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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13343 |
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| _version_ | 1866911177430269952 |
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| author | Čop, Andrej Bertalanič, Blaž Grobelnik, Marko Fortuna, Carolina |
| author_facet | Čop, Andrej Bertalanič, Blaž Grobelnik, Marko Fortuna, Carolina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MRM3: Machine Readable ML Model Metadata Čop, Andrej Bertalanič, Blaž Grobelnik, Marko Fortuna, Carolina Machine Learning 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. |
| title | MRM3: Machine Readable ML Model Metadata |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.13343 |