2023-10-25 00:11:18,614 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,615 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(64001, 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): 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)
            )
          )
          (1): 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)
            )
          )
          (2): 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)
            )
          )
          (3): 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)
            )
          )
          (4): 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)
            )
          )
          (5): 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)
            )
          )
          (6): 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)
            )
          )
          (7): 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)
            )
          )
          (8): 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)
            )
          )
          (9): 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)
            )
          )
          (10): 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)
            )
          )
          (11): 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=13, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-25 00:11:18,615 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,615 MultiCorpus: 5777 train + 722 dev + 723 test sentences
 - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /home/ubuntu/.flair/datasets/ner_icdar_europeana/nl
2023-10-25 00:11:18,615 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 Train:  5777 sentences
2023-10-25 00:11:18,616         (train_with_dev=False, train_with_test=False)
2023-10-25 00:11:18,616 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 Training Params:
2023-10-25 00:11:18,616  - learning_rate: "3e-05" 
2023-10-25 00:11:18,616  - mini_batch_size: "4"
2023-10-25 00:11:18,616  - max_epochs: "10"
2023-10-25 00:11:18,616  - shuffle: "True"
2023-10-25 00:11:18,616 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 Plugins:
2023-10-25 00:11:18,616  - TensorboardLogger
2023-10-25 00:11:18,616  - LinearScheduler | warmup_fraction: '0.1'
2023-10-25 00:11:18,616 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 Final evaluation on model from best epoch (best-model.pt)
2023-10-25 00:11:18,616  - metric: "('micro avg', 'f1-score')"
2023-10-25 00:11:18,616 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 Computation:
2023-10-25 00:11:18,616  - compute on device: cuda:0
2023-10-25 00:11:18,616  - embedding storage: none
2023-10-25 00:11:18,616 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-25 00:11:18,616 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 ----------------------------------------------------------------------------------------------------
2023-10-25 00:11:18,616 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-25 00:11:29,321 epoch 1 - iter 144/1445 - loss 1.38938163 - time (sec): 10.70 - samples/sec: 1720.98 - lr: 0.000003 - momentum: 0.000000
2023-10-25 00:11:39,360 epoch 1 - iter 288/1445 - loss 0.87654160 - time (sec): 20.74 - samples/sec: 1667.66 - lr: 0.000006 - momentum: 0.000000
