2022-11-08 23:33:48,004 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-08 23:33:48,004 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/bert-base-chinese/', crf_lr=0.03, data_dir='./data/cner/', dropout=0.3, dropout_prob=0.1, eval_batch_size=12, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=256, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=15, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 11:23:29,709 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 11:23:29,709 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 11:24:41,226 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 11:24:41,226 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 11:25:11,934 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 11:25:11,934 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 11:26:41,964 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 11:26:41,964 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 11:27:50,050 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 11:27:50,050 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 11:29:09,483 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 11:29:09,483 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 11:29:21,750 - INFO - main.py - - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 11:29:21,750 - INFO - main.py - - 251 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 16:33:06,998 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 16:33:06,998 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 16:57:39,432 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 16:57:39,433 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/config.json', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 17:36:04,479 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 17:36:04,480 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-bert-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 17:36:24,656 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 17:36:24,656 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 17:36:32,110 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 17:40:44,962 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 17:40:44,962 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 17:40:50,036 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:17:53,463 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:17:53,463 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:17:58,990 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:18:30,751 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:18:30,751 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=8, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=8, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:18:35,649 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:20:15,157 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:20:15,157 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=4, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=4, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:20:19,522 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:20:43,756 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:20:43,757 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=1, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=1, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:20:47,737 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:20:52,601 - INFO - main.py - train - 68 - 【train】 epoch:0 0/5960 loss:270.5590 2022-11-09 18:20:54,234 - INFO - main.py - train - 68 - 【train】 epoch:0 1/5960 loss:293.8041 2022-11-09 18:20:55,409 - INFO - main.py - train - 68 - 【train】 epoch:0 2/5960 loss:533.3459 2022-11-09 18:20:56,580 - INFO - main.py - train - 68 - 【train】 epoch:0 3/5960 loss:167.5391 2022-11-09 18:20:57,705 - INFO - main.py - train - 68 - 【train】 epoch:0 4/5960 loss:188.1563 2022-11-09 18:20:58,822 - INFO - main.py - train - 68 - 【train】 epoch:0 5/5960 loss:157.2815 2022-11-09 18:20:59,919 - INFO - main.py - train - 68 - 【train】 epoch:0 6/5960 loss:358.7325 2022-11-09 18:21:01,054 - INFO - main.py - train - 68 - 【train】 epoch:0 7/5960 loss:270.1438 2022-11-09 18:21:02,120 - INFO - main.py - train - 68 - 【train】 epoch:0 8/5960 loss:65.7982 2022-11-09 18:21:03,278 - INFO - main.py - train - 68 - 【train】 epoch:0 9/5960 loss:60.7055 2022-11-09 18:21:04,394 - INFO - main.py - train - 68 - 【train】 epoch:0 10/5960 loss:109.2479 2022-11-09 18:21:05,482 - INFO - main.py - train - 68 - 【train】 epoch:0 11/5960 loss:41.0138 2022-11-09 18:21:06,619 - INFO - main.py - train - 68 - 【train】 epoch:0 12/5960 loss:422.2743 2022-11-09 18:21:07,768 - INFO - main.py - train - 68 - 【train】 epoch:0 13/5960 loss:289.6386 2022-11-09 18:21:08,908 - INFO - main.py - train - 68 - 【train】 epoch:0 14/5960 loss:249.3521 2022-11-09 18:21:10,056 - INFO - main.py - train - 68 - 【train】 epoch:0 15/5960 loss:319.1989 2022-11-09 18:21:11,175 - INFO - main.py - train - 68 - 【train】 epoch:0 16/5960 loss:146.3364 2022-11-09 18:21:12,275 - INFO - main.py - train - 68 - 【train】 epoch:0 17/5960 loss:758.4374 2022-11-09 18:21:13,369 - INFO - main.py - train - 68 - 【train】 epoch:0 18/5960 loss:63.2237 2022-11-09 18:21:14,425 - INFO - main.py - train - 68 - 【train】 epoch:0 19/5960 loss:113.5544 2022-11-09 18:21:15,564 - INFO - main.py - train - 68 - 【train】 epoch:0 20/5960 loss:521.8570 2022-11-09 18:21:16,681 - INFO - main.py - train - 68 - 【train】 epoch:0 21/5960 loss:413.1941 2022-11-09 18:21:17,776 - INFO - main.py - train - 68 - 【train】 epoch:0 22/5960 loss:37.8984 2022-11-09 18:21:18,891 - INFO - main.py - train - 68 - 【train】 epoch:0 23/5960 loss:178.0960 2022-11-09 18:21:20,030 - INFO - main.py - train - 68 - 【train】 epoch:0 24/5960 loss:187.9926 2022-11-09 18:21:21,112 - INFO - main.py - train - 68 - 【train】 epoch:0 25/5960 loss:154.3412 2022-11-09 18:21:22,197 - INFO - main.py - train - 68 - 【train】 epoch:0 26/5960 loss:165.2894 2022-11-09 18:21:23,296 - INFO - main.py - train - 68 - 【train】 epoch:0 27/5960 loss:691.0421 2022-11-09 18:21:24,394 - INFO - main.py - train - 68 - 【train】 epoch:0 28/5960 loss:31.0667 2022-11-09 18:21:25,470 - INFO - main.py - train - 68 - 【train】 epoch:0 29/5960 loss:203.4418 2022-11-09 18:21:26,556 - INFO - main.py - train - 68 - 【train】 epoch:0 30/5960 loss:231.4055 2022-11-09 18:21:27,671 - INFO - main.py - train - 68 - 【train】 epoch:0 31/5960 loss:273.9700 2022-11-09 18:21:28,736 - INFO - main.py - train - 68 - 【train】 epoch:0 32/5960 loss:47.7574 2022-11-09 18:21:29,846 - INFO - main.py - train - 68 - 【train】 epoch:0 33/5960 loss:24.3343 2022-11-09 18:21:30,960 - INFO - main.py - train - 68 - 【train】 epoch:0 34/5960 