Overview

SetFit few-shot learning enables efficient intent classification with minimal labeled data. It uses contrastive embedding fine-tuning and a lightweight classification head to deliver fast, deployable models without prompts.

How This Skill Works

The approach performs contrastive fine-tuning of sentence embeddings on a small labeled set, then trains a simple classification head on top. It supports configurable base models and training parameters, enabling iterative sampling and evaluation for robust intent detection.

When to Use It

• Bootstrapping a new product's user intents with 8-16 examples per class.
• Rapidly iterating on new or changing intents with limited labeled data.
• Deploying lightweight classifiers on resource-constrained devices or apps.
• Avoiding prompt engineering by using a pure few-shot learning pipeline.
• Evaluating performance on held-out data to validate classification quality.

Quick Start

1. Step 1: Select a base sentence transformer model and collect 8-16 diverse examples per class.
2. Step 2: Run SetFit fine-tuning with contrastive learning and train the classification head on your labeled data.
3. Step 3: Evaluate on held-out data, iterate with sampling adjustments, and deploy the lightweight classifier.

Best Practices

• Provide diverse few-shot examples for each class to cover variations in user intent.
• Balance the number of examples across all classes to prevent bias.
• Choose an appropriate base sentence transformer model for your domain.
• Validate performance on held-out data and adjust sampling strategies as needed.
• Experiment with contrastive epochs and training data size to fit your task.

Example Use Cases

• Routing customer chat queries to the correct support team with minimal labeled data.
• Classifying user intents in a chatbot to trigger appropriate responses.
• Lightweight on-device intent detection for offline mobile apps.
• Rapidly adding new intents after product updates without rewriting prompts.
• Evaluating intent coverage on held-out user interactions to ensure reliability.

Frequently Asked Questions