2023-10-15 04:09:08,570 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,571 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=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-15 04:09:08,571 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 Train:  3575 sentences
2023-10-15 04:09:08,572         (train_with_dev=False, train_with_test=False)
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 Training Params:
2023-10-15 04:09:08,572  - learning_rate: "0.00015" 
2023-10-15 04:09:08,572  - mini_batch_size: "4"
2023-10-15 04:09:08,572  - max_epochs: "10"
2023-10-15 04:09:08,572  - shuffle: "True"
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 Plugins:
2023-10-15 04:09:08,572  - TensorboardLogger
2023-10-15 04:09:08,572  - LinearScheduler | warmup_fraction: '0.1'
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 Final evaluation on model from best epoch (best-model.pt)
2023-10-15 04:09:08,572  - metric: "('micro avg', 'f1-score')"
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 Computation:
2023-10-15 04:09:08,572  - compute on device: cuda:0
2023-10-15 04:09:08,572  - embedding storage: none
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 Model training base path: "hmbench-hipe2020/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5"
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,572 ----------------------------------------------------------------------------------------------------
2023-10-15 04:09:08,573 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-15 04:09:25,277 epoch 1 - iter 89/894 - loss 3.01669693 - time (sec): 16.70 - samples/sec: 522.34 - lr: 0.000015 - momentum: 0.000000
2023-10-15 04:09:41,508 epoch 1 - iter 178/894 - loss 2.96493085 - time (sec): 32.93 - samples/sec: 509.70 - lr: 0.000030 - momentum: 0.000000
2023-10-15 04:09:58,222 epoch 1 - iter 267/894 - loss 2.79712665 - time (sec): 49.65 - samples/sec: 509.72 - lr: 0.000045 - momentum: 0.000000
2023-10-15 04:10:15,109 epoch 1 - iter 356/894 - loss 2.58130911 - time (sec): 66.54 - samples/sec: 518.52 - lr: 0.000060 - momentum: 0.000000
2023-10-15 04:10:32,072 epoch 1 - iter 445/894 - loss 2.34533400 - time (sec): 83.50 - samples/sec: 524.01 - lr: 0.000074 - momentum: 0.000000
2023-10-15 04:10:49,283 epoch 1 - iter 534/894 - loss 2.11215980 - time (sec): 100.71 - samples/sec: 522.58 - lr: 0.000089 - momentum: 0.000000
2023-10-15 04:11:05,165 epoch 1 - iter 623/894 - loss 1.92635674 - time (sec): 116.59 - samples/sec: 515.69 - lr: 0.000104 - momentum: 0.000000
2023-10-15 04:11:23,626 epoch 1 - iter 712/894 - loss 1.73388268 - time (sec): 135.05 - samples/sec: 513.95 - lr: 0.000119 - momentum: 0.000000
2023-10-15 04:11:39,671 epoch 1 - iter 801/894 - loss 1.61191467 - time (sec): 151.10 - samples/sec: 511.70 - lr: 0.000134 - momentum: 0.000000
2023-10-15 04:11:56,352 epoch 1 - iter 890/894 - loss 1.48758363 - time (sec): 167.78 - samples/sec: 513.37 - lr: 0.000149 - momentum: 0.000000
