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Models/RoBERTaGCN

RoBERTaGCN

Reported on 10 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 Processing5 results

  • Text ClassificationonOhsumed
    Accuracy· 2021-05-12
    72.8
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • Text ClassificationonR8
    Accuracy· 2021-05-12
    98.2
    best: 98.451 (DeBERTa)
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • Text Classificationon20 Newsgroups
    Accuracy· 2021-05-12
    89.5
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • Text Classificationon20NEWS
    Accuracy· 2021-05-12
    89.5
    best: 93 (LinearSVM+TFIDF)
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • Text ClassificationonMR
    Accuracy· 2021-05-12
    89.7
    best: 93.3 (VLAWE)
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727

Methodology5 results

  • ClassificationonOhsumed
    Accuracy· 2021-05-12
    72.8
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • ClassificationonR8
    Accuracy· 2021-05-12
    98.2
    best: 98.451 (DeBERTa)
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • Classificationon20 Newsgroups
    Accuracy· 2021-05-12
    89.5
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • Classificationon20NEWS
    Accuracy· 2021-05-12
    89.5
    best: 93 (LinearSVM+TFIDF)
    SOTA
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727
  • ClassificationonMR
    Accuracy· 2021-05-12
    89.7
    best: 93.3 (VLAWE)
    BertGCN: Transductive Text Classification by Combining GCN and BERTarXiv:2105.05727