2022-10-09 14:35:47,018 ---------------------------------------------------------------------------------------------------- 2022-10-09 14:35:47,019 Model: "SequenceTagger( (embeddings): StackedEmbeddings( (list_embedding_0): TransformerWordEmbeddings( (model): DistilBertModel( (embeddings): Embeddings( (word_embeddings): Embedding(28996, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (transformer): Transformer( (layer): ModuleList( (0): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (1): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (2): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (3): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (4): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) (5): TransformerBlock( (attention): MultiHeadSelfAttention( (dropout): Dropout(p=0.1, inplace=False) (q_lin): Linear(in_features=768, out_features=768, bias=True) (k_lin): Linear(in_features=768, out_features=768, bias=True) (v_lin): Linear(in_features=768, out_features=768, bias=True) (out_lin): Linear(in_features=768, out_features=768, bias=True) ) (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (ffn): FFN( (dropout): Dropout(p=0.1, inplace=False) (lin1): Linear(in_features=768, out_features=3072, bias=True) (lin2): Linear(in_features=3072, out_features=768, bias=True) (activation): GELUActivation() ) (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) ) ) ) ) ) ) (word_dropout): WordDropout(p=0.05) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=21, bias=True) (loss_function): CrossEntropyLoss() )" 2022-10-09 14:35:47,020 ---------------------------------------------------------------------------------------------------- 2022-10-09 14:35:47,020 Corpus: "MultiCorpus: 126439 train + 28967 dev + 17625 test sentences - ColumnCorpus Corpus: 14896 train + 3444 dev + 3679 test sentences - ./ - ColumnCorpus Corpus: 1491 train + 166 dev + 184 test sentences - ./ - ColumnCorpus Corpus: 65087 train + 18419 dev + 9176 test sentences - ./datasets - ColumnCorpus Corpus: 44965 train + 6938 dev + 4586 test sentences - ./" 2022-10-09 14:35:47,020 ---------------------------------------------------------------------------------------------------- 2022-10-09 14:35:47,020 Parameters: 2022-10-09 14:35:47,020 - learning_rate: "0.000005" 2022-10-09 14:35:47,020 - mini_batch_size: "32" 2022-10-09 14:35:47,020 - patience: "3" 2022-10-09 14:35:47,020 - anneal_factor: "0.5" 2022-10-09 14:35:47,020 - max_epochs: "20" 2022-10-09 14:35:47,020 - shuffle: "True" 2022-10-09 14:35:47,020 - train_with_dev: "False" 2022-10-09 14:35:47,021 - batch_growth_annealing: "False" 2022-10-09 14:35:47,021 ---------------------------------------------------------------------------------------------------- 2022-10-09 14:35:47,021 Model training base path: "resources/taggers/privy-flair-transformers" 2022-10-09 14:35:47,021 ---------------------------------------------------------------------------------------------------- 2022-10-09 14:35:47,021 Device: cuda:0 2022-10-09 14:35:47,021 ---------------------------------------------------------------------------------------------------- 2022-10-09 14:35:47,021 Embeddings storage mode: none 2022-10-09 14:35:47,021 ---------------------------------------------------------------------------------------------------- 2022-10-09 14:41:45,282 epoch 1 - iter 395/3952 - loss 3.32419044 - samples/sec: 35.64 - lr: 0.000000 2022-10-09 14:50:42,225 epoch 1 - iter 790/3952 - loss 1.82346877 - samples/sec: 24.23 - lr: 0.000000 2022-10-09 15:00:44,300 epoch 1 - iter 1185/3952 - loss 1.06483796 - samples/sec: 21.66 - lr: 0.000001 2022-10-09 15:10:53,476 epoch 1 - iter 1580/3952 - loss 0.79311831 - samples/sec: 21.46 - lr: 0.000001 2022-10-09 15:20:53,647 epoch 1 - iter 1975/3952 - loss 0.65220017 - samples/sec: 21.79 - lr: 0.000001 2022-10-09 15:30:48,260 epoch 1 - iter 2370/3952 - loss 0.56201630 - samples/sec: 21.92 - lr: 0.000001 2022-10-09 15:38:37,611 epoch 1 - iter 2765/3952 - loss 0.53726885 - samples/sec: 27.75 - lr: 0.000002 2022-10-09 15:44:53,320 epoch 1 - iter 3160/3952 - loss 0.53328468 - samples/sec: 34.63 - lr: 0.000002 2022-10-09 15:51:16,972 epoch 1 - iter 3555/3952 - loss 0.52470503 - samples/sec: 33.80 - lr: 0.000002 2022-10-09 15:57:35,830 epoch 1 - iter 3950/3952 - loss 0.51681052 - samples/sec: 34.26 - lr: 0.000002 2022-10-09 15:57:37,275 ---------------------------------------------------------------------------------------------------- 2022-10-09 15:57:37,275 EPOCH 1 done: loss 0.5168 - lr 0.000002 2022-10-09 16:12:59,483 Evaluating as a multi-label problem: False 2022-10-09 16:12:59,975 DEV : loss 0.07261496782302856 - f1-score (micro avg) 0.7003 2022-10-09 16:13:31,047 BAD EPOCHS (no improvement): 4 2022-10-09 16:13:31,940 ---------------------------------------------------------------------------------------------------- 2022-10-09 16:21:39,781 epoch 2 - iter 395/3952 - loss 0.21218652 - samples/sec: 26.89 - lr: 0.000003 2022-10-09 16:29:46,206 epoch 2 - iter 790/3952 - loss 0.20655105 - samples/sec: 26.79 - lr: 0.000003 2022-10-09 16:37:52,936 epoch 2 - iter 1185/3952 - loss 0.20259102 - samples/sec: 26.73 - lr: 0.000003 2022-10-09 16:45:58,507 epoch 2 - iter 1580/3952 - loss 0.20005535 - samples/sec: 26.81 - lr: 0.000003 2022-10-09 16:53:52,122 epoch 2 - iter 1975/3952 - loss 0.19747189 - samples/sec: 27.49 - lr: 0.000004 2022-10-09 17:01:39,634 epoch 2 - iter 2370/3952 - loss 0.19566392 - samples/sec: 27.76 - lr: 0.000004 2022-10-09 17:09:30,471 epoch 2 - iter 2765/3952 - loss 0.19386487 - samples/sec: 27.73 - lr: 0.000004 2022-10-09 17:17:22,096 epoch 2 - iter 3160/3952 - loss 0.19249352 - samples/sec: 27.64 - lr: 0.000004 2022-10-09 17:25:14,518 epoch 2 - iter 3555/3952 - loss 0.19133590 - samples/sec: 27.53 - lr: 0.000005 2022-10-09 17:33:11,367 epoch 2 - iter 3950/3952 - loss 0.19002868 - samples/sec: 27.17 - lr: 0.000005 2022-10-09 17:33:13,051 ---------------------------------------------------------------------------------------------------- 2022-10-09 17:33:13,051 EPOCH 2 done: loss 0.1900 - lr 0.000005 2022-10-09 17:48:24,882 Evaluating as a multi-label problem: False 2022-10-09 17:48:25,337 DEV : loss 0.019461628049612045 - f1-score (micro avg) 0.9258 2022-10-09 17:48:56,512 BAD EPOCHS (no improvement): 4 2022-10-09 17:48:57,426 ---------------------------------------------------------------------------------------------------- 2022-10-09 17:57:02,355 epoch 3 - iter 395/3952 - loss 0.17847621 - samples/sec: 26.73 - lr: 0.000005 2022-10-09 18:05:03,813 epoch 3 - iter 790/3952 - loss 0.17591453 - samples/sec: 27.01 - lr: 0.000005 2022-10-09 18:13:04,328 epoch 3 - iter 1185/3952 - loss 0.17539734 - samples/sec: 27.16 - lr: 0.000005 2022-10-09 18:21:04,749 epoch 3 - iter 1580/3952 - loss 0.17471956 - samples/sec: 27.12 - lr: 0.000005 2022-10-09 18:29:08,781 epoch 3 - iter 1975/3952 - loss 0.17411210 - samples/sec: 26.88 - lr: 0.000005 2022-10-09 18:37:08,694 epoch 3 - iter 2370/3952 - loss 0.17357470 - samples/sec: 27.08 - lr: 0.000005 2022-10-09 18:45:04,417 epoch 3 - iter 2765/3952 - loss 0.17312875 - samples/sec: 27.38 - lr: 0.000005 2022-10-09 18:53:02,063 epoch 3 - iter 3160/3952 - loss 0.17256571 - samples/sec: 27.23 - lr: 0.000005 2022-10-09 19:01:03,502 epoch 3 - iter 3555/3952 - loss 0.17219397 - samples/sec: 26.99 - lr: 0.000005 2022-10-09 19:09:01,525 epoch 3 - iter 3950/3952 - loss 0.17175673 - samples/sec: 27.18 - lr: 0.000005 2022-10-09 19:09:02,937 ---------------------------------------------------------------------------------------------------- 2022-10-09 19:09:02,938 EPOCH 3 done: loss 0.1718 - lr 0.000005 2022-10-09 19:24:14,202 Evaluating as a multi-label problem: False 2022-10-09 19:24:14,636 DEV : loss 0.0132540138438344 - f1-score (micro avg) 0.9449 2022-10-09 19:24:46,251 BAD EPOCHS (no improvement): 4 2022-10-09 19:24:47,160 ---------------------------------------------------------------------------------------------------- 2022-10-09 19:32:49,924 epoch 4 - iter 395/3952 - loss 0.16804626 - samples/sec: 27.13 - lr: 0.000005 2022-10-09 19:40:52,439 epoch 4 - iter 790/3952 - loss 0.16663174 - samples/sec: 26.93 - lr: 0.000005 2022-10-09 19:48:52,963 epoch 4 - iter 1185/3952 - loss 0.16647828 - samples/sec: 26.96 - lr: 0.000005 2022-10-09 19:56:51,613 epoch 4 - iter 1580/3952 - loss 0.16639047 - samples/sec: 27.16 - lr: 0.000005 2022-10-09 20:04:52,753 epoch 4 - iter 1975/3952 - loss 0.16657475 - samples/sec: 27.08 - lr: 0.000005 2022-10-09 20:12:54,756 epoch 4 - iter 2370/3952 - loss 0.16632582 - samples/sec: 27.01 - lr: 0.000005 2022-10-09 20:20:58,248 epoch 4 - iter 2765/3952 - loss 0.16578692 - samples/sec: 26.90 - lr: 0.000005 2022-10-09 20:28:56,132 epoch 4 - iter 3160/3952 - loss 0.16538230 - samples/sec: 27.31 - lr: 0.000005 2022-10-09 