2023-09-03 19:29:16,528 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,529 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 768)
        (position_embeddings): Embedding(512, 768)
        (token_type_embeddings): Embedding(2, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-11): 12 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=768, out_features=768, bias=True)
                (key): Linear(in_features=768, out_features=768, bias=True)
                (value): Linear(in_features=768, out_features=768, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=768, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=3072, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-09-03 19:29:16,529 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,529 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-09-03 19:29:16,529 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,529 Train:  3575 sentences
2023-09-03 19:29:16,530         (train_with_dev=False, train_with_test=False)
2023-09-03 19:29:16,530 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,530 Training Params:
2023-09-03 19:29:16,530  - learning_rate: "3e-05" 
2023-09-03 19:29:16,530  - mini_batch_size: "8"
2023-09-03 19:29:16,530  - max_epochs: "10"
2023-09-03 19:29:16,530  - shuffle: "True"
2023-09-03 19:29:16,530 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,530 Plugins:
2023-09-03 19:29:16,530  - LinearScheduler | warmup_fraction: '0.1'
2023-09-03 19:29:16,530 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,530 Final evaluation on model from best epoch (best-model.pt)
2023-09-03 19:29:16,530  - metric: "('micro avg', 'f1-score')"
2023-09-03 19:29:16,530 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,530 Computation:
2023-09-03 19:29:16,530  - compute on device: cuda:0
2023-09-03 19:29:16,530  - embedding storage: none
2023-09-03 19:29:16,530 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,530 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
2023-09-03 19:29:16,530 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:16,530 ----------------------------------------------------------------------------------------------------
2023-09-03 19:29:23,448 epoch 1 - iter 44/447 - loss 3.19007209 - time (sec): 6.92 - samples/sec: 1152.59 - lr: 0.000003 - momentum: 0.000000
2023-09-03 19:29:30,626 epoch 1 - iter 88/447 - loss 2.53442556 - time (sec): 14.09 - samples/sec: 1130.73 - lr: 0.000006 - momentum: 0.000000
2023-09-03 19:29:38,072 epoch 1 - iter 132/447 - loss 1.80646532 - time (sec): 21.54 - samples/sec: 1146.61 - lr: 0.000009 - momentum: 0.000000
2023-09-03 19:29:45,084 epoch 1 - iter 176/447 - loss 1.48740869 - time (sec): 28.55 - samples/sec: 1142.99 - lr: 0.000012 - momentum: 0.000000
2023-09-03 19:29:52,496 epoch 1 - iter 220/447 - loss 1.25877196 - time (sec): 35.96 - samples/sec: 1147.28 - lr: 0.000015 - momentum: 0.000000
2023-09-03 19:30:01,853 epoch 1 - iter 264/447 - loss 1.07980864 - time (sec): 45.32 - samples/sec: 1138.90 - lr: 0.000018 - momentum: 0.000000
