2023-10-18 18:30:57,326 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,326 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=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 Train:  3575 sentences
2023-10-18 18:30:57,327         (train_with_dev=False, train_with_test=False)
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 Training Params:
2023-10-18 18:30:57,327  - learning_rate: "3e-05" 
2023-10-18 18:30:57,327  - mini_batch_size: "8"
2023-10-18 18:30:57,327  - max_epochs: "10"
2023-10-18 18:30:57,327  - shuffle: "True"
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 Plugins:
2023-10-18 18:30:57,327  - TensorboardLogger
2023-10-18 18:30:57,327  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 18:30:57,327  - metric: "('micro avg', 'f1-score')"
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 Computation:
2023-10-18 18:30:57,327  - compute on device: cuda:0
2023-10-18 18:30:57,327  - embedding storage: none
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 ----------------------------------------------------------------------------------------------------
2023-10-18 18:30:57,327 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 18:30:58,389 epoch 1 - iter 44/447 - loss 3.35537987 - time (sec): 1.06 - samples/sec: 8886.60 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:30:59,392 epoch 1 - iter 88/447 - loss 3.30743351 - time (sec): 2.06 - samples/sec: 8809.53 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:31:00,382 epoch 1 - iter 132/447 - loss 3.17927816 - time (sec): 3.05 - samples/sec: 8871.01 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:31:01,357 epoch 1 - iter 176/447 - loss 2.99703148 - time (sec): 4.03 - samples/sec: 8719.11 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:31:02,365 epoch 1 - iter 220/447 - loss 2.74692973 - time (sec): 5.04 - samples/sec: 8721.79 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:31:03,341 epoch 1 - iter 264/447 - loss 2.49918578 - time (sec): 6.01 - samples/sec: 8633.99 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:31:04,342 epoch 1 - iter 308/447 - loss 2.25178508 - time (sec): 7.01 - samples/sec: 8657.38 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:31:05,334 epoch 1 - iter 352/447 - loss 2.07493418 - time (sec): 8.01 - samples/sec: 8609.55 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:31:06,341 epoch 1 - iter 396/447 - loss 1.92933120 - time (sec): 9.01 - samples/sec: 8566.47 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:31:07,338 epoch 1 - iter 440/447 - loss 1.80685455 - time (sec): 10.01 - samples/sec: 8529.85 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:31:07,493 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:07,493 EPOCH 1 done: loss 1.7910 - lr: 0.000029
2023-10-18 18:31:09,707 DEV : loss 0.4836609661579132 - f1-score (micro avg)  0.0
2023-10-18 18:31:09,732 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:10,762 epoch 2 - iter 44/447 - loss 0.60346430 - time (sec): 1.03 - samples/sec: 9314.76 - lr: 0.000030 - momentum: 0.000000
2023-10-18 18:31:11,762 epoch 2 - iter 88/447 - loss 0.61427853 - time (sec): 2.03 - samples/sec: 9124.62 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:31:12,772 epoch 2 - iter 132/447 - loss 0.58695454 - time (sec): 3.04 - samples/sec: 8816.43 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:31:13,783 epoch 2 - iter 176/447 - loss 0.55566412 - time (sec): 4.05 - samples/sec: 8706.98 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:31:14,753 epoch 2 - iter 220/447 - loss 0.55262370 - time (sec): 5.02 - samples/sec: 8579.61 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:31:15,753 epoch 2 - iter 264/447 - loss 0.54627616 - time (sec): 6.02 - samples/sec: 8510.33 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:31:16,783 epoch 2 - iter 308/447 - loss 0.54522378 - time (sec): 7.05 - samples/sec: 8520.23 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:31:17,795 epoch 2 - iter 352/447 - loss 0.54685730 - time (sec): 8.06 - samples/sec: 8537.76 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:31:18,764 epoch 2 - iter 396/447 - loss 0.54330644 - time (sec): 9.03 - samples/sec: 8524.04 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:31:19,772 epoch 2 - iter 440/447 - loss 0.53883687 - time (sec): 10.04 - samples/sec: 8512.29 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:31:19,927 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:19,927 EPOCH 2 done: loss 0.5383 - lr: 0.000027
2023-10-18 18:31:25,102 DEV : loss 0.3944970965385437 - f1-score (micro avg)  0.0
