Agent skill · practicalswan
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.
What it needs
About 6k tokens when loaded.
What this skill does
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) Tiebreakers when the request is ambiguous: "embedding model" / "vector search" / "similarity" → [SentenceTransformer]. "rerank" / "ranker" / "two-stage" → [CrossEncoder]. "SPLADE" / "sparse" / "inverted index" → [SparseEncoder]. "ColBERT" / "late interaction" / "multi-vector" / "MaxSim" / "ColPali" / "ColQwen" → [MultiVectorEncoder]. If still unclear, ask. 2. Required reading Read these in full before writing any code. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills practicalswan/train-sentence-transformers