2023-10-17 15:44:35,152 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,154 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): ElectraModel(
      (embeddings): ElectraEmbeddings(
        (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): ElectraEncoder(
        (layer): ModuleList(
          (0-11): 12 x ElectraLayer(
            (attention): ElectraAttention(
              (self): ElectraSelfAttention(
                (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): ElectraSelfOutput(
                (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): ElectraIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): ElectraOutput(
              (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)
            )
          )
        )
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-17 15:44:35,154 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,155 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
2023-10-17 15:44:35,155 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,155 Train:  3575 sentences
2023-10-17 15:44:35,155         (train_with_dev=False, train_with_test=False)
2023-10-17 15:44:35,155 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,155 Training Params:
2023-10-17 15:44:35,155  - learning_rate: "5e-05" 
2023-10-17 15:44:35,155  - mini_batch_size: "4"
2023-10-17 15:44:35,155  - max_epochs: "10"
2023-10-17 15:44:35,155  - shuffle: "True"
2023-10-17 15:44:35,156 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,156 Plugins:
2023-10-17 15:44:35,156  - TensorboardLogger
2023-10-17 15:44:35,156  - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 15:44:35,156 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,156 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 15:44:35,156  - metric: "('micro avg', 'f1-score')"
2023-10-17 15:44:35,156 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,156 Computation:
2023-10-17 15:44:35,156  - compute on device: cuda:0
2023-10-17 15:44:35,156  - embedding storage: none
2023-10-17 15:44:35,156 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,156 Model training base path: "hmbench-hipe2020/de-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-17 15:44:35,156 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,156 ----------------------------------------------------------------------------------------------------
2023-10-17 15:44:35,157 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 15:44:42,344 epoch 1 - iter 89/894 - loss 2.91539600 - time (sec): 7.19 - samples/sec: 1246.80 - lr: 0.000005 - momentum: 0.000000
2023-10-17 15:44:49,272 epoch 1 - iter 178/894 - loss 1.80464316 - time (sec): 14.11 - samples/sec: 1222.26 - lr: 0.000010 - momentum: 0.000000
2023-10-17 15:44:56,189 epoch 1 - iter 267/894 - loss 1.36782446 - time (sec): 21.03 - samples/sec: 1228.23 - lr: 0.000015 - momentum: 0.000000
2023-10-17 15:45:03,113 epoch 1 - iter 356/894 - loss 1.10936834 - time (sec): 27.96 - samples/sec: 1247.79 - lr: 0.000020 - momentum: 0.000000
2023-10-17 15:45:10,564 epoch 1 - iter 445/894 - loss 0.93995891 - time (sec): 35.41 - samples/sec: 1236.66 - lr: 0.000025 - momentum: 0.000000
