Compara mètodes

	Classificació semi-supervisada basada en BERT	Classificació basada en BERT auto-supervisat
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2019–2020	2019
Autor original	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Tipus	Semi-supervised fine-tuning of pre-trained transformer	Pretrain-then-fine-tune transformer model
Font seminal	Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.	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, 4171–4186. Association for Computational Linguistics.
Àlies	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning	BERT fine-tuning for classification, BERT text classifier, self-supervised transformer classification, masked LM pretraining with classification head
Relacionats	6	0
Resum	Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.	Self-supervised BERT-based classification uses Google's Bidirectional Encoder Representations from Transformers (BERT), pretrained on massive unlabelled text via masked-language modelling, and fine-tunes it on labelled examples to assign text into categories. It consistently achieves state-of-the-art accuracy on sentiment analysis, topic classification, intent detection, and similar NLP tasks even with limited labelled data.