2023-09-03 19:30:09,492 epoch 1 - iter 308/447 - loss 0.97748992 - time (sec): 52.96 - samples/sec: 1125.74 - lr: 0.000021 - momentum: 0.000000
2023-09-03 19:30:16,350 epoch 1 - iter 352/447 - loss 0.88943186 - time (sec): 59.82 - samples/sec: 1137.59 - lr: 0.000024 - momentum: 0.000000
2023-09-03 19:30:23,843 epoch 1 - iter 396/447 - loss 0.82223418 - time (sec): 67.31 - samples/sec: 1135.66 - lr: 0.000027 - momentum: 0.000000
2023-09-03 19:30:30,904 epoch 1 - iter 440/447 - loss 0.76505808 - time (sec): 74.37 - samples/sec: 1138.13 - lr: 0.000029 - momentum: 0.000000
2023-09-03 19:30:32,376 ----------------------------------------------------------------------------------------------------
2023-09-03 19:30:32,376 EPOCH 1 done: loss 0.7533 - lr: 0.000029
2023-09-03 19:30:42,710 DEV : loss 0.2009868174791336 - f1-score (micro avg)  0.55
2023-09-03 19:30:42,736 saving best model
2023-09-03 19:30:43,196 ----------------------------------------------------------------------------------------------------
2023-09-03 19:30:50,362 epoch 2 - iter 44/447 - loss 0.24012949 - time (sec): 7.16 - samples/sec: 1189.44 - lr: 0.000030 - momentum: 0.000000
2023-09-03 19:30:57,854 epoch 2 - iter 88/447 - loss 0.22293035 - time (sec): 14.66 - samples/sec: 1151.20 - lr: 0.000029 - momentum: 0.000000
2023-09-03 19:31:04,563 epoch 2 - iter 132/447 - loss 0.20286926 - time (sec): 21.37 - samples/sec: 1170.63 - lr: 0.000029 - momentum: 0.000000
2023-09-03 19:31:11,883 epoch 2 - iter 176/447 - loss 0.19655009 - time (sec): 28.69 - samples/sec: 1177.57 - lr: 0.000029 - momentum: 0.000000
2023-09-03 19:31:18,584 epoch 2 - iter 220/447 - loss 0.18797064 - time (sec): 35.39 - samples/sec: 1174.86 - lr: 0.000028 - momentum: 0.000000
2023-09-03 19:31:26,678 epoch 2 - iter 264/447 - loss 0.18041665 - time (sec): 43.48 - samples/sec: 1170.98 - lr: 0.000028 - momentum: 0.000000
2023-09-03 19:31:33,651 epoch 2 - iter 308/447 - loss 0.17434352 - time (sec): 50.45 - samples/sec: 1174.62 - lr: 0.000028 - momentum: 0.000000
2023-09-03 19:31:41,504 epoch 2 - iter 352/447 - loss 0.17006885 - time (sec): 58.31 - samples/sec: 1176.33 - lr: 0.000027 - momentum: 0.000000
2023-09-03 19:31:49,385 epoch 2 - iter 396/447 - loss 0.16869020 - time (sec): 66.19 - samples/sec: 1162.87 - lr: 0.000027 - momentum: 0.000000
2023-09-03 19:31:56,494 epoch 2 - iter 440/447 - loss 0.16625334 - time (sec): 73.30 - samples/sec: 1162.89 - lr: 0.000027 - momentum: 0.000000
2023-09-03 19:31:57,490 ----------------------------------------------------------------------------------------------------
2023-09-03 19:31:57,490 EPOCH 2 done: loss 0.1657 - lr: 0.000027
2023-09-03 19:32:10,118 DEV : loss 0.12543398141860962 - f1-score (micro avg)  0.6997
2023-09-03 19:32:10,144 saving best model
2023-09-03 19:32:11,458 ----------------------------------------------------------------------------------------------------
2023-09-03 19:32:19,222 epoch 3 - iter 44/447 - loss 0.09271356 - time (sec): 7.76 - samples/sec: 1097.05 - lr: 0.000026 - momentum: 0.000000
2023-09-03 19:32:27,280 epoch 3 - iter 88/447 - loss 0.08576467 - time (sec): 15.82 - samples/sec: 1144.61 - lr: 0.000026 - momentum: 0.000000
