word2vec词向量处理英文语料
Posted chenlove
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word2vec介绍
word2vec官网：https://code.google.com/p/word2vec/
- word2vec是google的一个开源工具，能够根据输入的词的集合计算出词与词之间的距离。
- 它将term转换成向量形式，可以把对文本内容的处理简化为向量空间中的向量运算，计算出向量空间上的相似度，来表示文本语义上的相似度。
- word2vec计算的是余弦值，距离范围为0-1之间，值越大代表两个词关联度越高。
- 词向量：用Distributed Representation表示词，通常也被称为“Word Representation”或“Word Embedding（嵌入）”。
使用
运行和测试同样需要text8、questions-words.txt文件，语料下载地址：http://mattmahoney.net/dc/text8.zip
该语料编码格式UTF-8，存储为一行，语料训练信息：training on 85026035 raw words (62529137 effective words) took 197.4s, 316692 effective words/s
word2vec使用参数解释
-train 训练数据
-output 结果输入文件，即每个词的向量
-cbow 是否使用cbow模型，0表示使用skip-gram模型，1表示使用cbow模型，默认情况下是skip-gram模型，cbow模型快一些，skip-gram模型效果好一些
-size 表示输出的词向量维数
-window 为训练的窗口大小，8表示每个词考虑前8个词与后8个词（实际代码中还有一个随机选窗口的过程，窗口大小<=5)
-negative 表示是否使用NEG方，0表示不使用，其它的值目前还不是很清楚
-hs 是否使用HS方法，0表示不使用，1表示使用
-sample 表示 采样的阈值，如果一个词在训练样本中出现的频率越大，那么就越会被采样
-binary 表示输出的结果文件是否采用二进制存储，0表示不使用（即普通的文本存储，可以打开查看），1表示使用，即vectors.bin的存储类型
-alpha 表示 学习速率
-min-count 表示设置最低频率，默认为5，如果一个词语在文档中出现的次数小于该阈值，那么该词就会被舍弃
-classes 表示词聚类簇的个数，从相关源码中可以得出该聚类是采用k-means
代码——
1 # -*- coding: utf-8 -*- 2 3 """ 4 功能：测试gensim使用 5 时间：2016年5月2日 18:00:00 6 """ 7 8 from gensim.models import word2vec 9 import logging 10 11 # 主程序 12 logging.basicConfig(format=‘%(asctime)s : %(levelname)s : %(message)s‘, level=logging.INFO) 13 sentences = word2vec.Text8Corpus("data/text8") # 加载语料 14 model = word2vec.Word2Vec(sentences, size=200) # 训练skip-gram模型; 默认window=5 15 16 # 计算两个词的相似度/相关程度 17 y1 = model.similarity("woman", "man") 18 print u"woman和man的相似度为：", y1 19 print "-------- " 20 21 # 计算某个词的相关词列表 22 y2 = model.most_similar("good", topn=20) # 20个最相关的 23 print u"和good最相关的词有： " 24 for item in y2: 25 print item[0], item[1] 26 print "-------- " 27 28 # 寻找对应关系 29 print ‘ "boy" is to "father" as "girl" is to ...? ‘ 30 y3 = model.most_similar([‘girl‘, ‘father‘], [‘boy‘], topn=3) 31 for item in y3: 32 print item[0], item[1] 33 print "-------- " 34 35 more_examples = ["he his she", "big bigger bad", "going went being"] 36 for example in more_examples: 37 a, b, x = example.split() 38 predicted = model.most_similar([x, b], [a])[0][0] 39 print "‘%s‘ is to ‘%s‘ as ‘%s‘ is to ‘%s‘" % (a, b, x, predicted) 40 print "-------- " 41 42 # 寻找不合群的词 43 y4 = model.doesnt_match("breakfast cereal dinner lunch".split()) 44 print u"不合群的词：", y4 45 print "-------- " 46 47 # 保存模型，以便重用 48 model.save("text8.model") 49 # 对应的加载方式 50 # model_2 = word2vec.Word2Vec.load("text8.model") 51 52 # 以一种C语言可以解析的形式存储词向量 53 model.save_word2vec_format("text8.model.bin", binary=True) 54 # 对应的加载方式 55 # model_3 = word2vec.Word2Vec.load_word2vec_format("text8.model.bin", binary=True) 56 57 if __name__ == "__main__": 58 pass
Ubuntu16.04系统下运行结果
1 2016-5-2 18:56:19,332 : INFO : collecting all words and their counts 2 2016-5-2 18:56:19,334 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types 3 2016-5-2 18:56:27,431 : INFO : collected 253854 word types from a corpus of 17005207 raw words and 1701 sentences 4 2016-5-2 18:56:27,740 : INFO : min_count=5 retains 71290 unique words (drops 182564) 5 2016-5-2 18:56:27,740 : INFO : min_count leaves 16718844 word corpus (98% of original 17005207) 6 2016-5-2 18:56:27,914 : INFO : deleting the raw counts dictionary of 253854 items 7 2016-5-2 18:56:27,947 : INFO : sample=0.001 downsamples 38 most-common words 8 2016-5-2 18:56:27,947 : INFO : downsampling leaves estimated 12506280 word corpus (74.8% of prior 16718844) 9 2016-5-2 18:56:27,947 : INFO : estimated required memory for 71290 words and 200 dimensions: 149709000 bytes 10 2016-5-2 18:56:28,176 : INFO : resetting layer weights 11 2016-5-2 18:56:29,074 : INFO : training model with 3 workers on 71290 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 12 2016-5-2 18:56:29,074 : INFO : expecting 1701 sentences, matching count from corpus used for vocabulary survey 13 2016-5-2 18:56:30,086 : INFO : PROGRESS: at 0.86% examples, 531932 words/s, in_qsize 6, out_qsize 0 14 2016-5-2 18:56:31,088 : INFO : PROGRESS: at 1.72% examples, 528872 words/s, in_qsize 5, out_qsize 0 15 2016-5-2 18:56:32,108 : INFO : PROGRESS: at 2.68% examples, 549248 words/s, in_qsize 6, out_qsize 0 16 2016-5-2 18:56:33,113 : INFO : PROGRESS: at 3.47% examples, 534255 words/s, in_qsize 6, out_qsize 0 17 2016-5-2 18:56:34,135 : INFO : PROGRESS: at 4.43% examples, 545575 words/s, in_qsize 5, out_qsize 0 18 2016-5-2 18:56:35,145 : INFO : PROGRESS: at 5.40% examples, 555220 words/s, in_qsize 6, out_qsize 0 19 2016-5-2 18:56:36,147 : INFO : PROGRESS: at 6.34% examples, 560815 words/s, in_qsize 5, out_qsize 0 20 2016-5-2 18:56:37,155 : INFO : PROGRESS: at 7.28% examples, 564712 words/s, in_qsize 6, out_qsize 1 21 2016-5-2 18:56:38,172 : INFO : PROGRESS: at 8.24% examples, 568088 words/s, in_qsize 5, out_qsize 0 22 2016-5-2 18:56:39,169 : INFO : PROGRESS: at 9.19% examples, 570872 words/s, in_qsize 5, out_qsize 0 23 2016-5-2 18:56:40,191 : INFO : PROGRESS: at 10.16% examples, 573068 words/s, in_qsize 6, out_qsize 0 24 2016-5-2 18:56:41,203 : INFO : PROGRESS: at 11.12% examples, 575184 words/s, in_qsize 5, out_qsize 1 25 2016-5-2 18:56:42,217 : INFO : PROGRESS: at 12.09% examples, 577227 words/s, in_qsize 5, out_qsize 0 26 2016-5-2 18:56:43,220 : INFO : PROGRESS: at 13.04% examples, 578418 words/s, in_qsize 5, out_qsize 1 27 2016-5-2 18:56:44,235 : INFO : PROGRESS: at 14.00% examples, 579574 