Сравнение методов

	Классификация на основе дообученной модели RoBERTa	Классификация на основе BERT
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2019	2019
Автор метода	Liu, Y. et al. (Meta AI / University of Washington)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Тип	Pretrained transformer fine-tuned for classification	Pre-trained language model with fine-tuning
Основополагающий источник	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: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.
Другие названия	RoBERTa fine-tuning, RoBERTa classifier, fine-tuned RoBERTa, RoBERTa sequence classification	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Связанные	5	4
Сводка	Fine-tuned RoBERTa-based classification adapts the RoBERTa pretrained transformer — itself a robustly retrained variant of BERT — to a specific text classification task by appending a classification head and continuing training on labeled examples. It consistently achieves state-of-the-art or near-state-of-the-art performance on sentiment analysis, topic classification, toxicity detection, and similar NLP tasks.	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.