2023-10-14 19:31:38,478 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,479 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()
)"
2023-10-14 19:31:38,479 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,479 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
2023-10-14 19:31:38,480 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,480 Train:  3575 sentences
2023-10-14 19:31:38,480         (train_with_dev=False, train_with_test=False)
2023-10-14 19:31:38,480 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,480 Training Params:
2023-10-14 19:31:38,480  - learning_rate: "0.00015" 
2023-10-14 19:31:38,480  - mini_batch_size: "4"
2023-10-14 19:31:38,480  - max_epochs: "10"
2023-10-14 19:31:38,480  - shuffle: "True"
2023-10-14 19:31:38,480 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,480 Plugins:
2023-10-14 19:31:38,480  - TensorboardLogger
2023-10-14 19:31:38,480  - LinearScheduler | warmup_fraction: '0.1'
2023-10-14 19:31:38,480 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,480 Final evaluation on model from best epoch (best-model.pt)
2023-10-14 19:31:38,480  - metric: "('micro avg', 'f1-score')"
2023-10-14 19:31:38,480 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,480 Computation:
2023-10-14 19:31:38,480  - compute on device: cuda:0
2023-10-14 19:31:38,480  - embedding storage: none
2023-10-14 19:31:38,480 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,480 Model training base path: "hmbench-hipe2020/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1"
2023-10-14 19:31:38,480 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,481 ----------------------------------------------------------------------------------------------------
2023-10-14 19:31:38,481 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-14 19:31:55,583 epoch 1 - iter 89/894 - loss 3.04489387 - time (sec): 17.10 - samples/sec: 541.29 - lr: 0.000015 - momentum: 0.000000
2023-10-14 19:32:11,920 epoch 1 - iter 178/894 - loss 3.01135120 - time (sec): 33.44 - samples/sec: 522.36 - lr: 0.000030 - momentum: 0.000000
2023-10-14 19:32:28,430 epoch 1 - iter 267/894 - loss 2.86985611 - time (sec): 49.95 - samples/sec: 514.33 - lr: 0.000045 - momentum: 0.000000
2023-10-14 19:32:45,243 epoch 1 - iter 356/894 - loss 2.65587997 - time (sec): 66.76 - samples/sec: 515.06 - lr: 0.000060 - momentum: 0.000000
2023-10-14 19:33:01,082 epoch 1 - iter 445/894 - loss 2.44878120 - time (sec): 82.60 - samples/sec: 505.53 - lr: 0.000074 - momentum: 0.000000
2023-10-14 19:33:17,616 epoch 1 - iter 534/894 - loss 2.20154310 - time (sec): 99.13 - samples/sec: 504.78 - lr: 0.000089 - momentum: 0.000000
2023-10-14 19:33:34,891 epoch 1 - iter 623/894 - loss 1.94916014 - time (sec): 116.41 - samples/sec: 510.23 - lr: 0.000104 - momentum: 0.000000
2023-10-14 19:33:51,511 epoch 1 - iter 712/894 - loss 1.77451894 - time (sec): 133.03 - samples/sec: 511.33 - lr: 0.000119 - momentum: 0.000000
2023-10-14 19:34:10,506 epoch 1 - iter 801/894 - loss 1.61163646 - time (sec): 152.02 - samples/sec: 513.52 - lr: 0.000134 - momentum: 0.000000
