readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: koBERT-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. -->
  
  # koBERT-finetuned-wholemasking20
  
  This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4010
  
  ## 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        | 0.15  | 500   | 0.4369          |
  | No log        | 0.29  | 1000  | 0.4280          |
  | No log        | 0.44  | 1500  | 0.4214          |
  | No log        | 0.58  | 2000  | 0.4159          |
  | No log        | 0.73  | 2500  | 0.4144          |
  | No log        | 0.87  | 3000  | 0.4106          |
  | 0.474         | 1.02  | 3500  | 0.4142          |
  | 0.474         | 1.16  | 4000  | 0.4106          |
  | 0.474         | 1.31  | 4500  | 0.4106          |
  | 0.474         | 1.45  | 5000  | 0.4101          |
  | 0.474         | 1.6   | 5500  | 0.4087          |
  | 0.474         | 1.75  | 6000  | 0.4070          |
  | 0.474         | 1.89  | 6500  | 0.4065          |
  | 0.4122        | 2.04  | 7000  | 0.4088          |
  | 0.4122        | 2.18  | 7500  | 0.4073          |
  | 0.4122        | 2.33  | 8000  | 0.4058          |
  | 0.4122        | 2.47  | 8500  | 0.4025          |
  | 0.4122        | 2.62  | 9000  | 0.4032          |
  | 0.4122        | 2.76  | 9500  | 0.4062          |
  | 0.4122        | 2.91  | 10000 | 0.4059          |
  | 0.4081        | 3.05  | 10500 | 0.4040          |
  | 0.4081        | 3.2   | 11000 | 0.3993          |
  | 0.4081        | 3.35  | 11500 | 0.3982          |
  | 0.4081        | 3.49  | 12000 | 0.4041          |
  | 0.4081        | 3.64  | 12500 | 0.4026          |
  | 0.4081        | 3.78  | 13000 | 0.4009          |
  | 0.4081        | 3.93  | 13500 | 0.4011          |
  | 0.4041        | 4.07  | 14000 | 0.4001          |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  