Train Sentence Transformers
Train or fine-tune sentence-transformers models across `SentenceTransformer`...
Install
npx skills add huggingface/skills --skill train-sentence-transformers -g -a claude-code -y- Repository
- huggingface/skills11.2k
- Category
- AI and agents
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 2,446 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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_<type>_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.
1. Identify the model type
| Tag | Class | What it does | When to pick |
|---|---|---|---|
| [SentenceTransformer] | SentenceTransformer (bi-encoder) | Maps each input to a fixed-dim dense vector | Retrieval, similarity, clustering, classification, paraphrase mining, dedup |
| [CrossEncoder] | CrossEncoder (reranker) | Scores (query, passage) pairs jointly | Two-stage retrieval (rerank top-100 from bi-encoder), pair classification |
| [SparseEncoder] | SparseEncoder (SPLADE) | Sparse vectors over the vocabulary | Learned-sparse retrieval, inverted-index backends (Elasticsearch / OpenSearch / Lucene) |
| [MultiVectorEncoder] | MultiVectorEncoder (ColBERT) | One embedding per token, scored with MaxSim | Late-interaction retrieval, recall gains over bi-encoders at higher storage cost, multimodal (ColPali / ColQwen2) |
