2023-10-18 23:58:03,176 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 128)
        (position_embeddings): Embedding(512, 128)
        (token_type_embeddings): Embedding(2, 128)
        (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-1): 2 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=128, out_features=128, bias=True)
                (key): Linear(in_features=128, out_features=128, bias=True)
                (value): Linear(in_features=128, out_features=128, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=128, out_features=128, bias=True)
                (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=128, out_features=512, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=512, out_features=128, bias=True)
              (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=128, out_features=128, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=128, out_features=13, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
 - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 Train:  14465 sentences
2023-10-18 23:58:03,177         (train_with_dev=False, train_with_test=False)
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 Training Params:
2023-10-18 23:58:03,177  - learning_rate: "3e-05" 
2023-10-18 23:58:03,177  - mini_batch_size: "4"
2023-10-18 23:58:03,177  - max_epochs: "10"
2023-10-18 23:58:03,177  - shuffle: "True"
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 Plugins:
2023-10-18 23:58:03,177  - TensorboardLogger
2023-10-18 23:58:03,177  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 23:58:03,177  - metric: "('micro avg', 'f1-score')"
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 Computation:
2023-10-18 23:58:03,177  - compute on device: cuda:0
2023-10-18 23:58:03,177  - embedding storage: none
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,177 Model training base path: "hmbench-letemps/fr-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-18 23:58:03,177 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,178 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:03,178 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 23:58:08,803 epoch 1 - iter 361/3617 - loss 2.36997878 - time (sec): 5.63 - samples/sec: 6809.84 - lr: 0.000003 - momentum: 0.000000
2023-10-18 23:58:14,467 epoch 1 - iter 722/3617 - loss 1.77258874 - time (sec): 11.29 - samples/sec: 6600.74 - lr: 0.000006 - momentum: 0.000000
2023-10-18 23:58:20,123 epoch 1 - iter 1083/3617 - loss 1.31516970 - time (sec): 16.94 - samples/sec: 6619.34 - lr: 0.000009 - momentum: 0.000000
2023-10-18 23:58:25,656 epoch 1 - iter 1444/3617 - loss 1.06703131 - time (sec): 22.48 - samples/sec: 6702.33 - lr: 0.000012 - momentum: 0.000000
2023-10-18 23:58:31,174 epoch 1 - iter 1805/3617 - loss 0.91344974 - time (sec): 28.00 - samples/sec: 6727.80 - lr: 0.000015 - momentum: 0.000000
2023-10-18 23:58:36,755 epoch 1 - iter 2166/3617 - loss 0.81603607 - time (sec): 33.58 - samples/sec: 6675.28 - lr: 0.000018 - momentum: 0.000000
2023-10-18 23:58:42,404 epoch 1 - iter 2527/3617 - loss 0.73338690 - time (sec): 39.23 - samples/sec: 6673.64 - lr: 0.000021 - momentum: 0.000000
