESS: at 16.27% examples, 56243 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:18:43,665 : INFO : EPOCH 1 - PROGRESS: at 22.57% examples, 56277 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "2018-10-19 19:20:58,684 : INFO : EPOCH 1 - PROGRESS: at 64.08% examples, 56154 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:20:59,710 : INFO : EPOCH 1 - PROGRESS: at 64.40% examples, 56157 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:00,725 : INFO : EPOCH 1 - PROGRESS: at 64.71% examples, 56162 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:01,741 : INFO : EPOCH 1 - PROGRESS: at 65.02% examples, 56166 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:02,887 : INFO : EPOCH 1 - PROGRESS: at 65.33% examples, 56136 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:04,003 : INFO : EPOCH 1 - PROGRESS: at 65.67% examples, 56150 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:05,019 : INFO : EPOCH 1 - PROGRESS: at 65.98% examples, 56153 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:06,058 : INFO : EPOCH 1 - PROGRESS: at 66.31% examples, 56152 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:07,080 : INFO : EPOCH 1 - PROGRESS: at 66.62% examples, 56155 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:08,101 : INFO : EPOCH 1 - PROGRESS: at 66.94% examples, 56158 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:09,105 : INFO : EPOCH 1 - PROGRESS: at 67.24% examples, 56165 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:10,127 : INFO : EPOCH 1 - PROGRESS: at 67.56% examples, 56169 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:11,138 : INFO : EPOCH 1 - PROGRESS: at 67.86% examples, 56175 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:12,239 : INFO : EPOCH 1 - PROGRESS: at 68.19% examples, 56189 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:13,260 : INFO : EPOCH 1 - PROGRESS: at 68.51% examples, 56192 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:14,262 : INFO : EPOCH 1 - PROGRESS: at 68.82% examples, 56199 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:15,279 : INFO : EPOCH 1 - PROGRESS: at 69.13% examples, 56204 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:16,296 : INFO : EPOCH 1 - PROGRESS: at 69.44% examples, 56208 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:21:17,305 : INFO : EPOCH 1 - PROGRESS: at 69.75% examples, 56214 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:21:18,334 : INFO : EPOCH 1 - PROGRESS: at 70.07% examples, 56214 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:21:45,226 : INFO : EPOCH 1 - PROGRESS: at 78.30% examples, 56234 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:22:33,991 : INFO : EPOCH 1 - PROGRESS: at 93.22% examples, 56252 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:22:55,977 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 19:22:55,978 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 19:22:56,037 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 19:22:56,101 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 19:22:56,102 : INFO : EPOCH - 1 : training on 25242693 raw words (18336612 effective words) took 325.9s, 56271 effective words/s\n",
      "2018-10-19 19:22:57,159 : INFO : EPOCH 2 - PROGRESS: at 0.28% examples, 48623 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:23:09,564 : INFO : EPOCH 2 - PROGRESS: at 4.12% examples, 56641 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:23:15,769 : INFO : EPOCH 2 - PROGRESS: at 6.02% examples, 56072 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:23:18,835 : INFO : EPOCH 2 - PROGRESS: at 6.96% examples, 56164 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:23:19,862 : INFO : EPOCH 2 - PROGRESS: at 7.28% examples, 56179 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:23:31,305 : INFO : EPOCH 2 - PROGRESS: at 10.82% examples, 56253 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:23:32,323 : INFO : EPOCH 2 - PROGRESS: at 11.13% examples, 56274 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:23:34,368 : INFO : EPOCH 2 - PROGRESS: at 11.76% examples, 56300 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:23:35,389 : INFO : EPOCH 2 - PROGRESS: at 12.08% examples, 56310 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:23:36,469 : INFO : EPOCH 2 - PROGRESS: at 12.42% examples, 56238 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:23:42,599 : INFO : EPOCH 2 - PROGRESS: at 14.29% examples, 56145 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:23:43,658 : INFO : EPOCH 2 - PROGRESS: at 14.61% examples, 56118 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:23:44,771 : INFO : EPOCH 2 - PROGRESS: at 14.96% examples, 56171 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:23:45,810 : INFO : EPOCH 2 - PROGRESS: at 15.28% examples, 56160 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:25:01,994 : INFO : EPOCH 2 - PROGRESS: at 38.73% examples, 56078 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:26:00,195 : INFO : EPOCH 2 - PROGRESS: at 56.68% examples, 56173 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:26:07,394 : INFO : EPOCH 2 - PROGRESS: at 58.90% examples, 56185 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:26:08,499 : INFO : EPOCH 2 - PROGRESS: at 59.24% examples, 56200 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:26:09,519 : INFO : EPOCH 2 - PROGRESS: at 59.55% examples, 56203 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:26:12,696 : INFO : EPOCH 2 - PROGRESS: at 60.53% examples, 56216 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:26:14,881 : INFO : EPOCH 2 - PROGRESS: at 61.18% examples, 56218 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:26:16,978 : INFO : EPOCH 2 - PROGRESS: at 61.81% examples, 56211 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:26:17,991 : INFO : EPOCH 2 - PROGRESS: at 62.13% examples, 56216 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:26:19,122 : INFO : EPOCH 2 - PROGRESS: at 62.48% examples, 56224 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:27:35,931 : INFO : EPOCH 2 - PROGRESS: at 86.06% examples, 56340 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:28:20,650 : INFO : EPOCH 2 - PROGRESS: at 99.82% examples, 56397 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:21,046 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 19:28:21,047 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 19:28:21,108 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 19:28:21,168 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 19:28:21,169 : INFO : EPOCH - 2 : training on 25242693 raw words (18334797 effective words) took 325.1s, 56404 effective words/s\n",
      "2018-10-19 19:28:22,221 : INFO : EPOCH 3 - PROGRESS: at 0.28% examples, 48894 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:23,347 : INFO : EPOCH 3 - PROGRESS: at 0.63% examples, 53480 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:28:25,367 : INFO : EPOCH 3 - PROGRESS: at 1.21% examples, 53718 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:28:27,475 : INFO : EPOCH 3 - PROGRESS: at 1.87% examples, 55297 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:28,501 : INFO : EPOCH 3 - PROGRESS: at 2.20% examples, 55475 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:29,545 : INFO : EPOCH 3 - PROGRESS: at 2.52% examples, 55482 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:28:31,588 : INFO : EPOCH 3 - PROGRESS: at 3.15% examples, 55709 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:32,708 : INFO : EPOCH 3 - PROGRESS: at 3.50% examples, 55964 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:33,718 : INFO : EPOCH 3 - PROGRESS: at 3.81% examples, 56086 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:34,724 : INFO : EPOCH 3 - PROGRESS: at 4.12% examples, 56196 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:35,731 : INFO : EPOCH 3 - PROGRESS: at 4.43% examples, 56281 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:36,795 : INFO : EPOCH 3 - PROGRESS: at 4.77% examples, 56166 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:37,805 : INFO : EPOCH 3 - PROGRESS: at 5.09% examples, 56231 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:38,836 : INFO : EPOCH 3 - PROGRESS: at 5.40% examples, 56241 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:39,891 : INFO : EPOCH 3 - PROGRESS: at 5.73% examples, 56154 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:40,923 : INFO : EPOCH 3 - PROGRESS: at 6.05% examples, 56161 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:41,931 : INFO : EPOCH 3 - PROGRESS: at 6.37% examples, 56226 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:42,934 : INFO : EPOCH 3 - PROGRESS: at 6.68% examples, 56298 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:43,977 : INFO : EPOCH 3 - PROGRESS: at 7.00% examples, 56268 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:45,006 : INFO : EPOCH 3 - PROGRESS: at 7.32% examples, 56273 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:46,037 : INFO : EPOCH 3 - PROGRESS: at 7.64% examples, 56272 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:47,172 : INFO : EPOCH 3 - PROGRESS: at 7.99% examples, 56330 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:28:48,185 : INFO : EPOCH 3 - PROGRESS: at 8.30% examples, 56363 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:49,223 : INFO : EPOCH 3 - PROGRESS: at 8.63% examples, 56349 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:50,242 : INFO : EPOCH 3 - PROGRESS: at 8.95% examples, 56377 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:51,272 : INFO : EPOCH 3 - PROGRESS: at 9.23% examples, 56138 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:28:55,467 : INFO : EPOCH 3 - PROGRESS: at 10.54% examples, 56250 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:56,496 : INFO : EPOCH 3 - PROGRESS: at 10.87% examples, 56251 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:57,514 : INFO : EPOCH 3 - PROGRESS: at 11.18% examples, 56277 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:58,527 : INFO : EPOCH 3 - PROGRESS: at 11.49% examples, 56303 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:28:59,542 : INFO : EPOCH 3 - PROGRESS: at 11.80% examples, 56326 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:29:00,548 : INFO : EPOCH 3 - PROGRESS: at 12.11% examples, 56365 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:29:01,628 : INFO : EPOCH 3 - PROGRESS: at 12.46% examples, 56285 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:29:03,678 : INFO : EPOCH 3 - PROGRESS: at 13.10% examples, 56296 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:29:04,698 : INFO : EPOCH 3 - PROGRESS: at 13.41% examples, 56306 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:29:06,734 : INFO : EPOCH 3 - PROGRESS: at 14.04% examples, 56328 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:29:07,768 : INFO : EPOCH 3 - PROGRESS: at 14.37% examples, 56326 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:29:09,810 : INFO : EPOCH 3 - PROGRESS: at 15.00% examples, 56345 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:29:13,933 : INFO : EPOCH 3 - PROGRESS: at 16.27% examples, 56337 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:30:04,595 : INFO : EPOCH 3 - PROGRESS: at 31.88% examples, 56268 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:31:21,010 : INFO : EPOCH 3 - PROGRESS: at 55.47% examples, 56257 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "2018-10-19 19:33:19,314 : INFO : EPOCH 3 - PROGRESS: at 91.92% examples, 56508 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:20,405 : INFO : EPOCH 3 - PROGRESS: at 92.26% examples, 56520 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:33:22,459 : INFO : EPOCH 3 - PROGRESS: at 92.90% examples, 56521 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:33:25,613 : INFO : EPOCH 3 - PROGRESS: at 93.88% examples, 56531 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:26,644 : INFO : EPOCH 3 - PROGRESS: at 94.20% examples, 56530 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:33:29,824 : INFO : EPOCH 3 - PROGRESS: at 95.20% examples, 56536 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:30,859 : INFO : EPOCH 3 - PROGRESS: at 95.52% examples, 56535 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:31,871 : INFO : EPOCH 3 - PROGRESS: at 95.84% examples, 56537 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:32,893 : INFO : EPOCH 3 - PROGRESS: at 96.16% examples, 56538 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:33,939 : INFO : EPOCH 3 - PROGRESS: at 96.49% examples, 56533 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:34,976 : INFO : EPOCH 3 - PROGRESS: at 96.81% examples, 56531 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:36,075 : INFO : EPOCH 3 - PROGRESS: at 97.12% examples, 56518 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:37,194 : INFO : EPOCH 3 - PROGRESS: at 97.47% examples, 56525 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:38,306 : INFO : EPOCH 3 - PROGRESS: at 97.81% examples, 56532 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:39,308 : INFO : EPOCH 3 - PROGRESS: at 98.12% examples, 56535 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:40,405 : INFO : EPOCH 3 - PROGRESS: at 98.46% examples, 56546 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:41,419 : INFO : EPOCH 3 - PROGRESS: at 98.78% examples, 56548 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:42,521 : INFO : EPOCH 3 - PROGRESS: at 99.12% examples, 56557 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:43,537 : INFO : EPOCH 3 - PROGRESS: at 99.43% examples, 56559 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:44,548 : INFO : EPOCH 3 - PROGRESS: at 99.75% examples, 56561 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:45,187 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 19:33:45,189 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 19:33:45,249 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 19:33:45,311 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 19:33:45,312 : INFO : EPOCH - 3 : training on 25242693 raw words (18336272 effective words) took 324.1s, 56569 effective words/s\n",
