2023-10-24 13:26:12,906 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,907 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=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-24 13:26:12,907 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,907 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
 - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /home/ubuntu/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
2023-10-24 13:26:12,907 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,907 Train:  5901 sentences
2023-10-24 13:26:12,907         (train_with_dev=False, train_with_test=False)
2023-10-24 13:26:12,907 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,907 Training Params:
2023-10-24 13:26:12,907  - learning_rate: "5e-05" 
2023-10-24 13:26:12,907  - mini_batch_size: "8"
2023-10-24 13:26:12,907  - max_epochs: "10"
2023-10-24 13:26:12,907  - shuffle: "True"
2023-10-24 13:26:12,907 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,907 Plugins:
2023-10-24 13:26:12,907  - TensorboardLogger
2023-10-24 13:26:12,907  - LinearScheduler | warmup_fraction: '0.1'
2023-10-24 13:26:12,907 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,907 Final evaluation on model from best epoch (best-model.pt)
2023-10-24 13:26:12,907  - metric: "('micro avg', 'f1-score')"
2023-10-24 13:26:12,907 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,907 Computation:
2023-10-24 13:26:12,907  - compute on device: cuda:0
2023-10-24 13:26:12,907  - embedding storage: none
2023-10-24 13:26:12,907 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,908 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
2023-10-24 13:26:12,908 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,908 ----------------------------------------------------------------------------------------------------
2023-10-24 13:26:12,908 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-24 13:26:19,585 epoch 1 - iter 73/738 - loss 1.87843397 - time (sec): 6.68 - samples/sec: 2355.81 - lr: 0.000005 - momentum: 0.000000
2023-10-24 13:26:26,648 epoch 1 - iter 146/738 - loss 1.23669691 - time (sec): 13.74 - samples/sec: 2287.20 - lr: 0.000010 - momentum: 0.000000
2023-10-24 13:26:33,333 epoch 1 - iter 219/738 - loss 0.95046007 - time (sec): 20.42 - samples/sec: 2293.57 - lr: 0.000015 - momentum: 0.000000
2023-10-24 13:26:39,552 epoch 1 - iter 292/738 - loss 0.78741929 - time (sec): 26.64 - samples/sec: 2325.13 - lr: 0.000020 - momentum: 0.000000
2023-10-24 13:26:47,678 epoch 1 - iter 365/738 - loss 0.66312271 - time (sec): 34.77 - samples/sec: 2330.02 - lr: 0.000025 - momentum: 0.000000
2023-10-24 13:26:54,437 epoch 1 - iter 438/738 - loss 0.58329828 - time (sec): 41.53 - samples/sec: 2361.83 - lr: 0.000030 - momentum: 0.000000
2023-10-24 13:27:01,634 epoch 1 - iter 511/738 - loss 0.52013512 - time (sec): 48.73 - samples/sec: 2367.86 - lr: 0.000035 - momentum: 0.000000
2023-10-24 13:27:08,503 epoch 1 - iter 584/738 - loss 0.47832610 - time (sec): 55.59 - samples/sec: 2361.41 - lr: 0.000039 - momentum: 0.000000
