2023-10-18 17:57:48,044 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,044 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 128)
        (position_embeddings): Embedding(512, 128)
        (token_type_embeddings): Embedding(2, 128)
        (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-1): 2 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=128, out_features=128, bias=True)
                (key): Linear(in_features=128, out_features=128, bias=True)
                (value): Linear(in_features=128, out_features=128, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=128, out_features=128, bias=True)
                (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=128, out_features=512, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=512, out_features=128, bias=True)
              (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=128, out_features=128, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=128, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-18 17:57:48,044 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,044 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
2023-10-18 17:57:48,044 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,044 Train:  3575 sentences
2023-10-18 17:57:48,044         (train_with_dev=False, train_with_test=False)
2023-10-18 17:57:48,044 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,044 Training Params:
2023-10-18 17:57:48,044  - learning_rate: "5e-05" 
2023-10-18 17:57:48,045  - mini_batch_size: "8"
2023-10-18 17:57:48,045  - max_epochs: "10"
2023-10-18 17:57:48,045  - shuffle: "True"
2023-10-18 17:57:48,045 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,045 Plugins:
2023-10-18 17:57:48,045  - TensorboardLogger
2023-10-18 17:57:48,045  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 17:57:48,045 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,045 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 17:57:48,045  - metric: "('micro avg', 'f1-score')"
2023-10-18 17:57:48,045 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,045 Computation:
2023-10-18 17:57:48,045  - compute on device: cuda:0
2023-10-18 17:57:48,045  - embedding storage: none
2023-10-18 17:57:48,045 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,045 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-18 17:57:48,045 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,045 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:48,045 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 17:57:49,179 epoch 1 - iter 44/447 - loss 3.19284290 - time (sec): 1.13 - samples/sec: 7978.22 - lr: 0.000005 - momentum: 0.000000
2023-10-18 17:57:50,225 epoch 1 - iter 88/447 - loss 3.01363940 - time (sec): 2.18 - samples/sec: 8580.02 - lr: 0.000010 - momentum: 0.000000
2023-10-18 17:57:51,189 epoch 1 - iter 132/447 - loss 2.78665781 - time (sec): 3.14 - samples/sec: 8321.31 - lr: 0.000015 - momentum: 0.000000
2023-10-18 17:57:52,175 epoch 1 - iter 176/447 - loss 2.47324587 - time (sec): 4.13 - samples/sec: 8193.55 - lr: 0.000020 - momentum: 0.000000
2023-10-18 17:57:53,220 epoch 1 - iter 220/447 - loss 2.16020751 - time (sec): 5.17 - samples/sec: 8079.06 - lr: 0.000024 - momentum: 0.000000
2023-10-18 17:57:54,218 epoch 1 - iter 264/447 - loss 1.90715039 - time (sec): 6.17 - samples/sec: 8154.90 - lr: 0.000029 - momentum: 0.000000
2023-10-18 17:57:55,266 epoch 1 - iter 308/447 - loss 1.71530413 - time (sec): 7.22 - samples/sec: 8207.96 - lr: 0.000034 - momentum: 0.000000
