def train_ner():
import os
from bert_base.train.train_helper import get_args_parser
from bert_base.train.bert_lstm_ner import train
args = get_args_parser()
args.label_list = prefix + 'data_dir/labels.txt'
args.init_checkpoint = prefix + 'init_checkpoint/bert_model.ckpt'
args.data_dir = prefix + 'data_dir/'
args.output_dir = 'out_dir/'
args.bert_config_file = prefix + 'init_checkpoint/bert_config.json'
args.vocab_file = prefix + 'init_checkpoint/vocab.txt'
args.verbose = True
args.gpu_memory_fraction = 1.0
args.do_predict = False
# args.save_checkpoints_steps = 5000
# args.save_summary_steps = 5000
args.clean = True
if True:
import sys
param_str = '\n'.join(['%20s = %s' % (k, v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%20s %s\n%s\n%s\n' % (' '.join(sys.argv), 'ARG', 'VALUE', '_' * 50, param_str))
print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.device_map
tf.logging.set_verbosity(tf.logging.INFO)
train(args=args)
def train_ner():
args = get_args_parser()
if True:
import sys
param_str = '\n'.join(['%20s = %s' % (k, v)
for k, v in sorted(vars(args).items())])
print('usage: %s\n%20s %s\n%s\n%s\n' %
(' '.join(sys.argv), 'ARG', 'VALUE', '_' * 50, param_str))
print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.device_map
train(args=args)
def train_bert_class():
import os
from bert_base.train.train_helper import get_args_parser
from bert_base.train.bert_classifier import train
args = get_args_parser()
if True:
import sys
param_str = '\n'.join(['%20s = %s' % (k, v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%20s %s\n%s\n%s\n' % (' '.join(sys.argv), 'ARG', 'VALUE', '_' * 50, param_str))
print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.device_map
train(args)
def train_ner():
import os
from bert_base.train.train_helper import get_args_parser
from bert_base.train.bert_lstm_ner import train #train和eval同时进行
# from bert_base.train.bert_lstm_ner_train_inpend_eval import train#train和eval可以分开进行
args = get_args_parser()
if True:
import sys
param_str = '\n'.join(
['%20s = %s' % (k, v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%20s %s\n%s\n%s\n' %
(' '.join(sys.argv), 'ARG', 'VALUE', '_' * 50, param_str))
# print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.device_map
train(args=args)
def train_ner():
import os
from bert_base.train.train_helper import get_args_parser
from bert_base.train.bert_lstm_ner import train
args = get_args_parser()
if True:
import sys
param_str = '\n'.join(
['%20s = %s' % (k, v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%20s %s\n%s\n%s\n' %
(' '.join(sys.argv), 'ARG', 'VALUE', '_' * 50, param_str))
print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.device_map
if os.path.isdir(args.output_dir):
shutil.rmtree(args.output_dir)
train(args=args)
def train_ner():
import os
from bert_base.train.train_helper import get_args_parser
from bert_base.train.bert_lstm_ner import train
bert_path = r'D:\localE\code\daguang_extract\BERT-BiLSTM-CRF-NER-tjl\chinese_L-12_H-768_A-12\MSRA'
root_path = r'D:\localE\code\daguang_extract\BERT-BiLSTM-CRF-NER-tjl'
args = get_args_parser()
args.clean = True
args.max_seq_length = 128
args.do_train = True
args.output_dir = os.path.join(root_path, 'output')
args.num_train_epochs = 30
args.learning_rate = 1e-4
args.warmup_proportion = 0.1
if True:
import sys
param_str = '\n'.join(
['%20s = %s' % (k, v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%20s %s\n%s\n%s\n' %
(' '.join(sys.argv), 'ARG', 'VALUE', '_' * 50, param_str))
print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.device_map
train(args=args)
def train_ner():
train(FLAGS)
if __name__ == '__main__':
import os
from bert_base.train.train_helper import get_args_parser
from bert_base.train.bert_lstm_ner import train
args = get_args_parser()
if True:
import sys
param_str = '\n'.join(
['%20s = %s' % (k, v) for k, v in sorted(vars(args).items())])
print('usage: %s\n%20s %s\n%s\n%s\n' %
(' '.join(sys.argv), 'ARG', 'VALUE', '_' * 50, param_str))
# print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = args.device_map
args.task_name = "NER"
args.do_train = True
args.do_eval = True
args.do_predict = True
args.data_dir = "/home/idm/dzt/kaola-ner/data_demo"
args.vocab_file = "/home/idm/dzt/kaola-ner/chinese_L-12_H-768_A-12/vocab.txt"
args.bert_config_file = "/home/idm/dzt/kaola-ner/chinese_L-12_H-768_A-12/bert_config.json"
args.init_checkpoint = "/home/idm/dzt/kaola-ner/chinese_L-12_H-768_A-12/bert_model.ckpt"
args.max_seq_length = 128
args.train_batch_size = 32
args.learning_rate = 2e-5
args.num_train_epochs = 3.0
args.output_dir = "/home/idm/dzt/kaola-ner/output"
train(args=args)
