English | 简体中文
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, Google Colab tutorials, online demo, paper, slides and poster for beginners.
- ❗Want to use Large Language Models with DeepKE? Try DeepKE-LLM with KnowLM, 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!
- Table of Contents
- What's New
- Prediction Demo
- Model Framework
- Quick Start
- Notebook Tutorial
- Tips
- To do
- Reading Materials
- Related Toolkit
- Citation
- Contributors (Determined by the roll of the dice)
- Other Knowledge Extraction Open-Source Projects
-
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).
Previous News
-
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 addeddockerfile
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.
There is a demonstration of prediction. The GIF file is created by Terminalizer. Get the code.
- 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.
In the era of large models, DeepKE-LLM utilizes a completely new environment dependency.
conda create -n deepke-llm python=3.9
conda activate deepke-llm
cd example/llm
pip install -r requirements.txt
Please note that the requirements.txt
file is located in the example/llm
folder.
DeepKE supports pip install deepke
.
Take the fully supervised relation extraction for example.
Step1 Download the basic code
git clone --depth 1 https://github.com/zjunlp/DeepKE.git
Step2 Create a virtual environment using Anaconda
and enter it.
- ❗NOTE: We provide a Dockerfile with tutorials please refer to the Tips to speed up installation
conda create -n deepke python=3.8
conda activate deepke
-
Install DeepKE with source code (Recommended)
python setup.py install python setup.py develop
-
Install DeepKE with
pip
pip install deepke
Step3 Enter the task directory
cd DeepKE/example/re/standard
Step4 Download the dataset, or follow the annotation instructions to obtain data
wget 120.27.214.45/Data/re/standard/data.tar.gz
tar -xzvf data.tar.gz
Many types of data formats are supported,and details are in each part.
Step5 Training (Parameters for training can be changed in the conf
folder)
We support visual parameter tuning by using wandb.
python run.py
Step6 Prediction (Parameters for prediction can be changed in the conf
folder)
Modify the path of the trained model in predict.yaml
.The absolute path of the model needs to be used,such as xxx/checkpoints/2019-12-03_ 17-35-30/cnn_ epoch21.pth
.
python predict.py
- ❗NOTE: if you encounter any errors, please refer to the Tips or submit a GitHub issue.
python == 3.9
- torch==1.13.0
- accelerate==0.17.1
- transformers==4.28.1
- bitsandbytes==0.37.2
- peft==0.2.0
- gradio
- datasets
- sentencepiece
- fire
python == 3.8
- torch == 1.5
- hydra-core == 1.0.6
- tensorboard == 2.4.1
- matplotlib == 3.4.1
- transformers == 3.4.0
- jieba == 0.42.1
- scikit-learn == 0.24.1
- seqeval == 1.2.2
- tqdm == 4.60.0
- opt-einsum==3.3.0
- wandb==0.12.7
- ujson
-
Named entity recognition seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, organizations, etc.
-
The data is stored in
.txt
files. Some instances as following (Users can label data based on the tools Doccano, MarkTool, or they can use the Weak Supervision with DeepKE to obtain data automatically):Sentence Person Location Organization 本报北京9月4日讯记者杨涌报道:部分省区人民日报宣传发行工作座谈会9月3日在4日在京举行。 杨涌 北京 人民日报 《红楼梦》由王扶林导演,周汝昌、王蒙、周岭等多位专家参与制作。 王扶林,周汝昌,王蒙,周岭 秦始皇兵马俑位于陕西省西安市,是世界八大奇迹之一。 秦始皇 陕西省,西安市 -
Read the detailed process in specific README
-
We support LLM and provide the off-the-shelf model, DeepKE-cnSchema-NER, which will extract entities in cnSchema without training.
Step1 Enter
DeepKE/example/ner/standard
. Download the dataset.wget 120.27.214.45/Data/ner/standard/data.tar.gz tar -xzvf data.tar.gz
Step2 Training
The dataset and parameters can be customized in the
data
folder andconf
folder respectively.python run.py
Step3 Prediction
python predict.py
-
Step1 Enter
DeepKE/example/ner/few-shot
. Download the dataset.wget 120.27.214.45/Data/ner/few_shot/data.tar.gz tar -xzvf data.tar.gz
Step2 Training in the low-resouce setting
The directory where the model is loaded and saved and the configuration parameters can be cusomized in the
conf
folder.python run.py +train=few_shot
Users can modify
load_path
inconf/train/few_shot.yaml
to use existing loaded model.Step3 Add
- predict
toconf/config.yaml
, modifyloda_path
as the model path andwrite_path
as the path where the predicted results are saved inconf/predict.yaml
, and then runpython predict.py
python predict.py
-
Step1 Enter
DeepKE/example/ner/multimodal
. Download the dataset.wget 120.27.214.45/Data/ner/multimodal/data.tar.gz tar -xzvf data.tar.gz
We use RCNN detected objects and visual grounding objects from original images as visual local information, where RCNN via faster_rcnn and visual grounding via onestage_grounding.
