# SetFit

🤗 SetFit is an efficient and prompt-free framework for few-shot fine-tuning of [Sentence Transformers](https://sbert.net/). It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews 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:

* 🗣 **No prompts or verbalizers:** Current techniques for few-shot fine-tuning require handcrafted prompts or verbalizers to convert examples into a format suitable for the underlying language model. SetFit dispenses with prompts altogether by generating rich embeddings directly from text examples.
* 🏎 **Fast to train:** SetFit doesn't require large-scale models like T0, Llama or GPT-4 to achieve high accuracy. As a result, it is typically an order of magnitude (or more) faster to train and run inference with.
* 🌎 **Multilingual support**: SetFit can be used with any [Sentence Transformer](https://huggingface.co/models?library=sentence-transformers&sort=downloads) on the Hub, which means you can classify text in multiple languages by simply fine-tuning a multilingual checkpoint.

  
    Tutorials
      Learn the basics and become familiar with loading pretrained Sentence Transformers and fine-tuning them on data. Start here if you are using 🤗 SetFit for the first time!
    
    How-to guides
      Practical guides to help you achieve a specific goal. Take a look at these guides to learn how to use 🤗 SetFit to solve real-world problems.
    
    Conceptual guides
      High-level explanations for building a better understanding about important topics such as few-shot and contrastive learning.
   
    Reference
      Technical descriptions of how 🤗 SetFit classes and methods work.
    
  

