2023-09-03 20:00:50,026 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,027 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()
)"
2023-09-03 20:00:50,027 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,027 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-09-03 20:00:50,027 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,027 Train:  3575 sentences
2023-09-03 20:00:50,027         (train_with_dev=False, train_with_test=False)
2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,028 Training Params:
2023-09-03 20:00:50,028  - learning_rate: "3e-05" 
2023-09-03 20:00:50,028  - mini_batch_size: "4"
2023-09-03 20:00:50,028  - max_epochs: "10"
2023-09-03 20:00:50,028  - shuffle: "True"
2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,028 Plugins:
2023-09-03 20:00:50,028  - LinearScheduler | warmup_fraction: '0.1'
2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,028 Final evaluation on model from best epoch (best-model.pt)
2023-09-03 20:00:50,028  - metric: "('micro avg', 'f1-score')"
2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,028 Computation:
2023-09-03 20:00:50,028  - compute on device: cuda:0
2023-09-03 20:00:50,028  - embedding storage: none
2023-09-03 20:00:50,028 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,028 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
2023-09-03 20:00:50,029 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:50,029 ----------------------------------------------------------------------------------------------------
2023-09-03 20:00:58,521 epoch 1 - iter 89/894 - loss 3.11851080 - time (sec): 8.49 - samples/sec: 943.73 - lr: 0.000003 - momentum: 0.000000
2023-09-03 20:01:07,144 epoch 1 - iter 178/894 - loss 2.08316776 - time (sec): 17.11 - samples/sec: 938.27 - lr: 0.000006 - momentum: 0.000000
2023-09-03 20:01:16,041 epoch 1 - iter 267/894 - loss 1.49514732 - time (sec): 26.01 - samples/sec: 958.60 - lr: 0.000009 - momentum: 0.000000
2023-09-03 20:01:24,740 epoch 1 - iter 356/894 - loss 1.23073638 - time (sec): 34.71 - samples/sec: 951.73 - lr: 0.000012 - momentum: 0.000000
2023-09-03 20:01:33,715 epoch 1 - iter 445/894 - loss 1.04071512 - time (sec): 43.69 - samples/sec: 958.86 - lr: 0.000015 - momentum: 0.000000
2023-09-03 20:01:43,916 epoch 1 - iter 534/894 - loss 0.90265949 - time (sec): 53.89 - samples/sec: 968.58 - lr: 0.000018 - momentum: 0.000000
2023-09-03 20:01:53,021 epoch 1 - iter 623/894 - loss 0.81910891 - time (sec): 62.99 - samples/sec: 960.05 - lr: 0.000021 - momentum: 0.000000
2023-09-03 20:02:02,012 epoch 1 - iter 712/894 - loss 0.74839847 - time (sec): 71.98 - samples/sec: 961.89 - lr: 0.000024 - momentum: 0.000000
2023-09-03 20:02:10,769 epoch 1 - iter 801/894 - loss 0.69749666 - time (sec): 80.74 - samples/sec: 956.43 - lr: 0.000027 - momentum: 0.000000
2023-09-03 20:02:20,027 epoch 1 - iter 890/894 - loss 0.64658706 - time (sec): 90.00 - samples/sec: 956.03 - lr: 0.000030 - momentum: 0.000000