words/s, in_qsize 5, out_qsize 1 28 2016-5-2 18:56:45,239 : INFO : PROGRESS: at 14.96% examples, 580577 words/s, in_qsize 6, out_qsize 2 29 2016-5-2 18:56:46,243 : INFO : PROGRESS: at 15.86% examples, 578374 words/s, in_qsize 6, out_qsize 0 30 2016-5-2 18:56:47,252 : INFO : PROGRESS: at 16.70% examples, 574918 words/s, in_qsize 5, out_qsize 1 31 2016-5-2 18:56:48,256 : INFO : PROGRESS: at 17.66% examples, 576221 words/s, in_qsize 5, out_qsize 0 32 2016-5-2 18:56:49,258 : INFO : PROGRESS: at 18.61% examples, 577045 words/s, in_qsize 4, out_qsize 0 33 2016-5-2 18:56:50,260 : INFO : PROGRESS: at 19.54% examples, 576947 words/s, in_qsize 4, out_qsize 1 34 2016-5-2 18:56:51,261 : INFO : PROGRESS: at 20.47% examples, 577120 words/s, in_qsize 6, out_qsize 0 35 2016-5-2 18:56:52,284 : INFO : PROGRESS: at 21.43% examples, 577251 words/s, in_qsize 5, out_qsize 1 36 2016-5-2 18:56:53,287 : INFO : PROGRESS: at 22.34% examples, 576556 words/s, in_qsize 6, out_qsize 0 37 2016-5-2 18:56:54,308 : INFO : PROGRESS: at 23.20% examples, 574618 words/s, in_qsize 6, out_qsize 1 38 2016-5-2 18:56:55,306 : INFO : PROGRESS: at 24.15% examples, 575304 words/s, in_qsize 5, out_qsize 0 39 2016-5-2 18:56:56,329 : INFO : PROGRESS: at 25.09% examples, 575610 words/s, in_qsize 5, out_qsize 1 40 2016-5-2 18:56:57,333 : INFO : PROGRESS: at 26.04% examples, 576358 words/s, in_qsize 6, out_qsize 0 41 2016-5-2 18:56:58,340 : INFO : PROGRESS: at 26.97% examples, 576745 words/s, in_qsize 5, out_qsize 0 42 2016-5-2 18:56:59,337 : INFO : PROGRESS: at 27.91% examples, 577161 words/s, in_qsize 5, out_qsize 0 43 2016-5-2 18:57:00,338 : INFO : PROGRESS: at 28.84% examples, 577303 words/s, in_qsize 5, out_qsize 0 44 2016-5-2 18:57:01,346 : INFO : PROGRESS: at 29.65% examples, 575087 words/s, in_qsize 6, out_qsize 0 45 2016-5-2 18:57:02,353 : INFO : PROGRESS: at 30.55% examples, 574516 words/s, in_qsize 5, out_qsize 1 46 2016-5-2 18:57:03,356 : INFO : PROGRESS: at 31.36% examples, 572590 words/s, in_qsize 5, out_qsize 0 47 2016-5-2 18:57:04,371 : INFO : PROGRESS: at 32.10% examples, 569320 words/s, in_qsize 6, out_qsize 0 48 2016-5-2 18:57:05,380 : INFO : PROGRESS: at 32.95% examples, 568088 words/s, in_qsize 5, out_qsize 0 49 2016-5-2 18:57:06,389 : INFO : PROGRESS: at 33.78% examples, 566886 words/s, in_qsize 6, out_qsize 1 50 2016-5-2 18:57:07,399 : INFO : PROGRESS: at 34.60% examples, 565345 words/s, in_qsize 6, out_qsize 0 51 2016-5-2 18:57:08,418 : INFO : PROGRESS: at 35.51% examples, 564685 words/s, in_qsize 5, out_qsize 0 52 2016-5-2 18:57:09,432 : INFO : PROGRESS: at 36.39% examples, 564093 words/s, in_qsize 6, out_qsize 0 53 2016-5-2 18:57:10,441 : INFO : PROGRESS: at 37.21% examples, 562778 