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Machine learningDeep learning / NLP / CV

Klasifikasi Berbasis BERT Adaptif Domain

Klasifikasi berbasis BERT adaptif domain memperluas alur kerja penyempurnaan standar dengan pertama-tama melanjutkan pra-pelatihan model bahasa bertopeng (masked-language-model) BERT pada korpus besar teks tak berlabel dalam domain, kemudian menyempurnakan model yang telah diadaptasi pada contoh berlabel untuk tugas klasifikasi target. Pendekatan dua tahap ini menutup kesenjangan kosakata dan distribusi antara korpus pra-pelatihan umum BERT dan domain khusus seperti biomedis, hukum, keuangan, atau teks media sosial.

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Sumber

  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

Cara menyitasi halaman ini

ScholarGate. (2026, June 3). Domain-Adaptive Pre-training with BERT for Text Classification. ScholarGate. https://scholargate.app/id/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). Diakses 2026-06-15 dari https://scholargate.app/id/deep-learning/domain-adaptive-bert-based-classification · Set data: https://doi.org/10.5281/zenodo.20539026