readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: gpt_16_5_5.6e-5_lp5_nb10
    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. -->
  
  # gpt_16_5_5.6e-5_lp5_nb10
  
  This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.0126
  
  ## 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: 5.6e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.1407        | 0.38  | 1000  | 4.0864          |
  | 3.1639        | 0.76  | 2000  | 4.0867          |
  | 2.87          | 1.13  | 3000  | 4.0611          |
  | 2.8591        | 1.51  | 4000  | 4.0315          |
  | 2.8866        | 1.89  | 5000  | 4.0079          |
  | 2.6146        | 2.27  | 6000  | 4.0153          |
  | 2.6576        | 2.64  | 7000  | 4.0021          |
  | 2.5361        | 3.02  | 8000  | 4.0106          |
  | 2.4552        | 3.4   | 9000  | 4.0137          |
  | 2.4595        | 3.78  | 10000 | 3.9967          |
  | 2.3286        | 4.15  | 11000 | 4.0204          |
  | 2.2805        | 4.53  | 12000 | 4.0154          |
  | 2.3098        | 4.91  | 13000 | 4.0126          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.9.0+cu102
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  