inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np")
outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
```
The required output names (i.e. ["last_hidden_state"]) can be obtained by
taking a look at the ONNX configuration of each model. For example, for
DistilBERT we have:
```python
from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig
config = DistilBertConfig()
onnx_config = DistilBertOnnxConfig(config)
print(list(onnx_config.outputs.keys()))
["last_hidden_state"]
```
Selecting features for different model topologies
Each ready-made configuration comes with a set of features that enable you to
export models for different types of topologies or tasks. As shown in the table
below, each feature is associated with a different auto class:
