2023-09-03 21:29:12,311 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,312 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 21:29:12,312 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,312 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 21:29:12,312 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,312 Train:  3575 sentences
2023-09-03 21:29:12,312         (train_with_dev=False, train_with_test=False)
2023-09-03 21:29:12,312 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,312 Training Params:
2023-09-03 21:29:12,312  - learning_rate: "5e-05" 
2023-09-03 21:29:12,312  - mini_batch_size: "4"
2023-09-03 21:29:12,312  - max_epochs: "10"
2023-09-03 21:29:12,312  - shuffle: "True"
2023-09-03 21:29:12,312 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,313 Plugins:
2023-09-03 21:29:12,313  - LinearScheduler | warmup_fraction: '0.1'
2023-09-03 21:29:12,313 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,313 Final evaluation on model from best epoch (best-model.pt)
2023-09-03 21:29:12,313  - metric: "('micro avg', 'f1-score')"
2023-09-03 21:29:12,313 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,313 Computation:
2023-09-03 21:29:12,313  - compute on device: cuda:0
2023-09-03 21:29:12,313  - embedding storage: none
2023-09-03 21:29:12,313 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,313 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
2023-09-03 21:29:12,313 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:12,313 ----------------------------------------------------------------------------------------------------
2023-09-03 21:29:21,281 epoch 1 - iter 89/894 - loss 2.37573519 - time (sec): 8.97 - samples/sec: 935.65 - lr: 0.000005 - momentum: 0.000000
2023-09-03 21:29:30,672 epoch 1 - iter 178/894 - loss 1.43775852 - time (sec): 18.36 - samples/sec: 938.37 - lr: 0.000010 - momentum: 0.000000
2023-09-03 21:29:39,576 epoch 1 - iter 267/894 - loss 1.11155203 - time (sec): 27.26 - samples/sec: 925.17 - lr: 0.000015 - momentum: 0.000000
2023-09-03 21:29:49,321 epoch 1 - iter 356/894 - loss 0.88678663 - time (sec): 37.01 - samples/sec: 943.20 - lr: 0.000020 - momentum: 0.000000
2023-09-03 21:29:58,795 epoch 1 - iter 445/894 - loss 0.76216363 - time (sec): 46.48 - samples/sec: 939.28 - lr: 0.000025 - momentum: 0.000000
2023-09-03 21:30:08,426 epoch 1 - iter 534/894 - loss 0.67923090 - time (sec): 56.11 - samples/sec: 937.36 - lr: 0.000030 - momentum: 0.000000
2023-09-03 21:30:17,343 epoch 1 - iter 623/894 - loss 0.62299608 - time (sec): 65.03 - samples/sec: 932.84 - lr: 0.000035 - momentum: 0.000000
2023-09-03 21:30:26,620 epoch 1 - iter 712/894 - loss 0.58434755 - time (sec): 74.31 - samples/sec: 925.34 - lr: 0.000040 - momentum: 0.000000
2023-09-03 21:30:36,376 epoch 1 - iter 801/894 - loss 0.54340389 - time (sec): 84.06 - samples/sec: 927.55 - lr: 0.000045 - momentum: 0.000000
2023-09-03 21:30:45,270 epoch 1 - iter 890/894 - loss 0.51372191 - time (sec): 92.96 - samples/sec: 927.81 - lr: 0.000050 - momentum: 0.000000