20:37:02,675 epoch 4 - iter 3555/3952 - loss 0.16519531 - samples/sec: 26.71 - lr: 0.000004 2022-10-09 20:45:04,375 epoch 4 - iter 3950/3952 - loss 0.16516842 - samples/sec: 27.05 - lr: 0.000004 2022-10-09 20:45:05,820 ---------------------------------------------------------------------------------------------------- 2022-10-09 20:45:05,821 EPOCH 4 done: loss 0.1652 - lr 0.000004 2022-10-09 21:00:18,495 Evaluating as a multi-label problem: False 2022-10-09 21:00:18,914 DEV : loss 0.011177059262990952 - f1-score (micro avg) 0.9535 2022-10-09 21:00:51,130 BAD EPOCHS (no improvement): 4 2022-10-09 21:00:52,018 ---------------------------------------------------------------------------------------------------- 2022-10-09 21:08:44,713 epoch 5 - iter 395/3952 - loss 0.16331808 - samples/sec: 27.41 - lr: 0.000004 2022-10-09 21:16:38,390 epoch 5 - iter 790/3952 - loss 0.16221079 - samples/sec: 27.45 - lr: 0.000004 2022-10-09 21:24:27,598 epoch 5 - iter 1185/3952 - loss 0.16205464 - samples/sec: 27.72 - lr: 0.000004 2022-10-09 21:32:21,639 epoch 5 - iter 1580/3952 - loss 0.16189961 - samples/sec: 27.42 - lr: 0.000004 2022-10-09 21:40:09,976 epoch 5 - iter 1975/3952 - loss 0.16206946 - samples/sec: 27.79 - lr: 0.000004 2022-10-09 21:48:02,577 epoch 5 - iter 2370/3952 - loss 0.16196815 - samples/sec: 27.47 - lr: 0.000004 2022-10-09 21:55:56,886 epoch 5 - iter 2765/3952 - loss 0.16172381 - samples/sec: 27.34 - lr: 0.000004 2022-10-09 22:03:49,873 epoch 5 - iter 3160/3952 - loss 0.16156487 - samples/sec: 27.47 - lr: 0.000004 2022-10-09 22:11:41,572 epoch 5 - iter 3555/3952 - loss 0.16147326 - samples/sec: 27.64 - lr: 0.000004 2022-10-09 22:19:35,682 epoch 5 - iter 3950/3952 - loss 0.16130607 - samples/sec: 27.55 - lr: 0.000004 2022-10-09 22:19:37,189 ---------------------------------------------------------------------------------------------------- 2022-10-09 22:19:37,189 EPOCH 5 done: loss 0.1613 - lr 0.000004 2022-10-09 22:34:40,766 Evaluating as a multi-label problem: False 2022-10-09 22:34:41,185 DEV : loss 0.011488113552331924 - f1-score (micro avg) 0.9546 2022-10-09 22:35:13,419 BAD EPOCHS (no improvement): 4 2022-10-09 22:35:14,308 ---------------------------------------------------------------------------------------------------- 2022-10-09 22:43:06,921 epoch 6 - iter 395/3952 - loss 0.16063155 - samples/sec: 27.36 - lr: 0.000004 2022-10-09 22:50:59,349 epoch 6 - iter 790/3952 - loss 0.15984298 - samples/sec: 27.55 - lr: 0.000004 2022-10-09 22:58:46,877 epoch 6 - iter 1185/3952 - loss 0.15946325 - samples/sec: 27.80 - lr: 0.000004 2022-10-09 23:06:50,203 epoch 6 - iter 1580/3952 - loss 0.15917470 - samples/sec: 26.82 - lr: 0.000004 2022-10-09 23:14:51,992 epoch 6 - iter 1975/3952 - loss 0.15882030 - samples/sec: 27.09 - lr: 0.000004 2022-10-09 23:22:52,029 epoch 6 - iter 2370/3952 - loss 0.15876178 - samples/sec: 27.05 - lr: 0.000004 2022-10-09 23:30:58,678 epoch 6 - iter 2765/3952 - loss 0.15864742 - samples/sec: 26.76 - lr: 0.000004 2022-10-09 23:38:57,773 epoch 6 - iter 3160/3952 - loss 0.15842630 - samples/sec: 27.18 - lr: 0.000004 2022-10-09 23:46:58,724 epoch 6 - iter 3555/3952 - loss 0.15814869 - samples/sec: 27.02 - lr: 0.000004 2022-10-09 23:55:00,979 epoch 6 - iter 3950/3952 - loss 0.15800367 - samples/sec: 27.09 - lr: 0.000004 2022-10-09 23:55:02,321 ---------------------------------------------------------------------------------------------------- 2022-10-09 23:55:02,321 EPOCH 6 done: loss 0.1580 - lr 0.000004 2022-10-10 00:10:14,450 Evaluating as a multi-label problem: False 2022-10-10 00:10:14,910 DEV : loss 0.010615515522658825 - f1-score (micro avg) 0.9587 2022-10-10 00:10:46,276 BAD EPOCHS (no improvement): 4 2022-10-10 00:10:47,232 ---------------------------------------------------------------------------------------------------- 2022-10-10 00:18:56,370 epoch 7 - iter 395/3952 - loss 0.15533572 - samples/sec: 26.74 - lr: 0.000004 2022-10-10 00:26:53,533 epoch 7 - iter 790/3952 - loss 0.15567018 - samples/sec: 27.29 - lr: 0.000004 2022-10-10 00:34:55,929 epoch 7 - iter 1185/3952 - loss 0.15559902 - samples/sec: 26.92 - lr: 0.000004 2022-10-10 00:42:56,064 epoch 7 - iter 1580/3952 - loss 0.15526644 - samples/sec: 27.04 - lr: 0.000004 2022-10-10 00:50:56,575 epoch 7 - iter 1975/3952 - loss 0.15532544 - samples/sec: 27.04 - lr: 0.000004 2022-10-10 00:58:55,726 epoch 7 - iter 2370/3952 - loss 0.15538178 - samples/sec: 27.14 - lr: 0.000004 2022-10-10 01:06:54,255 epoch 7 - iter 2765/3952 - loss 0.15537470 - samples/sec: 27.15 - lr: 0.000004 2022-10-10 01:14:59,643 epoch 7 - iter 3160/3952 - loss 0.15531628 - samples/sec: 26.79 - lr: 0.000004 2022-10-10 01:23:03,037 epoch 7 - iter 3555/3952 - loss 0.15533451 - samples/sec: 26.86 - lr: 0.000004 2022-10-10 01:31:03,511 epoch 7 - iter 3950/3952 - loss 0.15511299 - samples/sec: 26.97 - lr: 0.000004 2022-10-10 01:31:05,040 ---------------------------------------------------------------------------------------------------- 2022-10-10 01:31:05,041 EPOCH 7 done: loss 0.1551 - lr 0.000004 2022-10-10 01:46:17,630 Evaluating as a multi-label problem: False 2022-10-10 01:46:18,057 DEV : loss 0.010866315104067326 - f1-score (micro avg) 0.9597 2022-10-10 01:46:49,834 BAD EPOCHS (no improvement): 4 2022-10-10 01:46:50,741 ---------------------------------------------------------------------------------------------------- 2022-10-10 01:54:49,387 epoch 8 - iter 395/3952 - loss 0.15339956 - samples/sec: 27.34 - lr: 0.000004 2022-10-10 02:02:54,436 epoch 8 - iter 790/3952 - loss 0.15357118 - samples/sec: 26.88 - lr: 0.000004 2022-10-10 02:10:57,380 epoch 8 - iter 1185/3952 - loss 0.15383618 - samples/sec: 26.86 - lr: 0.000004 2022-10-10 02:18:57,413 epoch 8 - iter 1580/3952 - loss 0.15388423 - samples/sec: 27.15 - lr: 0.000004 2022-10-10 02:26:58,665 epoch 8 - iter 1975/3952 - loss 0.15366022 - samples/sec: 26.96 - lr: 0.000003 2022-10-10 02:35:00,936 epoch 8 - iter 2370/3952 - loss 0.15388824 - samples/sec: 26.92 - lr: 0.000003 2022-10-10 02:43:03,179 epoch 8 - iter 2765/3952 - loss 0.15380049 - samples/sec: 27.06 - lr: 0.000003 2022-10-10 02:51:07,445 epoch 8 - iter 3160/3952 - loss 0.15356183 - samples/sec: 26.93 - lr: 0.000003 2022-10-10 02:59:09,568 epoch 8 - iter 3555/3952 - loss 0.15337591 - samples/sec: 26.91 - lr: 0.000003 2022-10-10 03:07:06,249 epoch 8 - iter 3950/3952 - loss 0.15327199 - samples/sec: 27.26 - lr: 0.000003 2022-10-10 03:07:07,508 ---------------------------------------------------------------------------------------------------- 2022-10-10 03:07:07,509 EPOCH 8 done: loss 0.1533 - lr 0.000003 2022-10-10 03:22:20,421 Evaluating as a multi-label problem: False 2022-10-10 03:22:20,849 DEV : loss 0.010451321490108967 - f1-score (micro avg) 0.9617 2022-10-10 03:22:53,399 BAD EPOCHS (no improvement): 4 2022-10-10 03:22:55,354 ---------------------------------------------------------------------------------------------------- 2022-10-10 03:31:03,911 epoch 9 - iter 395/3952 - loss 0.15095455 - samples/sec: 26.52 - lr: 0.000003 2022-10-10 03:39:03,919 epoch 9 - iter 790/3952 - loss 0.15100488 - samples/sec: 27.07 - lr: 0.000003 2022-10-10 03:46:57,642 epoch 9 - iter 1185/3952 - loss 0.15141407 - samples/sec: 27.49 - lr: 0.000003 2022-10-10 03:54:55,677 epoch 9 - iter 1580/3952 - loss 0.15153248 - samples/sec: 27.33 - lr: 0.000003 2022-10-10 04:02:55,192 epoch 9 - iter 1975/3952 - loss 0.15137991 - samples/sec: 27.30 - lr: 0.000003 2022-10-10 04:10:56,499 epoch 9 - iter 2370/3952 - loss 0.15134929 - samples/sec: 27.05 - lr: 0.000003 2022-10-10 04:18:51,998 epoch 9 - iter 2765/3952 - loss 0.15139573 - samples/sec: 27.48 - lr: 0.000003 2022-10-10 04:26:48,529 epoch 9 - iter 3160/3952 - loss 0.15141239 - samples/sec: 27.31 - lr: 0.000003 2022-10-10 04:34:41,608 epoch 9 - iter 3555/3952 - loss 0.15135720 - samples/sec: 27.53 - lr: 0.000003 2022-10-10 04:42:37,267 epoch 9 - iter 3950/3952 - loss 0.15132694 - samples/sec: 27.23 - lr: 0.000003 2022-10-10 04:42:38,593 ---------------------------------------------------------------------------------------------------- 2022-10-10 04:42:38,594 EPOCH 9 done: loss 0.1513 - lr 0.000003 2022-10-10 04:57:46,312 Evaluating as a multi-label problem: False 2022-10-10 04:57:46,749 DEV : loss 0.01064694207161665 - f1-score (micro avg) 0.9609 2022-10-10 04:58:17,984 BAD EPOCHS (no improvement): 4 2022-10-10 04:58:18,878 ---------------------------------------------------------------------------------------------------- 2022-10-10 05:06:19,341 epoch 10 - iter 395/3952 - loss 0.14934098 - samples/sec: 27.00 - lr: 0.000003 2022-10-10 05:14:13,052 epoch 10 - iter 790/3952 - loss 0.15047359 - samples/sec: 27.54 - lr: 0.000003 2022-10-10 05:22:09,904 epoch 10 - iter 1185/3952 - loss 0.15005411 - samples/sec: 27.27 - lr: 0.000003 2022-10-10 