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2023-10-25 21:15:35,815 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,816 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(
          (0-11): 12 x 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=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-25 21:15:35,816 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,816 MultiCorpus: 1166 train + 165 dev + 415 test sentences
 - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator
2023-10-25 21:15:35,816 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,816 Train:  1166 sentences
2023-10-25 21:15:35,816         (train_with_dev=False, train_with_test=False)
2023-10-25 21:15:35,816 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,816 Training Params:
2023-10-25 21:15:35,816  - learning_rate: "3e-05" 
2023-10-25 21:15:35,817  - mini_batch_size: "8"
2023-10-25 21:15:35,817  - max_epochs: "10"
2023-10-25 21:15:35,817  - shuffle: "True"
2023-10-25 21:15:35,817 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,817 Plugins:
2023-10-25 21:15:35,817  - TensorboardLogger
2023-10-25 21:15:35,817  - LinearScheduler | warmup_fraction: '0.1'
2023-10-25 21:15:35,817 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,817 Final evaluation on model from best epoch (best-model.pt)
2023-10-25 21:15:35,817  - metric: "('micro avg', 'f1-score')"
2023-10-25 21:15:35,817 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,817 Computation:
2023-10-25 21:15:35,817  - compute on device: cuda:0
2023-10-25 21:15:35,817  - embedding storage: none
2023-10-25 21:15:35,817 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,817 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-25 21:15:35,817 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,817 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:35,817 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-25 21:15:36,790 epoch 1 - iter 14/146 - loss 3.31880862 - time (sec): 0.97 - samples/sec: 4680.15 - lr: 0.000003 - momentum: 0.000000
2023-10-25 21:15:37,663 epoch 1 - iter 28/146 - loss 2.83940349 - time (sec): 1.85 - samples/sec: 4665.27 - lr: 0.000006 - momentum: 0.000000
2023-10-25 21:15:38,450 epoch 1 - iter 42/146 - loss 2.37233835 - time (sec): 2.63 - samples/sec: 4704.10 - lr: 0.000008 - momentum: 0.000000
2023-10-25 21:15:39,289 epoch 1 - iter 56/146 - loss 1.92518471 - time (sec): 3.47 - samples/sec: 4831.73 - lr: 0.000011 - momentum: 0.000000
2023-10-25 21:15:40,152 epoch 1 - iter 70/146 - loss 1.66955582 - time (sec): 4.33 - samples/sec: 4747.69 - lr: 0.000014 - momentum: 0.000000
2023-10-25 21:15:41,232 epoch 1 - iter 84/146 - loss 1.44430740 - time (sec): 5.41 - samples/sec: 4675.85 - lr: 0.000017 - momentum: 0.000000
2023-10-25 21:15:42,100 epoch 1 - iter 98/146 - loss 1.29525834 - time (sec): 6.28 - samples/sec: 4722.35 - lr: 0.000020 - momentum: 0.000000
2023-10-25 21:15:42,936 epoch 1 - iter 112/146 - loss 1.18172702 - time (sec): 7.12 - samples/sec: 4692.17 - lr: 0.000023 - momentum: 0.000000
2023-10-25 21:15:43,881 epoch 1 - iter 126/146 - loss 1.05862291 - time (sec): 8.06 - samples/sec: 4750.93 - lr: 0.000026 - momentum: 0.000000
2023-10-25 21:15:44,791 epoch 1 - iter 140/146 - loss 0.97914154 - time (sec): 8.97 - samples/sec: 4704.19 - lr: 0.000029 - momentum: 0.000000
