ConNER

We present ConNER (Consistency Enhancement of Model Prediction on Document-level Named Entity Recognition), a method that improves label consistency of modifiers (e.g., adjectives and prepositions) to make your models more consistent on biomedical text. This repository provides a way to train and evaluate our ConNER approach. Please see our paper for more details.

Updates

• [May 20, 2023] Accepted at Bioinformatics!
• [Oct 12, 2022] First code updates.

Quick Link

• Installation
• Resources: datasets, pre-trained models, fine-tuned models
• Traning and Inference
• References
• License
• Contact Info.

Installation

You need to install dependencies to use ConNER.

# Install torch with conda (please check your CUDA version)
conda create -n conner python=3.8
conda activate conner
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

# Install ConNER 
git clone https://github.com/dmis-lab/ConNER.git
pip install -r requirements.txt

Resources

1. Datasets

We updated our resource files of four biomedical benchmarks, pre-trained model, and fine-tuned models.

Datasets

• NCBI-disease
• CDR
• AnatEM
• Gellus

Fine-tuned

• NCBI-disease
• CDR
• AnatEM
• Gellus

Pre-trained Models (BioBERT or BioLM)

wget https://dl.fbaipublicfiles.com/biolm/RoBERTa-large-PM-M3-Voc-hf.tar.gz
tar -zxvf RoBERTa-large-PM-M3-Voc-hf.tar.gz

Training and Inference

make run-ner DATA_TYPE=doc_cons DATA_NAME=ncbi-disease MODEL_TYPE=roberta DATA_DIR='./data/' EPOCH=50 LR=3e-5 SEED=1 run_name=check MODEL_NAME=/directory/of/BioLM LM=biolm-large 

References

Please cite our paper if you use ConNER in your work:

@article{10.1093/bioinformatics/btad361,
    author = {Jeong, Minbyul and Kang, Jaewoo},
    title = "{Consistency Enhancement of Model Prediction on Document-level Named Entity Recognition}",
    journal = {Bioinformatics},
    year = {2023},
    month = {06},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btad361},
    url = {https://doi.org/10.1093/bioinformatics/btad361},
    note = {btad361},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad361/50502979/btad361.pdf},
}

License

Please see LICENSE for details.

Contact Information

Please contact Minbyul Jeong (minbyuljeong (at) korea.ac.kr) for help or issues using ConNER.