Train Sentence Transformers

Hugging FaceOfficialPublished by Hugging Face

Skill
Workflow
Data and AI
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From the author

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.

— the description by Hugging Face

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a standardized approach to training various embedding and ranking models using the sentence-transformers framework. It acts as a routing system that directs users to specific production templates and technical references based on the chosen model architecture. The workflow covers the entire lifecycle from model selection and data preparation to loss function configuration, evaluation, and publishing to the Hugging Face Hub.

What it does

Routes users to appropriate training templates for bi-encoders, cross-encoders, sparse encoders, and multi-vector encoders.
Provides technical references for loss functions, evaluators, and model architectures.
Offers guidance on hard-negative mining and dataset formatting for training.
Includes instructions for hardware-specific configurations and Hugging Face Hub integration.
Defines mandatory script requirements such as smoke testing, logging, and verdict reporting.

When to use it

When you need to train or fine-tune a sentence-transformers model for retrieval, similarity, or ranking tasks.
When you are unsure which model architecture (e.g., bi-encoder vs. cross-encoder) is best suited for your specific use case.
When you require a structured, repeatable process for training and evaluating embedding models.
When you need to integrate advanced training techniques like distillation, LoRA, or Matryoshka embeddings.

Advantages

Enforces consistent training practices through standardized production templates.
Reduces common errors by providing specific references for loss-to-data-shape mapping.
Facilitates systematic experimentation with built-in logging and verdict reporting.
Supports a wide range of model types including modern late-interaction and sparse architectures.

Usage examples

“I need to train a bi-encoder for semantic search on my internal documentation.”: The agent will identify the [SentenceTransformer] type, provide the relevant loss and evaluator references, and guide you to the bi-encoder production template.
“How do I implement a two-stage retrieval system using a reranker?”: The agent will direct you to the [CrossEncoder] documentation and template, explaining how to use it to score pairs for two-stage retrieval.
“I want to train a SPLADE model for sparse retrieval.”: The agent will provide the [SparseEncoder] references, including the required SpladeLoss wrapper and specific evaluators for sparse retrieval.
“How can I improve my retrieval model using hard negatives?”: The agent will guide you to the hard-negative mining script and explain how to generate n-tuple datasets for training.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/huggingface/train-sentence-transformers/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip train-sentence-transformers.zip -d .agents/skills

Documentation by Hugging Face

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. 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.

[MultiVectorEncoder]

references/losses_multi_vector_encoder.md: MaxSim scoring, scale choice per scoring mode (scale=1.0 for MaxSim, roughly the average query length for MeanMaxSim), MNRL / CachedMNRL / MarginMSE / DistillKLDiv, XTR-vs-ColBERT scoring, CachedMNRL ↔ gradient_checkpointing incompatibility.
references/evaluators_multi_vector_encoder.md: MultiVectorNanoBEIREvaluator (English-only) and the in-domain alternative, eval_NanoBEIR_mean_maxsim_ndcg@10 key format, distillation-eval spearman variant.
scripts/train_multi_vector_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.
[MultiVectorEncoder] Match scale to the scoring mode on any MNRL-family loss: near 1.0 for unnormalized MaxSim (do not copy scale=20.0 from bi-encoder MNRL), roughly the average query length with length-normalized MeanMaxSim, since each score is divided by its query's token count. XTRScores is a train-only similarity_fct: the evaluators reject it, so evaluation always scores with MaxSim, including for XTR-trained models.

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

pip install "sentence-transformers[train]>=5.0"        # add [train,image] / [audio] / [video] for [SentenceTransformer] multimodal
                                                       # [MultiVectorEncoder] requires >=6.0
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.

