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Machine learningDeep learning / NLP / CV

Domæne-adaptiv BERT-baseret klassifikation

Domæne-adaptiv BERT-baseret klassifikation udvider den standardiserede finjusteringspipeline ved først at fortsætte BERT's maskerede sprogmodel-prætræning på et stort korpus af umærkede tekster inden for domænet, og derefter finjustere den tilpassede model på mærkede eksempler til den pågældende klassifikationsopgave. Denne to-trins tilgang lukker vokabularet og den distributionelle kløft mellem BERT's generelle prætræningskorpus og specialiserede domæner som biomedicin, jura, finans eller tekster fra sociale 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/da/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/da/deep-learning/domain-adaptive-bert-based-classification · Datasæt: https://doi.org/10.5281/zenodo.20539026