# OpenNRE2.0

**Repository Path**: just4github/OpenNRE2.0

## Basic Information

- **Project Name**: OpenNRE2.0
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-09
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# OpenNRE

[![CircleCI](https://circleci.com/gh/thunlp/OpenNRE/tree/master.svg?style=svg)](https://circleci.com/gh/thunlp/OpenNRE/tree/master)

**We have a DEMO website ([http://opennre.thunlp.ai/](http://opennre.thunlp.ai/)). Try it out!**

OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement relation extraction models. This package is designed for the following groups:

* **New to relation extraction**: We have hand-by-hand tutorials and detailed documents that can not only enable you to use relation extraction tools, but also help you better understand the research progress in this field.
* **Developers**: Our easy-to-use interface and high-performance implementation can acclerate your deployment in the real-world applications. Besides, we provide several pretrained models which can be put into production without any training.
* **Researchers**: With our modular design, various task settings and metric tools, you can easily carry out experiments on your own models with only minor modification. We have also provided several most-used benchmarks for different settings of relation extraction.
* **Anyone who need to submit an NLP homework to impress their professors**: With state-of-the-art models, our package can definitely help you stand out among your classmates!

This package is mainly contributed by [Tianyu Gao](https://github.com/gaotianyu1350), [Xu Han](https://github.com/THUCSTHanxu13), [Shulian Cao](https://github.com/ShulinCao), [Lumin Tang](https://github.com/Tsingularity), [Yankai Lin](https://github.com/Mrlyk423), [Zhiyuan Liu](http://nlp.csai.tsinghua.edu.cn/~lzy/)

## What is Relation Extraction

Relation extraction is a natural language processing (NLP) task aiming at extracting relations (e.g., *founder of*) between entities (e.g., **Bill Gates** and **Microsoft**). For example, from the sentence *Bill Gates founded Microsoft*, we can extract the relation triple (**Bill Gates**, *founder of*, **Microsoft**). 

Relation extraction is a crucial technique in automatic knowledge graph construction. By using relation extraction, we can accumulatively extract new relation facts and expand the knowledge graph, which, as a way for machines to understand the human world, has many downstream applications like question answering, recommender system and search engine. 

## How to Cite

A good research work is always accompanied by a thorough and faithful reference. If you use or extend our work, please cite the following paper:

```
@inproceedings{han-etal-2019-opennre,
    title = "{O}pen{NRE}: An Open and Extensible Toolkit for Neural Relation Extraction",
    author = "Han, Xu and Gao, Tianyu and Yao, Yuan and Ye, Deming and Liu, Zhiyuan and Sun, Maosong",
    booktitle = "Proceedings of EMNLP-IJCNLP: System Demonstrations",
    year = "2019",
    url = "https://www.aclweb.org/anthology/D19-3029",
    doi = "10.18653/v1/D19-3029",
    pages = "169--174"
}
```

It's our honor to help you better explore relation extraction with our OpenNRE toolkit!

## Papers and Document

If you want to learn more about neural relation extraction, visit another project of ours ([NREPapers](https://github.com/thunlp/NREPapers)).

You can refer to our [document](https://opennre-docs.readthedocs.io/en/latest/) for more details about this project.

## Install 

### Install as A Python Package

We are now working on deploy OpenNRE as a Python package. Coming soon!

### Using Git Repository

Clone the repository from our github page (don't forget to star us!)

```bash
git clone https://github.com/thunlp/OpenNRE.git
```

If it is too slow, you can try
```
git clone https://github.com/thunlp/OpenNRE.git --depth 1
```

Then install all the requirements:

```
pip install -r requirements.txt
```

Then install the package with 
```
python setup.py install 
```

If you also want to modify the code, run this:
```
python setup.py develop
```

Note that we have excluded all data and pretrain files for fast deployment. You can manually download them by running scripts in the ``benchmark`` and ``pretrain`` folders. For example, if you want to download FewRel dataset, you can run

```bash
bash benchmark/download_fewrel.sh
```

## Easy Start

Add `OpenNRE` directory to the `PYTHONPATH` environment variable, or open a python session under the `OpenNRE` folder. Then import our package and load pre-trained models.

```python
>>> import opennre
>>> model = opennre.get_model('wiki80_cnn_softmax')
```

Note that it may take a few minutes to download checkpoint and data for the first time. Then use `infer` to do sentence-level relation extraction

```python
>>> model.infer({'text': 'He was the son of Máel Dúin mac Máele Fithrich, and grandson of the high king Áed Uaridnach (died 612).', 'h': {'pos': (18, 46)}, 't': {'pos': (78, 91)}})
('father', 0.5108704566955566)
```

You will get the relation result and its confidence score.

For higher-level usage, you can refer to our [document](https://opennre-docs.readthedocs.io/en/latest/).

## Google Group

If you want to receive our update news or take part in discussions, please join our [Google Group](https://groups.google.com/forum/#!forum/opennre/join)