Towards Pretraining Text Encoders for TabPFN
Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text features. Standard pipelines, therefore, embed text with a language model and compress the resulting vectors with PCA into a small number of scalar features before inputting them into TabPFN. This creates an information bottleneck: most embedding dimensions are discarded, and the compressed representation must then be expanded again by TabPFN's feature encoder. End-to-end alternatives can avoid PCA, but they require large amounts of pretraining data containing text cells and usually perform subpar compared to tabula...
| Authors | Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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