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2023-10-12 12:43:36,931 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,933 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=13, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-12 12:43:36,933 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,933 MultiCorpus: 7936 train + 992 dev + 992 test sentences
 - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
2023-10-12 12:43:36,933 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,933 Train:  7936 sentences
2023-10-12 12:43:36,933         (train_with_dev=False, train_with_test=False)
2023-10-12 12:43:36,934 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,934 Training Params:
2023-10-12 12:43:36,934  - learning_rate: "0.00016" 
2023-10-12 12:43:36,934  - mini_batch_size: "4"
2023-10-12 12:43:36,934  - max_epochs: "10"
2023-10-12 12:43:36,934  - shuffle: "True"
2023-10-12 12:43:36,934 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,934 Plugins:
2023-10-12 12:43:36,934  - TensorboardLogger
2023-10-12 12:43:36,934  - LinearScheduler | warmup_fraction: '0.1'
2023-10-12 12:43:36,934 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,934 Final evaluation on model from best epoch (best-model.pt)
2023-10-12 12:43:36,934  - metric: "('micro avg', 'f1-score')"
2023-10-12 12:43:36,934 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,934 Computation:
2023-10-12 12:43:36,935  - compute on device: cuda:0
2023-10-12 12:43:36,935  - embedding storage: none
2023-10-12 12:43:36,935 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,935 Model training base path: "hmbench-icdar/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1"
2023-10-12 12:43:36,935 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,935 ----------------------------------------------------------------------------------------------------
2023-10-12 12:43:36,935 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-12 12:44:29,444 epoch 1 - iter 198/1984 - loss 2.57872388 - time (sec): 52.51 - samples/sec: 294.57 - lr: 0.000016 - momentum: 0.000000
2023-10-12 12:45:23,159 epoch 1 - iter 396/1984 - loss 2.42977020 - time (sec): 106.22 - samples/sec: 296.79 - lr: 0.000032 - momentum: 0.000000
2023-10-12 12:46:17,601 epoch 1 - iter 594/1984 - loss 2.09651161 - time (sec): 160.66 - samples/sec: 298.18 - lr: 0.000048 - momentum: 0.000000
2023-10-12 12:47:19,176 epoch 1 - iter 792/1984 - loss 1.74658842 - time (sec): 222.24 - samples/sec: 289.43 - lr: 0.000064 - momentum: 0.000000
2023-10-12 12:48:12,650 epoch 1 - iter 990/1984 - loss 1.47898744 - time (sec): 275.71 - samples/sec: 291.16 - lr: 0.000080 - momentum: 0.000000
2023-10-12 12:49:03,780 epoch 1 - iter 1188/1984 - loss 1.27073991 - time (sec): 326.84 - samples/sec: 296.79 - lr: 0.000096 - momentum: 0.000000
2023-10-12 12:49:58,979 epoch 1 - iter 1386/1984 - loss 1.11087775 - time (sec): 382.04 - samples/sec: 299.34 - lr: 0.000112 - momentum: 0.000000
2023-10-12 12:51:00,244 epoch 1 - iter 1584/1984 - loss 0.99932054 - time (sec): 443.31 - samples/sec: 295.65 - lr: 0.000128 - momentum: 0.000000