2023-09-03 19:32:35,115 epoch 3 - iter 132/447 - loss 0.08869787 - time (sec): 23.65 - samples/sec: 1151.56 - lr: 0.000026 - momentum: 0.000000
2023-09-03 19:32:42,719 epoch 3 - iter 176/447 - loss 0.08093044 - time (sec): 31.26 - samples/sec: 1156.06 - lr: 0.000025 - momentum: 0.000000
2023-09-03 19:32:50,458 epoch 3 - iter 220/447 - loss 0.09036291 - time (sec): 39.00 - samples/sec: 1154.21 - lr: 0.000025 - momentum: 0.000000
2023-09-03 19:32:57,257 epoch 3 - iter 264/447 - loss 0.09249644 - time (sec): 45.80 - samples/sec: 1148.73 - lr: 0.000025 - momentum: 0.000000
2023-09-03 19:33:03,973 epoch 3 - iter 308/447 - loss 0.08893785 - time (sec): 52.51 - samples/sec: 1158.07 - lr: 0.000024 - momentum: 0.000000
2023-09-03 19:33:10,650 epoch 3 - iter 352/447 - loss 0.08855651 - time (sec): 59.19 - samples/sec: 1162.75 - lr: 0.000024 - momentum: 0.000000
2023-09-03 19:33:18,009 epoch 3 - iter 396/447 - loss 0.08761760 - time (sec): 66.55 - samples/sec: 1158.54 - lr: 0.000024 - momentum: 0.000000
2023-09-03 19:33:24,853 epoch 3 - iter 440/447 - loss 0.08937895 - time (sec): 73.39 - samples/sec: 1161.78 - lr: 0.000023 - momentum: 0.000000
2023-09-03 19:33:25,879 ----------------------------------------------------------------------------------------------------
2023-09-03 19:33:25,879 EPOCH 3 done: loss 0.0896 - lr: 0.000023
2023-09-03 19:33:38,507 DEV : loss 0.11516160517930984 - f1-score (micro avg)  0.7475
2023-09-03 19:33:38,533 saving best model
2023-09-03 19:33:39,852 ----------------------------------------------------------------------------------------------------
2023-09-03 19:33:46,198 epoch 4 - iter 44/447 - loss 0.05182455 - time (sec): 6.34 - samples/sec: 1190.63 - lr: 0.000023 - momentum: 0.000000
2023-09-03 19:33:54,167 epoch 4 - iter 88/447 - loss 0.04665842 - time (sec): 14.31 - samples/sec: 1175.05 - lr: 0.000023 - momentum: 0.000000
2023-09-03 19:34:01,223 epoch 4 - iter 132/447 - loss 0.05436150 - time (sec): 21.37 - samples/sec: 1173.70 - lr: 0.000022 - momentum: 0.000000
2023-09-03 19:34:08,302 epoch 4 - iter 176/447 - loss 0.05361792 - time (sec): 28.45 - samples/sec: 1182.21 - lr: 0.000022 - momentum: 0.000000
2023-09-03 19:34:14,821 epoch 4 - iter 220/447 - loss 0.05472694 - time (sec): 34.97 - samples/sec: 1174.09 - lr: 0.000022 - momentum: 0.000000
2023-09-03 19:34:23,348 epoch 4 - iter 264/447 - loss 0.05132701 - time (sec): 43.50 - samples/sec: 1172.89 - lr: 0.000021 - momentum: 0.000000
2023-09-03 19:34:31,640 epoch 4 - iter 308/447 - loss 0.05098314 - time (sec): 51.79 - samples/sec: 1154.36 - lr: 0.000021 - momentum: 0.000000
2023-09-03 19:34:38,448 epoch 4 - iter 352/447 - loss 0.05076573 - time (sec): 58.60 - samples/sec: 1157.75 - lr: 0.000021 - momentum: 0.000000
2023-09-03 19:34:46,013 epoch 4 - iter 396/447 - loss 0.04989549 - time (sec): 66.16 - samples/sec: 1165.38 - lr: 0.000020 - momentum: 0.000000
2023-09-03 19:34:52,977 epoch 4 - iter 440/447 - loss 0.04980707 - time (sec): 73.12 - samples/sec: 1167.43 - lr: 0.000020 - momentum: 0.000000
