2023-10-20 09:48:42,765 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,765 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 09:48:42,765 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,765 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 09:48:42,765 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,765 Train:  6183 sentences
2023-10-20 09:48:42,765         (train_with_dev=False, train_with_test=False)
2023-10-20 09:48:42,765 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,765 Training Params:
2023-10-20 09:48:42,765  - learning_rate: "5e-05" 
2023-10-20 09:48:42,765  - mini_batch_size: "8"
2023-10-20 09:48:42,765  - max_epochs: "10"
2023-10-20 09:48:42,765  - shuffle: "True"
2023-10-20 09:48:42,765 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,766 Plugins:
2023-10-20 09:48:42,766  - TensorboardLogger
2023-10-20 09:48:42,766  - LinearScheduler | warmup_fraction: '0.1'
2023-10-20 09:48:42,766 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,766 Final evaluation on model from best epoch (best-model.pt)
2023-10-20 09:48:42,766  - metric: "('micro avg', 'f1-score')"
2023-10-20 09:48:42,766 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,766 Computation:
2023-10-20 09:48:42,766  - compute on device: cuda:0
2023-10-20 09:48:42,766  - embedding storage: none
2023-10-20 09:48:42,766 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,766 Model training base path: "hmbench-topres19th/en-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-20 09:48:42,766 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,766 ----------------------------------------------------------------------------------------------------
2023-10-20 09:48:42,766 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-20 09:48:44,557 epoch 1 - iter 77/773 - loss 2.87926452 - time (sec): 1.79 - samples/sec: 6926.45 - lr: 0.000005 - momentum: 0.000000
2023-10-20 09:48:46,323 epoch 1 - iter 154/773 - loss 2.58090791 - time (sec): 3.56 - samples/sec: 7242.62 - lr: 0.000010 - momentum: 0.000000
2023-10-20 09:48:48,037 epoch 1 - iter 231/773 - loss 2.19347159 - time (sec): 5.27 - samples/sec: 7066.32 - lr: 0.000015 - momentum: 0.000000
2023-10-20 09:48:49,762 epoch 1 - iter 308/773 - loss 1.79530466 - time (sec): 7.00 - samples/sec: 6939.16 - lr: 0.000020 - momentum: 0.000000
2023-10-20 09:48:51,483 epoch 1 - iter 385/773 - loss 1.49005800 - time (sec): 8.72 - samples/sec: 6982.79 - lr: 0.000025 - momentum: 0.000000
2023-10-20 09:48:53,275 epoch 1 - iter 462/773 - loss 1.28696614 - time (sec): 10.51 - samples/sec: 6966.72 - lr: 0.000030 - momentum: 0.000000
2023-10-20 09:48:55,026 epoch 1 - iter 539/773 - loss 1.12674983 - time (sec): 12.26 - samples/sec: 7093.68 - lr: 0.000035 - momentum: 0.000000
2023-10-20 09:48:56,741 epoch 1 - iter 616/773 - loss 1.02283949 - time (sec): 13.97 - samples/sec: 7070.20 - lr: 0.000040 - momentum: 0.000000
