2021-08-16 14:11:42,025 - INFO - main.py - - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:11:42,026 - INFO - main.py - - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:16:26,807 - INFO - main.py - - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:16:26,808 - INFO - main.py - - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:19:54,863 - INFO - main.py - - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:19:55,070 - INFO - main.py - - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:20:48,788 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 14:20:51,144 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 14:20:53,065 - INFO - main.py - train - 55 - 【train】 epoch:0 1/53480 loss:108.4809 2021-08-16 14:20:54,953 - INFO - main.py - train - 55 - 【train】 epoch:0 2/53480 loss:112.7624 2021-08-16 14:20:56,847 - INFO - main.py - train - 55 - 【train】 epoch:0 3/53480 loss:102.9605 2021-08-16 14:20:58,773 - INFO - main.py - train - 55 - 【train】 epoch:0 4/53480 loss:112.5027 2021-08-16 14:21:00,692 - INFO - main.py - train - 55 - 【train】 epoch:0 5/53480 loss:110.6690 2021-08-16 14:21:02,589 - INFO - main.py - train - 55 - 【train】 epoch:0 6/53480 loss:112.0403 2021-08-16 14:21:04,476 - INFO - main.py - train - 55 - 【train】 epoch:0 7/53480 loss:99.9585 2021-08-16 14:21:06,356 - INFO - main.py - train - 55 - 【train】 epoch:0 8/53480 loss:93.6782 2021-08-16 14:21:08,257 - INFO - main.py - train - 55 - 【train】 epoch:0 9/53480 loss:98.5435 2021-08-16 14:21:10,156 - INFO - main.py - train - 55 - 【train】 epoch:0 10/53480 loss:105.8185 2021-08-16 14:21:12,057 - INFO - main.py - train - 55 - 【train】 epoch:0 11/53480 loss:98.5304 2021-08-16 14:21:13,977 - INFO - main.py - train - 55 - 【train】 epoch:0 12/53480 loss:108.9051 2021-08-16 14:21:15,878 - INFO - main.py - train - 55 - 【train】 epoch:0 13/53480 loss:109.9281 2021-08-16 14:21:17,785 - INFO - main.py - train - 55 - 【train】 epoch:0 14/53480 loss:112.1773 2021-08-16 14:21:19,687 - INFO - main.py - train - 55 - 【train】 epoch:0 15/53480 loss:97.4086 2021-08-16 14:21:21,605 - INFO - main.py - train - 55 - 【train】 epoch:0 16/53480 loss:130.0352 2021-08-16 14:21:23,520 - INFO - main.py - train - 55 - 【train】 epoch:0 17/53480 loss:106.4699 2021-08-16 14:21:25,442 - INFO - main.py - train - 55 - 【train】 epoch:0 18/53480 loss:106.5193 2021-08-16 14:21:27,371 - INFO - main.py - train - 55 - 【train】 epoch:0 19/53480 loss:122.9435 2021-08-16 14:21:29,298 - INFO - main.py - train - 55 - 【train】 epoch:0 20/53480 loss:94.8396 2021-08-16 14:21:31,230 - INFO - main.py - train - 55 - 【train】 epoch:0 21/53480 loss:96.2164 2021-08-16 14:21:33,171 - INFO - main.py - train - 55 - 【train】 epoch:0 22/53480 loss:95.3549 2021-08-16 14:21:35,118 - INFO - main.py - train - 55 - 【train】 epoch:0 23/53480 loss:83.7358 2021-08-16 14:21:37,069 - INFO - main.py - train - 55 - 【train】 epoch:0 24/53480 loss:113.1101 2021-08-16 14:21:39,017 - 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INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 14:23:33,979 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 14:23:35,894 - INFO - main.py - train - 55 - 【train】 epoch:0 1/53480 loss:108.4809 2021-08-16 14:23:37,822 - INFO - main.py - train - 55 - 【train】 epoch:0 2/53480 loss:112.7624 2021-08-16 14:23:39,755 - INFO - main.py - train - 55 - 【train】 epoch:0 3/53480 loss:102.9605 2021-08-16 14:23:41,691 - INFO - main.py - train - 55 - 【train】 epoch:0 4/53480 loss:112.5027 2021-08-16 14:23:43,630 - INFO - main.py - train - 55 - 【train】 epoch:0 5/53480 loss:110.6690 2021-08-16 14:23:45,570 - INFO - main.py - train - 55 - 【train】 epoch:0 6/53480 loss:112.0403 2021-08-16 14:23:47,518 - INFO - main.py - train - 55 - 【train】 epoch:0 7/53480 loss:99.9585 2021-08-16 14:23:49,470 - INFO - main.py - train - 55 - 【train】 epoch:0 8/53480 loss:93.6782 2021-08-16 14:23:51,426 - INFO - main.py - train - 55 - 【train】 epoch:0 9/53480 loss:98.5435 2021-08-16 14:23:53,401 - 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INFO - main.py - train - 55 - 【train】 epoch:0 32/53480 loss:80.9765 2021-08-16 14:24:40,131 - INFO - main.py - train - 55 - 【train】 epoch:0 33/53480 loss:114.3989 2021-08-16 14:24:42,224 - INFO - main.py - train - 55 - 【train】 epoch:0 34/53480 loss:93.9602 2021-08-16 14:24:44,331 - INFO - main.py - train - 55 - 【train】 epoch:0 35/53480 loss:111.1313 2021-08-16 14:24:46,468 - INFO - main.py - train - 55 - 【train】 epoch:0 36/53480 loss:108.2501 2021-08-16 14:24:48,580 - INFO - main.py - train - 55 - 【train】 epoch:0 37/53480 loss:99.9393 2021-08-16 14:24:50,701 - INFO - main.py - train - 55 - 【train】 epoch:0 38/53480 loss:100.8726 2021-08-16 14:24:52,832 - INFO - main.py - train - 55 - 【train】 epoch:0 39/53480 loss:123.0359 2021-08-16 14:24:54,939 - INFO - main.py - train - 55 - 【train】 epoch:0 40/53480 loss:91.7105 2021-08-16 14:24:57,058 - INFO - main.py - train - 55 - 【train】 epoch:0 41/53480 loss:108.0144 2021-08-16 14:24:59,163 - INFO - main.py - train - 55 - 【train】 epoch:0 42/53480 loss:84.0586 2021-08-16 14:25:01,307 - 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INFO - main.py - train - 55 - 【train】 epoch:0 87/53480 loss:68.4059 2021-08-16 14:26:39,283 - INFO - main.py - train - 55 - 【train】 epoch:0 88/53480 loss:81.0742 2021-08-16 14:26:41,534 - INFO - main.py - train - 55 - 【train】 epoch:0 89/53480 loss:85.1905 2021-08-16 14:31:36,445 - INFO - main.py - - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:31:36,446 - INFO - main.py - - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:32:20,221 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 14:32:22,580 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 14:32:24,594 - INFO - main.py - train - 55 - 【train】 epoch:0 1/53480 loss:108.4809 2021-08-16 14:32:26,600 - INFO - main.py - train - 55 - 【train】 epoch:0 2/53480 loss:112.7624 2021-08-16 14:32:28,619 - INFO - main.py - train - 55 - 【train】 epoch:0 3/53480 loss:102.9605 2021-08-16 14:32:30,639 - INFO - main.py - train - 55 - 【train】 epoch:0 4/53480 loss:112.5027 2021-08-16 14:32:32,650 - INFO - main.py - train - 55 - 【train】 epoch:0 5/53480 loss:110.6690 2021-08-16 14:32:34,665 - INFO - main.py - train - 55 - 【train】 epoch:0 6/53480 loss:112.0403 2021-08-16 14:32:36,684 - INFO - main.py - train - 55 - 【train】 epoch:0 7/53480 loss:99.9585 2021-08-16 14:32:38,705 - INFO - main.py - train - 55 - 【train】 epoch:0 8/53480 loss:93.6782 2021-08-16 14:32:40,747 - INFO - main.py - train - 55 - 【train】 epoch:0 9/53480 loss:98.5435 2021-08-16 14:32:42,788 - INFO - main.py - train - 55 - 【train】 epoch:0 10/53480 loss:105.8185 2021-08-16 14:32:44,851 - 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INFO - main.py - train - 55 - 【train】 epoch:0 88/53480 loss:81.0742 2021-08-16 14:35:37,072 - INFO - main.py - train - 55 - 【train】 epoch:0 89/53480 loss:85.1905 2021-08-16 14:51:22,439 - INFO - main.py - - 183 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:51:22,450 - INFO - main.py - - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:52:03,189 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 14:52:05,335 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 14:52:59,707 - INFO - main.py - - 183 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:52:59,720 - INFO - main.py - - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:53:35,871 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 14:53:38,025 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 14:55:35,274 - INFO - main.py - - 185 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:55:35,277 - INFO - main.py - - 187 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:56:12,931 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 14:56:15,118 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 14:58:37,410 - INFO - main.py - - 185 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 14:58:37,411 - INFO - main.py - - 187 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 14:59:15,132 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 14:59:17,318 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 15:01:13,264 - INFO - main.py - - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 15:01:13,265 - INFO - main.py - - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 15:01:49,537 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 15:01:51,697 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 15:11:12,161 - INFO - main.py - - 183 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 15:11:12,162 - INFO - main.py - - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 15:11:49,528 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 15:11:51,880 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 15:14:56,752 - INFO - main.py - - 183 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 15:14:56,753 - INFO - main.py - - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 15:46:37,230 - INFO - main.py - - 183 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 15:46:37,231 - INFO - main.py - - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 15:47:14,205 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 15:47:16,362 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 16:00:35,129 - INFO - main.py - - 186 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 16:00:35,130 - INFO - main.py - - 188 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 16:01:12,479 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 16:01:14,647 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 16:01:36,610 - INFO - main.py - - 186 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 16:01:36,610 - INFO - main.py - - 188 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 16:02:13,763 