2023-10-14 19:34:26,780 epoch 1 - iter 890/894 - loss 1.49843666 - time (sec): 168.30 - samples/sec: 511.53 - lr: 0.000149 - momentum: 0.000000
2023-10-14 19:34:27,544 ----------------------------------------------------------------------------------------------------
2023-10-14 19:34:27,545 EPOCH 1 done: loss 1.4929 - lr: 0.000149
2023-10-14 19:34:50,649 DEV : loss 0.3523489832878113 - f1-score (micro avg)  0.0556
2023-10-14 19:34:50,675 saving best model
2023-10-14 19:34:51,285 ----------------------------------------------------------------------------------------------------
2023-10-14 19:35:07,818 epoch 2 - iter 89/894 - loss 0.38002707 - time (sec): 16.53 - samples/sec: 515.60 - lr: 0.000148 - momentum: 0.000000
2023-10-14 19:35:24,321 epoch 2 - iter 178/894 - loss 0.36652988 - time (sec): 33.04 - samples/sec: 518.11 - lr: 0.000147 - momentum: 0.000000
2023-10-14 19:35:41,326 epoch 2 - iter 267/894 - loss 0.34661944 - time (sec): 50.04 - samples/sec: 530.03 - lr: 0.000145 - momentum: 0.000000
2023-10-14 19:35:59,999 epoch 2 - iter 356/894 - loss 0.33679946 - time (sec): 68.71 - samples/sec: 523.95 - lr: 0.000143 - momentum: 0.000000
2023-10-14 19:36:16,710 epoch 2 - iter 445/894 - loss 0.32450464 - time (sec): 85.42 - samples/sec: 522.29 - lr: 0.000142 - momentum: 0.000000
2023-10-14 19:36:33,787 epoch 2 - iter 534/894 - loss 0.31215668 - time (sec): 102.50 - samples/sec: 521.55 - lr: 0.000140 - momentum: 0.000000
2023-10-14 19:36:50,184 epoch 2 - iter 623/894 - loss 0.31216230 - time (sec): 118.90 - samples/sec: 517.47 - lr: 0.000138 - momentum: 0.000000
2023-10-14 19:37:06,813 epoch 2 - iter 712/894 - loss 0.30747611 - time (sec): 135.53 - samples/sec: 517.21 - lr: 0.000137 - momentum: 0.000000
2023-10-14 19:37:23,698 epoch 2 - iter 801/894 - loss 0.29758927 - time (sec): 152.41 - samples/sec: 516.39 - lr: 0.000135 - momentum: 0.000000
2023-10-14 19:37:39,603 epoch 2 - iter 890/894 - loss 0.29413445 - time (sec): 168.32 - samples/sec: 512.38 - lr: 0.000133 - momentum: 0.000000
2023-10-14 19:37:40,270 ----------------------------------------------------------------------------------------------------
2023-10-14 19:37:40,270 EPOCH 2 done: loss 0.2940 - lr: 0.000133
2023-10-14 19:38:05,408 DEV : loss 0.2095714658498764 - f1-score (micro avg)  0.5995
2023-10-14 19:38:05,434 saving best model
2023-10-14 19:38:10,190 ----------------------------------------------------------------------------------------------------
2023-10-14 19:38:26,702 epoch 3 - iter 89/894 - loss 0.21975580 - time (sec): 16.51 - samples/sec: 500.92 - lr: 0.000132 - momentum: 0.000000
2023-10-14 19:38:43,023 epoch 3 - iter 178/894 - loss 0.19576679 - time (sec): 32.83 - samples/sec: 501.22 - lr: 0.000130 - momentum: 0.000000
2023-10-14 19:39:00,122 epoch 3 - iter 267/894 - loss 0.19131051 - time (sec): 49.93 - samples/sec: 504.32 - lr: 0.000128 - momentum: 0.000000
2023-10-14 19:39:16,560 epoch 3 - iter 356/894 - loss 0.19069530 - time (sec): 66.37 - samples/sec: 507.18 - lr: 0.000127 - momentum: 0.000000
