train-sentence-transformers
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...
Profile →npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Train a sentence-transformers Model ## When to Use Use this skill when you need 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... **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
- When to Use
- 1. Identify the model type
- 2. Required reading
- Per-type — always required
- Cross-cutting — always required (regardless of task)
- Cross-cutting — load when applicable
- Variant scripts (open when the task matches)
- 3. Defaults
- 4. Constraints the produced script must satisfy
- 5. Workflow
- Prerequisites
- Limitations
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)
What does the train-sentence-transformers skill do?
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...
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill train-sentence-transformers --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.