2023-10-18 23:31:56,783 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,783 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 23:31:56,783 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,783 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 23:31:56,783 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,783 Train:  14465 sentences
2023-10-18 23:31:56,783         (train_with_dev=False, train_with_test=False)
2023-10-18 23:31:56,783 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,783 Training Params:
2023-10-18 23:31:56,783  - learning_rate: "5e-05" 
2023-10-18 23:31:56,783  - mini_batch_size: "4"
2023-10-18 23:31:56,783  - max_epochs: "10"
2023-10-18 23:31:56,783  - shuffle: "True"
2023-10-18 23:31:56,783 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,783 Plugins:
2023-10-18 23:31:56,783  - TensorboardLogger
2023-10-18 23:31:56,783  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 23:31:56,783 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,783 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 23:31:56,784  - metric: "('micro avg', 'f1-score')"
2023-10-18 23:31:56,784 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,784 Computation:
2023-10-18 23:31:56,784  - compute on device: cuda:0
2023-10-18 23:31:56,784  - embedding storage: none
2023-10-18 23:31:56,784 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,784 Model training base path: "hmbench-letemps/fr-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-18 23:31:56,784 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,784 ----------------------------------------------------------------------------------------------------
2023-10-18 23:31:56,784 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 23:32:02,616 epoch 1 - iter 361/3617 - loss 2.90306109 - time (sec): 5.83 - samples/sec: 6477.10 - lr: 0.000005 - momentum: 0.000000
2023-10-18 23:32:08,209 epoch 1 - iter 722/3617 - loss 1.99373894 - time (sec): 11.43 - samples/sec: 6675.78 - lr: 0.000010 - momentum: 0.000000
2023-10-18 23:32:13,920 epoch 1 - iter 1083/3617 - loss 1.43612253 - time (sec): 17.14 - samples/sec: 6767.81 - lr: 0.000015 - momentum: 0.000000
2023-10-18 23:32:19,644 epoch 1 - iter 1444/3617 - loss 1.15677364 - time (sec): 22.86 - samples/sec: 6745.40 - lr: 0.000020 - momentum: 0.000000
2023-10-18 23:32:24,914 epoch 1 - iter 1805/3617 - loss 0.98282051 - time (sec): 28.13 - samples/sec: 6867.21 - lr: 0.000025 - momentum: 0.000000
2023-10-18 23:32:30,238 epoch 1 - iter 2166/3617 - loss 0.86833073 - time (sec): 33.45 - samples/sec: 6872.25 - lr: 0.000030 - momentum: 0.000000
2023-10-18 23:32:35,906 epoch 1 - iter 2527/3617 - loss 0.77953327 - time (sec): 39.12 - samples/sec: 6811.87 - lr: 0.000035 - momentum: 0.000000
2023-10-18 23:32:41,535 epoch 1 - iter 2888/3617 - loss 0.71123326 - time (sec): 44.75 - samples/sec: 6776.65 - lr: 0.000040 - momentum: 0.000000
2023-10-18 23:32:47,198 epoch 1 - iter 3249/3617 - loss 0.65498355 - time (sec): 50.41 - samples/sec: 6744.88 - lr: 0.000045 - momentum: 0.000000