words/s, in_qsize 5, out_qsize 1 54 2016-5-2 18:57:11,453 : INFO : PROGRESS: at 38.14% examples, 563163 words/s, in_qsize 6, out_qsize 1 55 2016-5-2 18:57:12,449 : INFO : PROGRESS: at 38.98% examples, 562072 words/s, in_qsize 6, out_qsize 0 56 2016-5-2 18:57:13,461 : INFO : PROGRESS: at 39.88% examples, 561949 words/s, in_qsize 6, out_qsize 0 57 2016-5-2 18:57:14,464 : INFO : PROGRESS: at 40.75% examples, 561493 words/s, in_qsize 6, out_qsize 0 58 2016-5-2 18:57:15,482 : INFO : PROGRESS: at 41.60% examples, 560419 words/s, in_qsize 5, out_qsize 1 59 2016-5-2 18:57:16,503 : INFO : PROGRESS: at 42.40% examples, 558807 words/s, in_qsize 6, out_qsize 0 60 2016-5-2 18:57:17,520 : INFO : PROGRESS: at 43.27% examples, 558287 words/s, in_qsize 5, out_qsize 0 61 2016-5-2 18:57:18,534 : INFO : PROGRESS: at 44.13% examples, 557685 words/s, in_qsize 6, out_qsize 0 62 2016-5-2 18:57:19,538 : INFO : PROGRESS: at 44.93% examples, 556591 words/s, in_qsize 6, out_qsize 0 63 2016-5-2 18:57:20,540 : INFO : PROGRESS: at 45.83% examples, 556881 words/s, in_qsize 5, out_qsize 0 64 2016-5-2 18:57:21,541 : INFO : PROGRESS: at 46.75% examples, 557341 words/s, in_qsize 6, out_qsize 0 65 2016-5-2 18:57:22,553 : INFO : PROGRESS: at 47.69% examples, 557860 words/s, in_qsize 5, out_qsize 1 66 2016-5-2 18:57:23,557 : INFO : PROGRESS: at 48.51% examples, 557066 words/s, in_qsize 6, out_qsize 0 67 2016-5-2 18:57:24,564 : INFO : PROGRESS: at 49.42% examples, 557201 words/s, in_qsize 5, out_qsize 0 68 2016-5-2 18:57:25,571 : INFO : PROGRESS: at 50.31% examples, 557231 words/s, in_qsize 5, out_qsize 1 69 2016-5-2 18:57:26,585 : INFO : PROGRESS: at 51.26% examples, 557820 words/s, in_qsize 6, out_qsize 1 70 2016-5-2 18:57:27,586 : INFO : PROGRESS: at 52.22% examples, 558455 words/s, in_qsize 4, out_qsize 0 71 2016-5-2 18:57:28,588 : INFO : PROGRESS: at 53.16% examples, 558932 words/s, in_qsize 6, out_qsize 1 72 2016-5-2 18:57:29,609 : INFO : PROGRESS: at 54.11% examples, 559389 words/s, in_qsize 5, out_qsize 0 73 2016-5-2 18:57:30,616 : INFO : PROGRESS: at 55.01% examples, 559415 words/s, in_qsize 6, out_qsize 0 74 2016-5-2 18:57:31,642 : INFO : PROGRESS: at 55.87% examples, 558596 words/s, in_qsize 5, out_qsize 0 75 2016-5-2 18:57:32,647 : INFO : PROGRESS: at 56.78% examples, 558665 words/s, in_qsize 6, out_qsize 0 76 2016-5-2 18:57:33,656 : INFO : PROGRESS: at 57.57% examples, 557526 words/s, in_qsize 6, out_qsize 0 77 2016-5-2 18:57:34,660 : INFO : PROGRESS: at 58.39% examples, 556830 words/s, in_qsize 4, out_qsize 0 78 2016-5-2 18:57:35,664 : INFO : PROGRESS: at 59.31% examples, 557019 words/s, in_qsize 6, out_qsize 0 79 2016-5-2 18:57:36,670 : INFO : PROGRESS: at 60.12% examples, 556187 