PyTorch Frame vs XGBoost: which is better for tabular data?

PyTorch Frame's deep models can match GBDT accuracy on some datasets but train much slower; XGBoost is often faster and more interpretable for traditional tabular tasks, as shown in benchmarks.

How to use OpenAI embeddings with PyTorch Frame?

Use the text_embedded stype and follow the llm_embedding.py example, which shows integration with OpenAI's API for generating embeddings before model training.

What deep tabular models are included in PyTorch Frame?

It implements Trompt, FTTransformer, TabNet, ExcelFormer, TabTransformer, and ResNet, all accessible via a unified modular framework with example scripts.

Can PyTorch Frame handle image data in tables?

Yes, it supports image_embedded stypes, allowing pre-computed image embeddings to be integrated with other column types for multi-modal learning.

Is PyTorch Frame suitable for small datasets?

It can be overkill for very small datasets where GBDTs excel, but its modular design allows experimentation if you need deep learning features like text embeddings.

How does PyTorch Frame compare to PyTorch Tabular?

PyTorch Frame focuses on a modular architecture for heterogeneous data and LLM integration, while PyTorch Tabular may offer more traditional deep learning setups; choice depends on data complexity.

PyTorch Frame

MITPython0.3.0

A modular deep learning framework for PyTorch to build neural networks on heterogeneous tabular data.

Visit Website

What is PyTorch Frame?

PyTorch Frame is a deep learning extension for PyTorch designed specifically for heterogeneous tabular data, supporting diverse column types like numerical, categorical, text, timestamp, and images. It provides a modular framework to implement and experiment with deep tabular models, aiming to democratize deep learning research on tabular data beyond traditional tree-based models.

Target Audience

Researchers and practitioners working with tabular data who want to apply deep learning models, especially those dealing with multi-modal data types (e.g., text, images) alongside traditional columns. It also targets developers needing to integrate tabular models with other PyTorch libraries or large language models.

Value Proposition

PyTorch Frame offers a modular architecture that separates feature encoding, column interaction modeling, and decoding, enabling flexible experimentation and reusability. It uniquely supports integration with external APIs and models (e.g., OpenAI, Hugging Face) for text embedding, and includes benchmark datasets and implementations of state-of-the-art deep tabular models.

Overview

Tabular Deep Learning Library for PyTorch

Use Cases

Best For

Building deep learning models on tabular datasets with mixed column types (e.g., numerical, categorical, text, images).

Related Projects

Community-curated · Updated weekly · 100% open source

Found a gem we're missing?

Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.

Submit a project Star on GitHub

GitHub

780 stars71 forks0 contributors

Researchers benchmarking deep tabular models against gradient-boosted decision trees (GBDTs) like XGBoost or LightGBM.

Integrating large language model embeddings (e.g., from OpenAI or Cohere) into tabular data pipelines for enhanced feature representation.

Developing custom neural network architectures for tabular data using a modular framework of FeatureEncoder, TableConv, and Decoder components.

Training end-to-end models that combine tabular data with downstream PyTorch models, such as graph neural networks via PyG.

Experimenting with state-of-the-art deep tabular models like Trompt, FTTransformer, TabNet, ExcelFormer, and TabTransformer in a unified codebase.

Not Ideal For

Projects prioritizing fast training and inference over model flexibility, as deep tabular models are 5-100 times slower than GBDTs.
Applications with only numerical and categorical data, where simpler libraries like scikit-learn or XGBoost are more efficient and interpretable.
Environments with strict memory constraints, as models like Trompt can run out of memory on larger datasets per benchmark results.
Teams needing black-box model interpretability for compliance or debugging, since deep neural networks are less transparent than tree-based methods.

Pros & Cons

Pros

Modular Architecture

Separates models into FeatureEncoder, TableConv, and Decoder components, enabling flexible experimentation and reuse as shown in the ExampleTransformer implementation.

Heterogeneous Data Support

Handles diverse column types like text, images, and embeddings, allowing deep learning on complex tabular data that traditional methods struggle with.

LLM Integration

Seamlessly integrates with external APIs like OpenAI and Hugging Face for text embeddings, with code examples provided for each service.

Benchmark Datasets

Includes ready-to-use datasets and implementations of SOTA models like FTTransformer, facilitating research and comparison against GBDTs.

Cons

Slow Training Times

Deep models are significantly slower to train than GBDTs, as admitted in the benchmark, making them less suitable for time-sensitive applications.

Memory Scalability Issues

Some models cause out-of-memory errors on larger datasets, like Trompt in the benchmark, limiting use on big data without ample resources.

API Dependencies

Relies on third-party services for text embeddings, which can incur costs, require internet access, and add complexity to deployments.

Frequently Asked Questions

Home

Machine Learning

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Stars99,591

Forks27,643

Last commit9 hours ago

keras

Deep Learning for humans

Streamlit — A faster way to build and share data apps.

Stars44,427

Forks4,221

Last commit21 hours ago

gradio

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

Machine Learning72.2k