2023-10-13 14:08:11,374 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,375 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()
)"
2023-10-13 14:08:11,375 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,375 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
2023-10-13 14:08:11,375 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,375 Train:  3575 sentences
2023-10-13 14:08:11,375         (train_with_dev=False, train_with_test=False)
2023-10-13 14:08:11,375 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,375 Training Params:
2023-10-13 14:08:11,375  - learning_rate: "5e-05" 
2023-10-13 14:08:11,375  - mini_batch_size: "4"
2023-10-13 14:08:11,375  - max_epochs: "10"
2023-10-13 14:08:11,375  - shuffle: "True"
2023-10-13 14:08:11,375 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,375 Plugins:
2023-10-13 14:08:11,375  - LinearScheduler | warmup_fraction: '0.1'
2023-10-13 14:08:11,375 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,375 Final evaluation on model from best epoch (best-model.pt)
2023-10-13 14:08:11,375  - metric: "('micro avg', 'f1-score')"
2023-10-13 14:08:11,375 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,375 Computation:
2023-10-13 14:08:11,376  - compute on device: cuda:0
2023-10-13 14:08:11,376  - embedding storage: none
2023-10-13 14:08:11,376 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,376 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
2023-10-13 14:08:11,376 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:11,376 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:15,898 epoch 1 - iter 89/894 - loss 2.52923575 - time (sec): 4.52 - samples/sec: 2182.09 - lr: 0.000005 - momentum: 0.000000
2023-10-13 14:08:20,012 epoch 1 - iter 178/894 - loss 1.58342524 - time (sec): 8.64 - samples/sec: 2207.18 - lr: 0.000010 - momentum: 0.000000
2023-10-13 14:08:24,203 epoch 1 - iter 267/894 - loss 1.23639423 - time (sec): 12.83 - samples/sec: 2136.52 - lr: 0.000015 - momentum: 0.000000
2023-10-13 14:08:28,550 epoch 1 - iter 356/894 - loss 1.01715624 - time (sec): 17.17 - samples/sec: 2108.54 - lr: 0.000020 - momentum: 0.000000
2023-10-13 14:08:32,844 epoch 1 - iter 445/894 - loss 0.87794477 - time (sec): 21.47 - samples/sec: 2090.89 - lr: 0.000025 - momentum: 0.000000
2023-10-13 14:08:36,910 epoch 1 - iter 534/894 - loss 0.77886231 - time (sec): 25.53 - samples/sec: 2086.30 - lr: 0.000030 - momentum: 0.000000
2023-10-13 14:08:40,939 epoch 1 - iter 623/894 - loss 0.70820496 - time (sec): 29.56 - samples/sec: 2091.56 - lr: 0.000035 - momentum: 0.000000
2023-10-13 14:08:45,129 epoch 1 - iter 712/894 - loss 0.65158695 - time (sec): 33.75 - samples/sec: 2073.39 - lr: 0.000040 - momentum: 0.000000
2023-10-13 14:08:49,139 epoch 1 - iter 801/894 - loss 0.60985499 - time (sec): 37.76 - samples/sec: 2064.58 - lr: 0.000045 - momentum: 0.000000
2023-10-13 14:08:53,278 epoch 1 - iter 890/894 - loss 0.57386967 - time (sec): 41.90 - samples/sec: 2057.76 - lr: 0.000050 - momentum: 0.000000
2023-10-13 14:08:53,459 ----------------------------------------------------------------------------------------------------
2023-10-13 14:08:53,459 EPOCH 1 done: loss 0.5727 - lr: 0.000050
2023-10-13 14:08:58,500 DEV : loss 0.18978139758110046 - f1-score (micro avg)  0.5916
2023-10-13 14:08:58,525 saving best model
2023-10-13 14:08:58,878 ----------------------------------------------------------------------------------------------------
2023-10-13 14:09:03,117 epoch 2 - iter 89/894 - loss 0.21194614 - time (sec): 4.24 - samples/sec: 2181.41 - lr: 0.000049 - momentum: 0.000000
2023-10-13 14:09:07,553 epoch 2 - iter 178/894 - loss 0.19054416 - time (sec): 8.67 - samples/sec: 2045.48 - lr: 0.000049 - momentum: 0.000000
2023-10-13 14:09:12,119 epoch 2 - iter 267/894 - loss 0.17595858 - time (sec): 13.24 - samples/sec: 1960.01 - lr: 0.000048 - momentum: 0.000000
2023-10-13 14:09:16,730 epoch 2 - iter 356/894 - loss 0.17594605 - time (sec): 17.85 - samples/sec: 1879.92 - lr: 0.000048 - momentum: 0.000000
2023-10-13 14:09:21,206 epoch 2 - iter 445/894 - loss 0.17142673 - time (sec): 22.33 - samples/sec: 1886.32 - lr: 0.000047 - momentum: 0.000000
2023-10-13 14:09:25,650 epoch 2 - iter 534/894 - loss 0.16657106 - time (sec): 26.77 - samples/sec: 1893.29 - lr: 0.000047 - momentum: 0.000000
2023-10-13 14:09:29,793 epoch 2 - iter 623/894 - loss 0.16819979 - time (sec): 30.91 - samples/sec: 1920.54 - lr: 0.000046 - momentum: 0.000000
2023-10-13 14:09:33,961 epoch 2 - iter 712/894 - loss 0.16248898 - time (sec): 35.08 - samples/sec: 1942.69 - lr: 0.000046 - momentum: 0.000000
2023-10-13 14:09:38,270 epoch 2 - iter 801/894 - loss 0.16082101 - time (sec): 39.39 - samples/sec: 1957.79 - lr: 0.000045 - momentum: 0.000000
2023-10-13 14:09:42,495 epoch 2 - iter 890/894 - loss 0.15753470 - time (sec): 43.62 - samples/sec: 1972.33 - lr: 0.000044 - momentum: 0.000000
2023-10-13 14:09:42,682 ----------------------------------------------------------------------------------------------------
2023-10-13 14:09:42,682 EPOCH 2 done: loss 0.1577 - lr: 0.000044
2023-10-13 14:09:51,308 DEV : loss 0.16749663650989532 - f1-score (micro avg)  0.6977
2023-10-13 14:09:51,338 saving best model
2023-10-13 14:09:51,838 ----------------------------------------------------------------------------------------------------
2023-10-13 14:09:55,733 epoch 3 - iter 89/894 - loss 0.08007067 - time (sec): 3.89 - samples/sec: 2360.54 - lr: 0.000044 - momentum: 0.000000
2023-10-13 14:09:59,650 epoch 3 - iter 178/894 - loss 0.07640027 - time (sec): 7.81 - samples/sec: 2295.76 - lr: 0.000043 - momentum: 0.000000
2023-10-13 14:10:03,873 epoch 3 - iter 267/894 - loss 0.08683829 - time (sec): 12.03 - samples/sec: 2266.26 - lr: 0.000043 - momentum: 0.000000
2023-10-13 14:10:07,876 epoch 3 - iter 356/894 - loss 0.09438784 - time (sec): 16.04 - samples/sec: 2263.33 - lr: 0.000042 - momentum: 0.000000
2023-10-13 14:10:11,886 epoch 3 - iter 445/894 - loss 0.09349886 - time (sec): 20.05 - samples/sec: 2216.06 - lr: 0.000042 - momentum: 0.000000
