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README.md

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+---
+inference: false
+tags:
+- onnx
+- adapterhub:qa/narrativeqa
+- adapter-transformers
+- bart
+datasets:
+- narrativeqa
+---
+
+# ONNX export of Adapter `hSterz/narrativeqa` for facebook/bart-base
+## Conversion of [AdapterHub/narrativeqa](https://huggingface.co/AdapterHub/narrativeqa) for UKP SQuARE
+
+
+## Usage
+```python
+onnx_path = hf_hub_download(repo_id='UKP-SQuARE/narrativeqa-onnx', filename='model.onnx') # or model_quant.onnx for quantization
+onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
+
+context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
+question = 'What are advantages of ONNX?'
+tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/narrativeqa-onnx')
+
+inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
+outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)
+```
+
+## Architecture & Training
+
+<!-- Add some description here -->
+
+## Evaluation results
+
+<!-- Add some description here -->
+
+## Citation
+
+<!-- Add some description here -->