word2vec训练中文模型

时间：2024-07-07 17:33:20

--  这篇文章是一个学习、分析的博客 ---

1.准备数据与预处理

首先需要一份比较大的中文语料数据，可以考虑中文的*（也可以试试搜狗的新闻语料库）。中文*的打包文件地址为 
https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2

中文*的数据不是太大，xml的压缩文件大约1G左右。首先用 process_wiki_data.py处理这个XML压缩文件，执行：python process_wiki_data.py zhwiki-latest-pages-articles.xml.bz2 wiki.zh.text

  1. #!/usr/bin/env python
  2. # -*- coding: utf-8 -*-
  3. # process_wiki_data.py 用于解析XML，将XML的wiki数据转换为text格式胡2*!
  5. import logging
  6. import os.path
  7. import sys
  9. from gensim.corpora import WikiCorpus
  11. if __name__ == '__main__':
  12. program = os.path.basename(sys.argv[0])
  13. logger = logging.getLogger(program)
  15. logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
  16. logging.root.setLevel(level=logging.INFO)
  17. logger.info("running %s" % ' '.join(sys.argv))
  19. # check and process input arguments
  20. if len(sys.argv) < 3:
  21. print globals()['__doc__'] % locals()
  22. sys.exit(1)
  23. inp, outp = sys.argv[1:3]
  24. space = " "
  25. i = 0
  27. output = open(outp, 'w')
  28. wiki = WikiCorpus(inp, lemmatize=False, dictionary={})
  29. for text in wiki.get_texts():
  30. output.write(space.join(text) + "\n")
  31. i = i + 1
  32. if (i % 10000 == 0):
  33. logger.info("Saved " + str(i) + " articles")
  35. output.close()
  36. logger.info("Finished Saved " + str(i) + " articles")

得到信息：

  1. 2016-08-11 20:39:22,739: INFO: running process_wiki.py zhwiki-latest-pages-articles.xml.bz2 wiki.zh.text
  2. 2016-08-11 20:40:08,329: INFO: Saved 10000 articles
  3. 2016-08-11 20:40:45,501: INFO: Saved 20000 articles
  4. 2016-08-11 20:41:23,659: INFO: Saved 30000 articles
  5. 2016-08-11 20:42:01,748: INFO: Saved 40000 articles
  6. 2016-08-11 20:42:33,779: INFO: Saved 50000 articles
  7. ......
  8. 2016-08-11 20:55:23,094: INFO: Saved 200000 articles
  9. 2016-08-11 20:56:14,692: INFO: Saved 210000 articles
  10. 2016-08-11 20:57:04,614: INFO: Saved 220000 articles
  11. 2016-08-11 20:57:57,979: INFO: Saved 230000 articles
  12. 2016-08-11 20:58:16,621: INFO: finished iterating over Wikipedia corpus of 232894 documents with 51603419 positions (total 2581444 articles, 62177405 positions before pruning articles shorter than 50 words)
  13. 2016-08-11 20:58:16,622: INFO: Finished Saved 232894 articles

Python的话可用jieba完成分词，生成分词文件wiki.zh.text.seg 
接着用word2vec工具训练： 
python train_word2vec_model.py wiki.zh.text.seg wiki.zh.text.model wiki.zh.text.vector

  1. #!/usr/bin/env python
  2. # -*- coding: utf-8 -*-
  3. # train_word2vec_model.py用于训练模型
  5. import logging
  6. import os.path
  7. import sys
  8. import multiprocessing
  10. from gensim.corpora import WikiCorpus
  11. from gensim.models import Word2Vec
  12. from gensim.models.word2vec import LineSentence
  14. if __name__ == '__main__':
  15. program = os.path.basename(sys.argv[0])
  16. logger = logging.getLogger(program)
  18. logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
  19. logging.root.setLevel(level=logging.INFO)
  20. logger.info("running %s" % ' '.join(sys.argv))
  22. # check and process input arguments
  23. if len(sys.argv) < 4:
  24. print globals()['__doc__'] % locals()
  25. sys.exit(1)
  26. inp, outp1, outp2 = sys.argv[1:4]
  28. model = Word2Vec(LineSentence(inp), size=400, window=5, min_count=5,
  29. workers=multiprocessing.cpu_count())
  31. # trim unneeded model memory = use(much) less RAM
  32. #model.init_sims(replace=True)
  33. model.save(outp1)
  34. model.save_word2vec_format(outp2, binary=False)