2023-10-12 12:51:58,406 epoch 1 - iter 1782/1984 - loss 0.91329740 - time (sec): 501.47 - samples/sec: 293.81 - lr: 0.000144 - momentum: 0.000000
2023-10-12 12:52:52,746 epoch 1 - iter 1980/1984 - loss 0.83963990 - time (sec): 555.81 - samples/sec: 294.49 - lr: 0.000160 - momentum: 0.000000
2023-10-12 12:52:53,861 ----------------------------------------------------------------------------------------------------
2023-10-12 12:52:53,862 EPOCH 1 done: loss 0.8384 - lr: 0.000160
2023-10-12 12:53:19,383 DEV : loss 0.14055970311164856 - f1-score (micro avg)  0.6499
2023-10-12 12:53:19,431 saving best model
2023-10-12 12:53:20,360 ----------------------------------------------------------------------------------------------------
2023-10-12 12:54:14,361 epoch 2 - iter 198/1984 - loss 0.16528251 - time (sec): 54.00 - samples/sec: 305.04 - lr: 0.000158 - momentum: 0.000000
2023-10-12 12:55:10,170 epoch 2 - iter 396/1984 - loss 0.14401493 - time (sec): 109.81 - samples/sec: 297.71 - lr: 0.000156 - momentum: 0.000000
2023-10-12 12:56:08,489 epoch 2 - iter 594/1984 - loss 0.13710523 - time (sec): 168.13 - samples/sec: 291.73 - lr: 0.000155 - momentum: 0.000000
2023-10-12 12:57:06,323 epoch 2 - iter 792/1984 - loss 0.13661458 - time (sec): 225.96 - samples/sec: 290.86 - lr: 0.000153 - momentum: 0.000000
2023-10-12 12:58:01,146 epoch 2 - iter 990/1984 - loss 0.13287502 - time (sec): 280.78 - samples/sec: 293.36 - lr: 0.000151 - momentum: 0.000000
2023-10-12 12:58:55,564 epoch 2 - iter 1188/1984 - loss 0.13028398 - time (sec): 335.20 - samples/sec: 293.98 - lr: 0.000149 - momentum: 0.000000
2023-10-12 12:59:49,914 epoch 2 - iter 1386/1984 - loss 0.13061150 - time (sec): 389.55 - samples/sec: 295.42 - lr: 0.000148 - momentum: 0.000000
2023-10-12 13:00:43,318 epoch 2 - iter 1584/1984 - loss 0.12716756 - time (sec): 442.96 - samples/sec: 295.30 - lr: 0.000146 - momentum: 0.000000
2023-10-12 13:01:41,149 epoch 2 - iter 1782/1984 - loss 0.12425762 - time (sec): 500.79 - samples/sec: 293.51 - lr: 0.000144 - momentum: 0.000000
2023-10-12 13:02:40,598 epoch 2 - iter 1980/1984 - loss 0.12247253 - time (sec): 560.24 - samples/sec: 291.85 - lr: 0.000142 - momentum: 0.000000
2023-10-12 13:02:41,892 ----------------------------------------------------------------------------------------------------
2023-10-12 13:02:41,893 EPOCH 2 done: loss 0.1223 - lr: 0.000142
2023-10-12 13:03:09,818 DEV : loss 0.0859101414680481 - f1-score (micro avg)  0.7275
2023-10-12 13:03:09,869 saving best model
2023-10-12 13:03:10,961 ----------------------------------------------------------------------------------------------------
2023-10-12 13:04:04,131 epoch 3 - iter 198/1984 - loss 0.07669844 - time (sec): 53.17 - samples/sec: 296.03 - lr: 0.000140 - momentum: 0.000000
2023-10-12 13:04:57,976 epoch 3 - iter 396/1984 - loss 0.07779340 - time (sec): 107.01 - samples/sec: 298.62 - lr: 0.000139 - momentum: 0.000000
2023-10-12 13:05:53,748 epoch 3 - iter 594/1984 - loss 0.07883378 - time (sec): 162.78 - samples/sec: 299.80 - lr: 0.000137 - momentum: 0.000000
