方法对比

	微调 BERT 分类	[需翻译标题：BERT-based Classification...]
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2019	2019
提出者	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
类型	Pre-trained transformer fine-tuned for classification	Pre-trained language model with fine-tuning
开创性文献	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.	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 (pp. 4171–4186). Association for Computational Linguistics.
别名	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
相关	5	4
摘要	Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.