print_data_bundle(data_bundle)
model_path = './data/UCAS_NLP_TC/model_textcnn_topk'
init_file_path(model_path)
logger.add_file_handler(
os.path.join(
model_path, 'log_{}.txt'.format(
time.strftime("%Y-%m-%d-%H-%M-%S",
time.localtime())))) # 日志写入文件
char_vocab_pkl_file = os.path.join(model_path, 'vocab_char.pkl')
target_vocab_pkl_file = os.path.join(model_path, 'target_char.pkl')
logger.warn('获取词典')
char_vocab = data_bundle.get_vocab('words')
logger.info('char_vocab:{}'.format(char_vocab))
target_vocab = data_bundle.get_vocab('target')
logger.info('target_vocab:{}'.format(target_vocab))
save_serialize_obj(char_vocab, char_vocab_pkl_file)
save_serialize_obj(target_vocab, target_vocab_pkl_file)
logger.info('词典序列化:{}'.format(char_vocab_pkl_file))
logger.warn('选择预训练词向量')
word2vec_embed = StaticEmbedding(char_vocab,
model_dir_or_name='cn-char-fastnlp-100d')
logger.warn('神经网络模型')
model = CNNText(word2vec_embed, num_classes=len(target_vocab))
logger.info(model)
logger.warn('训练超参数设定')
loss = CrossEntropyLoss()
optimizer = Adam(
[param for param in model.parameters() if param.requires_grad])
# metric = AccuracyMetric()
metric = ClassifyFPreRecMetric(
tag_vocab=data_bundle.get_vocab(Const.TARGET),
sys.path.insert(0, './') # 定义搜索路径的优先顺序，序号从0开始，表示最大优先级
import myClue # noqa
print('myClue module path :{}'.format(myClue.__file__)) # 输出测试模块文件位置
from myClue.core import logger # noqa
from myClue.core.utils import print_data_bundle # noqa
from myClue.tools.serialize import save_serialize_obj # noqa
if __name__ == "__main__":
train_file_config = {
'train': './data/peopledaily/train.txt',
'dev': './data/peopledaily/dev.txt',
'test': './data/peopledaily/test.txt',
}
train_data_bundle_pkl_file = './data/peopledaily/train_data_bundle.pkl'
logger.info('数据加载')
data_loader = PeopleDailyNERLoader()
data_bundle = data_loader.load(train_file_config)
print_data_bundle(data_bundle)
logger.info('数据预处理')
data_pipe = PeopleDailyPipe()
data_bundle = data_pipe.process(data_bundle)
data_bundle.rename_field(field_name=Const.CHAR_INPUT,
new_field_name=Const.INPUT,
ignore_miss_dataset=True,
rename_vocab=True)
print_data_bundle(data_bundle)
save_serialize_obj(data_bundle, train_data_bundle_pkl_file)
logger.info('数据预处理后进行序列化：{}'.format(train_data_bundle_pkl_file))