2023-10-18 23:58:48,315 epoch 1 - iter 2888/3617 - loss 0.66902349 - time (sec): 45.14 - samples/sec: 6667.42 - lr: 0.000024 - momentum: 0.000000
2023-10-18 23:58:53,645 epoch 1 - iter 3249/3617 - loss 0.61751533 - time (sec): 50.47 - samples/sec: 6734.11 - lr: 0.000027 - momentum: 0.000000
2023-10-18 23:58:58,925 epoch 1 - iter 3610/3617 - loss 0.57566262 - time (sec): 55.75 - samples/sec: 6803.03 - lr: 0.000030 - momentum: 0.000000
2023-10-18 23:58:59,030 ----------------------------------------------------------------------------------------------------
2023-10-18 23:58:59,030 EPOCH 1 done: loss 0.5748 - lr: 0.000030
2023-10-18 23:59:01,374 DEV : loss 0.18418805301189423 - f1-score (micro avg)  0.1113
2023-10-18 23:59:01,406 saving best model
2023-10-18 23:59:01,439 ----------------------------------------------------------------------------------------------------
2023-10-18 23:59:07,237 epoch 2 - iter 361/3617 - loss 0.21498018 - time (sec): 5.80 - samples/sec: 6656.89 - lr: 0.000030 - momentum: 0.000000
2023-10-18 23:59:12,737 epoch 2 - iter 722/3617 - loss 0.21259486 - time (sec): 11.30 - samples/sec: 6753.82 - lr: 0.000029 - momentum: 0.000000
2023-10-18 23:59:18,069 epoch 2 - iter 1083/3617 - loss 0.21274781 - time (sec): 16.63 - samples/sec: 6784.57 - lr: 0.000029 - momentum: 0.000000
2023-10-18 23:59:23,748 epoch 2 - iter 1444/3617 - loss 0.21183692 - time (sec): 22.31 - samples/sec: 6787.56 - lr: 0.000029 - momentum: 0.000000
2023-10-18 23:59:29,474 epoch 2 - iter 1805/3617 - loss 0.20972085 - time (sec): 28.03 - samples/sec: 6761.65 - lr: 0.000028 - momentum: 0.000000
2023-10-18 23:59:35,061 epoch 2 - iter 2166/3617 - loss 0.20622936 - time (sec): 33.62 - samples/sec: 6745.79 - lr: 0.000028 - momentum: 0.000000
2023-10-18 23:59:40,816 epoch 2 - iter 2527/3617 - loss 0.20279605 - time (sec): 39.38 - samples/sec: 6723.03 - lr: 0.000028 - momentum: 0.000000
2023-10-18 23:59:46,581 epoch 2 - iter 2888/3617 - loss 0.20277512 - time (sec): 45.14 - samples/sec: 6715.36 - lr: 0.000027 - momentum: 0.000000
2023-10-18 23:59:52,401 epoch 2 - iter 3249/3617 - loss 0.19959401 - time (sec): 50.96 - samples/sec: 6718.71 - lr: 0.000027 - momentum: 0.000000
2023-10-18 23:59:58,072 epoch 2 - iter 3610/3617 - loss 0.19787867 - time (sec): 56.63 - samples/sec: 6698.97 - lr: 0.000027 - momentum: 0.000000
2023-10-18 23:59:58,174 ----------------------------------------------------------------------------------------------------
2023-10-18 23:59:58,174 EPOCH 2 done: loss 0.1979 - lr: 0.000027
2023-10-19 00:00:02,011 DEV : loss 0.17745766043663025 - f1-score (micro avg)  0.3099
2023-10-19 00:00:02,038 saving best model
2023-10-19 00:00:02,071 ----------------------------------------------------------------------------------------------------
2023-10-19 00:00:07,839 epoch 3 - iter 361/3617 - loss 0.17426994 - time (sec): 5.77 - samples/sec: 6426.35 - lr: 0.000026 - momentum: 0.000000
2023-10-19 00:00:13,678 epoch 3 - iter 722/3617 - loss 0.18366482 - time (sec): 11.61 - samples/sec: 6425.53 - lr: 0.000026 - momentum: 0.000000
