SetFit: Efficient Few-Shot Learning Without Prompts

SetFit introduces a novel approach for few-shot learning that eliminates the need for prompts, leveraging a dual encoder architecture. The model utilizes a pre-trained SentenceTransformer as the encoder and employs a contrastive learning framework to optimize performance on downstream tasks. This method demonstrates competitive results on benchmark datasets, significantly reducing the amount of labeled data required, which is crucial for practitioners aiming to deploy efficient AI solutions in low-data scenarios.