language: en
Model Card for BenjaminOcampo/task-implicit_task__model-deberta__aug_method-gm_revised
Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications
- Citation
- Glossary
- More Information
- Model Card Authors
- Model Card Contact
- How To Get Started With the Model
Model Details
Model Description
Classification results dev set
precision recall f1-score support
0 0.90 0.90 0.90 2680
1 0.82 0.84 0.83 1501
2 0.42 0.30 0.35 186
accuracy 0.85 4367
macro avg 0.71 0.68 0.69 4367
weighted avg 0.85 0.85 0.85 4367
Classification results test set
precision recall f1-score support
0 0.91 0.90 0.91 2681
1 0.83 0.86 0.84 1501
2 0.46 0.39 0.42 186
accuracy 0.87 4368
macro avg 0.73 0.72 0.72 4368
weighted avg 0.86 0.87 0.86 4368
- Developed by: Nicolás Benjamín Ocampo
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- Language(s) (NLP): en
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Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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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.
Training Details
Training Data
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Training Procedure [optional]
Preprocessing
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Speeds, Sizes, Times
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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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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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Compute Infrastructure
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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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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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How to Get Started with the Model
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