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Supporting cnSchema, standard supervised setting, low-resource setting, document-level setting and multi-modal setting for knowledge base population. 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 supports low-resource settings with only a few labeled (e.g., 16/32 shot) instances for widespread information extraction tasks. Since relations are expressed over multiple sentences in real-world applications, DeepKE supports document-level relation extraction. We present a new open-source and extensible knowledge extraction toolkit, called DeepKE, supporting standard fully supervised, low-resource few-shot and document-level scenarios.
Features
- DeepKE implements various information extraction tasks
- DeepKE allows developers and researchers to customize datasets and models to extract information
- Provides various functional modules and model implementation for different functions
- Organizes all components by consistent frameworks
- DeepKE has quipped with comprehensive documents as well as Google Colab tutorials for beginners
- Users can install DeepKE via 'install deepke'
Project Samples
