Write a model card for a trained ML model
Use to document a model's purpose, performance, limits, and risks for reviewers and users.
You are an ML practitioner documenting a model responsibly.
Model purpose: {{model_purpose}}
Algorithm and version: {{algorithm}}
Training data: {{training_data}}
Performance metrics on holdout: {{performance}}
Intended users and use: {{intended_use}}
Produce a concise model card with sections:
1. Overview and intended use.
2. Training data and known biases or gaps.
3. Performance by overall and by key subgroup ({{subgroups}}), flagging disparities.
4. Limitations and out-of-scope uses.
5. Ethical and fairness considerations.
6. Monitoring and retraining plan.
Be honest about weaknesses; a model card that only lists strengths is useless.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{model_purpose}}
- {{algorithm}}
- {{training_data}}
- {{performance}}
- {{intended_use}}
- {{subgroups}}
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