What is embedding-training?

A guide for fine-tuning SentenceTransformers bi-encoders, which turn text into vectors so similar documents can be found. It covers training, model selection, dataset formats, and retrieval testing.

Use it to train embedding models from paired or triplet examples, choose adapter modes, run local training, and measure recall, MRR, nDCG, or MAP.

It provides a repeatable command-line process for training and evaluating text-retrieval models without writing one-off Python scripts.

How ProfSynapse/Synaptic-Tuner configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to ProfSynapse/Synaptic-Tuner. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/ProfSynapse/Synaptic-Tuner/main/.agents/skills/embedding-training/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/ProfSynapse/Synaptic-Tuner

Made for: Claude Code.

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README.md

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Your own site · 80×15

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Per session 143 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,506 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00143	$0.02506
Opus 5.5	$0.00057	$0.01002
Sonnet 5.5	$0.00029	$0.00501
Haiku 4.5	$0.00014	$0.00251

Measured 2mo ago against content hash baa1104ee76d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

embedding-training 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 2mo 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.

.agents/skills/embedding-training/SKILL.md · 182 lines

How it starts

The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Embedding Training Pipeline

Fine-tune SentenceTransformer bi-encoders for retrieval with the embedding training method. This skill covers the end-to-end loop: obtain triplets → train (pick a base + adapter mode) → evaluate retrieval against a labeled corpus → read metrics → refine. Every step is a copy-pasteable CLI command or a YAML edit; there is no ad hoc Python.

The embedding method is a sibling to sft/kto/grpo/dpo — it is wired into the same method registry (shared/utilities/paths.py TRAINING_METHODS), the same recipe/local-run path (tuner.py local-run), and the same output layout (embedding_output/<timestamp>/...). What differs is the model family (encoder, not causal-LM), the loss (contrastive, not next-token), and the evaluation axis (corpus-level retrieval metrics, not per-completion correctness).

Quick Reference

Task	Command
Local Docker embedding smoke	`python tuner.py local-run --job-config Trainers/recipes/embedding_bge_base_smoke.yaml --yes`
List registered models	inspect `Trainers/embedding/configs/model_registry.yaml`
Validate a triplet dataset shape	run the smoke recipe `--dry-run` (loader validates the JSONL)
Retrieval eval (untrained base)	`python -m Evaluator.cli --scenario embedding_retrieval_smoke.yaml` (see `reference/retrieval-eval.md`)
Retrieval eval (trained adapter)	swap `model.registry_name` for `model.path` in the scenario

The exact local-run / eval flags follow the same surface as the SFT path — verify against tuner/cli/parser.py or --help before scripting; do not guess flags from memory.

Method at a Glance

Aspect	Embedding method
Model family	encoder bi-encoder (BERT / XLM-RoBERTa / decoder-as-embedder)
Loss	`multiple_negatives_ranking` (MNRL), optionally Matryoshka-wrapped
Dataset	triplets `{query, positive, negatives}` or pairs `{query, positive}`
Adapter modes	`full` · `lora` · `frozen_head` (no `qlora` in v1)
Evaluation	corpus-level retrieval: recall@k / MRR / nDCG@k / MAP against qrels
Output	`embedding_output/<timestamp>/` (adapter or merged encoder)

Read the full file on GitHub · 182 lines

Files

What ships with it

3 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.

Changes

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.

2mo ago First seen · 182 lines · 143 tokens per session scan A baa1104ee76d

Subscribe to this mod's changes

embedding-training is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (28 stars, last pushed 3d ago), licensed MIT. It adds 143 tokens to every session and 2,506 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.

Data and AI RAG and vectors

embedding-training next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
embedding-trainingProfSynapse/Synaptic-Tuner	Skill	28	3d ago	MIT	A	143	Claude Code
embeddingsruvnet/ruflo	Skill	81k	yesterday	MIT	A	62	Claude Code
agent-platform-rag-engine-managementgoogle/skills	Skill	22k	yesterday	Apache-2.0	A	85	Claude Code, Codex
llm-app-patternsdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	54	Claude Code, Codex
9router-embeddingsdecolua/9router	Skill	32k	9d ago	MIT	A	66	Claude Code, Codex
embedding-strategiesforyourhealth111-pixel/Vibe-Skills	Skill	3.8k	3d ago	Apache-2.0	A	37	Claude Code, Codex
similarity-search-patternsforyourhealth111-pixel/Vibe-Skills	Skill	3.8k	3d ago	Apache-2.0	A	30	Claude Code, Codex

Synaptic-Tuner: Skill for Claude Code