readme: ---
  inference: false
  tags:
  - onnx
  - adapterhub:qa/narrativeqa
  - adapter-transformers
  - bart
  datasets:
  - narrativeqa
  ---
  
  # ONNX export of Adapter `AdapterHub/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/bart-base-pf-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/bart-base-pf-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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