ScholarGate
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Machine learningDeep learning / NLP / CV

Transformer yang Ditalar Halus

Menalar halus Transformer bermaksud menyesuaikan model pra-latih berskala besar — seperti BERT, GPT, atau ViT — kepada tugasan hiliran tertentu dengan meneruskan latihan berasaskan kecerunan pada set data sasaran berlabel. Paradigma dua peringkat ini (pra-latih kemudian nalar halus) secara konsisten mencapai keputusan terkini merentasi tugasan NLP dan visi komputer dengan data khusus tugasan yang jauh lebih sedikit berbanding latihan dari awal.

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Sumber

  1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. link
  2. 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. link

Cara memetik halaman ini

ScholarGate. (2026, June 3). Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models). ScholarGate. https://scholargate.app/ms/deep-learning/fine-tuned-transformer

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ScholarGateFine-Tuned Transformer (Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/fine-tuned-transformer · Set data: https://doi.org/10.5281/zenodo.20539026