gensim Load embeddings

gensim package

from gensim.models.keyedvectors import KeyedVectors

twitter_embedding_path = 'twitter_embedding.emb'
twitter_vocab_path = 'twitter_model.vocab'
foursquare_embedding_path = 'foursquare_embedding.emb'
foursquare_vocab_path = 'foursquare_model.vocab'

# load the embedding vector using gensim
x_vectors = KeyedVectors.load_word2vec_format(foursquare_embedding_path, binary=False, fvocab=foursquare_vocab_path)
y_vectors = KeyedVectors.load_word2vec_format(twitter_embedding_path, binary=False, fvocab=twitter_vocab_path)

print('type(x_vectors)', type(x_vectors))
print('type(x_vectors.vocab)', type(x_vectors.vocab))
print('type(x_vectors.vocab.keys())', type(x_vectors.vocab.keys()))

Content in 'twitter_embedding.emb':

5120 64

BarackObama -0.079930 0.106491 -0.075812 -0.026447 ...

mashable 0.046692 -0.038019 -0.055519 ...

...

Content in 'twitter_model.vocab':

BarackObama 3475971

mashable 2668606

JonahLupton 2515250

instagram 2359886

TheEllenShow 2292545

cnnbrk 2157283

nytimes 2141588

foursquare 2021352

...

Write the embeddings into file

for writing the embeddings into file

ref code patch:

embedding_path = data_path + 'embedding/'
# ....
modelX = word2vec.Word2Vec(walkList_x, negative=10, sg=1, hs=0, size=100, window=4, min_count=0, workers=15, iter=30)
# save the embedding results
modelX.wv.save_word2vec_format(embedding_path + 'twitter.emb', fvocab=embedding_path + 'twitter.vocab')