stanford-star/relbench-preprocessed

RelBench, preprocessed

stanford-star/relbench-v1 in the tensor format read by the Relational Transformer: 7 databases, 21 forecast and 13 autocomplete tasks. The evaluation and validation data for RT-J and RT-PluRel. One directory per database:

<db>/  meta.json  table_info.json  column_index.json
       nodes.rkyv  offsets.rkyv  p2f_adj.rkyv
       text.json  text_emb_all-MiniLM-L12-v2.bin

legacy/ holds the same databases preprocessed with the boolean typing expected by the RT-v1 and PluRel-paper checkpoints.

Download, subset and revision pins: examples/README.md. Evaluation: examples/eval/.

License

CC BY 4.0. The RelBench databases keep their own licenses.

Citation

@inproceedings{ranjan2026rtj,
    title={{RT-J}: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions},
    author={Rishabh Ranjan and Vignesh Kothapalli and Harshvardhan Agarwal and Charilaos Kanatsoulis and Roshan Upendra and Tom Palczewski and Carlos Guestrin and Jure Leskovec},
    booktitle={The Fortieth Annual Conference on Neural Information Processing Systems},
    year={2026}
}

@inproceedings{ranjan2026relational,
    title={Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data},
    author={Rishabh Ranjan and Valter Hudovernik and Mark Znidar and Charilaos Kanatsoulis and Roshan Upendra and Mahmoud Mohammadi and Joe Meyer and Tom Palczewski and Carlos Guestrin and Jure Leskovec},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026}
}