2023-10-20 09:15:12,661 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,661 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()
)"
2023-10-20 09:15:12,661 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,661 MultiCorpus: 6183 train + 680 dev + 2113 test sentences
 - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator
2023-10-20 09:15:12,661 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,661 Train:  6183 sentences
2023-10-20 09:15:12,661         (train_with_dev=False, train_with_test=False)
2023-10-20 09:15:12,661 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,661 Training Params:
2023-10-20 09:15:12,661  - learning_rate: "3e-05" 
2023-10-20 09:15:12,661  - mini_batch_size: "8"
2023-10-20 09:15:12,661  - max_epochs: "10"
2023-10-20 09:15:12,661  - shuffle: "True"
2023-10-20 09:15:12,661 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,661 Plugins:
2023-10-20 09:15:12,661  - TensorboardLogger
2023-10-20 09:15:12,662  - LinearScheduler | warmup_fraction: '0.1'
2023-10-20 09:15:12,662 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,662 Final evaluation on model from best epoch (best-model.pt)
2023-10-20 09:15:12,662  - metric: "('micro avg', 'f1-score')"
2023-10-20 09:15:12,662 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,662 Computation:
2023-10-20 09:15:12,662  - compute on device: cuda:0
2023-10-20 09:15:12,662  - embedding storage: none
2023-10-20 09:15:12,662 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,662 Model training base path: "hmbench-topres19th/en-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-20 09:15:12,662 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,662 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:12,662 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-20 09:15:14,192 epoch 1 - iter 77/773 - loss 3.33952986 - time (sec): 1.53 - samples/sec: 8303.34 - lr: 0.000003 - momentum: 0.000000
2023-10-20 09:15:15,870 epoch 1 - iter 154/773 - loss 3.15376051 - time (sec): 3.21 - samples/sec: 7599.49 - lr: 0.000006 - momentum: 0.000000
2023-10-20 09:15:17,643 epoch 1 - iter 231/773 - loss 2.86285749 - time (sec): 4.98 - samples/sec: 7338.12 - lr: 0.000009 - momentum: 0.000000
2023-10-20 09:15:19,406 epoch 1 - iter 308/773 - loss 2.46722800 - time (sec): 6.74 - samples/sec: 7316.92 - lr: 0.000012 - momentum: 0.000000
2023-10-20 09:15:21,097 epoch 1 - iter 385/773 - loss 2.08159163 - time (sec): 8.43 - samples/sec: 7287.20 - lr: 0.000015 - momentum: 0.000000
2023-10-20 09:15:22,893 epoch 1 - iter 462/773 - loss 1.80370396 - time (sec): 10.23 - samples/sec: 7143.10 - lr: 0.000018 - momentum: 0.000000
2023-10-20 09:15:24,673 epoch 1 - iter 539/773 - loss 1.58250891 - time (sec): 12.01 - samples/sec: 7140.71 - lr: 0.000021 - momentum: 0.000000
2023-10-20 09:15:26,482 epoch 1 - iter 616/773 - loss 1.40958823 - time (sec): 13.82 - samples/sec: 7147.35 - lr: 0.000024 - momentum: 0.000000
2023-10-20 09:15:28,279 epoch 1 - iter 693/773 - loss 1.28574327 - time (sec): 15.62 - samples/sec: 7086.91 - lr: 0.000027 - momentum: 0.000000
