2023-10-17 13:42:53,754 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,755 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): ElectraModel( (embeddings): ElectraEmbeddings( (word_embeddings): Embedding(32001, 768) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): ElectraEncoder( (layer): ModuleList( (0-11): 12 x ElectraLayer( (attention): ElectraAttention( (self): ElectraSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): ElectraSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): ElectraIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): ElectraOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-17 13:42:53,755 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,755 MultiCorpus: 7936 train + 992 dev + 992 test sentences - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 Train: 7936 sentences 2023-10-17 13:42:53,756 (train_with_dev=False, train_with_test=False) 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 Training Params: 2023-10-17 13:42:53,756 - learning_rate: "5e-05" 2023-10-17 13:42:53,756 - mini_batch_size: "4" 2023-10-17 13:42:53,756 - max_epochs: "10" 2023-10-17 13:42:53,756 - shuffle: "True" 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 Plugins: 2023-10-17 13:42:53,756 - TensorboardLogger 2023-10-17 13:42:53,756 - LinearScheduler | warmup_fraction: '0.1' 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 Final evaluation on model from best epoch (best-model.pt) 2023-10-17 13:42:53,756 - metric: "('micro avg', 'f1-score')" 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 Computation: 2023-10-17 13:42:53,756 - compute on device: cuda:0 2023-10-17 13:42:53,756 - embedding storage: none 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 Model training base path: "hmbench-icdar/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:42:53,756 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-17 13:43:02,902 epoch 1 - iter 198/1984 - loss 1.99194816 - time (sec): 9.14 - samples/sec: 1812.16 - lr: 0.000005 - momentum: 0.000000 2023-10-17 13:43:12,040 epoch 1 - iter 396/1984 - loss 1.12529108 - time (sec): 18.28 - samples/sec: 1834.45 - lr: 0.000010 - momentum: 0.000000 2023-10-17 13:43:20,571 epoch 1 - iter 594/1984 - loss 0.84075243 - time (sec): 26.81 - samples/sec: 1828.97 - lr: 0.000015 - momentum: 0.000000 2023-10-17 13:43:29,636 epoch 1 - iter 792/1984 - loss 0.67948072 - time (sec): 35.88 - samples/sec: 1809.73 - lr: 0.000020 - momentum: 0.000000 2023-10-17 13:43:38,961 epoch 1 - iter 990/1984 - loss 0.56666412 - time (sec): 45.20 - samples/sec: 1828.99 - lr: 0.000025 - momentum: 0.000000 2023-10-17 13:43:47,711 epoch 1 - iter 1188/1984 - loss 0.50179248 - time (sec): 53.95 - samples/sec: 1829.62 - lr: 0.000030 - momentum: 0.000000 2023-10-17 13:43:56,331 epoch 1 - iter 1386/1984 - loss 0.45691609 - time (sec): 62.57 - samples/sec: 1836.93 - lr: 0.000035 - momentum: 0.000000 2023-10-17 13:44:05,242 epoch 1 - iter 1584/1984 - loss 0.41865425 - time (sec): 71.48 - samples/sec: 1841.02 - lr: 0.000040 - momentum: 0.000000 2023-10-17 13:44:14,524 epoch 1 - iter 1782/1984 - loss 0.38769661 - time (sec): 80.77 - samples/sec: 1834.44 - lr: 0.000045 - momentum: 0.000000 2023-10-17 13:44:23,569 epoch 1 - iter 1980/1984 - loss 0.36361201 - time (sec): 89.81 - samples/sec: 1822.66 - lr: 0.000050 - momentum: 0.000000 2023-10-17 13:44:23,744 