2023-09-04 15:07:57,477 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,478 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 15:07:57,478 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,478 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 15:07:57,478 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,478 Train:  5901 sentences
2023-09-04 15:07:57,478         (train_with_dev=False, train_with_test=False)
2023-09-04 15:07:57,478 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,478 Training Params:
2023-09-04 15:07:57,478  - learning_rate: "3e-05" 
2023-09-04 15:07:57,478  - mini_batch_size: "8"
2023-09-04 15:07:57,479  - max_epochs: "10"
2023-09-04 15:07:57,479  - shuffle: "True"
2023-09-04 15:07:57,479 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,479 Plugins:
2023-09-04 15:07:57,479  - LinearScheduler | warmup_fraction: '0.1'
2023-09-04 15:07:57,479 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,479 Final evaluation on model from best epoch (best-model.pt)
2023-09-04 15:07:57,479  - metric: "('micro avg', 'f1-score')"
2023-09-04 15:07:57,479 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,479 Computation:
2023-09-04 15:07:57,479  - compute on device: cuda:0
2023-09-04 15:07:57,479  - embedding storage: none
2023-09-04 15:07:57,479 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,479 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
2023-09-04 15:07:57,479 ----------------------------------------------------------------------------------------------------
2023-09-04 15:07:57,479 ----------------------------------------------------------------------------------------------------
2023-09-04 15:08:11,872 epoch 1 - iter 73/738 - loss 2.86248977 - time (sec): 14.39 - samples/sec: 1188.37 - lr: 0.000003 - momentum: 0.000000
2023-09-04 15:08:28,062 epoch 1 - iter 146/738 - loss 1.87378558 - time (sec): 30.58 - samples/sec: 1176.56 - lr: 0.000006 - momentum: 0.000000
2023-09-04 15:08:41,224 epoch 1 - iter 219/738 - loss 1.44076320 - time (sec): 43.74 - samples/sec: 1190.68 - lr: 0.000009 - momentum: 0.000000
2023-09-04 15:08:53,992 epoch 1 - iter 292/738 - loss 1.19510265 - time (sec): 56.51 - samples/sec: 1200.16 - lr: 0.000012 - momentum: 0.000000
2023-09-04 15:09:06,658 epoch 1 - iter 365/738 - loss 1.02747532 - time (sec): 69.18 - samples/sec: 1206.88 - lr: 0.000015 - momentum: 0.000000
2023-09-04 15:09:20,422 epoch 1 - iter 438/738 - loss 0.90817250 - time (sec): 82.94 - samples/sec: 1205.48 - lr: 0.000018 - momentum: 0.000000
2023-09-04 15:09:32,092 epoch 1 - iter 511/738 - loss 0.82740367 - time (sec): 94.61 - samples/sec: 1209.87 - lr: 0.000021 - momentum: 0.000000
2023-09-04 15:09:45,830 epoch 1 - iter 584/738 - loss 0.75485949 - time (sec): 108.35 - samples/sec: 1204.68 - lr: 0.000024 - momentum: 0.000000
2023-09-04 15:09:59,433 epoch 1 - iter 657/738 - loss 0.69355197 - time (sec): 121.95 - samples/sec: 1203.67 - lr: 0.000027 - momentum: 0.000000
