| 2023-10-15 21:09:01,347 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,348 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=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-15 21:09:01,348 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,348 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences | |
| - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator | |
| 2023-10-15 21:09:01,348 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,348 Train: 20847 sentences | |
| 2023-10-15 21:09:01,348 (train_with_dev=False, train_with_test=False) | |
| 2023-10-15 21:09:01,348 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,348 Training Params: | |
| 2023-10-15 21:09:01,349 - learning_rate: "5e-05" | |
| 2023-10-15 21:09:01,349 - mini_batch_size: "4" | |
| 2023-10-15 21:09:01,349 - max_epochs: "10" | |
| 2023-10-15 21:09:01,349 - shuffle: "True" | |
| 2023-10-15 21:09:01,349 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,349 Plugins: | |
| 2023-10-15 21:09:01,349 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-15 21:09:01,349 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,349 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-15 21:09:01,349 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-15 21:09:01,349 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,349 Computation: | |
| 2023-10-15 21:09:01,349 - compute on device: cuda:0 | |
| 2023-10-15 21:09:01,349 - embedding storage: none | |
| 2023-10-15 21:09:01,349 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,349 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" | |
| 2023-10-15 21:09:01,349 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:01,349 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:09:26,219 epoch 1 - iter 521/5212 - loss 1.35418822 - time (sec): 24.87 - samples/sec: 1405.99 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-15 21:09:51,690 epoch 1 - iter 1042/5212 - loss 0.87173210 - time (sec): 50.34 - samples/sec: 1460.05 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-15 21:10:17,117 epoch 1 - iter 1563/5212 - loss 0.68359880 - time (sec): 75.77 - samples/sec: 1436.88 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-15 21:10:42,614 epoch 1 - iter 2084/5212 - loss 0.57980752 - time (sec): 101.26 - samples/sec: 1431.39 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-15 21:11:08,079 epoch 1 - iter 2605/5212 - loss 0.50915925 - time (sec): 126.73 - samples/sec: 1450.07 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-15 21:11:33,037 epoch 1 - iter 3126/5212 - loss 0.46695555 - time (sec): 151.69 - samples/sec: 1443.28 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-15 21:11:57,932 epoch 1 - iter 3647/5212 - loss 0.43068088 - time (sec): 176.58 - samples/sec: 1443.12 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-15 21:12:23,688 epoch 1 - iter 4168/5212 - loss 0.40031908 - time (sec): 202.34 - samples/sec: 1444.07 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-15 21:12:48,715 epoch 1 - iter 4689/5212 - loss 0.38161901 - time (sec): 227.37 - samples/sec: 1445.17 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-15 21:13:15,489 epoch 1 - iter 5210/5212 - loss 0.36415325 - time (sec): 254.14 - samples/sec: 1445.58 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-15 21:13:15,572 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:13:15,573 EPOCH 1 done: loss 0.3641 - lr: 0.000050 | |
| 2023-10-15 21:13:21,332 DEV : loss 0.12803316116333008 - f1-score (micro avg) 0.2579 | |
| 2023-10-15 21:13:21,357 saving best model | |
| 2023-10-15 21:13:21,729 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:13:47,367 epoch 2 - iter 521/5212 - loss 0.21777270 - time (sec): 25.64 - samples/sec: 1486.64 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-15 21:14:13,018 epoch 2 - iter 1042/5212 - loss 0.18749115 - time (sec): 51.29 - samples/sec: 1487.45 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-15 21:14:38,321 epoch 2 - iter 1563/5212 - loss 0.18643203 - time (sec): 76.59 - samples/sec: 1481.92 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-15 21:15:03,555 epoch 2 - iter 2084/5212 - loss 0.18814099 - time (sec): 101.82 - samples/sec: 1457.01 