2023-10-25 11:49:28,311 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,312 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(64001, 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): 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)
            )
          )
          (1): 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)
            )
          )
          (2): 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)
            )
          )
          (3): 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)
            )
          )
          (4): 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)
            )
          )
          (5): 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)
            )
          )
          (6): 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)
            )
          )
          (7): 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)
            )
          )
          (8): 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)
            )
          )
          (9): 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)
            )
          )
          (10): 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)
            )
          )
          (11): 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=13, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-25 11:49:28,312 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,312 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
 - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /home/ubuntu/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
2023-10-25 11:49:28,312 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,312 Train:  14465 sentences
2023-10-25 11:49:28,312         (train_with_dev=False, train_with_test=False)
2023-10-25 11:49:28,312 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,312 Training Params:
2023-10-25 11:49:28,312  - learning_rate: "5e-05" 
2023-10-25 11:49:28,312  - mini_batch_size: "4"
2023-10-25 11:49:28,312  - max_epochs: "10"
2023-10-25 11:49:28,312  - shuffle: "True"
2023-10-25 11:49:28,312 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,312 Plugins:
2023-10-25 11:49:28,312  - TensorboardLogger
2023-10-25 11:49:28,312  - LinearScheduler | warmup_fraction: '0.1'
2023-10-25 11:49:28,312 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,312 Final evaluation on model from best epoch (best-model.pt)
2023-10-25 11:49:28,312  - metric: "('micro avg', 'f1-score')"
2023-10-25 11:49:28,312 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,312 Computation:
2023-10-25 11:49:28,312  - compute on device: cuda:0
2023-10-25 11:49:28,312  - embedding storage: none
2023-10-25 11:49:28,312 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,313 Model training base path: "hmbench-letemps/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-25 11:49:28,313 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,313 ----------------------------------------------------------------------------------------------------
2023-10-25 11:49:28,313 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-25 11:49:50,809 epoch 1 - iter 361/3617 - loss 0.86993127 - time (sec): 22.50 - samples/sec: 1710.07 - lr: 0.000005 - momentum: 0.000000
