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 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

Identify the model type (§1). Ask if ambiguous.
Load the §2 required-reading files for that type.
Open
```
scripts/train_<type>_example.py
```
and copy it as your starting point.
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}
```
).
Smoke-test (
```
max_steps=1
```
).
Run.
After the run, append to
```
logs/experiments.md
```
and propose iteration if the verdict is weak/marginal.

Prerequisites

bash

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

train-sentence-transformers

NPX Install

Tags

SKILL.md Content