Custom ONNX Runtime Operations

If you are looking to customize a pretrained embedding model by augmenting with pre-processing and post-processing operations, Oracle supports tokenization of an embedding model as a pre-processing operation and pooling and normalization as post-processing custom ONNX Runtime operations for version 1.15.1.

Oracle offers a Python utility that provides a mechanism to augment a pretrained model with tokenization, pooling and normalization. The Python utility can augment the model with pre-processing and post-processing operations and convert a pretrained model to an ONNX format. Models using any other custom operations will fail on import. For details on how to use the Python utility, see Convert Pretrained Models to ONNX Format.