2023-10-25 11:50:13,270 epoch 1 - iter 722/3617 - loss 0.52060995 - time (sec): 44.96 - samples/sec: 1692.19 - lr: 0.000010 - momentum: 0.000000
2023-10-25 11:50:35,928 epoch 1 - iter 1083/3617 - loss 0.39422738 - time (sec): 67.61 - samples/sec: 1687.75 - lr: 0.000015 - momentum: 0.000000
2023-10-25 11:50:58,635 epoch 1 - iter 1444/3617 - loss 0.32755860 - time (sec): 90.32 - samples/sec: 1677.54 - lr: 0.000020 - momentum: 0.000000
2023-10-25 11:51:20,989 epoch 1 - iter 1805/3617 - loss 0.28743077 - time (sec): 112.68 - samples/sec: 1664.91 - lr: 0.000025 - momentum: 0.000000
2023-10-25 11:51:43,571 epoch 1 - iter 2166/3617 - loss 0.25847487 - time (sec): 135.26 - samples/sec: 1665.35 - lr: 0.000030 - momentum: 0.000000
2023-10-25 11:52:06,563 epoch 1 - iter 2527/3617 - loss 0.23733988 - time (sec): 158.25 - samples/sec: 1674.91 - lr: 0.000035 - momentum: 0.000000
2023-10-25 11:52:29,326 epoch 1 - iter 2888/3617 - loss 0.22332063 - time (sec): 181.01 - samples/sec: 1678.95 - lr: 0.000040 - momentum: 0.000000
2023-10-25 11:52:51,919 epoch 1 - iter 3249/3617 - loss 0.21156741 - time (sec): 203.61 - samples/sec: 1675.79 - lr: 0.000045 - momentum: 0.000000
2023-10-25 11:53:14,580 epoch 1 - iter 3610/3617 - loss 0.20214765 - time (sec): 226.27 - samples/sec: 1676.00 - lr: 0.000050 - momentum: 0.000000
2023-10-25 11:53:15,001 ----------------------------------------------------------------------------------------------------
2023-10-25 11:53:15,001 EPOCH 1 done: loss 0.2021 - lr: 0.000050
2023-10-25 11:53:19,501 DEV : loss 0.14469869434833527 - f1-score (micro avg)  0.5759
2023-10-25 11:53:19,522 saving best model
2023-10-25 11:53:20,071 ----------------------------------------------------------------------------------------------------
2023-10-25 11:53:42,599 epoch 2 - iter 361/3617 - loss 0.10828242 - time (sec): 22.53 - samples/sec: 1681.04 - lr: 0.000049 - momentum: 0.000000
2023-10-25 11:54:05,556 epoch 2 - iter 722/3617 - loss 0.11062343 - time (sec): 45.48 - samples/sec: 1692.62 - lr: 0.000049 - momentum: 0.000000
2023-10-25 11:54:28,432 epoch 2 - iter 1083/3617 - loss 0.11343990 - time (sec): 68.36 - samples/sec: 1689.62 - lr: 0.000048 - momentum: 0.000000
2023-10-25 11:54:51,191 epoch 2 - iter 1444/3617 - loss 0.11147450 - time (sec): 91.12 - samples/sec: 1693.49 - lr: 0.000048 - momentum: 0.000000
2023-10-25 11:55:13,719 epoch 2 - iter 1805/3617 - loss 0.11268183 - time (sec): 113.65 - samples/sec: 1686.21 - lr: 0.000047 - momentum: 0.000000
2023-10-25 11:55:36,251 epoch 2 - iter 2166/3617 - loss 0.11108706 - time (sec): 136.18 - samples/sec: 1685.34 - lr: 0.000047 - momentum: 0.000000
2023-10-25 11:55:58,771 epoch 2 - iter 2527/3617 - loss 0.10962742 - time (sec): 158.70 - samples/sec: 1678.37 - lr: 0.000046 - momentum: 0.000000
2023-10-25 11:56:21,330 epoch 2 - iter 2888/3617 - loss 0.10930890 - time (sec): 181.26 - samples/sec: 1677.39 - lr: 0.000046 - momentum: 0.000000
2023-10-25 11:56:43,852 epoch 2 - iter 3249/3617 - loss 0.10745796 - time (sec): 203.78 - samples/sec: 1678.49 - lr: 0.000045 - momentum: 0.000000
2023-10-25 11:57:06,613 epoch 2 - iter 3610/3617 - loss 0.10586077 - time (sec): 226.54 - samples/sec: 1673.92 - lr: 0.000044 - momentum: 0.000000
