metadata
license: apache-2.0
library_name: relational-transformers
pipeline_tag: tabular-classification
tags:
- relational-transformers
- onnx
- relational-data
RT-J ONNX
This repository contains the classification checkpoint from
RelativeDB/rt-j-fp16, exported
to ONNX for framework-neutral target prediction over caller-provided relational
cell embeddings.
Load and cache it automatically with:
from relational_transformers import RelationalTransformer
model = RelationalTransformer(backend="onnx")
predictions = model.predict(batch)
The graph accepts the canonical RelationalBatch tensor fields. Batch size and
cell count are dynamic; the text and column-embedding width is fixed at 384.
Callers remain responsible for producing the model-ready cell embeddings and
relations described in the
relational-transformers input contract.
model.onnx is exported from the full published checkpoint. The release
process verifies PyTorch and ONNX Runtime output parity at multiple dynamic
context lengths before publishing the file.