2023-10-18 22:51:24,698 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,698 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(32001, 128) (position_embeddings): Embedding(512, 128) (token_type_embeddings): Embedding(2, 128) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-1): 2 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=128, out_features=128, bias=True) (key): Linear(in_features=128, out_features=128, bias=True) (value): Linear(in_features=128, out_features=128, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=128, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=128, out_features=512, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=512, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=128, out_features=128, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=128, out_features=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-18 22:51:24,698 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 MultiCorpus: 5777 train + 722 dev + 723 test sentences - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 Train: 5777 sentences 2023-10-18 22:51:24,699 (train_with_dev=False, train_with_test=False) 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 Training Params: 2023-10-18 22:51:24,699 - learning_rate: "5e-05" 2023-10-18 22:51:24,699 - mini_batch_size: "4" 2023-10-18 22:51:24,699 - max_epochs: "10" 2023-10-18 22:51:24,699 - shuffle: "True" 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 Plugins: 2023-10-18 22:51:24,699 - TensorboardLogger 2023-10-18 22:51:24,699 - LinearScheduler | warmup_fraction: '0.1' 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 Final evaluation on model from best epoch (best-model.pt) 2023-10-18 22:51:24,699 - metric: "('micro avg', 'f1-score')" 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 Computation: 2023-10-18 22:51:24,699 - compute on device: cuda:0 2023-10-18 22:51:24,699 - embedding storage: none 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 Model training base path: "hmbench-icdar/nl-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:24,699 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-18 22:51:27,059 epoch 1 - iter 144/1445 - loss 3.29539325 - time (sec): 2.36 - samples/sec: 6871.85 - lr: 0.000005 - momentum: 0.000000 2023-10-18 22:51:29,599 epoch 1 - iter 288/1445 - loss 2.80667047 - time (sec): 4.90 - samples/sec: 6851.93 - lr: 0.000010 - momentum: 0.000000 2023-10-18 22:51:32,037 epoch 1 - iter 432/1445 - loss 2.17298777 - time (sec): 7.34 - samples/sec: 7006.80 - lr: 0.000015 - momentum: 0.000000 2023-10-18 22:51:34,467 epoch 1 - iter 576/1445 - loss 1.69993643 - time (sec): 9.77 - samples/sec: 7136.03 - lr: 0.000020 - momentum: 0.000000 2023-10-18 22:51:36,827 epoch 1 - iter 720/1445 - loss 1.42956310 - time (sec): 12.13 - samples/sec: 7137.99 - lr: 0.000025 - momentum: 0.000000 2023-10-18 22:51:39,163 epoch 1 - iter 864/1445 - loss 1.24708491 - time (sec): 14.46 - samples/sec: 7154.15 - lr: 0.000030 - momentum: 0.000000 2023-10-18 22:51:41,607 epoch 1 - iter 1008/1445 - loss 1.11449457 - time (sec): 16.91 - samples/sec: 7121.10 - lr: 0.000035 - momentum: 0.000000 2023-10-18 22:51:44,080 epoch 1 - iter 1152/1445 - loss 1.00767761 - time (sec): 19.38 - samples/sec: 7180.56 - lr: 0.000040 - momentum: 0.000000 2023-10-18 22:51:46,593 epoch 1 - iter 1296/1445 - loss 0.91487367 - time (sec): 21.89 - samples/sec: 7218.53 - lr: 