2023-10-20 09:15:30,017 epoch 1 - iter 770/773 - loss 1.17895676 - time (sec): 17.35 - samples/sec: 7130.19 - lr: 0.000030 - momentum: 0.000000
2023-10-20 09:15:30,086 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:30,086 EPOCH 1 done: loss 1.1740 - lr: 0.000030
2023-10-20 09:15:31,013 DEV : loss 0.14998705685138702 - f1-score (micro avg)  0.0
2023-10-20 09:15:31,024 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:32,728 epoch 2 - iter 77/773 - loss 0.24465311 - time (sec): 1.70 - samples/sec: 7292.65 - lr: 0.000030 - momentum: 0.000000
2023-10-20 09:15:34,422 epoch 2 - iter 154/773 - loss 0.23774020 - time (sec): 3.40 - samples/sec: 7056.70 - lr: 0.000029 - momentum: 0.000000
2023-10-20 09:15:36,135 epoch 2 - iter 231/773 - loss 0.24048091 - time (sec): 5.11 - samples/sec: 6938.55 - lr: 0.000029 - momentum: 0.000000
2023-10-20 09:15:37,877 epoch 2 - iter 308/773 - loss 0.22885706 - time (sec): 6.85 - samples/sec: 7056.28 - lr: 0.000029 - momentum: 0.000000
2023-10-20 09:15:39,449 epoch 2 - iter 385/773 - loss 0.22732366 - time (sec): 8.42 - samples/sec: 7262.06 - lr: 0.000028 - momentum: 0.000000
2023-10-20 09:15:41,053 epoch 2 - iter 462/773 - loss 0.22283623 - time (sec): 10.03 - samples/sec: 7270.02 - lr: 0.000028 - momentum: 0.000000
2023-10-20 09:15:42,778 epoch 2 - iter 539/773 - loss 0.22091189 - time (sec): 11.75 - samples/sec: 7218.38 - lr: 0.000028 - momentum: 0.000000
2023-10-20 09:15:44,509 epoch 2 - iter 616/773 - loss 0.21641942 - time (sec): 13.49 - samples/sec: 7231.27 - lr: 0.000027 - momentum: 0.000000
2023-10-20 09:15:46,296 epoch 2 - iter 693/773 - loss 0.21432633 - time (sec): 15.27 - samples/sec: 7192.00 - lr: 0.000027 - momentum: 0.000000
2023-10-20 09:15:48,070 epoch 2 - iter 770/773 - loss 0.20934767 - time (sec): 17.05 - samples/sec: 7251.00 - lr: 0.000027 - momentum: 0.000000
2023-10-20 09:15:48,144 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:48,145 EPOCH 2 done: loss 0.2087 - lr: 0.000027
2023-10-20 09:15:49,200 DEV : loss 0.10451915115118027 - f1-score (micro avg)  0.2064
2023-10-20 09:15:49,212 saving best model
2023-10-20 09:15:49,242 ----------------------------------------------------------------------------------------------------
2023-10-20 09:15:50,941 epoch 3 - iter 77/773 - loss 0.18760397 - time (sec): 1.70 - samples/sec: 6733.13 - lr: 0.000026 - momentum: 0.000000
2023-10-20 09:15:52,749 epoch 3 - iter 154/773 - loss 0.17290474 - time (sec): 3.51 - samples/sec: 6870.26 - lr: 0.000026 - momentum: 0.000000
2023-10-20 09:15:54,525 epoch 3 - iter 231/773 - loss 0.16574787 - time (sec): 5.28 - samples/sec: 6901.08 - lr: 0.000026 - momentum: 0.000000
2023-10-20 09:15:56,360 epoch 3 - iter 308/773 - loss 0.17220049 - time (sec): 7.12 - samples/sec: 6944.67 - lr: 0.000025 - momentum: 0.000000
2023-10-20 09:15:58,130 epoch 3 - iter 385/773 - loss 0.17055457 - time (sec): 8.89 - samples/sec: 6903.34 - lr: 0.000025 - momentum: 0.000000