2023-10-19 00:00:19,477 epoch 3 - iter 1083/3617 - loss 0.18265066 - time (sec): 17.41 - samples/sec: 6527.22 - lr: 0.000026 - momentum: 0.000000
2023-10-19 00:00:25,181 epoch 3 - iter 1444/3617 - loss 0.17944185 - time (sec): 23.11 - samples/sec: 6560.81 - lr: 0.000025 - momentum: 0.000000
2023-10-19 00:00:30,946 epoch 3 - iter 1805/3617 - loss 0.17792967 - time (sec): 28.87 - samples/sec: 6492.08 - lr: 0.000025 - momentum: 0.000000
2023-10-19 00:00:36,733 epoch 3 - iter 2166/3617 - loss 0.17586984 - time (sec): 34.66 - samples/sec: 6505.40 - lr: 0.000025 - momentum: 0.000000
2023-10-19 00:00:42,495 epoch 3 - iter 2527/3617 - loss 0.17550889 - time (sec): 40.42 - samples/sec: 6522.07 - lr: 0.000024 - momentum: 0.000000
2023-10-19 00:00:47,896 epoch 3 - iter 2888/3617 - loss 0.17374263 - time (sec): 45.82 - samples/sec: 6581.15 - lr: 0.000024 - momentum: 0.000000
2023-10-19 00:00:53,585 epoch 3 - iter 3249/3617 - loss 0.17360638 - time (sec): 51.51 - samples/sec: 6613.70 - lr: 0.000024 - momentum: 0.000000
2023-10-19 00:00:59,356 epoch 3 - iter 3610/3617 - loss 0.17167513 - time (sec): 57.28 - samples/sec: 6619.96 - lr: 0.000023 - momentum: 0.000000
2023-10-19 00:00:59,468 ----------------------------------------------------------------------------------------------------
2023-10-19 00:00:59,468 EPOCH 3 done: loss 0.1717 - lr: 0.000023
2023-10-19 00:01:02,699 DEV : loss 0.1712975651025772 - f1-score (micro avg)  0.3656
2023-10-19 00:01:02,726 saving best model
2023-10-19 00:01:02,762 ----------------------------------------------------------------------------------------------------
2023-10-19 00:01:08,557 epoch 4 - iter 361/3617 - loss 0.17147098 - time (sec): 5.79 - samples/sec: 6526.12 - lr: 0.000023 - momentum: 0.000000
2023-10-19 00:01:14,379 epoch 4 - iter 722/3617 - loss 0.16727696 - time (sec): 11.62 - samples/sec: 6680.36 - lr: 0.000023 - momentum: 0.000000
2023-10-19 00:01:20,265 epoch 4 - iter 1083/3617 - loss 0.15984749 - time (sec): 17.50 - samples/sec: 6583.25 - lr: 0.000022 - momentum: 0.000000
2023-10-19 00:01:25,975 epoch 4 - iter 1444/3617 - loss 0.15449183 - time (sec): 23.21 - samples/sec: 6553.37 - lr: 0.000022 - momentum: 0.000000
2023-10-19 00:01:31,736 epoch 4 - iter 1805/3617 - loss 0.15441485 - time (sec): 28.97 - samples/sec: 6545.80 - lr: 0.000022 - momentum: 0.000000
2023-10-19 00:01:37,433 epoch 4 - iter 2166/3617 - loss 0.15394200 - time (sec): 34.67 - samples/sec: 6594.35 - lr: 0.000021 - momentum: 0.000000
2023-10-19 00:01:42,927 epoch 4 - iter 2527/3617 - loss 0.15538219 - time (sec): 40.16 - samples/sec: 6635.54 - lr: 0.000021 - momentum: 0.000000
2023-10-19 00:01:48,596 epoch 4 - iter 2888/3617 - loss 0.15441080 - time (sec): 45.83 - samples/sec: 6624.20 - lr: 0.000021 - momentum: 0.000000
2023-10-19 00:01:54,263 epoch 4 - iter 3249/3617 - loss 0.15565106 - time (sec): 51.50 - samples/sec: 6621.64 - lr: 0.000020 - momentum: 0.000000
2023-10-19 00:02:00,045 epoch 4 - iter 3610/3617 - loss 0.15428029 - time (sec): 57.28 - samples/sec: 6620.88 - lr: 0.000020 - momentum: 0.000000
2023-10-19 00:02:00,158 ----------------------------------------------------------------------------------------------------
