2023-10-13 12:41:31,655 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,656 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 12:41:31,656 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,656 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 12:41:31,656 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,656 Train:  3575 sentences
2023-10-13 12:41:31,656         (train_with_dev=False, train_with_test=False)
2023-10-13 12:41:31,656 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,656 Training Params:
2023-10-13 12:41:31,656  - learning_rate: "3e-05" 
2023-10-13 12:41:31,656  - mini_batch_size: "8"
2023-10-13 12:41:31,656  - max_epochs: "10"
2023-10-13 12:41:31,656  - shuffle: "True"
2023-10-13 12:41:31,656 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,656 Plugins:
2023-10-13 12:41:31,656  - LinearScheduler | warmup_fraction: '0.1'
2023-10-13 12:41:31,656 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,656 Final evaluation on model from best epoch (best-model.pt)
2023-10-13 12:41:31,657  - metric: "('micro avg', 'f1-score')"
2023-10-13 12:41:31,657 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,657 Computation:
2023-10-13 12:41:31,657  - compute on device: cuda:0
2023-10-13 12:41:31,657  - embedding storage: none
2023-10-13 12:41:31,657 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,657 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-13 12:41:31,657 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:31,657 ----------------------------------------------------------------------------------------------------
2023-10-13 12:41:34,383 epoch 1 - iter 44/447 - loss 2.86252177 - time (sec): 2.73 - samples/sec: 3026.88 - lr: 0.000003 - momentum: 0.000000
2023-10-13 12:41:37,304 epoch 1 - iter 88/447 - loss 2.08375789 - time (sec): 5.65 - samples/sec: 3029.24 - lr: 0.000006 - momentum: 0.000000
2023-10-13 12:41:39,959 epoch 1 - iter 132/447 - loss 1.59708301 - time (sec): 8.30 - samples/sec: 3013.37 - lr: 0.000009 - momentum: 0.000000
2023-10-13 12:41:42,919 epoch 1 - iter 176/447 - loss 1.27742097 - time (sec): 11.26 - samples/sec: 3070.12 - lr: 0.000012 - momentum: 0.000000
2023-10-13 12:41:45,863 epoch 1 - iter 220/447 - loss 1.09002352 - time (sec): 14.21 - samples/sec: 3048.86 - lr: 0.000015 - momentum: 0.000000
2023-10-13 12:41:48,731 epoch 1 - iter 264/447 - loss 0.96186677 - time (sec): 17.07 - samples/sec: 3044.44 - lr: 0.000018 - momentum: 0.000000
2023-10-13 12:41:51,437 epoch 1 - iter 308/447 - loss 0.87551581 - time (sec): 19.78 - samples/sec: 3033.31 - lr: 0.000021 - momentum: 0.000000
2023-10-13 12:41:54,379 epoch 1 - iter 352/447 - loss 0.81029456 - time (sec): 22.72 - samples/sec: 2996.66 - lr: 0.000024 - momentum: 0.000000
2023-10-13 12:41:57,492 epoch 1 - iter 396/447 - loss 0.74501254 - time (sec): 25.83 - samples/sec: 2986.20 - lr: 0.000027 - momentum: 0.000000
2023-10-13 12:42:00,160 epoch 1 - iter 440/447 - loss 0.69746191 - time (sec): 28.50 - samples/sec: 2996.51 - lr: 0.000029 - momentum: 0.000000
