Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Klasifikace textu s malým počtem příkladů× | Doménová adaptace× | |
|---|---|---|
| Obor | Dolování textu | Dolování textu |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku | — | — |
| Tvůrce | — | — |
| Typ≠ | NLP text-classification task (low-resource) | NLP transfer-learning / fine-tuning pipeline |
| Původní zdroj≠ | Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL. DOI ↗ | Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics. DOI ↗ |
| Další názvy≠ | few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot) | Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | 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. | Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model. |
| ScholarGateDatová sada ↗ |
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