2023-10-17 15:45:17,587 epoch 1 - iter 534/894 - loss 0.90914551 - time (sec): 42.43 - samples/sec: 1232.63 - lr: 0.000030 - momentum: 0.000000
2023-10-17 15:45:24,470 epoch 1 - iter 623/894 - loss 0.83544821 - time (sec): 49.31 - samples/sec: 1231.63 - lr: 0.000035 - momentum: 0.000000
2023-10-17 15:45:31,417 epoch 1 - iter 712/894 - loss 0.76823309 - time (sec): 56.26 - samples/sec: 1224.39 - lr: 0.000040 - momentum: 0.000000
2023-10-17 15:45:38,609 epoch 1 - iter 801/894 - loss 0.70255786 - time (sec): 63.45 - samples/sec: 1234.50 - lr: 0.000045 - momentum: 0.000000
2023-10-17 15:45:45,502 epoch 1 - iter 890/894 - loss 0.65383812 - time (sec): 70.34 - samples/sec: 1225.86 - lr: 0.000050 - momentum: 0.000000
2023-10-17 15:45:45,802 ----------------------------------------------------------------------------------------------------
2023-10-17 15:45:45,802 EPOCH 1 done: loss 0.6525 - lr: 0.000050
2023-10-17 15:45:52,567 DEV : loss 0.18383200466632843 - f1-score (micro avg)  0.5787
2023-10-17 15:45:52,644 saving best model
2023-10-17 15:45:53,292 ----------------------------------------------------------------------------------------------------
2023-10-17 15:46:00,489 epoch 2 - iter 89/894 - loss 0.23639322 - time (sec): 7.19 - samples/sec: 1197.73 - lr: 0.000049 - momentum: 0.000000
2023-10-17 15:46:08,042 epoch 2 - iter 178/894 - loss 0.20566560 - time (sec): 14.75 - samples/sec: 1141.12 - lr: 0.000049 - momentum: 0.000000
2023-10-17 15:46:15,151 epoch 2 - iter 267/894 - loss 0.20004145 - time (sec): 21.85 - samples/sec: 1156.49 - lr: 0.000048 - momentum: 0.000000
2023-10-17 15:46:22,373 epoch 2 - iter 356/894 - loss 0.18486982 - time (sec): 29.08 - samples/sec: 1166.08 - lr: 0.000048 - momentum: 0.000000
2023-10-17 15:46:29,598 epoch 2 - iter 445/894 - loss 0.17676887 - time (sec): 36.30 - samples/sec: 1166.69 - lr: 0.000047 - momentum: 0.000000
2023-10-17 15:46:36,817 epoch 2 - iter 534/894 - loss 0.17708823 - time (sec): 43.52 - samples/sec: 1184.92 - lr: 0.000047 - momentum: 0.000000
2023-10-17 15:46:44,026 epoch 2 - iter 623/894 - loss 0.17121058 - time (sec): 50.73 - samples/sec: 1188.24 - lr: 0.000046 - momentum: 0.000000
2023-10-17 15:46:51,313 epoch 2 - iter 712/894 - loss 0.16652271 - time (sec): 58.02 - samples/sec: 1195.63 - lr: 0.000046 - momentum: 0.000000
2023-10-17 15:46:58,497 epoch 2 - iter 801/894 - loss 0.16303052 - time (sec): 65.20 - samples/sec: 1184.14 - lr: 0.000045 - momentum: 0.000000
2023-10-17 15:47:05,835 epoch 2 - iter 890/894 - loss 0.15787277 - time (sec): 72.54 - samples/sec: 1188.83 - lr: 0.000044 - momentum: 0.000000
2023-10-17 15:47:06,145 ----------------------------------------------------------------------------------------------------
2023-10-17 15:47:06,146 EPOCH 2 done: loss 0.1579 - lr: 0.000044
2023-10-17 15:47:17,287 DEV : loss 0.14334321022033691 - f1-score (micro avg)  0.7085
2023-10-17 15:47:17,354 saving best model
2023-10-17 15:47:18,820 ----------------------------------------------------------------------------------------------------
2023-10-17 15:47:26,280 epoch 3 - iter 89/894 - loss 0.07813350 - time (sec): 7.46 - samples/sec: 1282.70 - lr: 0.000044 - momentum: 0.000000
