| 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 --> |