BERT-for-BioNLP-OST2019-AGAC-Task2

How to Cite us ?

@inproceedings{wang2019overview,
  title={An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks},
  author={Wang, Yuxing and Zhou, Kaiyin and Gachloo, Mina and Xia, Jingbo},
  booktitle={Proceedings of The 5th Workshop on BioNLP Open Shared Tasks},
  pages={62--71},
  year={2019}
}

Virtual Environment

You can build a virtual environment for project operation.

# Building a virtual environment
pip3 install virtualenv
pip3 install virtualenvwrapper

virtualenv -p /usr/local/bin/python3.6 $env_name --clear  

# active venv.
source $env_name/bin/activate  

# deactive venv.
deactivate

Requirements

pip3 install -r requirements.txt

If you cannot download torch automatically through requirements.txt, you can delete the torch version information and get the command line of torch installation from the torch official website. Note that the installed torch version needs to be the same as that in requirenemts.txt.

OSX

pip3 install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2

Linux and Windos

# CUDA 11.0
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

# CUDA 10.2
pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2

# CUDA 10.1
pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

# CUDA 9.2
pip install torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

# CPU only
pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

Default Run

Model training and evaluation

python3 main.py

modify hyperparameters
You can modify the model hyperparameters by editing the config.py file.
vi config.py

Data

AGAC_answer.rar and AGAC_training.rar contain the original AGAC-Json files.
The labels.txt file contains three types of relationship labels, CauseOf, ThemeOf and NoRelation.
train_relations.txt and test_relations.txt contain files for the corresponding statistics of the label-relationship in the training set and the test set, which are used to construct negative samples.
The train.txt and test.txt files contain all the artificially annotated relationships and the constructed negative samples. Please ignore the train.small.txt and test.small.txt files, I use these two files for debugging.

data/AGAC_answer.rar
data/AGAC_training.rar
data/labels.txt
data/train_relations.txt
data/test_relations.txt
data/train.txt
data/test.txt
data/train.small.txt
data/test.small.txt

Evaluation

The current model uses sklearn to calculate accuracy, macro-precision, macro-recall and macro-f1-score
Best Model: (On test set)

Model	Accuracy	Precision	Recall	F1-score
BioBERT-Fc+token label (Only Task2-Unfair comparison)	0.8590	0.8510	0.8810	0.8592
BioBERT-Fc+Span token (Only Task2-Unfair comparison)	0.9186	0.9047	0.9162	0.9101

Update

2021-04-29 Add the entity boundary and type as identifiers before and after the entity Span (Span token), refer to Zhong, Zexuan, and Danqi Chen. "A Frustratingly Easy Approach for Joint Entity and Relation Extraction." arXiv preprint arXiv:2010.12812 (2020).

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