readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: roberta-large-finetuned-wholemasking20
    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. -->
  
  # roberta-large-finetuned-wholemasking20
  
  This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3625
  
  ## 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: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | No log        | 0.08  | 500   | 0.4278          |
  | No log        | 0.16  | 1000  | 0.4203          |
  | No log        | 0.24  | 1500  | 0.4130          |
  | No log        | 0.33  | 2000  | 0.4065          |
  | No log        | 0.41  | 2500  | 0.4047          |
  | No log        | 0.49  | 3000  | 0.4036          |
  | No log        | 0.57  | 3500  | 0.4022          |
  | No log        | 0.65  | 4000  | 0.3942          |
  | No log        | 0.73  | 4500  | 0.3964          |
  | No log        | 0.81  | 5000  | 0.3927          |
  | No log        | 0.89  | 5500  | 0.3911          |
  | No log        | 0.98  | 6000  | 0.3924          |
  | 0.4211        | 1.06  | 6500  | 0.3923          |
  | 0.4211        | 1.14  | 7000  | 0.3873          |
  | 0.4211        | 1.22  | 7500  | 0.3864          |
  | 0.4211        | 1.3   | 8000  | 0.3838          |
  | 0.4211        | 1.38  | 8500  | 0.3848          |
  | 0.4211        | 1.46  | 9000  | 0.3859          |
  | 0.4211        | 1.54  | 9500  | 0.3815          |
  | 0.4211        | 1.63  | 10000 | 0.3776          |
  | 0.4211        | 1.71  | 10500 | 0.3806          |
  | 0.4211        | 1.79  | 11000 | 0.3759          |
  | 0.4211        | 1.87  | 11500 | 0.3724          |
  | 0.4211        | 1.95  | 12000 | 0.3698          |
  | 0.3907        | 2.03  | 12500 | 0.3746          |
  | 0.3907        | 2.11  | 13000 | 0.3713          |
  | 0.3907        | 2.2   | 13500 | 0.3746          |
  | 0.3907        | 2.28  | 14000 | 0.3697          |
  | 0.3907        | 2.36  | 14500 | 0.3721          |
  | 0.3907        | 2.44  | 15000 | 0.3720          |
  | 0.3907        | 2.52  | 15500 | 0.3698          |
  | 0.3907        | 2.6   | 16000 | 0.3688          |
  | 0.3907        | 2.68  | 16500 | 0.3705          |
  | 0.3907        | 2.76  | 17000 | 0.3683          |
  | 0.3907        | 2.85  | 17500 | 0.3661          |
  | 0.3907        | 2.93  | 18000 | 0.3653          |
  | 0.3775        | 3.01  | 18500 | 0.3667          |
  | 0.3775        | 3.09  | 19000 | 0.3644          |
  | 0.3775        | 3.17  | 19500 | 0.3612          |
  | 0.3775        | 3.25  | 20000 | 0.3635          |
  | 0.3775        | 3.33  | 20500 | 0.3669          |
  | 0.3775        | 3.42  | 21000 | 0.3674          |
  | 0.3775        | 3.5   | 21500 | 0.3637          |
  | 0.3775        | 3.58  | 22000 | 0.3583          |
  | 0.3775        | 3.66  | 22500 | 0.3628          |
  | 0.3775        | 3.74  | 23000 | 0.3626          |
  | 0.3775        | 3.82  | 23500 | 0.3604          |
  | 0.3775        | 3.9   | 24000 | 0.3591          |
  | 0.3775        | 3.98  | 24500 | 0.3591          |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  