2023-10-24 13:27:15,950 epoch 1 - iter 657/738 - loss 0.44013722 - time (sec): 63.04 - samples/sec: 2355.32 - lr: 0.000044 - momentum: 0.000000
2023-10-24 13:27:22,402 epoch 1 - iter 730/738 - loss 0.41193232 - time (sec): 69.49 - samples/sec: 2358.74 - lr: 0.000049 - momentum: 0.000000
2023-10-24 13:27:23,423 ----------------------------------------------------------------------------------------------------
2023-10-24 13:27:23,423 EPOCH 1 done: loss 0.4080 - lr: 0.000049
2023-10-24 13:27:29,681 DEV : loss 0.10953915119171143 - f1-score (micro avg)  0.6996
2023-10-24 13:27:29,702 saving best model
2023-10-24 13:27:30,252 ----------------------------------------------------------------------------------------------------
2023-10-24 13:27:36,790 epoch 2 - iter 73/738 - loss 0.12856043 - time (sec): 6.54 - samples/sec: 2401.39 - lr: 0.000049 - momentum: 0.000000
2023-10-24 13:27:43,668 epoch 2 - iter 146/738 - loss 0.12842399 - time (sec): 13.42 - samples/sec: 2351.92 - lr: 0.000049 - momentum: 0.000000
2023-10-24 13:27:50,487 epoch 2 - iter 219/738 - loss 0.12815504 - time (sec): 20.23 - samples/sec: 2361.80 - lr: 0.000048 - momentum: 0.000000
2023-10-24 13:27:57,248 epoch 2 - iter 292/738 - loss 0.12409229 - time (sec): 27.00 - samples/sec: 2339.92 - lr: 0.000048 - momentum: 0.000000
2023-10-24 13:28:03,997 epoch 2 - iter 365/738 - loss 0.12199118 - time (sec): 33.74 - samples/sec: 2347.12 - lr: 0.000047 - momentum: 0.000000
2023-10-24 13:28:10,826 epoch 2 - iter 438/738 - loss 0.11973631 - time (sec): 40.57 - samples/sec: 2342.24 - lr: 0.000047 - momentum: 0.000000
2023-10-24 13:28:18,157 epoch 2 - iter 511/738 - loss 0.12078151 - time (sec): 47.90 - samples/sec: 2360.10 - lr: 0.000046 - momentum: 0.000000
2023-10-24 13:28:25,872 epoch 2 - iter 584/738 - loss 0.11713377 - time (sec): 55.62 - samples/sec: 2358.47 - lr: 0.000046 - momentum: 0.000000
2023-10-24 13:28:32,609 epoch 2 - iter 657/738 - loss 0.11656313 - time (sec): 62.36 - samples/sec: 2357.16 - lr: 0.000045 - momentum: 0.000000
2023-10-24 13:28:40,269 epoch 2 - iter 730/738 - loss 0.11463726 - time (sec): 70.02 - samples/sec: 2350.59 - lr: 0.000045 - momentum: 0.000000
2023-10-24 13:28:41,017 ----------------------------------------------------------------------------------------------------
2023-10-24 13:28:41,017 EPOCH 2 done: loss 0.1145 - lr: 0.000045
2023-10-24 13:28:49,488 DEV : loss 0.11087270081043243 - f1-score (micro avg)  0.7895
2023-10-24 13:28:49,509 saving best model
2023-10-24 13:28:50,218 ----------------------------------------------------------------------------------------------------
2023-10-24 13:28:56,322 epoch 3 - iter 73/738 - loss 0.05848905 - time (sec): 6.10 - samples/sec: 2528.26 - lr: 0.000044 - momentum: 0.000000
2023-10-24 13:29:03,544 epoch 3 - iter 146/738 - loss 0.06303135 - time (sec): 13.33 - samples/sec: 2409.83 - lr: 0.000043 - momentum: 0.000000
2023-10-24 13:29:11,131 epoch 3 - iter 219/738 - loss 0.06634706 - time (sec): 20.91 - samples/sec: 2351.64 - lr: 0.000043 - momentum: 0.000000
