MRM3: Machine Readable ML Model Metadata

TL;DR

This paper introduces MRM3, a structured, machine-readable schema for ML model metadata that facilitates better organization, querying, and environmental impact assessment, exemplified through a wireless localization models dataset.

Contribution

It defines a new structured schema for ML model metadata supporting machine readability and knowledge graph integration, enhancing model documentation and reuse.

Findings

Created a schema for ML model metadata

Built a Neo4j knowledge graph with 113 nodes

Integrated 22 models from 4 datasets

Abstract

As the complexity and number of machine learning (ML) models grows, well-documented ML models are essential for developers and companies to use or adapt them to their specific use cases. Model metadata, already present in unstructured format as model cards in online repositories such as Hugging Face, could be more structured and machine readable while also incorporating environmental impact metrics such as energy consumption and carbon footprint. Our work extends the existing State of the Art by defining a structured schema for ML model metadata focusing on machine-readable format and support for integration into a knowledge graph (KG) for better organization and querying, enabling a wider set of use cases. Furthermore, we present an example wireless localization model metadata dataset consisting of 22 models trained on 4 datasets, integrated into a Neo4j-based KG with 113 nodes and 199 relations.