      "2018-10-19 19:33:46,359 : INFO : EPOCH 4 - PROGRESS: at 0.28% examples, 49057 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:47,468 : INFO : EPOCH 4 - PROGRESS: at 0.63% examples, 54079 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:48,583 : INFO : EPOCH 4 - PROGRESS: at 0.98% examples, 55631 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:49,707 : INFO : EPOCH 4 - PROGRESS: at 1.33% examples, 56199 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:33:50,718 : INFO : EPOCH 4 - PROGRESS: at 1.65% examples, 56409 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:51,834 : INFO : EPOCH 4 - PROGRESS: at 2.00% examples, 56803 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:33:58,131 : INFO : EPOCH 4 - PROGRESS: at 3.96% examples, 57174 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:33:59,171 : INFO : EPOCH 4 - PROGRESS: at 4.28% examples, 57052 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:34:00,186 : INFO : EPOCH 4 - PROGRESS: at 4.59% examples, 57076 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:34:04,292 : INFO : EPOCH 4 - PROGRESS: at 5.86% examples, 56573 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:34:05,317 : INFO : EPOCH 4 - PROGRESS: at 6.17% examples, 56564 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:34:08,356 : INFO : EPOCH 4 - PROGRESS: at 7.12% examples, 56660 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:34:09,391 : INFO : EPOCH 4 - PROGRESS: at 7.45% examples, 56628 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:34:10,414 : INFO : EPOCH 4 - PROGRESS: at 7.76% examples, 56633 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:34:56,371 : INFO : EPOCH 4 - PROGRESS: at 22.02% examples, 56735 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:35:06,008 : INFO : EPOCH 4 - PROGRESS: at 25.00% examples, 56602 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:35:07,033 : INFO : EPOCH 4 - PROGRESS: at 25.32% examples, 56600 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:35:08,058 : INFO : EPOCH 4 - PROGRESS: at 25.65% examples, 56599 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:35:45,357 : INFO : EPOCH 4 - PROGRESS: at 37.25% examples, 56573 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:36:24,554 : INFO : EPOCH 4 - PROGRESS: at 49.48% examples, 56566 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "2018-10-19 19:38:18,530 : INFO : EPOCH 4 - PROGRESS: at 84.16% examples, 56443 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:19,559 : INFO : EPOCH 4 - PROGRESS: at 84.48% examples, 56443 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:20,609 : INFO : EPOCH 4 - PROGRESS: at 84.81% examples, 56438 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:21,644 : INFO : EPOCH 4 - PROGRESS: at 85.08% examples, 56410 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:22,652 : INFO : EPOCH 4 - PROGRESS: at 85.40% examples, 56413 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:23,684 : INFO : EPOCH 4 - PROGRESS: at 85.72% examples, 56413 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:24,696 : INFO : EPOCH 4 - PROGRESS: at 86.03% examples, 56416 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:25,708 : INFO : EPOCH 4 - PROGRESS: at 86.34% examples, 56420 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:26,763 : INFO : EPOCH 4 - PROGRESS: at 86.66% examples, 56415 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:27,791 : INFO : EPOCH 4 - PROGRESS: at 86.98% examples, 56415 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:28,796 : INFO : EPOCH 4 - PROGRESS: at 87.29% examples, 56420 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:38:35,982 : INFO : EPOCH 4 - PROGRESS: at 89.49% examples, 56423 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:38:43,121 : INFO : EPOCH 4 - PROGRESS: at 91.69% examples, 56435 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:38:48,478 : INFO : EPOCH 4 - PROGRESS: at 93.29% examples, 56418 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:49,511 : INFO : EPOCH 4 - PROGRESS: at 93.61% examples, 56418 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:50,627 : INFO : EPOCH 4 - PROGRESS: at 93.95% examples, 56425 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:51,689 : INFO : EPOCH 4 - PROGRESS: at 94.29% examples, 56419 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:52,721 : INFO : EPOCH 4 - PROGRESS: at 94.60% examples, 56418 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:53,739 : INFO : EPOCH 4 - PROGRESS: at 94.91% examples, 56420 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:54,793 : INFO : EPOCH 4 - PROGRESS: at 95.23% examples, 56415 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:55,887 : INFO : EPOCH 4 - PROGRESS: at 95.56% examples, 56404 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:56,923 : INFO : EPOCH 4 - PROGRESS: at 95.88% examples, 56402 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:38:57,959 : INFO : EPOCH 4 - PROGRESS: at 96.20% examples, 56401 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:38:59,023 : INFO : EPOCH 4 - PROGRESS: at 96.53% examples, 56394 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:39:10,191 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 19:39:10,192 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 19:39:10,253 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 19:39:10,314 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 19:39:10,314 : INFO : EPOCH - 4 : training on 25242693 raw words (18338810 effective words) took 325.0s, 56428 effective words/s\n",
      "2018-10-19 19:39:11,373 : INFO : EPOCH 5 - PROGRESS: at 0.28% examples, 48447 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:40:09,767 : INFO : EPOCH 5 - PROGRESS: at 18.25% examples, 56108 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:40:10,799 : INFO : EPOCH 5 - PROGRESS: at 18.56% examples, 56102 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:41:27,506 : INFO : EPOCH 5 - PROGRESS: at 42.19% examples, 56048 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
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     "output_type": "stream",
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      "2018-10-19 19:43:14,922 : INFO : EPOCH 5 - PROGRESS: at 75.14% examples, 56246 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:15,939 : INFO : EPOCH 5 - PROGRESS: at 75.46% examples, 56249 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:16,973 : INFO : EPOCH 5 - PROGRESS: at 75.78% examples, 56249 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:18,002 : INFO : EPOCH 5 - PROGRESS: at 76.10% examples, 56248 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:19,032 : INFO : EPOCH 5 - PROGRESS: at 76.42% examples, 56248 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:20,149 : INFO : EPOCH 5 - PROGRESS: at 76.76% examples, 56259 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:21,164 : INFO : EPOCH 5 - PROGRESS: at 77.08% examples, 56262 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:43:24,248 : INFO : EPOCH 5 - PROGRESS: at 78.02% examples, 56266 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:25,276 : INFO : EPOCH 5 - PROGRESS: at 78.34% examples, 56267 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:43:44,938 : INFO : EPOCH 5 - PROGRESS: at 84.36% examples, 56278 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:45,981 : INFO : EPOCH 5 - PROGRESS: at 84.68% examples, 56275 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:47,006 : INFO : EPOCH 5 - PROGRESS: at 85.00% examples, 56276 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:48,034 : INFO : EPOCH 5 - PROGRESS: at 85.32% examples, 56277 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:49,060 : INFO : EPOCH 5 - PROGRESS: at 85.64% examples, 56279 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:50,068 : INFO : EPOCH 5 - PROGRESS: at 85.95% examples, 56283 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:51,074 : INFO : EPOCH 5 - PROGRESS: at 86.26% examples, 56288 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:43:53,154 : INFO : EPOCH 5 - PROGRESS: at 86.90% examples, 56285 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:54,156 : INFO : EPOCH 5 - PROGRESS: at 87.20% examples, 56290 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:55,178 : INFO : EPOCH 5 - PROGRESS: at 87.53% examples, 56292 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:43:57,218 : INFO : EPOCH 5 - PROGRESS: at 88.16% examples, 56297 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:58,233 : INFO : EPOCH 5 - PROGRESS: at 88.46% examples, 56300 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:43:59,359 : INFO : EPOCH 5 - PROGRESS: at 88.81% examples, 56305 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 19:44:00,389 : INFO : EPOCH 5 - PROGRESS: at 89.09% examples, 56281 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 19:44:01,398 : INFO : EPOCH 5 - PROGRESS: at 89.41% examples, 56285 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:44:07,636 : INFO : EPOCH 5 - PROGRESS: at 91.32% examples, 56300 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:44:13,922 : INFO : EPOCH 5 - PROGRESS: at 93.25% examples, 56303 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:44:16,995 : INFO : EPOCH 5 - PROGRESS: at 94.20% examples, 56306 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 19:44:35,793 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 19:44:35,794 : INFO : EPOCH 5 - PROGRESS: at 99.95% examples, 56305 words/s, in_qsize 2, out_qsize 1\n",
      "2018-10-19 19:44:35,794 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 19:44:35,855 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 19:44:35,916 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 19:44:35,917 : INFO : EPOCH - 5 : training on 25242693 raw words (18335741 effective words) took 325.6s, 56314 effective words/s\n",
      "2018-10-19 19:44:35,917 : INFO : training on a 126213465 raw words (91682232 effective words) took 1625.7s, 56396 effective words/s\n",
      "2018-10-19 19:44:38,291 : INFO : saving FastText object under 300features_40minwords_10context_fasttext, separately None\n",
      "2018-10-19 19:44:38,292 : INFO : storing np array 'vectors' to 300features_40minwords_10context_fasttext.wv.vectors.npy\n",
      "2018-10-19 19:44:38,598 : INFO : storing np array 'vectors_vocab' to 300features_40minwords_10context_fasttext.wv.vectors_vocab.npy\n",
      "2018-10-19 19:44:38,953 : INFO : storing np array 'vectors_ngrams' to 300features_40minwords_10context_fasttext.wv.vectors_ngrams.npy\n",
      "2018-10-19 19:44:40,770 : INFO : not storing attribute vectors_norm\n",
      "2018-10-19 19:44:40,771 : INFO : not storing attribute vectors_vocab_norm\n",
      "2018-10-19 19:44:40,771 : INFO : not storing attribute vectors_ngrams_norm\n",
      "2018-10-19 19:44:40,772 : INFO : not storing attribute buckets_word\n",
      "2018-10-19 19:44:40,772 : INFO : storing np array 'syn1neg' to 300features_40minwords_10context_fasttext.trainables.syn1neg.npy\n",
      "2018-10-19 19:44:41,079 : INFO : storing np array 'vectors_vocab_lockf' to 300features_40minwords_10context_fasttext.trainables.vectors_vocab_lockf.npy\n",
      "2018-10-19 19:44:41,396 : INFO : storing np array 'vectors_ngrams_lockf' to 300features_40minwords_10context_fasttext.trainables.vectors_ngrams_lockf.npy\n",
      "2018-10-19 19:44:43,243 : INFO : saved 300features_40minwords_10context_fasttext\n"
     ]
    }
   ],
   "source": [
    "from gensim.models.fasttext import FastText\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\\\n",
    "    level=logging.INFO)\n",
    "\n",
    "num_features = 300    # Word vector dimensionality                      \n",
    "min_word_count = 5   # Minimum word count                        \n",
    "num_workers = 4       # Number of threads to run in parallel\n",
    "context = 10          # Context window size                                                                                    \n",
    "downsampling = 1e-3   # Downsample setting for frequent words\n",
    "sg = 1\n",
    "hs = 0\n",
    "\n",
    "print(\"Training model...\")\n",
    "model = FastText(sentences, workers=num_workers, \\\n",
    "            size=num_features, min_count = min_word_count, \\\n",
    "            window = context, sample = downsampling, sg = sg)\n",
    "\n",
    "model_name = \"300features_40minwords_10context_fasttext\"\n",
    "model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n",
      "2018-10-19 17:47:08,140 : INFO : 'pattern' package not found; tag filters are not available for English\n",
      "2018-10-19 17:47:08,144 : INFO : collecting all words and their counts\n",
      "2018-10-19 17:47:08,155 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 17:47:10,691 : INFO : PROGRESS: at sentence #10000, processed 241944 words, keeping 18449 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.happierabroad.com\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 17:47:13,243 : INFO : PROGRESS: at sentence #20000, processed 483720 words, keeping 26155 word types\n",
      "2018-10-19 17:47:15,750 : INFO : PROGRESS: at sentence #30000, processed 718909 words, keeping 31608 word types\n",
      "2018-10-19 17:47:18,222 : INFO : PROGRESS: at sentence #40000, processed 961259 words, keeping 36357 word types\n",
      "2018-10-19 17:47:20,724 : INFO : PROGRESS: at sentence #50000, processed 1196249 words, keeping 40156 word types\n",
      "2018-10-19 17:47:23,216 : INFO : PROGRESS: at sentence #60000, processed 1433089 words, keeping 43426 word types\n",
      "2018-10-19 17:47:25,754 : INFO : PROGRESS: at sentence #70000, processed 1671705 words, keeping 46385 word types\n",
      "2018-10-19 17:47:28,272 : INFO : PROGRESS: at sentence #80000, processed 1906221 words, keeping 49064 word types\n",
      "2018-10-19 17:47:30,781 : INFO : PROGRESS: at sentence #90000, processed 2146491 words, keeping 51812 word types\n",
      "2018-10-19 17:47:33,232 : INFO : PROGRESS: at sentence #100000, processed 2384047 words, keeping 54189 word types\n",
      "2018-10-19 17:47:35,724 : INFO : PROGRESS: at sentence #110000, processed 2619479 words, keeping 56320 word types\n",
      "2018-10-19 17:47:38,222 : INFO : PROGRESS: at sentence #120000, processed 2857001 words, keeping 58637 word types\n",
      "2018-10-19 17:47:40,718 : INFO : PROGRESS: at sentence #130000, processed 3099128 words, keeping 60668 word types\n",
      "2018-10-19 17:47:43,181 : INFO : PROGRESS: at sentence #140000, processed 3326972 words, keeping 62397 word types\n",
      "2018-10-19 17:47:45,644 : INFO : PROGRESS: at sentence #150000, processed 3568211 words, keeping 64402 word types\n",
      "2018-10-19 17:47:48,083 : INFO : PROGRESS: at sentence #160000, processed 3806344 words, keeping 66233 word types\n",
      "2018-10-19 17:47:50,549 : INFO : PROGRESS: at sentence #170000, processed 4045170 words, keeping 67946 word types\n",
      "2018-10-19 17:47:53,013 : INFO : PROGRESS: at sentence #180000, processed 4280999 words, keeping 69624 word types\n",
      "2018-10-19 17:47:55,521 : INFO : PROGRESS: at sentence #190000, processed 4522051 words, keeping 71164 word types\n",
      "2018-10-19 17:47:58,054 : INFO : PROGRESS: at sentence #200000, processed 4762236 words, keeping 72681 word types\n",
      "2018-10-19 17:48:00,492 : INFO : PROGRESS: at sentence #210000, processed 4999023 words, keeping 74193 word types\n",
      "2018-10-19 17:48:02,959 : INFO : PROGRESS: at sentence #220000, processed 5239521 words, keeping 75757 word types\n",
      "2018-10-19 17:48:05,518 : INFO : PROGRESS: at sentence #230000, processed 5477612 words, keeping 77242 word types\n",
      "2018-10-19 17:48:08,006 : INFO : PROGRESS: at sentence #240000, processed 5720948 words, keeping 78750 word types\n",
      "2018-10-19 17:48:10,501 : INFO : PROGRESS: at sentence #250000, processed 5950179 words, keeping 80157 word types\n",
      "2018-10-19 17:48:13,023 : INFO : PROGRESS: at sentence #260000, processed 6186197 words, keeping 81506 word types\n",
      "2018-10-19 17:48:15,551 : INFO : PROGRESS: at sentence #270000, processed 6419502 words, keeping 83037 word types\n",
      "2018-10-19 17:48:18,053 : INFO : PROGRESS: at sentence #280000, processed 6662919 words, keeping 84850 word types\n",
      "2018-10-19 17:48:20,600 : INFO : PROGRESS: at sentence #290000, processed 6897354 words, keeping 86549 word types\n",
      "2018-10-19 17:48:23,147 : INFO : PROGRESS: at sentence #300000, processed 7134237 words, keeping 88213 word types\n",
      "2018-10-19 17:48:25,603 : INFO : PROGRESS: at sentence #310000, processed 7368457 words, keeping 89590 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'..'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 17:48:28,035 : INFO : PROGRESS: at sentence #320000, processed 7606474 words, keeping 91017 word types\n",
      "2018-10-19 17:48:30,492 : INFO : PROGRESS: at sentence #330000, processed 7846961 words, keeping 92546 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'....................................................................'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 17:48:32,928 : INFO : PROGRESS: at sentence #340000, processed 8082966 words, keeping 93885 word types\n",
      "2018-10-19 17:48:35,344 : INFO : PROGRESS: at sentence #350000, processed 8313720 words, keeping 95109 word types\n",
      "2018-10-19 17:48:37,776 : INFO : PROGRESS: at sentence #360000, processed 8551469 words, keeping 96462 word types\n",
      "2018-10-19 17:48:40,193 : INFO : PROGRESS: at sentence #370000, processed 8781534 words, keeping 97632 word types\n",
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      "2018-10-19 17:48:45,108 : INFO : PROGRESS: at sentence #390000, processed 9258408 words, keeping 99886 word types\n",
      "2018-10-19 17:48:47,548 : INFO : PROGRESS: at sentence #400000, processed 9494786 words, keeping 101123 word types\n",
      "2018-10-19 17:48:49,987 : INFO : PROGRESS: at sentence #410000, processed 9731245 words, keeping 102230 word types\n",
      "2018-10-19 17:48:52,442 : INFO : PROGRESS: at sentence #420000, processed 9970063 words, keeping 103433 word types\n",
      "2018-10-19 17:48:54,900 : INFO : PROGRESS: at sentence #430000, processed 10209043 words, keeping 104632 word types\n",
      "2018-10-19 17:48:57,383 : INFO : PROGRESS: at sentence #440000, processed 10444358 words, keeping 105734 word types\n",
      "2018-10-19 17:48:59,883 : INFO : PROGRESS: at sentence #450000, processed 10675921 words, keeping 106737 word types\n",
      "2018-10-19 17:49:02,365 : INFO : PROGRESS: at sentence #460000, processed 10914086 words, keeping 107868 word types\n",
      "2018-10-19 17:49:04,902 : INFO : PROGRESS: at sentence #470000, processed 11151508 words, keeping 108936 word types\n",
      "2018-10-19 17:49:07,412 : INFO : PROGRESS: at sentence #480000, processed 11382150 words, keeping 109977 word types\n",
