readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: korean_sentiment_analysis_dataset2_best2
    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. -->
  
  # korean_sentiment_analysis_dataset2_best2
  
  This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7740
  - Micro f1 score: 72.1152
  - Auprc: 71.8249
  - Accuracy: 0.7212
  
  ## 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: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Micro f1 score | Auprc   | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:-------:|:--------:|
  | 1.7701        | 0.12  | 100  | 1.4289          | 46.2971        | 43.4539 | 0.4630   |
  | 1.1903        | 0.24  | 200  | 0.9895          | 64.3811        | 63.2681 | 0.6438   |
  | 0.9564        | 0.36  | 300  | 0.8893          | 67.9333        | 69.4034 | 0.6793   |
  | 0.9108        | 0.49  | 400  | 0.8565          | 69.6017        | 71.7887 | 0.6960   |
  | 0.8831        | 0.61  | 500  | 0.8922          | 68.3477        | 72.4941 | 0.6835   |
  | 0.871         | 0.73  | 600  | 0.8252          | 70.3606        | 73.2323 | 0.7036   |
  | 0.8478        | 0.85  | 700  | 0.8490          | 70.1668        | 73.9074 | 0.7017   |
  | 0.8447        | 0.97  | 800  | 0.8024          | 71.4424        | 74.2617 | 0.7144   |
  | 0.7889        | 1.09  | 900  | 0.8192          | 71.8999        | 74.9934 | 0.7190   |
  | 0.7439        | 1.22  | 1000 | 0.7974          | 71.6738        | 74.6182 | 0.7167   |
  | 0.7329        | 1.34  | 1100 | 0.7874          | 71.7277        | 75.9467 | 0.7173   |
  | 0.7407        | 1.46  | 1200 | 0.8042          | 71.8730        | 75.3910 | 0.7187   |
  | 0.7416        | 1.58  | 1300 | 0.7787          | 72.5511        | 75.3113 | 0.7255   |
  | 0.7343        | 1.7   | 1400 | 0.7743          | 72.4273        | 75.7219 | 0.7243   |
  | 0.719         | 1.82  | 1500 | 0.7755          | 72.4973        | 75.9991 | 0.7250   |
  | 0.7274        | 1.95  | 1600 | 0.7684          | 72.8956        | 76.3442 | 0.7290   |
  | 0.6441        | 2.07  | 1700 | 0.7774          | 73.1593        | 76.5503 | 0.7316   |
  | 0.5555        | 2.19  | 1800 | 0.8335          | 72.1206        | 75.9935 | 0.7212   |
  | 0.5714        | 2.31  | 1900 | 0.7817          | 73.1216        | 76.1657 | 0.7312   |
  | 0.559         | 2.43  | 2000 | 0.7965          | 72.3735        | 76.0846 | 0.7237   |
  | 0.5633        | 2.55  | 2100 | 0.7959          | 72.9494        | 76.2179 | 0.7295   |
  | 0.5708        | 2.68  | 2200 | 0.7862          | 73.2939        | 76.8286 | 0.7329   |
  | 0.5573        | 2.8   | 2300 | 0.7899          | 73.2885        | 76.6268 | 0.7329   |
  | 0.5827        | 2.92  | 2400 | 0.7990          | 73.1324        | 76.5779 | 0.7313   |
  | 0.5083        | 3.04  | 2500 | 0.8681          | 72.7503        | 76.9610 | 0.7275   |
  | 0.3871        | 3.16  | 2600 | 0.9124          | 72.6749        | 76.2192 | 0.7267   |
  | 0.4003        | 3.28  | 2700 | 0.8817          | 72.7718        | 75.8442 | 0.7277   |
  | 0.4127        | 3.41  | 2800 | 0.8814          | 71.7492        | 75.6727 | 0.7175   |
  | 0.3965        | 3.53  | 2900 | 0.8819          | 72.7287        | 75.8708 | 0.7273   |
  | 0.4132        | 3.65  | 3000 | 0.8942          | 72.4273        | 75.8448 | 0.7243   |
  | 0.4042        | 3.77  | 3100 | 0.8820          | 72.6211        | 75.8217 | 0.7262   |
  | 0.3926        | 3.89  | 3200 | 0.9042          | 71.9914        | 76.2493 | 0.7199   |
  | 0.3904        | 4.01  | 3300 | 0.9631          | 72.8149        | 76.0438 | 0.7281   |
  | 0.2472        | 4.14  | 3400 | 1.0481          | 71.5608        | 75.0840 | 0.7156   |
  | 0.2569        | 4.26  | 3500 | 1.0334          | 72.2336        | 74.9394 | 0.7223   |
  | 0.2573        | 4.38  | 3600 | 1.0519          | 72.0237        | 74.7108 | 0.7202   |
  | 0.2624        | 4.5   | 3700 | 1.0345          | 72.2982        | 75.1365 | 0.7230   |
  | 0.2565        | 4.62  | 3800 | 1.0399          | 71.9914        | 74.8740 | 0.7199   |
  | 0.2628        | 4.74  | 3900 | 1.0436          | 71.6900        | 74.4578 | 0.7169   |
  | 0.272         | 4.87  | 4000 | 1.0666          | 71.5770        | 74.7498 | 0.7158   |
  | 0.2659        | 4