2023-10-25 11:57:07,071 ----------------------------------------------------------------------------------------------------
2023-10-25 11:57:07,071 EPOCH 2 done: loss 0.1058 - lr: 0.000044
2023-10-25 11:57:12,310 DEV : loss 0.14430101215839386 - f1-score (micro avg)  0.6079
2023-10-25 11:57:12,332 saving best model
2023-10-25 11:57:13,111 ----------------------------------------------------------------------------------------------------
2023-10-25 11:57:35,931 epoch 3 - iter 361/3617 - loss 0.07144153 - time (sec): 22.82 - samples/sec: 1734.60 - lr: 0.000044 - momentum: 0.000000
2023-10-25 11:57:58,787 epoch 3 - iter 722/3617 - loss 0.08074855 - time (sec): 45.68 - samples/sec: 1719.95 - lr: 0.000043 - momentum: 0.000000
2023-10-25 11:58:21,681 epoch 3 - iter 1083/3617 - loss 0.08263674 - time (sec): 68.57 - samples/sec: 1716.05 - lr: 0.000043 - momentum: 0.000000
2023-10-25 11:58:44,087 epoch 3 - iter 1444/3617 - loss 0.08288668 - time (sec): 90.97 - samples/sec: 1695.55 - lr: 0.000042 - momentum: 0.000000
2023-10-25 11:59:06,702 epoch 3 - iter 1805/3617 - loss 0.10363913 - time (sec): 113.59 - samples/sec: 1690.82 - lr: 0.000042 - momentum: 0.000000
2023-10-25 11:59:29,167 epoch 3 - iter 2166/3617 - loss 0.13603267 - time (sec): 136.06 - samples/sec: 1683.24 - lr: 0.000041 - momentum: 0.000000
2023-10-25 11:59:51,558 epoch 3 - iter 2527/3617 - loss 0.15873752 - time (sec): 158.45 - samples/sec: 1675.58 - lr: 0.000041 - momentum: 0.000000
2023-10-25 12:00:14,538 epoch 3 - iter 2888/3617 - loss 0.17453687 - time (sec): 181.43 - samples/sec: 1674.80 - lr: 0.000040 - momentum: 0.000000
2023-10-25 12:00:37,065 epoch 3 - iter 3249/3617 - loss 0.18855528 - time (sec): 203.95 - samples/sec: 1676.48 - lr: 0.000039 - momentum: 0.000000
2023-10-25 12:00:59,529 epoch 3 - iter 3610/3617 - loss 0.20118879 - time (sec): 226.42 - samples/sec: 1675.07 - lr: 0.000039 - momentum: 0.000000
2023-10-25 12:00:59,959 ----------------------------------------------------------------------------------------------------
2023-10-25 12:00:59,960 EPOCH 3 done: loss 0.2013 - lr: 0.000039
2023-10-25 12:01:05,163 DEV : loss 0.2843758761882782 - f1-score (micro avg)  0.0046
2023-10-25 12:01:05,185 ----------------------------------------------------------------------------------------------------
2023-10-25 12:01:27,882 epoch 4 - iter 361/3617 - loss 0.31486142 - time (sec): 22.70 - samples/sec: 1706.40 - lr: 0.000038 - momentum: 0.000000
2023-10-25 12:01:50,468 epoch 4 - iter 722/3617 - loss 0.30266314 - time (sec): 45.28 - samples/sec: 1686.83 - lr: 0.000038 - momentum: 0.000000
2023-10-25 12:02:13,127 epoch 4 - iter 1083/3617 - loss 0.30220104 - time (sec): 67.94 - samples/sec: 1689.02 - lr: 0.000037 - momentum: 0.000000
2023-10-25 12:02:35,756 epoch 4 - iter 1444/3617 - loss 0.29844936 - time (sec): 90.57 - samples/sec: 1670.74 - lr: 0.000037 - momentum: 0.000000
2023-10-25 12:02:58,524 epoch 4 - iter 1805/3617 - loss 0.29550689 - time (sec): 113.34 - samples/sec: 1672.63 - lr: 0.000036 - momentum: 0.000000
2023-10-25 12:03:21,138 epoch 4 - iter 2166/3617 - loss 0.29482135 - time (sec): 135.95 - samples/sec: 1663.58 - lr: 0.000036 - momentum: 0.000000