      "2018-10-19 17:49:09,902 : INFO : PROGRESS: at sentence #490000, processed 11620335 words, keeping 111008 word types\n",
      "2018-10-19 17:49:12,341 : INFO : PROGRESS: at sentence #500000, processed 11857067 words, keeping 111995 word types\n",
      "2018-10-19 17:49:14,775 : INFO : PROGRESS: at sentence #510000, processed 12094353 words, keeping 112995 word types\n",
      "2018-10-19 17:49:17,207 : INFO : PROGRESS: at sentence #520000, processed 12329373 words, keeping 113983 word types\n",
      "2018-10-19 17:49:19,643 : INFO : PROGRESS: at sentence #530000, processed 12572975 words, keeping 114981 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.archive.org/details/LovefromaStranger\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 17:49:22,095 : INFO : PROGRESS: at sentence #540000, processed 12814200 words, keeping 116268 word types\n",
      "2018-10-19 17:49:24,560 : INFO : PROGRESS: at sentence #550000, processed 13053410 words, keeping 117505 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.loosechangeguide.com/LooseChangeGuide.html\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 17:49:27,017 : INFO : PROGRESS: at sentence #560000, processed 13296240 words, keeping 118743 word types\n",
      "2018-10-19 17:49:29,467 : INFO : PROGRESS: at sentence #570000, processed 13536737 words, keeping 119869 word types\n",
      "2018-10-19 17:49:31,919 : INFO : PROGRESS: at sentence #580000, processed 13776414 words, keeping 120994 word types\n",
      "2018-10-19 17:49:34,403 : INFO : PROGRESS: at sentence #590000, processed 14013310 words, keeping 122095 word types\n",
      "2018-10-19 17:49:36,892 : INFO : PROGRESS: at sentence #600000, processed 14259336 words, keeping 123261 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'... ...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 17:49:39,364 : INFO : PROGRESS: at sentence #610000, processed 14499025 words, keeping 124344 word types\n",
      "2018-10-19 17:49:41,812 : INFO : PROGRESS: at sentence #620000, processed 14735172 words, keeping 125389 word types\n",
      "2018-10-19 17:49:44,269 : INFO : PROGRESS: at sentence #630000, processed 14977916 words, keeping 126383 word types\n",
      "2018-10-19 17:49:46,719 : INFO : PROGRESS: at sentence #640000, processed 15217915 words, keeping 127427 word types\n",
      "2018-10-19 17:49:49,176 : INFO : PROGRESS: at sentence #650000, processed 15463760 words, keeping 128437 word types\n",
      "2018-10-19 17:49:51,619 : INFO : PROGRESS: at sentence #660000, processed 15703944 words, keeping 129417 word types\n",
      "2018-10-19 17:49:54,064 : INFO : PROGRESS: at sentence #670000, processed 15938096 words, keeping 130337 word types\n",
      "2018-10-19 17:49:56,503 : INFO : PROGRESS: at sentence #680000, processed 16177011 words, keeping 131253 word types\n",
      "2018-10-19 17:49:58,941 : INFO : PROGRESS: at sentence #690000, processed 16418855 words, keeping 132214 word types\n",
      "2018-10-19 17:50:01,387 : INFO : PROGRESS: at sentence #700000, processed 16661740 words, keeping 133201 word types\n",
      "2018-10-19 17:50:03,869 : INFO : PROGRESS: at sentence #710000, processed 16902469 words, keeping 134253 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'....'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 17:50:06,328 : INFO : PROGRESS: at sentence #720000, processed 17149826 words, keeping 135291 word types\n",
      "2018-10-19 17:50:08,790 : INFO : PROGRESS: at sentence #730000, processed 17392794 words, keeping 136121 word types\n",
      "2018-10-19 17:50:11,241 : INFO : PROGRESS: at sentence #740000, processed 17634670 words, keeping 137070 word types\n",
      "2018-10-19 17:50:13,701 : INFO : PROGRESS: at sentence #750000, processed 17873593 words, keeping 138098 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.msnbc.msn.com/id/4972055/site/newsweek/\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 17:50:16,174 : INFO : PROGRESS: at sentence #760000, processed 18111463 words, keeping 138932 word types\n",
      "2018-10-19 17:50:18,657 : INFO : PROGRESS: at sentence #770000, processed 18351797 words, keeping 139906 word types\n",
      "2018-10-19 17:50:21,122 : INFO : PROGRESS: at sentence #780000, processed 18592424 words, keeping 140858 word types\n",
      "2018-10-19 17:50:23,586 : INFO : PROGRESS: at sentence #790000, processed 18834700 words, keeping 141726 word types\n",
      "2018-10-19 17:50:26,030 : INFO : PROGRESS: at sentence #800000, processed 19071544 words, keeping 142662 word types\n",
      "2018-10-19 17:50:28,475 : INFO : PROGRESS: at sentence #810000, processed 19310448 words, keeping 143576 word types\n",
      "2018-10-19 17:50:30,941 : INFO : PROGRESS: at sentence #820000, processed 19550803 words, keeping 144444 word types\n",
      "2018-10-19 17:50:33,396 : INFO : PROGRESS: at sentence #830000, processed 19793367 words, keeping 145258 word types\n",
      "2018-10-19 17:50:35,841 : INFO : PROGRESS: at sentence #840000, processed 20034333 words, keeping 146131 word types\n",
      "2018-10-19 17:50:38,286 : INFO : PROGRESS: at sentence #850000, processed 20272773 words, keeping 146980 word types\n",
      "2018-10-19 17:50:40,732 : INFO : PROGRESS: at sentence #860000, processed 20512067 words, keeping 147754 word types\n",
      "2018-10-19 17:50:43,206 : INFO : PROGRESS: at sentence #870000, processed 20749689 words, keeping 148674 word types\n",
      "2018-10-19 17:50:45,649 : INFO : PROGRESS: at sentence #880000, processed 20991083 words, keeping 149467 word types\n",
      "2018-10-19 17:50:48,098 : INFO : PROGRESS: at sentence #890000, processed 21230330 words, keeping 150250 word types\n",
      "2018-10-19 17:50:50,544 : INFO : PROGRESS: at sentence #900000, processed 21465215 words, keeping 151121 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://www.youtube.com/watch?v=a0KSqelmgN8\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 17:50:53,017 : INFO : PROGRESS: at sentence #910000, processed 21708735 words, keeping 151943 word types\n",
      "2018-10-19 17:50:55,493 : INFO : PROGRESS: at sentence #920000, processed 21945702 words, keeping 152728 word types\n",
      "2018-10-19 17:50:57,970 : INFO : PROGRESS: at sentence #930000, processed 22185870 words, keeping 153492 word types\n",
      "2018-10-19 17:51:00,452 : INFO : PROGRESS: at sentence #940000, processed 22428839 words, keeping 154248 word types\n",
      "2018-10-19 17:51:02,908 : INFO : PROGRESS: at sentence #950000, processed 22664717 words, keeping 154975 word types\n",
      "2018-10-19 17:51:05,355 : INFO : PROGRESS: at sentence #960000, processed 22908377 words, keeping 155854 word types\n",
      "2018-10-19 17:51:07,805 : INFO : PROGRESS: at sentence #970000, processed 23147777 words, keeping 156565 word types\n",
      "2018-10-19 17:51:10,252 : INFO : PROGRESS: at sentence #980000, processed 23391163 words, keeping 157226 word types\n",
      "2018-10-19 17:51:12,697 : INFO : PROGRESS: at sentence #990000, processed 23628855 words, keeping 157993 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:219: UserWarning: \"b'.. .'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "2018-10-19 17:51:15,139 : INFO : PROGRESS: at sentence #1000000, processed 23866627 words, keeping 158724 word types\n",
      "2018-10-19 17:51:17,578 : INFO : PROGRESS: at sentence #1010000, processed 24103110 words, keeping 159410 word types\n",
      "2018-10-19 17:51:20,010 : INFO : PROGRESS: at sentence #1020000, processed 24337454 words, keeping 160132 word types\n",
      "2018-10-19 17:51:22,488 : INFO : PROGRESS: at sentence #1030000, processed 24579174 words, keeping 160915 word types\n",
      "2018-10-19 17:51:24,932 : INFO : PROGRESS: at sentence #1040000, processed 24824563 words, keeping 161719 word types\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:282: UserWarning: \"http://jake-weird.blogspot.com/2007/08/beneath.html\"\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  ' that document to Beautiful Soup.' % decoded_markup\n",
      "2018-10-19 17:51:27,385 : INFO : PROGRESS: at sentence #1050000, processed 25068033 words, keeping 162429 word types\n",
      "2018-10-19 17:51:29,173 : INFO : collected 163009 word types from a corpus of 25242693 raw words and 1057275 sentences\n",
      "2018-10-19 17:51:29,174 : INFO : Loading a fresh vocabulary\n",
      "2018-10-19 17:51:29,304 : INFO : min_count=5 retains 55895 unique words (34% of original 163009, drops 107114)\n",
      "2018-10-19 17:51:29,304 : INFO : min_count=5 leaves 25075676 word corpus (99% of original 25242693, drops 167017)\n",
      "2018-10-19 17:51:29,430 : INFO : deleting the raw counts dictionary of 163009 items\n",
      "2018-10-19 17:51:29,434 : INFO : sample=0.001 downsamples 51 most-common words\n",
      "2018-10-19 17:51:29,435 : INFO : downsampling leaves estimated 18336060 word corpus (73.1% of prior 25075676)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 17:51:29,567 : INFO : estimated required memory for 55895 words and 300 dimensions: 162095500 bytes\n",
      "2018-10-19 17:51:29,567 : INFO : resetting layer weights\n",
      "2018-10-19 17:51:30,203 : INFO : training model with 4 workers on 55895 vocabulary and 300 features, using sg=1 hs=0 sample=0.001 negative=5 window=10\n",
      "2018-10-19 17:51:31,258 : INFO : EPOCH 1 - PROGRESS: at 0.35% examples, 62409 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 17:52:47,224 : INFO : EPOCH 1 - PROGRESS: at 27.94% examples, 66247 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 17:54:06,497 : INFO : EPOCH 1 - PROGRESS: at 56.72% examples, 66214 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:55:24,015 : INFO : EPOCH 1 - PROGRESS: at 84.72% examples, 66382 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:55:25,127 : INFO : EPOCH 1 - PROGRESS: at 85.13% examples, 66376 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 17:55:26,191 : INFO : EPOCH 1 - PROGRESS: at 85.52% examples, 66384 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:55:27,281 : INFO : EPOCH 1 - PROGRESS: at 85.91% examples, 66385 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:55:28,378 : INFO : EPOCH 1 - PROGRESS: at 86.29% examples, 66383 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:55:30,560 : INFO : EPOCH 1 - PROGRESS: at 87.09% examples, 66385 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:55:32,726 : INFO : EPOCH 1 - PROGRESS: at 87.88% examples, 66390 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:55:33,800 : INFO : EPOCH 1 - PROGRESS: at 88.27% examples, 66396 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:55:34,860 : INFO : EPOCH 1 - PROGRESS: at 88.65% examples, 66404 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:55:35,958 : INFO : EPOCH 1 - PROGRESS: at 89.05% examples, 66403 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:55:52,950 : INFO : EPOCH 1 - PROGRESS: at 95.20% examples, 66413 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:55:56,134 : INFO : EPOCH 1 - PROGRESS: at 96.36% examples, 66410 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:56:06,111 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 17:56:06,112 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 17:56:06,147 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 17:56:06,169 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 17:56:06,170 : INFO : EPOCH - 1 : training on 25242693 raw words (18336612 effective words) took 276.0s, 66446 effective words/s\n",
      "2018-10-19 17:56:07,226 : INFO : EPOCH 2 - PROGRESS: at 0.35% examples, 62556 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:56:08,293 : INFO : EPOCH 2 - PROGRESS: at 0.74% examples, 65324 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:56:21,166 : INFO : EPOCH 2 - PROGRESS: at 5.44% examples, 66798 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:56:23,268 : INFO : EPOCH 2 - PROGRESS: at 6.21% examples, 66626 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:56:28,625 : INFO : EPOCH 2 - PROGRESS: at 8.15% examples, 66560 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:56:33,985 : INFO : EPOCH 2 - PROGRESS: at 10.10% examples, 66505 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:56:35,076 : INFO : EPOCH 2 - PROGRESS: at 10.50% examples, 66508 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:56:37,251 : INFO : EPOCH 2 - PROGRESS: at 11.30% examples, 66518 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:56:38,354 : INFO : EPOCH 2 - PROGRESS: at 11.69% examples, 66490 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:56:39,431 : INFO : EPOCH 2 - PROGRESS: at 12.08% examples, 66518 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 17:56:40,456 : INFO : EPOCH 2 - PROGRESS: at 12.46% examples, 66427 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:56:41,538 : INFO : EPOCH 2 - PROGRESS: at 12.86% examples, 66443 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:57:37,168 : INFO : EPOCH 2 - PROGRESS: at 33.10% examples, 66346 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:57:42,562 : INFO : EPOCH 2 - PROGRESS: at 35.08% examples, 66321 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 17:57:51,108 : INFO : EPOCH 2 - PROGRESS: at 38.18% examples, 66311 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 17:57:52,155 : INFO : EPOCH 2 - PROGRESS: at 38.56% examples, 66339 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 17:57:59,697 : INFO : EPOCH 2 - PROGRESS: at 41.31% examples, 66330 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "2018-10-19 18:00:25,459 : INFO : EPOCH 2 - PROGRESS: at 94.25% examples, 66620 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 18:00:27,621 : INFO : EPOCH 2 - PROGRESS: at 95.03% examples, 66624 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:28,653 : INFO : EPOCH 2 - PROGRESS: at 95.40% examples, 66611 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:29,659 : INFO : EPOCH 2 - PROGRESS: at 95.76% examples, 66604 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:30,748 : INFO : EPOCH 2 - PROGRESS: at 96.16% examples, 66604 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:31,775 : INFO : EPOCH 2 - PROGRESS: at 96.53% examples, 66592 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:32,857 : INFO : EPOCH 2 - PROGRESS: at 96.92% examples, 66594 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:33,928 : INFO : EPOCH 2 - PROGRESS: at 97.31% examples, 66598 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:35,004 : INFO : EPOCH 2 - PROGRESS: at 97.70% examples, 66601 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:36,065 : INFO : EPOCH 2 - PROGRESS: at 98.08% examples, 66608 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:37,108 : INFO : EPOCH 2 - PROGRESS: at 98.46% examples, 66619 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:00:38,183 : INFO : EPOCH 2 - PROGRESS: at 98.85% examples, 66623 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:39,255 : INFO : EPOCH 2 - PROGRESS: at 99.25% examples, 66628 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:40,257 : INFO : EPOCH 2 - PROGRESS: at 99.59% examples, 66623 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:41,291 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 18:00:41,291 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 18:00:41,327 : INFO : EPOCH 2 - PROGRESS: at 99.96% examples, 66610 words/s, in_qsize 1, out_qsize 1\n",
      "2018-10-19 18:00:41,328 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 18:00:41,347 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 18:00:41,348 : INFO : EPOCH - 2 : training on 25242693 raw words (18334797 effective words) took 275.2s, 66631 effective words/s\n",
