2023-10-17 16:32:46,824 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,826 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): ElectraModel(
      (embeddings): ElectraEmbeddings(
        (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): ElectraEncoder(
        (layer): ModuleList(
          (0-11): 12 x ElectraLayer(
            (attention): ElectraAttention(
              (self): ElectraSelfAttention(
                (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): ElectraSelfOutput(
                (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): ElectraIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): ElectraOutput(
              (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)
            )
          )
        )
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-17 16:32:46,826 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,826 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-10-17 16:32:46,826 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,826 Train:  3575 sentences
2023-10-17 16:32:46,827         (train_with_dev=False, train_with_test=False)
2023-10-17 16:32:46,827 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,827 Training Params:
2023-10-17 16:32:46,827  - learning_rate: "5e-05" 
2023-10-17 16:32:46,827  - mini_batch_size: "4"
2023-10-17 16:32:46,827  - max_epochs: "10"
2023-10-17 16:32:46,827  - shuffle: "True"
2023-10-17 16:32:46,827 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,827 Plugins:
2023-10-17 16:32:46,827  - TensorboardLogger
2023-10-17 16:32:46,827  - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 16:32:46,827 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,827 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 16:32:46,827  - metric: "('micro avg', 'f1-score')"
2023-10-17 16:32:46,827 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,828 Computation:
2023-10-17 16:32:46,828  - compute on device: cuda:0
2023-10-17 16:32:46,828  - embedding storage: none
2023-10-17 16:32:46,828 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,828 Model training base path: "hmbench-hipe2020/de-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-17 16:32:46,828 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,828 ----------------------------------------------------------------------------------------------------
2023-10-17 16:32:46,828 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 16:32:53,873 epoch 1 - iter 89/894 - loss 3.11410852 - time (sec): 7.04 - samples/sec: 1268.20 - lr: 0.000005 - momentum: 0.000000
2023-10-17 16:33:00,942 epoch 1 - iter 178/894 - loss 1.92954730 - time (sec): 14.11 - samples/sec: 1229.02 - lr: 0.000010 - momentum: 0.000000
2023-10-17 16:33:08,109 epoch 1 - iter 267/894 - loss 1.46044506 - time (sec): 21.28 - samples/sec: 1176.16 - lr: 0.000015 - momentum: 0.000000
2023-10-17 16:33:15,623 epoch 1 - iter 356/894 - loss 1.15857887 - time (sec): 28.79 - samples/sec: 1202.71 - lr: 0.000020 - momentum: 0.000000
2023-10-17 16:33:22,680 epoch 1 - iter 445/894 - loss 0.99646697 - time (sec): 35.85 - samples/sec: 1201.91 - lr: 0.000025 - momentum: 0.000000