2023-10-14 19:39:35,222 epoch 3 - iter 445/894 - loss 0.18443738 - time (sec): 85.03 - samples/sec: 515.97 - lr: 0.000125 - momentum: 0.000000
2023-10-14 19:39:51,748 epoch 3 - iter 534/894 - loss 0.18034120 - time (sec): 101.56 - samples/sec: 514.35 - lr: 0.000123 - momentum: 0.000000
2023-10-14 19:40:07,984 epoch 3 - iter 623/894 - loss 0.17040918 - time (sec): 117.79 - samples/sec: 511.08 - lr: 0.000122 - momentum: 0.000000
2023-10-14 19:40:24,143 epoch 3 - iter 712/894 - loss 0.16379017 - time (sec): 133.95 - samples/sec: 509.43 - lr: 0.000120 - momentum: 0.000000
2023-10-14 19:40:41,141 epoch 3 - iter 801/894 - loss 0.15879809 - time (sec): 150.95 - samples/sec: 512.98 - lr: 0.000118 - momentum: 0.000000
2023-10-14 19:40:57,726 epoch 3 - iter 890/894 - loss 0.15341354 - time (sec): 167.53 - samples/sec: 513.65 - lr: 0.000117 - momentum: 0.000000
2023-10-14 19:40:58,514 ----------------------------------------------------------------------------------------------------
2023-10-14 19:40:58,515 EPOCH 3 done: loss 0.1532 - lr: 0.000117
2023-10-14 19:41:23,911 DEV : loss 0.17346230149269104 - f1-score (micro avg)  0.7064
2023-10-14 19:41:23,937 saving best model
2023-10-14 19:41:28,273 ----------------------------------------------------------------------------------------------------
2023-10-14 19:41:45,109 epoch 4 - iter 89/894 - loss 0.09844153 - time (sec): 16.83 - samples/sec: 501.59 - lr: 0.000115 - momentum: 0.000000
2023-10-14 19:42:01,109 epoch 4 - iter 178/894 - loss 0.10260624 - time (sec): 32.83 - samples/sec: 492.17 - lr: 0.000113 - momentum: 0.000000
2023-10-14 19:42:17,395 epoch 4 - iter 267/894 - loss 0.09857008 - time (sec): 49.12 - samples/sec: 495.86 - lr: 0.000112 - momentum: 0.000000
2023-10-14 19:42:33,912 epoch 4 - iter 356/894 - loss 0.09918121 - time (sec): 65.64 - samples/sec: 496.75 - lr: 0.000110 - momentum: 0.000000
2023-10-14 19:42:50,110 epoch 4 - iter 445/894 - loss 0.09246905 - time (sec): 81.84 - samples/sec: 496.73 - lr: 0.000108 - momentum: 0.000000
2023-10-14 19:43:06,959 epoch 4 - iter 534/894 - loss 0.08940730 - time (sec): 98.68 - samples/sec: 503.28 - lr: 0.000107 - momentum: 0.000000
2023-10-14 19:43:23,411 epoch 4 - iter 623/894 - loss 0.08504805 - time (sec): 115.14 - samples/sec: 503.30 - lr: 0.000105 - momentum: 0.000000
2023-10-14 19:43:39,743 epoch 4 - iter 712/894 - loss 0.08499619 - time (sec): 131.47 - samples/sec: 503.29 - lr: 0.000103 - momentum: 0.000000
2023-10-14 19:43:58,439 epoch 4 - iter 801/894 - loss 0.08586194 - time (sec): 150.17 - samples/sec: 507.58 - lr: 0.000102 - momentum: 0.000000
2023-10-14 19:44:16,248 epoch 4 - iter 890/894 - loss 0.08240795 - time (sec): 167.97 - samples/sec: 512.64 - lr: 0.000100 - momentum: 0.000000
2023-10-14 19:44:17,005 ----------------------------------------------------------------------------------------------------
2023-10-14 19:44:17,006 EPOCH 4 done: loss 0.0821 - lr: 0.000100
2023-10-14 19:44:42,068 DEV : loss 0.1738642454147339 - f1-score (micro avg)  0.7313
