readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - klue
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: KR-FinBert-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: klue
        type: klue
        config: ner
        split: train
        args: ner
      metrics:
      - name: Precision
        type: precision
        value: 0.70817831734221
      - name: Recall
        type: recall
        value: 0.7610296696359683
      - name: F1
        type: f1
        value: 0.7336533910338766
      - name: Accuracy
        type: accuracy
        value: 0.9504335292160994
  ---
  
  <!-- 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. -->
  
  # KR-FinBert-finetuned-ner
  
  This model is a fine-tuned version of [snunlp/KR-FinBert](https://huggingface.co/snunlp/KR-FinBert) on the klue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1634
  - Precision: 0.7082
  - Recall: 0.7610
  - F1: 0.7337
  - Accuracy: 0.9504
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2028        | 1.0   | 1313 | 0.1852          | 0.6650    | 0.7060 | 0.6849 | 0.9406   |
  | 0.1232        | 2.0   | 2626 | 0.1627          | 0.7028    | 0.7459 | 0.7237 | 0.9487   |
  | 0.0942        | 3.0   | 3939 | 0.1634          | 0.7082    | 0.7610 | 0.7337 | 0.9504   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  