05:30:10,047 epoch 10 - iter 1580/3952 - loss 0.14970562 - samples/sec: 27.14 - lr: 0.000003 2022-10-10 05:38:05,869 epoch 10 - iter 1975/3952 - loss 0.14954158 - samples/sec: 27.29 - lr: 0.000003 2022-10-10 05:46:05,902 epoch 10 - iter 2370/3952 - loss 0.14932048 - samples/sec: 27.11 - lr: 0.000003 2022-10-10 05:54:05,041 epoch 10 - iter 2765/3952 - loss 0.14927630 - samples/sec: 27.19 - lr: 0.000003 2022-10-10 06:02:04,693 epoch 10 - iter 3160/3952 - loss 0.14935304 - samples/sec: 27.16 - lr: 0.000003 2022-10-10 06:10:01,212 epoch 10 - iter 3555/3952 - loss 0.14941757 - samples/sec: 27.25 - lr: 0.000003 2022-10-10 06:17:54,179 epoch 10 - iter 3950/3952 - loss 0.14953843 - samples/sec: 27.53 - lr: 0.000003 2022-10-10 06:17:55,747 ---------------------------------------------------------------------------------------------------- 2022-10-10 06:17:55,747 EPOCH 10 done: loss 0.1495 - lr 0.000003 2022-10-10 06:33:03,662 Evaluating as a multi-label problem: False 2022-10-10 06:33:04,089 DEV : loss 0.010687584988772869 - f1-score (micro avg) 0.9623 2022-10-10 06:33:35,248 BAD EPOCHS (no improvement): 4 2022-10-10 06:33:36,135 ---------------------------------------------------------------------------------------------------- 2022-10-10 06:41:36,387 epoch 11 - iter 395/3952 - loss 0.14722548 - samples/sec: 27.24 - lr: 0.000003 2022-10-10 06:49:32,701 epoch 11 - iter 790/3952 - loss 0.14792717 - samples/sec: 27.36 - lr: 0.000003 2022-10-10 06:57:28,372 epoch 11 - iter 1185/3952 - loss 0.14804400 - samples/sec: 27.34 - lr: 0.000003 2022-10-10 07:05:28,768 epoch 11 - iter 1580/3952 - loss 0.14822560 - samples/sec: 27.11 - lr: 0.000003 2022-10-10 07:13:27,055 epoch 11 - iter 1975/3952 - loss 0.14845261 - samples/sec: 27.25 - lr: 0.000003 2022-10-10 07:21:21,803 epoch 11 - iter 2370/3952 - loss 0.14860234 - samples/sec: 27.39 - lr: 0.000003 2022-10-10 07:29:18,530 epoch 11 - iter 2765/3952 - loss 0.14881168 - samples/sec: 27.27 - lr: 0.000003 2022-10-10 07:37:14,641 epoch 11 - iter 3160/3952 - loss 0.14859987 - samples/sec: 27.27 - lr: 0.000003 2022-10-10 07:45:11,011 epoch 11 - iter 3555/3952 - loss 0.14841785 - samples/sec: 27.30 - lr: 0.000003 2022-10-10 07:53:06,062 epoch 11 - iter 3950/3952 - loss 0.14839159 - samples/sec: 27.46 - lr: 0.000003 2022-10-10 07:53:07,694 ---------------------------------------------------------------------------------------------------- 2022-10-10 07:53:07,694 EPOCH 11 done: loss 0.1484 - lr 0.000003 2022-10-10 08:08:15,642 Evaluating as a multi-label problem: False 2022-10-10 08:08:16,078 DEV : loss 0.010935463011264801 - f1-score (micro avg) 0.962 2022-10-10 08:08:47,374 BAD EPOCHS (no improvement): 4 2022-10-10 08:08:48,267 ---------------------------------------------------------------------------------------------------- 2022-10-10 08:16:43,768 epoch 12 - iter 395/3952 - loss 0.14779592 - samples/sec: 27.35 - lr: 0.000002 2022-10-10 08:24:44,096 epoch 12 - iter 790/3952 - loss 0.14727136 - samples/sec: 27.09 - lr: 0.000002 2022-10-10 08:32:40,480 epoch 12 - iter 1185/3952 - loss 0.14742119 - samples/sec: 27.30 - lr: 0.000002 2022-10-10 08:40:34,998 epoch 12 - iter 1580/3952 - loss 0.14735918 - samples/sec: 27.41 - lr: 0.000002 2022-10-10 08:48:29,447 epoch 12 - iter 1975/3952 - loss 0.14739904 - samples/sec: 27.37 - lr: 0.000002 2022-10-10 08:56:21,930 epoch 12 - iter 2370/3952 - loss 0.14746441 - samples/sec: 27.64 - lr: 0.000002 2022-10-10 09:04:20,566 epoch 12 - iter 2765/3952 - loss 0.14727131 - samples/sec: 27.27 - lr: 0.000002 2022-10-10 09:12:17,286 epoch 12 - iter 3160/3952 - loss 0.14733990 - samples/sec: 27.30 - lr: 0.000002 2022-10-10 09:20:14,749 epoch 12 - iter 3555/3952 - loss 0.14706041 - samples/sec: 27.28 - lr: 0.000002 2022-10-10 09:28:08,079 epoch 12 - iter 3950/3952 - loss 0.14700556 - samples/sec: 27.52 - lr: 0.000002 2022-10-10 09:28:09,718 ---------------------------------------------------------------------------------------------------- 2022-10-10 09:28:09,718 EPOCH 12 done: loss 0.1470 - lr 0.000002 2022-10-10 09:43:14,910 Evaluating as a multi-label problem: False 2022-10-10 09:43:15,334 DEV : loss 0.011056484654545784 - f1-score (micro avg) 0.9634 2022-10-10 09:43:47,802 BAD EPOCHS (no improvement): 4 2022-10-10 09:43:48,705 ---------------------------------------------------------------------------------------------------- 2022-10-10 09:51:46,179 epoch 