2023-09-03 20:02:20,448 ----------------------------------------------------------------------------------------------------
2023-09-03 20:02:20,448 EPOCH 1 done: loss 0.6437 - lr: 0.000030
2023-09-03 20:02:31,359 DEV : loss 0.19128236174583435 - f1-score (micro avg)  0.5819
2023-09-03 20:02:31,389 saving best model
2023-09-03 20:02:31,854 ----------------------------------------------------------------------------------------------------
2023-09-03 20:02:40,979 epoch 2 - iter 89/894 - loss 0.22375762 - time (sec): 9.12 - samples/sec: 942.97 - lr: 0.000030 - momentum: 0.000000
2023-09-03 20:02:50,306 epoch 2 - iter 178/894 - loss 0.20586503 - time (sec): 18.45 - samples/sec: 923.13 - lr: 0.000029 - momentum: 0.000000
2023-09-03 20:02:59,128 epoch 2 - iter 267/894 - loss 0.19298164 - time (sec): 27.27 - samples/sec: 924.96 - lr: 0.000029 - momentum: 0.000000
2023-09-03 20:03:08,378 epoch 2 - iter 356/894 - loss 0.18768150 - time (sec): 36.52 - samples/sec: 932.60 - lr: 0.000029 - momentum: 0.000000
2023-09-03 20:03:17,225 epoch 2 - iter 445/894 - loss 0.17916969 - time (sec): 45.37 - samples/sec: 929.52 - lr: 0.000028 - momentum: 0.000000
2023-09-03 20:03:26,962 epoch 2 - iter 534/894 - loss 0.17513122 - time (sec): 55.11 - samples/sec: 934.07 - lr: 0.000028 - momentum: 0.000000
2023-09-03 20:03:35,795 epoch 2 - iter 623/894 - loss 0.16785798 - time (sec): 63.94 - samples/sec: 935.39 - lr: 0.000028 - momentum: 0.000000
2023-09-03 20:03:45,615 epoch 2 - iter 712/894 - loss 0.16499300 - time (sec): 73.76 - samples/sec: 937.30 - lr: 0.000027 - momentum: 0.000000
2023-09-03 20:03:55,283 epoch 2 - iter 801/894 - loss 0.16353301 - time (sec): 83.43 - samples/sec: 933.55 - lr: 0.000027 - momentum: 0.000000
2023-09-03 20:04:04,298 epoch 2 - iter 890/894 - loss 0.16254544 - time (sec): 92.44 - samples/sec: 931.93 - lr: 0.000027 - momentum: 0.000000
2023-09-03 20:04:04,696 ----------------------------------------------------------------------------------------------------
2023-09-03 20:04:04,696 EPOCH 2 done: loss 0.1622 - lr: 0.000027
2023-09-03 20:04:18,263 DEV : loss 0.13599510490894318 - f1-score (micro avg)  0.6873
2023-09-03 20:04:18,290 saving best model
2023-09-03 20:04:19,610 ----------------------------------------------------------------------------------------------------
2023-09-03 20:04:29,061 epoch 3 - iter 89/894 - loss 0.08706196 - time (sec): 9.45 - samples/sec: 913.08 - lr: 0.000026 - momentum: 0.000000
2023-09-03 20:04:38,966 epoch 3 - iter 178/894 - loss 0.07931150 - time (sec): 19.35 - samples/sec: 943.85 - lr: 0.000026 - momentum: 0.000000
2023-09-03 20:04:48,571 epoch 3 - iter 267/894 - loss 0.08634813 - time (sec): 28.96 - samples/sec: 949.99 - lr: 0.000026 - momentum: 0.000000
2023-09-03 20:04:58,032 epoch 3 - iter 356/894 - loss 0.08190028 - time (sec): 38.42 - samples/sec: 949.48 - lr: 0.000025 - momentum: 0.000000
2023-09-03 20:05:07,602 epoch 3 - iter 445/894 - loss 0.08993317 - time (sec): 47.99 - samples/sec: 947.59 - lr: 0.000025 - momentum: 0.000000
