2023-10-17 22:29:04,133 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,134 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 22:29:04,134 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 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 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 Train:  5901 sentences
2023-10-17 22:29:04,135         (train_with_dev=False, train_with_test=False)
2023-10-17 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 Training Params:
2023-10-17 22:29:04,135  - learning_rate: "5e-05" 
2023-10-17 22:29:04,135  - mini_batch_size: "8"
2023-10-17 22:29:04,135  - max_epochs: "10"
2023-10-17 22:29:04,135  - shuffle: "True"
2023-10-17 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 Plugins:
2023-10-17 22:29:04,135  - TensorboardLogger
2023-10-17 22:29:04,135  - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 22:29:04,135  - metric: "('micro avg', 'f1-score')"
2023-10-17 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 Computation:
2023-10-17 22:29:04,135  - compute on device: cuda:0
2023-10-17 22:29:04,135  - embedding storage: none
2023-10-17 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 Model training base path: "hmbench-hipe2020/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-17 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:04,135 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 22:29:09,478 epoch 1 - iter 73/738 - loss 2.99837415 - time (sec): 5.34 - samples/sec: 3166.80 - lr: 0.000005 - momentum: 0.000000
2023-10-17 22:29:13,992 epoch 1 - iter 146/738 - loss 1.93510546 - time (sec): 9.86 - samples/sec: 3213.05 - lr: 0.000010 - momentum: 0.000000
2023-10-17 22:29:18,605 epoch 1 - iter 219/738 - loss 1.45592106 - time (sec): 14.47 - samples/sec: 3237.90 - lr: 0.000015 - momentum: 0.000000
2023-10-17 22:29:23,729 epoch 1 - iter 292/738 - loss 1.16902073 - time (sec): 19.59 - samples/sec: 3260.44 - lr: 0.000020 - momentum: 0.000000
2023-10-17 22:29:29,137 epoch 1 - iter 365/738 - loss 0.97478204 - time (sec): 25.00 - samples/sec: 3288.96 - lr: 0.000025 - momentum: 0.000000
2023-10-17 22:29:34,394 epoch 1 - iter 438/738 - loss 0.85064874 - time (sec): 30.26 - samples/sec: 3282.58 - lr: 0.000030 - momentum: 0.000000
2023-10-17 22:29:38,954 epoch 1 - iter 511/738 - loss 0.76809817 - time (sec): 34.82 - samples/sec: 3284.37 - lr: 0.000035 - momentum: 0.000000
2023-10-17 22:29:43,774 epoch 1 - iter 584/738 - loss 0.69380777 - time (sec): 39.64 - samples/sec: 3297.78 - lr: 0.000039 - momentum: 0.000000
2023-10-17 22:29:48,885 epoch 1 - iter 657/738 - loss 0.63363005 - time (sec): 44.75 - samples/sec: 3296.16 - lr: 0.000044 - momentum: 0.000000
2023-10-17 22:29:53,841 epoch 1 - iter 730/738 - loss 0.58383020 - time (sec): 49.70 - samples/sec: 3320.47 - lr: 0.000049 - momentum: 0.000000
2023-10-17 22:29:54,280 ----------------------------------------------------------------------------------------------------
2023-10-17 22:29:54,280 EPOCH 1 done: loss 0.5810 - lr: 0.000049