0.000045 - momentum: 0.000000 2023-10-18 22:51:49,002 epoch 1 - iter 1440/1445 - loss 0.84936645 - time (sec): 24.30 - samples/sec: 7229.17 - lr: 0.000050 - momentum: 0.000000 2023-10-18 22:51:49,078 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:49,079 EPOCH 1 done: loss 0.8477 - lr: 0.000050 2023-10-18 22:51:50,368 DEV : loss 0.28886058926582336 - f1-score (micro avg) 0.0363 2023-10-18 22:51:50,382 saving best model 2023-10-18 22:51:50,413 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:51:52,534 epoch 2 - iter 144/1445 - loss 0.20771123 - time (sec): 2.12 - samples/sec: 8378.68 - lr: 0.000049 - momentum: 0.000000 2023-10-18 22:51:54,705 epoch 2 - iter 288/1445 - loss 0.21302193 - time (sec): 4.29 - samples/sec: 8059.18 - lr: 0.000049 - momentum: 0.000000 2023-10-18 22:51:57,097 epoch 2 - iter 432/1445 - loss 0.21308278 - time (sec): 6.68 - samples/sec: 7668.37 - lr: 0.000048 - momentum: 0.000000 2023-10-18 22:51:59,685 epoch 2 - iter 576/1445 - loss 0.20687075 - time (sec): 9.27 - samples/sec: 7600.92 - lr: 0.000048 - momentum: 0.000000 2023-10-18 22:52:02,218 epoch 2 - iter 720/1445 - loss 0.20244105 - time (sec): 11.80 - samples/sec: 7393.23 - lr: 0.000047 - momentum: 0.000000 2023-10-18 22:52:04,652 epoch 2 - iter 864/1445 - loss 0.19886336 - time (sec): 14.24 - samples/sec: 7394.02 - lr: 0.000047 - momentum: 0.000000 2023-10-18 22:52:06,878 epoch 2 - iter 1008/1445 - loss 0.19645336 - time (sec): 16.46 - samples/sec: 7481.26 - lr: 0.000046 - momentum: 0.000000 2023-10-18 22:52:09,110 epoch 2 - iter 1152/1445 - loss 0.20134357 - time (sec): 18.70 - samples/sec: 7488.90 - lr: 0.000046 - momentum: 0.000000 2023-10-18 22:52:11,497 epoch 2 - iter 1296/1445 - loss 0.19916074 - time (sec): 21.08 - samples/sec: 7494.59 - lr: 0.000045 - momentum: 0.000000 2023-10-18 22:52:13,935 epoch 2 - iter 1440/1445 - loss 0.19916757 - time (sec): 23.52 - samples/sec: 7459.46 - lr: 0.000044 - momentum: 0.000000 2023-10-18 22:52:14,018 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:52:14,019 EPOCH 2 done: loss 0.1991 - lr: 0.000044 2023-10-18 22:52:15,801 DEV : loss 0.2434382140636444 - f1-score (micro avg) 0.3074 2023-10-18 22:52:15,815 saving best model 2023-10-18 22:52:15,849 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:52:18,303 epoch 3 - iter 144/1445 - loss 0.16824685 - time (sec): 2.45 - samples/sec: 7641.85 - lr: 0.000044 - momentum: 0.000000 2023-10-18 22:52:20,724 epoch 3 - iter 288/1445 - loss 0.17832135 - time (sec): 4.87 - samples/sec: 7722.80 - lr: 0.000043 - momentum: 0.000000 2023-10-18 22:52:23,482 epoch 3 - iter 432/1445 - loss 0.17388072 - time (sec): 7.63 - samples/sec: 7228.05 - lr: 0.000043 - momentum: 0.000000 2023-10-18 22:52:25,913 epoch 3 - iter 576/1445 - loss 0.16949600 - time (sec): 10.06 - samples/sec: 7262.71 - lr: 0.000042 - momentum: 0.000000 2023-10-18 22:52:28,376 epoch 3 - iter 720/1445 - loss 0.17010711 - time (sec): 12.53 - samples/sec: 7214.37 - lr: 0.000042 - momentum: 0.000000 2023-10-18 22:52:30,724 epoch 3 - iter 864/1445 - loss 0.16944462 - time (sec): 14.87 - samples/sec: 7161.25 - lr: 0.000041 - momentum: 0.000000 2023-10-18 22:52:33,038 epoch 3 - iter 1008/1445 - loss 0.17031912 - time (sec): 17.19 - samples/sec: 7241.37 - lr: 0.000041 - momentum: 0.000000 2023-10-18 22:52:35,207 epoch 3 - iter 1152/1445 - loss 0.16944422 - time (sec): 19.36 - samples/sec: 7297.31 - lr: 0.000040 - momentum: 