words/s, in_qsize 6, out_qsize 0 80 2016-5-2 18:57:37,683 : INFO : PROGRESS: at 60.94% examples, 555461 words/s, in_qsize 6, out_qsize 0 81 2016-5-2 18:57:38,686 : INFO : PROGRESS: at 61.78% examples, 554836 words/s, in_qsize 6, out_qsize 0 82 2016-5-2 18:57:39,705 : INFO : PROGRESS: at 62.54% examples, 553555 words/s, in_qsize 6, out_qsize 0 83 2016-5-2 18:57:40,710 : INFO : PROGRESS: at 63.35% examples, 552863 words/s, in_qsize 6, out_qsize 0 84 2016-5-2 18:57:41,719 : INFO : PROGRESS: at 64.12% examples, 551760 words/s, in_qsize 6, out_qsize 0 85 2016-5-2 18:57:42,726 : INFO : PROGRESS: at 64.93% examples, 551152 words/s, in_qsize 5, out_qsize 0 86 2016-5-2 18:57:43,741 : INFO : PROGRESS: at 65.74% examples, 550535 words/s, in_qsize 6, out_qsize 0 87 2016-5-2 18:57:44,743 : INFO : PROGRESS: at 66.51% examples, 549746 words/s, in_qsize 6, out_qsize 0 88 2016-5-2 18:57:45,743 : INFO : PROGRESS: at 67.23% examples, 548498 words/s, in_qsize 6, out_qsize 0 89 2016-5-2 18:57:46,773 : INFO : PROGRESS: at 67.98% examples, 547297 words/s, in_qsize 6, out_qsize 0 90 2016-5-2 18:57:47,786 : INFO : PROGRESS: at 68.81% examples, 546808 words/s, in_qsize 6, out_qsize 0 91 2016-5-2 18:57:48,792 : INFO : PROGRESS: at 69.58% examples, 546028 words/s, in_qsize 6, out_qsize 0 92 2016-5-2 18:57:49,798 : INFO : PROGRESS: at 70.37% examples, 545344 words/s, in_qsize 6, out_qsize 0 93 2016-5-2 18:57:50,807 : INFO : PROGRESS: at 71.19% examples, 545012 words/s, in_qsize 6, out_qsize 1 94 2016-5-2 18:57:51,802 : INFO : PROGRESS: at 72.09% examples, 545184 words/s, in_qsize 6, out_qsize 0 95 2016-5-2 18:57:52,806 : INFO : PROGRESS: at 72.98% examples, 545315 words/s, in_qsize 5, out_qsize 0 96 2016-5-2 18:57:53,827 : INFO : PROGRESS: at 73.92% examples, 545714 words/s, in_qsize 5, out_qsize 0 97 2016-5-2 18:57:54,827 : INFO : PROGRESS: at 74.86% examples, 546256 words/s, in_qsize 5, out_qsize 0 98 2016-5-2 18:57:55,840 : INFO : PROGRESS: at 75.79% examples, 546379 words/s, in_qsize 5, out_qsize 0 99 2016-5-2 18:57:56,851 : INFO : PROGRESS: at 76.73% examples, 546823 words/s, in_qsize 5, out_qsize 0 100 2016-5-2 18:57:57,843 : INFO : PROGRESS: at 77.66% examples, 547189 words/s, in_qsize 6, out_qsize 0 101 2016-5-2 18:57:58,847 : INFO : PROGRESS: at 78.50% examples, 546858 words/s, in_qsize 6, out_qsize 0 102 2016-5-2 18:57:59,849 : INFO : PROGRESS: at 79.39% examples, 546959 words/s, in_qsize 5, out_qsize 0 103 2016-5-2 18:58:00,854 : INFO : PROGRESS: at 80.27% examples, 546954 words/s, in_qsize 5, out_qsize 1 104 2016-5-2 18:58:01,856 : INFO : PROGRESS: at 81.22% examples, 547394 words/s, in_qsize 3, out_qsize 0 105 2016-5-2 18:58:02,875 : INFO : PROGRESS: at 82.13% examples, 547429 