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hmbench-icdar-nl-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3

Token Classification
Flair
PyTorch
Dutch
sequence-tagger-model
Model card Files Community
1
hmbench-icdar-nl-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
File size: 23,776 Bytes 
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2023-10-14 10:19:19,794 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,795 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=13, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-14 10:19:19,795 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,795 MultiCorpus: 5777 train + 722 dev + 723 test sentences
 - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl
2023-10-14 10:19:19,795 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,795 Train:  5777 sentences
2023-10-14 10:19:19,795         (train_with_dev=False, train_with_test=False)
2023-10-14 10:19:19,795 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,795 Training Params:
2023-10-14 10:19:19,795  - learning_rate: "5e-05" 
2023-10-14 10:19:19,795  - mini_batch_size: "8"
2023-10-14 10:19:19,795  - max_epochs: "10"
2023-10-14 10:19:19,795  - shuffle: "True"
2023-10-14 10:19:19,795 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,795 Plugins:
2023-10-14 10:19:19,795  - LinearScheduler | warmup_fraction: '0.1'
2023-10-14 10:19:19,795 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,795 Final evaluation on model from best epoch (best-model.pt)
2023-10-14 10:19:19,795  - metric: "('micro avg', 'f1-score')"
2023-10-14 10:19:19,795 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,795 Computation:
2023-10-14 10:19:19,796  - compute on device: cuda:0
2023-10-14 10:19:19,796  - embedding storage: none
2023-10-14 10:19:19,796 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,796 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-14 10:19:19,796 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:19,796 ----------------------------------------------------------------------------------------------------
2023-10-14 10:19:25,609 epoch 1 - iter 72/723 - loss 1.72327249 - time (sec): 5.81 - samples/sec: 2983.38 - lr: 0.000005 - momentum: 0.000000
2023-10-14 10:19:31,228 epoch 1 - iter 144/723 - loss 1.00146801 - time (sec): 11.43 - samples/sec: 3045.82 - lr: 0.000010 - momentum: 0.000000
2023-10-14 10:19:36,758 epoch 1 - iter 216/723 - loss 0.73554490 - time (sec): 16.96 - samples/sec: 3059.06 - lr: 0.000015 - momentum: 0.000000
2023-10-14 10:19:42,581 epoch 1 - iter 288/723 - loss 0.60215162 - time (sec): 22.78 - samples/sec: 3047.48 - lr: 0.000020 - momentum: 0.000000
2023-10-14 10:19:48,999 epoch 1 - iter 360/723 - loss 0.50523257 - time (sec): 29.20 - samples/sec: 3045.82 - lr: 0.000025 - momentum: 0.000000
2023-10-14 10:19:55,159 epoch 1 - iter 432/723 - loss 0.45270535 - time (sec): 35.36 - samples/sec: 2992.60 - lr: 0.000030 - momentum: 0.000000
2023-10-14 10:20:00,904 epoch 1 - iter 504/723 - loss 0.41268390 - time (sec): 41.11 - samples/sec: 2980.35 - lr: 0.000035 - momentum: 0.000000