0.000000 2023-10-18 22:52:37,602 epoch 3 - iter 1296/1445 - loss 0.16674107 - time (sec): 21.75 - samples/sec: 7316.34 - lr: 0.000039 - momentum: 0.000000 2023-10-18 22:52:39,958 epoch 3 - iter 1440/1445 - loss 0.16647381 - time (sec): 24.11 - samples/sec: 7278.75 - lr: 0.000039 - momentum: 0.000000 2023-10-18 22:52:40,039 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:52:40,039 EPOCH 3 done: loss 0.1663 - lr: 0.000039 2023-10-18 22:52:41,821 DEV : loss 0.21055997908115387 - f1-score (micro avg) 0.4219 2023-10-18 22:52:41,835 saving best model 2023-10-18 22:52:41,870 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:52:44,200 epoch 4 - iter 144/1445 - loss 0.15283862 - time (sec): 2.33 - samples/sec: 7307.47 - lr: 0.000038 - momentum: 0.000000 2023-10-18 22:52:46,578 epoch 4 - iter 288/1445 - loss 0.14799565 - time (sec): 4.71 - samples/sec: 7015.01 - lr: 0.000038 - momentum: 0.000000 2023-10-18 22:52:48,944 epoch 4 - iter 432/1445 - loss 0.15105169 - time (sec): 7.07 - samples/sec: 7106.38 - lr: 0.000037 - momentum: 0.000000 2023-10-18 22:52:51,342 epoch 4 - iter 576/1445 - loss 0.15217093 - time (sec): 9.47 - samples/sec: 7174.33 - lr: 0.000037 - momentum: 0.000000 2023-10-18 22:52:53,783 epoch 4 - iter 720/1445 - loss 0.15166536 - time (sec): 11.91 - samples/sec: 7162.93 - lr: 0.000036 - momentum: 0.000000 2023-10-18 22:52:56,210 epoch 4 - iter 864/1445 - loss 0.15237011 - time (sec): 14.34 - samples/sec: 7158.14 - lr: 0.000036 - momentum: 0.000000 2023-10-18 22:52:58,867 epoch 4 - iter 1008/1445 - loss 0.15079524 - time (sec): 17.00 - samples/sec: 7087.84 - lr: 0.000035 - momentum: 0.000000 2023-10-18 22:53:01,592 epoch 4 - iter 1152/1445 - loss 0.15204078 - time (sec): 19.72 - samples/sec: 7121.06 - lr: 0.000034 - momentum: 0.000000 2023-10-18 22:53:03,966 epoch 4 - iter 1296/1445 - loss 0.15103163 - time (sec): 22.10 - samples/sec: 7125.45 - lr: 0.000034 - momentum: 0.000000 2023-10-18 22:53:06,380 epoch 4 - iter 1440/1445 - loss 0.15143506 - time (sec): 24.51 - samples/sec: 7168.62 - lr: 0.000033 - momentum: 0.000000 2023-10-18 22:53:06,461 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:53:06,461 EPOCH 4 done: loss 0.1515 - lr: 0.000033 2023-10-18 22:53:08,244 DEV : loss 0.19626548886299133 - f1-score (micro avg) 0.4869 2023-10-18 22:53:08,258 saving best model 2023-10-18 22:53:08,293 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:53:10,756 epoch 5 - iter 144/1445 - loss 0.14791715 - time (sec): 2.46 - samples/sec: 7246.53 - lr: 0.000033 - momentum: 0.000000 2023-10-18 22:53:13,168 epoch 5 - iter 288/1445 - loss 0.14595192 - time (sec): 4.87 - samples/sec: 7399.69 - lr: 0.000032 - momentum: 0.000000 2023-10-18 22:53:15,540 epoch 5 - iter 432/1445 - loss 0.14209987 - time (sec): 7.25 - samples/sec: 7414.07 - lr: 0.000032 - momentum: 0.000000 2023-10-18 22:53:17,946 epoch 5 - iter 576/1445 - loss 0.14142116 - time (sec): 9.65 - samples/sec: 7301.39 - lr: 0.000031 - momentum: 0.000000 2023-10-18 22:53:20,291 epoch 5 - iter 720/1445 - loss 0.13858369 - time (sec): 12.00 - samples/sec: 7226.89 - lr: 0.000031 - momentum: 0.000000 2023-10-18 22:53:22,707 epoch 5 - iter 864/1445 - loss 0.13944449 - time (sec): 14.41 - samples/sec: 7219.53 - lr: 0.000030 - momentum: 0.000000 2023-10-18 22:53:25,161 epoch 5 - iter 1008/1445 - loss 0.13904087 - time (sec): 16.87 - samples/sec: 7204.05 - lr: 0.000029 - momentum: 0.000000 2023-10-18 