2023-10-17 20:35:39,058 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,058 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 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
 - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 Train:  5901 sentences
2023-10-17 20:35:39,059         (train_with_dev=False, train_with_test=False)
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 Training Params:
2023-10-17 20:35:39,059  - learning_rate: "3e-05" 
2023-10-17 20:35:39,059  - mini_batch_size: "8"
2023-10-17 20:35:39,059  - max_epochs: "10"
2023-10-17 20:35:39,059  - shuffle: "True"
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 Plugins:
2023-10-17 20:35:39,059  - TensorboardLogger
2023-10-17 20:35:39,059  - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 20:35:39,059  - metric: "('micro avg', 'f1-score')"
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 Computation:
2023-10-17 20:35:39,059  - compute on device: cuda:0
2023-10-17 20:35:39,059  - embedding storage: none
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 Model training base path: "hmbench-hipe2020/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,059 ----------------------------------------------------------------------------------------------------
2023-10-17 20:35:39,060 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 20:35:44,132 epoch 1 - iter 73/738 - loss 3.64116980 - time (sec): 5.07 - samples/sec: 3162.96 - lr: 0.000003 - momentum: 0.000000
2023-10-17 20:35:49,571 epoch 1 - iter 146/738 - loss 2.18983601 - time (sec): 10.51 - samples/sec: 3329.36 - lr: 0.000006 - momentum: 0.000000
2023-10-17 20:35:54,419 epoch 1 - iter 219/738 - loss 1.69219914 - time (sec): 15.36 - samples/sec: 3248.03 - lr: 0.000009 - momentum: 0.000000
2023-10-17 20:35:59,996 epoch 1 - iter 292/738 - loss 1.37862460 - time (sec): 20.94 - samples/sec: 3185.56 - lr: 0.000012 - momentum: 0.000000
2023-10-17 20:36:05,134 epoch 1 - iter 365/738 - loss 1.18355123 - time (sec): 26.07 - samples/sec: 3175.50 - lr: 0.000015 - momentum: 0.000000
2023-10-17 20:36:10,456 epoch 1 - iter 438/738 - loss 1.04194700 - time (sec): 31.39 - samples/sec: 3148.63 - lr: 0.000018 - momentum: 0.000000
2023-10-17 20:36:15,936 epoch 1 - iter 511/738 - loss 0.93151159 - time (sec): 36.88 - samples/sec: 3129.66 - lr: 0.000021 - momentum: 0.000000
2023-10-17 20:36:20,907 epoch 1 - iter 584/738 - loss 0.84481528 - time (sec): 41.85 - samples/sec: 3135.43 - lr: 0.000024 - momentum: 0.000000
2023-10-17 20:36:25,940 epoch 1 - iter 657/738 - loss 0.77413535 - time (sec): 46.88 - samples/sec: 3158.04 - lr: 0.000027 - momentum: 0.000000
2023-10-17 20:36:31,086 epoch 1 - iter 730/738 - loss 0.71881625 - time (sec): 52.03 - samples/sec: 3151.08 - lr: 0.000030 - momentum: 0.000000
2023-10-17 20:36:31,913 ----------------------------------------------------------------------------------------------------
2023-10-17 20:36:31,914 EPOCH 1 done: loss 0.7104 - lr: 0.000030