2023-10-18 23:32:53,052 epoch 1 - iter 3610/3617 - loss 0.60698836 - time (sec): 56.27 - samples/sec: 6741.68 - lr: 0.000050 - momentum: 0.000000
2023-10-18 23:32:53,154 ----------------------------------------------------------------------------------------------------
2023-10-18 23:32:53,154 EPOCH 1 done: loss 0.6062 - lr: 0.000050
2023-10-18 23:32:55,378 DEV : loss 0.17521269619464874 - f1-score (micro avg)  0.2814
2023-10-18 23:32:55,404 saving best model
2023-10-18 23:32:55,433 ----------------------------------------------------------------------------------------------------
2023-10-18 23:33:01,085 epoch 2 - iter 361/3617 - loss 0.18186471 - time (sec): 5.65 - samples/sec: 6671.55 - lr: 0.000049 - momentum: 0.000000
2023-10-18 23:33:06,832 epoch 2 - iter 722/3617 - loss 0.17555892 - time (sec): 11.40 - samples/sec: 6699.64 - lr: 0.000049 - momentum: 0.000000
2023-10-18 23:33:12,500 epoch 2 - iter 1083/3617 - loss 0.18267083 - time (sec): 17.07 - samples/sec: 6654.16 - lr: 0.000048 - momentum: 0.000000
2023-10-18 23:33:18,133 epoch 2 - iter 1444/3617 - loss 0.17984379 - time (sec): 22.70 - samples/sec: 6659.33 - lr: 0.000048 - momentum: 0.000000
2023-10-18 23:33:23,799 epoch 2 - iter 1805/3617 - loss 0.17830915 - time (sec): 28.37 - samples/sec: 6679.06 - lr: 0.000047 - momentum: 0.000000
2023-10-18 23:33:29,543 epoch 2 - iter 2166/3617 - loss 0.17452639 - time (sec): 34.11 - samples/sec: 6706.68 - lr: 0.000047 - momentum: 0.000000
2023-10-18 23:33:35,235 epoch 2 - iter 2527/3617 - loss 0.17431735 - time (sec): 39.80 - samples/sec: 6692.15 - lr: 0.000046 - momentum: 0.000000
2023-10-18 23:33:40,836 epoch 2 - iter 2888/3617 - loss 0.17180705 - time (sec): 45.40 - samples/sec: 6680.76 - lr: 0.000046 - momentum: 0.000000
2023-10-18 23:33:46,319 epoch 2 - iter 3249/3617 - loss 0.17124739 - time (sec): 50.89 - samples/sec: 6708.37 - lr: 0.000045 - momentum: 0.000000
2023-10-18 23:33:52,038 epoch 2 - iter 3610/3617 - loss 0.16926488 - time (sec): 56.60 - samples/sec: 6699.72 - lr: 0.000044 - momentum: 0.000000
2023-10-18 23:33:52,139 ----------------------------------------------------------------------------------------------------
2023-10-18 23:33:52,139 EPOCH 2 done: loss 0.1692 - lr: 0.000044
2023-10-18 23:33:55,914 DEV : loss 0.16764183342456818 - f1-score (micro avg)  0.3918
2023-10-18 23:33:55,943 saving best model
2023-10-18 23:33:55,982 ----------------------------------------------------------------------------------------------------
2023-10-18 23:34:01,814 epoch 3 - iter 361/3617 - loss 0.16873540 - time (sec): 5.83 - samples/sec: 6497.65 - lr: 0.000044 - momentum: 0.000000
2023-10-18 23:34:07,470 epoch 3 - iter 722/3617 - loss 0.15570685 - time (sec): 11.49 - samples/sec: 6513.90 - lr: 0.000043 - momentum: 0.000000
2023-10-18 23:34:13,134 epoch 3 - iter 1083/3617 - loss 0.15229953 - time (sec): 17.15 - samples/sec: 6685.33 - lr: 0.000043 - momentum: 0.000000
2023-10-18 23:34:18,815 epoch 3 - iter 1444/3617 - loss 0.15006958 - time (sec): 22.83 - samples/sec: 6651.49 - lr: 0.000042 - momentum: 0.000000