运行信息

  1. 2016-08-12 09:50:02,586: INFO: running python train_word2vec_model.py wiki.zh.text.seg wiki.zh.text.model wiki.zh.text.vector
  2. 2016-08-12 09:50:02,592: INFO: collecting all words and their counts
  3. 2016-08-12 09:50:02,592: INFO: PROGRESS: at sentence #0, processed 0 words and 0 word types
  4. 2016-08-12 09:50:12,476: INFO: PROGRESS: at sentence #10000, processed 12914562 words and 254662 word types
  5. 2016-08-12 09:50:20,215: INFO: PROGRESS: at sentence #20000, processed 22308801 words and 373573 word types
  6. 2016-08-12 09:50:28,448: INFO: PROGRESS: at sentence #30000, processed 30724902 words and 460837 word types
  7. ...
  8. 2016-08-12 09:52:03,498: INFO: PROGRESS: at sentence #210000, processed 143804601 words and 1483608 word types
  9. 2016-08-12 09:52:07,772: INFO: PROGRESS: at sentence #220000, processed 149352283 words and 1521199 word types
  10. 2016-08-12 09:52:11,639: INFO: PROGRESS: at sentence #230000, processed 154741839 words and 1563584 word types
  11. 2016-08-12 09:52:12,746: INFO: collected 1575172 word types from a corpus of 156430908 words and 232894 sentences
  12. 2016-08-12 09:52:13,672: INFO: total 278291 word types after removing those with count<5
  13. 2016-08-12 09:52:13,673: INFO: constructing a huffman tree from 278291 words
  14. 2016-08-12 09:52:29,323: INFO: built huffman tree with maximum node depth 25
  15. 2016-08-12 09:52:29,683: INFO: resetting layer weights
  16. 2016-08-12 09:52:38,805: INFO: training model with 4 workers on 278291 vocabulary and 400 features, using 'skipgram'=1 'hierarchical softmax'=1 'subsample'=0 and 'negative sampling'=0
  17. 2016-08-12 09:52:49,504: INFO: PROGRESS: at 0.10% words, alpha 0.02500, 15008 words/s
  18. 2016-08-12 09:52:51,935: INFO: PROGRESS: at 0.38% words, alpha 0.02500, 44434 words/s
  19. 2016-08-12 09:52:54,779: INFO: PROGRESS: at 0.56% words, alpha 0.02500, 53965 words/s
  20. 2016-08-12 09:52:57,240: INFO: PROGRESS: at 0.62% words, alpha 0.02491, 52116 words/s
  21. 2016-08-12 09:52:58,823: INFO: PROGRESS: at 0.72% words, alpha 0.02494, 55804 words/s
  22. 2016-08-12 09:53:03,649: INFO: PROGRESS: at 0.94% words, alpha 0.02486, 58277 words/s
  23. 2016-08-12 09:53:07,357: INFO: PROGRESS: at 1.03% words, alpha 0.02479, 56036 words/s
  24. ......
  25. 2016-08-12 19:22:09,002: INFO: PROGRESS: at 98.38% words, alpha 0.00044, 85936 words/s
  26. 2016-08-12 19:22:10,321: INFO: PROGRESS: at 98.50% words, alpha 0.00044, 85971 words/s
  27. 2016-08-12 19:22:11,934: INFO: PROGRESS: at 98.55% words, alpha 0.00039, 85940 words/s
  28. 2016-08-12 19:22:13,384: INFO: PROGRESS: at 98.65% words, alpha 0.00036, 85960 words/s
  29. 2016-08-12 19:22:13,883: INFO: training on 152625573 words took 1775.1s, 85982 words/s
  30. 2016-08-12 19:22:13,883: INFO: saving Word2Vec object under wiki.zh.text.model, separately None
  31. 2016-08-12 19:22:13,884: INFO: not storing attribute syn0norm
  32. 2016-08-12 19:22:13,884: INFO: storing numpy array 'syn0' to wiki.zh.text.model.syn0.npy
  33. 2016-08-12 19:22:20,797: INFO: storing numpy array 'syn1' to wiki.zh.text.model.syn1.npy
  34. 2016-08-12 19:22:40,667: INFO: storing 278291x400 projection weights into wiki.zh.text.vector

