2023-10-23 21:13:14,094 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,095 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()
)"
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /home/ubuntu/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 Train:  3575 sentences
2023-10-23 21:13:14,096         (train_with_dev=False, train_with_test=False)
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 Training Params:
2023-10-23 21:13:14,096  - learning_rate: "5e-05" 
2023-10-23 21:13:14,096  - mini_batch_size: "8"
2023-10-23 21:13:14,096  - max_epochs: "10"
2023-10-23 21:13:14,096  - shuffle: "True"
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 Plugins:
2023-10-23 21:13:14,096  - TensorboardLogger
2023-10-23 21:13:14,096  - LinearScheduler | warmup_fraction: '0.1'
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 Final evaluation on model from best epoch (best-model.pt)
2023-10-23 21:13:14,096  - metric: "('micro avg', 'f1-score')"
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 Computation:
2023-10-23 21:13:14,096  - compute on device: cuda:0
2023-10-23 21:13:14,096  - embedding storage: none
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:14,096 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-23 21:13:17,881 epoch 1 - iter 44/447 - loss 2.49175659 - time (sec): 3.78 - samples/sec: 2075.50 - lr: 0.000005 - momentum: 0.000000
2023-10-23 21:13:21,992 epoch 1 - iter 88/447 - loss 1.47620142 - time (sec): 7.89 - samples/sec: 2092.81 - lr: 0.000010 - momentum: 0.000000
2023-10-23 21:13:26,073 epoch 1 - iter 132/447 - loss 1.09754009 - time (sec): 11.98 - samples/sec: 2087.54 - lr: 0.000015 - momentum: 0.000000
2023-10-23 21:13:30,090 epoch 1 - iter 176/447 - loss 0.91399479 - time (sec): 15.99 - samples/sec: 2082.94 - lr: 0.000020 - momentum: 0.000000
2023-10-23 21:13:33,970 epoch 1 - iter 220/447 - loss 0.79606073 - time (sec): 19.87 - samples/sec: 2107.84 - lr: 0.000024 - momentum: 0.000000
2023-10-23 21:13:37,765 epoch 1 - iter 264/447 - loss 0.71083825 - time (sec): 23.67 - samples/sec: 2107.04 - lr: 0.000029 - momentum: 0.000000
2023-10-23 21:13:41,687 epoch 1 - iter 308/447 - loss 0.64270297 - time (sec): 27.59 - samples/sec: 2109.42 - lr: 0.000034 - momentum: 0.000000
2023-10-23 21:13:45,652 epoch 1 - iter 352/447 - loss 0.58337536 - time (sec): 31.56 - samples/sec: 2111.17 - lr: 0.000039 - momentum: 0.000000
2023-10-23 21:13:50,089 epoch 1 - iter 396/447 - loss 0.53741990 - time (sec): 35.99 - samples/sec: 2126.06 - lr: 0.000044 - momentum: 0.000000
2023-10-23 21:13:53,893 epoch 1 - iter 440/447 - loss 0.50360530 - time (sec): 39.80 - samples/sec: 2139.48 - lr: 0.000049 - momentum: 0.000000
2023-10-23 21:13:54,507 ----------------------------------------------------------------------------------------------------
2023-10-23 21:13:54,507 EPOCH 1 done: loss 0.4979 - lr: 0.000049
2023-10-23 21:13:59,344 DEV : loss 0.14264939725399017 - f1-score (micro avg)  0.649
