readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: frases-bertimbau-v0.4
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # frases-bertimbau-v0.4
  
  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4380
  - F1: 0.8653
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 20.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.4919        | 0.67  | 500   | 0.6711          | 0.7934 |
  | 0.5458        | 1.34  | 1000  | 0.4631          | 0.8445 |
  | 0.4346        | 2.01  | 1500  | 0.3971          | 0.8619 |
  | 0.3282        | 2.68  | 2000  | 0.3943          | 0.8647 |
  | 0.2738        | 3.35  | 2500  | 0.4260          | 0.8651 |
  | 0.2261        | 4.02  | 3000  | 0.4071          | 0.8716 |
  | 0.1539        | 4.68  | 3500  | 0.4522          | 0.8687 |
  | 0.1354        | 5.35  | 4000  | 0.5319          | 0.8633 |
  | 0.1132        | 6.02  | 4500  | 0.5306          | 0.8660 |
  | 0.0834        | 6.69  | 5000  | 0.5935          | 0.8633 |
  | 0.0756        | 7.36  | 5500  | 0.6532          | 0.8593 |
  | 0.0692        | 8.03  | 6000  | 0.6492          | 0.8650 |
  | 0.0541        | 8.7   | 6500  | 0.6708          | 0.8648 |
  | 0.0451        | 9.37  | 7000  | 0.7084          | 0.8667 |
  | 0.046         | 10.04 | 7500  | 0.7482          | 0.8655 |
  | 0.0356        | 10.71 | 8000  | 0.7802          | 0.8631 |
  | 0.0332        | 11.38 | 8500  | 0.8112          | 0.8623 |
  | 0.0282        | 12.05 | 9000  | 0.8070          | 0.8664 |
  | 0.0251        | 12.72 | 9500  | 0.8332          | 0.8640 |
  | 0.0215        | 13.39 | 10000 | 0.8487          | 0.8678 |
  | 0.0203        | 14.06 | 10500 | 0.8883          | 0.8604 |
  | 0.0168        | 14.73 | 11000 | 0.8870          | 0.8637 |
  | 0.0124        | 15.39 | 11500 | 0.8986          | 0.8678 |
  | 0.0137        | 16.06 | 12000 | 0.9093          | 0.8670 |
  | 0.0104        | 16.73 | 12500 | 0.9145          | 0.8659 |
  | 0.0071        | 17.4  | 13000 | 0.9380          | 0.8676 |
  | 0.0076        | 18.07 | 13500 | 0.9496          | 0.8686 |
  | 0.0072        | 18.74 | 14000 | 0.9589          | 0.8698 |
  | 0.0067        | 19.41 | 14500 | 0.9571          | 0.8687 |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.13.0
  - Datasets 2.2.1
  - Tokenizers 0.13.2
  