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:44:23,744 EPOCH 1 done: loss 0.3635 - lr: 0.000050 2023-10-17 13:44:26,902 DEV : loss 0.1107097640633583 - f1-score (micro avg) 0.7147 2023-10-17 13:44:26,923 saving best model 2023-10-17 13:44:27,399 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:44:36,392 epoch 2 - iter 198/1984 - loss 0.12198282 - time (sec): 8.99 - samples/sec: 1816.59 - lr: 0.000049 - momentum: 0.000000 2023-10-17 13:44:45,686 epoch 2 - iter 396/1984 - loss 0.12288858 - time (sec): 18.29 - samples/sec: 1802.37 - lr: 0.000049 - momentum: 0.000000 2023-10-17 13:44:54,485 epoch 2 - iter 594/1984 - loss 0.12158637 - time (sec): 27.08 - samples/sec: 1790.77 - lr: 0.000048 - momentum: 0.000000 2023-10-17 13:45:03,453 epoch 2 - iter 792/1984 - loss 0.12378321 - time (sec): 36.05 - samples/sec: 1793.41 - lr: 0.000048 - momentum: 0.000000 2023-10-17 13:45:12,572 epoch 2 - iter 990/1984 - loss 0.11981922 - time (sec): 45.17 - samples/sec: 1793.52 - lr: 0.000047 - momentum: 0.000000 2023-10-17 13:45:21,725 epoch 2 - iter 1188/1984 - loss 0.11892503 - time (sec): 54.32 - samples/sec: 1795.43 - lr: 0.000047 - momentum: 0.000000 2023-10-17 13:45:30,864 epoch 2 - iter 1386/1984 - loss 0.11995066 - time (sec): 63.46 - samples/sec: 1807.05 - lr: 0.000046 - momentum: 0.000000 2023-10-17 13:45:40,092 epoch 2 - iter 1584/1984 - loss 0.11971087 - time (sec): 72.69 - samples/sec: 1801.71 - lr: 0.000046 - momentum: 0.000000 2023-10-17 13:45:49,162 epoch 2 - iter 1782/1984 - loss 0.11939199 - time (sec): 81.76 - samples/sec: 1797.41 - lr: 0.000045 - momentum: 0.000000 2023-10-17 13:45:58,300 epoch 2 - iter 1980/1984 - loss 0.11867051 - time (sec): 90.90 - samples/sec: 1802.10 - lr: 0.000044 - momentum: 0.000000 2023-10-17 13:45:58,474 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:45:58,474 EPOCH 2 done: loss 0.1188 - lr: 0.000044 2023-10-17 13:46:02,349 DEV : loss 0.09292253851890564 - f1-score (micro avg) 0.7461 2023-10-17 13:46:02,372 saving best model 2023-10-17 13:46:02,884 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:46:12,137 epoch 3 - iter 198/1984 - loss 0.08093006 - time (sec): 9.25 - samples/sec: 1833.94 - lr: 0.000044 - momentum: 0.000000 2023-10-17 13:46:21,419 epoch 3 - iter 396/1984 - loss 0.08776510 - time (sec): 18.53 - samples/sec: 1800.42 - lr: 0.000043 - momentum: 0.000000 2023-10-17 13:46:30,417 epoch 3 - iter 594/1984 - loss 0.08770434 - time (sec): 27.53 - samples/sec: 1795.44 - lr: 0.000043 - momentum: 0.000000 2023-10-17 13:46:39,552 epoch 3 - iter 792/1984 - loss 0.08713512 - time (sec): 36.67 - samples/sec: 1797.16 - lr: 0.000042 - momentum: 0.000000 2023-10-17 13:46:48,582 epoch 3 - iter 990/1984 - loss 0.08915958 - time (sec): 45.70 - samples/sec: 1808.80 - lr: 0.000042 - momentum: 0.000000 2023-10-17 13:46:57,705 epoch 3 - iter 1188/1984 - loss 0.08975126 - time (sec): 54.82 - samples/sec: 1817.72 - lr: 0.000041 - momentum: 0.000000 2023-10-17 13:47:06,969 epoch 3 - iter 1386/1984 - loss 0.08933047 - time (sec): 64.08 - samples/sec: 1822.52 - lr: 0.000041 - momentum: 0.000000 2023-10-17 13:47:16,293 epoch 3 - iter 1584/1984 - loss 0.08968171 - time (sec): 73.41 - samples/sec: 1814.20 - lr: 0.000040 - momentum: 0.000000 2023-10-17 13:47:25,592 epoch 3 - iter 1782/1984 - loss 0.09022258 - time (sec): 82.71 - samples/sec: 1798.16 - lr: 0.000039 - momentum: 