| 2023-10-27 18:44:31,683 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,685 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): XLMRobertaModel( | |
| (embeddings): XLMRobertaEmbeddings( | |
| (word_embeddings): Embedding(250003, 1024) | |
| (position_embeddings): Embedding(514, 1024, padding_idx=1) | |
| (token_type_embeddings): Embedding(1, 1024) | |
| (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (encoder): XLMRobertaEncoder( | |
| (layer): ModuleList( | |
| (0-23): 24 x XLMRobertaLayer( | |
| (attention): XLMRobertaAttention( | |
| (self): XLMRobertaSelfAttention( | |
| (query): Linear(in_features=1024, out_features=1024, bias=True) | |
| (key): Linear(in_features=1024, out_features=1024, bias=True) | |
| (value): Linear(in_features=1024, out_features=1024, bias=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (output): XLMRobertaSelfOutput( | |
| (dense): Linear(in_features=1024, out_features=1024, bias=True) | |
| (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): XLMRobertaIntermediate( | |
| (dense): Linear(in_features=1024, out_features=4096, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): XLMRobertaOutput( | |
| (dense): Linear(in_features=4096, out_features=1024, bias=True) | |
| (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): XLMRobertaPooler( | |
| (dense): Linear(in_features=1024, out_features=1024, bias=True) | |
| (activation): Tanh() | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=1024, out_features=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-27 18:44:31,685 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,685 Corpus: 14903 train + 3449 dev + 3658 test sentences | |
| 2023-10-27 18:44:31,685 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,685 Train: 14903 sentences | |
| 2023-10-27 18:44:31,685 (train_with_dev=False, train_with_test=False) | |
| 2023-10-27 18:44:31,685 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,685 Training Params: | |
| 2023-10-27 18:44:31,685 - learning_rate: "5e-06" | |
| 2023-10-27 18:44:31,685 - mini_batch_size: "4" | |
| 2023-10-27 18:44:31,685 - max_epochs: "10" | |
| 2023-10-27 18:44:31,685 - shuffle: "True" | |
| 2023-10-27 18:44:31,685 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,685 Plugins: | |
| 2023-10-27 18:44:31,685 - TensorboardLogger | |
| 2023-10-27 18:44:31,686 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-27 18:44:31,686 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,686 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-27 18:44:31,686 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-27 18:44:31,686 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,686 Computation: | |
| 2023-10-27 18:44:31,686 - compute on device: cuda:0 | |
| 2023-10-27 18:44:31,686 - embedding storage: none | |
| 2023-10-27 18:44:31,686 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,686 Model training base path: "flair-clean-conll-lr5e-06-bs4-4" | |
| 2023-10-27 18:44:31,686 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,686 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:44:31,686 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-27 18:45:17,500 epoch 1 - iter 372/3726 - loss 2.56008396 - time (sec): 45.81 - samples/sec: 437.21 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 18:46:02,660 epoch 1 - iter 744/3726 - loss 1.77263965 - time (sec): 90.97 - samples/sec: 440.01 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 18:46:48,640 epoch 1 - iter 1116/3726 - loss 1.37087487 - time (sec): 136.95 - samples/sec: 443.33 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 18:47:34,220 epoch 1 - iter 1488/3726 - loss 1.13762799 - time (sec): 182.53 - samples/sec: 445.61 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 18:48:19,175 epoch 1 - iter 1860/3726 - loss 0.97055504 - time (sec): 227.49 - samples/sec: 446.76 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 18:49:04,645 epoch 1 - iter 2232/3726 - loss 0.83989727 - time (sec): 272.96 - samples/sec: 449.00 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 18:49:49,378 epoch 1 - iter 2604/3726 - loss 0.74197502 - time (sec): 317.69 - samples/sec: 450.38 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 18:50:34,930 epoch 1 - iter 2976/3726 - loss 0.66837341 - time (sec): 363.24 - samples/sec: 449.48 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 18:51:20,133 epoch 1 - iter 3348/3726 - loss 0.60924784 - time (sec): 408.45 - samples/sec: 448.31 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 18:52:08,456 epoch 1 - iter 3720/3726 - loss 0.55515076 - time (sec): 456.77 - samples/sec: 447.31 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:52:09,145 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:52:09,145 EPOCH 1 done: loss 0.5546 - lr: 0.000005 | |
