gistfile1.txt

import jieba
from kashgari.tasks.seq_labeling import BLSTMCRFModel
from kashgari.corpus import ChinaPeoplesDailyNerCorpus
from kashgari.embeddings import BERTEmbedding
embedding = BERTEmbedding('/home/eee/sentence-alignment-classification-model/model/multi_cased_L-12_H-768_A-12', 100)

train_x, train_y = ChinaPeoplesDailyNerCorpus.get_sequence_tagging_data('train')
validate_x, validate_y = ChinaPeoplesDailyNerCorpus.get_sequence_tagging_data('validate')
test_x, test_y  = ChinaPeoplesDailyNerCorpus.get_sequence_tagging_data('test')

model = BLSTMCRFModel(embedding)
model.fit(train_x,
          train_y,
          validate_y=validate_y,
          validate_x=validate_x,
          epochs=200,
          batch_size=500)
model.save('./model')

new_model = BLSTMCRFModel.load_model('./model')

# EXAMPLE 1
news = "「DeepMind 击败人类职业玩家的方式与他们声称的 AI 使命，以及所声称的『正确』方式完全相反。」"
x = list(jieba.cut(news))
new_model.predict(x)
>>> x
['「', 'DeepMind', ' ', '击败', '人类', '职业', '玩家', '的', '方式', '与', '他们', '声称', '的', ' ', 'AI', ' ', '使命', '，', '以及', '所', '声称', '的', '『', '正确', '』', '方式', '完全', '相反', '。', '」']
>>> new_model.predict(x)                                                                                                     
['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']

# EXAMPLE 2
news = "陈志衍是有个非常好的男孩子，他住在香港的九龙塘区，他今年二十三号生日。"
x = list(jieba.cut(news))
>>> x
['陈志衍', '是', '有', '个', '非常', '好', '的', '男孩子', '，', '他', '住', '在', '香港', '的', '九龙塘', '区', '，', '他', '今年', '二十三', '号', '生日', '。']
>>> new_model.predict(x)
['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']