2023-10-13 14:10:15,754 epoch 3 - iter 534/894 - loss 0.09086137 - time (sec): 23.91 - samples/sec: 2207.82 - lr: 0.000041 - momentum: 0.000000
2023-10-13 14:10:19,673 epoch 3 - iter 623/894 - loss 0.09013375 - time (sec): 27.83 - samples/sec: 2181.57 - lr: 0.000041 - momentum: 0.000000
2023-10-13 14:10:23,738 epoch 3 - iter 712/894 - loss 0.09024790 - time (sec): 31.90 - samples/sec: 2171.31 - lr: 0.000040 - momentum: 0.000000
2023-10-13 14:10:27,643 epoch 3 - iter 801/894 - loss 0.09257071 - time (sec): 35.80 - samples/sec: 2178.38 - lr: 0.000039 - momentum: 0.000000
2023-10-13 14:10:31,533 epoch 3 - iter 890/894 - loss 0.09331365 - time (sec): 39.69 - samples/sec: 2172.02 - lr: 0.000039 - momentum: 0.000000
2023-10-13 14:10:31,707 ----------------------------------------------------------------------------------------------------
2023-10-13 14:10:31,707 EPOCH 3 done: loss 0.0939 - lr: 0.000039
2023-10-13 14:10:40,166 DEV : loss 0.1999683529138565 - f1-score (micro avg)  0.7291
2023-10-13 14:10:40,193 saving best model
2023-10-13 14:10:40,669 ----------------------------------------------------------------------------------------------------
2023-10-13 14:10:44,976 epoch 4 - iter 89/894 - loss 0.07161300 - time (sec): 4.30 - samples/sec: 2161.37 - lr: 0.000038 - momentum: 0.000000
2023-10-13 14:10:49,749 epoch 4 - iter 178/894 - loss 0.06237632 - time (sec): 9.07 - samples/sec: 1927.98 - lr: 0.000038 - momentum: 0.000000
2023-10-13 14:10:54,687 epoch 4 - iter 267/894 - loss 0.06571349 - time (sec): 14.01 - samples/sec: 1972.83 - lr: 0.000037 - momentum: 0.000000
2023-10-13 14:10:58,893 epoch 4 - iter 356/894 - loss 0.06326807 - time (sec): 18.22 - samples/sec: 1959.55 - lr: 0.000037 - momentum: 0.000000
2023-10-13 14:11:03,057 epoch 4 - iter 445/894 - loss 0.06462152 - time (sec): 22.38 - samples/sec: 1972.59 - lr: 0.000036 - momentum: 0.000000
2023-10-13 14:11:07,375 epoch 4 - iter 534/894 - loss 0.06356342 - time (sec): 26.70 - samples/sec: 2004.24 - lr: 0.000036 - momentum: 0.000000
2023-10-13 14:11:11,407 epoch 4 - iter 623/894 - loss 0.06359237 - time (sec): 30.73 - samples/sec: 2007.14 - lr: 0.000035 - momentum: 0.000000
2023-10-13 14:11:15,432 epoch 4 - iter 712/894 - loss 0.06394484 - time (sec): 34.76 - samples/sec: 2010.72 - lr: 0.000034 - momentum: 0.000000
2023-10-13 14:11:19,477 epoch 4 - iter 801/894 - loss 0.06383650 - time (sec): 38.80 - samples/sec: 2010.42 - lr: 0.000034 - momentum: 0.000000
2023-10-13 14:11:23,578 epoch 4 - iter 890/894 - loss 0.06368701 - time (sec): 42.90 - samples/sec: 2009.83 - lr: 0.000033 - momentum: 0.000000
2023-10-13 14:11:23,755 ----------------------------------------------------------------------------------------------------
2023-10-13 14:11:23,755 EPOCH 4 done: loss 0.0638 - lr: 0.000033
2023-10-13 14:11:32,038 DEV : loss 0.22368095815181732 - f1-score (micro avg)  0.7451
2023-10-13 14:11:32,065 saving best model
2023-10-13 14:11:32,473 ----------------------------------------------------------------------------------------------------
2023-10-13 14:11:36,581 epoch 5 - iter 89/894 - loss 0.05326395 - time (sec): 4.10 - samples/sec: 2126.63 - lr: 0.000033 - momentum: 0.000000
2023-10-13 14:11:40,582 epoch 5 - iter 178/894 - loss 0.04466412 - time (sec): 8.10 - samples/sec: 2102.13 - lr: 0.000032 - momentum: 0.000000
2023-10-13 14:11:44,899 epoch 5 - iter 267/894 - loss 0.04574289 - time (sec): 12.42 - samples/sec: 2112.80 - lr: 0.000032 - momentum: 0.000000
2023-10-13 14:11:48,919 epoch 5 - iter 356/894 - loss 0.04504252 - time (sec): 16.44 - samples/sec: 2116.26 - lr: 0.000031 - momentum: 0.000000
2023-10-13 14:11:53,162 epoch 5 - iter 445/894 - loss 0.04436721 - time (sec): 20.68 - samples/sec: 2130.98 - lr: 0.000031 - momentum: 0.000000
2023-10-13 14:11:57,502 epoch 5 - iter 534/894 - loss 0.04372206 - time (sec): 25.02 - samples/sec: 2107.92 - lr: 0.000030 - momentum: 0.000000
2023-10-13 14:12:01,606 epoch 5 - iter 623/894 - loss 0.04442546 - time (sec): 29.13 - samples/sec: 2096.61 - lr: 0.000029 - momentum: 0.000000
2023-10-13 14:12:05,829 epoch 5 - iter 712/894 - loss 0.04469383 - time (sec): 33.35 - samples/sec: 2095.05 - lr: 0.000029 - momentum: 0.000000
2023-10-13 14:12:09,867 epoch 5 - iter 801/894 - loss 0.04557369 - time (sec): 37.39 - samples/sec: 2080.06 - lr: 0.000028 - momentum: 0.000000
2023-10-13 14:12:14,004 epoch 5 - iter 890/894 - loss 0.04426329 - time (sec): 41.52 - samples/sec: 2074.21 - lr: 0.000028 - momentum: 0.000000
2023-10-13 14:12:14,193 ----------------------------------------------------------------------------------------------------
2023-10-13 14:12:14,193 EPOCH 5 done: loss 0.0441 - lr: 0.000028
2023-10-13 14:12:22,702 DEV : loss 0.23852457106113434 - f1-score (micro avg)  0.7475
2023-10-13 14:12:22,729 saving best model
2023-10-13 14:12:23,193 ----------------------------------------------------------------------------------------------------
2023-10-13 14:12:27,238 epoch 6 - iter 89/894 - loss 0.01889556 - time (sec): 4.04 - samples/sec: 2069.29 - lr: 0.000027 - momentum: 0.000000
2023-10-13 14:12:31,500 epoch 6 - iter 178/894 - loss 0.02626898 - time (sec): 8.30 - samples/sec: 2019.45 - lr: 0.000027 - momentum: 0.000000
2023-10-13 14:12:36,124 epoch 6 - iter 267/894 - loss 0.02606331 - time (sec): 12.93 - samples/sec: 2097.55 - lr: 0.000026 - momentum: 0.000000
2023-10-13 14:12:40,260 epoch 6 - iter 356/894 - loss 0.02937353 - time (sec): 17.06 - samples/sec: 2099.91 - lr: 0.000026 - momentum: 0.000000
2023-10-13 14:12:44,576 epoch 6 - iter 445/894 - loss 0.02750190 - time (sec): 21.38 - samples/sec: 2134.02 - lr: 0.000025 - momentum: 0.000000
2023-10-13 14:12:48,628 epoch 6 - iter 534/894 - loss 0.02780421 - time (sec): 25.43 - samples/sec: 2105.98 - lr: 0.000024 - momentum: 0.000000
2023-10-13 14:12:52,732 epoch 6 - iter 623/894 - loss 0.02856228 - time (sec): 29.54 - samples/sec: 2085.01 - lr: 0.000024 - momentum: 0.000000
2023-10-13 14:12:56,924 epoch 6 - iter 712/894 - loss 0.02984563 - time (sec): 33.73 - samples/sec: 2073.64 - lr: 0.000023 - momentum: 0.000000
