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Machine learningDeep learning / NLP / CV

Domene-adaptiv BERT-basert klassifisering

Domene-adaptiv BERT-basert klassifisering utvider den standardiserte finjusteringspipelinen ved først å fortsette BERTs maskerte språkmodellerings-pretrening på et stort korpus av umerkede tekster innenfor domenet, deretter finjustere den tilpassede modellen på merkede eksempler for den aktuelle klassifiseringsoppgaven. Denne to-trinns tilnærmingen lukker vokabular- og distribusjonsgapet mellom BERTs generelle pretreningkorpus og spesialiserte domener som biomedisin, jus, finans eller sosiale medier.

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  1. Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360. DOI: 10.18653/v1/2020.acl-main.740
  2. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240. DOI: 10.1093/bioinformatics/btz682

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ScholarGate. (2026, June 3). Domain-Adaptive Pre-training with BERT for Text Classification. ScholarGate. https://scholargate.app/no/deep-learning/domain-adaptive-bert-based-classification

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ScholarGateDomain-adaptive BERT-based Classification (Domain-Adaptive Pre-training with BERT for Text Classification). Hentet 2026-06-15 fra https://scholargate.app/no/deep-learning/domain-adaptive-bert-based-classification · Datasett: https://doi.org/10.5281/zenodo.20539026