2023-09-03 19:44:59,244 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,245 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 19:44:59,245 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,246 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 19:44:59,246 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,246 Train:  3575 sentences
2023-09-03 19:44:59,246         (train_with_dev=False, train_with_test=False)
2023-09-03 19:44:59,246 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,246 Training Params:
2023-09-03 19:44:59,246  - learning_rate: "5e-05" 
2023-09-03 19:44:59,246  - mini_batch_size: "8"
2023-09-03 19:44:59,246  - max_epochs: "10"
2023-09-03 19:44:59,246  - shuffle: "True"
2023-09-03 19:44:59,246 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,246 Plugins:
2023-09-03 19:44:59,246  - LinearScheduler | warmup_fraction: '0.1'
2023-09-03 19:44:59,246 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,246 Final evaluation on model from best epoch (best-model.pt)
2023-09-03 19:44:59,246  - metric: "('micro avg', 'f1-score')"
2023-09-03 19:44:59,247 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,247 Computation:
2023-09-03 19:44:59,247  - compute on device: cuda:0
2023-09-03 19:44:59,247  - embedding storage: none
2023-09-03 19:44:59,247 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,247 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-09-03 19:44:59,247 ----------------------------------------------------------------------------------------------------
2023-09-03 19:44:59,247 ----------------------------------------------------------------------------------------------------
2023-09-03 19:45:05,857 epoch 1 - iter 44/447 - loss 3.05895178 - time (sec): 6.61 - samples/sec: 1206.22 - lr: 0.000005 - momentum: 0.000000
2023-09-03 19:45:12,691 epoch 1 - iter 88/447 - loss 2.11073299 - time (sec): 13.44 - samples/sec: 1185.47 - lr: 0.000010 - momentum: 0.000000
2023-09-03 19:45:19,769 epoch 1 - iter 132/447 - loss 1.51691462 - time (sec): 20.52 - samples/sec: 1203.61 - lr: 0.000015 - momentum: 0.000000
2023-09-03 19:45:26,516 epoch 1 - iter 176/447 - loss 1.24634762 - time (sec): 27.27 - samples/sec: 1196.86 - lr: 0.000020 - momentum: 0.000000
2023-09-03 19:45:33,690 epoch 1 - iter 220/447 - loss 1.05616858 - time (sec): 34.44 - samples/sec: 1197.99 - lr: 0.000024 - momentum: 0.000000
2023-09-03 19:45:42,842 epoch 1 - iter 264/447 - loss 0.91207757 - time (sec): 43.59 - samples/sec: 1184.02 - lr: 0.000029 - momentum: 0.000000
2023-09-03 19:45:50,353 epoch 1 - iter 308/447 - loss 0.82591020 - time (sec): 51.11 - samples/sec: 1166.61 - lr: 0.000034 - momentum: 0.000000
2023-09-03 19:45:57,145 epoch 1 - iter 352/447 - loss 0.75411732 - time (sec): 57.90 - samples/sec: 1175.35 - lr: 0.000039 - momentum: 0.000000
2023-09-03 19:46:04,586 epoch 1 - iter 396/447 - loss 0.69733732 - time (sec): 65.34 - samples/sec: 1169.96 - lr: 0.000044 - momentum: 0.000000
2023-09-03 19:46:11,651 epoch 1 - iter 440/447 - loss 0.65001169 - time (sec): 72.40 - samples/sec: 1169.08 - lr: 0.000049 - momentum: 0.000000