2023-10-19 00:02:00,158 EPOCH 4 done: loss 0.1544 - lr: 0.000020
2023-10-19 00:02:03,402 DEV : loss 0.16668201982975006 - f1-score (micro avg)  0.4035
2023-10-19 00:02:03,430 saving best model
2023-10-19 00:02:03,466 ----------------------------------------------------------------------------------------------------
2023-10-19 00:02:09,207 epoch 5 - iter 361/3617 - loss 0.12821978 - time (sec): 5.74 - samples/sec: 6756.08 - lr: 0.000020 - momentum: 0.000000
2023-10-19 00:02:14,914 epoch 5 - iter 722/3617 - loss 0.13135493 - time (sec): 11.45 - samples/sec: 6814.87 - lr: 0.000019 - momentum: 0.000000
2023-10-19 00:02:20,535 epoch 5 - iter 1083/3617 - loss 0.12991170 - time (sec): 17.07 - samples/sec: 6702.45 - lr: 0.000019 - momentum: 0.000000
2023-10-19 00:02:26,234 epoch 5 - iter 1444/3617 - loss 0.13451604 - time (sec): 22.77 - samples/sec: 6671.61 - lr: 0.000019 - momentum: 0.000000
2023-10-19 00:02:31,990 epoch 5 - iter 1805/3617 - loss 0.13846108 - time (sec): 28.52 - samples/sec: 6607.20 - lr: 0.000018 - momentum: 0.000000
2023-10-19 00:02:37,703 epoch 5 - iter 2166/3617 - loss 0.13989470 - time (sec): 34.24 - samples/sec: 6625.27 - lr: 0.000018 - momentum: 0.000000
2023-10-19 00:02:43,353 epoch 5 - iter 2527/3617 - loss 0.14171266 - time (sec): 39.89 - samples/sec: 6608.77 - lr: 0.000018 - momentum: 0.000000
2023-10-19 00:02:48,838 epoch 5 - iter 2888/3617 - loss 0.14217721 - time (sec): 45.37 - samples/sec: 6650.03 - lr: 0.000017 - momentum: 0.000000
2023-10-19 00:02:54,617 epoch 5 - iter 3249/3617 - loss 0.14146502 - time (sec): 51.15 - samples/sec: 6645.97 - lr: 0.000017 - momentum: 0.000000
2023-10-19 00:03:00,552 epoch 5 - iter 3610/3617 - loss 0.14181052 - time (sec): 57.08 - samples/sec: 6643.68 - lr: 0.000017 - momentum: 0.000000
2023-10-19 00:03:00,655 ----------------------------------------------------------------------------------------------------
2023-10-19 00:03:00,655 EPOCH 5 done: loss 0.1417 - lr: 0.000017
2023-10-19 00:03:04,530 DEV : loss 0.18115580081939697 - f1-score (micro avg)  0.4293
2023-10-19 00:03:04,560 saving best model
2023-10-19 00:03:04,596 ----------------------------------------------------------------------------------------------------
2023-10-19 00:03:10,102 epoch 6 - iter 361/3617 - loss 0.14639806 - time (sec): 5.51 - samples/sec: 6548.38 - lr: 0.000016 - momentum: 0.000000
2023-10-19 00:03:15,855 epoch 6 - iter 722/3617 - loss 0.13502357 - time (sec): 11.26 - samples/sec: 6600.08 - lr: 0.000016 - momentum: 0.000000
2023-10-19 00:03:21,611 epoch 6 - iter 1083/3617 - loss 0.13028085 - time (sec): 17.01 - samples/sec: 6543.90 - lr: 0.000016 - momentum: 0.000000
2023-10-19 00:03:27,331 epoch 6 - iter 1444/3617 - loss 0.13278529 - time (sec): 22.74 - samples/sec: 6549.58 - lr: 0.000015 - momentum: 0.000000
2023-10-19 00:03:33,378 epoch 6 - iter 1805/3617 - loss 0.13346841 - time (sec): 28.78 - samples/sec: 6538.15 - lr: 0.000015 - momentum: 0.000000
2023-10-19 00:03:39,165 epoch 6 - iter 2166/3617 - loss 0.13376779 - time (sec): 34.57 - samples/sec: 6533.47 - lr: 0.000015 - momentum: 0.000000
