2023-10-17 18:29:21,229 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,230 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): ElectraModel(
      (embeddings): ElectraEmbeddings(
        (word_embeddings): Embedding(32001, 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): ElectraEncoder(
        (layer): ModuleList(
          (0-11): 12 x ElectraLayer(
            (attention): ElectraAttention(
              (self): ElectraSelfAttention(
                (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): ElectraSelfOutput(
                (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): ElectraIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): ElectraOutput(
              (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)
            )
          )
        )
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-17 18:29:21,230 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,230 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
2023-10-17 18:29:21,230 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,230 Train:  1166 sentences
2023-10-17 18:29:21,230         (train_with_dev=False, train_with_test=False)
2023-10-17 18:29:21,230 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,230 Training Params:
2023-10-17 18:29:21,230  - learning_rate: "3e-05" 
2023-10-17 18:29:21,230  - mini_batch_size: "8"
2023-10-17 18:29:21,230  - max_epochs: "10"
2023-10-17 18:29:21,231  - shuffle: "True"
2023-10-17 18:29:21,231 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,231 Plugins:
2023-10-17 18:29:21,231  - TensorboardLogger
2023-10-17 18:29:21,231  - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 18:29:21,231 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,231 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 18:29:21,231  - metric: "('micro avg', 'f1-score')"
2023-10-17 18:29:21,231 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,231 Computation:
2023-10-17 18:29:21,231  - compute on device: cuda:0
2023-10-17 18:29:21,231  - embedding storage: none
2023-10-17 18:29:21,231 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,231 Model training base path: "hmbench-newseye/fi-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
2023-10-17 18:29:21,231 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,231 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:21,231 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 18:29:22,716 epoch 1 - iter 14/146 - loss 3.50355148 - time (sec): 1.48 - samples/sec: 2833.13 - lr: 0.000003 - momentum: 0.000000
2023-10-17 18:29:24,029 epoch 1 - iter 28/146 - loss 3.25324592 - time (sec): 2.80 - samples/sec: 3022.82 - lr: 0.000006 - momentum: 0.000000
2023-10-17 18:29:25,560 epoch 1 - iter 42/146 - loss 2.83306395 - time (sec): 4.33 - samples/sec: 2941.17 - lr: 0.000008 - momentum: 0.000000
2023-10-17 18:29:26,952 epoch 1 - iter 56/146 - loss 2.34617359 - time (sec): 5.72 - samples/sec: 2903.91 - lr: 0.000011 - momentum: 0.000000