2023-10-20 09:16:00,200 epoch 3 - iter 462/773 - loss 0.17102499 - time (sec): 10.96 - samples/sec: 6834.09 - lr: 0.000025 - momentum: 0.000000
2023-10-20 09:16:02,000 epoch 3 - iter 539/773 - loss 0.17156964 - time (sec): 12.76 - samples/sec: 6850.19 - lr: 0.000024 - momentum: 0.000000
2023-10-20 09:16:03,740 epoch 3 - iter 616/773 - loss 0.16973025 - time (sec): 14.50 - samples/sec: 6895.67 - lr: 0.000024 - momentum: 0.000000
2023-10-20 09:16:05,425 epoch 3 - iter 693/773 - loss 0.16937423 - time (sec): 16.18 - samples/sec: 6868.31 - lr: 0.000024 - momentum: 0.000000
2023-10-20 09:16:07,199 epoch 3 - iter 770/773 - loss 0.16819305 - time (sec): 17.96 - samples/sec: 6887.55 - lr: 0.000023 - momentum: 0.000000
2023-10-20 09:16:07,273 ----------------------------------------------------------------------------------------------------
2023-10-20 09:16:07,273 EPOCH 3 done: loss 0.1680 - lr: 0.000023
2023-10-20 09:16:08,371 DEV : loss 0.0877489447593689 - f1-score (micro avg)  0.4861
2023-10-20 09:16:08,383 saving best model
2023-10-20 09:16:08,418 ----------------------------------------------------------------------------------------------------
2023-10-20 09:16:10,216 epoch 4 - iter 77/773 - loss 0.16112576 - time (sec): 1.80 - samples/sec: 7014.62 - lr: 0.000023 - momentum: 0.000000
2023-10-20 09:16:11,957 epoch 4 - iter 154/773 - loss 0.15054189 - time (sec): 3.54 - samples/sec: 7020.61 - lr: 0.000023 - momentum: 0.000000
2023-10-20 09:16:13,681 epoch 4 - iter 231/773 - loss 0.15757889 - time (sec): 5.26 - samples/sec: 6798.16 - lr: 0.000022 - momentum: 0.000000
2023-10-20 09:16:15,454 epoch 4 - iter 308/773 - loss 0.15835643 - time (sec): 7.04 - samples/sec: 6887.47 - lr: 0.000022 - momentum: 0.000000
2023-10-20 09:16:17,147 epoch 4 - iter 385/773 - loss 0.15939542 - time (sec): 8.73 - samples/sec: 6952.52 - lr: 0.000022 - momentum: 0.000000
2023-10-20 09:16:18,879 epoch 4 - iter 462/773 - loss 0.15526926 - time (sec): 10.46 - samples/sec: 6969.98 - lr: 0.000021 - momentum: 0.000000
2023-10-20 09:16:20,636 epoch 4 - iter 539/773 - loss 0.15163147 - time (sec): 12.22 - samples/sec: 7051.10 - lr: 0.000021 - momentum: 0.000000
2023-10-20 09:16:22,428 epoch 4 - iter 616/773 - loss 0.15053741 - time (sec): 14.01 - samples/sec: 7075.84 - lr: 0.000021 - momentum: 0.000000
2023-10-20 09:16:24,135 epoch 4 - iter 693/773 - loss 0.15041744 - time (sec): 15.72 - samples/sec: 7074.15 - lr: 0.000020 - momentum: 0.000000
2023-10-20 09:16:25,852 epoch 4 - iter 770/773 - loss 0.14997795 - time (sec): 17.43 - samples/sec: 7104.06 - lr: 0.000020 - momentum: 0.000000
2023-10-20 09:16:25,917 ----------------------------------------------------------------------------------------------------
2023-10-20 09:16:25,917 EPOCH 4 done: loss 0.1499 - lr: 0.000020
2023-10-20 09:16:26,996 DEV : loss 0.08475784212350845 - f1-score (micro avg)  0.5195
2023-10-20 09:16:27,007 saving best model
2023-10-20 09:16:27,046 ----------------------------------------------------------------------------------------------------