2023-10-18 23:34:24,508 epoch 3 - iter 1805/3617 - loss 0.14545533 - time (sec): 28.53 - samples/sec: 6701.89 - lr: 0.000042 - momentum: 0.000000
2023-10-18 23:34:29,907 epoch 3 - iter 2166/3617 - loss 0.14270393 - time (sec): 33.92 - samples/sec: 6781.32 - lr: 0.000041 - momentum: 0.000000
2023-10-18 23:34:35,612 epoch 3 - iter 2527/3617 - loss 0.14114846 - time (sec): 39.63 - samples/sec: 6742.66 - lr: 0.000041 - momentum: 0.000000
2023-10-18 23:34:41,129 epoch 3 - iter 2888/3617 - loss 0.14188453 - time (sec): 45.15 - samples/sec: 6746.12 - lr: 0.000040 - momentum: 0.000000
2023-10-18 23:34:46,190 epoch 3 - iter 3249/3617 - loss 0.14157282 - time (sec): 50.21 - samples/sec: 6815.93 - lr: 0.000039 - momentum: 0.000000
2023-10-18 23:34:51,682 epoch 3 - iter 3610/3617 - loss 0.14134114 - time (sec): 55.70 - samples/sec: 6809.87 - lr: 0.000039 - momentum: 0.000000
2023-10-18 23:34:51,787 ----------------------------------------------------------------------------------------------------
2023-10-18 23:34:51,787 EPOCH 3 done: loss 0.1415 - lr: 0.000039
2023-10-18 23:34:55,000 DEV : loss 0.16292423009872437 - f1-score (micro avg)  0.4771
2023-10-18 23:34:55,028 saving best model
2023-10-18 23:34:55,069 ----------------------------------------------------------------------------------------------------
2023-10-18 23:35:00,739 epoch 4 - iter 361/3617 - loss 0.13410168 - time (sec): 5.67 - samples/sec: 6389.88 - lr: 0.000038 - momentum: 0.000000
2023-10-18 23:35:06,521 epoch 4 - iter 722/3617 - loss 0.13650069 - time (sec): 11.45 - samples/sec: 6598.29 - lr: 0.000038 - momentum: 0.000000
2023-10-18 23:35:12,008 epoch 4 - iter 1083/3617 - loss 0.13257245 - time (sec): 16.94 - samples/sec: 6715.49 - lr: 0.000037 - momentum: 0.000000
2023-10-18 23:35:17,740 epoch 4 - iter 1444/3617 - loss 0.13291513 - time (sec): 22.67 - samples/sec: 6642.34 - lr: 0.000037 - momentum: 0.000000
2023-10-18 23:35:23,361 epoch 4 - iter 1805/3617 - loss 0.13001197 - time (sec): 28.29 - samples/sec: 6659.44 - lr: 0.000036 - momentum: 0.000000
2023-10-18 23:35:29,139 epoch 4 - iter 2166/3617 - loss 0.12690191 - time (sec): 34.07 - samples/sec: 6663.86 - lr: 0.000036 - momentum: 0.000000
2023-10-18 23:35:34,906 epoch 4 - iter 2527/3617 - loss 0.12813304 - time (sec): 39.84 - samples/sec: 6686.69 - lr: 0.000035 - momentum: 0.000000
2023-10-18 23:35:40,607 epoch 4 - iter 2888/3617 - loss 0.12911785 - time (sec): 45.54 - samples/sec: 6658.28 - lr: 0.000034 - momentum: 0.000000
2023-10-18 23:35:46,281 epoch 4 - iter 3249/3617 - loss 0.12698770 - time (sec): 51.21 - samples/sec: 6679.26 - lr: 0.000034 - momentum: 0.000000
2023-10-18 23:35:52,052 epoch 4 - iter 3610/3617 - loss 0.12601547 - time (sec): 56.98 - samples/sec: 6651.46 - lr: 0.000033 - momentum: 0.000000
2023-10-18 23:35:52,170 ----------------------------------------------------------------------------------------------------
2023-10-18 23:35:52,170 EPOCH 4 done: loss 0.1259 - lr: 0.000033
2023-10-18 23:35:56,007 DEV : loss 0.17158399522304535 - f1-score (micro avg)  0.4922