2023-10-17 20:36:38,260 DEV : loss 0.12364602833986282 - f1-score (micro avg)  0.7098
2023-10-17 20:36:38,292 saving best model
2023-10-17 20:36:38,694 ----------------------------------------------------------------------------------------------------
2023-10-17 20:36:43,332 epoch 2 - iter 73/738 - loss 0.13875612 - time (sec): 4.64 - samples/sec: 3216.42 - lr: 0.000030 - momentum: 0.000000
2023-10-17 20:36:48,882 epoch 2 - iter 146/738 - loss 0.15187587 - time (sec): 10.19 - samples/sec: 3250.23 - lr: 0.000029 - momentum: 0.000000
2023-10-17 20:36:54,024 epoch 2 - iter 219/738 - loss 0.15074889 - time (sec): 15.33 - samples/sec: 3273.00 - lr: 0.000029 - momentum: 0.000000
2023-10-17 20:36:59,600 epoch 2 - iter 292/738 - loss 0.14401873 - time (sec): 20.91 - samples/sec: 3264.88 - lr: 0.000029 - momentum: 0.000000
2023-10-17 20:37:05,153 epoch 2 - iter 365/738 - loss 0.14152811 - time (sec): 26.46 - samples/sec: 3253.39 - lr: 0.000028 - momentum: 0.000000
2023-10-17 20:37:10,261 epoch 2 - iter 438/738 - loss 0.13657078 - time (sec): 31.57 - samples/sec: 3243.60 - lr: 0.000028 - momentum: 0.000000
2023-10-17 20:37:15,103 epoch 2 - iter 511/738 - loss 0.13404559 - time (sec): 36.41 - samples/sec: 3230.17 - lr: 0.000028 - momentum: 0.000000
2023-10-17 20:37:19,610 epoch 2 - iter 584/738 - loss 0.13244950 - time (sec): 40.92 - samples/sec: 3243.80 - lr: 0.000027 - momentum: 0.000000
2023-10-17 20:37:24,161 epoch 2 - iter 657/738 - loss 0.12901636 - time (sec): 45.47 - samples/sec: 3265.18 - lr: 0.000027 - momentum: 0.000000
2023-10-17 20:37:29,190 epoch 2 - iter 730/738 - loss 0.12733023 - time (sec): 50.49 - samples/sec: 3266.90 - lr: 0.000027 - momentum: 0.000000
2023-10-17 20:37:29,645 ----------------------------------------------------------------------------------------------------
2023-10-17 20:37:29,646 EPOCH 2 done: loss 0.1272 - lr: 0.000027
2023-10-17 20:37:41,241 DEV : loss 0.11324623972177505 - f1-score (micro avg)  0.7878
2023-10-17 20:37:41,272 saving best model
2023-10-17 20:37:41,764 ----------------------------------------------------------------------------------------------------
2023-10-17 20:37:46,421 epoch 3 - iter 73/738 - loss 0.08078576 - time (sec): 4.66 - samples/sec: 3186.13 - lr: 0.000026 - momentum: 0.000000
2023-10-17 20:37:51,451 epoch 3 - iter 146/738 - loss 0.08098219 - time (sec): 9.69 - samples/sec: 3153.82 - lr: 0.000026 - momentum: 0.000000
2023-10-17 20:37:56,715 epoch 3 - iter 219/738 - loss 0.07988876 - time (sec): 14.95 - samples/sec: 3177.16 - lr: 0.000026 - momentum: 0.000000
2023-10-17 20:38:01,548 epoch 3 - iter 292/738 - loss 0.08049920 - time (sec): 19.78 - samples/sec: 3235.71 - lr: 0.000025 - momentum: 0.000000
2023-10-17 20:38:06,420 epoch 3 - iter 365/738 - loss 0.07427187 - time (sec): 24.65 - samples/sec: 3258.39 - lr: 0.000025 - momentum: 0.000000
2023-10-17 20:38:11,210 epoch 3 - iter 438/738 - loss 0.07767398 - time (sec): 29.44 - samples/sec: 3263.15 - lr: 0.000025 - momentum: 0.000000