13 - iter 395/3952 - loss 0.14506338 - samples/sec: 27.10 - lr: 0.000002 2022-10-10 09:59:43,717 epoch 13 - iter 790/3952 - loss 0.14619048 - samples/sec: 27.23 - lr: 0.000002 2022-10-10 10:07:33,958 epoch 13 - iter 1185/3952 - loss 0.14639748 - samples/sec: 27.70 - lr: 0.000002 2022-10-10 10:15:01,211 epoch 13 - iter 1580/3952 - loss 0.14615405 - samples/sec: 29.14 - lr: 0.000002 2022-10-10 10:22:17,577 epoch 13 - iter 1975/3952 - loss 0.14620482 - samples/sec: 29.92 - lr: 0.000002 2022-10-10 10:29:43,376 epoch 13 - iter 2370/3952 - loss 0.14616699 - samples/sec: 29.29 - lr: 0.000002 2022-10-10 10:37:06,729 epoch 13 - iter 2765/3952 - loss 0.14605036 - samples/sec: 29.39 - lr: 0.000002 2022-10-10 10:44:37,315 epoch 13 - iter 3160/3952 - loss 0.14597794 - samples/sec: 28.97 - lr: 0.000002 2022-10-10 10:52:01,383 epoch 13 - iter 3555/3952 - loss 0.14602289 - samples/sec: 29.36 - lr: 0.000002 2022-10-10 10:59:26,413 epoch 13 - iter 3950/3952 - loss 0.14605007 - samples/sec: 29.23 - lr: 0.000002 2022-10-10 10:59:27,781 ---------------------------------------------------------------------------------------------------- 2022-10-10 10:59:27,782 EPOCH 13 done: loss 0.1460 - lr 0.000002 2022-10-10 11:14:26,437 Evaluating as a multi-label problem: False 2022-10-10 11:14:26,846 DEV : loss 0.011409671977162361 - f1-score (micro avg) 0.9628 2022-10-10 11:14:57,380 BAD EPOCHS (no improvement): 4 2022-10-10 11:14:58,218 ---------------------------------------------------------------------------------------------------- 2022-10-10 11:22:28,388 epoch 14 - iter 395/3952 - loss 0.14532304 - samples/sec: 29.11 - lr: 0.000002 2022-10-10 11:29:54,824 epoch 14 - iter 790/3952 - loss 0.14560920 - samples/sec: 29.11 - lr: 0.000002 2022-10-10 11:37:22,235 epoch 14 - iter 1185/3952 - loss 0.14518057 - samples/sec: 29.05 - lr: 0.000002 2022-10-10 11:44:50,891 epoch 14 - iter 1580/3952 - loss 0.14527092 - samples/sec: 28.98 - lr: 0.000002 2022-10-10 11:52:18,549 epoch 14 - iter 1975/3952 - loss 0.14511930 - samples/sec: 29.20 - lr: 0.000002 2022-10-10 11:59:56,465 epoch 14 - iter 2370/3952 - loss 0.14523496 - samples/sec: 28.44 - lr: 0.000002 2022-10-10 12:07:18,925 epoch 14 - iter 2765/3952 - loss 0.14524068 - samples/sec: 29.46 - lr: 0.000002 2022-10-10 12:14:42,038 epoch 14 - iter 3160/3952 - loss 0.14516594 - samples/sec: 29.36 - lr: 0.000002 2022-10-10 12:22:07,540 epoch 14 - iter 3555/3952 - loss 0.14526955 - samples/sec: 29.18 - lr: 0.000002 2022-10-10 12:29:36,124 epoch 14 - iter 3950/3952 - loss 0.14518783 - samples/sec: 29.17 - lr: 0.000002 2022-10-10 12:29:37,533 ---------------------------------------------------------------------------------------------------- 2022-10-10 12:29:37,533 EPOCH 14 done: loss 0.1452 - lr 0.000002 2022-10-10 12:44:22,577 Evaluating as a multi-label problem: False 2022-10-10 12:44:22,990 DEV : loss 0.011419754475355148 - f1-score (micro avg) 0.9637 2022-10-10 12:44:53,663 BAD EPOCHS (no improvement): 4 2022-10-10 12:44:54,557 ---------------------------------------------------------------------------------------------------- 2022-10-10 12:52:25,951 epoch 15 - iter 395/3952 - loss 0.14181725 - samples/sec: 28.90 - lr: 0.000002 2022-10-10 12:59:53,144 epoch 15 - iter 790/3952 - loss 0.14383060 - samples/sec: 29.20 - lr: 0.000002 2022-10-10 13:08:18,071 epoch 15 - iter 1185/3952 - loss 0.14395256 - samples/sec: 25.82 - lr: 0.000002 2022-10-10 13:16:43,174 epoch 15 - iter 1580/3952 - loss 0.14433998 - samples/sec: 25.78 - lr: 0.000002 2022-10-10 13:25:14,818 epoch 15 - iter 1975/3952 - loss 0.14428390 - samples/sec: 25.37 - lr: 0.000002 2022-10-10 13:33:46,506 epoch 15 - iter 2370/3952 - loss 0.14440542 - samples/sec: 25.41 - lr: 0.000002 2022-10-10 13:42:09,041 epoch 15 - iter 2765/3952 - loss 0.14445593 - samples/sec: 26.01 - lr: 0.000001 2022-10-10 13:50:39,620 epoch 15 - iter 3160/3952 - loss 0.14456461 - samples/sec: 25.44 - lr: 0.000001 2022-10-10 13:59:09,404 epoch 15 - iter 3555/3952 - loss 0.14444586 - samples/sec: 25.61 - lr: 0.000001 2022-10-10 14:07:41,706 epoch 15 - iter 3950/3952 - loss 0.14432217 - samples/sec: 25.45 - lr: 0.000001 2022-10-10 14:07:43,149 ---------------------------------------------------------------------------------------------------- 2022-10-10 14:07:43,150 EPOCH 15 done: loss 0.1443 - lr 0.000001 2022-10-10 14:23:33,181 Evaluating as a multi-label problem: False 