2023-10-20 09:48:58,434 epoch 1 - iter 693/773 - loss 0.93498457 - time (sec): 15.67 - samples/sec: 7095.90 - lr: 0.000045 - momentum: 0.000000
2023-10-20 09:49:00,161 epoch 1 - iter 770/773 - loss 0.86157976 - time (sec): 17.39 - samples/sec: 7118.20 - lr: 0.000050 - momentum: 0.000000
2023-10-20 09:49:00,225 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:00,225 EPOCH 1 done: loss 0.8589 - lr: 0.000050
2023-10-20 09:49:00,920 DEV : loss 0.13765180110931396 - f1-score (micro avg)  0.0
2023-10-20 09:49:00,931 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:02,640 epoch 2 - iter 77/773 - loss 0.21698829 - time (sec): 1.71 - samples/sec: 7968.37 - lr: 0.000049 - momentum: 0.000000
2023-10-20 09:49:04,280 epoch 2 - iter 154/773 - loss 0.20811745 - time (sec): 3.35 - samples/sec: 7749.56 - lr: 0.000049 - momentum: 0.000000
2023-10-20 09:49:06,063 epoch 2 - iter 231/773 - loss 0.20796981 - time (sec): 5.13 - samples/sec: 7473.80 - lr: 0.000048 - momentum: 0.000000
2023-10-20 09:49:08,149 epoch 2 - iter 308/773 - loss 0.20289547 - time (sec): 7.22 - samples/sec: 6999.97 - lr: 0.000048 - momentum: 0.000000
2023-10-20 09:49:09,929 epoch 2 - iter 385/773 - loss 0.20413434 - time (sec): 9.00 - samples/sec: 6985.69 - lr: 0.000047 - momentum: 0.000000
2023-10-20 09:49:11,712 epoch 2 - iter 462/773 - loss 0.19808681 - time (sec): 10.78 - samples/sec: 7019.02 - lr: 0.000047 - momentum: 0.000000
2023-10-20 09:49:13,398 epoch 2 - iter 539/773 - loss 0.19621468 - time (sec): 12.47 - samples/sec: 7043.99 - lr: 0.000046 - momentum: 0.000000
2023-10-20 09:49:15,069 epoch 2 - iter 616/773 - loss 0.19508540 - time (sec): 14.14 - samples/sec: 7028.74 - lr: 0.000046 - momentum: 0.000000
2023-10-20 09:49:16,802 epoch 2 - iter 693/773 - loss 0.19324075 - time (sec): 15.87 - samples/sec: 7033.26 - lr: 0.000045 - momentum: 0.000000
2023-10-20 09:49:18,554 epoch 2 - iter 770/773 - loss 0.18967567 - time (sec): 17.62 - samples/sec: 7025.86 - lr: 0.000044 - momentum: 0.000000
2023-10-20 09:49:18,621 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:18,621 EPOCH 2 done: loss 0.1896 - lr: 0.000044
2023-10-20 09:49:19,698 DEV : loss 0.09595523029565811 - f1-score (micro avg)  0.3657
2023-10-20 09:49:19,709 saving best model
2023-10-20 09:49:19,738 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:21,498 epoch 3 - iter 77/773 - loss 0.18262781 - time (sec): 1.76 - samples/sec: 6333.46 - lr: 0.000044 - momentum: 0.000000
2023-10-20 09:49:23,277 epoch 3 - iter 154/773 - loss 0.16257290 - time (sec): 3.54 - samples/sec: 6881.12 - lr: 0.000043 - momentum: 0.000000
2023-10-20 09:49:25,002 epoch 3 - iter 231/773 - loss 0.16928954 - time (sec): 5.26 - samples/sec: 6976.00 - lr: 0.000043 - momentum: 0.000000
2023-10-20 09:49:26,737 epoch 3 - iter 308/773 - loss 0.16039613 - time (sec): 7.00 - samples/sec: 6906.62 - lr: 0.000042 - momentum: 0.000000
2023-10-20 09:49:28,481 epoch 3 - iter 385/773 - loss 0.15828599 - time (sec): 8.74 - samples/sec: 6981.46 - lr: 0.000042 - momentum: 0.000000
