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微调 BERT 分类

微调 BERT 分类通过添加一个轻量级输出层并基于标注样本继续进行梯度下降训练，来将预训练的 BERT Transformer 模型适配到特定的文本分类任务。在情感分析、主题分类、意图识别和其他自然语言处理分类任务中，该方法在标注数据集相对较小的情况下，始终能达到接近最先进水平的准确率。

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微调 BERT 分类
[需翻译标题：BERT-based Classi…RoBERTa-based 分类微调微调Transformer基于RoBERTa的分类句子嵌入基于领域自适应BERT的分类可解释的BERT分类微调LDA主题模型微调命名实体识别微调问答

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来源

  1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186. DOI: 10.18653/v1/N19-1423 ↗
  2. Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Proceedings of CCL 2019, LNCS 11856, 194–206. DOI: 10.1007/978-3-030-32381-3_16 ↗

如何引用本页

ScholarGate. (2026, June 3). Fine-Tuned BERT-based Text Classification. ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-bert-based-classification

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被引用于

基于领域自适应BERT的分类可解释的BERT分类微调LDA主题模型微调命名实体识别微调问答微调强化学习RoBERTa-based 分类微调微调句嵌入微调文本摘要微调主题建模微调Transformer微调 Word2Vec (Fine-Tuned Word2Vec)半监督式BERT分类BERT 기반 전이 학습弱监督BERT分类

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ScholarGate — Fine-Tuned BERT-based Classification (Fine-Tuned BERT-based Text Classification). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/fine-tuned-bert-based-classification · 数据集： https://doi.org/10.5281/zenodo.20539026