README.md

Document Embedding

A python wrapper for embedding text documents using sent2vec, which draws on FastText.

To embed a list of strings documents, use:

from nk_sent2vec import Sent2Vec 

vectorizer = Sent2Vec(path = '/home/nk-sent2vec/models/torontobooks_unigrams.bin')

print(vectorizer.embed_sentences(sentences=[documents]))

Testing

Tests can be run using nosetests -s