Files

ATTRIBUTION.mdAdded by AutoGineer898 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md10.3 KiB
references/base_model_selection.md7.5 KiB
references/dataset_formats.md6.3 KiB
references/evaluators_cross_encoder.md5.5 KiB
references/evaluators_multi_vector_encoder.md6.6 KiB
references/evaluators_sentence_transformer.md5.8 KiB
references/evaluators_sparse_encoder.md4.9 KiB
references/hardware_guide.md5.9 KiB
references/hf_jobs_execution.md7.1 KiB
references/losses_cross_encoder.md9.5 KiB
references/losses_multi_vector_encoder.md10.5 KiB
references/losses_sentence_transformer.md10.7 KiB
references/losses_sparse_encoder.md5.1 KiB
references/model_architectures.md9.0 KiB
references/prompts_and_instructions.md6.4 KiB
references/training_args.md15.0 KiB
references/troubleshooting.md12.9 KiB
scripts/mine_hard_negatives.pyScript8.3 KiB
scripts/train_cross_encoder_distillation_example.pyScript9.2 KiB
scripts/train_cross_encoder_example.pyScript9.0 KiB
scripts/train_cross_encoder_listwise_example.pyScript10.2 KiB
scripts/train_multi_vector_encoder_example.pyScript8.8 KiB
scripts/train_sentence_transformer_distillation_example.pyScript11.9 KiB
scripts/train_sentence_transformer_example.pyScript7.5 KiB
scripts/train_sentence_transformer_make_multilingual_example.pyScript11.5 KiB
scripts/train_sentence_transformer_matryoshka_example.pyScript7.1 KiB
scripts/train_sentence_transformer_multi_dataset_example.pyScript9.9 KiB
scripts/train_sentence_transformer_static_embedding_example.pyScript10.3 KiB
scripts/train_sentence_transformer_with_lora_example.pyScript10.3 KiB
scripts/train_sparse_encoder_distillation_example.pyScript9.4 KiB
scripts/train_sparse_encoder_example.pyScript8.1 KiB

references/evaluators_sentence_transformer.md

Evaluators (Bi-Encoder)

All bi-encoder evaluators live in sentence_transformers.sentence_transformer.evaluation.

Choosing the right evaluator

Task	Evaluator
Retrieval (nDCG, MRR, Recall), fast default	`NanoBEIREvaluator`
Retrieval on your own corpus / qrels	`InformationRetrievalEvaluator`
STS / continuous similarity	`EmbeddingSimilarityEvaluator`
Binary classification	`BinaryClassificationEvaluator`
Triplet accuracy	`TripletEvaluator`
Reranking (from retrieval candidates)	`RerankingEvaluator`
MSE vs. teacher (distillation)	`MSEEvaluator`, `MSEEvaluatorFromDataFrame`
Paraphrase mining	`ParaphraseMiningEvaluator`
Translation (cross-lingual alignment)	`TranslationEvaluator`
Label accuracy (classification during training)	`LabelAccuracyEvaluator`

Wrap multiple evaluators in SequentialEvaluator to track all of them together:

from sentence_transformers.sentence_transformer.evaluation import SequentialEvaluator
evaluator = SequentialEvaluator([evaluator1, evaluator2, evaluator3])

The big three

`NanoBEIREvaluator` (retrieval)

Small, fast subset of BEIR. Typically runs in <1 minute on a mid-range GPU. Default choice for retrieval training.

from sentence_transformers.sentence_transformer.evaluation import NanoBEIREvaluator

evaluator = NanoBEIREvaluator(
    dataset_names=["msmarco", "nfcorpus", "nq"],   # default: all 13 NanoBEIR datasets
    batch_size=128,
    show_progress_bar=False,
)

Default dataset list covers 13 tasks. Pick a subset for speed during training.
Output key for metric_for_best_model: eval_NanoBEIR_mean_cosine_ndcg@10 (bi-encoder default = cosine similarity).

`EmbeddingSimilarityEvaluator` (STS-style)

Computes Pearson/Spearman correlation between model cosine similarities and gold labels.

from sentence_transformers.sentence_transformer.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers.util.similarity import SimilarityFunction

evaluator = EmbeddingSimilarityEvaluator(
    sentences1=stsb["sentence1"],
    sentences2=stsb["sentence2"],
    scores=stsb["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-dev",
)

main_similarity can be COSINE, DOT_PRODUCT, EUCLIDEAN, MANHATTAN.
name is used in the output key: eval_sts-dev_spearman_cosine, eval_sts-dev_pearson_cosine, etc.

