2023-10-11 00:50:21,777 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,779 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 00:50:21,779 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,780 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
2023-10-11 00:50:21,780 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,780 Train:  1166 sentences
2023-10-11 00:50:21,780         (train_with_dev=False, train_with_test=False)
2023-10-11 00:50:21,780 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,780 Training Params:
2023-10-11 00:50:21,780  - learning_rate: "0.00015" 
2023-10-11 00:50:21,780  - mini_batch_size: "4"
2023-10-11 00:50:21,780  - max_epochs: "10"
2023-10-11 00:50:21,780  - shuffle: "True"
2023-10-11 00:50:21,780 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,780 Plugins:
2023-10-11 00:50:21,780  - TensorboardLogger
2023-10-11 00:50:21,781  - LinearScheduler | warmup_fraction: '0.1'
2023-10-11 00:50:21,781 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,781 Final evaluation on model from best epoch (best-model.pt)
2023-10-11 00:50:21,781  - metric: "('micro avg', 'f1-score')"
2023-10-11 00:50:21,781 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,781 Computation:
2023-10-11 00:50:21,781  - compute on device: cuda:0
2023-10-11 00:50:21,781  - embedding storage: none
2023-10-11 00:50:21,781 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,781 Model training base path: "hmbench-newseye/fi-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3"
2023-10-11 00:50:21,781 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,781 ----------------------------------------------------------------------------------------------------
2023-10-11 00:50:21,781 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-11 00:50:30,994 epoch 1 - iter 29/292 - loss 2.82159671 - time (sec): 9.21 - samples/sec: 420.37 - lr: 0.000014 - momentum: 0.000000
2023-10-11 00:50:41,271 epoch 1 - iter 58/292 - loss 2.81150796 - time (sec): 19.49 - samples/sec: 431.35 - lr: 0.000029 - momentum: 0.000000
2023-10-11 00:50:51,081 epoch 1 - iter 87/292 - loss 2.79154959 - time (sec): 29.30 - samples/sec: 427.89 - lr: 0.000044 - momentum: 0.000000
2023-10-11 00:51:00,476 epoch 1 - iter 116/292 - loss 2.73211083 - time (sec): 38.69 - samples/sec: 434.21 - lr: 0.000059 - momentum: 0.000000
2023-10-11 00:51:10,963 epoch 1 - iter 145/292 - loss 2.63886376 - time (sec): 49.18 - samples/sec: 436.01 - lr: 0.000074 - momentum: 0.000000
2023-10-11 00:51:21,819 epoch 1 - iter 174/292 - loss 2.53457496 - time (sec): 60.04 - samples/sec: 444.40 - lr: 0.000089 - momentum: 0.000000
2023-10-11 00:51:32,024 epoch 1 - iter 203/292 - loss 2.42260030 - time (sec): 70.24 - samples/sec: 447.65 - lr: 0.000104 - momentum: 0.000000
2023-10-11 00:51:41,242 epoch 1 - iter 232/292 - loss 2.32706750 - time (sec): 79.46 - samples/sec: 443.27 - lr: 0.000119 - momentum: 0.000000
2023-10-11 00:51:51,124 epoch 1 - iter 261/292 - loss 2.20506010 - time (sec): 89.34 - samples/sec: 442.90 - lr: 0.000134 - momentum: 0.000000
