This repository implements the TabTransformer architecture for tabular data, leveraging self-attention mechanisms from transformers. The model handles both categorical and continuous features, using depth-wise attention layers followed by MLP layers for prediction. It also includes the FT-Transformer variant, which applies transformer architecture more broadly to tabular data problems.