readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2002
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-base-bne-finetuned-ner-finetuned2-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2002
        type: conll2002
        args: es
      metrics:
      - name: Precision
        type: precision
        value: 0.8697727272727273
      - name: Recall
        type: recall
        value: 0.8793658088235294
      - name: F1
        type: f1
        value: 0.8745429616087752
      - name: Accuracy
        type: accuracy
        value: 0.9808778791829639
  ---
  
  <!-- 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. -->
  
  # roberta-base-bne-finetuned-ner-finetuned2-ner
  
  This model is a fine-tuned version of [StivenLancheros/roberta-base-bne-finetuned-ner](https://huggingface.co/StivenLancheros/roberta-base-bne-finetuned-ner) on the conll2002 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1067
  - Precision: 0.8698
  - Recall: 0.8794
  - F1: 0.8745
  - Accuracy: 0.9809
  
  ## 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: 3e-05
  - train_batch_size: 5
  - eval_batch_size: 5
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0582        | 1.0   | 1665 | 0.0852          | 0.8697    | 0.8759 | 0.8728 | 0.9800   |
  | 0.0297        | 2.0   | 3330 | 0.0919          | 0.8841    | 0.8867 | 0.8854 | 0.9817   |
  | 0.0121        | 3.0   | 4995 | 0.0950          | 0.8751    | 0.8807 | 0.8779 | 0.9812   |
  | 0.0056        | 4.0   | 6660 | 0.1067          | 0.8698    | 0.8794 | 0.8745 | 0.9809   |
  
  
  ### Framework versions
  
  - Transformers 4.12.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.15.1
  - Tokenizers 0.10.3
  