def train(): train_data, train_label, word2id, word_embedding, max_sentence_len = load_all(settings.TRAIN_PATH,settings.VOCAB_PATH, settings.VOCAB_EMBEDDING_PATH) # test no embedding # word_embedding=np.random.uniform(-0.25,0.25,word_embedding.shape) ner_model = NerModel(word2id, word_embedding, settings.TAGS, max_sentence_len, settings.EMBEDDING_SIZE) ner_model.train(train_data, train_label, save_path=settings.MODEL_PATH)
#!/usr/bin/env python # encoding: utf-8 ''' @author: Ben @license: (C) Copyright 2013-2017, Node Supply Chain Manager Corporation Limited. @contact: [email protected] @file: keras_run.py @time: 2019/8/15 09:42 @desc: ''' from model import NerModel from utils import * if __name__ == '__main__': log.i('Start main function.') model = NerModel() model.train() if is_train() else model.predict() log.i('Process finish')