0.000000 2023-10-17 13:47:34,674 epoch 3 - iter 1980/1984 - loss 0.08989172 - time (sec): 91.79 - samples/sec: 1783.16 - lr: 0.000039 - momentum: 0.000000 2023-10-17 13:47:34,856 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:47:34,856 EPOCH 3 done: loss 0.0899 - lr: 0.000039 2023-10-17 13:47:38,254 DEV : loss 0.11529310792684555 - f1-score (micro avg) 0.7554 2023-10-17 13:47:38,275 saving best model 2023-10-17 13:47:38,842 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:47:47,509 epoch 4 - iter 198/1984 - loss 0.05901225 - time (sec): 8.66 - samples/sec: 1941.15 - lr: 0.000038 - momentum: 0.000000 2023-10-17 13:47:56,529 epoch 4 - iter 396/1984 - loss 0.07172775 - time (sec): 17.68 - samples/sec: 1856.82 - lr: 0.000038 - momentum: 0.000000 2023-10-17 13:48:05,245 epoch 4 - iter 594/1984 - loss 0.06965845 - time (sec): 26.40 - samples/sec: 1845.67 - lr: 0.000037 - momentum: 0.000000 2023-10-17 13:48:13,933 epoch 4 - iter 792/1984 - loss 0.07214150 - time (sec): 35.09 - samples/sec: 1873.43 - lr: 0.000037 - momentum: 0.000000 2023-10-17 13:48:22,554 epoch 4 - iter 990/1984 - loss 0.07156799 - time (sec): 43.71 - samples/sec: 1878.28 - lr: 0.000036 - momentum: 0.000000 2023-10-17 13:48:31,704 epoch 4 - iter 1188/1984 - loss 0.07472710 - time (sec): 52.86 - samples/sec: 1874.11 - lr: 0.000036 - momentum: 0.000000 2023-10-17 13:48:40,821 epoch 4 - iter 1386/1984 - loss 0.07315463 - time (sec): 61.97 - samples/sec: 1855.64 - lr: 0.000035 - momentum: 0.000000 2023-10-17 13:48:49,969 epoch 4 - iter 1584/1984 - loss 0.07446253 - time (sec): 71.12 - samples/sec: 1846.18 - lr: 0.000034 - momentum: 0.000000 2023-10-17 13:48:59,484 epoch 4 - iter 1782/1984 - loss 0.07318315 - time (sec): 80.64 - samples/sec: 1826.73 - lr: 0.000034 - momentum: 0.000000 2023-10-17 13:49:09,059 epoch 4 - iter 1980/1984 - loss 0.07161599 - time (sec): 90.21 - samples/sec: 1815.30 - lr: 0.000033 - momentum: 0.000000 2023-10-17 13:49:09,239 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:49:09,239 EPOCH 4 done: loss 0.0716 - lr: 0.000033 2023-10-17 13:49:12,748 DEV : loss 0.16965167224407196 - f1-score (micro avg) 0.7562 2023-10-17 13:49:12,770 saving best model 2023-10-17 13:49:13,358 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:49:22,504 epoch 5 - iter 198/1984 - loss 0.05142115 - time (sec): 9.14 - samples/sec: 1744.82 - lr: 0.000033 - momentum: 0.000000 2023-10-17 13:49:31,639 epoch 5 - iter 396/1984 - loss 0.05326253 - time (sec): 18.28 - samples/sec: 1783.51 - lr: 0.000032 - momentum: 0.000000 2023-10-17 13:49:40,726 epoch 5 - iter 594/1984 - loss 0.05234995 - time (sec): 27.36 - samples/sec: 1766.40 - lr: 0.000032 - momentum: 0.000000 2023-10-17 13:49:49,869 epoch 5 - iter 792/1984 - loss 0.05493024 - time (sec): 36.51 - samples/sec: 1766.69 - lr: 0.000031 - momentum: 0.000000 2023-10-17 13:49:59,222 epoch 5 - iter 990/1984 - loss 0.05547255 - time (sec): 45.86 - samples/sec: 1768.21 - lr: 0.000031 - momentum: 0.000000 2023-10-17 13:50:08,703 epoch 5 - iter 1188/1984 - loss 0.05535415 - time (sec): 55.34 - samples/sec: 1775.28 - lr: 0.000030 - momentum: 0.000000 2023-10-17 13:50:17,849 epoch 5 - iter 1386/1984 - loss 0.05553716 - time (sec): 64.49 - samples/sec: 1780.41 - lr: 0.000029 - momentum: 0.000000 2023-10-17 13:50:27,041 epoch 5 - iter 1584/1984 - loss 0.05438664 - time (sec): 73.68 - samples/sec: 