2023-10-12 13:06:53,717 epoch 3 - iter 792/1984 - loss 0.07827758 - time (sec): 222.75 - samples/sec: 292.63 - lr: 0.000135 - momentum: 0.000000
2023-10-12 13:07:52,154 epoch 3 - iter 990/1984 - loss 0.07681269 - time (sec): 281.19 - samples/sec: 289.45 - lr: 0.000133 - momentum: 0.000000
2023-10-12 13:08:51,767 epoch 3 - iter 1188/1984 - loss 0.07663353 - time (sec): 340.80 - samples/sec: 285.97 - lr: 0.000132 - momentum: 0.000000
2023-10-12 13:09:47,186 epoch 3 - iter 1386/1984 - loss 0.07734261 - time (sec): 396.22 - samples/sec: 285.72 - lr: 0.000130 - momentum: 0.000000
2023-10-12 13:10:44,118 epoch 3 - iter 1584/1984 - loss 0.07604814 - time (sec): 453.15 - samples/sec: 288.78 - lr: 0.000128 - momentum: 0.000000
2023-10-12 13:11:40,292 epoch 3 - iter 1782/1984 - loss 0.07555546 - time (sec): 509.33 - samples/sec: 290.07 - lr: 0.000126 - momentum: 0.000000
2023-10-12 13:12:38,011 epoch 3 - iter 1980/1984 - loss 0.07603113 - time (sec): 567.05 - samples/sec: 288.67 - lr: 0.000125 - momentum: 0.000000
2023-10-12 13:12:39,271 ----------------------------------------------------------------------------------------------------
2023-10-12 13:12:39,271 EPOCH 3 done: loss 0.0760 - lr: 0.000125
2023-10-12 13:13:08,663 DEV : loss 0.10180744528770447 - f1-score (micro avg)  0.7625
2023-10-12 13:13:08,710 saving best model
2023-10-12 13:13:11,483 ----------------------------------------------------------------------------------------------------
2023-10-12 13:14:09,654 epoch 4 - iter 198/1984 - loss 0.04829137 - time (sec): 58.16 - samples/sec: 294.70 - lr: 0.000123 - momentum: 0.000000
2023-10-12 13:15:02,659 epoch 4 - iter 396/1984 - loss 0.05173970 - time (sec): 111.17 - samples/sec: 306.37 - lr: 0.000121 - momentum: 0.000000
2023-10-12 13:15:57,996 epoch 4 - iter 594/1984 - loss 0.05651052 - time (sec): 166.51 - samples/sec: 300.16 - lr: 0.000119 - momentum: 0.000000
2023-10-12 13:16:53,481 epoch 4 - iter 792/1984 - loss 0.05517049 - time (sec): 221.99 - samples/sec: 296.01 - lr: 0.000117 - momentum: 0.000000
2023-10-12 13:17:52,233 epoch 4 - iter 990/1984 - loss 0.05634728 - time (sec): 280.74 - samples/sec: 291.99 - lr: 0.000116 - momentum: 0.000000
2023-10-12 13:18:49,209 epoch 4 - iter 1188/1984 - loss 0.05607999 - time (sec): 337.72 - samples/sec: 290.31 - lr: 0.000114 - momentum: 0.000000
2023-10-12 13:19:46,175 epoch 4 - iter 1386/1984 - loss 0.05551788 - time (sec): 394.69 - samples/sec: 290.32 - lr: 0.000112 - momentum: 0.000000
2023-10-12 13:20:44,641 epoch 4 - iter 1584/1984 - loss 0.05700501 - time (sec): 453.15 - samples/sec: 288.52 - lr: 0.000110 - momentum: 0.000000
2023-10-12 13:21:46,416 epoch 4 - iter 1782/1984 - loss 0.05565357 - time (sec): 514.93 - samples/sec: 286.92 - lr: 0.000109 - momentum: 0.000000
2023-10-12 13:22:42,025 epoch 4 - iter 1980/1984 - loss 0.05607850 - time (sec): 570.53 - samples/sec: 287.00 - lr: 0.000107 - momentum: 0.000000
2023-10-12 13:22:43,130 ----------------------------------------------------------------------------------------------------