2023-10-23 21:13:59,365 saving best model
2023-10-23 21:13:59,841 ----------------------------------------------------------------------------------------------------
2023-10-23 21:14:03,572 epoch 2 - iter 44/447 - loss 0.17136363 - time (sec): 3.73 - samples/sec: 2204.31 - lr: 0.000049 - momentum: 0.000000
2023-10-23 21:14:07,600 epoch 2 - iter 88/447 - loss 0.14665351 - time (sec): 7.76 - samples/sec: 2168.58 - lr: 0.000049 - momentum: 0.000000
2023-10-23 21:14:11,683 epoch 2 - iter 132/447 - loss 0.13770205 - time (sec): 11.84 - samples/sec: 2165.41 - lr: 0.000048 - momentum: 0.000000
2023-10-23 21:14:15,810 epoch 2 - iter 176/447 - loss 0.13975349 - time (sec): 15.97 - samples/sec: 2152.86 - lr: 0.000048 - momentum: 0.000000
2023-10-23 21:14:19,583 epoch 2 - iter 220/447 - loss 0.13141036 - time (sec): 19.74 - samples/sec: 2131.99 - lr: 0.000047 - momentum: 0.000000
2023-10-23 21:14:23,712 epoch 2 - iter 264/447 - loss 0.13359602 - time (sec): 23.87 - samples/sec: 2136.28 - lr: 0.000047 - momentum: 0.000000
2023-10-23 21:14:27,728 epoch 2 - iter 308/447 - loss 0.13099589 - time (sec): 27.89 - samples/sec: 2141.32 - lr: 0.000046 - momentum: 0.000000
2023-10-23 21:14:31,359 epoch 2 - iter 352/447 - loss 0.13108938 - time (sec): 31.52 - samples/sec: 2145.97 - lr: 0.000046 - momentum: 0.000000
2023-10-23 21:14:35,837 epoch 2 - iter 396/447 - loss 0.13272083 - time (sec): 35.99 - samples/sec: 2139.98 - lr: 0.000045 - momentum: 0.000000
2023-10-23 21:14:39,654 epoch 2 - iter 440/447 - loss 0.13058338 - time (sec): 39.81 - samples/sec: 2138.26 - lr: 0.000045 - momentum: 0.000000
2023-10-23 21:14:40,251 ----------------------------------------------------------------------------------------------------
2023-10-23 21:14:40,251 EPOCH 2 done: loss 0.1303 - lr: 0.000045
2023-10-23 21:14:46,728 DEV : loss 0.12430483102798462 - f1-score (micro avg)  0.7252
2023-10-23 21:14:46,749 saving best model
2023-10-23 21:14:47,345 ----------------------------------------------------------------------------------------------------
2023-10-23 21:14:51,416 epoch 3 - iter 44/447 - loss 0.06343382 - time (sec): 4.07 - samples/sec: 2147.46 - lr: 0.000044 - momentum: 0.000000
2023-10-23 21:14:55,502 epoch 3 - iter 88/447 - loss 0.07942642 - time (sec): 8.16 - samples/sec: 2140.20 - lr: 0.000043 - momentum: 0.000000
2023-10-23 21:14:59,641 epoch 3 - iter 132/447 - loss 0.07644302 - time (sec): 12.30 - samples/sec: 2162.74 - lr: 0.000043 - momentum: 0.000000
2023-10-23 21:15:03,560 epoch 3 - iter 176/447 - loss 0.07415197 - time (sec): 16.21 - samples/sec: 2126.47 - lr: 0.000042 - momentum: 0.000000
2023-10-23 21:15:07,434 epoch 3 - iter 220/447 - loss 0.07458049 - time (sec): 20.09 - samples/sec: 2144.74 - lr: 0.000042 - momentum: 0.000000
2023-10-23 21:15:11,199 epoch 3 - iter 264/447 - loss 0.07373489 - time (sec): 23.85 - samples/sec: 2150.21 - lr: 0.000041 - momentum: 0.000000
2023-10-23 21:15:15,070 epoch 3 - iter 308/447 - loss 0.07485896 - time (sec): 27.72 - samples/sec: 2142.33 - lr: 0.000041 - momentum: 0.000000
