readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - tapaco
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: punctuation-taboa-bert
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: tapaco
        type: tapaco
        config: all_languages
        split: train
        args: all_languages
      metrics:
      - name: Precision
        type: precision
        value: 0.9849559686888454
      - name: Recall
        type: recall
        value: 0.9836325882496642
      - name: F1
        type: f1
        value: 0.9842938336490864
      - name: Accuracy
        type: accuracy
        value: 0.9945622875893589
  ---
  
  <!-- 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. -->
  
  # punctuation-taboa-bert
  
  This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the tapaco dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0181
  - Precision: 0.9850
  - Recall: 0.9836
  - F1: 0.9843
  - Accuracy: 0.9946
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0272        | 1.0   | 17438 | 0.0181          | 0.9850    | 0.9836 | 0.9843 | 0.9946   |
  | 0.0234        | 2.0   | 34876 | 0.0196          | 0.9870    | 0.9853 | 0.9862 | 0.9948   |
  | 0.0092        | 3.0   | 52314 | 0.0233          | 0.9874    | 0.9853 | 0.9864 | 0.9950   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.13.1
  