2023-10-13 16:46:35,065 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,066 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()
)"
2023-10-13 16:46:35,066 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,066 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
2023-10-13 16:46:35,066 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,067 Train:  5901 sentences
2023-10-13 16:46:35,067         (train_with_dev=False, train_with_test=False)
2023-10-13 16:46:35,067 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,067 Training Params:
2023-10-13 16:46:35,067  - learning_rate: "3e-05" 
2023-10-13 16:46:35,067  - mini_batch_size: "8"
2023-10-13 16:46:35,067  - max_epochs: "10"
2023-10-13 16:46:35,067  - shuffle: "True"
2023-10-13 16:46:35,067 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,067 Plugins:
2023-10-13 16:46:35,067  - LinearScheduler | warmup_fraction: '0.1'
2023-10-13 16:46:35,067 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,067 Final evaluation on model from best epoch (best-model.pt)
2023-10-13 16:46:35,067  - metric: "('micro avg', 'f1-score')"
2023-10-13 16:46:35,067 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,067 Computation:
2023-10-13 16:46:35,067  - compute on device: cuda:0
2023-10-13 16:46:35,067  - embedding storage: none
2023-10-13 16:46:35,067 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,067 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-13 16:46:35,067 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:35,067 ----------------------------------------------------------------------------------------------------
2023-10-13 16:46:39,446 epoch 1 - iter 73/738 - loss 2.75876894 - time (sec): 4.38 - samples/sec: 3483.41 - lr: 0.000003 - momentum: 0.000000
2023-10-13 16:46:44,320 epoch 1 - iter 146/738 - loss 1.80884627 - time (sec): 9.25 - samples/sec: 3527.43 - lr: 0.000006 - momentum: 0.000000
2023-10-13 16:46:48,901 epoch 1 - iter 219/738 - loss 1.39634718 - time (sec): 13.83 - samples/sec: 3449.28 - lr: 0.000009 - momentum: 0.000000
2023-10-13 16:46:54,271 epoch 1 - iter 292/738 - loss 1.13609859 - time (sec): 19.20 - samples/sec: 3334.39 - lr: 0.000012 - momentum: 0.000000
2023-10-13 16:46:59,241 epoch 1 - iter 365/738 - loss 0.96487120 - time (sec): 24.17 - samples/sec: 3356.19 - lr: 0.000015 - momentum: 0.000000
2023-10-13 16:47:04,235 epoch 1 - iter 438/738 - loss 0.84886263 - time (sec): 29.17 - samples/sec: 3364.99 - lr: 0.000018 - momentum: 0.000000
2023-10-13 16:47:09,592 epoch 1 - iter 511/738 - loss 0.75569434 - time (sec): 34.52 - samples/sec: 3353.23 - lr: 0.000021 - momentum: 0.000000
2023-10-13 16:47:14,207 epoch 1 - iter 584/738 - loss 0.69445623 - time (sec): 39.14 - samples/sec: 3355.84 - lr: 0.000024 - momentum: 0.000000
2023-10-13 16:47:19,650 epoch 1 - iter 657/738 - loss 0.63336459 - time (sec): 44.58 - samples/sec: 3347.84 - lr: 0.000027 - momentum: 0.000000
2023-10-13 16:47:24,389 epoch 1 - iter 730/738 - loss 0.59174752 - time (sec): 49.32 - samples/sec: 3337.28 - lr: 0.000030 - momentum: 0.000000