`InformationRetrievalEvaluator` (full retrieval)

Use when you have your own corpus + queries + qrels (not one of the NanoBEIR tasks).

from sentence_transformers.sentence_transformer.evaluation import InformationRetrievalEvaluator

evaluator = InformationRetrievalEvaluator(
    queries={qid: query_text for qid, query_text in ...},
    corpus={doc_id: doc_text for doc_id, doc_text in ...},
    relevant_docs={qid: {doc_id, ...} for qid in ...},   # qid -> set of relevant doc_ids
    name="my-retrieval",
    mrr_at_k=[10],
    ndcg_at_k=[10],
    accuracy_at_k=[1, 5, 10],
    precision_recall_at_k=[1, 5, 10],
    map_at_k=[100],
    show_progress_bar=False,
    batch_size=64,
)

Output keys: eval_{name}_cosine_ndcg@10, eval_{name}_cosine_mrr@10, etc.

Heavy for large corpora: each eval encodes the full corpus. Don't run it every 100 steps. Use NanoBEIREvaluator for frequent evaluation during training and reserve full IR for milestones / post-training.

Other bi-encoder evaluators

`BinaryClassificationEvaluator`

For labeled pair classification (e.g. duplicate detection, entailment as binary). Reports accuracy, F1, precision/recall, AP. Supports all distance metrics. Finds the best threshold per metric.

`TripletEvaluator`

For (anchor, positive, negative) triplets. Reports the fraction of triplets where the positive is closer to the anchor than the negative.

`RerankingEvaluator`

For custom re-ranking datasets: you provide candidates per query, the evaluator computes MAP and MRR. Good for measuring retrieval-quality on a held-out set.

`MSEEvaluator` / `MSEEvaluatorFromDataFrame`

For distillation setups. Compares student embeddings against teacher embeddings (or teacher scores), reports MSE.

`ParaphraseMiningEvaluator`

For paraphrase-mining tasks. Given a corpus of labeled paraphrase pairs, computes mining quality (F1 at various thresholds).

`TranslationEvaluator`

For cross-lingual / make_multilingual-style alignment checking. Measures whether the student aligns sentences across languages.

`LabelAccuracyEvaluator`

For a SoftmaxLoss-trained classifier head. Reports accuracy on held-out data.

Writing `metric_for_best_model`

Pattern: f"eval_{evaluator.primary_metric}". Inspect after construction: print(evaluator.primary_metric). Common values:

eval_NanoBEIR_mean_cosine_ndcg@10: NanoBEIREvaluator
eval_sts-dev_spearman_cosine: EmbeddingSimilarityEvaluator(name="sts-dev")
eval_{name}_cosine_ndcg@10: InformationRetrievalEvaluator(name=...)

Multi-dimensional evaluation (Matryoshka)

For Matryoshka-trained models, evaluate at each target dimension:

per_dim_evaluators = [
    EmbeddingSimilarityEvaluator(
        sentences1=..., sentences2=..., scores=...,
        main_similarity=SimilarityFunction.COSINE,
        name=f"sts-dev-{dim}",
        truncate_dim=dim,
    ) for dim in [768, 512, 256, 128, 64]
]
evaluator = SequentialEvaluator(per_dim_evaluators, main_score_function=lambda scores: scores[0])

The first evaluator's score drives load_best_model_at_end.

Gotchas

Always run evaluator(model) once before training: pre-training baseline. If the post-training delta is tiny, the loss/data/base is wrong.
Don't run InformationRetrievalEvaluator with a large corpus (>100k docs) at frequent eval_steps. Use NanoBEIREvaluator during training, reserve full IR for end-of-training.
greater_is_better=True is the default, right for nDCG / MRR / accuracy.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by

Hugging Face

Publisher

Hugging Face (Official)

Source

huggingface/skills · skills/train-sentence-transformers · commit 25910b3

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

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