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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.

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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

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