gensim中word2vec一些补充

存储和加载模型

存储、加载模型的方法如下:

>>>model.save('/tmp/mymodel')

>>>new_model = gensim.models.Word2Vec.load('/tmp/mymodel')

保存为txt格式：

model.wv.save_word2vec_format('wordvec.txt')

可以直接加载由C生成的模型:

model = Word2Vec.load_word2vec_format('/tmp/vectors.txt', binary=False) 

#using gzipped/bz2 input works too, no need to unzip:

model=Word2Vec.load_word2vec_format('/tmp/vectors.bin.gz', binary=True)

可以在加载模型之后使用另外的句子来进一步训练模型

model = gensim.models.Word2Vec.load('/tmp/mymodel')

model.train(more_sentences)

不能对C生成的模型再训练

model.most_similar(positive=['woman','king'], negative=['man'], topn=1)

[('queen',0.50882536)]

model.doesnt_match("breakfast cereal dinner lunch".split())

'cereal'

model.similarity('woman','man')

.73723527