SetFit: Efficient Few-Shot Learning with Sentence Transformers

SetFit is an efficient few-shot learning method that utilizes Sentence Transformers for text semantic matching. The method aims to address the inefficiency of traditional few-shot learning methods while achieving high accuracy on small datasets. SetFit achieves this by converting each class's samples into vector representations and performing classification by minimizing the distances between samples. Experiments demonstrate that SetFit outperforms other methods across multiple datasets.