2023-10-24 13:29:18,504 epoch 3 - iter 292/738 - loss 0.06169395 - time (sec): 28.28 - samples/sec: 2350.13 - lr: 0.000042 - momentum: 0.000000
2023-10-24 13:29:25,686 epoch 3 - iter 365/738 - loss 0.06197945 - time (sec): 35.47 - samples/sec: 2337.71 - lr: 0.000042 - momentum: 0.000000
2023-10-24 13:29:32,841 epoch 3 - iter 438/738 - loss 0.06398829 - time (sec): 42.62 - samples/sec: 2338.06 - lr: 0.000041 - momentum: 0.000000
2023-10-24 13:29:39,705 epoch 3 - iter 511/738 - loss 0.06397235 - time (sec): 49.49 - samples/sec: 2340.00 - lr: 0.000041 - momentum: 0.000000
2023-10-24 13:29:46,062 epoch 3 - iter 584/738 - loss 0.06498214 - time (sec): 55.84 - samples/sec: 2350.07 - lr: 0.000040 - momentum: 0.000000
2023-10-24 13:29:52,694 epoch 3 - iter 657/738 - loss 0.06519288 - time (sec): 62.48 - samples/sec: 2347.72 - lr: 0.000040 - momentum: 0.000000
2023-10-24 13:29:59,865 epoch 3 - iter 730/738 - loss 0.06817157 - time (sec): 69.65 - samples/sec: 2355.77 - lr: 0.000039 - momentum: 0.000000
2023-10-24 13:30:01,021 ----------------------------------------------------------------------------------------------------
2023-10-24 13:30:01,021 EPOCH 3 done: loss 0.0681 - lr: 0.000039
2023-10-24 13:30:09,509 DEV : loss 0.11597760021686554 - f1-score (micro avg)  0.8001
2023-10-24 13:30:09,530 saving best model
2023-10-24 13:30:10,242 ----------------------------------------------------------------------------------------------------
2023-10-24 13:30:16,724 epoch 4 - iter 73/738 - loss 0.04098436 - time (sec): 6.48 - samples/sec: 2324.44 - lr: 0.000038 - momentum: 0.000000
2023-10-24 13:30:23,121 epoch 4 - iter 146/738 - loss 0.04478723 - time (sec): 12.88 - samples/sec: 2354.37 - lr: 0.000038 - momentum: 0.000000
2023-10-24 13:30:29,752 epoch 4 - iter 219/738 - loss 0.04880260 - time (sec): 19.51 - samples/sec: 2348.94 - lr: 0.000037 - momentum: 0.000000
2023-10-24 13:30:36,153 epoch 4 - iter 292/738 - loss 0.04742649 - time (sec): 25.91 - samples/sec: 2352.20 - lr: 0.000037 - momentum: 0.000000
2023-10-24 13:30:43,540 epoch 4 - iter 365/738 - loss 0.05043582 - time (sec): 33.30 - samples/sec: 2364.88 - lr: 0.000036 - momentum: 0.000000
2023-10-24 13:30:51,397 epoch 4 - iter 438/738 - loss 0.05054486 - time (sec): 41.15 - samples/sec: 2350.36 - lr: 0.000036 - momentum: 0.000000
2023-10-24 13:30:59,099 epoch 4 - iter 511/738 - loss 0.04763501 - time (sec): 48.86 - samples/sec: 2349.85 - lr: 0.000035 - momentum: 0.000000
2023-10-24 13:31:06,696 epoch 4 - iter 584/738 - loss 0.04763938 - time (sec): 56.45 - samples/sec: 2357.22 - lr: 0.000035 - momentum: 0.000000
2023-10-24 13:31:13,947 epoch 4 - iter 657/738 - loss 0.04791332 - time (sec): 63.70 - samples/sec: 2351.16 - lr: 0.000034 - momentum: 0.000000
2023-10-24 13:31:20,299 epoch 4 - iter 730/738 - loss 0.04703381 - time (sec): 70.06 - samples/sec: 2353.03 - lr: 0.000033 - momentum: 0.000000
2023-10-24 13:31:20,938 ----------------------------------------------------------------------------------------------------
2023-10-24 13:31:20,938 EPOCH 4 done: loss 0.0472 - lr: 0.000033