2023-10-18 23:35:56,035 saving best model
2023-10-18 23:35:56,074 ----------------------------------------------------------------------------------------------------
2023-10-18 23:36:01,842 epoch 5 - iter 361/3617 - loss 0.10767703 - time (sec): 5.77 - samples/sec: 6797.38 - lr: 0.000033 - momentum: 0.000000
2023-10-18 23:36:07,648 epoch 5 - iter 722/3617 - loss 0.11538990 - time (sec): 11.57 - samples/sec: 6711.32 - lr: 0.000032 - momentum: 0.000000
2023-10-18 23:36:13,368 epoch 5 - iter 1083/3617 - loss 0.10959815 - time (sec): 17.29 - samples/sec: 6725.29 - lr: 0.000032 - momentum: 0.000000
2023-10-18 23:36:19,117 epoch 5 - iter 1444/3617 - loss 0.10966842 - time (sec): 23.04 - samples/sec: 6697.82 - lr: 0.000031 - momentum: 0.000000
2023-10-18 23:36:24,772 epoch 5 - iter 1805/3617 - loss 0.11133696 - time (sec): 28.70 - samples/sec: 6661.49 - lr: 0.000031 - momentum: 0.000000
2023-10-18 23:36:30,368 epoch 5 - iter 2166/3617 - loss 0.11272880 - time (sec): 34.29 - samples/sec: 6649.42 - lr: 0.000030 - momentum: 0.000000
2023-10-18 23:36:36,026 epoch 5 - iter 2527/3617 - loss 0.11283559 - time (sec): 39.95 - samples/sec: 6669.94 - lr: 0.000029 - momentum: 0.000000
2023-10-18 23:36:41,522 epoch 5 - iter 2888/3617 - loss 0.11205737 - time (sec): 45.45 - samples/sec: 6696.03 - lr: 0.000029 - momentum: 0.000000
2023-10-18 23:36:46,562 epoch 5 - iter 3249/3617 - loss 0.11215470 - time (sec): 50.49 - samples/sec: 6768.50 - lr: 0.000028 - momentum: 0.000000
2023-10-18 23:36:52,200 epoch 5 - iter 3610/3617 - loss 0.11228934 - time (sec): 56.13 - samples/sec: 6756.66 - lr: 0.000028 - momentum: 0.000000
2023-10-18 23:36:52,312 ----------------------------------------------------------------------------------------------------
2023-10-18 23:36:52,312 EPOCH 5 done: loss 0.1122 - lr: 0.000028
2023-10-18 23:36:55,548 DEV : loss 0.18035954236984253 - f1-score (micro avg)  0.5056
2023-10-18 23:36:55,576 saving best model
2023-10-18 23:36:55,615 ----------------------------------------------------------------------------------------------------
2023-10-18 23:37:01,448 epoch 6 - iter 361/3617 - loss 0.10582480 - time (sec): 5.83 - samples/sec: 6762.34 - lr: 0.000027 - momentum: 0.000000
2023-10-18 23:37:07,097 epoch 6 - iter 722/3617 - loss 0.10344347 - time (sec): 11.48 - samples/sec: 6653.64 - lr: 0.000027 - momentum: 0.000000
2023-10-18 23:37:12,714 epoch 6 - iter 1083/3617 - loss 0.10584413 - time (sec): 17.10 - samples/sec: 6554.94 - lr: 0.000026 - momentum: 0.000000
2023-10-18 23:37:18,518 epoch 6 - iter 1444/3617 - loss 0.10479511 - time (sec): 22.90 - samples/sec: 6591.41 - lr: 0.000026 - momentum: 0.000000
2023-10-18 23:37:24,193 epoch 6 - iter 1805/3617 - loss 0.10413115 - time (sec): 28.58 - samples/sec: 6591.53 - lr: 0.000025 - momentum: 0.000000
2023-10-18 23:37:29,887 epoch 6 - iter 2166/3617 - loss 0.10251088 - time (sec): 34.27 - samples/sec: 6634.11 - lr: 0.000024 - momentum: 0.000000
2023-10-18 23:37:35,572 epoch 6 - iter 2527/3617 - loss 0.09916624 - time (sec): 39.96 - samples/sec: 6621.63 - lr: 0.000024 - momentum: 0.000000
