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

Fine-Tuned BERT-based Classification

Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. 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 ↗
  2. Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Proceedings of CCL 2019, LNCS 11856, 194–206. DOI: 10.1007/978-3-030-32381-3_16 ↗

Related methods

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

Referenced by

Domain-adaptive BERT-based ClassificationExplainable BERT-based ClassificationFine-Tuned LDA Topic ModelFine-Tuned Named Entity RecognitionFine-Tuned Question AnsweringFine-Tuned Reinforcement LearningFine-Tuned RoBERTa-based ClassificationFine-Tuned Sentence EmbeddingsFine-Tuned Text SummarizationFine-Tuned Topic ModelingFine-Tuned TransformerFine-Tuned Word2VecSemi-supervised BERT-based ClassificationTransfer Learning with BERT-based ClassificationWeakly supervised BERT-based classification

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

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