      "2018-10-19 18:00:42,399 : INFO : EPOCH 3 - PROGRESS: at 0.35% examples, 62865 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:43,469 : INFO : EPOCH 3 - PROGRESS: at 0.74% examples, 65242 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:44,552 : INFO : EPOCH 3 - PROGRESS: at 1.14% examples, 65878 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:45,629 : INFO : EPOCH 3 - PROGRESS: at 1.53% examples, 66183 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:46,679 : INFO : EPOCH 3 - PROGRESS: at 1.92% examples, 66797 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:47,779 : INFO : EPOCH 3 - PROGRESS: at 2.32% examples, 66646 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:48,861 : INFO : EPOCH 3 - PROGRESS: at 2.72% examples, 66682 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:49,949 : INFO : EPOCH 3 - PROGRESS: at 3.12% examples, 66659 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:50,986 : INFO : EPOCH 3 - PROGRESS: at 3.50% examples, 67013 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:51,992 : INFO : EPOCH 3 - PROGRESS: at 3.85% examples, 66808 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:53,066 : INFO : EPOCH 3 - PROGRESS: at 4.24% examples, 66854 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:54,179 : INFO : EPOCH 3 - PROGRESS: at 4.66% examples, 66711 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:55,251 : INFO : EPOCH 3 - PROGRESS: at 5.05% examples, 66777 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:56,329 : INFO : EPOCH 3 - PROGRESS: at 5.44% examples, 66810 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:57,342 : INFO : EPOCH 3 - PROGRESS: at 5.82% examples, 66643 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:58,430 : INFO : EPOCH 3 - PROGRESS: at 6.21% examples, 66654 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:00:59,501 : INFO : EPOCH 3 - PROGRESS: at 6.60% examples, 66709 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:00,589 : INFO : EPOCH 3 - PROGRESS: at 7.00% examples, 66703 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:01,682 : INFO : EPOCH 3 - PROGRESS: at 7.40% examples, 66683 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:02,763 : INFO : EPOCH 3 - PROGRESS: at 7.80% examples, 66709 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:03,772 : INFO : EPOCH 3 - PROGRESS: at 8.15% examples, 66614 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:04,853 : INFO : EPOCH 3 - PROGRESS: at 8.55% examples, 66642 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:05,948 : INFO : EPOCH 3 - PROGRESS: at 8.95% examples, 66631 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:07,035 : INFO : EPOCH 3 - PROGRESS: at 9.34% examples, 66640 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:08,053 : INFO : EPOCH 3 - PROGRESS: at 9.72% examples, 66545 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:09,125 : INFO : EPOCH 3 - PROGRESS: at 10.10% examples, 66589 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:10,215 : INFO : EPOCH 3 - PROGRESS: at 10.50% examples, 66586 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:11,316 : INFO : EPOCH 3 - PROGRESS: at 10.91% examples, 66556 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:12,396 : INFO : EPOCH 3 - PROGRESS: at 11.30% examples, 66581 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:13,457 : INFO : EPOCH 3 - PROGRESS: at 11.69% examples, 66637 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:14,577 : INFO : EPOCH 3 - PROGRESS: at 12.08% examples, 66579 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:15,604 : INFO : EPOCH 3 - PROGRESS: at 12.46% examples, 66480 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:16,684 : INFO : EPOCH 3 - PROGRESS: at 12.86% examples, 66497 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:17,685 : INFO : EPOCH 3 - PROGRESS: at 13.23% examples, 66457 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:18,739 : INFO : EPOCH 3 - PROGRESS: at 13.61% examples, 66517 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:19,821 : INFO : EPOCH 3 - PROGRESS: at 14.00% examples, 66526 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:20,906 : INFO : EPOCH 3 - PROGRESS: at 14.40% examples, 66535 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:21,993 : INFO : EPOCH 3 - PROGRESS: at 14.80% examples, 66541 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:23,080 : INFO : EPOCH 3 - PROGRESS: at 15.20% examples, 66543 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:24,083 : INFO : EPOCH 3 - PROGRESS: at 15.56% examples, 66512 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:25,156 : INFO : EPOCH 3 - PROGRESS: at 15.95% examples, 66536 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:26,183 : INFO : EPOCH 3 - PROGRESS: at 16.31% examples, 66464 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:27,272 : INFO : EPOCH 3 - PROGRESS: at 16.71% examples, 66468 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:28,359 : INFO : EPOCH 3 - PROGRESS: at 17.11% examples, 66474 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:29,433 : INFO : EPOCH 3 - PROGRESS: at 17.50% examples, 66497 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:30,496 : INFO : EPOCH 3 - PROGRESS: at 17.88% examples, 66532 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:31,590 : INFO : EPOCH 3 - PROGRESS: at 18.28% examples, 66527 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:32,661 : INFO : EPOCH 3 - PROGRESS: at 18.67% examples, 66541 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:33,746 : INFO : EPOCH 3 - PROGRESS: at 19.07% examples, 66542 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:34,762 : INFO : EPOCH 3 - PROGRESS: at 19.44% examples, 66505 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:35,829 : INFO : EPOCH 3 - PROGRESS: at 19.82% examples, 66535 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:36,913 : INFO : EPOCH 3 - PROGRESS: at 20.22% examples, 66546 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:37,925 : INFO : EPOCH 3 - PROGRESS: at 20.58% examples, 66508 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:38,984 : INFO : EPOCH 3 - PROGRESS: at 20.96% examples, 66543 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:40,085 : INFO : EPOCH 3 - PROGRESS: at 21.36% examples, 66530 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:41,165 : INFO : EPOCH 3 - PROGRESS: at 21.76% examples, 66542 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:42,218 : INFO : EPOCH 3 - PROGRESS: at 22.14% examples, 66578 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:43,309 : INFO : EPOCH 3 - PROGRESS: at 22.54% examples, 66574 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:44,398 : INFO : EPOCH 3 - PROGRESS: at 22.94% examples, 66575 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:45,432 : INFO : EPOCH 3 - PROGRESS: at 23.31% examples, 66519 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:46,553 : INFO : EPOCH 3 - PROGRESS: at 23.73% examples, 66488 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:47,639 : INFO : EPOCH 3 - PROGRESS: at 24.12% examples, 66491 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:48,707 : INFO : EPOCH 3 - PROGRESS: at 24.51% examples, 66508 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:49,746 : INFO : EPOCH 3 - PROGRESS: at 24.88% examples, 66451 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:50,758 : INFO : EPOCH 3 - PROGRESS: at 25.25% examples, 66420 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:01:51,843 : INFO : EPOCH 3 - PROGRESS: at 25.65% examples, 66425 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:01:52,895 : INFO : EPOCH 3 - PROGRESS: at 26.02% examples, 66457 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 18:02:10,188 : INFO : EPOCH 3 - PROGRESS: at 32.32% examples, 66404 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 18:02:12,350 : INFO : EPOCH 3 - PROGRESS: at 33.10% examples, 66344 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:03:11,357 : INFO : EPOCH 3 - PROGRESS: at 54.57% examples, 66331 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:04:30,727 : INFO : EPOCH 3 - PROGRESS: at 83.34% examples, 66559 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 18:05:17,791 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 18:05:17,792 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 18:05:17,828 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 18:05:17,848 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 18:05:17,848 : INFO : EPOCH - 3 : training on 25242693 raw words (18336272 effective words) took 276.5s, 66317 effective words/s\n",
      "2018-10-19 18:05:18,943 : INFO : EPOCH 4 - PROGRESS: at 0.35% examples, 60214 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:05:44,564 : INFO : EPOCH 4 - PROGRESS: at 9.59% examples, 65721 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "2018-10-19 18:08:00,437 : INFO : EPOCH 4 - PROGRESS: at 58.35% examples, 65489 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:01,507 : INFO : EPOCH 4 - PROGRESS: at 58.74% examples, 65504 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:02,513 : INFO : EPOCH 4 - PROGRESS: at 59.08% examples, 65497 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:03,624 : INFO : EPOCH 4 - PROGRESS: at 59.47% examples, 65496 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:04,629 : INFO : EPOCH 4 - PROGRESS: at 59.83% examples, 65494 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:05,694 : INFO : EPOCH 4 - PROGRESS: at 60.21% examples, 65509 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:06,694 : INFO : EPOCH 4 - PROGRESS: at 60.57% examples, 65507 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:07,758 : INFO : EPOCH 4 - PROGRESS: at 60.96% examples, 65522 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:08,818 : INFO : EPOCH 4 - PROGRESS: at 61.35% examples, 65541 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:09,895 : INFO : EPOCH 4 - PROGRESS: at 61.73% examples, 65554 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:10,974 : INFO : EPOCH 4 - PROGRESS: at 62.13% examples, 65564 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:12,083 : INFO : EPOCH 4 - PROGRESS: at 62.52% examples, 65563 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:13,130 : INFO : EPOCH 4 - PROGRESS: at 62.88% examples, 65542 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:14,192 : INFO : EPOCH 4 - PROGRESS: at 63.24% examples, 65518 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:15,302 : INFO : EPOCH 4 - PROGRESS: at 63.64% examples, 65517 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:16,323 : INFO : EPOCH 4 - PROGRESS: at 64.00% examples, 65508 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:17,324 : INFO : EPOCH 4 - PROGRESS: at 64.36% examples, 65507 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:18,324 : INFO : EPOCH 4 - PROGRESS: at 64.71% examples, 65504 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:19,342 : INFO : EPOCH 4 - PROGRESS: at 65.06% examples, 65498 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:20,419 : INFO : EPOCH 4 - PROGRESS: at 65.44% examples, 65508 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:08:21,425 : INFO : EPOCH 4 - PROGRESS: at 65.79% examples, 65505 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:22,434 : INFO : EPOCH 4 - PROGRESS: at 66.15% examples, 65499 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 18:08:39,100 : INFO : EPOCH 4 - PROGRESS: at 72.00% examples, 65485 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:08:40,113 : INFO : EPOCH 4 - PROGRESS: at 72.36% examples, 65481 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:09:39,762 : INFO : EPOCH 4 - PROGRESS: at 93.65% examples, 65555 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:09:40,821 : INFO : EPOCH 4 - PROGRESS: at 94.04% examples, 65566 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 18:09:57,520 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 18:09:57,521 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 18:09:57,554 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 18:09:57,573 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 18:09:57,573 : INFO : EPOCH - 4 : training on 25242693 raw words (18338810 effective words) took 279.7s, 65561 effective words/s\n",
      "2018-10-19 18:09:58,622 : INFO : EPOCH 5 - PROGRESS: at 0.35% examples, 62867 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:10:54,552 : INFO : EPOCH 5 - PROGRESS: at 20.73% examples, 66555 words/s, in_qsize 0, out_qsize 0\n",
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     ]
    },
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      "2018-10-19 18:13:01,307 : INFO : EPOCH 5 - PROGRESS: at 66.89% examples, 66551 words/s, in_qsize 0, out_qsize 0\n",
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      "2018-10-19 18:13:03,440 : INFO : EPOCH 5 - PROGRESS: at 67.67% examples, 66568 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:04,509 : INFO : EPOCH 5 - PROGRESS: at 68.04% examples, 66572 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:05,559 : INFO : EPOCH 5 - PROGRESS: at 68.43% examples, 66587 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:06,635 : INFO : EPOCH 5 - PROGRESS: at 68.82% examples, 66591 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:07,696 : INFO : EPOCH 5 - PROGRESS: at 69.20% examples, 66602 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:08,764 : INFO : EPOCH 5 - PROGRESS: at 69.59% examples, 66610 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:09,860 : INFO : EPOCH 5 - PROGRESS: at 69.99% examples, 66605 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:10,964 : INFO : EPOCH 5 - PROGRESS: at 70.39% examples, 66600 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:12,023 : INFO : EPOCH 5 - PROGRESS: at 70.77% examples, 66611 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:13,115 : INFO : EPOCH 5 - PROGRESS: at 71.17% examples, 66610 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:14,172 : INFO : EPOCH 5 - PROGRESS: at 71.55% examples, 66620 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:15,230 : INFO : EPOCH 5 - PROGRESS: at 71.92% examples, 66595 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:16,301 : INFO : EPOCH 5 - PROGRESS: at 72.32% examples, 66603 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:17,401 : INFO : EPOCH 5 - PROGRESS: at 72.72% examples, 66600 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:18,456 : INFO : EPOCH 5 - PROGRESS: at 73.10% examples, 66611 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:19,548 : INFO : EPOCH 5 - PROGRESS: at 73.50% examples, 66609 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:20,628 : INFO : EPOCH 5 - PROGRESS: at 73.90% examples, 66613 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:21,696 : INFO : EPOCH 5 - PROGRESS: at 74.29% examples, 66619 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:22,760 : INFO : EPOCH 5 - PROGRESS: at 74.67% examples, 66628 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:23,859 : INFO : EPOCH 5 - PROGRESS: at 75.07% examples, 66625 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:24,917 : INFO : EPOCH 5 - PROGRESS: at 75.46% examples, 66634 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:26,011 : INFO : EPOCH 5 - PROGRESS: at 75.86% examples, 66633 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:27,037 : INFO : EPOCH 5 - PROGRESS: at 76.22% examples, 66616 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:28,104 : INFO : EPOCH 5 - PROGRESS: at 76.61% examples, 66623 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:29,151 : INFO : EPOCH 5 - PROGRESS: at 76.99% examples, 66638 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:30,260 : INFO : EPOCH 5 - PROGRESS: at 77.40% examples, 66631 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:31,326 : INFO : EPOCH 5 - PROGRESS: at 77.79% examples, 66639 words/s, in_qsize 0, out_qsize 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:13:32,399 : INFO : EPOCH 5 - PROGRESS: at 78.18% examples, 66644 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:33,462 : INFO : EPOCH 5 - PROGRESS: at 78.57% examples, 66652 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:34,524 : INFO : EPOCH 5 - PROGRESS: at 78.95% examples, 66659 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:35,602 : INFO : EPOCH 5 - PROGRESS: at 79.35% examples, 66662 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:36,688 : INFO : EPOCH 5 - PROGRESS: at 79.75% examples, 66664 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:37,764 : INFO : EPOCH 5 - PROGRESS: at 80.14% examples, 66668 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:38,868 : INFO : EPOCH 5 - PROGRESS: at 80.53% examples, 66661 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:39,950 : INFO : EPOCH 5 - PROGRESS: at 80.93% examples, 66663 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:41,032 : INFO : EPOCH 5 - PROGRESS: at 81.32% examples, 66665 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:42,125 : INFO : EPOCH 5 - PROGRESS: at 81.73% examples, 66663 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:43,233 : INFO : EPOCH 5 - PROGRESS: at 82.13% examples, 66657 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:44,298 : INFO : EPOCH 5 - PROGRESS: at 