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Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
- Developed by: Lina Saba
- Model type: bert for multi-label classification
- Language(s) (NLP): Python
- Finetuned from model: distilbert-base-pwc-task-multi-label-classification
Model Sources [optional]
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Uses
This model aims to fine-tune BERT to predict one or more labels for a given piece of text. The related notebook illustrates how to fine-tune a distilbert-base-pwc-task-multi-label-classification model, Knowing that it's the same way to fine-tune a RoBERTa, DeBERTa, DistilBERT, CANINE, ... checkpoint.
Direct Use
Predict the labels of a piece of text from this list = { 0: 'aspersion', 1: 'hyperbole', 2: 'lying', 3: 'namecalling', 4: 'noncooperation', 5: 'offtopic', 6: 'other_incivility', 7: 'pejorative', 8: 'sarcasm', 9: 'vulgarity' }
Downstream Use [optional]
This model is fine-tuned on a dataset; a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.
Bias, Risks, and Limitations
Technical limitations : - Can't print more than one identified label.
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.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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