2023-10-17 15:47:33,860 epoch 3 - iter 178/894 - loss 0.09913616 - time (sec): 15.04 - samples/sec: 1249.92 - lr: 0.000043 - momentum: 0.000000
2023-10-17 15:47:41,689 epoch 3 - iter 267/894 - loss 0.10793971 - time (sec): 22.86 - samples/sec: 1173.68 - lr: 0.000043 - momentum: 0.000000
2023-10-17 15:47:49,341 epoch 3 - iter 356/894 - loss 0.10704836 - time (sec): 30.52 - samples/sec: 1139.29 - lr: 0.000042 - momentum: 0.000000
2023-10-17 15:47:56,425 epoch 3 - iter 445/894 - loss 0.10184390 - time (sec): 37.60 - samples/sec: 1144.76 - lr: 0.000042 - momentum: 0.000000
2023-10-17 15:48:03,494 epoch 3 - iter 534/894 - loss 0.10054332 - time (sec): 44.67 - samples/sec: 1143.14 - lr: 0.000041 - momentum: 0.000000
2023-10-17 15:48:10,617 epoch 3 - iter 623/894 - loss 0.09942243 - time (sec): 51.79 - samples/sec: 1145.24 - lr: 0.000041 - momentum: 0.000000
2023-10-17 15:48:17,781 epoch 3 - iter 712/894 - loss 0.10174860 - time (sec): 58.96 - samples/sec: 1161.79 - lr: 0.000040 - momentum: 0.000000
2023-10-17 15:48:24,921 epoch 3 - iter 801/894 - loss 0.10063278 - time (sec): 66.10 - samples/sec: 1171.38 - lr: 0.000039 - momentum: 0.000000
2023-10-17 15:48:32,239 epoch 3 - iter 890/894 - loss 0.09898124 - time (sec): 73.42 - samples/sec: 1174.64 - lr: 0.000039 - momentum: 0.000000
2023-10-17 15:48:32,558 ----------------------------------------------------------------------------------------------------
2023-10-17 15:48:32,559 EPOCH 3 done: loss 0.0991 - lr: 0.000039
2023-10-17 15:48:43,839 DEV : loss 0.1510310173034668 - f1-score (micro avg)  0.7216
2023-10-17 15:48:43,898 saving best model
2023-10-17 15:48:45,360 ----------------------------------------------------------------------------------------------------
2023-10-17 15:48:52,496 epoch 4 - iter 89/894 - loss 0.07471403 - time (sec): 7.13 - samples/sec: 1171.78 - lr: 0.000038 - momentum: 0.000000
2023-10-17 15:48:59,548 epoch 4 - iter 178/894 - loss 0.07152783 - time (sec): 14.18 - samples/sec: 1174.20 - lr: 0.000038 - momentum: 0.000000
2023-10-17 15:49:06,441 epoch 4 - iter 267/894 - loss 0.06660839 - time (sec): 21.08 - samples/sec: 1147.78 - lr: 0.000037 - momentum: 0.000000
2023-10-17 15:49:13,392 epoch 4 - iter 356/894 - loss 0.06963329 - time (sec): 28.03 - samples/sec: 1179.18 - lr: 0.000037 - momentum: 0.000000
2023-10-17 15:49:20,860 epoch 4 - iter 445/894 - loss 0.07210417 - time (sec): 35.50 - samples/sec: 1226.25 - lr: 0.000036 - momentum: 0.000000
2023-10-17 15:49:27,936 epoch 4 - iter 534/894 - loss 0.06863732 - time (sec): 42.57 - samples/sec: 1214.14 - lr: 0.000036 - momentum: 0.000000
2023-10-17 15:49:34,875 epoch 4 - iter 623/894 - loss 0.06728191 - time (sec): 49.51 - samples/sec: 1222.35 - lr: 0.000035 - momentum: 0.000000
2023-10-17 15:49:41,896 epoch 4 - iter 712/894 - loss 0.06607071 - time (sec): 56.53 - samples/sec: 1227.05 - lr: 0.000034 - momentum: 0.000000
2023-10-17 15:49:49,075 epoch 4 - iter 801/894 - loss 0.06540782 - time (sec): 63.71 - samples/sec: 1219.84 - lr: 0.000034 - momentum: 0.000000
2023-10-17 15:49:56,132 epoch 4 - iter 890/894 - loss 0.06487201 - time (sec): 70.77 - samples/sec: 1218.20 - lr: 0.000033 - momentum: 0.000000
2023-10-17 15:49:56,434 ----------------------------------------------------------------------------------------------------
2023-10-17 15:49:56,435 EPOCH 4 done: loss 0.0649 - lr: 0.000033
2023-10-17 15:50:07,665 DEV : loss 0.18753261864185333 - f1-score (micro avg)  0.7598
2023-10-17 15:50:07,723 saving best model
2023-10-17 15:50:09,202 ----------------------------------------------------------------------------------------------------
2023-10-17 15:50:16,314 epoch 5 - iter 89/894 - loss 0.04554912 - time (sec): 7.11 - samples/sec: 1258.23 - lr: 0.000033 - momentum: 0.000000
2023-10-17 15:50:23,393 epoch 5 - iter 178/894 - loss 0.04649557 - time (sec): 14.19 - samples/sec: 1242.98 - lr: 0.000032 - momentum: 0.000000
2023-10-17 15:50:30,683 epoch 5 - iter 267/894 - loss 0.04140939 - time (sec): 21.48 - samples/sec: 1238.00 - lr: 0.000032 - momentum: 0.000000
2023-10-17 15:50:37,854 epoch 5 - iter 356/894 - loss 0.04138492 - time (sec): 28.65 - samples/sec: 1204.31 - lr: 0.000031 - momentum: 0.000000
2023-10-17 15:50:45,123 epoch 5 - iter 445/894 - loss 0.05411284 - time (sec): 35.92 - samples/sec: 1222.37 - lr: 0.000031 - momentum: 0.000000
2023-10-17 15:50:52,169 epoch 5 - iter 534/894 - loss 0.05080553 - time (sec): 42.96 - samples/sec: 1211.48 - lr: 0.000030 - momentum: 0.000000
2023-10-17 15:50:59,144 epoch 5 - iter 623/894 - loss 0.04833544 - time (sec): 49.94 - samples/sec: 1212.08 - lr: 0.000029 - momentum: 0.000000
2023-10-17 15:51:06,093 epoch 5 - iter 712/894 - loss 0.04820027 - time (sec): 56.89 - samples/sec: 1215.38 - lr: 0.000029 - momentum: 0.000000
2023-10-17 15:51:13,045 epoch 5 - iter 801/894 - loss 0.04783057 - time (sec): 63.84 - samples/sec: 1215.05 - lr: 0.000028 - momentum: 0.000000
2023-10-17 15:51:19,921 epoch 5 - iter 890/894 - loss 0.04664670 - time (sec): 70.71 - samples/sec: 1220.16 - lr: 0.000028 - momentum: 0.000000
2023-10-17 15:51:20,217 ----------------------------------------------------------------------------------------------------
2023-10-17 15:51:20,217 EPOCH 5 done: loss 0.0465 - lr: 0.000028
2023-10-17 15:51:31,594 DEV : loss 0.22483567893505096 - f1-score (micro avg)  0.7747
2023-10-17 15:51:31,657 saving best model
2023-10-17 15:51:33,125 ----------------------------------------------------------------------------------------------------
2023-10-17 15:51:40,106 epoch 6 - iter 89/894 - loss 0.02224176 - time (sec): 6.98 - samples/sec: 1197.71 - lr: 0.000027 - momentum: 0.000000
2023-10-17 15:51:47,068 epoch 6 - iter 178/894 - loss 0.02790633 - time (sec): 13.94 - samples/sec: 1212.37 - lr: 0.000027 - momentum: 0.000000
2023-10-17 15:51:53,957 epoch 6 - iter 267/894 - loss 0.03349122 - time (sec): 20.83 - samples/sec: 1237.02 - lr: 0.000026 - momentum: 0.000000
2023-10-17 15:52:00,898 epoch 6 - iter 356/894 - loss 0.03376256 - time (sec): 27.77 - samples/sec: 1243.48 - lr: 0.000026 - momentum: 0.000000
2023-10-17 15:52:07,970 epoch 6 - iter 445/894 - loss 0.03080036 - time (sec): 34.84 - samples/sec: 1250.73 - lr: 0.000025 - momentum: 0.000000