2023-09-03 19:46:13,120 ----------------------------------------------------------------------------------------------------
2023-09-03 19:46:13,120 EPOCH 1 done: loss 0.6402 - lr: 0.000049
2023-09-03 19:46:23,336 DEV : loss 0.17735987901687622 - f1-score (micro avg)  0.5967
2023-09-03 19:46:23,362 saving best model
2023-09-03 19:46:23,836 ----------------------------------------------------------------------------------------------------
2023-09-03 19:46:31,012 epoch 2 - iter 44/447 - loss 0.20544633 - time (sec): 7.17 - samples/sec: 1187.90 - lr: 0.000049 - momentum: 0.000000
2023-09-03 19:46:38,511 epoch 2 - iter 88/447 - loss 0.18673012 - time (sec): 14.67 - samples/sec: 1149.88 - lr: 0.000049 - momentum: 0.000000
2023-09-03 19:46:45,256 epoch 2 - iter 132/447 - loss 0.17694085 - time (sec): 21.42 - samples/sec: 1167.73 - lr: 0.000048 - momentum: 0.000000
2023-09-03 19:46:52,651 epoch 2 - iter 176/447 - loss 0.17433877 - time (sec): 28.81 - samples/sec: 1172.35 - lr: 0.000048 - momentum: 0.000000
2023-09-03 19:46:59,416 epoch 2 - iter 220/447 - loss 0.16657912 - time (sec): 35.58 - samples/sec: 1168.52 - lr: 0.000047 - momentum: 0.000000
2023-09-03 19:47:07,929 epoch 2 - iter 264/447 - loss 0.16283126 - time (sec): 44.09 - samples/sec: 1154.74 - lr: 0.000047 - momentum: 0.000000
2023-09-03 19:47:14,974 epoch 2 - iter 308/447 - loss 0.15741588 - time (sec): 51.14 - samples/sec: 1158.92 - lr: 0.000046 - momentum: 0.000000
2023-09-03 19:47:22,920 epoch 2 - iter 352/447 - loss 0.15314425 - time (sec): 59.08 - samples/sec: 1160.86 - lr: 0.000046 - momentum: 0.000000
2023-09-03 19:47:30,918 epoch 2 - iter 396/447 - loss 0.15205093 - time (sec): 67.08 - samples/sec: 1147.38 - lr: 0.000045 - momentum: 0.000000
2023-09-03 19:47:38,144 epoch 2 - iter 440/447 - loss 0.15048232 - time (sec): 74.31 - samples/sec: 1147.07 - lr: 0.000045 - momentum: 0.000000
2023-09-03 19:47:39,160 ----------------------------------------------------------------------------------------------------
2023-09-03 19:47:39,160 EPOCH 2 done: loss 0.1503 - lr: 0.000045
2023-09-03 19:47:51,877 DEV : loss 0.12148022651672363 - f1-score (micro avg)  0.6901
2023-09-03 19:47:51,904 saving best model
2023-09-03 19:47:53,220 ----------------------------------------------------------------------------------------------------
2023-09-03 19:48:01,161 epoch 3 - iter 44/447 - loss 0.09924091 - time (sec): 7.94 - samples/sec: 1072.62 - lr: 0.000044 - momentum: 0.000000
2023-09-03 19:48:09,418 epoch 3 - iter 88/447 - loss 0.08969886 - time (sec): 16.20 - samples/sec: 1118.01 - lr: 0.000043 - momentum: 0.000000
2023-09-03 19:48:17,460 epoch 3 - iter 132/447 - loss 0.09047889 - time (sec): 24.24 - samples/sec: 1123.80 - lr: 0.000043 - momentum: 0.000000
2023-09-03 19:48:25,295 epoch 3 - iter 176/447 - loss 0.08321965 - time (sec): 32.07 - samples/sec: 1126.69 - lr: 0.000042 - momentum: 0.000000