2023-10-11 00:52:01,243 epoch 1 - iter 290/292 - loss 2.08434498 - time (sec): 99.46 - samples/sec: 442.73 - lr: 0.000148 - momentum: 0.000000
2023-10-11 00:52:01,954 ----------------------------------------------------------------------------------------------------
2023-10-11 00:52:01,954 EPOCH 1 done: loss 2.0728 - lr: 0.000148
2023-10-11 00:52:07,532 DEV : loss 0.7312660813331604 - f1-score (micro avg)  0.0
2023-10-11 00:52:07,542 ----------------------------------------------------------------------------------------------------
2023-10-11 00:52:16,725 epoch 2 - iter 29/292 - loss 0.76128386 - time (sec): 9.18 - samples/sec: 429.57 - lr: 0.000148 - momentum: 0.000000
2023-10-11 00:52:26,150 epoch 2 - iter 58/292 - loss 0.70992578 - time (sec): 18.61 - samples/sec: 428.77 - lr: 0.000147 - momentum: 0.000000
2023-10-11 00:52:35,816 epoch 2 - iter 87/292 - loss 0.67722563 - time (sec): 28.27 - samples/sec: 432.27 - lr: 0.000145 - momentum: 0.000000
2023-10-11 00:52:45,386 epoch 2 - iter 116/292 - loss 0.65637471 - time (sec): 37.84 - samples/sec: 439.59 - lr: 0.000143 - momentum: 0.000000
2023-10-11 00:52:55,475 epoch 2 - iter 145/292 - loss 0.60692602 - time (sec): 47.93 - samples/sec: 444.72 - lr: 0.000142 - momentum: 0.000000
2023-10-11 00:53:05,279 epoch 2 - iter 174/292 - loss 0.60829187 - time (sec): 57.74 - samples/sec: 448.68 - lr: 0.000140 - momentum: 0.000000
2023-10-11 00:53:14,906 epoch 2 - iter 203/292 - loss 0.58745485 - time (sec): 67.36 - samples/sec: 447.07 - lr: 0.000138 - momentum: 0.000000
2023-10-11 00:53:24,801 epoch 2 - iter 232/292 - loss 0.56126617 - time (sec): 77.26 - samples/sec: 449.20 - lr: 0.000137 - momentum: 0.000000
2023-10-11 00:53:34,328 epoch 2 - iter 261/292 - loss 0.54068959 - time (sec): 86.78 - samples/sec: 448.11 - lr: 0.000135 - momentum: 0.000000
2023-10-11 00:53:44,906 epoch 2 - iter 290/292 - loss 0.52073465 - time (sec): 97.36 - samples/sec: 453.12 - lr: 0.000134 - momentum: 0.000000
2023-10-11 00:53:45,475 ----------------------------------------------------------------------------------------------------
2023-10-11 00:53:45,475 EPOCH 2 done: loss 0.5195 - lr: 0.000134
2023-10-11 00:53:51,452 DEV : loss 0.2922310531139374 - f1-score (micro avg)  0.2024
2023-10-11 00:53:51,462 saving best model
2023-10-11 00:53:52,758 ----------------------------------------------------------------------------------------------------
2023-10-11 00:54:03,115 epoch 3 - iter 29/292 - loss 0.37983079 - time (sec): 10.35 - samples/sec: 491.11 - lr: 0.000132 - momentum: 0.000000
2023-10-11 00:54:13,462 epoch 3 - iter 58/292 - loss 0.33791672 - time (sec): 20.70 - samples/sec: 499.93 - lr: 0.000130 - momentum: 0.000000
2023-10-11 00:54:23,057 epoch 3 - iter 87/292 - loss 0.37366879 - time (sec): 30.30 - samples/sec: 491.97 - lr: 0.000128 - momentum: 0.000000
2023-10-11 00:54:32,279 epoch 3 - iter 116/292 - loss 0.35491092 - time (sec): 39.52 - samples/sec: 478.21 - lr: 0.000127 - momentum: 0.000000
