TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/BERT Base

BERT Base

Reported on 7 benchmarks across 6 tasks · 2 papers

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

Medical3 results

  • Length-of-Stay predictiononClinical Admission Notes from MIMIC-III
    AUROC· uses extra data· 2021-02-08
    70.4
    best: 72.53 (CORe)
    Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge IntegrationarXiv:2102.04110
  • Mortality PredictiononClinical Admission Notes from MIMIC-III
    AUROC· uses extra data· 2021-02-08
    81.13
    best: 84.04 (CORe)
    Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge IntegrationarXiv:2102.04110
  • Medical ProcedureonClinical Admission Notes from MIMIC-III
    AUROC· uses extra data· 2021-02-08
    85.84
    best: 88.37 (CORe)
    Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge IntegrationarXiv:2102.04110

Methodology2 results

  • Electrocardiography (ECG)onClinical Admission Notes from MIMIC-III
    AUROC· uses extra data· 2021-02-08
    81.13
    best: 84.04 (CORe)
    Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge IntegrationarXiv:2102.04110
  • Medical waveform analysisonClinical Admission Notes from MIMIC-III
    AUROC· uses extra data· 2021-02-08
    81.13
    best: 84.04 (CORe)
    Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge IntegrationarXiv:2102.04110

Natural Language Processing2 results

  • Sentiment AnalysisonSST-5 Fine-grained classification
    Accuracy· 2019-10-04
    53.2
    best: 62.27 (Llama-3.3-70B + CAPO)
    Fine-grained Sentiment Classification using BERTarXiv:1910.03474
  • Sentiment AnalysisonSST-2 Binary classification
    Accuracy· 2019-10-04
    91.2
    best: 97.5 (T5-11B)
    Fine-grained Sentiment Classification using BERTarXiv:1910.03474