/setfit-few-shot

name: setfit-few-shot
description: SetFit few-shot learning for efficient intent classification with minimal data
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graph:
  domains: [domain:software-engineering]
  specializations: [specialization:ai-agents-conversational]
  skillAreas: [skill-area:ml-fine-tuning, skill-area:natural-language-processing]
  roles: [role:ml-engineer, role:backend-engineer]
  workflows: [workflow:ml-model-lifecycle, workflow:feature-development]

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