2023-10-10 22:19:18,056 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,058 Model: "SequenceTagger(
  (embeddings): ByT5Embeddings(
    (model): T5EncoderModel(
      (shared): Embedding(384, 1472)
      (encoder): T5Stack(
        (embed_tokens): Embedding(384, 1472)
        (block): ModuleList(
          (0): T5Block(
            (layer): ModuleList(
              (0): T5LayerSelfAttention(
                (SelfAttention): T5Attention(
                  (q): Linear(in_features=1472, out_features=384, bias=False)
                  (k): Linear(in_features=1472, out_features=384, bias=False)
                  (v): Linear(in_features=1472, out_features=384, bias=False)
                  (o): Linear(in_features=384, out_features=1472, bias=False)
                  (relative_attention_bias): Embedding(32, 6)
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (1): T5LayerFF(
                (DenseReluDense): T5DenseGatedActDense(
                  (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
                  (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
                  (wo): Linear(in_features=3584, out_features=1472, bias=False)
                  (dropout): Dropout(p=0.1, inplace=False)
                  (act): NewGELUActivation()
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
          (1-11): 11 x T5Block(
            (layer): ModuleList(
              (0): T5LayerSelfAttention(
                (SelfAttention): T5Attention(
                  (q): Linear(in_features=1472, out_features=384, bias=False)
                  (k): Linear(in_features=1472, out_features=384, bias=False)
                  (v): Linear(in_features=1472, out_features=384, bias=False)
                  (o): Linear(in_features=384, out_features=1472, bias=False)
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (1): T5LayerFF(
                (DenseReluDense): T5DenseGatedActDense(
                  (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
                  (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
                  (wo): Linear(in_features=3584, out_features=1472, bias=False)
                  (dropout): Dropout(p=0.1, inplace=False)
                  (act): NewGELUActivation()
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
        )
        (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=1472, out_features=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-10 22:19:18,059 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,059 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
2023-10-10 22:19:18,059 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,059 Train:  1166 sentences
2023-10-10 22:19:18,059         (train_with_dev=False, train_with_test=False)
2023-10-10 22:19:18,059 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,059 Training Params:
2023-10-10 22:19:18,059  - learning_rate: "0.00015" 
2023-10-10 22:19:18,059  - mini_batch_size: "4"
2023-10-10 22:19:18,059  - max_epochs: "10"
2023-10-10 22:19:18,060  - shuffle: "True"
2023-10-10 22:19:18,060 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,060 Plugins:
2023-10-10 22:19:18,060  - TensorboardLogger
2023-10-10 22:19:18,060  - LinearScheduler | warmup_fraction: '0.1'
2023-10-10 22:19:18,060 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,060 Final evaluation on model from best epoch (best-model.pt)
2023-10-10 22:19:18,060  - metric: "('micro avg', 'f1-score')"
2023-10-10 22:19:18,060 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,060 Computation:
2023-10-10 22:19:18,060  - compute on device: cuda:0
2023-10-10 22:19:18,060  - embedding storage: none
2023-10-10 22:19:18,060 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,060 Model training base path: "hmbench-newseye/fi-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1"
2023-10-10 22:19:18,060 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,061 ----------------------------------------------------------------------------------------------------
2023-10-10 22:19:18,061 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-10 22:19:28,613 epoch 1 - iter 29/292 - loss 2.82926505 - time (sec): 10.55 - samples/sec: 484.14 - lr: 0.000014 - momentum: 0.000000
2023-10-10 22:19:38,229 epoch 1 - iter 58/292 - loss 2.82094593 - time (sec): 20.17 - samples/sec: 452.45 - lr: 0.000029 - momentum: 0.000000
2023-10-10 22:19:49,440 epoch 1 - iter 87/292 - loss 2.79891280 - time (sec): 31.38 - samples/sec: 466.73 - lr: 0.000044 - momentum: 0.000000
2023-10-10 22:19:59,444 epoch 1 - iter 116/292 - loss 2.76576425 - time (sec): 41.38 - samples/sec: 450.64 - lr: 0.000059 - momentum: 0.000000
2023-10-10 22:20:08,988 epoch 1 - iter 145/292 - loss 2.69583102 - time (sec): 50.93 - samples/sec: 442.63 - lr: 0.000074 - momentum: 0.000000
2023-10-10 22:20:18,043 epoch 1 - iter 174/292 - loss 2.60800245 - time (sec): 59.98 - samples/sec: 438.04 - lr: 0.000089 - momentum: 0.000000
2023-10-10 22:20:27,984 epoch 1 - iter 203/292 - loss 2.47632788 - time (sec): 69.92 - samples/sec: 444.58 - lr: 0.000104 - momentum: 0.000000
2023-10-10 22:20:37,938 epoch 1 - iter 232/292 - loss 2.35519679 - time (sec): 79.88 - samples/sec: 447.56 - lr: 0.000119 - momentum: 0.000000
2023-10-10 22:20:47,347 epoch 1 - iter 261/292 - loss 2.23758720 - time (sec): 89.28 - samples/sec: 447.63 - lr: 0.000134 - momentum: 0.000000
2023-10-10 22:20:56,952 epoch 1 - iter 290/292 - loss 2.12289055 - time (sec): 98.89 - samples/sec: 447.60 - lr: 0.000148 - momentum: 0.000000
2023-10-10 22:20:57,414 ----------------------------------------------------------------------------------------------------
2023-10-10 22:20:57,414 EPOCH 1 done: loss 2.1182 - lr: 0.000148
2023-10-10 22:21:02,854 DEV : loss 0.7325010299682617 - f1-score (micro avg)  0.0
2023-10-10 22:21:02,865 ----------------------------------------------------------------------------------------------------
2023-10-10 22:21:13,161 epoch 2 - iter 29/292 - loss 0.79297456 - time (sec): 10.29 - samples/sec: 454.36 - lr: 0.000148 - momentum: 0.000000
2023-10-10 22:21:23,229 epoch 2 - iter 58/292 - loss 0.82708352 - time (sec): 20.36 - samples/sec: 451.29 - lr: 0.000147 - momentum: 0.000000
2023-10-10 22:21:33,413 epoch 2 - iter 87/292 - loss 0.72111522 - time (sec): 30.55 - samples/sec: 448.51 - lr: 0.000145 - momentum: 0.000000
2023-10-10 22:21:43,537 epoch 2 - iter 116/292 - loss 0.64880272 - time (sec): 40.67 - samples/sec: 454.17 - lr: 0.000143 - momentum: 0.000000
2023-10-10 22:21:54,263 epoch 2 - iter 145/292 - loss 0.61803861 - time (sec): 51.40 - samples/sec: 458.36 - lr: 0.000142 - momentum: 0.000000
2023-10-10 22:22:03,618 epoch 2 - iter 174/292 - loss 0.57992157 - time (sec): 60.75 - samples/sec: 455.90 - lr: 0.000140 - momentum: 0.000000
