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autotrain-flair-hipe2022-de-hmbert

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autotrain-flair-hipe2022-de-hmbert
File size: 24,004 Bytes 
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2023-09-03 20:19:20,809 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,810 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 20:19:20,810 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,810 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 20:19:20,810 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,810 Train:  3575 sentences
2023-09-03 20:19:20,810         (train_with_dev=False, train_with_test=False)
2023-09-03 20:19:20,810 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,810 Training Params:
2023-09-03 20:19:20,810  - learning_rate: "5e-05" 
2023-09-03 20:19:20,811  - mini_batch_size: "4"
2023-09-03 20:19:20,811  - max_epochs: "10"
2023-09-03 20:19:20,811  - shuffle: "True"
2023-09-03 20:19:20,811 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,811 Plugins:
2023-09-03 20:19:20,811  - LinearScheduler | warmup_fraction: '0.1'
2023-09-03 20:19:20,811 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,811 Final evaluation on model from best epoch (best-model.pt)
2023-09-03 20:19:20,811  - metric: "('micro avg', 'f1-score')"
2023-09-03 20:19:20,811 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,811 Computation:
2023-09-03 20:19:20,811  - compute on device: cuda:0
2023-09-03 20:19:20,811  - embedding storage: none
2023-09-03 20:19:20,811 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,811 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-09-03 20:19:20,811 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:20,811 ----------------------------------------------------------------------------------------------------
2023-09-03 20:19:29,527 epoch 1 - iter 89/894 - loss 2.89989312 - time (sec): 8.71 - samples/sec: 919.60 - lr: 0.000005 - momentum: 0.000000
2023-09-03 20:19:38,330 epoch 1 - iter 178/894 - loss 1.76162593 - time (sec): 17.52 - samples/sec: 916.67 - lr: 0.000010 - momentum: 0.000000
2023-09-03 20:19:47,454 epoch 1 - iter 267/894 - loss 1.27223185 - time (sec): 26.64 - samples/sec: 935.95 - lr: 0.000015 - momentum: 0.000000
2023-09-03 20:19:56,329 epoch 1 - iter 356/894 - loss 1.05292482 - time (sec): 35.52 - samples/sec: 930.12 - lr: 0.000020 - momentum: 0.000000
2023-09-03 20:20:05,560 epoch 1 - iter 445/894 - loss 0.89522602 - time (sec): 44.75 - samples/sec: 936.09 - lr: 0.000025 - momentum: 0.000000
2023-09-03 20:20:16,121 epoch 1 - iter 534/894 - loss 0.78258018 - time (sec): 55.31 - samples/sec: 943.67 - lr: 0.000030 - momentum: 0.000000
2023-09-03 20:20:25,496 epoch 1 - iter 623/894 - loss 0.71307594 - time (sec): 64.68 - samples/sec: 934.93 - lr: 0.000035 - momentum: 0.000000
2023-09-03 20:20:34,781 epoch 1 - iter 712/894 - loss 0.65376441 - time (sec): 73.97 - samples/sec: 936.05 - lr: 0.000040 - momentum: 0.000000