2023-10-24 13:31:29,454 DEV : loss 0.1576639711856842 - f1-score (micro avg)  0.8054
2023-10-24 13:31:29,475 saving best model
2023-10-24 13:31:30,138 ----------------------------------------------------------------------------------------------------
2023-10-24 13:31:36,865 epoch 5 - iter 73/738 - loss 0.03814569 - time (sec): 6.73 - samples/sec: 2415.22 - lr: 0.000033 - momentum: 0.000000
2023-10-24 13:31:44,135 epoch 5 - iter 146/738 - loss 0.03447645 - time (sec): 14.00 - samples/sec: 2422.92 - lr: 0.000032 - momentum: 0.000000
2023-10-24 13:31:51,116 epoch 5 - iter 219/738 - loss 0.03261789 - time (sec): 20.98 - samples/sec: 2354.95 - lr: 0.000032 - momentum: 0.000000
2023-10-24 13:31:57,985 epoch 5 - iter 292/738 - loss 0.03744440 - time (sec): 27.85 - samples/sec: 2360.46 - lr: 0.000031 - momentum: 0.000000
2023-10-24 13:32:05,585 epoch 5 - iter 365/738 - loss 0.03675531 - time (sec): 35.45 - samples/sec: 2368.90 - lr: 0.000031 - momentum: 0.000000
2023-10-24 13:32:12,307 epoch 5 - iter 438/738 - loss 0.03564464 - time (sec): 42.17 - samples/sec: 2369.51 - lr: 0.000030 - momentum: 0.000000
2023-10-24 13:32:18,805 epoch 5 - iter 511/738 - loss 0.03487785 - time (sec): 48.67 - samples/sec: 2360.38 - lr: 0.000030 - momentum: 0.000000
2023-10-24 13:32:26,703 epoch 5 - iter 584/738 - loss 0.03512836 - time (sec): 56.56 - samples/sec: 2340.57 - lr: 0.000029 - momentum: 0.000000
2023-10-24 13:32:33,278 epoch 5 - iter 657/738 - loss 0.03480734 - time (sec): 63.14 - samples/sec: 2353.68 - lr: 0.000028 - momentum: 0.000000
2023-10-24 13:32:40,558 epoch 5 - iter 730/738 - loss 0.03464501 - time (sec): 70.42 - samples/sec: 2341.86 - lr: 0.000028 - momentum: 0.000000
2023-10-24 13:32:41,297 ----------------------------------------------------------------------------------------------------
2023-10-24 13:32:41,298 EPOCH 5 done: loss 0.0346 - lr: 0.000028
2023-10-24 13:32:49,820 DEV : loss 0.17847341299057007 - f1-score (micro avg)  0.8278
2023-10-24 13:32:49,842 saving best model
2023-10-24 13:32:50,561 ----------------------------------------------------------------------------------------------------
2023-10-24 13:32:57,858 epoch 6 - iter 73/738 - loss 0.01898276 - time (sec): 7.30 - samples/sec: 2357.37 - lr: 0.000027 - momentum: 0.000000
2023-10-24 13:33:03,897 epoch 6 - iter 146/738 - loss 0.02062329 - time (sec): 13.34 - samples/sec: 2388.72 - lr: 0.000027 - momentum: 0.000000
2023-10-24 13:33:11,244 epoch 6 - iter 219/738 - loss 0.01861392 - time (sec): 20.68 - samples/sec: 2330.38 - lr: 0.000026 - momentum: 0.000000
2023-10-24 13:33:19,200 epoch 6 - iter 292/738 - loss 0.02328625 - time (sec): 28.64 - samples/sec: 2365.49 - lr: 0.000026 - momentum: 0.000000
2023-10-24 13:33:25,704 epoch 6 - iter 365/738 - loss 0.02353453 - time (sec): 35.14 - samples/sec: 2361.28 - lr: 0.000025 - momentum: 0.000000
2023-10-24 13:33:32,111 epoch 6 - iter 438/738 - loss 0.02268378 - time (sec): 41.55 - samples/sec: 2356.15 - lr: 0.000025 - momentum: 0.000000
2023-10-24 13:33:38,223 epoch 6 - iter 511/738 - loss 0.02475660 - time (sec): 47.66 - samples/sec: 2350.05 - lr: 0.000024 - momentum: 0.000000