2023-10-12 13:22:43,130 EPOCH 4 done: loss 0.0560 - lr: 0.000107
2023-10-12 13:23:11,653 DEV : loss 0.12707574665546417 - f1-score (micro avg)  0.7592
2023-10-12 13:23:11,704 ----------------------------------------------------------------------------------------------------
2023-10-12 13:24:12,175 epoch 5 - iter 198/1984 - loss 0.04138412 - time (sec): 60.47 - samples/sec: 267.17 - lr: 0.000105 - momentum: 0.000000
2023-10-12 13:25:08,068 epoch 5 - iter 396/1984 - loss 0.03535050 - time (sec): 116.36 - samples/sec: 277.92 - lr: 0.000103 - momentum: 0.000000
2023-10-12 13:26:04,766 epoch 5 - iter 594/1984 - loss 0.03648738 - time (sec): 173.06 - samples/sec: 281.12 - lr: 0.000101 - momentum: 0.000000
2023-10-12 13:27:01,501 epoch 5 - iter 792/1984 - loss 0.03686215 - time (sec): 229.79 - samples/sec: 283.29 - lr: 0.000100 - momentum: 0.000000
2023-10-12 13:27:53,816 epoch 5 - iter 990/1984 - loss 0.03625039 - time (sec): 282.11 - samples/sec: 288.19 - lr: 0.000098 - momentum: 0.000000
2023-10-12 13:28:46,678 epoch 5 - iter 1188/1984 - loss 0.03960505 - time (sec): 334.97 - samples/sec: 292.34 - lr: 0.000096 - momentum: 0.000000
2023-10-12 13:29:40,681 epoch 5 - iter 1386/1984 - loss 0.04001738 - time (sec): 388.97 - samples/sec: 294.20 - lr: 0.000094 - momentum: 0.000000
2023-10-12 13:30:35,002 epoch 5 - iter 1584/1984 - loss 0.04082094 - time (sec): 443.29 - samples/sec: 296.26 - lr: 0.000093 - momentum: 0.000000
2023-10-12 13:31:28,676 epoch 5 - iter 1782/1984 - loss 0.04148610 - time (sec): 496.97 - samples/sec: 297.49 - lr: 0.000091 - momentum: 0.000000
2023-10-12 13:32:21,692 epoch 5 - iter 1980/1984 - loss 0.04251132 - time (sec): 549.99 - samples/sec: 297.50 - lr: 0.000089 - momentum: 0.000000
2023-10-12 13:32:22,804 ----------------------------------------------------------------------------------------------------
2023-10-12 13:32:22,804 EPOCH 5 done: loss 0.0425 - lr: 0.000089
2023-10-12 13:32:48,621 DEV : loss 0.14472655951976776 - f1-score (micro avg)  0.7461
2023-10-12 13:32:48,680 ----------------------------------------------------------------------------------------------------
2023-10-12 13:33:42,335 epoch 6 - iter 198/1984 - loss 0.02627233 - time (sec): 53.65 - samples/sec: 291.97 - lr: 0.000087 - momentum: 0.000000
2023-10-12 13:34:34,598 epoch 6 - iter 396/1984 - loss 0.02760424 - time (sec): 105.92 - samples/sec: 302.33 - lr: 0.000085 - momentum: 0.000000
2023-10-12 13:35:26,843 epoch 6 - iter 594/1984 - loss 0.02865242 - time (sec): 158.16 - samples/sec: 304.60 - lr: 0.000084 - momentum: 0.000000
2023-10-12 13:36:21,745 epoch 6 - iter 792/1984 - loss 0.02818054 - time (sec): 213.06 - samples/sec: 305.87 - lr: 0.000082 - momentum: 0.000000
2023-10-12 13:37:16,009 epoch 6 - iter 990/1984 - loss 0.02691623 - time (sec): 267.33 - samples/sec: 303.51 - lr: 0.000080 - momentum: 0.000000
2023-10-12 13:38:10,624 epoch 6 - iter 1188/1984 - loss 0.02670693 - time (sec): 321.94 - samples/sec: 303.88 - lr: 0.000078 - momentum: 0.000000
