PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning

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...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Hu, Weihua, Yuan, Yiwen, Zhang, Zecheng, Nitta, Akihiro, Cao, Kaidi, Vid Kocijan, Jinu Sunil, Leskovec, Jure, Fey, Matthias
Format: Article
Language:English
Subjects:
Columns (structural)
Data structures
Deep learning
Graph neural networks
Machine learning
Relational data bases
Tables (data)
Online Access:Get full text
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Summary: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.
ISSN:2331-8422