AI Development
Model Card
A model card is a short document published with a trained machine learning model that explains its intended uses, its limitations, and how it performs across different conditions and groups.
In depth
A model card is a short document published with a trained machine learning model that explains its intended uses, its limitations, and how it performs across different conditions and groups. Margaret Mitchell, Timnit Gebru, and co-authors proposed the format in the 2018 paper Model Cards for Model Reporting. On Hugging Face, a model's README serves as its model card and usually covers training data, license, evaluation results, and known risks. Read it before you adopt an open-weight model.
Primary source: arxiv.org
Example
In practice, developers reach for Model Card when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Model Card?
A model card is a short document published with a trained machine learning model that explains its intended uses, its limitations, and how it performs across different conditions and groups.
Why does Model Card matter for AI developers?
Model Card sits in the AI Development part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Model Card?
Developers Digest publishes tutorials and videos that cover AI Development topics including Model Card. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
AI Development
Model Collapse
A phenomenon where AI models trained on AI-generated data progressively lose quality and diversity over generations.
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Chinchilla Scaling
The finding from DeepMind's 2022 Chinchilla paper that, for a fixed compute budget, model size and training tokens should grow in equal proportion: double the parameters, double the data.
AI Development
Max Tokens
The request parameter that sets a hard ceiling on how many tokens a model may generate in one response.
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