Machine learningDeep learning / NLP / CV
基于域自适应的 RoBERTa 分类
基于域自适应的 RoBERTa 分类通过在特定领域语料库上继续进行掩码语言模型预训练，然后再针对分类任务进行微调，来扩展 RoBERTa Transformer。这种两阶段的自适应能够弥合通用网络爬取训练数据与生物医学、法律或科学文本等专业领域之间的差距，在有目标领域文本可用时，其性能始终优于标准的 RoBERTa 微调。
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来源
- 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 ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-Adaptive RoBERTa-based Text Classification. ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-roberta-based-classification
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