Model Card for Model ID
Model Details
Model Description
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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
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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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Speeds, Sizes, Times [optional]
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Evaluation
| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 |
|---|
| 1 | No log | 1.022442 | 1.000000 | 0.200000 | 0.333333 |
| 2 | No log | 0.474007 | 1.000000 | 0.525000 | 0.688525 |
| 3 | No log | 0.353309 | 1.000000 | 0.550000 | 0.709677 |
| 4 | No log | 0.279153 | 1.000000 | 0.600000 | 0.750000 |
| 5 | No log | 0.246459 | 1.000000 | 0.650000 | 0.787879 |
| 6 | No log | 0.216441 | 1.000000 | 0.650000 | 0.787879 |
| 7 | No log | 0.191740 | 1.000000 | 0.675000 | 0.805970 |
| 8 | No log | 0.188013 | 1.000000 | 0.700000 | 0.823529 |
| 9 | No log | 0.183922 | 1.000000 | 0.700000 | 0.823529 |
| 10 | 0.635200 | 0.185600 | 1.000000 | 0.700000 | 0.823529 |
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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Compute Infrastructure
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Hardware
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Software
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Framework versions
