Model Card for Model ID

Structured Sentiment Analysis (SemEval-2022 Task 10) as Semantic Dependency Parsing.

Model Details

Model Description

  • Developed by: Agüero-Torales, Marvin M. & Vilares, David.
  • Model type: Semantic Dependency Parsing.
  • Language(s) (NLP): English, Spanish, Catalan, Basque, and Norwegian.
  • License: MIT.
  • Finetuned from model [optional]: xml-roberta-base, infoxlm-base, cardiffnlp/twitter-xlm-roberta-base-sentiment, bert-base-multilingual-cased, dccuchile/bert-base-spanish-wwm-cased, NbAiLab/nb-bert-base, PlanTL-GOB-ES/roberta-base-ca, ixa-ehu/berteus-base-cased.

Model Sources

  • Repository: structured-sentiment-analysis-bis.

    Direct Use

    Structured Sentiment Analysis.

    Out-of-Scope Use

    This model is specific for Structured Sentiment Analysis on social media texts.

    Bias, Risks, and Limitations

    This model is specific for Structured Sentiment Analysis on social media texts.

    Recommendations

    Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

    How to Get Started with the Model

    Use the code below to get started with the model.

    from supar import Parser
    parser = Parser.load('models/opener_en/all/model_vi_es_roberta_6')
    dataset = parser.predict('especially the team of the main restaurant was really professionell and nice.', lang='en', prob=True, verbose=True)
    dataset[0], type(dataset[0])
    

    Training Details

    Training Data

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    Evaluation

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    Summary

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    Environmental Impact

    Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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