2023-09-03 19:34:53,997 ----------------------------------------------------------------------------------------------------
2023-09-03 19:34:53,997 EPOCH 4 done: loss 0.0495 - lr: 0.000020
2023-09-03 19:35:06,658 DEV : loss 0.14562876522541046 - f1-score (micro avg)  0.7768
2023-09-03 19:35:06,684 saving best model
2023-09-03 19:35:08,033 ----------------------------------------------------------------------------------------------------
2023-09-03 19:35:15,237 epoch 5 - iter 44/447 - loss 0.04532975 - time (sec): 7.20 - samples/sec: 1122.51 - lr: 0.000020 - momentum: 0.000000
2023-09-03 19:35:22,154 epoch 5 - iter 88/447 - loss 0.03636320 - time (sec): 14.12 - samples/sec: 1125.32 - lr: 0.000019 - momentum: 0.000000
2023-09-03 19:35:29,738 epoch 5 - iter 132/447 - loss 0.03369699 - time (sec): 21.70 - samples/sec: 1129.49 - lr: 0.000019 - momentum: 0.000000
2023-09-03 19:35:36,874 epoch 5 - iter 176/447 - loss 0.03420293 - time (sec): 28.84 - samples/sec: 1132.14 - lr: 0.000019 - momentum: 0.000000
2023-09-03 19:35:45,113 epoch 5 - iter 220/447 - loss 0.03139251 - time (sec): 37.08 - samples/sec: 1146.34 - lr: 0.000018 - momentum: 0.000000
2023-09-03 19:35:51,745 epoch 5 - iter 264/447 - loss 0.03132039 - time (sec): 43.71 - samples/sec: 1162.95 - lr: 0.000018 - momentum: 0.000000
2023-09-03 19:35:59,757 epoch 5 - iter 308/447 - loss 0.03075044 - time (sec): 51.72 - samples/sec: 1155.52 - lr: 0.000018 - momentum: 0.000000
2023-09-03 19:36:08,207 epoch 5 - iter 352/447 - loss 0.03108055 - time (sec): 60.17 - samples/sec: 1147.45 - lr: 0.000017 - momentum: 0.000000
2023-09-03 19:36:15,447 epoch 5 - iter 396/447 - loss 0.03125927 - time (sec): 67.41 - samples/sec: 1153.15 - lr: 0.000017 - momentum: 0.000000
2023-09-03 19:36:21,846 epoch 5 - iter 440/447 - loss 0.03113833 - time (sec): 73.81 - samples/sec: 1154.74 - lr: 0.000017 - momentum: 0.000000
2023-09-03 19:36:22,896 ----------------------------------------------------------------------------------------------------
2023-09-03 19:36:22,896 EPOCH 5 done: loss 0.0309 - lr: 0.000017
2023-09-03 19:36:35,956 DEV : loss 0.16441383957862854 - f1-score (micro avg)  0.7662
2023-09-03 19:36:35,983 ----------------------------------------------------------------------------------------------------
2023-09-03 19:36:43,439 epoch 6 - iter 44/447 - loss 0.02358977 - time (sec): 7.45 - samples/sec: 1151.83 - lr: 0.000016 - momentum: 0.000000
2023-09-03 19:36:50,732 epoch 6 - iter 88/447 - loss 0.02454260 - time (sec): 14.75 - samples/sec: 1137.84 - lr: 0.000016 - momentum: 0.000000
2023-09-03 19:36:57,815 epoch 6 - iter 132/447 - loss 0.02258539 - time (sec): 21.83 - samples/sec: 1136.93 - lr: 0.000016 - momentum: 0.000000
2023-09-03 19:37:05,179 epoch 6 - iter 176/447 - loss 0.02114354 - time (sec): 29.19 - samples/sec: 1136.33 - lr: 0.000015 - momentum: 0.000000
2023-09-03 19:37:13,071 epoch 6 - iter 220/447 - loss 0.02077686 - time (sec): 37.09 - samples/sec: 1122.22 - lr: 0.000015 - momentum: 0.000000