2023-10-18 17:57:56,340 epoch 1 - iter 352/447 - loss 1.55384500 - time (sec): 8.29 - samples/sec: 8290.74 - lr: 0.000039 - momentum: 0.000000
2023-10-18 17:57:57,336 epoch 1 - iter 396/447 - loss 1.44428539 - time (sec): 9.29 - samples/sec: 8304.00 - lr: 0.000044 - momentum: 0.000000
2023-10-18 17:57:58,334 epoch 1 - iter 440/447 - loss 1.36744870 - time (sec): 10.29 - samples/sec: 8290.89 - lr: 0.000049 - momentum: 0.000000
2023-10-18 17:57:58,494 ----------------------------------------------------------------------------------------------------
2023-10-18 17:57:58,494 EPOCH 1 done: loss 1.3584 - lr: 0.000049
2023-10-18 17:58:00,741 DEV : loss 0.43687644600868225 - f1-score (micro avg)  0.0
2023-10-18 17:58:00,766 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:01,735 epoch 2 - iter 44/447 - loss 0.48119690 - time (sec): 0.97 - samples/sec: 8619.57 - lr: 0.000049 - momentum: 0.000000
2023-10-18 17:58:02,731 epoch 2 - iter 88/447 - loss 0.50993594 - time (sec): 1.96 - samples/sec: 8729.02 - lr: 0.000049 - momentum: 0.000000
2023-10-18 17:58:03,741 epoch 2 - iter 132/447 - loss 0.50165300 - time (sec): 2.97 - samples/sec: 8368.61 - lr: 0.000048 - momentum: 0.000000
2023-10-18 17:58:04,741 epoch 2 - iter 176/447 - loss 0.49887066 - time (sec): 3.97 - samples/sec: 8304.89 - lr: 0.000048 - momentum: 0.000000
2023-10-18 17:58:05,794 epoch 2 - iter 220/447 - loss 0.50462302 - time (sec): 5.03 - samples/sec: 8465.43 - lr: 0.000047 - momentum: 0.000000
2023-10-18 17:58:06,779 epoch 2 - iter 264/447 - loss 0.49268319 - time (sec): 6.01 - samples/sec: 8482.14 - lr: 0.000047 - momentum: 0.000000
2023-10-18 17:58:07,825 epoch 2 - iter 308/447 - loss 0.48765160 - time (sec): 7.06 - samples/sec: 8639.41 - lr: 0.000046 - momentum: 0.000000
2023-10-18 17:58:08,801 epoch 2 - iter 352/447 - loss 0.48463496 - time (sec): 8.03 - samples/sec: 8531.89 - lr: 0.000046 - momentum: 0.000000
2023-10-18 17:58:09,788 epoch 2 - iter 396/447 - loss 0.48158940 - time (sec): 9.02 - samples/sec: 8544.51 - lr: 0.000045 - momentum: 0.000000
2023-10-18 17:58:10,794 epoch 2 - iter 440/447 - loss 0.47795623 - time (sec): 10.03 - samples/sec: 8519.62 - lr: 0.000045 - momentum: 0.000000
2023-10-18 17:58:10,947 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:10,947 EPOCH 2 done: loss 0.4791 - lr: 0.000045
2023-10-18 17:58:15,860 DEV : loss 0.33681756258010864 - f1-score (micro avg)  0.1217
2023-10-18 17:58:15,886 saving best model
2023-10-18 17:58:15,920 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:16,685 epoch 3 - iter 44/447 - loss 0.42838384 - time (sec): 0.76 - samples/sec: 11780.38 - lr: 0.000044 - momentum: 0.000000
2023-10-18 17:58:17,389 epoch 3 - iter 88/447 - loss 0.42564911 - time (sec): 1.47 - samples/sec: 11937.67 - lr: 0.000043 - momentum: 0.000000
2023-10-18 17:58:18,327 epoch 3 - iter 132/447 - loss 0.42033282 - time (sec): 2.41 - samples/sec: 10893.04 - lr: 0.000043 - momentum: 0.000000
2023-10-18 17:58:19,293 epoch 3 - iter 176/447 - loss 0.43188443 - time (sec): 3.37 - samples/sec: 10082.16 - lr: 0.000042 - momentum: 0.000000
2023-10-18 17:58:20,281 epoch 3 - iter 220/447 - loss 0.42083961 - time (sec): 4.36 - samples/sec: 9613.01 - lr: 0.000042 - momentum: 0.000000
2023-10-18 17:58:21,325 epoch 3 - iter 264/447 - loss 0.42146771 - time (sec): 5.40 - samples/sec: 9324.08 - lr: 0.000041 - momentum: 0.000000
2023-10-18 17:58:22,389 epoch 3 - iter 308/447 - loss 0.41172801 - time (sec): 6.47 - samples/sec: 9113.86 - lr: 0.000041 - momentum: 0.000000