2023-10-13 16:47:25,005 ----------------------------------------------------------------------------------------------------
2023-10-13 16:47:25,005 EPOCH 1 done: loss 0.5882 - lr: 0.000030
2023-10-13 16:47:30,666 DEV : loss 0.15391376614570618 - f1-score (micro avg)  0.6929
2023-10-13 16:47:30,695 saving best model
2023-10-13 16:47:31,080 ----------------------------------------------------------------------------------------------------
2023-10-13 16:47:35,944 epoch 2 - iter 73/738 - loss 0.15186425 - time (sec): 4.86 - samples/sec: 3376.69 - lr: 0.000030 - momentum: 0.000000
2023-10-13 16:47:41,168 epoch 2 - iter 146/738 - loss 0.14890498 - time (sec): 10.09 - samples/sec: 3316.02 - lr: 0.000029 - momentum: 0.000000
2023-10-13 16:47:46,670 epoch 2 - iter 219/738 - loss 0.14354573 - time (sec): 15.59 - samples/sec: 3324.22 - lr: 0.000029 - momentum: 0.000000
2023-10-13 16:47:51,357 epoch 2 - iter 292/738 - loss 0.13792109 - time (sec): 20.28 - samples/sec: 3320.09 - lr: 0.000029 - momentum: 0.000000
2023-10-13 16:47:56,012 epoch 2 - iter 365/738 - loss 0.13912932 - time (sec): 24.93 - samples/sec: 3327.55 - lr: 0.000028 - momentum: 0.000000
2023-10-13 16:48:02,120 epoch 2 - iter 438/738 - loss 0.13583538 - time (sec): 31.04 - samples/sec: 3255.74 - lr: 0.000028 - momentum: 0.000000
2023-10-13 16:48:07,086 epoch 2 - iter 511/738 - loss 0.13334651 - time (sec): 36.00 - samples/sec: 3263.15 - lr: 0.000028 - momentum: 0.000000
2023-10-13 16:48:12,038 epoch 2 - iter 584/738 - loss 0.13084622 - time (sec): 40.96 - samples/sec: 3256.25 - lr: 0.000027 - momentum: 0.000000
2023-10-13 16:48:16,879 epoch 2 - iter 657/738 - loss 0.12811312 - time (sec): 45.80 - samples/sec: 3255.44 - lr: 0.000027 - momentum: 0.000000
2023-10-13 16:48:21,581 epoch 2 - iter 730/738 - loss 0.12603141 - time (sec): 50.50 - samples/sec: 3268.72 - lr: 0.000027 - momentum: 0.000000
2023-10-13 16:48:22,025 ----------------------------------------------------------------------------------------------------
2023-10-13 16:48:22,025 EPOCH 2 done: loss 0.1265 - lr: 0.000027
2023-10-13 16:48:33,074 DEV : loss 0.12796831130981445 - f1-score (micro avg)  0.7459
2023-10-13 16:48:33,102 saving best model
2023-10-13 16:48:33,579 ----------------------------------------------------------------------------------------------------
2023-10-13 16:48:38,061 epoch 3 - iter 73/738 - loss 0.06383906 - time (sec): 4.48 - samples/sec: 3386.43 - lr: 0.000026 - momentum: 0.000000
2023-10-13 16:48:42,819 epoch 3 - iter 146/738 - loss 0.07115006 - time (sec): 9.24 - samples/sec: 3445.06 - lr: 0.000026 - momentum: 0.000000
2023-10-13 16:48:47,708 epoch 3 - iter 219/738 - loss 0.07055549 - time (sec): 14.13 - samples/sec: 3413.41 - lr: 0.000026 - momentum: 0.000000
2023-10-13 16:48:53,245 epoch 3 - iter 292/738 - loss 0.06676871 - time (sec): 19.66 - samples/sec: 3430.11 - lr: 0.000025 - momentum: 0.000000
2023-10-13 16:48:58,338 epoch 3 - iter 365/738 - loss 0.07018788 - time (sec): 24.76 - samples/sec: 3386.28 - lr: 0.000025 - momentum: 0.000000
