探索与导航Hugging Face模型图谱

摘要

随着数百万个公开可用的神经网络模型的出现，搜索和分析大规模模型库变得愈发重要。要驾驭如此众多的模型，需要一份“地图”，但由于大多数模型缺乏完善的文档，绘制这样一份地图颇具挑战。为了挖掘模型库的潜在价值，我们绘制了一份初步的地图，代表了Hugging Face平台上已文档化的模型部分。这份地图提供了模型景观及其演变的惊艳可视化展示。我们展示了该地图的多种应用，包括预测模型属性（如准确率）以及分析计算机视觉模型的趋势。然而，鉴于当前地图仍不完整，我们提出了一种方法来绘制未文档化的区域。具体而言，我们基于现实世界中主导的模型训练实践，识别出高置信度的结构先验。利用这些先验知识，我们的方法能够精确映射地图中先前未文档化的区域。我们公开了我们的数据集、代码及交互式地图。

English

As there are now millions of publicly available neural networks, searching and analyzing large model repositories becomes increasingly important. Navigating so many models requires an atlas, but as most models are poorly documented charting such an atlas is challenging. To explore the hidden potential of model repositories, we chart a preliminary atlas representing the documented fraction of Hugging Face. It provides stunning visualizations of the model landscape and evolution. We demonstrate several applications of this atlas including predicting model attributes (e.g., accuracy), and analyzing trends in computer vision models. However, as the current atlas remains incomplete, we propose a method for charting undocumented regions. Specifically, we identify high-confidence structural priors based on dominant real-world model training practices. Leveraging these priors, our approach enables accurate mapping of previously undocumented areas of the atlas. We publicly release our datasets, code, and interactive atlas.

探索与导航Hugging Face模型图谱

Charting and Navigating Hugging Face's Model Atlas

摘要

Summary

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