2023-10-12 13:39:05,104 epoch 6 - iter 1386/1984 - loss 0.02728730 - time (sec): 376.42 - samples/sec: 304.70 - lr: 0.000077 - momentum: 0.000000
2023-10-12 13:40:00,408 epoch 6 - iter 1584/1984 - loss 0.02914735 - time (sec): 431.73 - samples/sec: 302.80 - lr: 0.000075 - momentum: 0.000000
2023-10-12 13:40:54,644 epoch 6 - iter 1782/1984 - loss 0.02922904 - time (sec): 485.96 - samples/sec: 303.18 - lr: 0.000073 - momentum: 0.000000
2023-10-12 13:41:50,958 epoch 6 - iter 1980/1984 - loss 0.02961781 - time (sec): 542.28 - samples/sec: 301.72 - lr: 0.000071 - momentum: 0.000000
2023-10-12 13:41:52,135 ----------------------------------------------------------------------------------------------------
2023-10-12 13:41:52,136 EPOCH 6 done: loss 0.0297 - lr: 0.000071
2023-10-12 13:42:19,073 DEV : loss 0.17014847695827484 - f1-score (micro avg)  0.7612
2023-10-12 13:42:19,119 ----------------------------------------------------------------------------------------------------
2023-10-12 13:43:11,962 epoch 7 - iter 198/1984 - loss 0.01757743 - time (sec): 52.84 - samples/sec: 308.61 - lr: 0.000069 - momentum: 0.000000
2023-10-12 13:44:03,780 epoch 7 - iter 396/1984 - loss 0.02000202 - time (sec): 104.66 - samples/sec: 315.03 - lr: 0.000068 - momentum: 0.000000
2023-10-12 13:44:57,934 epoch 7 - iter 594/1984 - loss 0.01900677 - time (sec): 158.81 - samples/sec: 308.16 - lr: 0.000066 - momentum: 0.000000
2023-10-12 13:45:54,285 epoch 7 - iter 792/1984 - loss 0.02132227 - time (sec): 215.16 - samples/sec: 305.10 - lr: 0.000064 - momentum: 0.000000
2023-10-12 13:46:51,268 epoch 7 - iter 990/1984 - loss 0.02065637 - time (sec): 272.15 - samples/sec: 299.69 - lr: 0.000062 - momentum: 0.000000
2023-10-12 13:47:50,086 epoch 7 - iter 1188/1984 - loss 0.02083419 - time (sec): 330.96 - samples/sec: 295.66 - lr: 0.000061 - momentum: 0.000000
2023-10-12 13:48:47,692 epoch 7 - iter 1386/1984 - loss 0.02064694 - time (sec): 388.57 - samples/sec: 295.32 - lr: 0.000059 - momentum: 0.000000
2023-10-12 13:49:41,352 epoch 7 - iter 1584/1984 - loss 0.02141756 - time (sec): 442.23 - samples/sec: 293.14 - lr: 0.000057 - momentum: 0.000000
2023-10-12 13:50:36,232 epoch 7 - iter 1782/1984 - loss 0.02092148 - time (sec): 497.11 - samples/sec: 294.32 - lr: 0.000055 - momentum: 0.000000
2023-10-12 13:51:31,622 epoch 7 - iter 1980/1984 - loss 0.02052943 - time (sec): 552.50 - samples/sec: 296.11 - lr: 0.000053 - momentum: 0.000000
2023-10-12 13:51:32,742 ----------------------------------------------------------------------------------------------------
2023-10-12 13:51:32,742 EPOCH 7 done: loss 0.0205 - lr: 0.000053
2023-10-12 13:51:59,673 DEV : loss 0.2085985541343689 - f1-score (micro avg)  0.7496
2023-10-12 13:51:59,718 ----------------------------------------------------------------------------------------------------
2023-10-12 13:52:54,707 epoch 8 - iter 198/1984 - loss 0.01667937 - time (sec): 54.99 - samples/sec: 307.84 - lr: 0.000052 - momentum: 0.000000
2023-10-12 13:53:50,334 epoch 8 - iter 396/1984 - loss 0.01438657 - time (sec): 110.61 - samples/sec: 291.56 - lr: 0.000050 - momentum: 0.000000