2023-10-11 00:54:42,347 epoch 3 - iter 145/292 - loss 0.34147437 - time (sec): 49.59 - samples/sec: 484.65 - lr: 0.000125 - momentum: 0.000000
2023-10-11 00:54:51,471 epoch 3 - iter 174/292 - loss 0.33747745 - time (sec): 58.71 - samples/sec: 476.29 - lr: 0.000123 - momentum: 0.000000
2023-10-11 00:55:00,729 epoch 3 - iter 203/292 - loss 0.32562316 - time (sec): 67.97 - samples/sec: 471.82 - lr: 0.000122 - momentum: 0.000000
2023-10-11 00:55:09,329 epoch 3 - iter 232/292 - loss 0.32501433 - time (sec): 76.57 - samples/sec: 464.71 - lr: 0.000120 - momentum: 0.000000
2023-10-11 00:55:17,972 epoch 3 - iter 261/292 - loss 0.32013085 - time (sec): 85.21 - samples/sec: 458.79 - lr: 0.000119 - momentum: 0.000000
2023-10-11 00:55:28,050 epoch 3 - iter 290/292 - loss 0.31021482 - time (sec): 95.29 - samples/sec: 463.04 - lr: 0.000117 - momentum: 0.000000
2023-10-11 00:55:28,624 ----------------------------------------------------------------------------------------------------
2023-10-11 00:55:28,624 EPOCH 3 done: loss 0.3091 - lr: 0.000117
2023-10-11 00:55:34,180 DEV : loss 0.21407955884933472 - f1-score (micro avg)  0.4866
2023-10-11 00:55:34,189 saving best model
2023-10-11 00:55:42,377 ----------------------------------------------------------------------------------------------------
2023-10-11 00:55:51,695 epoch 4 - iter 29/292 - loss 0.23965969 - time (sec): 9.31 - samples/sec: 427.66 - lr: 0.000115 - momentum: 0.000000
2023-10-11 00:56:01,955 epoch 4 - iter 58/292 - loss 0.22892559 - time (sec): 19.57 - samples/sec: 454.35 - lr: 0.000113 - momentum: 0.000000
2023-10-11 00:56:11,004 epoch 4 - iter 87/292 - loss 0.22086500 - time (sec): 28.62 - samples/sec: 442.91 - lr: 0.000112 - momentum: 0.000000
2023-10-11 00:56:20,715 epoch 4 - iter 116/292 - loss 0.22494750 - time (sec): 38.33 - samples/sec: 446.64 - lr: 0.000110 - momentum: 0.000000
2023-10-11 00:56:30,836 epoch 4 - iter 145/292 - loss 0.22468010 - time (sec): 48.45 - samples/sec: 460.35 - lr: 0.000108 - momentum: 0.000000
2023-10-11 00:56:39,803 epoch 4 - iter 174/292 - loss 0.22000229 - time (sec): 57.42 - samples/sec: 456.40 - lr: 0.000107 - momentum: 0.000000
2023-10-11 00:56:49,312 epoch 4 - iter 203/292 - loss 0.21233482 - time (sec): 66.93 - samples/sec: 457.76 - lr: 0.000105 - momentum: 0.000000
2023-10-11 00:56:58,687 epoch 4 - iter 232/292 - loss 0.21144903 - time (sec): 76.31 - samples/sec: 459.69 - lr: 0.000104 - momentum: 0.000000
2023-10-11 00:57:08,090 epoch 4 - iter 261/292 - loss 0.20979618 - time (sec): 85.71 - samples/sec: 457.83 - lr: 0.000102 - momentum: 0.000000
2023-10-11 00:57:18,747 epoch 4 - iter 290/292 - loss 0.20074265 - time (sec): 96.37 - samples/sec: 460.34 - lr: 0.000100 - momentum: 0.000000
2023-10-11 00:57:19,133 ----------------------------------------------------------------------------------------------------
2023-10-11 00:57:19,133 EPOCH 4 done: loss 0.2005 - lr: 0.000100
2023-10-11 00:57:25,028 DEV : loss 0.16525955498218536 - f1-score (micro avg)  0.6345