2023-09-03 19:48:33,299 epoch 3 - iter 220/447 - loss 0.08905560 - time (sec): 40.08 - samples/sec: 1123.12 - lr: 0.000042 - momentum: 0.000000
2023-09-03 19:48:40,616 epoch 3 - iter 264/447 - loss 0.08981123 - time (sec): 47.39 - samples/sec: 1110.01 - lr: 0.000041 - momentum: 0.000000
2023-09-03 19:48:47,565 epoch 3 - iter 308/447 - loss 0.08795390 - time (sec): 54.34 - samples/sec: 1119.05 - lr: 0.000041 - momentum: 0.000000
2023-09-03 19:48:54,505 epoch 3 - iter 352/447 - loss 0.08610301 - time (sec): 61.28 - samples/sec: 1123.02 - lr: 0.000040 - momentum: 0.000000
2023-09-03 19:49:02,184 epoch 3 - iter 396/447 - loss 0.08514806 - time (sec): 68.96 - samples/sec: 1118.00 - lr: 0.000040 - momentum: 0.000000
2023-09-03 19:49:09,340 epoch 3 - iter 440/447 - loss 0.08672027 - time (sec): 76.12 - samples/sec: 1120.18 - lr: 0.000039 - momentum: 0.000000
2023-09-03 19:49:10,414 ----------------------------------------------------------------------------------------------------
2023-09-03 19:49:10,415 EPOCH 3 done: loss 0.0869 - lr: 0.000039
2023-09-03 19:49:23,440 DEV : loss 0.14279478788375854 - f1-score (micro avg)  0.7312
2023-09-03 19:49:23,466 saving best model
2023-09-03 19:49:24,811 ----------------------------------------------------------------------------------------------------
2023-09-03 19:49:31,483 epoch 4 - iter 44/447 - loss 0.05469815 - time (sec): 6.67 - samples/sec: 1132.40 - lr: 0.000038 - momentum: 0.000000
2023-09-03 19:49:39,956 epoch 4 - iter 88/447 - loss 0.04959434 - time (sec): 15.14 - samples/sec: 1110.71 - lr: 0.000038 - momentum: 0.000000
2023-09-03 19:49:47,478 epoch 4 - iter 132/447 - loss 0.05492602 - time (sec): 22.67 - samples/sec: 1106.59 - lr: 0.000037 - momentum: 0.000000
2023-09-03 19:49:54,970 epoch 4 - iter 176/447 - loss 0.05135116 - time (sec): 30.16 - samples/sec: 1115.21 - lr: 0.000037 - momentum: 0.000000
2023-09-03 19:50:01,928 epoch 4 - iter 220/447 - loss 0.05098197 - time (sec): 37.12 - samples/sec: 1106.13 - lr: 0.000036 - momentum: 0.000000
2023-09-03 19:50:11,049 epoch 4 - iter 264/447 - loss 0.04879327 - time (sec): 46.24 - samples/sec: 1103.35 - lr: 0.000036 - momentum: 0.000000
2023-09-03 19:50:19,900 epoch 4 - iter 308/447 - loss 0.04932569 - time (sec): 55.09 - samples/sec: 1085.19 - lr: 0.000035 - momentum: 0.000000
2023-09-03 19:50:27,153 epoch 4 - iter 352/447 - loss 0.04866242 - time (sec): 62.34 - samples/sec: 1088.19 - lr: 0.000035 - momentum: 0.000000
2023-09-03 19:50:35,195 epoch 4 - iter 396/447 - loss 0.04767351 - time (sec): 70.38 - samples/sec: 1095.46 - lr: 0.000034 - momentum: 0.000000
2023-09-03 19:50:42,562 epoch 4 - iter 440/447 - loss 0.04931913 - time (sec): 77.75 - samples/sec: 1097.96 - lr: 0.000033 - momentum: 0.000000
2023-09-03 19:50:43,634 ----------------------------------------------------------------------------------------------------
2023-09-03 19:50:43,635 EPOCH 4 done: loss 0.0489 - lr: 0.000033
2023-09-03 19:50:57,187 DEV : loss 0.1648201197385788 - f1-score (micro avg)  0.7606
2023-09-03 19:50:57,213 saving best model