2023-10-17 20:38:17,124 epoch 3 - iter 511/738 - loss 0.07609348 - time (sec): 35.36 - samples/sec: 3250.97 - lr: 0.000024 - momentum: 0.000000
2023-10-17 20:38:22,355 epoch 3 - iter 584/738 - loss 0.07449298 - time (sec): 40.59 - samples/sec: 3244.97 - lr: 0.000024 - momentum: 0.000000
2023-10-17 20:38:27,736 epoch 3 - iter 657/738 - loss 0.07310394 - time (sec): 45.97 - samples/sec: 3231.28 - lr: 0.000024 - momentum: 0.000000
2023-10-17 20:38:32,682 epoch 3 - iter 730/738 - loss 0.07307713 - time (sec): 50.92 - samples/sec: 3234.62 - lr: 0.000023 - momentum: 0.000000
2023-10-17 20:38:33,210 ----------------------------------------------------------------------------------------------------
2023-10-17 20:38:33,211 EPOCH 3 done: loss 0.0731 - lr: 0.000023
2023-10-17 20:38:44,573 DEV : loss 0.10576911270618439 - f1-score (micro avg)  0.8202
2023-10-17 20:38:44,603 saving best model
2023-10-17 20:38:45,099 ----------------------------------------------------------------------------------------------------
2023-10-17 20:38:50,418 epoch 4 - iter 73/738 - loss 0.04149513 - time (sec): 5.31 - samples/sec: 3190.23 - lr: 0.000023 - momentum: 0.000000
2023-10-17 20:38:56,360 epoch 4 - iter 146/738 - loss 0.04069771 - time (sec): 11.26 - samples/sec: 3121.34 - lr: 0.000023 - momentum: 0.000000
2023-10-17 20:39:01,363 epoch 4 - iter 219/738 - loss 0.04207686 - time (sec): 16.26 - samples/sec: 3158.45 - lr: 0.000022 - momentum: 0.000000
2023-10-17 20:39:06,635 epoch 4 - iter 292/738 - loss 0.04550866 - time (sec): 21.53 - samples/sec: 3158.71 - lr: 0.000022 - momentum: 0.000000
2023-10-17 20:39:11,345 epoch 4 - iter 365/738 - loss 0.04746098 - time (sec): 26.24 - samples/sec: 3176.94 - lr: 0.000022 - momentum: 0.000000
2023-10-17 20:39:15,967 epoch 4 - iter 438/738 - loss 0.04785900 - time (sec): 30.86 - samples/sec: 3208.66 - lr: 0.000021 - momentum: 0.000000
2023-10-17 20:39:20,773 epoch 4 - iter 511/738 - loss 0.04796136 - time (sec): 35.67 - samples/sec: 3231.21 - lr: 0.000021 - momentum: 0.000000
2023-10-17 20:39:25,854 epoch 4 - iter 584/738 - loss 0.04916438 - time (sec): 40.75 - samples/sec: 3241.96 - lr: 0.000021 - momentum: 0.000000
2023-10-17 20:39:31,026 epoch 4 - iter 657/738 - loss 0.04802511 - time (sec): 45.92 - samples/sec: 3255.52 - lr: 0.000020 - momentum: 0.000000
2023-10-17 20:39:35,741 epoch 4 - iter 730/738 - loss 0.04827589 - time (sec): 50.64 - samples/sec: 3252.29 - lr: 0.000020 - momentum: 0.000000
2023-10-17 20:39:36,300 ----------------------------------------------------------------------------------------------------
2023-10-17 20:39:36,301 EPOCH 4 done: loss 0.0481 - lr: 0.000020
2023-10-17 20:39:47,755 DEV : loss 0.14439940452575684 - f1-score (micro avg)  0.8331
2023-10-17 20:39:47,786 saving best model
2023-10-17 20:39:48,346 ----------------------------------------------------------------------------------------------------
2023-10-17 20:39:53,447 epoch 5 - iter 73/738 - loss 0.03820328 - time (sec): 5.10 - samples/sec: 3420.65 - lr: 0.000020 - momentum: 0.000000