2023-10-25 21:15:45,285 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:45,285 EPOCH 1 done: loss 0.9399 - lr: 0.000029
2023-10-25 21:15:45,797 DEV : loss 0.17592628300189972 - f1-score (micro avg)  0.5057
2023-10-25 21:15:45,804 saving best model
2023-10-25 21:15:46,340 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:47,257 epoch 2 - iter 14/146 - loss 0.27459429 - time (sec): 0.92 - samples/sec: 4613.22 - lr: 0.000030 - momentum: 0.000000
2023-10-25 21:15:48,209 epoch 2 - iter 28/146 - loss 0.20873210 - time (sec): 1.87 - samples/sec: 4769.99 - lr: 0.000029 - momentum: 0.000000
2023-10-25 21:15:49,000 epoch 2 - iter 42/146 - loss 0.19579956 - time (sec): 2.66 - samples/sec: 4908.76 - lr: 0.000029 - momentum: 0.000000
2023-10-25 21:15:49,915 epoch 2 - iter 56/146 - loss 0.20388411 - time (sec): 3.57 - samples/sec: 4896.50 - lr: 0.000029 - momentum: 0.000000
2023-10-25 21:15:50,857 epoch 2 - iter 70/146 - loss 0.19597627 - time (sec): 4.52 - samples/sec: 4725.59 - lr: 0.000028 - momentum: 0.000000
2023-10-25 21:15:51,643 epoch 2 - iter 84/146 - loss 0.19552040 - time (sec): 5.30 - samples/sec: 4695.35 - lr: 0.000028 - momentum: 0.000000
2023-10-25 21:15:52,486 epoch 2 - iter 98/146 - loss 0.19191093 - time (sec): 6.14 - samples/sec: 4722.30 - lr: 0.000028 - momentum: 0.000000
2023-10-25 21:15:53,480 epoch 2 - iter 112/146 - loss 0.19199910 - time (sec): 7.14 - samples/sec: 4693.68 - lr: 0.000027 - momentum: 0.000000
2023-10-25 21:15:54,441 epoch 2 - iter 126/146 - loss 0.18518667 - time (sec): 8.10 - samples/sec: 4712.56 - lr: 0.000027 - momentum: 0.000000
2023-10-25 21:15:55,342 epoch 2 - iter 140/146 - loss 0.18152334 - time (sec): 9.00 - samples/sec: 4749.87 - lr: 0.000027 - momentum: 0.000000
2023-10-25 21:15:55,719 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:55,719 EPOCH 2 done: loss 0.1802 - lr: 0.000027
2023-10-25 21:15:56,802 DEV : loss 0.1134551540017128 - f1-score (micro avg)  0.6803
2023-10-25 21:15:56,807 saving best model
2023-10-25 21:15:57,478 ----------------------------------------------------------------------------------------------------
2023-10-25 21:15:58,384 epoch 3 - iter 14/146 - loss 0.09704610 - time (sec): 0.90 - samples/sec: 5208.79 - lr: 0.000026 - momentum: 0.000000
2023-10-25 21:15:59,243 epoch 3 - iter 28/146 - loss 0.10318376 - time (sec): 1.76 - samples/sec: 4982.24 - lr: 0.000026 - momentum: 0.000000
2023-10-25 21:16:00,029 epoch 3 - iter 42/146 - loss 0.10850531 - time (sec): 2.55 - samples/sec: 4960.64 - lr: 0.000026 - momentum: 0.000000
2023-10-25 21:16:01,034 epoch 3 - iter 56/146 - loss 0.12139462 - time (sec): 3.55 - samples/sec: 4812.73 - lr: 0.000025 - momentum: 0.000000
2023-10-25 21:16:01,943 epoch 3 - iter 70/146 - loss 0.12819076 - time (sec): 4.46 - samples/sec: 4826.37 - lr: 0.000025 - momentum: 0.000000
2023-10-25 21:16:02,713 epoch 3 - iter 84/146 - loss 0.12323830 - time (sec): 5.23 - samples/sec: 4776.90 - lr: 0.000025 - momentum: 0.000000
2023-10-25 21:16:03,519 epoch 3 - iter 98/146 - loss 0.11656376 - time (sec): 6.04 - samples/sec: 4809.24 - lr: 0.000024 - momentum: 0.000000
2023-10-25 21:16:04,396 epoch 3 - iter 112/146 - loss 0.11841299 - time (sec): 6.92 - samples/sec: 4791.93 - lr: 0.000024 - momentum: 0.000000
2023-10-25 21:16:05,416 epoch 3 - iter 126/146 - loss 0.12592799 - time (sec): 7.94 - samples/sec: 4751.77 - lr: 0.000024 - momentum: 0.000000