22:53:27,753 epoch 5 - iter 1152/1445 - loss 0.13936345 - time (sec): 19.46 - samples/sec: 7198.86 - lr: 0.000029 - momentum: 0.000000 2023-10-18 22:53:30,106 epoch 5 - iter 1296/1445 - loss 0.13877879 - time (sec): 21.81 - samples/sec: 7220.31 - lr: 0.000028 - momentum: 0.000000 2023-10-18 22:53:32,573 epoch 5 - iter 1440/1445 - loss 0.13778394 - time (sec): 24.28 - samples/sec: 7245.29 - lr: 0.000028 - momentum: 0.000000 2023-10-18 22:53:32,646 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:53:32,646 EPOCH 5 done: loss 0.1378 - lr: 0.000028 2023-10-18 22:53:34,776 DEV : loss 0.186900332570076 - f1-score (micro avg) 0.5261 2023-10-18 22:53:34,792 saving best model 2023-10-18 22:53:34,828 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:53:37,198 epoch 6 - iter 144/1445 - loss 0.12941650 - time (sec): 2.37 - samples/sec: 6979.43 - lr: 0.000027 - momentum: 0.000000 2023-10-18 22:53:39,571 epoch 6 - iter 288/1445 - loss 0.12211079 - time (sec): 4.74 - samples/sec: 7141.82 - lr: 0.000027 - momentum: 0.000000 2023-10-18 22:53:41,959 epoch 6 - iter 432/1445 - loss 0.12637372 - time (sec): 7.13 - samples/sec: 7336.97 - lr: 0.000026 - momentum: 0.000000 2023-10-18 22:53:44,418 epoch 6 - iter 576/1445 - loss 0.12653560 - time (sec): 9.59 - samples/sec: 7368.21 - lr: 0.000026 - momentum: 0.000000 2023-10-18 22:53:46,769 epoch 6 - iter 720/1445 - loss 0.12839698 - time (sec): 11.94 - samples/sec: 7326.25 - lr: 0.000025 - momentum: 0.000000 2023-10-18 22:53:49,154 epoch 6 - iter 864/1445 - loss 0.12535563 - time (sec): 14.32 - samples/sec: 7325.47 - lr: 0.000024 - momentum: 0.000000 2023-10-18 22:53:51,561 epoch 6 - iter 1008/1445 - loss 0.12897091 - time (sec): 16.73 - samples/sec: 7261.16 - lr: 0.000024 - momentum: 0.000000 2023-10-18 22:53:53,981 epoch 6 - iter 1152/1445 - loss 0.12610695 - time (sec): 19.15 - samples/sec: 7266.88 - lr: 0.000023 - momentum: 0.000000 2023-10-18 22:53:56,366 epoch 6 - iter 1296/1445 - loss 0.12806517 - time (sec): 21.54 - samples/sec: 7280.58 - lr: 0.000023 - momentum: 0.000000 2023-10-18 22:53:58,957 epoch 6 - iter 1440/1445 - loss 0.12929665 - time (sec): 24.13 - samples/sec: 7283.06 - lr: 0.000022 - momentum: 0.000000 2023-10-18 22:53:59,044 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:53:59,044 EPOCH 6 done: loss 0.1292 - lr: 0.000022 2023-10-18 22:54:00,808 DEV : loss 0.1867137998342514 - f1-score (micro avg) 0.5326 2023-10-18 22:54:00,822 saving best model 2023-10-18 22:54:00,858 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:54:03,301 epoch 7 - iter 144/1445 - loss 0.11727356 - time (sec): 2.44 - samples/sec: 6746.22 - lr: 0.000022 - momentum: 0.000000 2023-10-18 22:54:05,687 epoch 7 - iter 288/1445 - loss 0.11920637 - time (sec): 4.83 - samples/sec: 7110.68 - lr: 0.000021 - momentum: 0.000000 2023-10-18 22:54:08,078 epoch 7 - iter 432/1445 - loss 0.12142107 - time (sec): 7.22 - samples/sec: 7086.33 - lr: 0.000021 - momentum: 0.000000 2023-10-18 22:54:10,549 epoch 7 - iter 576/1445 - loss 0.12054887 - time (sec): 9.69 - samples/sec: 7176.58 - lr: 0.000020 - momentum: 0.000000 2023-10-18 22:54:12,850 epoch 7 - iter 720/1445 - loss 0.12121286 - time (sec): 11.99 - samples/sec: 7219.02 - lr: 0.000019 - momentum: 0.000000 2023-10-18 22:54:15,406 epoch 7 - iter 864/1445 - loss 0.12151057 - time (sec): 14.55 - samples/sec: 7155.32 - lr: 0.000019 - momentum: 0.000000 2023-10-18 22:54:17,870 