2023-10-20 09:49:30,238 epoch 3 - iter 462/773 - loss 0.15435750 - time (sec): 10.50 - samples/sec: 7057.56 - lr: 0.000041 - momentum: 0.000000
2023-10-20 09:49:31,993 epoch 3 - iter 539/773 - loss 0.15526689 - time (sec): 12.25 - samples/sec: 7053.55 - lr: 0.000041 - momentum: 0.000000
2023-10-20 09:49:33,650 epoch 3 - iter 616/773 - loss 0.15528455 - time (sec): 13.91 - samples/sec: 7090.48 - lr: 0.000040 - momentum: 0.000000
2023-10-20 09:49:35,406 epoch 3 - iter 693/773 - loss 0.15441732 - time (sec): 15.67 - samples/sec: 7124.04 - lr: 0.000039 - momentum: 0.000000
2023-10-20 09:49:37,244 epoch 3 - iter 770/773 - loss 0.15466267 - time (sec): 17.50 - samples/sec: 7073.24 - lr: 0.000039 - momentum: 0.000000
2023-10-20 09:49:37,308 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:37,309 EPOCH 3 done: loss 0.1552 - lr: 0.000039
2023-10-20 09:49:38,391 DEV : loss 0.09129446744918823 - f1-score (micro avg)  0.4545
2023-10-20 09:49:38,402 saving best model
2023-10-20 09:49:38,437 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:40,135 epoch 4 - iter 77/773 - loss 0.12897817 - time (sec): 1.70 - samples/sec: 8025.57 - lr: 0.000038 - momentum: 0.000000
2023-10-20 09:49:41,847 epoch 4 - iter 154/773 - loss 0.14162760 - time (sec): 3.41 - samples/sec: 7389.73 - lr: 0.000038 - momentum: 0.000000
2023-10-20 09:49:43,541 epoch 4 - iter 231/773 - loss 0.13190156 - time (sec): 5.10 - samples/sec: 7334.53 - lr: 0.000037 - momentum: 0.000000
2023-10-20 09:49:45,309 epoch 4 - iter 308/773 - loss 0.13315426 - time (sec): 6.87 - samples/sec: 7213.08 - lr: 0.000037 - momentum: 0.000000
2023-10-20 09:49:47,080 epoch 4 - iter 385/773 - loss 0.13213692 - time (sec): 8.64 - samples/sec: 7243.47 - lr: 0.000036 - momentum: 0.000000
2023-10-20 09:49:48,813 epoch 4 - iter 462/773 - loss 0.13554983 - time (sec): 10.38 - samples/sec: 7207.39 - lr: 0.000036 - momentum: 0.000000
2023-10-20 09:49:50,578 epoch 4 - iter 539/773 - loss 0.13667052 - time (sec): 12.14 - samples/sec: 7200.13 - lr: 0.000035 - momentum: 0.000000
2023-10-20 09:49:52,341 epoch 4 - iter 616/773 - loss 0.13714767 - time (sec): 13.90 - samples/sec: 7204.78 - lr: 0.000034 - momentum: 0.000000
2023-10-20 09:49:54,053 epoch 4 - iter 693/773 - loss 0.13504563 - time (sec): 15.62 - samples/sec: 7204.58 - lr: 0.000034 - momentum: 0.000000
2023-10-20 09:49:55,783 epoch 4 - iter 770/773 - loss 0.13630022 - time (sec): 17.35 - samples/sec: 7139.71 - lr: 0.000033 - momentum: 0.000000
2023-10-20 09:49:55,846 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:55,847 EPOCH 4 done: loss 0.1362 - lr: 0.000033
2023-10-20 09:49:56,915 DEV : loss 0.08750458806753159 - f1-score (micro avg)  0.5182
2023-10-20 09:49:56,927 saving best model
2023-10-20 09:49:56,969 ----------------------------------------------------------------------------------------------------
2023-10-20 09:49:58,649 epoch 5 - iter 77/773 - loss 0.11837622 - time (sec): 1.68 - samples/sec: 7426.52 - lr: 0.000033 - momentum: 0.000000
