Testing whether the fast version is installed:
#!python >>> from gensim.models import word2vec >>> assert word2vec.FAST_VERSION > -1
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.
#!python >>> bigram_transformer = gensim.models.Phrases(sentences) >>> model = Word2Vec(bigram_transformed[sentences], size=100, ...)
Vocab object contains a word and its frequency (
count) and other properties (e.g.
sample_int is used for sampling purpose)
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 class #
_do_train_job(self, job, alpha, inits):
job is just sentences.
the document vectors are stored in this object.
indexed_doctags(self, doctag_tokens): given
doctag_tokens (a list of document tags), return (integer index,