2023-09-03 19:37:20,210 epoch 6 - iter 264/447 - loss 0.02015972 - time (sec): 44.23 - samples/sec: 1129.70 - lr: 0.000015 - momentum: 0.000000
2023-09-03 19:37:27,081 epoch 6 - iter 308/447 - loss 0.02051455 - time (sec): 51.10 - samples/sec: 1131.68 - lr: 0.000014 - momentum: 0.000000
2023-09-03 19:37:34,883 epoch 6 - iter 352/447 - loss 0.02170789 - time (sec): 58.90 - samples/sec: 1127.99 - lr: 0.000014 - momentum: 0.000000
2023-09-03 19:37:43,150 epoch 6 - iter 396/447 - loss 0.02174907 - time (sec): 67.17 - samples/sec: 1117.14 - lr: 0.000014 - momentum: 0.000000
2023-09-03 19:37:52,285 epoch 6 - iter 440/447 - loss 0.02109897 - time (sec): 76.30 - samples/sec: 1114.19 - lr: 0.000013 - momentum: 0.000000
2023-09-03 19:37:53,648 ----------------------------------------------------------------------------------------------------
2023-09-03 19:37:53,648 EPOCH 6 done: loss 0.0209 - lr: 0.000013
2023-09-03 19:38:07,077 DEV : loss 0.1834675371646881 - f1-score (micro avg)  0.7753
2023-09-03 19:38:07,104 ----------------------------------------------------------------------------------------------------
2023-09-03 19:38:14,678 epoch 7 - iter 44/447 - loss 0.01475716 - time (sec): 7.57 - samples/sec: 1136.15 - lr: 0.000013 - momentum: 0.000000
2023-09-03 19:38:22,372 epoch 7 - iter 88/447 - loss 0.01538235 - time (sec): 15.27 - samples/sec: 1119.97 - lr: 0.000013 - momentum: 0.000000
2023-09-03 19:38:29,614 epoch 7 - iter 132/447 - loss 0.01281823 - time (sec): 22.51 - samples/sec: 1160.39 - lr: 0.000012 - momentum: 0.000000
2023-09-03 19:38:37,545 epoch 7 - iter 176/447 - loss 0.01584858 - time (sec): 30.44 - samples/sec: 1142.64 - lr: 0.000012 - momentum: 0.000000
2023-09-03 19:38:45,043 epoch 7 - iter 220/447 - loss 0.01466873 - time (sec): 37.94 - samples/sec: 1125.94 - lr: 0.000012 - momentum: 0.000000
2023-09-03 19:38:52,900 epoch 7 - iter 264/447 - loss 0.01357460 - time (sec): 45.79 - samples/sec: 1122.50 - lr: 0.000011 - momentum: 0.000000
2023-09-03 19:39:00,331 epoch 7 - iter 308/447 - loss 0.01390915 - time (sec): 53.23 - samples/sec: 1116.71 - lr: 0.000011 - momentum: 0.000000
2023-09-03 19:39:07,962 epoch 7 - iter 352/447 - loss 0.01346994 - time (sec): 60.86 - samples/sec: 1115.27 - lr: 0.000011 - momentum: 0.000000
2023-09-03 19:39:15,066 epoch 7 - iter 396/447 - loss 0.01405975 - time (sec): 67.96 - samples/sec: 1110.49 - lr: 0.000010 - momentum: 0.000000
2023-09-03 19:39:23,740 epoch 7 - iter 440/447 - loss 0.01354116 - time (sec): 76.63 - samples/sec: 1104.22 - lr: 0.000010 - momentum: 0.000000
2023-09-03 19:39:25,807 ----------------------------------------------------------------------------------------------------
2023-09-03 19:39:25,807 EPOCH 7 done: loss 0.0137 - lr: 0.000010
2023-09-03 19:39:38,952 DEV : loss 0.19323676824569702 - f1-score (micro avg)  0.7833
2023-09-03 19:39:38,979 saving best model
2023-09-03 19:39:40,323 ----------------------------------------------------------------------------------------------------
2023-09-03 19:39:47,391 epoch 8 - iter 44/447 - loss 0.00788687 - time (sec): 7.07 - samples/sec: 1181.25 - lr: 0.000010 - momentum: 0.000000
