2023-10-13 13:45:29,155 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,155 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 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 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 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 Train:  3575 sentences
2023-10-13 13:45:29,156         (train_with_dev=False, train_with_test=False)
2023-10-13 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 Training Params:
2023-10-13 13:45:29,156  - learning_rate: "3e-05" 
2023-10-13 13:45:29,156  - mini_batch_size: "8"
2023-10-13 13:45:29,156  - max_epochs: "10"
2023-10-13 13:45:29,156  - shuffle: "True"
2023-10-13 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 Plugins:
2023-10-13 13:45:29,156  - LinearScheduler | warmup_fraction: '0.1'
2023-10-13 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 Final evaluation on model from best epoch (best-model.pt)
2023-10-13 13:45:29,156  - metric: "('micro avg', 'f1-score')"
2023-10-13 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 Computation:
2023-10-13 13:45:29,156  - compute on device: cuda:0
2023-10-13 13:45:29,156  - embedding storage: none
2023-10-13 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
2023-10-13 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:29,156 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:32,664 epoch 1 - iter 44/447 - loss 2.93081395 - time (sec): 3.51 - samples/sec: 2794.79 - lr: 0.000003 - momentum: 0.000000
2023-10-13 13:45:35,519 epoch 1 - iter 88/447 - loss 2.21221631 - time (sec): 6.36 - samples/sec: 2952.52 - lr: 0.000006 - momentum: 0.000000
2023-10-13 13:45:38,330 epoch 1 - iter 132/447 - loss 1.70150545 - time (sec): 9.17 - samples/sec: 2955.93 - lr: 0.000009 - momentum: 0.000000
2023-10-13 13:45:41,390 epoch 1 - iter 176/447 - loss 1.40314630 - time (sec): 12.23 - samples/sec: 2923.74 - lr: 0.000012 - momentum: 0.000000
2023-10-13 13:45:44,060 epoch 1 - iter 220/447 - loss 1.19922858 - time (sec): 14.90 - samples/sec: 2973.78 - lr: 0.000015 - momentum: 0.000000
2023-10-13 13:45:46,870 epoch 1 - iter 264/447 - loss 1.05760240 - time (sec): 17.71 - samples/sec: 2976.98 - lr: 0.000018 - momentum: 0.000000
2023-10-13 13:45:49,718 epoch 1 - iter 308/447 - loss 0.95386364 - time (sec): 20.56 - samples/sec: 2968.22 - lr: 0.000021 - momentum: 0.000000
2023-10-13 13:45:52,428 epoch 1 - iter 352/447 - loss 0.87272127 - time (sec): 23.27 - samples/sec: 2979.06 - lr: 0.000024 - momentum: 0.000000
2023-10-13 13:45:55,040 epoch 1 - iter 396/447 - loss 0.81230539 - time (sec): 25.88 - samples/sec: 2980.96 - lr: 0.000027 - momentum: 0.000000
2023-10-13 13:45:57,785 epoch 1 - iter 440/447 - loss 0.75773565 - time (sec): 28.63 - samples/sec: 2986.90 - lr: 0.000029 - momentum: 0.000000
2023-10-13 13:45:58,180 ----------------------------------------------------------------------------------------------------
2023-10-13 13:45:58,181 EPOCH 1 done: loss 0.7513 - lr: 0.000029
