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hmbench-newseye-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4

Token Classification
Flair
PyTorch
German
sequence-tagger-model
Model card Files Community
2
hmbench-newseye-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
File size: 24,147 Bytes 
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2023-10-15 21:09:01,347 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,348 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=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-15 21:09:01,348 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,348 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences
 - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator
2023-10-15 21:09:01,348 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,348 Train:  20847 sentences
2023-10-15 21:09:01,348         (train_with_dev=False, train_with_test=False)
2023-10-15 21:09:01,348 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,348 Training Params:
2023-10-15 21:09:01,349  - learning_rate: "5e-05" 
2023-10-15 21:09:01,349  - mini_batch_size: "4"
2023-10-15 21:09:01,349  - max_epochs: "10"
2023-10-15 21:09:01,349  - shuffle: "True"
2023-10-15 21:09:01,349 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,349 Plugins:
2023-10-15 21:09:01,349  - LinearScheduler | warmup_fraction: '0.1'
2023-10-15 21:09:01,349 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,349 Final evaluation on model from best epoch (best-model.pt)
2023-10-15 21:09:01,349  - metric: "('micro avg', 'f1-score')"
2023-10-15 21:09:01,349 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,349 Computation:
2023-10-15 21:09:01,349  - compute on device: cuda:0
2023-10-15 21:09:01,349  - embedding storage: none
2023-10-15 21:09:01,349 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,349 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-15 21:09:01,349 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:01,349 ----------------------------------------------------------------------------------------------------
2023-10-15 21:09:26,219 epoch 1 - iter 521/5212 - loss 1.35418822 - time (sec): 24.87 - samples/sec: 1405.99 - lr: 0.000005 - momentum: 0.000000
2023-10-15 21:09:51,690 epoch 1 - iter 1042/5212 - loss 0.87173210 - time (sec): 50.34 - samples/sec: 1460.05 - lr: 0.000010 - momentum: 0.000000
2023-10-15 21:10:17,117 epoch 1 - iter 1563/5212 - loss 0.68359880 - time (sec): 75.77 - samples/sec: 1436.88 - lr: 0.000015 - momentum: 0.000000
2023-10-15 21:10:42,614 epoch 1 - iter 2084/5212 - loss 0.57980752 - time (sec): 101.26 - samples/sec: 1431.39 - lr: 0.000020 - momentum: 0.000000
2023-10-15 21:11:08,079 epoch 1 - iter 2605/5212 - loss 0.50915925 - time (sec): 126.73 - samples/sec: 1450.07 - lr: 0.000025 - momentum: 0.000000
2023-10-15 21:11:33,037 epoch 1 - iter 3126/5212 - loss 0.46695555 - time (sec): 151.69 - samples/sec: 1443.28 - lr: 0.000030 - momentum: 0.000000
2023-10-15 21:11:57,932 epoch 1 - iter 3647/5212 - loss 0.43068088 - time (sec): 176.58 - samples/sec: 1443.12 - lr: 0.000035 - momentum: 0.000000
