word2vec.word2vec()¶

word2vec.word2vec(train, output, size=100, window=5, sample=u'1e-3', hs=0, negative=5, threads=12, iter_=5, min_count=5, alpha=0.025, debug=2, binary=1, cbow=1, save_vocab=None, read_vocab=None, verbose=False)[source]¶

word2vec execution

Parameters for training:

train <file>

Use text data from <file> to train the model

output <file>

Use <file> to save the resulting word vectors / word clusters

size <int>

Set size of word vectors; default is 100

window <int>

Set max skip length between words; default is 5

sample <float>

Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; default is 0 (off), useful value is 1e-5

hs <int>

Use Hierarchical Softmax; default is 1 (0 = not used)

negative <int>

Number of negative examples; default is 0, common values are 5 - 10 (0 = not used)

threads <int>

Use <int> threads (default 1)

min_count <int>

This will discard words that appear less than <int> times; default is 5

alpha <float>

Set the starting learning rate; default is 0.025

debug <int>

Set the debug mode (default = 2 = more info during training)

binary <int>

Save the resulting vectors in binary moded; default is 0 (off)

cbow <int>

Use the continuous back of words model; default is 1 (skip-gram model)

save_vocab <file>

The vocabulary will be saved to <file>

read_vocab <file>

The vocabulary will be read from <file>, not constructed from the training data

verbose

Print output from training