2023-10-18 23:37:41,386 epoch 6 - iter 2888/3617 - loss 0.09901356 - time (sec): 45.77 - samples/sec: 6621.04 - lr: 0.000023 - momentum: 0.000000
2023-10-18 23:37:47,445 epoch 6 - iter 3249/3617 - loss 0.10012522 - time (sec): 51.83 - samples/sec: 6597.53 - lr: 0.000023 - momentum: 0.000000
2023-10-18 23:37:53,206 epoch 6 - iter 3610/3617 - loss 0.10172452 - time (sec): 57.59 - samples/sec: 6585.83 - lr: 0.000022 - momentum: 0.000000
2023-10-18 23:37:53,310 ----------------------------------------------------------------------------------------------------
2023-10-18 23:37:53,310 EPOCH 6 done: loss 0.1017 - lr: 0.000022
2023-10-18 23:37:56,522 DEV : loss 0.19675783812999725 - f1-score (micro avg)  0.52
2023-10-18 23:37:56,550 saving best model
2023-10-18 23:37:56,582 ----------------------------------------------------------------------------------------------------
2023-10-18 23:38:02,251 epoch 7 - iter 361/3617 - loss 0.09799474 - time (sec): 5.67 - samples/sec: 6829.25 - lr: 0.000022 - momentum: 0.000000
2023-10-18 23:38:07,989 epoch 7 - iter 722/3617 - loss 0.09523192 - time (sec): 11.41 - samples/sec: 6830.30 - lr: 0.000021 - momentum: 0.000000
2023-10-18 23:38:13,729 epoch 7 - iter 1083/3617 - loss 0.09715759 - time (sec): 17.15 - samples/sec: 6717.61 - lr: 0.000021 - momentum: 0.000000
2023-10-18 23:38:19,390 epoch 7 - iter 1444/3617 - loss 0.09700347 - time (sec): 22.81 - samples/sec: 6691.41 - lr: 0.000020 - momentum: 0.000000
2023-10-18 23:38:25,140 epoch 7 - iter 1805/3617 - loss 0.09588648 - time (sec): 28.56 - samples/sec: 6685.91 - lr: 0.000019 - momentum: 0.000000
2023-10-18 23:38:30,875 epoch 7 - iter 2166/3617 - loss 0.09457137 - time (sec): 34.29 - samples/sec: 6695.36 - lr: 0.000019 - momentum: 0.000000
2023-10-18 23:38:36,556 epoch 7 - iter 2527/3617 - loss 0.09554146 - time (sec): 39.97 - samples/sec: 6686.03 - lr: 0.000018 - momentum: 0.000000
2023-10-18 23:38:42,335 epoch 7 - iter 2888/3617 - loss 0.09478297 - time (sec): 45.75 - samples/sec: 6661.58 - lr: 0.000018 - momentum: 0.000000
2023-10-18 23:38:47,875 epoch 7 - iter 3249/3617 - loss 0.09584686 - time (sec): 51.29 - samples/sec: 6673.39 - lr: 0.000017 - momentum: 0.000000
2023-10-18 23:38:53,473 epoch 7 - iter 3610/3617 - loss 0.09639512 - time (sec): 56.89 - samples/sec: 6668.95 - lr: 0.000017 - momentum: 0.000000
2023-10-18 23:38:53,574 ----------------------------------------------------------------------------------------------------
2023-10-18 23:38:53,574 EPOCH 7 done: loss 0.0964 - lr: 0.000017
2023-10-18 23:38:57,413 DEV : loss 0.20494325459003448 - f1-score (micro avg)  0.5123
2023-10-18 23:38:57,441 ----------------------------------------------------------------------------------------------------
2023-10-18 23:39:03,091 epoch 8 - iter 361/3617 - loss 0.08316593 - time (sec): 5.65 - samples/sec: 6661.87 - lr: 0.000016 - momentum: 0.000000
2023-10-18 23:39:08,961 epoch 8 - iter 722/3617 - loss 0.08133314 - time (sec): 11.52 - samples/sec: 6552.02 - lr: 0.000016 - momentum: 0.000000