2023-10-19 00:03:44,876 epoch 6 - iter 2527/3617 - loss 0.13205667 - time (sec): 40.28 - samples/sec: 6534.50 - lr: 0.000014 - momentum: 0.000000
2023-10-19 00:03:50,571 epoch 6 - iter 2888/3617 - loss 0.13254041 - time (sec): 45.97 - samples/sec: 6528.00 - lr: 0.000014 - momentum: 0.000000
2023-10-19 00:03:56,310 epoch 6 - iter 3249/3617 - loss 0.13140323 - time (sec): 51.71 - samples/sec: 6560.47 - lr: 0.000014 - momentum: 0.000000
2023-10-19 00:04:02,061 epoch 6 - iter 3610/3617 - loss 0.13177254 - time (sec): 57.46 - samples/sec: 6601.78 - lr: 0.000013 - momentum: 0.000000
2023-10-19 00:04:02,157 ----------------------------------------------------------------------------------------------------
2023-10-19 00:04:02,157 EPOCH 6 done: loss 0.1317 - lr: 0.000013
2023-10-19 00:04:05,350 DEV : loss 0.17110641300678253 - f1-score (micro avg)  0.4568
2023-10-19 00:04:05,378 saving best model
2023-10-19 00:04:05,412 ----------------------------------------------------------------------------------------------------
2023-10-19 00:04:11,107 epoch 7 - iter 361/3617 - loss 0.12258612 - time (sec): 5.69 - samples/sec: 6561.55 - lr: 0.000013 - momentum: 0.000000
2023-10-19 00:04:16,503 epoch 7 - iter 722/3617 - loss 0.12769325 - time (sec): 11.09 - samples/sec: 6782.46 - lr: 0.000013 - momentum: 0.000000
2023-10-19 00:04:22,269 epoch 7 - iter 1083/3617 - loss 0.12762667 - time (sec): 16.86 - samples/sec: 6690.09 - lr: 0.000012 - momentum: 0.000000
2023-10-19 00:04:27,932 epoch 7 - iter 1444/3617 - loss 0.13095263 - time (sec): 22.52 - samples/sec: 6642.13 - lr: 0.000012 - momentum: 0.000000
2023-10-19 00:04:33,641 epoch 7 - iter 1805/3617 - loss 0.13129488 - time (sec): 28.23 - samples/sec: 6607.53 - lr: 0.000012 - momentum: 0.000000
2023-10-19 00:04:39,080 epoch 7 - iter 2166/3617 - loss 0.12959570 - time (sec): 33.67 - samples/sec: 6698.16 - lr: 0.000011 - momentum: 0.000000
2023-10-19 00:04:44,807 epoch 7 - iter 2527/3617 - loss 0.12880100 - time (sec): 39.39 - samples/sec: 6687.27 - lr: 0.000011 - momentum: 0.000000
2023-10-19 00:04:50,566 epoch 7 - iter 2888/3617 - loss 0.12959427 - time (sec): 45.15 - samples/sec: 6685.86 - lr: 0.000011 - momentum: 0.000000
2023-10-19 00:04:56,329 epoch 7 - iter 3249/3617 - loss 0.12780021 - time (sec): 50.92 - samples/sec: 6695.46 - lr: 0.000010 - momentum: 0.000000
2023-10-19 00:05:02,113 epoch 7 - iter 3610/3617 - loss 0.12697124 - time (sec): 56.70 - samples/sec: 6685.95 - lr: 0.000010 - momentum: 0.000000
2023-10-19 00:05:02,216 ----------------------------------------------------------------------------------------------------
2023-10-19 00:05:02,217 EPOCH 7 done: loss 0.1269 - lr: 0.000010
2023-10-19 00:05:06,100 DEV : loss 0.1817493438720703 - f1-score (micro avg)  0.4622
2023-10-19 00:05:06,128 saving best model
2023-10-19 00:05:06,166 ----------------------------------------------------------------------------------------------------
2023-10-19 00:05:11,879 epoch 8 - iter 361/3617 - loss 0.11567923 - time (sec): 5.71 - samples/sec: 6721.37 - lr: 0.000010 - momentum: 0.000000
2023-10-19 00:05:17,542 epoch 8 - iter 722/3617 - loss 0.11726755 - time (sec): 11.38 - samples/sec: 6656.67 - lr: 0.000009 - momentum: 0.000000