2023-10-24 13:33:45,381 epoch 6 - iter 584/738 - loss 0.02481615 - time (sec): 54.82 - samples/sec: 2350.71 - lr: 0.000023 - momentum: 0.000000
2023-10-24 13:33:53,219 epoch 6 - iter 657/738 - loss 0.02437100 - time (sec): 62.66 - samples/sec: 2352.59 - lr: 0.000023 - momentum: 0.000000
2023-10-24 13:34:00,631 epoch 6 - iter 730/738 - loss 0.02404331 - time (sec): 70.07 - samples/sec: 2350.44 - lr: 0.000022 - momentum: 0.000000
2023-10-24 13:34:01,283 ----------------------------------------------------------------------------------------------------
2023-10-24 13:34:01,284 EPOCH 6 done: loss 0.0239 - lr: 0.000022
2023-10-24 13:34:09,822 DEV : loss 0.19103363156318665 - f1-score (micro avg)  0.8177
2023-10-24 13:34:09,844 ----------------------------------------------------------------------------------------------------
2023-10-24 13:34:17,437 epoch 7 - iter 73/738 - loss 0.01986646 - time (sec): 7.59 - samples/sec: 2506.14 - lr: 0.000022 - momentum: 0.000000
2023-10-24 13:34:24,923 epoch 7 - iter 146/738 - loss 0.01706995 - time (sec): 15.08 - samples/sec: 2406.26 - lr: 0.000021 - momentum: 0.000000
2023-10-24 13:34:31,695 epoch 7 - iter 219/738 - loss 0.01522152 - time (sec): 21.85 - samples/sec: 2366.54 - lr: 0.000021 - momentum: 0.000000
2023-10-24 13:34:38,753 epoch 7 - iter 292/738 - loss 0.01709441 - time (sec): 28.91 - samples/sec: 2354.77 - lr: 0.000020 - momentum: 0.000000
2023-10-24 13:34:45,228 epoch 7 - iter 365/738 - loss 0.01655309 - time (sec): 35.38 - samples/sec: 2363.51 - lr: 0.000020 - momentum: 0.000000
2023-10-24 13:34:51,955 epoch 7 - iter 438/738 - loss 0.01608348 - time (sec): 42.11 - samples/sec: 2356.84 - lr: 0.000019 - momentum: 0.000000
2023-10-24 13:34:58,690 epoch 7 - iter 511/738 - loss 0.01628481 - time (sec): 48.84 - samples/sec: 2347.12 - lr: 0.000018 - momentum: 0.000000
2023-10-24 13:35:04,990 epoch 7 - iter 584/738 - loss 0.01670341 - time (sec): 55.15 - samples/sec: 2345.70 - lr: 0.000018 - momentum: 0.000000
2023-10-24 13:35:13,111 epoch 7 - iter 657/738 - loss 0.01686839 - time (sec): 63.27 - samples/sec: 2348.50 - lr: 0.000017 - momentum: 0.000000
2023-10-24 13:35:20,233 epoch 7 - iter 730/738 - loss 0.01751030 - time (sec): 70.39 - samples/sec: 2338.05 - lr: 0.000017 - momentum: 0.000000
2023-10-24 13:35:20,903 ----------------------------------------------------------------------------------------------------
2023-10-24 13:35:20,903 EPOCH 7 done: loss 0.0175 - lr: 0.000017
2023-10-24 13:35:29,453 DEV : loss 0.19701939821243286 - f1-score (micro avg)  0.8147
2023-10-24 13:35:29,474 ----------------------------------------------------------------------------------------------------
2023-10-24 13:35:36,177 epoch 8 - iter 73/738 - loss 0.00499695 - time (sec): 6.70 - samples/sec: 2239.76 - lr: 0.000016 - momentum: 0.000000
2023-10-24 13:35:43,356 epoch 8 - iter 146/738 - loss 0.00716743 - time (sec): 13.88 - samples/sec: 2271.84 - lr: 0.000016 - momentum: 0.000000