2023-10-17 20:39:58,482 epoch 5 - iter 146/738 - loss 0.03746306 - time (sec): 10.14 - samples/sec: 3361.46 - lr: 0.000019 - momentum: 0.000000
2023-10-17 20:40:03,991 epoch 5 - iter 219/738 - loss 0.03939664 - time (sec): 15.64 - samples/sec: 3326.58 - lr: 0.000019 - momentum: 0.000000
2023-10-17 20:40:09,113 epoch 5 - iter 292/738 - loss 0.03908194 - time (sec): 20.77 - samples/sec: 3301.73 - lr: 0.000019 - momentum: 0.000000
2023-10-17 20:40:14,194 epoch 5 - iter 365/738 - loss 0.03717953 - time (sec): 25.85 - samples/sec: 3302.44 - lr: 0.000018 - momentum: 0.000000
2023-10-17 20:40:19,366 epoch 5 - iter 438/738 - loss 0.03476013 - time (sec): 31.02 - samples/sec: 3294.32 - lr: 0.000018 - momentum: 0.000000
2023-10-17 20:40:23,875 epoch 5 - iter 511/738 - loss 0.03534706 - time (sec): 35.53 - samples/sec: 3291.70 - lr: 0.000018 - momentum: 0.000000
2023-10-17 20:40:28,537 epoch 5 - iter 584/738 - loss 0.03692831 - time (sec): 40.19 - samples/sec: 3287.11 - lr: 0.000017 - momentum: 0.000000
2023-10-17 20:40:33,247 epoch 5 - iter 657/738 - loss 0.03670076 - time (sec): 44.90 - samples/sec: 3298.03 - lr: 0.000017 - momentum: 0.000000
2023-10-17 20:40:38,478 epoch 5 - iter 730/738 - loss 0.03777783 - time (sec): 50.13 - samples/sec: 3291.89 - lr: 0.000017 - momentum: 0.000000
2023-10-17 20:40:38,944 ----------------------------------------------------------------------------------------------------
2023-10-17 20:40:38,944 EPOCH 5 done: loss 0.0376 - lr: 0.000017
2023-10-17 20:40:50,539 DEV : loss 0.1691051870584488 - f1-score (micro avg)  0.8473
2023-10-17 20:40:50,572 saving best model
2023-10-17 20:40:51,123 ----------------------------------------------------------------------------------------------------
2023-10-17 20:40:56,085 epoch 6 - iter 73/738 - loss 0.02494294 - time (sec): 4.96 - samples/sec: 3274.56 - lr: 0.000016 - momentum: 0.000000
2023-10-17 20:41:01,232 epoch 6 - iter 146/738 - loss 0.02742836 - time (sec): 10.11 - samples/sec: 3184.58 - lr: 0.000016 - momentum: 0.000000
2023-10-17 20:41:05,758 epoch 6 - iter 219/738 - loss 0.02446918 - time (sec): 14.63 - samples/sec: 3219.58 - lr: 0.000016 - momentum: 0.000000
2023-10-17 20:41:10,892 epoch 6 - iter 292/738 - loss 0.02160983 - time (sec): 19.77 - samples/sec: 3227.62 - lr: 0.000015 - momentum: 0.000000
2023-10-17 20:41:16,184 epoch 6 - iter 365/738 - loss 0.02527468 - time (sec): 25.06 - samples/sec: 3203.28 - lr: 0.000015 - momentum: 0.000000
2023-10-17 20:41:20,766 epoch 6 - iter 438/738 - loss 0.02394053 - time (sec): 29.64 - samples/sec: 3234.50 - lr: 0.000015 - momentum: 0.000000
2023-10-17 20:41:25,447 epoch 6 - iter 511/738 - loss 0.02404647 - time (sec): 34.32 - samples/sec: 3250.82 - lr: 0.000014 - momentum: 0.000000
2023-10-17 20:41:30,296 epoch 6 - iter 584/738 - loss 0.02483385 - time (sec): 39.17 - samples/sec: 3245.50 - lr: 0.000014 - momentum: 0.000000