2023-10-20 09:16:28,737 epoch 5 - iter 77/773 - loss 0.13296687 - time (sec): 1.69 - samples/sec: 7299.21 - lr: 0.000020 - momentum: 0.000000
2023-10-20 09:16:30,473 epoch 5 - iter 154/773 - loss 0.13988942 - time (sec): 3.43 - samples/sec: 6991.62 - lr: 0.000019 - momentum: 0.000000
2023-10-20 09:16:32,180 epoch 5 - iter 231/773 - loss 0.14188857 - time (sec): 5.13 - samples/sec: 7007.76 - lr: 0.000019 - momentum: 0.000000
2023-10-20 09:16:33,909 epoch 5 - iter 308/773 - loss 0.13800861 - time (sec): 6.86 - samples/sec: 7172.74 - lr: 0.000019 - momentum: 0.000000
2023-10-20 09:16:35,608 epoch 5 - iter 385/773 - loss 0.13726507 - time (sec): 8.56 - samples/sec: 7265.92 - lr: 0.000018 - momentum: 0.000000
2023-10-20 09:16:37,518 epoch 5 - iter 462/773 - loss 0.13563549 - time (sec): 10.47 - samples/sec: 7100.19 - lr: 0.000018 - momentum: 0.000000
2023-10-20 09:16:39,257 epoch 5 - iter 539/773 - loss 0.13353442 - time (sec): 12.21 - samples/sec: 7072.29 - lr: 0.000018 - momentum: 0.000000
2023-10-20 09:16:40,988 epoch 5 - iter 616/773 - loss 0.13630665 - time (sec): 13.94 - samples/sec: 7084.86 - lr: 0.000017 - momentum: 0.000000
2023-10-20 09:16:42,795 epoch 5 - iter 693/773 - loss 0.13728145 - time (sec): 15.75 - samples/sec: 7104.29 - lr: 0.000017 - momentum: 0.000000
2023-10-20 09:16:44,524 epoch 5 - iter 770/773 - loss 0.13822704 - time (sec): 17.48 - samples/sec: 7084.17 - lr: 0.000017 - momentum: 0.000000
2023-10-20 09:16:44,587 ----------------------------------------------------------------------------------------------------
2023-10-20 09:16:44,587 EPOCH 5 done: loss 0.1381 - lr: 0.000017
2023-10-20 09:16:45,681 DEV : loss 0.08033832907676697 - f1-score (micro avg)  0.5468
2023-10-20 09:16:45,693 saving best model
2023-10-20 09:16:45,728 ----------------------------------------------------------------------------------------------------
2023-10-20 09:16:47,419 epoch 6 - iter 77/773 - loss 0.11510914 - time (sec): 1.69 - samples/sec: 7063.17 - lr: 0.000016 - momentum: 0.000000
2023-10-20 09:16:49,141 epoch 6 - iter 154/773 - loss 0.12357112 - time (sec): 3.41 - samples/sec: 6951.14 - lr: 0.000016 - momentum: 0.000000
2023-10-20 09:16:50,894 epoch 6 - iter 231/773 - loss 0.13493875 - time (sec): 5.16 - samples/sec: 6930.16 - lr: 0.000016 - momentum: 0.000000
2023-10-20 09:16:52,666 epoch 6 - iter 308/773 - loss 0.13607145 - time (sec): 6.94 - samples/sec: 7020.17 - lr: 0.000015 - momentum: 0.000000
2023-10-20 09:16:54,438 epoch 6 - iter 385/773 - loss 0.14047184 - time (sec): 8.71 - samples/sec: 6924.28 - lr: 0.000015 - momentum: 0.000000
2023-10-20 09:16:56,257 epoch 6 - iter 462/773 - loss 0.13593672 - time (sec): 10.53 - samples/sec: 6963.41 - lr: 0.000015 - momentum: 0.000000
2023-10-20 09:16:57,977 epoch 6 - iter 539/773 - loss 0.13231574 - time (sec): 12.25 - samples/sec: 7002.98 - lr: 0.000014 - momentum: 0.000000
2023-10-20 09:16:59,741 epoch 6 - iter 616/773 - loss 0.13109122 - time (sec): 14.01 - samples/sec: 7059.51 - lr: 0.000014 - momentum: 0.000000