2023-10-15 04:11:57,084 ----------------------------------------------------------------------------------------------------
2023-10-15 04:11:57,084 EPOCH 1 done: loss 1.4828 - lr: 0.000149
2023-10-15 04:12:20,886 DEV : loss 0.3928602635860443 - f1-score (micro avg)  0.0
2023-10-15 04:12:20,914 ----------------------------------------------------------------------------------------------------
2023-10-15 04:12:37,848 epoch 2 - iter 89/894 - loss 0.40241694 - time (sec): 16.93 - samples/sec: 532.50 - lr: 0.000148 - momentum: 0.000000
2023-10-15 04:12:54,611 epoch 2 - iter 178/894 - loss 0.36809400 - time (sec): 33.70 - samples/sec: 517.95 - lr: 0.000147 - momentum: 0.000000
2023-10-15 04:13:10,826 epoch 2 - iter 267/894 - loss 0.37648462 - time (sec): 49.91 - samples/sec: 505.92 - lr: 0.000145 - momentum: 0.000000
2023-10-15 04:13:27,441 epoch 2 - iter 356/894 - loss 0.36497012 - time (sec): 66.53 - samples/sec: 511.54 - lr: 0.000143 - momentum: 0.000000
2023-10-15 04:13:43,772 epoch 2 - iter 445/894 - loss 0.34104738 - time (sec): 82.86 - samples/sec: 511.37 - lr: 0.000142 - momentum: 0.000000
2023-10-15 04:14:00,746 epoch 2 - iter 534/894 - loss 0.32763633 - time (sec): 99.83 - samples/sec: 511.90 - lr: 0.000140 - momentum: 0.000000
2023-10-15 04:14:19,135 epoch 2 - iter 623/894 - loss 0.32056524 - time (sec): 118.22 - samples/sec: 511.61 - lr: 0.000138 - momentum: 0.000000
2023-10-15 04:14:35,357 epoch 2 - iter 712/894 - loss 0.30819682 - time (sec): 134.44 - samples/sec: 509.93 - lr: 0.000137 - momentum: 0.000000
2023-10-15 04:14:51,947 epoch 2 - iter 801/894 - loss 0.29742880 - time (sec): 151.03 - samples/sec: 510.18 - lr: 0.000135 - momentum: 0.000000
2023-10-15 04:15:08,857 epoch 2 - iter 890/894 - loss 0.28678667 - time (sec): 167.94 - samples/sec: 512.85 - lr: 0.000133 - momentum: 0.000000
2023-10-15 04:15:09,584 ----------------------------------------------------------------------------------------------------
2023-10-15 04:15:09,585 EPOCH 2 done: loss 0.2860 - lr: 0.000133
2023-10-15 04:15:35,564 DEV : loss 0.19997233152389526 - f1-score (micro avg)  0.6136
2023-10-15 04:15:35,590 saving best model
2023-10-15 04:15:36,195 ----------------------------------------------------------------------------------------------------
2023-10-15 04:15:53,031 epoch 3 - iter 89/894 - loss 0.16944357 - time (sec): 16.83 - samples/sec: 518.92 - lr: 0.000132 - momentum: 0.000000
2023-10-15 04:16:09,384 epoch 3 - iter 178/894 - loss 0.15854015 - time (sec): 33.19 - samples/sec: 504.82 - lr: 0.000130 - momentum: 0.000000
2023-10-15 04:16:28,515 epoch 3 - iter 267/894 - loss 0.16442094 - time (sec): 52.32 - samples/sec: 522.63 - lr: 0.000128 - momentum: 0.000000
2023-10-15 04:16:45,676 epoch 3 - iter 356/894 - loss 0.15557875 - time (sec): 69.48 - samples/sec: 526.84 - lr: 0.000127 - momentum: 0.000000
2023-10-15 04:17:02,066 epoch 3 - iter 445/894 - loss 0.15267942 - time (sec): 85.87 - samples/sec: 522.35 - lr: 0.000125 - momentum: 0.000000