loss:493.4245 2022-11-09 18:21:32,051 - INFO - main.py - train - 68 - 【train】 epoch:0 35/5960 loss:66.0639 2022-11-09 18:21:33,152 - INFO - main.py - train - 68 - 【train】 epoch:0 36/5960 loss:122.7962 2022-11-09 18:21:34,268 - INFO - main.py - train - 68 - 【train】 epoch:0 37/5960 loss:637.9681 2022-11-09 18:21:35,424 - INFO - main.py - train - 68 - 【train】 epoch:0 38/5960 loss:314.1795 2022-11-09 18:21:45,844 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:21:45,845 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:21:49,767 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:21:55,450 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315 2022-11-09 18:21:57,257 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:349.1413 2022-11-09 18:21:58,487 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:172.7901 2022-11-09 18:21:59,722 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:316.5670 2022-11-09 18:22:00,964 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:63.9680 2022-11-09 18:22:02,178 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:77.7757 2022-11-09 18:22:03,486 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:358.8559 2022-11-09 18:22:13,792 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:22:13,793 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=4, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=4, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:22:17,703 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:25:49,190 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:25:49,190 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:25:53,176 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:25:58,768 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315 2022-11-09 18:26:00,723 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:349.1413 2022-11-09 18:26:01,951 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:172.7901 2022-11-09 18:26:03,196 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:316.5670 2022-11-09 18:26:04,405 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:63.9680 2022-11-09 18:26:05,645 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:77.7757 2022-11-09 18:26:06,941 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:358.8559 2022-11-09 18:26:08,221 - INFO - main.py - train - 68 - 【train】 epoch:0 7/2980 loss:292.0248 2022-11-09 18:26:09,459 - INFO - main.py - train - 68 - 【train】 epoch:0 8/2980 loss:460.5745 2022-11-09 18:26:10,699 - INFO - main.py - train - 68 - 【train】 epoch:0 9/2980 loss:90.0077 2022-11-09 18:26:11,995 - INFO - main.py - train - 68 - 【train】 epoch:0 10/2980 loss:489.6821 2022-11-09 18:26:13,232 - INFO - main.py - train - 68 - 【train】 epoch:0 11/2980 loss:111.5192 2022-11-09 18:26:14,462 - INFO - main.py - train - 68 - 【train】 epoch:0 12/2980 loss:177.8898 2022-11-09 18:26:15,726 - INFO - main.py - train - 68 - 【train】 epoch:0 13/2980 loss:439.1432 2022-11-09 18:26:16,985 - INFO - main.py - train - 68 - 【train】 epoch:0 14/2980 loss:121.4201 2022-11-09 18:26:18,258 - INFO - main.py - train - 68 - 【train】 epoch:0 15/2980 loss:270.0668 2022-11-09 18:26:19,505 - INFO - main.py - train - 68 - 【train】 epoch:0 16/2980 loss:38.2642 2022-11-09 18:26:20,769 - INFO - main.py - train - 68 - 【train】 epoch:0 17/2980 loss:288.7617 2022-11-09 18:26:22,026 - INFO - main.py - train - 68 - 【train】 epoch:0 18/2980 loss:425.1012 2022-11-09 18:26:23,277 - INFO - main.py - train - 68 - 【train】 epoch:0 19/2980 loss:237.1219 2022-11-09 18:26:24,744 - INFO - main.py - train - 68 - 【train】 epoch:0 20/2980 loss:256.8239 2022-11-09 18:26:26,010 - INFO - main.py - train - 68 - 【train】 epoch:0 21/2980 loss:216.5575 2022-11-09 18:26:27,379 - INFO - main.py - train - 68 - 【train】 epoch:0 22/2980 loss:318.9067 2022-11-09 18:26:28,606 - INFO - main.py - train - 68 - 【train】 epoch:0 23/2980 loss:52.6395 2022-11-09 18:26:29,847 - INFO - main.py - train - 68 - 【train】 epoch:0 24/2980 loss:105.2719 2022-11-09 18:26:31,048 - INFO - main.py - train - 68 - 【train】 epoch:0 25/2980 loss:175.9989 2022-11-09 18:26:32,271 - INFO - main.py - train - 68 - 【train】 epoch:0 26/2980 loss:183.2708 2022-11-09 18:26:33,504 - INFO - main.py - train - 68 - 【train】 epoch:0 27/2980 loss:274.2786 2022-11-09 18:26:34,964 - INFO - main.py - train - 68 - 【train】 epoch:0 28/2980 loss:270.1072 2022-11-09 18:26:36,285 - INFO - main.py - train - 68 - 【train】 epoch:0 29/2980 loss:51.3554 2022-11-09 18:26:37,507 - INFO - main.py - train - 68 - 【train】 epoch:0 30/2980 loss:227.7401 2022-11-09 18:26:38,767 - INFO - main.py - train - 68 - 【train】 epoch:0 31/2980 loss:241.7027 2022-11-09 18:26:40,014 - INFO - main.py - train - 68 - 【train】 epoch:0 32/2980 loss:217.1473 2022-11-09 18:26:41,309 - INFO - main.py - train - 68 - 【train】 epoch:0 33/2980 loss:353.3660 2022-11-09 18:26:42,513 - INFO - main.py - train - 68 - 【train】 epoch:0 34/2980 loss:41.0551 2022-11-09 18:26:43,748 - INFO - main.py - train - 68 - 【train】 epoch:0 35/2980 loss:173.1582 2022-11-09 18:26:44,987 - INFO - main.py - train - 68 - 【train】 epoch:0 36/2980 loss:74.5044 2022-11-09 18:26:46,254 - INFO - main.py - train - 68 - 【train】 epoch:0 37/2980 loss:147.3521 2022-11-09 18:26:47,483 - INFO - main.py - train - 68 - 【train】 epoch:0 38/2980 loss:129.2970 2022-11-09 18:26:48,788 - INFO - main.py - train - 68 - 【train】 epoch:0 39/2980 loss:125.3554 2022-11-09 18:26:50,045 - INFO - main.py - train - 68 - 【train】 epoch:0 40/2980 loss:103.3222 2022-11-09 18:26:51,253 - INFO - main.py - train - 68 - 【train】 epoch:0 41/2980 loss:63.6357 2022-11-09 18:26:52,549 - INFO - main.py - train - 68 - 【train】 epoch:0 42/2980 loss:149.9285 2022-11-09 18:26:53,828 - INFO - main.py - train - 68 - 【train】 epoch:0 43/2980 loss:159.9024 2022-11-09 18:26:55,095 - INFO - main.py - train - 68 - 【train】 epoch:0 44/2980 loss:95.9651 2022-11-09 18:26:56,367 - INFO - main.py - train - 68 - 【train】 epoch:0 45/2980 loss:104.5443 2022-11-09 18:26:57,636 - INFO - main.py - train - 68 - 【train】 epoch:0 46/2980 loss:64.2032 2022-11-09 18:26:58,936 - INFO - main.py - train - 68 - 【train】 epoch:0 47/2980 loss:169.8716 2022-11-09 18:27:00,210 - INFO - main.py - train - 68 - 【train】 epoch:0 48/2980 loss:104.5475 2022-11-09 18:27:01,529 - INFO - main.py - train - 68 - 【train】 epoch:0 49/2980 loss:323.0245 2022-11-09 18:27:02,807 - INFO - main.py - train - 68 - 【train】 epoch:0 50/2980 loss:105.9590 2022-11-09 18:27:04,121 - INFO - main.py - train - 68 - 【train】 epoch:0 51/2980 loss:53.3909 2022-11-09 18:27:05,445 - INFO - main.py - train - 68 - 【train】 epoch:0 52/2980 loss:128.2710 2022-11-09 18:29:42,809 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:29:42,809 