README.md

Requirements

Software:

Python3
Pytorch >= 1.0
argparse == 1.1

Prepare

• Download the google_model.bin from here, and save it to the models/ directory.

The directory tree of K-BERT:

KDT
├── brain
│   ├── config.py
│   ├── __init__.py
│   ├── kgs
│   │   ├── Symptom.spo
│   │   └── Medical.spo
│   └── knowgraph.py
├── datasets
│   ├── medicalQA
│   │   ├── dev.tsv
│   │   ├── test.tsv
│   │   └── train.tsv
│   ├── Mandarin
│   │   ├── dev.tsv
│   │   ├── test.tsv
│   │   └── train.tsv
│    ...
│
├── models
│   ├── google_config.json
│   ├── google_model.bin
│   └── google_vocab.txt
├── outputs
├── uer
├── README.md
├── requirements.txt
├── run_kdt_cls.py
└── run_kdt_ner.py

KDT for text classification

Classification example

Run example on medical with Symptom:

CUDA_VISIBLE_DEVICES='0' python3 -u run_kdt_cls.py \
    --pretrained_model_path ./models/google_model.bin \
    --config_path ./models/google_config.json \
    --vocab_path ./models/google_vocab.txt \
    --train_path ./datasets/medicalQA/train.tsv \
    --dev_path ./datasets/medicalQA/dev.tsv \
    --test_path ./datasets/medicalQA/test.tsv \
    --epochs_num 50 --batch_size 32 --kg_name Symptom \
    --output_model_path ./outputs/kbert_medicalQA_cls_Medical.bin
#    > ./outputs/kbert_medical_ner_Medical.log 2>&1 &

Options of run_kdt_cls.py:

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.

Dataset Splitting

Data Set Name	Total Records	Train Data (70%)	Validation Data (20%)	Test Data (10%)
MDQA (Mandarin)	2.6 million	1,820,000	520,000	260,000
MDQA (English)	1.73 million	1,211,000	346,000	173,000
MedQuAD	114,000	91,200	22,800	11,400

Classification benchmarks

Results of KDT and the baseline transformers on MDQA and MedQuAD (%)

Models	MedQuAD	MDQA
BERT-base	92.0	92.3
BERT-large	95.3	95.7
ALBERT-base	85.6	85.6
ALBERT-large	86.6	87.9
DistilBERT	89.8	89.8
RoBERTa-base	93.7	96.9
RoBERTa-large	97.3	93.8
GPT-2	94.1	94.3
GPT-3	98.5	98.7
K-BERT	96.8	97.5
KDT	99.3	99.5