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2023-10-16 10:55:48,313 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,314 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=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-16 10:55:48,314 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,314 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
 - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
2023-10-16 10:55:48,314 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,314 Train:  7142 sentences
2023-10-16 10:55:48,314         (train_with_dev=False, train_with_test=False)
2023-10-16 10:55:48,314 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,315 Training Params:
2023-10-16 10:55:48,315  - learning_rate: "5e-05" 
2023-10-16 10:55:48,315  - mini_batch_size: "4"
2023-10-16 10:55:48,315  - max_epochs: "10"
2023-10-16 10:55:48,315  - shuffle: "True"
2023-10-16 10:55:48,315 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,315 Plugins:
2023-10-16 10:55:48,315  - LinearScheduler | warmup_fraction: '0.1'
2023-10-16 10:55:48,315 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,315 Final evaluation on model from best epoch (best-model.pt)
2023-10-16 10:55:48,315  - metric: "('micro avg', 'f1-score')"
2023-10-16 10:55:48,315 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,315 Computation:
2023-10-16 10:55:48,315  - compute on device: cuda:0
2023-10-16 10:55:48,315  - embedding storage: none
2023-10-16 10:55:48,315 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,315 Model training base path: "hmbench-newseye/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-16 10:55:48,315 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:48,315 ----------------------------------------------------------------------------------------------------
2023-10-16 10:55:57,101 epoch 1 - iter 178/1786 - loss 1.86473963 - time (sec): 8.78 - samples/sec: 2798.20 - lr: 0.000005 - momentum: 0.000000
2023-10-16 10:56:05,989 epoch 1 - iter 356/1786 - loss 1.15143669 - time (sec): 17.67 - samples/sec: 2805.49 - lr: 0.000010 - momentum: 0.000000
2023-10-16 10:56:14,650 epoch 1 - iter 534/1786 - loss 0.89028895 - time (sec): 26.33 - samples/sec: 2786.50 - lr: 0.000015 - momentum: 0.000000
2023-10-16 10:56:23,319 epoch 1 - iter 712/1786 - loss 0.72907805 - time (sec): 35.00 - samples/sec: 2791.74 - lr: 0.000020 - momentum: 0.000000
2023-10-16 10:56:32,196 epoch 1 - iter 890/1786 - loss 0.61722937 - time (sec): 43.88 - samples/sec: 2795.47 - lr: 0.000025 - momentum: 0.000000
2023-10-16 10:56:40,923 epoch 1 - iter 1068/1786 - loss 0.54496368 - time (sec): 52.61 - samples/sec: 2804.43 - lr: 0.000030 - momentum: 0.000000
2023-10-16 10:56:49,517 epoch 1 - iter 1246/1786 - loss 0.49315943 - time (sec): 61.20 - samples/sec: 2810.17 - lr: 0.000035 - momentum: 0.000000
2023-10-16 10:56:58,516 epoch 1 - iter 1424/1786 - loss 0.44831232 - time (sec): 70.20 - samples/sec: 2810.84 - lr: 0.000040 - momentum: 0.000000
2023-10-16 10:57:07,385 epoch 1 - iter 1602/1786 - loss 0.41610604 - time (sec): 79.07 - samples/sec: 2819.82 - lr: 0.000045 - momentum: 0.000000