2023-10-18 17:58:23,791 epoch 3 - iter 352/447 - loss 0.41407969 - time (sec): 7.87 - samples/sec: 8626.39 - lr: 0.000040 - momentum: 0.000000
2023-10-18 17:58:24,857 epoch 3 - iter 396/447 - loss 0.41044893 - time (sec): 8.94 - samples/sec: 8581.94 - lr: 0.000040 - momentum: 0.000000
2023-10-18 17:58:25,880 epoch 3 - iter 440/447 - loss 0.40707204 - time (sec): 9.96 - samples/sec: 8581.10 - lr: 0.000039 - momentum: 0.000000
2023-10-18 17:58:26,031 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:26,031 EPOCH 3 done: loss 0.4069 - lr: 0.000039
2023-10-18 17:58:30,935 DEV : loss 0.3142178952693939 - f1-score (micro avg)  0.2985
2023-10-18 17:58:30,960 saving best model
2023-10-18 17:58:30,992 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:31,990 epoch 4 - iter 44/447 - loss 0.39689983 - time (sec): 1.00 - samples/sec: 8133.45 - lr: 0.000038 - momentum: 0.000000
2023-10-18 17:58:33,066 epoch 4 - iter 88/447 - loss 0.35623413 - time (sec): 2.07 - samples/sec: 8753.23 - lr: 0.000038 - momentum: 0.000000
2023-10-18 17:58:34,068 epoch 4 - iter 132/447 - loss 0.35755301 - time (sec): 3.07 - samples/sec: 8651.03 - lr: 0.000037 - momentum: 0.000000
2023-10-18 17:58:35,038 epoch 4 - iter 176/447 - loss 0.36151376 - time (sec): 4.05 - samples/sec: 8718.94 - lr: 0.000037 - momentum: 0.000000
2023-10-18 17:58:36,046 epoch 4 - iter 220/447 - loss 0.35271799 - time (sec): 5.05 - samples/sec: 8718.35 - lr: 0.000036 - momentum: 0.000000
2023-10-18 17:58:37,004 epoch 4 - iter 264/447 - loss 0.35719487 - time (sec): 6.01 - samples/sec: 8702.25 - lr: 0.000036 - momentum: 0.000000
2023-10-18 17:58:37,979 epoch 4 - iter 308/447 - loss 0.35411738 - time (sec): 6.99 - samples/sec: 8668.62 - lr: 0.000035 - momentum: 0.000000
2023-10-18 17:58:38,962 epoch 4 - iter 352/447 - loss 0.35874790 - time (sec): 7.97 - samples/sec: 8662.23 - lr: 0.000035 - momentum: 0.000000
2023-10-18 17:58:39,950 epoch 4 - iter 396/447 - loss 0.35964077 - time (sec): 8.96 - samples/sec: 8604.79 - lr: 0.000034 - momentum: 0.000000
2023-10-18 17:58:40,968 epoch 4 - iter 440/447 - loss 0.35906851 - time (sec): 9.97 - samples/sec: 8547.40 - lr: 0.000033 - momentum: 0.000000
2023-10-18 17:58:41,133 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:41,134 EPOCH 4 done: loss 0.3586 - lr: 0.000033
2023-10-18 17:58:46,412 DEV : loss 0.3010146915912628 - f1-score (micro avg)  0.329
2023-10-18 17:58:46,437 saving best model
2023-10-18 17:58:46,470 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:47,540 epoch 5 - iter 44/447 - loss 0.34061996 - time (sec): 1.07 - samples/sec: 8009.08 - lr: 0.000033 - momentum: 0.000000
2023-10-18 17:58:48,573 epoch 5 - iter 88/447 - loss 0.32307230 - time (sec): 2.10 - samples/sec: 8478.89 - lr: 0.000032 - momentum: 0.000000
2023-10-18 17:58:49,572 epoch 5 - iter 132/447 - loss 0.32451711 - time (sec): 3.10 - samples/sec: 8213.47 - lr: 0.000032 - momentum: 0.000000
2023-10-18 17:58:50,586 epoch 5 - iter 176/447 - loss 0.32488369 - time (sec): 4.12 - samples/sec: 8309.14 - lr: 0.000031 - momentum: 0.000000
2023-10-18 17:58:51,579 epoch 5 - iter 220/447 - loss 0.32269515 - time (sec): 5.11 - samples/sec: 8223.00 - lr: 0.000031 - momentum: 0.000000
2023-10-18 17:58:52,605 epoch 5 - iter 264/447 - loss 0.33241103 - time (sec): 6.13 - samples/sec: 8169.84 - lr: 0.000030 - momentum: 0.000000
2023-10-18 17:58:53,709 epoch 5 - iter 308/447 - loss 0.33192877 - time (sec): 7.24 - samples/sec: 8188.65 - lr: 0.000030 - momentum: 0.000000
