This repository provides the official implementation of the SAINT paper, introducing improved neural network architectures for tabular data. The model employs row-level attention mechanisms and contrastive pre-training strategies to enhance performance on tabular classification and regression tasks. Built in PyTorch, it supports various attention variants including column-only, row-only, and combined approaches, and can leverage unlabeled data for unsupervised pretraining to improve performance with limited labeled samples.