2023-01-08 08:23:21,495 ----------------------------------------------------------------------------------------------------
2023-01-08 08:23:21,498 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(100000, 768, padding_idx=0)
        (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)
      )

      (pooler): BertPooler(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (activation): Tanh()
      )
  )
  (word_dropout): WordDropout(p=0.05)
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=18, bias=True)
  (beta): 1.0
  (weights): None
  (weight_tensor) None
)"
2023-01-08 08:23:21,500 ----------------------------------------------------------------------------------------------------
2023-01-08 08:23:21,505 Corpus: "Corpus: 26116 train + 2902 dev + 1572 test sentences"
2023-01-08 08:23:21,506 ----------------------------------------------------------------------------------------------------
2023-01-08 08:23:21,506 Parameters:
2023-01-08 08:23:21,507  - learning_rate: "5e-06"
2023-01-08 08:23:21,509  - mini_batch_size: "4"
2023-01-08 08:23:21,510  - patience: "3"
2023-01-08 08:23:21,512  - anneal_factor: "0.5"
2023-01-08 08:23:21,513  - max_epochs: "25"
2023-01-08 08:23:21,513  - shuffle: "False"
2023-01-08 08:23:21,514  - train_with_dev: "False"
2023-01-08 08:23:21,515  - batch_growth_annealing: "False"
2023-01-08 08:23:21,516 ----------------------------------------------------------------------------------------------------
2023-01-08 08:23:21,517 Model training base path: "resources/taggers/NSURL-2019_25epochs"
2023-01-08 08:23:21,518 ----------------------------------------------------------------------------------------------------
2023-01-08 08:23:21,519 Device: cuda:0
2023-01-08 08:23:21,519 ----------------------------------------------------------------------------------------------------
2023-01-08 08:23:21,520 Embeddings storage mode: none
2023-01-08 18:00:13,690 ----------------------------------------------------------------------------------------------------
2023-01-08 18:02:30,863 epoch 25 - iter 652/6529 - loss 0.12185023 - samples/sec: 19.02 - lr: 0.000000
2023-01-08 18:04:48,105 epoch 25 - iter 1304/6529 - loss 0.12151675 - samples/sec: 19.01 - lr: 0.000000
2023-01-08 18:07:03,845 epoch 25 - iter 1956/6529 - loss 0.12293666 - samples/sec: 19.22 - lr: 0.000000
2023-01-08 18:09:20,797 epoch 25 - iter 2608/6529 - loss 0.12248209 - samples/sec: 19.05 - lr: 0.000000
2023-01-08 18:11:38,782 epoch 25 - iter 3260/6529 - loss 0.12236612 - samples/sec: 18.91 - lr: 0.000000
2023-01-08 18:13:57,739 epoch 25 - iter 3912/6529 - loss 0.12284535 - samples/sec: 18.78 - lr: 0.000000
2023-01-08 18:16:19,460 epoch 25 - iter 4564/6529 - loss 0.12312537 - samples/sec: 18.41 - lr: 0.000000
2023-01-08 18:18:34,844 epoch 25 - iter 5216/6529 - loss 0.12315613 - samples/sec: 19.27 - lr: 0.000000
2023-01-08 18:20:52,724 epoch 25 - iter 5868/6529 - loss 0.12280164 - samples/sec: 18.92 - lr: 0.000000
2023-01-08 18:23:11,733 epoch 25 - iter 6520/6529 - loss 0.12286952 - samples/sec: 18.77 - lr: 0.000000
2023-01-08 18:23:13,587 ----------------------------------------------------------------------------------------------------
2023-01-08 18:23:13,590 EPOCH 25 done: loss 0.1229 - lr 0.0000000
2023-01-08 18:24:28,587 DEV : loss 0.1607247292995453 - f1-score (micro avg)  0.9119
2023-01-08 18:24:28,641 BAD EPOCHS (no improvement): 4
2023-01-08 18:24:29,854 ----------------------------------------------------------------------------------------------------
2023-01-08 18:24:29,857 Testing using last state of model ...
2023-01-08 18:25:11,654 0.9081	0.8984	0.9033	0.8277
2023-01-08 18:25:11,656 
Results:
- F-score (micro) 0.9033
- F-score (macro) 0.8976
- Accuracy 0.8277

By class:
              precision    recall  f1-score   support

         ORG     0.9016    0.8667    0.8838      1523
         LOC     0.9113    0.9305    0.9208      1425
         PER     0.9216    0.9322    0.9269      1224
         DAT     0.8623    0.7958    0.8277       480
         MON     0.9665    0.9558    0.9611       181
         PCT     0.9375    0.9740    0.9554        77
         TIM     0.8235    0.7925    0.8077        53

   micro avg     0.9081    0.8984    0.9033      4963
   macro avg     0.9035    0.8925    0.8976      4963
weighted avg     0.9076    0.8984    0.9028      4963
 samples avg     0.8277    0.8277    0.8277      4963

2023-01-08 18:25:11,656 ----------------------------------------------------------------------------------------------------