DeepKE is a knowledge extraction toolkit supporting cnSchema, standard supervised, low-resource and document-level scenarios for entity, relation and attribution extraction. It allows developers and researchers to customize datasets and models to extract information from unstructured texts.
DeepKE provides various functional modules and reganizes all components by consistent frameworks. DeepKE provides off-the-shelf extraction models with Chinese pre-trained language models based cnSchema.
The training & evaluation codes and model implementation are separated for easy usage.
An off-the-shelf automatic hyperparameter tuning component is available.
Ningyu Zhang, Liankuan Tao, Xin Xu, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li, Xiaozhuan Liang, YunzhiYao, Shumin Deng, Peng Wang, Wen Zhang, Guozhou Zheng, Huajun Chen
Qiang Chen, Feiyu Xiong
Zhenru Zhang, Chuanqi Tan, Fei Huang