2023-10-20 09:50:00,345 epoch 5 - iter 154/773 - loss 0.11950342 - time (sec): 3.38 - samples/sec: 7326.73 - lr: 0.000032 - momentum: 0.000000
2023-10-20 09:50:02,083 epoch 5 - iter 231/773 - loss 0.12281718 - time (sec): 5.11 - samples/sec: 7176.13 - lr: 0.000032 - momentum: 0.000000
2023-10-20 09:50:03,759 epoch 5 - iter 308/773 - loss 0.12163778 - time (sec): 6.79 - samples/sec: 7307.34 - lr: 0.000031 - momentum: 0.000000
2023-10-20 09:50:05,449 epoch 5 - iter 385/773 - loss 0.11961392 - time (sec): 8.48 - samples/sec: 7367.32 - lr: 0.000031 - momentum: 0.000000
2023-10-20 09:50:07,225 epoch 5 - iter 462/773 - loss 0.12444845 - time (sec): 10.26 - samples/sec: 7314.02 - lr: 0.000030 - momentum: 0.000000
2023-10-20 09:50:08,940 epoch 5 - iter 539/773 - loss 0.12636329 - time (sec): 11.97 - samples/sec: 7281.68 - lr: 0.000029 - momentum: 0.000000
2023-10-20 09:50:10,627 epoch 5 - iter 616/773 - loss 0.12908626 - time (sec): 13.66 - samples/sec: 7262.66 - lr: 0.000029 - momentum: 0.000000
2023-10-20 09:50:12,304 epoch 5 - iter 693/773 - loss 0.12873301 - time (sec): 15.33 - samples/sec: 7273.24 - lr: 0.000028 - momentum: 0.000000
2023-10-20 09:50:14,033 epoch 5 - iter 770/773 - loss 0.12550489 - time (sec): 17.06 - samples/sec: 7257.85 - lr: 0.000028 - momentum: 0.000000
2023-10-20 09:50:14,093 ----------------------------------------------------------------------------------------------------
2023-10-20 09:50:14,093 EPOCH 5 done: loss 0.1256 - lr: 0.000028
2023-10-20 09:50:15,180 DEV : loss 0.08463426679372787 - f1-score (micro avg)  0.5385
2023-10-20 09:50:15,192 saving best model
2023-10-20 09:50:15,232 ----------------------------------------------------------------------------------------------------
2023-10-20 09:50:16,956 epoch 6 - iter 77/773 - loss 0.15202072 - time (sec): 1.72 - samples/sec: 7215.52 - lr: 0.000027 - momentum: 0.000000
2023-10-20 09:50:18,717 epoch 6 - iter 154/773 - loss 0.12842673 - time (sec): 3.48 - samples/sec: 7212.32 - lr: 0.000027 - momentum: 0.000000
2023-10-20 09:50:20,453 epoch 6 - iter 231/773 - loss 0.12246865 - time (sec): 5.22 - samples/sec: 7160.36 - lr: 0.000026 - momentum: 0.000000
2023-10-20 09:50:22,137 epoch 6 - iter 308/773 - loss 0.12273321 - time (sec): 6.90 - samples/sec: 7074.89 - lr: 0.000026 - momentum: 0.000000
2023-10-20 09:50:23,834 epoch 6 - iter 385/773 - loss 0.12023376 - time (sec): 8.60 - samples/sec: 7223.55 - lr: 0.000025 - momentum: 0.000000
2023-10-20 09:50:25,485 epoch 6 - iter 462/773 - loss 0.11889169 - time (sec): 10.25 - samples/sec: 7235.65 - lr: 0.000024 - momentum: 0.000000
2023-10-20 09:50:27,185 epoch 6 - iter 539/773 - loss 0.11869253 - time (sec): 11.95 - samples/sec: 7243.32 - lr: 0.000024 - momentum: 0.000000
2023-10-20 09:50:28,935 epoch 6 - iter 616/773 - loss 0.11634542 - time (sec): 13.70 - samples/sec: 7221.14 - lr: 0.000023 - momentum: 0.000000
