2023-10-19 01:10:59,040 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,040 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()
)"
2023-10-19 01:10:59,040 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,040 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
2023-10-19 01:10:59,040 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,040 Train:  14465 sentences
2023-10-19 01:10:59,040         (train_with_dev=False, train_with_test=False)
2023-10-19 01:10:59,040 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,040 Training Params:
2023-10-19 01:10:59,040  - learning_rate: "3e-05" 
2023-10-19 01:10:59,040  - mini_batch_size: "4"
2023-10-19 01:10:59,040  - max_epochs: "10"
2023-10-19 01:10:59,040  - shuffle: "True"
2023-10-19 01:10:59,040 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,040 Plugins:
2023-10-19 01:10:59,040  - TensorboardLogger
2023-10-19 01:10:59,040  - LinearScheduler | warmup_fraction: '0.1'
2023-10-19 01:10:59,040 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,040 Final evaluation on model from best epoch (best-model.pt)
2023-10-19 01:10:59,040  - metric: "('micro avg', 'f1-score')"
2023-10-19 01:10:59,041 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,041 Computation:
2023-10-19 01:10:59,041  - compute on device: cuda:0
2023-10-19 01:10:59,041  - embedding storage: none
2023-10-19 01:10:59,041 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,041 Model training base path: "hmbench-letemps/fr-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-19 01:10:59,041 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,041 ----------------------------------------------------------------------------------------------------
2023-10-19 01:10:59,041 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-19 01:11:04,907 epoch 1 - iter 361/3617 - loss 3.12252186 - time (sec): 5.87 - samples/sec: 6145.92 - lr: 0.000003 - momentum: 0.000000
2023-10-19 01:11:10,541 epoch 1 - iter 722/3617 - loss 2.41293036 - time (sec): 11.50 - samples/sec: 6537.22 - lr: 0.000006 - momentum: 0.000000
2023-10-19 01:11:16,281 epoch 1 - iter 1083/3617 - loss 1.79547173 - time (sec): 17.24 - samples/sec: 6533.93 - lr: 0.000009 - momentum: 0.000000
2023-10-19 01:11:21,954 epoch 1 - iter 1444/3617 - loss 1.44599180 - time (sec): 22.91 - samples/sec: 6519.84 - lr: 0.000012 - momentum: 0.000000
2023-10-19 01:11:27,623 epoch 1 - iter 1805/3617 - loss 1.22012401 - time (sec): 28.58 - samples/sec: 6522.30 - lr: 0.000015 - momentum: 0.000000
2023-10-19 01:11:33,316 epoch 1 - iter 2166/3617 - loss 1.06655197 - time (sec): 34.27 - samples/sec: 6522.49 - lr: 0.000018 - momentum: 0.000000
2023-10-19 01:11:39,043 epoch 1 - iter 2527/3617 - loss 0.95272625 - time (sec): 40.00 - samples/sec: 6529.96 - lr: 0.000021 - momentum: 0.000000
2023-10-19 01:11:44,824 epoch 1 - iter 2888/3617 - loss 0.85861658 - time (sec): 45.78 - samples/sec: 6579.78 - lr: 0.000024 - momentum: 0.000000
2023-10-19 01:11:50,477 epoch 1 - iter 3249/3617 - loss 0.78754238 - time (sec): 51.44 - samples/sec: 6619.51 - lr: 0.000027 - momentum: 0.000000