| 2023-10-27 18:52:30,774 DEV : loss 0.10047101974487305 - f1-score (micro avg) 0.931 | |
| 2023-10-27 18:52:30,829 saving best model | |
| 2023-10-27 18:52:33,017 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 18:53:18,587 epoch 2 - iter 372/3726 - loss 0.11546777 - time (sec): 45.57 - samples/sec: 460.22 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:54:05,289 epoch 2 - iter 744/3726 - loss 0.10660754 - time (sec): 92.27 - samples/sec: 449.30 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:54:51,000 epoch 2 - iter 1116/3726 - loss 0.10038157 - time (sec): 137.98 - samples/sec: 455.34 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:55:36,869 epoch 2 - iter 1488/3726 - loss 0.09684308 - time (sec): 183.85 - samples/sec: 451.24 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:56:23,094 epoch 2 - iter 1860/3726 - loss 0.09806463 - time (sec): 230.07 - samples/sec: 450.98 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:57:08,793 epoch 2 - iter 2232/3726 - loss 0.09423182 - time (sec): 275.77 - samples/sec: 450.87 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:57:53,912 epoch 2 - iter 2604/3726 - loss 0.09270500 - time (sec): 320.89 - samples/sec: 452.25 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:58:39,792 epoch 2 - iter 2976/3726 - loss 0.08995641 - time (sec): 366.77 - samples/sec: 451.59 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 18:59:26,428 epoch 2 - iter 3348/3726 - loss 0.08720003 - time (sec): 413.41 - samples/sec: 447.43 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-27 19:00:11,611 epoch 2 - iter 3720/3726 - loss 0.08576854 - time (sec): 458.59 - samples/sec: 445.51 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:00:12,346 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:00:12,347 EPOCH 2 done: loss 0.0857 - lr: 0.000004 | |
| 2023-10-27 19:00:35,384 DEV : loss 0.06790720671415329 - f1-score (micro avg) 0.9571 | |
| 2023-10-27 19:00:35,438 saving best model | |
| 2023-10-27 19:00:37,853 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:01:23,535 epoch 3 - iter 372/3726 - loss 0.07374127 - time (sec): 45.68 - samples/sec: 439.85 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:02:08,846 epoch 3 - iter 744/3726 - loss 0.06087161 - time (sec): 90.99 - samples/sec: 440.33 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:02:54,176 epoch 3 - iter 1116/3726 - loss 0.05718760 - time (sec): 136.32 - samples/sec: 444.62 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:03:40,248 epoch 3 - iter 1488/3726 - loss 0.05400073 - time (sec): 182.39 - samples/sec: 447.02 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:04:26,095 epoch 3 - iter 1860/3726 - loss 0.05589718 - time (sec): 228.24 - samples/sec: 447.08 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:05:12,308 epoch 3 - iter 2232/3726 - loss 0.05415602 - time (sec): 274.45 - samples/sec: 445.29 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:05:58,644 epoch 3 - iter 2604/3726 - loss 0.05279672 - time (sec): 320.79 - samples/sec: 445.89 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:06:44,498 epoch 3 - iter 2976/3726 - loss 0.05148716 - time (sec): 366.64 - samples/sec: 446.33 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:07:29,887 epoch 3 - iter 3348/3726 - loss 0.05136170 - time (sec): 412.03 - samples/sec: 446.61 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:08:16,632 epoch 3 - iter 3720/3726 - loss 0.05145199 - time (sec): 458.78 - samples/sec: 445.53 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:08:17,367 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:08:17,367 EPOCH 3 done: loss 0.0514 - lr: 0.000004 | |
| 2023-10-27 19:08:39,846 DEV : loss 0.052444763481616974 - f1-score (micro avg) 0.9626 | |
| 2023-10-27 19:08:39,896 saving best model | |
