ONNX 导出的适配器 AdapterHub/bert-base-uncased-pf-race（适用于 bert-base-uncased）

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

使用方法

onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-race-onnx', filename='model.onnx') # 或使用 model_quant.onnx 进行量化
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?'
choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-race-onnx')

raw_input = [[context, question +  + choice] for choice in choices]
inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
inputs['input_ids'] =  np.expand_dims(inputs['input_ids'], axis=0)
inputs['attention_mask'] =  np.expand_dims(inputs['attention_mask'], axis=0)
outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)

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找到。

评估结果

有关结果的更多信息，请参阅论文。

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"：

@inproceedings{poth-etal-2021-what-to-pre-train-on,
    title={What to Pre-Train on? Efficient Intermediate Task Selection},
    author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2104.08247",
    pages = "to appear",
}