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Machine learningDeep learning / NLP / CV

Klasifikasi Berasaskan RoBERTa Adaptif Domain

Klasifikasi berasaskan RoBERTa adaptif domain melanjutkan transformer RoBERTa dengan terlebih dahulu meneruskan prapelatihan model bahasa bertopengnya pada korpus khusus domain sebelum penalaan halus untuk tugas klasifikasi. Adaptasi dua peringkat ini merapatkan jurang antara data latihan yang dirangkak dari web secara umum dan bidang khusus seperti teks bioperubatan, undang-undang, atau saintifik, secara konsisten mengatasi penalaan halus RoBERTa standard apabila teks domain sasaran tersedia.

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Sumber

  1. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692. link
  2. Gururangan, 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 ACL 2020, pp. 8342–8360. DOI: 10.18653/v1/2020.acl-main.740

Cara memetik halaman ini

ScholarGate. (2026, June 3). Domain-Adaptive RoBERTa-based Text Classification. ScholarGate. https://scholargate.app/ms/deep-learning/domain-adaptive-roberta-based-classification

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ScholarGateDomain-adaptive RoBERTa-based Classification (Domain-Adaptive RoBERTa-based Text Classification). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/domain-adaptive-roberta-based-classification · Set data: https://doi.org/10.5281/zenodo.20539026