readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - nubes
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Negation_Scope_Detection_NubEs_Spanish_mBERT_fine_tuned
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: nubes
        type: nubes
        config: Nubes
        split: train
        args: Nubes
      metrics:
      - name: Precision
        type: precision
        value: 0.9012345679012346
      - name: Recall
        type: recall
        value: 0.9184315370098554
      - name: F1
        type: f1
        value: 0.909751791463288
      - name: Accuracy
        type: accuracy
        value: 0.9743603468579382
  ---
  
  <!-- 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. -->
  
  # Negation_Scope_Detection_NubEs_Spanish_mBERT_fine_tuned
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the nubes dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1624
  - Precision: 0.9012
  - Recall: 0.9184
  - F1: 0.9098
  - Accuracy: 0.9744
  
  ## 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: 5e-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: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1802        | 1.0   | 1726  | 0.1849          | 0.7843    | 0.8526 | 0.8170 | 0.9509   |
  | 0.1216        | 2.0   | 3452  | 0.1512          | 0.8706    | 0.8352 | 0.8525 | 0.9579   |
  | 0.0817        | 3.0   | 5178  | 0.1083          | 0.8845    | 0.9038 | 0.8940 | 0.9710   |
  | 0.0517        | 4.0   | 6904  | 0.1314          | 0.8858    | 0.8960 | 0.8909 | 0.9693   |
  | 0.0265        | 5.0   | 8630  | 0.1514          | 0.8963    | 0.9079 | 0.9021 | 0.9721   |
  | 0.0136        | 6.0   | 10356 | 0.1524          | 0.9045    | 0.9092 | 0.9068 | 0.9729   |
  | 0.0045        | 7.0   | 12082 | 0.1624          | 0.9012    | 0.9184 | 0.9098 | 0.9744   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  