readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: output
    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. -->
  
  # output
  
  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6440
  
  ## 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: 0.0001
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 10000
  - num_epochs: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss |
  |:-------------:|:-----:|:------:|:---------------:|
  | 1.1985        | 0.22  | 2500   | 1.0940          |
  | 1.0937        | 0.44  | 5000   | 1.0033          |
  | 1.0675        | 0.66  | 7500   | 0.9753          |
  | 1.0565        | 0.87  | 10000  | 0.9801          |
  | 1.0244        | 1.09  | 12500  | 0.9526          |
  | 0.9943        | 1.31  | 15000  | 0.9298          |
  | 0.9799        | 1.53  | 17500  | 0.9035          |
  | 0.95          | 1.75  | 20000  | 0.8835          |
  | 0.933         | 1.97  | 22500  | 0.8636          |
  | 0.9079        | 2.18  | 25000  | 0.8507          |
  | 0.8938        | 2.4   | 27500  | 0.8397          |
  | 0.8781        | 2.62  | 30000  | 0.8195          |
  | 0.8647        | 2.84  | 32500  | 0.8088          |
  | 0.8422        | 3.06  | 35000  | 0.7954          |
  | 0.831         | 3.28  | 37500  | 0.7871          |
  | 0.8173        | 3.5   | 40000  | 0.7721          |
  | 0.8072        | 3.71  | 42500  | 0.7611          |
  | 0.8011        | 3.93  | 45000  | 0.7532          |
  | 0.7828        | 4.15  | 47500  | 0.7431          |
  | 0.7691        | 4.37  | 50000  | 0.7367          |
  | 0.7659        | 4.59  | 52500  | 0.7292          |
  | 0.7606        | 4.81  | 55000  | 0.7245          |
  | 0.8082        | 5.02  | 57500  | 0.7696          |
  | 0.8114        | 5.24  | 60000  | 0.7695          |
  | 0.8022        | 5.46  | 62500  | 0.7613          |
  | 0.7986        | 5.68  | 65000  | 0.7558          |
  | 0.8018        | 5.9   | 67500  | 0.7478          |
  | 0.782         | 6.12  | 70000  | 0.7435          |
  | 0.7743        | 6.34  | 72500  | 0.7367          |
  | 0.774         | 6.55  | 75000  | 0.7313          |
  | 0.7692        | 6.77  | 77500  | 0.7270          |
  | 0.7604        | 6.99  | 80000  | 0.7200          |
  | 0.7468        | 7.21  | 82500  | 0.7164          |
  | 0.7486        | 7.43  | 85000  | 0.7117          |
  | 0.7399        | 7.65  | 87500  | 0.7043          |
  | 0.7306        | 7.86  | 90000  | 0.6956          |
  | 0.7243        | 8.08  | 92500  | 0.6959          |
  | 0.7132        | 8.3   | 95000  | 0.6916          |
  | 0.71          | 8.52  | 97500  | 0.6853          |
  | 0.7128        | 8.74  | 100000 | 0.6855          |
  | 0.7088        | 8.96  | 102500 | 0.6809          |
  | 0.7002        | 9.18  | 105000 | 0.6784          |
  | 0.6953        | 9.39  | 107500 | 0.6737          |
  | 0.695         | 9.61  | 110000 | 0.6714          |
  | 0.6871        | 9.83  | 112500 | 0.6687          |
  | 0.7161        | 10.05 | 115000 | 0.6961          |
  | 0.7265        | 10.27 | 117500 | 0.7006          |
  | 0.7284        | 10.49 | 120000 | 0.6941          |
  | 0.724         | 10.7  | 122500 | 0.6887          |
  | 0.7266        | 10.92 | 125000 | 0.6931          |
  | 0.7051        | 11.14 | 127500 | 0.6846          |
  | 0.7106        | 11.36 | 130000 | 0.6816          |
  | 0.7011        | 11.58 | 132500 | 0.6830          |
  | 0.6997        | 11.8  | 135000 | 0.6784          |
  | 0.6969        | 12.02 | 137500 | 0.6734          |
  | 0.6968        | 12.23 | 140000 | 0.6709          |
  | 0.6867        | 12.45 | 142500 | 0.6656          |
  | 0.6925        | 12.67 | 145000 | 0.6661          |
  | 0.6795        | 12.89 | 147500 | 0.6606          |
  | 0.6774        | 13.11 | 150000 | 0.6617          |
  | 0.6756        | 13.33 | 152500 | 0.6563          |
  | 0.6728        | 13.54 | 155000 | 0.6547          |
  | 0.6732        | 13.76 | 157500 | 0.6520          |
  | 0.6704        | 13.98 | 160000 | 0.6492          |
  | 0.6666        | 14.2  | 162500 | 0.6446          |
  | 0.6615        | 14.42 | 165000 | 0.6488          |
  | 0.6638        | 14.64 | 167500 | 0.6523          |
  | 0.6588        | 14.85 | 170000 | 0.6415          |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.10.1+cu113
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  