2023-10-16 10:57:16,356 epoch 1 - iter 1780/1786 - loss 0.38771319 - time (sec): 88.04 - samples/sec: 2818.72 - lr: 0.000050 - momentum: 0.000000
2023-10-16 10:57:16,622 ----------------------------------------------------------------------------------------------------
2023-10-16 10:57:16,623 EPOCH 1 done: loss 0.3871 - lr: 0.000050
2023-10-16 10:57:19,749 DEV : loss 0.14844156801700592 - f1-score (micro avg)  0.7097
2023-10-16 10:57:19,766 saving best model
2023-10-16 10:57:20,235 ----------------------------------------------------------------------------------------------------
2023-10-16 10:57:29,212 epoch 2 - iter 178/1786 - loss 0.11605918 - time (sec): 8.98 - samples/sec: 2852.83 - lr: 0.000049 - momentum: 0.000000
2023-10-16 10:57:38,138 epoch 2 - iter 356/1786 - loss 0.12100219 - time (sec): 17.90 - samples/sec: 2815.83 - lr: 0.000049 - momentum: 0.000000
2023-10-16 10:57:46,829 epoch 2 - iter 534/1786 - loss 0.12074401 - time (sec): 26.59 - samples/sec: 2813.99 - lr: 0.000048 - momentum: 0.000000
2023-10-16 10:57:55,616 epoch 2 - iter 712/1786 - loss 0.12193352 - time (sec): 35.38 - samples/sec: 2817.75 - lr: 0.000048 - momentum: 0.000000
2023-10-16 10:58:04,529 epoch 2 - iter 890/1786 - loss 0.12062941 - time (sec): 44.29 - samples/sec: 2848.46 - lr: 0.000047 - momentum: 0.000000
2023-10-16 10:58:13,257 epoch 2 - iter 1068/1786 - loss 0.12155219 - time (sec): 53.02 - samples/sec: 2842.58 - lr: 0.000047 - momentum: 0.000000
2023-10-16 10:58:21,966 epoch 2 - iter 1246/1786 - loss 0.12243129 - time (sec): 61.73 - samples/sec: 2853.56 - lr: 0.000046 - momentum: 0.000000
2023-10-16 10:58:30,614 epoch 2 - iter 1424/1786 - loss 0.12158950 - time (sec): 70.38 - samples/sec: 2851.52 - lr: 0.000046 - momentum: 0.000000
2023-10-16 10:58:39,167 epoch 2 - iter 1602/1786 - loss 0.12123775 - time (sec): 78.93 - samples/sec: 2850.42 - lr: 0.000045 - momentum: 0.000000
2023-10-16 10:58:47,748 epoch 2 - iter 1780/1786 - loss 0.11991333 - time (sec): 87.51 - samples/sec: 2836.16 - lr: 0.000044 - momentum: 0.000000
2023-10-16 10:58:48,021 ----------------------------------------------------------------------------------------------------
2023-10-16 10:58:48,022 EPOCH 2 done: loss 0.1201 - lr: 0.000044
2023-10-16 10:58:52,252 DEV : loss 0.12567166984081268 - f1-score (micro avg)  0.7381
2023-10-16 10:58:52,278 saving best model
2023-10-16 10:58:52,816 ----------------------------------------------------------------------------------------------------
2023-10-16 10:59:02,942 epoch 3 - iter 178/1786 - loss 0.07602399 - time (sec): 10.12 - samples/sec: 2507.17 - lr: 0.000044 - momentum: 0.000000
2023-10-16 10:59:12,927 epoch 3 - iter 356/1786 - loss 0.08707339 - time (sec): 20.11 - samples/sec: 2555.56 - lr: 0.000043 - momentum: 0.000000
2023-10-16 10:59:21,653 epoch 3 - iter 534/1786 - loss 0.08690813 - time (sec): 28.83 - samples/sec: 2648.10 - lr: 0.000043 - momentum: 0.000000
2023-10-16 10:59:30,119 epoch 3 - iter 712/1786 - loss 0.08815103 - time (sec): 37.30 - samples/sec: 2670.85 - lr: 0.000042 - momentum: 0.000000