2023-10-25 00:11:49,875 epoch 1 - iter 432/1445 - loss 0.65410083 - time (sec): 31.26 - samples/sec: 1658.76 - lr: 0.000009 - momentum: 0.000000
2023-10-25 00:12:00,072 epoch 1 - iter 576/1445 - loss 0.53644177 - time (sec): 41.45 - samples/sec: 1661.93 - lr: 0.000012 - momentum: 0.000000
2023-10-25 00:12:11,122 epoch 1 - iter 720/1445 - loss 0.45401570 - time (sec): 52.51 - samples/sec: 1670.87 - lr: 0.000015 - momentum: 0.000000
2023-10-25 00:12:21,401 epoch 1 - iter 864/1445 - loss 0.40781351 - time (sec): 62.78 - samples/sec: 1664.14 - lr: 0.000018 - momentum: 0.000000
2023-10-25 00:12:32,044 epoch 1 - iter 1008/1445 - loss 0.36577446 - time (sec): 73.43 - samples/sec: 1667.51 - lr: 0.000021 - momentum: 0.000000
2023-10-25 00:12:42,531 epoch 1 - iter 1152/1445 - loss 0.33817906 - time (sec): 83.91 - samples/sec: 1670.17 - lr: 0.000024 - momentum: 0.000000
2023-10-25 00:12:53,139 epoch 1 - iter 1296/1445 - loss 0.31541819 - time (sec): 94.52 - samples/sec: 1670.96 - lr: 0.000027 - momentum: 0.000000
2023-10-25 00:13:03,644 epoch 1 - iter 1440/1445 - loss 0.29840554 - time (sec): 105.03 - samples/sec: 1671.49 - lr: 0.000030 - momentum: 0.000000
2023-10-25 00:13:04,067 ----------------------------------------------------------------------------------------------------
2023-10-25 00:13:04,067 EPOCH 1 done: loss 0.2976 - lr: 0.000030
2023-10-25 00:13:07,349 DEV : loss 0.13481558859348297 - f1-score (micro avg)  0.5007
2023-10-25 00:13:07,360 saving best model
2023-10-25 00:13:07,833 ----------------------------------------------------------------------------------------------------
2023-10-25 00:13:18,284 epoch 2 - iter 144/1445 - loss 0.11213883 - time (sec): 10.45 - samples/sec: 1656.80 - lr: 0.000030 - momentum: 0.000000
2023-10-25 00:13:29,023 epoch 2 - iter 288/1445 - loss 0.12245620 - time (sec): 21.19 - samples/sec: 1687.57 - lr: 0.000029 - momentum: 0.000000
2023-10-25 00:13:39,836 epoch 2 - iter 432/1445 - loss 0.11723412 - time (sec): 32.00 - samples/sec: 1687.73 - lr: 0.000029 - momentum: 0.000000
2023-10-25 00:13:50,678 epoch 2 - iter 576/1445 - loss 0.11064019 - time (sec): 42.84 - samples/sec: 1685.23 - lr: 0.000029 - momentum: 0.000000
2023-10-25 00:14:01,208 epoch 2 - iter 720/1445 - loss 0.10928472 - time (sec): 53.37 - samples/sec: 1672.93 - lr: 0.000028 - momentum: 0.000000
2023-10-25 00:14:11,856 epoch 2 - iter 864/1445 - loss 0.10679391 - time (sec): 64.02 - samples/sec: 1678.60 - lr: 0.000028 - momentum: 0.000000
2023-10-25 00:14:22,186 epoch 2 - iter 1008/1445 - loss 0.10555065 - time (sec): 74.35 - samples/sec: 1666.89 - lr: 0.000028 - momentum: 0.000000
2023-10-25 00:14:32,456 epoch 2 - iter 1152/1445 - loss 0.10376908 - time (sec): 84.62 - samples/sec: 1666.77 - lr: 0.000027 - momentum: 0.000000
2023-10-25 00:14:42,859 epoch 2 - iter 1296/1445 - loss 0.10361640 - time (sec): 95.03 - samples/sec: 1664.12 - lr: 0.000027 - momentum: 0.000000
2023-10-25 00:14:53,345 epoch 2 - iter 1440/1445 - loss 0.10081503 - time (sec): 105.51 - samples/sec: 1664.11 - lr: 0.000027 - momentum: 0.000000
2023-10-25 00:14:53,797 ----------------------------------------------------------------------------------------------------
