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SetFit

Free

Few-shot contrastive fine-tuning

FreeFree tier
Inputs: textOutputs: text
Type
Open Source
Company
Hugging Face
Visit Website GitHub (38 stars)

About SetFit

SetFit is an open-source framework for efficient few-shot fine-tuning of Sentence Transformers, developed by Hugging Face. It is designed to achieve high accuracy in text classification tasks with very little labeled data, such as only 8 examples per class, and can be competitive with models fine-tuned on thousands of examples. SetFit operates without the need for handcrafted prompts or verbalizers, instead generating rich embeddings directly from text examples. It is fast to train and supports multilingual classification by leveraging any Sentence Transformer model available on the Hugging Face Hub. The framework is available as a Python library on GitHub and is intended for developers and researchers working on text classification with limited labeled data.

Key Features

Efficient few-shot learning with Sentence Transformers
No prompts or verbalizers required
Fast training and inference compared to large-scale models
Multilingual support via any Sentence Transformer on the Hub
Open-source and free to use
Achieves high accuracy with as few as 8 labeled examples per class

Pros & Cons

Pros
  • Open-source and free to use
  • No need for handcrafted prompts or verbalizers
  • Fast training and inference relative to large models
  • Achieves competitive accuracy with very few labeled examples
  • Supports multilingual text classification
Cons
  • Primarily designed for text classification; other tasks may require adaptation
  • Performance depends on the quality of the chosen Sentence Transformer model
  • Limited to few-shot scenarios; may not be optimal for large datasets
  • Requires Python and familiarity with Hugging Face ecosystem

Best For

Text classification with limited labeled dataSentiment analysis on small datasetsTopic categorization in low-resource languagesRapid prototyping of classification modelsResearch on few-shot learning techniques

FAQ

What is SetFit?
SetFit is an open-source framework for few-shot fine-tuning of Sentence Transformers, developed by Hugging Face. It enables efficient text classification with very little labeled data.
Does SetFit require prompts or verbalizers?
No, SetFit dispenses with prompts and verbalizers by generating embeddings directly from text examples, based on available documentation.
How much labeled data does SetFit need?
SetFit can achieve high accuracy with as few as 8 labeled examples per class, according to the project's README. Exact requirements may vary by task.
Is SetFit free to use?
Yes, SetFit is open-source and free to use under the Apache 2.0 license, as indicated by its GitHub repository.
Can SetFit be used for languages other than English?
Yes, SetFit supports multilingual classification by using any Sentence Transformer model available on the Hugging Face Hub, as stated in the documentation.
How does SetFit compare to other few-shot methods?
SetFit is designed to be faster and simpler than methods that require large models or handcrafted prompts, achieving competitive accuracy with less data and compute, based on the project's claims.