Deep Learning based
Knowledge Extraction Toolkit

Suporting cnSchema, standard supervised setting, low-resource setting, and document-level setting for knowledge base population

Features

DeepKE is a knowledge extraction toolkit supporting 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.

Low-resource

DeepKE supports low-resource setting with only a few labelled (e.g., 16/32 shot) instances for widespread information extraction tasks.

Document-Level

Since relations are expressed over multiple sentences in real-world applications, DeepKE supports document-level relation extraction.

Diverse Tasks

DeepKE achieves diverse information extraction tasks: named entity recognition, relation extraction and attribute extraction.

Online Demo

We provide an online system to extract knowledge from the text with friendly interactive interfaces and fast reaction speed. Click here to try it !!

Open-sourced toolkit to extract knowledge

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. 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 texts according to their requirements. Specifically, DeepKE not only provides various functional modules and model implementation for different functions and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility. Moreover, DeepKE has quipped with comprehensive documents as well as Google Colab tutorials for beginners. Users can install DeepKE via 'install deepke'. We will provide maintenance to meet new requests, add new tasks, and fix bugs in the future.

Flexible usage

DeepKE provides various functional modules and rganizes all components by consistent frameworks.

Sufficient modularity and extensibility

The training & evaluation codes and model implementation are separated for easy usage.

Support automatic hyperparameter tuning

An off-the-shelf automatic hyperparameter tuning component is available.

Issues

Stars

Forks

Application Scenarios

Toolkit Design and Implementation

Start Our Demo for New Cn-schema

Broad Areas

Typical Scenario

Contributors