2023-09-03 22:03:37,162 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,163 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 22:03:37,163 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,163 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 22:03:37,163 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,163 Train:  3575 sentences
2023-09-03 22:03:37,163         (train_with_dev=False, train_with_test=False)
2023-09-03 22:03:37,163 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,163 Training Params:
2023-09-03 22:03:37,163  - learning_rate: "5e-05" 
2023-09-03 22:03:37,163  - mini_batch_size: "8"
2023-09-03 22:03:37,163  - max_epochs: "10"
2023-09-03 22:03:37,163  - shuffle: "True"
2023-09-03 22:03:37,163 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,163 Plugins:
2023-09-03 22:03:37,163  - LinearScheduler | warmup_fraction: '0.1'
2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,164 Final evaluation on model from best epoch (best-model.pt)
2023-09-03 22:03:37,164  - metric: "('micro avg', 'f1-score')"
2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,164 Computation:
2023-09-03 22:03:37,164  - compute on device: cuda:0
2023-09-03 22:03:37,164  - embedding storage: none
2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,164 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:37,164 ----------------------------------------------------------------------------------------------------
2023-09-03 22:03:44,616 epoch 1 - iter 44/447 - loss 3.00461982 - time (sec): 7.45 - samples/sec: 1175.42 - lr: 0.000005 - momentum: 0.000000
2023-09-03 22:03:52,433 epoch 1 - iter 88/447 - loss 1.96254236 - time (sec): 15.27 - samples/sec: 1163.70 - lr: 0.000010 - momentum: 0.000000
2023-09-03 22:03:59,439 epoch 1 - iter 132/447 - loss 1.50254678 - time (sec): 22.27 - samples/sec: 1154.90 - lr: 0.000015 - momentum: 0.000000
2023-09-03 22:04:07,711 epoch 1 - iter 176/447 - loss 1.21259910 - time (sec): 30.55 - samples/sec: 1129.89 - lr: 0.000020 - momentum: 0.000000
2023-09-03 22:04:14,819 epoch 1 - iter 220/447 - loss 1.03584482 - time (sec): 37.65 - samples/sec: 1134.34 - lr: 0.000024 - momentum: 0.000000
2023-09-03 22:04:22,049 epoch 1 - iter 264/447 - loss 0.91897569 - time (sec): 44.88 - samples/sec: 1134.71 - lr: 0.000029 - momentum: 0.000000
2023-09-03 22:04:29,426 epoch 1 - iter 308/447 - loss 0.83278018 - time (sec): 52.26 - samples/sec: 1133.34 - lr: 0.000034 - momentum: 0.000000
2023-09-03 22:04:36,847 epoch 1 - iter 352/447 - loss 0.75967852 - time (sec): 59.68 - samples/sec: 1133.18 - lr: 0.000039 - momentum: 0.000000
2023-09-03 22:04:43,829 epoch 1 - iter 396/447 - loss 0.69877256 - time (sec): 66.66 - samples/sec: 1134.73 - lr: 0.000044 - momentum: 0.000000
2023-09-03 22:04:53,046 epoch 1 - iter 440/447 - loss 0.64902061 - time (sec): 75.88 - samples/sec: 1123.32 - lr: 0.000049 - momentum: 0.000000
2023-09-03 22:04:54,158 ----------------------------------------------------------------------------------------------------