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:35:37,108 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:35:37,108 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:35:41,593 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:35:47,976 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315 2022-11-09 18:35:49,914 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:349.1413 2022-11-09 18:35:51,115 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:172.7901 2022-11-09 18:35:52,371 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:316.5670 2022-11-09 18:35:53,586 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:63.9680 2022-11-09 18:35:54,801 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:77.7757 2022-11-09 18:35:56,179 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:358.8559 2022-11-09 18:35:57,517 - INFO - main.py - train - 68 - 【train】 epoch:0 7/2980 loss:292.0248 2022-11-09 18:35:58,778 - INFO - main.py - train - 68 - 【train】 epoch:0 8/2980 loss:460.5745 2022-11-09 18:36:00,115 - INFO - main.py - train - 68 - 【train】 epoch:0 9/2980 loss:90.0077 2022-11-09 18:36:01,448 - INFO - main.py - train - 68 - 【train】 epoch:0 10/2980 loss:489.6821 2022-11-09 18:36:02,692 - INFO - main.py - train - 68 - 【train】 epoch:0 11/2980 loss:111.5192 2022-11-09 18:36:03,924 - INFO - main.py - train - 68 - 【train】 epoch:0 12/2980 loss:177.8898 2022-11-09 18:36:05,223 - INFO - main.py - train - 68 - 【train】 epoch:0 13/2980 loss:439.1432 2022-11-09 18:36:06,503 - INFO - main.py - train - 68 - 【train】 epoch:0 14/2980 loss:121.4201 2022-11-09 18:36:07,777 - INFO - main.py - train - 68 - 【train】 epoch:0 15/2980 loss:270.0668 2022-11-09 18:36:09,042 - INFO - main.py - train - 68 - 【train】 epoch:0 16/2980 loss:38.2642 2022-11-09 18:36:10,335 - INFO - main.py - train - 68 - 【train】 epoch:0 17/2980 loss:288.7617 2022-11-09 18:36:11,556 - INFO - main.py - train - 68 - 【train】 epoch:0 18/2980 loss:425.1012 2022-11-09 18:36:12,770 - INFO - main.py - train - 68 - 【train】 epoch:0 19/2980 loss:237.1219 2022-11-09 18:36:14,011 - INFO - main.py - train - 68 - 【train】 epoch:0 20/2980 loss:256.8239 2022-11-09 18:36:15,287 - INFO - main.py - train - 68 - 【train】 epoch:0 21/2980 loss:216.5575 2022-11-09 18:36:16,596 - INFO - main.py - train - 68 - 【train】 epoch:0 22/2980 loss:318.9067 2022-11-09 18:36:17,806 - INFO - main.py - train - 68 - 【train】 epoch:0 23/2980 loss:52.6395 2022-11-09 18:36:19,008 - INFO - main.py - train - 68 - 【train】 epoch:0 24/2980 loss:105.2719 2022-11-09 18:36:20,214 - INFO - main.py - train - 68 - 【train】 epoch:0 25/2980 loss:175.9989 2022-11-09 18:36:21,466 - INFO - main.py - train - 68 - 【train】 epoch:0 26/2980 loss:183.2708 2022-11-09 18:36:22,778 - INFO - main.py - train - 68 - 【train】 epoch:0 27/2980 loss:274.2786 2022-11-09 18:36:24,067 - INFO - main.py - train - 68 - 【train】 epoch:0 28/2980 loss:270.1072 2022-11-09 18:36:25,324 - INFO - main.py - train - 68 - 【train】 epoch:0 29/2980 loss:51.3554 2022-11-09 18:36:26,577 - INFO - main.py - train - 68 - 【train】 epoch:0 30/2980 loss:227.7401 2022-11-09 18:36:27,952 - INFO - main.py - train - 68 - 【train】 epoch:0 31/2980 loss:241.7027 2022-11-09 18:36:29,183 - INFO - main.py - train - 68 - 【train】 epoch:0 32/2980 loss:217.1473 2022-11-09 18:36:30,482 - INFO - main.py - train - 68 - 【train】 epoch:0 33/2980 loss:353.3660 2022-11-09 18:36:31,713 - INFO - main.py - train - 68 - 【train】 epoch:0 34/2980 loss:41.0551 2022-11-09 18:36:32,984 - INFO - main.py - train - 68 - 【train】 epoch:0 35/2980 loss:173.1582 2022-11-09 18:36:34,199 - INFO - main.py - train - 68 - 【train】 epoch:0 36/2980 loss:74.5044 2022-11-09 18:36:35,469 - INFO - main.py - train - 68 - 【train】 epoch:0 37/2980 loss:147.3521 2022-11-09 18:36:36,739 - INFO - main.py - train - 68 - 【train】 epoch:0 38/2980 loss:129.2970 2022-11-09 18:36:37,989 - INFO - main.py - train - 68 - 【train】 epoch:0 39/2980 loss:125.3554 2022-11-09 18:36:39,212 - INFO - main.py - train - 68 - 【train】 epoch:0 40/2980 loss:103.3222 2022-11-09 18:36:40,417 - INFO - main.py - train - 68 - 【train】 epoch:0 41/2980 loss:63.6357 2022-11-09 18:36:41,727 - INFO - main.py - train - 68 - 【train】 epoch:0 42/2980 loss:149.9285 2022-11-09 18:36:43,005 - INFO - main.py - train - 68 - 【train】 epoch:0 43/2980 loss:159.9024 2022-11-09 18:36:44,203 - INFO - main.py - train - 68 - 【train】 epoch:0 44/2980 loss:95.9651 2022-11-09 18:36:45,454 - INFO - main.py - train - 68 - 【train】 epoch:0 45/2980 loss:104.5443 2022-11-09 18:36:46,733 - INFO - main.py - train - 68 - 【train】 epoch:0 46/2980 loss:64.2032 2022-11-09 18:36:48,110 - INFO - main.py - train - 68 - 【train】 epoch:0 47/2980 loss:169.8716 2022-11-09 18:36:49,362 - INFO - main.py - train - 68 - 【train】 epoch:0 48/2980 loss:104.5475 2022-11-09 18:36:50,667 - INFO - main.py - train - 68 - 【train】 epoch:0 49/2980 loss:323.0245 2022-11-09 18:36:51,937 - INFO - main.py - train - 68 - 【train】 epoch:0 50/2980 loss:105.9590 2022-11-09 18:36:53,218 - INFO - main.py - train - 68 - 【train】 epoch:0 51/2980 loss:53.3909 2022-11-09 18:36:54,461 - INFO - main.py - train - 68 - 【train】 epoch:0 52/2980 loss:128.2710 2022-11-09 18:36:55,765 - INFO - main.py - train - 68 - 【train】 epoch:0 53/2980 loss:93.4058 2022-11-09 18:36:57,086 - INFO - main.py - train - 68 - 【train】 epoch:0 54/2980 loss:184.9477 2022-11-09 18:36:58,432 - INFO - main.py - train - 68 - 【train】 epoch:0 55/2980 loss:241.6559 2022-11-09 18:36:59,675 - INFO - main.py - train - 68 - 【train】 epoch:0 56/2980 loss:43.0941 2022-11-09 18:37:00,930 - INFO - main.py - train - 68 - 【train】 epoch:0 57/2980 loss:145.7007 2022-11-09 18:37:16,233 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:37:16,233 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=4, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=4, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:37:20,178 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:37:39,169 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 18:37:39,169 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 18:37:43,141 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 18:37:48,925 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315 2022-11-09 18:37:50,656 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:349.1413 2022-11-09 18:37:51,882 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:172.7901 2022-11-09 18:37:53,162 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:316.5670 2022-11-09 18:37:54,390 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:63.9680 2022-11-09 18:37:55,637 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:77.7757 2022-11-09 18:37:57,002 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:358.8559 