2023-10-20 09:50:30,696 epoch 6 - iter 693/773 - loss 0.11714691 - time (sec): 15.46 - samples/sec: 7245.60 - lr: 0.000023 - momentum: 0.000000
2023-10-20 09:50:32,382 epoch 6 - iter 770/773 - loss 0.11633289 - time (sec): 17.15 - samples/sec: 7219.80 - lr: 0.000022 - momentum: 0.000000
2023-10-20 09:50:32,450 ----------------------------------------------------------------------------------------------------
2023-10-20 09:50:32,451 EPOCH 6 done: loss 0.1160 - lr: 0.000022
2023-10-20 09:50:33,540 DEV : loss 0.08559702336788177 - f1-score (micro avg)  0.5348
2023-10-20 09:50:33,551 ----------------------------------------------------------------------------------------------------
2023-10-20 09:50:35,193 epoch 7 - iter 77/773 - loss 0.13418942 - time (sec): 1.64 - samples/sec: 7322.35 - lr: 0.000022 - momentum: 0.000000
2023-10-20 09:50:36,886 epoch 7 - iter 154/773 - loss 0.11605993 - time (sec): 3.33 - samples/sec: 7552.83 - lr: 0.000021 - momentum: 0.000000
2023-10-20 09:50:38,581 epoch 7 - iter 231/773 - loss 0.11974916 - time (sec): 5.03 - samples/sec: 7346.15 - lr: 0.000021 - momentum: 0.000000
2023-10-20 09:50:40,361 epoch 7 - iter 308/773 - loss 0.11273530 - time (sec): 6.81 - samples/sec: 7309.01 - lr: 0.000020 - momentum: 0.000000
2023-10-20 09:50:42,109 epoch 7 - iter 385/773 - loss 0.11096649 - time (sec): 8.56 - samples/sec: 7100.74 - lr: 0.000019 - momentum: 0.000000
2023-10-20 09:50:43,841 epoch 7 - iter 462/773 - loss 0.11189813 - time (sec): 10.29 - samples/sec: 7072.68 - lr: 0.000019 - momentum: 0.000000
2023-10-20 09:50:45,715 epoch 7 - iter 539/773 - loss 0.11121427 - time (sec): 12.16 - samples/sec: 7043.16 - lr: 0.000018 - momentum: 0.000000
2023-10-20 09:50:47,508 epoch 7 - iter 616/773 - loss 0.11017605 - time (sec): 13.96 - samples/sec: 7059.63 - lr: 0.000018 - momentum: 0.000000
2023-10-20 09:50:49,317 epoch 7 - iter 693/773 - loss 0.10910748 - time (sec): 15.76 - samples/sec: 7072.09 - lr: 0.000017 - momentum: 0.000000
2023-10-20 09:50:51,038 epoch 7 - iter 770/773 - loss 0.11083517 - time (sec): 17.49 - samples/sec: 7079.64 - lr: 0.000017 - momentum: 0.000000
2023-10-20 09:50:51,101 ----------------------------------------------------------------------------------------------------
2023-10-20 09:50:51,101 EPOCH 7 done: loss 0.1107 - lr: 0.000017
2023-10-20 09:50:52,197 DEV : loss 0.08398106694221497 - f1-score (micro avg)  0.5572
2023-10-20 09:50:52,209 saving best model
2023-10-20 09:50:52,248 ----------------------------------------------------------------------------------------------------
2023-10-20 09:50:53,959 epoch 8 - iter 77/773 - loss 0.07735418 - time (sec): 1.71 - samples/sec: 7032.51 - lr: 0.000016 - momentum: 0.000000
2023-10-20 09:50:55,671 epoch 8 - iter 154/773 - loss 0.09799317 - time (sec): 3.42 - samples/sec: 6976.98 - lr: 0.000016 - momentum: 0.000000
2023-10-20 09:50:57,435 epoch 8 - iter 231/773 - loss 0.10334869 - time (sec): 5.19 - samples/sec: 7043.50 - lr: 0.000015 - momentum: 0.000000
