embedding

SapBERT

PubMedBERT trained with self-alignment on UMLS synonyms so that names of the same medical concept get close embeddings, for medical entity linking.

University of Cambridge

Levelmodel

Classembedding

Parameters109.48M

Contextnot in the notes

Released22 Oct 2020

Opennessopen weights permissive

Runs onnot in the notes

Statusactive

R&D Topics

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

Identity

Developer: University of Cambridge
Family: not in the notes
Released: 22 Oct 2020 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: dense transformer
Total parameters: 109.48M sources
Active parameters: –
Checkpoint parameters: –
Note: Parameter count from the safetensors index on Hugging Face.
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: open weights permissive
Licence: Apache License 2.0 sources
Access routes: open weights download

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: Model card languages: en

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

University of Cambridgeuniversity

Model

SapBERTUniversity of Cambridge, 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

University of CambridgeuniversityDeveloper

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/2010.11784 https://arxiv.org/abs/2010.11784
huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext
huggingface.co/api/models?pipeline_tag=feature-extraction&so... https://huggingface.co/api/models?pipeline_tag=feature-extraction&sort=downloads&limit=50

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "SapBERT", https://aurion.semai.id/models/cambridge-ltl-sapbert/, records of October 2026.