2023-10-12 13:54:45,708 epoch 8 - iter 594/1984 - loss 0.01421150 - time (sec): 165.99 - samples/sec: 287.68 - lr: 0.000048 - momentum: 0.000000
2023-10-12 13:55:41,536 epoch 8 - iter 792/1984 - loss 0.01432064 - time (sec): 221.82 - samples/sec: 287.50 - lr: 0.000046 - momentum: 0.000000
2023-10-12 13:56:38,668 epoch 8 - iter 990/1984 - loss 0.01369389 - time (sec): 278.95 - samples/sec: 288.69 - lr: 0.000045 - momentum: 0.000000
2023-10-12 13:57:37,033 epoch 8 - iter 1188/1984 - loss 0.01537915 - time (sec): 337.31 - samples/sec: 290.16 - lr: 0.000043 - momentum: 0.000000
2023-10-12 13:58:34,687 epoch 8 - iter 1386/1984 - loss 0.01524152 - time (sec): 394.97 - samples/sec: 288.48 - lr: 0.000041 - momentum: 0.000000
2023-10-12 13:59:30,605 epoch 8 - iter 1584/1984 - loss 0.01517160 - time (sec): 450.88 - samples/sec: 289.72 - lr: 0.000039 - momentum: 0.000000
2023-10-12 14:00:28,078 epoch 8 - iter 1782/1984 - loss 0.01553226 - time (sec): 508.36 - samples/sec: 288.49 - lr: 0.000037 - momentum: 0.000000
2023-10-12 14:01:27,127 epoch 8 - iter 1980/1984 - loss 0.01498313 - time (sec): 567.41 - samples/sec: 288.59 - lr: 0.000036 - momentum: 0.000000
2023-10-12 14:01:28,169 ----------------------------------------------------------------------------------------------------
2023-10-12 14:01:28,170 EPOCH 8 done: loss 0.0150 - lr: 0.000036
2023-10-12 14:01:58,438 DEV : loss 0.21621058881282806 - f1-score (micro avg)  0.757
2023-10-12 14:01:58,492 ----------------------------------------------------------------------------------------------------
2023-10-12 14:02:56,912 epoch 9 - iter 198/1984 - loss 0.01321939 - time (sec): 58.42 - samples/sec: 294.64 - lr: 0.000034 - momentum: 0.000000
2023-10-12 14:03:55,154 epoch 9 - iter 396/1984 - loss 0.01312927 - time (sec): 116.66 - samples/sec: 288.45 - lr: 0.000032 - momentum: 0.000000
2023-10-12 14:04:49,193 epoch 9 - iter 594/1984 - loss 0.01098511 - time (sec): 170.70 - samples/sec: 296.55 - lr: 0.000030 - momentum: 0.000000
2023-10-12 14:05:43,288 epoch 9 - iter 792/1984 - loss 0.01215431 - time (sec): 224.79 - samples/sec: 294.40 - lr: 0.000029 - momentum: 0.000000
2023-10-12 14:06:43,526 epoch 9 - iter 990/1984 - loss 0.01151383 - time (sec): 285.03 - samples/sec: 290.06 - lr: 0.000027 - momentum: 0.000000
2023-10-12 14:07:40,132 epoch 9 - iter 1188/1984 - loss 0.01094490 - time (sec): 341.64 - samples/sec: 289.99 - lr: 0.000025 - momentum: 0.000000
2023-10-12 14:08:35,756 epoch 9 - iter 1386/1984 - loss 0.01030513 - time (sec): 397.26 - samples/sec: 291.31 - lr: 0.000023 - momentum: 0.000000
2023-10-12 14:09:31,788 epoch 9 - iter 1584/1984 - loss 0.01081881 - time (sec): 453.29 - samples/sec: 289.50 - lr: 0.000021 - momentum: 0.000000
2023-10-12 14:10:30,471 epoch 9 - iter 1782/1984 - loss 0.01121344 - time (sec): 511.98 - samples/sec: 287.80 - lr: 0.000020 - momentum: 0.000000
2023-10-12 14:11:25,942 epoch 9 - iter 1980/1984 - loss 0.01074014 - time (sec): 567.45 - samples/sec: 288.38 - lr: 0.000018 - momentum: 0.000000
