huggingface/sentence-transformers

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

About the project

Sentence Transformers is a framework for turning text into numerical embeddings and for using embedding, reranking, sparse-encoding, and multi-vector models. Developers use it for applications such as semantic search, similarity measurement, and finding paraphrases, while also training or fine-tuning their own models. The catalogue contains one skill for working with Sentence Transformers.

Open on GitHub sbert.net

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train-sentence-transformers

huggingface/sentence-transformers

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Train or fine-tune sentence-transformers models across SentenceTransformer (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), CrossEncoder (reranker, pair scoring for two-stage retrieval / pair classification), SparseEncoder…

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