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autotrain-flair-hipe2022-fr-hmbert

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autotrain-flair-hipe2022-fr-hmbert
File size: 24,224 Bytes 
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2023-09-04 18:01:02,696 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,697 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()
)"
2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,698 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
 - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,698 Train:  5901 sentences
2023-09-04 18:01:02,698         (train_with_dev=False, train_with_test=False)
2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,698 Training Params:
2023-09-04 18:01:02,698  - learning_rate: "3e-05" 
2023-09-04 18:01:02,698  - mini_batch_size: "4"
2023-09-04 18:01:02,698  - max_epochs: "10"
2023-09-04 18:01:02,698  - shuffle: "True"
2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,698 Plugins:
2023-09-04 18:01:02,698  - LinearScheduler | warmup_fraction: '0.1'
2023-09-04 18:01:02,698 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,698 Final evaluation on model from best epoch (best-model.pt)
2023-09-04 18:01:02,699  - metric: "('micro avg', 'f1-score')"
2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,699 Computation:
2023-09-04 18:01:02,699  - compute on device: cuda:0
2023-09-04 18:01:02,699  - embedding storage: none
2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,699 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:02,699 ----------------------------------------------------------------------------------------------------
2023-09-04 18:01:18,094 epoch 1 - iter 147/1476 - loss 2.44376377 - time (sec): 15.39 - samples/sec: 1055.91 - lr: 0.000003 - momentum: 0.000000
2023-09-04 18:01:33,845 epoch 1 - iter 294/1476 - loss 1.52151398 - time (sec): 31.15 - samples/sec: 1050.94 - lr: 0.000006 - momentum: 0.000000
2023-09-04 18:01:49,471 epoch 1 - iter 441/1476 - loss 1.15461415 - time (sec): 46.77 - samples/sec: 1043.42 - lr: 0.000009 - momentum: 0.000000
2023-09-04 18:02:05,099 epoch 1 - iter 588/1476 - loss 0.94850083 - time (sec): 62.40 - samples/sec: 1041.15 - lr: 0.000012 - momentum: 0.000000
2023-09-04 18:02:21,896 epoch 1 - iter 735/1476 - loss 0.82532013 - time (sec): 79.20 - samples/sec: 1036.04 - lr: 0.000015 - momentum: 0.000000
2023-09-04 18:02:36,710 epoch 1 - iter 882/1476 - loss 0.73671035 - time (sec): 94.01 - samples/sec: 1030.80 - lr: 0.000018 - momentum: 0.000000
2023-09-04 18:02:53,060 epoch 1 - iter 1029/1476 - loss 0.66213797 - time (sec): 110.36 - samples/sec: 1036.86 - lr: 0.000021 - momentum: 0.000000
2023-09-04 18:03:09,409 epoch 1 - iter 1176/1476 - loss 0.60019447 - time (sec): 126.71 - samples/sec: 1042.95 - lr: 0.000024 - momentum: 0.000000
2023-09-04 18:03:24,876 epoch 1 - iter 1323/1476 - loss 0.55657173 - time (sec): 142.18 - samples/sec: 1045.22 - lr: 0.000027 - momentum: 0.000000
2023-09-04 18:03:41,637 epoch 1 - iter 1470/1476 - loss 0.51947340 - time (sec): 158.94 - samples/sec: 1043.39 - lr: 0.000030 - momentum: 0.000000