2023-09-03 21:30:45,630 ----------------------------------------------------------------------------------------------------
2023-09-03 21:30:45,631 EPOCH 1 done: loss 0.5132 - lr: 0.000050
2023-09-03 21:30:56,853 DEV : loss 0.1689286082983017 - f1-score (micro avg)  0.6159
2023-09-03 21:30:56,880 saving best model
2023-09-03 21:30:57,363 ----------------------------------------------------------------------------------------------------
2023-09-03 21:31:06,371 epoch 2 - iter 89/894 - loss 0.21469836 - time (sec): 9.01 - samples/sec: 913.74 - lr: 0.000049 - momentum: 0.000000
2023-09-03 21:31:15,545 epoch 2 - iter 178/894 - loss 0.19766265 - time (sec): 18.18 - samples/sec: 927.20 - lr: 0.000049 - momentum: 0.000000
2023-09-03 21:31:24,622 epoch 2 - iter 267/894 - loss 0.18306157 - time (sec): 27.26 - samples/sec: 921.12 - lr: 0.000048 - momentum: 0.000000
2023-09-03 21:31:34,328 epoch 2 - iter 356/894 - loss 0.17429021 - time (sec): 36.96 - samples/sec: 911.61 - lr: 0.000048 - momentum: 0.000000
2023-09-03 21:31:43,318 epoch 2 - iter 445/894 - loss 0.17695779 - time (sec): 45.95 - samples/sec: 912.36 - lr: 0.000047 - momentum: 0.000000
2023-09-03 21:31:52,520 epoch 2 - iter 534/894 - loss 0.17167934 - time (sec): 55.16 - samples/sec: 910.02 - lr: 0.000047 - momentum: 0.000000
2023-09-03 21:32:02,104 epoch 2 - iter 623/894 - loss 0.16552044 - time (sec): 64.74 - samples/sec: 914.13 - lr: 0.000046 - momentum: 0.000000
2023-09-03 21:32:11,624 epoch 2 - iter 712/894 - loss 0.16532424 - time (sec): 74.26 - samples/sec: 914.87 - lr: 0.000046 - momentum: 0.000000
2023-09-03 21:32:21,057 epoch 2 - iter 801/894 - loss 0.16436528 - time (sec): 83.69 - samples/sec: 926.36 - lr: 0.000045 - momentum: 0.000000
2023-09-03 21:32:30,345 epoch 2 - iter 890/894 - loss 0.16458228 - time (sec): 92.98 - samples/sec: 927.28 - lr: 0.000044 - momentum: 0.000000
2023-09-03 21:32:30,726 ----------------------------------------------------------------------------------------------------
2023-09-03 21:32:30,726 EPOCH 2 done: loss 0.1645 - lr: 0.000044
2023-09-03 21:32:44,212 DEV : loss 0.17144104838371277 - f1-score (micro avg)  0.6691
2023-09-03 21:32:44,238 saving best model
2023-09-03 21:32:45,558 ----------------------------------------------------------------------------------------------------
2023-09-03 21:32:54,539 epoch 3 - iter 89/894 - loss 0.09817251 - time (sec): 8.98 - samples/sec: 912.52 - lr: 0.000044 - momentum: 0.000000
2023-09-03 21:33:03,449 epoch 3 - iter 178/894 - loss 0.09055944 - time (sec): 17.89 - samples/sec: 898.89 - lr: 0.000043 - momentum: 0.000000
2023-09-03 21:33:13,015 epoch 3 - iter 267/894 - loss 0.09533582 - time (sec): 27.46 - samples/sec: 911.87 - lr: 0.000043 - momentum: 0.000000
2023-09-03 21:33:22,020 epoch 3 - iter 356/894 - loss 0.10463513 - time (sec): 36.46 - samples/sec: 917.81 - lr: 0.000042 - momentum: 0.000000
2023-09-03 21:33:31,026 epoch 3 - iter 445/894 - loss 0.10568464 - time (sec): 45.47 - samples/sec: 913.54 - lr: 0.000042 - momentum: 0.000000
