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Classificació basada en BERT adaptada al domini×Aprenentatge per transferència amb classificació basada en BERT×
CampAprenentatge profundAprenentatge profund
FamíliaMachine learningMachine learning
Any d'origen2019–20202019 (BERT); transfer learning paradigm established circa 2010
Autor originalGururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERTDevlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (BERT); Pan, S. J. & Yang, Q. (transfer learning survey)
TipusDomain-adaptive pre-training followed by supervised fine-tuningPre-trained transformer fine-tuned for classification
Font seminalGururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360. DOI ↗Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics. DOI ↗
ÀliesDAPT BERT classification, domain-adaptive pre-training, domain-specific BERT fine-tuning, BERT DAPTBERT fine-tuning for classification, BERT transfer learning classifier, pre-trained BERT classifier, BERT downstream classification
Relacionats64
ResumDomain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.Transfer Learning with BERT-based Classification adapts a large transformer language model, pre-trained on massive text corpora, to a target classification task by fine-tuning its weights on labeled examples. The pre-trained representations encode rich syntactic and semantic knowledge, enabling high accuracy even when the labeled dataset is small.
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ScholarGateCompara mètodes: Domain-adaptive BERT-based Classification · Transfer Learning with BERT-based Classification. Recuperat el 2026-06-15 de https://scholargate.app/ca/compare