2023-10-25 00:14:53,798 EPOCH 2 done: loss 0.1006 - lr: 0.000027
2023-10-25 00:14:57,509 DEV : loss 0.09922739118337631 - f1-score (micro avg)  0.7789
2023-10-25 00:14:57,521 saving best model
2023-10-25 00:14:58,112 ----------------------------------------------------------------------------------------------------
2023-10-25 00:15:08,688 epoch 3 - iter 144/1445 - loss 0.07401386 - time (sec): 10.58 - samples/sec: 1626.38 - lr: 0.000026 - momentum: 0.000000
2023-10-25 00:15:19,237 epoch 3 - iter 288/1445 - loss 0.07036989 - time (sec): 21.12 - samples/sec: 1663.55 - lr: 0.000026 - momentum: 0.000000
2023-10-25 00:15:29,743 epoch 3 - iter 432/1445 - loss 0.07228507 - time (sec): 31.63 - samples/sec: 1660.81 - lr: 0.000026 - momentum: 0.000000
2023-10-25 00:15:40,193 epoch 3 - iter 576/1445 - loss 0.06975233 - time (sec): 42.08 - samples/sec: 1661.84 - lr: 0.000025 - momentum: 0.000000
2023-10-25 00:15:50,838 epoch 3 - iter 720/1445 - loss 0.07320523 - time (sec): 52.72 - samples/sec: 1659.56 - lr: 0.000025 - momentum: 0.000000
2023-10-25 00:16:01,293 epoch 3 - iter 864/1445 - loss 0.07136932 - time (sec): 63.18 - samples/sec: 1654.97 - lr: 0.000025 - momentum: 0.000000
2023-10-25 00:16:11,569 epoch 3 - iter 1008/1445 - loss 0.07073740 - time (sec): 73.46 - samples/sec: 1657.77 - lr: 0.000024 - momentum: 0.000000
2023-10-25 00:16:22,487 epoch 3 - iter 1152/1445 - loss 0.06940519 - time (sec): 84.37 - samples/sec: 1667.92 - lr: 0.000024 - momentum: 0.000000
2023-10-25 00:16:33,200 epoch 3 - iter 1296/1445 - loss 0.06930457 - time (sec): 95.09 - samples/sec: 1665.11 - lr: 0.000024 - momentum: 0.000000
2023-10-25 00:16:43,676 epoch 3 - iter 1440/1445 - loss 0.06923652 - time (sec): 105.56 - samples/sec: 1664.57 - lr: 0.000023 - momentum: 0.000000
2023-10-25 00:16:44,021 ----------------------------------------------------------------------------------------------------
2023-10-25 00:16:44,021 EPOCH 3 done: loss 0.0692 - lr: 0.000023
2023-10-25 00:16:47,446 DEV : loss 0.13537803292274475 - f1-score (micro avg)  0.7799
2023-10-25 00:16:47,458 saving best model
2023-10-25 00:16:48,053 ----------------------------------------------------------------------------------------------------
2023-10-25 00:16:58,764 epoch 4 - iter 144/1445 - loss 0.05989448 - time (sec): 10.71 - samples/sec: 1663.20 - lr: 0.000023 - momentum: 0.000000
2023-10-25 00:17:09,630 epoch 4 - iter 288/1445 - loss 0.05059786 - time (sec): 21.58 - samples/sec: 1620.12 - lr: 0.000023 - momentum: 0.000000
2023-10-25 00:17:20,376 epoch 4 - iter 432/1445 - loss 0.04777464 - time (sec): 32.32 - samples/sec: 1653.35 - lr: 0.000022 - momentum: 0.000000
2023-10-25 00:17:31,029 epoch 4 - iter 576/1445 - loss 0.04689286 - time (sec): 42.98 - samples/sec: 1668.10 - lr: 0.000022 - momentum: 0.000000
2023-10-25 00:17:41,011 epoch 4 - iter 720/1445 - loss 0.04834085 - time (sec): 52.96 - samples/sec: 1655.32 - lr: 0.000022 - momentum: 0.000000
2023-10-25 00:17:51,641 epoch 4 - iter 864/1445 - loss 0.04645510 - time (sec): 63.59 - samples/sec: 1654.41 - lr: 0.000021 - momentum: 0.000000
2023-10-25 00:18:02,147 epoch 4 - iter 1008/1445 - loss 0.04662996 - time (sec): 74.09 - samples/sec: 1654.67 - lr: 0.000021 - momentum: 0.000000