Step2 Training in the multimodal setting
- The dataset and parameters can be customized in the
data
folder andconf
folder respectively. - Start with the model trained last time: modify
load_path
inconf/train.yaml
as the path where the model trained last time was saved. And the path saving logs generated in training can be customized bylog_dir
.
python run.py
Step3 Prediction
python predict.py
- The dataset and parameters can be customized in the
-
-
Relationship extraction is the task of extracting semantic relations between entities from a unstructured text.
-
The data is stored in
.csv
files. Some instances as following (Users can label data based on the tools Doccano, MarkTool, or they can use the Weak Supervision with DeepKE to obtain data automatically):Sentence Relation Head Head_offset Tail Tail_offset 《岳父也是爹》是王军执导的电视剧,由马恩然、范明主演。 导演 岳父也是爹 1 王军 8 《九玄珠》是在纵横中文网连载的一部小说,作者是龙马。 连载网站 九玄珠 1 纵横中文网 7 提起杭州的美景,西湖总是第一个映入脑海的词语。 所在城市 西湖 8 杭州 2 -
!NOTE: If there are multiple entity types for one relation, entity types can be prefixed with the relation as inputs.
-
Read the detailed process in specific README
-
We support LLM and provide the off-the-shelf model, DeepKE-cnSchema-RE, which will extract relations in cnSchema without training.
Step1 Enter the
DeepKE/example/re/standard
folder. Download the dataset.wget 120.27.214.45/Data/re/standard/data.tar.gz tar -xzvf data.tar.gz
Step2 Training
The dataset and parameters can be customized in the
data
folder andconf
folder respectively.python run.py
Step3 Prediction
python predict.py
-
Step1 Enter
DeepKE/example/re/few-shot
. Download the dataset.wget 120.27.214.45/Data/re/few_shot/data.tar.gz tar -xzvf data.tar.gz
Step 2 Training
- The dataset and parameters can be customized in the
data
folder andconf
folder respectively. - Start with the model trained last time: modify
train_from_saved_model
inconf/train.yaml
as the path where the model trained last time was saved. And the path saving logs generated in training can be customized bylog_dir
.
python run.py
Step3 Prediction
python predict.py
- The dataset and parameters can be customized in the
-
Step1 Enter
DeepKE/example/re/document
. Download the dataset.wget 120.27.214.45/Data/re/document/data.tar.gz tar -xzvf data.tar.gz
Step2 Training
- The dataset and parameters can be customized in the
data
folder andconf
folder respectively. - Start with the model trained last time: modify
train_from_saved_model
inconf/train.yaml
as the path where the model trained last time was saved. And the path saving logs generated in training can be customized bylog_dir
.
python run.py
Step3 Prediction
python predict.py
- The dataset and parameters can be customized in the
-
Step1 Enter
DeepKE/example/re/multimodal
. Download the dataset.wget 120.27.214.45/Data/re/multimodal/data.tar.gz tar -xzvf data.tar.gz
We use RCNN detected objects and visual grounding objects from original images as visual local information, where RCNN via faster_rcnn and visual grounding via onestage_grounding.
Step2 Training
- The dataset and parameters can be customized in the
data
folder andconf
folder respectively. - Start with the model trained last time: modify
load_path
inconf/train.yaml
as the path where the model trained last time was saved. And the path saving logs generated in training can be customized bylog_dir
.
python run.py
Step3 Prediction
python predict.py
- The dataset and parameters can be customized in the
-
-
Attribute extraction is to extract attributes for entities in a unstructed text.
-
The data is stored in
.csv
files. Some instances as following:Sentence Att Ent Ent_offset Val Val_offset 张冬梅,女,汉族,1968年2月生,河南淇县人 民族 张冬梅 0 汉族 6 诸葛亮,字孔明,三国时期杰出的军事家、文学家、发明家。 朝代 诸葛亮 0 三国时期 8 2014年10月1日许鞍华执导的电影《黄金时代》上映 上映时间 黄金时代 19 2014年10月1日 0 -
Read the detailed process in specific README
-
Step1 Enter the
DeepKE/example/ae/standard
folder. Download the dataset.wget 120.27.214.45/Data/ae/standard/data.tar.gz tar -xzvf data.tar.gz
Step2 Training
The dataset and parameters can be customized in the
data
folder andconf
folder respectively.python run.py
Step3 Prediction
python predict.py
-
- Event extraction is the task to extract event type, event trigger words, event arguments from a unstructed text.
