Sentence Transformers is a framework for turning text into numerical embeddings and for using embedding, reranking, sparse-encoding, and multi-vector models. Developers use it for applications such as semantic search, similarity measurement, and finding paraphrases, while also training or fine-tuning their own models. The catalogue contains one skill for working with Sentence Transformers.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add huggingface/sentence-transformers --skill train-sentence-transformersgit clone --depth 1 https://github.com/huggingface/sentence-transformersWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/huggingface/sentence-transformers/train-sentence-transformers)<a href="https://agentmods.dev/skills/huggingface/sentence-transformers/train-sentence-transformers"><img src="https://agentmods.dev/badge/skills/huggingface/sentence-transformers/train-sentence-transformers/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/huggingface/sentence-transformers/train-sentence-transformers"><img src="https://agentmods.dev/badge/skills/huggingface/sentence-transformers/train-sentence-transformers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00146 | $0.02541 |
| Opus 5.5 | $0.00058 | $0.01016 |
| Sonnet 5.5 | $0.00029 | $0.00508 |
| Haiku 4.5 | $0.00015 | $0.00254 |
Grade A, and why
train-sentence-transformers scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- train-sentence-transformers — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
30 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/base_model_selection.md 7.5 KB
- references/dataset_formats.md 6.3 KB
- references/evaluators_cross_encoder.md 5.5 KB
- references/evaluators_multi_vector_encoder.md 6.6 KB
- references/evaluators_sentence_transformer.md 5.8 KB
- references/evaluators_sparse_encoder.md 4.9 KB
- references/hardware_guide.md 5.9 KB
- references/hf_jobs_execution.md 7.0 KB
- references/losses_cross_encoder.md 9.5 KB
- references/losses_multi_vector_encoder.md 10 KB
- references/losses_sentence_transformer.md 11 KB
- references/losses_sparse_encoder.md 5.1 KB
- references/model_architectures.md 9.0 KB
- references/prompts_and_instructions.md 6.4 KB
- references/training_args.md 15 KB
- references/troubleshooting.md 13 KB
- scripts/mine_hard_negatives.py 8.3 KB runs code
- scripts/train_cross_encoder_distillation_example.py 9.2 KB runs code
- scripts/train_cross_encoder_example.py 9.0 KB runs code
- scripts/train_cross_encoder_listwise_example.py 10 KB runs code
- scripts/train_multi_vector_encoder_example.py 8.8 KB runs code
- scripts/train_sentence_transformer_distillation_example.py 12 KB runs code
- scripts/train_sentence_transformer_example.py 7.5 KB runs code
- scripts/train_sentence_transformer_make_multilingual_example.py 12 KB runs code
- scripts/train_sentence_transformer_matryoshka_example.py 7.1 KB runs code
- scripts/train_sentence_transformer_multi_dataset_example.py 9.9 KB runs code
- scripts/train_sentence_transformer_static_embedding_example.py 10 KB runs code
- scripts/train_sentence_transformer_with_lora_example.py 10 KB runs code
- scripts/train_sparse_encoder_distillation_example.py 9.4 KB runs code
- scripts/train_sparse_encoder_example.py 8.1 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 110 lines · 146 tokens per session scan A 9bd098d81049
train-sentence-transformers is a skill published in the GitHub repository huggingface/sentence-transformers (19,178 stars, last pushed today), licensed Apache-2.0. It adds 146 tokens to every session and 2,541 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.