readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: xlm-roberta-large-xnli-finetuned-mnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8548888888888889
  ---
  
  <!-- 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. -->
  
  # xlm-roberta-large-xnli-finetuned-mnli
  
  This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2542
  - Accuracy: 0.8549
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.7468        | 1.0   | 2250  | 0.8551          | 0.8348   |
  | 0.567         | 2.0   | 4500  | 0.8935          | 0.8377   |
  | 0.318         | 3.0   | 6750  | 0.9892          | 0.8492   |
  | 0.1146        | 4.0   | 9000  | 1.2373          | 0.8446   |
  | 0.0383        | 5.0   | 11250 | 1.2542          | 0.8549   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.0
  - Tokenizers 0.12.1
  