2023-09-03 20:20:43,688 epoch 1 - iter 801/894 - loss 0.61105348 - time (sec): 82.88 - samples/sec: 931.77 - lr: 0.000045 - momentum: 0.000000
2023-09-03 20:20:52,993 epoch 1 - iter 890/894 - loss 0.57058985 - time (sec): 92.18 - samples/sec: 933.39 - lr: 0.000050 - momentum: 0.000000
2023-09-03 20:20:53,415 ----------------------------------------------------------------------------------------------------
2023-09-03 20:20:53,416 EPOCH 1 done: loss 0.5683 - lr: 0.000050
2023-09-03 20:21:04,508 DEV : loss 0.17097648978233337 - f1-score (micro avg)  0.6162
2023-09-03 20:21:04,534 saving best model
2023-09-03 20:21:04,992 ----------------------------------------------------------------------------------------------------
2023-09-03 20:21:14,165 epoch 2 - iter 89/894 - loss 0.20605836 - time (sec): 9.17 - samples/sec: 938.05 - lr: 0.000049 - momentum: 0.000000
2023-09-03 20:21:23,535 epoch 2 - iter 178/894 - loss 0.19007741 - time (sec): 18.54 - samples/sec: 918.64 - lr: 0.000049 - momentum: 0.000000
2023-09-03 20:21:32,430 epoch 2 - iter 267/894 - loss 0.18247790 - time (sec): 27.44 - samples/sec: 919.45 - lr: 0.000048 - momentum: 0.000000
2023-09-03 20:21:41,692 epoch 2 - iter 356/894 - loss 0.18202885 - time (sec): 36.70 - samples/sec: 928.16 - lr: 0.000048 - momentum: 0.000000
2023-09-03 20:21:50,582 epoch 2 - iter 445/894 - loss 0.17738266 - time (sec): 45.59 - samples/sec: 925.06 - lr: 0.000047 - momentum: 0.000000
2023-09-03 20:22:00,394 epoch 2 - iter 534/894 - loss 0.17474082 - time (sec): 55.40 - samples/sec: 929.14 - lr: 0.000047 - momentum: 0.000000
2023-09-03 20:22:09,359 epoch 2 - iter 623/894 - loss 0.16799398 - time (sec): 64.37 - samples/sec: 929.21 - lr: 0.000046 - momentum: 0.000000
2023-09-03 20:22:19,256 epoch 2 - iter 712/894 - loss 0.16240455 - time (sec): 74.26 - samples/sec: 930.96 - lr: 0.000046 - momentum: 0.000000
2023-09-03 20:22:28,965 epoch 2 - iter 801/894 - loss 0.16209682 - time (sec): 83.97 - samples/sec: 927.51 - lr: 0.000045 - momentum: 0.000000
2023-09-03 20:22:37,984 epoch 2 - iter 890/894 - loss 0.16158978 - time (sec): 92.99 - samples/sec: 926.44 - lr: 0.000044 - momentum: 0.000000
2023-09-03 20:22:38,376 ----------------------------------------------------------------------------------------------------
2023-09-03 20:22:38,376 EPOCH 2 done: loss 0.1613 - lr: 0.000044
2023-09-03 20:22:51,909 DEV : loss 0.16291926801204681 - f1-score (micro avg)  0.6627
2023-09-03 20:22:51,935 saving best model
2023-09-03 20:22:53,255 ----------------------------------------------------------------------------------------------------
2023-09-03 20:23:02,702 epoch 3 - iter 89/894 - loss 0.08768279 - time (sec): 9.45 - samples/sec: 913.47 - lr: 0.000044 - momentum: 0.000000
2023-09-03 20:23:12,631 epoch 3 - iter 178/894 - loss 0.08725946 - time (sec): 19.37 - samples/sec: 942.88 - lr: 0.000043 - momentum: 0.000000
2023-09-03 20:23:22,249 epoch 3 - iter 267/894 - loss 0.09362897 - time (sec): 28.99 - samples/sec: 948.89 - lr: 0.000043 - momentum: 0.000000
2023-09-03 20:23:31,711 epoch 3 - iter 356/894 - loss 0.08860942 - time (sec): 38.45 - samples/sec: 948.63 - lr: 0.000042 - momentum: 0.000000