2023-10-10 22:22:13,432 epoch 2 - iter 203/292 - loss 0.56502344 - time (sec): 70.56 - samples/sec: 447.15 - lr: 0.000138 - momentum: 0.000000
2023-10-10 22:22:23,089 epoch 2 - iter 232/292 - loss 0.54830154 - time (sec): 80.22 - samples/sec: 444.07 - lr: 0.000137 - momentum: 0.000000
2023-10-10 22:22:33,044 epoch 2 - iter 261/292 - loss 0.52602966 - time (sec): 90.18 - samples/sec: 444.78 - lr: 0.000135 - momentum: 0.000000
2023-10-10 22:22:43,474 epoch 2 - iter 290/292 - loss 0.52486245 - time (sec): 100.61 - samples/sec: 441.05 - lr: 0.000134 - momentum: 0.000000
2023-10-10 22:22:43,929 ----------------------------------------------------------------------------------------------------
2023-10-10 22:22:43,930 EPOCH 2 done: loss 0.5243 - lr: 0.000134
2023-10-10 22:22:49,985 DEV : loss 0.30209726095199585 - f1-score (micro avg)  0.1084
2023-10-10 22:22:49,994 saving best model
2023-10-10 22:22:50,936 ----------------------------------------------------------------------------------------------------
2023-10-10 22:23:00,714 epoch 3 - iter 29/292 - loss 0.38517526 - time (sec): 9.77 - samples/sec: 379.75 - lr: 0.000132 - momentum: 0.000000
2023-10-10 22:23:11,354 epoch 3 - iter 58/292 - loss 0.30206939 - time (sec): 20.41 - samples/sec: 435.28 - lr: 0.000130 - momentum: 0.000000
2023-10-10 22:23:21,441 epoch 3 - iter 87/292 - loss 0.33227764 - time (sec): 30.50 - samples/sec: 429.17 - lr: 0.000128 - momentum: 0.000000
2023-10-10 22:23:31,122 epoch 3 - iter 116/292 - loss 0.32587409 - time (sec): 40.18 - samples/sec: 425.78 - lr: 0.000127 - momentum: 0.000000
2023-10-10 22:23:40,829 epoch 3 - iter 145/292 - loss 0.32100867 - time (sec): 49.89 - samples/sec: 432.90 - lr: 0.000125 - momentum: 0.000000
2023-10-10 22:23:50,144 epoch 3 - iter 174/292 - loss 0.33023055 - time (sec): 59.20 - samples/sec: 432.40 - lr: 0.000123 - momentum: 0.000000
2023-10-10 22:24:02,391 epoch 3 - iter 203/292 - loss 0.33747240 - time (sec): 71.45 - samples/sec: 435.51 - lr: 0.000122 - momentum: 0.000000
2023-10-10 22:24:13,507 epoch 3 - iter 232/292 - loss 0.33787885 - time (sec): 82.57 - samples/sec: 432.12 - lr: 0.000120 - momentum: 0.000000
2023-10-10 22:24:24,624 epoch 3 - iter 261/292 - loss 0.33054624 - time (sec): 93.68 - samples/sec: 432.20 - lr: 0.000119 - momentum: 0.000000
2023-10-10 22:24:34,917 epoch 3 - iter 290/292 - loss 0.32750486 - time (sec): 103.98 - samples/sec: 426.04 - lr: 0.000117 - momentum: 0.000000
2023-10-10 22:24:35,419 ----------------------------------------------------------------------------------------------------
2023-10-10 22:24:35,419 EPOCH 3 done: loss 0.3268 - lr: 0.000117
2023-10-10 22:24:41,411 DEV : loss 0.24043378233909607 - f1-score (micro avg)  0.2967
2023-10-10 22:24:41,424 saving best model
2023-10-10 22:24:50,241 ----------------------------------------------------------------------------------------------------
2023-10-10 22:25:01,073 epoch 4 - iter 29/292 - loss 0.23406478 - time (sec): 10.82 - samples/sec: 462.15 - lr: 0.000115 - momentum: 0.000000
2023-10-10 22:25:11,188 epoch 4 - iter 58/292 - loss 0.22046035 - time (sec): 20.94 - samples/sec: 435.43 - lr: 0.000113 - momentum: 0.000000
2023-10-10 22:25:22,136 epoch 4 - iter 87/292 - loss 0.26702786 - time (sec): 31.89 - samples/sec: 439.90 - lr: 0.000112 - momentum: 0.000000