2023-10-17 16:33:29,890 epoch 1 - iter 534/894 - loss 0.88147494 - time (sec): 43.06 - samples/sec: 1200.57 - lr: 0.000030 - momentum: 0.000000
2023-10-17 16:33:36,882 epoch 1 - iter 623/894 - loss 0.79966928 - time (sec): 50.05 - samples/sec: 1191.36 - lr: 0.000035 - momentum: 0.000000
2023-10-17 16:33:43,748 epoch 1 - iter 712/894 - loss 0.73134493 - time (sec): 56.92 - samples/sec: 1203.05 - lr: 0.000040 - momentum: 0.000000
2023-10-17 16:33:50,870 epoch 1 - iter 801/894 - loss 0.67281431 - time (sec): 64.04 - samples/sec: 1216.42 - lr: 0.000045 - momentum: 0.000000
2023-10-17 16:33:57,724 epoch 1 - iter 890/894 - loss 0.62977212 - time (sec): 70.89 - samples/sec: 1216.64 - lr: 0.000050 - momentum: 0.000000
2023-10-17 16:33:58,031 ----------------------------------------------------------------------------------------------------
2023-10-17 16:33:58,032 EPOCH 1 done: loss 0.6287 - lr: 0.000050
2023-10-17 16:34:04,362 DEV : loss 0.20323888957500458 - f1-score (micro avg)  0.6421
2023-10-17 16:34:04,418 saving best model
2023-10-17 16:34:04,952 ----------------------------------------------------------------------------------------------------
2023-10-17 16:34:11,744 epoch 2 - iter 89/894 - loss 0.16949173 - time (sec): 6.79 - samples/sec: 1248.93 - lr: 0.000049 - momentum: 0.000000
2023-10-17 16:34:19,004 epoch 2 - iter 178/894 - loss 0.17515983 - time (sec): 14.05 - samples/sec: 1281.61 - lr: 0.000049 - momentum: 0.000000
2023-10-17 16:34:25,969 epoch 2 - iter 267/894 - loss 0.17426985 - time (sec): 21.01 - samples/sec: 1246.46 - lr: 0.000048 - momentum: 0.000000
2023-10-17 16:34:33,351 epoch 2 - iter 356/894 - loss 0.17013608 - time (sec): 28.40 - samples/sec: 1246.77 - lr: 0.000048 - momentum: 0.000000
2023-10-17 16:34:40,525 epoch 2 - iter 445/894 - loss 0.16745688 - time (sec): 35.57 - samples/sec: 1239.00 - lr: 0.000047 - momentum: 0.000000
2023-10-17 16:34:47,700 epoch 2 - iter 534/894 - loss 0.16144297 - time (sec): 42.75 - samples/sec: 1217.63 - lr: 0.000047 - momentum: 0.000000
2023-10-17 16:34:54,935 epoch 2 - iter 623/894 - loss 0.15623398 - time (sec): 49.98 - samples/sec: 1222.12 - lr: 0.000046 - momentum: 0.000000
2023-10-17 16:35:02,179 epoch 2 - iter 712/894 - loss 0.15158756 - time (sec): 57.23 - samples/sec: 1228.90 - lr: 0.000046 - momentum: 0.000000
2023-10-17 16:35:09,277 epoch 2 - iter 801/894 - loss 0.15137973 - time (sec): 64.32 - samples/sec: 1219.42 - lr: 0.000045 - momentum: 0.000000
2023-10-17 16:35:16,421 epoch 2 - iter 890/894 - loss 0.15238150 - time (sec): 71.47 - samples/sec: 1207.65 - lr: 0.000044 - momentum: 0.000000
2023-10-17 16:35:16,734 ----------------------------------------------------------------------------------------------------
2023-10-17 16:35:16,735 EPOCH 2 done: loss 0.1522 - lr: 0.000044
2023-10-17 16:35:28,076 DEV : loss 0.18639299273490906 - f1-score (micro avg)  0.6714
2023-10-17 16:35:28,132 saving best model
2023-10-17 16:35:29,529 ----------------------------------------------------------------------------------------------------
2023-10-17 16:35:36,530 epoch 3 - iter 89/894 - loss 0.11203303 - time (sec): 7.00 - samples/sec: 1150.94 - lr: 0.000044 - momentum: 0.000000
