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Models/T5-Small

T5-Small

Reported on 8 benchmarks across 4 tasks · 1 paper

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

Natural Language Processing8 results

  • Question AnsweringonSQuAD1.1 dev
    EM· uses extra data· 2019-10-23
    79.1
    best: 90.06 (T5-11B)
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683
  • Question AnsweringonSQuAD1.1 dev
    F1· uses extra data· 2019-10-23
    87.24
    best: 95.77 (XLNet+DSC)
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683
  • Natural Language InferenceonMultiNLI
    Matched· 2019-10-23
    82.4
    best: 92.6 (Turing NLR v5 XXL 5.4B (fine-tuned))
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683
  • Natural Language InferenceonMultiNLI
    Mismatched· 2019-10-23
    82.3
    best: 92.4 (Turing NLR v5 XXL 5.4B (fine-tuned))
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683
  • Semantic Textual SimilarityonMRPC
    F1· 2019-10-23
    89.7
    best: 92.5 (T5-3B)
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683
  • Semantic Textual SimilarityonSTS Benchmark
    Pearson Correlation· 2019-10-23
    0.856
    best: 0.929 (MT-DNN-SMART)
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683
  • Semantic Textual SimilarityonSTS Benchmark
    Spearman Correlation· 2019-10-23
    0.85
    best: 0.931 (Mnet-Sim)
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683
  • Sentiment AnalysisonSST-2 Binary classification
    Accuracy· 2019-10-23
    91.8
    best: 97.5 (T5-11B)
    Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683