2023-10-25 21:16:06,354 epoch 3 - iter 140/146 - loss 0.12063353 - time (sec): 8.87 - samples/sec: 4763.85 - lr: 0.000024 - momentum: 0.000000
2023-10-25 21:16:06,780 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:06,781 EPOCH 3 done: loss 0.1174 - lr: 0.000024
2023-10-25 21:16:07,701 DEV : loss 0.09478442370891571 - f1-score (micro avg)  0.7702
2023-10-25 21:16:07,706 saving best model
2023-10-25 21:16:08,223 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:09,122 epoch 4 - iter 14/146 - loss 0.05203944 - time (sec): 0.90 - samples/sec: 4657.75 - lr: 0.000023 - momentum: 0.000000
2023-10-25 21:16:10,078 epoch 4 - iter 28/146 - loss 0.05457589 - time (sec): 1.85 - samples/sec: 4904.91 - lr: 0.000023 - momentum: 0.000000
2023-10-25 21:16:11,077 epoch 4 - iter 42/146 - loss 0.06328594 - time (sec): 2.85 - samples/sec: 4862.79 - lr: 0.000022 - momentum: 0.000000
2023-10-25 21:16:11,928 epoch 4 - iter 56/146 - loss 0.05976392 - time (sec): 3.70 - samples/sec: 4901.62 - lr: 0.000022 - momentum: 0.000000
2023-10-25 21:16:12,800 epoch 4 - iter 70/146 - loss 0.06522269 - time (sec): 4.58 - samples/sec: 4859.69 - lr: 0.000022 - momentum: 0.000000
2023-10-25 21:16:13,587 epoch 4 - iter 84/146 - loss 0.07044788 - time (sec): 5.36 - samples/sec: 4829.01 - lr: 0.000021 - momentum: 0.000000
2023-10-25 21:16:14,389 epoch 4 - iter 98/146 - loss 0.06828926 - time (sec): 6.16 - samples/sec: 4845.43 - lr: 0.000021 - momentum: 0.000000
2023-10-25 21:16:15,306 epoch 4 - iter 112/146 - loss 0.06561645 - time (sec): 7.08 - samples/sec: 4845.63 - lr: 0.000021 - momentum: 0.000000
2023-10-25 21:16:16,164 epoch 4 - iter 126/146 - loss 0.06604040 - time (sec): 7.94 - samples/sec: 4842.00 - lr: 0.000021 - momentum: 0.000000
2023-10-25 21:16:17,121 epoch 4 - iter 140/146 - loss 0.06471436 - time (sec): 8.90 - samples/sec: 4828.05 - lr: 0.000020 - momentum: 0.000000
2023-10-25 21:16:17,538 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:17,539 EPOCH 4 done: loss 0.0647 - lr: 0.000020
2023-10-25 21:16:18,464 DEV : loss 0.10669375211000443 - f1-score (micro avg)  0.7394
2023-10-25 21:16:18,469 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:19,473 epoch 5 - iter 14/146 - loss 0.02805617 - time (sec): 1.00 - samples/sec: 4532.92 - lr: 0.000020 - momentum: 0.000000
2023-10-25 21:16:20,406 epoch 5 - iter 28/146 - loss 0.04003122 - time (sec): 1.94 - samples/sec: 4597.96 - lr: 0.000019 - momentum: 0.000000
2023-10-25 21:16:21,326 epoch 5 - iter 42/146 - loss 0.03988139 - time (sec): 2.86 - samples/sec: 4730.62 - lr: 0.000019 - momentum: 0.000000
2023-10-25 21:16:22,332 epoch 5 - iter 56/146 - loss 0.03736783 - time (sec): 3.86 - samples/sec: 4693.02 - lr: 0.000019 - momentum: 0.000000
2023-10-25 21:16:23,266 epoch 5 - iter 70/146 - loss 0.03674800 - time (sec): 4.80 - samples/sec: 4753.66 - lr: 0.000018 - momentum: 0.000000
2023-10-25 21:16:24,118 epoch 5 - iter 84/146 - loss 0.03884696 - time (sec): 5.65 - samples/sec: 4730.77 - lr: 0.000018 - momentum: 0.000000
2023-10-25 21:16:24,936 epoch 5 - iter 98/146 - loss 0.03861859 - time (sec): 6.47 - samples/sec: 4694.59 - lr: 0.000018 - momentum: 0.000000
2023-10-25 21:16:25,745 epoch 5 - iter 112/146 - loss 0.04109054 - time (sec): 7.28 - samples/sec: 4749.93 - lr: 0.000018 - momentum: 0.000000
2023-10-25 21:16:26,576 epoch 5 - iter 126/146 - loss 0.04162815 - time (sec): 8.11 - samples/sec: 4742.72 - lr: 0.000017 - momentum: 0.000000
2023-10-25 21:16:27,416 epoch 5 - iter 140/146 - loss 0.04205143 - time (sec): 8.95 - samples/sec: 4769.31 - lr: 0.000017 - momentum: 0.000000
2023-10-25 21:16:27,784 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:27,784 EPOCH 5 done: loss 0.0419 - lr: 0.000017
2023-10-25 21:16:28,858 DEV : loss 0.10857772827148438 - f1-score (micro avg)  0.7352
2023-10-25 21:16:28,863 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:29,735 epoch 6 - iter 14/146 - loss 0.03746632 - time (sec): 0.87 - samples/sec: 4769.99 - lr: 0.000016 - momentum: 0.000000
2023-10-25 21:16:30,634 epoch 6 - iter 28/146 - loss 0.03397273 - time (sec): 1.77 - samples/sec: 4573.87 - lr: 0.000016 - momentum: 0.000000
2023-10-25 21:16:31,568 epoch 6 - iter 42/146 - loss 0.03073910 - time (sec): 2.70 - samples/sec: 4487.59 - lr: 0.000016 - momentum: 0.000000
2023-10-25 21:16:32,391 epoch 6 - iter 56/146 - loss 0.03333079 - time (sec): 3.53 - samples/sec: 4571.12 - lr: 0.000015 - momentum: 0.000000
2023-10-25 21:16:33,315 epoch 6 - iter 70/146 - loss 0.02997522 - time (sec): 4.45 - samples/sec: 4610.15 - lr: 0.000015 - momentum: 0.000000
2023-10-25 21:16:34,277 epoch 6 - iter 84/146 - loss 0.03191011 - time (sec): 5.41 - samples/sec: 4590.95 - lr: 0.000015 - momentum: 0.000000
2023-10-25 21:16:35,167 epoch 6 - iter 98/146 - loss 0.03171068 - time (sec): 6.30 - samples/sec: 4608.29 - lr: 0.000015 - momentum: 0.000000
2023-10-25 21:16:36,254 epoch 6 - iter 112/146 - loss 0.03140399 - time (sec): 7.39 - samples/sec: 4678.96 - lr: 0.000014 - momentum: 0.000000
2023-10-25 21:16:37,062 epoch 6 - iter 126/146 - loss 0.03108208 - time (sec): 8.20 - samples/sec: 4701.18 - lr: 0.000014 - momentum: 0.000000
2023-10-25 21:16:38,016 epoch 6 - iter 140/146 - loss 0.02980445 - time (sec): 9.15 - samples/sec: 4690.75 - lr: 0.000014 - momentum: 0.000000
2023-10-25 21:16:38,351 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:38,352 EPOCH 6 done: loss 0.0295 - lr: 0.000014
2023-10-25 21:16:39,271 DEV : loss 0.12395735830068588 - f1-score (micro avg)  0.738
2023-10-25 21:16:39,276 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:40,107 epoch 7 - iter 14/146 - loss 0.01140103 - time (sec): 0.83 - samples/sec: 4409.89 - lr: 0.000013 - momentum: 0.000000
2023-10-25 21:16:41,000 epoch 7 - iter 28/146 - loss 0.02379471 - time (sec): 1.72 - samples/sec: 4593.84 - lr: 0.000013 - momentum: 0.000000
2023-10-25 21:16:42,030 epoch 7 - iter 42/146 - loss 0.02046241 - time (sec): 2.75 - samples/sec: 4605.88 - lr: 0.000012 - momentum: 0.000000
2023-10-25 21:16:42,855 epoch 7 - iter 56/146 - loss 0.02186969 - time (sec): 3.58 - samples/sec: 4603.38 - lr: 0.000012 - momentum: 0.000000
2023-10-25 21:16:43,670 epoch 7 - iter 70/146 - loss 0.02110426 - time (sec): 4.39 - samples/sec: 4583.07 - lr: 0.000012 - momentum: 0.000000
2023-10-25 21:16:44,694 epoch 7 - iter 84/146 - loss 0.01970689 - time (sec): 5.42 - samples/sec: 4578.54 - lr: 0.000012 - momentum: 0.000000
2023-10-25 21:16:45,654 epoch 7 - iter 98/146 - loss 0.02011816 - time (sec): 6.38 - samples/sec: 4712.52 - lr: 0.000011 - momentum: 0.000000
2023-10-25 21:16:46,475 epoch 7 - iter 112/146 - loss 0.02094152 - time (sec): 7.20 - samples/sec: 4730.30 - lr: 0.000011 - momentum: 0.000000
2023-10-25 21:16:47,406 epoch 7 - iter 126/146 - loss 0.02223192 - time (sec): 8.13 - samples/sec: 4698.01 - lr: 0.000011 - momentum: 0.000000
