2023-09-04 18:01:02,696 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,697 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (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): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (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): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (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) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=21, bias=True) (loss_function): CrossEntropyLoss() )" 2023-09-04 18:01:02,698 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,698 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator 2023-09-04 18:01:02,698 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,698 Train: 5901 sentences 2023-09-04 18:01:02,698 (train_with_dev=False, train_with_test=False) 2023-09-04 18:01:02,698 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,698 Training Params: 2023-09-04 18:01:02,698 - learning_rate: "3e-05" 2023-09-04 18:01:02,698 - mini_batch_size: "4" 2023-09-04 18:01:02,698 - max_epochs: "10" 2023-09-04 18:01:02,698 - shuffle: "True" 2023-09-04 18:01:02,698 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,698 Plugins: 2023-09-04 18:01:02,698 - LinearScheduler | warmup_fraction: '0.1' 2023-09-04 18:01:02,698 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,698 Final evaluation on model from best epoch (best-model.pt) 2023-09-04 18:01:02,699 - metric: "('micro avg', 'f1-score')" 2023-09-04 18:01:02,699 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,699 Computation: 2023-09-04 18:01:02,699 - compute on device: cuda:0 2023-09-04 18:01:02,699 - embedding storage: none 2023-09-04 18:01:02,699 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,699 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" 2023-09-04 18:01:02,699 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:02,699 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:01:18,094 epoch 1 - iter 147/1476 - loss 2.44376377 - time (sec): 15.39 - samples/sec: 1055.91 - lr: 0.000003 - momentum: 0.000000 2023-09-04 18:01:33,845 epoch 1 - iter 294/1476 - loss 1.52151398 - time (sec): 31.15 - samples/sec: 1050.94 - lr: 0.000006 - momentum: 0.000000 2023-09-04 18:01:49,471 epoch 1 - iter 441/1476 - loss 1.15461415 - time (sec): 46.77 - samples/sec: 1043.42 - lr: 0.000009 - momentum: 0.000000 2023-09-04 18:02:05,099 epoch 1 - iter 588/1476 - loss 0.94850083 - time (sec): 62.40 - samples/sec: 1041.15 - lr: 0.000012 - momentum: 0.000000 2023-09-04 18:02:21,896 epoch 1 - iter 735/1476 - loss 0.82532013 - time (sec): 79.20 - samples/sec: 1036.04 - lr: 0.000015 - momentum: 0.000000 2023-09-04 18:02:36,710 epoch 1 - iter 882/1476 - loss 0.73671035 - time (sec): 94.01 - samples/sec: 1030.80 - lr: 0.000018 - momentum: 0.000000 2023-09-04 18:02:53,060 epoch 1 - iter 1029/1476 - loss 0.66213797 - time (sec): 110.36 - samples/sec: 1036.86 - lr: 0.000021 - momentum: 0.000000 2023-09-04 18:03:09,409 epoch 1 - iter 1176/1476 - loss 0.60019447 - time (sec): 126.71 - samples/sec: 1042.95 - lr: 0.000024 - momentum: 0.000000 2023-09-04 18:03:24,876 epoch 1 - iter 1323/1476 - loss 0.55657173 - time (sec): 142.18 - samples/sec: 1045.22 - lr: 0.000027 - momentum: 0.000000 2023-09-04 18:03:41,637 epoch 1 - iter 1470/1476 - loss 0.51947340 - time (sec): 158.94 - samples/sec: 1043.39 - lr: 0.000030 - momentum: 0.000000 2023-09-04 18:03:42,206 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:03:42,207 EPOCH 1 done: loss 0.5185 - lr: 0.000030 2023-09-04 18:03:56,790 DEV : loss 0.15415577590465546 - f1-score (micro avg) 0.6856 2023-09-04 18:03:56,837 saving best model 2023-09-04 18:03:57,328 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:04:13,436 epoch 2 - iter 147/1476 - loss 0.13762875 - time (sec): 16.11 - samples/sec: 1041.34 - lr: 0.000030 - momentum: 0.000000 2023-09-04 18:04:29,294 epoch 2 - iter 294/1476 - loss 0.14049549 - time (sec): 31.96 - samples/sec: 1038.45 - lr: 0.000029 - momentum: 0.000000 2023-09-04 18:04:45,489 epoch 2 - iter 441/1476 - loss 0.13824014 - time (sec): 48.16 - samples/sec: 1037.93 - lr: 0.000029 - momentum: 0.000000 2023-09-04 18:05:00,555 epoch 2 - iter 588/1476 - loss 0.13296353 - time (sec): 63.23 - samples/sec: 1035.21 - lr: 0.000029 - momentum: 0.000000 2023-09-04 18:05:16,929 epoch 2 - iter 735/1476 - loss 0.12947844 - time (sec): 79.60 - samples/sec: 1052.53 - lr: 0.000028 - momentum: 0.000000 2023-09-04 18:05:35,682 epoch 2 - iter 882/1476 - loss 0.13359823 - time (sec): 98.35 - samples/sec: 1063.30 - lr: 0.000028 - momentum: 0.000000 2023-09-04 18:05:50,461 epoch 2 - iter 1029/1476 - loss 0.13133894 - time (sec): 113.13 - samples/sec: 1058.87 - lr: 0.000028 - momentum: 0.000000 2023-09-04 18:06:06,506 epoch 2 - iter 1176/1476 - loss 0.13058551 - time (sec): 129.18 - samples/sec: 1059.23 - lr: 0.000027 - momentum: 0.000000 2023-09-04 18:06:20,816 epoch 2 - iter 1323/1476 - loss 0.13091216 - time (sec): 143.49 - samples/sec: 1053.59 - lr: 0.000027 - momentum: 0.000000 2023-09-04 18:06:35,935 epoch 2 - iter 1470/1476 - loss 0.12972873 - time (sec): 158.61 - samples/sec: 1046.77 - lr: 0.000027 - momentum: 0.000000 2023-09-04 18:06:36,455 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:06:36,455 EPOCH 2 done: loss 0.1295 - lr: 0.000027 2023-09-04 18:06:54,283 DEV : loss 0.13132880628108978 - f1-score (micro avg) 0.7834 2023-09-04 18:06:54,312 saving best model 2023-09-04 18:06:55,664 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:07:12,339 epoch 3 - iter 147/1476 - loss 0.06202239 - time (sec): 16.67 - samples/sec: 1111.70 - lr: 0.000026 - momentum: 0.000000 2023-09-04 18:07:28,604 epoch 3 - iter 294/1476 - loss 0.06649857 - time (sec): 32.94 - samples/sec: 1070.17 - lr: 0.000026 - momentum: 0.000000 2023-09-04 18:07:44,511 epoch 3 - iter 441/1476 - loss 0.06984059 - time (sec): 48.85 - samples/sec: 1065.42 - lr: 0.000026 - momentum: 0.000000 2023-09-04 18:08:01,651 epoch 3 - iter 588/1476 - loss 0.07783875 - time (sec): 65.99 - samples/sec: 1064.77 - lr: 0.000025 - momentum: 0.000000 2023-09-04 18:08:16,965 epoch 3 - iter 735/1476 - loss 0.07932757 - time (sec): 81.30 - samples/sec: 1054.93 - lr: 0.000025 - momentum: 0.000000 2023-09-04 18:08:32,668 epoch 3 - iter 882/1476 - loss 0.07615306 - time (sec): 97.00 - samples/sec: 1049.99 - lr: 0.000025 - momentum: 0.000000 2023-09-04 18:08:48,048 epoch 3 - iter 1029/1476 - loss 0.07522442 - time (sec): 112.38 - samples/sec: 1045.01 - lr: 0.000024 - momentum: 0.000000 2023-09-04 18:09:03,784 epoch 3 - iter 1176/1476 - loss 0.07465154 - time (sec): 128.12 - samples/sec: 1043.48 - lr: 0.000024 - momentum: 0.000000 2023-09-04 18:09:19,691 epoch 3 - iter 1323/1476 - loss 0.07700065 - time (sec): 144.03 - samples/sec: 1041.14 - lr: 0.000024 - momentum: 0.000000 2023-09-04 18:09:34,988 epoch 3 - iter 1470/1476 - loss 0.07972797 - time (sec): 159.32 - samples/sec: 1041.18 - lr: 0.000023 - momentum: 0.000000 2023-09-04 18:09:35,518 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:09:35,518 EPOCH 3 done: loss 0.0799 - lr: 0.000023 2023-09-04 18:09:53,055 DEV : loss 0.14578530192375183 - f1-score (micro avg) 0.7994 2023-09-04 18:09:53,083 saving best model 2023-09-04 18:09:54,422 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:10:10,140 epoch 4 - iter 147/1476 - loss 0.05746252 - time (sec): 15.72 - samples/sec: 1026.05 - lr: 0.000023 - momentum: 0.000000 2023-09-04 18:10:27,722 epoch 4 - iter 294/1476 - loss 0.06102346 - time (sec): 33.30 - samples/sec: 1071.19 - lr: 0.000023 - momentum: 0.000000 2023-09-04 18:10:43,854 epoch 4 - iter 441/1476 - loss 0.05967365 - time (sec): 49.43 - samples/sec: 1047.86 - lr: 0.000022 - momentum: 0.000000 2023-09-04 18:10:58,746 epoch 4 - iter 588/1476 - loss 0.06001754 - time (sec): 64.32 - samples/sec: 1030.36 - lr: 0.000022 - momentum: 0.000000 2023-09-04 18:11:15,070 epoch 4 - iter 735/1476 - loss 0.05838120 - time (sec): 80.65 - samples/sec: 1034.71 - lr: 0.000022 - momentum: 0.000000 2023-09-04 18:11:30,504 epoch 4 - iter 882/1476 - loss 0.05932716 - time (sec): 96.08 - samples/sec: 1036.16 - lr: 0.000021 - momentum: 0.000000 2023-09-04 18:11:45,730 epoch 4 - iter 1029/1476 - loss 0.05890917 - time (sec): 111.31 - samples/sec: 1029.82 - lr: 0.000021 - momentum: 0.000000 2023-09-04 18:12:01,064 epoch 4 - iter 1176/1476 - loss 0.05796255 - time (sec): 126.64 - samples/sec: 1031.35 - lr: 0.000021 - momentum: 0.000000 2023-09-04 18:12:17,046 epoch 4 - iter 1323/1476 - loss 0.05817651 - time (sec): 142.62 - samples/sec: 1029.12 - lr: 0.000020 - momentum: 0.000000 2023-09-04 18:12:34,542 epoch 4 - iter 1470/1476 - loss 0.05645201 - time (sec): 160.12 - samples/sec: 1035.95 - lr: 0.000020 - momentum: 0.000000 2023-09-04 18:12:35,105 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:12:35,105 EPOCH 4 done: loss 0.0566 - lr: 0.000020 2023-09-04 18:12:52,812 DEV : loss 0.18173334002494812 - f1-score (micro avg) 0.8055 2023-09-04 18:12:52,842 saving best model 2023-09-04 18:12:54,191 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:13:09,997 epoch 5 - iter 147/1476 - loss 0.05138894 - time (sec): 15.80 - samples/sec: 1064.24 - lr: 0.000020 - momentum: 0.000000 2023-09-04 18:13:24,951 epoch 5 - iter 294/1476 - loss 0.04721485 - time (sec): 30.76 - samples/sec: 1027.23 - lr: 0.000019 - momentum: 0.000000 2023-09-04 18:13:41,035 epoch 5 - iter 441/1476 - loss 0.04141632 - time (sec): 46.84 - samples/sec: 1030.23 - lr: 0.000019 - momentum: 0.000000 2023-09-04 18:13:56,918 epoch 5 - iter 588/1476 - loss 0.03965347 - time (sec): 62.73 - samples/sec: 1034.00 - lr: 0.000019 - momentum: 0.000000 2023-09-04 18:14:13,476 epoch 5 - iter 735/1476 - loss 0.04115344 - time (sec): 79.28 - samples/sec: 1035.68 - lr: 0.000018 - momentum: 0.000000 2023-09-04 18:14:29,425 epoch 5 - iter 882/1476 - loss 0.04010953 - time (sec): 95.23 - samples/sec: 1038.19 - lr: 0.000018 - momentum: 0.000000 2023-09-04 18:14:46,066 epoch 5 - iter 1029/1476 - loss 0.03987999 - time (sec): 111.87 - samples/sec: 1037.38 - lr: 0.000018 - momentum: 0.000000 2023-09-04 18:15:02,228 epoch 5 - iter 1176/1476 - loss 0.04089865 - time (sec): 