2023-09-04 15:10:14,975 epoch 1 - iter 730/738 - loss 0.63642854 - time (sec): 137.49 - samples/sec: 1199.07 - lr: 0.000030 - momentum: 0.000000
2023-09-04 15:10:16,267 ----------------------------------------------------------------------------------------------------
2023-09-04 15:10:16,267 EPOCH 1 done: loss 0.6324 - lr: 0.000030
2023-09-04 15:10:30,427 DEV : loss 0.13814392685890198 - f1-score (micro avg)  0.7202
2023-09-04 15:10:30,455 saving best model
2023-09-04 15:10:30,930 ----------------------------------------------------------------------------------------------------
2023-09-04 15:10:43,191 epoch 2 - iter 73/738 - loss 0.15642957 - time (sec): 12.26 - samples/sec: 1196.70 - lr: 0.000030 - momentum: 0.000000
2023-09-04 15:10:55,459 epoch 2 - iter 146/738 - loss 0.15187407 - time (sec): 24.53 - samples/sec: 1213.71 - lr: 0.000029 - momentum: 0.000000
2023-09-04 15:11:10,504 epoch 2 - iter 219/738 - loss 0.14818061 - time (sec): 39.57 - samples/sec: 1210.82 - lr: 0.000029 - momentum: 0.000000
2023-09-04 15:11:23,998 epoch 2 - iter 292/738 - loss 0.14577470 - time (sec): 53.07 - samples/sec: 1207.19 - lr: 0.000029 - momentum: 0.000000
2023-09-04 15:11:37,517 epoch 2 - iter 365/738 - loss 0.14070710 - time (sec): 66.59 - samples/sec: 1204.25 - lr: 0.000028 - momentum: 0.000000
2023-09-04 15:11:51,786 epoch 2 - iter 438/738 - loss 0.13613518 - time (sec): 80.85 - samples/sec: 1204.78 - lr: 0.000028 - momentum: 0.000000
2023-09-04 15:12:05,952 epoch 2 - iter 511/738 - loss 0.13285248 - time (sec): 95.02 - samples/sec: 1194.66 - lr: 0.000028 - momentum: 0.000000
2023-09-04 15:12:19,375 epoch 2 - iter 584/738 - loss 0.13200454 - time (sec): 108.44 - samples/sec: 1196.77 - lr: 0.000027 - momentum: 0.000000
2023-09-04 15:12:35,244 epoch 2 - iter 657/738 - loss 0.12861740 - time (sec): 124.31 - samples/sec: 1191.28 - lr: 0.000027 - momentum: 0.000000
2023-09-04 15:12:49,248 epoch 2 - iter 730/738 - loss 0.12694763 - time (sec): 138.32 - samples/sec: 1190.88 - lr: 0.000027 - momentum: 0.000000
2023-09-04 15:12:50,559 ----------------------------------------------------------------------------------------------------
2023-09-04 15:12:50,559 EPOCH 2 done: loss 0.1269 - lr: 0.000027
2023-09-04 15:13:08,319 DEV : loss 0.11461225152015686 - f1-score (micro avg)  0.7643
2023-09-04 15:13:08,347 saving best model
2023-09-04 15:13:09,723 ----------------------------------------------------------------------------------------------------
2023-09-04 15:13:23,207 epoch 3 - iter 73/738 - loss 0.08262600 - time (sec): 13.48 - samples/sec: 1206.96 - lr: 0.000026 - momentum: 0.000000
2023-09-04 15:13:37,922 epoch 3 - iter 146/738 - loss 0.07783046 - time (sec): 28.20 - samples/sec: 1199.25 - lr: 0.000026 - momentum: 0.000000
2023-09-04 15:13:50,649 epoch 3 - iter 219/738 - loss 0.07457843 - time (sec): 40.92 - samples/sec: 1202.63 - lr: 0.000026 - momentum: 0.000000
2023-09-04 15:14:05,710 epoch 3 - iter 292/738 - loss 0.08068010 - time (sec): 55.99 - samples/sec: 1198.49 - lr: 0.000025 - momentum: 0.000000
