2023-10-11 11:59:51,169 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,171 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()
)"
2023-10-11 11:59:51,171 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,171 MultiCorpus: 1085 train + 148 dev + 364 test sentences
 - NER_HIPE_2022 Corpus: 1085 train + 148 dev + 364 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/sv/with_doc_seperator
2023-10-11 11:59:51,171 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,172 Train:  1085 sentences
2023-10-11 11:59:51,172         (train_with_dev=False, train_with_test=False)
2023-10-11 11:59:51,172 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,172 Training Params:
2023-10-11 11:59:51,172  - learning_rate: "0.00015" 
2023-10-11 11:59:51,172  - mini_batch_size: "4"
2023-10-11 11:59:51,172  - max_epochs: "10"
2023-10-11 11:59:51,172  - shuffle: "True"
2023-10-11 11:59:51,172 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,172 Plugins:
2023-10-11 11:59:51,172  - TensorboardLogger
2023-10-11 11:59:51,172  - LinearScheduler | warmup_fraction: '0.1'
2023-10-11 11:59:51,172 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,172 Final evaluation on model from best epoch (best-model.pt)
2023-10-11 11:59:51,172  - metric: "('micro avg', 'f1-score')"
2023-10-11 11:59:51,173 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,173 Computation:
2023-10-11 11:59:51,173  - compute on device: cuda:0
2023-10-11 11:59:51,173  - embedding storage: none
2023-10-11 11:59:51,173 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,173 Model training base path: "hmbench-newseye/sv-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4"
2023-10-11 11:59:51,173 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,173 ----------------------------------------------------------------------------------------------------
2023-10-11 11:59:51,173 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-11 12:00:00,746 epoch 1 - iter 27/272 - loss 2.83808245 - time (sec): 9.57 - samples/sec: 582.50 - lr: 0.000014 - momentum: 0.000000
2023-10-11 12:00:10,911 epoch 1 - iter 54/272 - loss 2.83168516 - time (sec): 19.74 - samples/sec: 577.07 - lr: 0.000029 - momentum: 0.000000
2023-10-11 12:00:20,306 epoch 1 - iter 81/272 - loss 2.81100836 - time (sec): 29.13 - samples/sec: 572.17 - lr: 0.000044 - momentum: 0.000000
2023-10-11 12:00:29,430 epoch 1 - iter 108/272 - loss 2.76093772 - time (sec): 38.26 - samples/sec: 568.91 - lr: 0.000059 - momentum: 0.000000
2023-10-11 12:00:38,439 epoch 1 - iter 135/272 - loss 2.68694812 - time (sec): 47.26 - samples/sec: 557.33 - lr: 0.000074 - momentum: 0.000000
2023-10-11 12:00:47,327 epoch 1 - iter 162/272 - loss 2.59395144 - time (sec): 56.15 - samples/sec: 553.86 - lr: 0.000089 - momentum: 0.000000
2023-10-11 12:00:56,040 epoch 1 - iter 189/272 - loss 2.49110800 - time (sec): 64.86 - samples/sec: 551.64 - lr: 0.000104 - momentum: 0.000000
2023-10-11 12:01:05,617 epoch 1 - iter 216/272 - loss 2.37091645 - time (sec): 74.44 - samples/sec: 558.22 - lr: 0.000119 - momentum: 0.000000