1785.59 - lr: 0.000029 - momentum: 0.000000 2023-10-17 13:50:36,058 epoch 5 - iter 1782/1984 - loss 0.05420342 - time (sec): 82.70 - samples/sec: 1785.78 - lr: 0.000028 - momentum: 0.000000 2023-10-17 13:50:45,233 epoch 5 - iter 1980/1984 - loss 0.05491134 - time (sec): 91.87 - samples/sec: 1780.76 - lr: 0.000028 - momentum: 0.000000 2023-10-17 13:50:45,422 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:50:45,423 EPOCH 5 done: loss 0.0548 - lr: 0.000028 2023-10-17 13:50:48,854 DEV : loss 0.17186634242534637 - f1-score (micro avg) 0.7583 2023-10-17 13:50:48,875 saving best model 2023-10-17 13:50:49,394 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:50:58,709 epoch 6 - iter 198/1984 - loss 0.04210687 - time (sec): 9.31 - samples/sec: 1797.63 - lr: 0.000027 - momentum: 0.000000 2023-10-17 13:51:07,945 epoch 6 - iter 396/1984 - loss 0.04558327 - time (sec): 18.55 - samples/sec: 1786.69 - lr: 0.000027 - momentum: 0.000000 2023-10-17 13:51:17,296 epoch 6 - iter 594/1984 - loss 0.04411621 - time (sec): 27.90 - samples/sec: 1776.05 - lr: 0.000026 - momentum: 0.000000 2023-10-17 13:51:26,833 epoch 6 - iter 792/1984 - loss 0.04189826 - time (sec): 37.44 - samples/sec: 1766.81 - lr: 0.000026 - momentum: 0.000000 2023-10-17 13:51:35,758 epoch 6 - iter 990/1984 - loss 0.04186020 - time (sec): 46.36 - samples/sec: 1790.47 - lr: 0.000025 - momentum: 0.000000 2023-10-17 13:51:44,892 epoch 6 - iter 1188/1984 - loss 0.04179555 - time (sec): 55.50 - samples/sec: 1784.39 - lr: 0.000024 - momentum: 0.000000 2023-10-17 13:51:54,014 epoch 6 - iter 1386/1984 - loss 0.04062803 - time (sec): 64.62 - samples/sec: 1802.35 - lr: 0.000024 - momentum: 0.000000 2023-10-17 13:52:03,187 epoch 6 - iter 1584/1984 - loss 0.04047753 - time (sec): 73.79 - samples/sec: 1794.79 - lr: 0.000023 - momentum: 0.000000 2023-10-17 13:52:12,464 epoch 6 - iter 1782/1984 - loss 0.04096866 - time (sec): 83.07 - samples/sec: 1783.58 - lr: 0.000023 - momentum: 0.000000 2023-10-17 13:52:21,511 epoch 6 - iter 1980/1984 - loss 0.04137447 - time (sec): 92.11 - samples/sec: 1776.94 - lr: 0.000022 - momentum: 0.000000 2023-10-17 13:52:21,692 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:52:21,692 EPOCH 6 done: loss 0.0414 - lr: 0.000022 2023-10-17 13:52:25,752 DEV : loss 0.1979617029428482 - f1-score (micro avg) 0.7635 2023-10-17 13:52:25,775 saving best model 2023-10-17 13:52:26,295 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:52:35,522 epoch 7 - iter 198/1984 - loss 0.02932924 - time (sec): 9.22 - samples/sec: 1697.08 - lr: 0.000022 - momentum: 0.000000 2023-10-17 13:52:45,099 epoch 7 - iter 396/1984 - loss 0.02557373 - time (sec): 18.80 - samples/sec: 1741.96 - lr: 0.000021 - momentum: 0.000000 2023-10-17 13:52:54,288 epoch 7 - iter 594/1984 - loss 0.02664735 - time (sec): 27.99 - samples/sec: 1753.83 - lr: 0.000021 - momentum: 0.000000 2023-10-17 13:53:03,396 epoch 7 - iter 792/1984 - loss 0.02794794 - time (sec): 37.10 - samples/sec: 1758.17 - lr: 0.000020 - momentum: 0.000000 2023-10-17 13:53:12,526 epoch 7 - iter 990/1984 - loss 0.02878967 - time (sec): 46.23 - samples/sec: 1767.18 - lr: 0.000019 - momentum: 0.000000 2023-10-17 13:53:21,604 epoch 7 - iter 1188/1984 - loss 0.02864416 - time (sec): 55.30 - samples/sec: 1761.33 - lr: 0.000019 - momentum: 0.000000 2023-10-17 13:53:30,974 epoch 7 - iter 1386/1984 - loss 0.02877632 - time (sec): 64.67 - samples/sec: 1751.38 - lr: 0.000018 - momentum: 0.000000 2023-10-17 13:53:40,189 epoch 7 - iter 1584/1984 - loss 0.02875581 - time (sec): 73.89 - samples/sec: 1754.20 - lr: 0.000018 - momentum: 0.000000 2023-10-17 13:53:49,370 epoch 7 - iter 1782/1984 - loss 0.02874451 - time (sec): 83.07 - samples/sec: 1755.94 - lr: 0.000017 - momentum: 0.000000 2023-10-17 13:53:58,828 epoch 7 - iter 1980/1984 - loss 0.02854042 - time (sec): 92.53 - samples/sec: 1768.83 - lr: 0.000017 - momentum: 0.000000 2023-10-17 13:53:59,013 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:53:59,014 EPOCH 7 done: loss 0.0285 - lr: 0.000017 2023-10-17 13:54:02,405 DEV : loss 0.21675720810890198 - f1-score (micro avg) 0.756 2023-10-17 13:54:02,426 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:54:11,532 epoch 8 - iter 198/1984 - loss 0.02131513 - time (sec): 9.10 - samples/sec: 1755.79 - lr: 0.000016 - momentum: 0.000000 2023-10-17 13:54:20,688 epoch 8 - iter 396/1984 - loss 0.02216696 - time (sec): 18.26 - samples/sec: 1771.05 - lr: 0.000016 - momentum: 0.000000 2023-10-17 13:54:29,689 epoch 8 - iter 594/1984 - loss 0.02326467 - time (sec): 27.26 - samples/sec: 1759.32 - lr: 0.000015 - momentum: 0.000000 2023-10-17 13:54:38,755 epoch 8 - iter 792/1984 - loss 0.02241368 - time (sec): 36.33 - samples/sec: 1763.98 - lr: 0.000014 - momentum: 0.000000 2023-10-17 13:54:47,967 epoch 8 - iter 990/1984 - loss 0.02005196 - time (sec): 45.54 - samples/sec: 1771.30 - lr: 0.000014 - momentum: 0.000000 2023-10-17 13:54:57,291 epoch 8 - iter 1188/1984 - loss 0.01961340 - time (sec): 54.86 - samples/sec: 1775.08 - lr: 0.000013 - momentum: 0.000000 2023-10-17 13:55:06,861 epoch 8 - iter 1386/1984 - loss 0.02145710 - time (sec): 64.43 - samples/sec: 1753.37 - lr: 0.000013 - momentum: 0.000000 2023-10-17 13:55:16,241 epoch 8 - iter 1584/1984 - loss 0.02064329 - time (sec): 73.81 - samples/sec: 1762.80 - lr: 0.000012 - momentum: 0.000000 2023-10-17 13:55:25,486 epoch 8 - iter 1782/1984 - loss 0.01981026 - time (sec): 83.06 - samples/sec: 1766.73 - lr: 0.000012 - momentum: 0.000000 2023-10-17 13:55:34,752 epoch 8 - iter 1980/1984 - loss 0.02031700 - time (sec): 92.32 - samples/sec: 1772.93 - lr: 0.000011 - momentum: 0.000000 2023-10-17 13:55:34,925 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:55:34,925 EPOCH 8 done: loss 0.0203 - lr: 0.000011 2023-10-17 13:55:38,336 DEV : loss 0.22816428542137146 - f1-score (micro avg) 0.7689 2023-10-17 13:55:38,358 saving best model 2023-10-17 13:55:38,938 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:55:47,982 epoch 9 - iter 198/1984 - loss 0.01192882 - time (sec): 9.04 - samples/sec: 1734.20 - lr: 0.000011 - momentum: 0.000000 2023-10-17 13:55:56,918 epoch 9 - iter 396/1984 - loss 0.01286841 - time (sec): 17.97 - samples/sec: 1798.99 - lr: 0.000010 - momentum: 0.000000 2023-10-17 13:56:05,849 epoch 9 - iter 594/1984 - loss 0.01438189 - time (sec): 26.91 - samples/sec: 1772.52 - lr: 0.000009 - momentum: 0.000000 2023-10-17 13:56:14,942 epoch 9 - iter 792/1984 - loss 0.01318238 - time (sec): 36.00 - samples/sec: 1770.21 - lr: 0.000009 - momentum: 0.000000 2023-10-17 13:56:24,030 epoch 9 - iter 990/1984 - loss 0.01351039 - time (sec): 45.09 - samples/sec: 1778.59 - lr: 0.000008 - momentum: 0.000000 2023-10-17 13:56:32,852 epoch 9 - iter 1188/1984 - loss 0.01319278 - time (sec): 53.91 - samples/sec: 1793.12 - lr: 0.000008 - momentum: 0.000000 2023-10-17 13:56:41,731 epoch 9 - iter 1386/1984 - loss 0.01309930 - time (sec): 62.79 - samples/sec: 1811.55 - lr: 0.000007 - momentum: 0.000000 2023-10-17 13:56:51,161 epoch 9 - iter 1584/1984 - loss 0.01255843 - time (sec): 72.22 - samples/sec: 1810.15 - lr: 0.000007 - momentum: 0.000000 2023-10-17 13:57:00,359 epoch 9 - iter 1782/1984 - loss 0.01307612 - time (sec): 81.42 - samples/sec: 1803.41 - lr: 0.000006 - momentum: 0.000000 2023-10-17 13:57:09,450 epoch 9 - iter 1980/1984 - loss 0.01305751 - time (sec): 90.51 - samples/sec: 1807.63 - lr: 0.000006 - momentum: 0.000000 2023-10-17 13:57:09,640 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:57:09,640 EPOCH 9 done: loss 0.0130 - lr: 0.000006 2023-10-17 13:57:13,058 DEV : loss 0.23943665623664856 - f1-score (micro avg) 0.7711 2023-10-17 13:57:13,081 saving best model 2023-10-17 13:57:13,702 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:57:22,874 epoch 10 - iter 198/1984 - loss 0.00790504 - time (sec): 9.17 - samples/sec: 1775.44 - lr: 0.000005 - momentum: 0.000000 2023-10-17 13:57:32,117 epoch 10 - iter 396/1984 - loss 0.00810567 - time (sec): 18.41 - samples/sec: 1819.26 - lr: 0.000004 - momentum: 0.000000 2023-10-17 13:57:41,199 epoch 10 - iter 594/1984 - loss 0.00780535 - time (sec): 27.49 - samples/sec: 1803.46 - lr: 0.000004 - momentum: 0.000000 2023-10-17 13:57:50,230 epoch 10 - iter 792/1984 - loss 0.00784589 - time (sec): 36.53 - samples/sec: 1788.64 - lr: 0.000003 - momentum: 0.000000 2023-10-17 13:57:59,203 epoch 10 - iter 990/1984 - loss 0.00744212 - time (sec): 45.50 - samples/sec: 1799.38 - lr: 0.000003 - momentum: 0.000000 2023-10-17 13:58:08,284 epoch 10 - iter 1188/1984 - loss 0.00828425 - time (sec): 54.58 - samples/sec: 1802.08 - lr: 0.000002 - momentum: 0.000000 2023-10-17 13:58:17,328 epoch 10 - iter 1386/1984 - loss 0.00823271 - time (sec): 63.62 - samples/sec: 1805.08 - lr: 0.000002 - momentum: 0.000000 2023-10-17 13:58:26,441 epoch 10 - iter 1584/1984 - loss 0.00807062 - time (sec): 72.74 - samples/sec: 1805.28 - lr: 0.000001 - momentum: 0.000000 2023-10-17 13:58:35,408 epoch 10 - iter 1782/1984 - loss 0.00842240 - time (sec): 81.70 - samples/sec: 1803.89 - lr: 0.000001 - momentum: 0.000000 2023-10-17 13:58:44,477 epoch 10 - iter 1980/1984 - loss 0.00880008 - time (sec): 90.77 - samples/sec: 1803.88 - lr: 0.000000 - momentum: 0.000000 2023-10-17 13:58:44,651 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:58:44,651 EPOCH 10 done: loss 0.0088 - lr: 0.000000 2023-10-17 13:58:48,192 DEV : loss 0.2471495419740677 - f1-score (micro avg) 0.7779 2023-10-17 13:58:48,221 saving best model 2023-10-17 13:58:49,148 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:58:49,150 Loading model from best epoch ... 2023-10-17 13:58:51,929 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG 2023-10-17 13:58:54,795 Results: - F-score (micro) 0.7672 - F-score (macro) 0.6813 - Accuracy 0.653 By class: precision recall f1-score support LOC 0.8318 0.8382 0.8350 655 PER 0.6811 0.7758 0.7254 223 ORG 0.5043 0.4646 0.4836 127 micro avg 0.7575 0.7771 0.7672 1005 macro avg 0.6724 0.6928 0.6813 1005 weighted avg 0.7570 0.7771 0.7663 1005 2023-10-17 13:58:54,795 ----------------------------------------------------------------------------------------------------