2023-10-11 00:57:25,039 saving best model
2023-10-11 00:57:35,183 ----------------------------------------------------------------------------------------------------
2023-10-11 00:57:45,167 epoch 5 - iter 29/292 - loss 0.17002188 - time (sec): 9.98 - samples/sec: 466.46 - lr: 0.000098 - momentum: 0.000000
2023-10-11 00:57:55,039 epoch 5 - iter 58/292 - loss 0.14429493 - time (sec): 19.85 - samples/sec: 451.54 - lr: 0.000097 - momentum: 0.000000
2023-10-11 00:58:04,598 epoch 5 - iter 87/292 - loss 0.15299122 - time (sec): 29.41 - samples/sec: 437.80 - lr: 0.000095 - momentum: 0.000000
2023-10-11 00:58:14,352 epoch 5 - iter 116/292 - loss 0.16624329 - time (sec): 39.16 - samples/sec: 432.72 - lr: 0.000093 - momentum: 0.000000
2023-10-11 00:58:24,628 epoch 5 - iter 145/292 - loss 0.15294436 - time (sec): 49.44 - samples/sec: 436.70 - lr: 0.000092 - momentum: 0.000000
2023-10-11 00:58:35,266 epoch 5 - iter 174/292 - loss 0.14924424 - time (sec): 60.08 - samples/sec: 447.08 - lr: 0.000090 - momentum: 0.000000
2023-10-11 00:58:45,086 epoch 5 - iter 203/292 - loss 0.14572104 - time (sec): 69.90 - samples/sec: 451.00 - lr: 0.000089 - momentum: 0.000000
2023-10-11 00:58:55,405 epoch 5 - iter 232/292 - loss 0.14222555 - time (sec): 80.22 - samples/sec: 447.48 - lr: 0.000087 - momentum: 0.000000
2023-10-11 00:59:05,798 epoch 5 - iter 261/292 - loss 0.13996227 - time (sec): 90.61 - samples/sec: 448.04 - lr: 0.000085 - momentum: 0.000000
2023-10-11 00:59:14,850 epoch 5 - iter 290/292 - loss 0.13738637 - time (sec): 99.66 - samples/sec: 443.93 - lr: 0.000084 - momentum: 0.000000
2023-10-11 00:59:15,341 ----------------------------------------------------------------------------------------------------
2023-10-11 00:59:15,341 EPOCH 5 done: loss 0.1373 - lr: 0.000084
2023-10-11 00:59:21,247 DEV : loss 0.15909573435783386 - f1-score (micro avg)  0.75
2023-10-11 00:59:21,258 saving best model
2023-10-11 00:59:29,578 ----------------------------------------------------------------------------------------------------
2023-10-11 00:59:39,736 epoch 6 - iter 29/292 - loss 0.07533053 - time (sec): 10.15 - samples/sec: 488.63 - lr: 0.000082 - momentum: 0.000000
2023-10-11 00:59:49,011 epoch 6 - iter 58/292 - loss 0.08647009 - time (sec): 19.43 - samples/sec: 465.26 - lr: 0.000080 - momentum: 0.000000
2023-10-11 00:59:58,345 epoch 6 - iter 87/292 - loss 0.08324412 - time (sec): 28.76 - samples/sec: 456.79 - lr: 0.000078 - momentum: 0.000000
2023-10-11 01:00:08,495 epoch 6 - iter 116/292 - loss 0.08045100 - time (sec): 38.91 - samples/sec: 457.03 - lr: 0.000077 - momentum: 0.000000
2023-10-11 01:00:17,971 epoch 6 - iter 145/292 - loss 0.09413655 - time (sec): 48.39 - samples/sec: 447.21 - lr: 0.000075 - momentum: 0.000000
2023-10-11 01:00:30,185 epoch 6 - iter 174/292 - loss 0.10256312 - time (sec): 60.60 - samples/sec: 452.71 - lr: 0.000074 - momentum: 0.000000
2023-10-11 01:00:39,954 epoch 6 - iter 203/292 - loss 0.10433424 - time (sec): 70.37 - samples/sec: 449.83 - lr: 0.000072 - momentum: 0.000000