2023-10-25 12:03:43,665 epoch 4 - iter 2527/3617 - loss 0.29559234 - time (sec): 158.48 - samples/sec: 1663.63 - lr: 0.000035 - momentum: 0.000000
2023-10-25 12:04:06,283 epoch 4 - iter 2888/3617 - loss 0.29737787 - time (sec): 181.10 - samples/sec: 1660.70 - lr: 0.000034 - momentum: 0.000000
2023-10-25 12:04:29,216 epoch 4 - iter 3249/3617 - loss 0.29938044 - time (sec): 204.03 - samples/sec: 1667.11 - lr: 0.000034 - momentum: 0.000000
2023-10-25 12:04:52,016 epoch 4 - iter 3610/3617 - loss 0.29694305 - time (sec): 226.83 - samples/sec: 1672.49 - lr: 0.000033 - momentum: 0.000000
2023-10-25 12:04:52,435 ----------------------------------------------------------------------------------------------------
2023-10-25 12:04:52,436 EPOCH 4 done: loss 0.2968 - lr: 0.000033
2023-10-25 12:04:57,151 DEV : loss 0.2834990918636322 - f1-score (micro avg)  0.0023
2023-10-25 12:04:57,173 ----------------------------------------------------------------------------------------------------
2023-10-25 12:05:20,646 epoch 5 - iter 361/3617 - loss 0.30958497 - time (sec): 23.47 - samples/sec: 1681.65 - lr: 0.000033 - momentum: 0.000000
2023-10-25 12:05:43,246 epoch 5 - iter 722/3617 - loss 0.29814374 - time (sec): 46.07 - samples/sec: 1668.88 - lr: 0.000032 - momentum: 0.000000
2023-10-25 12:06:05,856 epoch 5 - iter 1083/3617 - loss 0.29063581 - time (sec): 68.68 - samples/sec: 1663.40 - lr: 0.000032 - momentum: 0.000000
2023-10-25 12:06:28,402 epoch 5 - iter 1444/3617 - loss 0.28726657 - time (sec): 91.23 - samples/sec: 1668.57 - lr: 0.000031 - momentum: 0.000000
2023-10-25 12:06:50,962 epoch 5 - iter 1805/3617 - loss 0.28881196 - time (sec): 113.79 - samples/sec: 1669.95 - lr: 0.000031 - momentum: 0.000000
2023-10-25 12:07:13,960 epoch 5 - iter 2166/3617 - loss 0.28967773 - time (sec): 136.79 - samples/sec: 1673.87 - lr: 0.000030 - momentum: 0.000000
2023-10-25 12:07:36,621 epoch 5 - iter 2527/3617 - loss 0.29134561 - time (sec): 159.45 - samples/sec: 1668.53 - lr: 0.000029 - momentum: 0.000000
2023-10-25 12:07:59,257 epoch 5 - iter 2888/3617 - loss 0.29414601 - time (sec): 182.08 - samples/sec: 1667.95 - lr: 0.000029 - momentum: 0.000000
2023-10-25 12:08:21,892 epoch 5 - iter 3249/3617 - loss 0.29373568 - time (sec): 204.72 - samples/sec: 1663.60 - lr: 0.000028 - momentum: 0.000000
2023-10-25 12:08:44,658 epoch 5 - iter 3610/3617 - loss 0.29343011 - time (sec): 227.48 - samples/sec: 1667.69 - lr: 0.000028 - momentum: 0.000000
2023-10-25 12:08:45,069 ----------------------------------------------------------------------------------------------------
2023-10-25 12:08:45,069 EPOCH 5 done: loss 0.2934 - lr: 0.000028
2023-10-25 12:08:49,763 DEV : loss 0.27164599299430847 - f1-score (micro avg)  0.0
2023-10-25 12:08:49,785 ----------------------------------------------------------------------------------------------------
2023-10-25 12:09:12,514 epoch 6 - iter 361/3617 - loss 0.31431851 - time (sec): 22.73 - samples/sec: 1671.84 - lr: 0.000027 - momentum: 0.000000
2023-10-25 12:09:35,378 epoch 6 - iter 722/3617 - loss 0.29910863 - time (sec): 45.59 - samples/sec: 1692.45 - lr: 0.000027 - momentum: 0.000000
2023-10-25 12:09:58,193 epoch 6 - iter 1083/3617 - loss 0.29460583 - time (sec): 68.41 - samples/sec: 1692.25 - lr: 0.000026 - momentum: 0.000000