2023-09-03 19:50:58,556 ----------------------------------------------------------------------------------------------------
2023-09-03 19:51:06,145 epoch 5 - iter 44/447 - loss 0.05705444 - time (sec): 7.59 - samples/sec: 1065.57 - lr: 0.000033 - momentum: 0.000000
2023-09-03 19:51:13,447 epoch 5 - iter 88/447 - loss 0.04319302 - time (sec): 14.89 - samples/sec: 1067.09 - lr: 0.000032 - momentum: 0.000000
2023-09-03 19:51:21,475 epoch 5 - iter 132/447 - loss 0.03634302 - time (sec): 22.92 - samples/sec: 1069.66 - lr: 0.000032 - momentum: 0.000000
2023-09-03 19:51:28,956 epoch 5 - iter 176/447 - loss 0.03666310 - time (sec): 30.40 - samples/sec: 1074.08 - lr: 0.000031 - momentum: 0.000000
2023-09-03 19:51:37,660 epoch 5 - iter 220/447 - loss 0.03444196 - time (sec): 39.10 - samples/sec: 1087.00 - lr: 0.000031 - momentum: 0.000000
2023-09-03 19:51:44,587 epoch 5 - iter 264/447 - loss 0.03435495 - time (sec): 46.03 - samples/sec: 1104.34 - lr: 0.000030 - momentum: 0.000000
2023-09-03 19:51:52,881 epoch 5 - iter 308/447 - loss 0.03424624 - time (sec): 54.32 - samples/sec: 1100.18 - lr: 0.000030 - momentum: 0.000000
2023-09-03 19:52:01,766 epoch 5 - iter 352/447 - loss 0.03319613 - time (sec): 63.21 - samples/sec: 1092.33 - lr: 0.000029 - momentum: 0.000000
2023-09-03 19:52:09,298 epoch 5 - iter 396/447 - loss 0.03312079 - time (sec): 70.74 - samples/sec: 1098.90 - lr: 0.000028 - momentum: 0.000000
2023-09-03 19:52:15,926 epoch 5 - iter 440/447 - loss 0.03388957 - time (sec): 77.37 - samples/sec: 1101.65 - lr: 0.000028 - momentum: 0.000000
2023-09-03 19:52:17,020 ----------------------------------------------------------------------------------------------------
2023-09-03 19:52:17,020 EPOCH 5 done: loss 0.0335 - lr: 0.000028
2023-09-03 19:52:30,551 DEV : loss 0.17944809794425964 - f1-score (micro avg)  0.7624
2023-09-03 19:52:30,577 saving best model
2023-09-03 19:52:31,909 ----------------------------------------------------------------------------------------------------
2023-09-03 19:52:39,597 epoch 6 - iter 44/447 - loss 0.02485402 - time (sec): 7.69 - samples/sec: 1116.90 - lr: 0.000027 - momentum: 0.000000
2023-09-03 19:52:47,114 epoch 6 - iter 88/447 - loss 0.02547663 - time (sec): 15.20 - samples/sec: 1103.64 - lr: 0.000027 - momentum: 0.000000
2023-09-03 19:52:54,357 epoch 6 - iter 132/447 - loss 0.02496451 - time (sec): 22.45 - samples/sec: 1105.73 - lr: 0.000026 - momentum: 0.000000
2023-09-03 19:53:01,908 epoch 6 - iter 176/447 - loss 0.02252033 - time (sec): 30.00 - samples/sec: 1105.92 - lr: 0.000026 - momentum: 0.000000
2023-09-03 19:53:09,954 epoch 6 - iter 220/447 - loss 0.02461582 - time (sec): 38.04 - samples/sec: 1093.96 - lr: 0.000025 - momentum: 0.000000
2023-09-03 19:53:17,244 epoch 6 - iter 264/447 - loss 0.02321987 - time (sec): 45.33 - samples/sec: 1102.08 - lr: 0.000025 - momentum: 0.000000
2023-09-03 19:53:24,181 epoch 6 - iter 308/447 - loss 0.02317291 - time (sec): 52.27 - samples/sec: 1106.26 - lr: 0.000024 - momentum: 0.000000
