train-sentence-transformers

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.