2023-10-25 21:16:48,363 epoch 7 - iter 140/146 - loss 0.02211426 - time (sec): 9.09 - samples/sec: 4691.89 - lr: 0.000010 - momentum: 0.000000
2023-10-25 21:16:48,706 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:48,706 EPOCH 7 done: loss 0.0220 - lr: 0.000010
2023-10-25 21:16:49,627 DEV : loss 0.14144070446491241 - f1-score (micro avg)  0.7588
2023-10-25 21:16:49,632 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:50,520 epoch 8 - iter 14/146 - loss 0.01067314 - time (sec): 0.89 - samples/sec: 4530.35 - lr: 0.000010 - momentum: 0.000000
2023-10-25 21:16:51,508 epoch 8 - iter 28/146 - loss 0.01377843 - time (sec): 1.87 - samples/sec: 4914.76 - lr: 0.000009 - momentum: 0.000000
2023-10-25 21:16:52,324 epoch 8 - iter 42/146 - loss 0.01796832 - time (sec): 2.69 - samples/sec: 4805.15 - lr: 0.000009 - momentum: 0.000000
2023-10-25 21:16:53,172 epoch 8 - iter 56/146 - loss 0.01572929 - time (sec): 3.54 - samples/sec: 4887.26 - lr: 0.000009 - momentum: 0.000000
2023-10-25 21:16:54,011 epoch 8 - iter 70/146 - loss 0.01527731 - time (sec): 4.38 - samples/sec: 4881.07 - lr: 0.000009 - momentum: 0.000000
2023-10-25 21:16:55,023 epoch 8 - iter 84/146 - loss 0.01649619 - time (sec): 5.39 - samples/sec: 4840.89 - lr: 0.000008 - momentum: 0.000000
2023-10-25 21:16:55,942 epoch 8 - iter 98/146 - loss 0.01594214 - time (sec): 6.31 - samples/sec: 4775.98 - lr: 0.000008 - momentum: 0.000000
2023-10-25 21:16:56,808 epoch 8 - iter 112/146 - loss 0.01578526 - time (sec): 7.18 - samples/sec: 4732.45 - lr: 0.000008 - momentum: 0.000000
2023-10-25 21:16:57,713 epoch 8 - iter 126/146 - loss 0.01606300 - time (sec): 8.08 - samples/sec: 4749.22 - lr: 0.000007 - momentum: 0.000000
2023-10-25 21:16:58,790 epoch 8 - iter 140/146 - loss 0.01643747 - time (sec): 9.16 - samples/sec: 4721.15 - lr: 0.000007 - momentum: 0.000000
2023-10-25 21:16:59,104 ----------------------------------------------------------------------------------------------------
2023-10-25 21:16:59,104 EPOCH 8 done: loss 0.0161 - lr: 0.000007
2023-10-25 21:17:00,024 DEV : loss 0.15467968583106995 - f1-score (micro avg)  0.7424
2023-10-25 21:17:00,029 ----------------------------------------------------------------------------------------------------
2023-10-25 21:17:01,157 epoch 9 - iter 14/146 - loss 0.00288768 - time (sec): 1.13 - samples/sec: 3985.42 - lr: 0.000006 - momentum: 0.000000
2023-10-25 21:17:02,071 epoch 9 - iter 28/146 - loss 0.00859361 - time (sec): 2.04 - samples/sec: 4161.83 - lr: 0.000006 - momentum: 0.000000
2023-10-25 21:17:02,916 epoch 9 - iter 42/146 - loss 0.00973239 - time (sec): 2.89 - samples/sec: 4379.12 - lr: 0.000006 - momentum: 0.000000
2023-10-25 21:17:03,871 epoch 9 - iter 56/146 - loss 0.00984653 - time (sec): 3.84 - samples/sec: 4530.98 - lr: 0.000006 - momentum: 0.000000
2023-10-25 21:17:04,712 epoch 9 - iter 70/146 - loss 0.01184400 - time (sec): 4.68 - samples/sec: 4625.32 - lr: 0.000005 - momentum: 0.000000
2023-10-25 21:17:05,616 epoch 9 - iter 84/146 - loss 0.01206385 - time (sec): 5.59 - samples/sec: 4615.17 - lr: 0.000005 - momentum: 0.000000
2023-10-25 21:17:06,444 epoch 9 - iter 98/146 - loss 0.01143554 - time (sec): 6.41 - samples/sec: 4663.53 - lr: 0.000005 - momentum: 0.000000
2023-10-25 21:17:07,191 epoch 9 - iter 112/146 - loss 0.01143731 - time (sec): 7.16 - samples/sec: 4613.30 - lr: 0.000004 - momentum: 0.000000