2023-10-13 12:42:00,562 ----------------------------------------------------------------------------------------------------
2023-10-13 12:42:00,563 EPOCH 1 done: loss 0.6927 - lr: 0.000029
2023-10-13 12:42:05,856 DEV : loss 0.19505180418491364 - f1-score (micro avg)  0.6267
2023-10-13 12:42:05,882 saving best model
2023-10-13 12:42:06,236 ----------------------------------------------------------------------------------------------------
2023-10-13 12:42:09,017 epoch 2 - iter 44/447 - loss 0.21696724 - time (sec): 2.78 - samples/sec: 2924.66 - lr: 0.000030 - momentum: 0.000000
2023-10-13 12:42:11,819 epoch 2 - iter 88/447 - loss 0.20487957 - time (sec): 5.58 - samples/sec: 2946.26 - lr: 0.000029 - momentum: 0.000000
2023-10-13 12:42:14,636 epoch 2 - iter 132/447 - loss 0.19251978 - time (sec): 8.40 - samples/sec: 2958.63 - lr: 0.000029 - momentum: 0.000000
2023-10-13 12:42:17,702 epoch 2 - iter 176/447 - loss 0.17906352 - time (sec): 11.46 - samples/sec: 2901.31 - lr: 0.000029 - momentum: 0.000000
2023-10-13 12:42:20,409 epoch 2 - iter 220/447 - loss 0.17778899 - time (sec): 14.17 - samples/sec: 2925.37 - lr: 0.000028 - momentum: 0.000000
2023-10-13 12:42:23,246 epoch 2 - iter 264/447 - loss 0.17239185 - time (sec): 17.01 - samples/sec: 2930.20 - lr: 0.000028 - momentum: 0.000000
2023-10-13 12:42:25,994 epoch 2 - iter 308/447 - loss 0.17298603 - time (sec): 19.76 - samples/sec: 2936.17 - lr: 0.000028 - momentum: 0.000000
2023-10-13 12:42:29,071 epoch 2 - iter 352/447 - loss 0.16720639 - time (sec): 22.83 - samples/sec: 2937.10 - lr: 0.000027 - momentum: 0.000000
2023-10-13 12:42:31,912 epoch 2 - iter 396/447 - loss 0.16724068 - time (sec): 25.67 - samples/sec: 2988.06 - lr: 0.000027 - momentum: 0.000000
2023-10-13 12:42:34,661 epoch 2 - iter 440/447 - loss 0.16528204 - time (sec): 28.42 - samples/sec: 3000.44 - lr: 0.000027 - momentum: 0.000000
2023-10-13 12:42:35,128 ----------------------------------------------------------------------------------------------------
2023-10-13 12:42:35,128 EPOCH 2 done: loss 0.1650 - lr: 0.000027
2023-10-13 12:42:43,746 DEV : loss 0.14146439731121063 - f1-score (micro avg)  0.6657
2023-10-13 12:42:43,775 saving best model
2023-10-13 12:42:44,193 ----------------------------------------------------------------------------------------------------
2023-10-13 12:42:46,901 epoch 3 - iter 44/447 - loss 0.10202154 - time (sec): 2.71 - samples/sec: 3013.94 - lr: 0.000026 - momentum: 0.000000
2023-10-13 12:42:49,545 epoch 3 - iter 88/447 - loss 0.09540967 - time (sec): 5.35 - samples/sec: 2993.34 - lr: 0.000026 - momentum: 0.000000
2023-10-13 12:42:52,480 epoch 3 - iter 132/447 - loss 0.09869171 - time (sec): 8.28 - samples/sec: 2988.36 - lr: 0.000026 - momentum: 0.000000
2023-10-13 12:42:55,163 epoch 3 - iter 176/447 - loss 0.10009313 - time (sec): 10.97 - samples/sec: 3015.11 - lr: 0.000025 - momentum: 0.000000
2023-10-13 12:42:57,900 epoch 3 - iter 220/447 - loss 0.10031670 - time (sec): 13.70 - samples/sec: 2996.70 - lr: 0.000025 - momentum: 0.000000
