PluRel, preprocessed (plurel-preprocessed)

Phase 1 of RT-J's pretraining, and the pretraining corpus of RT-PluRel: the synthetic relational databases of stanford-star/plurel run through the rustler preprocessor, in the on-disk format the Relational Transformer dataloaders read.

• Code: https://github.com/stanford-star/relational-transformer
• Papers: Relational Transformer (arXiv:2510.06377) · PluRel (arXiv:2602.04029)

What it is

Databases	2,000 synthetic (plurel-3000 … plurel-4999)
Files	16,005
Size	43,218,968,510 bytes (~40.2 GiB)
Text embedder	sentence-transformers/all-MiniLM-L12-v2 (384-d)

Per-file-kind totals: nodes.rkyv ~35.2 GiB, p2f_adj.rkyv ~4.1 GiB, offsets.rkyv ~0.8 GiB, text_emb_all-MiniLM-L12-v2.bin ~0.1 GiB (synthetic databases carry little text).

The curated pretraining mixture is plurel/rt-plurel-train.json, vendored in the rt package: 86,211 (db, task) pairs over 1,900 of the 2,000 databases — the filtered subset RT-J's phase 1 and RT-PluRel actually train on. The remaining 100 databases are present but excluded by the filter.

Relation to the raw dataset

Derived from stanford-star/plurel — synthetic databases in RelBench format (parquet + manifest.yaml), generated by PluRel. That repository is the one to generate from or cite; this one is a build artifact and is not a substitute.

A 2026-09-12 commit dropped the older generation of databases, leaving only plurel-3000 … plurel-4999; the same round regenerated the task lists with autocomplete task manifests and the filtered rt-plurel-train.json. An earlier revision is a different collection.

File layout

plurel-<n>/              # one per synthetic database, n = 3000..4999
  meta.json  table_info.json  column_index.json
  nodes.rkyv  offsets.rkyv  p2f_adj.rkyv
  text_emb_all-MiniLM-L12-v2.bin
  text.json

The task lists are not in this repository. The curated (db, task) mixtures are vendored in the rt package instead, so a list can no longer drift from the data or the code that reads it:

from rt.data import get_mixture_path, list_mixtures

list_mixtures()                                    # every (collection, name)
path = get_mixture_path("plurel", "rt-plurel-train")

Pass that path as db_task_list.

For this collection the vendored lists are rt-plurel-train (86,211 pairs over 1,900 databases — the mixture RT-J phase 1 and RT-PluRel train on), all and autocomplete (116,088 over 1,973 each; every task here is an autocompletion task, so forecast is empty).

pre_dir is always a local directory; download with hf download --local-dir, nothing is fetched on demand.

How it was produced

# examples/preprocess.py, preprocess_a_collection()
many(repo="stanford-star/plurel", out_dir="data/plurel-preprocessed",
     shard=0, num_shards=1, skip_existing=True, embedder="all-MiniLM-L12-v2", ...)

There is no CLI: copy examples/preprocess.py, edit the call, pixi run python examples/preprocess.py.

The rt-plurel-train mixture is vendored in the package, so you do not have to rebuild it, but the filter that produced it is worth knowing because it is what separates the 1,900 trained-on databases from the 2,000 present. Taking plurel-3000 … plurel-4999 in order, a database is dropped if any of its tables has more than 5 foreign keys, and the first 1,900 survivors are kept. A column of a surviving database becomes a task only if it is a bool, int64 or double column whose name contains feature, it is not a source-node column, and it has at least 2 distinct values — then, for a classification task (the bool columns), at least 2 classes and a majority class no larger than 99%; for a regression task, a standard deviation of at least 1e-4. That yields the 86,211 pairs in the vendored list.

Preprocessing commit. The 2026-09-12 content was written by rustler at repository commit 970167c (2026-09-09, rustler: no boolean sem type in sampler output; bools are z-scored numbers end to end), the last preprocessing-code commit before that rebuild.

rustler z-scores numeric and datetime cells. Since PR #4 (rustler: column stats from the train period only, commit 8030aa8) the statistics come from the train period alone; PluRel manifests carry a real val_timestamp, so that change does affect this collection. The tree published at this revision predates 8030aa8; a regenerated one is on its way and will be uploaded here, with the preprocessing commit above updated to match. Pin the revision below for the published numbers.

Revisions

The RT-J paper's results were produced against revision 9d70172425b44053f1270b19094ba6dcf7d66464 (2026-09-12). Pin it:

pixi run hf download stanford-star/plurel-preprocessed --repo-type dataset \
  --revision 9d70172425b44053f1270b19094ba6dcf7d66464 \
  --local-dir data/plurel-preprocessed

Licence

CC BY 4.0 — attribution only, commercial use permitted.

Note the asymmetry with the upstream collection, which is deliberate. The databases here are entirely synthetic: they were generated by PluRel, contain no third-party data, and carry no inherited terms. stanford-star holds the rights in both the upstream collection and this derived artifact, so this repository can be offered under the more permissive CC BY 4.0 even though stanford-star/plurel is published as CC BY-SA 4.0. The upstream collection keeps its own CC BY-SA 4.0 terms when you redistribute it; CC BY 4.0 here applies to this preprocessed build.

The preprocessed builds of real-world data, the-join-preprocessed and relbench-preprocessed, are CC BY 4.0 as well, but for a different reason: their sources are third-party and share-alike, so those repositories ship no verbatim source content — only embeddings of it — and omit the text.json string table entirely. Here that was never necessary, so text.json ships and you can re-embed this tree with a different text embedder without regenerating it.

Citation

@article{ranjan2026rtj,
  title     = {RT-J: Large-Scale Pretraining of Relational Transformers for
               Context-Efficient Predictions},
  author    = {Ranjan, Rishabh and Kothapalli, Vignesh and Agarwal,
               Harshvardhan and Kanatsoulis, Charilaos and Upendra, Roshan and
               Palczewski, Tom and Guestrin, Carlos and Leskovec, Jure},
  year      = {2026},
}