2023-09-03 20:23:41,240 epoch 3 - iter 445/894 - loss 0.09576555 - time (sec): 47.98 - samples/sec: 947.72 - lr: 0.000042 - momentum: 0.000000
2023-09-03 20:23:50,052 epoch 3 - iter 534/894 - loss 0.10063010 - time (sec): 56.80 - samples/sec: 935.90 - lr: 0.000041 - momentum: 0.000000
2023-09-03 20:23:58,894 epoch 3 - iter 623/894 - loss 0.09832043 - time (sec): 65.64 - samples/sec: 937.51 - lr: 0.000041 - momentum: 0.000000
2023-09-03 20:24:07,624 epoch 3 - iter 712/894 - loss 0.09817305 - time (sec): 74.37 - samples/sec: 934.63 - lr: 0.000040 - momentum: 0.000000
2023-09-03 20:24:16,848 epoch 3 - iter 801/894 - loss 0.10180100 - time (sec): 83.59 - samples/sec: 931.72 - lr: 0.000039 - momentum: 0.000000
2023-09-03 20:24:25,737 epoch 3 - iter 890/894 - loss 0.10148223 - time (sec): 92.48 - samples/sec: 931.35 - lr: 0.000039 - momentum: 0.000000
2023-09-03 20:24:26,139 ----------------------------------------------------------------------------------------------------
2023-09-03 20:24:26,139 EPOCH 3 done: loss 0.1014 - lr: 0.000039
2023-09-03 20:24:39,577 DEV : loss 0.1718726009130478 - f1-score (micro avg)  0.7266
2023-09-03 20:24:39,603 saving best model
2023-09-03 20:24:40,951 ----------------------------------------------------------------------------------------------------
2023-09-03 20:24:49,636 epoch 4 - iter 89/894 - loss 0.07154590 - time (sec): 8.68 - samples/sec: 877.87 - lr: 0.000038 - momentum: 0.000000
2023-09-03 20:24:59,559 epoch 4 - iter 178/894 - loss 0.06212479 - time (sec): 18.61 - samples/sec: 912.49 - lr: 0.000038 - momentum: 0.000000
2023-09-03 20:25:08,695 epoch 4 - iter 267/894 - loss 0.06975820 - time (sec): 27.74 - samples/sec: 912.20 - lr: 0.000037 - momentum: 0.000000
2023-09-03 20:25:17,849 epoch 4 - iter 356/894 - loss 0.06914589 - time (sec): 36.90 - samples/sec: 920.20 - lr: 0.000037 - momentum: 0.000000
2023-09-03 20:25:26,598 epoch 4 - iter 445/894 - loss 0.06933892 - time (sec): 45.65 - samples/sec: 910.82 - lr: 0.000036 - momentum: 0.000000
2023-09-03 20:25:37,152 epoch 4 - iter 534/894 - loss 0.06671475 - time (sec): 56.20 - samples/sec: 924.15 - lr: 0.000036 - momentum: 0.000000
2023-09-03 20:25:46,669 epoch 4 - iter 623/894 - loss 0.06771971 - time (sec): 65.72 - samples/sec: 920.47 - lr: 0.000035 - momentum: 0.000000
2023-09-03 20:25:55,680 epoch 4 - iter 712/894 - loss 0.06810537 - time (sec): 74.73 - samples/sec: 918.03 - lr: 0.000034 - momentum: 0.000000
2023-09-03 20:26:05,114 epoch 4 - iter 801/894 - loss 0.06697660 - time (sec): 84.16 - samples/sec: 924.22 - lr: 0.000034 - momentum: 0.000000
2023-09-03 20:26:14,205 epoch 4 - iter 890/894 - loss 0.06654671 - time (sec): 93.25 - samples/sec: 924.97 - lr: 0.000033 - momentum: 0.000000
2023-09-03 20:26:14,594 ----------------------------------------------------------------------------------------------------
2023-09-03 20:26:14,594 EPOCH 4 done: loss 0.0663 - lr: 0.000033
2023-09-03 20:26:28,159 DEV : loss 0.21245643496513367 - f1-score (micro avg)  0.7368