2023-10-13 13:46:03,831 DEV : loss 0.19987231492996216 - f1-score (micro avg)  0.5867
2023-10-13 13:46:03,858 saving best model
2023-10-13 13:46:04,222 ----------------------------------------------------------------------------------------------------
2023-10-13 13:46:07,216 epoch 2 - iter 44/447 - loss 0.22838690 - time (sec): 2.99 - samples/sec: 3057.96 - lr: 0.000030 - momentum: 0.000000
2023-10-13 13:46:09,923 epoch 2 - iter 88/447 - loss 0.21485306 - time (sec): 5.70 - samples/sec: 3078.06 - lr: 0.000029 - momentum: 0.000000
2023-10-13 13:46:12,699 epoch 2 - iter 132/447 - loss 0.19643660 - time (sec): 8.48 - samples/sec: 3033.96 - lr: 0.000029 - momentum: 0.000000
2023-10-13 13:46:15,236 epoch 2 - iter 176/447 - loss 0.19333987 - time (sec): 11.01 - samples/sec: 3017.71 - lr: 0.000029 - momentum: 0.000000
2023-10-13 13:46:18,157 epoch 2 - iter 220/447 - loss 0.18690042 - time (sec): 13.93 - samples/sec: 2971.59 - lr: 0.000028 - momentum: 0.000000
2023-10-13 13:46:21,020 epoch 2 - iter 264/447 - loss 0.18521400 - time (sec): 16.80 - samples/sec: 2981.18 - lr: 0.000028 - momentum: 0.000000
2023-10-13 13:46:23,773 epoch 2 - iter 308/447 - loss 0.18183687 - time (sec): 19.55 - samples/sec: 3008.95 - lr: 0.000028 - momentum: 0.000000
2023-10-13 13:46:26,581 epoch 2 - iter 352/447 - loss 0.17669129 - time (sec): 22.36 - samples/sec: 3011.66 - lr: 0.000027 - momentum: 0.000000
2023-10-13 13:46:29,613 epoch 2 - iter 396/447 - loss 0.17173028 - time (sec): 25.39 - samples/sec: 3008.50 - lr: 0.000027 - momentum: 0.000000
2023-10-13 13:46:32,544 epoch 2 - iter 440/447 - loss 0.16743004 - time (sec): 28.32 - samples/sec: 3001.78 - lr: 0.000027 - momentum: 0.000000
2023-10-13 13:46:33,009 ----------------------------------------------------------------------------------------------------
2023-10-13 13:46:33,009 EPOCH 2 done: loss 0.1658 - lr: 0.000027
2023-10-13 13:46:41,867 DEV : loss 0.13758938014507294 - f1-score (micro avg)  0.6982
2023-10-13 13:46:41,893 saving best model
2023-10-13 13:46:42,353 ----------------------------------------------------------------------------------------------------
2023-10-13 13:46:45,070 epoch 3 - iter 44/447 - loss 0.09046529 - time (sec): 2.71 - samples/sec: 3343.86 - lr: 0.000026 - momentum: 0.000000
2023-10-13 13:46:47,780 epoch 3 - iter 88/447 - loss 0.08742309 - time (sec): 5.42 - samples/sec: 3275.41 - lr: 0.000026 - momentum: 0.000000
2023-10-13 13:46:50,919 epoch 3 - iter 132/447 - loss 0.09040866 - time (sec): 8.56 - samples/sec: 3157.90 - lr: 0.000026 - momentum: 0.000000
2023-10-13 13:46:53,787 epoch 3 - iter 176/447 - loss 0.09359270 - time (sec): 11.43 - samples/sec: 3147.53 - lr: 0.000025 - momentum: 0.000000
2023-10-13 13:46:56,667 epoch 3 - iter 220/447 - loss 0.09236157 - time (sec): 14.31 - samples/sec: 3069.82 - lr: 0.000025 - momentum: 0.000000
2023-10-13 13:46:59,353 epoch 3 - iter 264/447 - loss 0.09220849 - time (sec): 17.00 - samples/sec: 3079.50 - lr: 0.000025 - momentum: 0.000000
