Porovnať metódy

	Klasifikácia založená na doménovo adaptovanom RoBERTa	Klasifikácia založená na RoBERTa
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2019–2020	2019
Tvorca	Liu et al. (RoBERTa); Gururangan et al. (domain-adaptive pretraining)	Liu, Y. et al. (Facebook AI Research / University of Washington)
Typ	Pre-trained transformer with domain-adaptive pretraining and task fine-tuning	Pre-trained transformer fine-tuned for sequence classification
Pôvodný zdroj	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.	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.
Ďalšie názvy	DA-RoBERTa, domain-adapted RoBERTa classifier, RoBERTa domain adaptation, domain-specific RoBERTa fine-tuning	RoBERTa classifier, RoBERTa text classification, Robustly Optimized BERT Classification, RoBERTa fine-tuning for classification
Príbuzné	5	5
Zhrnutie	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.	RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks.