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

corpus = api.load('text8')
model = Word2Vec(corpus)
model.wv.save("text8vectors.wordvectors")

8072c709f042a0be7a05b727bd0f32fae852eb78
<mask token>

<mask token>
model.wv.save('text8vectors.wordvectors')

<mask token>
corpus = api.load('text8')
model = Word2Vec(corpus)
model.wv.save('text8vectors.wordvectors')

from gensim.models import Word2Vec
import gensim.downloader as api
corpus = api.load('text8')
model = Word2Vec(corpus)
model.wv.save('text8vectors.wordvectors')

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

corpus = api.load('text8')
model = Word2Vec(corpus)
model.wv.save("text8vectors.wordvectors")
