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/Vicuna-33B

Vicuna-33B

Reported on 12 benchmarks across 3 tasks

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

Methodology8 results

  • Multi-Label Text ClassificationonCC3M-TagMask
    Accuracy
    75.9
    best: 91 (TTD (w/o fine-tuning))
  • Multi-Label Text ClassificationonCC3M-TagMask
    F1
    60.4
    best: 82.8 (TTD (w/ fine-tuning))
  • Multi-Label Text ClassificationonCC3M-TagMask
    Precision
    52.7
    best: 88.3 (TTD (w/ fine-tuning))
  • Multi-Label Text ClassificationonCC3M-TagMask
    Recall
    70.7
    best: 83.7 (NLTK)
  • ClassificationonCC3M-TagMask
    Accuracy
    75.9
    best: 91 (TTD (w/o fine-tuning))
  • ClassificationonCC3M-TagMask
    F1
    60.4
    best: 82.8 (TTD (w/ fine-tuning))
  • ClassificationonCC3M-TagMask
    Precision
    52.7
    best: 88.3 (TTD (w/ fine-tuning))
  • ClassificationonCC3M-TagMask
    Recall
    70.7
    best: 83.7 (NLTK)

Natural Language Processing4 results

  • Text ClassificationonCC3M-TagMask
    Accuracy
    75.9
    best: 91 (TTD (w/o fine-tuning))
  • Text ClassificationonCC3M-TagMask
    F1
    60.4
    best: 82.8 (TTD (w/ fine-tuning))
  • Text ClassificationonCC3M-TagMask
    Precision
    52.7
    best: 88.3 (TTD (w/ fine-tuning))
  • Text ClassificationonCC3M-TagMask
    Recall
    70.7
    best: 83.7 (NLTK)