2023-10-18 17:58:54,814 epoch 5 - iter 352/447 - loss 0.33433260 - time (sec): 8.34 - samples/sec: 8220.85 - lr: 0.000029 - momentum: 0.000000
2023-10-18 17:58:55,825 epoch 5 - iter 396/447 - loss 0.33154866 - time (sec): 9.35 - samples/sec: 8252.27 - lr: 0.000028 - momentum: 0.000000
2023-10-18 17:58:56,791 epoch 5 - iter 440/447 - loss 0.32622718 - time (sec): 10.32 - samples/sec: 8221.07 - lr: 0.000028 - momentum: 0.000000
2023-10-18 17:58:56,967 ----------------------------------------------------------------------------------------------------
2023-10-18 17:58:56,967 EPOCH 5 done: loss 0.3247 - lr: 0.000028
2023-10-18 17:59:02,199 DEV : loss 0.2914736866950989 - f1-score (micro avg)  0.3482
2023-10-18 17:59:02,224 saving best model
2023-10-18 17:59:02,256 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:03,191 epoch 6 - iter 44/447 - loss 0.30115644 - time (sec): 0.93 - samples/sec: 8943.48 - lr: 0.000027 - momentum: 0.000000
2023-10-18 17:59:03,991 epoch 6 - iter 88/447 - loss 0.30685456 - time (sec): 1.73 - samples/sec: 9404.40 - lr: 0.000027 - momentum: 0.000000
2023-10-18 17:59:04,865 epoch 6 - iter 132/447 - loss 0.29321190 - time (sec): 2.61 - samples/sec: 9170.89 - lr: 0.000026 - momentum: 0.000000
2023-10-18 17:59:05,845 epoch 6 - iter 176/447 - loss 0.31362991 - time (sec): 3.59 - samples/sec: 8985.59 - lr: 0.000026 - momentum: 0.000000
2023-10-18 17:59:06,829 epoch 6 - iter 220/447 - loss 0.31663488 - time (sec): 4.57 - samples/sec: 8843.88 - lr: 0.000025 - momentum: 0.000000
2023-10-18 17:59:07,875 epoch 6 - iter 264/447 - loss 0.32593502 - time (sec): 5.62 - samples/sec: 8930.99 - lr: 0.000025 - momentum: 0.000000
2023-10-18 17:59:08,867 epoch 6 - iter 308/447 - loss 0.31944221 - time (sec): 6.61 - samples/sec: 8921.37 - lr: 0.000024 - momentum: 0.000000
2023-10-18 17:59:09,908 epoch 6 - iter 352/447 - loss 0.30844029 - time (sec): 7.65 - samples/sec: 8814.12 - lr: 0.000023 - momentum: 0.000000
2023-10-18 17:59:11,009 epoch 6 - iter 396/447 - loss 0.31191116 - time (sec): 8.75 - samples/sec: 8792.22 - lr: 0.000023 - momentum: 0.000000
2023-10-18 17:59:12,066 epoch 6 - iter 440/447 - loss 0.30921094 - time (sec): 9.81 - samples/sec: 8704.33 - lr: 0.000022 - momentum: 0.000000
2023-10-18 17:59:12,223 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:12,223 EPOCH 6 done: loss 0.3085 - lr: 0.000022
2023-10-18 17:59:17,516 DEV : loss 0.29000064730644226 - f1-score (micro avg)  0.3639
2023-10-18 17:59:17,540 saving best model
2023-10-18 17:59:17,571 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:18,576 epoch 7 - iter 44/447 - loss 0.28426196 - time (sec): 1.00 - samples/sec: 8758.45 - lr: 0.000022 - momentum: 0.000000
2023-10-18 17:59:19,615 epoch 7 - iter 88/447 - loss 0.30391801 - time (sec): 2.04 - samples/sec: 8399.06 - lr: 0.000021 - momentum: 0.000000
2023-10-18 17:59:20,678 epoch 7 - iter 132/447 - loss 0.30469420 - time (sec): 3.11 - samples/sec: 8215.37 - lr: 0.000021 - momentum: 0.000000
2023-10-18 17:59:21,698 epoch 7 - iter 176/447 - loss 0.30084472 - time (sec): 4.13 - samples/sec: 8124.85 - lr: 0.000020 - momentum: 0.000000
2023-10-18 17:59:22,736 epoch 7 - iter 220/447 - loss 0.29345470 - time (sec): 5.16 - samples/sec: 8110.09 - lr: 0.000020 - momentum: 0.000000
2023-10-18 17:59:23,759 epoch 7 - iter 264/447 - loss 0.29138463 - time (sec): 6.19 - samples/sec: 8252.21 - lr: 0.000019 - momentum: 0.000000
2023-10-18 17:59:24,754 epoch 7 - iter 308/447 - loss 0.29351467 - time (sec): 7.18 - samples/sec: 8305.01 - lr: 0.000018 - momentum: 0.000000
