A simple and readable implementation of doc2vec [1], using Python 3, Keras and TensorFlow.
pip install -r requirements.txt
python setup.py install
This implementation assumes that your documents are all in the same directory,
and named with monotonicall
6227
y incrementing integer IDs, e.g. 0.txt, 1.txt.
Each file should contain an ordinary text document, i.e. without any special
preprocessing.
doc2vec path/to/docs/ \
--save path/to/save/model.hdf5 \
--save_vocab path/to/save/vocab.vocab \
--train
By default, this will use the Distributed Memory (DM) model. You can also use the Distributed Bag-Of-Words (DBOW) model with:
doc2vec path/to/docs/ --model dbow
Previously trained models can be loaded with:
doc2vec path/to/docs/ \
--load path/to/load/model.hdf5 \
--load_vocab path/to/load/vocab.vocab
And document embeddings can be written to file as follows:
doc2vec path/to/docs/ \
--load path/to/load/model.hdf5 \
--load_vocab path/to/load/vocab.vocab \
--save_doc_embeddings path/to/save/embeddings.hdf5
Finally, you can see all available options and model parameters with:
doc2vec -h
- Le, Quoc, and Mikolov, Tomas. "Distributed representations of sentences and documents." International Conference on Machine Learning. 2014.
- Bird, Steven, Loper, Edward and Klein, Ewan. "Natural Language Processing with Python." O’Reilly Media Inc. 2009.