2023-09-03 20:05:16,437 epoch 3 - iter 534/894 - loss 0.09358812 - time (sec): 56.83 - samples/sec: 935.41 - lr: 0.000025 - momentum: 0.000000
2023-09-03 20:05:25,291 epoch 3 - iter 623/894 - loss 0.09173455 - time (sec): 65.68 - samples/sec: 936.92 - lr: 0.000024 - momentum: 0.000000
2023-09-03 20:05:34,095 epoch 3 - iter 712/894 - loss 0.09180758 - time (sec): 74.48 - samples/sec: 933.17 - lr: 0.000024 - momentum: 0.000000
2023-09-03 20:05:43,327 epoch 3 - iter 801/894 - loss 0.09299434 - time (sec): 83.71 - samples/sec: 930.35 - lr: 0.000024 - momentum: 0.000000
2023-09-03 20:05:52,294 epoch 3 - iter 890/894 - loss 0.09212889 - time (sec): 92.68 - samples/sec: 929.32 - lr: 0.000023 - momentum: 0.000000
2023-09-03 20:05:52,755 ----------------------------------------------------------------------------------------------------
2023-09-03 20:05:52,755 EPOCH 3 done: loss 0.0917 - lr: 0.000023
2023-09-03 20:06:06,532 DEV : loss 0.1533261090517044 - f1-score (micro avg)  0.739
2023-09-03 20:06:06,558 saving best model
2023-09-03 20:06:07,888 ----------------------------------------------------------------------------------------------------
2023-09-03 20:06:16,539 epoch 4 - iter 89/894 - loss 0.05568495 - time (sec): 8.65 - samples/sec: 881.27 - lr: 0.000023 - momentum: 0.000000
2023-09-03 20:06:26,449 epoch 4 - iter 178/894 - loss 0.05265981 - time (sec): 18.56 - samples/sec: 914.74 - lr: 0.000023 - momentum: 0.000000
2023-09-03 20:06:35,557 epoch 4 - iter 267/894 - loss 0.06012918 - time (sec): 27.67 - samples/sec: 914.68 - lr: 0.000022 - momentum: 0.000000
2023-09-03 20:06:44,626 epoch 4 - iter 356/894 - loss 0.06063329 - time (sec): 36.74 - samples/sec: 924.20 - lr: 0.000022 - momentum: 0.000000
2023-09-03 20:06:53,287 epoch 4 - iter 445/894 - loss 0.06192230 - time (sec): 45.40 - samples/sec: 915.79 - lr: 0.000022 - momentum: 0.000000
2023-09-03 20:07:03,572 epoch 4 - iter 534/894 - loss 0.05840812 - time (sec): 55.68 - samples/sec: 932.72 - lr: 0.000021 - momentum: 0.000000
2023-09-03 20:07:12,832 epoch 4 - iter 623/894 - loss 0.05596147 - time (sec): 64.94 - samples/sec: 931.43 - lr: 0.000021 - momentum: 0.000000
2023-09-03 20:07:21,523 epoch 4 - iter 712/894 - loss 0.05656731 - time (sec): 73.63 - samples/sec: 931.66 - lr: 0.000021 - momentum: 0.000000
2023-09-03 20:07:30,668 epoch 4 - iter 801/894 - loss 0.05657801 - time (sec): 82.78 - samples/sec: 939.65 - lr: 0.000020 - momentum: 0.000000
2023-09-03 20:07:39,471 epoch 4 - iter 890/894 - loss 0.05608795 - time (sec): 91.58 - samples/sec: 941.83 - lr: 0.000020 - momentum: 0.000000
2023-09-03 20:07:39,842 ----------------------------------------------------------------------------------------------------
2023-09-03 20:07:39,842 EPOCH 4 done: loss 0.0560 - lr: 0.000020
2023-09-03 20:07:52,581 DEV : loss 0.18926754593849182 - f1-score (micro avg)  0.7655
2023-09-03 20:07:52,608 saving best model
2023-09-03 20:07:53,942 ----------------------------------------------------------------------------------------------------