2023-10-25 21:17:08,147 epoch 9 - iter 126/146 - loss 0.01086007 - time (sec): 8.12 - samples/sec: 4661.50 - lr: 0.000004 - momentum: 0.000000
2023-10-25 21:17:09,170 epoch 9 - iter 140/146 - loss 0.01234488 - time (sec): 9.14 - samples/sec: 4657.04 - lr: 0.000004 - momentum: 0.000000
2023-10-25 21:17:09,545 ----------------------------------------------------------------------------------------------------
2023-10-25 21:17:09,545 EPOCH 9 done: loss 0.0121 - lr: 0.000004
2023-10-25 21:17:10,469 DEV : loss 0.1692580282688141 - f1-score (micro avg)  0.7277
2023-10-25 21:17:10,474 ----------------------------------------------------------------------------------------------------
2023-10-25 21:17:11,401 epoch 10 - iter 14/146 - loss 0.01200424 - time (sec): 0.93 - samples/sec: 4743.15 - lr: 0.000003 - momentum: 0.000000
2023-10-25 21:17:12,233 epoch 10 - iter 28/146 - loss 0.01257013 - time (sec): 1.76 - samples/sec: 4679.71 - lr: 0.000003 - momentum: 0.000000
2023-10-25 21:17:13,044 epoch 10 - iter 42/146 - loss 0.01023577 - time (sec): 2.57 - samples/sec: 4679.81 - lr: 0.000003 - momentum: 0.000000
2023-10-25 21:17:14,009 epoch 10 - iter 56/146 - loss 0.01250361 - time (sec): 3.53 - samples/sec: 4676.31 - lr: 0.000002 - momentum: 0.000000
2023-10-25 21:17:14,859 epoch 10 - iter 70/146 - loss 0.01150593 - time (sec): 4.38 - samples/sec: 4809.32 - lr: 0.000002 - momentum: 0.000000
2023-10-25 21:17:15,772 epoch 10 - iter 84/146 - loss 0.01022687 - time (sec): 5.30 - samples/sec: 4821.53 - lr: 0.000002 - momentum: 0.000000
2023-10-25 21:17:16,892 epoch 10 - iter 98/146 - loss 0.01001899 - time (sec): 6.42 - samples/sec: 4800.88 - lr: 0.000001 - momentum: 0.000000
2023-10-25 21:17:17,707 epoch 10 - iter 112/146 - loss 0.01060283 - time (sec): 7.23 - samples/sec: 4758.86 - lr: 0.000001 - momentum: 0.000000
2023-10-25 21:17:18,601 epoch 10 - iter 126/146 - loss 0.01066012 - time (sec): 8.13 - samples/sec: 4714.79 - lr: 0.000001 - momentum: 0.000000
2023-10-25 21:17:19,602 epoch 10 - iter 140/146 - loss 0.01073663 - time (sec): 9.13 - samples/sec: 4665.36 - lr: 0.000000 - momentum: 0.000000
2023-10-25 21:17:19,923 ----------------------------------------------------------------------------------------------------
2023-10-25 21:17:19,923 EPOCH 10 done: loss 0.0103 - lr: 0.000000
2023-10-25 21:17:20,844 DEV : loss 0.17254038155078888 - f1-score (micro avg)  0.7242
2023-10-25 21:17:21,379 ----------------------------------------------------------------------------------------------------
2023-10-25 21:17:21,381 Loading model from best epoch ...
2023-10-25 21:17:23,108 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
2023-10-25 21:17:24,656 
Results:
- F-score (micro) 0.7652
- F-score (macro) 0.6761
- Accuracy 0.6435

By class:
              precision    recall  f1-score   support

         PER     0.7813    0.8420    0.8105       348
         LOC     0.7536    0.7969    0.7747       261
         ORG     0.4222    0.3654    0.3918        52
   HumanProd     0.7273    0.7273    0.7273        22

   micro avg     0.7465    0.7848    0.7652       683
   macro avg     0.6711    0.6829    0.6761       683
weighted avg     0.7417    0.7848    0.7623       683

2023-10-25 21:17:24,656 ----------------------------------------------------------------------------------------------------