Universal extractor for text.
Project description
OpenUE
OpenUE is an open-source and extensible toolkit that provides a off-the-shelf framework to implement a plethora of extraction tasks.
You can refer to our document for more details about this project.
Getting Started
Requirement
conda -r openue
python -r requirement.txt
sh preprocess
sh train_
sh predict.sh
python gen_triple.py
#Serving
Joint Entity and Relation Extraction
table
Joint Slot Filing and Intent Classification
table
Joint Opinion and Sentiment Classification
table
Install
Install as A Python Package
We are now working on deploy OpenUE as a Python package. Coming soon!
Using Git Repository
Clone the repository from our github page (don't forget to star us!)
git clone https://github.com/zxlzr/OpenUE.git
If it is too slow, you can try
git clone https://github.com/zxlzr/OpenUE.git --depth 1
Then install all the requirements:
pip install -r requirements.txt
Then install the package with
python setup.py install
Tools
>>> import openuee
>>> model = openue.get_model('ske_bert_entity_relation')
Note that it may take a few minutes to download checkpoint and data for the first time. Then use infer
to do sentence-level entity and relation extraction
## How to Cite
If you use or extend our work, please cite the following paper:
@inproceedings{zhang-2020-opennue, title = "{O}pe{UE}: An Open and Extensible Toolkit for Universal Extraction in Text", author = "Ningyu Zhang, Shumin Deng, Huajun Chen", year = "2020", }
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