2023-09-03 20:08:02,799 epoch 5 - iter 89/894 - loss 0.05861320 - time (sec): 8.86 - samples/sec: 918.71 - lr: 0.000020 - momentum: 0.000000
2023-09-03 20:08:11,444 epoch 5 - iter 178/894 - loss 0.04567469 - time (sec): 17.50 - samples/sec: 916.80 - lr: 0.000019 - momentum: 0.000000
2023-09-03 20:08:20,508 epoch 5 - iter 267/894 - loss 0.04306743 - time (sec): 26.56 - samples/sec: 931.58 - lr: 0.000019 - momentum: 0.000000
2023-09-03 20:08:30,195 epoch 5 - iter 356/894 - loss 0.04255572 - time (sec): 36.25 - samples/sec: 940.70 - lr: 0.000019 - momentum: 0.000000
2023-09-03 20:08:39,048 epoch 5 - iter 445/894 - loss 0.03995375 - time (sec): 45.10 - samples/sec: 953.32 - lr: 0.000018 - momentum: 0.000000
2023-09-03 20:08:47,607 epoch 5 - iter 534/894 - loss 0.03930735 - time (sec): 53.66 - samples/sec: 958.07 - lr: 0.000018 - momentum: 0.000000
2023-09-03 20:08:56,976 epoch 5 - iter 623/894 - loss 0.03752091 - time (sec): 63.03 - samples/sec: 959.33 - lr: 0.000018 - momentum: 0.000000
2023-09-03 20:09:06,688 epoch 5 - iter 712/894 - loss 0.03698791 - time (sec): 72.74 - samples/sec: 959.71 - lr: 0.000017 - momentum: 0.000000
2023-09-03 20:09:15,510 epoch 5 - iter 801/894 - loss 0.03588804 - time (sec): 81.57 - samples/sec: 961.66 - lr: 0.000017 - momentum: 0.000000
2023-09-03 20:09:24,037 epoch 5 - iter 890/894 - loss 0.03634887 - time (sec): 90.09 - samples/sec: 956.82 - lr: 0.000017 - momentum: 0.000000
2023-09-03 20:09:24,392 ----------------------------------------------------------------------------------------------------
2023-09-03 20:09:24,393 EPOCH 5 done: loss 0.0365 - lr: 0.000017
2023-09-03 20:09:37,245 DEV : loss 0.20267988741397858 - f1-score (micro avg)  0.7533
2023-09-03 20:09:37,271 ----------------------------------------------------------------------------------------------------
2023-09-03 20:09:46,219 epoch 6 - iter 89/894 - loss 0.03095029 - time (sec): 8.95 - samples/sec: 970.06 - lr: 0.000016 - momentum: 0.000000
2023-09-03 20:09:54,982 epoch 6 - iter 178/894 - loss 0.02722403 - time (sec): 17.71 - samples/sec: 956.34 - lr: 0.000016 - momentum: 0.000000
2023-09-03 20:10:03,718 epoch 6 - iter 267/894 - loss 0.02513090 - time (sec): 26.45 - samples/sec: 948.52 - lr: 0.000016 - momentum: 0.000000
2023-09-03 20:10:12,687 epoch 6 - iter 356/894 - loss 0.02334869 - time (sec): 35.41 - samples/sec: 952.61 - lr: 0.000015 - momentum: 0.000000
2023-09-03 20:10:21,633 epoch 6 - iter 445/894 - loss 0.02369827 - time (sec): 44.36 - samples/sec: 946.44 - lr: 0.000015 - momentum: 0.000000
2023-09-03 20:10:30,370 epoch 6 - iter 534/894 - loss 0.02301427 - time (sec): 53.10 - samples/sec: 949.91 - lr: 0.000015 - momentum: 0.000000
2023-09-03 20:10:39,144 epoch 6 - iter 623/894 - loss 0.02362530 - time (sec): 61.87 - samples/sec: 946.65 - lr: 0.000014 - momentum: 0.000000
2023-09-03 20:10:48,318 epoch 6 - iter 712/894 - loss 0.02570622 - time (sec): 71.05 - samples/sec: 942.81 - lr: 0.000014 - momentum: 0.000000