2023-10-20 09:17:01,314 epoch 6 - iter 693/773 - loss 0.13015771 - time (sec): 15.58 - samples/sec: 7092.09 - lr: 0.000014 - momentum: 0.000000
2023-10-20 09:17:03,013 epoch 6 - iter 770/773 - loss 0.13172020 - time (sec): 17.28 - samples/sec: 7161.60 - lr: 0.000013 - momentum: 0.000000
2023-10-20 09:17:03,081 ----------------------------------------------------------------------------------------------------
2023-10-20 09:17:03,082 EPOCH 6 done: loss 0.1316 - lr: 0.000013
2023-10-20 09:17:04,172 DEV : loss 0.07792612910270691 - f1-score (micro avg)  0.5833
2023-10-20 09:17:04,184 saving best model
2023-10-20 09:17:04,222 ----------------------------------------------------------------------------------------------------
2023-10-20 09:17:05,950 epoch 7 - iter 77/773 - loss 0.12524588 - time (sec): 1.73 - samples/sec: 7772.00 - lr: 0.000013 - momentum: 0.000000
2023-10-20 09:17:07,631 epoch 7 - iter 154/773 - loss 0.12230570 - time (sec): 3.41 - samples/sec: 7297.43 - lr: 0.000013 - momentum: 0.000000
2023-10-20 09:17:09,492 epoch 7 - iter 231/773 - loss 0.11811755 - time (sec): 5.27 - samples/sec: 7190.68 - lr: 0.000012 - momentum: 0.000000
2023-10-20 09:17:11,333 epoch 7 - iter 308/773 - loss 0.12745433 - time (sec): 7.11 - samples/sec: 6958.52 - lr: 0.000012 - momentum: 0.000000
2023-10-20 09:17:12,836 epoch 7 - iter 385/773 - loss 0.12730702 - time (sec): 8.61 - samples/sec: 7215.29 - lr: 0.000012 - momentum: 0.000000
2023-10-20 09:17:14,570 epoch 7 - iter 462/773 - loss 0.12624463 - time (sec): 10.35 - samples/sec: 7225.78 - lr: 0.000011 - momentum: 0.000000
2023-10-20 09:17:16,355 epoch 7 - iter 539/773 - loss 0.12879208 - time (sec): 12.13 - samples/sec: 7200.24 - lr: 0.000011 - momentum: 0.000000
2023-10-20 09:17:18,072 epoch 7 - iter 616/773 - loss 0.12718972 - time (sec): 13.85 - samples/sec: 7220.27 - lr: 0.000011 - momentum: 0.000000
2023-10-20 09:17:19,851 epoch 7 - iter 693/773 - loss 0.12645319 - time (sec): 15.63 - samples/sec: 7147.23 - lr: 0.000010 - momentum: 0.000000
2023-10-20 09:17:21,728 epoch 7 - iter 770/773 - loss 0.12643290 - time (sec): 17.51 - samples/sec: 7072.56 - lr: 0.000010 - momentum: 0.000000
2023-10-20 09:17:21,794 ----------------------------------------------------------------------------------------------------
2023-10-20 09:17:21,795 EPOCH 7 done: loss 0.1262 - lr: 0.000010
2023-10-20 09:17:22,891 DEV : loss 0.0776781365275383 - f1-score (micro avg)  0.5872
2023-10-20 09:17:22,903 saving best model
2023-10-20 09:17:22,938 ----------------------------------------------------------------------------------------------------
2023-10-20 09:17:24,719 epoch 8 - iter 77/773 - loss 0.10583346 - time (sec): 1.78 - samples/sec: 6834.39 - lr: 0.000010 - momentum: 0.000000
2023-10-20 09:17:26,504 epoch 8 - iter 154/773 - loss 0.12530026 - time (sec): 3.57 - samples/sec: 6987.51 - lr: 0.000009 - momentum: 0.000000
