readme: ---
  inference: false
  tags:
  - onnx
  - question-answering
  - roberta
  - adapter-transformers
  datasets:
  - newsqa
  language:
  - en
  ---
  
  # ONNX export of Adapter `AdapterHub/roberta-base-pf-newsqa` for roberta-base
  ## Conversion of [AdapterHub/roberta-base-pf-newsqa](https://huggingface.co/AdapterHub/roberta-base-pf-newsqa) for UKP SQuARE
  
  
  ## Usage
  ```python
  onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-newsqa-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/roberta-base-pf-newsqa-onnx')
  
  inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
  inputs_int64 = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}
  outputs = onnx_model.run(input_feed=dict(inputs_int64), output_names=None)
  ```
  
  ## Architecture & Training
  
  The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
  In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
  
  
  ## Evaluation results
  
  Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
  
  ## Citation
  
  If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
  
  ```bibtex
  @inproceedings{poth-etal-2021-pre,
      title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
      author = {Poth, Clifton  and
        Pfeiffer, Jonas  and
        R{"u}ckl{'e}, Andreas  and
        Gurevych, Iryna},
      booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
      month = nov,
      year = "2021",
      address = "Online and Punta Cana, Dominican Republic",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2021.emnlp-main.827",
      pages = "10585--10605",
  }
  ```