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')