| --- |
| 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`](https://huggingface.co/RelativeDB/rt-j-fp16), exported |
| to ONNX for framework-neutral target prediction over caller-provided relational |
| cell embeddings. |
|
|
| Load and cache it automatically with: |
|
|
| ```python |
| 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](https://relationaltransformers.com/docs/relational_transformer/usage/batches.html). |
|
|
| `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. |
|
|
