readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: bert-portuguese-ner-archive
    results:
    - task:
        name: Token Classification
        type: token-classification
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9700325118974698
  ---
  
  <!-- 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-portuguese-ner-archive
  
  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased)
  It achieves the following results on the evaluation set:
  - Loss: 0.1140
  - Precision: 0.9147
  - Recall: 0.9483
  - F1: 0.9312
  - Accuracy: 0.9700
  
  ## Model description
  
  This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization
  
  ### Datasets
  
  All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/
  
  
  ### 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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 192  | 0.1438          | 0.8917    | 0.9392 | 0.9148 | 0.9633   |
  | 0.2454        | 2.0   | 384  | 0.1222          | 0.8985    | 0.9417 | 0.9196 | 0.9671   |
  | 0.0526        | 3.0   | 576  | 0.1098          | 0.9150    | 0.9481 | 0.9312 | 0.9698   |
  | 0.0372        | 4.0   | 768  | 0.1140          | 0.9147    | 0.9483 | 0.9312 | 0.9700   |
  
  
  ### Framework versions
  
  - Transformers 4.10.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.10.2
  - Tokenizers 0.10.3
  ### Citation
  
  @InProceedings{10.1007/978-3-031-04819-7_33,
  author="da Costa Cunha, Lu{\'i}s Filipe
  and Ramalho, Jos{\'e} Carlos",
  editor="Rocha, Alvaro
  and Adeli, Hojjat
  and Dzemyda, Gintautas
  and Moreira, Fernando",
  title="NER in Archival Finding Aids: Next Level",
  booktitle="Information Systems and Technologies",
  year="2022",
  publisher="Springer International Publishing",
  address="Cham",
  pages="333--342",
  isbn="978-3-031-04819-7"
  }
  
  
  