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tensorflow port of the lda2vec model for unsupervised learning of document + topic + word embeddings

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TensorFlow implementation of Christopher Moody's lda2vec, a hybrid of Latent Dirichlet Allocation & word2vec

The lda2vec model simultaneously learns embeddings (continuous dense vector representations) for: * words (based on word and document context), * topics (in the same latent word space), and * documents (as sparse distributions over topics).

[ + integrated with the tf Embeddings Projector to interactively visualize results ]

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Check back for updated docs and a walk-through example.

Meanwhile, read the paper and see the excellent README @ the original repo.


  • Python 3
  • TensorFlow 0.12.0+
  • numpy
  • pandas

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