2023-10-17 22:30:00,666 DEV : loss 0.11302945762872696 - f1-score (micro avg)  0.7588
2023-10-17 22:30:00,696 saving best model
2023-10-17 22:30:01,082 ----------------------------------------------------------------------------------------------------
2023-10-17 22:30:06,731 epoch 2 - iter 73/738 - loss 0.13556365 - time (sec): 5.65 - samples/sec: 3264.65 - lr: 0.000049 - momentum: 0.000000
2023-10-17 22:30:11,564 epoch 2 - iter 146/738 - loss 0.12924036 - time (sec): 10.48 - samples/sec: 3272.73 - lr: 0.000049 - momentum: 0.000000
2023-10-17 22:30:16,659 epoch 2 - iter 219/738 - loss 0.12904174 - time (sec): 15.58 - samples/sec: 3216.43 - lr: 0.000048 - momentum: 0.000000
2023-10-17 22:30:21,829 epoch 2 - iter 292/738 - loss 0.12726226 - time (sec): 20.75 - samples/sec: 3213.20 - lr: 0.000048 - momentum: 0.000000
2023-10-17 22:30:26,397 epoch 2 - iter 365/738 - loss 0.12773492 - time (sec): 25.31 - samples/sec: 3210.16 - lr: 0.000047 - momentum: 0.000000
2023-10-17 22:30:31,342 epoch 2 - iter 438/738 - loss 0.12584037 - time (sec): 30.26 - samples/sec: 3214.18 - lr: 0.000047 - momentum: 0.000000
2023-10-17 22:30:35,893 epoch 2 - iter 511/738 - loss 0.12575633 - time (sec): 34.81 - samples/sec: 3231.66 - lr: 0.000046 - momentum: 0.000000
2023-10-17 22:30:40,717 epoch 2 - iter 584/738 - loss 0.12360271 - time (sec): 39.63 - samples/sec: 3238.30 - lr: 0.000046 - momentum: 0.000000
2023-10-17 22:30:45,964 epoch 2 - iter 657/738 - loss 0.12251002 - time (sec): 44.88 - samples/sec: 3232.19 - lr: 0.000045 - momentum: 0.000000
2023-10-17 22:30:51,263 epoch 2 - iter 730/738 - loss 0.12156812 - time (sec): 50.18 - samples/sec: 3246.27 - lr: 0.000045 - momentum: 0.000000
2023-10-17 22:30:52,229 ----------------------------------------------------------------------------------------------------
2023-10-17 22:30:52,230 EPOCH 2 done: loss 0.1202 - lr: 0.000045
2023-10-17 22:31:04,398 DEV : loss 0.10620676726102829 - f1-score (micro avg)  0.7916
2023-10-17 22:31:04,432 saving best model
2023-10-17 22:31:04,975 ----------------------------------------------------------------------------------------------------
2023-10-17 22:31:10,816 epoch 3 - iter 73/738 - loss 0.07909692 - time (sec): 5.83 - samples/sec: 3171.41 - lr: 0.000044 - momentum: 0.000000
2023-10-17 22:31:15,736 epoch 3 - iter 146/738 - loss 0.07174765 - time (sec): 10.76 - samples/sec: 3220.31 - lr: 0.000043 - momentum: 0.000000
2023-10-17 22:31:20,225 epoch 3 - iter 219/738 - loss 0.07516061 - time (sec): 15.24 - samples/sec: 3229.49 - lr: 0.000043 - momentum: 0.000000
2023-10-17 22:31:25,006 epoch 3 - iter 292/738 - loss 0.07592855 - time (sec): 20.02 - samples/sec: 3258.50 - lr: 0.000042 - momentum: 0.000000
2023-10-17 22:31:29,791 epoch 3 - iter 365/738 - loss 0.07330696 - time (sec): 24.81 - samples/sec: 3265.19 - lr: 0.000042 - momentum: 0.000000
2023-10-17 22:31:35,103 epoch 3 - iter 438/738 - loss 0.07170697 - time (sec): 30.12 - samples/sec: 3259.41 - lr: 0.000041 - momentum: 0.000000
