Machine learning
T5（Text-to-Text Transfer Transformer）
T5 是由 Google Brain 的 Raffel 等人在 2020 年推出、发表于《机器学习研究期刊》（Journal of Machine Learning Research, Vol. 21, No. 140）的一个统一序列到序列深度学习框架。它将所有自然语言处理（NLP）任务——分类、翻译、摘要、问答等——重构为文本到文本问题：输入和输出始终是字符序列，使得单个编码器-解码器 Transformer 能够预训练一次，然后以一致的接口跨任务进行微调。T5 引入了跨度损坏预训练（span-corruption pre-training）和 C4 语料库，其最大的变体（110亿参数）在发布时横跨广泛的 NLP 基准测试取得了最先进的结果。
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来源
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67. link ↗
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & 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). T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. ScholarGate. https://scholargate.app/zh/deep-learning/t5
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