2023-10-19 00:05:23,292 epoch 8 - iter 1083/3617 - loss 0.11848005 - time (sec): 17.13 - samples/sec: 6656.08 - lr: 0.000009 - momentum: 0.000000
2023-10-19 00:05:29,007 epoch 8 - iter 1444/3617 - loss 0.12044334 - time (sec): 22.84 - samples/sec: 6662.48 - lr: 0.000009 - momentum: 0.000000
2023-10-19 00:05:34,761 epoch 8 - iter 1805/3617 - loss 0.12046610 - time (sec): 28.59 - samples/sec: 6689.38 - lr: 0.000008 - momentum: 0.000000
2023-10-19 00:05:40,271 epoch 8 - iter 2166/3617 - loss 0.12042788 - time (sec): 34.10 - samples/sec: 6663.89 - lr: 0.000008 - momentum: 0.000000
2023-10-19 00:05:45,980 epoch 8 - iter 2527/3617 - loss 0.11893839 - time (sec): 39.81 - samples/sec: 6641.06 - lr: 0.000008 - momentum: 0.000000
2023-10-19 00:05:51,718 epoch 8 - iter 2888/3617 - loss 0.11857905 - time (sec): 45.55 - samples/sec: 6625.96 - lr: 0.000007 - momentum: 0.000000
2023-10-19 00:05:57,262 epoch 8 - iter 3249/3617 - loss 0.12005226 - time (sec): 51.10 - samples/sec: 6673.14 - lr: 0.000007 - momentum: 0.000000
2023-10-19 00:06:02,972 epoch 8 - iter 3610/3617 - loss 0.12081155 - time (sec): 56.81 - samples/sec: 6675.12 - lr: 0.000007 - momentum: 0.000000
2023-10-19 00:06:03,084 ----------------------------------------------------------------------------------------------------
2023-10-19 00:06:03,084 EPOCH 8 done: loss 0.1209 - lr: 0.000007
2023-10-19 00:06:06,316 DEV : loss 0.18522560596466064 - f1-score (micro avg)  0.4627
2023-10-19 00:06:06,345 saving best model
2023-10-19 00:06:06,383 ----------------------------------------------------------------------------------------------------
2023-10-19 00:06:12,202 epoch 9 - iter 361/3617 - loss 0.11578345 - time (sec): 5.82 - samples/sec: 6446.83 - lr: 0.000006 - momentum: 0.000000
2023-10-19 00:06:17,868 epoch 9 - iter 722/3617 - loss 0.11658425 - time (sec): 11.48 - samples/sec: 6471.78 - lr: 0.000006 - momentum: 0.000000
2023-10-19 00:06:23,568 epoch 9 - iter 1083/3617 - loss 0.11552563 - time (sec): 17.18 - samples/sec: 6563.38 - lr: 0.000006 - momentum: 0.000000
2023-10-19 00:06:29,261 epoch 9 - iter 1444/3617 - loss 0.11439846 - time (sec): 22.88 - samples/sec: 6626.62 - lr: 0.000005 - momentum: 0.000000
2023-10-19 00:06:35,034 epoch 9 - iter 1805/3617 - loss 0.11471886 - time (sec): 28.65 - samples/sec: 6609.74 - lr: 0.000005 - momentum: 0.000000
2023-10-19 00:06:40,941 epoch 9 - iter 2166/3617 - loss 0.11616035 - time (sec): 34.56 - samples/sec: 6631.29 - lr: 0.000005 - momentum: 0.000000
2023-10-19 00:06:46,712 epoch 9 - iter 2527/3617 - loss 0.11804181 - time (sec): 40.33 - samples/sec: 6595.53 - lr: 0.000004 - momentum: 0.000000
2023-10-19 00:06:52,350 epoch 9 - iter 2888/3617 - loss 0.11740974 - time (sec): 45.97 - samples/sec: 6639.79 - lr: 0.000004 - momentum: 0.000000
2023-10-19 00:06:57,979 epoch 9 - iter 3249/3617 - loss 0.11893294 - time (sec): 51.60 - samples/sec: 6632.09 - lr: 0.000004 - momentum: 0.000000
2023-10-19 00:07:03,694 epoch 9 - iter 3610/3617 - loss 0.11887323 - time (sec): 57.31 - samples/sec: 6621.10 - lr: 0.000003 - momentum: 0.000000