2022-11-09 18:37:58,316 - INFO - main.py - train - 68 - 【train】 epoch:0 7/2980 loss:292.0248 2022-11-09 18:37:59,598 - INFO - main.py - train - 68 - 【train】 epoch:0 8/2980 loss:460.5745 2022-11-09 18:38:00,856 - INFO - main.py - train - 68 - 【train】 epoch:0 9/2980 loss:90.0077 2022-11-09 18:38:02,174 - 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INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 19:02:33,540 - INFO - main.py - train - 68 - 【train】 epoch:0 0/1490 loss:316.3720 2022-11-09 19:03:27,388 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 19:03:27,388 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 19:03:31,350 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 19:03:35,927 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315 2022-11-09 19:03:37,161 - 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96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 20:08:54,760 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 21:37:53,600 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 21:37:53,600 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 21:37:57,643 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 21:37:59,143 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 21:37:59,768 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:37:49,595 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 22:37:49,595 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 22:37:53,581 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:37:55,112 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 22:37:55,693 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:44:05,053 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 22:44:05,054 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 22:44:09,079 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:44:10,614 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 22:44:11,206 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:45:38,330 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 22:45:38,330 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 22:45:42,299 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:45:43,847 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 22:45:44,393 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:47:02,323 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 22:47:02,323 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 22:47:06,237 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:47:07,696 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 22:47:08,229 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:49:05,456 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 22:49:05,456 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 22:49:09,362 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 22:49:10,878 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 22:49:11,346 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:17:03,674 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:17:03,674 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:17:07,565 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:17:09,097 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:17:09,643 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:18:40,565 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:18:40,565 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:18:44,455 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:18:45,987 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:18:46,518 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:20:17,022 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:20:17,022 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:20:20,893 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:20:22,409 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:20:22,940 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:21:07,205 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:21:07,205 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:21:11,067 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:21:12,518 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:21:13,046 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:27:41,545 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:27:41,545 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:27:45,405 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:27:46,892 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:27:47,393 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:28:42,829 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:28:42,829 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:28:46,784 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:28:48,315 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:28:48,857 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:29:20,645 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:29:20,645 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:29:24,581 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:29:26,112 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:29:26,643 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:30:18,268 - INFO - main.py - - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:30:18,268 - INFO - main.py - - 254 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:30:22,170 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:30:23,643 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:30:24,174 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:35:30,357 - INFO - main.py - - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:35:30,357 - INFO - main.py - - 255 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:35:34,280 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:35:35,819 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:35:36,394 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:36:15,214 - INFO - main.py - - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:36:15,214 - INFO - main.py - - 255 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:36:19,079 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:36:20,595 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:36:21,110 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:40:29,200 - INFO - main.py - - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:40:29,200 - INFO - main.py - - 255 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:40:33,156 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:40:34,687 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:40:35,187 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:45:34,236 - INFO - main.py - - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-09 23:45:34,236 - INFO - main.py - - 255 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-09 23:45:38,056 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-09 23:45:39,585 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-09 23:45:40,057 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 08:53:56,178 - INFO - main.py - - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 08:53:56,178 - INFO - main.py - - 255 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 08:54:01,277 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 08:54:02,838 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 08:54:04,448 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:02:56,380 - INFO - main.py - - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:02:56,381 - INFO - main.py - - 255 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:03:01,165 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:03:02,705 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:03:03,269 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:19:59,929 - INFO - main.py - - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:19:59,929 - INFO - main.py - - 255 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:20:03,933 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:20:05,457 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:20:05,982 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:30:57,117 - INFO - main.py - - 256 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:30:57,117 - INFO - main.py - - 258 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:31:01,047 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:31:02,594 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:31:03,130 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:33:20,901 - INFO - main.py - - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:33:20,902 - INFO - main.py - - 259 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:33:24,827 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:33:26,373 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:33:26,905 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:34:27,530 - INFO - main.py - - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:34:27,530 - INFO - main.py - - 259 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:34:31,426 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:34:32,957 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:34:33,498 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:42:21,670 - INFO - main.py - - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:42:21,671 - INFO - main.py - - 260 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:42:25,605 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:42:27,143 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:42:27,686 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:45:15,502 - INFO - main.py - - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:45:15,502 - INFO - main.py - - 260 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:45:19,398 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:45:20,903 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:45:21,430 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:50:21,829 - INFO - main.py - - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:50:21,829 - INFO - main.py - - 260 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:50:25,735 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:50:27,276 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:50:27,804 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:51:34,635 - INFO - main.py - - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:51:34,635 - INFO - main.py - - 260 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:51:38,533 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:51:40,079 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:51:40,609 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:52:29,472 - INFO - main.py - - 259 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 09:52:29,472 - INFO - main.py - - 261 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 09:52:33,374 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 09:52:34,898 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 09:52:35,417 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:37:57,114 - INFO - main.py - - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 16:37:57,114 - INFO - main.py - - 260 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 16:38:01,299 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:38:02,895 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 16:38:03,510 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:39:07,040 - INFO - main.py - - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 16:39:07,040 - INFO - main.py - - 260 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 16:39:11,073 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:39:12,666 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 16:39:13,202 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:42:37,278 - INFO - main.py - - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 16:42:37,279 - INFO - main.py - - 260 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 16:42:41,207 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:42:42,744 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 16:42:43,275 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:46:56,985 - INFO - main.py - - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 16:46:56,985 - INFO - main.py - - 259 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 16:47:00,951 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:47:02,523 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 16:47:03,027 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:50:00,285 - INFO - main.py - - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 16:50:00,285 - INFO - main.py - - 259 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 16:50:04,225 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 16:50:05,793 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 16:50:06,315 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:00:50,069 - INFO - main.py - - 260 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:00:50,070 - INFO - main.py - - 262 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:00:54,073 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:00:55,663 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:00:56,196 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:04:13,463 - INFO - main.py - - 260 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:04:13,463 - INFO - main.py - - 262 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:04:17,395 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:04:18,954 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:04:19,483 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:05:36,614 - INFO - main.py - - 295 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:05:38,144 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:05:38,663 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:12:28,419 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:12:28,419 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:12:32,363 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:12:33,932 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:12:34,457 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:13:49,742 - INFO - main.py - - 298 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:13:51,243 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:13:51,757 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:18:45,679 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:18:45,679 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:18:49,568 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:18:51,174 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:18:51,741 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:20:08,393 - INFO - main.py - - 298 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:20:09,878 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:20:10,400 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:22:26,782 - INFO - main.py - - 266 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:22:26,782 - INFO - main.py - - 268 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:22:30,712 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:22:32,304 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:22:32,839 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:23:48,238 - INFO - main.py - - 301 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:23:49,732 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:23:50,220 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:27:05,063 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:27:05,063 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:27:08,989 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:27:10,547 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:27:11,062 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:28:26,585 - INFO - main.py - - 298 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:28:28,096 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:28:28,570 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:29:19,770 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:29:19,770 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:29:23,625 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:29:25,172 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:29:25,676 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:30:39,837 - INFO - main.py - - 298 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:30:41,305 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:30:41,767 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:34:37,153 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:34:37,153 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:34:41,097 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:34:42,637 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:34:43,171 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:35:59,620 - INFO - main.py - - 298 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:36:01,118 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:36:01,589 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:43:29,099 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:43:29,099 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:43:33,072 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:43:34,634 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:43:35,164 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:44:53,860 - INFO - main.py - - 298 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:44:55,338 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:44:55,847 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:45:18,986 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:45:18,986 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:45:22,928 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:45:24,449 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:45:24,948 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:46:20,522 - INFO - main.py - - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:46:20,522 - INFO - main.py - - 265 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:46:24,406 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:46:25,927 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:46:26,493 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:47:44,235 - INFO - main.py - - 298 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:47:45,681 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:47:46,180 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:55:42,885 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 17:55:42,885 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 17:55:46,791 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:55:48,383 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:55:48,913 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 17:57:05,653 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 17:57:07,150 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 17:57:07,665 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:17:11,930 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:17:11,930 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:17:15,986 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:17:17,550 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:17:18,073 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:18:32,384 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:18:33,885 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:18:34,405 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:23:13,296 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:23:13,296 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:23:17,229 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:23:18,805 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:23:19,339 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:24:36,791 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:24:38,241 