2023-10-17 15:52:14,857 epoch 6 - iter 534/894 - loss 0.03219363 - time (sec): 41.73 - samples/sec: 1230.66 - lr: 0.000024 - momentum: 0.000000
2023-10-17 15:52:22,139 epoch 6 - iter 623/894 - loss 0.03300328 - time (sec): 49.01 - samples/sec: 1246.28 - lr: 0.000024 - momentum: 0.000000
2023-10-17 15:52:29,253 epoch 6 - iter 712/894 - loss 0.03197960 - time (sec): 56.12 - samples/sec: 1241.63 - lr: 0.000023 - momentum: 0.000000
2023-10-17 15:52:36,303 epoch 6 - iter 801/894 - loss 0.03059184 - time (sec): 63.17 - samples/sec: 1237.76 - lr: 0.000023 - momentum: 0.000000
2023-10-17 15:52:43,298 epoch 6 - iter 890/894 - loss 0.02966133 - time (sec): 70.17 - samples/sec: 1228.24 - lr: 0.000022 - momentum: 0.000000
2023-10-17 15:52:43,613 ----------------------------------------------------------------------------------------------------
2023-10-17 15:52:43,613 EPOCH 6 done: loss 0.0297 - lr: 0.000022
2023-10-17 15:52:54,626 DEV : loss 0.23626892268657684 - f1-score (micro avg)  0.7682
2023-10-17 15:52:54,688 ----------------------------------------------------------------------------------------------------
2023-10-17 15:53:01,842 epoch 7 - iter 89/894 - loss 0.01453486 - time (sec): 7.15 - samples/sec: 1341.37 - lr: 0.000022 - momentum: 0.000000
2023-10-17 15:53:08,799 epoch 7 - iter 178/894 - loss 0.01426282 - time (sec): 14.11 - samples/sec: 1278.77 - lr: 0.000021 - momentum: 0.000000
2023-10-17 15:53:16,409 epoch 7 - iter 267/894 - loss 0.01170213 - time (sec): 21.72 - samples/sec: 1236.79 - lr: 0.000021 - momentum: 0.000000
2023-10-17 15:53:23,894 epoch 7 - iter 356/894 - loss 0.01502251 - time (sec): 29.20 - samples/sec: 1246.83 - lr: 0.000020 - momentum: 0.000000
2023-10-17 15:53:30,914 epoch 7 - iter 445/894 - loss 0.01592358 - time (sec): 36.22 - samples/sec: 1241.76 - lr: 0.000019 - momentum: 0.000000
2023-10-17 15:53:37,929 epoch 7 - iter 534/894 - loss 0.01506050 - time (sec): 43.24 - samples/sec: 1227.46 - lr: 0.000019 - momentum: 0.000000
2023-10-17 15:53:44,802 epoch 7 - iter 623/894 - loss 0.01569285 - time (sec): 50.11 - samples/sec: 1225.12 - lr: 0.000018 - momentum: 0.000000
2023-10-17 15:53:51,714 epoch 7 - iter 712/894 - loss 0.01598849 - time (sec): 57.02 - samples/sec: 1229.78 - lr: 0.000018 - momentum: 0.000000
2023-10-17 15:53:58,543 epoch 7 - iter 801/894 - loss 0.01505426 - time (sec): 63.85 - samples/sec: 1225.65 - lr: 0.000017 - momentum: 0.000000
2023-10-17 15:54:05,377 epoch 7 - iter 890/894 - loss 0.01634400 - time (sec): 70.69 - samples/sec: 1219.04 - lr: 0.000017 - momentum: 0.000000
2023-10-17 15:54:05,690 ----------------------------------------------------------------------------------------------------
2023-10-17 15:54:05,690 EPOCH 7 done: loss 0.0163 - lr: 0.000017
2023-10-17 15:54:17,064 DEV : loss 0.2253272533416748 - f1-score (micro avg)  0.7808
2023-10-17 15:54:17,123 saving best model
2023-10-17 15:54:18,629 ----------------------------------------------------------------------------------------------------
2023-10-17 15:54:25,480 epoch 8 - iter 89/894 - loss 0.01251303 - time (sec): 6.85 - samples/sec: 1215.36 - lr: 0.000016 - momentum: 0.000000