2023-10-11 12:01:14,799 epoch 1 - iter 243/272 - loss 2.25247641 - time (sec): 83.62 - samples/sec: 556.31 - lr: 0.000133 - momentum: 0.000000
2023-10-11 12:01:24,116 epoch 1 - iter 270/272 - loss 2.12945547 - time (sec): 92.94 - samples/sec: 555.84 - lr: 0.000148 - momentum: 0.000000
2023-10-11 12:01:24,641 ----------------------------------------------------------------------------------------------------
2023-10-11 12:01:24,641 EPOCH 1 done: loss 2.1220 - lr: 0.000148
2023-10-11 12:01:29,446 DEV : loss 0.787132978439331 - f1-score (micro avg)  0.0
2023-10-11 12:01:29,453 ----------------------------------------------------------------------------------------------------
2023-10-11 12:01:38,673 epoch 2 - iter 27/272 - loss 0.76434729 - time (sec): 9.22 - samples/sec: 580.18 - lr: 0.000148 - momentum: 0.000000
2023-10-11 12:01:47,492 epoch 2 - iter 54/272 - loss 0.73284299 - time (sec): 18.04 - samples/sec: 560.18 - lr: 0.000147 - momentum: 0.000000
2023-10-11 12:01:55,521 epoch 2 - iter 81/272 - loss 0.69848232 - time (sec): 26.07 - samples/sec: 536.84 - lr: 0.000145 - momentum: 0.000000
2023-10-11 12:02:05,664 epoch 2 - iter 108/272 - loss 0.64417120 - time (sec): 36.21 - samples/sec: 558.51 - lr: 0.000143 - momentum: 0.000000
2023-10-11 12:02:15,021 epoch 2 - iter 135/272 - loss 0.61592547 - time (sec): 45.57 - samples/sec: 553.77 - lr: 0.000142 - momentum: 0.000000
2023-10-11 12:02:24,945 epoch 2 - iter 162/272 - loss 0.56608904 - time (sec): 55.49 - samples/sec: 560.30 - lr: 0.000140 - momentum: 0.000000
2023-10-11 12:02:33,341 epoch 2 - iter 189/272 - loss 0.54253010 - time (sec): 63.89 - samples/sec: 553.92 - lr: 0.000138 - momentum: 0.000000
2023-10-11 12:02:42,502 epoch 2 - iter 216/272 - loss 0.51543417 - time (sec): 73.05 - samples/sec: 552.83 - lr: 0.000137 - momentum: 0.000000
2023-10-11 12:02:51,397 epoch 2 - iter 243/272 - loss 0.50075965 - time (sec): 81.94 - samples/sec: 551.15 - lr: 0.000135 - momentum: 0.000000
2023-10-11 12:03:01,869 epoch 2 - iter 270/272 - loss 0.48128104 - time (sec): 92.41 - samples/sec: 560.52 - lr: 0.000134 - momentum: 0.000000
2023-10-11 12:03:02,288 ----------------------------------------------------------------------------------------------------
2023-10-11 12:03:02,288 EPOCH 2 done: loss 0.4809 - lr: 0.000134
2023-10-11 12:03:08,051 DEV : loss 0.2902776598930359 - f1-score (micro avg)  0.3249
2023-10-11 12:03:08,059 saving best model
2023-10-11 12:03:08,905 ----------------------------------------------------------------------------------------------------
2023-10-11 12:03:18,487 epoch 3 - iter 27/272 - loss 0.25324371 - time (sec): 9.58 - samples/sec: 532.44 - lr: 0.000132 - momentum: 0.000000
2023-10-11 12:03:28,503 epoch 3 - iter 54/272 - loss 0.29239049 - time (sec): 19.60 - samples/sec: 552.15 - lr: 0.000130 - momentum: 0.000000
2023-10-11 12:03:38,111 epoch 3 - iter 81/272 - loss 0.28983456 - time (sec): 29.20 - samples/sec: 552.59 - lr: 0.000128 - momentum: 0.000000
