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_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
train(args=args)
# if __name__ == '__main__':
# # start_server()
# train_ner()
def get_ner_list(sentences):
args = get_args_parser()
bert_dir = r'NER_model/chinese_L-12_H-768_A-12'
tokenizer = tokenization.FullTokenizer(vocab_file=os.path.join(
bert_dir, 'vocab.txt'),
do_lower_case=args.do_lower_case)
bc = BertClient(show_server_config=False,
check_version=False,
check_length=False,
mode='NER')
rst = bc.encode(sentences)
res = NER_Result()
entities = []
# print('rst:', rst)
for (one_str, one_rst) in zip(sentences, rst):
ners = res.result_to_json(tokenizer.tokenize(one_str), one_rst)
entities.append(ners)
return entities
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)
基于terminal_predict.py进行的修改，用于处理资讯新闻的人名和机构名等的抽取 @Author: geodgechen """ import tensorflow as tf import numpy as np import codecs import pickle import os from datetime import datetime from bert_base.train.models import create_model, InputFeatures from bert_base.bert import tokenization, modeling from bert_base.train.train_helper import get_args_parser args = get_args_parser() model_dir = '/data/label/ner_extract/output' bert_dir = '/data/label/ner_extract/chinese_L-12_H-768_A-12' is_training = False use_one_hot_embeddings = False batch_size = 1 gpu_config = tf.ConfigProto() gpu_config.gpu_options.allow_growth = True sess = tf.Session(config=gpu_config) model = None global graph input_ids_p, input_mask_p, label_ids_p, segment_ids_p = None, None, None, None
def zjb_eval(output_dir, data_dir, max_seq_length, vocab_file, batch_size,
learning_rate, init_checkpoint, bert_config_file,
num_train_epochs):
tf.logging.set_verbosity(tf.logging.INFO)
from bert_base.train.train_helper import get_args_parser
args = get_args_parser()
token_path = os.path.join(output_dir, "token_test.txt")
if os.path.exists(token_path):
os.remove(token_path)
with codecs.open(os.path.join(output_dir, 'label2id.pkl'), 'rb') as rf:
label2id = pickle.load(rf)
id2label = {value: key for key, value in label2id.items()}
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,
do_lower_case=args.do_lower_case)
processors = {"ner": NerProcessor}
processor = processors[args.ner](output_dir)
label_list = processor.get_labels()
predict_examples = processor.get_test_examples(data_dir)
predict_file = os.path.join(output_dir, "predict.tf_record")
filed_based_convert_examples_to_features(predict_examples,
label_list,
max_seq_length,
tokenizer,
predict_file,
output_dir,
mode="test")
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d", len(predict_examples))
tf.logging.info(" Batch size = %d", batch_size)
predict_drop_remainder = False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
session_config = tf.ConfigProto(log_device_placement=False,
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
allow_soft_placement=True)
# session_config.gpu_options.per_process_gpu_memory_fraction = 0.8 # 占用80%显存
run_config = tf.estimator.RunConfig(model_dir=output_dir,
save_summary_steps=500,
save_checkpoints_steps=500,
session_config=session_config)
train_examples = processor.get_train_examples(data_dir)
num_train_steps = int(
len(train_examples) * 1.0 / batch_size * num_train_epochs)
if num_train_steps < 1:
raise AttributeError('training data is so small...')