| 2023-10-27 19:08:42,267 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:09:28,802 epoch 4 - iter 372/3726 - loss 0.03871254 - time (sec): 46.53 - samples/sec: 454.07 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:10:14,349 epoch 4 - iter 744/3726 - loss 0.04116213 - time (sec): 92.08 - samples/sec: 449.79 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:11:00,008 epoch 4 - iter 1116/3726 - loss 0.04020488 - time (sec): 137.74 - samples/sec: 454.42 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:11:45,448 epoch 4 - iter 1488/3726 - loss 0.04174189 - time (sec): 183.18 - samples/sec: 445.99 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:12:31,270 epoch 4 - iter 1860/3726 - loss 0.04211643 - time (sec): 229.00 - samples/sec: 444.26 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:13:16,951 epoch 4 - iter 2232/3726 - loss 0.04082069 - time (sec): 274.68 - samples/sec: 443.61 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:14:02,493 epoch 4 - iter 2604/3726 - loss 0.03901976 - time (sec): 320.22 - samples/sec: 446.31 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-27 19:14:48,002 epoch 4 - iter 2976/3726 - loss 0.03797498 - time (sec): 365.73 - samples/sec: 446.53 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:15:33,644 epoch 4 - iter 3348/3726 - loss 0.03778630 - time (sec): 411.37 - samples/sec: 445.67 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:16:19,632 epoch 4 - iter 3720/3726 - loss 0.03709369 - time (sec): 457.36 - samples/sec: 446.74 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:16:20,375 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:16:20,375 EPOCH 4 done: loss 0.0372 - lr: 0.000003 | |
| 2023-10-27 19:16:43,692 DEV : loss 0.05450737103819847 - f1-score (micro avg) 0.9647 | |
| 2023-10-27 19:16:43,745 saving best model | |
| 2023-10-27 19:16:46,767 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:17:32,579 epoch 5 - iter 372/3726 - loss 0.02845766 - time (sec): 45.81 - samples/sec: 459.66 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:18:18,467 epoch 5 - iter 744/3726 - loss 0.02421414 - time (sec): 91.70 - samples/sec: 448.91 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:19:04,523 epoch 5 - iter 1116/3726 - loss 0.02470444 - time (sec): 137.75 - samples/sec: 447.01 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:19:50,020 epoch 5 - iter 1488/3726 - loss 0.02877377 - time (sec): 183.25 - samples/sec: 446.70 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:20:35,406 epoch 5 - iter 1860/3726 - loss 0.02956343 - time (sec): 228.64 - samples/sec: 445.95 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:21:21,375 epoch 5 - iter 2232/3726 - loss 0.02911303 - time (sec): 274.61 - samples/sec: 444.00 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:22:06,761 epoch 5 - iter 2604/3726 - loss 0.02983439 - time (sec): 319.99 - samples/sec: 446.51 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:22:52,640 epoch 5 - iter 2976/3726 - loss 0.02953542 - time (sec): 365.87 - samples/sec: 447.29 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:23:37,972 epoch 5 - iter 3348/3726 - loss 0.02932321 - time (sec): 411.20 - samples/sec: 448.46 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:24:24,955 epoch 5 - iter 3720/3726 - loss 0.02916844 - time (sec): 458.19 - samples/sec: 445.99 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:24:25,687 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:24:25,688 EPOCH 5 done: loss 0.0292 - lr: 0.000003 | |
| 2023-10-27 19:24:48,031 DEV : loss 0.05429258942604065 - f1-score (micro avg) 0.9647 | |
| 2023-10-27 19:24:48,084 saving best model | |
| 2023-10-27 19:24:50,724 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:25:36,994 epoch 6 - iter 372/3726 - loss 0.01703796 - time (sec): 46.27 - samples/sec: 456.14 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:26:22,882 epoch 6 - iter 744/3726 - loss 0.02169011 - time (sec): 92.16 - samples/sec: 454.83 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:27:07,925 epoch 6 - iter 1116/3726 - loss 0.02127737 - time (sec): 137.20 - samples/sec: 451.49 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:27:53,410 epoch 6 - iter 1488/3726 - loss 0.02157007 - time (sec): 182.68 - samples/sec: 452.54 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:28:39,521 epoch 6 - iter 1860/3726 - loss 0.02169603 - time (sec): 228.79 - samples/sec: 449.67 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-27 19:29:24,934 epoch 6 - iter 2232/3726 - loss 0.02182002 - time (sec): 274.21 - samples/sec: 451.13 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:30:11,317 epoch 6 - iter 2604/3726 - loss 0.02225228 - time (sec): 320.59 - samples/sec: 448.74 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:30:56,485 epoch 6 - iter 2976/3726 - loss 0.02130458 - time (sec): 365.76 - samples/sec: 450.41 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:31:42,001 epoch 6 - iter 3348/3726 - loss 0.02151336 - time (sec): 411.28 - samples/sec: 448.25 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:32:28,009 epoch 6 - iter 3720/3726 - loss 0.02092486 - time (sec): 457.28 - samples/sec: 446.72 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:32:28,753 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:32:28,753 EPOCH 6 done: loss 0.0209 - lr: 0.000002 | |