2023-09-03 22:04:54,158 EPOCH 1 done: loss 0.6427 - lr: 0.000049
2023-09-03 22:05:05,059 DEV : loss 0.17456364631652832 - f1-score (micro avg)  0.6213
2023-09-03 22:05:05,086 saving best model
2023-09-03 22:05:05,541 ----------------------------------------------------------------------------------------------------
2023-09-03 22:05:13,911 epoch 2 - iter 44/447 - loss 0.19617148 - time (sec): 8.37 - samples/sec: 1070.23 - lr: 0.000049 - momentum: 0.000000
2023-09-03 22:05:22,808 epoch 2 - iter 88/447 - loss 0.17967532 - time (sec): 17.27 - samples/sec: 1071.74 - lr: 0.000049 - momentum: 0.000000
2023-09-03 22:05:29,712 epoch 2 - iter 132/447 - loss 0.16966610 - time (sec): 24.17 - samples/sec: 1083.68 - lr: 0.000048 - momentum: 0.000000
2023-09-03 22:05:36,876 epoch 2 - iter 176/447 - loss 0.16937622 - time (sec): 31.33 - samples/sec: 1100.26 - lr: 0.000048 - momentum: 0.000000
2023-09-03 22:05:44,718 epoch 2 - iter 220/447 - loss 0.16592335 - time (sec): 39.18 - samples/sec: 1099.72 - lr: 0.000047 - momentum: 0.000000
2023-09-03 22:05:51,731 epoch 2 - iter 264/447 - loss 0.15644282 - time (sec): 46.19 - samples/sec: 1120.15 - lr: 0.000047 - momentum: 0.000000
2023-09-03 22:05:58,701 epoch 2 - iter 308/447 - loss 0.15370065 - time (sec): 53.16 - samples/sec: 1123.35 - lr: 0.000046 - momentum: 0.000000
2023-09-03 22:06:05,322 epoch 2 - iter 352/447 - loss 0.15345269 - time (sec): 59.78 - samples/sec: 1133.48 - lr: 0.000046 - momentum: 0.000000
2023-09-03 22:06:13,741 epoch 2 - iter 396/447 - loss 0.14938778 - time (sec): 68.20 - samples/sec: 1126.58 - lr: 0.000045 - momentum: 0.000000
2023-09-03 22:06:20,624 epoch 2 - iter 440/447 - loss 0.14932727 - time (sec): 75.08 - samples/sec: 1135.40 - lr: 0.000045 - momentum: 0.000000
2023-09-03 22:06:21,930 ----------------------------------------------------------------------------------------------------
2023-09-03 22:06:21,930 EPOCH 2 done: loss 0.1485 - lr: 0.000045
2023-09-03 22:06:34,648 DEV : loss 0.1355467289686203 - f1-score (micro avg)  0.6927
2023-09-03 22:06:34,674 saving best model
2023-09-03 22:06:35,991 ----------------------------------------------------------------------------------------------------
2023-09-03 22:06:43,373 epoch 3 - iter 44/447 - loss 0.11084828 - time (sec): 7.38 - samples/sec: 1159.33 - lr: 0.000044 - momentum: 0.000000
2023-09-03 22:06:51,829 epoch 3 - iter 88/447 - loss 0.09617220 - time (sec): 15.84 - samples/sec: 1127.10 - lr: 0.000043 - momentum: 0.000000
2023-09-03 22:06:59,166 epoch 3 - iter 132/447 - loss 0.08541625 - time (sec): 23.17 - samples/sec: 1131.09 - lr: 0.000043 - momentum: 0.000000
2023-09-03 22:07:06,261 epoch 3 - iter 176/447 - loss 0.08791110 - time (sec): 30.27 - samples/sec: 1138.58 - lr: 0.000042 - momentum: 0.000000
2023-09-03 22:07:12,844 epoch 3 - iter 220/447 - loss 0.08725166 - time (sec): 36.85 - samples/sec: 1141.37 - lr: 0.000042 - momentum: 0.000000
2023-09-03 22:07:20,076 epoch 3 - iter 264/447 - loss 0.08591226 - time (sec): 44.08 - samples/sec: 1146.82 - lr: 0.000041 - momentum: 0.000000