2023-09-04 15:14:18,785 epoch 3 - iter 365/738 - loss 0.07733871 - time (sec): 69.06 - samples/sec: 1199.55 - lr: 0.000025 - momentum: 0.000000
2023-09-04 15:14:32,483 epoch 3 - iter 438/738 - loss 0.07510957 - time (sec): 82.76 - samples/sec: 1193.29 - lr: 0.000025 - momentum: 0.000000
2023-09-04 15:14:45,867 epoch 3 - iter 511/738 - loss 0.07428255 - time (sec): 96.14 - samples/sec: 1198.61 - lr: 0.000024 - momentum: 0.000000
2023-09-04 15:15:00,677 epoch 3 - iter 584/738 - loss 0.07258361 - time (sec): 110.95 - samples/sec: 1195.74 - lr: 0.000024 - momentum: 0.000000
2023-09-04 15:15:13,673 epoch 3 - iter 657/738 - loss 0.07018245 - time (sec): 123.95 - samples/sec: 1196.07 - lr: 0.000024 - momentum: 0.000000
2023-09-04 15:15:27,981 epoch 3 - iter 730/738 - loss 0.07051097 - time (sec): 138.26 - samples/sec: 1193.67 - lr: 0.000023 - momentum: 0.000000
2023-09-04 15:15:29,057 ----------------------------------------------------------------------------------------------------
2023-09-04 15:15:29,058 EPOCH 3 done: loss 0.0706 - lr: 0.000023
2023-09-04 15:15:46,860 DEV : loss 0.12629321217536926 - f1-score (micro avg)  0.7847
2023-09-04 15:15:46,888 saving best model
2023-09-04 15:15:48,249 ----------------------------------------------------------------------------------------------------
2023-09-04 15:16:02,993 epoch 4 - iter 73/738 - loss 0.04289321 - time (sec): 14.74 - samples/sec: 1210.88 - lr: 0.000023 - momentum: 0.000000
2023-09-04 15:16:15,752 epoch 4 - iter 146/738 - loss 0.04495463 - time (sec): 27.50 - samples/sec: 1204.09 - lr: 0.000023 - momentum: 0.000000
2023-09-04 15:16:32,912 epoch 4 - iter 219/738 - loss 0.04311179 - time (sec): 44.66 - samples/sec: 1184.67 - lr: 0.000022 - momentum: 0.000000
2023-09-04 15:16:47,592 epoch 4 - iter 292/738 - loss 0.04838265 - time (sec): 59.34 - samples/sec: 1173.84 - lr: 0.000022 - momentum: 0.000000
2023-09-04 15:17:00,520 epoch 4 - iter 365/738 - loss 0.04798678 - time (sec): 72.27 - samples/sec: 1181.69 - lr: 0.000022 - momentum: 0.000000
2023-09-04 15:17:15,753 epoch 4 - iter 438/738 - loss 0.04663458 - time (sec): 87.50 - samples/sec: 1182.61 - lr: 0.000021 - momentum: 0.000000
2023-09-04 15:17:28,483 epoch 4 - iter 511/738 - loss 0.04610997 - time (sec): 100.23 - samples/sec: 1187.93 - lr: 0.000021 - momentum: 0.000000
2023-09-04 15:17:41,349 epoch 4 - iter 584/738 - loss 0.04766936 - time (sec): 113.10 - samples/sec: 1185.21 - lr: 0.000021 - momentum: 0.000000
2023-09-04 15:17:53,035 epoch 4 - iter 657/738 - loss 0.04878602 - time (sec): 124.78 - samples/sec: 1191.55 - lr: 0.000020 - momentum: 0.000000
2023-09-04 15:18:06,125 epoch 4 - iter 730/738 - loss 0.04813588 - time (sec): 137.87 - samples/sec: 1195.65 - lr: 0.000020 - momentum: 0.000000
2023-09-04 15:18:07,361 ----------------------------------------------------------------------------------------------------
2023-09-04 15:18:07,361 EPOCH 4 done: loss 0.0478 - lr: 0.000020
2023-09-04 15:18:25,148 DEV : loss 0.1449851542711258 - f1-score (micro avg)  0.8246
2023-09-04 15:18:25,176 saving best model