2023-10-13 13:47:02,086 epoch 3 - iter 308/447 - loss 0.09035981 - time (sec): 19.73 - samples/sec: 3045.30 - lr: 0.000024 - momentum: 0.000000
2023-10-13 13:47:04,963 epoch 3 - iter 352/447 - loss 0.08957548 - time (sec): 22.61 - samples/sec: 3022.73 - lr: 0.000024 - momentum: 0.000000
2023-10-13 13:47:07,667 epoch 3 - iter 396/447 - loss 0.08950727 - time (sec): 25.31 - samples/sec: 3047.51 - lr: 0.000024 - momentum: 0.000000
2023-10-13 13:47:10,328 epoch 3 - iter 440/447 - loss 0.08942085 - time (sec): 27.97 - samples/sec: 3047.77 - lr: 0.000023 - momentum: 0.000000
2023-10-13 13:47:10,806 ----------------------------------------------------------------------------------------------------
2023-10-13 13:47:10,806 EPOCH 3 done: loss 0.0894 - lr: 0.000023
2023-10-13 13:47:19,441 DEV : loss 0.13164587318897247 - f1-score (micro avg)  0.7433
2023-10-13 13:47:19,469 saving best model
2023-10-13 13:47:19,885 ----------------------------------------------------------------------------------------------------
2023-10-13 13:47:22,845 epoch 4 - iter 44/447 - loss 0.05596531 - time (sec): 2.96 - samples/sec: 3100.82 - lr: 0.000023 - momentum: 0.000000
2023-10-13 13:47:25,456 epoch 4 - iter 88/447 - loss 0.05366549 - time (sec): 5.57 - samples/sec: 3109.90 - lr: 0.000023 - momentum: 0.000000
2023-10-13 13:47:28,711 epoch 4 - iter 132/447 - loss 0.05216138 - time (sec): 8.82 - samples/sec: 3101.36 - lr: 0.000022 - momentum: 0.000000
2023-10-13 13:47:31,433 epoch 4 - iter 176/447 - loss 0.05205191 - time (sec): 11.54 - samples/sec: 3060.90 - lr: 0.000022 - momentum: 0.000000
2023-10-13 13:47:34,196 epoch 4 - iter 220/447 - loss 0.05390396 - time (sec): 14.31 - samples/sec: 3051.40 - lr: 0.000022 - momentum: 0.000000
2023-10-13 13:47:37,277 epoch 4 - iter 264/447 - loss 0.05351340 - time (sec): 17.39 - samples/sec: 3050.35 - lr: 0.000021 - momentum: 0.000000
2023-10-13 13:47:39,967 epoch 4 - iter 308/447 - loss 0.05467544 - time (sec): 20.08 - samples/sec: 3047.85 - lr: 0.000021 - momentum: 0.000000
2023-10-13 13:47:42,750 epoch 4 - iter 352/447 - loss 0.05475463 - time (sec): 22.86 - samples/sec: 3022.27 - lr: 0.000021 - momentum: 0.000000
2023-10-13 13:47:45,448 epoch 4 - iter 396/447 - loss 0.05579707 - time (sec): 25.56 - samples/sec: 3012.72 - lr: 0.000020 - momentum: 0.000000
2023-10-13 13:47:48,323 epoch 4 - iter 440/447 - loss 0.05437413 - time (sec): 28.44 - samples/sec: 2999.50 - lr: 0.000020 - momentum: 0.000000
2023-10-13 13:47:48,774 ----------------------------------------------------------------------------------------------------
2023-10-13 13:47:48,774 EPOCH 4 done: loss 0.0544 - lr: 0.000020
2023-10-13 13:47:57,440 DEV : loss 0.1452960968017578 - f1-score (micro avg)  0.7585
2023-10-13 13:47:57,475 saving best model
2023-10-13 13:47:57,967 ----------------------------------------------------------------------------------------------------
2023-10-13 13:48:00,973 epoch 5 - iter 44/447 - loss 0.03517186 - time (sec): 3.00 - samples/sec: 2869.42 - lr: 0.000020 - momentum: 0.000000
2023-10-13 13:48:03,740 epoch 5 - iter 88/447 - loss 0.03773064 - time (sec): 5.77 - samples/sec: 2919.25 - lr: 0.000019 - momentum: 0.000000
2023-10-13 13:48:06,570 epoch 5 - iter 132/447 - loss 0.03356338 - time (sec): 8.60 - samples/sec: 2890.74 - lr: 0.000019 - momentum: 0.000000
2023-10-13 13:48:09,544 epoch 5 - iter 176/447 - loss 0.03500474 - time (sec): 11.57 - samples/sec: 2969.91 - lr: 0.000019 - momentum: 0.000000
2023-10-13 13:48:12,444 epoch 5 - iter 220/447 - loss 0.03438478 - time (sec): 14.48 - samples/sec: 2998.12 - lr: 0.000018 - momentum: 0.000000
2023-10-13 13:48:15,455 epoch 5 - iter 264/447 - loss 0.03589972 - time (sec): 17.49 - samples/sec: 2992.34 - lr: 0.000018 - momentum: 0.000000
2023-10-13 13:48:18,072 epoch 5 - iter 308/447 - loss 0.03587490 - time (sec): 20.10 - samples/sec: 3006.27 - lr: 0.000018 - momentum: 0.000000
2023-10-13 13:48:20,894 epoch 5 - iter 352/447 - loss 0.03540735 - time (sec): 22.92 - samples/sec: 3014.00 - lr: 0.000017 - momentum: 0.000000
2023-10-13 13:48:23,570 epoch 5 - iter 396/447 - loss 0.03499206 - time (sec): 25.60 - samples/sec: 3008.37 - lr: 0.000017 - momentum: 0.000000
2023-10-13 13:48:26,331 epoch 5 - iter 440/447 - loss 0.03401512 - time (sec): 28.36 - samples/sec: 3004.98 - lr: 0.000017 - momentum: 0.000000
2023-10-13 13:48:26,751 ----------------------------------------------------------------------------------------------------
2023-10-13 13:48:26,751 EPOCH 5 done: loss 0.0337 - lr: 0.000017
2023-10-13 13:48:35,451 DEV : loss 0.1682368963956833 - f1-score (micro avg)  0.7564
2023-10-13 13:48:35,478 ----------------------------------------------------------------------------------------------------
2023-10-13 13:48:38,149 epoch 6 - iter 44/447 - loss 0.02451337 - time (sec): 2.67 - samples/sec: 3095.65 - lr: 0.000016 - momentum: 0.000000
2023-10-13 13:48:41,006 epoch 6 - iter 88/447 - loss 0.02301274 - time (sec): 5.53 - samples/sec: 3017.27 - lr: 0.000016 - momentum: 0.000000
2023-10-13 13:48:44,494 epoch 6 - iter 132/447 - loss 0.01930332 - time (sec): 9.02 - samples/sec: 2980.08 - lr: 0.000016 - momentum: 0.000000
2023-10-13 13:48:47,421 epoch 6 - iter 176/447 - loss 0.01984188 - time (sec): 11.94 - samples/sec: 2973.45 - lr: 0.000015 - momentum: 0.000000
2023-10-13 13:48:50,346 epoch 6 - iter 220/447 - loss 0.01856509 - time (sec): 14.87 - samples/sec: 3011.30 - lr: 0.000015 - momentum: 0.000000
2023-10-13 13:48:53,243 epoch 6 - iter 264/447 - loss 0.01980529 - time (sec): 17.76 - samples/sec: 2984.20 - lr: 0.000015 - momentum: 0.000000
2023-10-13 13:48:55,997 epoch 6 - iter 308/447 - loss 0.02127022 - time (sec): 20.52 - samples/sec: 2969.36 - lr: 0.000014 - momentum: 0.000000
2023-10-13 13:48:58,601 epoch 6 - iter 352/447 - loss 0.02325254 - time (sec): 23.12 - samples/sec: 2992.88 - lr: 0.000014 - momentum: 0.000000
2023-10-13 13:49:01,377 epoch 6 - iter 396/447 - loss 0.02395462 - time (sec): 25.90 - samples/sec: 2998.56 - lr: 0.000014 - momentum: 0.000000