2023-10-10 22:25:32,876 epoch 4 - iter 116/292 - loss 0.27026625 - time (sec): 42.63 - samples/sec: 431.15 - lr: 0.000110 - momentum: 0.000000
2023-10-10 22:25:43,367 epoch 4 - iter 145/292 - loss 0.26372476 - time (sec): 53.12 - samples/sec: 430.58 - lr: 0.000108 - momentum: 0.000000
2023-10-10 22:25:54,534 epoch 4 - iter 174/292 - loss 0.26533000 - time (sec): 64.28 - samples/sec: 421.48 - lr: 0.000107 - momentum: 0.000000
2023-10-10 22:26:05,644 epoch 4 - iter 203/292 - loss 0.25791821 - time (sec): 75.39 - samples/sec: 420.71 - lr: 0.000105 - momentum: 0.000000
2023-10-10 22:26:16,097 epoch 4 - iter 232/292 - loss 0.25332297 - time (sec): 85.85 - samples/sec: 420.57 - lr: 0.000104 - momentum: 0.000000
2023-10-10 22:26:25,223 epoch 4 - iter 261/292 - loss 0.25035247 - time (sec): 94.97 - samples/sec: 418.14 - lr: 0.000102 - momentum: 0.000000
2023-10-10 22:26:34,882 epoch 4 - iter 290/292 - loss 0.24508743 - time (sec): 104.63 - samples/sec: 422.74 - lr: 0.000100 - momentum: 0.000000
2023-10-10 22:26:35,337 ----------------------------------------------------------------------------------------------------
2023-10-10 22:26:35,337 EPOCH 4 done: loss 0.2441 - lr: 0.000100
2023-10-10 22:26:41,068 DEV : loss 0.1876380443572998 - f1-score (micro avg)  0.547
2023-10-10 22:26:41,079 saving best model
2023-10-10 22:26:48,720 ----------------------------------------------------------------------------------------------------
2023-10-10 22:26:58,116 epoch 5 - iter 29/292 - loss 0.20545870 - time (sec): 9.39 - samples/sec: 462.55 - lr: 0.000098 - momentum: 0.000000
2023-10-10 22:27:08,281 epoch 5 - iter 58/292 - loss 0.22599380 - time (sec): 19.56 - samples/sec: 478.78 - lr: 0.000097 - momentum: 0.000000
2023-10-10 22:27:17,552 epoch 5 - iter 87/292 - loss 0.21763180 - time (sec): 28.83 - samples/sec: 471.25 - lr: 0.000095 - momentum: 0.000000
2023-10-10 22:27:26,972 epoch 5 - iter 116/292 - loss 0.18956745 - time (sec): 38.25 - samples/sec: 468.52 - lr: 0.000093 - momentum: 0.000000
2023-10-10 22:27:36,660 epoch 5 - iter 145/292 - loss 0.18521650 - time (sec): 47.93 - samples/sec: 474.60 - lr: 0.000092 - momentum: 0.000000
2023-10-10 22:27:45,741 epoch 5 - iter 174/292 - loss 0.17898882 - time (sec): 57.02 - samples/sec: 464.50 - lr: 0.000090 - momentum: 0.000000
2023-10-10 22:27:56,010 epoch 5 - iter 203/292 - loss 0.17521317 - time (sec): 67.29 - samples/sec: 467.17 - lr: 0.000089 - momentum: 0.000000
2023-10-10 22:28:06,581 epoch 5 - iter 232/292 - loss 0.17531990 - time (sec): 77.86 - samples/sec: 462.76 - lr: 0.000087 - momentum: 0.000000
2023-10-10 22:28:16,357 epoch 5 - iter 261/292 - loss 0.17062989 - time (sec): 87.63 - samples/sec: 453.22 - lr: 0.000085 - momentum: 0.000000
2023-10-10 22:28:26,404 epoch 5 - iter 290/292 - loss 0.16869967 - time (sec): 97.68 - samples/sec: 454.22 - lr: 0.000084 - momentum: 0.000000
2023-10-10 22:28:26,797 ----------------------------------------------------------------------------------------------------
2023-10-10 22:28:26,798 EPOCH 5 done: loss 0.1685 - lr: 0.000084
2023-10-10 22:28:32,756 DEV : loss 0.15193822979927063 - f1-score (micro avg)  0.674
2023-10-10 22:28:32,766 saving best model
2023-10-10 22:28:40,480 ----------------------------------------------------------------------------------------------------
