readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: portuguese-archival-finding-aids
    results:
    - task:
        name: Token Classification
        type: token-classification
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9617770479839446
  ---
  
  <!-- 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. -->
  
  # portuguese-archival-finding-aids
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1812
  - Precision: 0.8624
  - Recall: 0.9557
  - F1: 0.9067
  - Accuracy: 0.9618
  
  ## 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
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 192  | 0.1565          | 0.8511    | 0.9327 | 0.8900 | 0.9563   |
  | 0.1849        | 2.0   | 384  | 0.1594          | 0.8634    | 0.9543 | 0.9065 | 0.9619   |
  | 0.0454        | 3.0   | 576  | 0.1812          | 0.8624    | 0.9557 | 0.9067 | 0.9618   |
  
  
  ### Framework versions
  
  - Transformers 4.10.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.10.2
  - Tokenizers 0.10.3
  