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-15 21:15:28,943 epoch 2 - iter 2605/5212 - loss 0.19599826 - time (sec): 127.21 - samples/sec: 1459.62 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-15 21:15:53,824 epoch 2 - iter 3126/5212 - loss 0.19471761 - time (sec): 152.09 - samples/sec: 1454.98 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-15 21:16:18,931 epoch 2 - iter 3647/5212 - loss 0.19295220 - time (sec): 177.20 - samples/sec: 1466.52 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-15 21:16:42,937 epoch 2 - iter 4168/5212 - loss 0.19677761 - time (sec): 201.21 - samples/sec: 1467.46 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-15 21:17:07,768 epoch 2 - iter 4689/5212 - loss 0.19258009 - time (sec): 226.04 - samples/sec: 1476.98 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-15 21:17:31,714 epoch 2 - iter 5210/5212 - loss 0.19109860 - time (sec): 249.98 - samples/sec: 1469.65 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-15 21:17:31,800 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:17:31,800 EPOCH 2 done: loss 0.1911 - lr: 0.000044 | |
| 2023-10-15 21:17:40,715 DEV : loss 0.12834765017032623 - f1-score (micro avg) 0.3234 | |
| 2023-10-15 21:17:40,741 saving best model | |
| 2023-10-15 21:17:41,160 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:18:06,231 epoch 3 - iter 521/5212 - loss 0.16680848 - time (sec): 25.07 - samples/sec: 1455.11 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-15 21:18:31,447 epoch 3 - iter 1042/5212 - loss 0.15227978 - time (sec): 50.28 - samples/sec: 1454.38 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-15 21:18:56,965 epoch 3 - iter 1563/5212 - loss 0.15045944 - time (sec): 75.80 - samples/sec: 1457.53 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-15 21:19:22,788 epoch 3 - iter 2084/5212 - loss 0.15461880 - time (sec): 101.63 - samples/sec: 1460.91 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-15 21:19:47,794 epoch 3 - iter 2605/5212 - loss 0.14819648 - time (sec): 126.63 - samples/sec: 1460.43 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-15 21:20:12,632 epoch 3 - iter 3126/5212 - loss 0.14802839 - time (sec): 151.47 - samples/sec: 1457.06 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-15 21:20:37,526 epoch 3 - iter 3647/5212 - loss 0.14888939 - time (sec): 176.36 - samples/sec: 1455.73 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-15 21:21:03,199 epoch 3 - iter 4168/5212 - loss 0.14477081 - time (sec): 202.04 - samples/sec: 1463.02 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-15 21:21:28,311 epoch 3 - iter 4689/5212 - loss 0.14379556 - time (sec): 227.15 - samples/sec: 1463.51 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-15 21:21:53,162 epoch 3 - iter 5210/5212 - loss 0.14317646 - time (sec): 252.00 - samples/sec: 1457.92 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-15 21:21:53,250 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:21:53,250 EPOCH 3 done: loss 0.1432 - lr: 0.000039 | |
| 2023-10-15 21:22:01,515 DEV : loss 0.16754454374313354 - f1-score (micro avg) 0.3436 | |
| 2023-10-15 21:22:01,544 saving best model | |
| 2023-10-15 21:22:02,149 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:22:27,751 epoch 4 - iter 521/5212 - loss 0.11278945 - time (sec): 25.60 - samples/sec: 1434.22 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-15 21:22:52,628 epoch 4 - iter 1042/5212 - loss 0.10789964 - time (sec): 50.48 - samples/sec: 1413.48 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-15 21:23:17,727 epoch 4 - iter 1563/5212 - loss 0.11007241 - time (sec): 75.58 - samples/sec: 1421.41 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-15 21:23:43,808 epoch 4 - iter 2084/5212 - loss 0.10613979 - time (sec): 101.66 - samples/sec: 1422.18 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-15 21:24:08,589 epoch 4 - iter 2605/5212 - loss 0.10636173 - time (sec): 126.44 - samples/sec: 1423.89 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-15 21:24:33,295 epoch 4 - iter 3126/5212 - loss 0.11023475 - time (sec): 151.14 - samples/sec: 1428.26 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-15 21:24:58,631 epoch 4 - iter 3647/5212 - loss 0.11185702 - time (sec): 176.48 - samples/sec: 