What is a Model Card?

A Model Card is a standardized document that provides essential information about a machine learning model, including its intended use, performance metrics, limitations, and ethical considerations to ensure transparency.

Does it generate usage examples?

Yes, the skill automatically includes a 'How to Get Started' section with runnable Python code snippets to help users load and use the documented model.

Can this help with ML model compliance?

Yes, by providing structured sections for data bias, fairness, and environmental impact, it helps teams meet internal and external transparency and auditing requirements.

Does this skill support specific ML frameworks?

Yes, it is designed to document models from any major framework, including scikit-learn, XGBoost, PyTorch, TensorFlow, and more.

Why use the HuggingFace and NVIDIA formats?

These formats are industry standards that ensure your documentation covers critical aspects of model performance and safety, making it easier for stakeholders to evaluate the model.

Model Card Documentation

Name: Model Card Documentation
Author: andikarachman

byandikarachman

•

Ciencia de Datos y ML

Generates standardized machine learning model documentation following HuggingFace and NVIDIA Model Card++ formats for professional deployment and handoff.

This skill empowers Claude to create comprehensive, standardized documentation for machine learning models, ensuring transparency and reproducibility in ML workflows. By adhering to industry-standard frameworks like HuggingFace Model Cards and NVIDIA Model Card++ extensions, it guides users through documenting critical metadata, training data lineage, performance metrics, and ethical considerations. It is an essential tool for data scientists and ML engineers who need to maintain high-quality audit trails, satisfy compliance requirements, or facilitate seamless handoffs between research and production teams.

Características Principales

01Structured metric reporting with baseline comparisons and confidence intervals

02Comprehensive sections for training/evaluation data provenance and data hashes

03Standardized HuggingFace and NVIDIA Model Card++ formatting

04In-depth ethical risk assessment and fairness documentation

059 GitHub stars

06Automated 'How to Get Started' code snippet generation for model loading

Casos de Uso

01Documenting trained models for handoff between data science and MLOps teams

02Creating transparency reports for regulatory compliance in high-stakes ML applications

03Standardizing model metadata and performance tracking across enterprise data teams

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add andikarachman/data-science-plugin model-card

For use in Claude.ai and ChatGPT

Características Principales

01Structured metric reporting with baseline comparisons and confidence intervals

02Comprehensive sections for training/evaluation data provenance and data hashes

03Standardized HuggingFace and NVIDIA Model Card++ formatting

04In-depth ethical risk assessment and fairness documentation

059 GitHub stars

06Automated 'How to Get Started' code snippet generation for model loading

Casos de Uso

01Documenting trained models for handoff between data science and MLOps teams

02Creating transparency reports for regulatory compliance in high-stakes ML applications

03Standardizing model metadata and performance tracking across enterprise data teams