TabPFN-2.5 is a tabular foundation model based on the Transformer architecture. It utilizes context learning technology and can solve tabular prediction problems in a single forward propagation, providing efficient regression and classification solutions for structured tabular data.

TabPFN is a Transformer-based foundational model for tabular data. Through prior-data-based learning methods, it achieves strong performance on small-scale tabular regression tasks without requiring task-specific training.

TabPFN is a Transformer-based foundational model for tabular data. Through its prior data learning mechanism, it achieves outstanding performance on small-scale tabular datasets without requiring task-specific training.

TabPFNMix is a tabular foundation model pretrained on purely synthetic datasets, utilizing an encoder-decoder Transformer architecture, suitable for tabular data regression tasks.

Scientific ComputingSafetensors

A foundational model for tabular data, pretrained on synthetic datasets generated by mixing random classifiers

Scientific ComputingSafetensors