2023-10-25 00:18:12,811 epoch 4 - iter 1152/1445 - loss 0.04833320 - time (sec): 84.76 - samples/sec: 1657.00 - lr: 0.000021 - momentum: 0.000000
2023-10-25 00:18:23,313 epoch 4 - iter 1296/1445 - loss 0.04773193 - time (sec): 95.26 - samples/sec: 1661.15 - lr: 0.000020 - momentum: 0.000000
2023-10-25 00:18:33,848 epoch 4 - iter 1440/1445 - loss 0.04746787 - time (sec): 105.79 - samples/sec: 1661.63 - lr: 0.000020 - momentum: 0.000000
2023-10-25 00:18:34,192 ----------------------------------------------------------------------------------------------------
2023-10-25 00:18:34,192 EPOCH 4 done: loss 0.0475 - lr: 0.000020
2023-10-25 00:18:37,622 DEV : loss 0.12032151222229004 - f1-score (micro avg)  0.8011
2023-10-25 00:18:37,634 saving best model
2023-10-25 00:18:38,207 ----------------------------------------------------------------------------------------------------
2023-10-25 00:18:48,599 epoch 5 - iter 144/1445 - loss 0.02983013 - time (sec): 10.39 - samples/sec: 1631.93 - lr: 0.000020 - momentum: 0.000000
2023-10-25 00:18:58,968 epoch 5 - iter 288/1445 - loss 0.02899526 - time (sec): 20.76 - samples/sec: 1639.27 - lr: 0.000019 - momentum: 0.000000
2023-10-25 00:19:09,760 epoch 5 - iter 432/1445 - loss 0.03130541 - time (sec): 31.55 - samples/sec: 1659.53 - lr: 0.000019 - momentum: 0.000000
2023-10-25 00:19:20,186 epoch 5 - iter 576/1445 - loss 0.03109030 - time (sec): 41.98 - samples/sec: 1650.29 - lr: 0.000019 - momentum: 0.000000
2023-10-25 00:19:30,854 epoch 5 - iter 720/1445 - loss 0.03453446 - time (sec): 52.65 - samples/sec: 1651.54 - lr: 0.000018 - momentum: 0.000000
2023-10-25 00:19:41,861 epoch 5 - iter 864/1445 - loss 0.03446052 - time (sec): 63.65 - samples/sec: 1662.69 - lr: 0.000018 - momentum: 0.000000
2023-10-25 00:19:52,258 epoch 5 - iter 1008/1445 - loss 0.03609405 - time (sec): 74.05 - samples/sec: 1661.99 - lr: 0.000018 - momentum: 0.000000
2023-10-25 00:20:02,565 epoch 5 - iter 1152/1445 - loss 0.03627626 - time (sec): 84.36 - samples/sec: 1661.97 - lr: 0.000017 - momentum: 0.000000
2023-10-25 00:20:13,190 epoch 5 - iter 1296/1445 - loss 0.03556309 - time (sec): 94.98 - samples/sec: 1666.39 - lr: 0.000017 - momentum: 0.000000
2023-10-25 00:20:23,734 epoch 5 - iter 1440/1445 - loss 0.03679685 - time (sec): 105.53 - samples/sec: 1665.75 - lr: 0.000017 - momentum: 0.000000
2023-10-25 00:20:24,070 ----------------------------------------------------------------------------------------------------
2023-10-25 00:20:24,070 EPOCH 5 done: loss 0.0368 - lr: 0.000017
2023-10-25 00:20:27,795 DEV : loss 0.1298297941684723 - f1-score (micro avg)  0.8238
2023-10-25 00:20:27,807 saving best model
2023-10-25 00:20:28,405 ----------------------------------------------------------------------------------------------------
2023-10-25 00:20:39,126 epoch 6 - iter 144/1445 - loss 0.01601013 - time (sec): 10.72 - samples/sec: 1683.95 - lr: 0.000016 - momentum: 0.000000
2023-10-25 00:20:49,460 epoch 6 - iter 288/1445 - loss 0.02373592 - time (sec): 21.05 - samples/sec: 1666.69 - lr: 0.000016 - momentum: 0.000000
2023-10-25 00:20:59,889 epoch 6 - iter 432/1445 - loss 0.02608343 - time (sec): 31.48 - samples/sec: 1673.09 - lr: 0.000016 - momentum: 0.000000