2023-10-17 22:31:40,196 epoch 3 - iter 511/738 - loss 0.07160535 - time (sec): 35.21 - samples/sec: 3281.25 - lr: 0.000041 - momentum: 0.000000
2023-10-17 22:31:44,916 epoch 3 - iter 584/738 - loss 0.07165988 - time (sec): 39.93 - samples/sec: 3271.92 - lr: 0.000040 - momentum: 0.000000
2023-10-17 22:31:50,041 epoch 3 - iter 657/738 - loss 0.07097376 - time (sec): 45.06 - samples/sec: 3262.94 - lr: 0.000040 - momentum: 0.000000
2023-10-17 22:31:55,409 epoch 3 - iter 730/738 - loss 0.06940338 - time (sec): 50.43 - samples/sec: 3262.11 - lr: 0.000039 - momentum: 0.000000
2023-10-17 22:31:55,939 ----------------------------------------------------------------------------------------------------
2023-10-17 22:31:55,939 EPOCH 3 done: loss 0.0693 - lr: 0.000039
2023-10-17 22:32:07,887 DEV : loss 0.13108941912651062 - f1-score (micro avg)  0.8242
2023-10-17 22:32:07,925 saving best model
2023-10-17 22:32:08,507 ----------------------------------------------------------------------------------------------------
2023-10-17 22:32:13,135 epoch 4 - iter 73/738 - loss 0.03703471 - time (sec): 4.63 - samples/sec: 3340.02 - lr: 0.000038 - momentum: 0.000000
2023-10-17 22:32:18,045 epoch 4 - iter 146/738 - loss 0.04150633 - time (sec): 9.54 - samples/sec: 3270.43 - lr: 0.000038 - momentum: 0.000000
2023-10-17 22:32:23,041 epoch 4 - iter 219/738 - loss 0.04123992 - time (sec): 14.53 - samples/sec: 3241.31 - lr: 0.000037 - momentum: 0.000000
2023-10-17 22:32:29,054 epoch 4 - iter 292/738 - loss 0.04410608 - time (sec): 20.54 - samples/sec: 3232.21 - lr: 0.000037 - momentum: 0.000000
2023-10-17 22:32:34,235 epoch 4 - iter 365/738 - loss 0.04659042 - time (sec): 25.73 - samples/sec: 3244.52 - lr: 0.000036 - momentum: 0.000000
2023-10-17 22:32:38,717 epoch 4 - iter 438/738 - loss 0.04619151 - time (sec): 30.21 - samples/sec: 3250.55 - lr: 0.000036 - momentum: 0.000000
2023-10-17 22:32:43,578 epoch 4 - iter 511/738 - loss 0.04635101 - time (sec): 35.07 - samples/sec: 3264.21 - lr: 0.000035 - momentum: 0.000000
2023-10-17 22:32:48,576 epoch 4 - iter 584/738 - loss 0.04536034 - time (sec): 40.07 - samples/sec: 3252.74 - lr: 0.000035 - momentum: 0.000000
2023-10-17 22:32:53,408 epoch 4 - iter 657/738 - loss 0.04699631 - time (sec): 44.90 - samples/sec: 3255.70 - lr: 0.000034 - momentum: 0.000000
2023-10-17 22:32:59,545 epoch 4 - iter 730/738 - loss 0.04985825 - time (sec): 51.04 - samples/sec: 3227.83 - lr: 0.000033 - momentum: 0.000000
2023-10-17 22:33:00,140 ----------------------------------------------------------------------------------------------------
2023-10-17 22:33:00,141 EPOCH 4 done: loss 0.0499 - lr: 0.000033
2023-10-17 22:33:11,846 DEV : loss 0.14125068485736847 - f1-score (micro avg)  0.815
2023-10-17 22:33:11,880 ----------------------------------------------------------------------------------------------------
2023-10-17 22:33:16,573 epoch 5 - iter 73/738 - loss 0.02879190 - time (sec): 4.69 - samples/sec: 3375.41 - lr: 0.000033 - momentum: 0.000000