2023-10-10 22:28:51,531 epoch 6 - iter 29/292 - loss 0.13346566 - time (sec): 11.05 - samples/sec: 486.42 - lr: 0.000082 - momentum: 0.000000
2023-10-10 22:29:00,612 epoch 6 - iter 58/292 - loss 0.13900519 - time (sec): 20.13 - samples/sec: 452.48 - lr: 0.000080 - momentum: 0.000000
2023-10-10 22:29:10,836 epoch 6 - iter 87/292 - loss 0.12975395 - time (sec): 30.35 - samples/sec: 458.54 - lr: 0.000078 - momentum: 0.000000
2023-10-10 22:29:20,807 epoch 6 - iter 116/292 - loss 0.12347829 - time (sec): 40.32 - samples/sec: 458.73 - lr: 0.000077 - momentum: 0.000000
2023-10-10 22:29:30,877 epoch 6 - iter 145/292 - loss 0.12149791 - time (sec): 50.39 - samples/sec: 454.71 - lr: 0.000075 - momentum: 0.000000
2023-10-10 22:29:40,980 epoch 6 - iter 174/292 - loss 0.12536062 - time (sec): 60.50 - samples/sec: 448.45 - lr: 0.000074 - momentum: 0.000000
2023-10-10 22:29:50,814 epoch 6 - iter 203/292 - loss 0.12241815 - time (sec): 70.33 - samples/sec: 438.94 - lr: 0.000072 - momentum: 0.000000
2023-10-10 22:30:00,514 epoch 6 - iter 232/292 - loss 0.12167750 - time (sec): 80.03 - samples/sec: 437.37 - lr: 0.000070 - momentum: 0.000000
2023-10-10 22:30:10,869 epoch 6 - iter 261/292 - loss 0.12098743 - time (sec): 90.39 - samples/sec: 440.57 - lr: 0.000069 - momentum: 0.000000
2023-10-10 22:30:20,772 epoch 6 - iter 290/292 - loss 0.11981467 - time (sec): 100.29 - samples/sec: 439.95 - lr: 0.000067 - momentum: 0.000000
2023-10-10 22:30:21,347 ----------------------------------------------------------------------------------------------------
2023-10-10 22:30:21,348 EPOCH 6 done: loss 0.1204 - lr: 0.000067
2023-10-10 22:30:27,448 DEV : loss 0.13653677701950073 - f1-score (micro avg)  0.6985
2023-10-10 22:30:27,458 saving best model
2023-10-10 22:30:35,269 ----------------------------------------------------------------------------------------------------
2023-10-10 22:30:45,651 epoch 7 - iter 29/292 - loss 0.10364982 - time (sec): 10.38 - samples/sec: 472.45 - lr: 0.000065 - momentum: 0.000000
2023-10-10 22:30:54,861 epoch 7 - iter 58/292 - loss 0.09344940 - time (sec): 19.59 - samples/sec: 444.41 - lr: 0.000063 - momentum: 0.000000
2023-10-10 22:31:04,398 epoch 7 - iter 87/292 - loss 0.09750181 - time (sec): 29.12 - samples/sec: 450.75 - lr: 0.000062 - momentum: 0.000000
2023-10-10 22:31:14,866 epoch 7 - iter 116/292 - loss 0.08693134 - time (sec): 39.59 - samples/sec: 465.92 - lr: 0.000060 - momentum: 0.000000
2023-10-10 22:31:23,789 epoch 7 - iter 145/292 - loss 0.08318189 - time (sec): 48.52 - samples/sec: 463.39 - lr: 0.000059 - momentum: 0.000000
2023-10-10 22:31:33,095 epoch 7 - iter 174/292 - loss 0.09163479 - time (sec): 57.82 - samples/sec: 457.97 - lr: 0.000057 - momentum: 0.000000
2023-10-10 22:31:42,534 epoch 7 - iter 203/292 - loss 0.08993163 - time (sec): 67.26 - samples/sec: 454.05 - lr: 0.000055 - momentum: 0.000000
2023-10-10 22:31:52,643 epoch 7 - iter 232/292 - loss 0.08882304 - time (sec): 77.37 - samples/sec: 458.73 - lr: 0.000054 - momentum: 0.000000
2023-10-10 22:32:02,192 epoch 7 - iter 261/292 - loss 0.08995054 - time (sec): 86.92 - samples/sec: 458.07 - lr: 0.000052 - momentum: 0.000000
2023-10-10 22:32:11,752 epoch 7 - iter 290/292 - loss 0.09260519 - time (sec): 96.48 - samples/sec: 458.64 - lr: 0.000050 - momentum: 0.000000
2023-10-10 22:32:12,222 ----------------------------------------------------------------------------------------------------
