  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.