2023-10-14 10:20:07,154 epoch 1 - iter 576/723 - loss 0.37700903 - time (sec): 47.36 - samples/sec: 2966.93 - lr: 0.000040 - momentum: 0.000000
2023-10-14 10:20:13,057 epoch 1 - iter 648/723 - loss 0.34991301 - time (sec): 53.26 - samples/sec: 2976.79 - lr: 0.000045 - momentum: 0.000000
2023-10-14 10:20:18,955 epoch 1 - iter 720/723 - loss 0.32824400 - time (sec): 59.16 - samples/sec: 2973.43 - lr: 0.000050 - momentum: 0.000000
2023-10-14 10:20:19,104 ----------------------------------------------------------------------------------------------------
2023-10-14 10:20:19,104 EPOCH 1 done: loss 0.3283 - lr: 0.000050
2023-10-14 10:20:22,627 DEV : loss 0.1315712332725525 - f1-score (micro avg)  0.6755
2023-10-14 10:20:22,650 saving best model
2023-10-14 10:20:23,102 ----------------------------------------------------------------------------------------------------
2023-10-14 10:20:29,239 epoch 2 - iter 72/723 - loss 0.12632996 - time (sec): 6.13 - samples/sec: 2913.10 - lr: 0.000049 - momentum: 0.000000
2023-10-14 10:20:35,225 epoch 2 - iter 144/723 - loss 0.11494873 - time (sec): 12.12 - samples/sec: 2893.63 - lr: 0.000049 - momentum: 0.000000
2023-10-14 10:20:40,755 epoch 2 - iter 216/723 - loss 0.11125540 - time (sec): 17.65 - samples/sec: 2962.74 - lr: 0.000048 - momentum: 0.000000
2023-10-14 10:20:46,994 epoch 2 - iter 288/723 - loss 0.10970521 - time (sec): 23.89 - samples/sec: 2965.22 - lr: 0.000048 - momentum: 0.000000
2023-10-14 10:20:53,342 epoch 2 - iter 360/723 - loss 0.10475284 - time (sec): 30.24 - samples/sec: 2955.70 - lr: 0.000047 - momentum: 0.000000
2023-10-14 10:20:59,111 epoch 2 - iter 432/723 - loss 0.10224698 - time (sec): 36.01 - samples/sec: 2961.87 - lr: 0.000047 - momentum: 0.000000
2023-10-14 10:21:04,544 epoch 2 - iter 504/723 - loss 0.10046143 - time (sec): 41.44 - samples/sec: 2974.07 - lr: 0.000046 - momentum: 0.000000
2023-10-14 10:21:10,196 epoch 2 - iter 576/723 - loss 0.09752484 - time (sec): 47.09 - samples/sec: 2995.32 - lr: 0.000046 - momentum: 0.000000
2023-10-14 10:21:16,127 epoch 2 - iter 648/723 - loss 0.09941523 - time (sec): 53.02 - samples/sec: 2991.38 - lr: 0.000045 - momentum: 0.000000
2023-10-14 10:21:21,791 epoch 2 - iter 720/723 - loss 0.09855220 - time (sec): 58.69 - samples/sec: 2994.78 - lr: 0.000044 - momentum: 0.000000
2023-10-14 10:21:21,952 ----------------------------------------------------------------------------------------------------
2023-10-14 10:21:21,952 EPOCH 2 done: loss 0.0985 - lr: 0.000044
2023-10-14 10:21:26,345 DEV : loss 0.11473622173070908 - f1-score (micro avg)  0.7558
2023-10-14 10:21:26,361 saving best model
2023-10-14 10:21:26,963 ----------------------------------------------------------------------------------------------------
2023-10-14 10:21:32,825 epoch 3 - iter 72/723 - loss 0.07907433 - time (sec): 5.86 - samples/sec: 2993.20 - lr: 0.000044 - momentum: 0.000000
2023-10-14 10:21:38,751 epoch 3 - iter 144/723 - loss 0.07464467 - time (sec): 11.78 - samples/sec: 2959.55 - lr: 0.000043 - momentum: 0.000000
