lda2vec – flexible & interpretable NLP models¶
This is the documentation for lda2vec, a framework for useful flexible and interpretable NLP models.
Defining the model is simple and quick:
model = LDA2Vec(n_words, max_length, n_hidden, counts)
model.add_component(n_docs, n_topics, name='document id')
model.fit(clean, components=[doc_ids])
While visualizing the feature is similarly straightforward:
topics = model.prepare_topics('document_id', vocab)
prepared = pyLDAvis.prepare(topics)
pyLDAvis.display(prepared)
Resources¶
See this Jupyter Notebook for an example of an end-to-end demonstration.
See this presentation for a presentation focused on the benefits of word2vec, LDA, and lda2vec.
See the API reference docs
See the GitHub repo