SetFit
A few-shot text classification method based on contrastive learning over sentence embeddings. It first constructs positive and negative pairs from a small number of labeled samples for contrastive learning, then trains a lightweight classification head, featuring high sample efficiency and low training cost.
Timeline (last 90 days)
In the course, the routing layer does not use LLM calls but is built on small encoders (ModernBERT, SetFit), runs on CPU, and keeps latency under 15 milliseconds.
SetFit is an efficient method for few-shot text classification, proposed by the Hugging Face team, requiring only 8 to 16 labeled samples per class to achieve results close to full fine-tuning.
All Facts (2)
In the course, the routing layer does not use LLM calls but is built on small encoders (ModernBERT, SetFit), runs on CPU, and keeps latency under 15 milliseconds.
50%UnverifiedSetFit is an efficient method for few-shot text classification, proposed by the Hugging Face team, requiring only 8 to 16 labeled samples per class to achieve results close to full fine-tuning.
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