2023-10-17 15:54:32,319 epoch 8 - iter 178/894 - loss 0.01211563 - time (sec): 13.69 - samples/sec: 1204.97 - lr: 0.000016 - momentum: 0.000000
2023-10-17 15:54:39,189 epoch 8 - iter 267/894 - loss 0.01317594 - time (sec): 20.56 - samples/sec: 1191.22 - lr: 0.000015 - momentum: 0.000000
2023-10-17 15:54:46,270 epoch 8 - iter 356/894 - loss 0.01525423 - time (sec): 27.64 - samples/sec: 1207.62 - lr: 0.000014 - momentum: 0.000000
2023-10-17 15:54:53,283 epoch 8 - iter 445/894 - loss 0.01504989 - time (sec): 34.65 - samples/sec: 1219.32 - lr: 0.000014 - momentum: 0.000000
2023-10-17 15:55:00,195 epoch 8 - iter 534/894 - loss 0.01361242 - time (sec): 41.56 - samples/sec: 1214.36 - lr: 0.000013 - momentum: 0.000000
2023-10-17 15:55:07,127 epoch 8 - iter 623/894 - loss 0.01464410 - time (sec): 48.49 - samples/sec: 1212.03 - lr: 0.000013 - momentum: 0.000000
2023-10-17 15:55:13,952 epoch 8 - iter 712/894 - loss 0.01438402 - time (sec): 55.32 - samples/sec: 1213.86 - lr: 0.000012 - momentum: 0.000000
2023-10-17 15:55:20,917 epoch 8 - iter 801/894 - loss 0.01467751 - time (sec): 62.28 - samples/sec: 1230.73 - lr: 0.000012 - momentum: 0.000000
2023-10-17 15:55:27,954 epoch 8 - iter 890/894 - loss 0.01365605 - time (sec): 69.32 - samples/sec: 1244.28 - lr: 0.000011 - momentum: 0.000000
2023-10-17 15:55:28,260 ----------------------------------------------------------------------------------------------------
2023-10-17 15:55:28,260 EPOCH 8 done: loss 0.0136 - lr: 0.000011
2023-10-17 15:55:39,303 DEV : loss 0.25934016704559326 - f1-score (micro avg)  0.7752
2023-10-17 15:55:39,365 ----------------------------------------------------------------------------------------------------
2023-10-17 15:55:46,115 epoch 9 - iter 89/894 - loss 0.00235891 - time (sec): 6.75 - samples/sec: 1149.86 - lr: 0.000011 - momentum: 0.000000
2023-10-17 15:55:52,881 epoch 9 - iter 178/894 - loss 0.00468143 - time (sec): 13.51 - samples/sec: 1194.17 - lr: 0.000010 - momentum: 0.000000
2023-10-17 15:55:59,717 epoch 9 - iter 267/894 - loss 0.00575664 - time (sec): 20.35 - samples/sec: 1207.81 - lr: 0.000009 - momentum: 0.000000
2023-10-17 15:56:06,612 epoch 9 - iter 356/894 - loss 0.00558437 - time (sec): 27.25 - samples/sec: 1214.75 - lr: 0.000009 - momentum: 0.000000
2023-10-17 15:56:14,079 epoch 9 - iter 445/894 - loss 0.00553854 - time (sec): 34.71 - samples/sec: 1250.09 - lr: 0.000008 - momentum: 0.000000
2023-10-17 15:56:21,118 epoch 9 - iter 534/894 - loss 0.00506110 - time (sec): 41.75 - samples/sec: 1259.30 - lr: 0.000008 - momentum: 0.000000
2023-10-17 15:56:28,035 epoch 9 - iter 623/894 - loss 0.00692322 - time (sec): 48.67 - samples/sec: 1247.83 - lr: 0.000007 - momentum: 0.000000
2023-10-17 15:56:35,176 epoch 9 - iter 712/894 - loss 0.00657232 - time (sec): 55.81 - samples/sec: 1232.76 - lr: 0.000007 - momentum: 0.000000
2023-10-17 15:56:42,129 epoch 9 - iter 801/894 - loss 0.00603686 - time (sec): 62.76 - samples/sec: 1233.58 - lr: 0.000006 - momentum: 0.000000
2023-10-17 15:56:49,231 epoch 9 - iter 890/894 - loss 0.00573165 - time (sec): 69.86 - samples/sec: 1231.63 - lr: 0.000006 - momentum: 0.000000