2023-09-04 18:03:42,206 ----------------------------------------------------------------------------------------------------
2023-09-04 18:03:42,207 EPOCH 1 done: loss 0.5185 - lr: 0.000030
2023-09-04 18:03:56,790 DEV : loss 0.15415577590465546 - f1-score (micro avg)  0.6856
2023-09-04 18:03:56,837 saving best model
2023-09-04 18:03:57,328 ----------------------------------------------------------------------------------------------------
2023-09-04 18:04:13,436 epoch 2 - iter 147/1476 - loss 0.13762875 - time (sec): 16.11 - samples/sec: 1041.34 - lr: 0.000030 - momentum: 0.000000
2023-09-04 18:04:29,294 epoch 2 - iter 294/1476 - loss 0.14049549 - time (sec): 31.96 - samples/sec: 1038.45 - lr: 0.000029 - momentum: 0.000000
2023-09-04 18:04:45,489 epoch 2 - iter 441/1476 - loss 0.13824014 - time (sec): 48.16 - samples/sec: 1037.93 - lr: 0.000029 - momentum: 0.000000
2023-09-04 18:05:00,555 epoch 2 - iter 588/1476 - loss 0.13296353 - time (sec): 63.23 - samples/sec: 1035.21 - lr: 0.000029 - momentum: 0.000000
2023-09-04 18:05:16,929 epoch 2 - iter 735/1476 - loss 0.12947844 - time (sec): 79.60 - samples/sec: 1052.53 - lr: 0.000028 - momentum: 0.000000
2023-09-04 18:05:35,682 epoch 2 - iter 882/1476 - loss 0.13359823 - time (sec): 98.35 - samples/sec: 1063.30 - lr: 0.000028 - momentum: 0.000000
2023-09-04 18:05:50,461 epoch 2 - iter 1029/1476 - loss 0.13133894 - time (sec): 113.13 - samples/sec: 1058.87 - lr: 0.000028 - momentum: 0.000000
2023-09-04 18:06:06,506 epoch 2 - iter 1176/1476 - loss 0.13058551 - time (sec): 129.18 - samples/sec: 1059.23 - lr: 0.000027 - momentum: 0.000000
2023-09-04 18:06:20,816 epoch 2 - iter 1323/1476 - loss 0.13091216 - time (sec): 143.49 - samples/sec: 1053.59 - lr: 0.000027 - momentum: 0.000000
2023-09-04 18:06:35,935 epoch 2 - iter 1470/1476 - loss 0.12972873 - time (sec): 158.61 - samples/sec: 1046.77 - lr: 0.000027 - momentum: 0.000000
2023-09-04 18:06:36,455 ----------------------------------------------------------------------------------------------------
2023-09-04 18:06:36,455 EPOCH 2 done: loss 0.1295 - lr: 0.000027
2023-09-04 18:06:54,283 DEV : loss 0.13132880628108978 - f1-score (micro avg)  0.7834
2023-09-04 18:06:54,312 saving best model
2023-09-04 18:06:55,664 ----------------------------------------------------------------------------------------------------
2023-09-04 18:07:12,339 epoch 3 - iter 147/1476 - loss 0.06202239 - time (sec): 16.67 - samples/sec: 1111.70 - lr: 0.000026 - momentum: 0.000000
2023-09-04 18:07:28,604 epoch 3 - iter 294/1476 - loss 0.06649857 - time (sec): 32.94 - samples/sec: 1070.17 - lr: 0.000026 - momentum: 0.000000
2023-09-04 18:07:44,511 epoch 3 - iter 441/1476 - loss 0.06984059 - time (sec): 48.85 - samples/sec: 1065.42 - lr: 0.000026 - momentum: 0.000000
2023-09-04 18:08:01,651 epoch 3 - iter 588/1476 - loss 0.07783875 - time (sec): 65.99 - samples/sec: 1064.77 - lr: 0.000025 - momentum: 0.000000
2023-09-04 18:08:16,965 epoch 3 - iter 735/1476 - loss 0.07932757 - time (sec): 81.30 - samples/sec: 1054.93 - lr: 0.000025 - momentum: 0.000000