2023-10-13 13:49:03,917 epoch 6 - iter 440/447 - loss 0.02470748 - time (sec): 28.44 - samples/sec: 2995.43 - lr: 0.000013 - momentum: 0.000000
2023-10-13 13:49:04,342 ----------------------------------------------------------------------------------------------------
2023-10-13 13:49:04,342 EPOCH 6 done: loss 0.0244 - lr: 0.000013
2023-10-13 13:49:12,554 DEV : loss 0.18090558052062988 - f1-score (micro avg)  0.7619
2023-10-13 13:49:12,582 saving best model
2023-10-13 13:49:13,002 ----------------------------------------------------------------------------------------------------
2023-10-13 13:49:15,547 epoch 7 - iter 44/447 - loss 0.01799003 - time (sec): 2.54 - samples/sec: 3025.09 - lr: 0.000013 - momentum: 0.000000
2023-10-13 13:49:18,238 epoch 7 - iter 88/447 - loss 0.01481873 - time (sec): 5.23 - samples/sec: 2955.08 - lr: 0.000013 - momentum: 0.000000
2023-10-13 13:49:21,888 epoch 7 - iter 132/447 - loss 0.01452826 - time (sec): 8.88 - samples/sec: 2818.32 - lr: 0.000012 - momentum: 0.000000
2023-10-13 13:49:24,671 epoch 7 - iter 176/447 - loss 0.01521788 - time (sec): 11.67 - samples/sec: 2888.97 - lr: 0.000012 - momentum: 0.000000
2023-10-13 13:49:27,614 epoch 7 - iter 220/447 - loss 0.01545290 - time (sec): 14.61 - samples/sec: 2908.87 - lr: 0.000012 - momentum: 0.000000
2023-10-13 13:49:30,558 epoch 7 - iter 264/447 - loss 0.01487671 - time (sec): 17.55 - samples/sec: 2892.61 - lr: 0.000011 - momentum: 0.000000
2023-10-13 13:49:33,270 epoch 7 - iter 308/447 - loss 0.01548150 - time (sec): 20.27 - samples/sec: 2914.17 - lr: 0.000011 - momentum: 0.000000
2023-10-13 13:49:36,465 epoch 7 - iter 352/447 - loss 0.01551647 - time (sec): 23.46 - samples/sec: 2901.51 - lr: 0.000011 - momentum: 0.000000
2023-10-13 13:49:39,150 epoch 7 - iter 396/447 - loss 0.01499973 - time (sec): 26.15 - samples/sec: 2927.73 - lr: 0.000010 - momentum: 0.000000
2023-10-13 13:49:41,933 epoch 7 - iter 440/447 - loss 0.01549702 - time (sec): 28.93 - samples/sec: 2944.57 - lr: 0.000010 - momentum: 0.000000
2023-10-13 13:49:42,378 ----------------------------------------------------------------------------------------------------
2023-10-13 13:49:42,378 EPOCH 7 done: loss 0.0156 - lr: 0.000010
2023-10-13 13:49:50,741 DEV : loss 0.2054622322320938 - f1-score (micro avg)  0.7834
2023-10-13 13:49:50,771 saving best model
2023-10-13 13:49:51,167 ----------------------------------------------------------------------------------------------------
2023-10-13 13:49:53,918 epoch 8 - iter 44/447 - loss 0.02317903 - time (sec): 2.75 - samples/sec: 3031.64 - lr: 0.000010 - momentum: 0.000000
2023-10-13 13:49:56,645 epoch 8 - iter 88/447 - loss 0.01597198 - time (sec): 5.48 - samples/sec: 3026.22 - lr: 0.000009 - momentum: 0.000000
2023-10-13 13:49:59,631 epoch 8 - iter 132/447 - loss 0.01364282 - time (sec): 8.46 - samples/sec: 3052.97 - lr: 0.000009 - momentum: 0.000000
