metadata
license: cc-by-nc-sa-4.0
tags:
- relational-deep-learning
- relational-databases
- tabular
- tabular-classification
- tabular-regression
- foundation-model
- in-context-learning
- few-shot
- relbench
- relational-transformer
datasets:
- stanford-star/the-join
- stanford-star/relbench
pipeline_tag: tabular-classification
RT-P (rt-p)
RT-P is a Relational Transformer checkpoint pair for in-context / few-shot entity
prediction over multi-table relational databases (no per-task gradient training).
Same architecture and file layout as stanford-star/rt-j
— drop-in replacement.
| Variant | Folder | Task |
|---|---|---|
| Classifier | classification/ |
binary entity classification |
| Regressor | regression/ |
entity regression |
Architecture: ~85.6M params · bfloat16 · 12 blocks, d_model 512, 8 heads, d_ff 2048 ·
text columns embedded with all-MiniLM-L12-v2 (d_text 384).
Each folder contains model.safetensors (weights) and config.json
(dims + text-embedding model).
Usage
from rt.model import RelationalTransformer
model = RelationalTransformer.from_pretrained("stanford-star/rt-p/classification")
# or: RelationalTransformer.from_pretrained("stanford-star/rt-p", subfolder="regression")
pixi run eval --model.load-ckpt-path stanford-star/rt-p/classification ...
Related
- Training data: stanford-star/the-join
- Evaluation: RelBench
- Reference model: stanford-star/rt-j