readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - klue
  metrics:
  - accuracy
  model-index:
  - name: kcbert-base-finetuned
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: klue
        type: klue
        args: ynat
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8329856154606347
  ---
  
  <!-- 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. -->
  
  # kcbert-base-finetuned
  
  This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the klue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7393
  - Accuracy: 0.8330
  
  ## 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: 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.4612        | 1.0   | 2855  | 0.5216          | 0.8143   |
  | 0.3061        | 2.0   | 5710  | 0.5130          | 0.8248   |
  | 0.2129        | 3.0   | 8565  | 0.6062          | 0.8257   |
  | 0.1337        | 4.0   | 11420 | 0.7393          | 0.8330   |
  | 0.0653        | 5.0   | 14275 | 0.8651          | 0.8302   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.0+cu111
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  