# facebookresearch/LASER

Language-Agnostic SEntence Representations

Repository: https://github.com/facebookresearch/LASER
Canonical: https://ross.abutalabs.com/products/laser
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Archived: true
Last push: 2024-05-02T20:24:47+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2996, "days_push": 853, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3660, forks 462 (observed 2026-08-28T04:08:13.278175+00:00)

## What it is
LASER is a Python library from Facebook Research for computing multilingual, language-agnostic sentence embeddings supporting 200+ languages. It includes the pip-installable laser_encoders package plus pipelines for tasks like bitext mining and multilingual similarity search.

## Use cases
- compute multilingual sentence embeddings
- find parallel sentences across languages
- do cross-lingual similarity search
- encode sentences in 200+ languages with one model
- mine bitext from monolingual corpora
- build cross-lingual retrieval or zero-shot classification

## When to choose
- you need sentence embeddings across many languages including low-resource ones
- you want to mine parallel corpora or do cross-lingual search
- you want a lightweight pip-installable encoder with minimal dependencies

## When to avoid
- you only need English sentence embeddings
- you need state-of-the-art LLM-based embeddings rather than a fixed encoder
- you need a maintained project with frequent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, search-engine
- domain: machine-learning
- platform: python, cross-platform
- tags: sentence-embeddings, multilingual, multilingual-nlp, bitext-mining, cross-lingual, pytorch, natural-language-processing, search

## Member repositories
- facebookresearch/LASER (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:13.278175+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:31:35.528408+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/LASER (fetched 2026-08-28T04:08:13.278175+00:00, sha 2d0d8bc066b2)
- Data as of 2026-08-30T08:39:29.467469+00:00.