| 2023-10-27 19:32:52,231 DEV : loss 0.052240729331970215 - f1-score (micro avg) 0.9692 | |
| 2023-10-27 19:32:52,286 saving best model | |
| 2023-10-27 19:32:55,357 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:33:41,068 epoch 7 - iter 372/3726 - loss 0.02108834 - time (sec): 45.71 - samples/sec: 450.92 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:34:26,441 epoch 7 - iter 744/3726 - loss 0.01802067 - time (sec): 91.08 - samples/sec: 447.77 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:35:12,147 epoch 7 - iter 1116/3726 - loss 0.01632005 - time (sec): 136.79 - samples/sec: 445.34 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:35:57,225 epoch 7 - iter 1488/3726 - loss 0.01518131 - time (sec): 181.87 - samples/sec: 444.13 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:36:42,626 epoch 7 - iter 1860/3726 - loss 0.01620599 - time (sec): 227.27 - samples/sec: 445.22 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:37:28,781 epoch 7 - iter 2232/3726 - loss 0.01630849 - time (sec): 273.42 - samples/sec: 446.32 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:38:14,783 epoch 7 - iter 2604/3726 - loss 0.01643476 - time (sec): 319.42 - samples/sec: 447.07 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:39:00,647 epoch 7 - iter 2976/3726 - loss 0.01586704 - time (sec): 365.29 - samples/sec: 447.99 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:39:46,421 epoch 7 - iter 3348/3726 - loss 0.01547939 - time (sec): 411.06 - samples/sec: 448.19 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:40:32,073 epoch 7 - iter 3720/3726 - loss 0.01513403 - time (sec): 456.71 - samples/sec: 447.41 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:40:32,785 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:40:32,785 EPOCH 7 done: loss 0.0151 - lr: 0.000002 | |
| 2023-10-27 19:40:55,727 DEV : loss 0.050821226090192795 - f1-score (micro avg) 0.9714 | |
| 2023-10-27 19:40:55,781 saving best model | |
| 2023-10-27 19:40:58,713 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:41:44,020 epoch 8 - iter 372/3726 - loss 0.01532970 - time (sec): 45.30 - samples/sec: 450.04 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:42:29,890 epoch 8 - iter 744/3726 - loss 0.01208467 - time (sec): 91.18 - samples/sec: 452.34 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:43:16,086 epoch 8 - iter 1116/3726 - loss 0.01337061 - time (sec): 137.37 - samples/sec: 446.08 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-27 19:44:02,334 epoch 8 - iter 1488/3726 - loss 0.01184764 - time (sec): 183.62 - samples/sec: 444.41 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:44:48,481 epoch 8 - iter 1860/3726 - loss 0.01162265 - time (sec): 229.77 - samples/sec: 444.22 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:45:33,897 epoch 8 - iter 2232/3726 - loss 0.01116723 - time (sec): 275.18 - samples/sec: 447.92 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:46:19,563 epoch 8 - iter 2604/3726 - loss 0.01237156 - time (sec): 320.85 - samples/sec: 448.45 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:47:06,257 epoch 8 - iter 2976/3726 - loss 0.01216515 - time (sec): 367.54 - samples/sec: 445.88 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:47:51,860 epoch 8 - iter 3348/3726 - loss 0.01228458 - time (sec): 413.15 - samples/sec: 445.41 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:48:37,302 epoch 8 - iter 3720/3726 - loss 0.01173645 - time (sec): 458.59 - samples/sec: 445.55 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:48:38,037 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:48:38,038 EPOCH 8 done: loss 0.0117 - lr: 0.000001 | |
| 2023-10-27 19:49:01,125 DEV : loss 0.05222569778561592 - f1-score (micro avg) 0.9726 | |
| 2023-10-27 19:49:01,178 saving best model | |