2023-10-19 01:11:56,201 epoch 1 - iter 3610/3617 - loss 0.73000898 - time (sec): 57.16 - samples/sec: 6635.44 - lr: 0.000030 - momentum: 0.000000
2023-10-19 01:11:56,311 ----------------------------------------------------------------------------------------------------
2023-10-19 01:11:56,311 EPOCH 1 done: loss 0.7293 - lr: 0.000030
2023-10-19 01:11:58,584 DEV : loss 0.1803603321313858 - f1-score (micro avg)  0.1925
2023-10-19 01:11:58,611 saving best model
2023-10-19 01:11:58,640 ----------------------------------------------------------------------------------------------------
2023-10-19 01:12:04,265 epoch 2 - iter 361/3617 - loss 0.20098825 - time (sec): 5.62 - samples/sec: 6528.17 - lr: 0.000030 - momentum: 0.000000
2023-10-19 01:12:09,986 epoch 2 - iter 722/3617 - loss 0.20199769 - time (sec): 11.35 - samples/sec: 6570.12 - lr: 0.000029 - momentum: 0.000000
2023-10-19 01:12:15,686 epoch 2 - iter 1083/3617 - loss 0.19444887 - time (sec): 17.05 - samples/sec: 6553.53 - lr: 0.000029 - momentum: 0.000000
2023-10-19 01:12:21,265 epoch 2 - iter 1444/3617 - loss 0.19472453 - time (sec): 22.62 - samples/sec: 6574.65 - lr: 0.000029 - momentum: 0.000000
2023-10-19 01:12:26,799 epoch 2 - iter 1805/3617 - loss 0.19141549 - time (sec): 28.16 - samples/sec: 6668.61 - lr: 0.000028 - momentum: 0.000000
2023-10-19 01:12:32,506 epoch 2 - iter 2166/3617 - loss 0.18942948 - time (sec): 33.87 - samples/sec: 6651.96 - lr: 0.000028 - momentum: 0.000000
2023-10-19 01:12:38,393 epoch 2 - iter 2527/3617 - loss 0.19177342 - time (sec): 39.75 - samples/sec: 6633.01 - lr: 0.000028 - momentum: 0.000000
2023-10-19 01:12:44,107 epoch 2 - iter 2888/3617 - loss 0.18890055 - time (sec): 45.47 - samples/sec: 6637.92 - lr: 0.000027 - momentum: 0.000000
2023-10-19 01:12:49,797 epoch 2 - iter 3249/3617 - loss 0.18726829 - time (sec): 51.16 - samples/sec: 6664.34 - lr: 0.000027 - momentum: 0.000000
2023-10-19 01:12:55,492 epoch 2 - iter 3610/3617 - loss 0.18564262 - time (sec): 56.85 - samples/sec: 6673.24 - lr: 0.000027 - momentum: 0.000000
2023-10-19 01:12:55,588 ----------------------------------------------------------------------------------------------------
2023-10-19 01:12:55,588 EPOCH 2 done: loss 0.1857 - lr: 0.000027
2023-10-19 01:12:59,509 DEV : loss 0.17232035100460052 - f1-score (micro avg)  0.3846
2023-10-19 01:12:59,536 saving best model
2023-10-19 01:12:59,569 ----------------------------------------------------------------------------------------------------
2023-10-19 01:13:05,350 epoch 3 - iter 361/3617 - loss 0.15068487 - time (sec): 5.78 - samples/sec: 6657.47 - lr: 0.000026 - momentum: 0.000000
2023-10-19 01:13:11,111 epoch 3 - iter 722/3617 - loss 0.16512105 - time (sec): 11.54 - samples/sec: 6531.24 - lr: 0.000026 - momentum: 0.000000
2023-10-19 01:13:16,903 epoch 3 - iter 1083/3617 - loss 0.16734512 - time (sec): 17.33 - samples/sec: 6649.78 - lr: 0.000026 - momentum: 0.000000
2023-10-19 01:13:22,599 epoch 3 - iter 1444/3617 - loss 0.16745629 - time (sec): 23.03 - samples/sec: 6608.73 - lr: 0.000025 - momentum: 0.000000