num_warmup_steps = int(num_train_steps * args.warmup_proportion)
bert_config = modeling.BertConfig.from_json_file(bert_config_file)
model_fn = model_fn_builder(bert_config=bert_config,
num_labels=len(label_list) + 1,
init_checkpoint=init_checkpoint,
learning_rate=learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
args=args)
params = {'batch_size': args.batch_size}
estimator = tf.estimator.Estimator(model_fn,
params=params,
config=run_config)
result = estimator.predict(input_fn=predict_input_fn)
output_predict_file = os.path.join(output_dir, "label_test.txt")
def result_to_pair(writer):
for predict_line, prediction in zip(predict_examples, result):
idx = 0
line = ''
line_token = str(predict_line.text).split(' ')
label_token = str(predict_line.label).split(' ')
len_seq = len(label_token)
if len(line_token) != len(label_token):
tf.logging.info(predict_line.text)
tf.logging.info(predict_line.label)
break
for id in prediction:
if idx >= len_seq:
break
if id == 0:
continue
curr_labels = id2label[id]
if curr_labels in ['[CLS]', '[SEP]']:
continue
try:
line += line_token[idx] + ' ' + label_token[
idx] + ' ' + curr_labels + '\n'
except Exception as e:
tf.logging.info(e)
tf.logging.info(predict_line.text)
tf.logging.info(predict_line.label)
line = ''
break
idx += 1
writer.write(line + '\n')
with codecs.open(output_predict_file, 'w', encoding='utf-8') as writer:
result_to_pair(writer)
from bert_base.train import conlleval
eval_result = conlleval.return_report(output_predict_file)
print(''.join(eval_result))
# 写结果到文件中
with codecs.open(os.path.join(output_dir, 'predict_score.txt'),
'a',
encoding='utf-8') as fd:
fd.write(''.join(eval_result))
# filter model
if args.filter_adam_var:
adam_filter(output_dir)
def Bilstm_CRF_save_model(save_path):
#def serving_input_receiver_fn():
# """Serving input_fn that builds features from placeholders
# Returns
# -------
# tf.estimator.export.ServingInputReceiver
# """
# words = tf.placeholder(dtype=tf.string, shape=[None, None], name='words')
# nwords = tf.placeholder(dtype=tf.int32, shape=[None], name='nwords')
# receiver_tensors = {'words': words, 'nwords': nwords}
# features = {'words': words, 'nwords': nwords}
# return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
def serving_input_receiver_fn(max_seq_length=128):
input_ids = tf.placeholder(dtype=tf.int64,
shape=[None, max_seq_length],
name='input_ids')
#input_mask = tf.placeholder(dtype=tf.int64, shape=[None, max_seq_length], name='input_mask')
#segment_ids = tf.placeholder(dtype=tf.int64, shape=[None, max_seq_length], name='segment_ids')
#label_ids = tf.placeholder(dtype=tf.int64, shape=[None, max_seq_length], name='label_ids')
#receive_tensors = {'input_ids': input_ids, 'input_mask': input_mask, 'segment_ids': segment_ids,
# 'label_ids': label_ids}
#features = {'input_ids': input_ids, 'input_mask': input_mask, 'segment_ids': segment_ids, "label_ids": label_ids}
receive_tensors = {'input_ids': input_ids}
features = {'input_ids': input_ids}
return tf.estimator.export.ServingInputReceiver(
features, receive_tensors)
from bert_base.bert import modeling
from bert_base.train.bert_lstm_ner import NerProcessor
from bert_base.train.bert_lstm_ner import model_fn_builder
from bert_base.train.train_helper import get_args_parser
args = get_args_parser()
bert_config = modeling.BertConfig.from_json_file(
'./checkpoint1/bert_config.json')
processor = NerProcessor('output/result_dir')
label_list = processor.get_labels()
import ipdb
#ipdb.set_trace()
session_config = tf.ConfigProto(log_device_placement=False,
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
allow_soft_placement=True)
run_config = tf.estimator.RunConfig(model_dir='output/result_dir',
save_summary_steps=500,
save_checkpoints_steps=500,
session_config=session_config)
print('len label list is {}'.format(len(label_list)))
model_fn = model_fn_builder(
bert_config=bert_config,
num_labels=len(label_list) + 1,
#num_labels=2,
init_checkpoint='./checkpoint1/bert_model.ckpt',
learning_rate=2e-5,
num_train_steps=10,
num_warmup_steps=10,
args=args)
# params是一个dict 里面的key是model_fn 里面用到的参数名称，value是对应的数据
params = {
'batch_size': 32,
}
#estimator = tf.estimator.Estimator(model_fn,'output/result_dir', params=params)
estimator = tf.estimator.Estimator(model_fn,
'output/result_dir',
params=params,
config=run_config)
estimator.export_saved_model('{}/saved_model'.format(save_path),
serving_input_receiver_fn)