2022-10-10 14:23:33,654 DEV : loss 0.011627680622041225 - f1-score (micro avg) 0.9637 2022-10-10 14:24:07,996 BAD EPOCHS (no improvement): 4 2022-10-10 14:24:09,032 ---------------------------------------------------------------------------------------------------- 2022-10-10 14:32:40,414 epoch 16 - iter 395/3952 - loss 0.14350737 - samples/sec: 25.61 - lr: 0.000001 2022-10-10 14:41:10,956 epoch 16 - iter 790/3952 - loss 0.14341419 - samples/sec: 25.59 - lr: 0.000001 2022-10-10 14:49:40,914 epoch 16 - iter 1185/3952 - loss 0.14370127 - samples/sec: 25.52 - lr: 0.000001 2022-10-10 14:58:09,406 epoch 16 - iter 1580/3952 - loss 0.14378459 - samples/sec: 25.57 - lr: 0.000001 2022-10-10 15:06:40,193 epoch 16 - iter 1975/3952 - loss 0.14360404 - samples/sec: 25.52 - lr: 0.000001 2022-10-10 15:15:11,603 epoch 16 - iter 2370/3952 - loss 0.14360062 - samples/sec: 25.44 - lr: 0.000001 2022-10-10 15:23:44,499 epoch 16 - iter 2765/3952 - loss 0.14356139 - samples/sec: 25.37 - lr: 0.000001 2022-10-10 15:32:14,460 epoch 16 - iter 3160/3952 - loss 0.14361871 - samples/sec: 25.48 - lr: 0.000001 2022-10-10 15:40:46,346 epoch 16 - iter 3555/3952 - loss 0.14360176 - samples/sec: 25.51 - lr: 0.000001 2022-10-10 15:49:16,072 epoch 16 - iter 3950/3952 - loss 0.14352181 - samples/sec: 25.55 - lr: 0.000001 2022-10-10 15:49:18,082 ---------------------------------------------------------------------------------------------------- 2022-10-10 15:49:18,082 EPOCH 16 done: loss 0.1435 - lr 0.000001 2022-10-10 16:05:01,512 Evaluating as a multi-label problem: False 2022-10-10 16:05:01,984 DEV : loss 0.011783876456320286 - f1-score (micro avg) 0.9644 2022-10-10 16:05:36,459 BAD EPOCHS (no improvement): 4 2022-10-10 16:05:37,421 ---------------------------------------------------------------------------------------------------- 2022-10-10 16:14:08,530 epoch 17 - iter 395/3952 - loss 0.14367645 - samples/sec: 25.33 - lr: 0.000001 2022-10-10 16:22:34,521 epoch 17 - iter 790/3952 - loss 0.14312751 - samples/sec: 25.71 - lr: 0.000001 2022-10-10 16:31:01,690 epoch 17 - iter 1185/3952 - loss 0.14363484 - samples/sec: 25.68 - lr: 0.000001 2022-10-10 16:39:26,318 epoch 17 - iter 1580/3952 - loss 0.14329122 - samples/sec: 25.77 - lr: 0.000001 2022-10-10 16:47:51,245 epoch 17 - iter 1975/3952 - loss 0.14338973 - samples/sec: 25.84 - lr: 0.000001 2022-10-10 16:56:18,671 epoch 17 - iter 2370/3952 - loss 0.14364105 - samples/sec: 25.62 - lr: 0.000001 2022-10-10 17:04:48,817 epoch 17 - iter 2765/3952 - loss 0.14374600 - samples/sec: 25.48 - lr: 0.000001 2022-10-10 17:13:21,802 epoch 17 - iter 3160/3952 - loss 0.14369645 - samples/sec: 25.31 - lr: 0.000001 2022-10-10 17:21:51,309 epoch 17 - iter 3555/3952 - loss 0.14360598 - samples/sec: 25.59 - lr: 0.000001 2022-10-10 17:30:20,509 epoch 17 - iter 3950/3952 - loss 0.14356029 - samples/sec: 25.54 - lr: 0.000001 2022-10-10 17:30:22,113 ---------------------------------------------------------------------------------------------------- 2022-10-10 17:30:22,114 EPOCH 17 done: loss 0.1436 - lr 0.000001 2022-10-10 17:46:12,566 Evaluating as a multi-label problem: False 2022-10-10 17:46:13,046 DEV : loss 0.011797642335295677 - f1-score (micro avg) 0.9643 2022-10-10 17:46:47,683 BAD EPOCHS (no improvement): 4 2022-10-10 17:46:48,723 ---------------------------------------------------------------------------------------------------- 2022-10-10 17:55:28,142 epoch 18 - iter 395/3952 - loss 0.14306617 - samples/sec: 25.20 - lr: 0.000001 2022-10-10 18:03:57,902 epoch 18 - iter 790/3952 - loss 0.14196615 - samples/sec: 25.53 - lr: 0.000001 2022-10-10 18:12:31,453 epoch 18 - iter 1185/3952 - loss 0.14182625 - samples/sec: 25.38 - lr: 0.000001 2022-10-10 18:20:57,991 epoch 18 - iter 1580/3952 - loss 0.14185926 - samples/sec: 25.62 - lr: 0.000001 2022-10-10 18:29:28,131 epoch 18 - iter 1975/3952 - loss 0.14207068 - samples/sec: 25.46 - lr: 0.000001 2022-10-10 18:37:54,888 epoch 18 - iter 2370/3952 - loss 0.14229279 - samples/sec: 25.71 - lr: 0.000001 2022-10-10 18:46:22,698 epoch 18 - iter 2765/3952 - loss 0.14234187 - samples/sec: 25.65 - lr: 0.000001 2022-10-10 18:54:50,839 epoch 18 - iter 3160/3952 - loss 0.14240556 - samples/sec: 25.65 - lr: 0.000001 2022-10-10 19:03:22,482 epoch 18 - iter 3555/3952 - loss 0.14233153 - samples/sec: 25.48 - lr: 0.000001 2022-10-10 19:11:53,854 epoch 18 - iter 3950/3952 - loss 0.14236278 - samples/sec: 25.30 - lr: 0.000001 2022-10-10 19:11:56,073 ---------------------------------------------------------------------------------------------------- 2022-10-10 19:11:56,074 EPOCH 18 done: loss 0.1424 - lr 0.000001 2022-10-10 19:27:45,449 Evaluating as a multi-label problem: False 2022-10-10 19:27:45,930 DEV : loss 0.011939478106796741 - f1-score (micro avg) 0.964 2022-10-10 19:28:18,875 BAD EPOCHS (no improvement): 4 2022-10-10 19:28:19,941 ---------------------------------------------------------------------------------------------------- 2022-10-10 19:36:53,864 epoch 19 - iter 395/3952 - loss 0.14362086 - samples/sec: 25.29 - lr: 0.000001 2022-10-10 19:45:24,479 epoch 19 - iter 790/3952 - loss 0.14325958 - samples/sec: 25.49 - lr: 0.000001 2022-10-10 19:53:54,808 epoch 19 - iter 1185/3952 - loss 0.14310735 - samples/sec: 25.48 - lr: 0.000000 2022-10-10 20:02:24,384 epoch 19 - iter 1580/3952 - loss 0.14293734 - samples/sec: 25.47 - lr: 0.000000 2022-10-10 20:10:51,221 epoch 19 - iter 1975/3952 - loss 0.14306481 - samples/sec: 25.77 - lr: 0.000000 2022-10-10 20:19:18,624 epoch 19 - iter 2370/3952 - loss 0.14291352 - samples/sec: 25.72 - lr: 0.000000 2022-10-10 20:27:46,259 epoch 19 - iter 2765/3952 - loss 0.14298740 - samples/sec: 25.60 - lr: 0.000000 2022-10-10 20:36:16,560 epoch 19 - iter 3160/3952 - loss 0.14288623 - samples/sec: 25.52 - lr: 0.000000 2022-10-10 20:44:47,260 epoch 19 - iter 3555/3952 - loss 0.14282900 - samples/sec: 25.45 - lr: 0.000000 2022-10-10 20:53:18,466 epoch 19 - iter 3950/3952 - loss 0.14288617 - samples/sec: 25.54 - lr: 0.000000 2022-10-10 20:53:19,964 ---------------------------------------------------------------------------------------------------- 2022-10-10 20:53:19,964 EPOCH 19 done: loss 0.1429 - lr 0.000000 2022-10-10 21:09:08,715 Evaluating as a multi-label problem: False 2022-10-10 21:09:09,202 DEV : loss 0.012016847729682922 - f1-score (micro avg) 0.9643 2022-10-10 21:09:43,778 BAD EPOCHS (no improvement): 4 2022-10-10 21:09:44,810 ---------------------------------------------------------------------------------------------------- 2022-10-10 21:18:11,781 epoch 20 - iter 395/3952 - loss 0.14263110 - samples/sec: 25.65 - lr: 0.000000 2022-10-10 21:26:40,891 epoch 20 - iter 790/3952 - loss 0.14225428 - samples/sec: 25.60 - lr: 0.000000 2022-10-10 21:35:08,495 epoch 20 - iter 1185/3952 - loss 0.14205051 - samples/sec: 25.66 - lr: 0.000000 2022-10-10 21:43:34,108 epoch 20 - iter 1580/3952 - loss 0.14228947 - samples/sec: 25.71 - lr: 0.000000 2022-10-10 21:52:11,211 epoch 20 - iter 1975/3952 - loss 0.14209594 - samples/sec: 25.19 - lr: 0.000000 2022-10-10 22:00:41,644 epoch 20 - iter 2370/3952 - loss 0.14227931 - samples/sec: 25.63 - lr: 0.000000 2022-10-10 22:09:10,266 epoch 20 - iter 2765/3952 - loss 0.14254834 - samples/sec: 25.65 - lr: 0.000000 2022-10-10 22:17:38,261 epoch 20 - iter 3160/3952 - loss 0.14259954 - samples/sec: 25.71 - lr: 0.000000 2022-10-10 22:26:05,321 epoch 20 - iter 3555/3952 - loss 0.14252244 - samples/sec: 25.59 - lr: 0.000000 2022-10-10 22:34:35,781 epoch 20 - iter 3950/3952 - loss 0.14238758 - samples/sec: 25.47 - lr: 0.000000 2022-10-10 22:34:37,421 ---------------------------------------------------------------------------------------------------- 2022-10-10 22:34:37,422 EPOCH 20 done: loss 0.1424 - lr 0.000000 2022-10-10 22:50:27,724 Evaluating as a multi-label problem: False 2022-10-10 22:50:28,207 DEV : loss 0.012119622901082039 - f1-score (micro avg) 0.964 2022-10-10 22:51:01,203 BAD EPOCHS (no improvement): 4 2022-10-10 22:51:03,269 ---------------------------------------------------------------------------------------------------- 2022-10-10 22:51:03,271 Testing using last state of model ... 2022-10-10 22:59:53,131 Evaluating as a multi-label problem: False 2022-10-10 22:59:53,392 0.945 0.9596 0.9522 0.9179 2022-10-10 22:59:53,392 Results: - F-score (micro) 0.9522 - F-score (macro) 0.9468 - Accuracy 0.9179 By class: precision recall f1-score support LOC 0.9643 0.9671 0.9657 11823 PER 0.9722 0.9736 0.9729 7836 DATE_TIME 0.9152 0.9458 0.9303 4746 ORG 0.8720 0.9196 0.8952 4565 NRP 0.9633 0.9766 0.9699 2905 micro avg 0.9450 0.9596 0.9522 31875 macro avg 0.9374 0.9565 0.9468 31875 weighted avg 0.9456 0.9596 0.9525 31875 2022-10-10 22:59:53,392 ----------------------------------------------------------------------------------------------------