2023-10-13 12:43:00,688 epoch 3 - iter 264/447 - loss 0.09570394 - time (sec): 16.49 - samples/sec: 3021.04 - lr: 0.000025 - momentum: 0.000000
2023-10-13 12:43:03,521 epoch 3 - iter 308/447 - loss 0.09263551 - time (sec): 19.32 - samples/sec: 3015.52 - lr: 0.000024 - momentum: 0.000000
2023-10-13 12:43:06,404 epoch 3 - iter 352/447 - loss 0.09283022 - time (sec): 22.21 - samples/sec: 3003.11 - lr: 0.000024 - momentum: 0.000000
2023-10-13 12:43:09,295 epoch 3 - iter 396/447 - loss 0.08983680 - time (sec): 25.10 - samples/sec: 3009.43 - lr: 0.000024 - momentum: 0.000000
2023-10-13 12:43:12,073 epoch 3 - iter 440/447 - loss 0.09082591 - time (sec): 27.88 - samples/sec: 3018.36 - lr: 0.000023 - momentum: 0.000000
2023-10-13 12:43:12,851 ----------------------------------------------------------------------------------------------------
2023-10-13 12:43:12,851 EPOCH 3 done: loss 0.0897 - lr: 0.000023
2023-10-13 12:43:22,018 DEV : loss 0.1257464587688446 - f1-score (micro avg)  0.7384
2023-10-13 12:43:22,051 saving best model
2023-10-13 12:43:22,459 ----------------------------------------------------------------------------------------------------
2023-10-13 12:43:25,496 epoch 4 - iter 44/447 - loss 0.06020034 - time (sec): 3.04 - samples/sec: 2684.53 - lr: 0.000023 - momentum: 0.000000
2023-10-13 12:43:28,410 epoch 4 - iter 88/447 - loss 0.06022402 - time (sec): 5.95 - samples/sec: 2785.84 - lr: 0.000023 - momentum: 0.000000
2023-10-13 12:43:31,098 epoch 4 - iter 132/447 - loss 0.05742048 - time (sec): 8.64 - samples/sec: 2869.36 - lr: 0.000022 - momentum: 0.000000
2023-10-13 12:43:34,344 epoch 4 - iter 176/447 - loss 0.05712786 - time (sec): 11.88 - samples/sec: 2929.12 - lr: 0.000022 - momentum: 0.000000
2023-10-13 12:43:37,147 epoch 4 - iter 220/447 - loss 0.05283087 - time (sec): 14.69 - samples/sec: 2943.10 - lr: 0.000022 - momentum: 0.000000
2023-10-13 12:43:40,002 epoch 4 - iter 264/447 - loss 0.05318470 - time (sec): 17.54 - samples/sec: 2944.15 - lr: 0.000021 - momentum: 0.000000
2023-10-13 12:43:42,801 epoch 4 - iter 308/447 - loss 0.05227936 - time (sec): 20.34 - samples/sec: 2954.65 - lr: 0.000021 - momentum: 0.000000
2023-10-13 12:43:45,653 epoch 4 - iter 352/447 - loss 0.05160845 - time (sec): 23.19 - samples/sec: 2949.49 - lr: 0.000021 - momentum: 0.000000
2023-10-13 12:43:48,609 epoch 4 - iter 396/447 - loss 0.05224703 - time (sec): 26.15 - samples/sec: 2957.04 - lr: 0.000020 - momentum: 0.000000
2023-10-13 12:43:51,250 epoch 4 - iter 440/447 - loss 0.05330500 - time (sec): 28.79 - samples/sec: 2965.16 - lr: 0.000020 - momentum: 0.000000
2023-10-13 12:43:51,663 ----------------------------------------------------------------------------------------------------
2023-10-13 12:43:51,663 EPOCH 4 done: loss 0.0535 - lr: 0.000020
2023-10-13 12:44:00,532 DEV : loss 0.14387159049510956 - f1-score (micro avg)  0.7677
2023-10-13 12:44:00,559 saving best model
2023-10-13 12:44:00,978 ----------------------------------------------------------------------------------------------------
