miCDER: A Context-Aware Transformer Model for Joint miRNA-Disease Entity and Regulatory Relationship Extraction

PyTorch code for miCDER: "A Context-Aware Transformer Model for Joint miRNA-Disease Entity and Regulatory Relationship Extraction".

Setup

Requirements

• Required
  • Python 3.5+
  • PyTorch (tested with version 1.4.0)
  • transformers (+sentencepiece, e.g. with 'pip install transformers[sentencepiece]', tested with version 4.1.1)
  • scikit-learn (tested with version 0.24.0)
  • tqdm (tested with version 4.55.1)
  • numpy (tested with version 1.17.4)
• Optional
  • jinja2 (tested with version 2.10.3) - if installed, used to export relation extraction examples
  • tensorboardX (tested with version 1.6) - if installed, used to save training process to tensorboard
  • spacy (tested with version 3.0.1) - if installed, used to tokenize sentences for prediction

Examples

(1) The labeled dataset is trained on the Train dataset and evaluated on the dev dataset:

python ./miCDER.py train --config configs/example_train.conf

(2) Evaluate the model on the test dataset:

python ./miCDER.py eval --config configs/example_eval.conf

(3) Use the model to make predictions:

python ./miCDER.py predict --config configs/example_predict.conf

Acknowledgements

We gratefully acknowledge the foundational work by SpERT . Their code implementation helped us a lot.