2023-10-15 21:12:23,688 epoch 1 - iter 4168/5212 - loss 0.40031908 - time (sec): 202.34 - samples/sec: 1444.07 - lr: 0.000040 - momentum: 0.000000
2023-10-15 21:12:48,715 epoch 1 - iter 4689/5212 - loss 0.38161901 - time (sec): 227.37 - samples/sec: 1445.17 - lr: 0.000045 - momentum: 0.000000
2023-10-15 21:13:15,489 epoch 1 - iter 5210/5212 - loss 0.36415325 - time (sec): 254.14 - samples/sec: 1445.58 - lr: 0.000050 - momentum: 0.000000
2023-10-15 21:13:15,572 ----------------------------------------------------------------------------------------------------
2023-10-15 21:13:15,573 EPOCH 1 done: loss 0.3641 - lr: 0.000050
2023-10-15 21:13:21,332 DEV : loss 0.12803316116333008 - f1-score (micro avg)  0.2579
2023-10-15 21:13:21,357 saving best model
2023-10-15 21:13:21,729 ----------------------------------------------------------------------------------------------------
2023-10-15 21:13:47,367 epoch 2 - iter 521/5212 - loss 0.21777270 - time (sec): 25.64 - samples/sec: 1486.64 - lr: 0.000049 - momentum: 0.000000
2023-10-15 21:14:13,018 epoch 2 - iter 1042/5212 - loss 0.18749115 - time (sec): 51.29 - samples/sec: 1487.45 - lr: 0.000049 - momentum: 0.000000
2023-10-15 21:14:38,321 epoch 2 - iter 1563/5212 - loss 0.18643203 - time (sec): 76.59 - samples/sec: 1481.92 - lr: 0.000048 - momentum: 0.000000
2023-10-15 21:15:03,555 epoch 2 - iter 2084/5212 - loss 0.18814099 - time (sec): 101.82 - samples/sec: 1457.01 - lr: 0.000048 - momentum: 0.000000
2023-10-15 21:15:28,943 epoch 2 - iter 2605/5212 - loss 0.19599826 - time (sec): 127.21 - samples/sec: 1459.62 - lr: 0.000047 - momentum: 0.000000
2023-10-15 21:15:53,824 epoch 2 - iter 3126/5212 - loss 0.19471761 - time (sec): 152.09 - samples/sec: 1454.98 - lr: 0.000047 - momentum: 0.000000
2023-10-15 21:16:18,931 epoch 2 - iter 3647/5212 - loss 0.19295220 - time (sec): 177.20 - samples/sec: 1466.52 - lr: 0.000046 - momentum: 0.000000
2023-10-15 21:16:42,937 epoch 2 - iter 4168/5212 - loss 0.19677761 - time (sec): 201.21 - samples/sec: 1467.46 - lr: 0.000046 - momentum: 0.000000
2023-10-15 21:17:07,768 epoch 2 - iter 4689/5212 - loss 0.19258009 - time (sec): 226.04 - samples/sec: 1476.98 - lr: 0.000045 - momentum: 0.000000
2023-10-15 21:17:31,714 epoch 2 - iter 5210/5212 - loss 0.19109860 - time (sec): 249.98 - samples/sec: 1469.65 - lr: 0.000044 - momentum: 0.000000
2023-10-15 21:17:31,800 ----------------------------------------------------------------------------------------------------
2023-10-15 21:17:31,800 EPOCH 2 done: loss 0.1911 - lr: 0.000044
2023-10-15 21:17:40,715 DEV : loss 0.12834765017032623 - f1-score (micro avg)  0.3234
2023-10-15 21:17:40,741 saving best model
2023-10-15 21:17:41,160 ----------------------------------------------------------------------------------------------------
2023-10-15 21:18:06,231 epoch 3 - iter 521/5212 - loss 0.16680848 - time (sec): 25.07 - samples/sec: 1455.11 - lr: 0.000044 - momentum: 0.000000
2023-10-15 21:18:31,447 epoch 3 - iter 1042/5212 - loss 0.15227978 - time (sec): 50.28 - samples/sec: 1454.38 - lr: 0.000043 - momentum: 0.000000