2023-10-10 22:32:12,222 EPOCH 7 done: loss 0.0929 - lr: 0.000050
2023-10-10 22:32:18,044 DEV : loss 0.13227997720241547 - f1-score (micro avg)  0.7339
2023-10-10 22:32:18,055 saving best model
2023-10-10 22:32:26,797 ----------------------------------------------------------------------------------------------------
2023-10-10 22:32:37,310 epoch 8 - iter 29/292 - loss 0.07389758 - time (sec): 10.51 - samples/sec: 481.09 - lr: 0.000048 - momentum: 0.000000
2023-10-10 22:32:47,318 epoch 8 - iter 58/292 - loss 0.07781066 - time (sec): 20.52 - samples/sec: 483.16 - lr: 0.000047 - momentum: 0.000000
2023-10-10 22:32:57,022 epoch 8 - iter 87/292 - loss 0.07989627 - time (sec): 30.22 - samples/sec: 476.35 - lr: 0.000045 - momentum: 0.000000
2023-10-10 22:33:05,754 epoch 8 - iter 116/292 - loss 0.07567283 - time (sec): 38.95 - samples/sec: 463.02 - lr: 0.000044 - momentum: 0.000000
2023-10-10 22:33:15,391 epoch 8 - iter 145/292 - loss 0.07437945 - time (sec): 48.59 - samples/sec: 469.54 - lr: 0.000042 - momentum: 0.000000
2023-10-10 22:33:24,636 epoch 8 - iter 174/292 - loss 0.07758936 - time (sec): 57.84 - samples/sec: 466.94 - lr: 0.000040 - momentum: 0.000000
2023-10-10 22:33:33,871 epoch 8 - iter 203/292 - loss 0.07636403 - time (sec): 67.07 - samples/sec: 460.98 - lr: 0.000039 - momentum: 0.000000
2023-10-10 22:33:44,131 epoch 8 - iter 232/292 - loss 0.07443540 - time (sec): 77.33 - samples/sec: 462.34 - lr: 0.000037 - momentum: 0.000000
2023-10-10 22:33:53,435 epoch 8 - iter 261/292 - loss 0.07286737 - time (sec): 86.63 - samples/sec: 458.54 - lr: 0.000035 - momentum: 0.000000
2023-10-10 22:34:03,445 epoch 8 - iter 290/292 - loss 0.07492659 - time (sec): 96.64 - samples/sec: 458.25 - lr: 0.000034 - momentum: 0.000000
2023-10-10 22:34:03,886 ----------------------------------------------------------------------------------------------------
2023-10-10 22:34:03,886 EPOCH 8 done: loss 0.0749 - lr: 0.000034
2023-10-10 22:34:09,835 DEV : loss 0.1255185902118683 - f1-score (micro avg)  0.7106
2023-10-10 22:34:09,844 ----------------------------------------------------------------------------------------------------
2023-10-10 22:34:20,242 epoch 9 - iter 29/292 - loss 0.05795037 - time (sec): 10.40 - samples/sec: 448.34 - lr: 0.000032 - momentum: 0.000000
2023-10-10 22:34:30,842 epoch 9 - iter 58/292 - loss 0.05725661 - time (sec): 21.00 - samples/sec: 446.10 - lr: 0.000030 - momentum: 0.000000
2023-10-10 22:34:40,729 epoch 9 - iter 87/292 - loss 0.06037949 - time (sec): 30.88 - samples/sec: 447.63 - lr: 0.000029 - momentum: 0.000000
2023-10-10 22:34:51,425 epoch 9 - iter 116/292 - loss 0.05494455 - time (sec): 41.58 - samples/sec: 442.41 - lr: 0.000027 - momentum: 0.000000
2023-10-10 22:35:00,997 epoch 9 - iter 145/292 - loss 0.05906472 - time (sec): 51.15 - samples/sec: 437.78 - lr: 0.000025 - momentum: 0.000000
2023-10-10 22:35:12,000 epoch 9 - iter 174/292 - loss 0.06382807 - time (sec): 62.15 - samples/sec: 437.79 - lr: 0.000024 - momentum: 0.000000
2023-10-10 22:35:21,289 epoch 9 - iter 203/292 - loss 0.06188120 - time (sec): 71.44 - samples/sec: 434.30 - lr: 0.000022 - momentum: 0.000000
2023-10-10 22:35:31,447 epoch 9 - iter 232/292 - loss 0.06116108 - time (sec): 81.60 - samples/sec: 441.26 - lr: 0.000020 - momentum: 0.000000
