embedding

LASER

Language-agnostic sentence embeddings from one encoder covering 93 languages, for bitext mining and cross-lingual classification.

MetaUSA

Levelmodel

Classembedding

Parametersnot in the notes

Contextnot in the notes

Released26 Dec 2018

Opennessunknown

Runs onnot in the notes

Statusactive

R&D Topics

Retrieval-augmented generation (RAG)used by Text embeddings for semantic searchproduced by

Identity

Developer: Meta
Family: not in the notes
Released: 26 Dec 2018 sourcesannouncement page
Weights published: –
Record checked: 12 Oct 2026

Where this comes from

Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture: other BiLSTM encoder shared across languages
Total parameters: – sources
Active parameters: –
Checkpoint parameters: –
Context length: not in the notes
Input: text
Output: text

Where this comes from

Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness: unknown
Licence: not in the notes sources
Access routes: not in the notes

Where this comes from

The licence file governs over registry tags and aggregator labels.

Deployment

Runs on: not in the notes
Footprint: not in the notes
Runtimes: not in the notes

Where this comes from

Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support: not stated
Regional languages: not in the notes
Jakarta hosting: unknown
Languages: not in the notes

Where this comes from

Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Links

Developer

Metabig tech, USA

Model

LASERMeta, embedding

R&D TopicsRetrieval-Augmented Generation (RAG)Text Embeddings for Semantic Search

Use CasesEmbeddings and Retrieval for SEA LanguagesPublic SectorUse cases of models in the same R&D topic; none is recorded for this model itself.

Who Runs Itnone recordedDeployments of these use cases; none names this model.

Graph

Organisations 1

Metabig tech, USADeveloper

R&D topics 2

Retrieval-Augmented Generation (RAG)Grounding generation in retrieved documents.used by Text Embeddings for Semantic SearchTraining encoders that turn text in many languages into vectors so that search, clusterin…produced by

Sources

arxiv.org/abs/1812.10464 https://arxiv.org/abs/1812.10464
github.com/facebookresearch/LASER https://github.com/facebookresearch/LASER
modelzoo.co/model/laser https://modelzoo.co/model/laser

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "LASER", https://aurion.semai.id/models/meta-laser/, records of October 2026.