2023-10-15 21:18:56,965 epoch 3 - iter 1563/5212 - loss 0.15045944 - time (sec): 75.80 - samples/sec: 1457.53 - lr: 0.000043 - momentum: 0.000000
2023-10-15 21:19:22,788 epoch 3 - iter 2084/5212 - loss 0.15461880 - time (sec): 101.63 - samples/sec: 1460.91 - lr: 0.000042 - momentum: 0.000000
2023-10-15 21:19:47,794 epoch 3 - iter 2605/5212 - loss 0.14819648 - time (sec): 126.63 - samples/sec: 1460.43 - lr: 0.000042 - momentum: 0.000000
2023-10-15 21:20:12,632 epoch 3 - iter 3126/5212 - loss 0.14802839 - time (sec): 151.47 - samples/sec: 1457.06 - lr: 0.000041 - momentum: 0.000000
2023-10-15 21:20:37,526 epoch 3 - iter 3647/5212 - loss 0.14888939 - time (sec): 176.36 - samples/sec: 1455.73 - lr: 0.000041 - momentum: 0.000000
2023-10-15 21:21:03,199 epoch 3 - iter 4168/5212 - loss 0.14477081 - time (sec): 202.04 - samples/sec: 1463.02 - lr: 0.000040 - momentum: 0.000000
2023-10-15 21:21:28,311 epoch 3 - iter 4689/5212 - loss 0.14379556 - time (sec): 227.15 - samples/sec: 1463.51 - lr: 0.000039 - momentum: 0.000000
2023-10-15 21:21:53,162 epoch 3 - iter 5210/5212 - loss 0.14317646 - time (sec): 252.00 - samples/sec: 1457.92 - lr: 0.000039 - momentum: 0.000000
2023-10-15 21:21:53,250 ----------------------------------------------------------------------------------------------------
2023-10-15 21:21:53,250 EPOCH 3 done: loss 0.1432 - lr: 0.000039
2023-10-15 21:22:01,515 DEV : loss 0.16754454374313354 - f1-score (micro avg)  0.3436
2023-10-15 21:22:01,544 saving best model
2023-10-15 21:22:02,149 ----------------------------------------------------------------------------------------------------
2023-10-15 21:22:27,751 epoch 4 - iter 521/5212 - loss 0.11278945 - time (sec): 25.60 - samples/sec: 1434.22 - lr: 0.000038 - momentum: 0.000000
2023-10-15 21:22:52,628 epoch 4 - iter 1042/5212 - loss 0.10789964 - time (sec): 50.48 - samples/sec: 1413.48 - lr: 0.000038 - momentum: 0.000000
2023-10-15 21:23:17,727 epoch 4 - iter 1563/5212 - loss 0.11007241 - time (sec): 75.58 - samples/sec: 1421.41 - lr: 0.000037 - momentum: 0.000000
2023-10-15 21:23:43,808 epoch 4 - iter 2084/5212 - loss 0.10613979 - time (sec): 101.66 - samples/sec: 1422.18 - lr: 0.000037 - momentum: 0.000000
2023-10-15 21:24:08,589 epoch 4 - iter 2605/5212 - loss 0.10636173 - time (sec): 126.44 - samples/sec: 1423.89 - lr: 0.000036 - momentum: 0.000000
2023-10-15 21:24:33,295 epoch 4 - iter 3126/5212 - loss 0.11023475 - time (sec): 151.14 - samples/sec: 1428.26 - lr: 0.000036 - momentum: 0.000000
2023-10-15 21:24:58,631 epoch 4 - iter 3647/5212 - loss 0.11185702 - time (sec): 176.48 - samples/sec: 1441.00 - lr: 0.000035 - momentum: 0.000000
2023-10-15 21:25:23,843 epoch 4 - iter 4168/5212 - loss 0.11237802 - time (sec): 201.69 - samples/sec: 1437.97 - lr: 0.000034 - momentum: 0.000000
2023-10-15 21:25:49,127 epoch 4 - iter 4689/5212 - loss 0.11101056 - time (sec): 226.98 - samples/sec: 1445.77 - lr: 0.000034 - momentum: 0.000000
2023-10-15 21:26:14,931 epoch 4 - iter 5210/5212 - loss 0.10870647 - time (sec): 252.78 - samples/sec: 1453.35 - lr: 0.000033 - momentum: 0.000000