2023-09-04 15:18:26,491 ----------------------------------------------------------------------------------------------------
2023-09-04 15:18:40,829 epoch 5 - iter 73/738 - loss 0.03757261 - time (sec): 14.34 - samples/sec: 1175.64 - lr: 0.000020 - momentum: 0.000000
2023-09-04 15:18:53,223 epoch 5 - iter 146/738 - loss 0.03398262 - time (sec): 26.73 - samples/sec: 1202.24 - lr: 0.000019 - momentum: 0.000000
2023-09-04 15:19:05,893 epoch 5 - iter 219/738 - loss 0.03453452 - time (sec): 39.40 - samples/sec: 1221.49 - lr: 0.000019 - momentum: 0.000000
2023-09-04 15:19:20,376 epoch 5 - iter 292/738 - loss 0.03535832 - time (sec): 53.88 - samples/sec: 1214.70 - lr: 0.000019 - momentum: 0.000000
2023-09-04 15:19:34,656 epoch 5 - iter 365/738 - loss 0.03277805 - time (sec): 68.16 - samples/sec: 1196.75 - lr: 0.000018 - momentum: 0.000000
2023-09-04 15:19:48,355 epoch 5 - iter 438/738 - loss 0.03204077 - time (sec): 81.86 - samples/sec: 1192.57 - lr: 0.000018 - momentum: 0.000000
2023-09-04 15:20:05,121 epoch 5 - iter 511/738 - loss 0.03323374 - time (sec): 98.63 - samples/sec: 1183.15 - lr: 0.000018 - momentum: 0.000000
2023-09-04 15:20:16,779 epoch 5 - iter 584/738 - loss 0.03395649 - time (sec): 110.29 - samples/sec: 1193.38 - lr: 0.000017 - momentum: 0.000000
2023-09-04 15:20:31,468 epoch 5 - iter 657/738 - loss 0.03392424 - time (sec): 124.98 - samples/sec: 1189.35 - lr: 0.000017 - momentum: 0.000000
2023-09-04 15:20:44,868 epoch 5 - iter 730/738 - loss 0.03386304 - time (sec): 138.38 - samples/sec: 1191.00 - lr: 0.000017 - momentum: 0.000000
2023-09-04 15:20:46,072 ----------------------------------------------------------------------------------------------------
2023-09-04 15:20:46,072 EPOCH 5 done: loss 0.0336 - lr: 0.000017
2023-09-04 15:21:03,892 DEV : loss 0.16297703981399536 - f1-score (micro avg)  0.8206
2023-09-04 15:21:03,921 ----------------------------------------------------------------------------------------------------
2023-09-04 15:21:18,215 epoch 6 - iter 73/738 - loss 0.02179470 - time (sec): 14.29 - samples/sec: 1178.36 - lr: 0.000016 - momentum: 0.000000
2023-09-04 15:21:32,516 epoch 6 - iter 146/738 - loss 0.02530039 - time (sec): 28.59 - samples/sec: 1155.41 - lr: 0.000016 - momentum: 0.000000
2023-09-04 15:21:44,583 epoch 6 - iter 219/738 - loss 0.02335614 - time (sec): 40.66 - samples/sec: 1171.33 - lr: 0.000016 - momentum: 0.000000
2023-09-04 15:21:57,035 epoch 6 - iter 292/738 - loss 0.02558086 - time (sec): 53.11 - samples/sec: 1182.77 - lr: 0.000015 - momentum: 0.000000
2023-09-04 15:22:11,708 epoch 6 - iter 365/738 - loss 0.02563490 - time (sec): 67.79 - samples/sec: 1178.91 - lr: 0.000015 - momentum: 0.000000
2023-09-04 15:22:22,999 epoch 6 - iter 438/738 - loss 0.02483109 - time (sec): 79.08 - samples/sec: 1188.60 - lr: 0.000015 - momentum: 0.000000
2023-09-04 15:22:37,280 epoch 6 - iter 511/738 - loss 0.02354249 - time (sec): 93.36 - samples/sec: 1190.62 - lr: 0.000014 - momentum: 0.000000
