Machine learningDeep learning / NLP / CV
基于RoBERTa的分类
基于RoBERTa的分类将RoBERTa预训练的Transformer模型（通过动态掩码和更大的批次进行比BERT更稳健的训练）应用于文本分类任务，方法是在[CLS]标记的表示之上添加一个轻量级的分类头，并对整个模型进行有标签示例的微调。它在标准的NLP基准测试上始终能与BERT相匹配或超越BERT。
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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 ↗
- 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 (pp. 4171–4186). Association for Computational Linguistics. DOI: 10.18653/v1/N19-1423 ↗
如何引用本页
ScholarGate. (2026, June 3). RoBERTa-based Text Classification (Robustly Optimized BERT Pretraining Approach). ScholarGate. https://scholargate.app/zh/deep-learning/roberta-based-classification
Which method?
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.
- [需翻译标题：BERT-based Classification...]深度学习↔ compare
- RoBERTa-based 分类微调深度学习↔ compare
- 门控循环单元 (GRU)深度学习↔ compare
- 长短期记忆网络（LSTM）深度学习↔ compare
- 句子嵌入深度学习↔ compare
被引用于
[需翻译标题：BERT-based Classification...]基于领域自适应BERT的分类领域自适应问答基于域自适应的 RoBERTa 分类领域自适应句子嵌入 (Domain-Adaptive Sentence Embeddings)领域自适应情感分析可解释的BERT分类可解释问答可解释的 RoBERTa 分类可解释情感分析微调 BERT 分类微调命名实体识别微调问答RoBERTa-based 分类微调微调句嵌入微调文本摘要微调Transformer多语言问答基于多语言 RoBERTa 的分类多语言情感分析多语言 Transformer多模态 RoBERTa 分类自监督Transformer半监督式BERT分类基于RoBERTa的半监督分类半监督式 Transformer句子嵌入BERT 기반 전이 학습命名实体识别的迁移学习基于句子嵌入的迁移学习弱监督BERT分类弱监督 RoBERTa 分类