2023-10-16 10:59:38,819 epoch 3 - iter 890/1786 - loss 0.08755368 - time (sec): 46.00 - samples/sec: 2695.07 - lr: 0.000042 - momentum: 0.000000
2023-10-16 10:59:47,465 epoch 3 - iter 1068/1786 - loss 0.08864883 - time (sec): 54.65 - samples/sec: 2710.43 - lr: 0.000041 - momentum: 0.000000
2023-10-16 10:59:56,372 epoch 3 - iter 1246/1786 - loss 0.08622224 - time (sec): 63.55 - samples/sec: 2735.57 - lr: 0.000041 - momentum: 0.000000
2023-10-16 11:00:05,220 epoch 3 - iter 1424/1786 - loss 0.08740866 - time (sec): 72.40 - samples/sec: 2750.50 - lr: 0.000040 - momentum: 0.000000
2023-10-16 11:00:13,804 epoch 3 - iter 1602/1786 - loss 0.08628163 - time (sec): 80.98 - samples/sec: 2760.57 - lr: 0.000039 - momentum: 0.000000
2023-10-16 11:00:22,397 epoch 3 - iter 1780/1786 - loss 0.08517479 - time (sec): 89.58 - samples/sec: 2763.43 - lr: 0.000039 - momentum: 0.000000
2023-10-16 11:00:22,747 ----------------------------------------------------------------------------------------------------
2023-10-16 11:00:22,747 EPOCH 3 done: loss 0.0851 - lr: 0.000039
2023-10-16 11:00:28,065 DEV : loss 0.14893780648708344 - f1-score (micro avg)  0.763
2023-10-16 11:00:28,095 saving best model
2023-10-16 11:00:28,742 ----------------------------------------------------------------------------------------------------
2023-10-16 11:00:37,510 epoch 4 - iter 178/1786 - loss 0.05930779 - time (sec): 8.77 - samples/sec: 2921.05 - lr: 0.000038 - momentum: 0.000000
2023-10-16 11:00:46,255 epoch 4 - iter 356/1786 - loss 0.06322745 - time (sec): 17.51 - samples/sec: 2865.97 - lr: 0.000038 - momentum: 0.000000
2023-10-16 11:00:54,769 epoch 4 - iter 534/1786 - loss 0.06363083 - time (sec): 26.03 - samples/sec: 2834.26 - lr: 0.000037 - momentum: 0.000000
2023-10-16 11:01:03,344 epoch 4 - iter 712/1786 - loss 0.06394879 - time (sec): 34.60 - samples/sec: 2818.00 - lr: 0.000037 - momentum: 0.000000
2023-10-16 11:01:12,317 epoch 4 - iter 890/1786 - loss 0.06327639 - time (sec): 43.57 - samples/sec: 2815.14 - lr: 0.000036 - momentum: 0.000000
2023-10-16 11:01:21,136 epoch 4 - iter 1068/1786 - loss 0.06455310 - time (sec): 52.39 - samples/sec: 2814.42 - lr: 0.000036 - momentum: 0.000000
2023-10-16 11:01:29,981 epoch 4 - iter 1246/1786 - loss 0.06478828 - time (sec): 61.24 - samples/sec: 2837.72 - lr: 0.000035 - momentum: 0.000000
2023-10-16 11:01:38,780 epoch 4 - iter 1424/1786 - loss 0.06518193 - time (sec): 70.04 - samples/sec: 2829.75 - lr: 0.000034 - momentum: 0.000000
2023-10-16 11:01:47,514 epoch 4 - iter 1602/1786 - loss 0.06559590 - time (sec): 78.77 - samples/sec: 2834.31 - lr: 0.000034 - momentum: 0.000000
2023-10-16 11:01:56,245 epoch 4 - iter 1780/1786 - loss 0.06474763 - time (sec): 87.50 - samples/sec: 2832.00 - lr: 0.000033 - momentum: 0.000000
2023-10-16 11:01:56,584 ----------------------------------------------------------------------------------------------------
2023-10-16 11:01:56,584 EPOCH 4 done: loss 0.0647 - lr: 0.000033
2023-10-16 11:02:01,327 DEV : loss 0.17378392815589905 - f1-score (micro avg)  0.7717