2023-10-17 18:29:28,446 epoch 1 - iter 70/146 - loss 1.98648803 - time (sec): 7.21 - samples/sec: 2891.78 - lr: 0.000014 - momentum: 0.000000
2023-10-17 18:29:29,805 epoch 1 - iter 84/146 - loss 1.76467730 - time (sec): 8.57 - samples/sec: 2921.92 - lr: 0.000017 - momentum: 0.000000
2023-10-17 18:29:31,676 epoch 1 - iter 98/146 - loss 1.55755458 - time (sec): 10.44 - samples/sec: 2869.23 - lr: 0.000020 - momentum: 0.000000
2023-10-17 18:29:33,154 epoch 1 - iter 112/146 - loss 1.39712517 - time (sec): 11.92 - samples/sec: 2890.02 - lr: 0.000023 - momentum: 0.000000
2023-10-17 18:29:34,404 epoch 1 - iter 126/146 - loss 1.28447747 - time (sec): 13.17 - samples/sec: 2904.82 - lr: 0.000026 - momentum: 0.000000
2023-10-17 18:29:35,692 epoch 1 - iter 140/146 - loss 1.20091206 - time (sec): 14.46 - samples/sec: 2894.54 - lr: 0.000029 - momentum: 0.000000
2023-10-17 18:29:36,539 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:36,539 EPOCH 1 done: loss 1.1413 - lr: 0.000029
2023-10-17 18:29:37,573 DEV : loss 0.2010345607995987 - f1-score (micro avg)  0.4167
2023-10-17 18:29:37,578 saving best model
2023-10-17 18:29:37,947 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:39,304 epoch 2 - iter 14/146 - loss 0.21316978 - time (sec): 1.36 - samples/sec: 3106.80 - lr: 0.000030 - momentum: 0.000000
2023-10-17 18:29:40,935 epoch 2 - iter 28/146 - loss 0.31888932 - time (sec): 2.99 - samples/sec: 3057.61 - lr: 0.000029 - momentum: 0.000000
2023-10-17 18:29:42,400 epoch 2 - iter 42/146 - loss 0.29320937 - time (sec): 4.45 - samples/sec: 3055.95 - lr: 0.000029 - momentum: 0.000000
2023-10-17 18:29:43,927 epoch 2 - iter 56/146 - loss 0.26948633 - time (sec): 5.98 - samples/sec: 3026.62 - lr: 0.000029 - momentum: 0.000000
2023-10-17 18:29:45,777 epoch 2 - iter 70/146 - loss 0.25509469 - time (sec): 7.83 - samples/sec: 2887.24 - lr: 0.000028 - momentum: 0.000000
2023-10-17 18:29:47,099 epoch 2 - iter 84/146 - loss 0.24355715 - time (sec): 9.15 - samples/sec: 2854.39 - lr: 0.000028 - momentum: 0.000000
2023-10-17 18:29:48,415 epoch 2 - iter 98/146 - loss 0.23666148 - time (sec): 10.47 - samples/sec: 2871.26 - lr: 0.000028 - momentum: 0.000000
2023-10-17 18:29:49,979 epoch 2 - iter 112/146 - loss 0.22451920 - time (sec): 12.03 - samples/sec: 2868.03 - lr: 0.000027 - momentum: 0.000000
2023-10-17 18:29:51,508 epoch 2 - iter 126/146 - loss 0.21799436 - time (sec): 13.56 - samples/sec: 2858.93 - lr: 0.000027 - momentum: 0.000000
2023-10-17 18:29:52,916 epoch 2 - iter 140/146 - loss 0.21515085 - time (sec): 14.97 - samples/sec: 2836.38 - lr: 0.000027 - momentum: 0.000000
2023-10-17 18:29:53,623 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:53,623 EPOCH 2 done: loss 0.2128 - lr: 0.000027
2023-10-17 18:29:54,918 DEV : loss 0.12932813167572021 - f1-score (micro avg)  0.6043
2023-10-17 18:29:54,925 saving best model
2023-10-17 18:29:55,394 ----------------------------------------------------------------------------------------------------
2023-10-17 18:29:57,051 epoch 3 - iter 14/146 - loss 0.14055569 - time (sec): 1.66 - samples/sec: 3029.42 - lr: 0.000026 - momentum: 0.000000
