Skip to main content

Embeds text documents using sent2vec

Project description

Sentence Embedding

A python wrapper for embedding short texts or sentences using sent2vec, which draws on FastText.

To embed a list of strings documents, use:

from nk_sent2vec import Sent2Vec 

vectorizer = Sent2Vec(path = '/root/models/torontobooks_unigrams.bin')

print(vectorizer.embed_sentences(sentences=documents))

Testing

Tests can be run using pytest -s tests

Also see makefile for default commands

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nk_sent2vec-1.4.2.tar.gz (3.3 kB view hashes)

Uploaded Source

Built Distribution

nk_sent2vec-1.4.2-py3-none-any.whl (4.1 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page