2023-10-20 09:17:28,247 epoch 8 - iter 231/773 - loss 0.12693031 - time (sec): 5.31 - samples/sec: 6952.20 - lr: 0.000009 - momentum: 0.000000
2023-10-20 09:17:29,969 epoch 8 - iter 308/773 - loss 0.12130837 - time (sec): 7.03 - samples/sec: 7006.83 - lr: 0.000009 - momentum: 0.000000
2023-10-20 09:17:31,722 epoch 8 - iter 385/773 - loss 0.11920464 - time (sec): 8.78 - samples/sec: 7095.66 - lr: 0.000008 - momentum: 0.000000
2023-10-20 09:17:33,516 epoch 8 - iter 462/773 - loss 0.12199633 - time (sec): 10.58 - samples/sec: 7154.64 - lr: 0.000008 - momentum: 0.000000
2023-10-20 09:17:35,189 epoch 8 - iter 539/773 - loss 0.12080649 - time (sec): 12.25 - samples/sec: 7113.25 - lr: 0.000008 - momentum: 0.000000
2023-10-20 09:17:37,007 epoch 8 - iter 616/773 - loss 0.12327536 - time (sec): 14.07 - samples/sec: 7011.62 - lr: 0.000007 - momentum: 0.000000
2023-10-20 09:17:38,708 epoch 8 - iter 693/773 - loss 0.12158899 - time (sec): 15.77 - samples/sec: 7028.09 - lr: 0.000007 - momentum: 0.000000
2023-10-20 09:17:40,487 epoch 8 - iter 770/773 - loss 0.12160387 - time (sec): 17.55 - samples/sec: 7063.84 - lr: 0.000007 - momentum: 0.000000
2023-10-20 09:17:40,546 ----------------------------------------------------------------------------------------------------
2023-10-20 09:17:40,547 EPOCH 8 done: loss 0.1214 - lr: 0.000007
2023-10-20 09:17:41,635 DEV : loss 0.078594870865345 - f1-score (micro avg)  0.594
2023-10-20 09:17:41,648 saving best model
2023-10-20 09:17:41,684 ----------------------------------------------------------------------------------------------------
2023-10-20 09:17:43,444 epoch 9 - iter 77/773 - loss 0.11865783 - time (sec): 1.76 - samples/sec: 6953.78 - lr: 0.000006 - momentum: 0.000000
2023-10-20 09:17:45,352 epoch 9 - iter 154/773 - loss 0.11779580 - time (sec): 3.67 - samples/sec: 6731.92 - lr: 0.000006 - momentum: 0.000000
2023-10-20 09:17:47,177 epoch 9 - iter 231/773 - loss 0.11079074 - time (sec): 5.49 - samples/sec: 6945.20 - lr: 0.000006 - momentum: 0.000000
2023-10-20 09:17:48,925 epoch 9 - iter 308/773 - loss 0.11269628 - time (sec): 7.24 - samples/sec: 6912.83 - lr: 0.000005 - momentum: 0.000000
2023-10-20 09:17:50,690 epoch 9 - iter 385/773 - loss 0.11775128 - time (sec): 9.01 - samples/sec: 7031.36 - lr: 0.000005 - momentum: 0.000000
2023-10-20 09:17:52,442 epoch 9 - iter 462/773 - loss 0.12178041 - time (sec): 10.76 - samples/sec: 6994.68 - lr: 0.000005 - momentum: 0.000000
2023-10-20 09:17:54,230 epoch 9 - iter 539/773 - loss 0.12266438 - time (sec): 12.55 - samples/sec: 7009.50 - lr: 0.000004 - momentum: 0.000000
2023-10-20 09:17:55,948 epoch 9 - iter 616/773 - loss 0.12347143 - time (sec): 14.26 - samples/sec: 7020.21 - lr: 0.000004 - momentum: 0.000000
2023-10-20 09:17:57,590 epoch 9 - iter 693/773 - loss 0.12164974 - time (sec): 15.91 - samples/sec: 7013.24 - lr: 0.000004 - momentum: 0.000000
2023-10-20 09:17:59,268 epoch 9 - iter 770/773 - loss 0.12028417 - time (sec): 17.58 - samples/sec: 7045.20 - lr: 0.000003 - momentum: 0.000000
