Model Card Completeness Checker
Check a model card against the fields deployers and regulators actually need, covering identification, intended use, training data, evaluation and limitations, with the ones that cannot be omitted.
Last reviewed by the Radiatus Cloud team
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A card without limitations is not a card
The original model cards proposal put limitations and disaggregated evaluation at the centre, because those are the sections that let someone decide whether a model is suitable for their use rather than merely impressive. Cards produced since have tended to expand the performance sections and shrink the limitations, which inverts the purpose: every model has failure modes, and a card that omits them transfers the entire discovery cost to the deployer, who will find them in production.
Aggregate scores hide the subgroup where the harm lands
A single accuracy figure describes the average case, and the average case is not where models cause problems. Disaggregating results across the groups relevant to the application is what turns an evaluation into evidence about fairness, and it is the recommendation most often acknowledged and least often followed. A card reporting one number per benchmark has documented capability while leaving the question the card exists to answer untouched.
Licence and training data are the fields deployers most often assume
Open-weight licences vary widely and several restrict commercial use, downstream training or particular applications, yet they are routinely treated as interchangeable. Training data provenance sits underneath almost every downstream question, about copyright, personal data, bias and benchmark contamination, and a card silent on it cannot answer any of them. These are also the two fields most likely to matter in a dispute, which is exactly when the card is read closely for the first time.
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Frequently Asked Questions
What is the most commonly missing section?
Limitations, followed by disaggregated evaluation results. Both are central to the original proposal and both are the sections that let a deployer judge suitability rather than capability.
Why does disaggregation matter?
Because an aggregate score describes the average case and models cause problems in the subgroups the average hides. Disaggregating is what turns an evaluation into evidence about fairness.
Are open-weight licences interchangeable?
No. Several restrict commercial use, downstream training or particular applications. The licence is one of the two fields deployers most often assume rather than read.
What is benchmark contamination?
Evaluation data appearing in the training set, which makes the resulting scores meaningless. It is common enough that a card silent on the question is itself informative.
Does this satisfy regulatory documentation requirements?
No. It checks completeness against widely accepted model card practice. Regimes such as the EU AI Act specify their own technical documentation content, which overlaps with this but is not the same list.
Privacy & Security
Everything runs in your browser; nothing is uploaded.
How to Use
Tick the sections your model card contains to score it.
Disclaimer: This tool is provided "as is" without warranty of any kind. Results are for educational and utility purposes.
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