2023-09-04 18:08:32,668 epoch 3 - iter 882/1476 - loss 0.07615306 - time (sec): 97.00 - samples/sec: 1049.99 - lr: 0.000025 - momentum: 0.000000
2023-09-04 18:08:48,048 epoch 3 - iter 1029/1476 - loss 0.07522442 - time (sec): 112.38 - samples/sec: 1045.01 - lr: 0.000024 - momentum: 0.000000
2023-09-04 18:09:03,784 epoch 3 - iter 1176/1476 - loss 0.07465154 - time (sec): 128.12 - samples/sec: 1043.48 - lr: 0.000024 - momentum: 0.000000
2023-09-04 18:09:19,691 epoch 3 - iter 1323/1476 - loss 0.07700065 - time (sec): 144.03 - samples/sec: 1041.14 - lr: 0.000024 - momentum: 0.000000
2023-09-04 18:09:34,988 epoch 3 - iter 1470/1476 - loss 0.07972797 - time (sec): 159.32 - samples/sec: 1041.18 - lr: 0.000023 - momentum: 0.000000
2023-09-04 18:09:35,518 ----------------------------------------------------------------------------------------------------
2023-09-04 18:09:35,518 EPOCH 3 done: loss 0.0799 - lr: 0.000023
2023-09-04 18:09:53,055 DEV : loss 0.14578530192375183 - f1-score (micro avg)  0.7994
2023-09-04 18:09:53,083 saving best model
2023-09-04 18:09:54,422 ----------------------------------------------------------------------------------------------------
2023-09-04 18:10:10,140 epoch 4 - iter 147/1476 - loss 0.05746252 - time (sec): 15.72 - samples/sec: 1026.05 - lr: 0.000023 - momentum: 0.000000
2023-09-04 18:10:27,722 epoch 4 - iter 294/1476 - loss 0.06102346 - time (sec): 33.30 - samples/sec: 1071.19 - lr: 0.000023 - momentum: 0.000000
2023-09-04 18:10:43,854 epoch 4 - iter 441/1476 - loss 0.05967365 - time (sec): 49.43 - samples/sec: 1047.86 - lr: 0.000022 - momentum: 0.000000
2023-09-04 18:10:58,746 epoch 4 - iter 588/1476 - loss 0.06001754 - time (sec): 64.32 - samples/sec: 1030.36 - lr: 0.000022 - momentum: 0.000000
2023-09-04 18:11:15,070 epoch 4 - iter 735/1476 - loss 0.05838120 - time (sec): 80.65 - samples/sec: 1034.71 - lr: 0.000022 - momentum: 0.000000
2023-09-04 18:11:30,504 epoch 4 - iter 882/1476 - loss 0.05932716 - time (sec): 96.08 - samples/sec: 1036.16 - lr: 0.000021 - momentum: 0.000000
2023-09-04 18:11:45,730 epoch 4 - iter 1029/1476 - loss 0.05890917 - time (sec): 111.31 - samples/sec: 1029.82 - lr: 0.000021 - momentum: 0.000000
2023-09-04 18:12:01,064 epoch 4 - iter 1176/1476 - loss 0.05796255 - time (sec): 126.64 - samples/sec: 1031.35 - lr: 0.000021 - momentum: 0.000000
2023-09-04 18:12:17,046 epoch 4 - iter 1323/1476 - loss 0.05817651 - time (sec): 142.62 - samples/sec: 1029.12 - lr: 0.000020 - momentum: 0.000000
2023-09-04 18:12:34,542 epoch 4 - iter 1470/1476 - loss 0.05645201 - time (sec): 160.12 - samples/sec: 1035.95 - lr: 0.000020 - momentum: 0.000000
2023-09-04 18:12:35,105 ----------------------------------------------------------------------------------------------------
2023-09-04 18:12:35,105 EPOCH 4 done: loss 0.0566 - lr: 0.000020
2023-09-04 18:12:52,812 DEV : loss 0.18173334002494812 - f1-score (micro avg)  0.8055
2023-09-04 18:12:52,842 saving best model
