---
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`](https://huggingface.co/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

```python
from rt.model import RelationalTransformer

model = RelationalTransformer.from_pretrained("stanford-star/rt-p/classification")
# or: RelationalTransformer.from_pretrained("stanford-star/rt-p", subfolder="regression")
```

```bash
pixi run eval --model.load-ckpt-path stanford-star/rt-p/classification ...
```

## Related

- Training data: [stanford-star/the-join](https://huggingface.co/datasets/stanford-star/the-join)
- Evaluation: [RelBench](https://huggingface.co/datasets/stanford-star/relbench)
- Reference model: [stanford-star/rt-j](https://huggingface.co/stanford-star/rt-j)