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:24:38,785 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:29:09,874 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:29:09,874 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:29:13,878 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:29:15,406 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:29:15,985 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:30:35,949 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:30:37,469 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:30:38,006 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:31:27,576 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:31:27,576 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:31:31,514 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:31:33,005 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:31:33,557 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:32:49,325 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:32:50,802 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:32:51,316 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:37:30,106 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:37:30,106 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:37:33,997 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:37:35,552 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:37:36,079 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:38:53,870 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:38:55,371 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:38:55,871 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:40:05,112 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:40:05,112 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:40:08,970 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:40:10,545 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:40:11,064 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:41:28,041 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:41:29,519 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:41:29,951 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:42:27,691 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:42:27,692 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:42:31,621 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:42:33,195 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:42:33,754 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:43:48,161 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:43:49,658 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:43:50,181 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:45:02,275 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:45:02,275 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:45:06,144 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:45:07,673 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:45:08,190 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:46:25,087 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:46:26,594 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:46:27,102 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:54:55,404 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 18:54:55,405 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 18:54:59,321 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:55:00,899 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:55:01,454 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 18:56:15,954 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 18:56:17,430 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 18:56:17,895 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:23:14,893 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 19:23:14,893 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 19:23:18,931 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:23:20,457 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:23:21,012 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:24:35,945 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 19:24:37,432 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:24:37,946 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:26:49,648 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 19:26:49,649 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 19:26:53,503 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:26:55,024 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:26:55,557 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:28:12,016 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 19:28:13,501 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:28:14,011 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:29:08,244 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 19:29:08,244 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 19:29:12,151 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:29:13,648 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:29:14,195 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:30:29,093 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-10 19:30:30,548 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:30:31,010 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:30:31,342 - INFO - main.py - predict - 198 - [('暖风鼓风机', 21, 'subject'), ('有异常响声', 29, 'object'), ('鼓风机', 48, 'subject'), ('异响', 55, 'object'), ('鼓风机', 69, 'subject'), ('故障', 72, 'object'), ('2鼓风', 75, 'subject'), ('内有杂', 79, 'object')] 2022-11-10 19:40:03,510 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 19:40:03,511 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 19:40:07,424 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:40:08,988 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:40:09,513 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:46:03,178 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 19:46:03,178 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 19:46:07,445 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 19:46:09,083 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 19:46:09,610 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:03:40,961 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 20:03:40,961 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 20:03:44,954 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:03:46,524 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 20:03:47,074 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:12:12,450 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 20:12:12,451 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 20:12:16,386 