theano-word2vec 0.1.2

word2vec using Theano and Lasagne

To get started with word2vec, import and instantiate a Word2Vec object:

>>> from word2vec import Word2Vec
>>> word2vec = Word2Vec()

You can train on a corpus by passing it as a string:

>>> word2vec.train_on_corpus(
...     open('my-corpus.txt').read(),
...     num_embedding_dimensions=500
... )

Convert any string into a set of vectors using the embedding learned during training:

>>> embeddings = word2vec.embed(
...     'this will produce a list of vectors.  If the input is a string it '
...     'gets tokenized'
... )

Optionally pre-tokenize the string to be embedded:

>>> other_embeddings = word2vec.embed([
...     'control', 'tokenization', 'by', 'passing', 'a', 'tokenized', 'list'
... ])

Do anological arithmetic, and find the word having the nearest embedding to a given vector:

>>> king, man, woman = word2vec.embed('king man woman')
>>> queen = king - man + woman
>>> print word2vec.nearest(queen)
'queen'

Get a fresh Lasagne layer out of the trained model, and use it seemlessly with other Lasagne layers:

>>> some_input_var = theano.dmatrix('input')
>>> input_layer = lasagne.layers.InputLayer(some_input_var, shape)
>>> embedding_layer = word2vec.layer(input_layer)
>>> my_cool_architecture = lasagne.layers.DenseLayer(embedding_layer)