2023-09-03 19:39:57,517 epoch 8 - iter 88/447 - loss 0.00844500 - time (sec): 17.19 - samples/sec: 1038.49 - lr: 0.000009 - momentum: 0.000000
2023-09-03 19:40:05,024 epoch 8 - iter 132/447 - loss 0.00996625 - time (sec): 24.70 - samples/sec: 1056.67 - lr: 0.000009 - momentum: 0.000000
2023-09-03 19:40:12,354 epoch 8 - iter 176/447 - loss 0.00894889 - time (sec): 32.03 - samples/sec: 1078.01 - lr: 0.000009 - momentum: 0.000000
2023-09-03 19:40:19,563 epoch 8 - iter 220/447 - loss 0.00890931 - time (sec): 39.24 - samples/sec: 1080.80 - lr: 0.000008 - momentum: 0.000000
2023-09-03 19:40:28,021 epoch 8 - iter 264/447 - loss 0.00897967 - time (sec): 47.70 - samples/sec: 1073.69 - lr: 0.000008 - momentum: 0.000000
2023-09-03 19:40:35,717 epoch 8 - iter 308/447 - loss 0.00944563 - time (sec): 55.39 - samples/sec: 1084.63 - lr: 0.000008 - momentum: 0.000000
2023-09-03 19:40:43,176 epoch 8 - iter 352/447 - loss 0.01053706 - time (sec): 62.85 - samples/sec: 1085.21 - lr: 0.000007 - momentum: 0.000000
2023-09-03 19:40:50,859 epoch 8 - iter 396/447 - loss 0.01097928 - time (sec): 70.53 - samples/sec: 1088.35 - lr: 0.000007 - momentum: 0.000000
2023-09-03 19:40:58,497 epoch 8 - iter 440/447 - loss 0.01105219 - time (sec): 78.17 - samples/sec: 1090.57 - lr: 0.000007 - momentum: 0.000000
2023-09-03 19:40:59,655 ----------------------------------------------------------------------------------------------------
2023-09-03 19:40:59,656 EPOCH 8 done: loss 0.0111 - lr: 0.000007
2023-09-03 19:41:12,783 DEV : loss 0.21089980006217957 - f1-score (micro avg)  0.7903
2023-09-03 19:41:12,811 saving best model
2023-09-03 19:41:14,132 ----------------------------------------------------------------------------------------------------
2023-09-03 19:41:21,736 epoch 9 - iter 44/447 - loss 0.00372544 - time (sec): 7.60 - samples/sec: 1125.59 - lr: 0.000006 - momentum: 0.000000
2023-09-03 19:41:28,680 epoch 9 - iter 88/447 - loss 0.00446647 - time (sec): 14.55 - samples/sec: 1156.86 - lr: 0.000006 - momentum: 0.000000
2023-09-03 19:41:36,360 epoch 9 - iter 132/447 - loss 0.00581485 - time (sec): 22.23 - samples/sec: 1126.69 - lr: 0.000006 - momentum: 0.000000
2023-09-03 19:41:43,841 epoch 9 - iter 176/447 - loss 0.00668476 - time (sec): 29.71 - samples/sec: 1133.31 - lr: 0.000005 - momentum: 0.000000
2023-09-03 19:41:53,294 epoch 9 - iter 220/447 - loss 0.00663822 - time (sec): 39.16 - samples/sec: 1106.30 - lr: 0.000005 - momentum: 0.000000
2023-09-03 19:42:00,743 epoch 9 - iter 264/447 - loss 0.00613799 - time (sec): 46.61 - samples/sec: 1110.05 - lr: 0.000005 - momentum: 0.000000
2023-09-03 19:42:08,618 epoch 9 - iter 308/447 - loss 0.00629927 - time (sec): 54.49 - samples/sec: 1097.29 - lr: 0.000004 - momentum: 0.000000
2023-09-03 19:42:16,780 epoch 9 - iter 352/447 - loss 0.00623439 - time (sec): 62.65 - samples/sec: 1097.60 - lr: 0.000004 - momentum: 0.000000
2023-09-03 19:42:23,837 epoch 9 - iter 396/447 - loss 0.00632364 - time (sec): 69.70 - samples/sec: 1099.34 - lr: 0.000004 - momentum: 0.000000
