defined in the configuration. We generally find numerical agreement in the 1e-6
to 1e-4 range, although anything smaller than 1e-3 is likely to be OK.
Contributing a new configuration to 🤗 Transformers
We are looking to expand the set of ready-made configurations and welcome
contributions from the community! If you would like to contribute your addition
to the library, you will need to:
Implement the ONNX configuration in the corresponding configuration_<model_name>.py
file
Include the model architecture and corresponding features in [~onnx.features.FeatureManager]
Add your model architecture to the tests in test_onnx_v2.py
Check out how the configuration for IBERT was
contributed to
get an idea of what's involved.
TorchScript
This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with