2023-10-15 21:26:15,016 ----------------------------------------------------------------------------------------------------
2023-10-15 21:26:15,016 EPOCH 4 done: loss 0.1087 - lr: 0.000033
2023-10-15 21:26:23,320 DEV : loss 0.23736144602298737 - f1-score (micro avg)  0.3947
2023-10-15 21:26:23,351 saving best model
2023-10-15 21:26:23,983 ----------------------------------------------------------------------------------------------------
2023-10-15 21:26:48,489 epoch 5 - iter 521/5212 - loss 0.07596090 - time (sec): 24.50 - samples/sec: 1403.26 - lr: 0.000033 - momentum: 0.000000
2023-10-15 21:27:13,356 epoch 5 - iter 1042/5212 - loss 0.08242943 - time (sec): 49.37 - samples/sec: 1408.42 - lr: 0.000032 - momentum: 0.000000
2023-10-15 21:27:38,336 epoch 5 - iter 1563/5212 - loss 0.08169838 - time (sec): 74.35 - samples/sec: 1414.40 - lr: 0.000032 - momentum: 0.000000
2023-10-15 21:28:03,345 epoch 5 - iter 2084/5212 - loss 0.08443360 - time (sec): 99.36 - samples/sec: 1432.55 - lr: 0.000031 - momentum: 0.000000
2023-10-15 21:28:29,132 epoch 5 - iter 2605/5212 - loss 0.08304632 - time (sec): 125.15 - samples/sec: 1438.85 - lr: 0.000031 - momentum: 0.000000
2023-10-15 21:28:54,172 epoch 5 - iter 3126/5212 - loss 0.08296829 - time (sec): 150.18 - samples/sec: 1435.67 - lr: 0.000030 - momentum: 0.000000
2023-10-15 21:29:19,404 epoch 5 - iter 3647/5212 - loss 0.08179795 - time (sec): 175.42 - samples/sec: 1442.82 - lr: 0.000029 - momentum: 0.000000
2023-10-15 21:29:45,496 epoch 5 - iter 4168/5212 - loss 0.08011600 - time (sec): 201.51 - samples/sec: 1445.91 - lr: 0.000029 - momentum: 0.000000
2023-10-15 21:30:11,175 epoch 5 - iter 4689/5212 - loss 0.07905627 - time (sec): 227.19 - samples/sec: 1449.26 - lr: 0.000028 - momentum: 0.000000
2023-10-15 21:30:36,583 epoch 5 - iter 5210/5212 - loss 0.08040599 - time (sec): 252.60 - samples/sec: 1454.09 - lr: 0.000028 - momentum: 0.000000
2023-10-15 21:30:36,676 ----------------------------------------------------------------------------------------------------
2023-10-15 21:30:36,676 EPOCH 5 done: loss 0.0804 - lr: 0.000028
2023-10-15 21:30:45,177 DEV : loss 0.32270362973213196 - f1-score (micro avg)  0.3444
2023-10-15 21:30:45,211 ----------------------------------------------------------------------------------------------------
2023-10-15 21:31:10,971 epoch 6 - iter 521/5212 - loss 0.07154848 - time (sec): 25.76 - samples/sec: 1455.64 - lr: 0.000027 - momentum: 0.000000
2023-10-15 21:31:36,495 epoch 6 - iter 1042/5212 - loss 0.08555596 - time (sec): 51.28 - samples/sec: 1482.06 - lr: 0.000027 - momentum: 0.000000
2023-10-15 21:32:01,396 epoch 6 - iter 1563/5212 - loss 0.07751863 - time (sec): 76.18 - samples/sec: 1462.55 - lr: 0.000026 - momentum: 0.000000
2023-10-15 21:32:26,938 epoch 6 - iter 2084/5212 - loss 0.07146560 - time (sec): 101.72 - samples/sec: 1481.82 - lr: 0.000026 - momentum: 0.000000
2023-10-15 21:32:51,955 epoch 6 - iter 2605/5212 - loss 0.06915037 - time (sec): 126.74 - samples/sec: 1475.93 - lr: 0.000025 - momentum: 0.000000
2023-10-15 21:33:17,101 epoch 6 - iter 3126/5212 - loss 0.06782335 - time (sec): 151.89 - samples/sec: 1469.97 - lr: 0.000024 - momentum: 0.000000
