readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - xnli
  metrics:
  - accuracy
  model-index:
  - name: spanish-TinyBERT-betito-finetuned-xnli-es
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: xnli
        type: xnli
        args: es
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7475049900199601
  ---
  
  <!-- 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. -->
  
  # spanish-TinyBERT-betito-finetuned-xnli-es
  
  This model is a fine-tuned version of [mrm8488/spanish-TinyBERT-betito](https://huggingface.co/mrm8488/spanish-TinyBERT-betito) on the xnli dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7104
  - Accuracy: 0.7475
  
  ## 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: 2.50838112218154e-05
  - train_batch_size: 8
  - eval_batch_size: 64
  - seed: 13
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|
  | 0.7191        | 1.0   | 49399  | 0.6829          | 0.7112   |
  | 0.6323        | 2.0   | 98798  | 0.6527          | 0.7305   |
  | 0.5727        | 3.0   | 148197 | 0.6531          | 0.7465   |
  | 0.4964        | 4.0   | 197596 | 0.7079          | 0.7427   |
  | 0.4929        | 5.0   | 246995 | 0.7104          | 0.7475   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  