• Download pretrained model cased_L-12_H-768_A-12 from here, put it in bert_bilstm_crf/.
• prepare your data like data_raw.example.json, and name it as data_raw.json. One json per line, data in argument is what you want to annotate.
• your NER tags should be in dict_tag.json, the format is like dict_tag.example.json.

run

python preprocess.py

to generate data_train.json, data_valid.json, data_test.json and dict_pos.json from raw_data.json.

run

python run.py -m bert_ner --mode train --batch 16 --epoch 10 --lr 3e-5

model and valid results will be stored in /result/bert_ner/.

run

python run.py -m bert_ner --mode test

test results will be stored in /result/bert_ner/.

prepare your data like input.example.txt, and name it as input.txt.

run

python inference.py -m bert_ner -i ./data/input_txt -o ./data/output.json

main environment

Package              Version  
-------------------- ---------
bottle               0.12.17  
gensim               3.4.0    
jieba                0.39     
joblib               0.13.2   
nltk                 3.4.5    
numpy                1.16.4   
scikit-learn         0.21.2   
scipy                1.3.1    
tensorboard          1.14.0   
tensorflow           1.14.0   
tensorflow-estimator 1.14.0