2023-10-20 09:50:59,177 epoch 8 - iter 308/773 - loss 0.09645731 - time (sec): 6.93 - samples/sec: 7215.93 - lr: 0.000014 - momentum: 0.000000
2023-10-20 09:51:00,995 epoch 8 - iter 385/773 - loss 0.09864950 - time (sec): 8.75 - samples/sec: 7181.08 - lr: 0.000014 - momentum: 0.000000
2023-10-20 09:51:02,709 epoch 8 - iter 462/773 - loss 0.10127673 - time (sec): 10.46 - samples/sec: 7190.72 - lr: 0.000013 - momentum: 0.000000
2023-10-20 09:51:04,451 epoch 8 - iter 539/773 - loss 0.10595667 - time (sec): 12.20 - samples/sec: 7126.05 - lr: 0.000013 - momentum: 0.000000
2023-10-20 09:51:06,293 epoch 8 - iter 616/773 - loss 0.10535196 - time (sec): 14.05 - samples/sec: 7091.97 - lr: 0.000012 - momentum: 0.000000
2023-10-20 09:51:08,042 epoch 8 - iter 693/773 - loss 0.10603763 - time (sec): 15.79 - samples/sec: 7039.09 - lr: 0.000012 - momentum: 0.000000
2023-10-20 09:51:09,809 epoch 8 - iter 770/773 - loss 0.10450961 - time (sec): 17.56 - samples/sec: 7048.84 - lr: 0.000011 - momentum: 0.000000
2023-10-20 09:51:09,879 ----------------------------------------------------------------------------------------------------
2023-10-20 09:51:09,880 EPOCH 8 done: loss 0.1044 - lr: 0.000011
2023-10-20 09:51:10,964 DEV : loss 0.08721306174993515 - f1-score (micro avg)  0.557
2023-10-20 09:51:10,977 ----------------------------------------------------------------------------------------------------
2023-10-20 09:51:12,693 epoch 9 - iter 77/773 - loss 0.08508022 - time (sec): 1.72 - samples/sec: 6831.62 - lr: 0.000011 - momentum: 0.000000
2023-10-20 09:51:14,449 epoch 9 - iter 154/773 - loss 0.10086226 - time (sec): 3.47 - samples/sec: 7068.70 - lr: 0.000010 - momentum: 0.000000
2023-10-20 09:51:16,184 epoch 9 - iter 231/773 - loss 0.10411885 - time (sec): 5.21 - samples/sec: 7041.88 - lr: 0.000009 - momentum: 0.000000
2023-10-20 09:51:17,985 epoch 9 - iter 308/773 - loss 0.10360436 - time (sec): 7.01 - samples/sec: 7036.34 - lr: 0.000009 - momentum: 0.000000
2023-10-20 09:51:19,766 epoch 9 - iter 385/773 - loss 0.10607327 - time (sec): 8.79 - samples/sec: 7123.12 - lr: 0.000008 - momentum: 0.000000
2023-10-20 09:51:21,608 epoch 9 - iter 462/773 - loss 0.10462746 - time (sec): 10.63 - samples/sec: 6995.10 - lr: 0.000008 - momentum: 0.000000
2023-10-20 09:51:23,423 epoch 9 - iter 539/773 - loss 0.10355217 - time (sec): 12.45 - samples/sec: 6945.51 - lr: 0.000007 - momentum: 0.000000
2023-10-20 09:51:25,251 epoch 9 - iter 616/773 - loss 0.10298710 - time (sec): 14.27 - samples/sec: 6947.34 - lr: 0.000007 - momentum: 0.000000
2023-10-20 09:51:26,965 epoch 9 - iter 693/773 - loss 0.10206767 - time (sec): 15.99 - samples/sec: 6974.89 - lr: 0.000006 - momentum: 0.000000
2023-10-20 09:51:28,689 epoch 9 - iter 770/773 - loss 0.10193084 - time (sec): 17.71 - samples/sec: 6991.53 - lr: 0.000006 - momentum: 0.000000
2023-10-20 09:51:28,754 ----------------------------------------------------------------------------------------------------