1441.00 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-15 21:25:23,843 epoch 4 - iter 4168/5212 - loss 0.11237802 - time (sec): 201.69 - samples/sec: 1437.97 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-15 21:25:49,127 epoch 4 - iter 4689/5212 - loss 0.11101056 - time (sec): 226.98 - samples/sec: 1445.77 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-15 21:26:14,931 epoch 4 - iter 5210/5212 - loss 0.10870647 - time (sec): 252.78 - samples/sec: 1453.35 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-15 21:26:15,016 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:26:15,016 EPOCH 4 done: loss 0.1087 - lr: 0.000033 | |
| 2023-10-15 21:26:23,320 DEV : loss 0.23736144602298737 - f1-score (micro avg) 0.3947 | |
| 2023-10-15 21:26:23,351 saving best model | |
| 2023-10-15 21:26:23,983 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:26:48,489 epoch 5 - iter 521/5212 - loss 0.07596090 - time (sec): 24.50 - samples/sec: 1403.26 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-15 21:27:13,356 epoch 5 - iter 1042/5212 - loss 0.08242943 - time (sec): 49.37 - samples/sec: 1408.42 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-15 21:27:38,336 epoch 5 - iter 1563/5212 - loss 0.08169838 - time (sec): 74.35 - samples/sec: 1414.40 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-15 21:28:03,345 epoch 5 - iter 2084/5212 - loss 0.08443360 - time (sec): 99.36 - samples/sec: 1432.55 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-15 21:28:29,132 epoch 5 - iter 2605/5212 - loss 0.08304632 - time (sec): 125.15 - samples/sec: 1438.85 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-15 21:28:54,172 epoch 5 - iter 3126/5212 - loss 0.08296829 - time (sec): 150.18 - samples/sec: 1435.67 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-15 21:29:19,404 epoch 5 - iter 3647/5212 - loss 0.08179795 - time (sec): 175.42 - samples/sec: 1442.82 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-15 21:29:45,496 epoch 5 - iter 4168/5212 - loss 0.08011600 - time (sec): 201.51 - samples/sec: 1445.91 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-15 21:30:11,175 epoch 5 - iter 4689/5212 - loss 0.07905627 - time (sec): 227.19 - samples/sec: 1449.26 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-15 21:30:36,583 epoch 5 - iter 5210/5212 - loss 0.08040599 - time (sec): 252.60 - samples/sec: 1454.09 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-15 21:30:36,676 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:30:36,676 EPOCH 5 done: loss 0.0804 - lr: 0.000028 | |
| 2023-10-15 21:30:45,177 DEV : loss 0.32270362973213196 - f1-score (micro avg) 0.3444 | |
| 2023-10-15 21:30:45,211 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:31:10,971 epoch 6 - iter 521/5212 - loss 0.07154848 - time (sec): 25.76 - samples/sec: 1455.64 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-15 21:31:36,495 epoch 6 - iter 1042/5212 - loss 0.08555596 - time (sec): 51.28 - samples/sec: 1482.06 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-15 21:32:01,396 epoch 6 - iter 1563/5212 - loss 0.07751863 - time (sec): 76.18 - samples/sec: 1462.55 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-15 21:32:26,938 epoch 6 - iter 2084/5212 - loss 0.07146560 - time (sec): 101.72 - samples/sec: 1481.82 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-15 21:32:51,955 epoch 6 - iter 2605/5212 - loss 0.06915037 - time (sec): 126.74 - samples/sec: 1475.93 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-15 21:33:17,101 epoch 6 - iter 3126/5212 - loss 0.06782335 - time (sec): 151.89 - samples/sec: 1469.97 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-15 21:33:41,720 epoch 6 - iter 3647/5212 - loss 0.06809506 - time (sec): 176.51 - samples/sec: 1461.46 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-15 21:34:06,826 epoch 6 - iter 4168/5212 - loss 0.06739184 - time (sec): 201.61 - samples/sec: 1455.29 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-15 21:34:31,898 epoch 6 - iter 4689/5212 - loss 0.06720584 - time (sec): 226.68 - samples/sec: 1449.42 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-15 21:34:57,524 epoch 6 - iter 5210/5212 - loss 0.06716812 - time (sec): 252.31 - samples/sec: 1454.78 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-15 21:34:57,655 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:34:57,655 EPOCH 6 done: loss 0.0671 - lr: 0.000022 | |
| 2023-10-15 21:35:06,686 DEV : loss 0.3467041552066803 - f1-score (micro avg) 0.3468 | |
| 2023-10-15 21:35:06,712 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:35:31,692 epoch 7 - iter 521/5212 - loss 0.04008855 - time (sec): 24.98 - samples/sec: 1510.39 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-15 21:35:57,463 epoch 7 - iter 1042/5212 - loss 0.04042192 - time (sec): 50.75 - samples/sec: 1495.14 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-15 21:36:22,413 epoch 7 - iter 1563/5212 - loss 0.04452128 - time (sec): 75.70 - samples/sec: 1475.67 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-15 21:36:47,471 epoch 7 - iter 2084/5212 - loss 0.04517209 - time (sec): 100.76 - samples/sec: 1444.65 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-15 21:37:12,770 epoch 7 - iter 2605/5212 - loss 0.04565804 - time (sec): 126.06 - samples/sec: 1454.67 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-15 21:37:37,812 epoch 7 - iter 3126/5212 - loss 0.04487907 - time (sec): 151.10 - samples/sec: 1446.65 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-15 21:38:03,557 epoch 7 - iter 3647/5212 - loss 0.04501311 - time (sec): 176.84 - samples/sec: 1459.11 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-15 21:38:28,512 epoch 7 - iter 4168/5212 - loss 0.04461942 - time (sec): 201.80 - samples/sec: 1448.64 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-15 21:38:54,620 epoch 7 - iter 4689/5212 - loss 0.04411942 - time (sec): 227.91 - samples/sec: 1452.78 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-15 21:39:19,467 epoch 7 - iter 5210/5212 - loss 0.04338822 - time (sec): 252.75 - samples/sec: 1453.50 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-15 21:39:19,553 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:39:19,553 EPOCH 7 done: loss 0.0434 - lr: 0.000017 | |
| 2023-10-15 21:39:28,681 DEV : loss 0.3810468912124634 - f1-score (micro avg) 0.3437 | |
| 2023-10-15 21:39:28,710 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:39:53,580 epoch 8 - iter 521/5212 - loss 0.03094489 - time (sec): 24.87 - samples/sec: 1372.29 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-15 21:40:18,965 epoch 8 - iter 1042/5212 - loss 0.03266070 - time (sec): 50.25 - samples/sec: 1449.21 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-15 21:40:44,068 epoch 8 - iter 1563/5212 - loss 0.03082353 - time (sec): 75.36 - samples/sec: 1445.56 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-15 21:41:09,236 epoch 8 - iter 2084/5212 - loss 0.03189132 - time (sec): 100.52 - samples/sec: 1443.37 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-15 21:41:34,729 epoch 8 - iter 2605/5212 - loss 0.03187351 - time (sec): 126.02 - samples/sec: 1451.00 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-15 21:42:00,318 epoch 8 - iter 3126/5212 - loss 0.03141481 - time (sec): 151.61 - samples/sec: 1456.23 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-15 21:42:25,218 epoch 8 - iter 3647/5212 - loss 0.03152484 - time (sec): 176.51 - samples/sec: 1459.90 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-15 21:42:50,184 epoch 8 - iter 4168/5212 - loss 0.03161608 - time (sec): 201.47 - samples/sec: 1459.49 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-15 21:43:15,609 epoch 8 - iter 4689/5212 - loss 0.03114092 - time (sec): 226.90 - samples/sec: 1458.94 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-15 21:43:40,617 epoch 8 - iter 5210/5212 - loss 0.03225916 - time (sec): 251.91 - samples/sec: 1457.75 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-15 21:43:40,714 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:43:40,715 EPOCH 8 done: loss 0.0323 - lr: 0.000011 | |
| 2023-10-15 21:43:49,762 DEV : loss 0.3573097884654999 - f1-score (micro avg) 0.3737 | |
