readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - nsmc
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: kcbert-base-finetuned-nsmc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: nsmc
        type: nsmc
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.90198
      - name: F1
        type: f1
        value: 0.9033161705233671
      - name: Recall
        type: recall
        value: 0.9095062169785088
      - name: Precision
        type: precision
        value: 0.8972098126812446
  ---
  
  <!-- 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-nsmc
  
  This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the nsmc dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4197
  - Accuracy: 0.9020
  - F1: 0.9033
  - Recall: 0.9095
  - Precision: 0.8972
  
  ## 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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.3028        | 0.32  | 3000  | 0.2994          | 0.8769   | 0.8732 | 0.8422 | 0.9066    |
  | 0.2833        | 0.64  | 6000  | 0.2766          | 0.8880   | 0.8844 | 0.8512 | 0.9203    |
  | 0.2719        | 0.96  | 9000  | 0.2527          | 0.8980   | 0.8981 | 0.8933 | 0.9030    |
  | 0.1938        | 1.28  | 12000 | 0.2934          | 0.8969   | 0.8965 | 0.8869 | 0.9062    |
  | 0.1907        | 1.6   | 15000 | 0.3141          | 0.8992   | 0.8999 | 0.9003 | 0.8996    |
  | 0.1824        | 1.92  | 18000 | 0.3537          | 0.8986   | 0.8964 | 0.8711 | 0.9232    |
  | 0.1261        | 2.24  | 21000 | 0.4197          | 0.9020   | 0.9033 | 0.9095 | 0.8972    |
  | 0.1237        | 2.56  | 24000 | 0.4170          | 0.8995   | 0.9017 | 0.9156 | 0.8882    |
  | 0.1182        | 2.88  | 27000 | 0.4165          | 0.9020   | 0.9036 | 0.9130 | 0.8945    |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  