The rapid proliferation of AI models has underscored the importance of thorough documentation, which enables users to understand, trust and effectively use these models in various applications. Although developers are encouraged to produce model cards, it’s not clear how much or what information these cards contain. In this study we conduct a comprehensive analysis of 32,111 AI model documentations on Hugging Face, a leading platform for distributing and deploying AI models. Our investigation sheds light on the prevailing model card documentation practices. Most AI models with a substantial number of downloads provide model cards, although with uneven informativeness. We find that sections addressing environmental impact, limitations and evaluation exhibit the lowest filled-out rates, whereas the training section is the one most consistently filled-out. We analyse the content of each section to characterize practitioners’ priorities. Interestingly, there are considerable discussions of data, sometimes with equal or even greater emphasis than the model itself. Our study provides a systematic assessment of community norms and practices surroinding model documentation through large-scale data science and linguistic analysis.

人工智能模型的快速扩散凸显了完整文档的重要性，它使用户能够理解、信任并在各种应用中有效地使用这些模型。尽管鼓励开发人员制作模型卡，但尚不清楚这些卡包含多少或哪些信息。在这项研究中，我们对 Hugging Face（一个领先的 AI 模型分发和部署平台）上的 32,111 个 AI 模型文档进行了全面分析。我们的调查揭示了流行的模型卡文档实践。大多数下载量较大的人工智能模型都会提供模型卡，但信息量参差不齐。我们发现，涉及环境影响、限制和评估的部分填写率最低，而培训部分的填写率最高。我们分析每个部分的内容来描述从业者的优先事项。有趣的是，有大量关于数据的讨论，有时与模型本身同等甚至更重要。我们的研究通过大规模数据科学和语言分析，对围绕模型文档的社区规范和实践进行了系统评估。