2023-09-03 21:33:40,255 epoch 3 - iter 534/894 - loss 0.10337665 - time (sec): 54.70 - samples/sec: 920.17 - lr: 0.000041 - momentum: 0.000000
2023-09-03 21:33:49,512 epoch 3 - iter 623/894 - loss 0.09850664 - time (sec): 63.95 - samples/sec: 918.61 - lr: 0.000041 - momentum: 0.000000
2023-09-03 21:33:59,018 epoch 3 - iter 712/894 - loss 0.09784361 - time (sec): 73.46 - samples/sec: 916.66 - lr: 0.000040 - momentum: 0.000000
2023-09-03 21:34:08,480 epoch 3 - iter 801/894 - loss 0.09360190 - time (sec): 82.92 - samples/sec: 920.71 - lr: 0.000039 - momentum: 0.000000
2023-09-03 21:34:18,615 epoch 3 - iter 890/894 - loss 0.09635431 - time (sec): 93.05 - samples/sec: 925.96 - lr: 0.000039 - momentum: 0.000000
2023-09-03 21:34:19,037 ----------------------------------------------------------------------------------------------------
2023-09-03 21:34:19,037 EPOCH 3 done: loss 0.0961 - lr: 0.000039
2023-09-03 21:34:32,624 DEV : loss 0.184891939163208 - f1-score (micro avg)  0.6806
2023-09-03 21:34:32,650 saving best model
2023-09-03 21:34:33,968 ----------------------------------------------------------------------------------------------------
2023-09-03 21:34:43,458 epoch 4 - iter 89/894 - loss 0.07799970 - time (sec): 9.49 - samples/sec: 876.00 - lr: 0.000038 - momentum: 0.000000
2023-09-03 21:34:52,551 epoch 4 - iter 178/894 - loss 0.06755105 - time (sec): 18.58 - samples/sec: 900.10 - lr: 0.000038 - momentum: 0.000000
2023-09-03 21:35:01,492 epoch 4 - iter 267/894 - loss 0.06069600 - time (sec): 27.52 - samples/sec: 911.39 - lr: 0.000037 - momentum: 0.000000
2023-09-03 21:35:11,552 epoch 4 - iter 356/894 - loss 0.06416203 - time (sec): 37.58 - samples/sec: 938.16 - lr: 0.000037 - momentum: 0.000000
2023-09-03 21:35:20,461 epoch 4 - iter 445/894 - loss 0.06358131 - time (sec): 46.49 - samples/sec: 935.92 - lr: 0.000036 - momentum: 0.000000
2023-09-03 21:35:29,741 epoch 4 - iter 534/894 - loss 0.06641721 - time (sec): 55.77 - samples/sec: 935.32 - lr: 0.000036 - momentum: 0.000000
2023-09-03 21:35:38,782 epoch 4 - iter 623/894 - loss 0.06367007 - time (sec): 64.81 - samples/sec: 939.43 - lr: 0.000035 - momentum: 0.000000
2023-09-03 21:35:47,600 epoch 4 - iter 712/894 - loss 0.06216577 - time (sec): 73.63 - samples/sec: 939.53 - lr: 0.000034 - momentum: 0.000000
2023-09-03 21:35:57,451 epoch 4 - iter 801/894 - loss 0.06304291 - time (sec): 83.48 - samples/sec: 938.26 - lr: 0.000034 - momentum: 0.000000
2023-09-03 21:36:06,188 epoch 4 - iter 890/894 - loss 0.06540605 - time (sec): 92.22 - samples/sec: 935.29 - lr: 0.000033 - momentum: 0.000000
2023-09-03 21:36:06,552 ----------------------------------------------------------------------------------------------------
2023-09-03 21:36:06,552 EPOCH 4 done: loss 0.0653 - lr: 0.000033
2023-09-03 21:36:19,272 DEV : loss 0.2018679976463318 - f1-score (micro avg)  0.7237
2023-09-03 21:36:19,298 saving best model
