Methoden vergelijken

	Domeinadaptieve RoBERTa-gebaseerde classificatie	BERT-gebaseerde Classificatie
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2019–2020	2019
Grondlegger	Liu et al. (RoBERTa); Gururangan et al. (domain-adaptive pretraining)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Type	Pre-trained transformer with domain-adaptive pretraining and task fine-tuning	Pre-trained language model with fine-tuning
Oorspronkelijke bron	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.	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.
Aliassen	DA-RoBERTa, domain-adapted RoBERTa classifier, RoBERTa domain adaptation, domain-specific RoBERTa fine-tuning	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Verwant	5	4
Samenvatting	Domain-adaptive RoBERTa-based classification extends the RoBERTa transformer by first continuing its masked-language-model pretraining on a domain-specific corpus before fine-tuning for a classification task. This two-stage adaptation bridges the gap between general web-crawled training data and specialized fields such as biomedical, legal, or scientific text, consistently outperforming standard RoBERTa fine-tuning when target-domain text is available.	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.