2023-10-10 22:35:41,511 epoch 9 - iter 261/292 - loss 0.05935939 - time (sec): 91.66 - samples/sec: 439.74 - lr: 0.000019 - momentum: 0.000000
2023-10-10 22:35:50,707 epoch 9 - iter 290/292 - loss 0.06218808 - time (sec): 100.86 - samples/sec: 438.73 - lr: 0.000017 - momentum: 0.000000
2023-10-10 22:35:51,183 ----------------------------------------------------------------------------------------------------
2023-10-10 22:35:51,183 EPOCH 9 done: loss 0.0622 - lr: 0.000017
2023-10-10 22:35:57,859 DEV : loss 0.1263173669576645 - f1-score (micro avg)  0.7729
2023-10-10 22:35:57,869 saving best model
2023-10-10 22:36:06,588 ----------------------------------------------------------------------------------------------------
2023-10-10 22:36:17,008 epoch 10 - iter 29/292 - loss 0.04466337 - time (sec): 10.42 - samples/sec: 443.76 - lr: 0.000015 - momentum: 0.000000
2023-10-10 22:36:26,256 epoch 10 - iter 58/292 - loss 0.05701445 - time (sec): 19.67 - samples/sec: 435.39 - lr: 0.000014 - momentum: 0.000000
2023-10-10 22:36:35,703 epoch 10 - iter 87/292 - loss 0.05271940 - time (sec): 29.11 - samples/sec: 440.61 - lr: 0.000012 - momentum: 0.000000
2023-10-10 22:36:46,382 epoch 10 - iter 116/292 - loss 0.04764948 - time (sec): 39.79 - samples/sec: 456.08 - lr: 0.000010 - momentum: 0.000000
2023-10-10 22:36:56,840 epoch 10 - iter 145/292 - loss 0.04801644 - time (sec): 50.25 - samples/sec: 456.55 - lr: 0.000009 - momentum: 0.000000
2023-10-10 22:37:06,730 epoch 10 - iter 174/292 - loss 0.04758792 - time (sec): 60.14 - samples/sec: 453.98 - lr: 0.000007 - momentum: 0.000000
2023-10-10 22:37:17,217 epoch 10 - iter 203/292 - loss 0.04879119 - time (sec): 70.63 - samples/sec: 452.69 - lr: 0.000005 - momentum: 0.000000
2023-10-10 22:37:28,159 epoch 10 - iter 232/292 - loss 0.05379231 - time (sec): 81.57 - samples/sec: 441.09 - lr: 0.000004 - momentum: 0.000000
2023-10-10 22:37:38,659 epoch 10 - iter 261/292 - loss 0.05272024 - time (sec): 92.07 - samples/sec: 433.95 - lr: 0.000002 - momentum: 0.000000
2023-10-10 22:37:48,216 epoch 10 - iter 290/292 - loss 0.05624633 - time (sec): 101.62 - samples/sec: 435.19 - lr: 0.000000 - momentum: 0.000000
2023-10-10 22:37:48,784 ----------------------------------------------------------------------------------------------------
2023-10-10 22:37:48,785 EPOCH 10 done: loss 0.0566 - lr: 0.000000
2023-10-10 22:37:54,495 DEV : loss 0.12670132517814636 - f1-score (micro avg)  0.7355
2023-10-10 22:37:55,464 ----------------------------------------------------------------------------------------------------
2023-10-10 22:37:55,466 Loading model from best epoch ...
2023-10-10 22:37:59,258 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
2023-10-10 22:38:11,937 
Results:
- F-score (micro) 0.732
- F-score (macro) 0.661
- Accuracy 0.5936

By class:
              precision    recall  f1-score   support

         PER     0.7811    0.8305    0.8050       348
         LOC     0.6645    0.7663    0.7117       261
         ORG     0.3091    0.3269    0.3178        52
   HumanProd     0.8500    0.7727    0.8095        22

   micro avg     0.7011    0.7657    0.7320       683
   macro avg     0.6512    0.6741    0.6610       683
weighted avg     0.7028    0.7657    0.7324       683

2023-10-10 22:38:11,937 ----------------------------------------------------------------------------------------------------