2023-10-17 22:33:21,534 epoch 5 - iter 146/738 - loss 0.03371840 - time (sec): 9.65 - samples/sec: 3230.98 - lr: 0.000032 - momentum: 0.000000
2023-10-17 22:33:26,804 epoch 5 - iter 219/738 - loss 0.02945090 - time (sec): 14.92 - samples/sec: 3219.82 - lr: 0.000032 - momentum: 0.000000
2023-10-17 22:33:32,072 epoch 5 - iter 292/738 - loss 0.03027760 - time (sec): 20.19 - samples/sec: 3232.69 - lr: 0.000031 - momentum: 0.000000
2023-10-17 22:33:37,069 epoch 5 - iter 365/738 - loss 0.03506228 - time (sec): 25.19 - samples/sec: 3268.76 - lr: 0.000031 - momentum: 0.000000
2023-10-17 22:33:42,047 epoch 5 - iter 438/738 - loss 0.03543225 - time (sec): 30.17 - samples/sec: 3282.24 - lr: 0.000030 - momentum: 0.000000
2023-10-17 22:33:46,992 epoch 5 - iter 511/738 - loss 0.03479354 - time (sec): 35.11 - samples/sec: 3270.66 - lr: 0.000030 - momentum: 0.000000
2023-10-17 22:33:52,994 epoch 5 - iter 584/738 - loss 0.03639606 - time (sec): 41.11 - samples/sec: 3261.04 - lr: 0.000029 - momentum: 0.000000
2023-10-17 22:33:57,710 epoch 5 - iter 657/738 - loss 0.03555295 - time (sec): 45.83 - samples/sec: 3261.10 - lr: 0.000028 - momentum: 0.000000
2023-10-17 22:34:02,438 epoch 5 - iter 730/738 - loss 0.03486226 - time (sec): 50.56 - samples/sec: 3265.54 - lr: 0.000028 - momentum: 0.000000
2023-10-17 22:34:02,901 ----------------------------------------------------------------------------------------------------
2023-10-17 22:34:02,901 EPOCH 5 done: loss 0.0347 - lr: 0.000028
2023-10-17 22:34:15,042 DEV : loss 0.19004222750663757 - f1-score (micro avg)  0.8312
2023-10-17 22:34:15,088 saving best model
2023-10-17 22:34:15,573 ----------------------------------------------------------------------------------------------------
2023-10-17 22:34:20,441 epoch 6 - iter 73/738 - loss 0.02813745 - time (sec): 4.86 - samples/sec: 3296.59 - lr: 0.000027 - momentum: 0.000000
2023-10-17 22:34:25,602 epoch 6 - iter 146/738 - loss 0.02518261 - time (sec): 10.03 - samples/sec: 3225.85 - lr: 0.000027 - momentum: 0.000000
2023-10-17 22:34:31,064 epoch 6 - iter 219/738 - loss 0.02167308 - time (sec): 15.49 - samples/sec: 3216.28 - lr: 0.000026 - momentum: 0.000000
2023-10-17 22:34:35,610 epoch 6 - iter 292/738 - loss 0.02310727 - time (sec): 20.03 - samples/sec: 3242.49 - lr: 0.000026 - momentum: 0.000000
2023-10-17 22:34:40,377 epoch 6 - iter 365/738 - loss 0.02350712 - time (sec): 24.80 - samples/sec: 3262.02 - lr: 0.000025 - momentum: 0.000000
2023-10-17 22:34:45,894 epoch 6 - iter 438/738 - loss 0.02568561 - time (sec): 30.32 - samples/sec: 3251.89 - lr: 0.000025 - momentum: 0.000000
2023-10-17 22:34:50,637 epoch 6 - iter 511/738 - loss 0.02533559 - time (sec): 35.06 - samples/sec: 3248.03 - lr: 0.000024 - momentum: 0.000000
2023-10-17 22:34:55,852 epoch 6 - iter 584/738 - loss 0.02483365 - time (sec): 40.28 - samples/sec: 3242.98 - lr: 0.000023 - momentum: 0.000000
