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微调Transformer

微调Transformer是指通过在标记的目标数据集上继续进行基于梯度的训练，将大型预训练模型（如BERT、GPT或ViT）适配到特定的下游任务。这种两阶段范式（预训练后微调）在NLP和计算机视觉任务中始终能取得最先进的结果，且所需的特定任务数据远少于从头训练。

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微调Transformer

[需翻译标题：BERT-based Classi…微调 BERT 分类微调循环神经网络基于RoBERTa的分类微调门控循环单元 (Fine-Tuned GRU)微调长短期记忆网络 (Fine-Tuned LS…微调多层感知机微调强化学习 RoBERTa-based 分类微调微调句嵌入

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来源

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. link ↗
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. link ↗

如何引用本页

ScholarGate. (2026, June 3). Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models). ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-transformer

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
微调 BERT 分类深度学习↔ compare
微调循环神经网络深度学习↔ compare
基于RoBERTa的分类深度学习↔ compare

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被引用于

微调 BERT 分类微调门控循环单元 (Fine-Tuned GRU)微调长短期记忆网络 (Fine-Tuned LSTM)微调多层感知机微调循环神经网络微调强化学习 RoBERTa-based 分类微调微调句嵌入微调变分自编码器自监督Transformer 半监督式 Transformer 弱监督 Transformer

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