<p align="center"> <a href="https://github.com/zjunlp/deepke"> <img src="pics/logo.png" width="400"/></a> <p> <p align="center"> <a href="http://deepke.zjukg.cn"> <img alt="Documentation" src="https://img.shields.io/badge/demo-website-blue"> </a> <a href="https://pypi.org/project/deepke/#files"> <img alt="PyPI" src="https://img.shields.io/pypi/v/deepke"> </a> <a href="https://github.com/zjunlp/DeepKE/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/zjunlp/deepke"> </a> <a href="http://zjunlp.github.io/DeepKE"> <img alt="Documentation" src="https://img.shields.io/badge/doc-website-red"> </a> <a href="https://colab.research.google.com/drive/1vS8YJhJltzw3hpJczPt24O0Azcs3ZpRi?usp=sharing"> <img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"> </a> </p> <p align="center"> <b> English | <a href="https://github.com/zjunlp/DeepKE/blob/main/README_CN.md">简体中文</a> </b> </p> <h1 align="center"> <p>A Deep Learning Based Knowledge Extraction Toolkit<br>for Knowledge Graph Construction</p> </h1>

DeepKE is a knowledge extraction toolkit for knowledge graph construction supporting cnSchema，low-resource, document-level and multimodal scenarios for entity, relation and attribute extraction. We provide documents, online demo, paper, slides and poster for beginners.

• ❗Want to use Large Language Models with DeepKE? Try DeepKE-LLM and OneKE, have fun!
• ❗Want to train supervised models? Try Quick Start, we provide the NER models (e.g, LightNER(COLING'22), W2NER(AAAI'22)), relation extraction models (e.g., KnowPrompt(WWW'22)), relational triple extraction models (e.g., ASP(EMNLP'22), PRGC(ACL'21), PURE(NAACL'21)), and release off-the-shelf models at DeepKE-cnSchema, have fun!
• We recommend using Linux; if using Windows, please use \\ in file paths;
• If HuggingFace is inaccessible, please consider using wisemodel or modescape.

If you encounter any issues during the installation of DeepKE and DeepKE-LLM, please check Tips or promptly submit an issue, and we will assist you with resolving the problem!

Table of Contents

• Table of Contents
• What's New
• Prediction Demo
• Model Framework
• Quick Start
  • DeepKE-LLM
  • DeepKE-MCP-Tools
  • DeepKE
    • 🔧Manual Environment Configuration
    • 🐳Building With Docker Images
  • Requirements
    • DeepKE
  • Introduction of Three Functions
    • 1. Named Entity Recognition
    • 2. Relation Extraction
    • 3. Attribute Extraction
    • 4. Event Extraction
• Tips
• To do
• Reading Materials
• Related Toolkit
• Citation
• Contributors
• Other Knowledge Extraction Open-Source Projects

<br>

What's New

• June, 2025 We integrate the MCP service tools into DeepKE, enabling knowledge extraction through large language models (LLMs) as tool callers for lightweight models.
• December, 2024 We open source the OneKE knowledge extraction framework, supporting multi-agent knowledge extraction across various scenarios.
• April, 2024 We release a new bilingual (Chinese and English) schema-based information extraction model called OneKE based on Chinese-Alpaca-2-13B.
• Feb, 2024 We release a large-scale (0.32B tokens) high-quality bilingual (Chinese and English) Information Extraction (IE) instruction dataset named IEPile, along with two models trained with IEPile, baichuan2-13b-iepile-lora and llama2-13b-iepile-lora.
• Sep 2023 a bilingual Chinese English Information Extraction (IE) instruction dataset called InstructIE was released for the Instruction based Knowledge Graph Construction Task (Instruction based KGC), as detailed in here.
• June, 2023 We update DeepKE-LLM to support knowledge extraction with KnowLM, ChatGLM, LLaMA-series, GPT-series etc.
• Apr, 2023 We have added new models, including CP-NER(IJCAI'23), ASP(EMNLP'22), PRGC(ACL'21), PURE(NAACL'21), provided event extraction capabilities (Chinese and English), and offered compatibility with higher versions of Python packages (e.g., Transformers).
• Feb, 2023 We have supported using LLM (GPT-3) with in-context learning (based on EasyInstruct) & data generation, added a NER model W2NER(AAAI'22).

<details> <summary><b>Previous News</b></summary>

• Nov, 2022 Add data annotation instructions for entity recognition and relation extraction, automatic labelling of weakly supervised data (entity extraction and relation extraction), and optimize multi-GPU training.

• Sept, 2022 The paper DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population has been accepted by the EMNLP 2022 System Demonstration Track.

• Aug, 2022 We have added data augmentation (Chinese, English) support for low-resource relation extraction.

• June, 2022 We have added multimodal support for entity and relation extraction.

• May, 2022 We have released DeepKE-cnschema with off-the-shelf knowledge extraction models.

• Jan, 2022 We have released a paper DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

• Dec, 2021 We have added dockerfile to create the enviroment automatically.

• Nov, 2021 The demo of DeepKE, supporting real-time extration without deploying and training, has been released.

• The documentation of DeepKE, containing the details of DeepKE such as source codes and datasets, has been released.

• Oct, 2021 pip install deepke

• The codes of deepke-v2.0 have been released.

• Aug, 2019 The codes of deepke-v1.0 have been released.

• Aug, 2018 The project DeepKE startup and codes of deepke-v0.1 have been released.

</details>

Prediction Demo

There is a demonstration of prediction. The GIF file is created by Terminalizer. Get the code. <img src="pics/demo.gif" width="636" height="494" align=center>

<br>

Model Framework

<h3 align="center"> <img src="pics/architectures.png"> </h3>

• DeepKE contains a unified framework for named entity recognition, relation extraction and attribute extraction, the three knowledge extraction functions.
• Each task can be implemented in different scenarios. For example, we can achieve relation extraction in standard, low-resource (few-shot), document-level and multimodal settings.
• Each application scenario comprises of three components: Data including Tokenizer, Preprocessor and Loader, Model including Module, Encoder and Forwarder, Core including Training, Evaluation and Prediction.

<br>

Quick Start

DeepKE-LLM

In the era of large models, DeepKE-LLM utilizes a completel