Model Card for {{ model_id | default("Model ID", true) }}
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Model Details
Model Description
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- Model type: {{ model_type | default("[More Information Needed]", true)}}
- Language(s) (NLP): {{ language | default("[More Information Needed]", true)}}
- License: {{ license | default("[More Information Needed]", true)}}
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Model Sources [optional]
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Uses
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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
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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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Preprocessing [optional]
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Training Hyperparameters
- Training regime: {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]
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Evaluation
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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
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Model Examination [optional]
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Environmental Impact
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: {{ hardware | default("[More Information Needed]", true)}}
- Hours used: {{ hours_used | default("[More Information Needed]", true)}}
- Cloud Provider: {{ cloud_provider | default("[More Information Needed]", true)}}
- Compute Region: {{ cloud_region | default("[More Information Needed]", true)}}
- Carbon Emitted: {{ co2_emitted | default("[More Information Needed]", true)}}
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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BibTeX:
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APA:
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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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