2023-10-11 12:03:47,503 epoch 3 - iter 108/272 - loss 0.28946884 - time (sec): 38.60 - samples/sec: 544.15 - lr: 0.000127 - momentum: 0.000000
2023-10-11 12:03:57,218 epoch 3 - iter 135/272 - loss 0.28735415 - time (sec): 48.31 - samples/sec: 542.24 - lr: 0.000125 - momentum: 0.000000
2023-10-11 12:04:06,661 epoch 3 - iter 162/272 - loss 0.29499243 - time (sec): 57.75 - samples/sec: 542.78 - lr: 0.000123 - momentum: 0.000000
2023-10-11 12:04:16,599 epoch 3 - iter 189/272 - loss 0.29299739 - time (sec): 67.69 - samples/sec: 546.15 - lr: 0.000122 - momentum: 0.000000
2023-10-11 12:04:25,978 epoch 3 - iter 216/272 - loss 0.28805671 - time (sec): 77.07 - samples/sec: 543.70 - lr: 0.000120 - momentum: 0.000000
2023-10-11 12:04:35,125 epoch 3 - iter 243/272 - loss 0.29343930 - time (sec): 86.22 - samples/sec: 543.21 - lr: 0.000119 - momentum: 0.000000
2023-10-11 12:04:44,272 epoch 3 - iter 270/272 - loss 0.28780910 - time (sec): 95.37 - samples/sec: 541.74 - lr: 0.000117 - momentum: 0.000000
2023-10-11 12:04:44,825 ----------------------------------------------------------------------------------------------------
2023-10-11 12:04:44,825 EPOCH 3 done: loss 0.2863 - lr: 0.000117
2023-10-11 12:04:50,364 DEV : loss 0.21922904253005981 - f1-score (micro avg)  0.5105
2023-10-11 12:04:50,372 saving best model
2023-10-11 12:04:52,880 ----------------------------------------------------------------------------------------------------
2023-10-11 12:05:02,022 epoch 4 - iter 27/272 - loss 0.21394877 - time (sec): 9.14 - samples/sec: 533.16 - lr: 0.000115 - momentum: 0.000000
2023-10-11 12:05:11,546 epoch 4 - iter 54/272 - loss 0.17957342 - time (sec): 18.66 - samples/sec: 554.65 - lr: 0.000113 - momentum: 0.000000
2023-10-11 12:05:21,430 epoch 4 - iter 81/272 - loss 0.20307451 - time (sec): 28.55 - samples/sec: 568.62 - lr: 0.000112 - momentum: 0.000000
2023-10-11 12:05:30,670 epoch 4 - iter 108/272 - loss 0.19980419 - time (sec): 37.79 - samples/sec: 568.55 - lr: 0.000110 - momentum: 0.000000
2023-10-11 12:05:40,288 epoch 4 - iter 135/272 - loss 0.18968939 - time (sec): 47.40 - samples/sec: 570.33 - lr: 0.000108 - momentum: 0.000000
2023-10-11 12:05:49,026 epoch 4 - iter 162/272 - loss 0.19031204 - time (sec): 56.14 - samples/sec: 561.51 - lr: 0.000107 - momentum: 0.000000
2023-10-11 12:05:58,645 epoch 4 - iter 189/272 - loss 0.19085205 - time (sec): 65.76 - samples/sec: 564.64 - lr: 0.000105 - momentum: 0.000000
2023-10-11 12:06:07,826 epoch 4 - iter 216/272 - loss 0.18864700 - time (sec): 74.94 - samples/sec: 557.99 - lr: 0.000103 - momentum: 0.000000
2023-10-11 12:06:16,802 epoch 4 - iter 243/272 - loss 0.18605938 - time (sec): 83.92 - samples/sec: 553.83 - lr: 0.000102 - momentum: 0.000000
2023-10-11 12:06:26,347 epoch 4 - iter 270/272 - loss 0.18742508 - time (sec): 93.46 - samples/sec: 554.00 - lr: 0.000100 - momentum: 0.000000
2023-10-11 12:06:26,780 ----------------------------------------------------------------------------------------------------
2023-10-11 12:06:26,780 EPOCH 4 done: loss 0.1869 - lr: 0.000100
2023-10-11 12:06:32,242 DEV : loss 0.15904375910758972 - f1-score (micro avg)  0.6264
