2023-09-03 20:54:20,902 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,903 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 20:54:20,903 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,903 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 20:54:20,903 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,903 Train:  3575 sentences
2023-09-03 20:54:20,903         (train_with_dev=False, train_with_test=False)
2023-09-03 20:54:20,903 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,904 Training Params:
2023-09-03 20:54:20,904  - learning_rate: "5e-05" 
2023-09-03 20:54:20,904  - mini_batch_size: "8"
2023-09-03 20:54:20,904  - max_epochs: "10"
2023-09-03 20:54:20,904  - shuffle: "True"
2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,904 Plugins:
2023-09-03 20:54:20,904  - LinearScheduler | warmup_fraction: '0.1'
2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,904 Final evaluation on model from best epoch (best-model.pt)
2023-09-03 20:54:20,904  - metric: "('micro avg', 'f1-score')"
2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,904 Computation:
2023-09-03 20:54:20,904  - compute on device: cuda:0
2023-09-03 20:54:20,904  - embedding storage: none
2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,904 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:20,904 ----------------------------------------------------------------------------------------------------
2023-09-03 20:54:27,726 epoch 1 - iter 44/447 - loss 2.73716447 - time (sec): 6.82 - samples/sec: 1209.49 - lr: 0.000005 - momentum: 0.000000
2023-09-03 20:54:35,545 epoch 1 - iter 88/447 - loss 1.76275074 - time (sec): 14.64 - samples/sec: 1168.40 - lr: 0.000010 - momentum: 0.000000
2023-09-03 20:54:42,402 epoch 1 - iter 132/447 - loss 1.36332375 - time (sec): 21.50 - samples/sec: 1163.62 - lr: 0.000015 - momentum: 0.000000
2023-09-03 20:54:50,104 epoch 1 - iter 176/447 - loss 1.09517221 - time (sec): 29.20 - samples/sec: 1184.11 - lr: 0.000020 - momentum: 0.000000
2023-09-03 20:54:57,824 epoch 1 - iter 220/447 - loss 0.93311472 - time (sec): 36.92 - samples/sec: 1173.15 - lr: 0.000024 - momentum: 0.000000
2023-09-03 20:55:05,310 epoch 1 - iter 264/447 - loss 0.82310200 - time (sec): 44.40 - samples/sec: 1170.57 - lr: 0.000029 - momentum: 0.000000
2023-09-03 20:55:12,130 epoch 1 - iter 308/447 - loss 0.75010593 - time (sec): 51.22 - samples/sec: 1171.28 - lr: 0.000034 - momentum: 0.000000
2023-09-03 20:55:19,673 epoch 1 - iter 352/447 - loss 0.69595179 - time (sec): 58.77 - samples/sec: 1158.62 - lr: 0.000039 - momentum: 0.000000
2023-09-03 20:55:27,711 epoch 1 - iter 396/447 - loss 0.64348224 - time (sec): 66.81 - samples/sec: 1154.79 - lr: 0.000044 - momentum: 0.000000
2023-09-03 20:55:34,411 epoch 1 - iter 440/447 - loss 0.60334405 - time (sec): 73.51 - samples/sec: 1161.93 - lr: 0.000049 - momentum: 0.000000
2023-09-03 20:55:35,425 ----------------------------------------------------------------------------------------------------