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 16:02:15,927 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 16:03:15,794 - INFO - main.py - - 188 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 16:03:15,795 - INFO - main.py - - 190 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 16:03:53,069 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 16:03:55,247 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 16:04:25,287 - INFO - main.py - - 188 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 16:04:25,288 - INFO - main.py - - 190 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 16:05:02,701 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 16:05:04,888 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 16:07:01,588 - INFO - main.py - - 191 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 16:07:01,592 - INFO - main.py - - 193 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 16:07:38,433 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 16:07:40,586 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 16:19:24,864 - INFO - main.py - - 193 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 16:19:24,865 - INFO - main.py - - 195 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 16:20:02,878 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 16:20:05,099 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-16 16:25:45,379 - INFO - main.py - - 193 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-16 16:25:45,380 - INFO - main.py - - 195 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-16 16:26:23,250 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-16 16:26:25,453 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-17 13:10:50,899 - INFO - main.py - - 193 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-17 13:10:51,203 - INFO - main.py - - 195 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-17 13:11:54,297 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-17 13:11:56,606 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-17 13:13:44,224 - INFO - main.py - - 195 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-17 13:13:44,225 - INFO - main.py - - 197 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-17 13:14:34,158 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-17 13:14:36,401 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663 2021-08-17 13:22:51,814 - INFO - main.py - - 195 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-17 13:22:51,815 - INFO - main.py - - 197 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-17 23:49:37,988 - INFO - main.py - - 198 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-17 23:49:38,422 - INFO - main.py - - 200 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-17 23:50:41,216 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-17 23:50:43,618 - INFO - main.py - train - 55 - 【train】 epoch:0 0/5348 loss:97.5663 2021-08-17 23:50:45,557 - INFO - main.py - train - 55 - 【train】 epoch:0 1/5348 loss:108.4809 2021-08-17 23:50:47,481 - INFO - main.py - train - 55 - 【train】 epoch:0 2/5348 loss:112.7200 2021-08-17 23:50:49,432 - INFO - main.py - train - 55 - 【train】 epoch:0 3/5348 loss:102.8487 2021-08-17 23:50:51,374 - INFO - main.py - train - 55 - 【train】 epoch:0 4/5348 loss:112.2536 2021-08-17 23:50:53,311 - INFO - main.py - train - 55 - 【train】 epoch:0 5/5348 loss:110.2309 2021-08-17 23:50:55,251 - INFO - main.py - train - 55 - 【train】 epoch:0 6/5348 loss:111.3611 2021-08-17 23:50:57,195 - INFO - main.py - train - 55 - 【train】 epoch:0 7/5348 loss:99.1784 2021-08-17 23:50:59,134 - INFO - main.py - train - 55 - 【train】 epoch:0 8/5348 loss:92.7188 2021-08-17 23:51:01,055 - INFO - main.py - train - 55 - 【train】 epoch:0 9/5348 loss:97.2472 2021-08-17 23:51:02,991 - 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INFO - main.py - train - 55 - 【train】 epoch:0 5345/5348 loss:2.4169 2021-08-18 02:56:52,219 - INFO - main.py - train - 55 - 【train】 epoch:0 5346/5348 loss:3.5758 2021-08-18 02:56:54,270 - INFO - main.py - train - 55 - 【train】 epoch:0 5347/5348 loss:5.5199 2021-08-18 02:56:54,520 - INFO - trainUtils.py - save_model - 70 - Saving model checkpoint to ./checkpoints/bert_bilstm_crf 2021-08-18 11:19:55,422 - INFO - main.py - - 209 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 11:19:55,861 - INFO - main.py - - 211 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 11:20:56,432 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 11:21:03,329 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 11:21:08,816 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 11:22:33,666 - INFO - main.py - - 209 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 11:22:33,667 - INFO - main.py - - 211 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 11:23:16,795 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 11:23:18,294 