2023-09-03 20:10:58,080 epoch 6 - iter 801/894 - loss 0.02593093 - time (sec): 80.81 - samples/sec: 939.92 - lr: 0.000014 - momentum: 0.000000
2023-09-03 20:11:08,282 epoch 6 - iter 890/894 - loss 0.02550216 - time (sec): 91.01 - samples/sec: 944.89 - lr: 0.000013 - momentum: 0.000000
2023-09-03 20:11:08,787 ----------------------------------------------------------------------------------------------------
2023-09-03 20:11:08,787 EPOCH 6 done: loss 0.0256 - lr: 0.000013
2023-09-03 20:11:22,117 DEV : loss 0.21390819549560547 - f1-score (micro avg)  0.7688
2023-09-03 20:11:22,144 saving best model
2023-09-03 20:11:23,484 ----------------------------------------------------------------------------------------------------
2023-09-03 20:11:32,539 epoch 7 - iter 89/894 - loss 0.02358594 - time (sec): 9.05 - samples/sec: 959.67 - lr: 0.000013 - momentum: 0.000000
2023-09-03 20:11:41,614 epoch 7 - iter 178/894 - loss 0.02018242 - time (sec): 18.13 - samples/sec: 951.83 - lr: 0.000013 - momentum: 0.000000
2023-09-03 20:11:50,578 epoch 7 - iter 267/894 - loss 0.01855891 - time (sec): 27.09 - samples/sec: 970.83 - lr: 0.000012 - momentum: 0.000000
2023-09-03 20:12:00,093 epoch 7 - iter 356/894 - loss 0.01796381 - time (sec): 36.61 - samples/sec: 958.74 - lr: 0.000012 - momentum: 0.000000
2023-09-03 20:12:09,251 epoch 7 - iter 445/894 - loss 0.01543157 - time (sec): 45.77 - samples/sec: 944.04 - lr: 0.000012 - momentum: 0.000000
2023-09-03 20:12:18,564 epoch 7 - iter 534/894 - loss 0.01541795 - time (sec): 55.08 - samples/sec: 940.59 - lr: 0.000011 - momentum: 0.000000
2023-09-03 20:12:27,674 epoch 7 - iter 623/894 - loss 0.01583522 - time (sec): 64.19 - samples/sec: 935.79 - lr: 0.000011 - momentum: 0.000000
2023-09-03 20:12:36,956 epoch 7 - iter 712/894 - loss 0.01637177 - time (sec): 73.47 - samples/sec: 931.78 - lr: 0.000011 - momentum: 0.000000
2023-09-03 20:12:45,869 epoch 7 - iter 801/894 - loss 0.01638907 - time (sec): 82.38 - samples/sec: 925.24 - lr: 0.000010 - momentum: 0.000000
2023-09-03 20:12:56,566 epoch 7 - iter 890/894 - loss 0.01589087 - time (sec): 93.08 - samples/sec: 924.56 - lr: 0.000010 - momentum: 0.000000
2023-09-03 20:12:57,013 ----------------------------------------------------------------------------------------------------
2023-09-03 20:12:57,014 EPOCH 7 done: loss 0.0158 - lr: 0.000010
2023-09-03 20:13:10,562 DEV : loss 0.2357018142938614 - f1-score (micro avg)  0.7773
2023-09-03 20:13:10,590 saving best model
2023-09-03 20:13:11,916 ----------------------------------------------------------------------------------------------------
2023-09-03 20:13:20,796 epoch 8 - iter 89/894 - loss 0.01168538 - time (sec): 8.88 - samples/sec: 945.49 - lr: 0.000010 - momentum: 0.000000
2023-09-03 20:13:31,458 epoch 8 - iter 178/894 - loss 0.01122629 - time (sec): 19.54 - samples/sec: 925.43 - lr: 0.000009 - momentum: 0.000000
2023-09-03 20:13:40,609 epoch 8 - iter 267/894 - loss 0.01210043 - time (sec): 28.69 - samples/sec: 919.98 - lr: 0.000009 - momentum: 0.000000
