readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - klue
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: koelectra-base-v3-discriminator-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: klue
        type: klue
        args: ner
      metrics:
      - name: Precision
        type: precision
        value: 0.6665182546749777
      - name: Recall
        type: recall
        value: 0.7350073648032546
      - name: F1
        type: f1
        value: 0.6990893625537877
      - name: Accuracy
        type: accuracy
        value: 0.9395764497172635
  ---
  
  <!-- 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-discriminator-finetuned-ner
  
  This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1957
  - Precision: 0.6665
  - Recall: 0.7350
  - F1: 0.6991
  - Accuracy: 0.9396
  
  ## 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: 48
  - eval_batch_size: 48
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 438  | 0.2588          | 0.5701    | 0.6655 | 0.6141 | 0.9212   |
  | 0.4333        | 2.0   | 876  | 0.2060          | 0.6671    | 0.7134 | 0.6895 | 0.9373   |
  | 0.1944        | 3.0   | 1314 | 0.1957          | 0.6665    | 0.7350 | 0.6991 | 0.9396   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.12.0+cu102
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  