2023-09-03 19:53:32,115 epoch 6 - iter 352/447 - loss 0.02274179 - time (sec): 60.20 - samples/sec: 1103.52 - lr: 0.000023 - momentum: 0.000000
2023-09-03 19:53:40,466 epoch 6 - iter 396/447 - loss 0.02264507 - time (sec): 68.56 - samples/sec: 1094.47 - lr: 0.000023 - momentum: 0.000000
2023-09-03 19:53:49,679 epoch 6 - iter 440/447 - loss 0.02271494 - time (sec): 77.77 - samples/sec: 1093.16 - lr: 0.000022 - momentum: 0.000000
2023-09-03 19:53:51,045 ----------------------------------------------------------------------------------------------------
2023-09-03 19:53:51,045 EPOCH 6 done: loss 0.0225 - lr: 0.000022
2023-09-03 19:54:04,567 DEV : loss 0.20019599795341492 - f1-score (micro avg)  0.7655
2023-09-03 19:54:04,597 saving best model
2023-09-03 19:54:05,905 ----------------------------------------------------------------------------------------------------
2023-09-03 19:54:13,458 epoch 7 - iter 44/447 - loss 0.01666146 - time (sec): 7.55 - samples/sec: 1139.14 - lr: 0.000022 - momentum: 0.000000
2023-09-03 19:54:21,169 epoch 7 - iter 88/447 - loss 0.01354020 - time (sec): 15.26 - samples/sec: 1120.25 - lr: 0.000021 - momentum: 0.000000
2023-09-03 19:54:28,391 epoch 7 - iter 132/447 - loss 0.01334057 - time (sec): 22.49 - samples/sec: 1161.60 - lr: 0.000021 - momentum: 0.000000
2023-09-03 19:54:36,296 epoch 7 - iter 176/447 - loss 0.01659884 - time (sec): 30.39 - samples/sec: 1144.50 - lr: 0.000020 - momentum: 0.000000
2023-09-03 19:54:43,739 epoch 7 - iter 220/447 - loss 0.01532998 - time (sec): 37.83 - samples/sec: 1129.04 - lr: 0.000020 - momentum: 0.000000
2023-09-03 19:54:51,605 epoch 7 - iter 264/447 - loss 0.01415707 - time (sec): 45.70 - samples/sec: 1124.83 - lr: 0.000019 - momentum: 0.000000
2023-09-03 19:54:59,015 epoch 7 - iter 308/447 - loss 0.01444906 - time (sec): 53.11 - samples/sec: 1119.15 - lr: 0.000018 - momentum: 0.000000
2023-09-03 19:55:06,627 epoch 7 - iter 352/447 - loss 0.01551328 - time (sec): 60.72 - samples/sec: 1117.77 - lr: 0.000018 - momentum: 0.000000
2023-09-03 19:55:13,724 epoch 7 - iter 396/447 - loss 0.01473543 - time (sec): 67.82 - samples/sec: 1112.82 - lr: 0.000017 - momentum: 0.000000
2023-09-03 19:55:22,391 epoch 7 - iter 440/447 - loss 0.01420854 - time (sec): 76.49 - samples/sec: 1106.37 - lr: 0.000017 - momentum: 0.000000
2023-09-03 19:55:24,456 ----------------------------------------------------------------------------------------------------
2023-09-03 19:55:24,456 EPOCH 7 done: loss 0.0143 - lr: 0.000017
2023-09-03 19:55:37,962 DEV : loss 0.2281445562839508 - f1-score (micro avg)  0.7771
2023-09-03 19:55:37,989 saving best model
2023-09-03 19:55:39,292 ----------------------------------------------------------------------------------------------------
2023-09-03 19:55:46,351 epoch 8 - iter 44/447 - loss 0.00442347 - time (sec): 7.06 - samples/sec: 1182.60 - lr: 0.000016 - momentum: 0.000000
2023-09-03 19:55:56,134 epoch 8 - iter 88/447 - loss 0.00807419 - time (sec): 16.84 - samples/sec: 1060.16 - lr: 0.000016 - momentum: 0.000000
