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')