2023-10-13 13:50:03,019 epoch 8 - iter 176/447 - loss 0.01296909 - time (sec): 11.85 - samples/sec: 2963.07 - lr: 0.000009 - momentum: 0.000000
2023-10-13 13:50:05,926 epoch 8 - iter 220/447 - loss 0.01238780 - time (sec): 14.76 - samples/sec: 2958.16 - lr: 0.000008 - momentum: 0.000000
2023-10-13 13:50:08,399 epoch 8 - iter 264/447 - loss 0.01314139 - time (sec): 17.23 - samples/sec: 3003.92 - lr: 0.000008 - momentum: 0.000000
2023-10-13 13:50:11,150 epoch 8 - iter 308/447 - loss 0.01219720 - time (sec): 19.98 - samples/sec: 3006.95 - lr: 0.000008 - momentum: 0.000000
2023-10-13 13:50:13,836 epoch 8 - iter 352/447 - loss 0.01233694 - time (sec): 22.67 - samples/sec: 3019.24 - lr: 0.000007 - momentum: 0.000000
2023-10-13 13:50:16,541 epoch 8 - iter 396/447 - loss 0.01193033 - time (sec): 25.37 - samples/sec: 3024.64 - lr: 0.000007 - momentum: 0.000000
2023-10-13 13:50:19,511 epoch 8 - iter 440/447 - loss 0.01165387 - time (sec): 28.34 - samples/sec: 3012.13 - lr: 0.000007 - momentum: 0.000000
2023-10-13 13:50:19,893 ----------------------------------------------------------------------------------------------------
2023-10-13 13:50:19,893 EPOCH 8 done: loss 0.0115 - lr: 0.000007
2023-10-13 13:50:28,185 DEV : loss 0.2043541818857193 - f1-score (micro avg)  0.7832
2023-10-13 13:50:28,214 ----------------------------------------------------------------------------------------------------
2023-10-13 13:50:31,021 epoch 9 - iter 44/447 - loss 0.00821035 - time (sec): 2.81 - samples/sec: 2915.87 - lr: 0.000006 - momentum: 0.000000
2023-10-13 13:50:34,171 epoch 9 - iter 88/447 - loss 0.00704373 - time (sec): 5.96 - samples/sec: 2937.96 - lr: 0.000006 - momentum: 0.000000
2023-10-13 13:50:37,236 epoch 9 - iter 132/447 - loss 0.00630963 - time (sec): 9.02 - samples/sec: 2947.79 - lr: 0.000006 - momentum: 0.000000
2023-10-13 13:50:40,151 epoch 9 - iter 176/447 - loss 0.00673535 - time (sec): 11.94 - samples/sec: 2936.36 - lr: 0.000005 - momentum: 0.000000
2023-10-13 13:50:43,007 epoch 9 - iter 220/447 - loss 0.00678672 - time (sec): 14.79 - samples/sec: 2919.96 - lr: 0.000005 - momentum: 0.000000
2023-10-13 13:50:45,713 epoch 9 - iter 264/447 - loss 0.00638557 - time (sec): 17.50 - samples/sec: 2939.90 - lr: 0.000005 - momentum: 0.000000
2023-10-13 13:50:48,377 epoch 9 - iter 308/447 - loss 0.00699517 - time (sec): 20.16 - samples/sec: 2973.46 - lr: 0.000004 - momentum: 0.000000
2023-10-13 13:50:51,086 epoch 9 - iter 352/447 - loss 0.00728302 - time (sec): 22.87 - samples/sec: 2999.27 - lr: 0.000004 - momentum: 0.000000
2023-10-13 13:50:53,984 epoch 9 - iter 396/447 - loss 0.00737181 - time (sec): 25.77 - samples/sec: 2987.05 - lr: 0.000004 - momentum: 0.000000
2023-10-13 13:50:57,195 epoch 9 - iter 440/447 - loss 0.00763102 - time (sec): 28.98 - samples/sec: 2943.56 - lr: 0.000003 - momentum: 0.000000
2023-10-13 13:50:57,624 ----------------------------------------------------------------------------------------------------
2023-10-13 13:50:57,624 EPOCH 9 done: loss 0.0075 - lr: 0.000003