2023-09-03 20:13:49,786 epoch 8 - iter 356/894 - loss 0.01094018 - time (sec): 37.87 - samples/sec: 924.94 - lr: 0.000009 - momentum: 0.000000
2023-09-03 20:13:58,653 epoch 8 - iter 445/894 - loss 0.01076425 - time (sec): 46.74 - samples/sec: 915.81 - lr: 0.000008 - momentum: 0.000000
2023-09-03 20:14:08,387 epoch 8 - iter 534/894 - loss 0.01045307 - time (sec): 56.47 - samples/sec: 916.66 - lr: 0.000008 - momentum: 0.000000
2023-09-03 20:14:17,535 epoch 8 - iter 623/894 - loss 0.01021745 - time (sec): 65.62 - samples/sec: 923.75 - lr: 0.000008 - momentum: 0.000000
2023-09-03 20:14:26,697 epoch 8 - iter 712/894 - loss 0.01172481 - time (sec): 74.78 - samples/sec: 921.17 - lr: 0.000007 - momentum: 0.000000
2023-09-03 20:14:35,900 epoch 8 - iter 801/894 - loss 0.01208878 - time (sec): 83.98 - samples/sec: 922.74 - lr: 0.000007 - momentum: 0.000000
2023-09-03 20:14:45,183 epoch 8 - iter 890/894 - loss 0.01172491 - time (sec): 93.27 - samples/sec: 924.22 - lr: 0.000007 - momentum: 0.000000
2023-09-03 20:14:45,566 ----------------------------------------------------------------------------------------------------
2023-09-03 20:14:45,566 EPOCH 8 done: loss 0.0117 - lr: 0.000007
2023-09-03 20:14:59,155 DEV : loss 0.2376643270254135 - f1-score (micro avg)  0.7778
2023-09-03 20:14:59,181 saving best model
2023-09-03 20:15:00,503 ----------------------------------------------------------------------------------------------------
2023-09-03 20:15:09,698 epoch 9 - iter 89/894 - loss 0.00658409 - time (sec): 9.19 - samples/sec: 941.04 - lr: 0.000006 - momentum: 0.000000
2023-09-03 20:15:18,535 epoch 9 - iter 178/894 - loss 0.00659844 - time (sec): 18.03 - samples/sec: 945.85 - lr: 0.000006 - momentum: 0.000000
2023-09-03 20:15:27,665 epoch 9 - iter 267/894 - loss 0.00935523 - time (sec): 27.16 - samples/sec: 932.87 - lr: 0.000006 - momentum: 0.000000
2023-09-03 20:15:36,752 epoch 9 - iter 356/894 - loss 0.00888493 - time (sec): 36.25 - samples/sec: 939.41 - lr: 0.000005 - momentum: 0.000000
2023-09-03 20:15:47,156 epoch 9 - iter 445/894 - loss 0.00905866 - time (sec): 46.65 - samples/sec: 939.24 - lr: 0.000005 - momentum: 0.000000
2023-09-03 20:15:56,230 epoch 9 - iter 534/894 - loss 0.00892902 - time (sec): 55.73 - samples/sec: 937.12 - lr: 0.000005 - momentum: 0.000000
2023-09-03 20:16:05,440 epoch 9 - iter 623/894 - loss 0.00921909 - time (sec): 64.94 - samples/sec: 932.93 - lr: 0.000004 - momentum: 0.000000
2023-09-03 20:16:14,937 epoch 9 - iter 712/894 - loss 0.00903307 - time (sec): 74.43 - samples/sec: 933.31 - lr: 0.000004 - momentum: 0.000000
2023-09-03 20:16:23,744 epoch 9 - iter 801/894 - loss 0.00922263 - time (sec): 83.24 - samples/sec: 931.39 - lr: 0.000004 - momentum: 0.000000
2023-09-03 20:16:33,239 epoch 9 - iter 890/894 - loss 0.00914557 - time (sec): 92.74 - samples/sec: 929.37 - lr: 0.000003 - momentum: 0.000000
2023-09-03 20:16:33,641 ----------------------------------------------------------------------------------------------------