2023-10-24 13:35:50,568 epoch 8 - iter 219/738 - loss 0.00825664 - time (sec): 21.09 - samples/sec: 2323.82 - lr: 0.000015 - momentum: 0.000000
2023-10-24 13:35:58,122 epoch 8 - iter 292/738 - loss 0.01241157 - time (sec): 28.65 - samples/sec: 2372.68 - lr: 0.000015 - momentum: 0.000000
2023-10-24 13:36:04,517 epoch 8 - iter 365/738 - loss 0.01179330 - time (sec): 35.04 - samples/sec: 2374.44 - lr: 0.000014 - momentum: 0.000000
2023-10-24 13:36:11,878 epoch 8 - iter 438/738 - loss 0.01120012 - time (sec): 42.40 - samples/sec: 2367.55 - lr: 0.000013 - momentum: 0.000000
2023-10-24 13:36:18,294 epoch 8 - iter 511/738 - loss 0.01084709 - time (sec): 48.82 - samples/sec: 2364.83 - lr: 0.000013 - momentum: 0.000000
2023-10-24 13:36:25,096 epoch 8 - iter 584/738 - loss 0.01068412 - time (sec): 55.62 - samples/sec: 2365.31 - lr: 0.000012 - momentum: 0.000000
2023-10-24 13:36:32,706 epoch 8 - iter 657/738 - loss 0.01037388 - time (sec): 63.23 - samples/sec: 2359.64 - lr: 0.000012 - momentum: 0.000000
2023-10-24 13:36:39,555 epoch 8 - iter 730/738 - loss 0.01018278 - time (sec): 70.08 - samples/sec: 2347.80 - lr: 0.000011 - momentum: 0.000000
2023-10-24 13:36:40,255 ----------------------------------------------------------------------------------------------------
2023-10-24 13:36:40,255 EPOCH 8 done: loss 0.0101 - lr: 0.000011
2023-10-24 13:36:48,796 DEV : loss 0.2113467901945114 - f1-score (micro avg)  0.8322
2023-10-24 13:36:48,817 saving best model
2023-10-24 13:36:49,516 ----------------------------------------------------------------------------------------------------
2023-10-24 13:36:56,498 epoch 9 - iter 73/738 - loss 0.00505270 - time (sec): 6.98 - samples/sec: 2316.96 - lr: 0.000011 - momentum: 0.000000
2023-10-24 13:37:04,786 epoch 9 - iter 146/738 - loss 0.00820157 - time (sec): 15.27 - samples/sec: 2400.03 - lr: 0.000010 - momentum: 0.000000
2023-10-24 13:37:11,208 epoch 9 - iter 219/738 - loss 0.00649651 - time (sec): 21.69 - samples/sec: 2406.43 - lr: 0.000010 - momentum: 0.000000
2023-10-24 13:37:17,526 epoch 9 - iter 292/738 - loss 0.00547378 - time (sec): 28.01 - samples/sec: 2418.99 - lr: 0.000009 - momentum: 0.000000
2023-10-24 13:37:24,116 epoch 9 - iter 365/738 - loss 0.00612415 - time (sec): 34.60 - samples/sec: 2391.03 - lr: 0.000008 - momentum: 0.000000
2023-10-24 13:37:31,210 epoch 9 - iter 438/738 - loss 0.00698846 - time (sec): 41.69 - samples/sec: 2378.01 - lr: 0.000008 - momentum: 0.000000
2023-10-24 13:37:37,809 epoch 9 - iter 511/738 - loss 0.00675961 - time (sec): 48.29 - samples/sec: 2378.54 - lr: 0.000007 - momentum: 0.000000
2023-10-24 13:37:44,996 epoch 9 - iter 584/738 - loss 0.00753027 - time (sec): 55.48 - samples/sec: 2370.53 - lr: 0.000007 - momentum: 0.000000
2023-10-24 13:37:52,346 epoch 9 - iter 657/738 - loss 0.00763591 - time (sec): 62.83 - samples/sec: 2367.35 - lr: 0.000006 - momentum: 0.000000
2023-10-24 13:37:59,594 epoch 9 - iter 730/738 - loss 0.00777319 - time (sec): 70.08 - samples/sec: 2353.84 - lr: 0.000006 - momentum: 0.000000
2023-10-24 13:38:00,322 ----------------------------------------------------------------------------------------------------