测试模型效果：

    1. In [1]: import gensim
    3. In [2]: model = gensim.models.Word2Vec.load("wiki.zh.text.model")
    5. In [3]: model.most_similar(u"足球")
    6. Out[3]:
    7. [(u'\u8054\u8d5b', 0.6553816199302673),
    8. (u'\u7532\u7ea7', 0.6530429720878601),
    9. (u'\u7bee\u7403', 0.5967546701431274),
    10. (u'\u4ff1\u4e50\u90e8', 0.5872289538383484),
    11. (u'\u4e59\u7ea7', 0.5840631723403931),
    12. (u'\u8db3\u7403\u961f', 0.5560152530670166),
    13. (u'\u4e9a\u8db3\u8054', 0.5308005809783936),
    14. (u'allsvenskan', 0.5249762535095215),
    15. (u'\u4ee3\u8868\u961f', 0.5214947462081909),
    16. (u'\u7532\u7ec4', 0.5177896022796631)]
    18. In [4]: result = model.most_similar(u"足球")
    20. In [5]: for e in result:
    21. print e[0], e[1]
    22. ....:
    23. 联赛 0.65538161993
    24. 甲级 0.653042972088
    25. 篮球 0.596754670143
    26. 俱乐部 0.587228953838
    27. 乙级 0.58406317234
    28. 足球队 0.556015253067
    29. 亚足联 0.530800580978
    30. allsvenskan 0.52497625351
    31. 代表队 0.521494746208
    32. 甲组 0.51778960228
    34. In [6]: result = model.most_similar(u"男人")
    36. In [7]: for e in result:
    37. print e[0], e[1]
    38. ....:
    39. 女人 0.77537125349
    40. 家伙 0.617369174957
    41. 妈妈 0.567102909088
    42. 漂亮 0.560832381248
    43. 잘했어 0.540875017643
    44. 谎言 0.538448691368
    45. 爸爸 0.53660941124
    46. 傻瓜 0.535608053207
    47. 예쁘다 0.535151124001
    48. mc刘 0.529670000076
    50. In [8]: result = model.most_similar(u"女人")
    52. In [9]: for e in result:
    53. print e[0], e[1]
    54. ....:
    55. 男人 0.77537125349
    56. 我的某 0.589010596275
    57. 妈妈 0.576344847679
    58. 잘했어 0.562340974808
    59. 美丽 0.555426716805
    60. 爸爸 0.543958246708
    61. 新娘 0.543640494347
    62. 谎言 0.540272831917
    63. 妞儿 0.531066179276
    64. 老婆 0.528521537781
    66. In [10]: result = model.most_similar(u"青蛙")
    68. In [11]: for e in result:
    69. print e[0], e[1]
    70. ....:
    71. 老鼠 0.559612870216
    72. 乌龟 0.489831030369
    73. 蜥蜴 0.478990525007
    74. 猫 0.46728849411
    75. 鳄鱼 0.461885392666
    76. 蟾蜍 0.448014199734
    77. 猴子 0.436584025621
    78. 白雪公主 0.434905380011
    79. 蚯蚓 0.433413207531
    80. 螃蟹 0.4314712286
    82. In [12]: result = model.most_similar(u"姨夫")
    84. In [13]: for e in result:
    85. print e[0], e[1]
    86. ....:
    87. 堂伯 0.583935439587
    88. 祖父 0.574735701084
    89. 妃所生 0.569327116013
    90. 内弟 0.562012672424
    91. 早卒 0.558042645454
    92. 曕 0.553856015205
    93. 胤祯 0.553288519382
    94. 陈潜 0.550716996193
    95. 愔之 0.550510883331
    96. 叔父 0.550032019615
    98. In [14]: result = model.most_similar(u"衣服")
    100. In [15]: for e in result:
    101. print e[0], e[1]
    102. ....:
    103. 鞋子 0.686688780785
    104. 穿着 0.672499775887
    105. 衣物 0.67173999548
    106. 大衣 0.667605519295
    107. 裤子 0.662670075893
    108. 内裤 0.662210345268
    109. 裙子 0.659705817699
    110. 西装 0.648508131504
    111. 洋装 0.647238850594
    112. 围裙 0.642895817757
    114. In [16]: result = model.most_similar(u"*局")
    116. In [17]: for e in result:
    117. print e[0], e[1]
    118. ....:
    119. 司法局 0.730189085007
    120. *厅 0.634275555611
    121. * 0.612798035145
    122. 房管局 0.597343325615
    123. 商业局 0.597183346748
    124. 军管会 0.59476184845
    125. 体育局 0.59283208847
    126. 财政局 0.588721752167