2023-10-17 18:29:58,264 epoch 3 - iter 28/146 - loss 0.13162903 - time (sec): 2.87 - samples/sec: 3054.59 - lr: 0.000026 - momentum: 0.000000
2023-10-17 18:29:59,912 epoch 3 - iter 42/146 - loss 0.11361109 - time (sec): 4.52 - samples/sec: 3029.30 - lr: 0.000026 - momentum: 0.000000
2023-10-17 18:30:01,662 epoch 3 - iter 56/146 - loss 0.12750318 - time (sec): 6.27 - samples/sec: 2868.35 - lr: 0.000025 - momentum: 0.000000
2023-10-17 18:30:03,094 epoch 3 - iter 70/146 - loss 0.12967160 - time (sec): 7.70 - samples/sec: 2920.32 - lr: 0.000025 - momentum: 0.000000
2023-10-17 18:30:04,308 epoch 3 - iter 84/146 - loss 0.13277965 - time (sec): 8.91 - samples/sec: 2928.00 - lr: 0.000025 - momentum: 0.000000
2023-10-17 18:30:05,767 epoch 3 - iter 98/146 - loss 0.13399034 - time (sec): 10.37 - samples/sec: 2944.70 - lr: 0.000024 - momentum: 0.000000
2023-10-17 18:30:07,214 epoch 3 - iter 112/146 - loss 0.13409185 - time (sec): 11.82 - samples/sec: 2917.61 - lr: 0.000024 - momentum: 0.000000
2023-10-17 18:30:08,579 epoch 3 - iter 126/146 - loss 0.12926495 - time (sec): 13.18 - samples/sec: 2926.05 - lr: 0.000024 - momentum: 0.000000
2023-10-17 18:30:09,889 epoch 3 - iter 140/146 - loss 0.12582582 - time (sec): 14.49 - samples/sec: 2948.69 - lr: 0.000024 - momentum: 0.000000
2023-10-17 18:30:10,469 ----------------------------------------------------------------------------------------------------
2023-10-17 18:30:10,470 EPOCH 3 done: loss 0.1239 - lr: 0.000024
2023-10-17 18:30:11,759 DEV : loss 0.11435481905937195 - f1-score (micro avg)  0.6991
2023-10-17 18:30:11,766 saving best model
2023-10-17 18:30:12,263 ----------------------------------------------------------------------------------------------------
2023-10-17 18:30:13,869 epoch 4 - iter 14/146 - loss 0.09119870 - time (sec): 1.60 - samples/sec: 3135.17 - lr: 0.000023 - momentum: 0.000000
2023-10-17 18:30:15,299 epoch 4 - iter 28/146 - loss 0.08194537 - time (sec): 3.03 - samples/sec: 3091.34 - lr: 0.000023 - momentum: 0.000000
2023-10-17 18:30:16,620 epoch 4 - iter 42/146 - loss 0.08062505 - time (sec): 4.36 - samples/sec: 3004.00 - lr: 0.000022 - momentum: 0.000000
2023-10-17 18:30:17,929 epoch 4 - iter 56/146 - loss 0.08080567 - time (sec): 5.66 - samples/sec: 3013.90 - lr: 0.000022 - momentum: 0.000000
2023-10-17 18:30:19,776 epoch 4 - iter 70/146 - loss 0.08296659 - time (sec): 7.51 - samples/sec: 2889.81 - lr: 0.000022 - momentum: 0.000000
2023-10-17 18:30:21,196 epoch 4 - iter 84/146 - loss 0.08505015 - time (sec): 8.93 - samples/sec: 2843.59 - lr: 0.000021 - momentum: 0.000000
2023-10-17 18:30:22,632 epoch 4 - iter 98/146 - loss 0.08597811 - time (sec): 10.37 - samples/sec: 2861.21 - lr: 0.000021 - momentum: 0.000000
2023-10-17 18:30:24,375 epoch 4 - iter 112/146 - loss 0.08396738 - time (sec): 12.11 - samples/sec: 2831.51 - lr: 0.000021 - momentum: 0.000000
2023-10-17 18:30:25,867 epoch 4 - iter 126/146 - loss 0.08482342 - time (sec): 13.60 - samples/sec: 2837.10 - lr: 0.000021 - momentum: 0.000000