2023-10-15 04:17:17,826 epoch 3 - iter 534/894 - loss 0.14961009 - time (sec): 101.63 - samples/sec: 514.91 - lr: 0.000123 - momentum: 0.000000
2023-10-15 04:17:33,775 epoch 3 - iter 623/894 - loss 0.15096735 - time (sec): 117.58 - samples/sec: 511.73 - lr: 0.000122 - momentum: 0.000000
2023-10-15 04:17:50,817 epoch 3 - iter 712/894 - loss 0.14472220 - time (sec): 134.62 - samples/sec: 517.05 - lr: 0.000120 - momentum: 0.000000
2023-10-15 04:18:07,136 epoch 3 - iter 801/894 - loss 0.14338584 - time (sec): 150.94 - samples/sec: 514.75 - lr: 0.000118 - momentum: 0.000000
2023-10-15 04:18:23,674 epoch 3 - iter 890/894 - loss 0.13872605 - time (sec): 167.48 - samples/sec: 514.60 - lr: 0.000117 - momentum: 0.000000
2023-10-15 04:18:24,379 ----------------------------------------------------------------------------------------------------
2023-10-15 04:18:24,379 EPOCH 3 done: loss 0.1388 - lr: 0.000117
2023-10-15 04:18:50,277 DEV : loss 0.1811581701040268 - f1-score (micro avg)  0.7212
2023-10-15 04:18:50,303 saving best model
2023-10-15 04:18:53,369 ----------------------------------------------------------------------------------------------------
2023-10-15 04:19:10,048 epoch 4 - iter 89/894 - loss 0.07264337 - time (sec): 16.68 - samples/sec: 496.21 - lr: 0.000115 - momentum: 0.000000
2023-10-15 04:19:26,851 epoch 4 - iter 178/894 - loss 0.08074117 - time (sec): 33.48 - samples/sec: 513.75 - lr: 0.000113 - momentum: 0.000000
2023-10-15 04:19:45,305 epoch 4 - iter 267/894 - loss 0.08175014 - time (sec): 51.93 - samples/sec: 516.80 - lr: 0.000112 - momentum: 0.000000
2023-10-15 04:20:02,733 epoch 4 - iter 356/894 - loss 0.07864725 - time (sec): 69.36 - samples/sec: 522.87 - lr: 0.000110 - momentum: 0.000000
2023-10-15 04:20:19,324 epoch 4 - iter 445/894 - loss 0.08009647 - time (sec): 85.95 - samples/sec: 519.46 - lr: 0.000108 - momentum: 0.000000
2023-10-15 04:20:36,313 epoch 4 - iter 534/894 - loss 0.07890887 - time (sec): 102.94 - samples/sec: 519.76 - lr: 0.000107 - momentum: 0.000000
2023-10-15 04:20:52,676 epoch 4 - iter 623/894 - loss 0.07867496 - time (sec): 119.30 - samples/sec: 517.47 - lr: 0.000105 - momentum: 0.000000
2023-10-15 04:21:09,372 epoch 4 - iter 712/894 - loss 0.07953826 - time (sec): 136.00 - samples/sec: 513.81 - lr: 0.000103 - momentum: 0.000000
2023-10-15 04:21:25,511 epoch 4 - iter 801/894 - loss 0.08107232 - time (sec): 152.14 - samples/sec: 510.97 - lr: 0.000102 - momentum: 0.000000
2023-10-15 04:21:42,113 epoch 4 - iter 890/894 - loss 0.08004768 - time (sec): 168.74 - samples/sec: 511.37 - lr: 0.000100 - momentum: 0.000000
2023-10-15 04:21:42,769 ----------------------------------------------------------------------------------------------------
2023-10-15 04:21:42,769 EPOCH 4 done: loss 0.0798 - lr: 0.000100
2023-10-15 04:22:08,721 DEV : loss 0.1746881902217865 - f1-score (micro avg)  0.7458
2023-10-15 04:22:08,747 saving best model
2023-10-15 04:22:12,600 ----------------------------------------------------------------------------------------------------