2023-09-03 19:56:03,654 epoch 8 - iter 132/447 - loss 0.01003653 - time (sec): 24.36 - samples/sec: 1071.34 - lr: 0.000015 - momentum: 0.000000
2023-09-03 19:56:10,984 epoch 8 - iter 176/447 - loss 0.00929882 - time (sec): 31.69 - samples/sec: 1089.54 - lr: 0.000015 - momentum: 0.000000
2023-09-03 19:56:18,155 epoch 8 - iter 220/447 - loss 0.00972771 - time (sec): 38.86 - samples/sec: 1091.29 - lr: 0.000014 - momentum: 0.000000
2023-09-03 19:56:26,590 epoch 8 - iter 264/447 - loss 0.00984762 - time (sec): 47.30 - samples/sec: 1082.78 - lr: 0.000013 - momentum: 0.000000
2023-09-03 19:56:34,284 epoch 8 - iter 308/447 - loss 0.01092729 - time (sec): 54.99 - samples/sec: 1092.57 - lr: 0.000013 - momentum: 0.000000
2023-09-03 19:56:41,737 epoch 8 - iter 352/447 - loss 0.01138641 - time (sec): 62.44 - samples/sec: 1092.31 - lr: 0.000012 - momentum: 0.000000
2023-09-03 19:56:49,454 epoch 8 - iter 396/447 - loss 0.01142584 - time (sec): 70.16 - samples/sec: 1094.14 - lr: 0.000012 - momentum: 0.000000
2023-09-03 19:56:57,103 epoch 8 - iter 440/447 - loss 0.01077808 - time (sec): 77.81 - samples/sec: 1095.66 - lr: 0.000011 - momentum: 0.000000
2023-09-03 19:56:58,265 ----------------------------------------------------------------------------------------------------
2023-09-03 19:56:58,265 EPOCH 8 done: loss 0.0106 - lr: 0.000011
2023-09-03 19:57:11,784 DEV : loss 0.23382732272148132 - f1-score (micro avg)  0.7852
2023-09-03 19:57:11,811 saving best model
2023-09-03 19:57:13,138 ----------------------------------------------------------------------------------------------------
2023-09-03 19:57:20,737 epoch 9 - iter 44/447 - loss 0.00165931 - time (sec): 7.60 - samples/sec: 1126.33 - lr: 0.000011 - momentum: 0.000000
2023-09-03 19:57:27,693 epoch 9 - iter 88/447 - loss 0.00338486 - time (sec): 14.55 - samples/sec: 1156.33 - lr: 0.000010 - momentum: 0.000000
2023-09-03 19:57:35,368 epoch 9 - iter 132/447 - loss 0.00456257 - time (sec): 22.23 - samples/sec: 1126.60 - lr: 0.000010 - momentum: 0.000000
2023-09-03 19:57:42,847 epoch 9 - iter 176/447 - loss 0.00458564 - time (sec): 29.71 - samples/sec: 1133.33 - lr: 0.000009 - momentum: 0.000000
2023-09-03 19:57:52,291 epoch 9 - iter 220/447 - loss 0.00481797 - time (sec): 39.15 - samples/sec: 1106.53 - lr: 0.000008 - momentum: 0.000000
2023-09-03 19:57:59,739 epoch 9 - iter 264/447 - loss 0.00481335 - time (sec): 46.60 - samples/sec: 1110.30 - lr: 0.000008 - momentum: 0.000000
2023-09-03 19:58:07,268 epoch 9 - iter 308/447 - loss 0.00589621 - time (sec): 54.13 - samples/sec: 1104.51 - lr: 0.000007 - momentum: 0.000000
2023-09-03 19:58:15,233 epoch 9 - iter 352/447 - loss 0.00584651 - time (sec): 62.09 - samples/sec: 1107.37 - lr: 0.000007 - momentum: 0.000000
2023-09-03 19:58:22,095 epoch 9 - iter 396/447 - loss 0.00623178 - time (sec): 68.96 - samples/sec: 1111.26 - lr: 0.000006 - momentum: 0.000000
2023-09-03 19:58:29,284 epoch 9 - iter 440/447 - loss 0.00617847 - time (sec): 76.14 - samples/sec: 1117.26 - lr: 0.000006 - momentum: 0.000000