2023-10-13 12:44:03,885 epoch 5 - iter 44/447 - loss 0.04206078 - time (sec): 2.91 - samples/sec: 2913.13 - lr: 0.000020 - momentum: 0.000000
2023-10-13 12:44:06,598 epoch 5 - iter 88/447 - loss 0.03343777 - time (sec): 5.62 - samples/sec: 2946.94 - lr: 0.000019 - momentum: 0.000000
2023-10-13 12:44:09,347 epoch 5 - iter 132/447 - loss 0.03138511 - time (sec): 8.37 - samples/sec: 2986.43 - lr: 0.000019 - momentum: 0.000000
2023-10-13 12:44:12,075 epoch 5 - iter 176/447 - loss 0.03300357 - time (sec): 11.10 - samples/sec: 2967.47 - lr: 0.000019 - momentum: 0.000000
2023-10-13 12:44:15,150 epoch 5 - iter 220/447 - loss 0.03298000 - time (sec): 14.17 - samples/sec: 2974.89 - lr: 0.000018 - momentum: 0.000000
2023-10-13 12:44:17,806 epoch 5 - iter 264/447 - loss 0.03394846 - time (sec): 16.83 - samples/sec: 2980.12 - lr: 0.000018 - momentum: 0.000000
2023-10-13 12:44:20,488 epoch 5 - iter 308/447 - loss 0.03350148 - time (sec): 19.51 - samples/sec: 2994.44 - lr: 0.000018 - momentum: 0.000000
2023-10-13 12:44:23,656 epoch 5 - iter 352/447 - loss 0.03406508 - time (sec): 22.68 - samples/sec: 3003.88 - lr: 0.000017 - momentum: 0.000000
2023-10-13 12:44:26,614 epoch 5 - iter 396/447 - loss 0.03446389 - time (sec): 25.63 - samples/sec: 3012.96 - lr: 0.000017 - momentum: 0.000000
2023-10-13 12:44:29,587 epoch 5 - iter 440/447 - loss 0.03444354 - time (sec): 28.61 - samples/sec: 2983.31 - lr: 0.000017 - momentum: 0.000000
2023-10-13 12:44:29,981 ----------------------------------------------------------------------------------------------------
2023-10-13 12:44:29,981 EPOCH 5 done: loss 0.0340 - lr: 0.000017
2023-10-13 12:44:38,623 DEV : loss 0.16171278059482574 - f1-score (micro avg)  0.7783
2023-10-13 12:44:38,651 saving best model
2023-10-13 12:44:39,090 ----------------------------------------------------------------------------------------------------
2023-10-13 12:44:42,275 epoch 6 - iter 44/447 - loss 0.02199878 - time (sec): 3.18 - samples/sec: 3022.58 - lr: 0.000016 - momentum: 0.000000
2023-10-13 12:44:45,000 epoch 6 - iter 88/447 - loss 0.01791271 - time (sec): 5.91 - samples/sec: 3016.28 - lr: 0.000016 - momentum: 0.000000
2023-10-13 12:44:47,967 epoch 6 - iter 132/447 - loss 0.01908459 - time (sec): 8.88 - samples/sec: 3001.10 - lr: 0.000016 - momentum: 0.000000
2023-10-13 12:44:50,765 epoch 6 - iter 176/447 - loss 0.02371749 - time (sec): 11.67 - samples/sec: 3033.68 - lr: 0.000015 - momentum: 0.000000
2023-10-13 12:44:53,605 epoch 6 - iter 220/447 - loss 0.02416858 - time (sec): 14.51 - samples/sec: 3058.44 - lr: 0.000015 - momentum: 0.000000
2023-10-13 12:44:56,283 epoch 6 - iter 264/447 - loss 0.02335894 - time (sec): 17.19 - samples/sec: 3043.29 - lr: 0.000015 - momentum: 0.000000
2023-10-13 12:44:59,072 epoch 6 - iter 308/447 - loss 0.02268652 - time (sec): 19.98 - samples/sec: 3016.88 - lr: 0.000014 - momentum: 0.000000
2023-10-13 12:45:01,767 epoch 6 - iter 352/447 - loss 0.02424439 - time (sec): 22.68 - samples/sec: 3009.53 - lr: 0.000014 - momentum: 0.000000