2023-10-14 10:21:44,800 epoch 3 - iter 216/723 - loss 0.06887957 - time (sec): 17.83 - samples/sec: 2967.64 - lr: 0.000043 - momentum: 0.000000
2023-10-14 10:21:51,036 epoch 3 - iter 288/723 - loss 0.06980865 - time (sec): 24.07 - samples/sec: 2938.66 - lr: 0.000042 - momentum: 0.000000
2023-10-14 10:21:57,209 epoch 3 - iter 360/723 - loss 0.06595517 - time (sec): 30.24 - samples/sec: 2912.85 - lr: 0.000042 - momentum: 0.000000
2023-10-14 10:22:02,975 epoch 3 - iter 432/723 - loss 0.06369977 - time (sec): 36.01 - samples/sec: 2932.84 - lr: 0.000041 - momentum: 0.000000
2023-10-14 10:22:09,498 epoch 3 - iter 504/723 - loss 0.06320356 - time (sec): 42.53 - samples/sec: 2916.78 - lr: 0.000041 - momentum: 0.000000
2023-10-14 10:22:15,167 epoch 3 - iter 576/723 - loss 0.06249681 - time (sec): 48.20 - samples/sec: 2918.13 - lr: 0.000040 - momentum: 0.000000
2023-10-14 10:22:21,126 epoch 3 - iter 648/723 - loss 0.06292183 - time (sec): 54.16 - samples/sec: 2926.70 - lr: 0.000039 - momentum: 0.000000
2023-10-14 10:22:27,060 epoch 3 - iter 720/723 - loss 0.06275594 - time (sec): 60.09 - samples/sec: 2924.25 - lr: 0.000039 - momentum: 0.000000
2023-10-14 10:22:27,243 ----------------------------------------------------------------------------------------------------
2023-10-14 10:22:27,244 EPOCH 3 done: loss 0.0627 - lr: 0.000039
2023-10-14 10:22:30,796 DEV : loss 0.08838976919651031 - f1-score (micro avg)  0.7896
2023-10-14 10:22:30,815 saving best model
2023-10-14 10:22:31,393 ----------------------------------------------------------------------------------------------------
2023-10-14 10:22:37,402 epoch 4 - iter 72/723 - loss 0.04342449 - time (sec): 6.00 - samples/sec: 2874.63 - lr: 0.000038 - momentum: 0.000000
2023-10-14 10:22:43,827 epoch 4 - iter 144/723 - loss 0.04558890 - time (sec): 12.43 - samples/sec: 2795.95 - lr: 0.000038 - momentum: 0.000000
2023-10-14 10:22:50,441 epoch 4 - iter 216/723 - loss 0.04282584 - time (sec): 19.04 - samples/sec: 2683.29 - lr: 0.000037 - momentum: 0.000000
2023-10-14 10:22:56,600 epoch 4 - iter 288/723 - loss 0.04327687 - time (sec): 25.20 - samples/sec: 2751.68 - lr: 0.000037 - momentum: 0.000000
2023-10-14 10:23:02,680 epoch 4 - iter 360/723 - loss 0.04398015 - time (sec): 31.28 - samples/sec: 2781.27 - lr: 0.000036 - momentum: 0.000000
2023-10-14 10:23:09,050 epoch 4 - iter 432/723 - loss 0.04594075 - time (sec): 37.65 - samples/sec: 2802.25 - lr: 0.000036 - momentum: 0.000000
2023-10-14 10:23:15,185 epoch 4 - iter 504/723 - loss 0.04565317 - time (sec): 43.79 - samples/sec: 2831.76 - lr: 0.000035 - momentum: 0.000000
2023-10-14 10:23:20,960 epoch 4 - iter 576/723 - loss 0.04572753 - time (sec): 49.56 - samples/sec: 2833.82 - lr: 0.000034 - momentum: 0.000000
2023-10-14 10:23:26,585 epoch 4 - iter 648/723 - loss 0.04471750 - time (sec): 55.19 - samples/sec: 2854.56 - lr: 0.000034 - momentum: 0.000000
2023-10-14 10:23:32,857 epoch 4 - iter 720/723 - loss 0.04552874 - time (sec): 61.46 - samples/sec: 2861.20 - lr: 0.000033 - momentum: 0.000000
