Popular GitHub Repositories for Tabular Data

? TabPFN: Foundation Model for Tabular Data ?

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台，我们欢迎开源爱好者发起任何有意义的pr！

Automatic architecture search and hyperparameter optimization for PyTorch

Algorithms for outlier, adversarial and drift detection

AI code-writing assistant that understands data content

Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains

A standard framework for modelling Deep Learning Models for tabular data

Conditional GAN for generating synthetic tabular data.

A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.

Research on Tabular Deep Learning: Papers & Packages

Implementation of TabTransformer, attention network for tabular data, in Pytorch

Multimodal model for text and tabular data with HuggingFace transformers as building block for text data

本项目旨在收集开源的表格智能任务数据集（比如表格问答、表格-文本生成等），将原始数据整理为指令微调格式的数据并微调LLM，进而增强LLM对于表格数据的理解，最终构建出专门面向表格智能任务的大型语言模型。

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

DeltaPy - Tabular Data Augmentation (by @firmai)

A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.

The official PyTorch implementation of recent paper - SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training