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We present \emph{TabRet}, a pre-trainable Transformer-based model for tabulardata. TabRet is designed to work on a downstream task that contains columns notseen in pre-training. Unlike other methods, TabRet has an extra learning stepbefore fine-tuning called \emph{retokenizing}, which calibrates featureembeddings based on the masked autoencoding loss. In experiments, wepre-trained TabRet with a large collection of public health surveys andfine-tuned it on classification tasks in healthcare, and TabRet achieved thebest AUC performance on four datasets. In addition, an ablation study showsretokenizing and random shuffle augmentation of columns during pre-trainingcontributed to performance gains. The code is available athttps://github.com/pfnet-research/tabret .

TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns