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bert_crf.log

bert_crf.log 703 KB
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
  1. 2022-11-08 23:33:48,004 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    2. 

  2. 2022-11-08 23:33:48,004 - INFO - main.py - <module> - 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)
    3. 

  3. 2022-11-09 11:23:29,709 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    4. 

  4. 2022-11-09 11:23:29,709 - INFO - main.py - <module> - 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)
    5. 

  5. 2022-11-09 11:24:41,226 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    6. 

  6. 2022-11-09 11:24:41,226 - INFO - main.py - <module> - 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)
    7. 

  7. 2022-11-09 11:25:11,934 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    8. 

  8. 2022-11-09 11:25:11,934 - INFO - main.py - <module> - 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)
    9. 

  9. 2022-11-09 11:26:41,964 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    10. 

  10. 2022-11-09 11:26:41,964 - INFO - main.py - <module> - 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)
    11. 

  11. 2022-11-09 11:27:50,050 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    12. 

  12. 2022-11-09 11:27:50,050 - INFO - main.py - <module> - 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)
    13. 

  13. 2022-11-09 11:29:09,483 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    14. 

  14. 2022-11-09 11:29:09,483 - INFO - main.py - <module> - 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)
    15. 

  15. 2022-11-09 11:29:21,750 - INFO - main.py - <module> - 249 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    16. 

  16. 2022-11-09 11:29:21,750 - INFO - main.py - <module> - 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)
    17. 

  17. 2022-11-09 16:33:06,998 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    18. 

  18. 2022-11-09 16:33:06,998 - INFO - main.py - <module> - 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)
    19. 

  19. 2022-11-09 16:57:39,432 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    20. 

  20. 2022-11-09 16:57:39,433 - INFO - main.py - <module> - 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)
    21. 

  21. 2022-11-09 17:36:04,479 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    22. 

  22. 2022-11-09 17:36:04,480 - INFO - main.py - <module> - 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)
    23. 

  23. 2022-11-09 17:36:24,656 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    24. 

  24. 2022-11-09 17:36:24,656 - INFO - main.py - <module> - 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)
    25. 

  25. 2022-11-09 17:36:32,110 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    26. 

  26. 2022-11-09 17:40:44,962 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    27. 

  27. 2022-11-09 17:40:44,962 - INFO - main.py - <module> - 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)
    28. 

  28. 2022-11-09 17:40:50,036 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    29. 

  29. 2022-11-09 18:17:53,463 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    30. 

  30. 2022-11-09 18:17:53,463 - INFO - main.py - <module> - 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)
    31. 

  31. 2022-11-09 18:17:58,990 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    32. 

  32. 2022-11-09 18:18:30,751 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    33. 

  33. 2022-11-09 18:18:30,751 - INFO - main.py - <module> - 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)
    34. 

  34. 2022-11-09 18:18:35,649 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    35. 

  35. 2022-11-09 18:20:15,157 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    36. 

  36. 2022-11-09 18:20:15,157 - INFO - main.py - <module> - 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)
    37. 

  37. 2022-11-09 18:20:19,522 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    38. 

  38. 2022-11-09 18:20:43,756 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    39. 

  39. 2022-11-09 18:20:43,757 - INFO - main.py - <module> - 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)
    40. 

  40. 2022-11-09 18:20:47,737 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    41. 

  41. 2022-11-09 18:20:52,601 - INFO - main.py - train - 68 - 【train】 epoch:0 0/5960 loss:270.5590
    42. 

  42. 2022-11-09 18:20:54,234 - INFO - main.py - train - 68 - 【train】 epoch:0 1/5960 loss:293.8041
    43. 

  43. 2022-11-09 18:20:55,409 - INFO - main.py - train - 68 - 【train】 epoch:0 2/5960 loss:533.3459
    44. 

  44. 2022-11-09 18:20:56,580 - INFO - main.py - train - 68 - 【train】 epoch:0 3/5960 loss:167.5391
    45. 

  45. 2022-11-09 18:20:57,705 - INFO - main.py - train - 68 - 【train】 epoch:0 4/5960 loss:188.1563
    46. 

  46. 2022-11-09 18:20:58,822 - INFO - main.py - train - 68 - 【train】 epoch:0 5/5960 loss:157.2815
    47. 

  47. 2022-11-09 18:20:59,919 - INFO - main.py - train - 68 - 【train】 epoch:0 6/5960 loss:358.7325
    48. 

  48. 2022-11-09 18:21:01,054 - INFO - main.py - train - 68 - 【train】 epoch:0 7/5960 loss:270.1438
    49. 

  49. 2022-11-09 18:21:02,120 - INFO - main.py - train - 68 - 【train】 epoch:0 8/5960 loss:65.7982
    50. 

  50. 2022-11-09 18:21:03,278 - INFO - main.py - train - 68 - 【train】 epoch:0 9/5960 loss:60.7055
    51. 

  51. 2022-11-09 18:21:04,394 - INFO - main.py - train - 68 - 【train】 epoch:0 10/5960 loss:109.2479
    52. 

  52. 2022-11-09 18:21:05,482 - INFO - main.py - train - 68 - 【train】 epoch:0 11/5960 loss:41.0138
    53. 

  53. 2022-11-09 18:21:06,619 - INFO - main.py - train - 68 - 【train】 epoch:0 12/5960 loss:422.2743
    54. 

  54. 2022-11-09 18:21:07,768 - INFO - main.py - train - 68 - 【train】 epoch:0 13/5960 loss:289.6386
    55. 

  55. 2022-11-09 18:21:08,908 - INFO - main.py - train - 68 - 【train】 epoch:0 14/5960 loss:249.3521
    56. 

  56. 2022-11-09 18:21:10,056 - INFO - main.py - train - 68 - 【train】 epoch:0 15/5960 loss:319.1989
    57. 

  57. 2022-11-09 18:21:11,175 - INFO - main.py - train - 68 - 【train】 epoch:0 16/5960 loss:146.3364
    58. 

  58. 2022-11-09 18:21:12,275 - INFO - main.py - train - 68 - 【train】 epoch:0 17/5960 loss:758.4374
    59. 

  59. 2022-11-09 18:21:13,369 - INFO - main.py - train - 68 - 【train】 epoch:0 18/5960 loss:63.2237
    60. 

  60. 2022-11-09 18:21:14,425 - INFO - main.py - train - 68 - 【train】 epoch:0 19/5960 loss:113.5544
    61. 

  61. 2022-11-09 18:21:15,564 - INFO - main.py - train - 68 - 【train】 epoch:0 20/5960 loss:521.8570
    62. 

  62. 2022-11-09 18:21:16,681 - INFO - main.py - train - 68 - 【train】 epoch:0 21/5960 loss:413.1941
    63. 

  63. 2022-11-09 18:21:17,776 - INFO - main.py - train - 68 - 【train】 epoch:0 22/5960 loss:37.8984
    64. 

  64. 2022-11-09 18:21:18,891 - INFO - main.py - train - 68 - 【train】 epoch:0 23/5960 loss:178.0960
    65. 

  65. 2022-11-09 18:21:20,030 - INFO - main.py - train - 68 - 【train】 epoch:0 24/5960 loss:187.9926
    66. 

  66. 2022-11-09 18:21:21,112 - INFO - main.py - train - 68 - 【train】 epoch:0 25/5960 loss:154.3412
    67. 

  67. 2022-11-09 18:21:22,197 - INFO - main.py - train - 68 - 【train】 epoch:0 26/5960 loss:165.2894
    68. 

  68. 2022-11-09 18:21:23,296 - INFO - main.py - train - 68 - 【train】 epoch:0 27/5960 loss:691.0421
    69. 

  69. 2022-11-09 18:21:24,394 - INFO - main.py - train - 68 - 【train】 epoch:0 28/5960 loss:31.0667
    70. 

  70. 2022-11-09 18:21:25,470 - INFO - main.py - train - 68 - 【train】 epoch:0 29/5960 loss:203.4418
    71. 

  71. 2022-11-09 18:21:26,556 - INFO - main.py - train - 68 - 【train】 epoch:0 30/5960 loss:231.4055
    72. 

  72. 2022-11-09 18:21:27,671 - INFO - main.py - train - 68 - 【train】 epoch:0 31/5960 loss:273.9700
    73. 

  73. 2022-11-09 18:21:28,736 - INFO - main.py - train - 68 - 【train】 epoch:0 32/5960 loss:47.7574
    74. 

  74. 2022-11-09 18:21:29,846 - INFO - main.py - train - 68 - 【train】 epoch:0 33/5960 loss:24.3343
    75. 

  75. 2022-11-09 18:21:30,960 - INFO - main.py - train - 68 - 【train】 epoch:0 34/5960 loss:493.4245
    76. 

  76. 2022-11-09 18:21:32,051 - INFO - main.py - train - 68 - 【train】 epoch:0 35/5960 loss:66.0639
    77. 

  77. 2022-11-09 18:21:33,152 - INFO - main.py - train - 68 - 【train】 epoch:0 36/5960 loss:122.7962
    78. 

  78. 2022-11-09 18:21:34,268 - INFO - main.py - train - 68 - 【train】 epoch:0 37/5960 loss:637.9681
    79. 

  79. 2022-11-09 18:21:35,424 - INFO - main.py - train - 68 - 【train】 epoch:0 38/5960 loss:314.1795
    80. 

  80. 2022-11-09 18:21:45,844 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    81. 

  81. 2022-11-09 18:21:45,845 - INFO - main.py - <module> - 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)
    82. 

  82. 2022-11-09 18:21:49,767 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    83. 

  83. 2022-11-09 18:21:55,450 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315
    84. 

  84. 2022-11-09 18:21:57,257 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:349.1413
    85. 

  85. 2022-11-09 18:21:58,487 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:172.7901
    86. 

  86. 2022-11-09 18:21:59,722 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:316.5670
    87. 

  87. 2022-11-09 18:22:00,964 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:63.9680
    88. 

  88. 2022-11-09 18:22:02,178 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:77.7757
    89. 

  89. 2022-11-09 18:22:03,486 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:358.8559
    90. 

  90. 2022-11-09 18:22:13,792 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    91. 

  91. 2022-11-09 18:22:13,793 - INFO - main.py - <module> - 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)
    92. 

  92. 2022-11-09 18:22:17,703 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    93. 

  93. 2022-11-09 18:25:49,190 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    94. 

  94. 2022-11-09 18:25:49,190 - INFO - main.py - <module> - 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)
    95. 

  95. 2022-11-09 18:25:53,176 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    96. 

  96. 2022-11-09 18:25:58,768 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315
    97. 

  97. 2022-11-09 18:26:00,723 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:349.1413
    98. 

  98. 2022-11-09 18:26:01,951 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:172.7901
    99. 

  99. 2022-11-09 18:26:03,196 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:316.5670
    100. 

  100. 2022-11-09 18:26:04,405 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:63.9680
    101. 

  101. 2022-11-09 18:26:05,645 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:77.7757
    102. 

  102. 2022-11-09 18:26:06,941 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:358.8559
    103. 

  103. 2022-11-09 18:26:08,221 - INFO - main.py - train - 68 - 【train】 epoch:0 7/2980 loss:292.0248
    104. 

  104. 2022-11-09 18:26:09,459 - INFO - main.py - train - 68 - 【train】 epoch:0 8/2980 loss:460.5745
    105. 

  105. 2022-11-09 18:26:10,699 - INFO - main.py - train - 68 - 【train】 epoch:0 9/2980 loss:90.0077
    106. 

  106. 2022-11-09 18:26:11,995 - INFO - main.py - train - 68 - 【train】 epoch:0 10/2980 loss:489.6821
    107. 

  107. 2022-11-09 18:26:13,232 - INFO - main.py - train - 68 - 【train】 epoch:0 11/2980 loss:111.5192
    108. 

  108. 2022-11-09 18:26:14,462 - INFO - main.py - train - 68 - 【train】 epoch:0 12/2980 loss:177.8898
    109. 

  109. 2022-11-09 18:26:15,726 - INFO - main.py - train - 68 - 【train】 epoch:0 13/2980 loss:439.1432
    110. 

  110. 2022-11-09 18:26:16,985 - INFO - main.py - train - 68 - 【train】 epoch:0 14/2980 loss:121.4201
    111. 

  111. 2022-11-09 18:26:18,258 - INFO - main.py - train - 68 - 【train】 epoch:0 15/2980 loss:270.0668
    112. 

  112. 2022-11-09 18:26:19,505 - INFO - main.py - train - 68 - 【train】 epoch:0 16/2980 loss:38.2642
    113. 

  113. 2022-11-09 18:26:20,769 - INFO - main.py - train - 68 - 【train】 epoch:0 17/2980 loss:288.7617
    114. 

  114. 2022-11-09 18:26:22,026 - INFO - main.py - train - 68 - 【train】 epoch:0 18/2980 loss:425.1012
    115. 

  115. 2022-11-09 18:26:23,277 - INFO - main.py - train - 68 - 【train】 epoch:0 19/2980 loss:237.1219
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  116. 2022-11-09 18:26:24,744 - INFO - main.py - train - 68 - 【train】 epoch:0 20/2980 loss:256.8239
    117. 

  117. 2022-11-09 18:26:26,010 - INFO - main.py - train - 68 - 【train】 epoch:0 21/2980 loss:216.5575
    118. 

  118. 2022-11-09 18:26:27,379 - INFO - main.py - train - 68 - 【train】 epoch:0 22/2980 loss:318.9067
    119. 

  119. 2022-11-09 18:26:28,606 - INFO - main.py - train - 68 - 【train】 epoch:0 23/2980 loss:52.6395
    120. 

  120. 2022-11-09 18:26:29,847 - INFO - main.py - train - 68 - 【train】 epoch:0 24/2980 loss:105.2719
    121. 

  121. 2022-11-09 18:26:31,048 - INFO - main.py - train - 68 - 【train】 epoch:0 25/2980 loss:175.9989
    122. 

  122. 2022-11-09 18:26:32,271 - INFO - main.py - train - 68 - 【train】 epoch:0 26/2980 loss:183.2708
    123. 

  123. 2022-11-09 18:26:33,504 - INFO - main.py - train - 68 - 【train】 epoch:0 27/2980 loss:274.2786
    124. 

  124. 2022-11-09 18:26:34,964 - INFO - main.py - train - 68 - 【train】 epoch:0 28/2980 loss:270.1072
    125. 

  125. 2022-11-09 18:26:36,285 - INFO - main.py - train - 68 - 【train】 epoch:0 29/2980 loss:51.3554
    126. 

  126. 2022-11-09 18:26:37,507 - INFO - main.py - train - 68 - 【train】 epoch:0 30/2980 loss:227.7401
    127. 

  127. 2022-11-09 18:26:38,767 - INFO - main.py - train - 68 - 【train】 epoch:0 31/2980 loss:241.7027
    128. 

  128. 2022-11-09 18:26:40,014 - INFO - main.py - train - 68 - 【train】 epoch:0 32/2980 loss:217.1473
    129. 

  129. 2022-11-09 18:26:41,309 - INFO - main.py - train - 68 - 【train】 epoch:0 33/2980 loss:353.3660
    130. 

  130. 2022-11-09 18:26:42,513 - INFO - main.py - train - 68 - 【train】 epoch:0 34/2980 loss:41.0551
    131. 

  131. 2022-11-09 18:26:43,748 - INFO - main.py - train - 68 - 【train】 epoch:0 35/2980 loss:173.1582
    132. 

  132. 2022-11-09 18:26:44,987 - INFO - main.py - train - 68 - 【train】 epoch:0 36/2980 loss:74.5044
    133. 

  133. 2022-11-09 18:26:46,254 - INFO - main.py - train - 68 - 【train】 epoch:0 37/2980 loss:147.3521
    134. 

  134. 2022-11-09 18:26:47,483 - INFO - main.py - train - 68 - 【train】 epoch:0 38/2980 loss:129.2970
    135. 

  135. 2022-11-09 18:26:48,788 - INFO - main.py - train - 68 - 【train】 epoch:0 39/2980 loss:125.3554
    136. 

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    137. 

  137. 2022-11-09 18:26:51,253 - INFO - main.py - train - 68 - 【train】 epoch:0 41/2980 loss:63.6357
    138. 

  138. 2022-11-09 18:26:52,549 - INFO - main.py - train - 68 - 【train】 epoch:0 42/2980 loss:149.9285
    139. 

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    140. 

  140. 2022-11-09 18:26:55,095 - INFO - main.py - train - 68 - 【train】 epoch:0 44/2980 loss:95.9651
    141. 

  141. 2022-11-09 18:26:56,367 - INFO - main.py - train - 68 - 【train】 epoch:0 45/2980 loss:104.5443
    142. 

  142. 2022-11-09 18:26:57,636 - INFO - main.py - train - 68 - 【train】 epoch:0 46/2980 loss:64.2032
    143. 

  143. 2022-11-09 18:26:58,936 - INFO - main.py - train - 68 - 【train】 epoch:0 47/2980 loss:169.8716
    144. 

  144. 2022-11-09 18:27:00,210 - INFO - main.py - train - 68 - 【train】 epoch:0 48/2980 loss:104.5475
    145. 

  145. 2022-11-09 18:27:01,529 - INFO - main.py - train - 68 - 【train】 epoch:0 49/2980 loss:323.0245
    146. 

  146. 2022-11-09 18:27:02,807 - INFO - main.py - train - 68 - 【train】 epoch:0 50/2980 loss:105.9590
    147. 

  147. 2022-11-09 18:27:04,121 - INFO - main.py - train - 68 - 【train】 epoch:0 51/2980 loss:53.3909
    148. 

  148. 2022-11-09 18:27:05,445 - INFO - main.py - train - 68 - 【train】 epoch:0 52/2980 loss:128.2710
    149. 

  149. 2022-11-09 18:29:42,809 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    150. 

  150. 2022-11-09 18:29:42,809 - INFO - main.py - <module> - 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)
    151. 

  151. 2022-11-09 18:35:37,108 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    152. 

  152. 2022-11-09 18:35:37,108 - INFO - main.py - <module> - 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)
    153. 

  153. 2022-11-09 18:35:41,593 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    154. 

  154. 2022-11-09 18:35:47,976 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:282.4315
    155. 

  155. 2022-11-09 18:35:49,914 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:349.1413
    156. 

  156. 2022-11-09 18:35:51,115 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:172.7901
    157. 

  157. 2022-11-09 18:35:52,371 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:316.5670
    158. 

  158. 2022-11-09 18:35:53,586 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:63.9680
    159. 

  159. 2022-11-09 18:35:54,801 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:77.7757
    160. 

  160. 2022-11-09 18:35:56,179 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:358.8559
    161. 

  161. 2022-11-09 18:35:57,517 - INFO - main.py - train - 68 - 【train】 epoch:0 7/2980 loss:292.0248
    162. 

  162. 2022-11-09 18:35:58,778 - INFO - main.py - train - 68 - 【train】 epoch:0 8/2980 loss:460.5745
    163. 

  163. 2022-11-09 18:36:00,115 - INFO - main.py - train - 68 - 【train】 epoch:0 9/2980 loss:90.0077
    164. 

  164. 2022-11-09 18:36:01,448 - INFO - main.py - train - 68 - 【train】 epoch:0 10/2980 loss:489.6821
    165. 

  165. 2022-11-09 18:36:02,692 - INFO - main.py - train - 68 - 【train】 epoch:0 11/2980 loss:111.5192
    166. 

  166. 2022-11-09 18:36:03,924 - INFO - main.py - train - 68 - 【train】 epoch:0 12/2980 loss:177.8898
    167. 

  167. 2022-11-09 18:36:05,223 - INFO - main.py - train - 68 - 【train】 epoch:0 13/2980 loss:439.1432
    168. 

  168. 2022-11-09 18:36:06,503 - INFO - main.py - train - 68 - 【train】 epoch:0 14/2980 loss:121.4201
    169. 

  169. 2022-11-09 18:36:07,777 - INFO - main.py - train - 68 - 【train】 epoch:0 15/2980 loss:270.0668
    170. 

  170. 2022-11-09 18:36:09,042 - INFO - main.py - train - 68 - 【train】 epoch:0 16/2980 loss:38.2642
    171. 

  171. 2022-11-09 18:36:10,335 - INFO - main.py - train - 68 - 【train】 epoch:0 17/2980 loss:288.7617
    172. 

  172. 2022-11-09 18:36:11,556 - INFO - main.py - train - 68 - 【train】 epoch:0 18/2980 loss:425.1012
    173. 

  173. 2022-11-09 18:36:12,770 - INFO - main.py - train - 68 - 【train】 epoch:0 19/2980 loss:237.1219
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  174. 2022-11-09 18:36:14,011 - INFO - main.py - train - 68 - 【train】 epoch:0 20/2980 loss:256.8239
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  175. 2022-11-09 18:36:15,287 - INFO - main.py - train - 68 - 【train】 epoch:0 21/2980 loss:216.5575
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  176. 2022-11-09 18:36:16,596 - INFO - main.py - train - 68 - 【train】 epoch:0 22/2980 loss:318.9067
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  177. 2022-11-09 18:36:17,806 - INFO - main.py - train - 68 - 【train】 epoch:0 23/2980 loss:52.6395
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  178. 2022-11-09 18:36:19,008 - INFO - main.py - train - 68 - 【train】 epoch:0 24/2980 loss:105.2719
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  183. 2022-11-09 18:36:25,324 - INFO - main.py - train - 68 - 【train】 epoch:0 29/2980 loss:51.3554
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  184. 2022-11-09 18:36:26,577 - INFO - main.py - train - 68 - 【train】 epoch:0 30/2980 loss:227.7401
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  185. 2022-11-09 18:36:27,952 - INFO - main.py - train - 68 - 【train】 epoch:0 31/2980 loss:241.7027
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    189. 

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    191. 

  191. 2022-11-09 18:36:35,469 - INFO - main.py - train - 68 - 【train】 epoch:0 37/2980 loss:147.3521
    192. 

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    193. 

  193. 2022-11-09 18:36:37,989 - INFO - main.py - train - 68 - 【train】 epoch:0 39/2980 loss:125.3554
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  194. 2022-11-09 18:36:39,212 - INFO - main.py - train - 68 - 【train】 epoch:0 40/2980 loss:103.3222
    195. 

  195. 2022-11-09 18:36:40,417 - INFO - main.py - train - 68 - 【train】 epoch:0 41/2980 loss:63.6357
    196. 

  196. 2022-11-09 18:36:41,727 - INFO - main.py - train - 68 - 【train】 epoch:0 42/2980 loss:149.9285
    197. 

  197. 2022-11-09 18:36:43,005 - INFO - main.py - train - 68 - 【train】 epoch:0 43/2980 loss:159.9024
    198. 

  198. 2022-11-09 18:36:44,203 - INFO - main.py - train - 68 - 【train】 epoch:0 44/2980 loss:95.9651
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  199. 2022-11-09 18:36:45,454 - INFO - main.py - train - 68 - 【train】 epoch:0 45/2980 loss:104.5443
    200. 

  200. 2022-11-09 18:36:46,733 - INFO - main.py - train - 68 - 【train】 epoch:0 46/2980 loss:64.2032
    201. 

  201. 2022-11-09 18:36:48,110 - INFO - main.py - train - 68 - 【train】 epoch:0 47/2980 loss:169.8716
    202. 

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  203. 2022-11-09 18:36:50,667 - INFO - main.py - train - 68 - 【train】 epoch:0 49/2980 loss:323.0245
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    212. 

  212. 2022-11-09 18:37:16,233 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    213. 

  213. 2022-11-09 18:37:16,233 - INFO - main.py - <module> - 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)
    214. 

  214. 2022-11-09 18:37:20,178 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    215. 

  215. 2022-11-09 18:37:39,169 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    216. 

  216. 2022-11-09 18:37:39,169 - INFO - main.py - <module> - 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)
    217. 

  217. 2022-11-09 18:37:43,141 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    218. 

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  489. 2022-11-09 19:02:23,376 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    490. 

  490. 2022-11-09 19:02:23,376 - INFO - main.py - <module> - 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)
    491. 

  491. 2022-11-09 19:02:28,432 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    492. 

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    493. 

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    494. 

  494. 2022-11-09 19:03:27,388 - INFO - main.py - <module> - 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)
    495. 

  495. 2022-11-09 19:03:31,350 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    496. 

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    1990. 

  1990. 2022-11-09 19:35:55,279 - INFO - main.py - train - 68 - 【train】 epoch:2 1494/2980 loss:3.8279
    1991. 

  1991. 2022-11-09 19:35:56,482 - INFO - main.py - train - 68 - 【train】 epoch:2 1495/2980 loss:6.0278
    1992. 

  1992. 2022-11-09 19:35:57,700 - INFO - main.py - train - 68 - 【train】 epoch:2 1496/2980 loss:4.7884
    1993. 

  1993. 2022-11-09 19:35:59,247 - INFO - main.py - train - 68 - 【train】 epoch:2 1497/2980 loss:8.3655
    1994. 

  1994. 2022-11-09 19:36:00,559 - INFO - main.py - train - 68 - 【train】 epoch:2 1498/2980 loss:11.3722
    1995. 

  1995. 2022-11-09 19:36:01,762 - INFO - main.py - train - 68 - 【train】 epoch:2 1499/2980 loss:4.9509
    1996. 

  1996. 2022-11-09 19:36:03,027 - INFO - main.py - train - 68 - 【train】 epoch:2 1500/2980 loss:6.9220
    1997. 

  1997. 2022-11-09 19:36:04,323 - INFO - main.py - train - 68 - 【train】 epoch:2 1501/2980 loss:30.1602
    1998. 

  1998. 2022-11-09 19:36:05,495 - INFO - main.py - train - 68 - 【train】 epoch:2 1502/2980 loss:1.4575
    1999. 

  1999. 2022-11-09 19:36:06,917 - INFO - main.py - train - 68 - 【train】 epoch:2 1503/2980 loss:6.2479
    2000. 

  2000. 2022-11-09 19:36:08,182 - INFO - main.py - train - 68 - 【train】 epoch:2 1504/2980 loss:11.7579
    2001. 

  2001. 2022-11-09 19:36:10,010 - INFO - main.py - train - 68 - 【train】 epoch:2 1505/2980 loss:3.3624
    2002. 

  2002. 2022-11-09 19:36:11,228 - INFO - main.py - train - 68 - 【train】 epoch:2 1506/2980 loss:9.2679
    2003. 

  2003. 2022-11-09 19:36:12,650 - INFO - main.py - train - 68 - 【train】 epoch:2 1507/2980 loss:7.4666
    2004. 

  2004. 2022-11-09 19:36:13,852 - INFO - main.py - train - 68 - 【train】 epoch:2 1508/2980 loss:3.1688
    2005. 

  2005. 2022-11-09 19:36:15,133 - INFO - main.py - train - 68 - 【train】 epoch:2 1509/2980 loss:6.2498
    2006. 