2023-10-14 10:23:33,032 ----------------------------------------------------------------------------------------------------
2023-10-14 10:23:33,032 EPOCH 4 done: loss 0.0455 - lr: 0.000033
2023-10-14 10:23:36,576 DEV : loss 0.08705586940050125 - f1-score (micro avg)  0.8236
2023-10-14 10:23:36,597 saving best model
2023-10-14 10:23:37,122 ----------------------------------------------------------------------------------------------------
2023-10-14 10:23:43,697 epoch 5 - iter 72/723 - loss 0.03314608 - time (sec): 6.57 - samples/sec: 2845.13 - lr: 0.000033 - momentum: 0.000000
2023-10-14 10:23:49,408 epoch 5 - iter 144/723 - loss 0.02748621 - time (sec): 12.28 - samples/sec: 2921.11 - lr: 0.000032 - momentum: 0.000000
2023-10-14 10:23:55,570 epoch 5 - iter 216/723 - loss 0.02779545 - time (sec): 18.45 - samples/sec: 2927.77 - lr: 0.000032 - momentum: 0.000000
2023-10-14 10:24:01,691 epoch 5 - iter 288/723 - loss 0.02991449 - time (sec): 24.57 - samples/sec: 2917.20 - lr: 0.000031 - momentum: 0.000000
2023-10-14 10:24:07,775 epoch 5 - iter 360/723 - loss 0.03006041 - time (sec): 30.65 - samples/sec: 2908.32 - lr: 0.000031 - momentum: 0.000000
2023-10-14 10:24:13,849 epoch 5 - iter 432/723 - loss 0.03092723 - time (sec): 36.72 - samples/sec: 2904.76 - lr: 0.000030 - momentum: 0.000000
2023-10-14 10:24:19,930 epoch 5 - iter 504/723 - loss 0.03033433 - time (sec): 42.81 - samples/sec: 2882.81 - lr: 0.000029 - momentum: 0.000000
2023-10-14 10:24:25,916 epoch 5 - iter 576/723 - loss 0.03189491 - time (sec): 48.79 - samples/sec: 2887.10 - lr: 0.000029 - momentum: 0.000000
2023-10-14 10:24:31,822 epoch 5 - iter 648/723 - loss 0.03147341 - time (sec): 54.70 - samples/sec: 2897.07 - lr: 0.000028 - momentum: 0.000000
2023-10-14 10:24:38,005 epoch 5 - iter 720/723 - loss 0.03282684 - time (sec): 60.88 - samples/sec: 2885.40 - lr: 0.000028 - momentum: 0.000000
2023-10-14 10:24:38,183 ----------------------------------------------------------------------------------------------------
2023-10-14 10:24:38,184 EPOCH 5 done: loss 0.0328 - lr: 0.000028
2023-10-14 10:24:42,528 DEV : loss 0.11852852255105972 - f1-score (micro avg)  0.7987
2023-10-14 10:24:42,545 ----------------------------------------------------------------------------------------------------
2023-10-14 10:24:48,512 epoch 6 - iter 72/723 - loss 0.02109962 - time (sec): 5.97 - samples/sec: 2932.99 - lr: 0.000027 - momentum: 0.000000
2023-10-14 10:24:55,286 epoch 6 - iter 144/723 - loss 0.02464690 - time (sec): 12.74 - samples/sec: 2851.35 - lr: 0.000027 - momentum: 0.000000
2023-10-14 10:25:01,278 epoch 6 - iter 216/723 - loss 0.02651401 - time (sec): 18.73 - samples/sec: 2861.92 - lr: 0.000026 - momentum: 0.000000
2023-10-14 10:25:07,586 epoch 6 - iter 288/723 - loss 0.02500530 - time (sec): 25.04 - samples/sec: 2837.34 - lr: 0.000026 - momentum: 0.000000
2023-10-14 10:25:13,880 epoch 6 - iter 360/723 - loss 0.02550617 - time (sec): 31.33 - samples/sec: 2820.00 - lr: 0.000025 - momentum: 0.000000
2023-10-14 10:25:20,227 epoch 6 - iter 432/723 - loss 0.02588593 - time (sec): 37.68 - samples/sec: 2835.76 - lr: 0.000024 - momentum: 0.000000