128.04 - samples/sec: 1036.12 - lr: 0.000017 - momentum: 0.000000 2023-09-04 18:15:18,305 epoch 5 - iter 1323/1476 - loss 0.04058762 - time (sec): 144.11 - samples/sec: 1038.65 - lr: 0.000017 - momentum: 0.000000 2023-09-04 18:15:33,710 epoch 5 - iter 1470/1476 - loss 0.04089062 - time (sec): 159.52 - samples/sec: 1039.41 - lr: 0.000017 - momentum: 0.000000 2023-09-04 18:15:34,350 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:15:34,351 EPOCH 5 done: loss 0.0407 - lr: 0.000017 2023-09-04 18:15:52,045 DEV : loss 0.17798171937465668 - f1-score (micro avg) 0.8282 2023-09-04 18:15:52,073 saving best model 2023-09-04 18:15:53,401 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:16:09,370 epoch 6 - iter 147/1476 - loss 0.03077746 - time (sec): 15.97 - samples/sec: 1068.35 - lr: 0.000016 - momentum: 0.000000 2023-09-04 18:16:24,765 epoch 6 - iter 294/1476 - loss 0.02870391 - time (sec): 31.36 - samples/sec: 1035.23 - lr: 0.000016 - momentum: 0.000000 2023-09-04 18:16:40,709 epoch 6 - iter 441/1476 - loss 0.02709286 - time (sec): 47.31 - samples/sec: 1033.40 - lr: 0.000016 - momentum: 0.000000 2023-09-04 18:16:56,794 epoch 6 - iter 588/1476 - loss 0.02749007 - time (sec): 63.39 - samples/sec: 1030.11 - lr: 0.000015 - momentum: 0.000000 2023-09-04 18:17:12,164 epoch 6 - iter 735/1476 - loss 0.02642923 - time (sec): 78.76 - samples/sec: 1024.79 - lr: 0.000015 - momentum: 0.000000 2023-09-04 18:17:27,358 epoch 6 - iter 882/1476 - loss 0.02612535 - time (sec): 93.96 - samples/sec: 1022.85 - lr: 0.000015 - momentum: 0.000000 2023-09-04 18:17:43,902 epoch 6 - iter 1029/1476 - loss 0.02662349 - time (sec): 110.50 - samples/sec: 1029.41 - lr: 0.000014 - momentum: 0.000000 2023-09-04 18:17:59,949 epoch 6 - iter 1176/1476 - loss 0.02660086 - time (sec): 126.55 - samples/sec: 1029.02 - lr: 0.000014 - momentum: 0.000000 2023-09-04 18:18:16,060 epoch 6 - iter 1323/1476 - loss 0.02752760 - time (sec): 142.66 - samples/sec: 1028.15 - lr: 0.000014 - momentum: 0.000000 2023-09-04 18:18:32,511 epoch 6 - iter 1470/1476 - loss 0.02784187 - time (sec): 159.11 - samples/sec: 1037.65 - lr: 0.000013 - momentum: 0.000000 2023-09-04 18:18:33,705 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:18:33,706 EPOCH 6 done: loss 0.0278 - lr: 0.000013 2023-09-04 18:18:51,348 DEV : loss 0.2169143557548523 - f1-score (micro avg) 0.8137 2023-09-04 18:18:51,377 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:19:07,437 epoch 7 - iter 147/1476 - loss 0.02004925 - time (sec): 16.06 - samples/sec: 1087.97 - lr: 0.000013 - momentum: 0.000000 2023-09-04 18:19:25,106 epoch 7 - iter 294/1476 - loss 0.01928499 - time (sec): 33.73 - samples/sec: 1067.37 - lr: 0.000013 - momentum: 0.000000 2023-09-04 18:19:41,853 epoch 7 - iter 441/1476 - loss 0.01863859 - time (sec): 50.47 - samples/sec: 1059.56 - lr: 0.000012 - momentum: 0.000000 2023-09-04 18:19:58,276 epoch 7 - iter 588/1476 - loss 0.02152433 - time (sec): 66.90 - samples/sec: 1068.83 - lr: 0.000012 - momentum: 0.000000 2023-09-04 18:20:12,850 epoch 7 - iter 735/1476 - loss 0.02091597 - time (sec): 81.47 - samples/sec: 1061.81 - lr: 0.000012 - momentum: 0.000000 2023-09-04 18:20:29,285 epoch 7 - iter 882/1476 - loss 0.02099112 - time (sec): 97.91 - samples/sec: 1056.78 - lr: 0.000011 - momentum: 0.000000 2023-09-04 18:20:44,364 epoch 7 - iter 1029/1476 - loss 0.02043229 - time (sec): 112.99 - samples/sec: 1051.36 - lr: 0.000011 - momentum: 0.000000 2023-09-04 18:20:59,863 epoch 7 - iter 1176/1476 - loss 0.01948168 - time (sec): 128.48 - samples/sec: 1046.27 - lr: 0.000011 - momentum: 0.000000 2023-09-04 18:21:15,257 epoch 7 - iter 1323/1476 - loss 0.02009420 - time (sec): 143.88 - samples/sec: 1043.76 - lr: 0.000010 - momentum: 0.000000 2023-09-04 18:21:30,772 epoch 7 - iter 1470/1476 - loss 0.01960339 - time (sec): 159.39 - samples/sec: 1040.65 - lr: 0.000010 - momentum: 0.000000 2023-09-04 18:21:31,414 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:21:31,415 EPOCH 7 done: loss 0.0196 - lr: 0.000010 2023-09-04 18:21:49,585 DEV : loss 0.20429323613643646 - f1-score (micro avg) 0.8278 2023-09-04 18:21:49,614 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:22:05,738 epoch 8 - iter 147/1476 - loss 0.01227334 - time (sec): 16.12 - samples/sec: 1097.99 - lr: 0.000010 - momentum: 0.000000 2023-09-04 18:22:20,978 epoch 8 - iter 294/1476 - loss 0.00862639 - time (sec): 31.36 - samples/sec: 1054.47 - lr: 0.000009 - momentum: 0.000000 2023-09-04 18:22:38,095 epoch 8 - iter 441/1476 - loss 0.01267560 - time (sec): 48.48 - samples/sec: 1069.95 - lr: 0.000009 - momentum: 0.000000 2023-09-04 18:22:53,899 epoch 8 - iter 588/1476 - loss 0.01122963 - time (sec): 64.28 - samples/sec: 1049.34 - lr: 0.000009 - momentum: 0.000000 2023-09-04 18:23:08,476 epoch 8 - iter 735/1476 - loss 0.01313610 - time (sec): 78.86 - samples/sec: 1037.64 - lr: 0.000008 - momentum: 0.000000 2023-09-04 18:23:25,524 epoch 8 - iter 882/1476 - loss 0.01420155 - time (sec): 95.91 - samples/sec: 1040.54 - lr: 0.000008 - momentum: 0.000000 2023-09-04 18:23:41,088 epoch 8 - iter 1029/1476 - loss 0.01313444 - time (sec): 111.47 - samples/sec: 1040.35 - lr: 0.000008 - momentum: 0.000000 2023-09-04 18:23:56,612 epoch 8 - iter 1176/1476 - loss 0.01285575 - time (sec): 127.00 - samples/sec: 1037.89 - lr: 0.000007 - momentum: 0.000000 2023-09-04 18:24:12,668 epoch 8 - iter 1323/1476 - loss 0.01287226 - time (sec): 143.05 - samples/sec: 1036.18 - lr: 0.000007 - momentum: 0.000000 2023-09-04 18:24:29,072 epoch 8 - iter 1470/1476 - loss 0.01249485 - time (sec): 159.46 - samples/sec: 1040.11 - lr: 0.000007 - momentum: 0.000000 2023-09-04 18:24:29,617 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:24:29,617 EPOCH 8 done: loss 0.0125 - lr: 0.000007 2023-09-04 18:24:47,345 DEV : loss 0.21515436470508575 - f1-score (micro avg) 0.8236 2023-09-04 18:24:47,374 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:25:03,113 epoch 9 - iter 147/1476 - loss 0.01194312 - time (sec): 15.74 - samples/sec: 1021.80 - lr: 0.000006 - momentum: 0.000000 2023-09-04 18:25:18,626 epoch 9 - iter 294/1476 - loss 0.01113643 - time (sec): 31.25 - samples/sec: 1034.37 - lr: 0.000006 - momentum: 0.000000 2023-09-04 18:25:33,931 epoch 9 - iter 441/1476 - loss 0.00892407 - time (sec): 46.56 - samples/sec: 1010.97 - lr: 0.000006 - momentum: 0.000000 2023-09-04 18:25:50,615 epoch 9 - iter 588/1476 - loss 0.01054983 - time (sec): 63.24 - samples/sec: 1015.46 - lr: 0.000005 - momentum: 0.000000 2023-09-04 18:26:05,989 epoch 9 - iter 735/1476 - loss 0.01020447 - time (sec): 78.61 - samples/sec: 1014.85 - lr: 0.000005 - momentum: 0.000000 2023-09-04 18:26:21,846 epoch 9 - iter 882/1476 - loss 0.00970575 - time (sec): 94.47 - samples/sec: 1016.08 - lr: 0.000005 - momentum: 0.000000 