Model Card for Model ID
Model Details
Model Description
Quality Model
Model description
Evaluation metrics
| precision | recall | f1-score | support |
|---|
| 0 | 0.83 | 0.94 | 0.88 | 311 |
| 1 | 0.61 | 0.57 | 0.59 | 65 |
| 2 | 0.75 | 0.77 | 0.76 | 87 |
| 3 | 0.82 | 0.18 | 0.29 | 51 |
| -- | -- | -- | -- | -- |
| accuracy | | | 0.79 | 514 |
| macro | avg | 0.75 | 0.61 | 0.63 |
| weighted | avg | 0.79 | 0.79 | 0.77 |
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- Language(s) (NLP): en
- License: apache-2.0
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Model Sources [optional]
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Uses
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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.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
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Factors
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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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Software
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