2023-10-18 18:31:25,127 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:26,144 epoch 3 - iter 44/447 - loss 0.47928256 - time (sec): 1.02 - samples/sec: 8573.52 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:31:27,140 epoch 3 - iter 88/447 - loss 0.48481466 - time (sec): 2.01 - samples/sec: 8455.08 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:31:28,157 epoch 3 - iter 132/447 - loss 0.47210746 - time (sec): 3.03 - samples/sec: 8451.26 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:31:29,130 epoch 3 - iter 176/447 - loss 0.45204171 - time (sec): 4.00 - samples/sec: 8404.07 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:31:30,125 epoch 3 - iter 220/447 - loss 0.45951440 - time (sec): 5.00 - samples/sec: 8504.07 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:31:31,104 epoch 3 - iter 264/447 - loss 0.45609998 - time (sec): 5.98 - samples/sec: 8498.35 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:31:32,130 epoch 3 - iter 308/447 - loss 0.45039667 - time (sec): 7.00 - samples/sec: 8587.72 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:31:33,139 epoch 3 - iter 352/447 - loss 0.44769705 - time (sec): 8.01 - samples/sec: 8519.35 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:31:34,149 epoch 3 - iter 396/447 - loss 0.45480444 - time (sec): 9.02 - samples/sec: 8549.84 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:31:35,141 epoch 3 - iter 440/447 - loss 0.44997278 - time (sec): 10.01 - samples/sec: 8531.73 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:31:35,299 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:35,299 EPOCH 3 done: loss 0.4484 - lr: 0.000023
2023-10-18 18:31:40,464 DEV : loss 0.3369644582271576 - f1-score (micro avg)  0.1104
2023-10-18 18:31:40,487 saving best model
2023-10-18 18:31:40,521 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:41,556 epoch 4 - iter 44/447 - loss 0.39542898 - time (sec): 1.03 - samples/sec: 8636.65 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:31:42,561 epoch 4 - iter 88/447 - loss 0.38736877 - time (sec): 2.04 - samples/sec: 8628.03 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:31:43,608 epoch 4 - iter 132/447 - loss 0.38899063 - time (sec): 3.09 - samples/sec: 8799.06 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:31:44,585 epoch 4 - iter 176/447 - loss 0.40082774 - time (sec): 4.06 - samples/sec: 8811.42 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:31:45,571 epoch 4 - iter 220/447 - loss 0.40145089 - time (sec): 5.05 - samples/sec: 8656.73 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:31:46,581 epoch 4 - iter 264/447 - loss 0.40563358 - time (sec): 6.06 - samples/sec: 8696.86 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:31:47,552 epoch 4 - iter 308/447 - loss 0.40938382 - time (sec): 7.03 - samples/sec: 8654.08 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:31:48,541 epoch 4 - iter 352/447 - loss 0.41291327 - time (sec): 8.02 - samples/sec: 8564.35 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:31:49,574 epoch 4 - iter 396/447 - loss 0.41249266 - time (sec): 9.05 - samples/sec: 8505.34 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:31:50,537 epoch 4 - iter 440/447 - loss 0.41073576 - time (sec): 10.02 - samples/sec: 8526.40 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:31:50,686 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:50,686 EPOCH 4 done: loss 0.4120 - lr: 0.000020
2023-10-18 18:31:55,601 DEV : loss 0.32354578375816345 - f1-score (micro avg)  0.2093
2023-10-18 18:31:55,626 saving best model
2023-10-18 18:31:55,665 ----------------------------------------------------------------------------------------------------
2023-10-18 18:31:56,674 epoch 5 - iter 44/447 - loss 0.43829726 - time (sec): 1.01 - samples/sec: 7584.40 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:31:57,661 epoch 5 - iter 88/447 - loss 0.40228181 - time (sec): 2.00 - samples/sec: 7864.32 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:31:58,638 epoch 5 - iter 132/447 - loss 0.40280517 - time (sec): 2.97 - samples/sec: 7990.30 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:31:59,710 epoch 5 - iter 176/447 - loss 0.37902870 - time (sec): 4.04 - samples/sec: 8287.50 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:32:00,769 epoch 5 - iter 220/447 - loss 0.37122151 - time (sec): 5.10 - samples/sec: 8416.45 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:32:01,778 epoch 5 - iter 264/447 - loss 0.37513444 - time (sec): 6.11 - samples/sec: 8523.10 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:32:02,807 epoch 5 - iter 308/447 - loss 0.37468618 - time (sec): 7.14 - samples/sec: 8462.69 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:32:03,762 epoch 5 - iter 352/447 - loss 0.37705908 - time (sec): 8.10 - samples/sec: 8454.46 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:32:04,801 epoch 5 - iter 396/447 - loss 0.37945783 - time (sec): 9.14 - samples/sec: 8410.18 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:32:05,811 epoch 5 - iter 440/447 - loss 0.38037714 - time (sec): 10.15 - samples/sec: 8405.36 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:32:05,962 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:05,962 EPOCH 5 done: loss 0.3803 - lr: 0.000017
2023-10-18 18:32:11,182 DEV : loss 0.32035842537879944 - f1-score (micro avg)  0.2888