2023-10-13 16:49:02,977 epoch 3 - iter 438/738 - loss 0.06934785 - time (sec): 29.40 - samples/sec: 3383.76 - lr: 0.000025 - momentum: 0.000000
2023-10-13 16:49:08,177 epoch 3 - iter 511/738 - loss 0.07101091 - time (sec): 34.60 - samples/sec: 3374.95 - lr: 0.000024 - momentum: 0.000000
2023-10-13 16:49:12,936 epoch 3 - iter 584/738 - loss 0.06876706 - time (sec): 39.36 - samples/sec: 3370.31 - lr: 0.000024 - momentum: 0.000000
2023-10-13 16:49:17,960 epoch 3 - iter 657/738 - loss 0.07113615 - time (sec): 44.38 - samples/sec: 3355.59 - lr: 0.000024 - momentum: 0.000000
2023-10-13 16:49:22,960 epoch 3 - iter 730/738 - loss 0.06952763 - time (sec): 49.38 - samples/sec: 3333.48 - lr: 0.000023 - momentum: 0.000000
2023-10-13 16:49:23,508 ----------------------------------------------------------------------------------------------------
2023-10-13 16:49:23,508 EPOCH 3 done: loss 0.0696 - lr: 0.000023
2023-10-13 16:49:34,647 DEV : loss 0.12844610214233398 - f1-score (micro avg)  0.7915
2023-10-13 16:49:34,675 saving best model
2023-10-13 16:49:35,156 ----------------------------------------------------------------------------------------------------
2023-10-13 16:49:40,547 epoch 4 - iter 73/738 - loss 0.03685895 - time (sec): 5.39 - samples/sec: 3208.48 - lr: 0.000023 - momentum: 0.000000
2023-10-13 16:49:45,283 epoch 4 - iter 146/738 - loss 0.04110187 - time (sec): 10.12 - samples/sec: 3323.16 - lr: 0.000023 - momentum: 0.000000
2023-10-13 16:49:50,345 epoch 4 - iter 219/738 - loss 0.03920650 - time (sec): 15.19 - samples/sec: 3346.58 - lr: 0.000022 - momentum: 0.000000
2023-10-13 16:49:55,013 epoch 4 - iter 292/738 - loss 0.04046397 - time (sec): 19.85 - samples/sec: 3354.58 - lr: 0.000022 - momentum: 0.000000
2023-10-13 16:49:59,423 epoch 4 - iter 365/738 - loss 0.04108074 - time (sec): 24.26 - samples/sec: 3360.44 - lr: 0.000022 - momentum: 0.000000
2023-10-13 16:50:04,661 epoch 4 - iter 438/738 - loss 0.04132374 - time (sec): 29.50 - samples/sec: 3344.43 - lr: 0.000021 - momentum: 0.000000
2023-10-13 16:50:10,310 epoch 4 - iter 511/738 - loss 0.04572909 - time (sec): 35.15 - samples/sec: 3343.93 - lr: 0.000021 - momentum: 0.000000
2023-10-13 16:50:14,944 epoch 4 - iter 584/738 - loss 0.04580991 - time (sec): 39.79 - samples/sec: 3334.62 - lr: 0.000021 - momentum: 0.000000
2023-10-13 16:50:19,795 epoch 4 - iter 657/738 - loss 0.04521327 - time (sec): 44.64 - samples/sec: 3324.21 - lr: 0.000020 - momentum: 0.000000
2023-10-13 16:50:24,586 epoch 4 - iter 730/738 - loss 0.04616849 - time (sec): 49.43 - samples/sec: 3331.38 - lr: 0.000020 - momentum: 0.000000
2023-10-13 16:50:25,175 ----------------------------------------------------------------------------------------------------
2023-10-13 16:50:25,175 EPOCH 4 done: loss 0.0459 - lr: 0.000020
2023-10-13 16:50:36,330 DEV : loss 0.1346866935491562 - f1-score (micro avg)  0.8128
2023-10-13 16:50:36,360 saving best model
2023-10-13 16:50:36,832 ----------------------------------------------------------------------------------------------------