82.52% examples, 66663 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:45,366 : INFO : EPOCH 5 - PROGRESS: at 82.90% examples, 66669 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:46,450 : INFO : EPOCH 5 - PROGRESS: at 83.30% examples, 66670 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:47,508 : INFO : EPOCH 5 - PROGRESS: at 83.68% examples, 66679 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:48,521 : INFO : EPOCH 5 - PROGRESS: at 84.05% examples, 66669 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:49,593 : INFO : EPOCH 5 - PROGRESS: at 84.44% examples, 66675 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:50,623 : INFO : EPOCH 5 - PROGRESS: at 84.81% examples, 66659 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:51,706 : INFO : EPOCH 5 - PROGRESS: at 85.20% examples, 66661 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:52,791 : INFO : EPOCH 5 - PROGRESS: at 85.59% examples, 66663 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:53,862 : INFO : EPOCH 5 - PROGRESS: at 85.99% examples, 66667 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:54,930 : INFO : EPOCH 5 - PROGRESS: at 86.37% examples, 66673 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:56,035 : INFO : EPOCH 5 - PROGRESS: at 86.78% examples, 66669 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:57,107 : INFO : EPOCH 5 - PROGRESS: at 87.17% examples, 66672 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:58,198 : INFO : EPOCH 5 - PROGRESS: at 87.57% examples, 66671 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:13:59,280 : INFO : EPOCH 5 - PROGRESS: at 87.96% examples, 66673 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:00,351 : INFO : EPOCH 5 - PROGRESS: at 88.35% examples, 66678 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:01,404 : INFO : EPOCH 5 - PROGRESS: at 88.73% examples, 66687 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:02,433 : INFO : EPOCH 5 - PROGRESS: at 89.09% examples, 66674 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:03,527 : INFO : EPOCH 5 - PROGRESS: at 89.49% examples, 66672 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:04,622 : INFO : EPOCH 5 - PROGRESS: at 89.89% examples, 66670 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:05,680 : INFO : EPOCH 5 - PROGRESS: at 90.28% examples, 66677 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:06,764 : INFO : EPOCH 5 - PROGRESS: at 90.67% examples, 66680 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:07,820 : INFO : EPOCH 5 - PROGRESS: at 91.05% examples, 66688 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:08,895 : INFO : EPOCH 5 - PROGRESS: at 91.44% examples, 66690 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:10,002 : INFO : EPOCH 5 - PROGRESS: at 91.85% examples, 66684 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:11,028 : INFO : EPOCH 5 - PROGRESS: at 92.22% examples, 66699 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:12,093 : INFO : EPOCH 5 - PROGRESS: at 92.61% examples, 66706 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:13,146 : INFO : EPOCH 5 - PROGRESS: at 92.98% examples, 66686 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:14,223 : INFO : EPOCH 5 - PROGRESS: at 93.37% examples, 66689 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:15,303 : INFO : EPOCH 5 - PROGRESS: at 93.77% examples, 66691 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:16,388 : INFO : EPOCH 5 - PROGRESS: at 94.16% examples, 66691 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:17,394 : INFO : EPOCH 5 - PROGRESS: at 94.53% examples, 66683 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:18,450 : INFO : EPOCH 5 - PROGRESS: at 94.91% examples, 66691 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:19,552 : INFO : EPOCH 5 - PROGRESS: at 95.32% examples, 66688 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:20,554 : INFO : EPOCH 5 - PROGRESS: at 95.69% examples, 66682 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:21,623 : INFO : EPOCH 5 - PROGRESS: at 96.08% examples, 66687 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:22,736 : INFO : EPOCH 5 - PROGRESS: at 96.49% examples, 66680 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:23,815 : INFO : EPOCH 5 - PROGRESS: at 96.88% examples, 66681 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:24,821 : INFO : EPOCH 5 - PROGRESS: at 97.23% examples, 66675 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:25,890 : INFO : EPOCH 5 - PROGRESS: at 97.62% examples, 66679 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:26,957 : INFO : EPOCH 5 - PROGRESS: at 98.01% examples, 66685 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:28,013 : INFO : EPOCH 5 - PROGRESS: at 98.39% examples, 66692 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:29,086 : INFO : EPOCH 5 - PROGRESS: at 98.78% examples, 66695 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:30,156 : INFO : EPOCH 5 - PROGRESS: at 99.16% examples, 66701 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:31,235 : INFO : EPOCH 5 - PROGRESS: at 99.55% examples, 66702 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:32,325 : INFO : EPOCH 5 - PROGRESS: at 99.95% examples, 66701 words/s, in_qsize 0, out_qsize 0\n",
      "2018-10-19 18:14:32,380 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 18:14:32,381 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 18:14:32,413 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 18:14:32,434 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 18:14:32,434 : INFO : EPOCH - 5 : training on 25242693 raw words (18335741 effective words) took 274.9s, 66710 effective words/s\n",
      "2018-10-19 18:14:32,435 : INFO : training on a 126213465 raw words (91682232 effective words) took 1382.2s, 66329 effective words/s\n",
      "2018-10-19 18:14:32,435 : INFO : saving Word2Vec object under 300features_40minwords_10context_sg, separately None\n",
      "2018-10-19 18:14:32,436 : INFO : storing np array 'vectors' to 300features_40minwords_10context_sg.wv.vectors.npy\n",
      "2018-10-19 18:14:32,753 : INFO : not storing attribute vectors_norm\n",
      "2018-10-19 18:14:32,753 : INFO : storing np array 'syn1neg' to 300features_40minwords_10context_sg.trainables.syn1neg.npy\n",
      "2018-10-19 18:14:33,065 : INFO : not storing attribute cum_table\n",
      "2018-10-19 18:14:33,159 : INFO : saved 300features_40minwords_10context_sg\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\\\n",
    "    level=logging.INFO)\n",
    "\n",
    "num_features = 300    # Word vector dimensionality                      \n",
    "min_word_count = 5   # Minimum word count                        \n",
    "num_workers = 4       # Number of threads to run in parallel\n",
    "context = 10          # Context window size                                                                                    \n",
    "downsampling = 1e-3   # Downsample setting for frequent words\n",
    "sg = 1\n",
    "hs = 0\n",
    "\n",
    "\n",
    "from gensim.models import word2vec\n",
    "print(\"Training model...\")\n",
    "model = word2vec.Word2Vec(sentences, workers=num_workers, \\\n",
    "            size=num_features, min_count = min_word_count, \\\n",
    "            window = context, sample = downsampling, sg = sg)\n",
    "\n",
    "model_name = \"300features_40minwords_10context_sg\"\n",
    "model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 16:13:39,199 : INFO : collecting all words and their counts\n",
      "2018-10-19 16:13:39,200 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2018-10-19 16:13:39,237 : INFO : PROGRESS: at sentence #10000, processed 241944 words, keeping 18449 word types\n",
      "2018-10-19 16:13:39,278 : INFO : PROGRESS: at sentence #20000, processed 483720 words, keeping 26155 word types\n",
      "2018-10-19 16:13:39,314 : INFO : PROGRESS: at sentence #30000, processed 718909 words, keeping 31608 word types\n",
      "2018-10-19 16:13:39,352 : INFO : PROGRESS: at sentence #40000, processed 961259 words, keeping 36357 word types\n",
      "2018-10-19 16:13:39,388 : INFO : PROGRESS: at sentence #50000, processed 1196249 words, keeping 40156 word types\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 16:13:39,424 : INFO : PROGRESS: at sentence #60000, processed 1433089 words, keeping 43426 word types\n",
      "2018-10-19 16:13:39,465 : INFO : PROGRESS: at sentence #70000, processed 1671705 words, keeping 46385 word types\n",
      "2018-10-19 16:13:39,499 : INFO : PROGRESS: at sentence #80000, processed 1906221 words, keeping 49064 word types\n",
      "2018-10-19 16:13:39,536 : INFO : PROGRESS: at sentence #90000, processed 2146491 words, keeping 51812 word types\n",
      "2018-10-19 16:13:39,573 : INFO : PROGRESS: at sentence #100000, processed 2384047 words, keeping 54189 word types\n",
      "2018-10-19 16:13:39,607 : INFO : PROGRESS: at sentence #110000, processed 2619479 words, keeping 56320 word types\n",
      "2018-10-19 16:13:39,646 : INFO : PROGRESS: at sentence #120000, processed 2857001 words, keeping 58637 word types\n",
      "2018-10-19 16:13:39,684 : INFO : PROGRESS: at sentence #130000, processed 3099128 words, keeping 60668 word types\n",
      "2018-10-19 16:13:39,717 : INFO : PROGRESS: at sentence #140000, processed 3326972 words, keeping 62397 word types\n",
      "2018-10-19 16:13:39,757 : INFO : PROGRESS: at sentence #150000, processed 3568211 words, keeping 64402 word types\n",
      "2018-10-19 16:13:39,792 : INFO : PROGRESS: at sentence #160000, processed 3806344 words, keeping 66233 word types\n",
      "2018-10-19 16:13:39,831 : INFO : PROGRESS: at sentence #170000, processed 4045170 words, keeping 67946 word types\n",
      "2018-10-19 16:13:39,871 : INFO : PROGRESS: at sentence #180000, processed 4280999 words, keeping 69624 word types\n",
      "2018-10-19 16:13:39,910 : INFO : PROGRESS: at sentence #190000, processed 4522051 words, keeping 71164 word types\n",
      "2018-10-19 16:13:39,949 : INFO : PROGRESS: at sentence #200000, processed 4762236 words, keeping 72681 word types\n",
      "2018-10-19 16:13:39,988 : INFO : PROGRESS: at sentence #210000, processed 4999023 words, keeping 74193 word types\n",
      "2018-10-19 16:13:40,028 : INFO : PROGRESS: at sentence #220000, processed 5239521 words, keeping 75757 word types\n",
      "2018-10-19 16:13:40,066 : INFO : PROGRESS: at sentence #230000, processed 5477612 words, keeping 77242 word types\n",
      "2018-10-19 16:13:40,103 : INFO : PROGRESS: at sentence #240000, processed 5720948 words, keeping 78750 word types\n",
      "2018-10-19 16:13:40,138 : INFO : PROGRESS: at sentence #250000, processed 5950179 words, keeping 80157 word types\n",
      "2018-10-19 16:13:40,175 : INFO : PROGRESS: at sentence #260000, processed 6186197 words, keeping 81506 word types\n",
      "2018-10-19 16:13:40,212 : INFO : PROGRESS: at sentence #270000, processed 6419502 words, keeping 83037 word types\n",
      "2018-10-19 16:13:40,255 : INFO : PROGRESS: at sentence #280000, processed 6662919 words, keeping 84850 word types\n",
      "2018-10-19 16:13:40,293 : INFO : PROGRESS: at sentence #290000, processed 6897354 words, keeping 86549 word types\n",
      "2018-10-19 16:13:40,337 : INFO : PROGRESS: at sentence #300000, processed 7134237 words, keeping 88213 word types\n",
      "2018-10-19 16:13:40,377 : INFO : PROGRESS: at sentence #310000, processed 7368457 words, keeping 89590 word types\n",
      "2018-10-19 16:13:40,415 : INFO : PROGRESS: at sentence #320000, processed 7606474 words, keeping 91017 word types\n",
      "2018-10-19 16:13:40,455 : INFO : PROGRESS: at sentence #330000, processed 7846961 words, keeping 92546 word types\n",
      "2018-10-19 16:13:40,494 : INFO : PROGRESS: at sentence #340000, processed 8082966 words, keeping 93885 word types\n",
      "2018-10-19 16:13:40,532 : INFO : PROGRESS: at sentence #350000, processed 8313720 words, keeping 95109 word types\n",
      "2018-10-19 16:13:40,569 : INFO : PROGRESS: at sentence #360000, processed 8551469 words, keeping 96462 word types\n",
      "2018-10-19 16:13:40,607 : INFO : PROGRESS: at sentence #370000, processed 8781534 words, keeping 97632 word types\n",
      "2018-10-19 16:13:40,646 : INFO : PROGRESS: at sentence #380000, processed 9018037 words, keeping 98708 word types\n",
      "2018-10-19 16:13:40,683 : INFO : PROGRESS: at sentence #390000, processed 9258408 words, keeping 99886 word types\n",
      "2018-10-19 16:13:40,721 : INFO : PROGRESS: at sentence #400000, processed 9494786 words, keeping 101123 word types\n",
      "2018-10-19 16:13:40,760 : INFO : PROGRESS: at sentence #410000, processed 9731245 words, keeping 102230 word types\n",
      "2018-10-19 16:13:40,799 : INFO : PROGRESS: at sentence #420000, processed 9970063 words, keeping 103433 word types\n",
      "2018-10-19 16:13:40,841 : INFO : PROGRESS: at sentence #430000, processed 10209043 words, keeping 104632 word types\n",
      "2018-10-19 16:13:40,881 : INFO : PROGRESS: at sentence #440000, processed 10444358 words, keeping 105734 word types\n",
      "2018-10-19 16:13:40,921 : INFO : PROGRESS: at sentence #450000, processed 10675921 words, keeping 106737 word types\n",
      "2018-10-19 16:13:40,963 : INFO : PROGRESS: at sentence #460000, processed 10914086 words, keeping 107868 word types\n",
      "2018-10-19 16:13:41,003 : INFO : PROGRESS: at sentence #470000, processed 11151508 words, keeping 108936 word types\n",
      "2018-10-19 16:13:41,044 : INFO : PROGRESS: at sentence #480000, processed 11382150 words, keeping 109977 word types\n",
      "2018-10-19 16:13:41,083 : INFO : PROGRESS: at sentence #490000, processed 11620335 words, keeping 111008 word types\n",
      "2018-10-19 16:13:41,123 : INFO : PROGRESS: at sentence #500000, processed 11857067 words, keeping 111995 word types\n",
      "2018-10-19 16:13:41,159 : INFO : PROGRESS: at sentence #510000, processed 12094353 words, keeping 112995 word types\n",
      "2018-10-19 16:13:41,197 : INFO : PROGRESS: at sentence #520000, processed 12329373 words, keeping 113983 word types\n",
      "2018-10-19 16:13:41,236 : INFO : PROGRESS: at sentence #530000, processed 12572975 words, keeping 114981 word types\n",
      "2018-10-19 16:13:41,278 : INFO : PROGRESS: at sentence #540000, processed 12814200 words, keeping 116268 word types\n",
      "2018-10-19 16:13:41,315 : INFO : PROGRESS: at sentence #550000, processed 13053410 words, keeping 117505 word types\n",
      "2018-10-19 16:13:41,354 : INFO : PROGRESS: at sentence #560000, processed 13296240 words, keeping 118743 word types\n",
      "2018-10-19 16:13:41,393 : INFO : PROGRESS: at sentence #570000, processed 13536737 words, keeping 119869 word types\n",
      "2018-10-19 16:13:41,431 : INFO : PROGRESS: at sentence #580000, processed 13776414 words, keeping 120994 word types\n",
      "2018-10-19 16:13:41,472 : INFO : PROGRESS: at sentence #590000, processed 14013310 words, keeping 122095 word types\n",
      "2018-10-19 16:13:41,514 : INFO : PROGRESS: at sentence #600000, processed 14259336 words, keeping 123261 word types\n",
      "2018-10-19 16:13:41,555 : INFO : PROGRESS: at sentence #610000, processed 14499025 words, keeping 124344 word types\n",
      "2018-10-19 16:13:41,595 : INFO : PROGRESS: at sentence #620000, processed 14735172 words, keeping 125389 word types\n",
      "2018-10-19 16:13:41,637 : INFO : PROGRESS: at sentence #630000, processed 14977916 words, keeping 126383 word types\n",
      "2018-10-19 16:13:41,679 : INFO : PROGRESS: at sentence #640000, processed 15217915 words, keeping 127427 word types\n",
      "2018-10-19 16:13:41,718 : INFO : PROGRESS: at sentence #650000, processed 15463760 words, keeping 128437 word types\n",
      "2018-10-19 16:13:41,757 : INFO : PROGRESS: at sentence #660000, processed 15703944 words, keeping 129417 word types\n",
      "2018-10-19 16:13:41,796 : INFO : PROGRESS: at sentence #670000, processed 15938096 words, keeping 130337 word types\n",
      "2018-10-19 16:13:41,832 : INFO : PROGRESS: at sentence #680000, processed 16177011 words, keeping 131253 word types\n",
      "2018-10-19 16:13:41,871 : INFO : PROGRESS: at sentence #690000, processed 16418855 words, keeping 132214 word types\n",
      "2018-10-19 16:13:41,911 : INFO : PROGRESS: at sentence #700000, processed 16661740 words, keeping 133201 word types\n",
      "2018-10-19 16:13:41,952 : INFO : PROGRESS: at sentence #710000, processed 16902469 words, keeping 134253 word types\n",