2023-10-18 17:59:25,825 epoch 7 - iter 352/447 - loss 0.29638985 - time (sec): 8.25 - samples/sec: 8355.19 - lr: 0.000018 - momentum: 0.000000
2023-10-18 17:59:26,879 epoch 7 - iter 396/447 - loss 0.29660285 - time (sec): 9.31 - samples/sec: 8329.27 - lr: 0.000017 - momentum: 0.000000
2023-10-18 17:59:27,902 epoch 7 - iter 440/447 - loss 0.29359818 - time (sec): 10.33 - samples/sec: 8260.63 - lr: 0.000017 - momentum: 0.000000
2023-10-18 17:59:28,060 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:28,061 EPOCH 7 done: loss 0.2939 - lr: 0.000017
2023-10-18 17:59:33,329 DEV : loss 0.2924981713294983 - f1-score (micro avg)  0.3632
2023-10-18 17:59:33,353 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:34,451 epoch 8 - iter 44/447 - loss 0.30018572 - time (sec): 1.10 - samples/sec: 8010.32 - lr: 0.000016 - momentum: 0.000000
2023-10-18 17:59:35,486 epoch 8 - iter 88/447 - loss 0.29132373 - time (sec): 2.13 - samples/sec: 8515.38 - lr: 0.000016 - momentum: 0.000000
2023-10-18 17:59:36,483 epoch 8 - iter 132/447 - loss 0.29254593 - time (sec): 3.13 - samples/sec: 8346.49 - lr: 0.000015 - momentum: 0.000000
2023-10-18 17:59:37,528 epoch 8 - iter 176/447 - loss 0.28696166 - time (sec): 4.17 - samples/sec: 8362.77 - lr: 0.000015 - momentum: 0.000000
2023-10-18 17:59:38,565 epoch 8 - iter 220/447 - loss 0.28064859 - time (sec): 5.21 - samples/sec: 8490.03 - lr: 0.000014 - momentum: 0.000000
2023-10-18 17:59:39,596 epoch 8 - iter 264/447 - loss 0.28058588 - time (sec): 6.24 - samples/sec: 8423.76 - lr: 0.000013 - momentum: 0.000000
2023-10-18 17:59:40,604 epoch 8 - iter 308/447 - loss 0.28283717 - time (sec): 7.25 - samples/sec: 8324.22 - lr: 0.000013 - momentum: 0.000000
2023-10-18 17:59:41,671 epoch 8 - iter 352/447 - loss 0.27965960 - time (sec): 8.32 - samples/sec: 8320.97 - lr: 0.000012 - momentum: 0.000000
2023-10-18 17:59:42,712 epoch 8 - iter 396/447 - loss 0.28457473 - time (sec): 9.36 - samples/sec: 8303.89 - lr: 0.000012 - momentum: 0.000000
2023-10-18 17:59:43,663 epoch 8 - iter 440/447 - loss 0.28032758 - time (sec): 10.31 - samples/sec: 8254.52 - lr: 0.000011 - momentum: 0.000000
2023-10-18 17:59:43,820 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:43,820 EPOCH 8 done: loss 0.2786 - lr: 0.000011
2023-10-18 17:59:48,835 DEV : loss 0.2947298288345337 - f1-score (micro avg)  0.3628
2023-10-18 17:59:48,860 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:49,885 epoch 9 - iter 44/447 - loss 0.21804140 - time (sec): 1.02 - samples/sec: 8158.29 - lr: 0.000011 - momentum: 0.000000
2023-10-18 17:59:50,878 epoch 9 - iter 88/447 - loss 0.24378083 - time (sec): 2.02 - samples/sec: 8148.24 - lr: 0.000010 - momentum: 0.000000
2023-10-18 17:59:51,953 epoch 9 - iter 132/447 - loss 0.26094469 - time (sec): 3.09 - samples/sec: 8394.90 - lr: 0.000010 - momentum: 0.000000
2023-10-18 17:59:52,966 epoch 9 - iter 176/447 - loss 0.27186542 - time (sec): 4.11 - samples/sec: 8515.92 - lr: 0.000009 - momentum: 0.000000
2023-10-18 17:59:53,966 epoch 9 - iter 220/447 - loss 0.27565457 - time (sec): 5.11 - samples/sec: 8388.76 - lr: 0.000008 - momentum: 0.000000
2023-10-18 17:59:54,956 epoch 9 - iter 264/447 - loss 0.27726648 - time (sec): 6.10 - samples/sec: 8356.24 - lr: 0.000008 - momentum: 0.000000
2023-10-18 17:59:55,952 epoch 9 - iter 308/447 - loss 0.27713457 - time (sec): 7.09 - samples/sec: 8417.32 - lr: 0.000007 - momentum: 0.000000
2023-10-18 17:59:56,953 epoch 9 - iter 352/447 - loss 0.26979378 - time (sec): 8.09 - samples/sec: 8536.18 - lr: 0.000007 - momentum: 0.000000