2023-10-25 12:10:20,897 epoch 6 - iter 1444/3617 - loss 0.29799880 - time (sec): 91.11 - samples/sec: 1687.46 - lr: 0.000026 - momentum: 0.000000
2023-10-25 12:10:43,359 epoch 6 - iter 1805/3617 - loss 0.29509303 - time (sec): 113.57 - samples/sec: 1680.84 - lr: 0.000025 - momentum: 0.000000
2023-10-25 12:11:05,929 epoch 6 - iter 2166/3617 - loss 0.29227878 - time (sec): 136.14 - samples/sec: 1674.83 - lr: 0.000024 - momentum: 0.000000
2023-10-25 12:11:28,526 epoch 6 - iter 2527/3617 - loss 0.29220228 - time (sec): 158.74 - samples/sec: 1669.69 - lr: 0.000024 - momentum: 0.000000
2023-10-25 12:11:51,388 epoch 6 - iter 2888/3617 - loss 0.28976457 - time (sec): 181.60 - samples/sec: 1677.10 - lr: 0.000023 - momentum: 0.000000
2023-10-25 12:12:13,993 epoch 6 - iter 3249/3617 - loss 0.28949741 - time (sec): 204.21 - samples/sec: 1674.39 - lr: 0.000023 - momentum: 0.000000
2023-10-25 12:12:36,781 epoch 6 - iter 3610/3617 - loss 0.29047095 - time (sec): 226.99 - samples/sec: 1670.50 - lr: 0.000022 - momentum: 0.000000
2023-10-25 12:12:37,217 ----------------------------------------------------------------------------------------------------
2023-10-25 12:12:37,218 EPOCH 6 done: loss 0.2905 - lr: 0.000022
2023-10-25 12:12:42,446 DEV : loss 0.2741363048553467 - f1-score (micro avg)  0.0
2023-10-25 12:12:42,469 ----------------------------------------------------------------------------------------------------
2023-10-25 12:13:05,031 epoch 7 - iter 361/3617 - loss 0.28801601 - time (sec): 22.56 - samples/sec: 1668.20 - lr: 0.000022 - momentum: 0.000000
2023-10-25 12:13:27,676 epoch 7 - iter 722/3617 - loss 0.28684817 - time (sec): 45.21 - samples/sec: 1642.00 - lr: 0.000021 - momentum: 0.000000
2023-10-25 12:13:50,389 epoch 7 - iter 1083/3617 - loss 0.28911761 - time (sec): 67.92 - samples/sec: 1636.66 - lr: 0.000021 - momentum: 0.000000
2023-10-25 12:14:12,991 epoch 7 - iter 1444/3617 - loss 0.28494759 - time (sec): 90.52 - samples/sec: 1647.46 - lr: 0.000020 - momentum: 0.000000
2023-10-25 12:14:35,764 epoch 7 - iter 1805/3617 - loss 0.28299654 - time (sec): 113.29 - samples/sec: 1651.43 - lr: 0.000019 - momentum: 0.000000
2023-10-25 12:14:58,250 epoch 7 - iter 2166/3617 - loss 0.28544928 - time (sec): 135.78 - samples/sec: 1648.64 - lr: 0.000019 - momentum: 0.000000
2023-10-25 12:15:21,100 epoch 7 - iter 2527/3617 - loss 0.28500039 - time (sec): 158.63 - samples/sec: 1656.30 - lr: 0.000018 - momentum: 0.000000
2023-10-25 12:15:44,065 epoch 7 - iter 2888/3617 - loss 0.28630743 - time (sec): 181.60 - samples/sec: 1662.84 - lr: 0.000018 - momentum: 0.000000
2023-10-25 12:16:06,597 epoch 7 - iter 3249/3617 - loss 0.28966796 - time (sec): 204.13 - samples/sec: 1667.85 - lr: 0.000017 - momentum: 0.000000
2023-10-25 12:16:29,444 epoch 7 - iter 3610/3617 - loss 0.29008210 - time (sec): 226.97 - samples/sec: 1671.33 - lr: 0.000017 - momentum: 0.000000
2023-10-25 12:16:29,851 ----------------------------------------------------------------------------------------------------
2023-10-25 12:16:29,852 EPOCH 7 done: loss 0.2903 - lr: 0.000017
2023-10-25 12:16:35,059 DEV : loss 0.26774144172668457 - f1-score (micro avg)  0.0