2023-09-04 18:12:54,191 ----------------------------------------------------------------------------------------------------
2023-09-04 18:13:09,997 epoch 5 - iter 147/1476 - loss 0.05138894 - time (sec): 15.80 - samples/sec: 1064.24 - lr: 0.000020 - momentum: 0.000000
2023-09-04 18:13:24,951 epoch 5 - iter 294/1476 - loss 0.04721485 - time (sec): 30.76 - samples/sec: 1027.23 - lr: 0.000019 - momentum: 0.000000
2023-09-04 18:13:41,035 epoch 5 - iter 441/1476 - loss 0.04141632 - time (sec): 46.84 - samples/sec: 1030.23 - lr: 0.000019 - momentum: 0.000000
2023-09-04 18:13:56,918 epoch 5 - iter 588/1476 - loss 0.03965347 - time (sec): 62.73 - samples/sec: 1034.00 - lr: 0.000019 - momentum: 0.000000
2023-09-04 18:14:13,476 epoch 5 - iter 735/1476 - loss 0.04115344 - time (sec): 79.28 - samples/sec: 1035.68 - lr: 0.000018 - momentum: 0.000000
2023-09-04 18:14:29,425 epoch 5 - iter 882/1476 - loss 0.04010953 - time (sec): 95.23 - samples/sec: 1038.19 - lr: 0.000018 - momentum: 0.000000
2023-09-04 18:14:46,066 epoch 5 - iter 1029/1476 - loss 0.03987999 - time (sec): 111.87 - samples/sec: 1037.38 - lr: 0.000018 - momentum: 0.000000
2023-09-04 18:15:02,228 epoch 5 - iter 1176/1476 - loss 0.04089865 - time (sec): 128.04 - samples/sec: 1036.12 - lr: 0.000017 - momentum: 0.000000
2023-09-04 18:15:18,305 epoch 5 - iter 1323/1476 - loss 0.04058762 - time (sec): 144.11 - samples/sec: 1038.65 - lr: 0.000017 - momentum: 0.000000
2023-09-04 18:15:33,710 epoch 5 - iter 1470/1476 - loss 0.04089062 - time (sec): 159.52 - samples/sec: 1039.41 - lr: 0.000017 - momentum: 0.000000
2023-09-04 18:15:34,350 ----------------------------------------------------------------------------------------------------
2023-09-04 18:15:34,351 EPOCH 5 done: loss 0.0407 - lr: 0.000017
2023-09-04 18:15:52,045 DEV : loss 0.17798171937465668 - f1-score (micro avg)  0.8282
2023-09-04 18:15:52,073 saving best model
2023-09-04 18:15:53,401 ----------------------------------------------------------------------------------------------------
2023-09-04 18:16:09,370 epoch 6 - iter 147/1476 - loss 0.03077746 - time (sec): 15.97 - samples/sec: 1068.35 - lr: 0.000016 - momentum: 0.000000
2023-09-04 18:16:24,765 epoch 6 - iter 294/1476 - loss 0.02870391 - time (sec): 31.36 - samples/sec: 1035.23 - lr: 0.000016 - momentum: 0.000000
2023-09-04 18:16:40,709 epoch 6 - iter 441/1476 - loss 0.02709286 - time (sec): 47.31 - samples/sec: 1033.40 - lr: 0.000016 - momentum: 0.000000
2023-09-04 18:16:56,794 epoch 6 - iter 588/1476 - loss 0.02749007 - time (sec): 63.39 - samples/sec: 1030.11 - lr: 0.000015 - momentum: 0.000000
2023-09-04 18:17:12,164 epoch 6 - iter 735/1476 - loss 0.02642923 - time (sec): 78.76 - samples/sec: 1024.79 - lr: 0.000015 - momentum: 0.000000
2023-09-04 18:17:27,358 epoch 6 - iter 882/1476 - loss 0.02612535 - time (sec): 93.96 - samples/sec: 1022.85 - lr: 0.000015 - momentum: 0.000000
2023-09-04 18:17:43,902 epoch 6 - iter 1029/1476 - loss 0.02662349 - time (sec): 110.50 - samples/sec: 1029.41 - lr: 0.000014 - momentum: 0.000000