2023-10-17 18:30:27,449 epoch 4 - iter 140/146 - loss 0.08515369 - time (sec): 15.18 - samples/sec: 2832.57 - lr: 0.000020 - momentum: 0.000000
2023-10-17 18:30:27,994 ----------------------------------------------------------------------------------------------------
2023-10-17 18:30:27,994 EPOCH 4 done: loss 0.0841 - lr: 0.000020
2023-10-17 18:30:29,328 DEV : loss 0.10569198429584503 - f1-score (micro avg)  0.7539
2023-10-17 18:30:29,334 saving best model
2023-10-17 18:30:29,848 ----------------------------------------------------------------------------------------------------
2023-10-17 18:30:31,801 epoch 5 - iter 14/146 - loss 0.05778226 - time (sec): 1.95 - samples/sec: 2693.83 - lr: 0.000020 - momentum: 0.000000
2023-10-17 18:30:32,972 epoch 5 - iter 28/146 - loss 0.06230529 - time (sec): 3.12 - samples/sec: 2757.51 - lr: 0.000019 - momentum: 0.000000
2023-10-17 18:30:34,248 epoch 5 - iter 42/146 - loss 0.06228412 - time (sec): 4.40 - samples/sec: 2778.13 - lr: 0.000019 - momentum: 0.000000
2023-10-17 18:30:35,907 epoch 5 - iter 56/146 - loss 0.05920739 - time (sec): 6.06 - samples/sec: 2743.48 - lr: 0.000019 - momentum: 0.000000
2023-10-17 18:30:37,441 epoch 5 - iter 70/146 - loss 0.05930941 - time (sec): 7.59 - samples/sec: 2838.75 - lr: 0.000018 - momentum: 0.000000
2023-10-17 18:30:38,805 epoch 5 - iter 84/146 - loss 0.06127520 - time (sec): 8.95 - samples/sec: 2880.65 - lr: 0.000018 - momentum: 0.000000
2023-10-17 18:30:40,183 epoch 5 - iter 98/146 - loss 0.05945058 - time (sec): 10.33 - samples/sec: 2873.88 - lr: 0.000018 - momentum: 0.000000
2023-10-17 18:30:41,453 epoch 5 - iter 112/146 - loss 0.05860727 - time (sec): 11.60 - samples/sec: 2891.09 - lr: 0.000018 - momentum: 0.000000
2023-10-17 18:30:42,816 epoch 5 - iter 126/146 - loss 0.05959648 - time (sec): 12.97 - samples/sec: 2918.13 - lr: 0.000017 - momentum: 0.000000
2023-10-17 18:30:44,239 epoch 5 - iter 140/146 - loss 0.06104726 - time (sec): 14.39 - samples/sec: 2958.14 - lr: 0.000017 - momentum: 0.000000
2023-10-17 18:30:44,915 ----------------------------------------------------------------------------------------------------
2023-10-17 18:30:44,915 EPOCH 5 done: loss 0.0597 - lr: 0.000017
2023-10-17 18:30:46,210 DEV : loss 0.10582081973552704 - f1-score (micro avg)  0.7652
2023-10-17 18:30:46,215 saving best model
2023-10-17 18:30:46,672 ----------------------------------------------------------------------------------------------------
2023-10-17 18:30:48,511 epoch 6 - iter 14/146 - loss 0.05051652 - time (sec): 1.84 - samples/sec: 2994.31 - lr: 0.000016 - momentum: 0.000000
2023-10-17 18:30:49,651 epoch 6 - iter 28/146 - loss 0.04479525 - time (sec): 2.98 - samples/sec: 3079.50 - lr: 0.000016 - momentum: 0.000000
2023-10-17 18:30:51,160 epoch 6 - iter 42/146 - loss 0.04342063 - time (sec): 4.49 - samples/sec: 3009.15 - lr: 0.000016 - momentum: 0.000000
2023-10-17 18:30:52,674 epoch 6 - iter 56/146 - loss 0.03906415 - time (sec): 6.00 - samples/sec: 3005.44 - lr: 0.000015 - momentum: 0.000000