2023-09-04 15:22:52,561 epoch 6 - iter 584/738 - loss 0.02407573 - time (sec): 108.64 - samples/sec: 1191.50 - lr: 0.000014 - momentum: 0.000000
2023-09-04 15:23:09,298 epoch 6 - iter 657/738 - loss 0.02428604 - time (sec): 125.37 - samples/sec: 1189.00 - lr: 0.000014 - momentum: 0.000000
2023-09-04 15:23:22,753 epoch 6 - iter 730/738 - loss 0.02498198 - time (sec): 138.83 - samples/sec: 1189.25 - lr: 0.000013 - momentum: 0.000000
2023-09-04 15:23:23,779 ----------------------------------------------------------------------------------------------------
2023-09-04 15:23:23,779 EPOCH 6 done: loss 0.0251 - lr: 0.000013
2023-09-04 15:23:41,509 DEV : loss 0.1827543079853058 - f1-score (micro avg)  0.8092
2023-09-04 15:23:41,538 ----------------------------------------------------------------------------------------------------
2023-09-04 15:23:54,237 epoch 7 - iter 73/738 - loss 0.02181177 - time (sec): 12.70 - samples/sec: 1212.83 - lr: 0.000013 - momentum: 0.000000
2023-09-04 15:24:06,417 epoch 7 - iter 146/738 - loss 0.02084610 - time (sec): 24.88 - samples/sec: 1200.09 - lr: 0.000013 - momentum: 0.000000
2023-09-04 15:24:20,568 epoch 7 - iter 219/738 - loss 0.02110233 - time (sec): 39.03 - samples/sec: 1209.11 - lr: 0.000012 - momentum: 0.000000
2023-09-04 15:24:33,733 epoch 7 - iter 292/738 - loss 0.01901752 - time (sec): 52.19 - samples/sec: 1204.34 - lr: 0.000012 - momentum: 0.000000
2023-09-04 15:24:48,456 epoch 7 - iter 365/738 - loss 0.02024199 - time (sec): 66.92 - samples/sec: 1188.39 - lr: 0.000012 - momentum: 0.000000
2023-09-04 15:25:02,191 epoch 7 - iter 438/738 - loss 0.02027023 - time (sec): 80.65 - samples/sec: 1189.40 - lr: 0.000011 - momentum: 0.000000
2023-09-04 15:25:15,277 epoch 7 - iter 511/738 - loss 0.02061434 - time (sec): 93.74 - samples/sec: 1196.34 - lr: 0.000011 - momentum: 0.000000
2023-09-04 15:25:29,003 epoch 7 - iter 584/738 - loss 0.02034981 - time (sec): 107.46 - samples/sec: 1196.32 - lr: 0.000011 - momentum: 0.000000
2023-09-04 15:25:45,325 epoch 7 - iter 657/738 - loss 0.02041326 - time (sec): 123.79 - samples/sec: 1195.65 - lr: 0.000010 - momentum: 0.000000
2023-09-04 15:25:59,500 epoch 7 - iter 730/738 - loss 0.01986392 - time (sec): 137.96 - samples/sec: 1191.73 - lr: 0.000010 - momentum: 0.000000
2023-09-04 15:26:01,204 ----------------------------------------------------------------------------------------------------
2023-09-04 15:26:01,204 EPOCH 7 done: loss 0.0198 - lr: 0.000010
2023-09-04 15:26:18,945 DEV : loss 0.19946980476379395 - f1-score (micro avg)  0.8101
2023-09-04 15:26:18,974 ----------------------------------------------------------------------------------------------------
2023-09-04 15:26:33,611 epoch 8 - iter 73/738 - loss 0.01320934 - time (sec): 14.63 - samples/sec: 1199.27 - lr: 0.000010 - momentum: 0.000000
2023-09-04 15:26:46,284 epoch 8 - iter 146/738 - loss 0.01087234 - time (sec): 27.31 - samples/sec: 1190.62 - lr: 0.000009 - momentum: 0.000000
2023-09-04 15:27:00,406 epoch 8 - iter 219/738 - loss 0.01118795 - time (sec): 41.43 - samples/sec: 1193.36 - lr: 0.000009 - momentum: 0.000000