2023-10-13 14:13:00,987 epoch 6 - iter 801/894 - loss 0.02950493 - time (sec): 37.79 - samples/sec: 2076.16 - lr: 0.000023 - momentum: 0.000000
2023-10-13 14:13:05,151 epoch 6 - iter 890/894 - loss 0.02941361 - time (sec): 41.96 - samples/sec: 2053.84 - lr: 0.000022 - momentum: 0.000000
2023-10-13 14:13:05,332 ----------------------------------------------------------------------------------------------------
2023-10-13 14:13:05,333 EPOCH 6 done: loss 0.0293 - lr: 0.000022
2023-10-13 14:13:13,835 DEV : loss 0.23937579989433289 - f1-score (micro avg)  0.7575
2023-10-13 14:13:13,863 saving best model
2023-10-13 14:13:14,366 ----------------------------------------------------------------------------------------------------
2023-10-13 14:13:18,512 epoch 7 - iter 89/894 - loss 0.01705401 - time (sec): 4.14 - samples/sec: 1884.74 - lr: 0.000022 - momentum: 0.000000
2023-10-13 14:13:22,533 epoch 7 - iter 178/894 - loss 0.01759034 - time (sec): 8.17 - samples/sec: 1917.37 - lr: 0.000021 - momentum: 0.000000
2023-10-13 14:13:27,065 epoch 7 - iter 267/894 - loss 0.01849552 - time (sec): 12.70 - samples/sec: 1997.91 - lr: 0.000021 - momentum: 0.000000
2023-10-13 14:13:31,220 epoch 7 - iter 356/894 - loss 0.02024248 - time (sec): 16.85 - samples/sec: 2024.97 - lr: 0.000020 - momentum: 0.000000
2023-10-13 14:13:35,408 epoch 7 - iter 445/894 - loss 0.02149739 - time (sec): 21.04 - samples/sec: 2048.75 - lr: 0.000019 - momentum: 0.000000
2023-10-13 14:13:39,801 epoch 7 - iter 534/894 - loss 0.01928417 - time (sec): 25.43 - samples/sec: 2018.47 - lr: 0.000019 - momentum: 0.000000
2023-10-13 14:13:44,296 epoch 7 - iter 623/894 - loss 0.02198540 - time (sec): 29.93 - samples/sec: 1990.90 - lr: 0.000018 - momentum: 0.000000
2023-10-13 14:13:49,168 epoch 7 - iter 712/894 - loss 0.02082455 - time (sec): 34.80 - samples/sec: 1978.14 - lr: 0.000018 - momentum: 0.000000
2023-10-13 14:13:53,734 epoch 7 - iter 801/894 - loss 0.02160685 - time (sec): 39.37 - samples/sec: 1966.85 - lr: 0.000017 - momentum: 0.000000
2023-10-13 14:13:58,424 epoch 7 - iter 890/894 - loss 0.02101591 - time (sec): 44.06 - samples/sec: 1955.83 - lr: 0.000017 - momentum: 0.000000
2023-10-13 14:13:58,627 ----------------------------------------------------------------------------------------------------
2023-10-13 14:13:58,627 EPOCH 7 done: loss 0.0209 - lr: 0.000017
2023-10-13 14:14:07,516 DEV : loss 0.2525382936000824 - f1-score (micro avg)  0.7615
2023-10-13 14:14:07,560 saving best model
2023-10-13 14:14:08,054 ----------------------------------------------------------------------------------------------------
2023-10-13 14:14:12,693 epoch 8 - iter 89/894 - loss 0.01225659 - time (sec): 4.64 - samples/sec: 1823.64 - lr: 0.000016 - momentum: 0.000000
2023-10-13 14:14:17,331 epoch 8 - iter 178/894 - loss 0.00698362 - time (sec): 9.28 - samples/sec: 1794.94 - lr: 0.000016 - momentum: 0.000000
2023-10-13 14:14:22,187 epoch 8 - iter 267/894 - loss 0.00689475 - time (sec): 14.13 - samples/sec: 1845.24 - lr: 0.000015 - momentum: 0.000000