2023-10-17 16:35:43,592 epoch 3 - iter 178/894 - loss 0.10878561 - time (sec): 14.06 - samples/sec: 1194.19 - lr: 0.000043 - momentum: 0.000000
2023-10-17 16:35:50,849 epoch 3 - iter 267/894 - loss 0.09676878 - time (sec): 21.32 - samples/sec: 1209.94 - lr: 0.000043 - momentum: 0.000000
2023-10-17 16:35:57,786 epoch 3 - iter 356/894 - loss 0.09079084 - time (sec): 28.25 - samples/sec: 1205.20 - lr: 0.000042 - momentum: 0.000000
2023-10-17 16:36:04,925 epoch 3 - iter 445/894 - loss 0.08840102 - time (sec): 35.39 - samples/sec: 1221.78 - lr: 0.000042 - momentum: 0.000000
2023-10-17 16:36:12,130 epoch 3 - iter 534/894 - loss 0.09039810 - time (sec): 42.60 - samples/sec: 1208.26 - lr: 0.000041 - momentum: 0.000000
2023-10-17 16:36:19,527 epoch 3 - iter 623/894 - loss 0.08822105 - time (sec): 49.99 - samples/sec: 1209.13 - lr: 0.000041 - momentum: 0.000000
2023-10-17 16:36:26,922 epoch 3 - iter 712/894 - loss 0.08881662 - time (sec): 57.39 - samples/sec: 1201.43 - lr: 0.000040 - momentum: 0.000000
2023-10-17 16:36:34,295 epoch 3 - iter 801/894 - loss 0.09266436 - time (sec): 64.76 - samples/sec: 1194.34 - lr: 0.000039 - momentum: 0.000000
2023-10-17 16:36:41,729 epoch 3 - iter 890/894 - loss 0.09358859 - time (sec): 72.20 - samples/sec: 1194.00 - lr: 0.000039 - momentum: 0.000000
2023-10-17 16:36:42,065 ----------------------------------------------------------------------------------------------------
2023-10-17 16:36:42,065 EPOCH 3 done: loss 0.0937 - lr: 0.000039
2023-10-17 16:36:53,475 DEV : loss 0.17432451248168945 - f1-score (micro avg)  0.7389
2023-10-17 16:36:53,531 saving best model
2023-10-17 16:36:54,922 ----------------------------------------------------------------------------------------------------
2023-10-17 16:37:02,198 epoch 4 - iter 89/894 - loss 0.05862225 - time (sec): 7.27 - samples/sec: 1317.64 - lr: 0.000038 - momentum: 0.000000
2023-10-17 16:37:09,386 epoch 4 - iter 178/894 - loss 0.05796894 - time (sec): 14.46 - samples/sec: 1320.39 - lr: 0.000038 - momentum: 0.000000
2023-10-17 16:37:16,288 epoch 4 - iter 267/894 - loss 0.05799161 - time (sec): 21.36 - samples/sec: 1272.90 - lr: 0.000037 - momentum: 0.000000
2023-10-17 16:37:23,185 epoch 4 - iter 356/894 - loss 0.05680783 - time (sec): 28.26 - samples/sec: 1239.46 - lr: 0.000037 - momentum: 0.000000
2023-10-17 16:37:30,107 epoch 4 - iter 445/894 - loss 0.05803381 - time (sec): 35.18 - samples/sec: 1236.96 - lr: 0.000036 - momentum: 0.000000
2023-10-17 16:37:37,232 epoch 4 - iter 534/894 - loss 0.05629713 - time (sec): 42.31 - samples/sec: 1236.71 - lr: 0.000036 - momentum: 0.000000
2023-10-17 16:37:44,144 epoch 4 - iter 623/894 - loss 0.05739990 - time (sec): 49.22 - samples/sec: 1231.42 - lr: 0.000035 - momentum: 0.000000
2023-10-17 16:37:51,253 epoch 4 - iter 712/894 - loss 0.05819088 - time (sec): 56.33 - samples/sec: 1231.58 - lr: 0.000034 - momentum: 0.000000
2023-10-17 16:37:58,226 epoch 4 - iter 801/894 - loss 0.05824462 - time (sec): 63.30 - samples/sec: 1229.49 - lr: 0.000034 - momentum: 0.000000
