setfit-few-shot

采用 SetFit 少样本学习方法，以极少量数据实现高效的意图分类

SKILL.md

--- frontmatter

name: setfit-few-shot
description: SetFit few-shot learning for efficient intent classification with minimal data
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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

•Few Examples: 8-16 examples per class
•No Prompts: No prompt engineering needed
•Fast Training: Minutes vs hours
•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