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Models/Turing NLR v5 XXL 5.4B (fine-tuned)

Turing NLR v5 XXL 5.4B (fine-tuned)

Reported on 13 benchmarks across 5 tasks · 1 paper · 3 SOTA

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

Natural Language Processing13 results

  • Common Sense ReasoningonReCoRD
    EM· 2022-12-04
    95.9
    SOTA
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Common Sense ReasoningonReCoRD
    F1· 2022-12-04
    96.4
    SOTA
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Natural Language InferenceonWNLI
    Accuracy· 2022-12-04
    95.9
    SOTA
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Question AnsweringonCOPA
    Accuracy· 2022-12-04
    98.2
    best: 100 (PaLM 540B (finetuned) )
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Question AnsweringonMultiRC
    EM· 2022-12-04
    63
    best: 69.2 (PaLM 540B (finetuned) )
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Question AnsweringonMultiRC
    F1· 2022-12-04
    88.4
    best: 90.1 (PaLM 540B (finetuned) )
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Question AnsweringonBoolQ
    Accuracy· 2022-12-04
    92
    best: 99.87 (Mistral-Nemo 12B (HPT))
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Word Sense DisambiguationonWords in Context
    Accuracy· 2022-12-04
    77.1
    best: 85.3 (COSINE + Transductive Learning)
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Natural Language InferenceonCommitmentBank
    Accuracy· 2022-12-04
    97.6
    best: 100 (PaLM 540B (finetuned))
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Natural Language InferenceonCommitmentBank
    F1· 2022-12-04
    95.9
    best: 100 (PaLM 540B (finetuned))
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Coreference ResolutiononWinograd Schema Challenge
    Accuracy· 2022-12-04
    97.3
    best: 100 (PaLM 540B (fine-tuned))
    Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEarXiv:2212.01853
  • Natural Language InferenceonMultiNLI
    Matched
    92.6
  • Natural Language InferenceonMultiNLI
    Mismatched
    92.4