2023-10-25 00:21:10,404 epoch 6 - iter 576/1445 - loss 0.02606470 - time (sec): 42.00 - samples/sec: 1670.48 - lr: 0.000015 - momentum: 0.000000
2023-10-25 00:21:21,085 epoch 6 - iter 720/1445 - loss 0.02539757 - time (sec): 52.68 - samples/sec: 1672.76 - lr: 0.000015 - momentum: 0.000000
2023-10-25 00:21:31,532 epoch 6 - iter 864/1445 - loss 0.02480823 - time (sec): 63.13 - samples/sec: 1668.80 - lr: 0.000015 - momentum: 0.000000
2023-10-25 00:21:41,901 epoch 6 - iter 1008/1445 - loss 0.02490080 - time (sec): 73.50 - samples/sec: 1665.35 - lr: 0.000014 - momentum: 0.000000
2023-10-25 00:21:52,431 epoch 6 - iter 1152/1445 - loss 0.02503213 - time (sec): 84.03 - samples/sec: 1667.30 - lr: 0.000014 - momentum: 0.000000
2023-10-25 00:22:03,316 epoch 6 - iter 1296/1445 - loss 0.02496600 - time (sec): 94.91 - samples/sec: 1676.20 - lr: 0.000014 - momentum: 0.000000
2023-10-25 00:22:13,716 epoch 6 - iter 1440/1445 - loss 0.02479026 - time (sec): 105.31 - samples/sec: 1669.96 - lr: 0.000013 - momentum: 0.000000
2023-10-25 00:22:14,016 ----------------------------------------------------------------------------------------------------
2023-10-25 00:22:14,017 EPOCH 6 done: loss 0.0249 - lr: 0.000013
2023-10-25 00:22:17,443 DEV : loss 0.20431096851825714 - f1-score (micro avg)  0.7895
2023-10-25 00:22:17,455 ----------------------------------------------------------------------------------------------------
2023-10-25 00:22:27,950 epoch 7 - iter 144/1445 - loss 0.01117684 - time (sec): 10.49 - samples/sec: 1663.75 - lr: 0.000013 - momentum: 0.000000
2023-10-25 00:22:38,150 epoch 7 - iter 288/1445 - loss 0.01571047 - time (sec): 20.69 - samples/sec: 1639.81 - lr: 0.000013 - momentum: 0.000000
2023-10-25 00:22:49,582 epoch 7 - iter 432/1445 - loss 0.01656906 - time (sec): 32.13 - samples/sec: 1676.46 - lr: 0.000012 - momentum: 0.000000
2023-10-25 00:23:00,018 epoch 7 - iter 576/1445 - loss 0.01503815 - time (sec): 42.56 - samples/sec: 1673.14 - lr: 0.000012 - momentum: 0.000000
2023-10-25 00:23:10,915 epoch 7 - iter 720/1445 - loss 0.01601736 - time (sec): 53.46 - samples/sec: 1671.50 - lr: 0.000012 - momentum: 0.000000
2023-10-25 00:23:21,092 epoch 7 - iter 864/1445 - loss 0.01576845 - time (sec): 63.64 - samples/sec: 1663.45 - lr: 0.000011 - momentum: 0.000000
2023-10-25 00:23:32,263 epoch 7 - iter 1008/1445 - loss 0.01739489 - time (sec): 74.81 - samples/sec: 1669.40 - lr: 0.000011 - momentum: 0.000000
2023-10-25 00:23:42,513 epoch 7 - iter 1152/1445 - loss 0.01767123 - time (sec): 85.06 - samples/sec: 1657.92 - lr: 0.000011 - momentum: 0.000000
2023-10-25 00:23:53,120 epoch 7 - iter 1296/1445 - loss 0.01754927 - time (sec): 95.66 - samples/sec: 1658.01 - lr: 0.000010 - momentum: 0.000000
2023-10-25 00:24:03,214 epoch 7 - iter 1440/1445 - loss 0.01693935 - time (sec): 105.76 - samples/sec: 1661.54 - lr: 0.000010 - momentum: 0.000000
2023-10-25 00:24:03,537 ----------------------------------------------------------------------------------------------------
2023-10-25 00:24:03,538 EPOCH 7 done: loss 0.0169 - lr: 0.000010
2023-10-25 00:24:06,979 DEV : loss 0.18424199521541595 - f1-score (micro avg)  0.8119