words/s, in_qsize 6, out_qsize 0 106 2016-5-2 18:58:03,888 : INFO : PROGRESS: at 83.07% examples, 547815 words/s, in_qsize 6, out_qsize 0 107 2016-5-2 18:58:04,880 : INFO : PROGRESS: at 84.00% examples, 548153 words/s, in_qsize 5, out_qsize 0 108 2016-5-2 18:58:05,895 : INFO : PROGRESS: at 84.91% examples, 548428 words/s, in_qsize 5, out_qsize 0 109 2016-5-2 18:58:06,888 : INFO : PROGRESS: at 85.77% examples, 548357 words/s, in_qsize 6, out_qsize 0 110 2016-5-2 18:58:07,901 : INFO : PROGRESS: at 86.64% examples, 548365 words/s, in_qsize 6, out_qsize 0 111 2016-5-2 18:58:08,897 : INFO : PROGRESS: at 87.50% examples, 548265 words/s, in_qsize 6, out_qsize 0 112 2016-5-2 18:58:09,902 : INFO : PROGRESS: at 88.42% examples, 548504 words/s, in_qsize 6, out_qsize 0 113 2016-5-2 18:58:10,916 : INFO : PROGRESS: at 89.18% examples, 547765 words/s, in_qsize 5, out_qsize 0 114 2016-5-2 18:58:11,921 : INFO : PROGRESS: at 89.94% examples, 547006 words/s, in_qsize 5, out_qsize 0 115 2016-5-2 18:58:12,923 : INFO : PROGRESS: at 90.81% examples, 546992 words/s, in_qsize 6, out_qsize 0 116 2016-5-2 18:58:13,930 : INFO : PROGRESS: at 91.72% examples, 547225 words/s, in_qsize 6, out_qsize 0 117 2016-5-2 18:58:14,935 : INFO : PROGRESS: at 92.59% examples, 547187 words/s, in_qsize 5, out_qsize 0 118 2016-5-2 18:58:15,939 : INFO : PROGRESS: at 93.46% examples, 547133 words/s, in_qsize 6, out_qsize 0 119 2016-5-2 18:58:16,944 : INFO : PROGRESS: at 94.18% examples, 546224 words/s, in_qsize 6, out_qsize 0 120 2016-5-2 18:58:17,953 : INFO : PROGRESS: at 94.93% examples, 545497 words/s, in_qsize 6, out_qsize 0 121 2016-5-2 18:58:18,959 : INFO : PROGRESS: at 95.70% examples, 544697 words/s, in_qsize 6, out_qsize 0 122 2016-5-2 18:58:19,967 : INFO : PROGRESS: at 96.40% examples, 543702 words/s, in_qsize 5, out_qsize 0 123 2016-5-2 18:58:20,974 : INFO : PROGRESS: at 97.26% examples, 543612 words/s, in_qsize 5, out_qsize 0 124 2016-5-2 18:58:21,978 : INFO : PROGRESS: at 98.17% examples, 543801 words/s, in_qsize 5, out_qsize 0 125 2016-5-2 18:58:22,994 : INFO : PROGRESS: at 99.07% examples, 543908 words/s, in_qsize 4, out_qsize 2 126 2016-5-2 18:58:23,989 : INFO : PROGRESS: at 99.91% examples, 543692 words/s, in_qsize 6, out_qsize 0 127 2016-5-2 18:58:24,067 : INFO : worker thread finished; awaiting finish of 2 more threads 128 2016-5-2 18:58:24,083 : INFO : worker thread finished; awaiting finish of 1 more threads 129 2016-5-2 18:58:24,086 : INFO : worker thread finished; awaiting finish of 0 more threads 130 2016-5-2 18:58:24,086 : INFO : training on 85026035 raw words (62534095 effective words) took 115.0s, 543725 effective words/s 131 2016-5-2 18:58:24,086 : INFO : precomputing L2-norms