2023-09-03 19:42:31,296 epoch 9 - iter 440/447 - loss 0.00680502 - time (sec): 77.16 - samples/sec: 1102.51 - lr: 0.000003 - momentum: 0.000000
2023-09-03 19:42:33,153 ----------------------------------------------------------------------------------------------------
2023-09-03 19:42:33,153 EPOCH 9 done: loss 0.0069 - lr: 0.000003
2023-09-03 19:42:46,375 DEV : loss 0.2204572707414627 - f1-score (micro avg)  0.7901
2023-09-03 19:42:46,401 ----------------------------------------------------------------------------------------------------
2023-09-03 19:42:54,639 epoch 10 - iter 44/447 - loss 0.00107852 - time (sec): 8.24 - samples/sec: 1110.82 - lr: 0.000003 - momentum: 0.000000
2023-09-03 19:43:01,983 epoch 10 - iter 88/447 - loss 0.00398631 - time (sec): 15.58 - samples/sec: 1101.53 - lr: 0.000003 - momentum: 0.000000
2023-09-03 19:43:09,675 epoch 10 - iter 132/447 - loss 0.00549052 - time (sec): 23.27 - samples/sec: 1089.55 - lr: 0.000002 - momentum: 0.000000
2023-09-03 19:43:18,909 epoch 10 - iter 176/447 - loss 0.00432581 - time (sec): 32.51 - samples/sec: 1084.96 - lr: 0.000002 - momentum: 0.000000
2023-09-03 19:43:26,150 epoch 10 - iter 220/447 - loss 0.00450331 - time (sec): 39.75 - samples/sec: 1094.80 - lr: 0.000002 - momentum: 0.000000
2023-09-03 19:43:33,036 epoch 10 - iter 264/447 - loss 0.00464585 - time (sec): 46.63 - samples/sec: 1110.24 - lr: 0.000001 - momentum: 0.000000
2023-09-03 19:43:40,136 epoch 10 - iter 308/447 - loss 0.00514209 - time (sec): 53.73 - samples/sec: 1108.01 - lr: 0.000001 - momentum: 0.000000
2023-09-03 19:43:48,593 epoch 10 - iter 352/447 - loss 0.00509009 - time (sec): 62.19 - samples/sec: 1096.76 - lr: 0.000001 - momentum: 0.000000
2023-09-03 19:43:56,066 epoch 10 - iter 396/447 - loss 0.00495633 - time (sec): 69.66 - samples/sec: 1095.69 - lr: 0.000000 - momentum: 0.000000
2023-09-03 19:44:04,386 epoch 10 - iter 440/447 - loss 0.00505250 - time (sec): 77.98 - samples/sec: 1095.08 - lr: 0.000000 - momentum: 0.000000
2023-09-03 19:44:05,515 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:05,515 EPOCH 10 done: loss 0.0050 - lr: 0.000000
2023-09-03 19:44:18,984 DEV : loss 0.22151651978492737 - f1-score (micro avg)  0.7885
2023-09-03 19:44:19,474 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:19,476 Loading model from best epoch ...
2023-09-03 19:44:21,229 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
2023-09-03 19:44:31,874 
Results:
- F-score (micro) 0.7482
- F-score (macro) 0.6664
- Accuracy 0.6188

By class:
              precision    recall  f1-score   support

         loc     0.8476    0.8490    0.8483       596
        pers     0.6667    0.7508    0.7062       333
         org     0.5038    0.5076    0.5057       132
        prod     0.6800    0.5152    0.5862        66
        time     0.6429    0.7347    0.6857        49

   micro avg     0.7374    0.7594    0.7482      1176
   macro avg     0.6682    0.6714    0.6664      1176
weighted avg     0.7398    0.7594    0.7481      1176

2023-09-03 19:44:31,874 ----------------------------------------------------------------------------------------------------