2023-10-25 00:24:06,991 ----------------------------------------------------------------------------------------------------
2023-10-25 00:24:17,311 epoch 8 - iter 144/1445 - loss 0.00930858 - time (sec): 10.32 - samples/sec: 1632.76 - lr: 0.000010 - momentum: 0.000000
2023-10-25 00:24:27,987 epoch 8 - iter 288/1445 - loss 0.01249980 - time (sec): 21.00 - samples/sec: 1632.85 - lr: 0.000009 - momentum: 0.000000
2023-10-25 00:24:38,238 epoch 8 - iter 432/1445 - loss 0.01219123 - time (sec): 31.25 - samples/sec: 1619.39 - lr: 0.000009 - momentum: 0.000000
2023-10-25 00:24:49,551 epoch 8 - iter 576/1445 - loss 0.01174793 - time (sec): 42.56 - samples/sec: 1649.32 - lr: 0.000009 - momentum: 0.000000
2023-10-25 00:24:59,973 epoch 8 - iter 720/1445 - loss 0.01162494 - time (sec): 52.98 - samples/sec: 1654.59 - lr: 0.000008 - momentum: 0.000000
2023-10-25 00:25:10,506 epoch 8 - iter 864/1445 - loss 0.01127335 - time (sec): 63.51 - samples/sec: 1656.66 - lr: 0.000008 - momentum: 0.000000
2023-10-25 00:25:21,054 epoch 8 - iter 1008/1445 - loss 0.01257425 - time (sec): 74.06 - samples/sec: 1661.14 - lr: 0.000008 - momentum: 0.000000
2023-10-25 00:25:31,601 epoch 8 - iter 1152/1445 - loss 0.01352298 - time (sec): 84.61 - samples/sec: 1660.77 - lr: 0.000007 - momentum: 0.000000
2023-10-25 00:25:41,995 epoch 8 - iter 1296/1445 - loss 0.01297552 - time (sec): 95.00 - samples/sec: 1660.40 - lr: 0.000007 - momentum: 0.000000
2023-10-25 00:25:52,563 epoch 8 - iter 1440/1445 - loss 0.01308558 - time (sec): 105.57 - samples/sec: 1664.94 - lr: 0.000007 - momentum: 0.000000
2023-10-25 00:25:52,893 ----------------------------------------------------------------------------------------------------
2023-10-25 00:25:52,894 EPOCH 8 done: loss 0.0132 - lr: 0.000007
2023-10-25 00:25:56,623 DEV : loss 0.18342554569244385 - f1-score (micro avg)  0.8154
2023-10-25 00:25:56,635 ----------------------------------------------------------------------------------------------------
2023-10-25 00:26:07,274 epoch 9 - iter 144/1445 - loss 0.00487700 - time (sec): 10.64 - samples/sec: 1686.80 - lr: 0.000006 - momentum: 0.000000
2023-10-25 00:26:17,719 epoch 9 - iter 288/1445 - loss 0.00618271 - time (sec): 21.08 - samples/sec: 1674.65 - lr: 0.000006 - momentum: 0.000000
2023-10-25 00:26:28,409 epoch 9 - iter 432/1445 - loss 0.00694467 - time (sec): 31.77 - samples/sec: 1668.72 - lr: 0.000006 - momentum: 0.000000
2023-10-25 00:26:39,233 epoch 9 - iter 576/1445 - loss 0.00703293 - time (sec): 42.60 - samples/sec: 1679.56 - lr: 0.000005 - momentum: 0.000000
2023-10-25 00:26:49,652 epoch 9 - iter 720/1445 - loss 0.00748005 - time (sec): 53.02 - samples/sec: 1667.43 - lr: 0.000005 - momentum: 0.000000
2023-10-25 00:27:00,096 epoch 9 - iter 864/1445 - loss 0.00812538 - time (sec): 63.46 - samples/sec: 1660.54 - lr: 0.000005 - momentum: 0.000000
2023-10-25 00:27:10,612 epoch 9 - iter 1008/1445 - loss 0.00850011 - time (sec): 73.98 - samples/sec: 1664.19 - lr: 0.000004 - momentum: 0.000000
2023-10-25 00:27:21,467 epoch 9 - iter 1152/1445 - loss 0.00876078 - time (sec): 84.83 - samples/sec: 1669.13 - lr: 0.000004 - momentum: 0.000000
2023-10-25 00:27:31,749 epoch 9 - iter 1296/1445 - loss 0.00983545 - time (sec): 95.11 - samples/sec: 1664.29 - lr: 0.000004 - momentum: 0.000000
