SetFit: Efficient Few-Shot Learning Without Prompts
SetFit is significantly more sample efficient and robust to noise than standard fine-tuning.
Few-shot learning with pretrained language models has emerged as a promising solution to every data scientist's nightmare: dealing with data that has few to no labels 😱.
Together with our research partners at Intel Labs and the UKP Lab, Hugging Face is excited to introduce SetFit: an efficient framework for few-shot fine-tuning of Sentence Transformers. SetFit achieves high accuracy with little labeled data - for example, with only 8 labeled examples per class on the Customer Reviews (CR) sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples 🤯!
Compared to other few-shot learning methods, SetFit has several unique features:
SetFit is significantly more sample efficient and robust to noise than standard fine-tuning.
Few-shot learning with pretrained language models has emerged as a promising solution to every data scientist's nightmare: dealing with data that has few to no labels 😱.
Together with our research partners at Intel Labs and the UKP Lab, Hugging Face is excited to introduce SetFit: an efficient framework for few-shot fine-tuning of Sentence Transformers. SetFit achieves high accuracy with little labeled data - for example, with only 8 labeled examples per class on the Customer Reviews (CR) sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples 🤯!
Compared to other few-shot learning methods, SetFit has several unique features: