readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner-es-en
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        config: es
        split: validation
        args: es
      metrics:
      - name: Precision
        type: precision
        value: 0.8694376140239549
      - name: Recall
        type: recall
        value: 0.8933170334148329
      - name: F1
        type: f1
        value: 0.8812155806568316
      - name: Accuracy
        type: accuracy
        value: 0.9448179020644737
  ---
  
  <!-- 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-finetuned-ner-es-en
  
  This model was trained from scratch on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2564
  - Precision: 0.8694
  - Recall: 0.8933
  - F1: 0.8812
  - Accuracy: 0.9448
  
  ## 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: 8
  - eval_batch_size: 8
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2459        | 1.0   | 2500 | 0.2378          | 0.8169    | 0.8606 | 0.8382 | 0.9304   |
  | 0.1441        | 2.0   | 5000 | 0.2468          | 0.8618    | 0.8876 | 0.8745 | 0.9429   |
  | 0.0972        | 3.0   | 7500 | 0.2564          | 0.8694    | 0.8933 | 0.8812 | 0.9448   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  