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Gensim #
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Installation #

Testing whether the fast version is installed:

>>> from gensim.models import word2vec
>>> assert word2vec.FAST_VERSION > -1

Models #

Phrases #

This model detects multi-word phrases that can be grouped, such as new_york_times. Can be used as a preprocessor for word2vec or doc2vec models.

>>> bigram_transformer = gensim.models.Phrases(sentences)
>>> model = Word2Vec(bigram_transformed[sentences], size=100, ...)

word2vec #

Let V as the size of the vocabulary and N as the dimension of the hidden layer (vector dimension).

  • model.syn0: \( V \times N \) matrix. model.syn0[wordindex] returns the word vector.

doc2vec #