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Models/Sudowoodo

Sudowoodo

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.

Knowledge Base4 results

  • Data IntegrationonAbt-Buy
    Candidate Set Size· 2022-07-08
    3276
    best: 54500 (Sparkly k=50)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122
  • Data IntegrationonAbt-Buy
    Recall· 2022-07-08
    88.6
    best: 99.5 (SC-Block)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122
  • Data IntegrationonAmazon-Google
    Candidate Set Size· 2022-07-08
    48390
    best: 165900 (Sparkly k=50)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122
  • Data IntegrationonAmazon-Google
    Recall· 2022-07-08
    97.3
    best: 99.6 (SC-Block)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122

Natural Language Processing4 results

  • Entity ResolutiononAbt-Buy
    Candidate Set Size· 2022-07-08
    3276
    best: 54500 (Sparkly k=50)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122
  • Entity ResolutiononAbt-Buy
    Recall· 2022-07-08
    88.6
    best: 99.5 (SC-Block)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122
  • Entity ResolutiononAmazon-Google
    Candidate Set Size· 2022-07-08
    48390
    best: 165900 (Sparkly k=50)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122
  • Entity ResolutiononAmazon-Google
    Recall· 2022-07-08
    97.3
    best: 99.6 (SC-Block)
    SOTA
    Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationarXiv:2207.04122