2023-10-25 00:27:42,233 epoch 9 - iter 1440/1445 - loss 0.00953351 - time (sec): 105.60 - samples/sec: 1663.77 - lr: 0.000003 - momentum: 0.000000
2023-10-25 00:27:42,560 ----------------------------------------------------------------------------------------------------
2023-10-25 00:27:42,560 EPOCH 9 done: loss 0.0097 - lr: 0.000003
2023-10-25 00:27:45,993 DEV : loss 0.18781644105911255 - f1-score (micro avg)  0.823
2023-10-25 00:27:46,005 ----------------------------------------------------------------------------------------------------
2023-10-25 00:27:56,502 epoch 10 - iter 144/1445 - loss 0.00437476 - time (sec): 10.50 - samples/sec: 1646.85 - lr: 0.000003 - momentum: 0.000000
2023-10-25 00:28:07,554 epoch 10 - iter 288/1445 - loss 0.00845703 - time (sec): 21.55 - samples/sec: 1620.52 - lr: 0.000003 - momentum: 0.000000
2023-10-25 00:28:17,904 epoch 10 - iter 432/1445 - loss 0.00618837 - time (sec): 31.90 - samples/sec: 1633.18 - lr: 0.000002 - momentum: 0.000000
2023-10-25 00:28:28,473 epoch 10 - iter 576/1445 - loss 0.00597168 - time (sec): 42.47 - samples/sec: 1649.52 - lr: 0.000002 - momentum: 0.000000
2023-10-25 00:28:38,916 epoch 10 - iter 720/1445 - loss 0.00539134 - time (sec): 52.91 - samples/sec: 1651.52 - lr: 0.000002 - momentum: 0.000000
2023-10-25 00:28:50,172 epoch 10 - iter 864/1445 - loss 0.00625082 - time (sec): 64.17 - samples/sec: 1667.60 - lr: 0.000001 - momentum: 0.000000
2023-10-25 00:29:00,734 epoch 10 - iter 1008/1445 - loss 0.00634905 - time (sec): 74.73 - samples/sec: 1667.31 - lr: 0.000001 - momentum: 0.000000
2023-10-25 00:29:11,282 epoch 10 - iter 1152/1445 - loss 0.00643576 - time (sec): 85.28 - samples/sec: 1669.39 - lr: 0.000001 - momentum: 0.000000
2023-10-25 00:29:21,407 epoch 10 - iter 1296/1445 - loss 0.00623537 - time (sec): 95.40 - samples/sec: 1662.12 - lr: 0.000000 - momentum: 0.000000
2023-10-25 00:29:31,911 epoch 10 - iter 1440/1445 - loss 0.00626251 - time (sec): 105.90 - samples/sec: 1660.30 - lr: 0.000000 - momentum: 0.000000
2023-10-25 00:29:32,215 ----------------------------------------------------------------------------------------------------
2023-10-25 00:29:32,216 EPOCH 10 done: loss 0.0063 - lr: 0.000000
2023-10-25 00:29:35,646 DEV : loss 0.19258512556552887 - f1-score (micro avg)  0.8221
2023-10-25 00:29:36,125 ----------------------------------------------------------------------------------------------------
2023-10-25 00:29:36,126 Loading model from best epoch ...
2023-10-25 00:29:37,790 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
2023-10-25 00:29:41,330 
Results:
- F-score (micro) 0.7947
- F-score (macro) 0.6981
- Accuracy 0.6764

By class:
              precision    recall  f1-score   support

         PER     0.7939    0.8071    0.8004       482
         LOC     0.8662    0.8057    0.8348       458
         ORG     0.5283    0.4058    0.4590        69

   micro avg     0.8111    0.7790    0.7947      1009
   macro avg     0.7295    0.6728    0.6981      1009
weighted avg     0.8085    0.7790    0.7927      1009

2023-10-25 00:29:41,330 ----------------------------------------------------------------------------------------------------