2023-10-13 16:50:42,063 epoch 5 - iter 73/738 - loss 0.04944024 - time (sec): 5.23 - samples/sec: 3460.34 - lr: 0.000020 - momentum: 0.000000
2023-10-13 16:50:46,854 epoch 5 - iter 146/738 - loss 0.03832551 - time (sec): 10.02 - samples/sec: 3393.40 - lr: 0.000019 - momentum: 0.000000
2023-10-13 16:50:52,140 epoch 5 - iter 219/738 - loss 0.03785219 - time (sec): 15.31 - samples/sec: 3357.37 - lr: 0.000019 - momentum: 0.000000
2023-10-13 16:50:56,835 epoch 5 - iter 292/738 - loss 0.03537582 - time (sec): 20.00 - samples/sec: 3338.30 - lr: 0.000019 - momentum: 0.000000
2023-10-13 16:51:01,384 epoch 5 - iter 365/738 - loss 0.03443213 - time (sec): 24.55 - samples/sec: 3350.21 - lr: 0.000018 - momentum: 0.000000
2023-10-13 16:51:06,156 epoch 5 - iter 438/738 - loss 0.03236903 - time (sec): 29.32 - samples/sec: 3334.08 - lr: 0.000018 - momentum: 0.000000
2023-10-13 16:51:11,273 epoch 5 - iter 511/738 - loss 0.03406980 - time (sec): 34.44 - samples/sec: 3317.60 - lr: 0.000018 - momentum: 0.000000
2023-10-13 16:51:16,186 epoch 5 - iter 584/738 - loss 0.03283544 - time (sec): 39.35 - samples/sec: 3303.95 - lr: 0.000017 - momentum: 0.000000
2023-10-13 16:51:20,707 epoch 5 - iter 657/738 - loss 0.03218738 - time (sec): 43.87 - samples/sec: 3316.58 - lr: 0.000017 - momentum: 0.000000
2023-10-13 16:51:26,258 epoch 5 - iter 730/738 - loss 0.03237876 - time (sec): 49.42 - samples/sec: 3331.87 - lr: 0.000017 - momentum: 0.000000
2023-10-13 16:51:26,764 ----------------------------------------------------------------------------------------------------
2023-10-13 16:51:26,764 EPOCH 5 done: loss 0.0328 - lr: 0.000017
2023-10-13 16:51:37,913 DEV : loss 0.1763695776462555 - f1-score (micro avg)  0.8236
2023-10-13 16:51:37,942 saving best model
2023-10-13 16:51:38,405 ----------------------------------------------------------------------------------------------------
2023-10-13 16:51:43,720 epoch 6 - iter 73/738 - loss 0.02851833 - time (sec): 5.31 - samples/sec: 3401.46 - lr: 0.000016 - momentum: 0.000000
2023-10-13 16:51:48,974 epoch 6 - iter 146/738 - loss 0.02463249 - time (sec): 10.56 - samples/sec: 3353.65 - lr: 0.000016 - momentum: 0.000000
2023-10-13 16:51:54,352 epoch 6 - iter 219/738 - loss 0.02827618 - time (sec): 15.94 - samples/sec: 3371.69 - lr: 0.000016 - momentum: 0.000000
2023-10-13 16:51:59,040 epoch 6 - iter 292/738 - loss 0.02592426 - time (sec): 20.63 - samples/sec: 3378.60 - lr: 0.000015 - momentum: 0.000000
2023-10-13 16:52:03,788 epoch 6 - iter 365/738 - loss 0.02549278 - time (sec): 25.38 - samples/sec: 3375.93 - lr: 0.000015 - momentum: 0.000000
2023-10-13 16:52:08,850 epoch 6 - iter 438/738 - loss 0.02522983 - time (sec): 30.44 - samples/sec: 3350.26 - lr: 0.000015 - momentum: 0.000000
2023-10-13 16:52:13,892 epoch 6 - iter 511/738 - loss 0.02559890 - time (sec): 35.48 - samples/sec: 3332.52 - lr: 0.000014 - momentum: 0.000000
2023-10-13 16:52:18,464 epoch 6 - iter 584/738 - loss 0.02545238 - time (sec): 40.05 - samples/sec: 3328.41 - lr: 0.000014 - momentum: 0.000000