2023-10-18 18:32:11,207 saving best model
2023-10-18 18:32:11,240 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:12,172 epoch 6 - iter 44/447 - loss 0.33483908 - time (sec): 0.93 - samples/sec: 9115.05 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:32:13,137 epoch 6 - iter 88/447 - loss 0.35899358 - time (sec): 1.90 - samples/sec: 9080.63 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:32:14,206 epoch 6 - iter 132/447 - loss 0.35690744 - time (sec): 2.96 - samples/sec: 9076.59 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:32:15,164 epoch 6 - iter 176/447 - loss 0.36576389 - time (sec): 3.92 - samples/sec: 8994.93 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:32:16,184 epoch 6 - iter 220/447 - loss 0.37498136 - time (sec): 4.94 - samples/sec: 8676.02 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:32:17,234 epoch 6 - iter 264/447 - loss 0.37144901 - time (sec): 5.99 - samples/sec: 8495.57 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:32:18,297 epoch 6 - iter 308/447 - loss 0.37135490 - time (sec): 7.06 - samples/sec: 8449.37 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:32:19,337 epoch 6 - iter 352/447 - loss 0.37580628 - time (sec): 8.10 - samples/sec: 8398.88 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:32:20,381 epoch 6 - iter 396/447 - loss 0.37311173 - time (sec): 9.14 - samples/sec: 8374.81 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:32:21,351 epoch 6 - iter 440/447 - loss 0.37059963 - time (sec): 10.11 - samples/sec: 8425.98 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:32:21,511 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:21,511 EPOCH 6 done: loss 0.3695 - lr: 0.000013
2023-10-18 18:32:26,767 DEV : loss 0.3224472403526306 - f1-score (micro avg)  0.3079
2023-10-18 18:32:26,792 saving best model
2023-10-18 18:32:26,834 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:27,937 epoch 7 - iter 44/447 - loss 0.38634909 - time (sec): 1.10 - samples/sec: 7574.69 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:32:28,992 epoch 7 - iter 88/447 - loss 0.35989766 - time (sec): 2.16 - samples/sec: 7720.36 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:32:30,010 epoch 7 - iter 132/447 - loss 0.35635785 - time (sec): 3.18 - samples/sec: 7724.99 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:32:31,063 epoch 7 - iter 176/447 - loss 0.34873541 - time (sec): 4.23 - samples/sec: 7992.13 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:32:32,072 epoch 7 - iter 220/447 - loss 0.35566055 - time (sec): 5.24 - samples/sec: 8143.25 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:32:33,110 epoch 7 - iter 264/447 - loss 0.34763579 - time (sec): 6.28 - samples/sec: 8178.77 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:32:34,137 epoch 7 - iter 308/447 - loss 0.35746887 - time (sec): 7.30 - samples/sec: 8187.89 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:32:35,174 epoch 7 - iter 352/447 - loss 0.35715054 - time (sec): 8.34 - samples/sec: 8266.95 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:32:36,181 epoch 7 - iter 396/447 - loss 0.35950966 - time (sec): 9.35 - samples/sec: 8263.77 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:32:37,181 epoch 7 - iter 440/447 - loss 0.35766237 - time (sec): 10.35 - samples/sec: 8235.62 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:32:37,340 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:37,341 EPOCH 7 done: loss 0.3576 - lr: 0.000010
2023-10-18 18:32:42,619 DEV : loss 0.3121800422668457 - f1-score (micro avg)  0.3244
2023-10-18 18:32:42,644 saving best model
2023-10-18 18:32:42,678 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:43,671 epoch 8 - iter 44/447 - loss 0.37422031 - time (sec): 0.99 - samples/sec: 8531.72 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:32:44,640 epoch 8 - iter 88/447 - loss 0.36592520 - time (sec): 1.96 - samples/sec: 8542.01 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:32:45,668 epoch 8 - iter 132/447 - loss 0.36290806 - time (sec): 2.99 - samples/sec: 8534.38 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:32:46,673 epoch 8 - iter 176/447 - loss 0.36281915 - time (sec): 3.99 - samples/sec: 8615.59 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:32:47,726 epoch 8 - iter 220/447 - loss 0.35917912 - time (sec): 5.05 - samples/sec: 8471.13 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:32:48,759 epoch 8 - iter 264/447 - loss 0.35714516 - time (sec): 6.08 - samples/sec: 8611.74 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:32:49,745 epoch 8 - iter 308/447 - loss 0.35463981 - time (sec): 7.07 - samples/sec: 8541.08 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:32:50,767 epoch 8 - iter 352/447 - loss 0.35443539 - time (sec): 8.09 - samples/sec: 8533.45 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:32:51,802 epoch 8 - iter 396/447 - loss 0.34812574 - time (sec): 9.12 - samples/sec: 8528.33 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:32:52,836 epoch 8 - iter 440/447 - loss 0.35044269 - time (sec): 10.16 - samples/sec: 8418.84 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:32:52,990 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:52,990 EPOCH 8 done: loss 0.3514 - lr: 0.000007
2023-10-18 18:32:58,218 DEV : loss 0.3059460520744324 - f1-score (micro avg)  0.3413
2023-10-18 18:32:58,244 saving best model
2023-10-18 18:32:58,278 ----------------------------------------------------------------------------------------------------
2023-10-18 18:32:59,317 epoch 9 - iter 44/447 - loss 0.33594296 - time (sec): 1.04 - samples/sec: 7558.99 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:33:00,364 epoch 9 - iter 88/447 - loss 0.35405140 - time (sec): 2.09 - samples/sec: 8481.46 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:33:01,374 epoch 9 - iter 132/447 - loss 0.37457981 - time (sec): 3.10 - samples/sec: 8446.61 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:33:02,440 epoch 9 - iter 176/447 - loss 0.36377188 - time (sec): 4.16 - samples/sec: 8311.21 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:33:03,383 epoch 9 - iter 220/447 - loss 0.35955115 - time (sec): 5.10 - samples/sec: 8335.73 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:33:04,496 epoch 9 - iter 264/447 - loss 0.35376935 - time (sec): 6.22 - samples/sec: 8381.32 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:33:05,535 epoch 9 - iter 308/447 - loss 0.34765466 - time (sec): 7.26 - samples/sec: 8417.96 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:33:06,610 epoch 9 - iter 352/447 - loss 0.34682011 - time (sec): 8.33 - samples/sec: 8303.00 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:33:07,629 epoch 9 - iter 396/447 - loss 0.34678332 - time (sec): 9.35 - samples/sec: 8308.58 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:33:08,589 epoch 9 - iter 440/447 - loss 0.34367673 - time (sec): 10.31 - samples/sec: 8285.48 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:33:08,741 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:08,741 EPOCH 9 done: loss 0.3429 - lr: 0.000003
2023-10-18 18:33:13,698 DEV : loss 0.3109874725341797 - f1-score (micro avg)  0.3371
2023-10-18 18:33:13,723 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:14,605 epoch 10 - iter 44/447 - loss 0.28949017 - time (sec): 0.88 - samples/sec: 10077.39 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:33:15,622 epoch 10 - iter 88/447 - loss 0.30867757 - time (sec): 1.90 - samples/sec: 9115.42 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:33:16,601 epoch 10 - iter 132/447 - loss 0.30207633 - time (sec): 2.88 - samples/sec: 8598.52 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:33:17,584 epoch 10 - iter 176/447 - loss 0.31228723 - time (sec): 3.86 - samples/sec: 8535.06 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:33:18,593 epoch 10 - iter 220/447 - loss 0.32028080 - time (sec): 4.87 - samples/sec: 8454.58 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:33:19,587 epoch 10 - iter 264/447 - loss 0.32693675 - time (sec): 5.86 - samples/sec: 8394.92 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:33:20,570 epoch 10 - iter 308/447 - loss 0.33101357 - time (sec): 6.85 - samples/sec: 8374.72 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:33:21,645 epoch 10 - iter 352/447 - loss 0.33462225 - time (sec): 7.92 - samples/sec: 8439.68 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:33:22,709 epoch 10 - iter 396/447 - loss 0.32984143 - time (sec): 8.99 - samples/sec: 8551.25 - lr: 0.000000 - momentum: 0.000000
2023-10-18 18:33:23,715 epoch 10 - iter 440/447 - loss 0.33800628 - time (sec): 9.99 - samples/sec: 8523.11 - lr: 0.000000 - momentum: 0.000000
2023-10-18 18:33:23,878 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:23,878 EPOCH 10 done: loss 0.3392 - lr: 0.000000
2023-10-18 18:33:29,185 DEV : loss 0.3085727393627167 - f1-score (micro avg)  0.3422
2023-10-18 18:33:29,211 saving best model
2023-10-18 18:33:29,272 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:29,273 Loading model from best epoch ...
2023-10-18 18:33:29,350 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
2023-10-18 18:33:31,641 
Results:
- F-score (micro) 0.3266
- F-score (macro) 0.1301
- Accuracy 0.2057

By class:
              precision    recall  f1-score   support

         loc     0.4855    0.4765    0.4809       596
        pers     0.1746    0.1652    0.1698       333
         org     0.0000    0.0000    0.0000       132
        prod     0.0000    0.0000    0.0000        66
        time     0.0000    0.0000    0.0000        49

   micro avg     0.3767    0.2883    0.3266      1176
   macro avg     0.1320    0.1283    0.1301      1176
weighted avg     0.2955    0.2883    0.2918      1176

2023-10-18 18:33:31,641 ----------------------------------------------------------------------------------------------------