2023-09-03 20:26:28,186 saving best model
2023-09-03 20:26:30,057 ----------------------------------------------------------------------------------------------------
2023-09-03 20:26:39,218 epoch 5 - iter 89/894 - loss 0.06250775 - time (sec): 9.16 - samples/sec: 888.32 - lr: 0.000033 - momentum: 0.000000
2023-09-03 20:26:48,153 epoch 5 - iter 178/894 - loss 0.04987455 - time (sec): 18.09 - samples/sec: 886.77 - lr: 0.000032 - momentum: 0.000000
2023-09-03 20:26:57,584 epoch 5 - iter 267/894 - loss 0.05005559 - time (sec): 27.53 - samples/sec: 899.07 - lr: 0.000032 - momentum: 0.000000
2023-09-03 20:27:07,691 epoch 5 - iter 356/894 - loss 0.05457406 - time (sec): 37.63 - samples/sec: 906.19 - lr: 0.000031 - momentum: 0.000000
2023-09-03 20:27:16,826 epoch 5 - iter 445/894 - loss 0.05184580 - time (sec): 46.77 - samples/sec: 919.43 - lr: 0.000031 - momentum: 0.000000
2023-09-03 20:27:25,722 epoch 5 - iter 534/894 - loss 0.05490556 - time (sec): 55.66 - samples/sec: 923.67 - lr: 0.000030 - momentum: 0.000000
2023-09-03 20:27:35,344 epoch 5 - iter 623/894 - loss 0.05272637 - time (sec): 65.28 - samples/sec: 926.24 - lr: 0.000029 - momentum: 0.000000
2023-09-03 20:27:45,383 epoch 5 - iter 712/894 - loss 0.05144270 - time (sec): 75.32 - samples/sec: 926.86 - lr: 0.000029 - momentum: 0.000000
2023-09-03 20:27:54,395 epoch 5 - iter 801/894 - loss 0.04983405 - time (sec): 84.34 - samples/sec: 930.09 - lr: 0.000028 - momentum: 0.000000
2023-09-03 20:28:03,140 epoch 5 - iter 890/894 - loss 0.04937796 - time (sec): 93.08 - samples/sec: 926.12 - lr: 0.000028 - momentum: 0.000000
2023-09-03 20:28:03,510 ----------------------------------------------------------------------------------------------------
2023-09-03 20:28:03,510 EPOCH 5 done: loss 0.0498 - lr: 0.000028
2023-09-03 20:28:17,043 DEV : loss 0.22385385632514954 - f1-score (micro avg)  0.7632
2023-09-03 20:28:17,070 saving best model
2023-09-03 20:28:18,382 ----------------------------------------------------------------------------------------------------
2023-09-03 20:28:27,680 epoch 6 - iter 89/894 - loss 0.03521194 - time (sec): 9.30 - samples/sec: 933.60 - lr: 0.000027 - momentum: 0.000000
2023-09-03 20:28:36,764 epoch 6 - iter 178/894 - loss 0.03279992 - time (sec): 18.38 - samples/sec: 921.40 - lr: 0.000027 - momentum: 0.000000
2023-09-03 20:28:45,721 epoch 6 - iter 267/894 - loss 0.03124356 - time (sec): 27.34 - samples/sec: 917.56 - lr: 0.000026 - momentum: 0.000000
2023-09-03 20:28:54,970 epoch 6 - iter 356/894 - loss 0.02894350 - time (sec): 36.59 - samples/sec: 922.08 - lr: 0.000026 - momentum: 0.000000
2023-09-03 20:29:04,198 epoch 6 - iter 445/894 - loss 0.03070838 - time (sec): 45.81 - samples/sec: 916.42 - lr: 0.000025 - momentum: 0.000000
2023-09-03 20:29:13,064 epoch 6 - iter 534/894 - loss 0.02981611 - time (sec): 54.68 - samples/sec: 922.41 - lr: 0.000024 - momentum: 0.000000
2023-09-03 20:29:21,897 epoch 6 - iter 623/894 - loss 0.02928313 - time (sec): 63.51 - samples/sec: 922.19 - lr: 0.000024 - momentum: 0.000000