2023-09-03 21:36:20,629 ----------------------------------------------------------------------------------------------------
2023-09-03 21:36:29,669 epoch 5 - iter 89/894 - loss 0.06341849 - time (sec): 9.04 - samples/sec: 945.59 - lr: 0.000033 - momentum: 0.000000
2023-09-03 21:36:38,304 epoch 5 - iter 178/894 - loss 0.05096189 - time (sec): 17.67 - samples/sec: 947.13 - lr: 0.000032 - momentum: 0.000000
2023-09-03 21:36:47,080 epoch 5 - iter 267/894 - loss 0.04933270 - time (sec): 26.45 - samples/sec: 956.65 - lr: 0.000032 - momentum: 0.000000
2023-09-03 21:36:55,787 epoch 5 - iter 356/894 - loss 0.04822761 - time (sec): 35.16 - samples/sec: 947.00 - lr: 0.000031 - momentum: 0.000000
2023-09-03 21:37:05,496 epoch 5 - iter 445/894 - loss 0.04917756 - time (sec): 44.87 - samples/sec: 949.21 - lr: 0.000031 - momentum: 0.000000
2023-09-03 21:37:14,334 epoch 5 - iter 534/894 - loss 0.04999502 - time (sec): 53.70 - samples/sec: 946.49 - lr: 0.000030 - momentum: 0.000000
2023-09-03 21:37:22,917 epoch 5 - iter 623/894 - loss 0.04679459 - time (sec): 62.29 - samples/sec: 944.92 - lr: 0.000029 - momentum: 0.000000
2023-09-03 21:37:32,733 epoch 5 - iter 712/894 - loss 0.05112854 - time (sec): 72.10 - samples/sec: 956.88 - lr: 0.000029 - momentum: 0.000000
2023-09-03 21:37:41,829 epoch 5 - iter 801/894 - loss 0.04918769 - time (sec): 81.20 - samples/sec: 961.63 - lr: 0.000028 - momentum: 0.000000
2023-09-03 21:37:50,744 epoch 5 - iter 890/894 - loss 0.04937617 - time (sec): 90.11 - samples/sec: 957.25 - lr: 0.000028 - momentum: 0.000000
2023-09-03 21:37:51,109 ----------------------------------------------------------------------------------------------------
2023-09-03 21:37:51,109 EPOCH 5 done: loss 0.0493 - lr: 0.000028
2023-09-03 21:38:03,742 DEV : loss 0.22591951489448547 - f1-score (micro avg)  0.7599
2023-09-03 21:38:03,772 saving best model
2023-09-03 21:38:05,084 ----------------------------------------------------------------------------------------------------
2023-09-03 21:38:14,901 epoch 6 - iter 89/894 - loss 0.03093745 - time (sec): 9.82 - samples/sec: 989.27 - lr: 0.000027 - momentum: 0.000000
2023-09-03 21:38:23,655 epoch 6 - iter 178/894 - loss 0.02538333 - time (sec): 18.57 - samples/sec: 971.10 - lr: 0.000027 - momentum: 0.000000
2023-09-03 21:38:32,849 epoch 6 - iter 267/894 - loss 0.02745734 - time (sec): 27.76 - samples/sec: 969.05 - lr: 0.000026 - momentum: 0.000000
2023-09-03 21:38:41,966 epoch 6 - iter 356/894 - loss 0.02870489 - time (sec): 36.88 - samples/sec: 972.17 - lr: 0.000026 - momentum: 0.000000
2023-09-03 21:38:50,897 epoch 6 - iter 445/894 - loss 0.02870487 - time (sec): 45.81 - samples/sec: 977.49 - lr: 0.000025 - momentum: 0.000000
2023-09-03 21:38:59,601 epoch 6 - iter 534/894 - loss 0.03025865 - time (sec): 54.52 - samples/sec: 968.70 - lr: 0.000024 - momentum: 0.000000
2023-09-03 21:39:08,517 epoch 6 - iter 623/894 - loss 0.02891946 - time (sec): 63.43 - samples/sec: 958.04 - lr: 0.000024 - momentum: 0.000000