2023-10-17 22:35:01,847 epoch 6 - iter 657/738 - loss 0.02444670 - time (sec): 46.27 - samples/sec: 3218.62 - lr: 0.000023 - momentum: 0.000000
2023-10-17 22:35:06,861 epoch 6 - iter 730/738 - loss 0.02362799 - time (sec): 51.28 - samples/sec: 3215.12 - lr: 0.000022 - momentum: 0.000000
2023-10-17 22:35:07,312 ----------------------------------------------------------------------------------------------------
2023-10-17 22:35:07,313 EPOCH 6 done: loss 0.0236 - lr: 0.000022
2023-10-17 22:35:19,340 DEV : loss 0.20369166135787964 - f1-score (micro avg)  0.8327
2023-10-17 22:35:19,377 saving best model
2023-10-17 22:35:19,893 ----------------------------------------------------------------------------------------------------
2023-10-17 22:35:25,515 epoch 7 - iter 73/738 - loss 0.01350406 - time (sec): 5.62 - samples/sec: 3361.02 - lr: 0.000022 - momentum: 0.000000
2023-10-17 22:35:30,665 epoch 7 - iter 146/738 - loss 0.01445533 - time (sec): 10.77 - samples/sec: 3283.82 - lr: 0.000021 - momentum: 0.000000
2023-10-17 22:35:35,388 epoch 7 - iter 219/738 - loss 0.01349065 - time (sec): 15.49 - samples/sec: 3296.44 - lr: 0.000021 - momentum: 0.000000
2023-10-17 22:35:40,791 epoch 7 - iter 292/738 - loss 0.01184519 - time (sec): 20.90 - samples/sec: 3258.88 - lr: 0.000020 - momentum: 0.000000
2023-10-17 22:35:45,268 epoch 7 - iter 365/738 - loss 0.01302031 - time (sec): 25.37 - samples/sec: 3278.92 - lr: 0.000020 - momentum: 0.000000
2023-10-17 22:35:50,359 epoch 7 - iter 438/738 - loss 0.01425148 - time (sec): 30.46 - samples/sec: 3268.83 - lr: 0.000019 - momentum: 0.000000
2023-10-17 22:35:55,534 epoch 7 - iter 511/738 - loss 0.01540854 - time (sec): 35.64 - samples/sec: 3248.78 - lr: 0.000018 - momentum: 0.000000
2023-10-17 22:36:00,485 epoch 7 - iter 584/738 - loss 0.01593386 - time (sec): 40.59 - samples/sec: 3255.73 - lr: 0.000018 - momentum: 0.000000
2023-10-17 22:36:05,813 epoch 7 - iter 657/738 - loss 0.01714758 - time (sec): 45.92 - samples/sec: 3233.90 - lr: 0.000017 - momentum: 0.000000
2023-10-17 22:36:11,021 epoch 7 - iter 730/738 - loss 0.01677898 - time (sec): 51.13 - samples/sec: 3219.64 - lr: 0.000017 - momentum: 0.000000
2023-10-17 22:36:11,589 ----------------------------------------------------------------------------------------------------
2023-10-17 22:36:11,590 EPOCH 7 done: loss 0.0167 - lr: 0.000017
2023-10-17 22:36:23,302 DEV : loss 0.20619168877601624 - f1-score (micro avg)  0.8309
2023-10-17 22:36:23,335 ----------------------------------------------------------------------------------------------------
2023-10-17 22:36:28,762 epoch 8 - iter 73/738 - loss 0.01436214 - time (sec): 5.43 - samples/sec: 3505.01 - lr: 0.000016 - momentum: 0.000000
2023-10-17 22:36:33,706 epoch 8 - iter 146/738 - loss 0.01406720 - time (sec): 10.37 - samples/sec: 3337.79 - lr: 0.000016 - momentum: 0.000000
2023-10-17 22:36:39,250 epoch 8 - iter 219/738 - loss 0.01357334 - time (sec): 15.91 - samples/sec: 3295.54 - lr: 0.000015 - momentum: 0.000000