    127. 戒毒所 0.575558543205
    128. 新闻办 0.573395550251
    130. In [18]: result = model.most_similar(u"铁道部")
    132. In [19]: for e in result:
    133. print e[0], e[1]
    134. ....:
    135. 盛光祖 0.565509021282
    136. 交通部 0.548688530922
    137. 批复 0.546967327595
    138. 刘志军 0.541010737419
    139. 立项 0.517836689949
    140. 报送 0.510296344757
    141. 计委 0.508456230164
    142. 水利部 0.503531932831
    143. 国务院 0.503227233887
    144. 经贸委 0.50156635046
    146. In [20]: result = model.most_similar(u"清华大学")
    148. In [21]: for e in result:
    149. print e[0], e[1]
    150. ....:
    151. 北京大学 0.763922810555
    152. 化学系 0.724210739136
    153. 物理系 0.694550514221
    154. 数学系 0.684280991554
    155. 中山大学 0.677202701569
    156. 复旦 0.657914161682
    157. 师范大学 0.656435549259
    158. 哲学系 0.654701948166
    159. 生物系 0.654403865337
    160. 中文系 0.653147578239
    162. In [22]: result = model.most_similar(u"卫视")
    164. In [23]: for e in result:
    165. print e[0], e[1]
    166. ....:
    167. 湖南 0.676812887192
    168. 中文台 0.626506924629
    169. 収蔵 0.621356606483
    170. 黄金档 0.582251906395
    171. cctv 0.536769032478
    172. 安徽 0.536752820015
    173. 非同凡响 0.534517168999
    174. 唱响 0.533438682556
    175. 最强音 0.532605051994
    176. 金鹰 0.531676828861
    178. In [24]: result = model.most_similar(u"习1*") //这里博客作了判断，不让包含 有国家*的信息
    180. In [25]: for e in result:
    181. print e[0], e[1]
    182. ....:
    183. 胡2* 0.809472680092
    184. 江3泽民 0.754633367062
    185. 李4克强 0.739740967751
    186. 贾5庆林 0.737033963203
    187. 曾6庆红 0.732847094536
    188. 吴7邦国 0.726941585541
    189. 总书记 0.719057679176
    190. 李8瑞环 0.716384887695
    191. 温9家宝 0.711952567101
    192. 王10岐山 0.703570842743
    194. In [26]: result = model.most_similar(u"林丹")
    196. In [27]: for e in result:
    197. print e[0], e[1]
    198. ....:
    199. 黄综翰 0.538035452366
    200. 蒋燕皎 0.52646958828
    201. 刘鑫 0.522252976894
    202. 韩晶娜 0.516120731831
    203. 王晓理 0.512289524078
    204. 王适 0.508560419083
    205. 杨影 0.508159279823
    206. 陈跃 0.507353425026
    207. 龚智超 0.503159761429
    208. 李敬元 0.50262516737
    210. In [28]: result = model.most_similar(u"语言学")
    212. In [29]: for e in result:
    213. print e[0], e[1]
    214. ....:
    215. 社会学 0.632598280907
    216. 人类学 0.623406708241
    217. 历史学 0.618442356586
    218. 比较文学 0.604823827744
    219. 心理学 0.600066184998
    220. 人文科学 0.577783346176
    221. 社会心理学 0.575571238995
    222. 政治学 0.574541330338
    223. 地理学 0.573896467686
    224. 哲学 0.573873817921
    226. In [30]: result = model.most_similar(u"计算机")
    228. In [31]: for e in result:
    229. print e[0], e[1]
    230. ....:
    231. 自动化 0.674171924591
    232. 应用 0.614087462425
    233. 自动化系 0.611132860184
    234. 材料科学 0.607891201973
    235. 集成电路 0.600370049477
    236. 技术 0.597518980503
    237. 电子学 0.591316461563
    238. 建模 0.577238917351
    239. 工程学 0.572855889797
    240. 微电子 0.570086717606
    242. In [32]: model.similarity(u"计算机", u"自动化")
    243. Out[32]: 0.67417196002404789
    245. In [33]: model.similarity(u"女人", u"男人")
    246. Out[33]: 0.77537125129824813
    248. In [34]: model.doesnt_match(u"早餐 晚餐 午餐 中心".split())
    249. Out[34]: u'\u4e2d\u5fc3'
    251. In [35]: print model.doesnt_match(u"早餐 晚餐 午餐 中心".split())
    252. 中心

来源：https://www.zybuluo.com/hanxiaoyang/note/472184

相关文章