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Models/KERMIT

KERMIT

Reported on 9 benchmarks across 2 tasks · 2 papers · 4 SOTA

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

Graphs8 results

  • Link PredictiononWN18RR
    Hits@1· 2023-09-26
    0.629
    best: 0.665 (MoCoKGC)
    SOTA
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770
  • Link PredictiononWN18RR
    Hits@10· 2023-09-26
    0.832
    best: 0.881 (MoCoKGC)
    SOTA
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770
  • Link PredictiononWN18RR
    Hits@3· 2023-09-26
    0.738
    best: 0.792 (MoCoKGC)
    SOTA
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770
  • Link PredictiononWN18RR
    MRR· 2023-09-26
    0.7
    best: 0.742 (MoCoKGC)
    SOTA
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770
  • Link PredictiononFB15k-237
    Hits@1· 2023-09-26
    0.266
    best: 0.321 (NBFNet)
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770
  • Link PredictiononFB15k-237
    Hits@10· 2023-09-26
    0.547
    best: 0.599 (NBFNet)
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770
  • Link PredictiononFB15k-237
    Hits@3· 2023-09-26
    0.396
    best: 0.454 (NBFNet)
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770
  • Link PredictiononFB15k-237
    MRR· 2023-09-26
    0.359
    best: 0.415 (NBFNet)
    KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationarXiv:2309.14770

Natural Language Processing1 result

  • Machine TranslationonWMT2014 English-German
    BLEU score· 2019-06-04
    28.7
    best: 35.14 (Transformer Cycle (Rev))
    KERMIT: Generative Insertion-Based Modeling for SequencesarXiv:1906.01604