epoch 7 - iter 1008/1445 - loss 0.12126598 - time (sec): 17.01 - samples/sec: 7193.07 - lr: 0.000018 - momentum: 0.000000 2023-10-18 22:54:20,281 epoch 7 - iter 1152/1445 - loss 0.12219795 - time (sec): 19.42 - samples/sec: 7208.87 - lr: 0.000018 - momentum: 0.000000 2023-10-18 22:54:22,749 epoch 7 - iter 1296/1445 - loss 0.12311442 - time (sec): 21.89 - samples/sec: 7217.81 - lr: 0.000017 - momentum: 0.000000 2023-10-18 22:54:25,139 epoch 7 - iter 1440/1445 - loss 0.12171338 - time (sec): 24.28 - samples/sec: 7233.22 - lr: 0.000017 - momentum: 0.000000 2023-10-18 22:54:25,219 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:54:25,220 EPOCH 7 done: loss 0.1216 - lr: 0.000017 2023-10-18 22:54:26,988 DEV : loss 0.19099119305610657 - f1-score (micro avg) 0.5487 2023-10-18 22:54:27,003 saving best model 2023-10-18 22:54:27,040 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:54:29,347 epoch 8 - iter 144/1445 - loss 0.13909632 - time (sec): 2.31 - samples/sec: 7149.79 - lr: 0.000016 - momentum: 0.000000 2023-10-18 22:54:31,777 epoch 8 - iter 288/1445 - loss 0.12875856 - time (sec): 4.74 - samples/sec: 7333.26 - lr: 0.000016 - momentum: 0.000000 2023-10-18 22:54:34,165 epoch 8 - iter 432/1445 - loss 0.12665044 - time (sec): 7.13 - samples/sec: 7443.17 - lr: 0.000015 - momentum: 0.000000 2023-10-18 22:54:36,574 epoch 8 - iter 576/1445 - loss 0.12517964 - time (sec): 9.53 - samples/sec: 7321.07 - lr: 0.000014 - momentum: 0.000000 2023-10-18 22:54:38,887 epoch 8 - iter 720/1445 - loss 0.12049045 - time (sec): 11.85 - samples/sec: 7417.55 - lr: 0.000014 - momentum: 0.000000 2023-10-18 22:54:41,339 epoch 8 - iter 864/1445 - loss 0.11769624 - time (sec): 14.30 - samples/sec: 7392.12 - lr: 0.000013 - momentum: 0.000000 2023-10-18 22:54:43,795 epoch 8 - iter 1008/1445 - loss 0.11773878 - time (sec): 16.76 - samples/sec: 7377.68 - lr: 0.000013 - momentum: 0.000000 2023-10-18 22:54:46,163 epoch 8 - iter 1152/1445 - loss 0.11579635 - time (sec): 19.12 - samples/sec: 7331.13 - lr: 0.000012 - momentum: 0.000000 2023-10-18 22:54:48,494 epoch 8 - iter 1296/1445 - loss 0.11660615 - time (sec): 21.45 - samples/sec: 7351.02 - lr: 0.000012 - momentum: 0.000000 2023-10-18 22:54:50,905 epoch 8 - iter 1440/1445 - loss 0.11664721 - time (sec): 23.86 - samples/sec: 7365.31 - lr: 0.000011 - momentum: 0.000000 2023-10-18 22:54:50,980 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:54:50,980 EPOCH 8 done: loss 0.1166 - lr: 0.000011 2023-10-18 22:54:53,066 DEV : loss 0.19956204295158386 - f1-score (micro avg) 0.5434 2023-10-18 22:54:53,080 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:54:55,471 epoch 9 - iter 144/1445 - loss 0.11520321 - time (sec): 2.39 - samples/sec: 7521.67 - lr: 0.000011 - momentum: 0.000000 2023-10-18 22:54:57,898 epoch 9 - iter 288/1445 - loss 0.12001827 - time (sec): 4.82 - samples/sec: 7468.77 - lr: 0.000010 - momentum: 0.000000 2023-10-18 22:55:00,238 epoch 9 - iter 432/1445 - loss 0.11044915 - time (sec): 7.16 - samples/sec: 7365.68 - lr: 0.000009 - momentum: 0.000000 2023-10-18 22:55:02,679 epoch 9 - iter 576/1445 - loss 0.10805370 - time (sec): 9.60 - samples/sec: 7325.82 - lr: 0.000009 - momentum: 0.000000 2023-10-18 22:55:05,091 epoch 9 - iter 720/1445 - loss 0.11126004 - time (sec): 12.01 - samples/sec: 7351.91 - lr: 0.000008 - momentum: 0.000000 2023-10-18 22:55:07,481 epoch 9 - iter 864/1445 - loss 0.11107034 - time (sec): 14.40 - samples/sec: 7406.58 - lr: 0.000008 - momentum: 0.000000 2023-10-18 22:55:09,864 epoch 9 - iter 1008/1445 - loss 0.11224647 - time (sec): 16.78 - samples/sec: 7416.01 - lr: 0.000007 - momentum: 0.000000 2023-10-18 22:55:12,182 epoch 9 - iter 1152/1445 - loss 0.11351795 - time (sec): 19.10 - samples/sec: 7395.91 - lr: 0.000007 - momentum: 0.000000 2023-10-18 22:55:14,679 epoch 9 - iter 1296/1445 - loss 0.11382066 - time (sec): 21.60 - samples/sec: 7353.71 - lr: 0.000006 - momentum: 0.000000 2023-10-18 22:55:17,000 epoch 9 - iter 1440/1445 - loss 0.11288922 - time (sec): 23.92 - samples/sec: 7345.54 - lr: 0.000006 - momentum: 0.000000 2023-10-18 22:55:17,074 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:55:17,075 EPOCH 9 done: loss 0.1130 - lr: 0.000006 2023-10-18 22:55:18,856 DEV : loss 0.19409048557281494 - f1-score (micro avg) 0.5636 2023-10-18 22:55:18,870 saving best model 2023-10-18 22:55:18,907 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:55:21,246 epoch 10 - iter 144/1445 - loss 0.11919407 - time (sec): 2.34 - samples/sec: 7369.04 - lr: 0.000005 - momentum: 0.000000 2023-10-18 22:55:23,624 epoch 10 - iter 288/1445 - loss 0.12311079 - time (sec): 4.72 - samples/sec: 7239.49 - lr: 0.000004 - momentum: 0.000000 2023-10-18 22:55:26,056 epoch 10 - iter 432/1445 - loss 0.12140389 - time (sec): 7.15 - samples/sec: 7266.09 - lr: 0.000004 - momentum: 0.000000 2023-10-18 22:55:28,507 epoch 10 - iter 576/1445 - loss 0.11657262 - time (sec): 9.60 - samples/sec: 7407.10 - lr: 0.000003 - momentum: 0.000000 2023-10-18 22:55:30,961 epoch 10 - iter 720/1445 - loss 0.11243952 - time (sec): 12.05 - samples/sec: 7385.04 - lr: 0.000003 - momentum: 0.000000 2023-10-18 22:55:33,205 epoch 10 - iter 864/1445 - loss 0.11354347 - time (sec): 14.30 - samples/sec: 7387.47 - lr: 0.000002 - momentum: 0.000000 2023-10-18 22:55:35,316 epoch 10 - iter 1008/1445 - loss 0.11085507 - time (sec): 16.41 - samples/sec: 7513.69 - lr: 0.000002 - momentum: 0.000000 2023-10-18 22:55:37,805 epoch 10 - iter 1152/1445 - loss 0.10959603 - time (sec): 18.90 - samples/sec: 7503.00 - lr: 0.000001 - momentum: 0.000000 2023-10-18 22:55:40,166 epoch 10 - iter 1296/1445 - loss 0.11097144 - time (sec): 21.26 - samples/sec: 7447.61 - lr: 0.000001 - momentum: 0.000000 2023-10-18 22:55:42,632 epoch 10 - iter 1440/1445 - loss 0.11183179 - time (sec): 23.72 - samples/sec: 7402.41 - lr: 0.000000 - momentum: 0.000000 2023-10-18 22:55:42,711 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:55:42,712 EPOCH 10 done: loss 0.1119 - lr: 0.000000 2023-10-18 22:55:44,486 DEV : loss 0.19939179718494415 - f1-score (micro avg) 0.5604 2023-10-18 22:55:44,530 ---------------------------------------------------------------------------------------------------- 2023-10-18 22:55:44,530 Loading model from best epoch ... 2023-10-18 22:55:44,613 SequenceTagger predicts: Dictionary with 13 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 2023-10-18 22:55:45,906 Results: - F-score (micro) 0.5542 - F-score (macro) 0.392 - Accuracy 0.393 By class: precision recall f1-score support LOC 0.6227 0.6594 0.6405 458 PER 0.5196 0.4959 0.5074 482 ORG 0.5000 0.0145 0.0282 69 micro avg 0.5723 0.5372 0.5542 1009 macro avg 0.5474 0.3899 0.3920 1009 weighted avg 0.5650 0.5372 0.5351 1009 2023-10-18 22:55:45,907 ----------------------------------------------------------------------------------------------------