2023-10-17 18:30:54,064 epoch 6 - iter 70/146 - loss 0.04181724 - time (sec): 7.39 - samples/sec: 2992.72 - lr: 0.000015 - momentum: 0.000000
2023-10-17 18:30:55,684 epoch 6 - iter 84/146 - loss 0.03969524 - time (sec): 9.01 - samples/sec: 2881.81 - lr: 0.000015 - momentum: 0.000000
2023-10-17 18:30:57,131 epoch 6 - iter 98/146 - loss 0.03973318 - time (sec): 10.46 - samples/sec: 2891.90 - lr: 0.000015 - momentum: 0.000000
2023-10-17 18:30:58,685 epoch 6 - iter 112/146 - loss 0.04223050 - time (sec): 12.01 - samples/sec: 2888.74 - lr: 0.000014 - momentum: 0.000000
2023-10-17 18:30:59,993 epoch 6 - iter 126/146 - loss 0.04220367 - time (sec): 13.32 - samples/sec: 2911.54 - lr: 0.000014 - momentum: 0.000000
2023-10-17 18:31:01,341 epoch 6 - iter 140/146 - loss 0.04001740 - time (sec): 14.67 - samples/sec: 2917.48 - lr: 0.000014 - momentum: 0.000000
2023-10-17 18:31:01,845 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:01,845 EPOCH 6 done: loss 0.0400 - lr: 0.000014
2023-10-17 18:31:03,126 DEV : loss 0.13629357516765594 - f1-score (micro avg)  0.7617
2023-10-17 18:31:03,131 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:04,738 epoch 7 - iter 14/146 - loss 0.02967247 - time (sec): 1.61 - samples/sec: 3051.52 - lr: 0.000013 - momentum: 0.000000
2023-10-17 18:31:06,216 epoch 7 - iter 28/146 - loss 0.03651477 - time (sec): 3.08 - samples/sec: 2906.74 - lr: 0.000013 - momentum: 0.000000
2023-10-17 18:31:07,491 epoch 7 - iter 42/146 - loss 0.03086782 - time (sec): 4.36 - samples/sec: 2997.40 - lr: 0.000012 - momentum: 0.000000
2023-10-17 18:31:09,037 epoch 7 - iter 56/146 - loss 0.02920746 - time (sec): 5.91 - samples/sec: 3029.61 - lr: 0.000012 - momentum: 0.000000
2023-10-17 18:31:10,394 epoch 7 - iter 70/146 - loss 0.02817667 - time (sec): 7.26 - samples/sec: 3058.22 - lr: 0.000012 - momentum: 0.000000
2023-10-17 18:31:12,016 epoch 7 - iter 84/146 - loss 0.03114981 - time (sec): 8.88 - samples/sec: 3020.00 - lr: 0.000012 - momentum: 0.000000
2023-10-17 18:31:13,347 epoch 7 - iter 98/146 - loss 0.03218262 - time (sec): 10.21 - samples/sec: 2969.90 - lr: 0.000011 - momentum: 0.000000
2023-10-17 18:31:14,705 epoch 7 - iter 112/146 - loss 0.03505462 - time (sec): 11.57 - samples/sec: 2968.48 - lr: 0.000011 - momentum: 0.000000
2023-10-17 18:31:16,034 epoch 7 - iter 126/146 - loss 0.03492020 - time (sec): 12.90 - samples/sec: 2968.80 - lr: 0.000011 - momentum: 0.000000
2023-10-17 18:31:17,535 epoch 7 - iter 140/146 - loss 0.03310629 - time (sec): 14.40 - samples/sec: 2978.57 - lr: 0.000010 - momentum: 0.000000
2023-10-17 18:31:18,069 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:18,069 EPOCH 7 done: loss 0.0330 - lr: 0.000010
2023-10-17 18:31:19,369 DEV : loss 0.12761062383651733 - f1-score (micro avg)  0.7659
2023-10-17 18:31:19,375 saving best model
2023-10-17 18:31:19,894 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:21,355 epoch 8 - iter 14/146 - loss 0.03349981 - time (sec): 1.46 - samples/sec: 3024.57 - lr: 0.000010 - momentum: 0.000000