2023-09-03 21:39:17,249 epoch 6 - iter 712/894 - loss 0.03124647 - time (sec): 72.16 - samples/sec: 954.38 - lr: 0.000023 - momentum: 0.000000
2023-09-03 21:39:26,268 epoch 6 - iter 801/894 - loss 0.03206784 - time (sec): 81.18 - samples/sec: 956.96 - lr: 0.000023 - momentum: 0.000000
2023-09-03 21:39:35,360 epoch 6 - iter 890/894 - loss 0.03264358 - time (sec): 90.27 - samples/sec: 955.85 - lr: 0.000022 - momentum: 0.000000
2023-09-03 21:39:35,701 ----------------------------------------------------------------------------------------------------
2023-09-03 21:39:35,701 EPOCH 6 done: loss 0.0326 - lr: 0.000022
2023-09-03 21:39:48,613 DEV : loss 0.2312653362751007 - f1-score (micro avg)  0.7403
2023-09-03 21:39:48,640 ----------------------------------------------------------------------------------------------------
2023-09-03 21:39:58,053 epoch 7 - iter 89/894 - loss 0.02565388 - time (sec): 9.41 - samples/sec: 991.53 - lr: 0.000022 - momentum: 0.000000
2023-09-03 21:40:08,304 epoch 7 - iter 178/894 - loss 0.02241187 - time (sec): 19.66 - samples/sec: 971.81 - lr: 0.000021 - momentum: 0.000000
2023-09-03 21:40:17,537 epoch 7 - iter 267/894 - loss 0.02101239 - time (sec): 28.90 - samples/sec: 967.66 - lr: 0.000021 - momentum: 0.000000
2023-09-03 21:40:26,817 epoch 7 - iter 356/894 - loss 0.02145189 - time (sec): 38.18 - samples/sec: 977.31 - lr: 0.000020 - momentum: 0.000000
2023-09-03 21:40:36,306 epoch 7 - iter 445/894 - loss 0.02018220 - time (sec): 47.66 - samples/sec: 965.86 - lr: 0.000019 - momentum: 0.000000
2023-09-03 21:40:45,201 epoch 7 - iter 534/894 - loss 0.01918311 - time (sec): 56.56 - samples/sec: 953.90 - lr: 0.000019 - momentum: 0.000000
2023-09-03 21:40:53,682 epoch 7 - iter 623/894 - loss 0.01939565 - time (sec): 65.04 - samples/sec: 946.67 - lr: 0.000018 - momentum: 0.000000
2023-09-03 21:41:02,868 epoch 7 - iter 712/894 - loss 0.01938977 - time (sec): 74.23 - samples/sec: 942.98 - lr: 0.000018 - momentum: 0.000000
2023-09-03 21:41:11,585 epoch 7 - iter 801/894 - loss 0.01854526 - time (sec): 82.94 - samples/sec: 940.82 - lr: 0.000017 - momentum: 0.000000
2023-09-03 21:41:20,690 epoch 7 - iter 890/894 - loss 0.01808458 - time (sec): 92.05 - samples/sec: 935.55 - lr: 0.000017 - momentum: 0.000000
2023-09-03 21:41:21,109 ----------------------------------------------------------------------------------------------------
2023-09-03 21:41:21,109 EPOCH 7 done: loss 0.0180 - lr: 0.000017
2023-09-03 21:41:34,685 DEV : loss 0.27939727902412415 - f1-score (micro avg)  0.7561
2023-09-03 21:41:34,712 ----------------------------------------------------------------------------------------------------
2023-09-03 21:41:43,958 epoch 8 - iter 89/894 - loss 0.01939659 - time (sec): 9.24 - samples/sec: 945.57 - lr: 0.000016 - momentum: 0.000000
2023-09-03 21:41:53,013 epoch 8 - iter 178/894 - loss 0.01689374 - time (sec): 18.30 - samples/sec: 924.46 - lr: 0.000016 - momentum: 0.000000
2023-09-03 21:42:03,422 epoch 8 - iter 267/894 - loss 0.01403347 - time (sec): 28.71 - samples/sec: 942.21 - lr: 0.000015 - momentum: 0.000000
