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Machine learning

Longformer / BigBird

Transformer jangka panjang seperti Longformer (Beltagy, Peters & Cohan, 2020) dan BigBird (Zaheer et al., 2020) menggantikan perhatian O(n²) standard Transformer dengan corak perhatian jarang yang berskala secara linear, O(n), dengan panjang jujukan. Ini membolehkan satu model tunggal memberi perhatian kepada ribuan token — dokumen penuh, teks undang-undang, atau jujukan genomik — yang tidak akan muat dalam Transformer konvensional.

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Sumber

  1. Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv. link
  2. Zaheer, M. et al. (2020). Big Bird: Transformers for Longer Sequences. NeurIPS. link

Cara memetik halaman ini

ScholarGate. (2026, June 1). Long-Sequence Transformers with Sparse Attention (Longformer / BigBird). ScholarGate. https://scholargate.app/ms/deep-learning/longformer-bigbird

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ScholarGateLongformer / BigBird (Long-Sequence Transformers with Sparse Attention (Longformer / BigBird)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/longformer-bigbird · Set data: https://doi.org/10.5281/zenodo.20539026