2023-10-17 15:56:49,545 ----------------------------------------------------------------------------------------------------
2023-10-17 15:56:49,545 EPOCH 9 done: loss 0.0057 - lr: 0.000006
2023-10-17 15:57:00,913 DEV : loss 0.27651283144950867 - f1-score (micro avg)  0.7847
2023-10-17 15:57:00,980 saving best model
2023-10-17 15:57:02,455 ----------------------------------------------------------------------------------------------------
2023-10-17 15:57:09,482 epoch 10 - iter 89/894 - loss 0.00371495 - time (sec): 7.02 - samples/sec: 1226.07 - lr: 0.000005 - momentum: 0.000000
2023-10-17 15:57:16,473 epoch 10 - iter 178/894 - loss 0.00299072 - time (sec): 14.01 - samples/sec: 1198.78 - lr: 0.000004 - momentum: 0.000000
2023-10-17 15:57:23,793 epoch 10 - iter 267/894 - loss 0.00302717 - time (sec): 21.33 - samples/sec: 1246.43 - lr: 0.000004 - momentum: 0.000000
2023-10-17 15:57:30,750 epoch 10 - iter 356/894 - loss 0.00372958 - time (sec): 28.29 - samples/sec: 1216.31 - lr: 0.000003 - momentum: 0.000000
2023-10-17 15:57:37,742 epoch 10 - iter 445/894 - loss 0.00318995 - time (sec): 35.28 - samples/sec: 1229.18 - lr: 0.000003 - momentum: 0.000000
2023-10-17 15:57:44,789 epoch 10 - iter 534/894 - loss 0.00338575 - time (sec): 42.33 - samples/sec: 1235.47 - lr: 0.000002 - momentum: 0.000000
2023-10-17 15:57:51,733 epoch 10 - iter 623/894 - loss 0.00351432 - time (sec): 49.27 - samples/sec: 1229.50 - lr: 0.000002 - momentum: 0.000000
2023-10-17 15:57:59,331 epoch 10 - iter 712/894 - loss 0.00332563 - time (sec): 56.87 - samples/sec: 1223.36 - lr: 0.000001 - momentum: 0.000000
2023-10-17 15:58:06,294 epoch 10 - iter 801/894 - loss 0.00327091 - time (sec): 63.83 - samples/sec: 1212.54 - lr: 0.000001 - momentum: 0.000000
2023-10-17 15:58:13,380 epoch 10 - iter 890/894 - loss 0.00345596 - time (sec): 70.92 - samples/sec: 1216.40 - lr: 0.000000 - momentum: 0.000000
2023-10-17 15:58:13,697 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:13,698 EPOCH 10 done: loss 0.0034 - lr: 0.000000
2023-10-17 15:58:25,218 DEV : loss 0.27436527609825134 - f1-score (micro avg)  0.7904
2023-10-17 15:58:25,278 saving best model
2023-10-17 15:58:27,359 ----------------------------------------------------------------------------------------------------
2023-10-17 15:58:27,361 Loading model from best epoch ...
2023-10-17 15:58:30,392 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
2023-10-17 15:58:36,273 
Results:
- F-score (micro) 0.7748
- F-score (macro) 0.675
- Accuracy 0.6531

By class:
              precision    recall  f1-score   support

         loc     0.8648    0.8909    0.8777       596
        pers     0.7072    0.7688    0.7367       333
         org     0.5462    0.4924    0.5179       132
        prod     0.6444    0.4394    0.5225        66
        time     0.7059    0.7347    0.7200        49

   micro avg     0.7699    0.7798    0.7748      1176
   macro avg     0.6937    0.6652    0.6750      1176
weighted avg     0.7654    0.7798    0.7709      1176

2023-10-17 15:58:36,273 ----------------------------------------------------------------------------------------------------