2023-09-03 22:07:27,072 epoch 3 - iter 308/447 - loss 0.08861516 - time (sec): 51.08 - samples/sec: 1149.11 - lr: 0.000041 - momentum: 0.000000
2023-09-03 22:07:34,653 epoch 3 - iter 352/447 - loss 0.08519341 - time (sec): 58.66 - samples/sec: 1151.20 - lr: 0.000040 - momentum: 0.000000
2023-09-03 22:07:41,422 epoch 3 - iter 396/447 - loss 0.08896865 - time (sec): 65.43 - samples/sec: 1159.03 - lr: 0.000040 - momentum: 0.000000
2023-09-03 22:07:49,673 epoch 3 - iter 440/447 - loss 0.08973667 - time (sec): 73.68 - samples/sec: 1157.81 - lr: 0.000039 - momentum: 0.000000
2023-09-03 22:07:50,684 ----------------------------------------------------------------------------------------------------
2023-09-03 22:07:50,684 EPOCH 3 done: loss 0.0893 - lr: 0.000039
2023-09-03 22:08:03,119 DEV : loss 0.13895265758037567 - f1-score (micro avg)  0.7329
2023-09-03 22:08:03,146 saving best model
2023-09-03 22:08:04,473 ----------------------------------------------------------------------------------------------------
2023-09-03 22:08:11,591 epoch 4 - iter 44/447 - loss 0.04648237 - time (sec): 7.12 - samples/sec: 1186.01 - lr: 0.000038 - momentum: 0.000000
2023-09-03 22:08:18,309 epoch 4 - iter 88/447 - loss 0.05241541 - time (sec): 13.84 - samples/sec: 1186.27 - lr: 0.000038 - momentum: 0.000000
2023-09-03 22:08:25,668 epoch 4 - iter 132/447 - loss 0.04548737 - time (sec): 21.19 - samples/sec: 1178.24 - lr: 0.000037 - momentum: 0.000000
2023-09-03 22:08:32,467 epoch 4 - iter 176/447 - loss 0.04325888 - time (sec): 27.99 - samples/sec: 1190.52 - lr: 0.000037 - momentum: 0.000000
2023-09-03 22:08:42,149 epoch 4 - iter 220/447 - loss 0.04812558 - time (sec): 37.68 - samples/sec: 1151.18 - lr: 0.000036 - momentum: 0.000000
2023-09-03 22:08:49,472 epoch 4 - iter 264/447 - loss 0.04853272 - time (sec): 45.00 - samples/sec: 1154.30 - lr: 0.000036 - momentum: 0.000000
2023-09-03 22:08:55,986 epoch 4 - iter 308/447 - loss 0.04945678 - time (sec): 51.51 - samples/sec: 1160.71 - lr: 0.000035 - momentum: 0.000000
2023-09-03 22:09:03,030 epoch 4 - iter 352/447 - loss 0.04862237 - time (sec): 58.56 - samples/sec: 1158.25 - lr: 0.000035 - momentum: 0.000000
2023-09-03 22:09:11,999 epoch 4 - iter 396/447 - loss 0.04969455 - time (sec): 67.52 - samples/sec: 1144.81 - lr: 0.000034 - momentum: 0.000000
2023-09-03 22:09:19,277 epoch 4 - iter 440/447 - loss 0.05061967 - time (sec): 74.80 - samples/sec: 1139.39 - lr: 0.000033 - momentum: 0.000000
2023-09-03 22:09:20,415 ----------------------------------------------------------------------------------------------------
2023-09-03 22:09:20,415 EPOCH 4 done: loss 0.0516 - lr: 0.000033
2023-09-03 22:09:33,207 DEV : loss 0.15090885758399963 - f1-score (micro avg)  0.7371
2023-09-03 22:09:33,233 saving best model
2023-09-03 22:09:34,589 ----------------------------------------------------------------------------------------------------
2023-09-03 22:09:42,646 epoch 5 - iter 44/447 - loss 0.03586095 - time (sec): 8.06 - samples/sec: 1113.62 - lr: 0.000033 - momentum: 0.000000