2023-10-17 16:38:05,069 epoch 4 - iter 890/894 - loss 0.06068208 - time (sec): 70.14 - samples/sec: 1228.00 - lr: 0.000033 - momentum: 0.000000
2023-10-17 16:38:05,382 ----------------------------------------------------------------------------------------------------
2023-10-17 16:38:05,382 EPOCH 4 done: loss 0.0612 - lr: 0.000033
2023-10-17 16:38:16,763 DEV : loss 0.17829285562038422 - f1-score (micro avg)  0.7578
2023-10-17 16:38:16,817 saving best model
2023-10-17 16:38:18,207 ----------------------------------------------------------------------------------------------------
2023-10-17 16:38:25,151 epoch 5 - iter 89/894 - loss 0.02982003 - time (sec): 6.94 - samples/sec: 1208.40 - lr: 0.000033 - momentum: 0.000000
2023-10-17 16:38:32,390 epoch 5 - iter 178/894 - loss 0.03304612 - time (sec): 14.18 - samples/sec: 1271.34 - lr: 0.000032 - momentum: 0.000000
2023-10-17 16:38:39,495 epoch 5 - iter 267/894 - loss 0.03586922 - time (sec): 21.28 - samples/sec: 1258.57 - lr: 0.000032 - momentum: 0.000000
2023-10-17 16:38:47,102 epoch 5 - iter 356/894 - loss 0.04145233 - time (sec): 28.89 - samples/sec: 1216.66 - lr: 0.000031 - momentum: 0.000000
2023-10-17 16:38:54,547 epoch 5 - iter 445/894 - loss 0.04280853 - time (sec): 36.33 - samples/sec: 1189.27 - lr: 0.000031 - momentum: 0.000000
2023-10-17 16:39:02,581 epoch 5 - iter 534/894 - loss 0.04154607 - time (sec): 44.37 - samples/sec: 1176.45 - lr: 0.000030 - momentum: 0.000000
2023-10-17 16:39:09,838 epoch 5 - iter 623/894 - loss 0.04203572 - time (sec): 51.63 - samples/sec: 1175.56 - lr: 0.000029 - momentum: 0.000000
2023-10-17 16:39:17,085 epoch 5 - iter 712/894 - loss 0.04172114 - time (sec): 58.87 - samples/sec: 1184.70 - lr: 0.000029 - momentum: 0.000000
2023-10-17 16:39:24,099 epoch 5 - iter 801/894 - loss 0.04233064 - time (sec): 65.89 - samples/sec: 1182.10 - lr: 0.000028 - momentum: 0.000000
2023-10-17 16:39:31,558 epoch 5 - iter 890/894 - loss 0.04024411 - time (sec): 73.35 - samples/sec: 1176.40 - lr: 0.000028 - momentum: 0.000000
2023-10-17 16:39:31,872 ----------------------------------------------------------------------------------------------------
2023-10-17 16:39:31,872 EPOCH 5 done: loss 0.0403 - lr: 0.000028
2023-10-17 16:39:43,356 DEV : loss 0.2713957130908966 - f1-score (micro avg)  0.7753
2023-10-17 16:39:43,411 saving best model
2023-10-17 16:39:44,800 ----------------------------------------------------------------------------------------------------
2023-10-17 16:39:51,905 epoch 6 - iter 89/894 - loss 0.03784356 - time (sec): 7.10 - samples/sec: 1252.78 - lr: 0.000027 - momentum: 0.000000
2023-10-17 16:39:59,301 epoch 6 - iter 178/894 - loss 0.03078874 - time (sec): 14.50 - samples/sec: 1213.14 - lr: 0.000027 - momentum: 0.000000
2023-10-17 16:40:06,519 epoch 6 - iter 267/894 - loss 0.02968316 - time (sec): 21.72 - samples/sec: 1191.18 - lr: 0.000026 - momentum: 0.000000
2023-10-17 16:40:13,613 epoch 6 - iter 356/894 - loss 0.03060611 - time (sec): 28.81 - samples/sec: 1188.95 - lr: 0.000026 - momentum: 0.000000
2023-10-17 16:40:21,175 epoch 6 - iter 445/894 - loss 0.02585728 - time (sec): 36.37 - samples/sec: 1182.02 - lr: 0.000025 - momentum: 0.000000
