# The State of Documentation Practices of Third-party Machine Learning Models and Datasets

arXiv: https://arxiv.org/abs/2312.15058 · HTML: https://mlsanity.com/paper/2312.15058/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Data Quality for Software Vulnerability Datasets](https://mlsanity.com/paper/2301.05456/) (2023)
- [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023)
- [Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards](https://mlsanity.com/paper/2108.07374/) (2021)
- [Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability](https://mlsanity.com/paper/2204.06425/) (2022)

## Built on by (most cited first, 5 of 5)

- [An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face](https://mlsanity.com/paper/2502.04484/) (2025)
- [OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering](https://mlsanity.com/paper/2512.15979/) (2025)
- [Ethical and Technical Limits of Deepfake Speech Datasets](https://mlsanity.com/paper/2606.10911/) (2026)
- [Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models](https://mlsanity.com/paper/2608.18086/) (2026)
- [Automatic Model Card Generation Using an LLM](https://mlsanity.com/paper/2608.24807/) (2026)

## Abstract (arXiv)

Model stores offer third-party ML models and datasets for easy project integration, minimizing coding efforts. One might hope to find detailed specifications of these models and datasets in the documentation, leveraging documentation standards such as model and dataset cards. In this study, we use statistical analysis and hybrid card sorting to assess the state of the practice of documenting model cards and dataset cards in one of the largest model stores in use today--Hugging Face (HF). Our findings show that only 21,902 models (39.62%) and 1,925 datasets (28.48%) have documentation. Furthermore, we observe inconsistency in ethics and transparency-related documentation for ML models and datasets.