2023-10-14 10:25:26,637 epoch 6 - iter 504/723 - loss 0.02626521 - time (sec): 44.09 - samples/sec: 2816.25 - lr: 0.000024 - momentum: 0.000000
2023-10-14 10:25:32,350 epoch 6 - iter 576/723 - loss 0.02545341 - time (sec): 49.80 - samples/sec: 2826.59 - lr: 0.000023 - momentum: 0.000000
2023-10-14 10:25:38,211 epoch 6 - iter 648/723 - loss 0.02546137 - time (sec): 55.67 - samples/sec: 2824.80 - lr: 0.000023 - momentum: 0.000000
2023-10-14 10:25:44,480 epoch 6 - iter 720/723 - loss 0.02448943 - time (sec): 61.93 - samples/sec: 2834.22 - lr: 0.000022 - momentum: 0.000000
2023-10-14 10:25:44,752 ----------------------------------------------------------------------------------------------------
2023-10-14 10:25:44,752 EPOCH 6 done: loss 0.0244 - lr: 0.000022
2023-10-14 10:25:48,334 DEV : loss 0.1726008951663971 - f1-score (micro avg)  0.7898
2023-10-14 10:25:48,364 ----------------------------------------------------------------------------------------------------
2023-10-14 10:25:54,132 epoch 7 - iter 72/723 - loss 0.01010894 - time (sec): 5.77 - samples/sec: 3009.11 - lr: 0.000022 - momentum: 0.000000
2023-10-14 10:26:00,546 epoch 7 - iter 144/723 - loss 0.01397475 - time (sec): 12.18 - samples/sec: 2880.79 - lr: 0.000021 - momentum: 0.000000
2023-10-14 10:26:06,233 epoch 7 - iter 216/723 - loss 0.01737460 - time (sec): 17.87 - samples/sec: 2927.54 - lr: 0.000021 - momentum: 0.000000
2023-10-14 10:26:12,698 epoch 7 - iter 288/723 - loss 0.01805427 - time (sec): 24.33 - samples/sec: 2887.43 - lr: 0.000020 - momentum: 0.000000
2023-10-14 10:26:18,352 epoch 7 - iter 360/723 - loss 0.01744268 - time (sec): 29.99 - samples/sec: 2920.78 - lr: 0.000019 - momentum: 0.000000
2023-10-14 10:26:24,396 epoch 7 - iter 432/723 - loss 0.01990357 - time (sec): 36.03 - samples/sec: 2932.22 - lr: 0.000019 - momentum: 0.000000
2023-10-14 10:26:30,242 epoch 7 - iter 504/723 - loss 0.01939631 - time (sec): 41.88 - samples/sec: 2936.64 - lr: 0.000018 - momentum: 0.000000
2023-10-14 10:26:35,761 epoch 7 - iter 576/723 - loss 0.01925559 - time (sec): 47.40 - samples/sec: 2951.69 - lr: 0.000018 - momentum: 0.000000
2023-10-14 10:26:41,965 epoch 7 - iter 648/723 - loss 0.01913244 - time (sec): 53.60 - samples/sec: 2949.86 - lr: 0.000017 - momentum: 0.000000
2023-10-14 10:26:48,111 epoch 7 - iter 720/723 - loss 0.01890120 - time (sec): 59.75 - samples/sec: 2942.22 - lr: 0.000017 - momentum: 0.000000
2023-10-14 10:26:48,270 ----------------------------------------------------------------------------------------------------
2023-10-14 10:26:48,271 EPOCH 7 done: loss 0.0189 - lr: 0.000017
2023-10-14 10:26:51,780 DEV : loss 0.16478076577186584 - f1-score (micro avg)  0.8158
2023-10-14 10:26:51,797 ----------------------------------------------------------------------------------------------------
2023-10-14 10:26:57,786 epoch 8 - iter 72/723 - loss 0.01381860 - time (sec): 5.99 - samples/sec: 3020.38 - lr: 0.000016 - momentum: 0.000000