2023-10-23 21:15:19,242 epoch 3 - iter 352/447 - loss 0.07147296 - time (sec): 31.90 - samples/sec: 2146.35 - lr: 0.000040 - momentum: 0.000000
2023-10-23 21:15:23,058 epoch 3 - iter 396/447 - loss 0.07105503 - time (sec): 35.71 - samples/sec: 2149.05 - lr: 0.000040 - momentum: 0.000000
2023-10-23 21:15:27,181 epoch 3 - iter 440/447 - loss 0.07201190 - time (sec): 39.83 - samples/sec: 2134.04 - lr: 0.000039 - momentum: 0.000000
2023-10-23 21:15:27,838 ----------------------------------------------------------------------------------------------------
2023-10-23 21:15:27,839 EPOCH 3 done: loss 0.0720 - lr: 0.000039
2023-10-23 21:15:34,341 DEV : loss 0.13549202680587769 - f1-score (micro avg)  0.7124
2023-10-23 21:15:34,361 ----------------------------------------------------------------------------------------------------
2023-10-23 21:15:38,090 epoch 4 - iter 44/447 - loss 0.04999690 - time (sec): 3.73 - samples/sec: 2136.07 - lr: 0.000038 - momentum: 0.000000
2023-10-23 21:15:42,151 epoch 4 - iter 88/447 - loss 0.04797379 - time (sec): 7.79 - samples/sec: 2115.30 - lr: 0.000038 - momentum: 0.000000
2023-10-23 21:15:46,201 epoch 4 - iter 132/447 - loss 0.04349472 - time (sec): 11.84 - samples/sec: 2134.54 - lr: 0.000037 - momentum: 0.000000
2023-10-23 21:15:50,375 epoch 4 - iter 176/447 - loss 0.04141184 - time (sec): 16.01 - samples/sec: 2118.29 - lr: 0.000037 - momentum: 0.000000
2023-10-23 21:15:54,562 epoch 4 - iter 220/447 - loss 0.04291677 - time (sec): 20.20 - samples/sec: 2110.96 - lr: 0.000036 - momentum: 0.000000
2023-10-23 21:15:58,592 epoch 4 - iter 264/447 - loss 0.04298733 - time (sec): 24.23 - samples/sec: 2118.23 - lr: 0.000036 - momentum: 0.000000
2023-10-23 21:16:02,830 epoch 4 - iter 308/447 - loss 0.04222533 - time (sec): 28.47 - samples/sec: 2119.43 - lr: 0.000035 - momentum: 0.000000
2023-10-23 21:16:06,714 epoch 4 - iter 352/447 - loss 0.04257038 - time (sec): 32.35 - samples/sec: 2124.33 - lr: 0.000035 - momentum: 0.000000
2023-10-23 21:16:10,630 epoch 4 - iter 396/447 - loss 0.04389888 - time (sec): 36.27 - samples/sec: 2125.21 - lr: 0.000034 - momentum: 0.000000
2023-10-23 21:16:14,412 epoch 4 - iter 440/447 - loss 0.04468753 - time (sec): 40.05 - samples/sec: 2129.68 - lr: 0.000033 - momentum: 0.000000
2023-10-23 21:16:15,005 ----------------------------------------------------------------------------------------------------
2023-10-23 21:16:15,005 EPOCH 4 done: loss 0.0453 - lr: 0.000033
2023-10-23 21:16:21,523 DEV : loss 0.16867585480213165 - f1-score (micro avg)  0.7202
2023-10-23 21:16:21,543 ----------------------------------------------------------------------------------------------------
2023-10-23 21:16:25,550 epoch 5 - iter 44/447 - loss 0.03485991 - time (sec): 4.01 - samples/sec: 2168.92 - lr: 0.000033 - momentum: 0.000000
2023-10-23 21:16:29,651 epoch 5 - iter 88/447 - loss 0.03804560 - time (sec): 8.11 - samples/sec: 2075.96 - lr: 0.000032 - momentum: 0.000000