      "2018-10-19 16:13:41,992 : INFO : PROGRESS: at sentence #720000, processed 17149826 words, keeping 135291 word types\n",
      "2018-10-19 16:13:42,031 : INFO : PROGRESS: at sentence #730000, processed 17392794 words, keeping 136121 word types\n",
      "2018-10-19 16:13:42,070 : INFO : PROGRESS: at sentence #740000, processed 17634670 words, keeping 137070 word types\n",
      "2018-10-19 16:13:42,108 : INFO : PROGRESS: at sentence #750000, processed 17873593 words, keeping 138098 word types\n",
      "2018-10-19 16:13:42,147 : INFO : PROGRESS: at sentence #760000, processed 18111463 words, keeping 138932 word types\n",
      "2018-10-19 16:13:42,184 : INFO : PROGRESS: at sentence #770000, processed 18351797 words, keeping 139906 word types\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 16:13:42,224 : INFO : PROGRESS: at sentence #780000, processed 18592424 words, keeping 140858 word types\n",
      "2018-10-19 16:13:42,261 : INFO : PROGRESS: at sentence #790000, processed 18834700 words, keeping 141726 word types\n",
      "2018-10-19 16:13:42,298 : INFO : PROGRESS: at sentence #800000, processed 19071544 words, keeping 142662 word types\n",
      "2018-10-19 16:13:42,339 : INFO : PROGRESS: at sentence #810000, processed 19310448 words, keeping 143576 word types\n",
      "2018-10-19 16:13:42,375 : INFO : PROGRESS: at sentence #820000, processed 19550803 words, keeping 144444 word types\n",
      "2018-10-19 16:13:42,417 : INFO : PROGRESS: at sentence #830000, processed 19793367 words, keeping 145258 word types\n",
      "2018-10-19 16:13:42,456 : INFO : PROGRESS: at sentence #840000, processed 20034333 words, keeping 146131 word types\n",
      "2018-10-19 16:13:42,496 : INFO : PROGRESS: at sentence #850000, processed 20272773 words, keeping 146980 word types\n",
      "2018-10-19 16:13:42,537 : INFO : PROGRESS: at sentence #860000, processed 20512067 words, keeping 147754 word types\n",
      "2018-10-19 16:13:42,578 : INFO : PROGRESS: at sentence #870000, processed 20749689 words, keeping 148674 word types\n",
      "2018-10-19 16:13:42,618 : INFO : PROGRESS: at sentence #880000, processed 20991083 words, keeping 149467 word types\n",
      "2018-10-19 16:13:42,655 : INFO : PROGRESS: at sentence #890000, processed 21230330 words, keeping 150250 word types\n",
      "2018-10-19 16:13:42,693 : INFO : PROGRESS: at sentence #900000, processed 21465215 words, keeping 151121 word types\n",
      "2018-10-19 16:13:42,733 : INFO : PROGRESS: at sentence #910000, processed 21708735 words, keeping 151943 word types\n",
      "2018-10-19 16:13:42,769 : INFO : PROGRESS: at sentence #920000, processed 21945702 words, keeping 152728 word types\n",
      "2018-10-19 16:13:42,809 : INFO : PROGRESS: at sentence #930000, processed 22185870 words, keeping 153492 word types\n",
      "2018-10-19 16:13:42,847 : INFO : PROGRESS: at sentence #940000, processed 22428839 words, keeping 154248 word types\n",
      "2018-10-19 16:13:42,887 : INFO : PROGRESS: at sentence #950000, processed 22664717 words, keeping 154975 word types\n",
      "2018-10-19 16:13:42,929 : INFO : PROGRESS: at sentence #960000, processed 22908377 words, keeping 155854 word types\n",
      "2018-10-19 16:13:42,968 : INFO : PROGRESS: at sentence #970000, processed 23147777 words, keeping 156565 word types\n",
      "2018-10-19 16:13:43,010 : INFO : PROGRESS: at sentence #980000, processed 23391163 words, keeping 157226 word types\n",
      "2018-10-19 16:13:43,051 : INFO : PROGRESS: at sentence #990000, processed 23628855 words, keeping 157993 word types\n",
      "2018-10-19 16:13:43,091 : INFO : PROGRESS: at sentence #1000000, processed 23866627 words, keeping 158724 word types\n",
      "2018-10-19 16:13:43,132 : INFO : PROGRESS: at sentence #1010000, processed 24103110 words, keeping 159410 word types\n",
      "2018-10-19 16:13:43,172 : INFO : PROGRESS: at sentence #1020000, processed 24337454 words, keeping 160132 word types\n",
      "2018-10-19 16:13:43,211 : INFO : PROGRESS: at sentence #1030000, processed 24579174 words, keeping 160915 word types\n",
      "2018-10-19 16:13:43,254 : INFO : PROGRESS: at sentence #1040000, processed 24824563 words, keeping 161719 word types\n",
      "2018-10-19 16:13:43,290 : INFO : PROGRESS: at sentence #1050000, processed 25068033 words, keeping 162429 word types\n",
      "2018-10-19 16:13:43,321 : INFO : collected 163009 word types from a corpus of 25242693 raw words and 1057275 sentences\n",
      "2018-10-19 16:13:43,322 : INFO : Loading a fresh vocabulary\n",
      "2018-10-19 16:13:43,424 : INFO : min_count=5 retains 55895 unique words (34% of original 163009, drops 107114)\n",
      "2018-10-19 16:13:43,425 : INFO : min_count=5 leaves 25075676 word corpus (99% of original 25242693, drops 167017)\n",
      "2018-10-19 16:13:43,555 : INFO : deleting the raw counts dictionary of 163009 items\n",
      "2018-10-19 16:13:43,559 : INFO : sample=0.001 downsamples 51 most-common words\n",
      "2018-10-19 16:13:43,559 : INFO : downsampling leaves estimated 18336060 word corpus (73.1% of prior 25075676)\n",
      "2018-10-19 16:13:43,704 : INFO : estimated required memory for 55895 words and 300 dimensions: 162095500 bytes\n",
      "2018-10-19 16:13:43,704 : INFO : resetting layer weights\n",
      "2018-10-19 16:13:44,367 : INFO : training model with 4 workers on 55895 vocabulary and 300 features, using sg=0 hs=0 sample=0.001 negative=5 window=10\n",
      "2018-10-19 16:13:45,382 : INFO : EPOCH 1 - PROGRESS: at 6.92% examples, 1256030 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:46,387 : INFO : EPOCH 1 - PROGRESS: at 14.00% examples, 1269490 words/s, in_qsize 7, out_qsize 1\n",
      "2018-10-19 16:13:47,389 : INFO : EPOCH 1 - PROGRESS: at 20.88% examples, 1265673 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:48,393 : INFO : EPOCH 1 - PROGRESS: at 27.87% examples, 1265112 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:49,396 : INFO : EPOCH 1 - PROGRESS: at 34.96% examples, 1267840 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:13:50,399 : INFO : EPOCH 1 - PROGRESS: at 41.87% examples, 1265923 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:51,400 : INFO : EPOCH 1 - PROGRESS: at 48.87% examples, 1266146 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:52,402 : INFO : EPOCH 1 - PROGRESS: at 55.71% examples, 1265117 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:53,406 : INFO : EPOCH 1 - PROGRESS: at 62.52% examples, 1264262 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:54,418 : INFO : EPOCH 1 - PROGRESS: at 69.36% examples, 1263301 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:55,421 : INFO : EPOCH 1 - PROGRESS: at 76.31% examples, 1264099 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:56,424 : INFO : EPOCH 1 - PROGRESS: at 83.10% examples, 1262962 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:57,435 : INFO : EPOCH 1 - PROGRESS: at 90.01% examples, 1262357 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:58,443 : INFO : EPOCH 1 - PROGRESS: at 96.96% examples, 1262635 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:13:58,869 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 16:13:58,874 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 16:13:58,876 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 16:13:58,879 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 16:13:58,879 : INFO : EPOCH - 1 : training on 25242693 raw words (18335187 effective words) took 14.5s, 1263842 effective words/s\n",
      "2018-10-19 16:13:59,889 : INFO : EPOCH 2 - PROGRESS: at 7.00% examples, 1276033 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:00,896 : INFO : EPOCH 2 - PROGRESS: at 14.16% examples, 1286165 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:01,902 : INFO : EPOCH 2 - PROGRESS: at 21.28% examples, 1289934 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:02,909 : INFO : EPOCH 2 - PROGRESS: at 28.46% examples, 1290973 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:03,916 : INFO : EPOCH 2 - PROGRESS: at 35.68% examples, 1291980 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:04,924 : INFO : EPOCH 2 - PROGRESS: at 42.84% examples, 1292448 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:05,938 : INFO : EPOCH 2 - PROGRESS: at 50.01% examples, 1291391 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:06,946 : INFO : EPOCH 2 - PROGRESS: at 57.07% examples, 1291760 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:07,948 : INFO : EPOCH 2 - PROGRESS: at 64.08% examples, 1291494 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:08,952 : INFO : EPOCH 2 - PROGRESS: at 71.01% examples, 1290816 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:09,954 : INFO : EPOCH 2 - PROGRESS: at 78.02% examples, 1290623 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:10,956 : INFO : EPOCH 2 - PROGRESS: at 84.72% examples, 1285581 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:11,960 : INFO : EPOCH 2 - PROGRESS: at 91.28% examples, 1279477 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:12,961 : INFO : EPOCH 2 - PROGRESS: at 97.92% examples, 1274997 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:13,262 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 16:14:13,264 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 16:14:13,267 : INFO : worker thread finished; awaiting finish of 1 more threads\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 16:14:13,277 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 16:14:13,278 : INFO : EPOCH - 2 : training on 25242693 raw words (18336279 effective words) took 14.4s, 1273841 effective words/s\n",
      "2018-10-19 16:14:14,290 : INFO : EPOCH 3 - PROGRESS: at 6.68% examples, 1220382 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:15,296 : INFO : EPOCH 3 - PROGRESS: at 13.61% examples, 1237482 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:16,300 : INFO : EPOCH 3 - PROGRESS: at 20.34% examples, 1233864 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:17,301 : INFO : EPOCH 3 - PROGRESS: at 27.14% examples, 1234701 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:18,309 : INFO : EPOCH 3 - PROGRESS: at 34.05% examples, 1236634 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:19,309 : INFO : EPOCH 3 - PROGRESS: at 41.08% examples, 1243012 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:20,312 : INFO : EPOCH 3 - PROGRESS: at 47.88% examples, 1240755 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:21,313 : INFO : EPOCH 3 - PROGRESS: at 54.61% examples, 1240529 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:22,323 : INFO : EPOCH 3 - PROGRESS: at 61.57% examples, 1244590 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:23,328 : INFO : EPOCH 3 - PROGRESS: at 68.43% examples, 1246517 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:24,331 : INFO : EPOCH 3 - PROGRESS: at 75.18% examples, 1246334 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:25,339 : INFO : EPOCH 3 - PROGRESS: at 82.05% examples, 1246830 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:26,348 : INFO : EPOCH 3 - PROGRESS: at 88.98% examples, 1248191 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:27,349 : INFO : EPOCH 3 - PROGRESS: at 95.72% examples, 1247590 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:27,952 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 16:14:27,956 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 16:14:27,963 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 16:14:27,967 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 16:14:27,967 : INFO : EPOCH - 3 : training on 25242693 raw words (18338164 effective words) took 14.7s, 1248978 effective words/s\n",
      "2018-10-19 16:14:28,976 : INFO : EPOCH 4 - PROGRESS: at 6.76% examples, 1237515 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:29,981 : INFO : EPOCH 4 - PROGRESS: at 13.49% examples, 1228883 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:30,985 : INFO : EPOCH 4 - PROGRESS: at 20.46% examples, 1242658 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:31,988 : INFO : EPOCH 4 - PROGRESS: at 27.38% examples, 1246148 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:33,001 : INFO : EPOCH 4 - PROGRESS: at 34.51% examples, 1251920 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:34,004 : INFO : EPOCH 4 - PROGRESS: at 41.47% examples, 1253762 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:35,004 : INFO : EPOCH 4 - PROGRESS: at 48.40% examples, 1253514 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:36,013 : INFO : EPOCH 4 - PROGRESS: at 55.31% examples, 1254844 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:37,021 : INFO : EPOCH 4 - PROGRESS: at 62.28% examples, 1258011 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:38,026 : INFO : EPOCH 4 - PROGRESS: at 69.09% examples, 1257578 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:39,030 : INFO : EPOCH 4 - PROGRESS: at 76.03% examples, 1258985 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:40,034 : INFO : EPOCH 4 - PROGRESS: at 82.79% examples, 1257626 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:41,038 : INFO : EPOCH 4 - PROGRESS: at 89.73% examples, 1258679 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:42,038 : INFO : EPOCH 4 - PROGRESS: at 96.69% examples, 1259897 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:42,500 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 16:14:42,504 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 16:14:42,512 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 16:14:42,515 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 16:14:42,515 : INFO : EPOCH - 4 : training on 25242693 raw words (18337654 effective words) took 14.5s, 1261051 effective words/s\n",
      "2018-10-19 16:14:43,521 : INFO : EPOCH 5 - PROGRESS: at 6.96% examples, 1273759 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:44,523 : INFO : EPOCH 5 - PROGRESS: at 14.08% examples, 1285559 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:45,535 : INFO : EPOCH 5 - PROGRESS: at 21.08% examples, 1279359 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:46,549 : INFO : EPOCH 5 - PROGRESS: at 28.26% examples, 1280605 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:47,550 : INFO : EPOCH 5 - PROGRESS: at 35.44% examples, 1283791 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:48,557 : INFO : EPOCH 5 - PROGRESS: at 42.31% examples, 1277157 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:49,559 : INFO : EPOCH 5 - PROGRESS: at 49.48% examples, 1279443 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:50,573 : INFO : EPOCH 5 - PROGRESS: at 56.40% examples, 1277868 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:51,585 : INFO : EPOCH 5 - PROGRESS: at 63.48% examples, 1279155 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:52,586 : INFO : EPOCH 5 - PROGRESS: at 70.27% examples, 1277251 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:53,590 : INFO : EPOCH 5 - PROGRESS: at 76.88% examples, 1271613 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:54,592 : INFO : EPOCH 5 - PROGRESS: at 83.49% examples, 1266979 words/s, in_qsize 8, out_qsize 0\n",
      "2018-10-19 16:14:55,596 : INFO : EPOCH 5 - PROGRESS: at 90.05% examples, 1261739 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:56,610 : INFO : EPOCH 5 - PROGRESS: at 97.15% examples, 1263553 words/s, in_qsize 7, out_qsize 0\n",
      "2018-10-19 16:14:56,997 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2018-10-19 16:14:57,003 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2018-10-19 16:14:57,013 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2018-10-19 16:14:57,014 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2018-10-19 16:14:57,015 : INFO : EPOCH - 5 : training on 25242693 raw words (18334962 effective words) took 14.5s, 1264902 effective words/s\n",
      "2018-10-19 16:14:57,015 : INFO : training on a 126213465 raw words (91682246 effective words) took 72.6s, 1261993 effective words/s\n",
      "2018-10-19 16:14:57,016 : INFO : saving Word2Vec object under 300features_40minwords_10context, separately None\n",
      "2018-10-19 16:14:57,016 : INFO : storing np array 'vectors' to 300features_40minwords_10context.wv.vectors.npy\n",
      "2018-10-19 16:14:57,318 : INFO : not storing attribute vectors_norm\n",
      "2018-10-19 16:14:57,319 : INFO : storing np array 'syn1neg' to 300features_40minwords_10context.trainables.syn1neg.npy\n",
      "2018-10-19 16:14:57,619 : INFO : not storing attribute cum_table\n",