2023-10-16 11:02:01,343 saving best model
2023-10-16 11:02:01,929 ----------------------------------------------------------------------------------------------------
2023-10-16 11:02:10,798 epoch 5 - iter 178/1786 - loss 0.04377203 - time (sec): 8.86 - samples/sec: 2853.88 - lr: 0.000033 - momentum: 0.000000
2023-10-16 11:02:19,500 epoch 5 - iter 356/1786 - loss 0.04754169 - time (sec): 17.56 - samples/sec: 2891.83 - lr: 0.000032 - momentum: 0.000000
2023-10-16 11:02:28,081 epoch 5 - iter 534/1786 - loss 0.04802223 - time (sec): 26.15 - samples/sec: 2810.91 - lr: 0.000032 - momentum: 0.000000
2023-10-16 11:02:36,836 epoch 5 - iter 712/1786 - loss 0.04911834 - time (sec): 34.90 - samples/sec: 2859.03 - lr: 0.000031 - momentum: 0.000000
2023-10-16 11:02:45,355 epoch 5 - iter 890/1786 - loss 0.04948678 - time (sec): 43.42 - samples/sec: 2844.94 - lr: 0.000031 - momentum: 0.000000
2023-10-16 11:02:54,112 epoch 5 - iter 1068/1786 - loss 0.04806990 - time (sec): 52.18 - samples/sec: 2848.29 - lr: 0.000030 - momentum: 0.000000
2023-10-16 11:03:02,929 epoch 5 - iter 1246/1786 - loss 0.04825570 - time (sec): 60.99 - samples/sec: 2841.27 - lr: 0.000029 - momentum: 0.000000
2023-10-16 11:03:11,923 epoch 5 - iter 1424/1786 - loss 0.04891461 - time (sec): 69.99 - samples/sec: 2857.31 - lr: 0.000029 - momentum: 0.000000
2023-10-16 11:03:20,483 epoch 5 - iter 1602/1786 - loss 0.04962470 - time (sec): 78.55 - samples/sec: 2857.09 - lr: 0.000028 - momentum: 0.000000
2023-10-16 11:03:29,083 epoch 5 - iter 1780/1786 - loss 0.04904968 - time (sec): 87.15 - samples/sec: 2845.88 - lr: 0.000028 - momentum: 0.000000
2023-10-16 11:03:29,351 ----------------------------------------------------------------------------------------------------
2023-10-16 11:03:29,351 EPOCH 5 done: loss 0.0490 - lr: 0.000028
2023-10-16 11:03:33,517 DEV : loss 0.17484842240810394 - f1-score (micro avg)  0.7728
2023-10-16 11:03:33,537 saving best model
2023-10-16 11:03:34,174 ----------------------------------------------------------------------------------------------------
2023-10-16 11:03:43,090 epoch 6 - iter 178/1786 - loss 0.03564048 - time (sec): 8.91 - samples/sec: 2725.57 - lr: 0.000027 - momentum: 0.000000
2023-10-16 11:03:52,442 epoch 6 - iter 356/1786 - loss 0.03852911 - time (sec): 18.26 - samples/sec: 2720.29 - lr: 0.000027 - momentum: 0.000000
2023-10-16 11:04:01,277 epoch 6 - iter 534/1786 - loss 0.04136964 - time (sec): 27.10 - samples/sec: 2738.01 - lr: 0.000026 - momentum: 0.000000
2023-10-16 11:04:09,800 epoch 6 - iter 712/1786 - loss 0.04024227 - time (sec): 35.62 - samples/sec: 2769.91 - lr: 0.000026 - momentum: 0.000000
2023-10-16 11:04:18,513 epoch 6 - iter 890/1786 - loss 0.04012505 - time (sec): 44.34 - samples/sec: 2778.95 - lr: 0.000025 - momentum: 0.000000
2023-10-16 11:04:27,194 epoch 6 - iter 1068/1786 - loss 0.04289492 - time (sec): 53.02 - samples/sec: 2807.61 - lr: 0.000024 - momentum: 0.000000
2023-10-16 11:04:36,047 epoch 6 - iter 1246/1786 - loss 0.04221536 - time (sec): 61.87 - samples/sec: 2818.13 - lr: 0.000024 - momentum: 0.000000
