Template

ML Model Card Template for Research (Interactive)

An interactive, research-focused model card template to capture purpose, data provenance, evaluation results, limitations, interpretability methods, reproducibility steps, licensing, and recommended use cases — designed to make ML models more transparent, reproducible, and easier to evaluate and reuse across teams.

Interactive Tool

ML Model Card Template for Research

This interactive model card captures the essential information researchers and teams need to evaluate, reproduce, and responsibly reuse a machine learning model. Complete the fields with concise, evidence-backed entries. Where appropriate, include links to artifacts (datasets, notebooks, trained weights, CI logs, benchmark code) so others can validate and reuse your work.

Tip: Keep each entry focused — one or two paragraphs for descriptive fields and short lists for metrics. Use the Reproduction Assets field to link artifacts and provide exact commands or CI steps in Reproducibility Steps.

Human-readable name, e.g. 'CellSegNet'
Semantic version or tag, e.g. 'v1.2.0'
One-sentence summary of what the model does and its primary domain.
Who should use this model and for what tasks? Be specific about contexts where it's appropriate.
Situations or decisions where the model must not be used (safety, legal, ethical concerns).
High-level description of datasets used (sources, size, collection dates, key labels/annotations). Include dataset versions where possible.
Key preprocessing steps, normalization, augmentation, feature selection, and any dataset filtering.
URLs, DOIs, or repository paths for datasets; note license or access restrictions.
How dataset versions are tracked and how to reproduce the exact training set (hashes, snapshots, SQL queries).
List metrics with numeric values and dataset splits. Example: 'Accuracy: train=0.93, val=0.89, test=0.87'. Include confidence intervals where available.
How this model compares to baselines or published results. Include datasets and metric names.
Known performance differences for important subpopulations or conditions (e.g., classes, demographics, operating ranges).
Whether outputs are calibrated and how uncertainty is estimated (e.g., Monte Carlo dropout, ensembles).
Concrete examples or conditions where the model fails or outputs are unreliable.
Adversarial tests, noise tolerance, out-of-distribution evaluations, or sensitivity analyses performed.
Techniques used (SHAP, LIME, saliency maps) and key findings.
Exact steps to reproduce training and evaluation (commands, environment specs, random seeds, hardware). Include CI commands if available.
Links to code, notebooks, checkpoints, container images, or CI logs that enable reproduction.
Exact datasets and split definitions used for reported evaluation numbers.
Notes about personal data, de-identification, retention, and consent constraints.
Potential societal impacts, misuse risks, fairness considerations, and mitigation measures.
Risks such as model inversion, data leakage, and recommended operational controls.
Model and artifact license or terms of use.
Provide license identifiers, third-party constraints, or IP ownership notes.
Person or team responsible for model stewardship.
Best contact for questions or incident reports.
Concrete examples of suitable applications and operational constraints.
Resource requirements, expected latency, recommended monitoring signals, and retraining cadence.
How model performance will be monitored, drift thresholds, alerting, and maintenance actions.
Yes if reproducibility and evaluation are automated via CI pipelines.
Current readiness/approval state for production use.
Summary of reviews, reviewers, and key remediation actions.
Any other information helpful to assess or reuse the model.
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