2023-09-04 18:17:59,949 epoch 6 - iter 1176/1476 - loss 0.02660086 - time (sec): 126.55 - samples/sec: 1029.02 - lr: 0.000014 - momentum: 0.000000
2023-09-04 18:18:16,060 epoch 6 - iter 1323/1476 - loss 0.02752760 - time (sec): 142.66 - samples/sec: 1028.15 - lr: 0.000014 - momentum: 0.000000
2023-09-04 18:18:32,511 epoch 6 - iter 1470/1476 - loss 0.02784187 - time (sec): 159.11 - samples/sec: 1037.65 - lr: 0.000013 - momentum: 0.000000
2023-09-04 18:18:33,705 ----------------------------------------------------------------------------------------------------
2023-09-04 18:18:33,706 EPOCH 6 done: loss 0.0278 - lr: 0.000013
2023-09-04 18:18:51,348 DEV : loss 0.2169143557548523 - f1-score (micro avg)  0.8137
2023-09-04 18:18:51,377 ----------------------------------------------------------------------------------------------------
2023-09-04 18:19:07,437 epoch 7 - iter 147/1476 - loss 0.02004925 - time (sec): 16.06 - samples/sec: 1087.97 - lr: 0.000013 - momentum: 0.000000
2023-09-04 18:19:25,106 epoch 7 - iter 294/1476 - loss 0.01928499 - time (sec): 33.73 - samples/sec: 1067.37 - lr: 0.000013 - momentum: 0.000000
2023-09-04 18:19:41,853 epoch 7 - iter 441/1476 - loss 0.01863859 - time (sec): 50.47 - samples/sec: 1059.56 - lr: 0.000012 - momentum: 0.000000
2023-09-04 18:19:58,276 epoch 7 - iter 588/1476 - loss 0.02152433 - time (sec): 66.90 - samples/sec: 1068.83 - lr: 0.000012 - momentum: 0.000000
2023-09-04 18:20:12,850 epoch 7 - iter 735/1476 - loss 0.02091597 - time (sec): 81.47 - samples/sec: 1061.81 - lr: 0.000012 - momentum: 0.000000
2023-09-04 18:20:29,285 epoch 7 - iter 882/1476 - loss 0.02099112 - time (sec): 97.91 - samples/sec: 1056.78 - lr: 0.000011 - momentum: 0.000000
2023-09-04 18:20:44,364 epoch 7 - iter 1029/1476 - loss 0.02043229 - time (sec): 112.99 - samples/sec: 1051.36 - lr: 0.000011 - momentum: 0.000000
2023-09-04 18:20:59,863 epoch 7 - iter 1176/1476 - loss 0.01948168 - time (sec): 128.48 - samples/sec: 1046.27 - lr: 0.000011 - momentum: 0.000000
2023-09-04 18:21:15,257 epoch 7 - iter 1323/1476 - loss 0.02009420 - time (sec): 143.88 - samples/sec: 1043.76 - lr: 0.000010 - momentum: 0.000000
2023-09-04 18:21:30,772 epoch 7 - iter 1470/1476 - loss 0.01960339 - time (sec): 159.39 - samples/sec: 1040.65 - lr: 0.000010 - momentum: 0.000000
2023-09-04 18:21:31,414 ----------------------------------------------------------------------------------------------------
2023-09-04 18:21:31,415 EPOCH 7 done: loss 0.0196 - lr: 0.000010
2023-09-04 18:21:49,585 DEV : loss 0.20429323613643646 - f1-score (micro avg)  0.8278
2023-09-04 18:21:49,614 ----------------------------------------------------------------------------------------------------
2023-09-04 18:22:05,738 epoch 8 - iter 147/1476 - loss 0.01227334 - time (sec): 16.12 - samples/sec: 1097.99 - lr: 0.000010 - momentum: 0.000000
2023-09-04 18:22:20,978 epoch 8 - iter 294/1476 - loss 0.00862639 - time (sec): 31.36 - samples/sec: 1054.47 - lr: 0.000009 - momentum: 0.000000
2023-09-04 18:22:38,095 epoch 8 - iter 441/1476 - loss 0.01267560 - time (sec): 48.48 - samples/sec: 1069.95 - lr: 0.000009 - momentum: 0.000000
