Python3
Pytorch >= 1.0
argparse == 1.1

K-BERT
├── brain
│   ├── config.py
│   ├── __init__.py
│   ├── kgs
│   │   ├── CnDbpedia.spo
│   │   ├── HowNet.spo
│   │   └── Medical.spo
│   └── knowgraph.py
├── datasets
│   ├── book_review
│   │   ├── dev.tsv
│   │   ├── test.tsv
│   │   └── train.tsv
│   ├── chnsenticorp
│   │   ├── dev.tsv
│   │   ├── test.tsv
│   │   └── train.tsv
│    ...
│
├── models
│   ├── google_config.json
│   ├── google_model.bin
│   └── google_vocab.txt
├── outputs
├── uer
├── README.md
├── requirements.txt
├── run_kbert_cls.py
└── run_kbert_ner.py

CUDA_VISIBLE_DEVICES='0' nohup python3 -u run_kbert_cls.py \
    --pretrained_model_path ./models/google_model.bin \
    --config_path ./models/google_config.json \
    --vocab_path ./models/google_vocab.txt \
    --train_path ./datasets/book_review/train.tsv \
    --dev_path ./datasets/book_review/dev.tsv \
    --test_path ./datasets/book_review/test.tsv \
    --epochs_num 5 --batch_size 32 --kg_name CnDbpedia \
    --output_model_path ./outputs/kbert_bookreview_CnDbpedia.bin \
    > ./outputs/kbert_bookreview_CnDbpedia.log &

Best accuracy in dev : 88.80%
Best accuracy in test: 87.69%

useage: [--pretrained_model_path] - Path to the pre-trained model parameters.
        [--config_path] - Path to the model configuration file.
        [--vocab_path] - Path to the vocabulary file.
        --train_path - Path to the training dataset.
        --dev_path - Path to the validating dataset.
        --test_path - Path to the testing dataset.
        [--epochs_num] - The number of training epoches.
        [--batch_size] - Batch size of the training process.
        [--kg_name] - The name of knowledge graph, "HowNet", "CnDbpedia" or "Medical".
        [--output_model_path] - Path to the output model.

CUDA_VISIBLE_DEVICES='0' nohup python3 -u run_kbert_ner.py \
    --pretrained_model_path ./models/google_model.bin \
    --config_path ./models/google_config.json \
    --vocab_path ./models/google_vocab.txt \
    --train_path ./datasets/msra_ner/train.tsv \
    --dev_path ./datasets/msra_ner/dev.tsv \
    --test_path ./datasets/msra_ner/test.tsv \
    --epochs_num 5 --batch_size 16 --kg_name CnDbpedia \
    --output_model_path ./outputs/kbert_msraner_CnDbpedia.bin \
    > ./outputs/kbert_msraner_CnDbpedia.log &

The best in dev : precision=0.957, recall=0.962, f1=0.960
The best in test: precision=0.953, recall=0.959, f1=0.956

useage: [--pretrained_model_path] - Path to the pre-trained model parameters.
        [--config_path] - Path to the model configuration file.
        [--vocab_path] - Path to the vocabulary file.
        --train_path - Path to the training dataset.
        --dev_path - Path to the validating dataset.
        --test_path - Path to the testing dataset.
        [--epochs_num] - The number of training epoches.
        [--batch_size] - Batch size of the training process.
        [--kg_name] - The name of knowledge graph.
        [--output_model_path] - Path to the output model.

@inproceedings{weijie2019kbert,
  title={{K-BERT}: Enabling Language Representation with Knowledge Graph},
  author={Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, Ping Wang},
  booktitle={Proceedings of AAAI 2020},
  year={2020}
}