ONNX 导出版适配器 AdapterHub/bert-base-uncased-pf-hotpotqa（适用于 bert-base-uncased）

将 AdapterHub/bert-base-uncased-pf-hotpotqa 转换为 UKP SQuARE

使用方法

onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-hotpotqa-onnx', filename='model.onnx') # 或使用 model_quant.onnx 进行量化
onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])

## 架构与训练

该适配器的训练代码可在 https://github.com/adapter-hub/efficient-task-transfer 获取。
具体而言，所有任务的训练配置可以在[这里](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs)找到。


## 评估结果

有关结果的更多信息，请参阅[论文](https://arxiv.org/pdf/2104.08247)。

## 引用

如果您使用此适配器，请引用我们的论文["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\",
}