  2006. 2022-11-09 19:36:16,336 - INFO - main.py - train - 68 - 【train】 epoch:2 1510/2980 loss:6.4912
    2007. 

  2007. 2022-11-09 19:36:17,602 - INFO - main.py - train - 68 - 【train】 epoch:2 1511/2980 loss:1.1950
    2008. 

  2008. 2022-11-09 19:36:19,148 - INFO - main.py - train - 68 - 【train】 epoch:2 1512/2980 loss:5.4696
    2009. 

  2009. 2022-11-09 19:36:20,382 - INFO - main.py - train - 68 - 【train】 epoch:2 1513/2980 loss:18.7745
    2010. 

  2010. 2022-11-09 19:36:21,648 - INFO - main.py - train - 68 - 【train】 epoch:2 1514/2980 loss:2.7113
    2011. 

  2011. 2022-11-09 19:36:22,975 - INFO - main.py - train - 68 - 【train】 epoch:2 1515/2980 loss:7.2943
    2012. 

  2012. 2022-11-09 19:36:24,209 - INFO - main.py - train - 68 - 【train】 epoch:2 1516/2980 loss:10.4109
    2013. 

  2013. 2022-11-09 19:36:25,428 - INFO - main.py - train - 68 - 【train】 epoch:2 1517/2980 loss:1.6450
    2014. 

  2014. 2022-11-09 19:36:26,678 - INFO - main.py - train - 68 - 【train】 epoch:2 1518/2980 loss:2.4336
    2015. 

  2015. 2022-11-09 19:36:27,927 - INFO - main.py - train - 68 - 【train】 epoch:2 1519/2980 loss:1.4648
    2016. 

  2016. 2022-11-09 19:36:29,755 - INFO - main.py - train - 68 - 【train】 epoch:2 1520/2980 loss:3.6874
    2017. 

  2017. 2022-11-09 19:36:30,942 - INFO - main.py - train - 68 - 【train】 epoch:2 1521/2980 loss:3.8806
    2018. 

  2018. 2022-11-09 19:36:32,254 - INFO - main.py - train - 68 - 【train】 epoch:2 1522/2980 loss:1.6294
    2019. 

  2019. 2022-11-09 19:36:33,817 - INFO - main.py - train - 68 - 【train】 epoch:2 1523/2980 loss:0.7809
    2020. 

  2020. 2022-11-09 19:36:35,051 - INFO - main.py - train - 68 - 【train】 epoch:2 1524/2980 loss:3.9964
    2021. 

  2021. 2022-11-09 19:36:36,253 - INFO - main.py - train - 68 - 【train】 epoch:2 1525/2980 loss:5.6733
    2022. 

  2022. 2022-11-09 19:36:37,659 - INFO - main.py - train - 68 - 【train】 epoch:2 1526/2980 loss:2.5758
    2023. 

  2023. 2022-11-09 19:36:39,081 - INFO - main.py - train - 68 - 【train】 epoch:2 1527/2980 loss:9.5719
    2024. 

  2024. 2022-11-09 19:36:40,299 - INFO - main.py - train - 68 - 【train】 epoch:2 1528/2980 loss:4.1550
    2025. 

  2025. 2022-11-09 19:36:41,924 - INFO - main.py - train - 68 - 【train】 epoch:2 1529/2980 loss:8.3913
    2026. 

  2026. 2022-11-09 19:36:43,182 - INFO - main.py - train - 68 - 【train】 epoch:2 1530/2980 loss:3.8897
    2027. 

  2027. 2022-11-09 19:36:44,650 - INFO - main.py - train - 68 - 【train】 epoch:2 1531/2980 loss:4.5290
    2028. 

  2028. 2022-11-09 19:36:45,916 - INFO - main.py - train - 68 - 【train】 epoch:2 1532/2980 loss:10.7979
    2029. 

  2029. 2022-11-09 19:36:47,353 - INFO - main.py - train - 68 - 【train】 epoch:2 1533/2980 loss:7.0484
    2030. 

  2030. 2022-11-09 19:36:48,571 - INFO - main.py - train - 68 - 【train】 epoch:2 1534/2980 loss:5.0957
    2031. 

  2031. 2022-11-09 19:36:49,790 - INFO - main.py - train - 68 - 【train】 epoch:2 1535/2980 loss:4.5475
    2032. 

  2032. 2022-11-09 19:36:51,196 - INFO - main.py - train - 68 - 【train】 epoch:2 1536/2980 loss:11.0533
    2033. 

  2033. 2022-11-09 19:36:52,555 - INFO - main.py - train - 68 - 【train】 epoch:2 1537/2980 loss:12.1459
    2034. 

  2034. 2022-11-09 19:36:53,867 - INFO - main.py - train - 68 - 【train】 epoch:2 1538/2980 loss:9.4324
    2035. 

  2035. 2022-11-09 19:36:55,164 - INFO - main.py - train - 68 - 【train】 epoch:2 1539/2980 loss:1.7077
    2036. 

  2036. 2022-11-09 19:36:56,585 - INFO - main.py - train - 68 - 【train】 epoch:2 1540/2980 loss:8.8502
    2037. 

  2037. 2022-11-09 19:36:57,882 - INFO - main.py - train - 68 - 【train】 epoch:2 1541/2980 loss:14.9248
    2038. 

  2038. 2022-11-09 19:36:59,350 - INFO - main.py - train - 68 - 【train】 epoch:2 1542/2980 loss:1.3760
    2039. 

  2039. 2022-11-09 19:37:00,584 - INFO - main.py - train - 68 - 【train】 epoch:2 1543/2980 loss:16.1577
    2040. 

  2040. 2022-11-09 19:37:01,756 - INFO - main.py - train - 68 - 【train】 epoch:2 1544/2980 loss:0.4490
    2041. 

  2041. 2022-11-09 19:37:02,974 - INFO - main.py - train - 68 - 【train】 epoch:2 1545/2980 loss:6.1854
    2042. 

  2042. 2022-11-09 19:37:04,208 - INFO - main.py - train - 68 - 【train】 epoch:2 1546/2980 loss:1.5579
    2043. 

  2043. 2022-11-09 19:37:05,427 - INFO - main.py - train - 68 - 【train】 epoch:2 1547/2980 loss:13.4670
    2044. 

  2044. 2022-11-09 19:37:06,817 - INFO - main.py - train - 68 - 【train】 epoch:2 1548/2980 loss:12.2180
    2045. 

  2045. 2022-11-09 19:37:08,082 - INFO - main.py - train - 68 - 【train】 epoch:2 1549/2980 loss:12.6025
    2046. 

  2046. 2022-11-09 19:37:09,301 - INFO - main.py - train - 68 - 【train】 epoch:2 1550/2980 loss:11.3885
    2047. 

  2047. 2022-11-09 19:37:10,551 - INFO - main.py - train - 68 - 【train】 epoch:2 1551/2980 loss:6.6250
    2048. 

  2048. 2022-11-09 19:37:11,832 - INFO - main.py - train - 68 - 【train】 epoch:2 1552/2980 loss:7.3215
    2049. 

  2049. 2022-11-09 19:37:13,019 - INFO - main.py - train - 68 - 【train】 epoch:2 1553/2980 loss:2.3705
    2050. 

  2050. 2022-11-09 19:37:14,237 - INFO - main.py - train - 68 - 【train】 epoch:2 1554/2980 loss:15.4381
    2051. 

  2051. 2022-11-09 19:37:15,440 - INFO - main.py - train - 68 - 【train】 epoch:2 1555/2980 loss:2.7182
    2052. 

  2052. 2022-11-09 19:37:16,659 - INFO - main.py - train - 68 - 【train】 epoch:2 1556/2980 loss:16.5300
    2053. 

  2053. 2022-11-09 19:37:17,955 - INFO - main.py - train - 68 - 【train】 epoch:2 1557/2980 loss:9.5238
    2054. 

  2054. 2022-11-09 19:37:19,267 - INFO - main.py - train - 68 - 【train】 epoch:2 1558/2980 loss:4.2491
    2055. 

  2055. 2022-11-09 19:37:20,455 - INFO - main.py - train - 68 - 【train】 epoch:2 1559/2980 loss:5.1103
    2056. 

  2056. 2022-11-09 19:37:21,720 - INFO - main.py - train - 68 - 【train】 epoch:2 1560/2980 loss:0.3819
    2057. 

  2057. 2022-11-09 19:37:22,954 - INFO - main.py - train - 68 - 【train】 epoch:2 1561/2980 loss:14.8600
    2058. 

  2058. 2022-11-09 19:37:24,282 - INFO - main.py - train - 68 - 【train】 epoch:2 1562/2980 loss:11.9108
    2059. 

  2059. 2022-11-09 19:37:25,485 - INFO - main.py - train - 68 - 【train】 epoch:2 1563/2980 loss:3.2090
    2060. 

  2060. 2022-11-09 19:37:26,922 - INFO - main.py - train - 68 - 【train】 epoch:2 1564/2980 loss:17.9613
    2061. 

  2061. 2022-11-09 19:37:28,156 - INFO - main.py - train - 68 - 【train】 epoch:2 1565/2980 loss:6.7761
    2062. 

  2062. 2022-11-09 19:37:29,452 - INFO - main.py - train - 68 - 【train】 epoch:2 1566/2980 loss:3.2298
    2063. 

  2063. 2022-11-09 19:37:30,655 - INFO - main.py - train - 68 - 【train】 epoch:2 1567/2980 loss:3.5609
    2064. 

  2064. 2022-11-09 19:37:31,952 - INFO - main.py - train - 68 - 【train】 epoch:2 1568/2980 loss:13.3151
    2065. 

  2065. 2022-11-09 19:37:33,170 - INFO - main.py - train - 68 - 【train】 epoch:2 1569/2980 loss:0.4172
    2066. 

  2066. 2022-11-09 19:37:34,623 - INFO - main.py - train - 68 - 【train】 epoch:2 1570/2980 loss:23.5598
    2067. 

  2067. 2022-11-09 19:37:35,920 - INFO - main.py - train - 68 - 【train】 epoch:2 1571/2980 loss:27.8597
    2068. 

  2068. 2022-11-09 19:37:37,201 - INFO - main.py - train - 68 - 【train】 epoch:2 1572/2980 loss:2.1358
    2069. 

  2069. 2022-11-09 19:37:38,435 - INFO - main.py - train - 68 - 【train】 epoch:2 1573/2980 loss:15.3207
    2070. 

  2070. 2022-11-09 19:37:39,637 - INFO - main.py - train - 68 - 【train】 epoch:2 1574/2980 loss:0.6016
    2071. 

  2071. 2022-11-09 19:37:40,903 - INFO - main.py - train - 68 - 【train】 epoch:2 1575/2980 loss:6.5639
    2072. 

  2072. 2022-11-09 19:37:42,715 - INFO - main.py - train - 68 - 【train】 epoch:2 1576/2980 loss:3.6579
    2073. 

  2073. 2022-11-09 19:37:43,933 - INFO - main.py - train - 68 - 【train】 epoch:2 1577/2980 loss:4.7820
    2074. 

  2074. 2022-11-09 19:37:45,136 - INFO - main.py - train - 68 - 【train】 epoch:2 1578/2980 loss:2.0977
    2075. 

  2075. 2022-11-09 19:37:46,417 - INFO - main.py - train - 68 - 【train】 epoch:2 1579/2980 loss:6.2633
    2076. 

  2076. 2022-11-09 19:37:47,761 - INFO - main.py - train - 68 - 【train】 epoch:2 1580/2980 loss:10.6429
    2077. 

  2077. 2022-11-09 19:37:49,120 - INFO - main.py - train - 68 - 【train】 epoch:2 1581/2980 loss:13.7162
    2078. 

  2078. 2022-11-09 19:37:50,338 - INFO - main.py - train - 68 - 【train】 epoch:2 1582/2980 loss:7.7870
    2079. 

  2079. 2022-11-09 19:37:51,619 - INFO - main.py - train - 68 - 【train】 epoch:2 1583/2980 loss:13.0133
    2080. 

  2080. 2022-11-09 19:37:52,838 - INFO - main.py - train - 68 - 【train】 epoch:2 1584/2980 loss:8.1330
    2081. 

  2081. 2022-11-09 19:37:54,150 - INFO - main.py - train - 68 - 【train】 epoch:2 1585/2980 loss:13.8452
    2082. 

  2082. 2022-11-09 19:37:55,431 - INFO - main.py - train - 68 - 【train】 epoch:2 1586/2980 loss:5.9103
    2083. 

  2083. 2022-11-09 19:37:56,665 - INFO - main.py - train - 68 - 【train】 epoch:2 1587/2980 loss:4.7986
    2084. 

  2084. 2022-11-09 19:37:57,914 - INFO - main.py - train - 68 - 【train】 epoch:2 1588/2980 loss:6.8932
    2085. 

  2085. 2022-11-09 19:37:59,273 - INFO - main.py - train - 68 - 【train】 epoch:2 1589/2980 loss:7.5110
    2086. 

  2086. 2022-11-09 19:38:00,508 - INFO - main.py - train - 68 - 【train】 epoch:2 1590/2980 loss:13.0655
    2087. 

  2087. 2022-11-09 19:38:01,851 - INFO - main.py - train - 68 - 【train】 epoch:2 1591/2980 loss:2.8706
    2088. 

  2088. 2022-11-09 19:38:03,085 - INFO - main.py - train - 68 - 【train】 epoch:2 1592/2980 loss:15.4536
    2089. 

  2089. 2022-11-09 19:38:04,335 - INFO - main.py - train - 68 - 【train】 epoch:2 1593/2980 loss:11.0938
    2090. 

  2090. 2022-11-09 19:38:05,600 - INFO - main.py - train - 68 - 【train】 epoch:2 1594/2980 loss:4.8637
    2091. 

  2091. 2022-11-09 19:38:06,803 - INFO - main.py - train - 68 - 【train】 epoch:2 1595/2980 loss:0.8563
    2092. 

  2092. 2022-11-09 19:38:08,490 - INFO - main.py - train - 68 - 【train】 epoch:2 1596/2980 loss:23.2700
    2093. 

  2093. 2022-11-09 19:38:09,912 - INFO - main.py - train - 68 - 【train】 epoch:2 1597/2980 loss:20.5530
    2094. 

  2094. 2022-11-09 19:38:11,396 - INFO - main.py - train - 68 - 【train】 epoch:2 1598/2980 loss:4.1632
    2095. 

  2095. 2022-11-09 19:38:12,802 - INFO - main.py - train - 68 - 【train】 epoch:2 1599/2980 loss:8.9677
    2096. 

  2096. 2022-11-09 19:38:14,129 - INFO - main.py - train - 68 - 【train】 epoch:2 1600/2980 loss:9.8652
    2097. 

  2097. 2022-11-09 19:38:15,613 - INFO - main.py - train - 68 - 【train】 epoch:2 1601/2980 loss:2.8466
    2098. 

  2098. 2022-11-09 19:38:16,957 - INFO - main.py - train - 68 - 【train】 epoch:2 1602/2980 loss:8.8157
    2099. 

  2099. 2022-11-09 19:38:18,285 - INFO - main.py - train - 68 - 【train】 epoch:2 1603/2980 loss:0.7183
    2100. 

  2100. 2022-11-09 19:38:19,597 - INFO - main.py - train - 68 - 【train】 epoch:2 1604/2980 loss:0.6484
    2101. 

  2101. 2022-11-09 19:38:21,175 - INFO - main.py - train - 68 - 【train】 epoch:2 1605/2980 loss:10.5781
    2102. 

  2102. 2022-11-09 19:38:22,502 - INFO - main.py - train - 68 - 【train】 epoch:2 1606/2980 loss:6.7119
    2103. 

  2103. 2022-11-09 19:38:23,924 - INFO - main.py - train - 68 - 【train】 epoch:2 1607/2980 loss:10.1706
    2104. 

  2104. 2022-11-09 19:38:25,330 - INFO - main.py - train - 68 - 【train】 epoch:2 1608/2980 loss:9.6469
    2105. 

  2105. 2022-11-09 19:38:26,673 - INFO - main.py - train - 68 - 【train】 epoch:2 1609/2980 loss:21.8677
    2106. 

  2106. 2022-11-09 19:38:28,126 - INFO - main.py - train - 68 - 【train】 epoch:2 1610/2980 loss:7.6371
    2107. 

  2107. 2022-11-09 19:38:29,423 - INFO - main.py - train - 68 - 【train】 epoch:2 1611/2980 loss:17.5643
    2108. 

  2108. 2022-11-09 19:38:30,860 - INFO - main.py - train - 68 - 【train】 epoch:2 1612/2980 loss:1.5125
    2109. 

  2109. 2022-11-09 19:38:32,422 - INFO - main.py - train - 68 - 【train】 epoch:2 1613/2980 loss:8.2371
    2110. 

  2110. 2022-11-09 19:38:33,797 - INFO - main.py - train - 68 - 【train】 epoch:2 1614/2980 loss:8.3551
    2111. 

  2111. 2022-11-09 19:38:35,187 - INFO - main.py - train - 68 - 【train】 epoch:2 1615/2980 loss:6.3841
    2112. 

  2112. 2022-11-09 19:38:36,515 - INFO - main.py - train - 68 - 【train】 epoch:2 1616/2980 loss:13.3133
    2113. 

  2113. 2022-11-09 19:38:37,936 - INFO - main.py - train - 68 - 【train】 epoch:2 1617/2980 loss:13.1829
    2114. 

  2114. 2022-11-09 19:38:39,483 - INFO - main.py - train - 68 - 【train】 epoch:2 1618/2980 loss:3.2250
    2115. 

  2115. 2022-11-09 19:38:40,748 - INFO - main.py - train - 68 - 【train】 epoch:2 1619/2980 loss:4.2770
    2116. 

  2116. 2022-11-09 19:38:41,967 - INFO - main.py - train - 68 - 【train】 epoch:2 1620/2980 loss:3.8124
    2117. 

  2117. 2022-11-09 19:38:43,591 - INFO - main.py - train - 68 - 【train】 epoch:2 1621/2980 loss:5.1748
    2118. 

  2118. 2022-11-09 19:38:44,857 - INFO - main.py - train - 68 - 【train】 epoch:2 1622/2980 loss:7.4533
    2119. 

  2119. 2022-11-09 19:38:46,106 - INFO - main.py - train - 68 - 【train】 epoch:2 1623/2980 loss:5.4316
    2120. 

  2120. 2022-11-09 19:38:47,372 - INFO - main.py - train - 68 - 【train】 epoch:2 1624/2980 loss:3.6036
    2121. 

  2121. 2022-11-09 19:38:48,621 - INFO - main.py - train - 68 - 【train】 epoch:2 1625/2980 loss:14.8389
    2122. 

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    2123. 

  2123. 2022-11-09 19:38:51,121 - INFO - main.py - train - 68 - 【train】 epoch:2 1627/2980 loss:20.6375
    2124. 

  2124. 2022-11-09 19:38:52,355 - INFO - main.py - train - 68 - 【train】 epoch:2 1628/2980 loss:1.7576
    2125. 

  2125. 2022-11-09 19:38:53,651 - INFO - main.py - train - 68 - 【train】 epoch:2 1629/2980 loss:7.7404
    2126. 

  2126. 2022-11-09 19:38:54,917 - INFO - main.py - train - 68 - 【train】 epoch:2 1630/2980 loss:7.4985
    2127. 

  2127. 2022-11-09 19:38:56,213 - INFO - main.py - train - 68 - 【train】 epoch:2 1631/2980 loss:9.8291
    2128. 

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    2129. 

  2129. 2022-11-09 19:38:58,697 - INFO - main.py - train - 68 - 【train】 epoch:2 1633/2980 loss:6.1685
    2130. 

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    2131. 

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    2133. 

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    2134. 

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    2135. 

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    2136. 

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  3440. 2022-11-09 20:08:04,959 - INFO - main.py - train - 68 - 【train】 epoch:4 2944/2980 loss:2.9856
    3441. 

  3441. 2022-11-09 20:08:06,381 - INFO - main.py - train - 68 - 【train】 epoch:4 2945/2980 loss:6.7960
    3442. 

  3442. 2022-11-09 20:08:07,849 - INFO - main.py - train - 68 - 【train】 epoch:4 2946/2980 loss:2.1157
    3443. 

  3443. 2022-11-09 20:08:09,287 - INFO - main.py - train - 68 - 【train】 epoch:4 2947/2980 loss:3.2768
    3444. 

  3444. 2022-11-09 20:08:10,755 - INFO - main.py - train - 68 - 【train】 epoch:4 2948/2980 loss:7.1061
    3445. 

  3445. 2022-11-09 20:08:11,989 - INFO - main.py - train - 68 - 【train】 epoch:4 2949/2980 loss:4.0073
    3446. 

  3446. 2022-11-09 20:08:13,582 - INFO - main.py - train - 68 - 【train】 epoch:4 2950/2980 loss:20.3640
    3447. 

  3447. 2022-11-09 20:08:14,832 - INFO - main.py - train - 68 - 【train】 epoch:4 2951/2980 loss:0.1124
    3448. 

  3448. 2022-11-09 20:08:16,051 - INFO - main.py - train - 68 - 【train】 epoch:4 2952/2980 loss:6.8030
    3449. 

  3449. 2022-11-09 20:08:17,394 - INFO - main.py - train - 68 - 【train】 epoch:4 2953/2980 loss:1.7045
    3450. 

  3450. 2022-11-09 20:08:18,675 - INFO - main.py - train - 68 - 【train】 epoch:4 2954/2980 loss:0.8841
    3451. 

  3451. 2022-11-09 20:08:19,909 - INFO - main.py - train - 68 - 【train】 epoch:4 2955/2980 loss:13.3774
    3452. 

  3452. 2022-11-09 20:08:21,127 - INFO - main.py - train - 68 - 【train】 epoch:4 2956/2980 loss:1.9520
    3453. 

  3453. 2022-11-09 20:08:22,580 - INFO - main.py - train - 68 - 【train】 epoch:4 2957/2980 loss:8.1010
    3454. 

  3454. 2022-11-09 20:08:23,814 - INFO - main.py - train - 68 - 【train】 epoch:4 2958/2980 loss:2.7235
    3455. 

  3455. 2022-11-09 20:08:25,002 - INFO - main.py - train - 68 - 【train】 epoch:4 2959/2980 loss:0.8775
    3456. 

  3456. 2022-11-09 20:08:26,298 - INFO - main.py - train - 68 - 【train】 epoch:4 2960/2980 loss:4.7177
    3457. 

  3457. 2022-11-09 20:08:27,688 - INFO - main.py - train - 68 - 【train】 epoch:4 2961/2980 loss:1.2038
    3458. 

  3458. 2022-11-09 20:08:29,063 - INFO - main.py - train - 68 - 【train】 epoch:4 2962/2980 loss:2.2772
    3459. 

  3459. 2022-11-09 20:08:30,282 - INFO - main.py - train - 68 - 【train】 epoch:4 2963/2980 loss:2.3886
    3460. 

  3460. 2022-11-09 20:08:31,609 - INFO - main.py - train - 68 - 【train】 epoch:4 2964/2980 loss:10.8579
    3461. 

  3461. 2022-11-09 20:08:32,922 - INFO - main.py - train - 68 - 【train】 epoch:4 2965/2980 loss:3.1771
    3462. 

  3462. 2022-11-09 20:08:34,406 - INFO - main.py - train - 68 - 【train】 epoch:4 2966/2980 loss:2.6416
    3463. 

  3463. 2022-11-09 20:08:35,749 - INFO - main.py - train - 68 - 【train】 epoch:4 2967/2980 loss:6.1592
    3464. 

  3464. 2022-11-09 20:08:37,264 - INFO - main.py - train - 68 - 【train】 epoch:4 2968/2980 loss:12.5800
    3465. 

  3465. 2022-11-09 20:08:38,483 - INFO - main.py - train - 68 - 【train】 epoch:4 2969/2980 loss:1.1993
    3466. 

  3466. 2022-11-09 20:08:39,920 - INFO - main.py - train - 68 - 【train】 epoch:4 2970/2980 loss:2.6484
    3467. 

  3467. 2022-11-09 20:08:41,232 - INFO - main.py - train - 68 - 【train】 epoch:4 2971/2980 loss:1.7165
    3468. 

  3468. 2022-11-09 20:08:42,482 - INFO - main.py - train - 68 - 【train】 epoch:4 2972/2980 loss:11.3746
    3469. 

  3469. 2022-11-09 20:08:43,778 - INFO - main.py - train - 68 - 【train】 epoch:4 2973/2980 loss:3.9966
    3470. 

  3470. 2022-11-09 20:08:45,325 - INFO - main.py - train - 68 - 【train】 epoch:4 2974/2980 loss:5.1066
    3471. 

  3471. 2022-11-09 20:08:46,637 - INFO - main.py - train - 68 - 【train】 epoch:4 2975/2980 loss:4.9227
    3472. 

  3472. 2022-11-09 20:08:47,840 - INFO - main.py - train - 68 - 【train】 epoch:4 2976/2980 loss:0.8795
    3473. 

  3473. 2022-11-09 20:08:49,277 - INFO - main.py - train - 68 - 【train】 epoch:4 2977/2980 loss:11.3107
    3474. 

  3474. 2022-11-09 20:08:50,730 - INFO - main.py - train - 68 - 【train】 epoch:4 2978/2980 loss:2.8808
    3475. 

  3475. 2022-11-09 20:08:51,948 - INFO - main.py - train - 68 - 【train】 epoch:4 2979/2980 loss:4.7416
    3476. 

  3476. 2022-11-09 20:08:52,167 - INFO - trainUtils.py - save_model - 70 - Saving model checkpoint to ./checkpoints/bert_crf
    3477. 

  3477. 2022-11-09 20:08:54,229 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3478. 

  3478. 2022-11-09 20:08:54,760 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3479. 

  3479. 2022-11-09 21:37:53,600 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3480. 

  3480. 2022-11-09 21:37:53,600 - INFO - main.py - <module> - 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)
    3481. 

  3481. 2022-11-09 21:37:57,643 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3482. 

  3482. 2022-11-09 21:37:59,143 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3483. 

  3483. 2022-11-09 21:37:59,768 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3484. 

  3484. 2022-11-09 22:37:49,595 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3485. 

  3485. 2022-11-09 22:37:49,595 - INFO - main.py - <module> - 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)
    3486. 

  3486. 2022-11-09 22:37:53,581 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3487. 

  3487. 2022-11-09 22:37:55,112 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3488. 

  3488. 2022-11-09 22:37:55,693 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3489. 

  3489. 2022-11-09 22:44:05,053 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3490. 

  3490. 2022-11-09 22:44:05,054 - INFO - main.py - <module> - 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)
    3491. 

  3491. 2022-11-09 22:44:09,079 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3492. 

  3492. 2022-11-09 22:44:10,614 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3493. 

  3493. 2022-11-09 22:44:11,206 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3494. 

  3494. 2022-11-09 22:45:38,330 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3495. 

  3495. 2022-11-09 22:45:38,330 - INFO - main.py - <module> - 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)
    3496. 

  3496. 2022-11-09 22:45:42,299 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3497. 

  3497. 2022-11-09 22:45:43,847 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3498. 

  3498. 2022-11-09 22:45:44,393 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3499. 

  3499. 2022-11-09 22:47:02,323 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3500. 

  3500. 2022-11-09 22:47:02,323 - INFO - main.py - <module> - 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)
    3501. 

  3501. 2022-11-09 22:47:06,237 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3502. 

  3502. 2022-11-09 22:47:07,696 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3503. 

  3503. 2022-11-09 22:47:08,229 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3504. 

  3504. 2022-11-09 22:49:05,456 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3505. 

  3505. 2022-11-09 22:49:05,456 - INFO - main.py - <module> - 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)
    3506. 

  3506. 2022-11-09 22:49:09,362 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3507. 

  3507. 2022-11-09 22:49:10,878 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3508. 

  3508. 2022-11-09 22:49:11,346 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3509. 

  3509. 2022-11-09 23:17:03,674 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3510. 

  3510. 2022-11-09 23:17:03,674 - INFO - main.py - <module> - 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)
    3511. 

  3511. 2022-11-09 23:17:07,565 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3512. 

  3512. 2022-11-09 23:17:09,097 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3513. 

  3513. 2022-11-09 23:17:09,643 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3514. 

  3514. 2022-11-09 23:18:40,565 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3515. 

  3515. 2022-11-09 23:18:40,565 - INFO - main.py - <module> - 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)
    3516. 

  3516. 2022-11-09 23:18:44,455 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3517. 

  3517. 2022-11-09 23:18:45,987 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3518. 

  3518. 2022-11-09 23:18:46,518 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3519. 

  3519. 2022-11-09 23:20:17,022 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3520. 

  3520. 2022-11-09 23:20:17,022 - INFO - main.py - <module> - 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)
    3521. 

  3521. 2022-11-09 23:20:20,893 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3522. 

  3522. 2022-11-09 23:20:22,409 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3523. 

  3523. 2022-11-09 23:20:22,940 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3524. 

  3524. 2022-11-09 23:21:07,205 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3525. 

  3525. 2022-11-09 23:21:07,205 - INFO - main.py - <module> - 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)
    3526. 

  3526. 2022-11-09 23:21:11,067 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3527. 

  3527. 2022-11-09 23:21:12,518 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3528. 

  3528. 2022-11-09 23:21:13,046 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3529. 

  3529. 2022-11-09 23:27:41,545 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3530. 

  3530. 2022-11-09 23:27:41,545 - INFO - main.py - <module> - 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)
    3531. 

  3531. 2022-11-09 23:27:45,405 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3532. 

  3532. 2022-11-09 23:27:46,892 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3533. 

  3533. 2022-11-09 23:27:47,393 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3534. 

  3534. 2022-11-09 23:28:42,829 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3535. 

  3535. 2022-11-09 23:28:42,829 - INFO - main.py - <module> - 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)
    3536. 

  3536. 2022-11-09 23:28:46,784 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3537. 

  3537. 2022-11-09 23:28:48,315 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3538. 

  3538. 2022-11-09 23:28:48,857 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3539. 

  3539. 2022-11-09 23:29:20,645 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3540. 

  3540. 2022-11-09 23:29:20,645 - INFO - main.py - <module> - 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)
    3541. 

  3541. 2022-11-09 23:29:24,581 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3542. 

  3542. 2022-11-09 23:29:26,112 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3543. 

  3543. 2022-11-09 23:29:26,643 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3544. 

  3544. 2022-11-09 23:30:18,268 - INFO - main.py - <module> - 252 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3545. 

  3545. 2022-11-09 23:30:18,268 - INFO - main.py - <module> - 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)
    3546. 

  3546. 2022-11-09 23:30:22,170 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3547. 

  3547. 2022-11-09 23:30:23,643 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3548. 

  3548. 2022-11-09 23:30:24,174 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3549. 

  3549. 2022-11-09 23:35:30,357 - INFO - main.py - <module> - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3550. 

  3550. 2022-11-09 23:35:30,357 - INFO - main.py - <module> - 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)
    3551. 

  3551. 2022-11-09 23:35:34,280 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3552. 

  3552. 2022-11-09 23:35:35,819 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3553. 

  3553. 2022-11-09 23:35:36,394 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3554. 

  3554. 2022-11-09 23:36:15,214 - INFO - main.py - <module> - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3555. 

  3555. 2022-11-09 23:36:15,214 - INFO - main.py - <module> - 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)
    3556. 

  3556. 2022-11-09 23:36:19,079 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3557. 

  3557. 2022-11-09 23:36:20,595 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3558. 

  3558. 2022-11-09 23:36:21,110 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3559. 

  3559. 2022-11-09 23:40:29,200 - INFO - main.py - <module> - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3560. 

  3560. 2022-11-09 23:40:29,200 - INFO - main.py - <module> - 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)
    3561. 

  3561. 2022-11-09 23:40:33,156 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3562. 

  3562. 2022-11-09 23:40:34,687 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3563. 

  3563. 2022-11-09 23:40:35,187 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3564. 

  3564. 2022-11-09 23:45:34,236 - INFO - main.py - <module> - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3565. 

  3565. 2022-11-09 23:45:34,236 - INFO - main.py - <module> - 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)
    3566. 

  3566. 2022-11-09 23:45:38,056 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3567. 

  3567. 2022-11-09 23:45:39,585 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3568. 

  3568. 2022-11-09 23:45:40,057 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3569. 

  3569. 2022-11-10 08:53:56,178 - INFO - main.py - <module> - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3570. 

  3570. 2022-11-10 08:53:56,178 - INFO - main.py - <module> - 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)
    3571. 

  3571. 2022-11-10 08:54:01,277 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3572. 

  3572. 2022-11-10 08:54:02,838 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3573. 

  3573. 2022-11-10 08:54:04,448 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3574. 

  3574. 2022-11-10 09:02:56,380 - INFO - main.py - <module> - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3575. 

  3575. 2022-11-10 09:02:56,381 - INFO - main.py - <module> - 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)
    3576. 

  3576. 2022-11-10 09:03:01,165 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3577. 

  3577. 2022-11-10 09:03:02,705 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3578. 

  3578. 2022-11-10 09:03:03,269 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3579. 

  3579. 2022-11-10 09:19:59,929 - INFO - main.py - <module> - 253 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3580. 

  3580. 2022-11-10 09:19:59,929 - INFO - main.py - <module> - 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)
    3581. 

  3581. 2022-11-10 09:20:03,933 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3582. 

  3582. 2022-11-10 09:20:05,457 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3583. 

  3583. 2022-11-10 09:20:05,982 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3584. 

  3584. 2022-11-10 09:30:57,117 - INFO - main.py - <module> - 256 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3585. 

  3585. 2022-11-10 09:30:57,117 - INFO - main.py - <module> - 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)
    3586. 

  3586. 2022-11-10 09:31:01,047 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3587. 

  3587. 2022-11-10 09:31:02,594 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3588. 

  3588. 2022-11-10 09:31:03,130 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3589. 

  3589. 2022-11-10 09:33:20,901 - INFO - main.py - <module> - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3590. 

  3590. 2022-11-10 09:33:20,902 - INFO - main.py - <module> - 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)
    3591. 

  3591. 2022-11-10 09:33:24,827 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3592. 

  3592. 2022-11-10 09:33:26,373 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3593. 

  3593. 2022-11-10 09:33:26,905 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3594. 

  3594. 2022-11-10 09:34:27,530 - INFO - main.py - <module> - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3595. 

  3595. 2022-11-10 09:34:27,530 - INFO - main.py - <module> - 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)
    3596. 

  3596. 2022-11-10 09:34:31,426 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3597. 

  3597. 2022-11-10 09:34:32,957 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3598. 

  3598. 2022-11-10 09:34:33,498 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3599. 

  3599. 2022-11-10 09:42:21,670 - INFO - main.py - <module> - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3600. 

  3600. 2022-11-10 09:42:21,671 - INFO - main.py - <module> - 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)
    3601. 

  3601. 2022-11-10 09:42:25,605 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3602. 

  3602. 2022-11-10 09:42:27,143 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3603. 

  3603. 2022-11-10 09:42:27,686 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3604. 

  3604. 2022-11-10 09:45:15,502 - INFO - main.py - <module> - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3605. 

  3605. 2022-11-10 09:45:15,502 - INFO - main.py - <module> - 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)
    3606. 

  3606. 2022-11-10 09:45:19,398 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3607. 

  3607. 2022-11-10 09:45:20,903 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3608. 

  3608. 2022-11-10 09:45:21,430 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3609. 

  3609. 2022-11-10 09:50:21,829 - INFO - main.py - <module> - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3610. 

  3610. 2022-11-10 09:50:21,829 - INFO - main.py - <module> - 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)
    3611. 

  3611. 2022-11-10 09:50:25,735 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3612. 

  3612. 2022-11-10 09:50:27,276 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3613. 

  3613. 2022-11-10 09:50:27,804 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3614. 

  3614. 2022-11-10 09:51:34,635 - INFO - main.py - <module> - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3615. 

  3615. 2022-11-10 09:51:34,635 - INFO - main.py - <module> - 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)
    3616. 

  3616. 2022-11-10 09:51:38,533 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3617. 

  3617. 2022-11-10 09:51:40,079 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3618. 

  3618. 2022-11-10 09:51:40,609 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3619. 

  3619. 2022-11-10 09:52:29,472 - INFO - main.py - <module> - 259 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3620. 

  3620. 2022-11-10 09:52:29,472 - INFO - main.py - <module> - 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)
    3621. 

  3621. 2022-11-10 09:52:33,374 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3622. 

  3622. 2022-11-10 09:52:34,898 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3623. 

  3623. 2022-11-10 09:52:35,417 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3624. 

  3624. 2022-11-10 16:37:57,114 - INFO - main.py - <module> - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3625. 

  3625. 2022-11-10 16:37:57,114 - INFO - main.py - <module> - 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)
    3626. 

  3626. 2022-11-10 16:38:01,299 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3627. 

  3627. 2022-11-10 16:38:02,895 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3628. 

  3628. 2022-11-10 16:38:03,510 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3629. 

  3629. 2022-11-10 16:39:07,040 - INFO - main.py - <module> - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3630. 

  3630. 2022-11-10 16:39:07,040 - INFO - main.py - <module> - 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)
    3631. 

  3631. 2022-11-10 16:39:11,073 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3632. 

  3632. 2022-11-10 16:39:12,666 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3633. 

  3633. 2022-11-10 16:39:13,202 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3634. 

  3634. 2022-11-10 16:42:37,278 - INFO - main.py - <module> - 258 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3635. 

  3635. 2022-11-10 16:42:37,279 - INFO - main.py - <module> - 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)
    3636. 

  3636. 2022-11-10 16:42:41,207 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3637. 

  3637. 2022-11-10 16:42:42,744 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3638. 

  3638. 2022-11-10 16:42:43,275 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3639. 

  3639. 2022-11-10 16:46:56,985 - INFO - main.py - <module> - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3640. 

  3640. 2022-11-10 16:46:56,985 - INFO - main.py - <module> - 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)
    3641. 

  3641. 2022-11-10 16:47:00,951 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3642. 

  3642. 2022-11-10 16:47:02,523 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3643. 

  3643. 2022-11-10 16:47:03,027 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3644. 

  3644. 2022-11-10 16:50:00,285 - INFO - main.py - <module> - 257 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3645. 

  3645. 2022-11-10 16:50:00,285 - INFO - main.py - <module> - 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)
    3646. 

  3646. 2022-11-10 16:50:04,225 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3647. 

  3647. 2022-11-10 16:50:05,793 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3648. 

  3648. 2022-11-10 16:50:06,315 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3649. 

  3649. 2022-11-10 17:00:50,069 - INFO - main.py - <module> - 260 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3650. 

  3650. 2022-11-10 17:00:50,070 - INFO - main.py - <module> - 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)
    3651. 

  3651. 2022-11-10 17:00:54,073 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3652. 

  3652. 2022-11-10 17:00:55,663 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3653. 

  3653. 2022-11-10 17:00:56,196 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3654. 

  3654. 2022-11-10 17:04:13,463 - INFO - main.py - <module> - 260 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3655. 

  3655. 2022-11-10 17:04:13,463 - INFO - main.py - <module> - 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)
    3656. 

  3656. 2022-11-10 17:04:17,395 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3657. 

  3657. 2022-11-10 17:04:18,954 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3658. 

  3658. 2022-11-10 17:04:19,483 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3659. 

  3659. 2022-11-10 17:05:36,614 - INFO - main.py - <module> - 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
    3660. 

  3660. 2022-11-10 17:05:38,144 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3661. 

  3661. 2022-11-10 17:05:38,663 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3662. 

  3662. 2022-11-10 17:12:28,419 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3663. 

  3663. 2022-11-10 17:12:28,419 - INFO - main.py - <module> - 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)
    3664. 

  3664. 2022-11-10 17:12:32,363 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3665. 

  3665. 2022-11-10 17:12:33,932 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3666. 

  3666. 2022-11-10 17:12:34,457 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3667. 

  3667. 2022-11-10 17:13:49,742 - INFO - main.py - <module> - 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
    3668. 

  3668. 2022-11-10 17:13:51,243 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3669. 

  3669. 2022-11-10 17:13:51,757 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3670. 

  3670. 2022-11-10 17:18:45,679 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3671. 

  3671. 2022-11-10 17:18:45,679 - INFO - main.py - <module> - 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)
    3672. 

  3672. 2022-11-10 17:18:49,568 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3673. 

  3673. 2022-11-10 17:18:51,174 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3674. 

  3674. 2022-11-10 17:18:51,741 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3675. 

  3675. 2022-11-10 17:20:08,393 - INFO - main.py - <module> - 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
    3676. 

  3676. 2022-11-10 17:20:09,878 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3677. 

  3677. 2022-11-10 17:20:10,400 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3678. 

  3678. 2022-11-10 17:22:26,782 - INFO - main.py - <module> - 266 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3679. 

  3679. 2022-11-10 17:22:26,782 - INFO - main.py - <module> - 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)
    3680. 

  3680. 2022-11-10 17:22:30,712 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3681. 

  3681. 2022-11-10 17:22:32,304 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3682. 

  3682. 2022-11-10 17:22:32,839 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3683. 

  3683. 2022-11-10 17:23:48,238 - INFO - main.py - <module> - 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
    3684. 

  3684. 2022-11-10 17:23:49,732 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3685. 

  3685. 2022-11-10 17:23:50,220 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3686. 

  3686. 2022-11-10 17:27:05,063 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3687. 

  3687. 2022-11-10 17:27:05,063 - INFO - main.py - <module> - 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)
    3688. 

  3688. 2022-11-10 17:27:08,989 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3689. 

  3689. 2022-11-10 17:27:10,547 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3690. 

  3690. 2022-11-10 17:27:11,062 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3691. 

  3691. 2022-11-10 17:28:26,585 - INFO - main.py - <module> - 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
    3692. 

  3692. 2022-11-10 17:28:28,096 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3693. 

  3693. 2022-11-10 17:28:28,570 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3694. 

  3694. 2022-11-10 17:29:19,770 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3695. 

  3695. 2022-11-10 17:29:19,770 - INFO - main.py - <module> - 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)
    3696. 

  3696. 2022-11-10 17:29:23,625 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3697. 

  3697. 2022-11-10 17:29:25,172 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3698. 

  3698. 2022-11-10 17:29:25,676 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3699. 

  3699. 2022-11-10 17:30:39,837 - INFO - main.py - <module> - 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
    3700. 

  3700. 2022-11-10 17:30:41,305 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3701. 

  3701. 2022-11-10 17:30:41,767 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3702. 

  3702. 2022-11-10 17:34:37,153 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3703. 

  3703. 2022-11-10 17:34:37,153 - INFO - main.py - <module> - 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)
    3704. 

  3704. 2022-11-10 17:34:41,097 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3705. 

  3705. 2022-11-10 17:34:42,637 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3706. 

  3706. 2022-11-10 17:34:43,171 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3707. 

  3707. 2022-11-10 17:35:59,620 - INFO - main.py - <module> - 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
    3708. 

  3708. 2022-11-10 17:36:01,118 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3709. 

  3709. 2022-11-10 17:36:01,589 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3710. 

  3710. 2022-11-10 17:43:29,099 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3711. 

  3711. 2022-11-10 17:43:29,099 - INFO - main.py - <module> - 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)
    3712. 

  3712. 2022-11-10 17:43:33,072 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3713. 

  3713. 2022-11-10 17:43:34,634 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3714. 

  3714. 2022-11-10 17:43:35,164 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3715. 

  3715. 2022-11-10 17:44:53,860 - INFO - main.py - <module> - 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
    3716. 

  3716. 2022-11-10 17:44:55,338 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3717. 

  3717. 2022-11-10 17:44:55,847 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3718. 

  3718. 2022-11-10 17:45:18,986 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3719. 

  3719. 2022-11-10 17:45:18,986 - INFO - main.py - <module> - 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)
    3720. 

  3720. 2022-11-10 17:45:22,928 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3721. 

  3721. 2022-11-10 17:45:24,449 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3722. 

  3722. 2022-11-10 17:45:24,948 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3723. 

  3723. 2022-11-10 17:46:20,522 - INFO - main.py - <module> - 263 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3724. 

  3724. 2022-11-10 17:46:20,522 - INFO - main.py - <module> - 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)
    3725. 

  3725. 2022-11-10 17:46:24,406 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3726. 

  3726. 2022-11-10 17:46:25,927 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3727. 

  3727. 2022-11-10 17:46:26,493 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3728. 

  3728. 2022-11-10 17:47:44,235 - INFO - main.py - <module> - 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
    3729. 

  3729. 2022-11-10 17:47:45,681 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3730. 

  3730. 2022-11-10 17:47:46,180 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3731. 

  3731. 2022-11-10 17:55:42,885 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3732. 

  3732. 2022-11-10 17:55:42,885 - INFO - main.py - <module> - 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)
    3733. 

  3733. 2022-11-10 17:55:46,791 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3734. 

  3734. 2022-11-10 17:55:48,383 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3735. 

  3735. 2022-11-10 17:55:48,913 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3736. 

  3736. 2022-11-10 17:57:05,653 - INFO - main.py - <module> - 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
    3737. 

  3737. 2022-11-10 17:57:07,150 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3738. 

  3738. 2022-11-10 17:57:07,665 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3739. 

  3739. 2022-11-10 18:17:11,930 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3740. 

  3740. 2022-11-10 18:17:11,930 - INFO - main.py - <module> - 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)
    3741. 

  3741. 2022-11-10 18:17:15,986 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3742. 

  3742. 2022-11-10 18:17:17,550 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3743. 

  3743. 2022-11-10 18:17:18,073 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3744. 

  3744. 2022-11-10 18:18:32,384 - INFO - main.py - <module> - 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
    3745. 

  3745. 2022-11-10 18:18:33,885 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3746. 

  3746. 2022-11-10 18:18:34,405 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3747. 

  3747. 2022-11-10 18:23:13,296 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3748. 

  3748. 2022-11-10 18:23:13,296 - INFO - main.py - <module> - 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)
    3749. 

  3749. 2022-11-10 18:23:17,229 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3750. 

  3750. 2022-11-10 18:23:18,805 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3751. 

  3751. 2022-11-10 18:23:19,339 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3752. 

  3752. 2022-11-10 18:24:36,791 - INFO - main.py - <module> - 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
    3753. 

  3753. 2022-11-10 18:24:38,241 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3754. 

  3754. 2022-11-10 18:24:38,785 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3755. 

  3755. 2022-11-10 18:29:09,874 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3756. 

  3756. 2022-11-10 18:29:09,874 - INFO - main.py - <module> - 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)
    3757. 

  3757. 2022-11-10 18:29:13,878 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3758. 

  3758. 2022-11-10 18:29:15,406 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3759. 

  3759. 2022-11-10 18:29:15,985 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3760. 

  3760. 2022-11-10 18:30:35,949 - INFO - main.py - <module> - 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
    3761. 

  3761. 2022-11-10 18:30:37,469 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3762. 

  3762. 2022-11-10 18:30:38,006 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3763. 

  3763. 2022-11-10 18:31:27,576 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3764. 

  3764. 2022-11-10 18:31:27,576 - INFO - main.py - <module> - 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)
    3765. 

  3765. 2022-11-10 18:31:31,514 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3766. 

  3766. 2022-11-10 18:31:33,005 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3767. 

  3767. 2022-11-10 18:31:33,557 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3768. 

  3768. 2022-11-10 18:32:49,325 - INFO - main.py - <module> - 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
    3769. 

  3769. 2022-11-10 18:32:50,802 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3770. 

  3770. 2022-11-10 18:32:51,316 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3771. 

  3771. 2022-11-10 18:37:30,106 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3772. 

  3772. 2022-11-10 18:37:30,106 - INFO - main.py - <module> - 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)
    3773. 

  3773. 2022-11-10 18:37:33,997 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3774. 

  3774. 2022-11-10 18:37:35,552 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3775. 

  3775. 2022-11-10 18:37:36,079 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3776. 

  3776. 2022-11-10 18:38:53,870 - INFO - main.py - <module> - 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
    3777. 

  3777. 2022-11-10 18:38:55,371 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3778. 

  3778. 2022-11-10 18:38:55,871 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3779. 

  3779. 2022-11-10 18:40:05,112 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3780. 

  3780. 2022-11-10 18:40:05,112 - INFO - main.py - <module> - 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)
    3781. 

  3781. 2022-11-10 18:40:08,970 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3782. 

  3782. 2022-11-10 18:40:10,545 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3783. 

  3783. 2022-11-10 18:40:11,064 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3784. 

  3784. 2022-11-10 18:41:28,041 - INFO - main.py - <module> - 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
    3785. 

  3785. 2022-11-10 18:41:29,519 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3786. 

  3786. 2022-11-10 18:41:29,951 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3787. 

  3787. 2022-11-10 18:42:27,691 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3788. 

  3788. 2022-11-10 18:42:27,692 - INFO - main.py - <module> - 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)
    3789. 

  3789. 2022-11-10 18:42:31,621 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3790. 

  3790. 2022-11-10 18:42:33,195 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3791. 

  3791. 2022-11-10 18:42:33,754 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3792. 

  3792. 2022-11-10 18:43:48,161 - INFO - main.py - <module> - 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
    3793. 

  3793. 2022-11-10 18:43:49,658 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3794. 

  3794. 2022-11-10 18:43:50,181 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3795. 

  3795. 2022-11-10 18:45:02,275 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3796. 

  3796. 2022-11-10 18:45:02,275 - INFO - main.py - <module> - 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)
    3797. 

  3797. 2022-11-10 18:45:06,144 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3798. 

  3798. 2022-11-10 18:45:07,673 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3799. 

  3799. 2022-11-10 18:45:08,190 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3800. 

  3800. 2022-11-10 18:46:25,087 - INFO - main.py - <module> - 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
    3801. 

  3801. 2022-11-10 18:46:26,594 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3802. 

  3802. 2022-11-10 18:46:27,102 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3803. 

  3803. 2022-11-10 18:54:55,404 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3804. 

  3804. 2022-11-10 18:54:55,405 - INFO - main.py - <module> - 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)
    3805. 

  3805. 2022-11-10 18:54:59,321 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3806. 

  3806. 2022-11-10 18:55:00,899 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3807. 

  3807. 2022-11-10 18:55:01,454 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3808. 

  3808. 2022-11-10 18:56:15,954 - INFO - main.py - <module> - 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
    3809. 

  3809. 2022-11-10 18:56:17,430 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3810. 

  3810. 2022-11-10 18:56:17,895 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3811. 

  3811. 2022-11-10 19:23:14,893 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3812. 

  3812. 2022-11-10 19:23:14,893 - INFO - main.py - <module> - 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)
    3813. 

  3813. 2022-11-10 19:23:18,931 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3814. 

  3814. 2022-11-10 19:23:20,457 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3815. 

  3815. 2022-11-10 19:23:21,012 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3816. 

  3816. 2022-11-10 19:24:35,945 - INFO - main.py - <module> - 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
    3817. 

  3817. 2022-11-10 19:24:37,432 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3818. 

  3818. 2022-11-10 19:24:37,946 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3819. 

  3819. 2022-11-10 19:26:49,648 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3820. 

  3820. 2022-11-10 19:26:49,649 - INFO - main.py - <module> - 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)
    3821. 

  3821. 2022-11-10 19:26:53,503 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3822. 

  3822. 2022-11-10 19:26:55,024 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3823. 

  3823. 2022-11-10 19:26:55,557 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3824. 

  3824. 2022-11-10 19:28:12,016 - INFO - main.py - <module> - 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
    3825. 

  3825. 2022-11-10 19:28:13,501 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3826. 

  3826. 2022-11-10 19:28:14,011 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3827. 

  3827. 2022-11-10 19:29:08,244 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3828. 

  3828. 2022-11-10 19:29:08,244 - INFO - main.py - <module> - 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)
    3829. 

  3829. 2022-11-10 19:29:12,151 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3830. 

  3830. 2022-11-10 19:29:13,648 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3831. 

  3831. 2022-11-10 19:29:14,195 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3832. 

  3832. 2022-11-10 19:30:29,093 - INFO - main.py - <module> - 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
    3833. 

  3833. 2022-11-10 19:30:30,548 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3834. 

  3834. 2022-11-10 19:30:31,010 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3835. 

  3835. 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')]
    3836. 

  3836. 2022-11-10 19:40:03,510 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3837. 

  3837. 2022-11-10 19:40:03,511 - INFO - main.py - <module> - 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)
    3838. 

  3838. 2022-11-10 19:40:07,424 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3839. 

  3839. 2022-11-10 19:40:08,988 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3840. 

  3840. 2022-11-10 19:40:09,513 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3841. 

  3841. 2022-11-10 19:46:03,178 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3842. 

  3842. 2022-11-10 19:46:03,178 - INFO - main.py - <module> - 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)
    3843. 

  3843. 2022-11-10 19:46:07,445 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3844. 

  3844. 2022-11-10 19:46:09,083 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3845. 

  3845. 2022-11-10 19:46:09,610 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3846. 

  3846. 2022-11-10 20:03:40,961 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3847. 

  3847. 2022-11-10 20:03:40,961 - INFO - main.py - <module> - 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)
    3848. 

  3848. 2022-11-10 20:03:44,954 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3849. 

  3849. 2022-11-10 20:03:46,524 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3850. 

  3850. 2022-11-10 20:03:47,074 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3851. 

  3851. 2022-11-10 20:12:12,450 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3852. 

  3852. 2022-11-10 20:12:12,451 - INFO - main.py - <module> - 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)
    3853. 

  3853. 2022-11-10 20:12:16,386 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3854. 

  3854. 2022-11-10 20:12:17,963 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3855. 

  3855. 2022-11-10 20:12:18,492 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3856. 

  3856. 2022-11-10 20:16:51,884 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3857. 

  3857. 2022-11-10 20:16:51,884 - INFO - main.py - <module> - 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)
    3858. 

  3858. 2022-11-10 20:16:55,874 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3859. 

  3859. 2022-11-10 20:16:57,444 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3860. 

  3860. 2022-11-10 20:16:57,975 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3861. 

  3861. 2022-11-10 20:20:54,021 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3862. 

  3862. 2022-11-10 20:20:54,021 - INFO - main.py - <module> - 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)
    3863. 

  3863. 2022-11-10 20:20:57,964 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3864. 

  3864. 2022-11-10 20:20:59,502 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3865. 

  3865. 2022-11-10 20:21:00,047 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3866. 

  3866. 2022-11-10 20:30:07,108 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3867. 

  3867. 2022-11-10 20:30:07,108 - INFO - main.py - <module> - 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)
    3868. 

  3868. 2022-11-10 20:30:10,989 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3869. 

  3869. 2022-11-10 20:30:12,528 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3870. 

  3870. 2022-11-10 20:30:13,048 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3871. 

  3871. 2022-11-11 10:33:12,211 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3872. 

  3872. 2022-11-11 10:33:12,211 - INFO - main.py - <module> - 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)
    3873. 

  3873. 2022-11-11 10:33:16,114 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3874. 

  3874. 2022-11-11 10:33:17,719 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3875. 

  3875. 2022-11-11 10:33:18,345 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3876. 

  3876. 2022-11-20 18:04:14,025 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3877. 

  3877. 2022-11-20 18:04:14,025 - INFO - main.py - <module> - 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)
    3878. 

  3878. 2022-11-20 18:04:19,333 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3879. 

  3879. 2022-11-20 18:04:20,876 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3880. 

  3880. 2022-11-20 18:04:21,453 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3881. 

  3881. 2022-11-21 23:26:20,302 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3882. 

  3882. 2022-11-21 23:26:20,302 - INFO - main.py - <module> - 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)
    3883. 

  3883. 2022-11-21 23:27:17,601 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3884. 

  3884. 2022-11-21 23:27:17,601 - INFO - main.py - <module> - 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)
    3885. 

  3885. 2022-11-21 23:27:22,658 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3886. 

  3886. 2022-11-21 23:27:24,168 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3887. 

  3887. 2022-11-21 23:27:24,777 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3888. 

  3888. 2022-11-21 23:28:41,197 - INFO - main.py - <module> - 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
    3889. 

  3889. 2022-11-21 23:28:42,790 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3890. 

  3890. 2022-11-21 23:28:43,431 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3891. 

  3891. 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')]
    3892. 

  3892. 2022-11-22 15:34:35,264 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3893. 

  3893. 2022-11-22 15:34:35,264 - INFO - main.py - <module> - 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)
    3894. 

  3894. 2022-11-23 21:12:45,244 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3895. 

  3895. 2022-11-23 21:18:10,782 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3896. 

  3896. 2022-11-23 21:18:11,485 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3897. 

  3897. 2022-11-23 22:21:01,725 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3898. 

  3898. 2022-11-23 22:21:01,725 - INFO - main.py - <module> - 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)
    3899. 

  3899. 2022-11-23 22:21:06,001 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3900. 

  3900. 2022-11-23 22:21:07,547 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3901. 

  3901. 2022-11-23 22:21:08,500 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3902. 

  3902. 2022-11-29 23:24:44,589 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3903. 

  3903. 2022-11-29 23:24:44,589 - INFO - main.py - <module> - 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)
    3904. 

  3904. 2022-11-29 23:34:26,938 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3905. 

  3905. 2022-11-29 23:34:26,938 - INFO - main.py - <module> - 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)
    3906. 

  3906. 2022-11-29 23:36:22,619 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3907. 

  3907. 2022-11-29 23:36:25,400 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    3908. 

  3908. 2022-11-29 23:36:26,071 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3909. 

  3909. 2022-11-30 11:08:47,186 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3910. 

  3910. 2022-11-30 11:08:47,186 - INFO - main.py - <module> - 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)
    3911. 

  3911. 2022-11-30 11:19:31,863 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3912. 

  3912. 2022-11-30 19:49:08,585 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3913. 

  3913. 2022-11-30 19:49:08,585 - INFO - main.py - <module> - 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)
    3914. 

  3914. 2022-11-30 19:56:23,301 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3915. 

  3915. 2022-11-30 19:56:23,301 - INFO - main.py - <module> - 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)
    3916. 

  3916. 2022-11-30 20:17:20,125 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3917. 

  3917. 2022-11-30 20:58:55,203 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3918. 

  3918. 2022-11-30 20:58:55,203 - INFO - main.py - <module> - 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)
    3919. 

  3919. 2022-11-30 20:59:02,055 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3920. 

  3920. 2023-03-16 21:05:02,026 - INFO - main.py - <module> - 264 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    3921. 

  3921. 2023-03-16 21:05:02,026 - INFO - main.py - <module> - 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')
    3922. 

  3922. 2023-03-16 21:05:07,179 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    3923. 

  3923. 2023-03-16 21:05:19,512 - INFO - main.py - train - 68 - 【train】 epoch:0 0/2980 loss:292.0276
    3924. 

  3924. 2023-03-16 21:05:20,547 - INFO - main.py - train - 68 - 【train】 epoch:0 1/2980 loss:226.9651
    3925. 

  3925. 2023-03-16 21:05:21,583 - INFO - main.py - train - 68 - 【train】 epoch:0 2/2980 loss:96.5961
    3926. 

  3926. 2023-03-16 21:05:22,773 - INFO - main.py - train - 68 - 【train】 epoch:0 3/2980 loss:193.5617
    3927. 

  3927. 2023-03-16 21:05:23,897 - INFO - main.py - train - 68 - 【train】 epoch:0 4/2980 loss:138.0916
    3928. 

  3928. 2023-03-16 21:05:25,019 - INFO - main.py - train - 68 - 【train】 epoch:0 5/2980 loss:69.7198
    3929. 

  3929. 2023-03-16 21:05:26,093 - INFO - main.py - train - 68 - 【train】 epoch:0 6/2980 loss:367.6134
    3930. 

  3930. 2023-03-16 21:05:27,193 - INFO - main.py - train - 68 - 【train】 epoch:0 7/2980 loss:137.3571
    3931. 

  3931. 2023-03-16 21:05:28,302 - INFO - main.py - train - 68 - 【train】 epoch:0 8/2980 loss:418.8170
    3932. 

  3932. 2023-03-16 21:05:29,372 - INFO - main.py - train - 68 - 【train】 epoch:0 9/2980 loss:230.4982
    3933. 

  3933. 2023-03-16 21:05:30,444 - INFO - main.py - train - 68 - 【train】 epoch:0 10/2980 loss:173.2603
    3934. 

  3934. 2023-03-16 21:05:31,533 - INFO - main.py - train - 68 - 【train】 epoch:0 11/2980 loss:180.9781
    3935. 

  3935. 2023-03-16 21:05:32,627 - INFO - main.py - train - 68 - 【train】 epoch:0 12/2980 loss:392.8346
    3936. 

  3936. 2023-03-16 21:05:33,795 - INFO - main.py - train - 68 - 【train】 epoch:0 13/2980 loss:290.8567
    3937. 

  3937. 2023-03-16 21:05:34,933 - INFO - main.py - train - 68 - 【train】 epoch:0 14/2980 loss:57.3965
    3938. 

  3938. 2023-03-16 21:05:36,066 - INFO - main.py - train - 68 - 【train】 epoch:0 15/2980 loss:385.7918
    3939. 

  3939. 2023-03-16 21:05:37,149 - INFO - main.py - train - 68 - 【train】 epoch:0 16/2980 loss:70.0209
    3940. 

  3940. 2023-03-16 21:05:38,246 - INFO - main.py - train - 68 - 【train】 epoch:0 17/2980 loss:127.1326
    3941. 

  3941. 2023-03-16 21:05:39,333 - INFO - main.py - train - 68 - 【train】 epoch:0 18/2980 loss:202.2739
    3942. 

  3942. 2023-03-16 21:05:40,459 - INFO - main.py - train - 68 - 【train】 epoch:0 19/2980 loss:229.5152
    3943. 

  3943. 2023-03-16 21:05:41,575 - INFO - main.py - train - 68 - 【train】 epoch:0 20/2980 loss:364.3724
    3944. 

  3944. 2023-03-16 21:05:42,691 - INFO - main.py - train - 68 - 【train】 epoch:0 21/2980 loss:333.0904
    3945. 

  3945. 2023-03-16 21:05:43,804 - INFO - main.py - train - 68 - 【train】 epoch:0 22/2980 loss:248.2384
    3946. 

  3946. 2023-03-16 21:05:44,887 - INFO - main.py - train - 68 - 【train】 epoch:0 23/2980 loss:118.4113
    3947. 

  3947. 2023-03-16 21:05:46,058 - INFO - main.py - train - 68 - 【train】 epoch:0 24/2980 loss:363.6166
    3948. 

  3948. 2023-03-16 21:05:47,189 - INFO - main.py - train - 68 - 【train】 epoch:0 25/2980 loss:339.0241
    3949. 

  3949. 2023-03-16 21:05:48,287 - INFO - main.py - train - 68 - 【train】 epoch:0 26/2980 loss:45.9091
    3950. 

  3950. 2023-03-16 21:05:49,374 - INFO - main.py - train - 68 - 【train】 epoch:0 27/2980 loss:145.5225
    3951. 

  3951. 2023-03-16 21:05:50,471 - INFO - main.py - train - 68 - 【train】 epoch:0 28/2980 loss:113.1599
    3952. 

  3952. 2023-03-16 21:05:51,561 - INFO - main.py - train - 68 - 【train】 epoch:0 29/2980 loss:113.0434
    3953. 

  3953. 2023-03-16 21:05:52,657 - INFO - main.py - train - 68 - 【train】 epoch:0 30/2980 loss:77.8018
    3954. 

  3954. 2023-03-16 21:05:53,758 - INFO - main.py - train - 68 - 【train】 epoch:0 31/2980 loss:108.3225
    3955. 

  3955. 2023-03-16 21:05:54,887 - INFO - main.py - train - 68 - 【train】 epoch:0 32/2980 loss:396.3494
    3956. 

  3956. 2023-03-16 21:05:56,017 - INFO - main.py - train - 68 - 【train】 epoch:0 33/2980 loss:249.8687
    3957. 

  3957. 2023-03-16 21:05:57,149 - INFO - main.py - train - 68 - 【train】 epoch:0 34/2980 loss:179.0801
    3958. 

  3958. 2023-03-16 21:05:58,277 - INFO - main.py - train - 68 - 【train】 epoch:0 35/2980 loss:148.0742
    3959. 

  3959. 2023-03-16 21:05:59,493 - INFO - main.py - train - 68 - 【train】 epoch:0 36/2980 loss:256.1973
    3960. 

  3960. 2023-03-16 21:06:00,655 - INFO - main.py - train - 68 - 【train】 epoch:0 37/2980 loss:268.5482
    3961. 

  3961. 2023-03-16 21:06:01,772 - INFO - main.py - train - 68 - 【train】 epoch:0 38/2980 loss:48.5073
    3962. 

  3962. 2023-03-16 21:06:02,906 - INFO - main.py - train - 68 - 【train】 epoch:0 39/2980 loss:252.2066
    3963. 

  3963. 2023-03-16 21:06:04,033 - INFO - main.py - train - 68 - 【train】 epoch:0 40/2980 loss:142.8852
    3964. 

  3964. 2023-03-16 21:06:05,197 - INFO - main.py - train - 68 - 【train】 epoch:0 41/2980 loss:117.0852
    3965. 

  3965. 2023-03-16 21:06:06,344 - INFO - main.py - train - 68 - 【train】 epoch:0 42/2980 loss:159.1487
    3966. 

  3966. 2023-03-16 21:06:07,444 - INFO - main.py - train - 68 - 【train】 epoch:0 43/2980 loss:102.0254
    3967. 

  3967. 2023-03-16 21:06:08,640 - INFO - main.py - train - 68 - 【train】 epoch:0 44/2980 loss:140.6444
    3968. 

  3968. 2023-03-16 21:06:09,763 - INFO - main.py - train - 68 - 【train】 epoch:0 45/2980 loss:55.1283
    3969. 

  3969. 2023-03-16 21:06:10,954 - INFO - main.py - train - 68 - 【train】 epoch:0 46/2980 loss:90.1131
    3970. 

  3970. 2023-03-16 21:06:12,114 - INFO - main.py - train - 68 - 【train】 epoch:0 47/2980 loss:25.0661
    3971. 

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  4264. 2023-03-16 21:12:30,344 - INFO - main.py - train - 68 - 【train】 epoch:0 341/2980 loss:45.6343
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    4267. 

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    4280. 

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  4314. 2023-03-16 21:13:43,870 - INFO - main.py - train - 68 - 【train】 epoch:0 391/2980 loss:14.3533
    4315. 

  4315. 2023-03-16 21:13:45,164 - INFO - main.py - train - 68 - 【train】 epoch:0 392/2980 loss:22.4962
    4316. 

  4316. 2023-03-16 21:13:46,445 - INFO - main.py - train - 68 - 【train】 epoch:0 393/2980 loss:5.8102
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    4318. 

