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), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
Install
Install this Claude and Codex skill with the command below. The command stays visible even when copy support is unavailable.
git clone https://github.com/huggingface/sentence-transformers /tmp/sentence-transformers && ln -s /tmp/sentence-transformers/skills/train-sentence-transformers ~/.claude/skills/train-sentence-transformersFrom README
Train a sentence-transformers Model This SKILL.md is a router, not a manual. It tells you which references and example scripts to load for your task. The actual content — recommended losses, evaluators, training-script structure, model selection, training-arg knobs, troubleshooting — lives in references/ and scripts/. Do not synthesize a training script from this file alone. Open the per-type production template (scripts/train example.py) and copy it as your starting point. The templates contain load-bearing scaffolding (autocast helper, model-card class, logger silencing list, force=True, seed, TF32, version-compatible imports, named-evaluator metric handling) that prior agent runs have repeatedly missed when rolling their own from a synthesized snippet.
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