2023-09-03 22:09:50,282 epoch 5 - iter 88/447 - loss 0.03374525 - time (sec): 15.69 - samples/sec: 1100.55 - lr: 0.000032 - momentum: 0.000000
2023-09-03 22:09:57,990 epoch 5 - iter 132/447 - loss 0.03495845 - time (sec): 23.40 - samples/sec: 1116.01 - lr: 0.000032 - momentum: 0.000000
2023-09-03 22:10:05,484 epoch 5 - iter 176/447 - loss 0.03476096 - time (sec): 30.89 - samples/sec: 1122.55 - lr: 0.000031 - momentum: 0.000000
2023-09-03 22:10:12,600 epoch 5 - iter 220/447 - loss 0.03837830 - time (sec): 38.01 - samples/sec: 1121.96 - lr: 0.000031 - momentum: 0.000000
2023-09-03 22:10:20,517 epoch 5 - iter 264/447 - loss 0.03833497 - time (sec): 45.93 - samples/sec: 1116.17 - lr: 0.000030 - momentum: 0.000000
2023-09-03 22:10:29,701 epoch 5 - iter 308/447 - loss 0.03847519 - time (sec): 55.11 - samples/sec: 1099.20 - lr: 0.000030 - momentum: 0.000000
2023-09-03 22:10:36,611 epoch 5 - iter 352/447 - loss 0.03810102 - time (sec): 62.02 - samples/sec: 1105.93 - lr: 0.000029 - momentum: 0.000000
2023-09-03 22:10:44,324 epoch 5 - iter 396/447 - loss 0.03738290 - time (sec): 69.73 - samples/sec: 1100.88 - lr: 0.000028 - momentum: 0.000000
2023-09-03 22:10:52,171 epoch 5 - iter 440/447 - loss 0.03626288 - time (sec): 77.58 - samples/sec: 1100.48 - lr: 0.000028 - momentum: 0.000000
2023-09-03 22:10:53,227 ----------------------------------------------------------------------------------------------------
2023-09-03 22:10:53,227 EPOCH 5 done: loss 0.0358 - lr: 0.000028
2023-09-03 22:11:06,510 DEV : loss 0.18703238666057587 - f1-score (micro avg)  0.7673
2023-09-03 22:11:06,536 saving best model
2023-09-03 22:11:07,864 ----------------------------------------------------------------------------------------------------
2023-09-03 22:11:15,663 epoch 6 - iter 44/447 - loss 0.01850349 - time (sec): 7.80 - samples/sec: 1103.69 - lr: 0.000027 - momentum: 0.000000
2023-09-03 22:11:23,891 epoch 6 - iter 88/447 - loss 0.01852900 - time (sec): 16.03 - samples/sec: 1099.21 - lr: 0.000027 - momentum: 0.000000
2023-09-03 22:11:31,268 epoch 6 - iter 132/447 - loss 0.01746134 - time (sec): 23.40 - samples/sec: 1109.71 - lr: 0.000026 - momentum: 0.000000
2023-09-03 22:11:40,403 epoch 6 - iter 176/447 - loss 0.01744129 - time (sec): 32.54 - samples/sec: 1101.18 - lr: 0.000026 - momentum: 0.000000
2023-09-03 22:11:47,974 epoch 6 - iter 220/447 - loss 0.01967293 - time (sec): 40.11 - samples/sec: 1081.05 - lr: 0.000025 - momentum: 0.000000
2023-09-03 22:11:55,216 epoch 6 - iter 264/447 - loss 0.01841148 - time (sec): 47.35 - samples/sec: 1087.19 - lr: 0.000025 - momentum: 0.000000
2023-09-03 22:12:03,093 epoch 6 - iter 308/447 - loss 0.01855352 - time (sec): 55.23 - samples/sec: 1085.87 - lr: 0.000024 - momentum: 0.000000
2023-09-03 22:12:10,507 epoch 6 - iter 352/447 - loss 0.02055176 - time (sec): 62.64 - samples/sec: 1086.07 - lr: 0.000023 - momentum: 0.000000
