README.md

---
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

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## Evaluation results

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## Citation

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