readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: bert-large-pt-archive
    results:
    - task:
        name: Token Classification
        type: token-classification
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9766762474673703
  ---
  
  <!-- 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. -->
  
  # bert-large-pt-archive
  
  This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unkown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0869
  - Precision: 0.9280
  - Recall: 0.9541
  - F1: 0.9409
  - Accuracy: 0.9767
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0665        | 1.0   | 765  | 0.1020          | 0.8928    | 0.9566 | 0.9236 | 0.9696   |
  | 0.0392        | 2.0   | 1530 | 0.0781          | 0.9229    | 0.9586 | 0.9404 | 0.9757   |
  | 0.0201        | 3.0   | 2295 | 0.0809          | 0.9278    | 0.9550 | 0.9412 | 0.9767   |
  | 0.0152        | 4.0   | 3060 | 0.0869          | 0.9280    | 0.9541 | 0.9409 | 0.9767   |
  
  
  ### Framework versions
  
  - Transformers 4.10.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.10.2
  - Tokenizers 0.10.3
  