2023-09-03 20:55:35,426 EPOCH 1 done: loss 0.5998 - lr: 0.000049
2023-09-03 20:55:45,844 DEV : loss 0.17524564266204834 - f1-score (micro avg)  0.5998
2023-09-03 20:55:45,870 saving best model
2023-09-03 20:55:46,360 ----------------------------------------------------------------------------------------------------
2023-09-03 20:55:53,314 epoch 2 - iter 44/447 - loss 0.18708094 - time (sec): 6.95 - samples/sec: 1168.87 - lr: 0.000049 - momentum: 0.000000
2023-09-03 20:56:00,420 epoch 2 - iter 88/447 - loss 0.17901121 - time (sec): 14.06 - samples/sec: 1169.74 - lr: 0.000049 - momentum: 0.000000
2023-09-03 20:56:07,576 epoch 2 - iter 132/447 - loss 0.17257848 - time (sec): 21.21 - samples/sec: 1171.26 - lr: 0.000048 - momentum: 0.000000
2023-09-03 20:56:15,224 epoch 2 - iter 176/447 - loss 0.16490695 - time (sec): 28.86 - samples/sec: 1152.42 - lr: 0.000048 - momentum: 0.000000
2023-09-03 20:56:22,217 epoch 2 - iter 220/447 - loss 0.16421404 - time (sec): 35.85 - samples/sec: 1156.16 - lr: 0.000047 - momentum: 0.000000
2023-09-03 20:56:29,655 epoch 2 - iter 264/447 - loss 0.15964720 - time (sec): 43.29 - samples/sec: 1151.14 - lr: 0.000047 - momentum: 0.000000
2023-09-03 20:56:36,715 epoch 2 - iter 308/447 - loss 0.16020622 - time (sec): 50.35 - samples/sec: 1151.99 - lr: 0.000046 - momentum: 0.000000
2023-09-03 20:56:45,118 epoch 2 - iter 352/447 - loss 0.15440493 - time (sec): 58.76 - samples/sec: 1141.39 - lr: 0.000046 - momentum: 0.000000
2023-09-03 20:56:52,814 epoch 2 - iter 396/447 - loss 0.15684350 - time (sec): 66.45 - samples/sec: 1154.44 - lr: 0.000045 - momentum: 0.000000
2023-09-03 20:57:00,179 epoch 2 - iter 440/447 - loss 0.15425075 - time (sec): 73.82 - samples/sec: 1155.31 - lr: 0.000045 - momentum: 0.000000
2023-09-03 20:57:01,430 ----------------------------------------------------------------------------------------------------
2023-09-03 20:57:01,430 EPOCH 2 done: loss 0.1545 - lr: 0.000045
2023-09-03 20:57:14,374 DEV : loss 0.1318657249212265 - f1-score (micro avg)  0.7219
2023-09-03 20:57:14,401 saving best model
2023-09-03 20:57:15,722 ----------------------------------------------------------------------------------------------------
2023-09-03 20:57:22,857 epoch 3 - iter 44/447 - loss 0.09910014 - time (sec): 7.13 - samples/sec: 1142.88 - lr: 0.000044 - momentum: 0.000000
2023-09-03 20:57:29,811 epoch 3 - iter 88/447 - loss 0.09095220 - time (sec): 14.09 - samples/sec: 1136.57 - lr: 0.000043 - momentum: 0.000000
2023-09-03 20:57:37,844 epoch 3 - iter 132/447 - loss 0.09474706 - time (sec): 22.12 - samples/sec: 1119.14 - lr: 0.000043 - momentum: 0.000000
2023-09-03 20:57:44,926 epoch 3 - iter 176/447 - loss 0.09662773 - time (sec): 29.20 - samples/sec: 1132.33 - lr: 0.000042 - momentum: 0.000000
2023-09-03 20:57:52,164 epoch 3 - iter 220/447 - loss 0.09890834 - time (sec): 36.44 - samples/sec: 1126.96 - lr: 0.000042 - momentum: 0.000000
