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huggingface-evaluate

Evaluate machine learning models and datasets using Hugging Face Evaluate metrics.

2|Updated Jan 15, 2026
Search Tags:#compliance#evaluation#datasets#huggingface#ai act#ml models
One-click install
npx skills add https://github.com/DTMC-marketplace/governance --skill huggingface-evaluate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-evaluate
Source: https://github.com/DTMC-marketplace/governance/tree/main/skills/huggingface-evaluate
Command: npx skills add https://github.com/DTMC-marketplace/governance --skill huggingface-evaluate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the evaluation of machine learning models and datasets, particularly those hosted on Hugging Face, ensuring performance and compliance.

Core Features & Use Cases

  • Model & Dataset Evaluation: Access over 100 metrics for accuracy, fairness, bias, and environmental impact.
  • Compliance Assessment: Evaluate AI systems against EU AI Act Article 15 requirements.
  • Risk Mitigation: Implement controls for performance-related risks.
  • Use Case: Evaluate a new natural language processing model for bias before deploying it in a customer-facing application.

Quick Start

Use the huggingface-evaluate skill to assess the bias of the model 'bert-base-uncased' on the dataset 'imdb'.

Frequently Asked Questions about huggingface-evaluate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate an ML model for bias using Hugging Face datasets?▼

You can evaluate ML models for bias using the Hugging Face Evaluate library, which provides access to over 100 metrics. It allows you to assess models like bert-base-uncased against datasets such as imdb for accuracy and fairness.

What metrics are needed for EU AI Act Article 15 compliance assessment?▼

EU AI Act Article 15 compliance assessment requires metrics for accuracy, fairness, bias, and environmental impact. This evaluation process helps implement controls for performance-related risks in AI systems.

Can I assess the environmental impact of my machine learning models?▼

Yes, you can assess the environmental impact of machine learning models using the Hugging Face Evaluate library. It includes specific metrics to measure the environmental footprint alongside accuracy and fairness.

Do I need to host my datasets on Hugging Face to use the evaluate library?▼

Yes, you need integration with the Hugging Face ecosystem to access the models and datasets required for evaluation. This integration is necessary to run the available metrics for accuracy and bias.

How many metrics are available for dataset evaluation?▼

There are over 100 metrics available for dataset evaluation. These metrics cover accuracy, fairness, bias, and environmental impact to help mitigate performance-related risks.

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