2023-09-03 22:12:18,228 epoch 6 - iter 396/447 - loss 0.02136484 - time (sec): 70.36 - samples/sec: 1093.17 - lr: 0.000023 - momentum: 0.000000
2023-09-03 22:12:26,017 epoch 6 - iter 440/447 - loss 0.02271157 - time (sec): 78.15 - samples/sec: 1091.54 - lr: 0.000022 - momentum: 0.000000
2023-09-03 22:12:27,092 ----------------------------------------------------------------------------------------------------
2023-09-03 22:12:27,092 EPOCH 6 done: loss 0.0226 - lr: 0.000022
2023-09-03 22:12:40,208 DEV : loss 0.2095835655927658 - f1-score (micro avg)  0.775
2023-09-03 22:12:40,235 saving best model
2023-09-03 22:12:41,848 ----------------------------------------------------------------------------------------------------
2023-09-03 22:12:51,322 epoch 7 - iter 44/447 - loss 0.02179964 - time (sec): 9.47 - samples/sec: 1048.43 - lr: 0.000022 - momentum: 0.000000
2023-09-03 22:12:58,904 epoch 7 - iter 88/447 - loss 0.01967603 - time (sec): 17.05 - samples/sec: 1046.02 - lr: 0.000021 - momentum: 0.000000
2023-09-03 22:13:06,822 epoch 7 - iter 132/447 - loss 0.01671162 - time (sec): 24.97 - samples/sec: 1064.77 - lr: 0.000021 - momentum: 0.000000
2023-09-03 22:13:14,693 epoch 7 - iter 176/447 - loss 0.01631688 - time (sec): 32.84 - samples/sec: 1078.88 - lr: 0.000020 - momentum: 0.000000
2023-09-03 22:13:22,185 epoch 7 - iter 220/447 - loss 0.01540574 - time (sec): 40.34 - samples/sec: 1089.38 - lr: 0.000020 - momentum: 0.000000
2023-09-03 22:13:29,431 epoch 7 - iter 264/447 - loss 0.01610182 - time (sec): 47.58 - samples/sec: 1085.33 - lr: 0.000019 - momentum: 0.000000
2023-09-03 22:13:36,929 epoch 7 - iter 308/447 - loss 0.01612637 - time (sec): 55.08 - samples/sec: 1091.99 - lr: 0.000018 - momentum: 0.000000
2023-09-03 22:13:44,464 epoch 7 - iter 352/447 - loss 0.01597313 - time (sec): 62.61 - samples/sec: 1091.97 - lr: 0.000018 - momentum: 0.000000
2023-09-03 22:13:51,503 epoch 7 - iter 396/447 - loss 0.01617994 - time (sec): 69.65 - samples/sec: 1096.51 - lr: 0.000017 - momentum: 0.000000
2023-09-03 22:13:59,075 epoch 7 - iter 440/447 - loss 0.01580620 - time (sec): 77.23 - samples/sec: 1106.63 - lr: 0.000017 - momentum: 0.000000
2023-09-03 22:14:00,041 ----------------------------------------------------------------------------------------------------
2023-09-03 22:14:00,041 EPOCH 7 done: loss 0.0157 - lr: 0.000017
2023-09-03 22:14:12,985 DEV : loss 0.21346184611320496 - f1-score (micro avg)  0.7756
2023-09-03 22:14:13,012 saving best model
2023-09-03 22:14:14,349 ----------------------------------------------------------------------------------------------------
2023-09-03 22:14:21,624 epoch 8 - iter 44/447 - loss 0.00559374 - time (sec): 7.27 - samples/sec: 1181.34 - lr: 0.000016 - momentum: 0.000000
2023-09-03 22:14:29,176 epoch 8 - iter 88/447 - loss 0.00569671 - time (sec): 14.83 - samples/sec: 1155.86 - lr: 0.000016 - momentum: 0.000000
2023-09-03 22:14:36,035 epoch 8 - iter 132/447 - loss 0.01018177 - time (sec): 21.68 - samples/sec: 1169.06 - lr: 0.000015 - momentum: 0.000000