2023-10-17 20:41:36,103 epoch 6 - iter 657/738 - loss 0.02533529 - time (sec): 44.98 - samples/sec: 3276.03 - lr: 0.000014 - momentum: 0.000000
2023-10-17 20:41:41,227 epoch 6 - iter 730/738 - loss 0.02453929 - time (sec): 50.10 - samples/sec: 3274.64 - lr: 0.000013 - momentum: 0.000000
2023-10-17 20:41:41,964 ----------------------------------------------------------------------------------------------------
2023-10-17 20:41:41,964 EPOCH 6 done: loss 0.0248 - lr: 0.000013
2023-10-17 20:41:53,305 DEV : loss 0.18655835092067719 - f1-score (micro avg)  0.8407
2023-10-17 20:41:53,335 ----------------------------------------------------------------------------------------------------
2023-10-17 20:41:58,205 epoch 7 - iter 73/738 - loss 0.01138158 - time (sec): 4.87 - samples/sec: 3127.58 - lr: 0.000013 - momentum: 0.000000
2023-10-17 20:42:02,805 epoch 7 - iter 146/738 - loss 0.01083901 - time (sec): 9.47 - samples/sec: 3295.87 - lr: 0.000013 - momentum: 0.000000
2023-10-17 20:42:07,435 epoch 7 - iter 219/738 - loss 0.01471937 - time (sec): 14.10 - samples/sec: 3243.52 - lr: 0.000012 - momentum: 0.000000
2023-10-17 20:42:12,317 epoch 7 - iter 292/738 - loss 0.01463951 - time (sec): 18.98 - samples/sec: 3268.16 - lr: 0.000012 - momentum: 0.000000
2023-10-17 20:42:17,142 epoch 7 - iter 365/738 - loss 0.01650812 - time (sec): 23.81 - samples/sec: 3279.92 - lr: 0.000012 - momentum: 0.000000
2023-10-17 20:42:22,200 epoch 7 - iter 438/738 - loss 0.01641925 - time (sec): 28.86 - samples/sec: 3325.52 - lr: 0.000011 - momentum: 0.000000
2023-10-17 20:42:28,377 epoch 7 - iter 511/738 - loss 0.01890794 - time (sec): 35.04 - samples/sec: 3327.95 - lr: 0.000011 - momentum: 0.000000
2023-10-17 20:42:33,160 epoch 7 - iter 584/738 - loss 0.01836577 - time (sec): 39.82 - samples/sec: 3322.87 - lr: 0.000011 - momentum: 0.000000
2023-10-17 20:42:38,103 epoch 7 - iter 657/738 - loss 0.01813755 - time (sec): 44.77 - samples/sec: 3319.80 - lr: 0.000010 - momentum: 0.000000
2023-10-17 20:42:43,114 epoch 7 - iter 730/738 - loss 0.01811864 - time (sec): 49.78 - samples/sec: 3308.21 - lr: 0.000010 - momentum: 0.000000
2023-10-17 20:42:43,689 ----------------------------------------------------------------------------------------------------
2023-10-17 20:42:43,690 EPOCH 7 done: loss 0.0186 - lr: 0.000010
2023-10-17 20:42:55,131 DEV : loss 0.1865713894367218 - f1-score (micro avg)  0.8508
2023-10-17 20:42:55,161 saving best model
2023-10-17 20:42:55,722 ----------------------------------------------------------------------------------------------------
2023-10-17 20:43:00,521 epoch 8 - iter 73/738 - loss 0.01395816 - time (sec): 4.79 - samples/sec: 3243.82 - lr: 0.000010 - momentum: 0.000000
2023-10-17 20:43:05,444 epoch 8 - iter 146/738 - loss 0.01071471 - time (sec): 9.72 - samples/sec: 3238.91 - lr: 0.000009 - momentum: 0.000000
2023-10-17 20:43:10,235 epoch 8 - iter 219/738 - loss 0.01056576 - time (sec): 14.51 - samples/sec: 3260.54 - lr: 0.000009 - momentum: 0.000000
