We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model abstraction to enable modular implementation of tabular models, and allowing external foundation models to be incorporated to handle complex columns (e.g., LLMs for text columns). We demonstrate the usefulness of PyTorch Frame by implementing diverse tabular models in a modular way, successfully applying these models to complex multi-modal tabular data, and integrating our framework with PyTorch Geometric, a PyTorch library for Graph Neural Networks (GNNs), to perform end-to-end learning over relational databases.

翻译：我们提出PyTorch Frame，一个基于PyTorch的深度学习框架，专为多模态表格数据设计。PyTorch Frame通过提供基于PyTorch的数据结构来处理复杂表格数据、引入模型抽象以实现表格模型的模块化实现，并允许集成外部基础模型（例如文本列的LLM）以处理复杂列，从而简化了表格深度学习。我们通过模块化方式实现多种表格模型、成功将这些模型应用于复杂多模态表格数据，并将我们的框架与图神经网络（GNN）的PyTorch库PyTorch Geometric集成以对关系数据库进行端到端学习，验证了PyTorch Frame的实用性。