from gensim.models.word2vec import Word2Vec
import gensim.downloader as api

corpus = api.load('text8')  # download the corpus and return it opened as an iterable
model = Word2Vec(corpus)  # train a model from the corpus
print(model.most_similar("car"))

model.save('twitter25-tag_vectors.model')