readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: 8_roberta-large_train_korquad-1_2_aihubf
    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. -->
  
  # 8_roberta-large_train_korquad-1_2_aihubf
  
  This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Exact Match: 70.8573
  - F1: 85.1795
  - Loss: 0.6914
  
  ## 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: 1e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Exact Match | F1      | Validation Loss |
  |:-------------:|:-----:|:------:|:-----------:|:-------:|:---------------:|
  | 1.7528        | 0.55  | 20000  | 66.9569     | 82.7721 | 0.7109          |
  | 0.6827        | 1.1   | 40000  | 69.1023     | 84.0073 | 0.6357          |
  | 0.6049        | 1.65  | 60000  | 70.0135     | 84.8732 | 0.5845          |
  | 0.5649        | 2.21  | 80000  | 70.4758     | 85.0331 | 0.5737          |
  | 0.5425        | 2.76  | 100000 | 70.3456     | 85.0041 | 0.5879          |
  | 0.5395        | 3.31  | 120000 | 70.5072     | 85.0318 | 0.5742          |
  | 0.5279        | 3.86  | 140000 | 70.7226     | 85.3219 | 0.5708          |
  | 0.4925        | 4.41  | 160000 | 70.9425     | 85.3718 | 0.5713          |
  | 0.4861        | 4.96  | 180000 | 71.0144     | 85.4729 | 0.5630          |
  | 0.4813        | 5.51  | 200000 | 70.7496     | 85.3388 | 0.5757          |
  | 0.4819        | 6.06  | 220000 | 71.1580     | 85.4708 | 0.5884          |
  | 0.4481        | 6.62  | 240000 | 71.1311     | 85.4844 | 0.5850          |
  | 0.4404        | 7.17  | 260000 | 71.2118     | 85.4463 | 0.5986          |
  | 0.4452        | 7.72  | 280000 | 71.0009     | 85.3122 | 0.5947          |
  | 0.4338        | 8.27  | 300000 | 71.1984     | 85.4052 | 0.6113          |
  | 0.4144        | 8.82  | 320000 | 71.2433     | 85.4699 | 0.6001          |
  | 0.4016        | 9.37  | 340000 | 71.2522     | 85.4297 | 0.6099          |
  | 0.4122        | 9.92  | 360000 | 71.1715     | 85.3448 | 0.5923          |
  | 0.3966        | 10.47 | 380000 | 71.1984     | 85.4874 | 0.6240          |
  | 0.3825        | 11.03 | 400000 | 71.4093     | 85.5420 | 0.6309          |
  | 0.3639        | 11.58 | 420000 | 70.9336     | 85.3509 | 0.6431          |
  | 0.3728        | 12.13 | 440000 | 70.9425     | 85.2109 | 0.6562          |
  | 0.3655        | 12.68 | 460000 | 71.0503     | 85.3442 | 0.6543          |
  | 0.3476        | 13.23 | 480000 | 71.0637     | 85.3476 | 0.6963          |
  | 0.332         | 13.78 | 500000 | 70.9560     | 85.2729 | 0.6963          |
  | 0.337         | 14.33 | 520000 | 70.5700     | 85.1620 | 0.7109          |
  | 0.3415        | 14.89 | 540000 | 70.8573     | 85.1795 | 0.6914          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  