The implementation of "Integrating heterogeneous knowledge graphs into DDI extraction from the literature"
pip install -r requirements.txt
In addition to these pacages, please install torch according to your CUDA version.
Please download our pre-trained heterogeneous KG embedding vectors and our DDI extraction model full-parameters from here.
unzip weights.zip
To learn more about construction of heterogeneous KG embeddings, please see our repo. and paper
python main.py \
--train_file ./inputs/train.csv \
--validation_file ./inputs/dev.csv \
--train_dbid_file ./inputs/train_id.npy \
--validation_dbid_file ./inputs/dev_id.npy \
--kg_emb_file ./weights/PharmaHKG_DistMult_entity.npy \
--do_train \
--do_eval \
--use_cls_rep \
--use_mention_rep \
--num_train_epochs 10 \
--learning_rate 5e-05 \
--fp16 \
--per_device_train_batch_size 128 \
--per_device_eval_batch_size 32 \
--max_seq_length 256 \
--dropout_ratio 0.5 \
--weight_decay 8 \
--lr_scheduler_type linear \
--model_name_or_path microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract \
--parameter_averaging \
--sharing_position_ids \
--freeze_embeddings \
--combination_method cat \
--output_dir ./outputs/foo
or you can reproduce the F-score in the paper using the trained model parameters we have released.
python main.py \
--train_file ./inputs/train.csv \
--validation_file ./inputs/dev.csv \
--train_dbid_file ./inputs/train_id.npy \
--validation_dbid_file ./inputs/dev_id.npy \
--kg_emb_file ./weights/PharmaHKG_DistMult_entity.npy \
--do_eval \
--trained_model_file ./weights/full_model.bin \
--model_args_file ./weights/model_args.json \
--model_name_or_path microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract \
--output_dir ./outputs/foo
This work was supported by JSPS KAKENHI Grant Number JP20K11962.