license: mit
language:
- ca
- 'no'
- eu
- es
- en
metrics:
- f1
widget:
- text: Best holidays ever !
library_name: supar
tags:
- supar
- SemEval-2022-Task-10
- structured-sentiment-analysis
- sentiment-analysis
base_model:
- facebook/xml-roberta-base
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
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Summary
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