2023-10-19 00:07:03,798 ----------------------------------------------------------------------------------------------------
2023-10-19 00:07:03,798 EPOCH 9 done: loss 0.1190 - lr: 0.000003
2023-10-19 00:07:07,005 DEV : loss 0.18721944093704224 - f1-score (micro avg)  0.47
2023-10-19 00:07:07,036 saving best model
2023-10-19 00:07:07,071 ----------------------------------------------------------------------------------------------------
2023-10-19 00:07:12,683 epoch 10 - iter 361/3617 - loss 0.11147538 - time (sec): 5.61 - samples/sec: 6402.00 - lr: 0.000003 - momentum: 0.000000
2023-10-19 00:07:18,285 epoch 10 - iter 722/3617 - loss 0.11257207 - time (sec): 11.21 - samples/sec: 6642.89 - lr: 0.000003 - momentum: 0.000000
2023-10-19 00:07:23,989 epoch 10 - iter 1083/3617 - loss 0.11426618 - time (sec): 16.92 - samples/sec: 6584.35 - lr: 0.000002 - momentum: 0.000000
2023-10-19 00:07:29,688 epoch 10 - iter 1444/3617 - loss 0.11495492 - time (sec): 22.62 - samples/sec: 6615.37 - lr: 0.000002 - momentum: 0.000000
2023-10-19 00:07:35,257 epoch 10 - iter 1805/3617 - loss 0.11633593 - time (sec): 28.19 - samples/sec: 6670.70 - lr: 0.000002 - momentum: 0.000000
2023-10-19 00:07:41,001 epoch 10 - iter 2166/3617 - loss 0.11656673 - time (sec): 33.93 - samples/sec: 6654.08 - lr: 0.000001 - momentum: 0.000000
2023-10-19 00:07:46,840 epoch 10 - iter 2527/3617 - loss 0.11556910 - time (sec): 39.77 - samples/sec: 6663.47 - lr: 0.000001 - momentum: 0.000000
2023-10-19 00:07:52,358 epoch 10 - iter 2888/3617 - loss 0.11448283 - time (sec): 45.29 - samples/sec: 6683.36 - lr: 0.000001 - momentum: 0.000000
2023-10-19 00:07:58,097 epoch 10 - iter 3249/3617 - loss 0.11653997 - time (sec): 51.03 - samples/sec: 6710.72 - lr: 0.000000 - momentum: 0.000000
2023-10-19 00:08:03,672 epoch 10 - iter 3610/3617 - loss 0.11682170 - time (sec): 56.60 - samples/sec: 6699.07 - lr: 0.000000 - momentum: 0.000000
2023-10-19 00:08:03,781 ----------------------------------------------------------------------------------------------------
2023-10-19 00:08:03,782 EPOCH 10 done: loss 0.1167 - lr: 0.000000
2023-10-19 00:08:07,665 DEV : loss 0.18911151587963104 - f1-score (micro avg)  0.4703
2023-10-19 00:08:07,693 saving best model
2023-10-19 00:08:07,758 ----------------------------------------------------------------------------------------------------
2023-10-19 00:08:07,758 Loading model from best epoch ...
2023-10-19 00:08:07,838 SequenceTagger predicts: Dictionary with 13 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
2023-10-19 00:08:11,239 
Results:
- F-score (micro) 0.5026
- F-score (macro) 0.3266
- Accuracy 0.3503

By class:
              precision    recall  f1-score   support

         loc     0.5060    0.7140    0.5923       591
        pers     0.3636    0.4146    0.3874       357
         org     0.0000    0.0000    0.0000        79

   micro avg     0.4593    0.5550    0.5026      1027
   macro avg     0.2899    0.3762    0.3266      1027
weighted avg     0.4176    0.5550    0.4755      1027

2023-10-19 00:08:11,239 ----------------------------------------------------------------------------------------------------