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setfit-few-shot

// SetFit few-shot learning for efficient intent classification with minimal data

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updated:March 4, 2026
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SKILL.md Frontmatter
namesetfit-few-shot
descriptionSetFit few-shot learning for efficient intent classification with minimal data
allowed-toolsRead,Write,Edit,Bash,Glob,Grep

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