2023-10-15 04:22:29,474 epoch 5 - iter 89/894 - loss 0.05705340 - time (sec): 16.87 - samples/sec: 527.26 - lr: 0.000098 - momentum: 0.000000
2023-10-15 04:22:46,746 epoch 5 - iter 178/894 - loss 0.05878435 - time (sec): 34.14 - samples/sec: 532.89 - lr: 0.000097 - momentum: 0.000000
2023-10-15 04:23:03,102 epoch 5 - iter 267/894 - loss 0.05451082 - time (sec): 50.50 - samples/sec: 526.04 - lr: 0.000095 - momentum: 0.000000
2023-10-15 04:23:19,958 epoch 5 - iter 356/894 - loss 0.05267294 - time (sec): 67.36 - samples/sec: 526.25 - lr: 0.000093 - momentum: 0.000000
2023-10-15 04:23:38,442 epoch 5 - iter 445/894 - loss 0.05334504 - time (sec): 85.84 - samples/sec: 524.68 - lr: 0.000092 - momentum: 0.000000
2023-10-15 04:23:55,070 epoch 5 - iter 534/894 - loss 0.05556145 - time (sec): 102.47 - samples/sec: 523.18 - lr: 0.000090 - momentum: 0.000000
2023-10-15 04:24:11,507 epoch 5 - iter 623/894 - loss 0.05529169 - time (sec): 118.90 - samples/sec: 520.68 - lr: 0.000088 - momentum: 0.000000
2023-10-15 04:24:27,838 epoch 5 - iter 712/894 - loss 0.05506619 - time (sec): 135.24 - samples/sec: 516.46 - lr: 0.000087 - momentum: 0.000000
2023-10-15 04:24:44,106 epoch 5 - iter 801/894 - loss 0.05292623 - time (sec): 151.50 - samples/sec: 513.68 - lr: 0.000085 - momentum: 0.000000
2023-10-15 04:25:00,787 epoch 5 - iter 890/894 - loss 0.05178979 - time (sec): 168.19 - samples/sec: 512.87 - lr: 0.000083 - momentum: 0.000000
2023-10-15 04:25:01,462 ----------------------------------------------------------------------------------------------------
2023-10-15 04:25:01,462 EPOCH 5 done: loss 0.0516 - lr: 0.000083
2023-10-15 04:25:27,317 DEV : loss 0.1961323320865631 - f1-score (micro avg)  0.7533
2023-10-15 04:25:27,343 saving best model
2023-10-15 04:25:30,115 ----------------------------------------------------------------------------------------------------
2023-10-15 04:25:46,977 epoch 6 - iter 89/894 - loss 0.02773905 - time (sec): 16.86 - samples/sec: 508.69 - lr: 0.000082 - momentum: 0.000000
2023-10-15 04:26:05,774 epoch 6 - iter 178/894 - loss 0.03176464 - time (sec): 35.66 - samples/sec: 503.28 - lr: 0.000080 - momentum: 0.000000
2023-10-15 04:26:22,897 epoch 6 - iter 267/894 - loss 0.03165230 - time (sec): 52.78 - samples/sec: 507.70 - lr: 0.000078 - momentum: 0.000000
2023-10-15 04:26:40,037 epoch 6 - iter 356/894 - loss 0.02837219 - time (sec): 69.92 - samples/sec: 512.12 - lr: 0.000077 - momentum: 0.000000
2023-10-15 04:26:56,297 epoch 6 - iter 445/894 - loss 0.02927562 - time (sec): 86.18 - samples/sec: 509.14 - lr: 0.000075 - momentum: 0.000000
2023-10-15 04:27:13,036 epoch 6 - iter 534/894 - loss 0.02852822 - time (sec): 102.92 - samples/sec: 511.49 - lr: 0.000073 - momentum: 0.000000
2023-10-15 04:27:30,324 epoch 6 - iter 623/894 - loss 0.03104351 - time (sec): 120.21 - samples/sec: 512.56 - lr: 0.000072 - momentum: 0.000000
2023-10-15 04:27:46,861 epoch 6 - iter 712/894 - loss 0.03080534 - time (sec): 136.75 - samples/sec: 510.43 - lr: 0.000070 - momentum: 0.000000