2023-09-03 20:57:59,695 epoch 3 - iter 264/447 - loss 0.09257574 - time (sec): 43.97 - samples/sec: 1133.04 - lr: 0.000041 - momentum: 0.000000
2023-09-03 20:58:07,414 epoch 3 - iter 308/447 - loss 0.09156175 - time (sec): 51.69 - samples/sec: 1127.34 - lr: 0.000041 - momentum: 0.000000
2023-09-03 20:58:15,315 epoch 3 - iter 352/447 - loss 0.09061217 - time (sec): 59.59 - samples/sec: 1119.14 - lr: 0.000040 - momentum: 0.000000
2023-09-03 20:58:23,229 epoch 3 - iter 396/447 - loss 0.08784546 - time (sec): 67.51 - samples/sec: 1118.91 - lr: 0.000040 - momentum: 0.000000
2023-09-03 20:58:30,739 epoch 3 - iter 440/447 - loss 0.08704201 - time (sec): 75.02 - samples/sec: 1121.66 - lr: 0.000039 - momentum: 0.000000
2023-09-03 20:58:33,106 ----------------------------------------------------------------------------------------------------
2023-09-03 20:58:33,107 EPOCH 3 done: loss 0.0860 - lr: 0.000039
2023-09-03 20:58:46,618 DEV : loss 0.13379663228988647 - f1-score (micro avg)  0.7175
2023-09-03 20:58:46,645 ----------------------------------------------------------------------------------------------------
2023-09-03 20:58:54,615 epoch 4 - iter 44/447 - loss 0.06277073 - time (sec): 7.97 - samples/sec: 1022.51 - lr: 0.000038 - momentum: 0.000000
2023-09-03 20:59:02,152 epoch 4 - iter 88/447 - loss 0.05648163 - time (sec): 15.51 - samples/sec: 1068.96 - lr: 0.000038 - momentum: 0.000000
2023-09-03 20:59:09,377 epoch 4 - iter 132/447 - loss 0.05312505 - time (sec): 22.73 - samples/sec: 1090.33 - lr: 0.000037 - momentum: 0.000000
2023-09-03 20:59:18,549 epoch 4 - iter 176/447 - loss 0.05349326 - time (sec): 31.90 - samples/sec: 1091.10 - lr: 0.000037 - momentum: 0.000000
2023-09-03 20:59:26,198 epoch 4 - iter 220/447 - loss 0.04992607 - time (sec): 39.55 - samples/sec: 1092.82 - lr: 0.000036 - momentum: 0.000000
2023-09-03 20:59:33,971 epoch 4 - iter 264/447 - loss 0.05119766 - time (sec): 47.33 - samples/sec: 1091.28 - lr: 0.000036 - momentum: 0.000000
2023-09-03 20:59:41,433 epoch 4 - iter 308/447 - loss 0.05149054 - time (sec): 54.79 - samples/sec: 1096.95 - lr: 0.000035 - momentum: 0.000000
2023-09-03 20:59:48,965 epoch 4 - iter 352/447 - loss 0.05063399 - time (sec): 62.32 - samples/sec: 1097.67 - lr: 0.000035 - momentum: 0.000000
2023-09-03 20:59:57,322 epoch 4 - iter 396/447 - loss 0.05013942 - time (sec): 70.68 - samples/sec: 1094.04 - lr: 0.000034 - momentum: 0.000000
2023-09-03 21:00:04,533 epoch 4 - iter 440/447 - loss 0.05169724 - time (sec): 77.89 - samples/sec: 1096.02 - lr: 0.000033 - momentum: 0.000000
2023-09-03 21:00:05,636 ----------------------------------------------------------------------------------------------------
2023-09-03 21:00:05,636 EPOCH 4 done: loss 0.0521 - lr: 0.000033
2023-09-03 21:00:19,119 DEV : loss 0.1692018061876297 - f1-score (micro avg)  0.7695
2023-09-03 21:00:19,153 saving best model
2023-09-03 21:00:20,479 ----------------------------------------------------------------------------------------------------
