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

2024-03-31Code Available4· sign in to hype

Weihua Hu, Yiwen Yuan, Zecheng Zhang, Akihiro Nitta, Kaidi Cao, Vid Kocijan, Jinu Sunil, Jure Leskovec, Matthias Fey

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Abstract

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.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
fakeFTTransformer + OpenAI embeddingAUROC0.91Unverified
fakeFTTransformer + RoBERTa embeddingAUROC0.94Unverified
fakeResNet + RoBERTa embeddingAUROC0.93Unverified
fakeResNet + OpenAI embeddingAUROC0.92Unverified
fakeTrompt + OpenAI embeddingAUROC0.98Unverified
fakeLightGBM + OpenAI embeddingAUROC0.97Unverified
fakeFTTransformer + RoBERTa fintuneAUROC0.96Unverified
fakeLightGBM + RoBERTa embeddingAUROC0.95Unverified
kickstarterResNet + RoBERTa finetuneAUROC0.79Unverified
kickstarterLightGBM + RoBERTa embeddingAUROC0.77Unverified
kickstarterTrompt + OpenAI embeddingAUROC0.81Unverified

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