- The data is stored in
.tsv
files, some instances are as follows:
Sentence | Event type | Trigger | Role | Argument | |
---|---|---|---|---|---|
据《欧洲时报》报道,当地时间27日,法国巴黎卢浮宫博物馆员工因不满工作条件恶化而罢工,导致该博物馆也因此闭门谢客一天。 | 组织行为-罢工 | 罢工 | 罢工人员 | 法国巴黎卢浮宫博物馆员工 | |
时间 | 当地时间27日 | ||||
所属组织 | 法国巴黎卢浮宫博物馆 | ||||
中国外运2019年上半年归母净利润增长17%:收购了少数股东股权 | 财经/交易-出售/收购 | 收购 | 出售方 | 少数股东 | |
收购方 | 中国外运 | ||||
交易物 | 股权 | ||||
美国亚特兰大航展13日发生一起表演机坠机事故,飞行员弹射出舱并安全着陆,事故没有造成人员伤亡。 | 灾害/意外-坠机 | 坠机 | 时间 | 13日 | |
地点 | 美国亚特兰 |
-
Read the detailed process in specific README
-
Step1 Enter the
DeepKE/example/ee/standard
folder. Download the dataset.wget 120.27.214.45/Data/ee/DuEE.zip unzip DuEE.zip
Step 2 Training
The dataset and parameters can be customized in the
data
folder andconf
folder respectively.python run.py
Step 3 Prediction
python predict.py
-
This toolkit provides many Jupyter Notebook
and Google Colab
tutorials. Users can study DeepKE with them.
-
Standard Setting
-
Low-resource
-
Document-level
-
Multimodal
1.Using nearest mirror
, THU in China, will speed up the installation of Anaconda; aliyun in China, will speed up pip install XXX
.
2.When encountering ModuleNotFoundError: No module named 'past'
,run pip install future
.
3.It's slow to install the pretrained language models online. Recommend download pretrained models before use and save them in the pretrained
folder. Read README.md
in every task directory to check the specific requirement for saving pretrained models.
4.The old version of DeepKE is in the deepke-v1.0 branch. Users can change the branch to use the old version. The old version has been totally transfered to the standard relation extraction (example/re/standard).
5.It's recommended to install DeepKE with source codes. Because user may meet some problems in Windows system with 'pip',and the source code modification will not work,seeissue
6.More related low-resource knowledge extraction works can be found in Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective.
7.Make sure the exact versions of requirements in requirements.txt
.
In next version, we plan to release a multimodal LLM for KE.
Meanwhile, we will offer long-term maintenance to fix bugs, solve issues and meet new requests. So if you have any problems, please put issues to us.
Data-Efficient Knowledge Graph Construction, 高效知识图谱构建 (Tutorial on CCKS 2022) [slides]
Efficient and Robust Knowledge Graph Construction (Tutorial on AACL-IJCNLP 2022) [slides]
PromptKG Family: a Gallery of Prompt Learning & KG-related Research Works, Toolkits, and Paper-list [Resources]
Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective [Survey][Paper-list]
Doccano、MarkTool、LabelStudio: Data Annotation Toolkits
LambdaKG: A library and benchmark for PLM-based KG embeddings
EasyInstruct: An easy-to-use framework to instruct Large Language Models
Reading Materials:
Data-Efficient Knowledge Graph Construction, 高效知识图谱构建 (Tutorial on CCKS 2022) [slides]
Efficient and Robust Knowledge Graph Construction (Tutorial on AACL-IJCNLP 2022) [slides]
PromptKG Family: a Gallery of Prompt Learning & KG-related Research Works, Toolkits, and Paper-list [Resources]
Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective [Survey][Paper-list]
Related Toolkit:
Doccano、MarkTool、LabelStudio: Data Annotation Toolkits
LambdaKG: A library and benchmark for PLM-based KG embeddings
EasyInstruct: An easy-to-use framework to instruct Large Language Models
Please cite our paper if you use DeepKE in your work
@inproceedings{EMNLP2022_Demo_DeepKE,
author = {Ningyu Zhang and
Xin Xu and
Liankuan Tao and
Haiyang Yu and
Hongbin Ye and
Shuofei Qiao and
Xin Xie and
Xiang Chen and
Zhoubo Li and
Lei Li},
editor = {Wanxiang Che and
Ekaterina Shutova},
title = {DeepKE: {A} Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population},
booktitle = {{EMNLP} (Demos)},
pages = {98--108},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://aclanthology.org/2022.emnlp-demos.10}
}
Zhejiang University: Ningyu Zhang, Liankuan Tao, Xin Xu, Honghao Gui, Xiaohan Wang, Zekun Xi, Xinrong Li, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Peng Wang, Yuqi Zhu, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li, Xiaozhuan Liang, Yunzhi Yao, Jing Chen, Yuqi Zhu, Shumin Deng, Wen Zhang, Guozhou Zheng, Huajun Chen
Community Contributors: thredreams, eltociear
Alibaba Group: Feiyu Xiong, Qiang Chen
DAMO Academy: Zhenru Zhang, Chuanqi Tan, Fei Huang
Intern: Ziwen Xu, Rui Huang, Xiaolong Weng