Few-Shot Text Classification
Also known as: few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot)
Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels.
Key highlights
- Learns from very few labelled examples — as little as one per class — where conventional supervised classifiers fail.
- Reuses the broad language knowledge inside a large pretrained model, so little task-specific data is needed.
- Flexible across strategies: prototypical networks, MAML, or lightweight fine-tuning all fit the same pipeline.
Intuition
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How it works
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When to use it
Use few-shot text classification when you have text to classify but only a few labelled examples per class — far fewer than a conventional supervised classifier would need. It assumes at least one example exists for every class and works best when a large pretrained model is available to supply the missing knowledge. With abundant labelled data, an ordinary supervised text classifier is the simpler choice.
Strengths & limitations
- Learns from very few labelled examples — as little as one per class — where conventional supervised classifiers fail.
- Reuses the broad language knowledge inside a large pretrained model, so little task-specific data is needed.
- Flexible across strategies: prototypical networks, MAML, or lightweight fine-tuning all fit the same pipeline.
- Needs at least one labelled example for every class; entirely unseen classes cannot be predicted.
- Quality depends heavily on the pretrained backbone — a weak or mismatched model limits performance.
- With extremely few examples, predictions can be unstable and sensitive to which examples were chosen.
Common pitfalls
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Applications
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Frequently asked
How few examples per class does it really need?
At least one labelled example per class is required, and the method is designed to work with only a few per class. The fewer you provide, the more it leans on the pretrained backbone's prior knowledge.
Which few-shot strategy should I use?
Prototypical networks build a prototype per class from the few examples and classify by nearest prototype; MAML meta-learns parameters that adapt quickly to new tasks; fine-tuning lightly updates a pretrained model on the small set. All three fit the same pipeline; the prompt-free SetFit style of fine-tuning is a practical default.
Why does the pretrained model matter so much?
With only a few labelled examples, most of the knowledge has to come from somewhere else. A large pretrained model supplies broad language understanding, so a stronger backbone generally gives better few-shot results.
When should I prefer a normal supervised classifier instead?
When you have abundant labelled data. Few-shot methods exist for the low-label regime; with a large balanced labelled set, a standard supervised text classifier is simpler and usually stronger.
Sources
- 1.Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL.
- 2.Tunstall, L., Reimers, N., Jo, U.E.S., Bates, L., Korat, D., Wasserblat, M. & Pereg, O. (2022). Efficient Few-Shot Learning Without Prompts. arXiv.
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Cite this page
ScholarGate. (2026, June 1). Few-Shot Text Classification. ScholarGate. https://scholargate.app/text-mining/few-shot-text-classification