2023-09-03 22:14:43,018 epoch 8 - iter 176/447 - loss 0.01136583 - time (sec): 28.67 - samples/sec: 1168.77 - lr: 0.000015 - momentum: 0.000000
2023-09-03 22:14:50,415 epoch 8 - iter 220/447 - loss 0.01046024 - time (sec): 36.06 - samples/sec: 1158.72 - lr: 0.000014 - momentum: 0.000000
2023-09-03 22:14:57,393 epoch 8 - iter 264/447 - loss 0.00983952 - time (sec): 43.04 - samples/sec: 1166.38 - lr: 0.000013 - momentum: 0.000000
2023-09-03 22:15:04,599 epoch 8 - iter 308/447 - loss 0.00938640 - time (sec): 50.25 - samples/sec: 1163.21 - lr: 0.000013 - momentum: 0.000000
2023-09-03 22:15:13,018 epoch 8 - iter 352/447 - loss 0.01105031 - time (sec): 58.67 - samples/sec: 1152.82 - lr: 0.000012 - momentum: 0.000000
2023-09-03 22:15:21,211 epoch 8 - iter 396/447 - loss 0.01042212 - time (sec): 66.86 - samples/sec: 1147.29 - lr: 0.000012 - momentum: 0.000000
2023-09-03 22:15:28,131 epoch 8 - iter 440/447 - loss 0.01008377 - time (sec): 73.78 - samples/sec: 1153.04 - lr: 0.000011 - momentum: 0.000000
2023-09-03 22:15:29,423 ----------------------------------------------------------------------------------------------------
2023-09-03 22:15:29,423 EPOCH 8 done: loss 0.0103 - lr: 0.000011
2023-09-03 22:15:42,125 DEV : loss 0.23662017285823822 - f1-score (micro avg)  0.7796
2023-09-03 22:15:42,152 saving best model
2023-09-03 22:15:43,477 ----------------------------------------------------------------------------------------------------
2023-09-03 22:15:50,431 epoch 9 - iter 44/447 - loss 0.00145890 - time (sec): 6.95 - samples/sec: 1193.03 - lr: 0.000011 - momentum: 0.000000
2023-09-03 22:15:58,205 epoch 9 - iter 88/447 - loss 0.00495495 - time (sec): 14.73 - samples/sec: 1152.95 - lr: 0.000010 - momentum: 0.000000
2023-09-03 22:16:04,837 epoch 9 - iter 132/447 - loss 0.00690696 - time (sec): 21.36 - samples/sec: 1166.87 - lr: 0.000010 - momentum: 0.000000
2023-09-03 22:16:12,174 epoch 9 - iter 176/447 - loss 0.00870116 - time (sec): 28.70 - samples/sec: 1159.17 - lr: 0.000009 - momentum: 0.000000
2023-09-03 22:16:20,096 epoch 9 - iter 220/447 - loss 0.00803870 - time (sec): 36.62 - samples/sec: 1148.72 - lr: 0.000008 - momentum: 0.000000
2023-09-03 22:16:27,725 epoch 9 - iter 264/447 - loss 0.00678798 - time (sec): 44.25 - samples/sec: 1140.18 - lr: 0.000008 - momentum: 0.000000
2023-09-03 22:16:34,699 epoch 9 - iter 308/447 - loss 0.00678808 - time (sec): 51.22 - samples/sec: 1151.45 - lr: 0.000007 - momentum: 0.000000
2023-09-03 22:16:43,889 epoch 9 - iter 352/447 - loss 0.00678058 - time (sec): 60.41 - samples/sec: 1143.77 - lr: 0.000007 - momentum: 0.000000
2023-09-03 22:16:51,250 epoch 9 - iter 396/447 - loss 0.00610103 - time (sec): 67.77 - samples/sec: 1141.24 - lr: 0.000006 - momentum: 0.000000
2023-09-03 22:16:58,407 epoch 9 - iter 440/447 - loss 0.00581456 - time (sec): 74.93 - samples/sec: 1140.58 - lr: 0.000006 - momentum: 0.000000
2023-09-03 22:16:59,353 ----------------------------------------------------------------------------------------------------
2023-09-03 22:16:59,354 EPOCH 9 done: loss 0.0058 - lr: 0.000006