2023-10-19 01:13:28,382 epoch 3 - iter 1805/3617 - loss 0.16513383 - time (sec): 28.81 - samples/sec: 6577.01 - lr: 0.000025 - momentum: 0.000000
2023-10-19 01:13:34,182 epoch 3 - iter 2166/3617 - loss 0.16581818 - time (sec): 34.61 - samples/sec: 6543.84 - lr: 0.000025 - momentum: 0.000000
2023-10-19 01:13:40,005 epoch 3 - iter 2527/3617 - loss 0.16204270 - time (sec): 40.44 - samples/sec: 6531.56 - lr: 0.000024 - momentum: 0.000000
2023-10-19 01:13:45,508 epoch 3 - iter 2888/3617 - loss 0.15884163 - time (sec): 45.94 - samples/sec: 6591.42 - lr: 0.000024 - momentum: 0.000000
2023-10-19 01:13:51,225 epoch 3 - iter 3249/3617 - loss 0.15690141 - time (sec): 51.66 - samples/sec: 6628.98 - lr: 0.000024 - momentum: 0.000000
2023-10-19 01:13:56,955 epoch 3 - iter 3610/3617 - loss 0.15791128 - time (sec): 57.38 - samples/sec: 6609.03 - lr: 0.000023 - momentum: 0.000000
2023-10-19 01:13:57,053 ----------------------------------------------------------------------------------------------------
2023-10-19 01:13:57,053 EPOCH 3 done: loss 0.1578 - lr: 0.000023
2023-10-19 01:14:00,254 DEV : loss 0.16959495842456818 - f1-score (micro avg)  0.4158
2023-10-19 01:14:00,282 saving best model
2023-10-19 01:14:00,315 ----------------------------------------------------------------------------------------------------
2023-10-19 01:14:06,037 epoch 4 - iter 361/3617 - loss 0.13799445 - time (sec): 5.72 - samples/sec: 6598.38 - lr: 0.000023 - momentum: 0.000000
2023-10-19 01:14:11,790 epoch 4 - iter 722/3617 - loss 0.13722792 - time (sec): 11.47 - samples/sec: 6581.33 - lr: 0.000023 - momentum: 0.000000
2023-10-19 01:14:17,606 epoch 4 - iter 1083/3617 - loss 0.14251973 - time (sec): 17.29 - samples/sec: 6618.47 - lr: 0.000022 - momentum: 0.000000
2023-10-19 01:14:23,333 epoch 4 - iter 1444/3617 - loss 0.14215649 - time (sec): 23.02 - samples/sec: 6612.71 - lr: 0.000022 - momentum: 0.000000
2023-10-19 01:14:29,047 epoch 4 - iter 1805/3617 - loss 0.14527769 - time (sec): 28.73 - samples/sec: 6567.33 - lr: 0.000022 - momentum: 0.000000
2023-10-19 01:14:34,901 epoch 4 - iter 2166/3617 - loss 0.14771726 - time (sec): 34.58 - samples/sec: 6575.48 - lr: 0.000021 - momentum: 0.000000
2023-10-19 01:14:40,765 epoch 4 - iter 2527/3617 - loss 0.14620802 - time (sec): 40.45 - samples/sec: 6581.63 - lr: 0.000021 - momentum: 0.000000
2023-10-19 01:14:46,464 epoch 4 - iter 2888/3617 - loss 0.14434453 - time (sec): 46.15 - samples/sec: 6577.15 - lr: 0.000021 - momentum: 0.000000
2023-10-19 01:14:52,181 epoch 4 - iter 3249/3617 - loss 0.14432907 - time (sec): 51.86 - samples/sec: 6602.27 - lr: 0.000020 - momentum: 0.000000
2023-10-19 01:14:57,873 epoch 4 - iter 3610/3617 - loss 0.14625128 - time (sec): 57.56 - samples/sec: 6587.12 - lr: 0.000020 - momentum: 0.000000
2023-10-19 01:14:57,984 ----------------------------------------------------------------------------------------------------
2023-10-19 01:14:57,984 EPOCH 4 done: loss 0.1464 - lr: 0.000020
2023-10-19 01:15:01,868 DEV : loss 0.16661077737808228 - f1-score (micro avg)  0.4483
