Graph Machine Learning
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Model Card for {{ model_id | default("Model ID", true) }}

{{ model_summary | default("", true) }}

Model Details

Model Description

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  • Developed by: {{ developers | default("[More Information Needed]", true)}}
  • Funded by [optional]: {{ funded_by | default("[More Information Needed]", true)}}
  • Shared by [optional]: {{ shared_by | default("[More Information Needed]", true)}}
  • 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)}}
  • Finetuned from model [optional]: {{ base_model | default("[More Information Needed]", true)}}

Model Sources [optional]

  • Repository: {{ repo | default("[More Information Needed]", true)}}
  • Paper [optional]: {{ paper | default("[More Information Needed]", true)}}
  • Demo [optional]: {{ demo | default("[More Information Needed]", true)}}

Uses

Direct Use

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Downstream Use [optional]

{{ downstream_use | default("[More Information Needed]", true)}}

Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}

How to Get Started with the Model

Use the code below to get started with the model.

{{ get_started_code | default("[More Information Needed]", true)}}

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

{{ preprocessing | default("[More Information Needed]", true)}}

Training Hyperparameters

  • Training regime: {{ training_regime | default("[More Information Needed]", true)}}

Speeds, Sizes, Times [optional]

{{ speeds_sizes_times | default("[More Information Needed]", true)}}

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

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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).

  • Hardware Type: {{ hardware_type | 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

{{ model_specs | default("[More Information Needed]", true)}}

Compute Infrastructure

{{ compute_infrastructure | default("[More Information Needed]", true)}}

Hardware

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Software

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Citation [optional]

@article{lang2024aifs, title={AIFS-ECMWF's data-driven forecasting system}, author={Lang, Simon and Alexe, Mihai and Chantry, Matthew and Dramsch, Jesper and Pinault, Florian and Raoult, Baudouin and Clare, Mariana CA and Lessig, Christian and Maier-Gerber, Michael and Magnusson, Linus and others}, journal={arXiv preprint arXiv:2406.01465}, year={2024} }

BibTeX:

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APA:

Lang, S., Alexe, M., Chantry, M., Dramsch, J., Pinault, F., Raoult, B., ... & Rabier, F. (2024). AIFS-ECMWF's data-driven forecasting system. arXiv preprint arXiv:2406.01465.

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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