setfit-few-shot
SetFit few-shot learning for efficient intent classification with minimal data
npx skills add majiayu000/claude-skill-registry --skill setfit-few-shot --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.
# SetFit Few-Shot Skill ## Capabilities - Train SetFit models with few examples per class - Configure contrastive learning settings - Implement efficient classification pipelines - Design few-shot training strategies - Set up model evaluation - Deploy lightweight classifiers ## Target Processes - intent-classification-system ## Implementation Details ### SetFit Advantages 1. **Few Examples**: 8-16 examples per class 2. **No Prompts**: No prompt engineering needed 3. **Fast Training**: Minutes vs hours 4. **Small Models**: Sentence transformer base ### Training Process - Contrastive fine-tuning of embeddings - Classification head training - Iterative sampling strategies ### Configuration Options - Base sentence transformer model - Number of training examples - Contrastive learning epochs - Classification head architecture - Evaluation metrics ### Best Practices - Diverse few-shot examples - Balance class examples - Use appropriate base model - Validate on held-out data ### Dependencies - setfit - sentence-transformers
- Capabilities
- Target Processes
- Implementation Details
- SetFit Advantages
- Training Process
- Configuration Options
- Best Practices
- Dependencies
What does the setfit-few-shot skill do?
SetFit few-shot learning for efficient intent classification with minimal data
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill setfit-few-shot --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 majiayu000/claude-skill-registry, a repository with 534 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.