2023-10-13 16:52:23,093 epoch 6 - iter 657/738 - loss 0.02493318 - time (sec): 44.68 - samples/sec: 3329.62 - lr: 0.000014 - momentum: 0.000000
2023-10-13 16:52:27,661 epoch 6 - iter 730/738 - loss 0.02518905 - time (sec): 49.25 - samples/sec: 3339.91 - lr: 0.000013 - momentum: 0.000000
2023-10-13 16:52:28,128 ----------------------------------------------------------------------------------------------------
2023-10-13 16:52:28,128 EPOCH 6 done: loss 0.0250 - lr: 0.000013
2023-10-13 16:52:39,285 DEV : loss 0.18539553880691528 - f1-score (micro avg)  0.817
2023-10-13 16:52:39,315 ----------------------------------------------------------------------------------------------------
2023-10-13 16:52:43,755 epoch 7 - iter 73/738 - loss 0.01011457 - time (sec): 4.44 - samples/sec: 3463.78 - lr: 0.000013 - momentum: 0.000000
2023-10-13 16:52:49,698 epoch 7 - iter 146/738 - loss 0.00991157 - time (sec): 10.38 - samples/sec: 3179.21 - lr: 0.000013 - momentum: 0.000000
2023-10-13 16:52:54,136 epoch 7 - iter 219/738 - loss 0.01639992 - time (sec): 14.82 - samples/sec: 3225.00 - lr: 0.000012 - momentum: 0.000000
2023-10-13 16:52:59,643 epoch 7 - iter 292/738 - loss 0.01771782 - time (sec): 20.33 - samples/sec: 3207.13 - lr: 0.000012 - momentum: 0.000000
2023-10-13 16:53:04,291 epoch 7 - iter 365/738 - loss 0.01626466 - time (sec): 24.98 - samples/sec: 3231.89 - lr: 0.000012 - momentum: 0.000000
2023-10-13 16:53:08,896 epoch 7 - iter 438/738 - loss 0.01633743 - time (sec): 29.58 - samples/sec: 3240.55 - lr: 0.000011 - momentum: 0.000000
2023-10-13 16:53:15,008 epoch 7 - iter 511/738 - loss 0.01619167 - time (sec): 35.69 - samples/sec: 3240.35 - lr: 0.000011 - momentum: 0.000000
2023-10-13 16:53:20,345 epoch 7 - iter 584/738 - loss 0.01642316 - time (sec): 41.03 - samples/sec: 3238.07 - lr: 0.000011 - momentum: 0.000000
2023-10-13 16:53:25,482 epoch 7 - iter 657/738 - loss 0.01739578 - time (sec): 46.17 - samples/sec: 3236.69 - lr: 0.000010 - momentum: 0.000000
2023-10-13 16:53:30,254 epoch 7 - iter 730/738 - loss 0.01737564 - time (sec): 50.94 - samples/sec: 3238.00 - lr: 0.000010 - momentum: 0.000000
2023-10-13 16:53:30,720 ----------------------------------------------------------------------------------------------------
2023-10-13 16:53:30,720 EPOCH 7 done: loss 0.0172 - lr: 0.000010
2023-10-13 16:53:41,882 DEV : loss 0.18756036460399628 - f1-score (micro avg)  0.8247
2023-10-13 16:53:41,911 saving best model
2023-10-13 16:53:42,390 ----------------------------------------------------------------------------------------------------
2023-10-13 16:53:47,374 epoch 8 - iter 73/738 - loss 0.00892796 - time (sec): 4.98 - samples/sec: 3368.70 - lr: 0.000010 - momentum: 0.000000
2023-10-13 16:53:52,625 epoch 8 - iter 146/738 - loss 0.01253336 - time (sec): 10.23 - samples/sec: 3345.02 - lr: 0.000009 - momentum: 0.000000
2023-10-13 16:53:57,957 epoch 8 - iter 219/738 - loss 0.01654324 - time (sec): 15.57 - samples/sec: 3364.89 - lr: 0.000009 - momentum: 0.000000