2023-09-03 21:00:28,391 epoch 5 - iter 44/447 - loss 0.03984379 - time (sec): 7.91 - samples/sec: 1069.76 - lr: 0.000033 - momentum: 0.000000
2023-09-03 21:00:35,671 epoch 5 - iter 88/447 - loss 0.03527843 - time (sec): 15.19 - samples/sec: 1089.96 - lr: 0.000032 - momentum: 0.000000
2023-09-03 21:00:43,033 epoch 5 - iter 132/447 - loss 0.03633554 - time (sec): 22.55 - samples/sec: 1107.96 - lr: 0.000032 - momentum: 0.000000
2023-09-03 21:00:50,223 epoch 5 - iter 176/447 - loss 0.03854755 - time (sec): 29.74 - samples/sec: 1107.05 - lr: 0.000031 - momentum: 0.000000
2023-09-03 21:00:58,991 epoch 5 - iter 220/447 - loss 0.03856388 - time (sec): 38.51 - samples/sec: 1094.62 - lr: 0.000031 - momentum: 0.000000
2023-09-03 21:01:06,149 epoch 5 - iter 264/447 - loss 0.03879315 - time (sec): 45.67 - samples/sec: 1098.02 - lr: 0.000030 - momentum: 0.000000
2023-09-03 21:01:13,548 epoch 5 - iter 308/447 - loss 0.03806475 - time (sec): 53.07 - samples/sec: 1100.83 - lr: 0.000030 - momentum: 0.000000
2023-09-03 21:01:22,543 epoch 5 - iter 352/447 - loss 0.03944462 - time (sec): 62.06 - samples/sec: 1097.56 - lr: 0.000029 - momentum: 0.000000
2023-09-03 21:01:30,581 epoch 5 - iter 396/447 - loss 0.03814469 - time (sec): 70.10 - samples/sec: 1101.77 - lr: 0.000028 - momentum: 0.000000
2023-09-03 21:01:38,343 epoch 5 - iter 440/447 - loss 0.03774412 - time (sec): 77.86 - samples/sec: 1096.09 - lr: 0.000028 - momentum: 0.000000
2023-09-03 21:01:39,351 ----------------------------------------------------------------------------------------------------
2023-09-03 21:01:39,351 EPOCH 5 done: loss 0.0374 - lr: 0.000028
2023-09-03 21:01:52,412 DEV : loss 0.17978209257125854 - f1-score (micro avg)  0.7549
2023-09-03 21:01:52,439 ----------------------------------------------------------------------------------------------------
2023-09-03 21:02:01,732 epoch 6 - iter 44/447 - loss 0.02980735 - time (sec): 9.29 - samples/sec: 1035.64 - lr: 0.000027 - momentum: 0.000000
2023-09-03 21:02:09,110 epoch 6 - iter 88/447 - loss 0.02423965 - time (sec): 16.67 - samples/sec: 1069.15 - lr: 0.000027 - momentum: 0.000000
2023-09-03 21:02:17,431 epoch 6 - iter 132/447 - loss 0.02298144 - time (sec): 24.99 - samples/sec: 1065.88 - lr: 0.000026 - momentum: 0.000000
2023-09-03 21:02:25,242 epoch 6 - iter 176/447 - loss 0.02273349 - time (sec): 32.80 - samples/sec: 1079.58 - lr: 0.000026 - momentum: 0.000000
2023-09-03 21:02:33,012 epoch 6 - iter 220/447 - loss 0.02157790 - time (sec): 40.57 - samples/sec: 1094.10 - lr: 0.000025 - momentum: 0.000000
2023-09-03 21:02:40,172 epoch 6 - iter 264/447 - loss 0.02048096 - time (sec): 47.73 - samples/sec: 1096.13 - lr: 0.000025 - momentum: 0.000000
2023-09-03 21:02:47,619 epoch 6 - iter 308/447 - loss 0.02034238 - time (sec): 55.18 - samples/sec: 1092.41 - lr: 0.000024 - momentum: 0.000000
2023-09-03 21:02:54,850 epoch 6 - iter 352/447 - loss 0.02168193 - time (sec): 62.41 - samples/sec: 1093.46 - lr: 0.000023 - momentum: 0.000000
