/train-sentence-transformers

name: train-sentence-transformers
description: "Train or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training tasks."
license: Apache-2.0 (modified; see UPSTREAMS.json)

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) |

Tiebreakers when the request is ambiguous: "embedding model" / "vector search" / "similarity" → **[SentenceTransformer]**. "rerank" / "ranker" / "two-stage" → **[CrossEncoder]**. "SPLADE" / "sparse" / "inverted index" → **[SparseEncoder]**. If still unclear, ask.

2. Required reading

**Read these in full before writing any code. Do not triage by perceived relevance.**

Per-type — always required

**[SentenceTransformer]**

• `references/losses_sentence_transformer.md` — loss-to-data-shape mapping; `BatchSamplers.NO_DUPLICATES` requirement for MNRL-family; `Cached*` ↔ `gradient_checkpointing` incompatibility.
• `references/evaluators_sentence_transformer.md` — evaluator-to-task mapping; `metric_for_best_model` key construction (named vs unnamed); per-evaluator `primary_metric` values.
• `references/model_architectures.md` — encoder vs decoder vs static vs Router pipelines; pooling rules (mean / cls / lasttoken); auto-mean-pooling behavior for fresh-start MLM bases.
• `scripts/train_sentence_transformer_example.py` — production template; copy this as your starting point.

**[CrossEncoder]**

• `references/losses_cross_encoder.md` — pointwise / pairwise / listwise / distillation; `pos_weight` derivation; `activation_fn=Identity()` mandatory for non-BCE losses (silent eval-rank collapse otherwise).
• `references/evaluators_cross_encoder.md` — `CrossEncoderRerankingEvaluator` recipe; named-evaluator key format `eval_{name}_{primary_metric}`.
• `scripts/train_cross_encoder_example.py` — production template; copy this as your starting point.

**[SparseEncoder]**

• `references/losses_sparse_encoder.md` — `SpladeLoss` wrapper requirement; FLOPS regularizer weights; smoke-test active-dim ramp behavior.
• `references/evaluators_sparse_encoder.md` — `SparseNanoBEIREvaluator` (English-only) and the in-domain alternative; `eval_{name}_{primary_metric}` key format.
• `scripts/train_sparse_encoder_example.py` — production template; copy this as your starting point.

Cross-cutting — always required (regardless of task)

• `references/training_args.md` — `TrainingArguments` knobs, precision rules (load fp32 + autocast bf16/fp16; never `torch_dtype=bfloat16`), `warmup_steps` (float) vs deprecated `warmup_ratio`, `save_steps` must be a multiple of `eval_steps` for `load_best_model_at_end`, schedulers, HPO, tracker, resume, hub-push variants.
• `references/dataset_formats.md` — column-matching rules (label name auto-detection; column-order-not-name); reshaping recipes; hard-negative mining options.
• `references/base_model_selection.md` — discovery commands; per-type model namespaces; ModernBERT-family `max_seq_length=8192` trap; `datasets >= 4` script-loader rejection; non-English starting-point shortcuts.
• `references/troubleshooting.md` — symptom-indexed failure recipes. Skim the section headings on every run, even a healthy one; the "Metrics don't improve" and "Hub push fails" entries cover bugs that bite frequently and are cheaper to recognize before they fire than to debug after.

Cross-cutting — load when applicable

• `references/hardware_guide.md` — VRAM sizing, multi-GPU, FSDP / DeepSpeed, HF Jobs flavors. Required for >24GB models, multi-GPU, or HF Jobs runs.
• `references/hf_jobs_execution.md` — required when running on HF Jobs.
• `references/prompts_and_instructions.md` — required when using prompt-tuned bases (E5, BGE, GTE, Qwen3-Embedding, Instructor, Nomic, etc.) or adding `query: ` / `passage: ` style prefixes.

Variant scripts (open when the task matches)

• **[SentenceTransformer]** `scripts/train_sentence_transformer_<matryoshka|multi_dataset|with_lora|distillation|make_multilingual|static_embedding>_example.py`.
• **[CrossEncoder]** `scripts/train_cross_encoder_<distillation|listwise>_example.py`.
• **[SparseEncoder]** `scripts/train_sparse_encoder_distillation_example.py`.
• Hard-negative mining CLI — `scripts/mine_hard_negatives.py`.

3. Defaults

Override only if the user specifies otherwise:

• **Local execution.** Pitch HF Jobs only if local hardware can't fit the job.
• **Single run.** After it completes, propose experimentation if the user would benefit (weak/marginal verdict, "see how high you can push it" framing, etc.). Iteration