ScholarGate
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Machine learning

Longformer / BigBird

Langsekvens-Transformere som Longformer (Beltagy, Peters & Cohan, 2020) og BigBird (Zaheer et al., 2020) erstatter den standard Transformer-arkitekturs O(n²)-opmærksomhed med sparsomme opmærksomhedsmønstre, der skalerer lineært, O(n), med sekvenslængden. Dette gør det muligt for en enkelt model at behandle tusindvis af tokens – hele dokumenter, juridiske tekster eller genomiske sekvenser – som ikke ville passe ind i en konventionel Transformer.

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Kilder

  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

Sådan citerer du denne side

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

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Refereret af

ScholarGateLongformer / BigBird (Long-Sequence Transformers with Sparse Attention (Longformer / BigBird)). Hentet 2026-06-15 fra https://scholargate.app/da/deep-learning/longformer-bigbird · Datasæt: https://doi.org/10.5281/zenodo.20539026