2023-09-03 21:03:02,551 epoch 6 - iter 396/447 - loss 0.02256137 - time (sec): 70.11 - samples/sec: 1097.72 - lr: 0.000023 - momentum: 0.000000
2023-09-03 21:03:09,393 epoch 6 - iter 440/447 - loss 0.02303606 - time (sec): 76.95 - samples/sec: 1102.62 - lr: 0.000022 - momentum: 0.000000
2023-09-03 21:03:11,217 ----------------------------------------------------------------------------------------------------
2023-09-03 21:03:11,217 EPOCH 6 done: loss 0.0231 - lr: 0.000022
2023-09-03 21:03:24,374 DEV : loss 0.19026526808738708 - f1-score (micro avg)  0.7761
2023-09-03 21:03:24,400 saving best model
2023-09-03 21:03:25,705 ----------------------------------------------------------------------------------------------------
2023-09-03 21:03:33,900 epoch 7 - iter 44/447 - loss 0.01327631 - time (sec): 8.19 - samples/sec: 1122.36 - lr: 0.000022 - momentum: 0.000000
2023-09-03 21:03:43,494 epoch 7 - iter 88/447 - loss 0.01391578 - time (sec): 17.79 - samples/sec: 1066.78 - lr: 0.000021 - momentum: 0.000000
2023-09-03 21:03:51,315 epoch 7 - iter 132/447 - loss 0.01429568 - time (sec): 25.61 - samples/sec: 1079.50 - lr: 0.000021 - momentum: 0.000000
2023-09-03 21:03:59,158 epoch 7 - iter 176/447 - loss 0.01322758 - time (sec): 33.45 - samples/sec: 1099.43 - lr: 0.000020 - momentum: 0.000000
2023-09-03 21:04:07,980 epoch 7 - iter 220/447 - loss 0.01222787 - time (sec): 42.27 - samples/sec: 1080.35 - lr: 0.000020 - momentum: 0.000000
2023-09-03 21:04:15,077 epoch 7 - iter 264/447 - loss 0.01097556 - time (sec): 49.37 - samples/sec: 1079.67 - lr: 0.000019 - momentum: 0.000000
2023-09-03 21:04:21,966 epoch 7 - iter 308/447 - loss 0.01200888 - time (sec): 56.26 - samples/sec: 1085.28 - lr: 0.000018 - momentum: 0.000000
2023-09-03 21:04:29,651 epoch 7 - iter 352/447 - loss 0.01374890 - time (sec): 63.94 - samples/sec: 1083.72 - lr: 0.000018 - momentum: 0.000000
2023-09-03 21:04:36,475 epoch 7 - iter 396/447 - loss 0.01349475 - time (sec): 70.77 - samples/sec: 1089.46 - lr: 0.000017 - momentum: 0.000000
2023-09-03 21:04:43,497 epoch 7 - iter 440/447 - loss 0.01326615 - time (sec): 77.79 - samples/sec: 1093.14 - lr: 0.000017 - momentum: 0.000000
2023-09-03 21:04:44,921 ----------------------------------------------------------------------------------------------------
2023-09-03 21:04:44,921 EPOCH 7 done: loss 0.0131 - lr: 0.000017
2023-09-03 21:04:58,031 DEV : loss 0.20857642590999603 - f1-score (micro avg)  0.7897
2023-09-03 21:04:58,060 saving best model
2023-09-03 21:04:59,399 ----------------------------------------------------------------------------------------------------
2023-09-03 21:05:06,891 epoch 8 - iter 44/447 - loss 0.00956675 - time (sec): 7.49 - samples/sec: 1153.98 - lr: 0.000016 - momentum: 0.000000
2023-09-03 21:05:14,219 epoch 8 - iter 88/447 - loss 0.00759491 - time (sec): 14.82 - samples/sec: 1132.50 - lr: 0.000016 - momentum: 0.000000
2023-09-03 21:05:23,510 epoch 8 - iter 132/447 - loss 0.00798829 - time (sec): 24.11 - samples/sec: 1110.17 - lr: 0.000015 - momentum: 0.000000