2023-09-04 18:26:38,280 epoch 9 - iter 1029/1476 - loss 0.00972550 - time (sec): 110.90 - samples/sec: 1026.61 - lr: 0.000004 - momentum: 0.000000 2023-09-04 18:26:55,525 epoch 9 - iter 1176/1476 - loss 0.01059971 - time (sec): 128.15 - samples/sec: 1031.92 - lr: 0.000004 - momentum: 0.000000 2023-09-04 18:27:10,728 epoch 9 - iter 1323/1476 - loss 0.01001943 - time (sec): 143.35 - samples/sec: 1029.52 - lr: 0.000004 - momentum: 0.000000 2023-09-04 18:27:26,756 epoch 9 - iter 1470/1476 - loss 0.00955175 - time (sec): 159.38 - samples/sec: 1035.45 - lr: 0.000003 - momentum: 0.000000 2023-09-04 18:27:27,865 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:27:27,866 EPOCH 9 done: loss 0.0095 - lr: 0.000003 2023-09-04 18:27:45,558 DEV : loss 0.20868642628192902 - f1-score (micro avg) 0.8292 2023-09-04 18:27:45,587 saving best model 2023-09-04 18:27:46,955 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:28:02,137 epoch 10 - iter 147/1476 - loss 0.00101060 - time (sec): 15.18 - samples/sec: 1010.93 - lr: 0.000003 - momentum: 0.000000 2023-09-04 18:28:18,974 epoch 10 - iter 294/1476 - loss 0.00390884 - time (sec): 32.02 - samples/sec: 1028.47 - lr: 0.000003 - momentum: 0.000000 2023-09-04 18:28:35,317 epoch 10 - iter 441/1476 - loss 0.00447299 - time (sec): 48.36 - samples/sec: 1024.21 - lr: 0.000002 - momentum: 0.000000 2023-09-04 18:28:51,711 epoch 10 - iter 588/1476 - loss 0.00417455 - time (sec): 64.75 - samples/sec: 1030.00 - lr: 0.000002 - momentum: 0.000000 2023-09-04 18:29:08,401 epoch 10 - iter 735/1476 - loss 0.00470003 - time (sec): 81.44 - samples/sec: 1039.68 - lr: 0.000002 - momentum: 0.000000 2023-09-04 18:29:23,342 epoch 10 - iter 882/1476 - loss 0.00479490 - time (sec): 96.38 - samples/sec: 1041.02 - lr: 0.000001 - momentum: 0.000000 2023-09-04 18:29:38,135 epoch 10 - iter 1029/1476 - loss 0.00653311 - time (sec): 111.18 - samples/sec: 1041.64 - lr: 0.000001 - momentum: 0.000000 2023-09-04 18:29:54,398 epoch 10 - iter 1176/1476 - loss 0.00644572 - time (sec): 127.44 - samples/sec: 1038.01 - lr: 0.000001 - momentum: 0.000000 2023-09-04 18:30:10,831 epoch 10 - iter 1323/1476 - loss 0.00614733 - time (sec): 143.87 - samples/sec: 1044.88 - lr: 0.000000 - momentum: 0.000000 2023-09-04 18:30:26,129 epoch 10 - iter 1470/1476 - loss 0.00651115 - time (sec): 159.17 - samples/sec: 1041.46 - lr: 0.000000 - momentum: 0.000000 2023-09-04 18:30:26,732 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:30:26,732 EPOCH 10 done: loss 0.0065 - lr: 0.000000 2023-09-04 18:30:44,728 DEV : loss 0.22322718799114227 - f1-score (micro avg) 0.8291 2023-09-04 18:30:45,239 ---------------------------------------------------------------------------------------------------- 2023-09-04 18:30:45,241 Loading model from best epoch ... 2023-09-04 18:30:47,124 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod 2023-09-04 18:31:01,874 Results: - F-score (micro) 0.7899 - F-score (macro) 0.6984 - Accuracy 0.6764 By class: precision recall f1-score support loc 0.8319 0.8765 0.8536 858 pers 0.7709 0.7896 0.7801 537 org 0.5034 0.5606 0.5305 132 time 0.5645 0.6481 0.6034 54 prod 0.7636 0.6885 0.7241 61 micro avg 0.7724 0.8082 0.7899 1642 macro avg 0.6869 0.7127 0.6984 1642 weighted avg 0.7742 0.8082 0.7905 1642 2023-09-04 18:31:01,875 ----------------------------------------------------------------------------------------------------