2023-10-19 01:15:01,897 saving best model
2023-10-19 01:15:01,929 ----------------------------------------------------------------------------------------------------
2023-10-19 01:15:07,423 epoch 5 - iter 361/3617 - loss 0.12987237 - time (sec): 5.49 - samples/sec: 6882.43 - lr: 0.000020 - momentum: 0.000000
2023-10-19 01:15:13,383 epoch 5 - iter 722/3617 - loss 0.12949253 - time (sec): 11.45 - samples/sec: 6532.00 - lr: 0.000019 - momentum: 0.000000
2023-10-19 01:15:19,029 epoch 5 - iter 1083/3617 - loss 0.13420185 - time (sec): 17.10 - samples/sec: 6506.71 - lr: 0.000019 - momentum: 0.000000
2023-10-19 01:15:24,691 epoch 5 - iter 1444/3617 - loss 0.13910024 - time (sec): 22.76 - samples/sec: 6542.09 - lr: 0.000019 - momentum: 0.000000
2023-10-19 01:15:30,487 epoch 5 - iter 1805/3617 - loss 0.13856134 - time (sec): 28.56 - samples/sec: 6558.16 - lr: 0.000018 - momentum: 0.000000
2023-10-19 01:15:36,168 epoch 5 - iter 2166/3617 - loss 0.13530390 - time (sec): 34.24 - samples/sec: 6550.97 - lr: 0.000018 - momentum: 0.000000
2023-10-19 01:15:41,913 epoch 5 - iter 2527/3617 - loss 0.13679379 - time (sec): 39.98 - samples/sec: 6590.17 - lr: 0.000018 - momentum: 0.000000
2023-10-19 01:15:47,672 epoch 5 - iter 2888/3617 - loss 0.13624011 - time (sec): 45.74 - samples/sec: 6604.87 - lr: 0.000017 - momentum: 0.000000
2023-10-19 01:15:53,434 epoch 5 - iter 3249/3617 - loss 0.13560627 - time (sec): 51.50 - samples/sec: 6616.88 - lr: 0.000017 - momentum: 0.000000
2023-10-19 01:15:59,190 epoch 5 - iter 3610/3617 - loss 0.13495541 - time (sec): 57.26 - samples/sec: 6624.56 - lr: 0.000017 - momentum: 0.000000
2023-10-19 01:15:59,302 ----------------------------------------------------------------------------------------------------
2023-10-19 01:15:59,303 EPOCH 5 done: loss 0.1351 - lr: 0.000017
2023-10-19 01:16:02,561 DEV : loss 0.1814231425523758 - f1-score (micro avg)  0.4586
2023-10-19 01:16:02,588 saving best model
2023-10-19 01:16:02,621 ----------------------------------------------------------------------------------------------------
2023-10-19 01:16:08,251 epoch 6 - iter 361/3617 - loss 0.13714181 - time (sec): 5.63 - samples/sec: 6642.75 - lr: 0.000016 - momentum: 0.000000
2023-10-19 01:16:13,926 epoch 6 - iter 722/3617 - loss 0.12680325 - time (sec): 11.30 - samples/sec: 6662.60 - lr: 0.000016 - momentum: 0.000000
2023-10-19 01:16:19,692 epoch 6 - iter 1083/3617 - loss 0.12500890 - time (sec): 17.07 - samples/sec: 6691.92 - lr: 0.000016 - momentum: 0.000000
2023-10-19 01:16:25,275 epoch 6 - iter 1444/3617 - loss 0.12471214 - time (sec): 22.65 - samples/sec: 6643.42 - lr: 0.000015 - momentum: 0.000000
2023-10-19 01:16:30,912 epoch 6 - iter 1805/3617 - loss 0.12052615 - time (sec): 28.29 - samples/sec: 6579.21 - lr: 0.000015 - momentum: 0.000000
2023-10-19 01:16:36,593 epoch 6 - iter 2166/3617 - loss 0.12279489 - time (sec): 33.97 - samples/sec: 6628.67 - lr: 0.000015 - momentum: 0.000000
2023-10-19 01:16:42,367 epoch 6 - iter 2527/3617 - loss 0.12224267 - time (sec): 39.74 - samples/sec: 6630.65 - lr: 0.000014 - momentum: 0.000000