| 2023-10-27 19:49:03,903 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:49:49,349 epoch 9 - iter 372/3726 - loss 0.00486713 - time (sec): 45.44 - samples/sec: 455.66 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:50:34,984 epoch 9 - iter 744/3726 - loss 0.00545741 - time (sec): 91.08 - samples/sec: 453.89 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:51:20,189 epoch 9 - iter 1116/3726 - loss 0.00787820 - time (sec): 136.28 - samples/sec: 452.07 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:52:05,623 epoch 9 - iter 1488/3726 - loss 0.00691940 - time (sec): 181.72 - samples/sec: 451.59 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:52:51,095 epoch 9 - iter 1860/3726 - loss 0.00761910 - time (sec): 227.19 - samples/sec: 452.40 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:53:36,717 epoch 9 - iter 2232/3726 - loss 0.00762123 - time (sec): 272.81 - samples/sec: 450.93 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:54:23,451 epoch 9 - iter 2604/3726 - loss 0.00784988 - time (sec): 319.55 - samples/sec: 448.92 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:55:08,672 epoch 9 - iter 2976/3726 - loss 0.00844099 - time (sec): 364.77 - samples/sec: 449.81 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:55:53,980 epoch 9 - iter 3348/3726 - loss 0.00836439 - time (sec): 410.07 - samples/sec: 449.23 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:56:39,471 epoch 9 - iter 3720/3726 - loss 0.00807807 - time (sec): 455.57 - samples/sec: 448.18 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:56:40,233 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:56:40,233 EPOCH 9 done: loss 0.0081 - lr: 0.000001 | |
| 2023-10-27 19:57:03,898 DEV : loss 0.05321481078863144 - f1-score (micro avg) 0.9742 | |
| 2023-10-27 19:57:03,949 saving best model | |
| 2023-10-27 19:57:06,562 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 19:57:52,150 epoch 10 - iter 372/3726 - loss 0.00368318 - time (sec): 45.59 - samples/sec: 447.26 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-27 19:58:37,058 epoch 10 - iter 744/3726 - loss 0.00490381 - time (sec): 90.49 - samples/sec: 441.64 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 19:59:22,616 epoch 10 - iter 1116/3726 - loss 0.00591526 - time (sec): 136.05 - samples/sec: 444.19 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:00:08,343 epoch 10 - iter 1488/3726 - loss 0.00634553 - time (sec): 181.78 - samples/sec: 443.45 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:00:54,241 epoch 10 - iter 1860/3726 - loss 0.00584248 - time (sec): 227.68 - samples/sec: 441.45 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:01:40,141 epoch 10 - iter 2232/3726 - loss 0.00572180 - time (sec): 273.58 - samples/sec: 443.18 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:02:25,686 epoch 10 - iter 2604/3726 - loss 0.00598793 - time (sec): 319.12 - samples/sec: 445.80 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:03:11,659 epoch 10 - iter 2976/3726 - loss 0.00597232 - time (sec): 365.09 - samples/sec: 445.70 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:03:57,027 epoch 10 - iter 3348/3726 - loss 0.00599237 - time (sec): 410.46 - samples/sec: 447.67 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:04:42,462 epoch 10 - iter 3720/3726 - loss 0.00636112 - time (sec): 455.90 - samples/sec: 448.08 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-27 20:04:43,220 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 20:04:43,220 EPOCH 10 done: loss 0.0064 - lr: 0.000000 | |
| 2023-10-27 20:05:06,157 DEV : loss 0.05384046211838722 - f1-score (micro avg) 0.9737 | |
| 2023-10-27 20:05:08,560 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-27 20:05:08,562 Loading model from best epoch ... | |
| 2023-10-27 20:05:16,264 SequenceTagger predicts: Dictionary with 17 tags: O, S-ORG, B-ORG, E-ORG, I-ORG, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-MISC, B-MISC, E-MISC, I-MISC | |
| 2023-10-27 20:05:38,883 | |
| Results: | |
| - F-score (micro) 0.9696 | |
| - F-score (macro) 0.9645 | |
| - Accuracy 0.9555 | |
| By class: | |
| precision recall f1-score support | |
| ORG 0.9665 0.9665 0.9665 1909 | |
| PER 0.9956 0.9956 0.9956 1591 | |
| LOC 0.9723 0.9674 0.9698 1413 | |
| MISC 0.9117 0.9409 0.9261 812 | |
| micro avg 0.9680 0.9712 0.9696 5725 | |
| macro avg 0.9615 0.9676 0.9645 5725 | |
| weighted avg 0.9682 0.9712 0.9697 5725 | |
| 2023-10-27 20:05:38,883 ---------------------------------------------------------------------------------------------------- | |