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 11:23:30,091 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 11:40:13,156 - INFO - main.py - - 209 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 11:40:13,158 - INFO - main.py - - 211 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 11:40:56,515 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 11:40:58,007 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 11:41:09,745 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:02:09,646 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:02:09,651 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:02:50,804 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:02:52,299 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:03:03,592 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:03:51,780 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:03:51,781 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:04:31,452 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:04:32,941 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:04:44,208 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:08:05,903 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:08:05,908 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:08:43,557 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:08:45,181 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:08:51,819 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:17:05,417 - INFO - main.py - test - 144 - precision recall f1-score support object 0.76 0.89 0.82 38656 subject 0.76 0.85 0.80 25103 micro avg 0.76 0.88 0.81 63759 macro avg 0.76 0.87 0.81 63759 weighted avg 0.76 0.88 0.81 63759 2021-08-18 12:35:52,912 - INFO - main.py - - 208 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:35:52,915 - INFO - main.py - - 210 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:36:32,932 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:36:32,936 - INFO - main.py - - 236 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:36:34,408 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:36:42,078 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:40:09,187 - INFO - main.py - - 208 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:40:09,191 - INFO - main.py - - 210 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:40:46,152 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:40:46,156 - INFO - main.py - - 236 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:40:47,636 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:40:53,854 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:41:57,908 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:41:57,909 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:42:34,894 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:42:34,898 - INFO - main.py - - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:42:36,383 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:42:43,950 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:45:56,680 - INFO - main.py - - 212 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:45:56,683 - INFO - main.py - - 214 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:46:33,166 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:46:33,170 - INFO - main.py - - 240 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:46:34,650 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:46:37,771 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:49:58,061 - INFO - main.py - - 210 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:49:58,062 - INFO - main.py - - 212 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:50:34,756 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:50:34,760 - INFO - main.py - - 238 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:50:36,258 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:50:44,329 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:50:44,452 - INFO - main.py - predict - 170 - [('身》', 2, 'B'), ('》是', 3, 'I'), ('ut', 6, 'B'), ('tsider演唱', 7, 'I'), ('辑M', 26, 'B'), ('Maestro》。', 27, 'I'), ('身的', 42, 'B'), ('的感', 43, 'I'), ('身即', 48, 'B'), ('即是', 49, 'I')] 2021-08-18 12:50:44,454 - INFO - main.py - - 241 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲 2021-08-18 12:50:45,853 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:50:46,692 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:50:46,774 - INFO - main.py - predict - 170 - [('写你', 4, 'B'), ('你的美》是', 5, 'I'), ('漠演', 14, 'B'), ('演唱', 15, 'I')] 2021-08-18 12:52:15,992 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:52:15,993 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:52:54,909 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:52:54,913 - INFO - main.py - - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:52:56,424 