2023-10-13 16:54:03,288 epoch 8 - iter 292/738 - loss 0.01734773 - time (sec): 20.90 - samples/sec: 3307.99 - lr: 0.000009 - momentum: 0.000000
2023-10-13 16:54:08,395 epoch 8 - iter 365/738 - loss 0.01551208 - time (sec): 26.00 - samples/sec: 3285.30 - lr: 0.000008 - momentum: 0.000000
2023-10-13 16:54:13,444 epoch 8 - iter 438/738 - loss 0.01347784 - time (sec): 31.05 - samples/sec: 3297.82 - lr: 0.000008 - momentum: 0.000000
2023-10-13 16:54:18,073 epoch 8 - iter 511/738 - loss 0.01260285 - time (sec): 35.68 - samples/sec: 3310.59 - lr: 0.000008 - momentum: 0.000000
2023-10-13 16:54:22,616 epoch 8 - iter 584/738 - loss 0.01238670 - time (sec): 40.22 - samples/sec: 3297.36 - lr: 0.000007 - momentum: 0.000000
2023-10-13 16:54:27,063 epoch 8 - iter 657/738 - loss 0.01286577 - time (sec): 44.67 - samples/sec: 3312.04 - lr: 0.000007 - momentum: 0.000000
2023-10-13 16:54:31,953 epoch 8 - iter 730/738 - loss 0.01257961 - time (sec): 49.56 - samples/sec: 3315.55 - lr: 0.000007 - momentum: 0.000000
2023-10-13 16:54:32,610 ----------------------------------------------------------------------------------------------------
2023-10-13 16:54:32,610 EPOCH 8 done: loss 0.0125 - lr: 0.000007
2023-10-13 16:54:43,760 DEV : loss 0.20449210703372955 - f1-score (micro avg)  0.834
2023-10-13 16:54:43,789 saving best model
2023-10-13 16:54:44,362 ----------------------------------------------------------------------------------------------------
2023-10-13 16:54:49,824 epoch 9 - iter 73/738 - loss 0.01064858 - time (sec): 5.46 - samples/sec: 3272.76 - lr: 0.000006 - momentum: 0.000000
2023-10-13 16:54:54,742 epoch 9 - iter 146/738 - loss 0.00779519 - time (sec): 10.37 - samples/sec: 3357.34 - lr: 0.000006 - momentum: 0.000000
2023-10-13 16:54:59,363 epoch 9 - iter 219/738 - loss 0.00658462 - time (sec): 15.00 - samples/sec: 3353.64 - lr: 0.000006 - momentum: 0.000000
2023-10-13 16:55:04,212 epoch 9 - iter 292/738 - loss 0.00639633 - time (sec): 19.84 - samples/sec: 3360.35 - lr: 0.000005 - momentum: 0.000000
2023-10-13 16:55:08,608 epoch 9 - iter 365/738 - loss 0.00784894 - time (sec): 24.24 - samples/sec: 3350.02 - lr: 0.000005 - momentum: 0.000000
2023-10-13 16:55:13,771 epoch 9 - iter 438/738 - loss 0.00753028 - time (sec): 29.40 - samples/sec: 3336.58 - lr: 0.000005 - momentum: 0.000000
2023-10-13 16:55:18,929 epoch 9 - iter 511/738 - loss 0.00848044 - time (sec): 34.56 - samples/sec: 3352.91 - lr: 0.000004 - momentum: 0.000000
2023-10-13 16:55:23,474 epoch 9 - iter 584/738 - loss 0.00849055 - time (sec): 39.11 - samples/sec: 3376.49 - lr: 0.000004 - momentum: 0.000000
2023-10-13 16:55:28,220 epoch 9 - iter 657/738 - loss 0.00861082 - time (sec): 43.85 - samples/sec: 3381.01 - lr: 0.000004 - momentum: 0.000000
2023-10-13 16:55:33,280 epoch 9 - iter 730/738 - loss 0.00844713 - time (sec): 48.91 - samples/sec: 3364.31 - lr: 0.000003 - momentum: 0.000000
2023-10-13 16:55:33,870 ----------------------------------------------------------------------------------------------------
