readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: xlmr_mask_punctuation
    results: []
  ---
  
  <!-- 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. -->
  
  # xlmr_mask_punctuation
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5160
  
  ## 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: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 2.6352        | 0.05  | 500  | 1.4744          |
  | 1.4623        | 0.11  | 1000 | 1.0987          |
  | 1.1947        | 0.16  | 1500 | 1.1878          |
  | 1.0693        | 0.21  | 2000 | 0.8077          |
  | 0.9465        | 0.26  | 2500 | 0.8038          |
  | 0.8394        | 0.32  | 3000 | 0.7772          |
  | 0.8184        | 0.37  | 3500 | 0.8529          |
  | 0.7773        | 0.42  | 4000 | 0.6255          |
  | 0.7338        | 0.47  | 4500 | 0.6993          |
  | 0.6935        | 0.53  | 5000 | 0.5952          |
  | 0.6713        | 0.58  | 5500 | 0.5605          |
  | 0.6636        | 0.63  | 6000 | 0.6588          |
  | 0.6169        | 0.68  | 6500 | 0.5154          |
  | 0.6045        | 0.74  | 7000 | 0.5374          |
  | 0.5853        | 0.79  | 7500 | 0.5033          |
  | 0.5752        | 0.84  | 8000 | 0.5002          |
  | 0.5263        | 0.89  | 8500 | 0.5300          |
  | 0.5512        | 0.95  | 9000 | 0.5138          |
  | 0.541         | 1.0   | 9500 | 0.5160          |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  