2023-09-03 21:42:12,750 epoch 8 - iter 356/894 - loss 0.01720869 - time (sec): 38.04 - samples/sec: 933.60 - lr: 0.000014 - momentum: 0.000000
2023-09-03 21:42:21,689 epoch 8 - iter 445/894 - loss 0.01536338 - time (sec): 46.98 - samples/sec: 937.88 - lr: 0.000014 - momentum: 0.000000
2023-09-03 21:42:31,262 epoch 8 - iter 534/894 - loss 0.01551848 - time (sec): 56.55 - samples/sec: 936.72 - lr: 0.000013 - momentum: 0.000000
2023-09-03 21:42:40,238 epoch 8 - iter 623/894 - loss 0.01450492 - time (sec): 65.52 - samples/sec: 936.97 - lr: 0.000013 - momentum: 0.000000
2023-09-03 21:42:49,068 epoch 8 - iter 712/894 - loss 0.01507741 - time (sec): 74.35 - samples/sec: 936.71 - lr: 0.000012 - momentum: 0.000000
2023-09-03 21:42:58,036 epoch 8 - iter 801/894 - loss 0.01430409 - time (sec): 83.32 - samples/sec: 941.84 - lr: 0.000012 - momentum: 0.000000
2023-09-03 21:43:06,614 epoch 8 - iter 890/894 - loss 0.01440714 - time (sec): 91.90 - samples/sec: 938.57 - lr: 0.000011 - momentum: 0.000000
2023-09-03 21:43:06,967 ----------------------------------------------------------------------------------------------------
2023-09-03 21:43:06,967 EPOCH 8 done: loss 0.0144 - lr: 0.000011
2023-09-03 21:43:19,717 DEV : loss 0.25227266550064087 - f1-score (micro avg)  0.7538
2023-09-03 21:43:19,743 ----------------------------------------------------------------------------------------------------
2023-09-03 21:43:28,846 epoch 9 - iter 89/894 - loss 0.00701860 - time (sec): 9.10 - samples/sec: 904.91 - lr: 0.000011 - momentum: 0.000000
2023-09-03 21:43:38,734 epoch 9 - iter 178/894 - loss 0.00405271 - time (sec): 18.99 - samples/sec: 932.90 - lr: 0.000010 - momentum: 0.000000
2023-09-03 21:43:47,869 epoch 9 - iter 267/894 - loss 0.00458330 - time (sec): 28.12 - samples/sec: 934.72 - lr: 0.000009 - momentum: 0.000000
2023-09-03 21:43:56,759 epoch 9 - iter 356/894 - loss 0.00756307 - time (sec): 37.01 - samples/sec: 938.88 - lr: 0.000009 - momentum: 0.000000
2023-09-03 21:44:05,409 epoch 9 - iter 445/894 - loss 0.00714667 - time (sec): 45.66 - samples/sec: 947.67 - lr: 0.000008 - momentum: 0.000000
2023-09-03 21:44:14,213 epoch 9 - iter 534/894 - loss 0.00708796 - time (sec): 54.47 - samples/sec: 946.20 - lr: 0.000008 - momentum: 0.000000
2023-09-03 21:44:22,932 epoch 9 - iter 623/894 - loss 0.00691866 - time (sec): 63.19 - samples/sec: 943.72 - lr: 0.000007 - momentum: 0.000000
2023-09-03 21:44:31,848 epoch 9 - iter 712/894 - loss 0.00610120 - time (sec): 72.10 - samples/sec: 947.24 - lr: 0.000007 - momentum: 0.000000
2023-09-03 21:44:41,509 epoch 9 - iter 801/894 - loss 0.00735687 - time (sec): 81.76 - samples/sec: 953.29 - lr: 0.000006 - momentum: 0.000000
2023-09-03 21:44:50,521 epoch 9 - iter 890/894 - loss 0.00763608 - time (sec): 90.78 - samples/sec: 949.23 - lr: 0.000006 - momentum: 0.000000
2023-09-03 21:44:50,930 ----------------------------------------------------------------------------------------------------
