Python实现word2Vec model过程解析

import gensim, logging, os
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
import nltk

corpus = nltk.corpus.brown.sents()

fname = 'brown_skipgram.model'
if os.path.exists(fname):
  # load the file if it has already been trained, to save repeating the slow training step below
  model = gensim.models.Word2Vec.load(fname)
else:
  # can take a few minutes, grab a cuppa
  model = gensim.models.Word2Vec(corpus, size=100, min_count=5, workers=2, iter=50)
  model.save(fname)

words = "woman women man girl boy green blue".split()
for w1 in words:
  for w2 in words:
    print(w1, w2, model.similarity(w1, w2))

print(model.most_similar(positive=['woman', ''], topn=1))
print(model.similarity('woman', 'girl'))girl