2023-10-17 16:40:28,273 epoch 6 - iter 534/894 - loss 0.02507947 - time (sec): 43.47 - samples/sec: 1177.02 - lr: 0.000024 - momentum: 0.000000
2023-10-17 16:40:35,216 epoch 6 - iter 623/894 - loss 0.02444122 - time (sec): 50.41 - samples/sec: 1173.00 - lr: 0.000024 - momentum: 0.000000
2023-10-17 16:40:42,613 epoch 6 - iter 712/894 - loss 0.02585665 - time (sec): 57.81 - samples/sec: 1180.03 - lr: 0.000023 - momentum: 0.000000
2023-10-17 16:40:49,698 epoch 6 - iter 801/894 - loss 0.02528235 - time (sec): 64.89 - samples/sec: 1180.84 - lr: 0.000023 - momentum: 0.000000
2023-10-17 16:40:57,157 epoch 6 - iter 890/894 - loss 0.02483238 - time (sec): 72.35 - samples/sec: 1191.34 - lr: 0.000022 - momentum: 0.000000
2023-10-17 16:40:57,481 ----------------------------------------------------------------------------------------------------
2023-10-17 16:40:57,481 EPOCH 6 done: loss 0.0247 - lr: 0.000022
2023-10-17 16:41:08,509 DEV : loss 0.23799937963485718 - f1-score (micro avg)  0.7797
2023-10-17 16:41:08,567 saving best model
2023-10-17 16:41:09,970 ----------------------------------------------------------------------------------------------------
2023-10-17 16:41:17,048 epoch 7 - iter 89/894 - loss 0.01634402 - time (sec): 7.07 - samples/sec: 1230.27 - lr: 0.000022 - momentum: 0.000000
2023-10-17 16:41:23,790 epoch 7 - iter 178/894 - loss 0.01132312 - time (sec): 13.82 - samples/sec: 1189.82 - lr: 0.000021 - momentum: 0.000000
2023-10-17 16:41:30,778 epoch 7 - iter 267/894 - loss 0.01278808 - time (sec): 20.80 - samples/sec: 1202.61 - lr: 0.000021 - momentum: 0.000000
2023-10-17 16:41:37,945 epoch 7 - iter 356/894 - loss 0.01168855 - time (sec): 27.97 - samples/sec: 1219.29 - lr: 0.000020 - momentum: 0.000000
2023-10-17 16:41:44,979 epoch 7 - iter 445/894 - loss 0.01495033 - time (sec): 35.00 - samples/sec: 1220.15 - lr: 0.000019 - momentum: 0.000000
2023-10-17 16:41:52,075 epoch 7 - iter 534/894 - loss 0.01544760 - time (sec): 42.10 - samples/sec: 1226.15 - lr: 0.000019 - momentum: 0.000000
2023-10-17 16:41:59,111 epoch 7 - iter 623/894 - loss 0.01402268 - time (sec): 49.14 - samples/sec: 1224.39 - lr: 0.000018 - momentum: 0.000000
2023-10-17 16:42:06,783 epoch 7 - iter 712/894 - loss 0.01470202 - time (sec): 56.81 - samples/sec: 1220.18 - lr: 0.000018 - momentum: 0.000000
2023-10-17 16:42:13,897 epoch 7 - iter 801/894 - loss 0.01567910 - time (sec): 63.92 - samples/sec: 1222.31 - lr: 0.000017 - momentum: 0.000000
2023-10-17 16:42:20,822 epoch 7 - iter 890/894 - loss 0.01542654 - time (sec): 70.85 - samples/sec: 1215.22 - lr: 0.000017 - momentum: 0.000000
2023-10-17 16:42:21,144 ----------------------------------------------------------------------------------------------------
2023-10-17 16:42:21,144 EPOCH 7 done: loss 0.0155 - lr: 0.000017
2023-10-17 16:42:32,075 DEV : loss 0.2671242356300354 - f1-score (micro avg)  0.7727
2023-10-17 16:42:32,135 ----------------------------------------------------------------------------------------------------
2023-10-17 16:42:38,984 epoch 8 - iter 89/894 - loss 0.00642232 - time (sec): 6.85 - samples/sec: 1286.01 - lr: 0.000016 - momentum: 0.000000