2023-10-23 21:16:33,407 epoch 5 - iter 132/447 - loss 0.03565243 - time (sec): 11.86 - samples/sec: 2092.94 - lr: 0.000032 - momentum: 0.000000
2023-10-23 21:16:37,779 epoch 5 - iter 176/447 - loss 0.03560807 - time (sec): 16.23 - samples/sec: 2102.25 - lr: 0.000031 - momentum: 0.000000
2023-10-23 21:16:41,635 epoch 5 - iter 220/447 - loss 0.03534608 - time (sec): 20.09 - samples/sec: 2103.62 - lr: 0.000031 - momentum: 0.000000
2023-10-23 21:16:45,439 epoch 5 - iter 264/447 - loss 0.03359264 - time (sec): 23.90 - samples/sec: 2104.31 - lr: 0.000030 - momentum: 0.000000
2023-10-23 21:16:49,900 epoch 5 - iter 308/447 - loss 0.03114445 - time (sec): 28.36 - samples/sec: 2110.97 - lr: 0.000030 - momentum: 0.000000
2023-10-23 21:16:53,842 epoch 5 - iter 352/447 - loss 0.03263717 - time (sec): 32.30 - samples/sec: 2113.97 - lr: 0.000029 - momentum: 0.000000
2023-10-23 21:16:57,757 epoch 5 - iter 396/447 - loss 0.03153033 - time (sec): 36.21 - samples/sec: 2127.60 - lr: 0.000028 - momentum: 0.000000
2023-10-23 21:17:01,544 epoch 5 - iter 440/447 - loss 0.03214773 - time (sec): 40.00 - samples/sec: 2136.26 - lr: 0.000028 - momentum: 0.000000
2023-10-23 21:17:02,105 ----------------------------------------------------------------------------------------------------
2023-10-23 21:17:02,105 EPOCH 5 done: loss 0.0318 - lr: 0.000028
2023-10-23 21:17:08,620 DEV : loss 0.19214682281017303 - f1-score (micro avg)  0.7459
2023-10-23 21:17:08,640 saving best model
2023-10-23 21:17:09,235 ----------------------------------------------------------------------------------------------------
2023-10-23 21:17:13,106 epoch 6 - iter 44/447 - loss 0.02105748 - time (sec): 3.87 - samples/sec: 2043.91 - lr: 0.000027 - momentum: 0.000000
2023-10-23 21:17:17,057 epoch 6 - iter 88/447 - loss 0.01726201 - time (sec): 7.82 - samples/sec: 2046.65 - lr: 0.000027 - momentum: 0.000000
2023-10-23 21:17:21,186 epoch 6 - iter 132/447 - loss 0.01949802 - time (sec): 11.95 - samples/sec: 2075.00 - lr: 0.000026 - momentum: 0.000000
2023-10-23 21:17:25,225 epoch 6 - iter 176/447 - loss 0.02190376 - time (sec): 15.99 - samples/sec: 2125.97 - lr: 0.000026 - momentum: 0.000000
2023-10-23 21:17:29,209 epoch 6 - iter 220/447 - loss 0.02099074 - time (sec): 19.97 - samples/sec: 2137.11 - lr: 0.000025 - momentum: 0.000000
2023-10-23 21:17:33,283 epoch 6 - iter 264/447 - loss 0.02173912 - time (sec): 24.05 - samples/sec: 2116.13 - lr: 0.000025 - momentum: 0.000000
2023-10-23 21:17:37,096 epoch 6 - iter 308/447 - loss 0.02086471 - time (sec): 27.86 - samples/sec: 2127.79 - lr: 0.000024 - momentum: 0.000000
2023-10-23 21:17:41,021 epoch 6 - iter 352/447 - loss 0.02351890 - time (sec): 31.79 - samples/sec: 2134.36 - lr: 0.000023 - momentum: 0.000000
2023-10-23 21:17:45,297 epoch 6 - iter 396/447 - loss 0.02301478 - time (sec): 36.06 - samples/sec: 2126.14 - lr: 0.000023 - momentum: 0.000000