2023-10-11 01:00:49,717 epoch 6 - iter 232/292 - loss 0.09984966 - time (sec): 80.13 - samples/sec: 451.42 - lr: 0.000070 - momentum: 0.000000
2023-10-11 01:00:58,833 epoch 6 - iter 261/292 - loss 0.09911210 - time (sec): 89.25 - samples/sec: 449.80 - lr: 0.000069 - momentum: 0.000000
2023-10-11 01:01:08,672 epoch 6 - iter 290/292 - loss 0.09772248 - time (sec): 99.09 - samples/sec: 447.11 - lr: 0.000067 - momentum: 0.000000
2023-10-11 01:01:09,105 ----------------------------------------------------------------------------------------------------
2023-10-11 01:01:09,105 EPOCH 6 done: loss 0.0977 - lr: 0.000067
2023-10-11 01:01:15,130 DEV : loss 0.1364792436361313 - f1-score (micro avg)  0.7425
2023-10-11 01:01:15,141 ----------------------------------------------------------------------------------------------------
2023-10-11 01:01:25,276 epoch 7 - iter 29/292 - loss 0.06875395 - time (sec): 10.13 - samples/sec: 470.05 - lr: 0.000065 - momentum: 0.000000
2023-10-11 01:01:35,793 epoch 7 - iter 58/292 - loss 0.07266422 - time (sec): 20.65 - samples/sec: 472.46 - lr: 0.000063 - momentum: 0.000000
2023-10-11 01:01:45,287 epoch 7 - iter 87/292 - loss 0.07403621 - time (sec): 30.14 - samples/sec: 462.39 - lr: 0.000062 - momentum: 0.000000
2023-10-11 01:01:54,612 epoch 7 - iter 116/292 - loss 0.06805350 - time (sec): 39.47 - samples/sec: 458.29 - lr: 0.000060 - momentum: 0.000000
2023-10-11 01:02:03,847 epoch 7 - iter 145/292 - loss 0.07208524 - time (sec): 48.70 - samples/sec: 456.64 - lr: 0.000059 - momentum: 0.000000
2023-10-11 01:02:12,537 epoch 7 - iter 174/292 - loss 0.07485159 - time (sec): 57.39 - samples/sec: 454.56 - lr: 0.000057 - momentum: 0.000000
2023-10-11 01:02:22,221 epoch 7 - iter 203/292 - loss 0.07712604 - time (sec): 67.08 - samples/sec: 458.86 - lr: 0.000055 - momentum: 0.000000
2023-10-11 01:02:31,161 epoch 7 - iter 232/292 - loss 0.07674406 - time (sec): 76.02 - samples/sec: 453.74 - lr: 0.000054 - momentum: 0.000000
2023-10-11 01:02:42,087 epoch 7 - iter 261/292 - loss 0.07805652 - time (sec): 86.94 - samples/sec: 459.18 - lr: 0.000052 - momentum: 0.000000
2023-10-11 01:02:51,824 epoch 7 - iter 290/292 - loss 0.07712795 - time (sec): 96.68 - samples/sec: 456.75 - lr: 0.000050 - momentum: 0.000000
2023-10-11 01:02:52,422 ----------------------------------------------------------------------------------------------------
2023-10-11 01:02:52,423 EPOCH 7 done: loss 0.0767 - lr: 0.000050
2023-10-11 01:02:58,358 DEV : loss 0.13527634739875793 - f1-score (micro avg)  0.7421
2023-10-11 01:02:58,368 ----------------------------------------------------------------------------------------------------
2023-10-11 01:03:09,439 epoch 8 - iter 29/292 - loss 0.05749003 - time (sec): 11.07 - samples/sec: 481.78 - lr: 0.000048 - momentum: 0.000000
2023-10-11 01:03:18,707 epoch 8 - iter 58/292 - loss 0.07179638 - time (sec): 20.34 - samples/sec: 457.10 - lr: 0.000047 - momentum: 0.000000