2023-10-15 04:28:03,322 epoch 6 - iter 801/894 - loss 0.03026852 - time (sec): 153.21 - samples/sec: 507.43 - lr: 0.000068 - momentum: 0.000000
2023-10-15 04:28:20,062 epoch 6 - iter 890/894 - loss 0.02999441 - time (sec): 169.95 - samples/sec: 507.21 - lr: 0.000067 - momentum: 0.000000
2023-10-15 04:28:20,777 ----------------------------------------------------------------------------------------------------
2023-10-15 04:28:20,777 EPOCH 6 done: loss 0.0301 - lr: 0.000067
2023-10-15 04:28:46,864 DEV : loss 0.21011091768741608 - f1-score (micro avg)  0.7392
2023-10-15 04:28:46,891 ----------------------------------------------------------------------------------------------------
2023-10-15 04:29:03,183 epoch 7 - iter 89/894 - loss 0.01509648 - time (sec): 16.29 - samples/sec: 497.89 - lr: 0.000065 - momentum: 0.000000
2023-10-15 04:29:20,466 epoch 7 - iter 178/894 - loss 0.01768168 - time (sec): 33.57 - samples/sec: 521.88 - lr: 0.000063 - momentum: 0.000000
2023-10-15 04:29:36,944 epoch 7 - iter 267/894 - loss 0.01723374 - time (sec): 50.05 - samples/sec: 515.64 - lr: 0.000062 - momentum: 0.000000
2023-10-15 04:29:53,834 epoch 7 - iter 356/894 - loss 0.01926101 - time (sec): 66.94 - samples/sec: 519.06 - lr: 0.000060 - momentum: 0.000000
2023-10-15 04:30:12,591 epoch 7 - iter 445/894 - loss 0.02063806 - time (sec): 85.70 - samples/sec: 519.28 - lr: 0.000058 - momentum: 0.000000
2023-10-15 04:30:29,223 epoch 7 - iter 534/894 - loss 0.02231732 - time (sec): 102.33 - samples/sec: 516.46 - lr: 0.000057 - momentum: 0.000000
2023-10-15 04:30:45,697 epoch 7 - iter 623/894 - loss 0.02186638 - time (sec): 118.80 - samples/sec: 515.38 - lr: 0.000055 - momentum: 0.000000
2023-10-15 04:31:02,125 epoch 7 - iter 712/894 - loss 0.02151176 - time (sec): 135.23 - samples/sec: 513.26 - lr: 0.000053 - momentum: 0.000000
2023-10-15 04:31:18,350 epoch 7 - iter 801/894 - loss 0.02251965 - time (sec): 151.46 - samples/sec: 510.54 - lr: 0.000052 - momentum: 0.000000
2023-10-15 04:31:35,670 epoch 7 - iter 890/894 - loss 0.02216851 - time (sec): 168.78 - samples/sec: 511.33 - lr: 0.000050 - momentum: 0.000000
2023-10-15 04:31:36,315 ----------------------------------------------------------------------------------------------------
2023-10-15 04:31:36,315 EPOCH 7 done: loss 0.0221 - lr: 0.000050
2023-10-15 04:32:02,469 DEV : loss 0.21213364601135254 - f1-score (micro avg)  0.7496
2023-10-15 04:32:02,496 ----------------------------------------------------------------------------------------------------
2023-10-15 04:32:19,708 epoch 8 - iter 89/894 - loss 0.01169276 - time (sec): 17.21 - samples/sec: 491.26 - lr: 0.000048 - momentum: 0.000000
2023-10-15 04:32:38,228 epoch 8 - iter 178/894 - loss 0.02044703 - time (sec): 35.73 - samples/sec: 515.58 - lr: 0.000047 - momentum: 0.000000
2023-10-15 04:32:54,827 epoch 8 - iter 267/894 - loss 0.01832686 - time (sec): 52.33 - samples/sec: 519.69 - lr: 0.000045 - momentum: 0.000000