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:52:57,563 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:52:57,671 - INFO - main.py - predict - 171 - [('身》', 2, 'B'), ('》是', 3, 'I'), ('ut', 6, 'B'), ('tsider演唱', 7, 'I'), ('辑M', 26, 'B'), ('Maestro》。', 27, 'I'), ('身的', 42, 'B'), ('的感', 43, 'I'), ('身即', 48, 'B'), ('即是', 49, 'I')] 2021-08-18 12:52:57,673 - INFO - main.py - - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲 2021-08-18 12:52:59,084 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:53:00,075 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:53:00,158 - INFO - main.py - predict - 171 - [('写你', 4, 'B'), ('你的美》是', 5, 'I'), ('漠演', 14, 'B'), ('演唱', 15, 'I')] 2021-08-18 12:54:04,343 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:54:04,344 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:54:47,898 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:54:47,902 - INFO - main.py - - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:54:49,415 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:54:50,514 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:54:50,620 - INFO - main.py - predict - 171 - [('身》是', 2, 'subject'), ('utsider演唱', 6, 'object'), ('辑Maestro》。', 26, 'object'), ('身的感', 42, 'subject'), ('身即是', 48, 'subject')] 2021-08-18 12:54:50,623 - INFO - main.py - - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲 2021-08-18 12:54:51,974 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:54:52,762 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:54:52,843 - INFO - main.py - predict - 171 - [('写你的美》是', 4, 'subject'), ('漠演唱', 14, 'object')] 2021-08-18 12:55:46,286 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:55:46,287 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:56:29,495 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:56:29,499 - INFO - main.py - - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:56:30,996 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:56:32,076 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:56:32,208 - INFO - main.py - predict - 171 - [('单身》', 1, 'subject'), ('Outsider演', 5, 'object'), ('2辑Maestro》', 25, 'object'), ('单身的', 41, 'subject'), ('单身即', 47, 'subject')] 2021-08-18 12:56:32,210 - INFO - main.py - - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲 2021-08-18 12:56:33,766 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:56:34,764 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:56:34,850 - INFO - main.py - predict - 171 - [('墨写你的美》', 3, 'subject'), ('冷漠演', 13, 'object')] 2021-08-18 12:58:01,458 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 12:58:01,459 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 12:58:37,558 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:58:37,562 - INFO - main.py - - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 12:58:39,044 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:58:40,212 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:58:40,318 - INFO - main.py - predict - 171 - [('单身', 1, 'subject'), ('Outsider', 5, 'object'), ('2辑Maestro', 25, 'object'), ('单身', 41, 'subject'), ('单身', 47, 'subject')] 2021-08-18 12:58:40,321 - INFO - main.py - - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲 2021-08-18 12:58:41,656 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 12:58:42,454 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0'] 2021-08-18 12:58:42,536 - INFO - main.py - predict - 171 - [('墨写你的美', 3, 'subject'), ('冷漠', 13, 'object')] 2021-08-18 13:20:10,327 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 13:20:10,560 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', 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=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 13:22:28,005 - INFO - main.py - - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'} 2021-08-18 13:22:28,006 - INFO - main.py - - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='-1', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01) 2021-08-18 13:23:07,063 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['-1'] 2021-08-18 13:23:07,068 - INFO - main.py - - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s 2021-08-18 13:23:09,576 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 13:23:15,200 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['-1'] 2021-08-18 13:23:17,002 - INFO - main.py - predict - 171 - [('单身', 1, 'subject'), ('Outsider', 5, 'object'), ('2辑Maestro', 25, 'object'), ('单身', 41, 'subject'), ('单身', 47, 'subject')] 2021-08-18 13:23:17,022 - INFO - main.py - - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲 2021-08-18 13:23:18,589 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt 2021-08-18 13:23:19,506 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['-1'] 2021-08-18 13:23:20,846 - INFO - main.py - predict - 171 - [('墨写你的美', 3, 'subject'), ('冷漠', 13, 'object')]