2023-10-13 12:45:04,569 epoch 6 - iter 396/447 - loss 0.02494761 - time (sec): 25.48 - samples/sec: 3020.77 - lr: 0.000014 - momentum: 0.000000
2023-10-13 12:45:07,181 epoch 6 - iter 440/447 - loss 0.02450484 - time (sec): 28.09 - samples/sec: 3020.74 - lr: 0.000013 - momentum: 0.000000
2023-10-13 12:45:07,807 ----------------------------------------------------------------------------------------------------
2023-10-13 12:45:07,807 EPOCH 6 done: loss 0.0244 - lr: 0.000013
2023-10-13 12:45:16,042 DEV : loss 0.17539535462856293 - f1-score (micro avg)  0.7765
2023-10-13 12:45:16,072 ----------------------------------------------------------------------------------------------------
2023-10-13 12:45:19,440 epoch 7 - iter 44/447 - loss 0.01357369 - time (sec): 3.37 - samples/sec: 2730.95 - lr: 0.000013 - momentum: 0.000000
2023-10-13 12:45:22,746 epoch 7 - iter 88/447 - loss 0.01403634 - time (sec): 6.67 - samples/sec: 2843.58 - lr: 0.000013 - momentum: 0.000000
2023-10-13 12:45:25,595 epoch 7 - iter 132/447 - loss 0.01539611 - time (sec): 9.52 - samples/sec: 2903.29 - lr: 0.000012 - momentum: 0.000000
2023-10-13 12:45:28,448 epoch 7 - iter 176/447 - loss 0.01710186 - time (sec): 12.38 - samples/sec: 2971.82 - lr: 0.000012 - momentum: 0.000000
2023-10-13 12:45:31,550 epoch 7 - iter 220/447 - loss 0.01634294 - time (sec): 15.48 - samples/sec: 2950.74 - lr: 0.000012 - momentum: 0.000000
2023-10-13 12:45:34,209 epoch 7 - iter 264/447 - loss 0.01602423 - time (sec): 18.14 - samples/sec: 2939.09 - lr: 0.000011 - momentum: 0.000000
2023-10-13 12:45:36,766 epoch 7 - iter 308/447 - loss 0.01550904 - time (sec): 20.69 - samples/sec: 2950.56 - lr: 0.000011 - momentum: 0.000000
2023-10-13 12:45:39,529 epoch 7 - iter 352/447 - loss 0.01612650 - time (sec): 23.46 - samples/sec: 2954.33 - lr: 0.000011 - momentum: 0.000000
2023-10-13 12:45:42,057 epoch 7 - iter 396/447 - loss 0.01558782 - time (sec): 25.98 - samples/sec: 2967.20 - lr: 0.000010 - momentum: 0.000000
2023-10-13 12:45:44,752 epoch 7 - iter 440/447 - loss 0.01530422 - time (sec): 28.68 - samples/sec: 2965.04 - lr: 0.000010 - momentum: 0.000000
2023-10-13 12:45:45,265 ----------------------------------------------------------------------------------------------------
2023-10-13 12:45:45,265 EPOCH 7 done: loss 0.0152 - lr: 0.000010
2023-10-13 12:45:53,827 DEV : loss 0.18422970175743103 - f1-score (micro avg)  0.7758
2023-10-13 12:45:53,857 ----------------------------------------------------------------------------------------------------
2023-10-13 12:45:56,629 epoch 8 - iter 44/447 - loss 0.00798763 - time (sec): 2.77 - samples/sec: 3120.73 - lr: 0.000010 - momentum: 0.000000
2023-10-13 12:45:59,360 epoch 8 - iter 88/447 - loss 0.00777261 - time (sec): 5.50 - samples/sec: 3050.62 - lr: 0.000009 - momentum: 0.000000
2023-10-13 12:46:03,058 epoch 8 - iter 132/447 - loss 0.00764591 - time (sec): 9.20 - samples/sec: 2909.35 - lr: 0.000009 - momentum: 0.000000
