方法对比

	BERT微调	XGBoost
领域	深度学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2019	2016
提出者	Devlin, J. et al.	Chen, T. & Guestrin, C.
类型	Transfer learning (fine-tuning a pre-trained transformer)	Ensemble (gradient-boosted decision trees)
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. NAACL.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
别名	BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT	XGBoost, extreme gradient boosting, scalable tree boosting
相关	5	5
摘要	BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.