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train-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` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

When to use

• Training SentenceTransformer models
• Fine-tuning CrossEncoder rerankers
• Training SparseEncoder models
• Training MultiVectorEncoder models
• Loss selection
• Hard-negative mining
• Evaluators
• Distillation

Skill page

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