2023-10-16 11:04:44,578 epoch 6 - iter 1424/1786 - loss 0.04182216 - time (sec): 70.40 - samples/sec: 2812.76 - lr: 0.000023 - momentum: 0.000000
2023-10-16 11:04:53,113 epoch 6 - iter 1602/1786 - loss 0.04171130 - time (sec): 78.94 - samples/sec: 2819.50 - lr: 0.000023 - momentum: 0.000000
2023-10-16 11:05:01,768 epoch 6 - iter 1780/1786 - loss 0.04099380 - time (sec): 87.59 - samples/sec: 2833.27 - lr: 0.000022 - momentum: 0.000000
2023-10-16 11:05:02,038 ----------------------------------------------------------------------------------------------------
2023-10-16 11:05:02,038 EPOCH 6 done: loss 0.0410 - lr: 0.000022
2023-10-16 11:05:07,359 DEV : loss 0.16918683052062988 - f1-score (micro avg)  0.7777
2023-10-16 11:05:07,384 saving best model
2023-10-16 11:05:08,130 ----------------------------------------------------------------------------------------------------
2023-10-16 11:05:16,990 epoch 7 - iter 178/1786 - loss 0.02648365 - time (sec): 8.86 - samples/sec: 3052.78 - lr: 0.000022 - momentum: 0.000000
2023-10-16 11:05:25,906 epoch 7 - iter 356/1786 - loss 0.02728552 - time (sec): 17.77 - samples/sec: 2869.74 - lr: 0.000021 - momentum: 0.000000
2023-10-16 11:05:34,815 epoch 7 - iter 534/1786 - loss 0.02817086 - time (sec): 26.68 - samples/sec: 2837.46 - lr: 0.000021 - momentum: 0.000000
2023-10-16 11:05:43,583 epoch 7 - iter 712/1786 - loss 0.02776279 - time (sec): 35.45 - samples/sec: 2851.27 - lr: 0.000020 - momentum: 0.000000
2023-10-16 11:05:52,253 epoch 7 - iter 890/1786 - loss 0.02711624 - time (sec): 44.12 - samples/sec: 2851.82 - lr: 0.000019 - momentum: 0.000000
2023-10-16 11:06:00,861 epoch 7 - iter 1068/1786 - loss 0.02876745 - time (sec): 52.73 - samples/sec: 2839.91 - lr: 0.000019 - momentum: 0.000000
2023-10-16 11:06:09,636 epoch 7 - iter 1246/1786 - loss 0.02936533 - time (sec): 61.50 - samples/sec: 2815.16 - lr: 0.000018 - momentum: 0.000000
2023-10-16 11:06:18,532 epoch 7 - iter 1424/1786 - loss 0.02812044 - time (sec): 70.40 - samples/sec: 2830.18 - lr: 0.000018 - momentum: 0.000000
2023-10-16 11:06:27,146 epoch 7 - iter 1602/1786 - loss 0.02927136 - time (sec): 79.01 - samples/sec: 2823.45 - lr: 0.000017 - momentum: 0.000000
2023-10-16 11:06:35,809 epoch 7 - iter 1780/1786 - loss 0.02918995 - time (sec): 87.68 - samples/sec: 2825.84 - lr: 0.000017 - momentum: 0.000000
2023-10-16 11:06:36,138 ----------------------------------------------------------------------------------------------------
2023-10-16 11:06:36,139 EPOCH 7 done: loss 0.0292 - lr: 0.000017
2023-10-16 11:06:40,964 DEV : loss 0.186806321144104 - f1-score (micro avg)  0.7809
2023-10-16 11:06:40,981 saving best model
2023-10-16 11:06:41,594 ----------------------------------------------------------------------------------------------------
2023-10-16 11:06:50,242 epoch 8 - iter 178/1786 - loss 0.02299188 - time (sec): 8.65 - samples/sec: 2815.85 - lr: 0.000016 - momentum: 0.000000
2023-10-16 11:06:58,827 epoch 8 - iter 356/1786 - loss 0.02359001 - time (sec): 17.23 - samples/sec: 2827.78 - lr: 0.000016 - momentum: 0.000000
