<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">
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<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">
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<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](https://arxiv.org/pdf/2201.03335.pdf) 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](https://zjunlp.github.io/DeepKE/), [Google Colab tutorials](https://colab.research.google.com/drive/1vS8YJhJltzw3hpJczPt24O0Azcs3ZpRi?usp=sharing), [online demo](http://deepke.zjukg.cn/), [paper](https://arxiv.org/pdf/2201.03335.pdf), [slides](https://drive.google.com/file/d/1IIeIZAbVduemqXc4zD40FUMoPHCJinLy/view?usp=sharing) and [poster](https://drive.google.com/file/d/1vd7xVHlWzoAxivN4T5qKrcqIGDcSM1_7/view?usp=sharing) for beginners.
- ❗New: We provide the LLM support with [EasyInstruct](https://github.com/zjunlp/EasyInstruct) at [DeepKE-LLM](https://github.com/zjunlp/DeepKE/tree/main/example/llm) including [InstructionKGC]() and [CodeKGC](), have fun!
- ❗New: We provide the relational triple extraction support (e.g., [ASP(EMNLP'22)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/ASP), [PRGC(ACL'21)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/PRGC), [PURE(NAACL'21)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/PURE)) and release off-the-shelf models at [DeepKE-cnSchema](https://github.com/zjunlp/DeepKE/tree/main/example/triple/cnschema), have fun!
**Reading Materials**:
Data-Efficient Knowledge Graph Construction, 高效知识图谱构建 ([Tutorial on CCKS 2022](http://sigkg.cn/ccks2022/?page_id=24)) \[[slides](https://drive.google.com/drive/folders/1xqeREw3dSiw-Y1rxLDx77r0hGUvHnuuE)\]
Efficient and Robust Knowledge Graph Construction ([Tutorial on AACL-IJCNLP 2022](https://www.aacl2022.org/Program/tutorials)) \[[slides](https://github.com/NLP-Tutorials/AACL-IJCNLP2022-KGC-Tutorial)\]
PromptKG Family: a Gallery of Prompt Learning & KG-related Research Works, Toolkits, and Paper-list [[Resources](https://github.com/zjunlp/PromptKG)\]
Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective \[[Survey](https://arxiv.org/abs/2202.08063)\]\[[Paper-list](https://github.com/zjunlp/Low-resource-KEPapers)\]
**Related Toolkit**:
[Doccano](https://github.com/doccano/doccano)、[MarkTool](https://github.com/FXLP/MarkTool)、[LabelStudio](https://labelstud.io/ ): Data Annotation Toolkits
[LambdaKG](https://github.com/zjunlp/PromptKG/tree/main/lambdaKG): A library and benchmark for PLM-based KG embeddings
[EasyInstruct](https://github.com/zjunlp/EasyInstruct): An easy-to-use framework to instruct Large Language Models
# Table of Contents
* [What's New](#whats-new)
* [Prediction Demo](#prediction-demo)
* [Model Framework](#model-framework)
* [Quick Start](#quick-start)
* [Requirements](#requirements)
* [Introduction of Three Functions](#introduction-of-three-functions)
* [1. Named Entity Recognition](#1-named-entity-recognition)
* [2. Relation Extraction](#2-relation-extraction)
* [3. Attribute Extraction](#3-attribute-extraction)
* [Notebook Tutorial](#notebook-tutorial)
* [Tips](#tips)
* [To do](#to-do)
* [Citation](#citation)
* [Contributors](#contributors)
* [Other Knowledge Extraction Open-Source Projects](#other-knowledge-extraction-open-source-projects)
<br>
# What's New
## Apr, 2023
* We have added new models, including [CP-NER(IJCAI'23)](https://github.com/zjunlp/DeepKE/blob/main/example/ner/cross), [ASP(EMNLP'22)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/ASP), [PRGC(ACL'21)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/PRGC), [PURE(NAACL'21)](https://github.com/zjunlp/DeepKE/tree/main/example/triple/PURE), provided [event extraction](https://github.com/zjunlp/DeepKE/tree/main/example/ee/standard) capabilities (Chinese and English), and offered compatibility with higher versions of Python packages (e.g., Transformers).
## Feb, 2023
* We have supported using [LLM](https://github.com/zjunlp/DeepKE/tree/main/example/llm) (GPT-3) with in-context learning (based on [EasyInstruct](https://github.com/zjunlp/EasyInstruct)) & data generation, added a NER model [W2NER(AAAI'22)](https://github.com/zjunlp/DeepKE/tree/main/example/ner/standard/w2ner).
## Nov, 2022
* Add data [annotation instructions](https://github.com/zjunlp/DeepKE/blob/main/README_TAG.md) for entity recognition and relation extraction, automatic labelling of weakly supervised data ([entity extraction](https://github.com/zjunlp/DeepKE/tree/main/example/ner/prepare-data) and [relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/prepare-data)), and optimize [multi-GPU training](https://github.com/zjunlp/DeepKE/tree/main/example/re/standard).
## Sept, 2022
* The paper [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://arxiv.org/abs/2201.03335) has been accepted by the EMNLP 2022 System Demonstration Track.
## Aug, 2022
* We have added [data augmentation](https://github.com/zjunlp/DeepKE/tree/main/example/re/few-shot/DA) (Chinese, English) support for [low-resource relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/few-shot).
## June, 2022
* We have added multimodal support for [entity](https://github.com/zjunlp/DeepKE/tree/main/example/ner/multimodal) and [relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/multimodal).
## May, 2022
* We have released [DeepKE-cnschema](https://github.com/zjunlp/DeepKE/blob/main/README_CNSCHEMA.md) 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](https://arxiv.org/abs/2201.03335)
## 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.
<br>
# Prediction Demo
There is a demonstration of prediction. The GIF file is created by [Terminalizer](https://github.com/faressoft/terminalizer). Get the [code](https://drive.google.com/file/d/1r4tWfAkpvynH3CBSgd-XG79rf-pB-KR3/view?usp=share_link).
<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 extrac
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DeepKE压缩包,支持NLP
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DeepKE压缩包,支持NLP (583个子文件)
run_finetune_ds.bash 1KB
make.bat 804B
CITATION.cff 2KB
custom.css 2KB
relation.csv 1KB
example.csv 404B
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fewshot_re_tutorial.ipynb 65KB
document_re_tutorial.ipynb 63KB
multimodal_ner_tutorial.ipynb 49KB
multimodal_re_tutorial.ipynb 46KB
standard_re_pcnn_tutorial.ipynb 33KB
standard_re_BERT_tutorial.ipynb 22KB
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example.json 571B
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config.json 234B
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README.md 29KB
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