2023-10-25 12:16:35,081 ----------------------------------------------------------------------------------------------------
2023-10-25 12:16:57,825 epoch 8 - iter 361/3617 - loss 0.27660386 - time (sec): 22.74 - samples/sec: 1704.22 - lr: 0.000016 - momentum: 0.000000
2023-10-25 12:17:20,392 epoch 8 - iter 722/3617 - loss 0.28079844 - time (sec): 45.31 - samples/sec: 1685.49 - lr: 0.000016 - momentum: 0.000000
2023-10-25 12:17:43,247 epoch 8 - iter 1083/3617 - loss 0.29020034 - time (sec): 68.17 - samples/sec: 1680.45 - lr: 0.000015 - momentum: 0.000000
2023-10-25 12:18:05,766 epoch 8 - iter 1444/3617 - loss 0.29429510 - time (sec): 90.68 - samples/sec: 1672.78 - lr: 0.000014 - momentum: 0.000000
2023-10-25 12:18:28,476 epoch 8 - iter 1805/3617 - loss 0.29349848 - time (sec): 113.39 - samples/sec: 1670.15 - lr: 0.000014 - momentum: 0.000000
2023-10-25 12:18:51,283 epoch 8 - iter 2166/3617 - loss 0.29446113 - time (sec): 136.20 - samples/sec: 1679.67 - lr: 0.000013 - momentum: 0.000000
2023-10-25 12:19:13,886 epoch 8 - iter 2527/3617 - loss 0.29023797 - time (sec): 158.80 - samples/sec: 1679.28 - lr: 0.000013 - momentum: 0.000000
2023-10-25 12:19:36,555 epoch 8 - iter 2888/3617 - loss 0.28871733 - time (sec): 181.47 - samples/sec: 1680.37 - lr: 0.000012 - momentum: 0.000000
2023-10-25 12:19:59,060 epoch 8 - iter 3249/3617 - loss 0.29028619 - time (sec): 203.98 - samples/sec: 1678.18 - lr: 0.000012 - momentum: 0.000000
2023-10-25 12:20:21,609 epoch 8 - iter 3610/3617 - loss 0.28929915 - time (sec): 226.53 - samples/sec: 1673.87 - lr: 0.000011 - momentum: 0.000000
2023-10-25 12:20:22,052 ----------------------------------------------------------------------------------------------------
2023-10-25 12:20:22,052 EPOCH 8 done: loss 0.2893 - lr: 0.000011
2023-10-25 12:20:27,261 DEV : loss 0.2751471996307373 - f1-score (micro avg)  0.0
2023-10-25 12:20:27,283 ----------------------------------------------------------------------------------------------------
2023-10-25 12:20:49,886 epoch 9 - iter 361/3617 - loss 0.30361453 - time (sec): 22.60 - samples/sec: 1645.83 - lr: 0.000011 - momentum: 0.000000
2023-10-25 12:21:12,574 epoch 9 - iter 722/3617 - loss 0.29195695 - time (sec): 45.29 - samples/sec: 1654.32 - lr: 0.000010 - momentum: 0.000000
2023-10-25 12:21:35,215 epoch 9 - iter 1083/3617 - loss 0.29457114 - time (sec): 67.93 - samples/sec: 1652.12 - lr: 0.000009 - momentum: 0.000000
2023-10-25 12:21:58,034 epoch 9 - iter 1444/3617 - loss 0.29407289 - time (sec): 90.75 - samples/sec: 1668.14 - lr: 0.000009 - momentum: 0.000000
2023-10-25 12:22:20,669 epoch 9 - iter 1805/3617 - loss 0.29048332 - time (sec): 113.39 - samples/sec: 1671.64 - lr: 0.000008 - momentum: 0.000000
2023-10-25 12:22:43,706 epoch 9 - iter 2166/3617 - loss 0.28277675 - time (sec): 136.42 - samples/sec: 1676.87 - lr: 0.000008 - momentum: 0.000000
2023-10-25 12:23:06,481 epoch 9 - iter 2527/3617 - loss 0.28719758 - time (sec): 159.20 - samples/sec: 1675.04 - lr: 0.000007 - momentum: 0.000000
2023-10-25 12:23:29,126 epoch 9 - iter 2888/3617 - loss 0.28670629 - time (sec): 181.84 - samples/sec: 1677.11 - lr: 0.000007 - momentum: 0.000000
2023-10-25 12:23:51,359 epoch 9 - iter 3249/3617 - loss 0.28859893 - time (sec): 204.08 - samples/sec: 1670.16 - lr: 0.000006 - momentum: 0.000000
