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BERT微调

BERT微调是基于Devlin及其同事于2019年推出的BERT模型，它在一个小型标注数据集上重新训练预训练的BERT模型，以完成分类、命名实体识别或问答等目标任务。通过迁移学习，即使只有相对较少的任务特定数据，它也能达到高性能。

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BERT微调
GPT模型微调LoRA 和 PEFT随机森林Vision TransformerXGBoost注意力机制检索增强生成（RAG）多头自注意力机制序列到序列模型

来源

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

如何引用本页

ScholarGate. (2026, June 1). Fine-Tuning of Pre-trained BERT (Bidirectional Encoder Representations from Transformers). ScholarGate. https://scholargate.app/zh/deep-learning/bert-finetuning

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注意力机制检索增强生成（RAG）多头自注意力机制序列到序列模型

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ScholarGate — BERT Fine-Tuning (Fine-Tuning of Pre-trained BERT (Bidirectional Encoder Representations from Transformers)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/bert-finetuning · 数据集： https://doi.org/10.5281/zenodo.20539026