2023-10-11 12:06:32,250 saving best model
2023-10-11 12:06:34,755 ----------------------------------------------------------------------------------------------------
2023-10-11 12:06:44,289 epoch 5 - iter 27/272 - loss 0.13640380 - time (sec): 9.53 - samples/sec: 588.07 - lr: 0.000098 - momentum: 0.000000
2023-10-11 12:06:53,372 epoch 5 - iter 54/272 - loss 0.14436985 - time (sec): 18.61 - samples/sec: 564.88 - lr: 0.000097 - momentum: 0.000000
2023-10-11 12:07:03,111 epoch 5 - iter 81/272 - loss 0.15175419 - time (sec): 28.35 - samples/sec: 574.28 - lr: 0.000095 - momentum: 0.000000
2023-10-11 12:07:13,161 epoch 5 - iter 108/272 - loss 0.14004454 - time (sec): 38.40 - samples/sec: 576.06 - lr: 0.000093 - momentum: 0.000000
2023-10-11 12:07:22,370 epoch 5 - iter 135/272 - loss 0.13536960 - time (sec): 47.61 - samples/sec: 569.30 - lr: 0.000092 - momentum: 0.000000
2023-10-11 12:07:31,360 epoch 5 - iter 162/272 - loss 0.13210074 - time (sec): 56.60 - samples/sec: 561.25 - lr: 0.000090 - momentum: 0.000000
2023-10-11 12:07:40,344 epoch 5 - iter 189/272 - loss 0.12674601 - time (sec): 65.58 - samples/sec: 555.01 - lr: 0.000088 - momentum: 0.000000
2023-10-11 12:07:49,701 epoch 5 - iter 216/272 - loss 0.12421874 - time (sec): 74.94 - samples/sec: 555.07 - lr: 0.000087 - momentum: 0.000000
2023-10-11 12:07:59,031 epoch 5 - iter 243/272 - loss 0.12779456 - time (sec): 84.27 - samples/sec: 556.30 - lr: 0.000085 - momentum: 0.000000
2023-10-11 12:08:07,940 epoch 5 - iter 270/272 - loss 0.12379983 - time (sec): 93.18 - samples/sec: 554.13 - lr: 0.000084 - momentum: 0.000000
2023-10-11 12:08:08,508 ----------------------------------------------------------------------------------------------------
2023-10-11 12:08:08,508 EPOCH 5 done: loss 0.1239 - lr: 0.000084
2023-10-11 12:08:14,112 DEV : loss 0.14260436594486237 - f1-score (micro avg)  0.6396
2023-10-11 12:08:14,120 saving best model
2023-10-11 12:08:16,636 ----------------------------------------------------------------------------------------------------
2023-10-11 12:08:25,967 epoch 6 - iter 27/272 - loss 0.09081814 - time (sec): 9.33 - samples/sec: 549.95 - lr: 0.000082 - momentum: 0.000000
2023-10-11 12:08:35,234 epoch 6 - iter 54/272 - loss 0.09044964 - time (sec): 18.59 - samples/sec: 549.29 - lr: 0.000080 - momentum: 0.000000
2023-10-11 12:08:45,497 epoch 6 - iter 81/272 - loss 0.08657561 - time (sec): 28.86 - samples/sec: 574.01 - lr: 0.000078 - momentum: 0.000000
2023-10-11 12:08:54,116 epoch 6 - iter 108/272 - loss 0.08572893 - time (sec): 37.48 - samples/sec: 565.04 - lr: 0.000077 - momentum: 0.000000
2023-10-11 12:09:02,994 epoch 6 - iter 135/272 - loss 0.08374920 - time (sec): 46.35 - samples/sec: 559.29 - lr: 0.000075 - momentum: 0.000000
2023-10-11 12:09:12,371 epoch 6 - iter 162/272 - loss 0.08312497 - time (sec): 55.73 - samples/sec: 558.08 - lr: 0.000073 - momentum: 0.000000
2023-10-11 12:09:21,244 epoch 6 - iter 189/272 - loss 0.08859240 - time (sec): 64.60 - samples/sec: 553.43 - lr: 0.000072 - momentum: 0.000000