2023-10-25 12:24:14,063 epoch 9 - iter 3610/3617 - loss 0.28826189 - time (sec): 226.78 - samples/sec: 1672.48 - lr: 0.000006 - momentum: 0.000000
2023-10-25 12:24:14,484 ----------------------------------------------------------------------------------------------------
2023-10-25 12:24:14,484 EPOCH 9 done: loss 0.2882 - lr: 0.000006
2023-10-25 12:24:19,699 DEV : loss 0.27169540524482727 - f1-score (micro avg)  0.0
2023-10-25 12:24:19,721 ----------------------------------------------------------------------------------------------------
2023-10-25 12:24:42,190 epoch 10 - iter 361/3617 - loss 0.26792150 - time (sec): 22.47 - samples/sec: 1653.50 - lr: 0.000005 - momentum: 0.000000
2023-10-25 12:25:05,071 epoch 10 - iter 722/3617 - loss 0.27746427 - time (sec): 45.35 - samples/sec: 1662.83 - lr: 0.000004 - momentum: 0.000000
2023-10-25 12:25:28,013 epoch 10 - iter 1083/3617 - loss 0.27477559 - time (sec): 68.29 - samples/sec: 1680.32 - lr: 0.000004 - momentum: 0.000000
2023-10-25 12:25:50,616 epoch 10 - iter 1444/3617 - loss 0.27361481 - time (sec): 90.89 - samples/sec: 1678.02 - lr: 0.000003 - momentum: 0.000000
2023-10-25 12:26:13,325 epoch 10 - iter 1805/3617 - loss 0.27593718 - time (sec): 113.60 - samples/sec: 1684.69 - lr: 0.000003 - momentum: 0.000000
2023-10-25 12:26:35,800 epoch 10 - iter 2166/3617 - loss 0.28020518 - time (sec): 136.08 - samples/sec: 1674.36 - lr: 0.000002 - momentum: 0.000000
2023-10-25 12:26:58,435 epoch 10 - iter 2527/3617 - loss 0.28002646 - time (sec): 158.71 - samples/sec: 1673.74 - lr: 0.000002 - momentum: 0.000000
2023-10-25 12:27:21,055 epoch 10 - iter 2888/3617 - loss 0.27933392 - time (sec): 181.33 - samples/sec: 1673.50 - lr: 0.000001 - momentum: 0.000000
2023-10-25 12:27:43,896 epoch 10 - iter 3249/3617 - loss 0.28251991 - time (sec): 204.17 - samples/sec: 1677.60 - lr: 0.000001 - momentum: 0.000000
2023-10-25 12:28:06,393 epoch 10 - iter 3610/3617 - loss 0.28721607 - time (sec): 226.67 - samples/sec: 1673.50 - lr: 0.000000 - momentum: 0.000000
2023-10-25 12:28:06,815 ----------------------------------------------------------------------------------------------------
2023-10-25 12:28:06,815 EPOCH 10 done: loss 0.2873 - lr: 0.000000
2023-10-25 12:28:11,509 DEV : loss 0.27294182777404785 - f1-score (micro avg)  0.0
2023-10-25 12:28:12,086 ----------------------------------------------------------------------------------------------------
2023-10-25 12:28:12,087 Loading model from best epoch ...
2023-10-25 12:28:13,838 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
2023-10-25 12:28:20,073 
Results:
- F-score (micro) 0.639
- F-score (macro) 0.4364
- Accuracy 0.4803

By class:
              precision    recall  f1-score   support

         loc     0.6601    0.7360    0.6960       591
        pers     0.5473    0.6975    0.6133       357
         org     0.0000    0.0000    0.0000        79

   micro avg     0.6140    0.6660    0.6390      1027
   macro avg     0.4024    0.4778    0.4364      1027
weighted avg     0.5701    0.6660    0.6137      1027

2023-10-25 12:28:20,073 ----------------------------------------------------------------------------------------------------