2023-09-03 21:05:31,762 epoch 8 - iter 176/447 - loss 0.00858372 - time (sec): 32.36 - samples/sec: 1087.71 - lr: 0.000015 - momentum: 0.000000
2023-09-03 21:05:39,184 epoch 8 - iter 220/447 - loss 0.00883033 - time (sec): 39.78 - samples/sec: 1095.55 - lr: 0.000014 - momentum: 0.000000
2023-09-03 21:05:47,717 epoch 8 - iter 264/447 - loss 0.00944911 - time (sec): 48.32 - samples/sec: 1083.50 - lr: 0.000013 - momentum: 0.000000
2023-09-03 21:05:55,417 epoch 8 - iter 308/447 - loss 0.00948601 - time (sec): 56.02 - samples/sec: 1087.55 - lr: 0.000013 - momentum: 0.000000
2023-09-03 21:06:02,467 epoch 8 - iter 352/447 - loss 0.00892223 - time (sec): 63.07 - samples/sec: 1092.67 - lr: 0.000012 - momentum: 0.000000
2023-09-03 21:06:10,154 epoch 8 - iter 396/447 - loss 0.00886820 - time (sec): 70.75 - samples/sec: 1094.43 - lr: 0.000012 - momentum: 0.000000
2023-09-03 21:06:16,959 epoch 8 - iter 440/447 - loss 0.00915381 - time (sec): 77.56 - samples/sec: 1100.56 - lr: 0.000011 - momentum: 0.000000
2023-09-03 21:06:17,966 ----------------------------------------------------------------------------------------------------
2023-09-03 21:06:17,967 EPOCH 8 done: loss 0.0092 - lr: 0.000011
2023-09-03 21:06:31,579 DEV : loss 0.2286161482334137 - f1-score (micro avg)  0.7771
2023-09-03 21:06:31,605 ----------------------------------------------------------------------------------------------------
2023-09-03 21:06:39,659 epoch 9 - iter 44/447 - loss 0.00534548 - time (sec): 8.05 - samples/sec: 1015.04 - lr: 0.000011 - momentum: 0.000000
2023-09-03 21:06:48,777 epoch 9 - iter 88/447 - loss 0.00425359 - time (sec): 17.17 - samples/sec: 1019.60 - lr: 0.000010 - momentum: 0.000000
2023-09-03 21:06:56,849 epoch 9 - iter 132/447 - loss 0.00444618 - time (sec): 25.24 - samples/sec: 1032.27 - lr: 0.000010 - momentum: 0.000000
2023-09-03 21:07:04,488 epoch 9 - iter 176/447 - loss 0.00461089 - time (sec): 32.88 - samples/sec: 1047.40 - lr: 0.000009 - momentum: 0.000000
2023-09-03 21:07:11,516 epoch 9 - iter 220/447 - loss 0.00539607 - time (sec): 39.91 - samples/sec: 1071.49 - lr: 0.000008 - momentum: 0.000000
2023-09-03 21:07:18,585 epoch 9 - iter 264/447 - loss 0.00492824 - time (sec): 46.98 - samples/sec: 1083.02 - lr: 0.000008 - momentum: 0.000000
2023-09-03 21:07:25,897 epoch 9 - iter 308/447 - loss 0.00432453 - time (sec): 54.29 - samples/sec: 1086.25 - lr: 0.000007 - momentum: 0.000000
2023-09-03 21:07:33,042 epoch 9 - iter 352/447 - loss 0.00427809 - time (sec): 61.44 - samples/sec: 1093.30 - lr: 0.000007 - momentum: 0.000000
2023-09-03 21:07:41,918 epoch 9 - iter 396/447 - loss 0.00449438 - time (sec): 70.31 - samples/sec: 1095.67 - lr: 0.000006 - momentum: 0.000000
2023-09-03 21:07:49,889 epoch 9 - iter 440/447 - loss 0.00470024 - time (sec): 78.28 - samples/sec: 1089.96 - lr: 0.000006 - momentum: 0.000000
2023-09-03 21:07:50,912 ----------------------------------------------------------------------------------------------------