2023-10-13 12:46:06,043 epoch 8 - iter 176/447 - loss 0.00851798 - time (sec): 12.18 - samples/sec: 2888.94 - lr: 0.000009 - momentum: 0.000000
2023-10-13 12:46:08,754 epoch 8 - iter 220/447 - loss 0.00875116 - time (sec): 14.90 - samples/sec: 2926.03 - lr: 0.000008 - momentum: 0.000000
2023-10-13 12:46:11,769 epoch 8 - iter 264/447 - loss 0.00970690 - time (sec): 17.91 - samples/sec: 2922.91 - lr: 0.000008 - momentum: 0.000000
2023-10-13 12:46:14,532 epoch 8 - iter 308/447 - loss 0.00932691 - time (sec): 20.67 - samples/sec: 2946.73 - lr: 0.000008 - momentum: 0.000000
2023-10-13 12:46:17,149 epoch 8 - iter 352/447 - loss 0.00886594 - time (sec): 23.29 - samples/sec: 2958.75 - lr: 0.000007 - momentum: 0.000000
2023-10-13 12:46:19,955 epoch 8 - iter 396/447 - loss 0.00894768 - time (sec): 26.10 - samples/sec: 2967.27 - lr: 0.000007 - momentum: 0.000000
2023-10-13 12:46:22,571 epoch 8 - iter 440/447 - loss 0.00921656 - time (sec): 28.71 - samples/sec: 2972.85 - lr: 0.000007 - momentum: 0.000000
2023-10-13 12:46:22,952 ----------------------------------------------------------------------------------------------------
2023-10-13 12:46:22,952 EPOCH 8 done: loss 0.0093 - lr: 0.000007
2023-10-13 12:46:31,318 DEV : loss 0.2055710256099701 - f1-score (micro avg)  0.7846
2023-10-13 12:46:31,349 saving best model
2023-10-13 12:46:31,806 ----------------------------------------------------------------------------------------------------
2023-10-13 12:46:34,789 epoch 9 - iter 44/447 - loss 0.00632862 - time (sec): 2.98 - samples/sec: 2741.13 - lr: 0.000006 - momentum: 0.000000
2023-10-13 12:46:38,014 epoch 9 - iter 88/447 - loss 0.00391427 - time (sec): 6.21 - samples/sec: 2820.55 - lr: 0.000006 - momentum: 0.000000
2023-10-13 12:46:40,912 epoch 9 - iter 132/447 - loss 0.00600799 - time (sec): 9.10 - samples/sec: 2861.99 - lr: 0.000006 - momentum: 0.000000
2023-10-13 12:46:43,703 epoch 9 - iter 176/447 - loss 0.00647352 - time (sec): 11.90 - samples/sec: 2895.16 - lr: 0.000005 - momentum: 0.000000
2023-10-13 12:46:46,314 epoch 9 - iter 220/447 - loss 0.00669799 - time (sec): 14.51 - samples/sec: 2947.90 - lr: 0.000005 - momentum: 0.000000
2023-10-13 12:46:48,942 epoch 9 - iter 264/447 - loss 0.00717437 - time (sec): 17.13 - samples/sec: 2969.34 - lr: 0.000005 - momentum: 0.000000
2023-10-13 12:46:51,618 epoch 9 - iter 308/447 - loss 0.00633304 - time (sec): 19.81 - samples/sec: 2976.81 - lr: 0.000004 - momentum: 0.000000
2023-10-13 12:46:54,292 epoch 9 - iter 352/447 - loss 0.00630348 - time (sec): 22.48 - samples/sec: 2987.23 - lr: 0.000004 - momentum: 0.000000
2023-10-13 12:46:57,800 epoch 9 - iter 396/447 - loss 0.00673031 - time (sec): 25.99 - samples/sec: 2963.79 - lr: 0.000004 - momentum: 0.000000
2023-10-13 12:47:00,643 epoch 9 - iter 440/447 - loss 0.00677427 - time (sec): 28.84 - samples/sec: 2958.97 - lr: 0.000003 - momentum: 0.000000
2023-10-13 12:47:01,044 ----------------------------------------------------------------------------------------------------