      "2018-10-19 16:14:57,726 : INFO : saved 300features_40minwords_10context\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\\\n",
    "    level=logging.INFO)\n",
    "\n",
    "num_features = 300    # Word vector dimensionality                      \n",
    "min_word_count = 5   # Minimum word count                        \n",
    "num_workers = 4       # Number of threads to run in parallel\n",
    "context = 10          # Context window size                                                                                    \n",
    "downsampling = 1e-3   # Downsample setting for frequent words\n",
    "\n",
    "\n",
    "from gensim.models import word2vec\n",
    "print(\"Training model...\")\n",
    "model = word2vec.Word2Vec(sentences, workers=num_workers, \\\n",
    "            size=num_features, min_count = min_word_count, \\\n",
    "            window = context, sample = downsampling)\n",
    "\n",
    "model_name = \"300features_40minwords_10context\"\n",
    "model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-10-19 18:20:12,075 : INFO : precomputing L2-norms of word weight vectors\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'kitchen'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.doesnt_match(\"man woman child kitchen\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'berlin'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.doesnt_match(\"france england germany berlin\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'london'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.doesnt_match(\"paris berlin london austria\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('woman', 0.5533495545387268),\n",
       " ('guy', 0.5364952683448792),\n",
       " ('men', 0.5345921516418457),\n",
       " ('kashmiri', 0.5059187412261963),\n",
       " ('chap', 0.4919845461845398),\n",
       " ('objector', 0.4828798770904541),\n",
       " ('romono', 0.48286956548690796),\n",
       " ('mans', 0.4813400208950043),\n",
       " ('crim', 0.47416964173316956),\n",
       " ('lagosi', 0.4738166928291321)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"man\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('latifah', 0.5422337055206299),\n",
       " ('ellery', 0.5357602834701538),\n",
       " ('viveca', 0.4887978136539459),\n",
       " ('sandahl', 0.4886631965637207),\n",
       " ('sheeba', 0.4884185791015625),\n",
       " ('amidala', 0.4856964945793152),\n",
       " ('queens', 0.48544973134994507),\n",
       " ('lindfors', 0.48037341237068176),\n",
       " ('gedren', 0.4802626073360443),\n",
       " ('king', 0.47563061118125916)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"queen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('terrible', 0.7587670683860779),\n",
       " ('horrible', 0.7220320105552673),\n",
       " ('atrocious', 0.6966452598571777),\n",
       " ('dreadful', 0.6448382139205933),\n",
       " ('appalling', 0.6320473551750183),\n",
       " ('abysmal', 0.6299008727073669),\n",
       " ('horrendous', 0.629846453666687),\n",
       " ('apalling', 0.6284140348434448),\n",
       " ('horrid', 0.6059893369674683),\n",
       " ('bad', 0.6028908491134644)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"awful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('great', 0.6640492677688599),\n",
       " ('decent', 0.6411788463592529),\n",
       " ('bad', 0.5928826332092285),\n",
       " (\"'great'\", 0.5730583667755127),\n",
       " ('nice', 0.5669047832489014),\n",
       " ('actualy', 0.5550702810287476),\n",
       " ('allright', 0.549493670463562),\n",
       " ('dissapointing', 0.5472931861877441),\n",
       " ('definate', 0.5460936427116394),\n",
       " ('awsome', 0.5422983169555664)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"good\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('worse', 0.651874303817749),\n",
       " ('edgier', 0.583482027053833),\n",
       " ('creepier', 0.5491827130317688),\n",
       " ('funnier', 0.5470982193946838),\n",
       " ('pornos', 0.5457853078842163),\n",
       " ('cb4', 0.5454648733139038),\n",
       " ('worser', 0.5402961373329163),\n",
       " ('classier', 0.5398609638214111),\n",
       " ('snappier', 0.539228618144989),\n",
       " ('salvageable', 0.530937910079956)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"better\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('pretzels', 0.5084955096244812),\n",
       " ('pecan', 0.501787543296814),\n",
       " ('cider', 0.4990856647491455),\n",
       " ('buttered', 0.49770110845565796),\n",
       " ('bop', 0.4866560995578766),\n",
       " ('macs', 0.48226624727249146),\n",
       " ('buzzard', 0.47121721506118774),\n",
       " ('baking', 0.4697813391685486),\n",
       " ('sausage', 0.4682459235191345),\n",
       " ('spoonful', 0.46704697608947754)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"apple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('queen', 0.47335630655288696)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(positive=['woman', 'king'], negative=['man'], topn=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5533495673999537"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.similarity('woman', 'man')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_vectors = model.wv\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_vectors.get_keras_embedding??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>7772_8</td>\n",
       "      <td>From the opening sequence, filled with black a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>11465_10</td>\n",
       "      <td>This is a great horror film for people who don...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                             review  sentiment\n",
       "49998    7772_8  From the opening sequence, filled with black a...          1\n",
       "49999  11465_10  This is a great horror film for people who don...          1"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "\n",
    "def clean_str(string):\n",
    "    \"\"\"\n",
    "    Tokenization/string cleaning for all datasets \n",
    "    Every dataset is lower cased\n",
    "    \"\"\"\n",
    "    string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)     \n",
    "    string = re.sub(r\"\\'s\", \" \\'s\", string) \n",
    "    string = re.sub(r\"\\'ve\", \" \\'ve\", string) \n",
    "    string = re.sub(r\"n\\'t\", \" n\\'t\", string) \n",
    "    string = re.sub(r\"\\'re\", \" \\'re\", string) \n",
    "    string = re.sub(r\"\\'d\", \" \\'d\", string) \n",
    "    string = re.sub(r\"\\'ll\", \" \\'ll\", string) \n",
    "    string = re.sub(r\",\", \" , \", string) \n",
    "    string = re.sub(r\"!\", \" ! \", string) \n",
    "    string = re.sub(r\"\\(\", \" \\( \", string) \n",
    "    string = re.sub(r\"\\)\", \" \\) \", string) \n",
    "    string = re.sub(r\"\\?\", \" \\? \", string) \n",
    "    string = re.sub(r\"\\s{2,}\", \" \", string)    \n",
    "    return string.strip().lower()\n",
    "\n",
    "texts = []\n",
    "labels = []\n",
    "for idx in range(data_all.review.shape[0]):\n",
    "    text = BeautifulSoup(data_all.review[idx], \"lxml\")\n",
    "    texts.append(clean_str(text.get_text()))\n",
    "    labels.append(data_all.sentiment[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Dense, Input, Flatten, concatenate, TimeDistributed\n",
    "from keras.layers import Conv1D, MaxPooling1D, Dropout, Concatenate, SpatialDropout1D, GlobalAveragePooling1D, GlobalMaxPooling1D\n",
    "from keras.layers import Bidirectional, LSTM, GRU, CuDNNLSTM, CuDNNGRU\n",
    "from keras.models import Model\n",
    "\n",
    "from keras import backend as K\n",
    "from keras.engine.topology import Layer, InputSpec\n",
    "from keras import initializers\n",
    "\n",
    "MAX_SEQUENCE_LENGTH = 1000\n",
    "MAX_NB_WORDS = 20000\n",
    "EMBEDDING_DIM = 300\n",
    "VALIDATION_SPLIT = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\keras_preprocessing\\text.py:175: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
      "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 114066 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "sequences = tokenizer.texts_to_sequences(texts)\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data tensor: (50000, 1000)\n",
      "Shape of label tensor: (50000, 2)\n"
     ]
    }
   ],
   "source": [
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "labels = to_categorical(np.asarray(labels))\n",
    "print('Shape of data tensor:', data.shape)\n",
    "print('Shape of label tensor:', labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of positive and negative reviews in traing and validation set \n",
      "[12500. 12500.]\n",
      "[12500. 12500.]\n"
     ]
    }
   ],
   "source": [
    "'''indices = np.arange(data.shape[0])\n",
    "np.random.shuffle(indices)\n",
    "data = data[indices]\n",
    "labels = labels[indices]\n",
    "nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])'''\n",
    "\n",
    "x_train = data[:25000]\n",
    "y_train = labels[:25000]\n",
    "x_val = data[25000:]\n",
    "y_val = labels[25000:]\n",
    "\n",
    "print('Number of positive and negative reviews in traing and validation set ')\n",
    "print(y_train.sum(axis=0))\n",
    "print(y_val.sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "EMBEDDING_DIM = 300\n",
    "embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\n",
    "j = 0\n",
    "for word, i in word_index.items():\n",
    "    try:\n",
    "        embedding_vector = model.wv.get_vector(word)\n",
    "    except:\n",
    "        j += 1\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix[i] = embedding_vector\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_layer = Embedding(len(word_index) + 1,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=MAX_SEQUENCE_LENGTH,\n",
    "                            trainable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model fitting - RCNN\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 1000, 300)    34220100    input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_2 (SpatialDro (None, 1000, 300)    0           embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 1000, 200)    241200      spatial_dropout1d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 999, 64)      25664       bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_2 (Glo (None, 64)           0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_2 (GlobalM (None, 64)           0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 128)          0           global_average_pooling1d_2[0][0] \n",
      "                                                                 global_max_pooling1d_2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 2)            258         concatenate_2[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 34,487,222\n",
      "Trainable params: 34,487,222\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 115s 5ms/step - loss: 0.4073 - acc: 0.8012 - val_loss: 0.2538 - val_acc: 0.8968\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.2162 - acc: 0.9146 - val_loss: 0.2323 - val_acc: 0.9074\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 115s 5ms/step - loss: 0.1262 - acc: 0.9560 - val_loss: 0.2471 - val_acc: 0.9100\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 116s 5ms/step - loss: 0.0643 - acc: 0.9778 - val_loss: 0.2940 - val_acc: 0.9065\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 118s 5ms/step - loss: 0.0267 - acc: 0.9914 - val_loss: 0.4523 - val_acc: 0.8890\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 115s 5ms/step - loss: 0.0163 - acc: 0.9948 - val_loss: 0.4353 - val_acc: 0.9018\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0112 - acc: 0.9965 - val_loss: 0.5091 - val_acc: 0.8993\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 115s 5ms/step - loss: 0.0095 - acc: 0.9964 - val_loss: 0.5332 - val_acc: 0.8983\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 116s 5ms/step - loss: 0.0110 - acc: 0.9960 - val_loss: 0.5910 - val_acc: 0.8962\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 115s 5ms/step - loss: 0.0081 - acc: 0.9971 - val_loss: 0.6223 - val_acc: 0.8957\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x28fd3401828>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use  fasttext\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "l_dropout = SpatialDropout1D(0.3)(embedded_sequences)\n",
    "l_gru = Bidirectional(CuDNNGRU(100, return_sequences=True))(l_dropout)\n",
    "l_cov = Conv1D(64, kernel_size = 2, padding = \"valid\", kernel_initializer = \"he_uniform\")(l_gru)\n",
    "avg_pool = GlobalAveragePooling1D()(l_cov)\n",
    "max_pool = GlobalMaxPooling1D()(l_cov)\n",
    "l_conc = concatenate([avg_pool, max_pool])\n",
    "preds = Dense(2, activation='softmax')(l_conc)\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"model fitting - RCNN\")\n",
    "model.summary()\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          nb_epoch=10, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model fitting - RCNN\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1000, 300)    34220100    input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_1 (SpatialDro (None, 1000, 300)    0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 1000, 200)    241200      spatial_dropout1d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 999, 64)      25664       bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_1 (Glo (None, 64)           0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 64)           0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 128)          0           global_average_pooling1d_1[0][0] \n",
      "                                                                 global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            258         concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 34,487,222\n",
      "Trainable params: 34,487,222\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 117s 5ms/step - loss: 0.3737 - acc: 0.8248 - val_loss: 0.2200 - val_acc: 0.9117\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.1942 - acc: 0.9246 - val_loss: 0.2245 - val_acc: 0.9100\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.1135 - acc: 0.9589 - val_loss: 0.2405 - val_acc: 0.9130\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 113s 5ms/step - loss: 0.0556 - acc: 0.9809 - val_loss: 0.2886 - val_acc: 0.9079\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0268 - acc: 0.9914 - val_loss: 0.3773 - val_acc: 0.9025\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0150 - acc: 0.9951 - val_loss: 0.5421 - val_acc: 0.8853\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0120 - acc: 0.9962 - val_loss: 0.5120 - val_acc: 0.8999\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0095 - acc: 0.9968 - val_loss: 0.5548 - val_acc: 0.8995\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0094 - acc: 0.9968 - val_loss: 0.5839 - val_acc: 0.8973\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 113s 5ms/step - loss: 0.0088 - acc: 0.9971 - val_loss: 0.5907 - val_acc: 0.8947\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x28fd51a5e80>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use word2vec_sg \n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "l_dropout = SpatialDropout1D(0.3)(embedded_sequences)\n",
    "l_gru = Bidirectional(CuDNNGRU(100, return_sequences=True))(l_dropout)\n",
    "l_cov = Conv1D(64, kernel_size = 2, padding = \"valid\", kernel_initializer = \"he_uniform\")(l_gru)\n",
    "avg_pool = GlobalAveragePooling1D()(l_cov)\n",
    "max_pool = GlobalMaxPooling1D()(l_cov)\n",
    "l_conc = concatenate([avg_pool, max_pool])\n",
    "preds = Dense(2, activation='softmax')(l_conc)\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"model fitting - RCNN\")\n",
    "model.summary()\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          nb_epoch=10, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model fitting - RCNN\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1000, 300)    34220100    input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_1 (SpatialDro (None, 1000, 300)    0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 1000, 200)    241200      spatial_dropout1d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 999, 64)      25664       bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_1 (Glo (None, 64)           0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 64)           0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 128)          0           global_average_pooling1d_1[0][0] \n",