2023-09-04 18:22:53,899 epoch 8 - iter 588/1476 - loss 0.01122963 - time (sec): 64.28 - samples/sec: 1049.34 - lr: 0.000009 - momentum: 0.000000
2023-09-04 18:23:08,476 epoch 8 - iter 735/1476 - loss 0.01313610 - time (sec): 78.86 - samples/sec: 1037.64 - lr: 0.000008 - momentum: 0.000000
2023-09-04 18:23:25,524 epoch 8 - iter 882/1476 - loss 0.01420155 - time (sec): 95.91 - samples/sec: 1040.54 - lr: 0.000008 - momentum: 0.000000
2023-09-04 18:23:41,088 epoch 8 - iter 1029/1476 - loss 0.01313444 - time (sec): 111.47 - samples/sec: 1040.35 - lr: 0.000008 - momentum: 0.000000
2023-09-04 18:23:56,612 epoch 8 - iter 1176/1476 - loss 0.01285575 - time (sec): 127.00 - samples/sec: 1037.89 - lr: 0.000007 - momentum: 0.000000
2023-09-04 18:24:12,668 epoch 8 - iter 1323/1476 - loss 0.01287226 - time (sec): 143.05 - samples/sec: 1036.18 - lr: 0.000007 - momentum: 0.000000
2023-09-04 18:24:29,072 epoch 8 - iter 1470/1476 - loss 0.01249485 - time (sec): 159.46 - samples/sec: 1040.11 - lr: 0.000007 - momentum: 0.000000
2023-09-04 18:24:29,617 ----------------------------------------------------------------------------------------------------
2023-09-04 18:24:29,617 EPOCH 8 done: loss 0.0125 - lr: 0.000007
2023-09-04 18:24:47,345 DEV : loss 0.21515436470508575 - f1-score (micro avg)  0.8236
2023-09-04 18:24:47,374 ----------------------------------------------------------------------------------------------------
2023-09-04 18:25:03,113 epoch 9 - iter 147/1476 - loss 0.01194312 - time (sec): 15.74 - samples/sec: 1021.80 - lr: 0.000006 - momentum: 0.000000
2023-09-04 18:25:18,626 epoch 9 - iter 294/1476 - loss 0.01113643 - time (sec): 31.25 - samples/sec: 1034.37 - lr: 0.000006 - momentum: 0.000000
2023-09-04 18:25:33,931 epoch 9 - iter 441/1476 - loss 0.00892407 - time (sec): 46.56 - samples/sec: 1010.97 - lr: 0.000006 - momentum: 0.000000
2023-09-04 18:25:50,615 epoch 9 - iter 588/1476 - loss 0.01054983 - time (sec): 63.24 - samples/sec: 1015.46 - lr: 0.000005 - momentum: 0.000000
2023-09-04 18:26:05,989 epoch 9 - iter 735/1476 - loss 0.01020447 - time (sec): 78.61 - samples/sec: 1014.85 - lr: 0.000005 - momentum: 0.000000
2023-09-04 18:26:21,846 epoch 9 - iter 882/1476 - loss 0.00970575 - time (sec): 94.47 - samples/sec: 1016.08 - lr: 0.000005 - momentum: 0.000000
2023-09-04 18:26:38,280 epoch 9 - iter 1029/1476 - loss 0.00972550 - time (sec): 110.90 - samples/sec: 1026.61 - lr: 0.000004 - momentum: 0.000000
2023-09-04 18:26:55,525 epoch 9 - iter 1176/1476 - loss 0.01059971 - time (sec): 128.15 - samples/sec: 1031.92 - lr: 0.000004 - momentum: 0.000000
2023-09-04 18:27:10,728 epoch 9 - iter 1323/1476 - loss 0.01001943 - time (sec): 143.35 - samples/sec: 1029.52 - lr: 0.000004 - momentum: 0.000000
2023-09-04 18:27:26,756 epoch 9 - iter 1470/1476 - loss 0.00955175 - time (sec): 159.38 - samples/sec: 1035.45 - lr: 0.000003 - momentum: 0.000000
2023-09-04 18:27:27,865 ----------------------------------------------------------------------------------------------------
2023-09-04 18:27:27,866 EPOCH 9 done: loss 0.0095 - lr: 0.000003