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:12:17,963 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 20:12:18,492 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:16:51,884 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 20:16:51,884 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 20:16:55,874 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:16:57,444 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 20:16:57,975 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:20:54,021 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 20:20:54,021 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 20:20:57,964 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:20:59,502 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 20:21:00,047 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:30:07,108 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-10 20:30:07,108 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-10 20:30:10,989 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-10 20:30:12,528 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-10 20:30:13,048 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-11 10:33:12,211 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-11 10:33:12,211 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-11 10:33:16,114 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-11 10:33:17,719 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-11 10:33:18,345 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-20 18:04:14,025 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-20 18:04:14,025 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-20 18:04:19,333 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-20 18:04:20,876 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-20 18:04:21,453 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-21 23:26:20,302 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-21 23:26:20,302 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-21 23:27:17,601 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-21 23:27:17,601 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-21 23:27:22,658 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-21 23:27:24,168 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-21 23:27:24,777 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-21 23:28:41,197 - INFO - main.py - - 299 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2022-11-21 23:28:42,790 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-21 23:28:43,431 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-21 23:28:43,853 - INFO - main.py - predict - 198 - [('暖风鼓风机', 21, 'subject'), ('有异常响声', 29, 'object'), ('鼓风机', 48, 'subject'), ('异响', 55, 'object'), ('鼓风机', 69, 'subject'), ('故障', 72, 'object'), ('2鼓风', 75, 'subject'), ('内有杂', 79, 'object')] 2022-11-22 15:34:35,264 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-22 15:34:35,264 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-23 21:12:45,244 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-23 21:18:10,782 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-23 21:18:11,485 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-23 22:21:01,725 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-23 22:21:01,725 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-23 22:21:06,001 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-23 22:21:07,547 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-23 22:21:08,500 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-29 23:24:44,589 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-29 23:24:44,589 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-29 23:34:26,938 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-29 23:34:26,938 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-29 23:36:22,619 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-29 23:36:25,400 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2022-11-29 23:36:26,071 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-30 11:08:47,186 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-30 11:08:47,186 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-30 11:19:31,863 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-30 19:49:08,585 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-30 19:49:08,585 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-30 19:56:23,301 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-30 19:56:23,301 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-30 20:17:20,125 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2022-11-30 20:58:55,203 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2022-11-30 20:58:55,203 - INFO - main.py - - 266 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/dgre/', dropout=0.3, dropout_prob=0.3, eval_batch_size=2, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1, max_seq_len=512, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=2, train_epochs=5, use_crf='True', use_lstm='False', warmup_proportion=0.1, weight_decay=0.01) 2022-11-30 20:59:02,055 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2023-03-16 21:05:02,026 - INFO - main.py - - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2023-03-16 21:05:02,026 - INFO - main.py - - 266 - Namespace(output_dir='./checkpoints/', bert_dir='../model_hub/chinese-roberta-wwm-ext/', data_dir='../data/dgre/', log_dir='./logs/', num_tags=5, seed=123, gpu_ids='0', max_seq_len=512, eval_batch_size=2, swa_start=3, train_epochs=5, dropout_prob=0.3, lr=3e-05, other_lr=0.0003, crf_lr=0.03, max_grad_norm=1, warmup_proportion=0.1, weight_decay=0.01, adam_epsilon=1e-08, train_batch_size=2, use_lstm='False', lstm_hidden=128, num_layers=1, dropout=0.3, use_crf='True') 2023-03-16 21:05:07,179 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2023-03-16 21:05:19,512 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:292.0276 2023-03-16 21:05:20,547 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:226.9651 2023-03-16 21:05:21,583 - 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trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2023-03-16 22:13:44,163 - INFO - main.py - - 302 - 211号汽车故障报告综合情况:故障现象:开暖风鼓风机运转时有异常响声。故障原因简要分析:该故障是鼓风机运转时有异响由此可以判断可能原因：1鼓风机故障 2鼓风机内有杂物 2023-03-16 22:13:45,606 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt 2023-03-16 22:13:46,203 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2023-03-16 22:13:46,593 - INFO - main.py - predict - 198 - [('鼓风机', 23, 'subject'), ('有异常响声', 29, 'object'), ('鼓风机', 48, 'subject'), ('异响', 55, 'object'), ('鼓风机', 69, 'subject'), ('故障', 72, 'object'), ('鼓风机', 76, 'subject'), ('有杂物', 80, 'object')]