So sánh phương pháp

	Transformer tự giám sát	Phân loại dựa trên BERT
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2017–2019	2019
Người khởi xướng	Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Loại	Self-supervised deep learning model	Pre-trained language model with fine-tuning
Công trình gốc	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.
Tên gọi khác	SSL Transformer, self-supervised pretraining, masked self-attention pretraining, contrastive transformer	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Liên quan	5	4
Tóm tắt	A self-supervised Transformer is a Transformer network pretrained using automatically constructed supervision signals — such as masked token prediction or next-sentence prediction — rather than human-annotated labels. The resulting representations are then fine-tuned or probed on downstream tasks. BERT, GPT, and ViT (Vision Transformer in masked-image modeling mode) are the most widely known instantiations of this paradigm.	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.