2023-10-13 12:47:01,044 EPOCH 9 done: loss 0.0071 - lr: 0.000003
2023-10-13 12:47:09,395 DEV : loss 0.21442939341068268 - f1-score (micro avg)  0.7876
2023-10-13 12:47:09,425 saving best model
2023-10-13 12:47:09,938 ----------------------------------------------------------------------------------------------------
2023-10-13 12:47:12,923 epoch 10 - iter 44/447 - loss 0.00347708 - time (sec): 2.98 - samples/sec: 3022.65 - lr: 0.000003 - momentum: 0.000000
2023-10-13 12:47:15,674 epoch 10 - iter 88/447 - loss 0.00567608 - time (sec): 5.73 - samples/sec: 3017.03 - lr: 0.000003 - momentum: 0.000000
2023-10-13 12:47:18,567 epoch 10 - iter 132/447 - loss 0.00482620 - time (sec): 8.63 - samples/sec: 2983.60 - lr: 0.000002 - momentum: 0.000000
2023-10-13 12:47:21,552 epoch 10 - iter 176/447 - loss 0.00604485 - time (sec): 11.61 - samples/sec: 2982.37 - lr: 0.000002 - momentum: 0.000000
2023-10-13 12:47:24,150 epoch 10 - iter 220/447 - loss 0.00591778 - time (sec): 14.21 - samples/sec: 3001.57 - lr: 0.000002 - momentum: 0.000000
2023-10-13 12:47:26,840 epoch 10 - iter 264/447 - loss 0.00602543 - time (sec): 16.90 - samples/sec: 3005.87 - lr: 0.000001 - momentum: 0.000000
2023-10-13 12:47:29,663 epoch 10 - iter 308/447 - loss 0.00564455 - time (sec): 19.72 - samples/sec: 3012.65 - lr: 0.000001 - momentum: 0.000000
2023-10-13 12:47:33,086 epoch 10 - iter 352/447 - loss 0.00531102 - time (sec): 23.15 - samples/sec: 3009.37 - lr: 0.000001 - momentum: 0.000000
2023-10-13 12:47:35,778 epoch 10 - iter 396/447 - loss 0.00565506 - time (sec): 25.84 - samples/sec: 2999.81 - lr: 0.000000 - momentum: 0.000000
2023-10-13 12:47:38,391 epoch 10 - iter 440/447 - loss 0.00523144 - time (sec): 28.45 - samples/sec: 2997.36 - lr: 0.000000 - momentum: 0.000000
2023-10-13 12:47:38,804 ----------------------------------------------------------------------------------------------------
2023-10-13 12:47:38,804 EPOCH 10 done: loss 0.0052 - lr: 0.000000
2023-10-13 12:47:47,440 DEV : loss 0.21124331653118134 - f1-score (micro avg)  0.7838
2023-10-13 12:47:47,791 ----------------------------------------------------------------------------------------------------
2023-10-13 12:47:47,792 Loading model from best epoch ...
2023-10-13 12:47:49,218 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 12:47:54,501 
Results:
- F-score (micro) 0.7514
- F-score (macro) 0.6796
- Accuracy 0.6216

By class:
              precision    recall  f1-score   support

         loc     0.8401    0.8289    0.8345       596
        pers     0.6885    0.7568    0.7210       333
         org     0.5455    0.5455    0.5455       132
        prod     0.7021    0.5000    0.5841        66
        time     0.6923    0.7347    0.7129        49

   micro avg     0.7485    0.7543    0.7514      1176
   macro avg     0.6937    0.6732    0.6796      1176
weighted avg     0.7502    0.7543    0.7508      1176

2023-10-13 12:47:54,502 ----------------------------------------------------------------------------------------------------