ScholarGate
⌘K
Assistant
EN
Machine learningDeep learning / NLP / CV

Fine-Tuned RoBERTa-based Classification

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.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. 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. link ↗
  2. 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. DOI: 10.18653/v1/N19-1423 ↗

Related methods

BERT-based ClassificationFine-Tuned BERT-based ClassificationFine-Tuned TransformerRoBERTa-based ClassificationSentence Embeddings

Referenced by

Domain-adaptive RoBERTa-based ClassificationFine-Tuned BERT-based ClassificationRoBERTa-based ClassificationSemi-supervised RoBERTa-based ClassificationWeakly Supervised RoBERTa-based Classification

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — Fine-Tuned RoBERTa-based Classification (Fine-Tuned RoBERTa-based Text Classification). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/fine-tuned-roberta-based-classification