2023-10-16 11:07:07,629 epoch 8 - iter 534/1786 - loss 0.02254523 - time (sec): 26.03 - samples/sec: 2811.01 - lr: 0.000015 - momentum: 0.000000
2023-10-16 11:07:16,444 epoch 8 - iter 712/1786 - loss 0.02199670 - time (sec): 34.85 - samples/sec: 2838.40 - lr: 0.000014 - momentum: 0.000000
2023-10-16 11:07:25,076 epoch 8 - iter 890/1786 - loss 0.02265221 - time (sec): 43.48 - samples/sec: 2842.73 - lr: 0.000014 - momentum: 0.000000
2023-10-16 11:07:33,588 epoch 8 - iter 1068/1786 - loss 0.02375428 - time (sec): 51.99 - samples/sec: 2838.22 - lr: 0.000013 - momentum: 0.000000
2023-10-16 11:07:42,171 epoch 8 - iter 1246/1786 - loss 0.02271185 - time (sec): 60.57 - samples/sec: 2838.00 - lr: 0.000013 - momentum: 0.000000
2023-10-16 11:07:50,996 epoch 8 - iter 1424/1786 - loss 0.02264176 - time (sec): 69.40 - samples/sec: 2830.78 - lr: 0.000012 - momentum: 0.000000
2023-10-16 11:07:59,917 epoch 8 - iter 1602/1786 - loss 0.02278864 - time (sec): 78.32 - samples/sec: 2834.50 - lr: 0.000012 - momentum: 0.000000
2023-10-16 11:08:08,823 epoch 8 - iter 1780/1786 - loss 0.02208015 - time (sec): 87.23 - samples/sec: 2842.31 - lr: 0.000011 - momentum: 0.000000
2023-10-16 11:08:09,133 ----------------------------------------------------------------------------------------------------
2023-10-16 11:08:09,133 EPOCH 8 done: loss 0.0221 - lr: 0.000011
2023-10-16 11:08:13,486 DEV : loss 0.19474762678146362 - f1-score (micro avg)  0.796
2023-10-16 11:08:13,506 saving best model
2023-10-16 11:08:14,028 ----------------------------------------------------------------------------------------------------
2023-10-16 11:08:23,840 epoch 9 - iter 178/1786 - loss 0.01798579 - time (sec): 9.81 - samples/sec: 2549.51 - lr: 0.000011 - momentum: 0.000000
2023-10-16 11:08:32,940 epoch 9 - iter 356/1786 - loss 0.01872623 - time (sec): 18.91 - samples/sec: 2662.25 - lr: 0.000010 - momentum: 0.000000
2023-10-16 11:08:42,168 epoch 9 - iter 534/1786 - loss 0.01727464 - time (sec): 28.14 - samples/sec: 2677.48 - lr: 0.000009 - momentum: 0.000000
2023-10-16 11:08:50,782 epoch 9 - iter 712/1786 - loss 0.01690762 - time (sec): 36.75 - samples/sec: 2698.90 - lr: 0.000009 - momentum: 0.000000
2023-10-16 11:08:59,465 epoch 9 - iter 890/1786 - loss 0.01729150 - time (sec): 45.43 - samples/sec: 2718.02 - lr: 0.000008 - momentum: 0.000000
2023-10-16 11:09:08,235 epoch 9 - iter 1068/1786 - loss 0.01759655 - time (sec): 54.20 - samples/sec: 2743.24 - lr: 0.000008 - momentum: 0.000000
2023-10-16 11:09:17,081 epoch 9 - iter 1246/1786 - loss 0.01836818 - time (sec): 63.05 - samples/sec: 2762.68 - lr: 0.000007 - momentum: 0.000000
2023-10-16 11:09:25,811 epoch 9 - iter 1424/1786 - loss 0.01880668 - time (sec): 71.78 - samples/sec: 2792.07 - lr: 0.000007 - momentum: 0.000000
2023-10-16 11:09:34,295 epoch 9 - iter 1602/1786 - loss 0.01846836 - time (sec): 80.26 - samples/sec: 2780.26 - lr: 0.000006 - momentum: 0.000000
2023-10-16 11:09:43,041 epoch 9 - iter 1780/1786 - loss 0.01794289 - time (sec): 89.01 - samples/sec: 2784.51 - lr: 0.000006 - momentum: 0.000000