2023-09-03 20:29:31,119 epoch 6 - iter 712/894 - loss 0.03070513 - time (sec): 72.74 - samples/sec: 920.91 - lr: 0.000023 - momentum: 0.000000
2023-09-03 20:29:40,917 epoch 6 - iter 801/894 - loss 0.03083902 - time (sec): 82.53 - samples/sec: 920.26 - lr: 0.000023 - momentum: 0.000000
2023-09-03 20:29:51,131 epoch 6 - iter 890/894 - loss 0.02973958 - time (sec): 92.75 - samples/sec: 927.19 - lr: 0.000022 - momentum: 0.000000
2023-09-03 20:29:51,649 ----------------------------------------------------------------------------------------------------
2023-09-03 20:29:51,649 EPOCH 6 done: loss 0.0304 - lr: 0.000022
2023-09-03 20:30:05,098 DEV : loss 0.21197949349880219 - f1-score (micro avg)  0.764
2023-09-03 20:30:05,132 saving best model
2023-09-03 20:30:06,446 ----------------------------------------------------------------------------------------------------
2023-09-03 20:30:15,471 epoch 7 - iter 89/894 - loss 0.02658940 - time (sec): 9.02 - samples/sec: 962.78 - lr: 0.000022 - momentum: 0.000000
2023-09-03 20:30:24,494 epoch 7 - iter 178/894 - loss 0.02246646 - time (sec): 18.05 - samples/sec: 956.13 - lr: 0.000021 - momentum: 0.000000
2023-09-03 20:30:33,443 epoch 7 - iter 267/894 - loss 0.02172733 - time (sec): 27.00 - samples/sec: 974.31 - lr: 0.000021 - momentum: 0.000000
2023-09-03 20:30:42,859 epoch 7 - iter 356/894 - loss 0.02104734 - time (sec): 36.41 - samples/sec: 963.89 - lr: 0.000020 - momentum: 0.000000
2023-09-03 20:30:51,845 epoch 7 - iter 445/894 - loss 0.02039607 - time (sec): 45.40 - samples/sec: 951.69 - lr: 0.000019 - momentum: 0.000000
2023-09-03 20:31:01,067 epoch 7 - iter 534/894 - loss 0.02106881 - time (sec): 54.62 - samples/sec: 948.49 - lr: 0.000019 - momentum: 0.000000
2023-09-03 20:31:10,101 epoch 7 - iter 623/894 - loss 0.02042294 - time (sec): 63.65 - samples/sec: 943.65 - lr: 0.000018 - momentum: 0.000000
2023-09-03 20:31:19,311 epoch 7 - iter 712/894 - loss 0.02048542 - time (sec): 72.86 - samples/sec: 939.55 - lr: 0.000018 - momentum: 0.000000
2023-09-03 20:31:28,143 epoch 7 - iter 801/894 - loss 0.02025357 - time (sec): 81.70 - samples/sec: 933.03 - lr: 0.000017 - momentum: 0.000000
2023-09-03 20:31:38,718 epoch 7 - iter 890/894 - loss 0.02006831 - time (sec): 92.27 - samples/sec: 932.68 - lr: 0.000017 - momentum: 0.000000
2023-09-03 20:31:39,169 ----------------------------------------------------------------------------------------------------
2023-09-03 20:31:39,169 EPOCH 7 done: loss 0.0200 - lr: 0.000017
2023-09-03 20:31:52,686 DEV : loss 0.22539937496185303 - f1-score (micro avg)  0.7688
2023-09-03 20:31:52,713 saving best model
2023-09-03 20:31:54,079 ----------------------------------------------------------------------------------------------------
2023-09-03 20:32:02,989 epoch 8 - iter 89/894 - loss 0.00996259 - time (sec): 8.91 - samples/sec: 942.28 - lr: 0.000016 - momentum: 0.000000
2023-09-03 20:32:13,683 epoch 8 - iter 178/894 - loss 0.01308135 - time (sec): 19.60 - samples/sec: 922.53 - lr: 0.000016 - momentum: 0.000000
