Update README.md

README.md

@@ -1,10 +1,110 @@
 ---
 license: mit
 task_categories:
-- zero-shot-classification
+- tabular-classification
+- tabular-regression
+- feature-extraction
+pretty_name: PluRel – Synthetic Relational Databases
+size_categories:
+- 1B<n<10B
 tags:
 - relational-data
 - synthetic-data
-size_categories:
-- 10B<n<100B
----
+- foundation-models
+- tabular
+- pretraining
+- structural-causal-model
+- relbench
+---
+
+# PluRel Dataset
+
+**Synthetic Data unlocks Scaling Laws for Relational Foundation Models**
+
+[![arXiv](https://img.shields.io/badge/arXiv-2602.04029-b31b1b?style=flat&logo=arxiv)](https://arxiv.org/abs/2602.04029)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue?style=flat&logo=github)](https://snap-stanford.github.io/plurel/)
+[![GitHub](https://img.shields.io/badge/Code-GitHub-black?style=flat&logo=github)](https://github.com/snap-stanford/plurel)
+[![Checkpoints](https://img.shields.io/badge/Checkpoints-HuggingFace-yellow?style=flat&logo=huggingface)](https://huggingface.co/kvignesh1420/relational-transformer-plurel)
+
+Preprocessed synthetic relational databases for pretraining relational foundation models, as introduced in:
+
+> **PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models**
+> Kothapalli, Ranjan, Hudovernik, Dwivedi, Hoffart, Guestrin, Leskovec — arXiv:2602.04029 (2026)
+
+---
+
+## Data Structure
+
+Each entry is a [relbench](https://github.com/snap-stanford/relbench)-compatible `Database` consisting of multiple relational tables.
+
+| Component | Description |
+|-----------|-------------|
+| Tables | 3–20 per database |
+| Primary keys | `row_idx` (auto-generated) |
+| Foreign keys | `foreign_row_0`, `foreign_row_1`, ... |
+| Feature columns | `feature_0`, `feature_1`, ... (categorical or numerical) |
+| Time column | `date` — on activity (leaf) tables only |
+
+**Schema topology** is sampled from: BarabasiAlbert, ReverseRandomTree, or WattsStrogatz graphs.
+
+**Data generation** uses Structural Causal Models (SCMs) — column dependencies are modeled as DAGs, with values propagated through randomly-initialized MLPs. Activity tables also include trend + cycle + noise time-series.
+
+| Parameter | Range |
+|-----------|-------|
+| Rows per entity table | 500–1,000 |
+| Rows per activity table | 2,000–5,000 |
+| Columns per table | 3–40 (power-law) |
+| Missing values | 1–10% of numerical columns |
+| Timestamp range | 1990–2025 |
+| Train / Val / Test | 80% / 10% / 10% |
+
+---
+
+## Download
+
+```bash
+huggingface-cli download kvignesh1420/plurel \
+    --repo-type dataset \
+    --local-dir ~/scratch/pre
+```
+
+---
+
+## Usage
+
+Databases are named `rel-synthetic-<seed>` and are fully reproducible:
+
+```python
+from plurel import SyntheticDataset, Config
+
+dataset = SyntheticDataset(seed=42, config=Config())
+db = dataset.make_db()
+
+for name, table in db.tables.items():
+    print(f"{name}: {table.df.shape}")
+```
+
+See [snap-stanford/plurel](https://github.com/snap-stanford/plurel) for installation, configuration, and training scripts.
+
+---
+
+## Related
+
+| Resource | Link |
+|----------|------|
+| Pretrained checkpoints | [kvignesh1420/relational-transformer-plurel](https://huggingface.co/kvignesh1420/relational-transformer-plurel) |
+| Real-world relbench data | [hvag976/relational-transformer](https://huggingface.co/datasets/hvag976/relational-transformer) |
+
+
+---
+
+## Citation
+
+```bibtex
+@article{kothapalli2026plurel,
+  title={{PluRel:} Synthetic Data unlocks Scaling Laws for Relational Foundation Models},
+  author={Kothapalli, Vignesh and Ranjan, Rishabh and Hudovernik, Valter and Dwivedi, Vijay Prakash and Hoffart, Johannes and Guestrin, Carlos and Leskovec, Jure},
+  journal={arXiv preprint arXiv:2602.04029},
+  year={2026}
+}
+```