2023-10-17 22:36:44,612 epoch 8 - iter 292/738 - loss 0.01362606 - time (sec): 21.28 - samples/sec: 3277.39 - lr: 0.000015 - momentum: 0.000000
2023-10-17 22:36:49,362 epoch 8 - iter 365/738 - loss 0.01258376 - time (sec): 26.03 - samples/sec: 3288.03 - lr: 0.000014 - momentum: 0.000000
2023-10-17 22:36:54,351 epoch 8 - iter 438/738 - loss 0.01195634 - time (sec): 31.01 - samples/sec: 3256.51 - lr: 0.000013 - momentum: 0.000000
2023-10-17 22:36:59,301 epoch 8 - iter 511/738 - loss 0.01131119 - time (sec): 35.97 - samples/sec: 3247.35 - lr: 0.000013 - momentum: 0.000000
2023-10-17 22:37:04,249 epoch 8 - iter 584/738 - loss 0.01151810 - time (sec): 40.91 - samples/sec: 3262.34 - lr: 0.000012 - momentum: 0.000000
2023-10-17 22:37:09,202 epoch 8 - iter 657/738 - loss 0.01136472 - time (sec): 45.87 - samples/sec: 3258.75 - lr: 0.000012 - momentum: 0.000000
2023-10-17 22:37:13,756 epoch 8 - iter 730/738 - loss 0.01129012 - time (sec): 50.42 - samples/sec: 3264.76 - lr: 0.000011 - momentum: 0.000000
2023-10-17 22:37:14,310 ----------------------------------------------------------------------------------------------------
2023-10-17 22:37:14,310 EPOCH 8 done: loss 0.0116 - lr: 0.000011
2023-10-17 22:37:25,955 DEV : loss 0.20618367195129395 - f1-score (micro avg)  0.8382
2023-10-17 22:37:25,994 saving best model
2023-10-17 22:37:26,535 ----------------------------------------------------------------------------------------------------
2023-10-17 22:37:31,040 epoch 9 - iter 73/738 - loss 0.00179149 - time (sec): 4.50 - samples/sec: 3318.58 - lr: 0.000011 - momentum: 0.000000
2023-10-17 22:37:35,954 epoch 9 - iter 146/738 - loss 0.00388670 - time (sec): 9.42 - samples/sec: 3296.02 - lr: 0.000010 - momentum: 0.000000
2023-10-17 22:37:42,573 epoch 9 - iter 219/738 - loss 0.00558100 - time (sec): 16.04 - samples/sec: 3216.48 - lr: 0.000010 - momentum: 0.000000
2023-10-17 22:37:46,905 epoch 9 - iter 292/738 - loss 0.00579759 - time (sec): 20.37 - samples/sec: 3248.07 - lr: 0.000009 - momentum: 0.000000
2023-10-17 22:37:51,672 epoch 9 - iter 365/738 - loss 0.00592703 - time (sec): 25.14 - samples/sec: 3250.17 - lr: 0.000008 - momentum: 0.000000
2023-10-17 22:37:57,350 epoch 9 - iter 438/738 - loss 0.00668828 - time (sec): 30.81 - samples/sec: 3240.67 - lr: 0.000008 - momentum: 0.000000
2023-10-17 22:38:02,024 epoch 9 - iter 511/738 - loss 0.00651091 - time (sec): 35.49 - samples/sec: 3235.47 - lr: 0.000007 - momentum: 0.000000
2023-10-17 22:38:07,184 epoch 9 - iter 584/738 - loss 0.00667925 - time (sec): 40.65 - samples/sec: 3233.36 - lr: 0.000007 - momentum: 0.000000
2023-10-17 22:38:12,714 epoch 9 - iter 657/738 - loss 0.00716367 - time (sec): 46.18 - samples/sec: 3221.77 - lr: 0.000006 - momentum: 0.000000
2023-10-17 22:38:17,563 epoch 9 - iter 730/738 - loss 0.00684617 - time (sec): 51.03 - samples/sec: 3226.78 - lr: 0.000006 - momentum: 0.000000
2023-10-17 22:38:18,091 ----------------------------------------------------------------------------------------------------
