train-sentence-transformers

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

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 rules in references/training_args.md (Experimentation section).
• Public Hub push at end-of-run, wrapped in try-except. On HF Jobs (ephemeral env) ALSO enable in-trainer push (push_to_hub=True + hub_strategy="every_save"); details in references/hf_jobs_execution.md.

4. Constraints the produced script must satisfy

These are non-negotiable contracts. Implementation lives in the production templates and references — do not reinvent.

• Capture the pre-training evaluator score as baseline_eval before trainer.train().
• Emit a single end-of-run line: VERDICT: WIN|MARGINAL|REGRESSION | score=... | baseline=... | delta=.... A monitor scrapes for this.
• Silence httpx, httpcore, huggingface_hub, urllib3, filelock, fsspec to WARNING (otherwise HF download URLs flood the agent's context).
• Tee logs to logs/{RUN_NAME}.log.
• End with model.push_to_hub(...) wrapped in try/except.
• Smoke-test before any long run (max_steps=1 + tiny dataset slice). The production templates show one common pattern (SMOKE_TEST env var).
• [CrossEncoder] Include EarlyStoppingCallback(patience>=3) — CE rerankers often peak mid-training and regress.
• [SparseEncoder] Log query_active_dims / corpus_active_dims on the verdict line; high nDCG with collapsed sparsity is not a win. The keys come back name-prefixed (e.g. ..._query_active_dims); use suffix matching to pluck them — see the SPARSE production template for the exact pattern.

5. Workflow

1. Identify the model type (§1). Ask if ambiguous.
2. Load the §2 required-reading files for that type.
3. Open scripts/train_<type>_example.py and copy it as your starting point.
4. Replace MODEL_NAME, DATASET_NAME, RUN_NAME, the loss, and the evaluator with the user's task. Cross-check loss/data-shape match against references/losses_<type>.md; cross-check the metric_for_best_model key against references/evaluators_<type>.md (named evaluators format the key as eval_{name}_{primary_metric}).
5. Smoke-test (max_steps=1).
6. Run.
7. After the run, append to logs/experiments.md and propose iteration if the verdict is weak/marginal.

Prerequisites

pip install "sentence-transformers[train]>=5.0"        # add [train,image] / [audio] / [video] for [SentenceTransformer] multimodal
pip install trackio                                    # optional tracker; or wandb / tensorboard / mlflow
hf auth login                                          # or set HF_TOKEN with write scope (for Hub push)

GPU strongly recommended. CPU works only for demos and [SentenceTransformer] StaticEmbedding.