setfit-few-shot

name	setfit-few-shot
description	SetFit few-shot learning for efficient intent classification with minimal data
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep"]
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