word2vec+维基百科语料库训练中文模型

训练中文词向量word2vec模型

1.准备数据

中文维基百科地址：https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2；
搜狗全网新闻预料地址：http://www.sogou.com/labs/resource/ca.php；
中文维基百科是xml格式的压缩文件，有1G左右。下面仅以中文维基预料进行模型训练。
1.将xml文件转为text。执行命令：python process_wiki_data.py zhwiki-latest-pages-articles.xml.bz2 wiki.zh.text

# process_wiki_data.py
# -*- coding: utf-8 -*-
import logging
import os.path
import sys
from gensim.corpora import WikiCorpus
if __name__ == '__main__':
    program = os.path.basename(sys.argv[0])
    logger = logging.getLogger(program)
    logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
    logging.root.setLevel(level=logging.INFO)
    logger.info("running %s" % ' '.join(sys.argv))
    # check and process input arguments
    if len(sys.argv) < 3:
        print(globals()['__doc__'] % locals())
        sys.exit(1)
    inp, outp = sys.argv[1:3]
    space = " "
    i = 0
    output = open(outp, 'w', encoding='utf-8')
    wiki = WikiCorpus(inp, lemmatize=False, dictionary={})
    for text in wiki.get_texts():
        output.write(space.join(text) + "\n")
        i = i + 1
        if (i % 10000 == 0):
            logger.info("Saved " + str(i) + " articles")
    output.close()
    logger.info("Finished Saved " + str(i) + " articles")
``
![处理数据结果](https://img-blog.csdnimg.cn/20190806175953398.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3hpZXppXzEwMTU=,size_16,color_FFFFFF,t_70)
2.对变换格式后的预料进行分词处理（中文分词以jieba为例）命令：python word_seg.py
```javascript
# -*- coding: utf-8  -*-
# 逐行读取文件数据进行jieba分词
# word_seg.py
import jieba
import jieba.analyse
import jieba.posseg as pseg #引入词性标注接口 
import codecs,sys


if __name__ == '__main__':
    f = codecs.open('zhwiki.zh.text', 'r', encoding='utf8')
    target = codecs.open('wiki.zh.text.seg', 'w', encoding='utf8')
    print('open files.')

    lineNum = 1
    line = f.readline()
    print("打印line：", line)
    while line:
        print('---processing ',lineNum,' article---')
        seg_list = jieba.cut(line,cut_all=False)
        line_seg = ' '.join(seg_list)
        print("分词", line_seg)
        target.writelines(line_seg)
        lineNum = lineNum + 1
        line = f.readline()
    print('well done.')
    f.close()
    target.close()

3.使用分割后的预料进行模型的训练，命令：python word2vecmodel.py wiki.zh.text.seg wiki.zh.text.model wiki.zh.text.vector

# -*- coding: utf-8 -*-
# word2vecmodel.py用于训练模型
import logging
import os.path
import sys
import multiprocessing
from gensim.corpora import WikiCorpus
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
if __name__ == '__main__':
    program = os.path.basename(sys.argv[0])
    logger = logging.getLogger(program)
    logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
    logging.root.setLevel(level=logging.INFO)
    logger.info("running %s" % ' '.join(sys.argv))
    # check and process input arguments
    if len(sys.argv) < 4:
        print(globals()['__doc__'] % locals())
        sys.exit(1)
    inp, outp1, outp2 = sys.argv[1:4]
    model = Word2Vec(LineSentence(inp), size=400, window=5, min_count=5,
            workers=multiprocessing.cpu_count())
    # trim unneeded model memory = use(much) less RAM
    #model.init_sims(replace=True)
    model.save(outp1)
    model.save_word2vec_format(outp2, binary=False)

4.测试模型效果，命令：python testmodel.py

# coding: utf-8
# testmodel.py
import gensim
model = gensim.models.Word2Vec.load("wiki.zh.text.model")
model.init_sims(replace = True)

result = model.most_similar(u"足球")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"男人")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"女人")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"青蛙")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"姨夫")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"衣服")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"公安局")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"铁道部")
for e in result:
    print(e[0], e[1])
result = model.most_similar(u"清华大学")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"卫视")
for e in result:
    print(e[0], e[1])
    
result = model.most_similar(u"语言学")
for e in result:
    print(e[0], e[1])

result = model.most_similar(u"计算机")
for e in result:
    print(e[0], e[1])

model.similarity(u"计算机", u"自动化")

model.similarity(u"女人", u"男人")

model.doesnt_match(u"早餐 晚餐 午餐 中心".split())

print(model.doesnt_match(u"早餐 晚餐 午餐 中心".split()))
运行结果：
排球 0.6024000644683838
足球运动 0.599421501159668
冰球 0.5516613721847534

所遇问题：
Traceback (most recent call last):
File “”, line 1, in
File “/usr/local/lib/python2.7/dist-packages/gensim/utils.py”, line 1422, in new_func1
return func(*args, **kwargs)
File “/usr/local/lib/python2.7/dist-packages/gensim/models/base_any2vec.py”, line 1397, in most_similar
return self.wv.most_similar(positive, negative, topn, restrict_vocab, indexer)
File “/usr/local/lib/python2.7/dist-packages/gensim/models/keyedvectors.py”, line 509, in most_similar
self.init_sims()
File “/usr/local/lib/python2.7/dist-packages/gensim/models/keyedvectors.py”, line 1366, in init_sims
self.vectors_norm = (self.vectors / sqrt((self.vectors ** 2).sum(-1))[…, newaxis]).astype(REAL)
MemoryError
解决方案：
在加载完Word2Vec模型后，接着输入这一行命令：model.init_sims(replace = True)