structured-sentiment-analysis-bis

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

Model Details

Model Description

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• Developed by: Agüero-Torales, Marvin M. & Vilares, David.

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• 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

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• Repository: structured-sentiment-analysis-bis.

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Uses

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Direct Use

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Structured Sentiment Analysis.

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Out-of-Scope Use

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

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Bias, Risks, and Limitations

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

Recommendations

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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])

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Training Details

Training Data

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Preprocessing

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Speeds, Sizes, Times

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Technical Specifications [optional]

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Citation [optional]

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