| 2023-10-15 21:43:49,788 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:44:15,283 epoch 9 - iter 521/5212 - loss 0.02137501 - time (sec): 25.49 - samples/sec: 1557.65 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-15 21:44:40,744 epoch 9 - iter 1042/5212 - loss 0.02521327 - time (sec): 50.95 - samples/sec: 1532.21 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-15 21:45:06,007 epoch 9 - iter 1563/5212 - loss 0.02417142 - time (sec): 76.22 - samples/sec: 1518.03 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-15 21:45:30,713 epoch 9 - iter 2084/5212 - loss 0.02264293 - time (sec): 100.92 - samples/sec: 1505.23 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-15 21:45:55,673 epoch 9 - iter 2605/5212 - loss 0.02338377 - time (sec): 125.88 - samples/sec: 1495.02 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-15 21:46:20,343 epoch 9 - iter 3126/5212 - loss 0.02317935 - time (sec): 150.55 - samples/sec: 1467.58 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-15 21:46:45,572 epoch 9 - iter 3647/5212 - loss 0.02373783 - time (sec): 175.78 - samples/sec: 1469.70 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-15 21:47:10,321 epoch 9 - iter 4168/5212 - loss 0.02314043 - time (sec): 200.53 - samples/sec: 1469.15 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-15 21:47:35,423 epoch 9 - iter 4689/5212 - loss 0.02314496 - time (sec): 225.63 - samples/sec: 1467.76 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-15 21:48:00,356 epoch 9 - iter 5210/5212 - loss 0.02267322 - time (sec): 250.57 - samples/sec: 1466.11 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-15 21:48:00,446 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:48:00,446 EPOCH 9 done: loss 0.0227 - lr: 0.000006 | |
| 2023-10-15 21:48:09,481 DEV : loss 0.42241212725639343 - f1-score (micro avg) 0.3776 | |
| 2023-10-15 21:48:09,509 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:48:34,392 epoch 10 - iter 521/5212 - loss 0.01795075 - time (sec): 24.88 - samples/sec: 1427.01 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-15 21:48:59,088 epoch 10 - iter 1042/5212 - loss 0.01766837 - time (sec): 49.58 - samples/sec: 1399.01 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-15 21:49:24,146 epoch 10 - iter 1563/5212 - loss 0.01546036 - time (sec): 74.64 - samples/sec: 1428.03 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-15 21:49:48,972 epoch 10 - iter 2084/5212 - loss 0.01551573 - time (sec): 99.46 - samples/sec: 1433.30 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-15 21:50:13,944 epoch 10 - iter 2605/5212 - loss 0.01520813 - time (sec): 124.43 - samples/sec: 1436.94 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-15 21:50:39,014 epoch 10 - iter 3126/5212 - loss 0.01608964 - time (sec): 149.50 - samples/sec: 1440.85 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-15 21:51:04,117 epoch 10 - iter 3647/5212 - loss 0.01586596 - time (sec): 174.61 - samples/sec: 1446.98 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-15 21:51:29,596 epoch 10 - iter 4168/5212 - loss 0.01541581 - time (sec): 200.09 - samples/sec: 1456.61 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-15 21:51:54,619 epoch 10 - iter 4689/5212 - loss 0.01533530 - time (sec): 225.11 - samples/sec: 1454.47 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-15 21:52:20,313 epoch 10 - iter 5210/5212 - loss 0.01573070 - time (sec): 250.80 - samples/sec: 1464.56 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-15 21:52:20,399 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:52:20,400 EPOCH 10 done: loss 0.0157 - lr: 0.000000 | |
| 2023-10-15 21:52:29,511 DEV : loss 0.42857325077056885 - f1-score (micro avg) 0.3698 | |
| 2023-10-15 21:52:29,962 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 21:52:29,963 Loading model from best epoch ... | |
| 2023-10-15 21:52:31,489 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd | |
| 2023-10-15 21:52:47,128 | |
| Results: | |
| - F-score (micro) 0.4359 | |
| - F-score (macro) 0.2917 | |
| - Accuracy 0.283 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.5601 0.5338 0.5466 1214 | |
| PER 0.3793 0.3676 0.3734 808 | |
| ORG 0.2152 0.2890 0.2467 353 | |
| HumanProd 0.0000 0.0000 0.0000 15 | |
| micro avg 0.4337 0.4381 0.4359 2390 | |
| macro avg 0.2886 0.2976 0.2917 2390 | |
| weighted avg 0.4445 0.4381 0.4403 2390 | |
| 2023-10-15 21:52:47,129 ---------------------------------------------------------------------------------------------------- | |