2023-10-15 21:33:41,720 epoch 6 - iter 3647/5212 - loss 0.06809506 - time (sec): 176.51 - samples/sec: 1461.46 - lr: 0.000024 - momentum: 0.000000
2023-10-15 21:34:06,826 epoch 6 - iter 4168/5212 - loss 0.06739184 - time (sec): 201.61 - samples/sec: 1455.29 - lr: 0.000023 - momentum: 0.000000
2023-10-15 21:34:31,898 epoch 6 - iter 4689/5212 - loss 0.06720584 - time (sec): 226.68 - samples/sec: 1449.42 - lr: 0.000023 - momentum: 0.000000
2023-10-15 21:34:57,524 epoch 6 - iter 5210/5212 - loss 0.06716812 - time (sec): 252.31 - samples/sec: 1454.78 - lr: 0.000022 - momentum: 0.000000
2023-10-15 21:34:57,655 ----------------------------------------------------------------------------------------------------
2023-10-15 21:34:57,655 EPOCH 6 done: loss 0.0671 - lr: 0.000022
2023-10-15 21:35:06,686 DEV : loss 0.3467041552066803 - f1-score (micro avg)  0.3468
2023-10-15 21:35:06,712 ----------------------------------------------------------------------------------------------------
2023-10-15 21:35:31,692 epoch 7 - iter 521/5212 - loss 0.04008855 - time (sec): 24.98 - samples/sec: 1510.39 - lr: 0.000022 - momentum: 0.000000
2023-10-15 21:35:57,463 epoch 7 - iter 1042/5212 - loss 0.04042192 - time (sec): 50.75 - samples/sec: 1495.14 - lr: 0.000021 - momentum: 0.000000
2023-10-15 21:36:22,413 epoch 7 - iter 1563/5212 - loss 0.04452128 - time (sec): 75.70 - samples/sec: 1475.67 - lr: 0.000021 - momentum: 0.000000
2023-10-15 21:36:47,471 epoch 7 - iter 2084/5212 - loss 0.04517209 - time (sec): 100.76 - samples/sec: 1444.65 - lr: 0.000020 - momentum: 0.000000
2023-10-15 21:37:12,770 epoch 7 - iter 2605/5212 - loss 0.04565804 - time (sec): 126.06 - samples/sec: 1454.67 - lr: 0.000019 - momentum: 0.000000
2023-10-15 21:37:37,812 epoch 7 - iter 3126/5212 - loss 0.04487907 - time (sec): 151.10 - samples/sec: 1446.65 - lr: 0.000019 - momentum: 0.000000
2023-10-15 21:38:03,557 epoch 7 - iter 3647/5212 - loss 0.04501311 - time (sec): 176.84 - samples/sec: 1459.11 - lr: 0.000018 - momentum: 0.000000
2023-10-15 21:38:28,512 epoch 7 - iter 4168/5212 - loss 0.04461942 - time (sec): 201.80 - samples/sec: 1448.64 - lr: 0.000018 - momentum: 0.000000
2023-10-15 21:38:54,620 epoch 7 - iter 4689/5212 - loss 0.04411942 - time (sec): 227.91 - samples/sec: 1452.78 - lr: 0.000017 - momentum: 0.000000
2023-10-15 21:39:19,467 epoch 7 - iter 5210/5212 - loss 0.04338822 - time (sec): 252.75 - samples/sec: 1453.50 - lr: 0.000017 - momentum: 0.000000
2023-10-15 21:39:19,553 ----------------------------------------------------------------------------------------------------
2023-10-15 21:39:19,553 EPOCH 7 done: loss 0.0434 - lr: 0.000017
2023-10-15 21:39:28,681 DEV : loss 0.3810468912124634 - f1-score (micro avg)  0.3437
2023-10-15 21:39:28,710 ----------------------------------------------------------------------------------------------------
2023-10-15 21:39:53,580 epoch 8 - iter 521/5212 - loss 0.03094489 - time (sec): 24.87 - samples/sec: 1372.29 - lr: 0.000016 - momentum: 0.000000
