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Models/GeoLayoutLM

GeoLayoutLM

Reported on 7 benchmarks across 4 tasks · 2 papers · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing7 results

  • Entity LinkingonEC-FUNSD
    F1· 2024-02-04
    86.18
    best: 87.42 (RORE (GeoLayoutLM))
    SOTA
    Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric PerspectivearXiv:2402.02379
  • Relation ExtractiononFUNSD
    F1· 2023-04-21
    89.45
    best: 90.81 (LayoutLMv3 large EM + BBO + RSF)
    SOTA
    GeoLayoutLM: Geometric Pre-training for Visual Information ExtractionarXiv:2304.10759
  • Entity LinkingonFUNSD
    F1· 2023-04-21
    89.45
    SOTA
    GeoLayoutLM: Geometric Pre-training for Visual Information ExtractionarXiv:2304.10759
  • Key Information ExtractiononCORD
    F1· 2023-04-21
    97.97
    best: 98.52 (RORE (GeoLayoutLM))
    SOTA
    GeoLayoutLM: Geometric Pre-training for Visual Information ExtractionarXiv:2304.10759
  • Key Information ExtractiononRFUND-EN
    key-value pair F1· 2023-04-21
    69.03
    best: 79.27 (PEneo (LayoutLMv3_base))
    SOTA
    GeoLayoutLM: Geometric Pre-training for Visual Information ExtractionarXiv:2304.10759
  • Semantic entity labelingonEC-FUNSD
    F1· 2024-02-04
    83.62
    best: 84.53 (RORE (LayoutLMv3-large))
    Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric PerspectivearXiv:2402.02379
  • Semantic entity labelingonFUNSD
    F1· 2023-04-21
    92.86
    best: 93.2 (LayoutMask (large))
    GeoLayoutLM: Geometric Pre-training for Visual Information ExtractionarXiv:2304.10759