2023-10-11 01:03:28,519 epoch 8 - iter 87/292 - loss 0.06961649 - time (sec): 30.15 - samples/sec: 439.32 - lr: 0.000045 - momentum: 0.000000
2023-10-11 01:03:38,114 epoch 8 - iter 116/292 - loss 0.07056370 - time (sec): 39.74 - samples/sec: 444.19 - lr: 0.000044 - momentum: 0.000000
2023-10-11 01:03:47,883 epoch 8 - iter 145/292 - loss 0.07275745 - time (sec): 49.51 - samples/sec: 448.83 - lr: 0.000042 - momentum: 0.000000
2023-10-11 01:03:57,135 epoch 8 - iter 174/292 - loss 0.07168536 - time (sec): 58.77 - samples/sec: 443.44 - lr: 0.000040 - momentum: 0.000000
2023-10-11 01:04:07,330 epoch 8 - iter 203/292 - loss 0.06796946 - time (sec): 68.96 - samples/sec: 442.53 - lr: 0.000039 - momentum: 0.000000
2023-10-11 01:04:16,921 epoch 8 - iter 232/292 - loss 0.06456009 - time (sec): 78.55 - samples/sec: 440.82 - lr: 0.000037 - momentum: 0.000000
2023-10-11 01:04:27,689 epoch 8 - iter 261/292 - loss 0.06065561 - time (sec): 89.32 - samples/sec: 445.33 - lr: 0.000035 - momentum: 0.000000
2023-10-11 01:04:37,905 epoch 8 - iter 290/292 - loss 0.06311309 - time (sec): 99.54 - samples/sec: 443.45 - lr: 0.000034 - momentum: 0.000000
2023-10-11 01:04:38,543 ----------------------------------------------------------------------------------------------------
2023-10-11 01:04:38,544 EPOCH 8 done: loss 0.0639 - lr: 0.000034
2023-10-11 01:04:44,596 DEV : loss 0.13573415577411652 - f1-score (micro avg)  0.7526
2023-10-11 01:04:44,606 saving best model
2023-10-11 01:04:53,186 ----------------------------------------------------------------------------------------------------
2023-10-11 01:05:04,113 epoch 9 - iter 29/292 - loss 0.06634260 - time (sec): 10.92 - samples/sec: 442.52 - lr: 0.000032 - momentum: 0.000000
2023-10-11 01:05:15,299 epoch 9 - iter 58/292 - loss 0.05300830 - time (sec): 22.11 - samples/sec: 429.58 - lr: 0.000030 - momentum: 0.000000
2023-10-11 01:05:25,289 epoch 9 - iter 87/292 - loss 0.04974619 - time (sec): 32.10 - samples/sec: 419.03 - lr: 0.000029 - momentum: 0.000000
2023-10-11 01:05:35,229 epoch 9 - iter 116/292 - loss 0.05095022 - time (sec): 42.04 - samples/sec: 431.58 - lr: 0.000027 - momentum: 0.000000
2023-10-11 01:05:45,780 epoch 9 - iter 145/292 - loss 0.05630806 - time (sec): 52.59 - samples/sec: 436.18 - lr: 0.000025 - momentum: 0.000000
2023-10-11 01:05:55,631 epoch 9 - iter 174/292 - loss 0.05258594 - time (sec): 62.44 - samples/sec: 438.89 - lr: 0.000024 - momentum: 0.000000
2023-10-11 01:06:04,960 epoch 9 - iter 203/292 - loss 0.05164650 - time (sec): 71.77 - samples/sec: 438.92 - lr: 0.000022 - momentum: 0.000000
2023-10-11 01:06:14,983 epoch 9 - iter 232/292 - loss 0.04999494 - time (sec): 81.79 - samples/sec: 439.41 - lr: 0.000020 - momentum: 0.000000
2023-10-11 01:06:24,698 epoch 9 - iter 261/292 - loss 0.05561816 - time (sec): 91.51 - samples/sec: 439.53 - lr: 0.000019 - momentum: 0.000000
2023-10-11 01:06:34,087 epoch 9 - iter 290/292 - loss 0.05665320 - time (sec): 100.90 - samples/sec: 438.57 - lr: 0.000017 - momentum: 0.000000