      "                                                                 global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            258         concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 34,487,222\n",
      "Trainable params: 34,487,222\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 118s 5ms/step - loss: 0.3750 - acc: 0.8310 - val_loss: 0.2563 - val_acc: 0.8972\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 115s 5ms/step - loss: 0.2428 - acc: 0.9026 - val_loss: 0.2330 - val_acc: 0.9074\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.1878 - acc: 0.9257 - val_loss: 0.2205 - val_acc: 0.9158\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.1508 - acc: 0.9424 - val_loss: 0.2494 - val_acc: 0.9093\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 113s 5ms/step - loss: 0.1122 - acc: 0.9590 - val_loss: 0.2498 - val_acc: 0.9129\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 112s 4ms/step - loss: 0.0776 - acc: 0.9726 - val_loss: 0.2597 - val_acc: 0.9146\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 112s 4ms/step - loss: 0.0547 - acc: 0.9811 - val_loss: 0.3037 - val_acc: 0.9104\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 115s 5ms/step - loss: 0.0360 - acc: 0.9879 - val_loss: 0.3444 - val_acc: 0.9084\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0273 - acc: 0.9913 - val_loss: 0.3562 - val_acc: 0.9120\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 114s 5ms/step - loss: 0.0216 - acc: 0.9927 - val_loss: 0.4332 - val_acc: 0.9025\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2561b5144e0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use word2vec\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "l_dropout = SpatialDropout1D(0.3)(embedded_sequences)\n",
    "l_gru = Bidirectional(CuDNNGRU(100, return_sequences=True))(l_dropout)\n",
    "l_cov = Conv1D(64, kernel_size = 2, padding = \"valid\", kernel_initializer = \"he_uniform\")(l_gru)\n",
    "avg_pool = GlobalAveragePooling1D()(l_cov)\n",
    "max_pool = GlobalMaxPooling1D()(l_cov)\n",
    "l_conc = concatenate([avg_pool, max_pool])\n",
    "preds = Dense(2, activation='softmax')(l_conc)\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"model fitting - RCNN\")\n",
    "model.summary()\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          nb_epoch=10, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model fitting - RCNN\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1000, 300)    34220100    input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_1 (SpatialDro (None, 1000, 300)    0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 1000, 200)    241200      spatial_dropout1d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 999, 64)      25664       bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_1 (Glo (None, 64)           0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 64)           0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 128)          0           global_average_pooling1d_1[0][0] \n",
      "                                                                 global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            258         concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 34,487,222\n",
      "Trainable params: 34,487,222\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 124s 5ms/step - loss: 0.3790 - acc: 0.8302 - val_loss: 0.2576 - val_acc: 0.8960\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 123s 5ms/step - loss: 0.2518 - acc: 0.8986 - val_loss: 0.2553 - val_acc: 0.9010\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 123s 5ms/step - loss: 0.1934 - acc: 0.9240 - val_loss: 0.2480 - val_acc: 0.9088\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 123s 5ms/step - loss: 0.1464 - acc: 0.9434 - val_loss: 0.2539 - val_acc: 0.9069\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 123s 5ms/step - loss: 0.1018 - acc: 0.9631 - val_loss: 0.2719 - val_acc: 0.9073\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 122s 5ms/step - loss: 0.0675 - acc: 0.9766 - val_loss: 0.2991 - val_acc: 0.9064\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 123s 5ms/step - loss: 0.0424 - acc: 0.9850 - val_loss: 0.3354 - val_acc: 0.9058\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 120s 5ms/step - loss: 0.0306 - acc: 0.9893 - val_loss: 0.3996 - val_acc: 0.8977\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 119s 5ms/step - loss: 0.0214 - acc: 0.9927 - val_loss: 0.4076 - val_acc: 0.9018\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 121s 5ms/step - loss: 0.0185 - acc: 0.9938 - val_loss: 0.4710 - val_acc: 0.9002\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x20960d27e80>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use word2vec not including number and punctuation\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "l_dropout = SpatialDropout1D(0.3)(embedded_sequences)\n",
    "l_gru = Bidirectional(CuDNNGRU(100, return_sequences=True))(l_dropout)\n",
    "l_cov = Conv1D(64, kernel_size = 2, padding = \"valid\", kernel_initializer = \"he_uniform\")(l_gru)\n",
    "avg_pool = GlobalAveragePooling1D()(l_cov)\n",
    "max_pool = GlobalMaxPooling1D()(l_cov)\n",
    "l_conc = concatenate([avg_pool, max_pool])\n",
    "preds = Dense(2, activation='softmax')(l_conc)\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"model fitting - RCNN\")\n",
    "model.summary()\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          nb_epoch=10, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=3)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=4)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=5)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model fitting - more complex convolutional neural network\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 1000, 300)    34220100    input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 998, 128)     115328      embedding_2[1][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 997, 128)     153728      embedding_2[1][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 996, 128)     192128      embedding_2[1][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 199, 128)     0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 199, 128)     0           conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 199, 128)     0           conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 597, 128)     0           max_pooling1d_1[0][0]            \n",
      "                                                                 max_pooling1d_2[0][0]            \n",
      "                                                                 max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_3 (GlobalM (None, 128)          0           concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 128)          16512       global_max_pooling1d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 2)            258         dense_3[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 34,698,054\n",
      "Trainable params: 34,698,054\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/8\n",
      "25000/25000 [==============================] - 65s 3ms/step - loss: 0.2458 - acc: 0.8972 - val_loss: 0.4025 - val_acc: 0.8609\n",
      "Epoch 2/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0786 - acc: 0.9705 - val_loss: 0.4180 - val_acc: 0.8715\n",
      "Epoch 3/8\n",
      "25000/25000 [==============================] - 65s 3ms/step - loss: 0.0316 - acc: 0.9892 - val_loss: 0.5363 - val_acc: 0.8776\n",
      "Epoch 4/8\n",
      "25000/25000 [==============================] - 65s 3ms/step - loss: 0.0129 - acc: 0.9959 - val_loss: 0.7399 - val_acc: 0.8764\n",
      "Epoch 5/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0056 - acc: 0.9980 - val_loss: 0.8589 - val_acc: 0.8788\n",
      "Epoch 6/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0025 - acc: 0.9991 - val_loss: 1.0094 - val_acc: 0.8726\n",
      "Epoch 7/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0021 - acc: 0.9994 - val_loss: 1.0152 - val_acc: 0.8760\n",
      "Epoch 8/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0027 - acc: 0.9994 - val_loss: 1.1731 - val_acc: 0.8710\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x28fe4d0d6d8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applying a more complex convolutional approach\n",
    "convs = []\n",
    "filter_sizes = [3,4,5]\n",
    "\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "\n",
    "for fsz in filter_sizes:\n",
    "    l_conv = Conv1D(nb_filter=128,filter_length=fsz,activation='relu')(embedded_sequences)\n",
    "    l_pool = MaxPooling1D(5)(l_conv)\n",
    "    convs.append(l_pool)\n",
    "l_merge = Concatenate(axis=1)(convs)    \n",
    "l_pool1 = GlobalMaxPooling1D()(l_merge)\n",
    "l_dense = Dense(128, activation='relu')(l_pool1)\n",
    "preds = Dense(2, activation='softmax')(l_dense)\n",
    "\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"model fitting - more complex convolutional neural network\")\n",
    "model.summary()\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          nb_epoch=8, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=3)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=4)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=5)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model fitting - more complex convolutional neural network\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1000, 300)    34220100    input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 998, 128)     115328      embedding_1[1][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 997, 128)     153728      embedding_1[1][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 996, 128)     192128      embedding_1[1][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 199, 128)     0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 199, 128)     0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 199, 128)     0           conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 597, 128)     0           max_pooling1d_1[0][0]            \n",
      "                                                                 max_pooling1d_2[0][0]            \n",
      "                                                                 max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_2 (GlobalM (None, 128)          0           concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 128)          16512       global_max_pooling1d_2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 2)            258         dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 34,698,054\n",
      "Trainable params: 34,698,054\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 1.9662 - acc: 0.7056 - val_loss: 0.3213 - val_acc: 0.8626\n",
      "Epoch 2/8\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.2953 - acc: 0.8791 - val_loss: 0.3309 - val_acc: 0.8652\n",
      "Epoch 3/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.2077 - acc: 0.9178 - val_loss: 0.2673 - val_acc: 0.8940\n",
      "Epoch 4/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.1462 - acc: 0.9447 - val_loss: 0.3072 - val_acc: 0.8931\n",
      "Epoch 5/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.1025 - acc: 0.9631 - val_loss: 0.3215 - val_acc: 0.9003\n",
      "Epoch 6/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0744 - acc: 0.9743 - val_loss: 0.6395 - val_acc: 0.8540\n",
      "Epoch 7/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0518 - acc: 0.9833 - val_loss: 0.4579 - val_acc: 0.8996\n",
      "Epoch 8/8\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.0447 - acc: 0.9866 - val_loss: 1.3892 - val_acc: 0.8066\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2561affb6a0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applying a more complex convolutional approach\n",
    "convs = []\n",
    "filter_sizes = [3,4,5]\n",
    "\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "\n",
    "for fsz in filter_sizes:\n",
    "    l_conv = Conv1D(nb_filter=128,filter_length=fsz,activation='relu')(embedded_sequences)\n",
    "    l_pool = MaxPooling1D(5)(l_conv)\n",
    "    convs.append(l_pool)\n",
    "l_merge = Concatenate(axis=1)(convs)    \n",
    "l_pool1 = GlobalMaxPooling1D()(l_merge)\n",
    "l_dense = Dense(128, activation='relu')(l_pool1)\n",
    "preds = Dense(2, activation='softmax')(l_dense)\n",
    "\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"model fitting - more complex convolutional neural network\")\n",
    "model.summary()\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          nb_epoch=8, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=3)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=4)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation=\"relu\", filters=128, kernel_size=5)`\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\sun\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model fitting - more complex convolutional neural network\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1000, 300)    34220100    input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 998, 128)     115328      embedding_1[2][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 997, 128)     153728      embedding_1[2][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 996, 128)     192128      embedding_1[2][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 199, 128)     0           conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 199, 128)     0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1D)  (None, 199, 128)     0           conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 597, 128)     0           max_pooling1d_4[0][0]            \n",
      "                                                                 max_pooling1d_5[0][0]            \n",
      "                                                                 max_pooling1d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_3 (GlobalM (None, 128)          0           concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 128)          16512       global_max_pooling1d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 2)            258         dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 34,698,054\n",
      "Trainable params: 34,698,054\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 65s 3ms/step - loss: 0.5390 - acc: 0.7803 - val_loss: 0.3130 - val_acc: 0.8678\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.2709 - acc: 0.8891 - val_loss: 0.2571 - val_acc: 0.8954\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 64s 3ms/step - loss: 0.1969 - acc: 0.9214 - val_loss: 0.3712 - val_acc: 0.8562\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.1376 - acc: 0.9480 - val_loss: 0.2772 - val_acc: 0.8932\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.0918 - acc: 0.9660 - val_loss: 0.3412 - val_acc: 0.8926\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.0639 - acc: 0.9782 - val_loss: 0.4176 - val_acc: 0.8971\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.0527 - acc: 0.9829 - val_loss: 0.7199 - val_acc: 0.8596\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.0464 - acc: 0.9859 - val_loss: 0.5446 - val_acc: 0.8967\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.0305 - acc: 0.9900 - val_loss: 0.6676 - val_acc: 0.8953\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 63s 3ms/step - loss: 0.0389 - acc: 0.9908 - val_loss: 0.7119 - val_acc: 0.8939\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x20af15b72b0>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applying a more complex convolutional approach\n",
    "convs = []\n",
    "filter_sizes = [3,4,5]\n",
    "\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "\n",
    "for fsz in filter_sizes:\n",
    "    l_conv = Conv1D(nb_filter=128,filter_length=fsz,activation='relu')(embedded_sequences)\n",
    "    l_pool = MaxPooling1D(5)(l_conv)\n",
    "    convs.append(l_pool)\n",
    "l_merge = Concatenate(axis=1)(convs)    \n",
    "l_pool1 = GlobalMaxPooling1D()(l_merge)\n",
    "l_dense = Dense(128, activation='relu')(l_pool1)\n",
    "preds = Dense(2, activation='softmax')(l_dense)\n",
    "\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"model fitting - more complex convolutional neural network\")\n",
    "model.summary()\n",
    "model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          nb_epoch=8, batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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