2023-10-19 01:16:48,183 epoch 6 - iter 2888/3617 - loss 0.12426780 - time (sec): 45.56 - samples/sec: 6629.71 - lr: 0.000014 - momentum: 0.000000
2023-10-19 01:16:53,829 epoch 6 - iter 3249/3617 - loss 0.12488997 - time (sec): 51.21 - samples/sec: 6654.26 - lr: 0.000014 - momentum: 0.000000
2023-10-19 01:16:59,084 epoch 6 - iter 3610/3617 - loss 0.12639819 - time (sec): 56.46 - samples/sec: 6717.91 - lr: 0.000013 - momentum: 0.000000
2023-10-19 01:16:59,180 ----------------------------------------------------------------------------------------------------
2023-10-19 01:16:59,181 EPOCH 6 done: loss 0.1264 - lr: 0.000013
2023-10-19 01:17:02,429 DEV : loss 0.1841202825307846 - f1-score (micro avg)  0.4741
2023-10-19 01:17:02,456 saving best model
2023-10-19 01:17:02,489 ----------------------------------------------------------------------------------------------------
2023-10-19 01:17:08,157 epoch 7 - iter 361/3617 - loss 0.12363306 - time (sec): 5.67 - samples/sec: 6708.62 - lr: 0.000013 - momentum: 0.000000
2023-10-19 01:17:13,867 epoch 7 - iter 722/3617 - loss 0.12287275 - time (sec): 11.38 - samples/sec: 6576.34 - lr: 0.000013 - momentum: 0.000000
2023-10-19 01:17:19,709 epoch 7 - iter 1083/3617 - loss 0.11963241 - time (sec): 17.22 - samples/sec: 6579.90 - lr: 0.000012 - momentum: 0.000000
2023-10-19 01:17:25,354 epoch 7 - iter 1444/3617 - loss 0.12218097 - time (sec): 22.86 - samples/sec: 6637.46 - lr: 0.000012 - momentum: 0.000000
2023-10-19 01:17:31,082 epoch 7 - iter 1805/3617 - loss 0.12399375 - time (sec): 28.59 - samples/sec: 6639.53 - lr: 0.000012 - momentum: 0.000000
2023-10-19 01:17:36,684 epoch 7 - iter 2166/3617 - loss 0.12278498 - time (sec): 34.19 - samples/sec: 6696.17 - lr: 0.000011 - momentum: 0.000000
2023-10-19 01:17:43,138 epoch 7 - iter 2527/3617 - loss 0.12015701 - time (sec): 40.65 - samples/sec: 6563.75 - lr: 0.000011 - momentum: 0.000000
2023-10-19 01:17:48,760 epoch 7 - iter 2888/3617 - loss 0.11877865 - time (sec): 46.27 - samples/sec: 6593.24 - lr: 0.000011 - momentum: 0.000000
2023-10-19 01:17:54,087 epoch 7 - iter 3249/3617 - loss 0.12139837 - time (sec): 51.60 - samples/sec: 6624.35 - lr: 0.000010 - momentum: 0.000000
2023-10-19 01:17:59,808 epoch 7 - iter 3610/3617 - loss 0.12214986 - time (sec): 57.32 - samples/sec: 6617.30 - lr: 0.000010 - momentum: 0.000000
2023-10-19 01:17:59,917 ----------------------------------------------------------------------------------------------------
2023-10-19 01:17:59,917 EPOCH 7 done: loss 0.1222 - lr: 0.000010
2023-10-19 01:18:03,123 DEV : loss 0.18754823505878448 - f1-score (micro avg)  0.4779
2023-10-19 01:18:03,151 saving best model
2023-10-19 01:18:03,186 ----------------------------------------------------------------------------------------------------
2023-10-19 01:18:09,163 epoch 8 - iter 361/3617 - loss 0.11552861 - time (sec): 5.98 - samples/sec: 6550.53 - lr: 0.000010 - momentum: 0.000000
2023-10-19 01:18:14,914 epoch 8 - iter 722/3617 - loss 0.11948353 - time (sec): 11.73 - samples/sec: 6616.21 - lr: 0.000009 - momentum: 0.000000