2023-09-03 20:32:22,805 epoch 8 - iter 267/894 - loss 0.01115617 - time (sec): 28.72 - samples/sec: 918.94 - lr: 0.000015 - momentum: 0.000000
2023-09-03 20:32:31,984 epoch 8 - iter 356/894 - loss 0.01062211 - time (sec): 37.90 - samples/sec: 924.07 - lr: 0.000014 - momentum: 0.000000
2023-09-03 20:32:40,824 epoch 8 - iter 445/894 - loss 0.01045602 - time (sec): 46.74 - samples/sec: 915.64 - lr: 0.000014 - momentum: 0.000000
2023-09-03 20:32:50,497 epoch 8 - iter 534/894 - loss 0.01026522 - time (sec): 56.42 - samples/sec: 917.51 - lr: 0.000013 - momentum: 0.000000
2023-09-03 20:32:59,618 epoch 8 - iter 623/894 - loss 0.01133332 - time (sec): 65.54 - samples/sec: 924.87 - lr: 0.000013 - momentum: 0.000000
2023-09-03 20:33:08,738 epoch 8 - iter 712/894 - loss 0.01245965 - time (sec): 74.66 - samples/sec: 922.67 - lr: 0.000012 - momentum: 0.000000
2023-09-03 20:33:17,904 epoch 8 - iter 801/894 - loss 0.01295115 - time (sec): 83.82 - samples/sec: 924.48 - lr: 0.000012 - momentum: 0.000000
2023-09-03 20:33:27,219 epoch 8 - iter 890/894 - loss 0.01278545 - time (sec): 93.14 - samples/sec: 925.48 - lr: 0.000011 - momentum: 0.000000
2023-09-03 20:33:27,604 ----------------------------------------------------------------------------------------------------
2023-09-03 20:33:27,604 EPOCH 8 done: loss 0.0127 - lr: 0.000011
2023-09-03 20:33:41,115 DEV : loss 0.23452451825141907 - f1-score (micro avg)  0.7825
2023-09-03 20:33:41,142 saving best model
2023-09-03 20:33:42,466 ----------------------------------------------------------------------------------------------------
2023-09-03 20:33:51,655 epoch 9 - iter 89/894 - loss 0.00216462 - time (sec): 9.19 - samples/sec: 941.78 - lr: 0.000011 - momentum: 0.000000
2023-09-03 20:34:00,559 epoch 9 - iter 178/894 - loss 0.00335217 - time (sec): 18.09 - samples/sec: 942.77 - lr: 0.000010 - momentum: 0.000000
2023-09-03 20:34:09,729 epoch 9 - iter 267/894 - loss 0.00587195 - time (sec): 27.26 - samples/sec: 929.47 - lr: 0.000009 - momentum: 0.000000
2023-09-03 20:34:18,819 epoch 9 - iter 356/894 - loss 0.00603339 - time (sec): 36.35 - samples/sec: 936.75 - lr: 0.000009 - momentum: 0.000000
2023-09-03 20:34:29,238 epoch 9 - iter 445/894 - loss 0.00533673 - time (sec): 46.77 - samples/sec: 936.89 - lr: 0.000008 - momentum: 0.000000
2023-09-03 20:34:38,377 epoch 9 - iter 534/894 - loss 0.00548625 - time (sec): 55.91 - samples/sec: 934.06 - lr: 0.000008 - momentum: 0.000000
2023-09-03 20:34:47,598 epoch 9 - iter 623/894 - loss 0.00623569 - time (sec): 65.13 - samples/sec: 930.15 - lr: 0.000007 - momentum: 0.000000
2023-09-03 20:34:57,098 epoch 9 - iter 712/894 - loss 0.00622240 - time (sec): 74.63 - samples/sec: 930.85 - lr: 0.000007 - momentum: 0.000000
2023-09-03 20:35:05,905 epoch 9 - iter 801/894 - loss 0.00690534 - time (sec): 83.44 - samples/sec: 929.19 - lr: 0.000006 - momentum: 0.000000
2023-09-03 20:35:15,382 epoch 9 - iter 890/894 - loss 0.00691071 - time (sec): 92.91 - samples/sec: 927.59 - lr: 0.000006 - momentum: 0.000000