2023-10-13 14:14:26,650 epoch 8 - iter 356/894 - loss 0.00651321 - time (sec): 18.59 - samples/sec: 1906.91 - lr: 0.000014 - momentum: 0.000000
2023-10-13 14:14:30,820 epoch 8 - iter 445/894 - loss 0.00846668 - time (sec): 22.76 - samples/sec: 1928.85 - lr: 0.000014 - momentum: 0.000000
2023-10-13 14:14:35,003 epoch 8 - iter 534/894 - loss 0.00904011 - time (sec): 26.95 - samples/sec: 1942.81 - lr: 0.000013 - momentum: 0.000000
2023-10-13 14:14:39,112 epoch 8 - iter 623/894 - loss 0.00884653 - time (sec): 31.06 - samples/sec: 1959.93 - lr: 0.000013 - momentum: 0.000000
2023-10-13 14:14:43,204 epoch 8 - iter 712/894 - loss 0.00918400 - time (sec): 35.15 - samples/sec: 1971.65 - lr: 0.000012 - momentum: 0.000000
2023-10-13 14:14:47,448 epoch 8 - iter 801/894 - loss 0.00950962 - time (sec): 39.39 - samples/sec: 1969.14 - lr: 0.000012 - momentum: 0.000000
2023-10-13 14:14:51,628 epoch 8 - iter 890/894 - loss 0.00947519 - time (sec): 43.57 - samples/sec: 1978.33 - lr: 0.000011 - momentum: 0.000000
2023-10-13 14:14:51,804 ----------------------------------------------------------------------------------------------------
2023-10-13 14:14:51,804 EPOCH 8 done: loss 0.0094 - lr: 0.000011
2023-10-13 14:15:00,785 DEV : loss 0.2568976879119873 - f1-score (micro avg)  0.7658
2023-10-13 14:15:00,815 saving best model
2023-10-13 14:15:01,340 ----------------------------------------------------------------------------------------------------
2023-10-13 14:15:05,956 epoch 9 - iter 89/894 - loss 0.00391659 - time (sec): 4.61 - samples/sec: 1787.79 - lr: 0.000011 - momentum: 0.000000
2023-10-13 14:15:10,412 epoch 9 - iter 178/894 - loss 0.00453941 - time (sec): 9.07 - samples/sec: 1958.05 - lr: 0.000010 - momentum: 0.000000
2023-10-13 14:15:14,661 epoch 9 - iter 267/894 - loss 0.00419557 - time (sec): 13.32 - samples/sec: 2014.76 - lr: 0.000009 - momentum: 0.000000
2023-10-13 14:15:18,739 epoch 9 - iter 356/894 - loss 0.00626206 - time (sec): 17.40 - samples/sec: 2030.77 - lr: 0.000009 - momentum: 0.000000
2023-10-13 14:15:22,908 epoch 9 - iter 445/894 - loss 0.00766227 - time (sec): 21.57 - samples/sec: 2017.53 - lr: 0.000008 - momentum: 0.000000
2023-10-13 14:15:26,928 epoch 9 - iter 534/894 - loss 0.00757263 - time (sec): 25.59 - samples/sec: 2035.91 - lr: 0.000008 - momentum: 0.000000
2023-10-13 14:15:31,064 epoch 9 - iter 623/894 - loss 0.00774753 - time (sec): 29.72 - samples/sec: 2044.80 - lr: 0.000007 - momentum: 0.000000
2023-10-13 14:15:35,087 epoch 9 - iter 712/894 - loss 0.00696736 - time (sec): 33.74 - samples/sec: 2055.60 - lr: 0.000007 - momentum: 0.000000
2023-10-13 14:15:39,286 epoch 9 - iter 801/894 - loss 0.00720776 - time (sec): 37.94 - samples/sec: 2045.59 - lr: 0.000006 - momentum: 0.000000
2023-10-13 14:15:43,335 epoch 9 - iter 890/894 - loss 0.00695611 - time (sec): 41.99 - samples/sec: 2052.94 - lr: 0.000006 - momentum: 0.000000
2023-10-13 14:15:43,509 ----------------------------------------------------------------------------------------------------
2023-10-13 14:15:43,509 EPOCH 9 done: loss 0.0069 - lr: 0.000006