2023-10-19 01:18:20,735 epoch 8 - iter 1083/3617 - loss 0.11968151 - time (sec): 17.55 - samples/sec: 6671.45 - lr: 0.000009 - momentum: 0.000000
2023-10-19 01:18:26,269 epoch 8 - iter 1444/3617 - loss 0.12114434 - time (sec): 23.08 - samples/sec: 6666.92 - lr: 0.000009 - momentum: 0.000000
2023-10-19 01:18:32,065 epoch 8 - iter 1805/3617 - loss 0.11664197 - time (sec): 28.88 - samples/sec: 6667.87 - lr: 0.000008 - momentum: 0.000000
2023-10-19 01:18:37,854 epoch 8 - iter 2166/3617 - loss 0.11725016 - time (sec): 34.67 - samples/sec: 6598.38 - lr: 0.000008 - momentum: 0.000000
2023-10-19 01:18:43,593 epoch 8 - iter 2527/3617 - loss 0.11944440 - time (sec): 40.41 - samples/sec: 6575.91 - lr: 0.000008 - momentum: 0.000000
2023-10-19 01:18:49,269 epoch 8 - iter 2888/3617 - loss 0.11884190 - time (sec): 46.08 - samples/sec: 6587.45 - lr: 0.000007 - momentum: 0.000000
2023-10-19 01:18:54,994 epoch 8 - iter 3249/3617 - loss 0.11785410 - time (sec): 51.81 - samples/sec: 6583.13 - lr: 0.000007 - momentum: 0.000000
2023-10-19 01:19:00,793 epoch 8 - iter 3610/3617 - loss 0.11660685 - time (sec): 57.61 - samples/sec: 6587.20 - lr: 0.000007 - momentum: 0.000000
2023-10-19 01:19:00,903 ----------------------------------------------------------------------------------------------------
2023-10-19 01:19:00,903 EPOCH 8 done: loss 0.1166 - lr: 0.000007
2023-10-19 01:19:04,160 DEV : loss 0.19483603537082672 - f1-score (micro avg)  0.4857
2023-10-19 01:19:04,188 saving best model
2023-10-19 01:19:04,220 ----------------------------------------------------------------------------------------------------
2023-10-19 01:19:10,105 epoch 9 - iter 361/3617 - loss 0.12224075 - time (sec): 5.88 - samples/sec: 6742.34 - lr: 0.000006 - momentum: 0.000000
2023-10-19 01:19:15,778 epoch 9 - iter 722/3617 - loss 0.10932434 - time (sec): 11.56 - samples/sec: 6748.35 - lr: 0.000006 - momentum: 0.000000
2023-10-19 01:19:21,542 epoch 9 - iter 1083/3617 - loss 0.11140440 - time (sec): 17.32 - samples/sec: 6728.86 - lr: 0.000006 - momentum: 0.000000
2023-10-19 01:19:27,301 epoch 9 - iter 1444/3617 - loss 0.10874201 - time (sec): 23.08 - samples/sec: 6632.17 - lr: 0.000005 - momentum: 0.000000
2023-10-19 01:19:32,820 epoch 9 - iter 1805/3617 - loss 0.10872015 - time (sec): 28.60 - samples/sec: 6732.84 - lr: 0.000005 - momentum: 0.000000
2023-10-19 01:19:38,447 epoch 9 - iter 2166/3617 - loss 0.11133825 - time (sec): 34.23 - samples/sec: 6714.00 - lr: 0.000005 - momentum: 0.000000
2023-10-19 01:19:44,220 epoch 9 - iter 2527/3617 - loss 0.11266491 - time (sec): 40.00 - samples/sec: 6670.77 - lr: 0.000004 - momentum: 0.000000
2023-10-19 01:19:50,013 epoch 9 - iter 2888/3617 - loss 0.11290616 - time (sec): 45.79 - samples/sec: 6672.15 - lr: 0.000004 - momentum: 0.000000
2023-10-19 01:19:55,727 epoch 9 - iter 3249/3617 - loss 0.11269907 - time (sec): 51.51 - samples/sec: 6647.26 - lr: 0.000004 - momentum: 0.000000
2023-10-19 01:20:01,410 epoch 9 - iter 3610/3617 - loss 0.11350583 - time (sec): 57.19 - samples/sec: 6630.19 - lr: 0.000003 - momentum: 0.000000