of word weight vectors 132 <span style="color:#FF0000;">woman和man的相似度为： 0.699695936218 133 -------- 134 和good最相关的词有： 135 136 bad 0.721469461918 137 poor 0.567566931248 138 safe 0.534923613071 139 luck 0.518905758858 140 courage 0.510788619518 141 useful 0.498157411814 142 quick 0.497716665268 143 easy 0.497328162193 144 everyone 0.485905945301 145 pleasure 0.483758479357 146 true 0.482762247324 147 simple 0.480014979839 148 practical 0.479516804218 149 fair 0.479104012251 150 happy 0.476968646049 151 wrong 0.476797521114 152 reasonable 0.476701617241 153 you 0.475801795721 154 fun 0.472196519375 155 helpful 0.471719056368 156 -------- 157 158 "boy" is to "father" as "girl" is to ...? 159 160 mother 0.76334130764 161 grandmother 0.690031766891 162 daughter 0.684129178524 163 -------- 164 165 ‘he‘ is to ‘his‘ as ‘she‘ is to ‘her‘ 166 ‘big‘ is to ‘bigger‘ as ‘bad‘ is to ‘worse‘ 167 ‘going‘ is to ‘went‘ as ‘being‘ is to ‘was‘ 168 -------- 169 170 不合群的词： cereal 171 --------</span> 172 173 2016-5-2 18:58:24,185 : INFO : saving Word2Vec object under text8.model, separately None 174 2016-5-2 18:58:24,185 : INFO : storing numpy array ‘syn1neg‘ to text8.model.syn1neg.npy 175 2016-5-2 18:58:24,235 : INFO : not storing attribute syn0norm 176 2016-5-2 18:58:24,235 : INFO : storing numpy array ‘syn0‘ to text8.model.syn0.npy 177 2016-5-2 18:58:24,278 : INFO : not storing attribute cum_table 178 2016-5-2 18:58:25,083 : INFO : storing 71290x200 projection weights into text8.model.bin
常用语料资源
下面提供一些网上能下载到的中文的好语料，供研究人员学习使用。
(1).中科院自动化所的中英文新闻语料库 http://www.datatang.com/data/13484
中文新闻分类语料库从凤凰、新浪、网易、腾讯等版面搜集。英语新闻分类语料库为Reuters-21578的ModApte版本。
(2).搜狗的中文新闻语料库 http://www.sogou.com/labs/dl/c.html
包括搜狐的大量新闻语料与对应的分类信息。有不同大小的版本可以下载。
(3).李荣陆老师的中文语料库 http://www.datatang.com/data/11968
压缩后有240M大小
(4).谭松波老师的中文文本分类语料 http://www.datatang.com/data/11970
不仅包含大的分类，例如经济、运动等等，每个大类下面还包含具体的小类，例如运动包含篮球、足球等等。能够作为层次分类的语料库，非常实用。这个网址免积分(谭松波老师的主页)：http://www.searchforum.org.cn/tansongbo/corpus1.PHP
(5).网易分类文本数据 http://www.datatang.com/data/11965
包含运动、汽车等六大类的4000条文本数据。
(6).中文文本分类语料 http://www.datatang.com/data/11963
包含Arts、Literature等类别的语料文本。
(7).更全的搜狗文本分类语料 http://www.sogou.com/labs/dl/c.html
搜狗实验室发布的文本分类语料，有不同大小的数据版本供免费下载
(8).2002年中文网页分类训练集 http://www.datatang.com/data/15021
2002年秋天北京大学网络与分布式实验室天网小组通过动员不同专业的几十个学生，人工选取形成了一个全新的基于层次模型的大规模中文网页样本集。它包括11678个训练网页实例和3630个测试网页实例，分布在11个大类别中。
常用分词工具
将预料库进行分词并去掉停用词，常用分词工具有：
StandardAnalyzer（中英文）、ChineseAnalyzer（中文）、CJKAnalyzer（中英文）、IKAnalyzer（中英文，兼容韩文，日文）、paoding（中文）、MMAnalyzer（中英文）、MMSeg4j（中英文）、imdict（中英文）、NLTK（中英文）、Jieba（中英文）。
提供一份DEMO语料资源
原始语料 http://pan.baidu.com/s/1nviuFc1
训练语料 http://pan.baidu.com/s/1kVEmNTd
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