2023-10-15 04:33:11,310 epoch 8 - iter 356/894 - loss 0.01846364 - time (sec): 68.81 - samples/sec: 517.95 - lr: 0.000043 - momentum: 0.000000
2023-10-15 04:33:27,641 epoch 8 - iter 445/894 - loss 0.01842268 - time (sec): 85.14 - samples/sec: 516.53 - lr: 0.000042 - momentum: 0.000000
2023-10-15 04:33:44,139 epoch 8 - iter 534/894 - loss 0.01721769 - time (sec): 101.64 - samples/sec: 513.08 - lr: 0.000040 - momentum: 0.000000
2023-10-15 04:34:01,049 epoch 8 - iter 623/894 - loss 0.01554985 - time (sec): 118.55 - samples/sec: 514.36 - lr: 0.000038 - momentum: 0.000000
2023-10-15 04:34:17,851 epoch 8 - iter 712/894 - loss 0.01536733 - time (sec): 135.35 - samples/sec: 516.76 - lr: 0.000037 - momentum: 0.000000
2023-10-15 04:34:34,020 epoch 8 - iter 801/894 - loss 0.01479547 - time (sec): 151.52 - samples/sec: 512.89 - lr: 0.000035 - momentum: 0.000000
2023-10-15 04:34:50,629 epoch 8 - iter 890/894 - loss 0.01443364 - time (sec): 168.13 - samples/sec: 513.01 - lr: 0.000033 - momentum: 0.000000
2023-10-15 04:34:51,277 ----------------------------------------------------------------------------------------------------
2023-10-15 04:34:51,277 EPOCH 8 done: loss 0.0144 - lr: 0.000033
2023-10-15 04:35:17,695 DEV : loss 0.23843105137348175 - f1-score (micro avg)  0.7599
2023-10-15 04:35:17,725 saving best model
2023-10-15 04:35:20,871 ----------------------------------------------------------------------------------------------------
2023-10-15 04:35:37,980 epoch 9 - iter 89/894 - loss 0.01583546 - time (sec): 17.11 - samples/sec: 526.25 - lr: 0.000032 - momentum: 0.000000
2023-10-15 04:35:54,638 epoch 9 - iter 178/894 - loss 0.01093487 - time (sec): 33.77 - samples/sec: 526.20 - lr: 0.000030 - momentum: 0.000000
2023-10-15 04:36:11,458 epoch 9 - iter 267/894 - loss 0.00930857 - time (sec): 50.59 - samples/sec: 519.11 - lr: 0.000028 - momentum: 0.000000
2023-10-15 04:36:27,958 epoch 9 - iter 356/894 - loss 0.00861726 - time (sec): 67.09 - samples/sec: 515.16 - lr: 0.000027 - momentum: 0.000000
2023-10-15 04:36:44,252 epoch 9 - iter 445/894 - loss 0.00776441 - time (sec): 83.38 - samples/sec: 511.51 - lr: 0.000025 - momentum: 0.000000
2023-10-15 04:37:01,051 epoch 9 - iter 534/894 - loss 0.00737818 - time (sec): 100.18 - samples/sec: 511.52 - lr: 0.000023 - momentum: 0.000000
2023-10-15 04:37:18,832 epoch 9 - iter 623/894 - loss 0.00817716 - time (sec): 117.96 - samples/sec: 514.90 - lr: 0.000022 - momentum: 0.000000
2023-10-15 04:37:35,299 epoch 9 - iter 712/894 - loss 0.00840400 - time (sec): 134.43 - samples/sec: 511.00 - lr: 0.000020 - momentum: 0.000000
2023-10-15 04:37:53,566 epoch 9 - iter 801/894 - loss 0.01000822 - time (sec): 152.69 - samples/sec: 509.87 - lr: 0.000019 - momentum: 0.000000
2023-10-15 04:38:10,401 epoch 9 - iter 890/894 - loss 0.01001162 - time (sec): 169.53 - samples/sec: 508.91 - lr: 0.000017 - momentum: 0.000000
2023-10-15 04:38:11,057 ----------------------------------------------------------------------------------------------------
