We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population. DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction. With a unified framework, DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements. Specifically, DeepKE not only provides various functional modules and model implementation for different tasks and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility. We release the source code at GitHub in https://github.com/zjunlp/DeepKE with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various tasks, and a demo video.

翻译：我们提出一个开源且可扩展的知识抽取工具包DeepKE，支持知识库群体构建中的复杂低资源场景、文档级场景及多模态场景。DeepKE实现了多种信息抽取任务，包括命名实体识别、关系抽取和属性抽取。通过统一框架，DeepKE允许开发者和研究人员根据需求自定义数据集和模型，从非结构化数据中抽取信息。具体而言，DeepKE不仅为不同任务和场景提供多种功能模块与模型实现，还通过一致的框架组织所有组件以保持充分的模块化和可扩展性。我们在GitHub上发布源代码（https://github.com/zjunlp/DeepKE），并提供Google Colab教程和面向初学者的综合文档。此外，我们提供在线系统（http://deepke.openkg.cn/EN/re_doc_show.html）用于多种任务的实时抽取，以及演示视频。