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Longformer / BigBird

Pikad Transformerid, nagu Longformer (Beltagy, Peters & Cohan, 2020) ja BigBird (Zaheer et al., 2020), asendavad standardse Transformeri O(n²) tähelepanuharjumuse harvaesineva tähelepanumustriga, mis skaalub lineaarselt, O(n), järjestuse pikkusega. See võimaldab ühel mudelil töödelda tuhandeid tokeneid – täisdokumente, juriidilisi tekste või genoomilisi järjestusi –, mis ei mahuks tavalisse Transformerisse.

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Allikad

  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

Kuidas sellele lehele viidata

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

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Sellele viitavad

ScholarGateLongformer / BigBird (Long-Sequence Transformers with Sparse Attention (Longformer / BigBird)). Loetud 2026-06-15 aadressilt https://scholargate.app/et/deep-learning/longformer-bigbird · Andmestik: https://doi.org/10.5281/zenodo.20539026