2023-10-17 18:31:22,790 epoch 8 - iter 28/146 - loss 0.02887435 - time (sec): 2.89 - samples/sec: 2974.56 - lr: 0.000009 - momentum: 0.000000
2023-10-17 18:31:24,403 epoch 8 - iter 42/146 - loss 0.02995794 - time (sec): 4.51 - samples/sec: 3121.61 - lr: 0.000009 - momentum: 0.000000
2023-10-17 18:31:26,018 epoch 8 - iter 56/146 - loss 0.02678266 - time (sec): 6.12 - samples/sec: 3077.72 - lr: 0.000009 - momentum: 0.000000
2023-10-17 18:31:27,347 epoch 8 - iter 70/146 - loss 0.02314035 - time (sec): 7.45 - samples/sec: 3105.83 - lr: 0.000009 - momentum: 0.000000
2023-10-17 18:31:28,770 epoch 8 - iter 84/146 - loss 0.02186221 - time (sec): 8.87 - samples/sec: 3062.90 - lr: 0.000008 - momentum: 0.000000
2023-10-17 18:31:30,290 epoch 8 - iter 98/146 - loss 0.02199109 - time (sec): 10.39 - samples/sec: 3008.44 - lr: 0.000008 - momentum: 0.000000
2023-10-17 18:31:31,605 epoch 8 - iter 112/146 - loss 0.02235855 - time (sec): 11.71 - samples/sec: 2972.83 - lr: 0.000008 - momentum: 0.000000
2023-10-17 18:31:32,957 epoch 8 - iter 126/146 - loss 0.02176303 - time (sec): 13.06 - samples/sec: 2974.26 - lr: 0.000007 - momentum: 0.000000
2023-10-17 18:31:34,601 epoch 8 - iter 140/146 - loss 0.02565869 - time (sec): 14.71 - samples/sec: 2940.76 - lr: 0.000007 - momentum: 0.000000
2023-10-17 18:31:35,066 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:35,066 EPOCH 8 done: loss 0.0257 - lr: 0.000007
2023-10-17 18:31:36,345 DEV : loss 0.12473238259553909 - f1-score (micro avg)  0.7709
2023-10-17 18:31:36,352 saving best model
2023-10-17 18:31:36,901 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:38,456 epoch 9 - iter 14/146 - loss 0.02290499 - time (sec): 1.55 - samples/sec: 2919.97 - lr: 0.000006 - momentum: 0.000000
2023-10-17 18:31:40,125 epoch 9 - iter 28/146 - loss 0.02113077 - time (sec): 3.22 - samples/sec: 2944.98 - lr: 0.000006 - momentum: 0.000000
2023-10-17 18:31:41,743 epoch 9 - iter 42/146 - loss 0.02181818 - time (sec): 4.84 - samples/sec: 2872.92 - lr: 0.000006 - momentum: 0.000000
2023-10-17 18:31:43,033 epoch 9 - iter 56/146 - loss 0.02005836 - time (sec): 6.13 - samples/sec: 2904.48 - lr: 0.000006 - momentum: 0.000000
2023-10-17 18:31:44,850 epoch 9 - iter 70/146 - loss 0.01847436 - time (sec): 7.95 - samples/sec: 2820.34 - lr: 0.000005 - momentum: 0.000000
2023-10-17 18:31:46,338 epoch 9 - iter 84/146 - loss 0.01856837 - time (sec): 9.43 - samples/sec: 2815.95 - lr: 0.000005 - momentum: 0.000000
2023-10-17 18:31:47,749 epoch 9 - iter 98/146 - loss 0.02083486 - time (sec): 10.85 - samples/sec: 2816.24 - lr: 0.000005 - momentum: 0.000000
2023-10-17 18:31:48,997 epoch 9 - iter 112/146 - loss 0.02077486 - time (sec): 12.09 - samples/sec: 2798.72 - lr: 0.000004 - momentum: 0.000000
2023-10-17 18:31:50,885 epoch 9 - iter 126/146 - loss 0.01948000 - time (sec): 13.98 - samples/sec: 2749.61 - lr: 0.000004 - momentum: 0.000000
2023-10-17 18:31:52,425 epoch 9 - iter 140/146 - loss 0.01950185 - time (sec): 15.52 - samples/sec: 2758.56 - lr: 0.000004 - momentum: 0.000000