2023-10-24 13:38:00,323 EPOCH 9 done: loss 0.0078 - lr: 0.000006
2023-10-24 13:38:08,878 DEV : loss 0.21783244609832764 - f1-score (micro avg)  0.8352
2023-10-24 13:38:08,900 saving best model
2023-10-24 13:38:09,600 ----------------------------------------------------------------------------------------------------
2023-10-24 13:38:16,920 epoch 10 - iter 73/738 - loss 0.00253360 - time (sec): 7.32 - samples/sec: 2295.08 - lr: 0.000005 - momentum: 0.000000
2023-10-24 13:38:23,358 epoch 10 - iter 146/738 - loss 0.00249723 - time (sec): 13.76 - samples/sec: 2342.58 - lr: 0.000004 - momentum: 0.000000
2023-10-24 13:38:30,009 epoch 10 - iter 219/738 - loss 0.00177836 - time (sec): 20.41 - samples/sec: 2356.11 - lr: 0.000004 - momentum: 0.000000
2023-10-24 13:38:36,780 epoch 10 - iter 292/738 - loss 0.00257288 - time (sec): 27.18 - samples/sec: 2357.09 - lr: 0.000003 - momentum: 0.000000
2023-10-24 13:38:43,614 epoch 10 - iter 365/738 - loss 0.00349932 - time (sec): 34.01 - samples/sec: 2339.08 - lr: 0.000003 - momentum: 0.000000
2023-10-24 13:38:50,533 epoch 10 - iter 438/738 - loss 0.00352785 - time (sec): 40.93 - samples/sec: 2317.89 - lr: 0.000002 - momentum: 0.000000
2023-10-24 13:38:57,255 epoch 10 - iter 511/738 - loss 0.00323887 - time (sec): 47.65 - samples/sec: 2327.57 - lr: 0.000002 - momentum: 0.000000
2023-10-24 13:39:03,819 epoch 10 - iter 584/738 - loss 0.00431697 - time (sec): 54.22 - samples/sec: 2329.50 - lr: 0.000001 - momentum: 0.000000
2023-10-24 13:39:11,019 epoch 10 - iter 657/738 - loss 0.00445023 - time (sec): 61.42 - samples/sec: 2355.78 - lr: 0.000001 - momentum: 0.000000
2023-10-24 13:39:19,476 epoch 10 - iter 730/738 - loss 0.00508714 - time (sec): 69.87 - samples/sec: 2356.15 - lr: 0.000000 - momentum: 0.000000
2023-10-24 13:39:20,153 ----------------------------------------------------------------------------------------------------
2023-10-24 13:39:20,153 EPOCH 10 done: loss 0.0050 - lr: 0.000000
2023-10-24 13:39:28,707 DEV : loss 0.22154250741004944 - f1-score (micro avg)  0.8321
2023-10-24 13:39:29,293 ----------------------------------------------------------------------------------------------------
2023-10-24 13:39:29,294 Loading model from best epoch ...
2023-10-24 13:39:31,161 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
2023-10-24 13:39:37,474 
Results:
- F-score (micro) 0.7844
- F-score (macro) 0.6892
- Accuracy 0.6719

By class:
              precision    recall  f1-score   support

         loc     0.8373    0.8695    0.8531       858
        pers     0.7276    0.7858    0.7556       537
         org     0.5926    0.6061    0.5993       132
        time     0.5231    0.6296    0.5714        54
        prod     0.7400    0.6066    0.6667        61

   micro avg     0.7664    0.8033    0.7844      1642
   macro avg     0.6841    0.6995    0.6892      1642
weighted avg     0.7678    0.8033    0.7846      1642

2023-10-24 13:39:37,474 ----------------------------------------------------------------------------------------------------