2023-09-03 19:58:31,053 ----------------------------------------------------------------------------------------------------
2023-09-03 19:58:31,054 EPOCH 9 done: loss 0.0063 - lr: 0.000006
2023-09-03 19:58:43,941 DEV : loss 0.2306753247976303 - f1-score (micro avg)  0.7844
2023-09-03 19:58:43,968 ----------------------------------------------------------------------------------------------------
2023-09-03 19:58:51,779 epoch 10 - iter 44/447 - loss 0.00222028 - time (sec): 7.81 - samples/sec: 1171.46 - lr: 0.000005 - momentum: 0.000000
2023-09-03 19:58:58,749 epoch 10 - iter 88/447 - loss 0.00444802 - time (sec): 14.78 - samples/sec: 1161.23 - lr: 0.000005 - momentum: 0.000000
2023-09-03 19:59:06,006 epoch 10 - iter 132/447 - loss 0.00413128 - time (sec): 22.04 - samples/sec: 1150.68 - lr: 0.000004 - momentum: 0.000000
2023-09-03 19:59:14,713 epoch 10 - iter 176/447 - loss 0.00339273 - time (sec): 30.74 - samples/sec: 1147.16 - lr: 0.000003 - momentum: 0.000000
2023-09-03 19:59:21,550 epoch 10 - iter 220/447 - loss 0.00445031 - time (sec): 37.58 - samples/sec: 1157.95 - lr: 0.000003 - momentum: 0.000000
2023-09-03 19:59:28,080 epoch 10 - iter 264/447 - loss 0.00448787 - time (sec): 44.11 - samples/sec: 1173.73 - lr: 0.000002 - momentum: 0.000000
2023-09-03 19:59:34,811 epoch 10 - iter 308/447 - loss 0.00440139 - time (sec): 50.84 - samples/sec: 1171.05 - lr: 0.000002 - momentum: 0.000000
2023-09-03 19:59:42,765 epoch 10 - iter 352/447 - loss 0.00399426 - time (sec): 58.79 - samples/sec: 1160.10 - lr: 0.000001 - momentum: 0.000000
2023-09-03 19:59:49,844 epoch 10 - iter 396/447 - loss 0.00395381 - time (sec): 65.87 - samples/sec: 1158.72 - lr: 0.000001 - momentum: 0.000000
2023-09-03 19:59:57,688 epoch 10 - iter 440/447 - loss 0.00420395 - time (sec): 73.72 - samples/sec: 1158.44 - lr: 0.000000 - momentum: 0.000000
2023-09-03 19:59:58,758 ----------------------------------------------------------------------------------------------------
2023-09-03 19:59:58,758 EPOCH 10 done: loss 0.0041 - lr: 0.000000
2023-09-03 20:00:11,492 DEV : loss 0.2330584079027176 - f1-score (micro avg)  0.7858
2023-09-03 20:00:11,520 saving best model
2023-09-03 20:00:13,308 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:13,309 Loading model from best epoch ...
2023-09-03 20:00:15,075 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 20:00:24,956 
Results:
- F-score (micro) 0.7411
- F-score (macro) 0.6789
- Accuracy 0.6072

By class:
              precision    recall  f1-score   support

         loc     0.8114    0.8372    0.8241       596
        pers     0.6623    0.7538    0.7051       333
         org     0.5285    0.4924    0.5098       132
        prod     0.6415    0.5152    0.5714        66
        time     0.7547    0.8163    0.7843        49

   micro avg     0.7269    0.7560    0.7411      1176
   macro avg     0.6797    0.6830    0.6789      1176
weighted avg     0.7255    0.7560    0.7393      1176

2023-09-03 20:00:24,956 ----------------------------------------------------------------------------------------------------