2023-10-19 01:20:01,524 ----------------------------------------------------------------------------------------------------
2023-10-19 01:20:01,524 EPOCH 9 done: loss 0.1135 - lr: 0.000003
2023-10-19 01:20:05,482 DEV : loss 0.19358040392398834 - f1-score (micro avg)  0.4833
2023-10-19 01:20:05,511 ----------------------------------------------------------------------------------------------------
2023-10-19 01:20:11,321 epoch 10 - iter 361/3617 - loss 0.11277088 - time (sec): 5.81 - samples/sec: 6246.22 - lr: 0.000003 - momentum: 0.000000
2023-10-19 01:20:17,167 epoch 10 - iter 722/3617 - loss 0.11290023 - time (sec): 11.66 - samples/sec: 6445.07 - lr: 0.000003 - momentum: 0.000000
2023-10-19 01:20:22,907 epoch 10 - iter 1083/3617 - loss 0.11198872 - time (sec): 17.40 - samples/sec: 6454.90 - lr: 0.000002 - momentum: 0.000000
2023-10-19 01:20:28,643 epoch 10 - iter 1444/3617 - loss 0.10861250 - time (sec): 23.13 - samples/sec: 6495.86 - lr: 0.000002 - momentum: 0.000000
2023-10-19 01:20:33,825 epoch 10 - iter 1805/3617 - loss 0.11091058 - time (sec): 28.31 - samples/sec: 6672.48 - lr: 0.000002 - momentum: 0.000000
2023-10-19 01:20:39,517 epoch 10 - iter 2166/3617 - loss 0.11258390 - time (sec): 34.01 - samples/sec: 6697.61 - lr: 0.000001 - momentum: 0.000000
2023-10-19 01:20:45,292 epoch 10 - iter 2527/3617 - loss 0.11155306 - time (sec): 39.78 - samples/sec: 6694.42 - lr: 0.000001 - momentum: 0.000000
2023-10-19 01:20:51,014 epoch 10 - iter 2888/3617 - loss 0.10931783 - time (sec): 45.50 - samples/sec: 6687.76 - lr: 0.000001 - momentum: 0.000000
2023-10-19 01:20:56,674 epoch 10 - iter 3249/3617 - loss 0.11004335 - time (sec): 51.16 - samples/sec: 6662.31 - lr: 0.000000 - momentum: 0.000000
2023-10-19 01:21:02,509 epoch 10 - iter 3610/3617 - loss 0.10947971 - time (sec): 57.00 - samples/sec: 6656.02 - lr: 0.000000 - momentum: 0.000000
2023-10-19 01:21:02,609 ----------------------------------------------------------------------------------------------------
2023-10-19 01:21:02,609 EPOCH 10 done: loss 0.1097 - lr: 0.000000
2023-10-19 01:21:05,822 DEV : loss 0.1967521756887436 - f1-score (micro avg)  0.4832
2023-10-19 01:21:05,881 ----------------------------------------------------------------------------------------------------
2023-10-19 01:21:05,881 Loading model from best epoch ...
2023-10-19 01:21:05,977 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
2023-10-19 01:21:10,135 
Results:
- F-score (micro) 0.5273
- F-score (macro) 0.3519
- Accuracy 0.37

By class:
              precision    recall  f1-score   support

         loc     0.5467    0.6734    0.6035       591
        pers     0.4155    0.4958    0.4521       357
         org     0.0000    0.0000    0.0000        79

   micro avg     0.4983    0.5599    0.5273      1027
   macro avg     0.3207    0.3897    0.3519      1027
weighted avg     0.4590    0.5599    0.5044      1027

2023-10-19 01:21:10,135 ----------------------------------------------------------------------------------------------------