<reponame>Cl3V0r/MLSeminar<gh_stars>0
import gensim

model = gensim.models.KeyedVectors.load_word2vec_format('../model/GoogleNews-vectors-negative300.bin.gz', binary=True)
model.init_sims(replace=True)

print(model.most_similar(positive=['woman', 'king'], negative=['man'], topn=1),
    model.doesnt_match("breakfast cereal dinner lunch".split()),
    model.similarity('woman', 'man'),
    model.get_vector('pizza'))

import gensim

model = gensim.models.KeyedVectors.load_word2vec_format('../model/GoogleNews-vectors-negative300.bin.gz', binary=True)
model.init_sims(replace=True)

print(model.most_similar(positive=['woman', 'king'], negative=['man'], topn=1),
    model.doesnt_match("breakfast cereal dinner lunch".split()),
    model.similarity('woman', 'man'),
    model.get_vector('pizza'))