2023-10-14 10:27:03,369 epoch 8 - iter 144/723 - loss 0.01061469 - time (sec): 11.57 - samples/sec: 3040.23 - lr: 0.000016 - momentum: 0.000000
2023-10-14 10:27:10,268 epoch 8 - iter 216/723 - loss 0.01332653 - time (sec): 18.47 - samples/sec: 2967.68 - lr: 0.000015 - momentum: 0.000000
2023-10-14 10:27:15,337 epoch 8 - iter 288/723 - loss 0.01314284 - time (sec): 23.54 - samples/sec: 2952.54 - lr: 0.000014 - momentum: 0.000000
2023-10-14 10:27:21,493 epoch 8 - iter 360/723 - loss 0.01343682 - time (sec): 29.70 - samples/sec: 2971.85 - lr: 0.000014 - momentum: 0.000000
2023-10-14 10:27:27,555 epoch 8 - iter 432/723 - loss 0.01231511 - time (sec): 35.76 - samples/sec: 2984.09 - lr: 0.000013 - momentum: 0.000000
2023-10-14 10:27:32,976 epoch 8 - iter 504/723 - loss 0.01164514 - time (sec): 41.18 - samples/sec: 3007.06 - lr: 0.000013 - momentum: 0.000000
2023-10-14 10:27:38,634 epoch 8 - iter 576/723 - loss 0.01222536 - time (sec): 46.84 - samples/sec: 3010.30 - lr: 0.000012 - momentum: 0.000000
2023-10-14 10:27:44,572 epoch 8 - iter 648/723 - loss 0.01264218 - time (sec): 52.77 - samples/sec: 3011.91 - lr: 0.000012 - momentum: 0.000000
2023-10-14 10:27:50,263 epoch 8 - iter 720/723 - loss 0.01283067 - time (sec): 58.47 - samples/sec: 3006.26 - lr: 0.000011 - momentum: 0.000000
2023-10-14 10:27:50,446 ----------------------------------------------------------------------------------------------------
2023-10-14 10:27:50,446 EPOCH 8 done: loss 0.0128 - lr: 0.000011
2023-10-14 10:27:54,343 DEV : loss 0.17232058942317963 - f1-score (micro avg)  0.8201
2023-10-14 10:27:54,359 ----------------------------------------------------------------------------------------------------
2023-10-14 10:28:00,250 epoch 9 - iter 72/723 - loss 0.00704046 - time (sec): 5.89 - samples/sec: 3061.31 - lr: 0.000011 - momentum: 0.000000
2023-10-14 10:28:06,250 epoch 9 - iter 144/723 - loss 0.00659264 - time (sec): 11.89 - samples/sec: 2995.37 - lr: 0.000010 - momentum: 0.000000
2023-10-14 10:28:11,950 epoch 9 - iter 216/723 - loss 0.00659604 - time (sec): 17.59 - samples/sec: 2989.48 - lr: 0.000009 - momentum: 0.000000
2023-10-14 10:28:18,181 epoch 9 - iter 288/723 - loss 0.00665508 - time (sec): 23.82 - samples/sec: 2980.52 - lr: 0.000009 - momentum: 0.000000
2023-10-14 10:28:23,869 epoch 9 - iter 360/723 - loss 0.00733282 - time (sec): 29.51 - samples/sec: 2978.60 - lr: 0.000008 - momentum: 0.000000
2023-10-14 10:28:30,360 epoch 9 - iter 432/723 - loss 0.00781952 - time (sec): 36.00 - samples/sec: 2974.28 - lr: 0.000008 - momentum: 0.000000
2023-10-14 10:28:35,920 epoch 9 - iter 504/723 - loss 0.00773609 - time (sec): 41.56 - samples/sec: 2971.56 - lr: 0.000007 - momentum: 0.000000
2023-10-14 10:28:41,901 epoch 9 - iter 576/723 - loss 0.00869847 - time (sec): 47.54 - samples/sec: 2978.58 - lr: 0.000007 - momentum: 0.000000
2023-10-14 10:28:47,424 epoch 9 - iter 648/723 - loss 0.00883473 - time (sec): 53.06 - samples/sec: 2988.14 - lr: 0.000006 - momentum: 0.000000
2023-10-14 10:28:53,233 epoch 9 - iter 720/723 - loss 0.00872089 - time (sec): 58.87 - samples/sec: 2987.10 - lr: 0.000006 - momentum: 0.000000