2023-10-17 20:43:14,954 epoch 8 - iter 292/738 - loss 0.01129541 - time (sec): 19.23 - samples/sec: 3257.05 - lr: 0.000009 - momentum: 0.000000
2023-10-17 20:43:21,213 epoch 8 - iter 365/738 - loss 0.01372072 - time (sec): 25.49 - samples/sec: 3246.49 - lr: 0.000008 - momentum: 0.000000
2023-10-17 20:43:27,166 epoch 8 - iter 438/738 - loss 0.01307143 - time (sec): 31.44 - samples/sec: 3231.89 - lr: 0.000008 - momentum: 0.000000
2023-10-17 20:43:32,257 epoch 8 - iter 511/738 - loss 0.01235288 - time (sec): 36.53 - samples/sec: 3215.28 - lr: 0.000008 - momentum: 0.000000
2023-10-17 20:43:37,396 epoch 8 - iter 584/738 - loss 0.01288183 - time (sec): 41.67 - samples/sec: 3220.07 - lr: 0.000007 - momentum: 0.000000
2023-10-17 20:43:42,270 epoch 8 - iter 657/738 - loss 0.01324443 - time (sec): 46.54 - samples/sec: 3209.11 - lr: 0.000007 - momentum: 0.000000
2023-10-17 20:43:46,637 epoch 8 - iter 730/738 - loss 0.01347051 - time (sec): 50.91 - samples/sec: 3231.77 - lr: 0.000007 - momentum: 0.000000
2023-10-17 20:43:47,173 ----------------------------------------------------------------------------------------------------
2023-10-17 20:43:47,173 EPOCH 8 done: loss 0.0134 - lr: 0.000007
2023-10-17 20:43:58,642 DEV : loss 0.18905526399612427 - f1-score (micro avg)  0.8473
2023-10-17 20:43:58,674 ----------------------------------------------------------------------------------------------------
2023-10-17 20:44:03,908 epoch 9 - iter 73/738 - loss 0.01099258 - time (sec): 5.23 - samples/sec: 3427.72 - lr: 0.000006 - momentum: 0.000000
2023-10-17 20:44:09,018 epoch 9 - iter 146/738 - loss 0.00781828 - time (sec): 10.34 - samples/sec: 3329.22 - lr: 0.000006 - momentum: 0.000000
2023-10-17 20:44:13,566 epoch 9 - iter 219/738 - loss 0.00703626 - time (sec): 14.89 - samples/sec: 3350.47 - lr: 0.000006 - momentum: 0.000000
2023-10-17 20:44:18,952 epoch 9 - iter 292/738 - loss 0.00747490 - time (sec): 20.28 - samples/sec: 3299.06 - lr: 0.000005 - momentum: 0.000000
2023-10-17 20:44:23,717 epoch 9 - iter 365/738 - loss 0.00926992 - time (sec): 25.04 - samples/sec: 3298.05 - lr: 0.000005 - momentum: 0.000000
2023-10-17 20:44:28,988 epoch 9 - iter 438/738 - loss 0.00910148 - time (sec): 30.31 - samples/sec: 3298.16 - lr: 0.000005 - momentum: 0.000000
2023-10-17 20:44:34,231 epoch 9 - iter 511/738 - loss 0.01035846 - time (sec): 35.56 - samples/sec: 3282.09 - lr: 0.000004 - momentum: 0.000000
2023-10-17 20:44:39,225 epoch 9 - iter 584/738 - loss 0.01077101 - time (sec): 40.55 - samples/sec: 3279.83 - lr: 0.000004 - momentum: 0.000000
2023-10-17 20:44:44,390 epoch 9 - iter 657/738 - loss 0.01029712 - time (sec): 45.71 - samples/sec: 3280.07 - lr: 0.000004 - momentum: 0.000000
2023-10-17 20:44:48,891 epoch 9 - iter 730/738 - loss 0.00965010 - time (sec): 50.22 - samples/sec: 3284.93 - lr: 0.000003 - momentum: 0.000000
2023-10-17 20:44:49,402 ----------------------------------------------------------------------------------------------------
2023-10-17 20:44:49,403 EPOCH 9 done: loss 0.0096 - lr: 0.000003