2023-09-03 20:35:15,781 ----------------------------------------------------------------------------------------------------
2023-09-03 20:35:15,782 EPOCH 9 done: loss 0.0071 - lr: 0.000006
2023-09-03 20:35:29,298 DEV : loss 0.2623580992221832 - f1-score (micro avg)  0.763
2023-09-03 20:35:29,325 ----------------------------------------------------------------------------------------------------
2023-09-03 20:35:38,945 epoch 10 - iter 89/894 - loss 0.00043241 - time (sec): 9.62 - samples/sec: 960.91 - lr: 0.000005 - momentum: 0.000000
2023-09-03 20:35:47,979 epoch 10 - iter 178/894 - loss 0.00207187 - time (sec): 18.65 - samples/sec: 929.48 - lr: 0.000004 - momentum: 0.000000
2023-09-03 20:35:57,573 epoch 10 - iter 267/894 - loss 0.00536179 - time (sec): 28.25 - samples/sec: 911.89 - lr: 0.000004 - momentum: 0.000000
2023-09-03 20:36:07,788 epoch 10 - iter 356/894 - loss 0.00509014 - time (sec): 38.46 - samples/sec: 923.95 - lr: 0.000003 - momentum: 0.000000
2023-09-03 20:36:16,901 epoch 10 - iter 445/894 - loss 0.00516233 - time (sec): 47.57 - samples/sec: 922.77 - lr: 0.000003 - momentum: 0.000000
2023-09-03 20:36:25,959 epoch 10 - iter 534/894 - loss 0.00504847 - time (sec): 56.63 - samples/sec: 925.35 - lr: 0.000002 - momentum: 0.000000
2023-09-03 20:36:34,875 epoch 10 - iter 623/894 - loss 0.00481702 - time (sec): 65.55 - samples/sec: 917.34 - lr: 0.000002 - momentum: 0.000000
2023-09-03 20:36:44,411 epoch 10 - iter 712/894 - loss 0.00432165 - time (sec): 75.08 - samples/sec: 915.89 - lr: 0.000001 - momentum: 0.000000
2023-09-03 20:36:53,441 epoch 10 - iter 801/894 - loss 0.00422815 - time (sec): 84.12 - samples/sec: 914.71 - lr: 0.000001 - momentum: 0.000000
2023-09-03 20:37:03,105 epoch 10 - iter 890/894 - loss 0.00396219 - time (sec): 93.78 - samples/sec: 919.69 - lr: 0.000000 - momentum: 0.000000
2023-09-03 20:37:03,504 ----------------------------------------------------------------------------------------------------
2023-09-03 20:37:03,504 EPOCH 10 done: loss 0.0040 - lr: 0.000000
2023-09-03 20:37:17,166 DEV : loss 0.2543531358242035 - f1-score (micro avg)  0.7802
2023-09-03 20:37:17,642 ----------------------------------------------------------------------------------------------------
2023-09-03 20:37:17,643 Loading model from best epoch ...
2023-09-03 20:37:19,438 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 20:37:30,123 
Results:
- F-score (micro) 0.7459
- F-score (macro) 0.6693
- Accuracy 0.6167

By class:
              precision    recall  f1-score   support

         loc     0.8527    0.8356    0.8441       596
        pers     0.6384    0.7688    0.6975       333
         org     0.5455    0.5000    0.5217       132
        prod     0.6600    0.5000    0.5690        66
        time     0.7143    0.7143    0.7143        49

   micro avg     0.7369    0.7551    0.7459      1176
   macro avg     0.6822    0.6637    0.6693      1176
weighted avg     0.7410    0.7551    0.7456      1176

2023-09-03 20:37:30,123 ----------------------------------------------------------------------------------------------------