2023-10-13 16:55:33,870 EPOCH 9 done: loss 0.0085 - lr: 0.000003
2023-10-13 16:55:45,043 DEV : loss 0.20950576663017273 - f1-score (micro avg)  0.8277
2023-10-13 16:55:45,073 ----------------------------------------------------------------------------------------------------
2023-10-13 16:55:50,272 epoch 10 - iter 73/738 - loss 0.00495315 - time (sec): 5.20 - samples/sec: 3446.07 - lr: 0.000003 - momentum: 0.000000
2023-10-13 16:55:54,764 epoch 10 - iter 146/738 - loss 0.00461775 - time (sec): 9.69 - samples/sec: 3396.23 - lr: 0.000003 - momentum: 0.000000
2023-10-13 16:55:59,868 epoch 10 - iter 219/738 - loss 0.00379146 - time (sec): 14.79 - samples/sec: 3375.10 - lr: 0.000002 - momentum: 0.000000
2023-10-13 16:56:04,753 epoch 10 - iter 292/738 - loss 0.00373819 - time (sec): 19.68 - samples/sec: 3317.15 - lr: 0.000002 - momentum: 0.000000
2023-10-13 16:56:10,064 epoch 10 - iter 365/738 - loss 0.00620564 - time (sec): 24.99 - samples/sec: 3360.08 - lr: 0.000002 - momentum: 0.000000
2023-10-13 16:56:14,865 epoch 10 - iter 438/738 - loss 0.00579438 - time (sec): 29.79 - samples/sec: 3358.73 - lr: 0.000001 - momentum: 0.000000
2023-10-13 16:56:19,725 epoch 10 - iter 511/738 - loss 0.00610258 - time (sec): 34.65 - samples/sec: 3364.26 - lr: 0.000001 - momentum: 0.000000
2023-10-13 16:56:24,577 epoch 10 - iter 584/738 - loss 0.00640510 - time (sec): 39.50 - samples/sec: 3374.56 - lr: 0.000001 - momentum: 0.000000
2023-10-13 16:56:29,147 epoch 10 - iter 657/738 - loss 0.00631027 - time (sec): 44.07 - samples/sec: 3382.29 - lr: 0.000000 - momentum: 0.000000
2023-10-13 16:56:34,418 epoch 10 - iter 730/738 - loss 0.00658051 - time (sec): 49.34 - samples/sec: 3345.91 - lr: 0.000000 - momentum: 0.000000
2023-10-13 16:56:34,856 ----------------------------------------------------------------------------------------------------
2023-10-13 16:56:34,856 EPOCH 10 done: loss 0.0066 - lr: 0.000000
2023-10-13 16:56:46,029 DEV : loss 0.2123527079820633 - f1-score (micro avg)  0.8358
2023-10-13 16:56:46,058 saving best model
2023-10-13 16:56:46,995 ----------------------------------------------------------------------------------------------------
2023-10-13 16:56:46,997 Loading model from best epoch ...
2023-10-13 16:56:48,450 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
2023-10-13 16:56:54,898 
Results:
- F-score (micro) 0.7941
- F-score (macro) 0.6902
- Accuracy 0.6827

By class:
              precision    recall  f1-score   support

         loc     0.8571    0.8741    0.8656       858
        pers     0.7535    0.8026    0.7773       537
         org     0.5405    0.6061    0.5714       132
        time     0.4783    0.6111    0.5366        54
        prod     0.7119    0.6885    0.7000        61

   micro avg     0.7754    0.8136    0.7941      1642
   macro avg     0.6683    0.7165    0.6902      1642
weighted avg     0.7799    0.8136    0.7961      1642

2023-10-13 16:56:54,898 ----------------------------------------------------------------------------------------------------