2023-10-18 23:39:14,650 epoch 8 - iter 1083/3617 - loss 0.08367591 - time (sec): 17.21 - samples/sec: 6644.50 - lr: 0.000015 - momentum: 0.000000
2023-10-18 23:39:20,408 epoch 8 - iter 1444/3617 - loss 0.08496144 - time (sec): 22.97 - samples/sec: 6612.09 - lr: 0.000014 - momentum: 0.000000
2023-10-18 23:39:26,204 epoch 8 - iter 1805/3617 - loss 0.08735194 - time (sec): 28.76 - samples/sec: 6628.41 - lr: 0.000014 - momentum: 0.000000
2023-10-18 23:39:31,922 epoch 8 - iter 2166/3617 - loss 0.09141252 - time (sec): 34.48 - samples/sec: 6648.80 - lr: 0.000013 - momentum: 0.000000
2023-10-18 23:39:37,572 epoch 8 - iter 2527/3617 - loss 0.09151271 - time (sec): 40.13 - samples/sec: 6675.19 - lr: 0.000013 - momentum: 0.000000
2023-10-18 23:39:43,240 epoch 8 - iter 2888/3617 - loss 0.09206089 - time (sec): 45.80 - samples/sec: 6672.07 - lr: 0.000012 - momentum: 0.000000
2023-10-18 23:39:48,868 epoch 8 - iter 3249/3617 - loss 0.09102678 - time (sec): 51.43 - samples/sec: 6683.90 - lr: 0.000012 - momentum: 0.000000
2023-10-18 23:39:54,734 epoch 8 - iter 3610/3617 - loss 0.08981402 - time (sec): 57.29 - samples/sec: 6623.49 - lr: 0.000011 - momentum: 0.000000
2023-10-18 23:39:54,831 ----------------------------------------------------------------------------------------------------
2023-10-18 23:39:54,831 EPOCH 8 done: loss 0.0898 - lr: 0.000011
2023-10-18 23:39:58,030 DEV : loss 0.22380779683589935 - f1-score (micro avg)  0.5261
2023-10-18 23:39:58,058 saving best model
2023-10-18 23:39:58,091 ----------------------------------------------------------------------------------------------------
2023-10-18 23:40:03,819 epoch 9 - iter 361/3617 - loss 0.07389080 - time (sec): 5.73 - samples/sec: 6734.41 - lr: 0.000011 - momentum: 0.000000
2023-10-18 23:40:09,496 epoch 9 - iter 722/3617 - loss 0.08048499 - time (sec): 11.40 - samples/sec: 6728.63 - lr: 0.000010 - momentum: 0.000000
2023-10-18 23:40:15,212 epoch 9 - iter 1083/3617 - loss 0.08163769 - time (sec): 17.12 - samples/sec: 6682.20 - lr: 0.000009 - momentum: 0.000000
2023-10-18 23:40:21,028 epoch 9 - iter 1444/3617 - loss 0.08528220 - time (sec): 22.94 - samples/sec: 6708.43 - lr: 0.000009 - momentum: 0.000000
2023-10-18 23:40:26,615 epoch 9 - iter 1805/3617 - loss 0.08612361 - time (sec): 28.52 - samples/sec: 6640.28 - lr: 0.000008 - momentum: 0.000000
2023-10-18 23:40:32,305 epoch 9 - iter 2166/3617 - loss 0.08425714 - time (sec): 34.21 - samples/sec: 6642.71 - lr: 0.000008 - momentum: 0.000000
2023-10-18 23:40:38,017 epoch 9 - iter 2527/3617 - loss 0.08439459 - time (sec): 39.93 - samples/sec: 6664.00 - lr: 0.000007 - momentum: 0.000000
2023-10-18 23:40:43,797 epoch 9 - iter 2888/3617 - loss 0.08538617 - time (sec): 45.70 - samples/sec: 6657.30 - lr: 0.000007 - momentum: 0.000000
2023-10-18 23:40:49,295 epoch 9 - iter 3249/3617 - loss 0.08512273 - time (sec): 51.20 - samples/sec: 6678.10 - lr: 0.000006 - momentum: 0.000000
2023-10-18 23:40:54,945 epoch 9 - iter 3610/3617 - loss 0.08581878 - time (sec): 56.85 - samples/sec: 6673.93 - lr: 0.000006 - momentum: 0.000000
