epfml/sent2vec

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1201 stars · 257 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Sent2vec is a C++ library with a Cython/Python interface that trains unsupervised distributed representations of sentences and short texts, built on Facebook's FastText. It includes pre-trained models for generating sentence and word embeddings usable as features in downstream machine learning tasks.

Use cases

• generate sentence embeddings for text classification
• get numerical features for short texts and sentences
• train an unsupervised sentence embedding model
• find nearest neighbour sentences and word analogies
• extract word embeddings from pre-trained models
• embed sentences in python while keeping the model in memory

When to choose

• you need fast, lightweight unsupervised sentence embeddings without a GPU
• you want to train custom embeddings on your own corpus
• you need embeddings as input features for classical ML models

When to avoid

• you need state-of-the-art contextual embeddings from transformer models
• you need cross-lingual sentence embeddings (use Bi-sent2vec instead)
• you need an actively developed project with recent updates

Facets

library · maturity maintenance

machine-learning nlp machine-learning python cpp cross-platform sentence-embeddings word-embeddings unsupervised-learning fasttext cython natural-language-processing

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markdown · JSON · MCP: product_card(name="epfml/sent2vec")