2023-10-14 19:44:42,095 saving best model
2023-10-14 19:44:45,496 ----------------------------------------------------------------------------------------------------
2023-10-14 19:45:01,636 epoch 5 - iter 89/894 - loss 0.04568145 - time (sec): 16.14 - samples/sec: 481.94 - lr: 0.000098 - momentum: 0.000000
2023-10-14 19:45:18,218 epoch 5 - iter 178/894 - loss 0.04121839 - time (sec): 32.72 - samples/sec: 494.46 - lr: 0.000097 - momentum: 0.000000
2023-10-14 19:45:35,198 epoch 5 - iter 267/894 - loss 0.04001861 - time (sec): 49.70 - samples/sec: 505.89 - lr: 0.000095 - momentum: 0.000000
2023-10-14 19:45:51,772 epoch 5 - iter 356/894 - loss 0.04722101 - time (sec): 66.28 - samples/sec: 508.26 - lr: 0.000093 - momentum: 0.000000
2023-10-14 19:46:08,157 epoch 5 - iter 445/894 - loss 0.04470699 - time (sec): 82.66 - samples/sec: 507.78 - lr: 0.000092 - momentum: 0.000000
2023-10-14 19:46:26,783 epoch 5 - iter 534/894 - loss 0.04750186 - time (sec): 101.29 - samples/sec: 509.56 - lr: 0.000090 - momentum: 0.000000
2023-10-14 19:46:43,080 epoch 5 - iter 623/894 - loss 0.04827928 - time (sec): 117.58 - samples/sec: 508.77 - lr: 0.000088 - momentum: 0.000000
2023-10-14 19:46:59,856 epoch 5 - iter 712/894 - loss 0.05022362 - time (sec): 134.36 - samples/sec: 511.75 - lr: 0.000087 - momentum: 0.000000
2023-10-14 19:47:16,471 epoch 5 - iter 801/894 - loss 0.05136390 - time (sec): 150.97 - samples/sec: 513.00 - lr: 0.000085 - momentum: 0.000000
2023-10-14 19:47:32,967 epoch 5 - iter 890/894 - loss 0.05218871 - time (sec): 167.47 - samples/sec: 513.90 - lr: 0.000083 - momentum: 0.000000
2023-10-14 19:47:33,747 ----------------------------------------------------------------------------------------------------
2023-10-14 19:47:33,747 EPOCH 5 done: loss 0.0532 - lr: 0.000083
2023-10-14 19:47:58,630 DEV : loss 0.22192847728729248 - f1-score (micro avg)  0.7529
2023-10-14 19:47:58,656 saving best model
2023-10-14 19:48:01,734 ----------------------------------------------------------------------------------------------------
2023-10-14 19:48:18,652 epoch 6 - iter 89/894 - loss 0.02019978 - time (sec): 16.92 - samples/sec: 512.06 - lr: 0.000082 - momentum: 0.000000
2023-10-14 19:48:34,931 epoch 6 - iter 178/894 - loss 0.02444540 - time (sec): 33.19 - samples/sec: 511.14 - lr: 0.000080 - momentum: 0.000000
2023-10-14 19:48:51,471 epoch 6 - iter 267/894 - loss 0.02913359 - time (sec): 49.73 - samples/sec: 511.58 - lr: 0.000078 - momentum: 0.000000
2023-10-14 19:49:07,947 epoch 6 - iter 356/894 - loss 0.02770752 - time (sec): 66.21 - samples/sec: 514.07 - lr: 0.000077 - momentum: 0.000000
2023-10-14 19:49:24,294 epoch 6 - iter 445/894 - loss 0.02840373 - time (sec): 82.56 - samples/sec: 510.46 - lr: 0.000075 - momentum: 0.000000
2023-10-14 19:49:42,688 epoch 6 - iter 534/894 - loss 0.03156363 - time (sec): 100.95 - samples/sec: 513.38 - lr: 0.000073 - momentum: 0.000000
2023-10-14 19:49:59,604 epoch 6 - iter 623/894 - loss 0.03216127 - time (sec): 117.87 - samples/sec: 517.42 - lr: 0.000072 - momentum: 0.000000