2023-10-14 10:28:53,391 ----------------------------------------------------------------------------------------------------
2023-10-14 10:28:53,391 EPOCH 9 done: loss 0.0087 - lr: 0.000006
2023-10-14 10:28:56,879 DEV : loss 0.18103523552417755 - f1-score (micro avg)  0.8237
2023-10-14 10:28:56,896 saving best model
2023-10-14 10:28:57,500 ----------------------------------------------------------------------------------------------------
2023-10-14 10:29:03,133 epoch 10 - iter 72/723 - loss 0.00332405 - time (sec): 5.63 - samples/sec: 2958.13 - lr: 0.000005 - momentum: 0.000000
2023-10-14 10:29:09,814 epoch 10 - iter 144/723 - loss 0.00473766 - time (sec): 12.31 - samples/sec: 2881.86 - lr: 0.000004 - momentum: 0.000000
2023-10-14 10:29:15,600 epoch 10 - iter 216/723 - loss 0.00599232 - time (sec): 18.10 - samples/sec: 2940.03 - lr: 0.000004 - momentum: 0.000000
2023-10-14 10:29:21,108 epoch 10 - iter 288/723 - loss 0.00549493 - time (sec): 23.61 - samples/sec: 2957.54 - lr: 0.000003 - momentum: 0.000000
2023-10-14 10:29:27,547 epoch 10 - iter 360/723 - loss 0.00683243 - time (sec): 30.04 - samples/sec: 2937.64 - lr: 0.000003 - momentum: 0.000000
2023-10-14 10:29:33,142 epoch 10 - iter 432/723 - loss 0.00611410 - time (sec): 35.64 - samples/sec: 2958.49 - lr: 0.000002 - momentum: 0.000000
2023-10-14 10:29:39,216 epoch 10 - iter 504/723 - loss 0.00646123 - time (sec): 41.71 - samples/sec: 2972.85 - lr: 0.000002 - momentum: 0.000000
2023-10-14 10:29:45,089 epoch 10 - iter 576/723 - loss 0.00655367 - time (sec): 47.59 - samples/sec: 2969.83 - lr: 0.000001 - momentum: 0.000000
2023-10-14 10:29:50,720 epoch 10 - iter 648/723 - loss 0.00634156 - time (sec): 53.22 - samples/sec: 2971.21 - lr: 0.000001 - momentum: 0.000000
2023-10-14 10:29:56,807 epoch 10 - iter 720/723 - loss 0.00633108 - time (sec): 59.31 - samples/sec: 2958.84 - lr: 0.000000 - momentum: 0.000000
2023-10-14 10:29:57,135 ----------------------------------------------------------------------------------------------------
2023-10-14 10:29:57,136 EPOCH 10 done: loss 0.0063 - lr: 0.000000
2023-10-14 10:30:00,608 DEV : loss 0.18696151673793793 - f1-score (micro avg)  0.821
2023-10-14 10:30:01,006 ----------------------------------------------------------------------------------------------------
2023-10-14 10:30:01,007 Loading model from best epoch ...
2023-10-14 10:30:02,647 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG
2023-10-14 10:30:05,788 
Results:
- F-score (micro) 0.815
- F-score (macro) 0.7275
- Accuracy 0.6978

By class:
              precision    recall  f1-score   support

         PER     0.8004    0.8402    0.8198       482
         LOC     0.8892    0.8231    0.8549       458
         ORG     0.5410    0.4783    0.5077        69

   micro avg     0.8224    0.8077    0.8150      1009
   macro avg     0.7435    0.7139    0.7275      1009
weighted avg     0.8229    0.8077    0.8144      1009

2023-10-14 10:30:05,789 ----------------------------------------------------------------------------------------------------