2023-10-23 21:17:49,167 epoch 6 - iter 440/447 - loss 0.02345262 - time (sec): 39.93 - samples/sec: 2139.26 - lr: 0.000022 - momentum: 0.000000
2023-10-23 21:17:49,731 ----------------------------------------------------------------------------------------------------
2023-10-23 21:17:49,731 EPOCH 6 done: loss 0.0235 - lr: 0.000022
2023-10-23 21:17:56,209 DEV : loss 0.20100656151771545 - f1-score (micro avg)  0.7432
2023-10-23 21:17:56,230 ----------------------------------------------------------------------------------------------------
2023-10-23 21:17:59,954 epoch 7 - iter 44/447 - loss 0.01434631 - time (sec): 3.72 - samples/sec: 2231.02 - lr: 0.000022 - momentum: 0.000000
2023-10-23 21:18:04,021 epoch 7 - iter 88/447 - loss 0.01477926 - time (sec): 7.79 - samples/sec: 2170.43 - lr: 0.000021 - momentum: 0.000000
2023-10-23 21:18:08,514 epoch 7 - iter 132/447 - loss 0.01339597 - time (sec): 12.28 - samples/sec: 2143.01 - lr: 0.000021 - momentum: 0.000000
2023-10-23 21:18:12,450 epoch 7 - iter 176/447 - loss 0.01300304 - time (sec): 16.22 - samples/sec: 2141.35 - lr: 0.000020 - momentum: 0.000000
2023-10-23 21:18:16,367 epoch 7 - iter 220/447 - loss 0.01258631 - time (sec): 20.14 - samples/sec: 2136.94 - lr: 0.000020 - momentum: 0.000000
2023-10-23 21:18:20,436 epoch 7 - iter 264/447 - loss 0.01333364 - time (sec): 24.21 - samples/sec: 2137.91 - lr: 0.000019 - momentum: 0.000000
2023-10-23 21:18:24,476 epoch 7 - iter 308/447 - loss 0.01331886 - time (sec): 28.25 - samples/sec: 2133.18 - lr: 0.000018 - momentum: 0.000000
2023-10-23 21:18:28,262 epoch 7 - iter 352/447 - loss 0.01320475 - time (sec): 32.03 - samples/sec: 2135.10 - lr: 0.000018 - momentum: 0.000000
2023-10-23 21:18:32,184 epoch 7 - iter 396/447 - loss 0.01306538 - time (sec): 35.95 - samples/sec: 2141.97 - lr: 0.000017 - momentum: 0.000000
2023-10-23 21:18:36,080 epoch 7 - iter 440/447 - loss 0.01247335 - time (sec): 39.85 - samples/sec: 2144.78 - lr: 0.000017 - momentum: 0.000000
2023-10-23 21:18:36,627 ----------------------------------------------------------------------------------------------------
2023-10-23 21:18:36,627 EPOCH 7 done: loss 0.0123 - lr: 0.000017
2023-10-23 21:18:43,096 DEV : loss 0.2460336685180664 - f1-score (micro avg)  0.7548
2023-10-23 21:18:43,116 saving best model
2023-10-23 21:18:43,685 ----------------------------------------------------------------------------------------------------
2023-10-23 21:18:47,540 epoch 8 - iter 44/447 - loss 0.01600473 - time (sec): 3.85 - samples/sec: 2174.01 - lr: 0.000016 - momentum: 0.000000
2023-10-23 21:18:51,458 epoch 8 - iter 88/447 - loss 0.01261638 - time (sec): 7.77 - samples/sec: 2168.27 - lr: 0.000016 - momentum: 0.000000
2023-10-23 21:18:55,297 epoch 8 - iter 132/447 - loss 0.01097643 - time (sec): 11.61 - samples/sec: 2125.13 - lr: 0.000015 - momentum: 0.000000
2023-10-23 21:18:59,926 epoch 8 - iter 176/447 - loss 0.00839399 - time (sec): 16.24 - samples/sec: 2140.26 - lr: 0.000015 - momentum: 0.000000
