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.
@article{arxiv.2404.00776,
title = {PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning},
author = {Weihua Hu and Yiwen Yuan and Zecheng Zhang and Akihiro Nitta and Kaidi Cao and Vid Kocijan and Jinu Sunil and Jure Leskovec and Matthias Fey},
journal= {arXiv preprint arXiv:2404.00776},
year = {2024}
}