2023-10-11 01:06:34,575 ----------------------------------------------------------------------------------------------------
2023-10-11 01:06:34,575 EPOCH 9 done: loss 0.0565 - lr: 0.000017
2023-10-11 01:06:40,540 DEV : loss 0.13554613292217255 - f1-score (micro avg)  0.7342
2023-10-11 01:06:40,552 ----------------------------------------------------------------------------------------------------
2023-10-11 01:06:50,750 epoch 10 - iter 29/292 - loss 0.04670804 - time (sec): 10.20 - samples/sec: 483.70 - lr: 0.000015 - momentum: 0.000000
2023-10-11 01:07:00,562 epoch 10 - iter 58/292 - loss 0.05131042 - time (sec): 20.01 - samples/sec: 475.56 - lr: 0.000014 - momentum: 0.000000
2023-10-11 01:07:10,586 epoch 10 - iter 87/292 - loss 0.05819764 - time (sec): 30.03 - samples/sec: 487.05 - lr: 0.000012 - momentum: 0.000000
2023-10-11 01:07:20,049 epoch 10 - iter 116/292 - loss 0.05376000 - time (sec): 39.50 - samples/sec: 481.95 - lr: 0.000010 - momentum: 0.000000
2023-10-11 01:07:29,378 epoch 10 - iter 145/292 - loss 0.05463939 - time (sec): 48.82 - samples/sec: 481.20 - lr: 0.000009 - momentum: 0.000000
2023-10-11 01:07:38,497 epoch 10 - iter 174/292 - loss 0.05237695 - time (sec): 57.94 - samples/sec: 475.74 - lr: 0.000007 - momentum: 0.000000
2023-10-11 01:07:47,611 epoch 10 - iter 203/292 - loss 0.05012569 - time (sec): 67.06 - samples/sec: 473.06 - lr: 0.000005 - momentum: 0.000000
2023-10-11 01:07:57,036 epoch 10 - iter 232/292 - loss 0.04789444 - time (sec): 76.48 - samples/sec: 472.60 - lr: 0.000004 - momentum: 0.000000
2023-10-11 01:08:05,894 epoch 10 - iter 261/292 - loss 0.04957126 - time (sec): 85.34 - samples/sec: 467.49 - lr: 0.000002 - momentum: 0.000000
2023-10-11 01:08:15,463 epoch 10 - iter 290/292 - loss 0.04988927 - time (sec): 94.91 - samples/sec: 467.25 - lr: 0.000000 - momentum: 0.000000
2023-10-11 01:08:15,844 ----------------------------------------------------------------------------------------------------
2023-10-11 01:08:15,845 EPOCH 10 done: loss 0.0499 - lr: 0.000000
2023-10-11 01:08:21,444 DEV : loss 0.13735945522785187 - f1-score (micro avg)  0.7489
2023-10-11 01:08:22,663 ----------------------------------------------------------------------------------------------------
2023-10-11 01:08:22,665 Loading model from best epoch ...
2023-10-11 01:08:26,661 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
2023-10-11 01:08:39,358 
Results:
- F-score (micro) 0.7334
- F-score (macro) 0.6963
- Accuracy 0.5943

By class:
              precision    recall  f1-score   support

         PER     0.8164    0.8305    0.8234       348
         LOC     0.5777    0.8123    0.6752       261
         ORG     0.4231    0.4231    0.4231        52
   HumanProd     0.8636    0.8636    0.8636        22

   micro avg     0.6818    0.7936    0.7334       683
   macro avg     0.6702    0.7324    0.6963       683
weighted avg     0.6967    0.7936    0.7375       683

2023-10-11 01:08:39,358 ----------------------------------------------------------------------------------------------------