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Models/SSR-PU

SSR-PU

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

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

Natural Language Processing8 results

  • Relation ExtractiononReDocRED
    F1· 2022-10-17
    78.86
    best: 84.01 (TTM-RE)
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709
  • Relation ExtractiononReDocRED
    Ign F1· 2022-10-17
    77.67
    best: 83.11 (TTM-RE)
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709
  • Relation ExtractiononChemDisGene
    F1· 2022-10-17
    48.56
    best: 53.59 (TTM-RE)
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709
  • Relation ExtractiononRe-DocRED
    F1· 2022-10-17
    59.5
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709
  • Relation ExtractiononRe-DocRED
    Ign F1· 2022-10-17
    58.68
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709
  • Document-level Relation ExtractiononChemDisGene
    F1· 2022-10-17
    48.56
    best: 53.59 (TTM-RE)
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709
  • Document-level Relation ExtractiononRe-DocRED
    F1· 2022-10-17
    59.5
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709
  • Document-level Relation ExtractiononRe-DocRED
    Ign F1· 2022-10-17
    58.68
    SOTA
    A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of LabelingarXiv:2210.08709