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Sent2Vec

sent2vec is a Python library that encodes sentences as high-dimensional vectors using a BERT model or supplied Word2Vec weights. It exposes the vectors for downstream NLP tasks and sentence-distance comparisons.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Sent2Vec — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use sent2vec when a Python workflow needs sentence vectors and you want to choose between its default DistilBERT-based setup, another supported BERT model, or locally supplied Word2Vec weights.

Who it is for

  • Python developers building NLP prototypes that need sentence representations.
  • Practitioners comparing sentence vectors or using embeddings as inputs to downstream workflows.

Documented uses

  • Convert a list of sentences into vector representations.
  • Compare sentence vectors with cosine distance.
  • Prepare sentence embeddings for downstream tasks such as sentiment analysis or summarization.
Before you choose

Requirements & limits

The project is an open-source Python package in a public GitHub repository with an MIT license. The README gives pip installation instructions.

  • The default vectorizer uses distilbert-base-uncased; the README also names distilbert-base-multilingual-cased as an alternative.
  • The Word2Vec route requires a valid model-weight file and averages the remaining word vectors by default.
  • The README lists gensim, numpy, spacy, transformers and torch as dependencies.
  • The repository includes an example that compares sentence vectors with cosine distance using SciPy.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Encode sentences with the default BERT model

Install sent2vec and obtain vector representations for a list of sentences.

Before you start

  • Python with pip
  • The README-listed dependencies: gensim, numpy, spacy, transformers and torch

Steps

  1. Install the package with the command below.

    Documented command
    pip3 install sent2vec

  2. Import Vectorizer from sent2vec.vectorizer and create a list of sentences.

  3. Create a default Vectorizer, call run(sentences), and read the resulting vectors attribute.

Expected outcome

A vector representation for each input sentence, using the default DistilBERT-based setup.

Guide limitations

  • The README documents encoding and vector retrieval; further downstream processing is left to the user.
  • The dependency list is documented, but this guide does not claim a tested environment or compatibility guarantee.
Documentation 1 ↗
Source-based practical guide

Encode sentences with supplied Word2Vec weights

Use a local Word2Vec model file to create sentence vectors with configurable stop words.

Before you start

  • Python with sent2vec and its listed dependencies installed
  • A valid local Word2Vec model-weight file

Steps

  1. Set pretrained_weights to the path of the model-weight file.

  2. Create a Vectorizer using that path.

  3. Call run(sentences) with remove_stop_words and add_stop_words lists.

  4. Retrieve the sentence vectors from the vectors attribute.

Expected outcome

Sentence vectors formed by averaging the vectors for the remaining words, as described in the README.

Guide limitations

  • A usable model-weight file must be supplied; the README does not provide one as part of this recipe.
  • The described aggregation is an average of the remaining word vectors.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
17 October 2026
Original website title
pdrm83/sent2vec · Official homepage title · retained in its source language
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