This repository contains the official implementation of a NeurIPS 2022 paper exploring how transforming scalar continuous features into vector embeddings before mixing them in the backbone can improve tabular deep learning models. The approach is applicable to conventional backbones like MLP and Transformer. The authors demonstrate that simple MLPs with embeddings can rival heavier Transformer-based models with better efficiency.