2023-10-11 12:09:30,609 epoch 6 - iter 216/272 - loss 0.08839713 - time (sec): 73.97 - samples/sec: 553.75 - lr: 0.000070 - momentum: 0.000000
2023-10-11 12:09:40,425 epoch 6 - iter 243/272 - loss 0.08829484 - time (sec): 83.78 - samples/sec: 556.94 - lr: 0.000069 - momentum: 0.000000
2023-10-11 12:09:49,758 epoch 6 - iter 270/272 - loss 0.08896194 - time (sec): 93.12 - samples/sec: 556.03 - lr: 0.000067 - momentum: 0.000000
2023-10-11 12:09:50,169 ----------------------------------------------------------------------------------------------------
2023-10-11 12:09:50,169 EPOCH 6 done: loss 0.0886 - lr: 0.000067
2023-10-11 12:09:55,682 DEV : loss 0.13805799186229706 - f1-score (micro avg)  0.7097
2023-10-11 12:09:55,690 saving best model
2023-10-11 12:09:58,183 ----------------------------------------------------------------------------------------------------
2023-10-11 12:10:07,627 epoch 7 - iter 27/272 - loss 0.06053447 - time (sec): 9.44 - samples/sec: 515.26 - lr: 0.000065 - momentum: 0.000000
2023-10-11 12:10:16,495 epoch 7 - iter 54/272 - loss 0.07179566 - time (sec): 18.31 - samples/sec: 509.11 - lr: 0.000063 - momentum: 0.000000
2023-10-11 12:10:25,250 epoch 7 - iter 81/272 - loss 0.06709301 - time (sec): 27.06 - samples/sec: 512.22 - lr: 0.000062 - momentum: 0.000000
2023-10-11 12:10:34,721 epoch 7 - iter 108/272 - loss 0.06756713 - time (sec): 36.53 - samples/sec: 522.78 - lr: 0.000060 - momentum: 0.000000
2023-10-11 12:10:44,611 epoch 7 - iter 135/272 - loss 0.06376829 - time (sec): 46.42 - samples/sec: 532.50 - lr: 0.000058 - momentum: 0.000000
2023-10-11 12:10:54,644 epoch 7 - iter 162/272 - loss 0.06218548 - time (sec): 56.46 - samples/sec: 540.48 - lr: 0.000057 - momentum: 0.000000
2023-10-11 12:11:04,197 epoch 7 - iter 189/272 - loss 0.06719311 - time (sec): 66.01 - samples/sec: 538.29 - lr: 0.000055 - momentum: 0.000000
2023-10-11 12:11:13,197 epoch 7 - iter 216/272 - loss 0.06548144 - time (sec): 75.01 - samples/sec: 529.93 - lr: 0.000053 - momentum: 0.000000
2023-10-11 12:11:23,292 epoch 7 - iter 243/272 - loss 0.06733417 - time (sec): 85.11 - samples/sec: 537.18 - lr: 0.000052 - momentum: 0.000000
2023-10-11 12:11:33,454 epoch 7 - iter 270/272 - loss 0.06807975 - time (sec): 95.27 - samples/sec: 543.97 - lr: 0.000050 - momentum: 0.000000
2023-10-11 12:11:33,852 ----------------------------------------------------------------------------------------------------
2023-10-11 12:11:33,852 EPOCH 7 done: loss 0.0682 - lr: 0.000050
2023-10-11 12:11:39,581 DEV : loss 0.13298040628433228 - f1-score (micro avg)  0.7486
2023-10-11 12:11:39,590 saving best model
2023-10-11 12:11:42,108 ----------------------------------------------------------------------------------------------------
2023-10-11 12:11:51,545 epoch 8 - iter 27/272 - loss 0.03607497 - time (sec): 9.43 - samples/sec: 545.51 - lr: 0.000048 - momentum: 0.000000
2023-10-11 12:12:01,924 epoch 8 - iter 54/272 - loss 0.05540413 - time (sec): 19.81 - samples/sec: 553.76 - lr: 0.000047 - momentum: 0.000000