2023-10-18 17:59:57,973 epoch 9 - iter 396/447 - loss 0.27600964 - time (sec): 9.11 - samples/sec: 8472.65 - lr: 0.000006 - momentum: 0.000000
2023-10-18 17:59:58,940 epoch 9 - iter 440/447 - loss 0.27663352 - time (sec): 10.08 - samples/sec: 8473.73 - lr: 0.000006 - momentum: 0.000000
2023-10-18 17:59:59,089 ----------------------------------------------------------------------------------------------------
2023-10-18 17:59:59,089 EPOCH 9 done: loss 0.2764 - lr: 0.000006
2023-10-18 18:00:04,399 DEV : loss 0.286447137594223 - f1-score (micro avg)  0.3807
2023-10-18 18:00:04,424 saving best model
2023-10-18 18:00:04,454 ----------------------------------------------------------------------------------------------------
2023-10-18 18:00:05,491 epoch 10 - iter 44/447 - loss 0.26465749 - time (sec): 1.04 - samples/sec: 9417.64 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:00:06,502 epoch 10 - iter 88/447 - loss 0.27584699 - time (sec): 2.05 - samples/sec: 8735.04 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:00:07,506 epoch 10 - iter 132/447 - loss 0.26228332 - time (sec): 3.05 - samples/sec: 8604.53 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:00:08,501 epoch 10 - iter 176/447 - loss 0.26937474 - time (sec): 4.05 - samples/sec: 8701.97 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:00:09,466 epoch 10 - iter 220/447 - loss 0.26880653 - time (sec): 5.01 - samples/sec: 8505.78 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:00:10,492 epoch 10 - iter 264/447 - loss 0.26811965 - time (sec): 6.04 - samples/sec: 8551.71 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:00:11,548 epoch 10 - iter 308/447 - loss 0.27210434 - time (sec): 7.09 - samples/sec: 8570.30 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:00:12,593 epoch 10 - iter 352/447 - loss 0.27503554 - time (sec): 8.14 - samples/sec: 8491.65 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:00:13,615 epoch 10 - iter 396/447 - loss 0.27246757 - time (sec): 9.16 - samples/sec: 8414.29 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:00:14,613 epoch 10 - iter 440/447 - loss 0.26857705 - time (sec): 10.16 - samples/sec: 8371.37 - lr: 0.000000 - momentum: 0.000000
2023-10-18 18:00:14,781 ----------------------------------------------------------------------------------------------------
2023-10-18 18:00:14,781 EPOCH 10 done: loss 0.2677 - lr: 0.000000
2023-10-18 18:00:20,055 DEV : loss 0.2858006954193115 - f1-score (micro avg)  0.3819
2023-10-18 18:00:20,079 saving best model
2023-10-18 18:00:20,140 ----------------------------------------------------------------------------------------------------
2023-10-18 18:00:20,140 Loading model from best epoch ...
2023-10-18 18:00:20,216 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
2023-10-18 18:00:22,461 
Results:
- F-score (micro) 0.3942
- F-score (macro) 0.1902
- Accuracy 0.2574

By class:
              precision    recall  f1-score   support

         loc     0.5108    0.5940    0.5493       596
        pers     0.2347    0.2763    0.2538       333
         org     0.0000    0.0000    0.0000       132
        time     0.1875    0.1224    0.1481        49
        prod     0.0000    0.0000    0.0000        66

   micro avg     0.4047    0.3844    0.3942      1176
   macro avg     0.1866    0.1985    0.1902      1176
weighted avg     0.3332    0.3844    0.3564      1176

2023-10-18 18:00:22,461 ----------------------------------------------------------------------------------------------------