2023-09-03 22:17:12,218 DEV : loss 0.23721420764923096 - f1-score (micro avg)  0.7917
2023-09-03 22:17:12,245 saving best model
2023-09-03 22:17:13,563 ----------------------------------------------------------------------------------------------------
2023-09-03 22:17:22,436 epoch 10 - iter 44/447 - loss 0.00717064 - time (sec): 8.87 - samples/sec: 1114.23 - lr: 0.000005 - momentum: 0.000000
2023-09-03 22:17:30,722 epoch 10 - iter 88/447 - loss 0.00593374 - time (sec): 17.16 - samples/sec: 1081.01 - lr: 0.000005 - momentum: 0.000000
2023-09-03 22:17:38,148 epoch 10 - iter 132/447 - loss 0.00468114 - time (sec): 24.58 - samples/sec: 1090.66 - lr: 0.000004 - momentum: 0.000000
2023-09-03 22:17:45,211 epoch 10 - iter 176/447 - loss 0.00470797 - time (sec): 31.65 - samples/sec: 1099.93 - lr: 0.000003 - momentum: 0.000000
2023-09-03 22:17:52,787 epoch 10 - iter 220/447 - loss 0.00429535 - time (sec): 39.22 - samples/sec: 1104.04 - lr: 0.000003 - momentum: 0.000000
2023-09-03 22:17:59,720 epoch 10 - iter 264/447 - loss 0.00404627 - time (sec): 46.16 - samples/sec: 1111.62 - lr: 0.000002 - momentum: 0.000000
2023-09-03 22:18:07,149 epoch 10 - iter 308/447 - loss 0.00420055 - time (sec): 53.58 - samples/sec: 1110.36 - lr: 0.000002 - momentum: 0.000000
2023-09-03 22:18:15,187 epoch 10 - iter 352/447 - loss 0.00415827 - time (sec): 61.62 - samples/sec: 1105.23 - lr: 0.000001 - momentum: 0.000000
2023-09-03 22:18:22,241 epoch 10 - iter 396/447 - loss 0.00376964 - time (sec): 68.68 - samples/sec: 1113.03 - lr: 0.000001 - momentum: 0.000000
2023-09-03 22:18:30,062 epoch 10 - iter 440/447 - loss 0.00369437 - time (sec): 76.50 - samples/sec: 1118.14 - lr: 0.000000 - momentum: 0.000000
2023-09-03 22:18:31,120 ----------------------------------------------------------------------------------------------------
2023-09-03 22:18:31,121 EPOCH 10 done: loss 0.0037 - lr: 0.000000
2023-09-03 22:18:44,578 DEV : loss 0.23381954431533813 - f1-score (micro avg)  0.7938
2023-09-03 22:18:44,605 saving best model
2023-09-03 22:18:46,420 ----------------------------------------------------------------------------------------------------
2023-09-03 22:18:46,422 Loading model from best epoch ...
2023-09-03 22:18:48,216 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 22:18:58,912 
Results:
- F-score (micro) 0.7375
- F-score (macro) 0.6636
- Accuracy 0.6048

By class:
              precision    recall  f1-score   support

         loc     0.8358    0.8540    0.8448       596
        pers     0.6085    0.7327    0.6649       333
         org     0.5500    0.5000    0.5238       132
        prod     0.6200    0.4697    0.5345        66
        time     0.7091    0.7959    0.7500        49

   micro avg     0.7198    0.7560    0.7375      1176
   macro avg     0.6647    0.6705    0.6636      1176
weighted avg     0.7220    0.7560    0.7365      1176

2023-09-03 22:18:58,912 ----------------------------------------------------------------------------------------------------