2023-10-12 14:11:27,169 ----------------------------------------------------------------------------------------------------
2023-10-12 14:11:27,169 EPOCH 9 done: loss 0.0107 - lr: 0.000018
2023-10-12 14:11:55,640 DEV : loss 0.22689764201641083 - f1-score (micro avg)  0.7614
2023-10-12 14:11:55,690 ----------------------------------------------------------------------------------------------------
2023-10-12 14:12:53,298 epoch 10 - iter 198/1984 - loss 0.00665429 - time (sec): 57.61 - samples/sec: 289.71 - lr: 0.000016 - momentum: 0.000000
2023-10-12 14:13:51,316 epoch 10 - iter 396/1984 - loss 0.00646911 - time (sec): 115.62 - samples/sec: 283.40 - lr: 0.000014 - momentum: 0.000000
2023-10-12 14:14:49,815 epoch 10 - iter 594/1984 - loss 0.00659430 - time (sec): 174.12 - samples/sec: 283.69 - lr: 0.000013 - momentum: 0.000000
2023-10-12 14:15:46,196 epoch 10 - iter 792/1984 - loss 0.00696637 - time (sec): 230.50 - samples/sec: 286.08 - lr: 0.000011 - momentum: 0.000000
2023-10-12 14:16:42,436 epoch 10 - iter 990/1984 - loss 0.00672449 - time (sec): 286.74 - samples/sec: 287.82 - lr: 0.000009 - momentum: 0.000000
2023-10-12 14:17:38,147 epoch 10 - iter 1188/1984 - loss 0.00643333 - time (sec): 342.45 - samples/sec: 287.35 - lr: 0.000007 - momentum: 0.000000
2023-10-12 14:18:34,399 epoch 10 - iter 1386/1984 - loss 0.00719725 - time (sec): 398.71 - samples/sec: 286.32 - lr: 0.000005 - momentum: 0.000000
2023-10-12 14:19:30,932 epoch 10 - iter 1584/1984 - loss 0.00759850 - time (sec): 455.24 - samples/sec: 286.89 - lr: 0.000004 - momentum: 0.000000
2023-10-12 14:20:26,329 epoch 10 - iter 1782/1984 - loss 0.00756874 - time (sec): 510.64 - samples/sec: 288.34 - lr: 0.000002 - momentum: 0.000000
2023-10-12 14:21:23,506 epoch 10 - iter 1980/1984 - loss 0.00842761 - time (sec): 567.81 - samples/sec: 288.42 - lr: 0.000000 - momentum: 0.000000
2023-10-12 14:21:24,509 ----------------------------------------------------------------------------------------------------
2023-10-12 14:21:24,509 EPOCH 10 done: loss 0.0085 - lr: 0.000000
2023-10-12 14:21:51,035 DEV : loss 0.2328162044286728 - f1-score (micro avg)  0.7578
2023-10-12 14:21:52,053 ----------------------------------------------------------------------------------------------------
2023-10-12 14:21:52,056 Loading model from best epoch ...
2023-10-12 14:21:56,378 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
2023-10-12 14:22:22,594 
Results:
- F-score (micro) 0.7368
- F-score (macro) 0.6608
- Accuracy 0.6204

By class:
              precision    recall  f1-score   support

         LOC     0.8380    0.7817    0.8088       655
         PER     0.6341    0.8161    0.7137       223
         ORG     0.4125    0.5197    0.4599       127

   micro avg     0.7183    0.7562    0.7368      1005
   macro avg     0.6282    0.7058    0.6608      1005
weighted avg     0.7390    0.7562    0.7436      1005

2023-10-12 14:22:22,594 ----------------------------------------------------------------------------------------------------