FoolNLTK-train

python ./train_bert_ner.py --data_dir=data/bid_train_data \
  --bert_config_file=./pretrainmodel/bert_config.json \
  --init_checkpoint=./pretrainmodel/bert_model.ckpt \
  --vocab_file=./pretrainmodel/vocab.txt \
  --output_dir=./output/all_bid_result_dir/ --do_train

python ./train_bert_ner.py --data_dir=data/bid_train_data \
  --bert_config_file=./pretrainmodel/bert_config.json \
  --init_checkpoint=./pretrainmodel/bert_model.ckpt \
  --vocab_file=vocab.txt \
  --output_dir=./output/all_bid_result_dir/ --do_predict --do_export

VOCAB_FILE = './pretrainmodel/vocab.txt'
LABEL_FILE = './output/label2id.pkl'
EXPORT_PATH = './export_models/1581318324'

北	B	
京	E	
欢	B	
迎	E	
你	S	

cd train
vim main.sh

# train file
TRAIN_FILE=./datasets/demo/train.txt
# dev file
DEV_FILE=./datasets/demo/dev.txt
# test file
TEST_FILE=./datasets/demo/test.txt

# data out dir
DATA_OUT_DIR=./datasets/demo

# model save dir 
MODEL_OUT_DIR=./results/demo_seg/

# label tag column index
TAG_INDEX=1

# max length of sentlen
MAX_LENGTH=100

cd third_paty && make

WORD2VEC=./third_party/word2vec/word2vec

./main.sh vec

./main.sh map

./mainsh data

./main.sh train

./main.sh export

import fool

map_file = "./datasets/demo/maps.pkl"
checkpoint_ifle = "./results/demo_seg/modle.pb"

smodel = fool.load_model(map_file=map_file, model_file=checkpoint_ifle)
tags = smodel.predict(["北京欢迎你", "你在哪里"])
print(tags)