2023-09-04 15:27:13,072 epoch 8 - iter 292/738 - loss 0.01173749 - time (sec): 54.10 - samples/sec: 1196.64 - lr: 0.000009 - momentum: 0.000000
2023-09-04 15:27:27,562 epoch 8 - iter 365/738 - loss 0.01497626 - time (sec): 68.59 - samples/sec: 1182.80 - lr: 0.000008 - momentum: 0.000000
2023-09-04 15:27:43,186 epoch 8 - iter 438/738 - loss 0.01439217 - time (sec): 84.21 - samples/sec: 1175.93 - lr: 0.000008 - momentum: 0.000000
2023-09-04 15:27:54,652 epoch 8 - iter 511/738 - loss 0.01407249 - time (sec): 95.68 - samples/sec: 1189.87 - lr: 0.000008 - momentum: 0.000000
2023-09-04 15:28:09,602 epoch 8 - iter 584/738 - loss 0.01366217 - time (sec): 110.63 - samples/sec: 1183.80 - lr: 0.000007 - momentum: 0.000000
2023-09-04 15:28:22,514 epoch 8 - iter 657/738 - loss 0.01343997 - time (sec): 123.54 - samples/sec: 1186.85 - lr: 0.000007 - momentum: 0.000000
2023-09-04 15:28:37,442 epoch 8 - iter 730/738 - loss 0.01419631 - time (sec): 138.47 - samples/sec: 1190.92 - lr: 0.000007 - momentum: 0.000000
2023-09-04 15:28:38,663 ----------------------------------------------------------------------------------------------------
2023-09-04 15:28:38,664 EPOCH 8 done: loss 0.0142 - lr: 0.000007
2023-09-04 15:28:56,470 DEV : loss 0.1939600259065628 - f1-score (micro avg)  0.8261
2023-09-04 15:28:56,500 saving best model
2023-09-04 15:28:57,845 ----------------------------------------------------------------------------------------------------
2023-09-04 15:29:11,878 epoch 9 - iter 73/738 - loss 0.01087719 - time (sec): 14.03 - samples/sec: 1194.75 - lr: 0.000006 - momentum: 0.000000
2023-09-04 15:29:26,380 epoch 9 - iter 146/738 - loss 0.01176628 - time (sec): 28.53 - samples/sec: 1173.05 - lr: 0.000006 - momentum: 0.000000
2023-09-04 15:29:38,093 epoch 9 - iter 219/738 - loss 0.01045319 - time (sec): 40.25 - samples/sec: 1199.96 - lr: 0.000006 - momentum: 0.000000
2023-09-04 15:29:50,788 epoch 9 - iter 292/738 - loss 0.01059609 - time (sec): 52.94 - samples/sec: 1200.20 - lr: 0.000005 - momentum: 0.000000
2023-09-04 15:30:04,953 epoch 9 - iter 365/738 - loss 0.01182372 - time (sec): 67.11 - samples/sec: 1182.59 - lr: 0.000005 - momentum: 0.000000
2023-09-04 15:30:20,455 epoch 9 - iter 438/738 - loss 0.01097159 - time (sec): 82.61 - samples/sec: 1174.63 - lr: 0.000005 - momentum: 0.000000
2023-09-04 15:30:35,578 epoch 9 - iter 511/738 - loss 0.01034011 - time (sec): 97.73 - samples/sec: 1173.99 - lr: 0.000004 - momentum: 0.000000
2023-09-04 15:30:48,048 epoch 9 - iter 584/738 - loss 0.01093906 - time (sec): 110.20 - samples/sec: 1182.16 - lr: 0.000004 - momentum: 0.000000
2023-09-04 15:31:01,277 epoch 9 - iter 657/738 - loss 0.01060596 - time (sec): 123.43 - samples/sec: 1182.92 - lr: 0.000004 - momentum: 0.000000
2023-09-04 15:31:15,726 epoch 9 - iter 730/738 - loss 0.01058406 - time (sec): 137.88 - samples/sec: 1193.37 - lr: 0.000003 - momentum: 0.000000
2023-09-04 15:31:17,063 ----------------------------------------------------------------------------------------------------