2023-10-11 12:12:11,888 epoch 8 - iter 81/272 - loss 0.05162211 - time (sec): 29.78 - samples/sec: 543.33 - lr: 0.000045 - momentum: 0.000000
2023-10-11 12:12:21,348 epoch 8 - iter 108/272 - loss 0.05054188 - time (sec): 39.24 - samples/sec: 533.64 - lr: 0.000043 - momentum: 0.000000
2023-10-11 12:12:31,201 epoch 8 - iter 135/272 - loss 0.04929120 - time (sec): 49.09 - samples/sec: 538.60 - lr: 0.000042 - momentum: 0.000000
2023-10-11 12:12:41,764 epoch 8 - iter 162/272 - loss 0.05142742 - time (sec): 59.65 - samples/sec: 548.02 - lr: 0.000040 - momentum: 0.000000
2023-10-11 12:12:50,755 epoch 8 - iter 189/272 - loss 0.05364109 - time (sec): 68.64 - samples/sec: 537.23 - lr: 0.000038 - momentum: 0.000000
2023-10-11 12:13:00,768 epoch 8 - iter 216/272 - loss 0.05394908 - time (sec): 78.66 - samples/sec: 538.88 - lr: 0.000037 - momentum: 0.000000
2023-10-11 12:13:10,105 epoch 8 - iter 243/272 - loss 0.05376396 - time (sec): 87.99 - samples/sec: 536.01 - lr: 0.000035 - momentum: 0.000000
2023-10-11 12:13:19,304 epoch 8 - iter 270/272 - loss 0.05261769 - time (sec): 97.19 - samples/sec: 532.25 - lr: 0.000034 - momentum: 0.000000
2023-10-11 12:13:19,767 ----------------------------------------------------------------------------------------------------
2023-10-11 12:13:19,768 EPOCH 8 done: loss 0.0524 - lr: 0.000034
2023-10-11 12:13:25,531 DEV : loss 0.1314464509487152 - f1-score (micro avg)  0.7656
2023-10-11 12:13:25,540 saving best model
2023-10-11 12:13:28,081 ----------------------------------------------------------------------------------------------------
2023-10-11 12:13:37,314 epoch 9 - iter 27/272 - loss 0.04742634 - time (sec): 9.23 - samples/sec: 530.51 - lr: 0.000032 - momentum: 0.000000
2023-10-11 12:13:46,234 epoch 9 - iter 54/272 - loss 0.05748414 - time (sec): 18.15 - samples/sec: 510.72 - lr: 0.000030 - momentum: 0.000000
2023-10-11 12:13:55,554 epoch 9 - iter 81/272 - loss 0.05158405 - time (sec): 27.47 - samples/sec: 510.11 - lr: 0.000028 - momentum: 0.000000
2023-10-11 12:14:05,486 epoch 9 - iter 108/272 - loss 0.04642078 - time (sec): 37.40 - samples/sec: 523.07 - lr: 0.000027 - momentum: 0.000000
2023-10-11 12:14:15,195 epoch 9 - iter 135/272 - loss 0.04661014 - time (sec): 47.11 - samples/sec: 530.66 - lr: 0.000025 - momentum: 0.000000
2023-10-11 12:14:24,961 epoch 9 - iter 162/272 - loss 0.04351579 - time (sec): 56.88 - samples/sec: 526.63 - lr: 0.000023 - momentum: 0.000000
2023-10-11 12:14:34,863 epoch 9 - iter 189/272 - loss 0.04215739 - time (sec): 66.78 - samples/sec: 526.63 - lr: 0.000022 - momentum: 0.000000
2023-10-11 12:14:44,981 epoch 9 - iter 216/272 - loss 0.04283431 - time (sec): 76.90 - samples/sec: 533.37 - lr: 0.000020 - momentum: 0.000000
2023-10-11 12:14:54,506 epoch 9 - iter 243/272 - loss 0.04609896 - time (sec): 86.42 - samples/sec: 531.67 - lr: 0.000019 - momentum: 0.000000
2023-10-11 12:15:04,548 epoch 9 - iter 270/272 - loss 0.04490903 - time (sec): 96.46 - samples/sec: 536.01 - lr: 0.000017 - momentum: 0.000000
2023-10-11 12:15:05,044 ----------------------------------------------------------------------------------------------------