2023-10-23 21:19:03,883 epoch 8 - iter 220/447 - loss 0.00700836 - time (sec): 20.20 - samples/sec: 2146.24 - lr: 0.000014 - momentum: 0.000000
2023-10-23 21:19:07,540 epoch 8 - iter 264/447 - loss 0.00658404 - time (sec): 23.85 - samples/sec: 2123.74 - lr: 0.000013 - momentum: 0.000000
2023-10-23 21:19:11,710 epoch 8 - iter 308/447 - loss 0.00672887 - time (sec): 28.02 - samples/sec: 2123.98 - lr: 0.000013 - momentum: 0.000000
2023-10-23 21:19:15,680 epoch 8 - iter 352/447 - loss 0.00730350 - time (sec): 31.99 - samples/sec: 2126.26 - lr: 0.000012 - momentum: 0.000000
2023-10-23 21:19:20,096 epoch 8 - iter 396/447 - loss 0.00757808 - time (sec): 36.41 - samples/sec: 2121.30 - lr: 0.000012 - momentum: 0.000000
2023-10-23 21:19:23,827 epoch 8 - iter 440/447 - loss 0.00721160 - time (sec): 40.14 - samples/sec: 2121.34 - lr: 0.000011 - momentum: 0.000000
2023-10-23 21:19:24,443 ----------------------------------------------------------------------------------------------------
2023-10-23 21:19:24,444 EPOCH 8 done: loss 0.0080 - lr: 0.000011
2023-10-23 21:19:30,630 DEV : loss 0.26853764057159424 - f1-score (micro avg)  0.7664
2023-10-23 21:19:30,651 saving best model
2023-10-23 21:19:31,246 ----------------------------------------------------------------------------------------------------
2023-10-23 21:19:34,871 epoch 9 - iter 44/447 - loss 0.00413003 - time (sec): 3.62 - samples/sec: 2217.26 - lr: 0.000011 - momentum: 0.000000
2023-10-23 21:19:39,173 epoch 9 - iter 88/447 - loss 0.00874300 - time (sec): 7.93 - samples/sec: 2054.24 - lr: 0.000010 - momentum: 0.000000
2023-10-23 21:19:43,410 epoch 9 - iter 132/447 - loss 0.00637939 - time (sec): 12.16 - samples/sec: 2070.73 - lr: 0.000010 - momentum: 0.000000
2023-10-23 21:19:47,231 epoch 9 - iter 176/447 - loss 0.00677074 - time (sec): 15.98 - samples/sec: 2104.57 - lr: 0.000009 - momentum: 0.000000
2023-10-23 21:19:51,199 epoch 9 - iter 220/447 - loss 0.00601290 - time (sec): 19.95 - samples/sec: 2121.37 - lr: 0.000008 - momentum: 0.000000
2023-10-23 21:19:55,456 epoch 9 - iter 264/447 - loss 0.00551771 - time (sec): 24.21 - samples/sec: 2124.91 - lr: 0.000008 - momentum: 0.000000
2023-10-23 21:19:59,708 epoch 9 - iter 308/447 - loss 0.00532185 - time (sec): 28.46 - samples/sec: 2127.58 - lr: 0.000007 - momentum: 0.000000
2023-10-23 21:20:03,460 epoch 9 - iter 352/447 - loss 0.00602318 - time (sec): 32.21 - samples/sec: 2127.14 - lr: 0.000007 - momentum: 0.000000
2023-10-23 21:20:07,201 epoch 9 - iter 396/447 - loss 0.00619570 - time (sec): 35.95 - samples/sec: 2132.79 - lr: 0.000006 - momentum: 0.000000
2023-10-23 21:20:11,227 epoch 9 - iter 440/447 - loss 0.00584523 - time (sec): 39.98 - samples/sec: 2135.91 - lr: 0.000006 - momentum: 0.000000
2023-10-23 21:20:11,853 ----------------------------------------------------------------------------------------------------
2023-10-23 21:20:11,854 EPOCH 9 done: loss 0.0058 - lr: 0.000006
2023-10-23 21:20:18,089 DEV : loss 0.259550541639328 - f1-score (micro avg)  0.768