2023-10-17 16:42:45,825 epoch 8 - iter 178/894 - loss 0.01547951 - time (sec): 13.69 - samples/sec: 1244.24 - lr: 0.000016 - momentum: 0.000000
2023-10-17 16:42:52,646 epoch 8 - iter 267/894 - loss 0.01183362 - time (sec): 20.51 - samples/sec: 1237.58 - lr: 0.000015 - momentum: 0.000000
2023-10-17 16:42:59,577 epoch 8 - iter 356/894 - loss 0.01316590 - time (sec): 27.44 - samples/sec: 1224.66 - lr: 0.000014 - momentum: 0.000000
2023-10-17 16:43:06,861 epoch 8 - iter 445/894 - loss 0.01148861 - time (sec): 34.72 - samples/sec: 1255.91 - lr: 0.000014 - momentum: 0.000000
2023-10-17 16:43:13,946 epoch 8 - iter 534/894 - loss 0.01218895 - time (sec): 41.81 - samples/sec: 1255.45 - lr: 0.000013 - momentum: 0.000000
2023-10-17 16:43:20,701 epoch 8 - iter 623/894 - loss 0.01256129 - time (sec): 48.56 - samples/sec: 1269.29 - lr: 0.000013 - momentum: 0.000000
2023-10-17 16:43:27,458 epoch 8 - iter 712/894 - loss 0.01163940 - time (sec): 55.32 - samples/sec: 1259.03 - lr: 0.000012 - momentum: 0.000000
2023-10-17 16:43:34,177 epoch 8 - iter 801/894 - loss 0.01183209 - time (sec): 62.04 - samples/sec: 1257.71 - lr: 0.000012 - momentum: 0.000000
2023-10-17 16:43:40,862 epoch 8 - iter 890/894 - loss 0.01144474 - time (sec): 68.72 - samples/sec: 1255.86 - lr: 0.000011 - momentum: 0.000000
2023-10-17 16:43:41,154 ----------------------------------------------------------------------------------------------------
2023-10-17 16:43:41,155 EPOCH 8 done: loss 0.0115 - lr: 0.000011
2023-10-17 16:43:52,982 DEV : loss 0.25983676314353943 - f1-score (micro avg)  0.7832
2023-10-17 16:43:53,059 saving best model
2023-10-17 16:43:54,471 ----------------------------------------------------------------------------------------------------
2023-10-17 16:44:01,588 epoch 9 - iter 89/894 - loss 0.00948902 - time (sec): 7.11 - samples/sec: 1194.84 - lr: 0.000011 - momentum: 0.000000
2023-10-17 16:44:08,811 epoch 9 - iter 178/894 - loss 0.00788875 - time (sec): 14.34 - samples/sec: 1195.01 - lr: 0.000010 - momentum: 0.000000
2023-10-17 16:44:16,000 epoch 9 - iter 267/894 - loss 0.00936076 - time (sec): 21.53 - samples/sec: 1154.95 - lr: 0.000009 - momentum: 0.000000
2023-10-17 16:44:23,123 epoch 9 - iter 356/894 - loss 0.00847747 - time (sec): 28.65 - samples/sec: 1180.69 - lr: 0.000009 - momentum: 0.000000
2023-10-17 16:44:30,220 epoch 9 - iter 445/894 - loss 0.00842446 - time (sec): 35.75 - samples/sec: 1194.06 - lr: 0.000008 - momentum: 0.000000
2023-10-17 16:44:37,433 epoch 9 - iter 534/894 - loss 0.00877574 - time (sec): 42.96 - samples/sec: 1201.69 - lr: 0.000008 - momentum: 0.000000
2023-10-17 16:44:44,464 epoch 9 - iter 623/894 - loss 0.00788580 - time (sec): 49.99 - samples/sec: 1201.20 - lr: 0.000007 - momentum: 0.000000
2023-10-17 16:44:51,571 epoch 9 - iter 712/894 - loss 0.00767381 - time (sec): 57.10 - samples/sec: 1204.23 - lr: 0.000007 - momentum: 0.000000
2023-10-17 16:44:58,906 epoch 9 - iter 801/894 - loss 0.00698938 - time (sec): 64.43 - samples/sec: 1205.26 - lr: 0.000006 - momentum: 0.000000
2023-10-17 16:45:06,127 epoch 9 - iter 890/894 - loss 0.00663373 - time (sec): 71.65 - samples/sec: 1203.66 - lr: 0.000006 - momentum: 0.000000
