readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: koelectra-base-v3-finetuned-korquad-finetuned-klue5
    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. -->
  
  # koelectra-base-v3-finetuned-korquad-finetuned-klue5
  
  This model is a fine-tuned version of [monologg/koelectra-base-v3-finetuned-korquad](https://huggingface.co/monologg/koelectra-base-v3-finetuned-korquad) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.0979
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 1.8314        | 0.36  | 500  | 1.5168          |
  | 1.5231        | 0.71  | 1000 | 1.4084          |
  | 1.3849        | 1.07  | 1500 | 1.4020          |
  | 1.0592        | 1.42  | 2000 | 1.4384          |
  | 1.0728        | 1.78  | 2500 | 1.3936          |
  | 0.9789        | 2.14  | 3000 | 1.6275          |
  | 0.8857        | 2.49  | 3500 | 1.5426          |
  | 0.8928        | 2.85  | 4000 | 1.5594          |
  | 0.8502        | 3.21  | 4500 | 1.6085          |
  | 0.8375        | 3.56  | 5000 | 1.6470          |
  | 0.8507        | 3.92  | 5500 | 1.7148          |
  | 0.7725        | 4.27  | 6000 | 1.8683          |
  | 0.7574        | 4.63  | 6500 | 1.7876          |
  | 0.8021        | 4.99  | 7000 | 1.8254          |
  | 0.7435        | 5.34  | 7500 | 2.0119          |
  | 0.7409        | 5.7   | 8000 | 2.0003          |
  | 0.7355        | 6.05  | 8500 | 2.0860          |
  | 0.6941        | 6.41  | 9000 | 2.1488          |
  | 0.6914        | 6.77  | 9500 | 2.0979          |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  