2023-10-13 14:15:52,300 DEV : loss 0.26707446575164795 - f1-score (micro avg)  0.7733
2023-10-13 14:15:52,332 saving best model
2023-10-13 14:15:52,842 ----------------------------------------------------------------------------------------------------
2023-10-13 14:15:56,987 epoch 10 - iter 89/894 - loss 0.01316612 - time (sec): 4.14 - samples/sec: 2068.39 - lr: 0.000005 - momentum: 0.000000
2023-10-13 14:16:01,198 epoch 10 - iter 178/894 - loss 0.00979750 - time (sec): 8.35 - samples/sec: 1970.51 - lr: 0.000004 - momentum: 0.000000
2023-10-13 14:16:05,313 epoch 10 - iter 267/894 - loss 0.00661329 - time (sec): 12.47 - samples/sec: 2005.21 - lr: 0.000004 - momentum: 0.000000
2023-10-13 14:16:09,316 epoch 10 - iter 356/894 - loss 0.00704547 - time (sec): 16.47 - samples/sec: 2011.59 - lr: 0.000003 - momentum: 0.000000
2023-10-13 14:16:13,661 epoch 10 - iter 445/894 - loss 0.00624802 - time (sec): 20.82 - samples/sec: 2058.36 - lr: 0.000003 - momentum: 0.000000
2023-10-13 14:16:18,017 epoch 10 - iter 534/894 - loss 0.00619476 - time (sec): 25.17 - samples/sec: 2071.72 - lr: 0.000002 - momentum: 0.000000
2023-10-13 14:16:22,076 epoch 10 - iter 623/894 - loss 0.00569995 - time (sec): 29.23 - samples/sec: 2070.04 - lr: 0.000002 - momentum: 0.000000
2023-10-13 14:16:26,128 epoch 10 - iter 712/894 - loss 0.00579249 - time (sec): 33.28 - samples/sec: 2059.38 - lr: 0.000001 - momentum: 0.000000
2023-10-13 14:16:30,235 epoch 10 - iter 801/894 - loss 0.00526208 - time (sec): 37.39 - samples/sec: 2083.28 - lr: 0.000001 - momentum: 0.000000
2023-10-13 14:16:34,265 epoch 10 - iter 890/894 - loss 0.00512287 - time (sec): 41.42 - samples/sec: 2080.80 - lr: 0.000000 - momentum: 0.000000
2023-10-13 14:16:34,441 ----------------------------------------------------------------------------------------------------
2023-10-13 14:16:34,441 EPOCH 10 done: loss 0.0051 - lr: 0.000000
2023-10-13 14:16:43,026 DEV : loss 0.2595662772655487 - f1-score (micro avg)  0.7736
2023-10-13 14:16:43,057 saving best model
2023-10-13 14:16:44,098 ----------------------------------------------------------------------------------------------------
2023-10-13 14:16:44,100 Loading model from best epoch ...
2023-10-13 14:16:45,623 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
2023-10-13 14:16:50,883 
Results:
- F-score (micro) 0.7347
- F-score (macro) 0.6609
- Accuracy 0.5974

By class:
              precision    recall  f1-score   support

         loc     0.8176    0.8423    0.8298       596
        pers     0.6575    0.7147    0.6849       333
         org     0.5225    0.4394    0.4774       132
        prod     0.6522    0.4545    0.5357        66
        time     0.7407    0.8163    0.7767        49

   micro avg     0.7313    0.7381    0.7347      1176
   macro avg     0.6781    0.6535    0.6609      1176
weighted avg     0.7266    0.7381    0.7305      1176

2023-10-13 14:16:50,883 ----------------------------------------------------------------------------------------------------