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Models/BERTweet

BERTweet

Reported on 27 benchmarks across 5 tasks · 2 papers · 11 SOTA

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

Natural Language Processing19 results

  • Part-Of-Speech TaggingonRitter
    Acc· 2020-05-20
    90.1
    best: 93.4 (ACE)
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Part-Of-Speech TaggingonTweebank
    Acc· 2020-05-20
    95.2
    best: 95.8 (ACE)
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Sentiment AnalysisonTweetEval
    ALL· 2020-05-20
    67.9
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Sentiment AnalysisonTweetEval
    Emoji· 2020-05-20
    33.4
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Sentiment AnalysisonTweetEval
    Emotion· 2020-05-20
    79.3
    best: 79.5 (RoB-RT)
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Sentiment AnalysisonTweetEval
    Irony· 2020-05-20
    82.1
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Sentiment AnalysisonTweetEval
    Offensive· 2020-05-20
    79.5
    best: 80.5 (RoB-RT)
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Sentiment AnalysisonTweetEval
    Sentiment· 2020-05-20
    73.4
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Sentiment AnalysisonTweetEval
    Stance· 2020-05-20
    71.2
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Named Entity Recognition (NER)onWNUT 2017
    F1· 2020-05-20
    56.5
    best: 60.45 (CL-KL)
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Named Entity Recognition (NER)onWNUT 2016
    F1· 2020-05-20
    52.1
    best: 59.5 (HGN)
    SOTA
    BERTweet: A pre-trained language model for English TweetsarXiv:2005.10200
  • Text ClassificationonCivil Comments
    AUROC· 2023-01-26
    0.979
    best: 0.9818 (RoBERTa Focal Loss)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • Text ClassificationonCivil Comments
    GMB BNSP· 2023-01-26
    0.9603
    best: 0.9644 (DistilBERT)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • Text ClassificationonCivil Comments
    GMB BPSN· 2023-01-26
    0.8945
    best: 0.901 (RoBERTa Focal Loss)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • Text ClassificationonCivil Comments
    GMB Subgroup· 2023-01-26
    0.878
    best: 0.8807 (RoBERTa Focal Loss)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • Text ClassificationonCivil Comments
    Macro F1· 2023-01-26
    0.3612
    best: 0.4749 (RoBERTa BCE)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • Text ClassificationonCivil Comments
    Micro F1· 2023-01-26
    0.4928
    best: 0.5958 (Unfreeze Glove ResNet 44)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • Text ClassificationonCivil Comments
    Precision· 2023-01-26
    0.3363
    best: 0.4835 (Unfreeze Glove ResNet 44)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • Text ClassificationonCivil Comments
    Recall· 2023-01-26
    0.9216
    best: 0.9254 (XLNet)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125

Methodology8 results

  • ClassificationonCivil Comments
    AUROC· 2023-01-26
    0.979
    best: 0.9818 (RoBERTa Focal Loss)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • ClassificationonCivil Comments
    GMB BNSP· 2023-01-26
    0.9603
    best: 0.9644 (DistilBERT)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • ClassificationonCivil Comments
    GMB BPSN· 2023-01-26
    0.8945
    best: 0.901 (RoBERTa Focal Loss)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • ClassificationonCivil Comments
    GMB Subgroup· 2023-01-26
    0.878
    best: 0.8807 (RoBERTa Focal Loss)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • ClassificationonCivil Comments
    Macro F1· 2023-01-26
    0.3612
    best: 0.4749 (RoBERTa BCE)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • ClassificationonCivil Comments
    Micro F1· 2023-01-26
    0.4928
    best: 0.5958 (Unfreeze Glove ResNet 44)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • ClassificationonCivil Comments
    Precision· 2023-01-26
    0.3363
    best: 0.4835 (Unfreeze Glove ResNet 44)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125
  • ClassificationonCivil Comments
    Recall· 2023-01-26
    0.9216
    best: 0.9254 (XLNet)
    A benchmark for toxic comment classification on Civil Comments datasetarXiv:2301.11125