2023-10-23 21:20:18,110 saving best model
2023-10-23 21:20:18,700 ----------------------------------------------------------------------------------------------------
2023-10-23 21:20:22,576 epoch 10 - iter 44/447 - loss 0.00028617 - time (sec): 3.88 - samples/sec: 2214.84 - lr: 0.000005 - momentum: 0.000000
2023-10-23 21:20:26,741 epoch 10 - iter 88/447 - loss 0.00036442 - time (sec): 8.04 - samples/sec: 2165.17 - lr: 0.000005 - momentum: 0.000000
2023-10-23 21:20:30,674 epoch 10 - iter 132/447 - loss 0.00123632 - time (sec): 11.97 - samples/sec: 2150.91 - lr: 0.000004 - momentum: 0.000000
2023-10-23 21:20:34,613 epoch 10 - iter 176/447 - loss 0.00181556 - time (sec): 15.91 - samples/sec: 2137.75 - lr: 0.000003 - momentum: 0.000000
2023-10-23 21:20:38,919 epoch 10 - iter 220/447 - loss 0.00290602 - time (sec): 20.22 - samples/sec: 2145.60 - lr: 0.000003 - momentum: 0.000000
2023-10-23 21:20:42,689 epoch 10 - iter 264/447 - loss 0.00253256 - time (sec): 23.99 - samples/sec: 2136.16 - lr: 0.000002 - momentum: 0.000000
2023-10-23 21:20:46,503 epoch 10 - iter 308/447 - loss 0.00278757 - time (sec): 27.80 - samples/sec: 2147.54 - lr: 0.000002 - momentum: 0.000000
2023-10-23 21:20:50,596 epoch 10 - iter 352/447 - loss 0.00244091 - time (sec): 31.90 - samples/sec: 2139.99 - lr: 0.000001 - momentum: 0.000000
2023-10-23 21:20:54,908 epoch 10 - iter 396/447 - loss 0.00254504 - time (sec): 36.21 - samples/sec: 2122.45 - lr: 0.000001 - momentum: 0.000000
2023-10-23 21:20:58,966 epoch 10 - iter 440/447 - loss 0.00280141 - time (sec): 40.27 - samples/sec: 2116.87 - lr: 0.000000 - momentum: 0.000000
2023-10-23 21:20:59,589 ----------------------------------------------------------------------------------------------------
2023-10-23 21:20:59,589 EPOCH 10 done: loss 0.0028 - lr: 0.000000
2023-10-23 21:21:05,787 DEV : loss 0.269205242395401 - f1-score (micro avg)  0.7664
2023-10-23 21:21:06,277 ----------------------------------------------------------------------------------------------------
2023-10-23 21:21:06,278 Loading model from best epoch ...
2023-10-23 21:21:07,876 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-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
2023-10-23 21:21:12,685 
Results:
- F-score (micro) 0.7505
- F-score (macro) 0.6658
- Accuracy 0.6185

By class:
              precision    recall  f1-score   support

         loc     0.8249    0.8456    0.8351       596
        pers     0.6959    0.7628    0.7278       333
         org     0.5469    0.5303    0.5385       132
        prod     0.6066    0.5606    0.5827        66
        time     0.6818    0.6122    0.6452        49

   micro avg     0.7403    0.7611    0.7505      1176
   macro avg     0.6712    0.6623    0.6658      1176
weighted avg     0.7389    0.7611    0.7494      1176

2023-10-23 21:21:12,685 ----------------------------------------------------------------------------------------------------