2023-10-17 20:45:00,841 DEV : loss 0.19005419313907623 - f1-score (micro avg)  0.8524
2023-10-17 20:45:00,871 saving best model
2023-10-17 20:45:01,430 ----------------------------------------------------------------------------------------------------
2023-10-17 20:45:06,530 epoch 10 - iter 73/738 - loss 0.00555099 - time (sec): 5.09 - samples/sec: 3200.00 - lr: 0.000003 - momentum: 0.000000
2023-10-17 20:45:12,684 epoch 10 - iter 146/738 - loss 0.00688196 - time (sec): 11.25 - samples/sec: 3108.59 - lr: 0.000003 - momentum: 0.000000
2023-10-17 20:45:17,624 epoch 10 - iter 219/738 - loss 0.00781010 - time (sec): 16.19 - samples/sec: 3112.06 - lr: 0.000002 - momentum: 0.000000
2023-10-17 20:45:22,889 epoch 10 - iter 292/738 - loss 0.00705490 - time (sec): 21.45 - samples/sec: 3145.34 - lr: 0.000002 - momentum: 0.000000
2023-10-17 20:45:27,707 epoch 10 - iter 365/738 - loss 0.00643071 - time (sec): 26.27 - samples/sec: 3163.51 - lr: 0.000002 - momentum: 0.000000
2023-10-17 20:45:32,265 epoch 10 - iter 438/738 - loss 0.00712227 - time (sec): 30.83 - samples/sec: 3201.88 - lr: 0.000001 - momentum: 0.000000
2023-10-17 20:45:37,454 epoch 10 - iter 511/738 - loss 0.00670318 - time (sec): 36.02 - samples/sec: 3182.86 - lr: 0.000001 - momentum: 0.000000
2023-10-17 20:45:42,386 epoch 10 - iter 584/738 - loss 0.00651760 - time (sec): 40.95 - samples/sec: 3202.32 - lr: 0.000001 - momentum: 0.000000
2023-10-17 20:45:47,406 epoch 10 - iter 657/738 - loss 0.00642097 - time (sec): 45.97 - samples/sec: 3209.18 - lr: 0.000000 - momentum: 0.000000
2023-10-17 20:45:52,833 epoch 10 - iter 730/738 - loss 0.00609764 - time (sec): 51.40 - samples/sec: 3207.79 - lr: 0.000000 - momentum: 0.000000
2023-10-17 20:45:53,346 ----------------------------------------------------------------------------------------------------
2023-10-17 20:45:53,346 EPOCH 10 done: loss 0.0060 - lr: 0.000000
2023-10-17 20:46:05,101 DEV : loss 0.19482703506946564 - f1-score (micro avg)  0.8552
2023-10-17 20:46:05,137 saving best model
2023-10-17 20:46:06,066 ----------------------------------------------------------------------------------------------------
2023-10-17 20:46:06,068 Loading model from best epoch ...
2023-10-17 20:46:07,473 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
2023-10-17 20:46:13,965 
Results:
- F-score (micro) 0.8082
- F-score (macro) 0.7155
- Accuracy 0.6981

By class:
              precision    recall  f1-score   support

         loc     0.8568    0.8788    0.8677       858
        pers     0.7727    0.8231    0.7971       537
         org     0.5970    0.6061    0.6015       132
        time     0.5625    0.6667    0.6102        54
        prod     0.7321    0.6721    0.7009        61

   micro avg     0.7931    0.8240    0.8082      1642
   macro avg     0.7042    0.7293    0.7155      1642
weighted avg     0.7941    0.8240    0.8085      1642

2023-10-17 20:46:13,965 ----------------------------------------------------------------------------------------------------