2023-10-17 22:38:18,091 EPOCH 9 done: loss 0.0068 - lr: 0.000006
2023-10-17 22:38:29,523 DEV : loss 0.20809388160705566 - f1-score (micro avg)  0.8473
2023-10-17 22:38:29,554 saving best model
2023-10-17 22:38:30,106 ----------------------------------------------------------------------------------------------------
2023-10-17 22:38:34,557 epoch 10 - iter 73/738 - loss 0.00157154 - time (sec): 4.45 - samples/sec: 3307.08 - lr: 0.000005 - momentum: 0.000000
2023-10-17 22:38:39,511 epoch 10 - iter 146/738 - loss 0.00473884 - time (sec): 9.40 - samples/sec: 3262.89 - lr: 0.000004 - momentum: 0.000000
2023-10-17 22:38:44,413 epoch 10 - iter 219/738 - loss 0.00435182 - time (sec): 14.30 - samples/sec: 3245.94 - lr: 0.000004 - momentum: 0.000000
2023-10-17 22:38:50,176 epoch 10 - iter 292/738 - loss 0.00461898 - time (sec): 20.06 - samples/sec: 3265.73 - lr: 0.000003 - momentum: 0.000000
2023-10-17 22:38:54,905 epoch 10 - iter 365/738 - loss 0.00437464 - time (sec): 24.79 - samples/sec: 3267.62 - lr: 0.000003 - momentum: 0.000000
2023-10-17 22:39:00,079 epoch 10 - iter 438/738 - loss 0.00585767 - time (sec): 29.97 - samples/sec: 3265.78 - lr: 0.000002 - momentum: 0.000000
2023-10-17 22:39:04,813 epoch 10 - iter 511/738 - loss 0.00504414 - time (sec): 34.70 - samples/sec: 3278.60 - lr: 0.000002 - momentum: 0.000000
2023-10-17 22:39:09,643 epoch 10 - iter 584/738 - loss 0.00486786 - time (sec): 39.53 - samples/sec: 3281.88 - lr: 0.000001 - momentum: 0.000000
2023-10-17 22:39:15,461 epoch 10 - iter 657/738 - loss 0.00498413 - time (sec): 45.35 - samples/sec: 3278.45 - lr: 0.000001 - momentum: 0.000000
2023-10-17 22:39:20,217 epoch 10 - iter 730/738 - loss 0.00471586 - time (sec): 50.11 - samples/sec: 3274.82 - lr: 0.000000 - momentum: 0.000000
2023-10-17 22:39:20,859 ----------------------------------------------------------------------------------------------------
2023-10-17 22:39:20,859 EPOCH 10 done: loss 0.0047 - lr: 0.000000
2023-10-17 22:39:32,442 DEV : loss 0.21777039766311646 - f1-score (micro avg)  0.8438
2023-10-17 22:39:32,871 ----------------------------------------------------------------------------------------------------
2023-10-17 22:39:32,873 Loading model from best epoch ...
2023-10-17 22:39:34,269 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 22:39:40,451 
Results:
- F-score (micro) 0.8122
- F-score (macro) 0.7141
- Accuracy 0.7007

By class:
              precision    recall  f1-score   support

         loc     0.8672    0.8904    0.8787       858
        pers     0.7737    0.8212    0.7967       537
         org     0.5714    0.6364    0.6022       132
        prod     0.6667    0.6885    0.6774        61
        time     0.5714    0.6667    0.6154        54

   micro avg     0.7929    0.8325    0.8122      1642
   macro avg     0.6901    0.7406    0.7141      1642
weighted avg     0.7957    0.8325    0.8135      1642

2023-10-17 22:39:40,451 ----------------------------------------------------------------------------------------------------