2023-10-15 21:40:18,965 epoch 8 - iter 1042/5212 - loss 0.03266070 - time (sec): 50.25 - samples/sec: 1449.21 - lr: 0.000016 - momentum: 0.000000
2023-10-15 21:40:44,068 epoch 8 - iter 1563/5212 - loss 0.03082353 - time (sec): 75.36 - samples/sec: 1445.56 - lr: 0.000015 - momentum: 0.000000
2023-10-15 21:41:09,236 epoch 8 - iter 2084/5212 - loss 0.03189132 - time (sec): 100.52 - samples/sec: 1443.37 - lr: 0.000014 - momentum: 0.000000
2023-10-15 21:41:34,729 epoch 8 - iter 2605/5212 - loss 0.03187351 - time (sec): 126.02 - samples/sec: 1451.00 - lr: 0.000014 - momentum: 0.000000
2023-10-15 21:42:00,318 epoch 8 - iter 3126/5212 - loss 0.03141481 - time (sec): 151.61 - samples/sec: 1456.23 - lr: 0.000013 - momentum: 0.000000
2023-10-15 21:42:25,218 epoch 8 - iter 3647/5212 - loss 0.03152484 - time (sec): 176.51 - samples/sec: 1459.90 - lr: 0.000013 - momentum: 0.000000
2023-10-15 21:42:50,184 epoch 8 - iter 4168/5212 - loss 0.03161608 - time (sec): 201.47 - samples/sec: 1459.49 - lr: 0.000012 - momentum: 0.000000
2023-10-15 21:43:15,609 epoch 8 - iter 4689/5212 - loss 0.03114092 - time (sec): 226.90 - samples/sec: 1458.94 - lr: 0.000012 - momentum: 0.000000
2023-10-15 21:43:40,617 epoch 8 - iter 5210/5212 - loss 0.03225916 - time (sec): 251.91 - samples/sec: 1457.75 - lr: 0.000011 - momentum: 0.000000
2023-10-15 21:43:40,714 ----------------------------------------------------------------------------------------------------
2023-10-15 21:43:40,715 EPOCH 8 done: loss 0.0323 - lr: 0.000011
2023-10-15 21:43:49,762 DEV : loss 0.3573097884654999 - f1-score (micro avg)  0.3737
2023-10-15 21:43:49,788 ----------------------------------------------------------------------------------------------------
2023-10-15 21:44:15,283 epoch 9 - iter 521/5212 - loss 0.02137501 - time (sec): 25.49 - samples/sec: 1557.65 - lr: 0.000011 - momentum: 0.000000
2023-10-15 21:44:40,744 epoch 9 - iter 1042/5212 - loss 0.02521327 - time (sec): 50.95 - samples/sec: 1532.21 - lr: 0.000010 - momentum: 0.000000
2023-10-15 21:45:06,007 epoch 9 - iter 1563/5212 - loss 0.02417142 - time (sec): 76.22 - samples/sec: 1518.03 - lr: 0.000009 - momentum: 0.000000
2023-10-15 21:45:30,713 epoch 9 - iter 2084/5212 - loss 0.02264293 - time (sec): 100.92 - samples/sec: 1505.23 - lr: 0.000009 - momentum: 0.000000
2023-10-15 21:45:55,673 epoch 9 - iter 2605/5212 - loss 0.02338377 - time (sec): 125.88 - samples/sec: 1495.02 - lr: 0.000008 - momentum: 0.000000
2023-10-15 21:46:20,343 epoch 9 - iter 3126/5212 - loss 0.02317935 - time (sec): 150.55 - samples/sec: 1467.58 - lr: 0.000008 - momentum: 0.000000
2023-10-15 21:46:45,572 epoch 9 - iter 3647/5212 - loss 0.02373783 - time (sec): 175.78 - samples/sec: 1469.70 - lr: 0.000007 - momentum: 0.000000
2023-10-15 21:47:10,321 epoch 9 - iter 4168/5212 - loss 0.02314043 - time (sec): 200.53 - samples/sec: 1469.15 - lr: 0.000007 - momentum: 0.000000
2023-10-15 21:47:35,423 epoch 9 - iter 4689/5212 - loss 0.02314496 - time (sec): 225.63 - samples/sec: 1467.76 - lr: 0.000006 - momentum: 0.000000
2023-10-15 21:48:00,356 epoch 9 - iter 5210/5212 - loss 0.02267322 - time (sec): 250.57 - samples/sec: 1466.11 - lr: 0.000006 - momentum: 0.000000
