ScholarGate
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Process / pipeline

Kratkoklasna klasifikaciјa teksta

Kratkoklasna klasifikaciјa teksta dodeљuјe dokumente klasama koristeći samo šačicu označenih primera po klasi. Nadovezuјući se na napretke Gao et al. (2021) i pristup SetFit bez uputstava Tunstall et al. (2022), oslaњa se na prototipske mreže, MAML ili fino podešavaњe velikog prethodno obučenog modela da bi učio iz oskudnih oznaka.

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Method map

The neighbourhood of related methods — select a node to explore.

Izvori

  1. Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL. DOI: 10.18653/v1/2021.acl-long.295
  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. DOI: 10.48550/arXiv.2209.11055

Kako citirati ovu stranicu

ScholarGate. (2026, June 1). Few-Shot Text Classification. ScholarGate. https://scholargate.app/sr/text-mining/few-shot-text-classification

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Citirana u

ScholarGateFew-Shot Text Classification (Few-Shot Text Classification). Preuzeto 2026-06-15 sa https://scholargate.app/sr/text-mining/few-shot-text-classification · Skup podataka: https://doi.org/10.5281/zenodo.20539026