2023-09-04 15:31:17,064 EPOCH 9 done: loss 0.0105 - lr: 0.000003
2023-09-04 15:31:34,810 DEV : loss 0.20513701438903809 - f1-score (micro avg)  0.8227
2023-09-04 15:31:34,839 ----------------------------------------------------------------------------------------------------
2023-09-04 15:31:48,240 epoch 10 - iter 73/738 - loss 0.00152471 - time (sec): 13.40 - samples/sec: 1191.48 - lr: 0.000003 - momentum: 0.000000
2023-09-04 15:32:01,756 epoch 10 - iter 146/738 - loss 0.00902703 - time (sec): 26.92 - samples/sec: 1210.66 - lr: 0.000003 - momentum: 0.000000
2023-09-04 15:32:13,703 epoch 10 - iter 219/738 - loss 0.00963469 - time (sec): 38.86 - samples/sec: 1221.31 - lr: 0.000002 - momentum: 0.000000
2023-09-04 15:32:28,695 epoch 10 - iter 292/738 - loss 0.01078399 - time (sec): 53.85 - samples/sec: 1218.41 - lr: 0.000002 - momentum: 0.000000
2023-09-04 15:32:44,713 epoch 10 - iter 365/738 - loss 0.01129022 - time (sec): 69.87 - samples/sec: 1201.46 - lr: 0.000002 - momentum: 0.000000
2023-09-04 15:32:57,832 epoch 10 - iter 438/738 - loss 0.01051066 - time (sec): 82.99 - samples/sec: 1200.25 - lr: 0.000001 - momentum: 0.000000
2023-09-04 15:33:11,789 epoch 10 - iter 511/738 - loss 0.00970689 - time (sec): 96.95 - samples/sec: 1203.46 - lr: 0.000001 - momentum: 0.000000
2023-09-04 15:33:27,378 epoch 10 - iter 584/738 - loss 0.00932251 - time (sec): 112.54 - samples/sec: 1194.23 - lr: 0.000001 - momentum: 0.000000
2023-09-04 15:33:40,681 epoch 10 - iter 657/738 - loss 0.00881038 - time (sec): 125.84 - samples/sec: 1193.25 - lr: 0.000000 - momentum: 0.000000
2023-09-04 15:33:52,640 epoch 10 - iter 730/738 - loss 0.00838896 - time (sec): 137.80 - samples/sec: 1195.44 - lr: 0.000000 - momentum: 0.000000
2023-09-04 15:33:53,938 ----------------------------------------------------------------------------------------------------
2023-09-04 15:33:53,938 EPOCH 10 done: loss 0.0083 - lr: 0.000000
2023-09-04 15:34:11,671 DEV : loss 0.20588278770446777 - f1-score (micro avg)  0.8215
2023-09-04 15:34:12,180 ----------------------------------------------------------------------------------------------------
2023-09-04 15:34:12,181 Loading model from best epoch ...
2023-09-04 15:34:14,139 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 15:34:29,363 
Results:
- F-score (micro) 0.7885
- F-score (macro) 0.688
- Accuracy 0.6729

By class:
              precision    recall  f1-score   support

         loc     0.8694    0.8613    0.8653       858
        pers     0.7423    0.8045    0.7721       537
         org     0.4765    0.6136    0.5364       132
        time     0.5231    0.6296    0.5714        54
        prod     0.7193    0.6721    0.6949        61

   micro avg     0.7697    0.8082    0.7885      1642
   macro avg     0.6661    0.7162    0.6880      1642
weighted avg     0.7793    0.8082    0.7924      1642

2023-09-04 15:34:29,363 ----------------------------------------------------------------------------------------------------