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基于自监督的BERT分类

基于自监督的BERT分类利用谷歌的BERT（Bidirectional Encoder Representations from Transformers），该模型已在海量无标签文本上通过掩码语言建模（masked-language modelling）进行了预训练，然后在一个有标签的示例集上进行微调，以将文本分配到不同的类别。即使在标记数据有限的情况下，它在情感分析、主题分类、意图检测以及类似的自然语言处理（NLP）任务上也能持续取得最先进的准确率。

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基于自监督的BERT分类
自监督句子嵌入半监督式BERT分类弱监督BERT分类

来源

  1. 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: 10.18653/v1/N19-1423 ↗
  2. Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? In China National Conference on Chinese Computational Linguistics (CCL 2019), LNCS 11856, 194–206. Springer. DOI: 10.1007/978-3-030-32381-3_16 ↗

如何引用本页

ScholarGate. (2026, June 3). Self-supervised BERT-based Text Classification (Pretrain then Fine-tune). ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-bert-based-classification

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自监督句子嵌入半监督式BERT分类弱监督BERT分类

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ScholarGate — Self-supervised BERT-based classification (Self-supervised BERT-based Text Classification (Pretrain then Fine-tune)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/self-supervised-bert-based-classification · 数据集： https://doi.org/10.5281/zenodo.20539026