```python
print(onnx_config.default_onnx_opset)
11
```
You can also view the outputs associated with the model as follows:
```python
print(onnx_config.outputs)
OrderedDict([("last_hidden_state", {0: "batch", 1: "sequence"})])
```
Notice that the outputs property follows the same structure as the inputs; it
returns an OrderedDict of named outputs and their shapes. The output structure
is linked to the choice of feature that the configuration is initialised with.
By default, the ONNX configuration is initialized with the default feature
that corresponds to exporting a model loaded with the AutoModel class. If you
want to export a different model topology, just provide a different feature to
the task argument when you initialize the ONNX configuration. For example, if
we wished to export DistilBERT with a sequence classification head, we could