2023-10-14 19:50:16,354 epoch 6 - iter 712/894 - loss 0.03207254 - time (sec): 134.62 - samples/sec: 515.54 - lr: 0.000070 - momentum: 0.000000
2023-10-14 19:50:32,553 epoch 6 - iter 801/894 - loss 0.03414003 - time (sec): 150.82 - samples/sec: 512.67 - lr: 0.000068 - momentum: 0.000000
2023-10-14 19:50:49,459 epoch 6 - iter 890/894 - loss 0.03363307 - time (sec): 167.72 - samples/sec: 513.86 - lr: 0.000067 - momentum: 0.000000
2023-10-14 19:50:50,160 ----------------------------------------------------------------------------------------------------
2023-10-14 19:50:50,160 EPOCH 6 done: loss 0.0336 - lr: 0.000067
2023-10-14 19:51:15,260 DEV : loss 0.2039400190114975 - f1-score (micro avg)  0.734
2023-10-14 19:51:15,286 ----------------------------------------------------------------------------------------------------
2023-10-14 19:51:33,717 epoch 7 - iter 89/894 - loss 0.03261519 - time (sec): 18.43 - samples/sec: 529.16 - lr: 0.000065 - momentum: 0.000000
2023-10-14 19:51:50,276 epoch 7 - iter 178/894 - loss 0.02744893 - time (sec): 34.99 - samples/sec: 526.69 - lr: 0.000063 - momentum: 0.000000
2023-10-14 19:52:06,313 epoch 7 - iter 267/894 - loss 0.03297580 - time (sec): 51.03 - samples/sec: 517.14 - lr: 0.000062 - momentum: 0.000000
2023-10-14 19:52:22,564 epoch 7 - iter 356/894 - loss 0.02788303 - time (sec): 67.28 - samples/sec: 516.79 - lr: 0.000060 - momentum: 0.000000
2023-10-14 19:52:39,137 epoch 7 - iter 445/894 - loss 0.02518628 - time (sec): 83.85 - samples/sec: 518.68 - lr: 0.000058 - momentum: 0.000000
2023-10-14 19:52:56,093 epoch 7 - iter 534/894 - loss 0.02351345 - time (sec): 100.81 - samples/sec: 520.30 - lr: 0.000057 - momentum: 0.000000
2023-10-14 19:53:12,453 epoch 7 - iter 623/894 - loss 0.02380859 - time (sec): 117.17 - samples/sec: 519.12 - lr: 0.000055 - momentum: 0.000000
2023-10-14 19:53:28,739 epoch 7 - iter 712/894 - loss 0.02328976 - time (sec): 133.45 - samples/sec: 519.08 - lr: 0.000053 - momentum: 0.000000
2023-10-14 19:53:45,255 epoch 7 - iter 801/894 - loss 0.02298144 - time (sec): 149.97 - samples/sec: 519.20 - lr: 0.000052 - momentum: 0.000000
2023-10-14 19:54:01,703 epoch 7 - iter 890/894 - loss 0.02165727 - time (sec): 166.42 - samples/sec: 517.90 - lr: 0.000050 - momentum: 0.000000
2023-10-14 19:54:02,416 ----------------------------------------------------------------------------------------------------
2023-10-14 19:54:02,416 EPOCH 7 done: loss 0.0218 - lr: 0.000050
2023-10-14 19:54:27,377 DEV : loss 0.22965174913406372 - f1-score (micro avg)  0.7541
2023-10-14 19:54:27,404 saving best model
2023-10-14 19:54:31,666 ----------------------------------------------------------------------------------------------------
2023-10-14 19:54:48,327 epoch 8 - iter 89/894 - loss 0.02163776 - time (sec): 16.66 - samples/sec: 497.44 - lr: 0.000048 - momentum: 0.000000
2023-10-14 19:55:05,147 epoch 8 - iter 178/894 - loss 0.02358524 - time (sec): 33.48 - samples/sec: 504.43 - lr: 0.000047 - momentum: 0.000000
2023-10-14 19:55:21,010 epoch 8 - iter 267/894 - loss 0.01955824 - time (sec): 49.34 - samples/sec: 505.08 - lr: 0.000045 - momentum: 0.000000
2023-10-14 19:55:37,848 epoch 8 - iter 356/894 - loss 0.01772872 - time (sec): 66.18 - samples/sec: 521.34 - lr: 0.000043 - momentum: 0.000000
2023-10-14 19:55:54,766 epoch 8 - iter 445/894 - loss 0.01662355 - time (sec): 83.10 - samples/sec: 525.63 - lr: 0.000042 - momentum: 0.000000
2023-10-14 19:56:10,901 epoch 8 - iter 534/894 - loss 0.01594968 - time (sec): 99.23 - samples/sec: 521.15 - lr: 0.000040 - momentum: 0.000000
2023-10-14 19:56:28,814 epoch 8 - iter 623/894 - loss 0.01663856 - time (sec): 117.15 - samples/sec: 517.27 - lr: 0.000038 - momentum: 0.000000
2023-10-14 19:56:45,270 epoch 8 - iter 712/894 - loss 0.01649056 - time (sec): 133.60 - samples/sec: 517.33 - lr: 0.000037 - momentum: 0.000000
2023-10-14 19:57:01,811 epoch 8 - iter 801/894 - loss 0.01565139 - time (sec): 150.14 - samples/sec: 515.49 - lr: 0.000035 - momentum: 0.000000
2023-10-14 19:57:18,575 epoch 8 - iter 890/894 - loss 0.01524975 - time (sec): 166.91 - samples/sec: 517.20 - lr: 0.000033 - momentum: 0.000000
2023-10-14 19:57:19,212 ----------------------------------------------------------------------------------------------------
2023-10-14 19:57:19,212 EPOCH 8 done: loss 0.0152 - lr: 0.000033
2023-10-14 19:57:44,067 DEV : loss 0.24145588278770447 - f1-score (micro avg)  0.7493
2023-10-14 19:57:44,093 ----------------------------------------------------------------------------------------------------
2023-10-14 19:58:02,689 epoch 9 - iter 89/894 - loss 0.01188183 - time (sec): 18.60 - samples/sec: 526.54 - lr: 0.000032 - momentum: 0.000000
2023-10-14 19:58:19,786 epoch 9 - iter 178/894 - loss 0.01216627 - time (sec): 35.69 - samples/sec: 531.41 - lr: 0.000030 - momentum: 0.000000
2023-10-14 19:58:36,437 epoch 9 - iter 267/894 - loss 0.00981157 - time (sec): 52.34 - samples/sec: 528.51 - lr: 0.000028 - momentum: 0.000000
2023-10-14 19:58:53,092 epoch 9 - iter 356/894 - loss 0.01043168 - time (sec): 69.00 - samples/sec: 530.26 - lr: 0.000027 - momentum: 0.000000
2023-10-14 19:59:09,041 epoch 9 - iter 445/894 - loss 0.01268618 - time (sec): 84.95 - samples/sec: 522.52 - lr: 0.000025 - momentum: 0.000000
2023-10-14 19:59:25,420 epoch 9 - iter 534/894 - loss 0.01178358 - time (sec): 101.33 - samples/sec: 519.39 - lr: 0.000023 - momentum: 0.000000
2023-10-14 19:59:41,540 epoch 9 - iter 623/894 - loss 0.01061545 - time (sec): 117.45 - samples/sec: 514.98 - lr: 0.000022 - momentum: 0.000000
2023-10-14 19:59:58,141 epoch 9 - iter 712/894 - loss 0.01084988 - time (sec): 134.05 - samples/sec: 515.67 - lr: 0.000020 - momentum: 0.000000
2023-10-14 20:00:14,586 epoch 9 - iter 801/894 - loss 0.01043690 - time (sec): 150.49 - samples/sec: 515.10 - lr: 0.000019 - momentum: 0.000000
2023-10-14 20:00:31,323 epoch 9 - iter 890/894 - loss 0.01119584 - time (sec): 167.23 - samples/sec: 515.84 - lr: 0.000017 - momentum: 0.000000
2023-10-14 20:00:31,987 ----------------------------------------------------------------------------------------------------