2023-09-03 21:44:50,931 EPOCH 9 done: loss 0.0079 - lr: 0.000006
2023-09-03 21:45:03,963 DEV : loss 0.2669627368450165 - f1-score (micro avg)  0.7631
2023-09-03 21:45:03,990 saving best model
2023-09-03 21:45:05,380 ----------------------------------------------------------------------------------------------------
2023-09-03 21:45:14,802 epoch 10 - iter 89/894 - loss 0.00553400 - time (sec): 9.42 - samples/sec: 971.28 - lr: 0.000005 - momentum: 0.000000
2023-09-03 21:45:23,626 epoch 10 - iter 178/894 - loss 0.00348736 - time (sec): 18.24 - samples/sec: 956.13 - lr: 0.000004 - momentum: 0.000000
2023-09-03 21:45:32,657 epoch 10 - iter 267/894 - loss 0.00270854 - time (sec): 27.28 - samples/sec: 962.01 - lr: 0.000004 - momentum: 0.000000
2023-09-03 21:45:41,872 epoch 10 - iter 356/894 - loss 0.00366180 - time (sec): 36.49 - samples/sec: 958.01 - lr: 0.000003 - momentum: 0.000000
2023-09-03 21:45:50,539 epoch 10 - iter 445/894 - loss 0.00392369 - time (sec): 45.16 - samples/sec: 953.26 - lr: 0.000003 - momentum: 0.000000
2023-09-03 21:45:59,537 epoch 10 - iter 534/894 - loss 0.00402871 - time (sec): 54.16 - samples/sec: 947.15 - lr: 0.000002 - momentum: 0.000000
2023-09-03 21:46:08,775 epoch 10 - iter 623/894 - loss 0.00360604 - time (sec): 63.39 - samples/sec: 951.43 - lr: 0.000002 - momentum: 0.000000
2023-09-03 21:46:19,239 epoch 10 - iter 712/894 - loss 0.00331703 - time (sec): 73.86 - samples/sec: 953.08 - lr: 0.000001 - momentum: 0.000000
2023-09-03 21:46:28,192 epoch 10 - iter 801/894 - loss 0.00357190 - time (sec): 82.81 - samples/sec: 944.85 - lr: 0.000001 - momentum: 0.000000
2023-09-03 21:46:36,938 epoch 10 - iter 890/894 - loss 0.00345665 - time (sec): 91.56 - samples/sec: 940.90 - lr: 0.000000 - momentum: 0.000000
2023-09-03 21:46:37,328 ----------------------------------------------------------------------------------------------------
2023-09-03 21:46:37,328 EPOCH 10 done: loss 0.0034 - lr: 0.000000
2023-09-03 21:46:50,655 DEV : loss 0.27164772152900696 - f1-score (micro avg)  0.7586
2023-09-03 21:46:51,154 ----------------------------------------------------------------------------------------------------
2023-09-03 21:46:51,155 Loading model from best epoch ...
2023-09-03 21:46:52,931 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 21:47:03,568 
Results:
- F-score (micro) 0.7364
- F-score (macro) 0.6707
- Accuracy 0.6043

By class:
              precision    recall  f1-score   support

         loc     0.8197    0.8238    0.8218       596
        pers     0.6509    0.7447    0.6947       333
         org     0.5833    0.4242    0.4912       132
        prod     0.6250    0.5303    0.5738        66
        time     0.7500    0.7959    0.7723        49

   micro avg     0.7340    0.7389    0.7364      1176
   macro avg     0.6858    0.6638    0.6707      1176
weighted avg     0.7315    0.7389    0.7327      1176

2023-09-03 21:47:03,568 ----------------------------------------------------------------------------------------------------