2023-10-11 12:15:05,044 EPOCH 9 done: loss 0.0447 - lr: 0.000017
2023-10-11 12:15:10,818 DEV : loss 0.13004814088344574 - f1-score (micro avg)  0.7729
2023-10-11 12:15:10,826 saving best model
2023-10-11 12:15:13,329 ----------------------------------------------------------------------------------------------------
2023-10-11 12:15:23,113 epoch 10 - iter 27/272 - loss 0.04807385 - time (sec): 9.78 - samples/sec: 545.79 - lr: 0.000015 - momentum: 0.000000
2023-10-11 12:15:32,509 epoch 10 - iter 54/272 - loss 0.04823045 - time (sec): 19.18 - samples/sec: 544.06 - lr: 0.000013 - momentum: 0.000000
2023-10-11 12:15:41,444 epoch 10 - iter 81/272 - loss 0.04320814 - time (sec): 28.11 - samples/sec: 537.12 - lr: 0.000012 - momentum: 0.000000
2023-10-11 12:15:50,755 epoch 10 - iter 108/272 - loss 0.04362312 - time (sec): 37.42 - samples/sec: 535.03 - lr: 0.000010 - momentum: 0.000000
2023-10-11 12:16:01,496 epoch 10 - iter 135/272 - loss 0.03948215 - time (sec): 48.16 - samples/sec: 546.87 - lr: 0.000008 - momentum: 0.000000
2023-10-11 12:16:10,578 epoch 10 - iter 162/272 - loss 0.03854345 - time (sec): 57.24 - samples/sec: 536.05 - lr: 0.000007 - momentum: 0.000000
2023-10-11 12:16:20,657 epoch 10 - iter 189/272 - loss 0.03769292 - time (sec): 67.32 - samples/sec: 537.82 - lr: 0.000005 - momentum: 0.000000
2023-10-11 12:16:30,189 epoch 10 - iter 216/272 - loss 0.03774164 - time (sec): 76.86 - samples/sec: 537.11 - lr: 0.000003 - momentum: 0.000000
2023-10-11 12:16:39,841 epoch 10 - iter 243/272 - loss 0.03886886 - time (sec): 86.51 - samples/sec: 538.09 - lr: 0.000002 - momentum: 0.000000
2023-10-11 12:16:49,410 epoch 10 - iter 270/272 - loss 0.03961577 - time (sec): 96.08 - samples/sec: 538.68 - lr: 0.000000 - momentum: 0.000000
2023-10-11 12:16:49,871 ----------------------------------------------------------------------------------------------------
2023-10-11 12:16:49,871 EPOCH 10 done: loss 0.0395 - lr: 0.000000
2023-10-11 12:16:55,638 DEV : loss 0.1334371417760849 - f1-score (micro avg)  0.7701
2023-10-11 12:16:56,489 ----------------------------------------------------------------------------------------------------
2023-10-11 12:16:56,490 Loading model from best epoch ...
2023-10-11 12:17:00,059 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd, S-ORG, B-ORG, E-ORG, I-ORG
2023-10-11 12:17:12,402 
Results:
- F-score (micro) 0.7609
- F-score (macro) 0.6793
- Accuracy 0.635

By class:
              precision    recall  f1-score   support

         LOC     0.7830    0.8558    0.8178       312
         PER     0.7000    0.8413    0.7642       208
         ORG     0.4314    0.4000    0.4151        55
   HumanProd     0.6429    0.8182    0.7200        22

   micro avg     0.7194    0.8074    0.7609       597
   macro avg     0.6393    0.7288    0.6793       597
weighted avg     0.7165    0.8074    0.7584       597

2023-10-11 12:17:12,403 ----------------------------------------------------------------------------------------------------