# huggingface/sentence-transformers

State-of-the-Art Embeddings, Retrieval, and Reranking

Repository: https://github.com/huggingface/sentence-transformers
Canonical: https://ross.abutalabs.com/products/sentence-transformers
Homepage: https://www.sbert.net
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-08-26T12:16:49+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 2597, "days_push": 7, "days_rel": 15, "gap_med": 21, "n_releases_24m": 28}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 19036, forks 2867 (observed 2026-08-28T04:11:27.375317+00:00)

## What it is
Sentence Transformers (SBERT) is a Python library for computing, using, and training state-of-the-art embedding, reranker, sparse encoder, and multi-vector (ColBERT-style) models built on PyTorch and Hugging Face Transformers. It provides access to thousands of pre-trained models on the Hugging Face Hub and supports semantic search, similarity, retrieval, and model finetuning workflows.

## Use cases
- compute sentence embeddings for semantic search
- rerank search results with a cross-encoder
- find semantically similar sentences or paraphrases
- build a RAG retrieval pipeline with dense embeddings
- finetune a custom embedding model on my own data
- do ColBERT-style late-interaction retrieval
- cluster or deduplicate documents by meaning
- search images with text using CLIP models

## When to choose
- you need text/image embeddings or reranking in Python with minimal code
- you want to train or finetune embedding or reranker models
- you want access to thousands of pre-trained models from the Hugging Face Hub
- you need dense, sparse, or multi-vector retrieval in one library

## When to avoid
- you need a production vector database or full search engine rather than an embedding library
- you work outside Python or cannot use PyTorch
- you need lightweight non-neural similarity like TF-IDF or BM25 only

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, nlp, rag, search-engine, image-processing, audio-processing, video-processing
- domain: machine-learning, deep-learning
- platform: python, cross-platform
- tags: embeddings, sentence-embeddings, reranking, cross-encoder, colbert, semantic-search, sparse-embeddings, transformers, pytorch, natural-language-processing, retrieval-augmented-generation, search, gpu

## Member repositories
- huggingface/sentence-transformers (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:27.375317+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 2026-08-29T17:00:38.876121+00:00, confidence not recorded.
  - readme: https://github.com/huggingface/sentence-transformers (fetched 2026-08-28T04:11:27.375317+00:00, sha 89515b75b4d3)
  - homepage: https://www.sbert.net (fetched 2026-08-29T07:59:00.303736+00:00, sha 28483f2b9a0d)
  - site_page: https://www.sbert.net/docs/sentence_transformer/usage/usage.html (fetched 2026-08-29T07:59:00.321696+00:00, sha 2d5f57bc8de2)
  - site_page: https://www.sbert.net/docs/sentence_transformer/usage/semantic_textual_similarity.html (fetched 2026-08-29T07:59:00.323470+00:00, sha 2e3728284ad3)
  - site_page: https://www.sbert.net/docs/installation.html (fetched 2026-08-29T07:59:00.313827+00:00, sha 25a9f3bee389)
  - site_page: https://www.sbert.net/docs/quickstart.html (fetched 2026-08-29T07:59:00.315981+00:00, sha 5b6b904b500d)
  - site_page: https://www.sbert.net/docs/migration_guide.html (fetched 2026-08-29T07:59:00.318422+00:00, sha c2218a021e90)
  - site_page: https://www.sbert.net/docs/sentence_transformer/usage/custom_models.html (fetched 2026-08-29T07:59:00.327674+00:00, sha 3d4a7ba15e97)
  - site_page: https://www.sbert.net/docs/sentence_transformer/usage/mteb_evaluation.html (fetched 2026-08-29T07:59:00.330272+00:00, sha a5fc43b5ac24)
  - registry_pypi: https://pypi.org/pypi/sentence-transformers/json (fetched 2026-08-29T07:59:00.332103+00:00, sha 497b26e96537)
- Data as of 2026-08-30T08:39:29.467469+00:00.