2023-10-20 09:51:28,755 EPOCH 9 done: loss 0.1020 - lr: 0.000006
2023-10-20 09:51:29,845 DEV : loss 0.08746206760406494 - f1-score (micro avg)  0.5598
2023-10-20 09:51:29,857 saving best model
2023-10-20 09:51:29,898 ----------------------------------------------------------------------------------------------------
2023-10-20 09:51:31,632 epoch 10 - iter 77/773 - loss 0.09052757 - time (sec): 1.73 - samples/sec: 7036.26 - lr: 0.000005 - momentum: 0.000000
2023-10-20 09:51:33,295 epoch 10 - iter 154/773 - loss 0.08568932 - time (sec): 3.40 - samples/sec: 6743.47 - lr: 0.000005 - momentum: 0.000000
2023-10-20 09:51:35,057 epoch 10 - iter 231/773 - loss 0.09090122 - time (sec): 5.16 - samples/sec: 7114.36 - lr: 0.000004 - momentum: 0.000000
2023-10-20 09:51:36,834 epoch 10 - iter 308/773 - loss 0.09401948 - time (sec): 6.94 - samples/sec: 7126.06 - lr: 0.000003 - momentum: 0.000000
2023-10-20 09:51:38,537 epoch 10 - iter 385/773 - loss 0.09521242 - time (sec): 8.64 - samples/sec: 7168.24 - lr: 0.000003 - momentum: 0.000000
2023-10-20 09:51:40,283 epoch 10 - iter 462/773 - loss 0.09812942 - time (sec): 10.38 - samples/sec: 7175.35 - lr: 0.000002 - momentum: 0.000000
2023-10-20 09:51:42,013 epoch 10 - iter 539/773 - loss 0.09933101 - time (sec): 12.11 - samples/sec: 7131.17 - lr: 0.000002 - momentum: 0.000000
2023-10-20 09:51:43,770 epoch 10 - iter 616/773 - loss 0.09792858 - time (sec): 13.87 - samples/sec: 7186.32 - lr: 0.000001 - momentum: 0.000000
2023-10-20 09:51:45,469 epoch 10 - iter 693/773 - loss 0.09892283 - time (sec): 15.57 - samples/sec: 7150.93 - lr: 0.000001 - momentum: 0.000000
2023-10-20 09:51:47,214 epoch 10 - iter 770/773 - loss 0.10051481 - time (sec): 17.32 - samples/sec: 7154.09 - lr: 0.000000 - momentum: 0.000000
2023-10-20 09:51:47,278 ----------------------------------------------------------------------------------------------------
2023-10-20 09:51:47,278 EPOCH 10 done: loss 0.1004 - lr: 0.000000
2023-10-20 09:51:48,362 DEV : loss 0.08812259882688522 - f1-score (micro avg)  0.565
2023-10-20 09:51:48,374 saving best model
2023-10-20 09:51:48,448 ----------------------------------------------------------------------------------------------------
2023-10-20 09:51:48,449 Loading model from best epoch ...
2023-10-20 09:51:48,522 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 09:51:51,447 
Results:
- F-score (micro) 0.5627
- F-score (macro) 0.3215
- Accuracy 0.4019

By class:
              precision    recall  f1-score   support

         LOC     0.6176    0.6438    0.6304       946
    BUILDING     0.2535    0.0973    0.1406       185
      STREET     1.0000    0.1071    0.1935        56

   micro avg     0.5955    0.5333    0.5627      1187
   macro avg     0.6237    0.2827    0.3215      1187
weighted avg     0.5789    0.5333    0.5335      1187

2023-10-20 09:51:51,447 ----------------------------------------------------------------------------------------------------