SetFit v1.2 released: 8 examples per class rivals RoBERTa-Large fine-tuned on 3k
tomaarsen · x · 2026-09-04
Hugging Face released SetFit v1.2.0, a prompt-free few-shot text classification framework: it builds positive/negative pairs from a handful of labeled sentences, contrastively fine-tunes a Sentence Transformer, and fits a classifier on the embeddings — no prompts or LLMs needed. The original paper shows 8 examples per class rivals RoBERTa-Large fine-tuned on 3k examples, training an order of magnitude faster than PEFT/PET. v1.2 adds support for transformers v5, Sentence Transformers v6, and huggingfacehub v1.
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