operators are used to construct a computational graph (often called an
intermediate representation) which represents the flow of data through the
neural network.
By exposing a graph with standardized operators and data types, ONNX makes it
easy to switch between frameworks. For example, a model trained in PyTorch can
be exported to ONNX format and then imported in TensorFlow (and vice versa).
🤗 Transformers provides a transformers.onnx package that enables you to
convert model checkpoints to an ONNX graph by leveraging configuration objects.
These configuration objects come ready made for a number of model architectures,
and are designed to be easily extendable to other architectures.
Ready-made configurations include the following architectures:
This table is automatically generated by make style, do not fill manually!