2023-09-04 18:27:45,558 DEV : loss 0.20868642628192902 - f1-score (micro avg)  0.8292
2023-09-04 18:27:45,587 saving best model
2023-09-04 18:27:46,955 ----------------------------------------------------------------------------------------------------
2023-09-04 18:28:02,137 epoch 10 - iter 147/1476 - loss 0.00101060 - time (sec): 15.18 - samples/sec: 1010.93 - lr: 0.000003 - momentum: 0.000000
2023-09-04 18:28:18,974 epoch 10 - iter 294/1476 - loss 0.00390884 - time (sec): 32.02 - samples/sec: 1028.47 - lr: 0.000003 - momentum: 0.000000
2023-09-04 18:28:35,317 epoch 10 - iter 441/1476 - loss 0.00447299 - time (sec): 48.36 - samples/sec: 1024.21 - lr: 0.000002 - momentum: 0.000000
2023-09-04 18:28:51,711 epoch 10 - iter 588/1476 - loss 0.00417455 - time (sec): 64.75 - samples/sec: 1030.00 - lr: 0.000002 - momentum: 0.000000
2023-09-04 18:29:08,401 epoch 10 - iter 735/1476 - loss 0.00470003 - time (sec): 81.44 - samples/sec: 1039.68 - lr: 0.000002 - momentum: 0.000000
2023-09-04 18:29:23,342 epoch 10 - iter 882/1476 - loss 0.00479490 - time (sec): 96.38 - samples/sec: 1041.02 - lr: 0.000001 - momentum: 0.000000
2023-09-04 18:29:38,135 epoch 10 - iter 1029/1476 - loss 0.00653311 - time (sec): 111.18 - samples/sec: 1041.64 - lr: 0.000001 - momentum: 0.000000
2023-09-04 18:29:54,398 epoch 10 - iter 1176/1476 - loss 0.00644572 - time (sec): 127.44 - samples/sec: 1038.01 - lr: 0.000001 - momentum: 0.000000
2023-09-04 18:30:10,831 epoch 10 - iter 1323/1476 - loss 0.00614733 - time (sec): 143.87 - samples/sec: 1044.88 - lr: 0.000000 - momentum: 0.000000
2023-09-04 18:30:26,129 epoch 10 - iter 1470/1476 - loss 0.00651115 - time (sec): 159.17 - samples/sec: 1041.46 - lr: 0.000000 - momentum: 0.000000
2023-09-04 18:30:26,732 ----------------------------------------------------------------------------------------------------
2023-09-04 18:30:26,732 EPOCH 10 done: loss 0.0065 - lr: 0.000000
2023-09-04 18:30:44,728 DEV : loss 0.22322718799114227 - f1-score (micro avg)  0.8291
2023-09-04 18:30:45,239 ----------------------------------------------------------------------------------------------------
2023-09-04 18:30:45,241 Loading model from best epoch ...
2023-09-04 18:30:47,124 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-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod
2023-09-04 18:31:01,874 
Results:
- F-score (micro) 0.7899
- F-score (macro) 0.6984
- Accuracy 0.6764

By class:
              precision    recall  f1-score   support

         loc     0.8319    0.8765    0.8536       858
        pers     0.7709    0.7896    0.7801       537
         org     0.5034    0.5606    0.5305       132
        time     0.5645    0.6481    0.6034        54
        prod     0.7636    0.6885    0.7241        61

   micro avg     0.7724    0.8082    0.7899      1642
   macro avg     0.6869    0.7127    0.6984      1642
weighted avg     0.7742    0.8082    0.7905      1642

2023-09-04 18:31:01,875 ----------------------------------------------------------------------------------------------------