2023-10-17 16:45:06,433 ----------------------------------------------------------------------------------------------------
2023-10-17 16:45:06,433 EPOCH 9 done: loss 0.0066 - lr: 0.000006
2023-10-17 16:45:18,138 DEV : loss 0.2585032284259796 - f1-score (micro avg)  0.7948
2023-10-17 16:45:18,200 saving best model
2023-10-17 16:45:19,640 ----------------------------------------------------------------------------------------------------
2023-10-17 16:45:26,775 epoch 10 - iter 89/894 - loss 0.00559870 - time (sec): 7.13 - samples/sec: 1289.77 - lr: 0.000005 - momentum: 0.000000
2023-10-17 16:45:33,768 epoch 10 - iter 178/894 - loss 0.00539965 - time (sec): 14.12 - samples/sec: 1226.33 - lr: 0.000004 - momentum: 0.000000
2023-10-17 16:45:40,756 epoch 10 - iter 267/894 - loss 0.00432438 - time (sec): 21.11 - samples/sec: 1202.75 - lr: 0.000004 - momentum: 0.000000
2023-10-17 16:45:47,738 epoch 10 - iter 356/894 - loss 0.00382682 - time (sec): 28.09 - samples/sec: 1211.09 - lr: 0.000003 - momentum: 0.000000
2023-10-17 16:45:54,757 epoch 10 - iter 445/894 - loss 0.00379707 - time (sec): 35.11 - samples/sec: 1216.64 - lr: 0.000003 - momentum: 0.000000
2023-10-17 16:46:02,045 epoch 10 - iter 534/894 - loss 0.00433668 - time (sec): 42.40 - samples/sec: 1232.01 - lr: 0.000002 - momentum: 0.000000
2023-10-17 16:46:09,021 epoch 10 - iter 623/894 - loss 0.00409876 - time (sec): 49.38 - samples/sec: 1213.61 - lr: 0.000002 - momentum: 0.000000
2023-10-17 16:46:16,175 epoch 10 - iter 712/894 - loss 0.00359853 - time (sec): 56.53 - samples/sec: 1215.64 - lr: 0.000001 - momentum: 0.000000
2023-10-17 16:46:23,161 epoch 10 - iter 801/894 - loss 0.00367913 - time (sec): 63.52 - samples/sec: 1210.80 - lr: 0.000001 - momentum: 0.000000
2023-10-17 16:46:30,292 epoch 10 - iter 890/894 - loss 0.00340485 - time (sec): 70.65 - samples/sec: 1218.69 - lr: 0.000000 - momentum: 0.000000
2023-10-17 16:46:30,607 ----------------------------------------------------------------------------------------------------
2023-10-17 16:46:30,607 EPOCH 10 done: loss 0.0034 - lr: 0.000000
2023-10-17 16:46:42,254 DEV : loss 0.27636414766311646 - f1-score (micro avg)  0.7941
2023-10-17 16:46:42,844 ----------------------------------------------------------------------------------------------------
2023-10-17 16:46:42,846 Loading model from best epoch ...
2023-10-17 16:46:45,143 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
2023-10-17 16:46:51,229 
Results:
- F-score (micro) 0.7627
- F-score (macro) 0.6782
- Accuracy 0.6355

By class:
              precision    recall  f1-score   support

         loc     0.8344    0.8540    0.8441       596
        pers     0.7230    0.7838    0.7522       333
         org     0.5345    0.4697    0.5000       132
        prod     0.6909    0.5758    0.6281        66
        time     0.6600    0.6735    0.6667        49

   micro avg     0.7576    0.7679    0.7627      1176
   macro avg     0.6886    0.6713    0.6782      1176
weighted avg     0.7539    0.7679    0.7599      1176

2023-10-17 16:46:51,230 ----------------------------------------------------------------------------------------------------