2023-10-13 13:51:05,821 DEV : loss 0.20693199336528778 - f1-score (micro avg)  0.7894
2023-10-13 13:51:05,850 saving best model
2023-10-13 13:51:06,241 ----------------------------------------------------------------------------------------------------
2023-10-13 13:51:09,038 epoch 10 - iter 44/447 - loss 0.00549616 - time (sec): 2.79 - samples/sec: 3056.58 - lr: 0.000003 - momentum: 0.000000
2023-10-13 13:51:11,718 epoch 10 - iter 88/447 - loss 0.00580800 - time (sec): 5.47 - samples/sec: 2968.75 - lr: 0.000003 - momentum: 0.000000
2023-10-13 13:51:14,523 epoch 10 - iter 132/447 - loss 0.00521287 - time (sec): 8.28 - samples/sec: 2999.29 - lr: 0.000002 - momentum: 0.000000
2023-10-13 13:51:17,082 epoch 10 - iter 176/447 - loss 0.00551659 - time (sec): 10.84 - samples/sec: 3018.95 - lr: 0.000002 - momentum: 0.000000
2023-10-13 13:51:20,311 epoch 10 - iter 220/447 - loss 0.00577789 - time (sec): 14.07 - samples/sec: 3014.52 - lr: 0.000002 - momentum: 0.000000
2023-10-13 13:51:23,519 epoch 10 - iter 264/447 - loss 0.00524429 - time (sec): 17.27 - samples/sec: 2988.18 - lr: 0.000001 - momentum: 0.000000
2023-10-13 13:51:26,181 epoch 10 - iter 308/447 - loss 0.00473462 - time (sec): 19.93 - samples/sec: 3002.31 - lr: 0.000001 - momentum: 0.000000
2023-10-13 13:51:28,755 epoch 10 - iter 352/447 - loss 0.00431197 - time (sec): 22.51 - samples/sec: 2999.77 - lr: 0.000001 - momentum: 0.000000
2023-10-13 13:51:31,655 epoch 10 - iter 396/447 - loss 0.00461146 - time (sec): 25.41 - samples/sec: 3029.63 - lr: 0.000000 - momentum: 0.000000
2023-10-13 13:51:34,370 epoch 10 - iter 440/447 - loss 0.00490048 - time (sec): 28.12 - samples/sec: 3034.79 - lr: 0.000000 - momentum: 0.000000
2023-10-13 13:51:34,805 ----------------------------------------------------------------------------------------------------
2023-10-13 13:51:34,805 EPOCH 10 done: loss 0.0048 - lr: 0.000000
2023-10-13 13:51:43,485 DEV : loss 0.21205270290374756 - f1-score (micro avg)  0.784
2023-10-13 13:51:43,833 ----------------------------------------------------------------------------------------------------
2023-10-13 13:51:43,834 Loading model from best epoch ...
2023-10-13 13:51:45,288 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 13:51:50,821 
Results:
- F-score (micro) 0.7487
- F-score (macro) 0.667
- Accuracy 0.6162

By class:
              precision    recall  f1-score   support

         loc     0.8279    0.8557    0.8416       596
        pers     0.6684    0.7508    0.7072       333
         org     0.5575    0.4773    0.5143       132
        prod     0.6400    0.4848    0.5517        66
        time     0.7059    0.7347    0.7200        49

   micro avg     0.7400    0.7577    0.7487      1176
   macro avg     0.6800    0.6607    0.6670      1176
weighted avg     0.7368    0.7577    0.7455      1176

2023-10-13 13:51:50,821 ----------------------------------------------------------------------------------------------------