2023-10-17 18:31:53,021 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:53,022 EPOCH 9 done: loss 0.0196 - lr: 0.000004
2023-10-17 18:31:54,356 DEV : loss 0.12521956861019135 - f1-score (micro avg)  0.7835
2023-10-17 18:31:54,362 saving best model
2023-10-17 18:31:54,886 ----------------------------------------------------------------------------------------------------
2023-10-17 18:31:56,635 epoch 10 - iter 14/146 - loss 0.01159532 - time (sec): 1.74 - samples/sec: 2794.62 - lr: 0.000003 - momentum: 0.000000
2023-10-17 18:31:58,076 epoch 10 - iter 28/146 - loss 0.01749210 - time (sec): 3.18 - samples/sec: 2809.62 - lr: 0.000003 - momentum: 0.000000
2023-10-17 18:31:59,503 epoch 10 - iter 42/146 - loss 0.02306435 - time (sec): 4.61 - samples/sec: 2729.46 - lr: 0.000003 - momentum: 0.000000
2023-10-17 18:32:01,069 epoch 10 - iter 56/146 - loss 0.02221024 - time (sec): 6.17 - samples/sec: 2772.54 - lr: 0.000002 - momentum: 0.000000
2023-10-17 18:32:02,694 epoch 10 - iter 70/146 - loss 0.02024334 - time (sec): 7.80 - samples/sec: 2787.96 - lr: 0.000002 - momentum: 0.000000
2023-10-17 18:32:04,062 epoch 10 - iter 84/146 - loss 0.01765259 - time (sec): 9.17 - samples/sec: 2861.79 - lr: 0.000002 - momentum: 0.000000
2023-10-17 18:32:05,575 epoch 10 - iter 98/146 - loss 0.01690867 - time (sec): 10.68 - samples/sec: 2848.31 - lr: 0.000001 - momentum: 0.000000
2023-10-17 18:32:06,896 epoch 10 - iter 112/146 - loss 0.01579067 - time (sec): 12.00 - samples/sec: 2862.39 - lr: 0.000001 - momentum: 0.000000
2023-10-17 18:32:08,298 epoch 10 - iter 126/146 - loss 0.01607607 - time (sec): 13.40 - samples/sec: 2867.29 - lr: 0.000001 - momentum: 0.000000
2023-10-17 18:32:09,749 epoch 10 - iter 140/146 - loss 0.01649865 - time (sec): 14.85 - samples/sec: 2872.24 - lr: 0.000000 - momentum: 0.000000
2023-10-17 18:32:10,433 ----------------------------------------------------------------------------------------------------
2023-10-17 18:32:10,434 EPOCH 10 done: loss 0.0164 - lr: 0.000000
2023-10-17 18:32:11,735 DEV : loss 0.12415261566638947 - f1-score (micro avg)  0.7756
2023-10-17 18:32:12,098 ----------------------------------------------------------------------------------------------------
2023-10-17 18:32:12,100 Loading model from best epoch ...
2023-10-17 18:32:13,828 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
2023-10-17 18:32:16,785 
Results:
- F-score (micro) 0.7545
- F-score (macro) 0.665
- Accuracy 0.6287

By class:
              precision    recall  f1-score   support

         PER     0.8127    0.8477    0.8298       348
         LOC     0.6505    0.8199    0.7254       261
         ORG     0.4444    0.3846    0.4124        52
   HumanProd     0.6000    0.8182    0.6923        22

   micro avg     0.7132    0.8009    0.7545       683
   macro avg     0.6269    0.7176    0.6650       683
weighted avg     0.7158    0.8009    0.7537       683

2023-10-17 18:32:16,786 ----------------------------------------------------------------------------------------------------