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

Semi-supervised BERT-based Classification

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.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. 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. link ↗
  2. Chen, J., Yang, Z., & Yang, D. (2020). MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2147–2157. DOI: 10.18653/v1/2020.acl-main.194 ↗

Related methods

BERT-based ClassificationFine-Tuned BERT-based ClassificationRoBERTa-based ClassificationSelf-supervised BERT-based classificationSemi-supervised TransformerWeakly supervised BERT-based classification

Referenced by

Semi-supervised GANSemi-supervised Question AnsweringSemi-supervised RoBERTa-based ClassificationSemi-supervised Sentence EmbeddingsSemi-supervised Sentiment AnalysisSemi-supervised Vision TransformerSemi-supervised Word2VecWeakly supervised BERT-based classification

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

ScholarGate — Semi-supervised BERT-based Classification (Semi-supervised BERT-based Text Classification). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/semi-supervised-bert-based-classification