2023-10-20 09:17:59,328 ----------------------------------------------------------------------------------------------------
2023-10-20 09:17:59,328 EPOCH 9 done: loss 0.1202 - lr: 0.000003
2023-10-20 09:18:00,418 DEV : loss 0.07745374739170074 - f1-score (micro avg)  0.5978
2023-10-20 09:18:00,430 saving best model
2023-10-20 09:18:00,463 ----------------------------------------------------------------------------------------------------
2023-10-20 09:18:02,196 epoch 10 - iter 77/773 - loss 0.13395817 - time (sec): 1.73 - samples/sec: 6936.02 - lr: 0.000003 - momentum: 0.000000
2023-10-20 09:18:03,975 epoch 10 - iter 154/773 - loss 0.12555449 - time (sec): 3.51 - samples/sec: 7085.61 - lr: 0.000003 - momentum: 0.000000
2023-10-20 09:18:05,577 epoch 10 - iter 231/773 - loss 0.12235248 - time (sec): 5.11 - samples/sec: 7455.37 - lr: 0.000002 - momentum: 0.000000
2023-10-20 09:18:07,115 epoch 10 - iter 308/773 - loss 0.11713785 - time (sec): 6.65 - samples/sec: 7646.12 - lr: 0.000002 - momentum: 0.000000
2023-10-20 09:18:08,958 epoch 10 - iter 385/773 - loss 0.11947063 - time (sec): 8.49 - samples/sec: 7426.05 - lr: 0.000002 - momentum: 0.000000
2023-10-20 09:18:10,695 epoch 10 - iter 462/773 - loss 0.11681252 - time (sec): 10.23 - samples/sec: 7355.83 - lr: 0.000001 - momentum: 0.000000
2023-10-20 09:18:12,412 epoch 10 - iter 539/773 - loss 0.11331848 - time (sec): 11.95 - samples/sec: 7367.65 - lr: 0.000001 - momentum: 0.000000
2023-10-20 09:18:14,164 epoch 10 - iter 616/773 - loss 0.11310343 - time (sec): 13.70 - samples/sec: 7264.87 - lr: 0.000001 - momentum: 0.000000
2023-10-20 09:18:15,932 epoch 10 - iter 693/773 - loss 0.11715195 - time (sec): 15.47 - samples/sec: 7228.65 - lr: 0.000000 - momentum: 0.000000
2023-10-20 09:18:17,637 epoch 10 - iter 770/773 - loss 0.11898981 - time (sec): 17.17 - samples/sec: 7219.65 - lr: 0.000000 - momentum: 0.000000
2023-10-20 09:18:17,698 ----------------------------------------------------------------------------------------------------
2023-10-20 09:18:17,698 EPOCH 10 done: loss 0.1189 - lr: 0.000000
2023-10-20 09:18:18,836 DEV : loss 0.07805749028921127 - f1-score (micro avg)  0.5968
2023-10-20 09:18:18,880 ----------------------------------------------------------------------------------------------------
2023-10-20 09:18:18,881 Loading model from best epoch ...
2023-10-20 09:18:18,958 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET
2023-10-20 09:18:21,824 
Results:
- F-score (micro) 0.5601
- F-score (macro) 0.2106
- Accuracy 0.4021

By class:
              precision    recall  f1-score   support

         LOC     0.6210    0.6427    0.6317       946
    BUILDING     0.0000    0.0000    0.0000       185
      STREET     0.0000    0.0000    0.0000        56

   micro avg     0.6179    0.5122    0.5601      1187
   macro avg     0.2070    0.2142    0.2106      1187
weighted avg     0.4949    0.5122    0.5034      1187

2023-10-20 09:18:21,824 ----------------------------------------------------------------------------------------------------