阅读理解 Reading Comprehension

对指定文本数据内容进行理解和分析并回答提出的问题。
Understand and analyze the content of specified text data and answer the questions.

使用 Usage

Pip install fengshen:

git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
cd Fengshenbang-LM
pip install --editable ./

Run the code:

import argparse
from fengshen import UbertPiplines

total_parser = argparse.ArgumentParser("TASK NAME")
total_parser = UbertPiplines.piplines_args(total_parser)
args = total_parser.parse_args()

args.pretrained_model_path = "IDEA-CCNL/Erlangshen-Ubert-110M-Chinese"

test_data=[
    {
        "task_type": "抽取任务", 
        "subtask_type": "实体识别", 
        "text": "这也让很多业主据此认为，雅清苑是政府公务员挤对了国家的经适房政策。", 
        "choices": [ 
            {"entity_type": "小区名字"}, 
            {"entity_type": "岗位职责"}
            ],
        "id": 0}
]

model = UbertPiplines(args)
result = model.predict(test_data)
for line in result:
    print(line)

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的论文：

If you are using the resource for your work, please cite the our paper:

@article{fengshenbang,
  author    = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}

也可以引用我们的网站:

You can also cite our website:

@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}

阅读理解 Reading Comprehension

推荐模型 Recommended Models

使用 Usage

引用 Citation