2023-10-16 11:09:43,337 ----------------------------------------------------------------------------------------------------
2023-10-16 11:09:43,338 EPOCH 9 done: loss 0.0180 - lr: 0.000006
2023-10-16 11:09:48,154 DEV : loss 0.1844618022441864 - f1-score (micro avg)  0.8048
2023-10-16 11:09:48,181 saving best model
2023-10-16 11:09:48,726 ----------------------------------------------------------------------------------------------------
2023-10-16 11:09:58,642 epoch 10 - iter 178/1786 - loss 0.01185496 - time (sec): 9.91 - samples/sec: 2381.04 - lr: 0.000005 - momentum: 0.000000
2023-10-16 11:10:08,696 epoch 10 - iter 356/1786 - loss 0.01135159 - time (sec): 19.97 - samples/sec: 2548.54 - lr: 0.000004 - momentum: 0.000000
2023-10-16 11:10:18,592 epoch 10 - iter 534/1786 - loss 0.01376928 - time (sec): 29.86 - samples/sec: 2545.00 - lr: 0.000004 - momentum: 0.000000
2023-10-16 11:10:28,392 epoch 10 - iter 712/1786 - loss 0.01268921 - time (sec): 39.66 - samples/sec: 2517.19 - lr: 0.000003 - momentum: 0.000000
2023-10-16 11:10:38,278 epoch 10 - iter 890/1786 - loss 0.01251690 - time (sec): 49.55 - samples/sec: 2514.27 - lr: 0.000003 - momentum: 0.000000
2023-10-16 11:10:48,164 epoch 10 - iter 1068/1786 - loss 0.01347864 - time (sec): 59.44 - samples/sec: 2506.01 - lr: 0.000002 - momentum: 0.000000
2023-10-16 11:10:58,148 epoch 10 - iter 1246/1786 - loss 0.01278922 - time (sec): 69.42 - samples/sec: 2507.14 - lr: 0.000002 - momentum: 0.000000
2023-10-16 11:11:07,753 epoch 10 - iter 1424/1786 - loss 0.01267902 - time (sec): 79.02 - samples/sec: 2494.79 - lr: 0.000001 - momentum: 0.000000
2023-10-16 11:11:17,680 epoch 10 - iter 1602/1786 - loss 0.01194308 - time (sec): 88.95 - samples/sec: 2510.87 - lr: 0.000001 - momentum: 0.000000
2023-10-16 11:11:27,393 epoch 10 - iter 1780/1786 - loss 0.01186500 - time (sec): 98.66 - samples/sec: 2515.20 - lr: 0.000000 - momentum: 0.000000
2023-10-16 11:11:27,704 ----------------------------------------------------------------------------------------------------
2023-10-16 11:11:27,704 EPOCH 10 done: loss 0.0118 - lr: 0.000000
2023-10-16 11:11:32,731 DEV : loss 0.18512549996376038 - f1-score (micro avg)  0.8024
2023-10-16 11:11:33,164 ----------------------------------------------------------------------------------------------------
2023-10-16 11:11:33,165 Loading model from best epoch ...
2023-10-16 11:11:34,828 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
2023-10-16 11:11:44,735 
Results:
- F-score (micro) 0.6916
- F-score (macro) 0.5961
- Accuracy 0.5475

By class:
              precision    recall  f1-score   support

         LOC     0.6627    0.7196    0.6900      1095
         PER     0.7735    0.7628    0.7682      1012
         ORG     0.5198    0.5154    0.5176       357
   HumanProd     0.3167    0.5758    0.4086        33

   micro avg     0.6778    0.7060    0.6916      2497
   macro avg     0.5682    0.6434    0.5961      2497
weighted avg     0.6826    0.7060    0.6933      2497

2023-10-16 11:11:44,735 ----------------------------------------------------------------------------------------------------