scripts.word2vec_standalone – Train word2vec on text file CORPUS

USAGE: %(program)s -train CORPUS -output VECTORS -size SIZE -window WINDOW -cbow CBOW -sample SAMPLE -hs HS -negative NEGATIVE -threads THREADS -iter ITER -min_count MIN-COUNT -alpha ALPHA -binary BINARY -accuracy FILE

Trains a neural embedding model on text file CORPUS. Parameters essentially reproduce those used by the original C tool (see

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 1e-3, useful range is (0, 1e-5)

-hs <int>

Use Hierarchical Softmax; default is 0 (not used)

-negative <int>

Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)

-threads <int>

Use <int> threads (default 3)

-iter <int>

Run more training iterations (default 5)

-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 for skip-gram and 0.05 for CBOW

-binary <int>

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

-cbow <int>

Use the continuous bag of words model; default is 1 (use 0 for skip-gram model)

-accuracy <file>

Compute accuracy of the resulting model analogical inference power on questions file <file> See an example of questions file at

Example: python -m gensim.scripts.word2vec_standalone -train data.txt -output vec.txt -size 200 -sample 1e-4 -binary 0 -iter 3