2023-10-15 21:48:00,446 ----------------------------------------------------------------------------------------------------
2023-10-15 21:48:00,446 EPOCH 9 done: loss 0.0227 - lr: 0.000006
2023-10-15 21:48:09,481 DEV : loss 0.42241212725639343 - f1-score (micro avg)  0.3776
2023-10-15 21:48:09,509 ----------------------------------------------------------------------------------------------------
2023-10-15 21:48:34,392 epoch 10 - iter 521/5212 - loss 0.01795075 - time (sec): 24.88 - samples/sec: 1427.01 - lr: 0.000005 - momentum: 0.000000
2023-10-15 21:48:59,088 epoch 10 - iter 1042/5212 - loss 0.01766837 - time (sec): 49.58 - samples/sec: 1399.01 - lr: 0.000004 - momentum: 0.000000
2023-10-15 21:49:24,146 epoch 10 - iter 1563/5212 - loss 0.01546036 - time (sec): 74.64 - samples/sec: 1428.03 - lr: 0.000004 - momentum: 0.000000
2023-10-15 21:49:48,972 epoch 10 - iter 2084/5212 - loss 0.01551573 - time (sec): 99.46 - samples/sec: 1433.30 - lr: 0.000003 - momentum: 0.000000
2023-10-15 21:50:13,944 epoch 10 - iter 2605/5212 - loss 0.01520813 - time (sec): 124.43 - samples/sec: 1436.94 - lr: 0.000003 - momentum: 0.000000
2023-10-15 21:50:39,014 epoch 10 - iter 3126/5212 - loss 0.01608964 - time (sec): 149.50 - samples/sec: 1440.85 - lr: 0.000002 - momentum: 0.000000
2023-10-15 21:51:04,117 epoch 10 - iter 3647/5212 - loss 0.01586596 - time (sec): 174.61 - samples/sec: 1446.98 - lr: 0.000002 - momentum: 0.000000
2023-10-15 21:51:29,596 epoch 10 - iter 4168/5212 - loss 0.01541581 - time (sec): 200.09 - samples/sec: 1456.61 - lr: 0.000001 - momentum: 0.000000
2023-10-15 21:51:54,619 epoch 10 - iter 4689/5212 - loss 0.01533530 - time (sec): 225.11 - samples/sec: 1454.47 - lr: 0.000001 - momentum: 0.000000
2023-10-15 21:52:20,313 epoch 10 - iter 5210/5212 - loss 0.01573070 - time (sec): 250.80 - samples/sec: 1464.56 - lr: 0.000000 - momentum: 0.000000
2023-10-15 21:52:20,399 ----------------------------------------------------------------------------------------------------
2023-10-15 21:52:20,400 EPOCH 10 done: loss 0.0157 - lr: 0.000000
2023-10-15 21:52:29,511 DEV : loss 0.42857325077056885 - f1-score (micro avg)  0.3698
2023-10-15 21:52:29,962 ----------------------------------------------------------------------------------------------------
2023-10-15 21:52:29,963 Loading model from best epoch ...
2023-10-15 21:52:31,489 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
2023-10-15 21:52:47,128 
Results:
- F-score (micro) 0.4359
- F-score (macro) 0.2917
- Accuracy 0.283

By class:
              precision    recall  f1-score   support

         LOC     0.5601    0.5338    0.5466      1214
         PER     0.3793    0.3676    0.3734       808
         ORG     0.2152    0.2890    0.2467       353
   HumanProd     0.0000    0.0000    0.0000        15

   micro avg     0.4337    0.4381    0.4359      2390
   macro avg     0.2886    0.2976    0.2917      2390
weighted avg     0.4445    0.4381    0.4403      2390

2023-10-15 21:52:47,129 ----------------------------------------------------------------------------------------------------