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  4319. 2023-03-16 21:13:50,238 - INFO - main.py - train - 68 - 【train】 epoch:0 396/2980 loss:11.9630
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    4325. 

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    4342. 

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    4353. 

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    4360. 

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    4361. 

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    4364. 

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    4400. 

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    4401. 

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    4402. 

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    4410. 

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    4411. 

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  4616. 2023-03-16 21:21:10,255 - INFO - main.py - train - 68 - 【train】 epoch:1 693/2980 loss:12.5134
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  4681. 2023-03-16 21:22:30,455 - INFO - main.py - train - 68 - 【train】 epoch:1 758/2980 loss:10.8162
    4682. 

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    4683. 

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  4703. 2023-03-16 21:22:57,283 - INFO - main.py - train - 68 - 【train】 epoch:1 780/2980 loss:40.0740
    4704. 

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    4705. 

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  4848. 2023-03-16 21:25:48,523 - INFO - main.py - train - 68 - 【train】 epoch:1 925/2980 loss:12.3059
    4849. 

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  4911. 2023-03-16 21:27:03,938 - INFO - main.py - train - 68 - 【train】 epoch:1 988/2980 loss:12.2105
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  4925. 2023-03-16 21:27:25,240 - INFO - main.py - train - 68 - 【train】 epoch:1 1002/2980 loss:11.7100
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    4941. 

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    4942. 

  4942. 2023-03-16 21:27:48,485 - INFO - main.py - train - 68 - 【train】 epoch:1 1019/2980 loss:19.2018
    4943. 

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  4960. 2023-03-16 21:28:19,089 - INFO - main.py - train - 68 - 【train】 epoch:1 1037/2980 loss:3.6557
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    4991. 

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  4993. 2023-03-16 21:29:01,186 - INFO - main.py - train - 68 - 【train】 epoch:1 1070/2980 loss:14.0748
    4994. 

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  5283. 2023-03-16 21:35:06,426 - INFO - main.py - train - 68 - 【train】 epoch:2 1360/2980 loss:6.6329
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  5360. 2023-03-16 21:36:41,529 - INFO - main.py - train - 68 - 【train】 epoch:2 1437/2980 loss:3.4568
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  5428. 2023-03-16 21:38:03,411 - INFO - main.py - train - 68 - 【train】 epoch:2 1505/2980 loss:10.2834
    5429. 

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    5573. 

  5573. 2023-03-16 21:41:11,763 - INFO - main.py - train - 68 - 【train】 epoch:2 1650/2980 loss:9.5490
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  5718. 2023-03-16 21:44:27,417 - INFO - main.py - train - 68 - 【train】 epoch:3 1795/2980 loss:5.5717
    5719. 

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  5864. 2023-03-16 21:47:26,668 - INFO - main.py - train - 68 - 【train】 epoch:3 1941/2980 loss:6.4584
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  5916. 2023-03-16 21:48:28,885 - INFO - main.py - train - 68 - 【train】 epoch:3 1993/2980 loss:3.0255
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  5917. 2023-03-16 21:48:30,051 - INFO - main.py - train - 68 - 【train】 epoch:3 1994/2980 loss:2.0472
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  5918. 2023-03-16 21:48:31,243 - INFO - main.py - train - 68 - 【train】 epoch:3 1995/2980 loss:10.0864
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  5919. 2023-03-16 21:48:32,433 - INFO - main.py - train - 68 - 【train】 epoch:3 1996/2980 loss:13.7977
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  5920. 2023-03-16 21:48:33,656 - INFO - main.py - train - 68 - 【train】 epoch:3 1997/2980 loss:6.7212
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  5925. 2023-03-16 21:48:39,658 - INFO - main.py - train - 68 - 【train】 epoch:3 2002/2980 loss:15.8660
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  5926. 2023-03-16 21:48:40,834 - INFO - main.py - train - 68 - 【train】 epoch:3 2003/2980 loss:5.9665
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  5927. 2023-03-16 21:48:42,015 - INFO - main.py - train - 68 - 【train】 epoch:3 2004/2980 loss:7.2970
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  5928. 2023-03-16 21:48:43,300 - INFO - main.py - train - 68 - 【train】 epoch:3 2005/2980 loss:8.1770
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  5930. 2023-03-16 21:48:45,699 - INFO - main.py - train - 68 - 【train】 epoch:3 2007/2980 loss:6.1259
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  5931. 2023-03-16 21:48:46,880 - INFO - main.py - train - 68 - 【train】 epoch:3 2008/2980 loss:6.2983
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  5932. 2023-03-16 21:48:48,065 - INFO - main.py - train - 68 - 【train】 epoch:3 2009/2980 loss:1.1197
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  5934. 2023-03-16 21:48:50,462 - INFO - main.py - train - 68 - 【train】 epoch:3 2011/2980 loss:25.6485
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  5935. 2023-03-16 21:48:51,687 - INFO - main.py - train - 68 - 【train】 epoch:3 2012/2980 loss:14.0013
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  5936. 2023-03-16 21:48:52,864 - INFO - main.py - train - 68 - 【train】 epoch:3 2013/2980 loss:3.4954
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  5937. 2023-03-16 21:48:54,084 - INFO - main.py - train - 68 - 【train】 epoch:3 2014/2980 loss:14.2868
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  5938. 2023-03-16 21:48:55,250 - INFO - main.py - train - 68 - 【train】 epoch:3 2015/2980 loss:3.6789
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  5939. 2023-03-16 21:48:56,445 - INFO - main.py - train - 68 - 【train】 epoch:3 2016/2980 loss:3.7371
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  5940. 2023-03-16 21:48:57,624 - INFO - main.py - train - 68 - 【train】 epoch:3 2017/2980 loss:0.4046
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  5941. 2023-03-16 21:48:58,805 - INFO - main.py - train - 68 - 【train】 epoch:3 2018/2980 loss:0.6445
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  5942. 2023-03-16 21:48:59,985 - INFO - main.py - train - 68 - 【train】 epoch:3 2019/2980 loss:0.6840
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  5943. 2023-03-16 21:49:01,173 - INFO - main.py - train - 68 - 【train】 epoch:3 2020/2980 loss:3.3179
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  5944. 2023-03-16 21:49:02,358 - INFO - main.py - train - 68 - 【train】 epoch:3 2021/2980 loss:16.5275
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  5945. 2023-03-16 21:49:03,541 - INFO - main.py - train - 68 - 【train】 epoch:3 2022/2980 loss:1.3730
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  5947. 2023-03-16 21:49:05,895 - INFO - main.py - train - 68 - 【train】 epoch:3 2024/2980 loss:2.7072
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  6010. 2023-03-16 21:50:21,204 - INFO - main.py - train - 68 - 【train】 epoch:3 2087/2980 loss:6.6616
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    6155. 

  6155. 2023-03-16 21:53:19,886 - INFO - main.py - train - 68 - 【train】 epoch:3 2232/2980 loss:4.4036
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    6299. 

  6299. 2023-03-16 21:56:53,434 - INFO - main.py - train - 68 - 【train】 epoch:3 2376/2980 loss:3.2422
    6300. 

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    6310. 

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    6375. 

  6375. 2023-03-16 21:58:50,254 - INFO - main.py - train - 68 - 【train】 epoch:4 2452/2980 loss:6.0678
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  6376. 2023-03-16 21:58:51,596 - INFO - main.py - train - 68 - 【train】 epoch:4 2453/2980 loss:20.1436
    6377. 

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    6378. 

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    6379. 

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    6380. 

  6380. 2023-03-16 21:58:56,983 - INFO - main.py - train - 68 - 【train】 epoch:4 2457/2980 loss:8.3555
    6381. 

  6381. 2023-03-16 21:58:58,350 - INFO - main.py - train - 68 - 【train】 epoch:4 2458/2980 loss:5.5362
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    6383. 

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    6444. 

  6444. 2023-03-16 22:00:23,222 - INFO - main.py - train - 68 - 【train】 epoch:4 2521/2980 loss:8.7818
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    6681. 

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    6733. 

  6733. 2023-03-16 22:07:27,976 - INFO - main.py - train - 68 - 【train】 epoch:4 2810/2980 loss:3.6978
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  6795. 2023-03-16 22:09:04,823 - INFO - main.py - train - 68 - 【train】 epoch:4 2872/2980 loss:6.2065
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  6796. 2023-03-16 22:09:06,155 - INFO - main.py - train - 68 - 【train】 epoch:4 2873/2980 loss:4.6603
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  6797. 2023-03-16 22:09:07,482 - INFO - main.py - train - 68 - 【train】 epoch:4 2874/2980 loss:2.0924
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  6810. 2023-03-16 22:09:27,441 - INFO - main.py - train - 68 - 【train】 epoch:4 2887/2980 loss:15.2141
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  6826. 2023-03-16 22:09:52,995 - INFO - main.py - train - 68 - 【train】 epoch:4 2903/2980 loss:3.3933
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  6878. 2023-03-16 22:11:10,033 - INFO - main.py - train - 68 - 【train】 epoch:4 2955/2980 loss:9.6282
    6879. 

  6879. 2023-03-16 22:11:11,473 - INFO - main.py - train - 68 - 【train】 epoch:4 2956/2980 loss:6.5213
    6880. 

  6880. 2023-03-16 22:11:12,855 - INFO - main.py - train - 68 - 【train】 epoch:4 2957/2980 loss:8.8274
    6881. 

  6881. 2023-03-16 22:11:14,343 - INFO - main.py - train - 68 - 【train】 epoch:4 2958/2980 loss:1.7905
    6882. 

  6882. 2023-03-16 22:11:15,723 - INFO - main.py - train - 68 - 【train】 epoch:4 2959/2980 loss:7.0458
    6883. 

  6883. 2023-03-16 22:11:17,082 - INFO - main.py - train - 68 - 【train】 epoch:4 2960/2980 loss:6.3148
    6884. 

  6884. 2023-03-16 22:11:18,473 - INFO - main.py - train - 68 - 【train】 epoch:4 2961/2980 loss:11.4154
    6885. 

  6885. 2023-03-16 22:11:19,833 - INFO - main.py - train - 68 - 【train】 epoch:4 2962/2980 loss:3.9497
    6886. 

  6886. 2023-03-16 22:11:21,123 - INFO - main.py - train - 68 - 【train】 epoch:4 2963/2980 loss:2.8411
    6887. 

  6887. 2023-03-16 22:11:22,443 - INFO - main.py - train - 68 - 【train】 epoch:4 2964/2980 loss:5.5801
    6888. 

  6888. 2023-03-16 22:11:23,798 - INFO - main.py - train - 68 - 【train】 epoch:4 2965/2980 loss:2.1789
    6889. 

  6889. 2023-03-16 22:11:25,234 - INFO - main.py - train - 68 - 【train】 epoch:4 2966/2980 loss:8.7305
    6890. 

  6890. 2023-03-16 22:11:26,692 - INFO - main.py - train - 68 - 【train】 epoch:4 2967/2980 loss:5.9604
    6891. 

  6891. 2023-03-16 22:11:28,116 - INFO - main.py - train - 68 - 【train】 epoch:4 2968/2980 loss:2.6953
    6892. 

  6892. 2023-03-16 22:11:29,463 - INFO - main.py - train - 68 - 【train】 epoch:4 2969/2980 loss:6.9024
    6893. 

  6893. 2023-03-16 22:11:30,792 - INFO - main.py - train - 68 - 【train】 epoch:4 2970/2980 loss:2.8682
    6894. 

  6894. 2023-03-16 22:11:32,161 - INFO - main.py - train - 68 - 【train】 epoch:4 2971/2980 loss:8.3969
    6895. 

  6895. 2023-03-16 22:11:33,663 - INFO - main.py - train - 68 - 【train】 epoch:4 2972/2980 loss:2.9724
    6896. 

  6896. 2023-03-16 22:11:35,197 - INFO - main.py - train - 68 - 【train】 epoch:4 2973/2980 loss:1.8177
    6897. 

  6897. 2023-03-16 22:11:36,614 - INFO - main.py - train - 68 - 【train】 epoch:4 2974/2980 loss:0.1093
    6898. 

  6898. 2023-03-16 22:11:38,142 - INFO - main.py - train - 68 - 【train】 epoch:4 2975/2980 loss:2.7382
    6899. 

  6899. 2023-03-16 22:11:39,792 - INFO - main.py - train - 68 - 【train】 epoch:4 2976/2980 loss:7.7669
    6900. 

  6900. 2023-03-16 22:11:41,261 - INFO - main.py - train - 68 - 【train】 epoch:4 2977/2980 loss:13.7039
    6901. 

  6901. 2023-03-16 22:11:42,652 - INFO - main.py - train - 68 - 【train】 epoch:4 2978/2980 loss:1.7054
    6902. 

  6902. 2023-03-16 22:11:44,128 - INFO - main.py - train - 68 - 【train】 epoch:4 2979/2980 loss:2.8405
    6903. 

  6903. 2023-03-16 22:11:44,754 - INFO - trainUtils.py - save_model - 70 - Saving model checkpoint to ./checkpoints/bert_crf
    6904. 

  6904. 2023-03-16 22:11:47,770 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    6905. 

  6905. 2023-03-16 22:11:48,424 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    6906. 

  6906. 2023-03-16 22:13:44,163 - INFO - main.py - <module> - 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
    6907. 

  6907. 2023-03-16 22:13:45,606 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_crf/model.pt
    6908. 

  6908. 2023-03-16 22:13:46,203 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    6909. 

  6909. 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')]
    6910.