```python
from transformers.onnx.features import FeaturesManager
distilbert_features = list(FeaturesManager.get_supported_features_for_model_type("distilbert").keys())
print(distilbert_features)
["default", "masked-lm", "causal-lm", "sequence-classification", "token-classification", "question-answering"]
```
You can then pass one of these features to the --feature argument in the
transformers.onnx package. For example, to export a text-classification model
we can pick a fine-tuned model from the Hub and run:
bash
python -m transformers.onnx --model=distilbert-base-uncased-finetuned-sst-2-english \
                            --feature=sequence-classification onnx/
which will display the following logs:
bash
Validating ONNX model...
        -[✓] ONNX model output names match reference model ({'logits'})
