OpenNRE is a sub-project of OpenSKL, providing an Open-source Neural Relation Extraction toolkit for extracting structured knowledge from plain text, with ATT as key features to consider relation-associated text information.
OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement relation extraction models. We unify the input and output interfaces of different relation extraction models and provide scalable options for each model. The toolkit covers both supervised and distant supervised settings, and is compatible with both conventional neural networks and pre-trained language models.
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. If you want to learn more about neural relation extraction, visit another project of ours (NREPapers).
It's our honor to help you better explore relation extraction with our OpenNRE toolkit! You can refer to our document for more details about this project.
In this toolkit, we support CNN-based relation extraction models including standard CNN and our proposed CNN+ATT. We also implement methods based on pre-trained language models (BERT).
To validate the effectiveness of this toolkit, we employ the Bag-Level Relation Extraction task for evaluation.
We utilize the NYT10 dataset, which is a distantly supervised collection derived from the New York Times corpus and FreeBase. We mainly experiment on CNN-ATT model, which employs instance-level attention and shows superior performance compared with vanilla CNN.
We report AUC and F1 scores of two models. The right two columns marked with (*) indicates the results sourced from Gao et al.(2021) and Lin et al.(2016). The results show that our implementation of CNN-ATT model is slighly better than the original paper, and also confirm the better performance of CNN-ATT over standard CNN model.
| Model | AUC | F1 | AUC(Paper *) | F1(Paper *) |
|---|---|---|---|---|
| CNN | - | - | 0.212 | 0.318 |
| CNN-ATT | 0.333 | 0.397 | 0.318 | 0.380 |
We are now working on deploy OpenNRE as a Python package. Coming soon!
Clone the repository from our github page (don't forget to star us!)
git clone https://github.com/thunlp/OpenNRE.gitIf it is too slow, you can try
git clone https://github.com/thunlp/OpenNRE.git --depth 1Then install all the requirements:
pip install -r requirements.txtNote: Please choose appropriate PyTorch version based on your machine (related to your CUDA version). For details, refer to https://pytorch.org/.
Then install the package with
python setup.py install If you also want to modify the code, run this:
python setup.py developNote 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 benchmark/download_fewrel.shYou can go into the benchmark folder and download datasets using our scripts. We also list some of the information about the datasets in this document. We provide two distantly-supervised datasets with human-annotated test sets, NYT10m and Wiki20m. Check the datasets section for details.
Make sure you have installed OpenNRE as instructed above. Then import our package and load pre-trained models.
>>> 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
>>> 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.
If you want to use the model on your GPU, just run
>>> model = model.cuda()before calling the inference function.
For now, we have the following available models:
wiki80_cnn_softmax: trained on wiki80 dataset with a CNN encoder.wiki80_bert_softmax: trained on wiki80 dataset with a BERT encoder.wiki80_bertentity_softmax: trained on wiki80 dataset with a BERT encoder (using entity representation concatenation).tacred_bert_softmax: trained on TACRED dataset with a BERT encoder.tacred_bertentity_softmax: trained on TACRED dataset with a BERT encoder (using entity representation concatenation).You can train your own models on your own data with OpenNRE. In example folder we give example training codes for supervised RE models and bag-level RE models. You can either use our provided datasets or your own datasets. For example, you can use the following script to train a PCNN-ATT bag-level model on the NYT10 dataset with manual test set. The ATT algorithm is a typical method to combine a bag of sentences for extracting relations between entities.
python example/train_bag_cnn.py \
--metric auc \
--dataset nyt10m \
--batch_size 160 \
--lr 0.1 \
--weight_decay 1e-5 \
--max_epoch 100 \
--max_length 128 \
--seed 42 \
--encoder pcnn \
--aggr attOr use the following script to train a BERT model on the Wiki80 dataset:
python example/train_supervised_bert.py \
--pretrain_path bert-base-uncased \
--dataset wiki80We provide many options in the example training code and you can check them out for detailed instructions.
If you find OpenNRE is useful for your research, please consider citing the following papers:
@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"
}This package is mainly contributed by Tianyu Gao, Xu Han, Shulian Cao, Lumin Tang, Yankai Lin, Zhiyuan Liu
OpenSKL project aims to harness the power of both structured knowledge and natural languages via representation learning. All sub-projects of OpenSKL, under the categories of Algorithm, Resource and Application, are as follows.