2023-10-15 04:38:11,057 EPOCH 9 done: loss 0.0100 - lr: 0.000017
2023-10-15 04:38:36,962 DEV : loss 0.24341297149658203 - f1-score (micro avg)  0.7577
2023-10-15 04:38:36,988 ----------------------------------------------------------------------------------------------------
2023-10-15 04:38:53,623 epoch 10 - iter 89/894 - loss 0.00631867 - time (sec): 16.63 - samples/sec: 496.94 - lr: 0.000015 - momentum: 0.000000
2023-10-15 04:39:10,822 epoch 10 - iter 178/894 - loss 0.00579084 - time (sec): 33.83 - samples/sec: 517.16 - lr: 0.000013 - momentum: 0.000000
2023-10-15 04:39:27,560 epoch 10 - iter 267/894 - loss 0.00739788 - time (sec): 50.57 - samples/sec: 521.17 - lr: 0.000012 - momentum: 0.000000
2023-10-15 04:39:46,005 epoch 10 - iter 356/894 - loss 0.00899480 - time (sec): 69.02 - samples/sec: 519.49 - lr: 0.000010 - momentum: 0.000000
2023-10-15 04:40:02,766 epoch 10 - iter 445/894 - loss 0.00779425 - time (sec): 85.78 - samples/sec: 512.73 - lr: 0.000008 - momentum: 0.000000
2023-10-15 04:40:18,916 epoch 10 - iter 534/894 - loss 0.00861217 - time (sec): 101.93 - samples/sec: 508.64 - lr: 0.000007 - momentum: 0.000000
2023-10-15 04:40:35,444 epoch 10 - iter 623/894 - loss 0.00785899 - time (sec): 118.45 - samples/sec: 512.67 - lr: 0.000005 - momentum: 0.000000
2023-10-15 04:40:51,915 epoch 10 - iter 712/894 - loss 0.00783975 - time (sec): 134.93 - samples/sec: 511.39 - lr: 0.000004 - momentum: 0.000000
2023-10-15 04:41:08,151 epoch 10 - iter 801/894 - loss 0.00785490 - time (sec): 151.16 - samples/sec: 507.30 - lr: 0.000002 - momentum: 0.000000
2023-10-15 04:41:25,522 epoch 10 - iter 890/894 - loss 0.00775187 - time (sec): 168.53 - samples/sec: 511.62 - lr: 0.000000 - momentum: 0.000000
2023-10-15 04:41:26,204 ----------------------------------------------------------------------------------------------------
2023-10-15 04:41:26,204 EPOCH 10 done: loss 0.0077 - lr: 0.000000
2023-10-15 04:41:52,334 DEV : loss 0.24051038920879364 - f1-score (micro avg)  0.7591
2023-10-15 04:41:52,948 ----------------------------------------------------------------------------------------------------
2023-10-15 04:41:52,949 Loading model from best epoch ...
2023-10-15 04:42:00,352 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
2023-10-15 04:42:22,999 
Results:
- F-score (micro) 0.7589
- F-score (macro) 0.6502
- Accuracy 0.629

By class:
              precision    recall  f1-score   support

         loc     0.8484    0.8641    0.8562       596
        pers     0.6733    0.8168    0.7381       333
         org     0.5600    0.5303    0.5447       132
        prod     0.5439    0.4697    0.5041        66
        time     0.5849    0.6327    0.6078        49

   micro avg     0.7376    0.7815    0.7589      1176
   macro avg     0.6421    0.6627    0.6502      1176
weighted avg     0.7384    0.7815    0.7577      1176

2023-10-15 04:42:22,999 ----------------------------------------------------------------------------------------------------