2023-10-14 20:00:31,987 EPOCH 9 done: loss 0.0112 - lr: 0.000017
2023-10-14 20:00:57,595 DEV : loss 0.2552002966403961 - f1-score (micro avg)  0.7442
2023-10-14 20:00:57,621 ----------------------------------------------------------------------------------------------------
2023-10-14 20:01:14,415 epoch 10 - iter 89/894 - loss 0.01578049 - time (sec): 16.79 - samples/sec: 525.81 - lr: 0.000015 - momentum: 0.000000
2023-10-14 20:01:30,506 epoch 10 - iter 178/894 - loss 0.01155094 - time (sec): 32.88 - samples/sec: 505.39 - lr: 0.000013 - momentum: 0.000000
2023-10-14 20:01:47,360 epoch 10 - iter 267/894 - loss 0.00867733 - time (sec): 49.74 - samples/sec: 503.14 - lr: 0.000012 - momentum: 0.000000
2023-10-14 20:02:04,763 epoch 10 - iter 356/894 - loss 0.00811223 - time (sec): 67.14 - samples/sec: 510.36 - lr: 0.000010 - momentum: 0.000000
2023-10-14 20:02:23,485 epoch 10 - iter 445/894 - loss 0.00905890 - time (sec): 85.86 - samples/sec: 514.33 - lr: 0.000008 - momentum: 0.000000
2023-10-14 20:02:40,436 epoch 10 - iter 534/894 - loss 0.00886633 - time (sec): 102.81 - samples/sec: 512.81 - lr: 0.000007 - momentum: 0.000000
2023-10-14 20:02:56,856 epoch 10 - iter 623/894 - loss 0.00827956 - time (sec): 119.23 - samples/sec: 507.53 - lr: 0.000005 - momentum: 0.000000
2023-10-14 20:03:12,609 epoch 10 - iter 712/894 - loss 0.00920467 - time (sec): 134.99 - samples/sec: 505.80 - lr: 0.000004 - momentum: 0.000000
2023-10-14 20:03:29,677 epoch 10 - iter 801/894 - loss 0.00867601 - time (sec): 152.05 - samples/sec: 510.91 - lr: 0.000002 - momentum: 0.000000
2023-10-14 20:03:45,854 epoch 10 - iter 890/894 - loss 0.01003975 - time (sec): 168.23 - samples/sec: 512.78 - lr: 0.000000 - momentum: 0.000000
2023-10-14 20:03:46,499 ----------------------------------------------------------------------------------------------------
2023-10-14 20:03:46,499 EPOCH 10 done: loss 0.0100 - lr: 0.000000
2023-10-14 20:04:11,876 DEV : loss 0.2602584660053253 - f1-score (micro avg)  0.7463
2023-10-14 20:04:12,521 ----------------------------------------------------------------------------------------------------
2023-10-14 20:04:12,523 Loading model from best epoch ...
2023-10-14 20:04:14,869 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
2023-10-14 20:04:36,455 
Results:
- F-score (micro) 0.7586
- F-score (macro) 0.6527
- Accuracy 0.6278

By class:
              precision    recall  f1-score   support

         loc     0.8445    0.8658    0.8550       596
        pers     0.6979    0.8048    0.7476       333
         org     0.4964    0.5227    0.5092       132
        prod     0.6000    0.5000    0.5455        66
        time     0.6000    0.6122    0.6061        49

   micro avg     0.7393    0.7789    0.7586      1176
   macro avg     0.6478    0.6611    0.6527      1176
weighted avg     0.7400    0.7789    0.7580      1176

2023-10-14 20:04:36,456 ----------------------------------------------------------------------------------------------------