2023-09-03 21:07:50,913 EPOCH 9 done: loss 0.0052 - lr: 0.000006
2023-09-03 21:08:04,501 DEV : loss 0.22261452674865723 - f1-score (micro avg)  0.7777
2023-09-03 21:08:04,528 ----------------------------------------------------------------------------------------------------
2023-09-03 21:08:12,555 epoch 10 - iter 44/447 - loss 0.00479591 - time (sec): 8.03 - samples/sec: 1123.45 - lr: 0.000005 - momentum: 0.000000
2023-09-03 21:08:19,833 epoch 10 - iter 88/447 - loss 0.00658392 - time (sec): 15.30 - samples/sec: 1130.43 - lr: 0.000005 - momentum: 0.000000
2023-09-03 21:08:27,230 epoch 10 - iter 132/447 - loss 0.00460342 - time (sec): 22.70 - samples/sec: 1133.81 - lr: 0.000004 - momentum: 0.000000
2023-09-03 21:08:35,120 epoch 10 - iter 176/447 - loss 0.00429682 - time (sec): 30.59 - samples/sec: 1132.08 - lr: 0.000003 - momentum: 0.000000
2023-09-03 21:08:41,797 epoch 10 - iter 220/447 - loss 0.00420025 - time (sec): 37.27 - samples/sec: 1144.52 - lr: 0.000003 - momentum: 0.000000
2023-09-03 21:08:48,910 epoch 10 - iter 264/447 - loss 0.00362146 - time (sec): 44.38 - samples/sec: 1144.62 - lr: 0.000002 - momentum: 0.000000
2023-09-03 21:08:56,331 epoch 10 - iter 308/447 - loss 0.00361059 - time (sec): 51.80 - samples/sec: 1147.07 - lr: 0.000002 - momentum: 0.000000
2023-09-03 21:09:05,620 epoch 10 - iter 352/447 - loss 0.00336808 - time (sec): 61.09 - samples/sec: 1140.21 - lr: 0.000001 - momentum: 0.000000
2023-09-03 21:09:12,495 epoch 10 - iter 396/447 - loss 0.00329159 - time (sec): 67.97 - samples/sec: 1140.42 - lr: 0.000001 - momentum: 0.000000
2023-09-03 21:09:19,057 epoch 10 - iter 440/447 - loss 0.00311645 - time (sec): 74.53 - samples/sec: 1144.22 - lr: 0.000000 - momentum: 0.000000
2023-09-03 21:09:20,071 ----------------------------------------------------------------------------------------------------
2023-09-03 21:09:20,072 EPOCH 10 done: loss 0.0031 - lr: 0.000000
2023-09-03 21:09:32,827 DEV : loss 0.2274623066186905 - f1-score (micro avg)  0.7878
2023-09-03 21:09:33,320 ----------------------------------------------------------------------------------------------------
2023-09-03 21:09:33,321 Loading model from best epoch ...
2023-09-03 21:09:35,181 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 21:09:45,092 
Results:
- F-score (micro) 0.7335
- F-score (macro) 0.6578
- Accuracy 0.5978

By class:
              precision    recall  f1-score   support

         loc     0.8115    0.8523    0.8314       596
        pers     0.6434    0.7477    0.6917       333
         org     0.4839    0.4545    0.4687       132
        prod     0.5690    0.5000    0.5323        66
        time     0.7358    0.7959    0.7647        49

   micro avg     0.7123    0.7560    0.7335      1176
   macro avg     0.6487    0.6701    0.6578      1176
weighted avg     0.7104    0.7560    0.7316      1176

2023-09-03 21:09:45,092 ----------------------------------------------------------------------------------------------------