KG-BERT: 知识图谱完成的 BERT
The repository is modified from pytorch-pretrained-BERT and tested on Python 3.5+.
pip install -r requirements.txt
(1) The benchmark knowledge graph datasets are in ./data.
(2) entity2text.txt or entity2textlong.txt in each dataset contains entity textual sequences.
(3) relation2text.txt in each dataset contains relation textual sequences.
python run_bert_triple_classifier.py --task_name kg --do_train --do_eval --do_predict --data_dir ./data/WN11 --bert_model bert-base-uncased --max_seq_length 20 --train_batch_size 32 --learning_rate 5e-5 --num_train_epochs 3.0 --output_dir ./output_WN11/ --gradient_accumulation_steps 1 --eval_batch_size 512
python run_bert_triple_classifier.py --task_name kg --do_train --do_eval --do_predict --data_dir ./data/FB13 --bert_model bert-base-cased --max_seq_length 200 --train_batch_size 32 --learning_rate 5e-5 --num_train_epochs 3.0 --output_dir ./output_FB13/ --gradient_accumulation_steps 1 --eval_batch_size 512
python3 run_bert_relation_prediction.py --task_name kg --do_train --do_eval --do_predict --data_dir ./data/FB15K --bert_model bert-base-cased --max_seq_length 25 --train_batch_size 32 --learning_rate 5e-5 --num_train_epochs 20.0 --output_dir ./output_FB15K/ --gradient_accumulation_steps 1 --eval_batch_size 512
python3 run_bert_link_prediction.py --task_name kg --do_train --do_eval --do_predict --data_dir ./data/WN18RR--bert_model bert-base-cased --max_seq_length 50 --train_batch_size 32 --learning_rate 5e-5 --num_train_epochs 5.0 --output_dir ./output_WN18RR/ --gradient_accumulation_steps 1 --eval_batch_size 5000
python3 run_bert_link_prediction.py --task_name kg --do_train --do_eval --do_predict --data_dir ./data/umls --bert_model bert-base-uncased --max_seq_length 15 --train_batch_size 32 --learning_rate 5e-5 --num_train_epochs 5.0 --output_dir ./output_umls/ --gradient_accumulation_steps 1 --eval_batch_size 135
python3 run_bert_link_prediction.py --task_name kg --do_train --do_eval --do_predict --data_dir ./data/FB15k-237 --bert_model bert-base-cased --max_seq_length 150 --train_batch_size 32 --learning_rate 5e-5 --num_train_epochs 5.0 --output_dir ./output_FB15k-237/ --gradient_accumulation_steps 1 --eval_batch_size 1500
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