2023-09-03 20:16:33,642 EPOCH 9 done: loss 0.0093 - lr: 0.000003
2023-09-03 20:16:47,247 DEV : loss 0.2397419661283493 - f1-score (micro avg)  0.7773
2023-09-03 20:16:47,274 ----------------------------------------------------------------------------------------------------
2023-09-03 20:16:56,973 epoch 10 - iter 89/894 - loss 0.00098514 - time (sec): 9.70 - samples/sec: 953.14 - lr: 0.000003 - momentum: 0.000000
2023-09-03 20:17:06,027 epoch 10 - iter 178/894 - loss 0.00223991 - time (sec): 18.75 - samples/sec: 924.58 - lr: 0.000003 - momentum: 0.000000
2023-09-03 20:17:15,258 epoch 10 - iter 267/894 - loss 0.00432898 - time (sec): 27.98 - samples/sec: 920.50 - lr: 0.000002 - momentum: 0.000000
2023-09-03 20:17:25,436 epoch 10 - iter 356/894 - loss 0.00405978 - time (sec): 38.16 - samples/sec: 931.25 - lr: 0.000002 - momentum: 0.000000
2023-09-03 20:17:34,526 epoch 10 - iter 445/894 - loss 0.00424425 - time (sec): 47.25 - samples/sec: 929.10 - lr: 0.000002 - momentum: 0.000000
2023-09-03 20:17:43,508 epoch 10 - iter 534/894 - loss 0.00505153 - time (sec): 56.23 - samples/sec: 931.94 - lr: 0.000001 - momentum: 0.000000
2023-09-03 20:17:52,376 epoch 10 - iter 623/894 - loss 0.00504540 - time (sec): 65.10 - samples/sec: 923.65 - lr: 0.000001 - momentum: 0.000000
2023-09-03 20:18:01,937 epoch 10 - iter 712/894 - loss 0.00501811 - time (sec): 74.66 - samples/sec: 921.08 - lr: 0.000001 - momentum: 0.000000
2023-09-03 20:18:10,996 epoch 10 - iter 801/894 - loss 0.00563425 - time (sec): 83.72 - samples/sec: 919.02 - lr: 0.000000 - momentum: 0.000000
2023-09-03 20:18:20,743 epoch 10 - iter 890/894 - loss 0.00575244 - time (sec): 93.47 - samples/sec: 922.76 - lr: 0.000000 - momentum: 0.000000
2023-09-03 20:18:21,147 ----------------------------------------------------------------------------------------------------
2023-09-03 20:18:21,147 EPOCH 10 done: loss 0.0059 - lr: 0.000000
2023-09-03 20:18:34,886 DEV : loss 0.23916852474212646 - f1-score (micro avg)  0.7865
2023-09-03 20:18:34,913 saving best model
2023-09-03 20:18:36,753 ----------------------------------------------------------------------------------------------------
2023-09-03 20:18:36,754 Loading model from best epoch ...
2023-09-03 20:18:38,576 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
2023-09-03 20:18:49,344 
Results:
- F-score (micro) 0.7454
- F-score (macro) 0.6684
- Accuracy 0.6184

By class:
              precision    recall  f1-score   support

         loc     0.8366    0.8507    0.8436       596
        pers     0.6684    0.7568    0.7099       333
         org     0.4752    0.5076    0.4908       132
        prod     0.5962    0.4697    0.5254        66
        time     0.7500    0.7959    0.7723        49

   micro avg     0.7296    0.7619    0.7454      1176
   macro avg     0.6653    0.6761    0.6684      1176
weighted avg     0.7313    0.7619    0.7453      1176

2023-09-03 20:18:49,344 ----------------------------------------------------------------------------------------------------