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Transfer Learning with BERT-based Classification

Transfer Learning with BERT-based Classification adapts a large transformer language model, pre-trained on massive text corpora, to a target classification task by fine-tuning its weights on labeled examples. The pre-trained representations encode rich syntactic and semantic knowledge, enabling high accuracy even when the labeled dataset is small.

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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. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics. DOI: 10.18653/v1/N19-1423 ↗
  2. Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗

Related methods

BERT-based ClassificationFine-Tuned BERT-based ClassificationRoBERTa-based ClassificationSentence Embeddings

Referenced by

Domain-adaptive BERT-based ClassificationDomain-adaptive Named Entity RecognitionDomain-adaptive Question AnsweringDomain-adaptive Sentiment AnalysisTransfer Learning with Graph Neural NetworkTransfer Learning with Named Entity RecognitionTransfer Learning with Sentence EmbeddingsTransfer Learning with Word2Vec

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ScholarGate — Transfer Learning with BERT-based Classification (Transfer Learning with BERT-based Text Classification). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/transfer-learning-with-bert-based-classification