2023-10-18 23:40:55,051 ----------------------------------------------------------------------------------------------------
2023-10-18 23:40:55,052 EPOCH 9 done: loss 0.0858 - lr: 0.000006
2023-10-18 23:40:58,910 DEV : loss 0.23317401111125946 - f1-score (micro avg)  0.5297
2023-10-18 23:40:58,938 saving best model
2023-10-18 23:40:58,977 ----------------------------------------------------------------------------------------------------
2023-10-18 23:41:05,019 epoch 10 - iter 361/3617 - loss 0.08574863 - time (sec): 6.04 - samples/sec: 6107.84 - lr: 0.000005 - momentum: 0.000000
2023-10-18 23:41:10,700 epoch 10 - iter 722/3617 - loss 0.08392903 - time (sec): 11.72 - samples/sec: 6349.28 - lr: 0.000004 - momentum: 0.000000
2023-10-18 23:41:16,371 epoch 10 - iter 1083/3617 - loss 0.07922998 - time (sec): 17.39 - samples/sec: 6427.48 - lr: 0.000004 - momentum: 0.000000
2023-10-18 23:41:21,800 epoch 10 - iter 1444/3617 - loss 0.08164333 - time (sec): 22.82 - samples/sec: 6551.76 - lr: 0.000003 - momentum: 0.000000
2023-10-18 23:41:27,491 epoch 10 - iter 1805/3617 - loss 0.07922620 - time (sec): 28.51 - samples/sec: 6589.30 - lr: 0.000003 - momentum: 0.000000
2023-10-18 23:41:33,314 epoch 10 - iter 2166/3617 - loss 0.08341687 - time (sec): 34.34 - samples/sec: 6620.00 - lr: 0.000002 - momentum: 0.000000
2023-10-18 23:41:38,972 epoch 10 - iter 2527/3617 - loss 0.08209356 - time (sec): 39.99 - samples/sec: 6620.51 - lr: 0.000002 - momentum: 0.000000
2023-10-18 23:41:44,642 epoch 10 - iter 2888/3617 - loss 0.08287713 - time (sec): 45.66 - samples/sec: 6626.57 - lr: 0.000001 - momentum: 0.000000
2023-10-18 23:41:50,388 epoch 10 - iter 3249/3617 - loss 0.08215472 - time (sec): 51.41 - samples/sec: 6663.09 - lr: 0.000001 - momentum: 0.000000
2023-10-18 23:41:55,996 epoch 10 - iter 3610/3617 - loss 0.08275437 - time (sec): 57.02 - samples/sec: 6654.92 - lr: 0.000000 - momentum: 0.000000
2023-10-18 23:41:56,096 ----------------------------------------------------------------------------------------------------
2023-10-18 23:41:56,097 EPOCH 10 done: loss 0.0830 - lr: 0.000000
2023-10-18 23:41:59,297 DEV : loss 0.2345695048570633 - f1-score (micro avg)  0.5275
2023-10-18 23:41:59,357 ----------------------------------------------------------------------------------------------------
2023-10-18 23:41:59,357 Loading model from best epoch ...
2023-10-18 23:41:59,438 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
2023-10-18 23:42:03,543 
Results:
- F-score (micro) 0.5325
- F-score (macro) 0.3573
- Accuracy 0.375

By class:
              precision    recall  f1-score   support

         loc     0.5215    0.6971    0.5967       591
        pers     0.4273    0.5350    0.4751       357
         org     0.0000    0.0000    0.0000        79

   micro avg     0.4871    0.5871    0.5325      1027
   macro avg     0.3163    0.4107    0.3573      1027
weighted avg     0.4486    0.5871    0.5085      1027

2023-10-18 23:42:03,543 ----------------------------------------------------------------------------------------------------