readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: 8_roberta-large_train_korquad-1_aihub10_final
    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_aihub10_final
  
  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: 82.0646
  - F1: 89.8532
  - Loss: 0.6855
  
  ## 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 |
  |:-------------:|:-----:|:------:|:-----------:|:-------:|:---------------:|
  | 5.2079        | 0.09  | 1000   | 16.8859     | 22.6297 | 3.7930          |
  | 1.6052        | 0.18  | 2000   | 69.8685     | 80.7048 | 1.0508          |
  | 0.981         | 0.27  | 3000   | 74.8904     | 85.0342 | 0.8213          |
  | 0.8183        | 0.36  | 4000   | 77.0028     | 86.6159 | 0.6270          |
  | 0.6779        | 0.45  | 5000   | 77.8663     | 87.1009 | 0.6006          |
  | 0.6535        | 0.54  | 6000   | 78.4775     | 87.4922 | 0.5645          |
  | 0.6171        | 0.63  | 7000   | 78.9159     | 87.7604 | 0.5454          |
  | 0.5953        | 0.72  | 8000   | 79.2082     | 87.9666 | 0.5474          |
  | 0.5967        | 0.81  | 9000   | 79.1949     | 88.0747 | 0.5449          |
  | 0.6081        | 0.9   | 10000  | 79.6067     | 88.4190 | 0.5483          |
  | 0.5652        | 0.99  | 11000  | 79.8857     | 88.4750 | 0.5527          |
  | 0.5434        | 1.08  | 12000  | 79.5005     | 88.3638 | 0.5483          |
  | 0.5618        | 1.17  | 13000  | 79.8592     | 88.6312 | 0.5396          |
  | 0.5301        | 1.26  | 14000  | 80.2312     | 88.7555 | 0.5229          |
  | 0.5335        | 1.35  | 15000  | 80.4172     | 88.9265 | 0.5171          |
  | 0.5           | 1.44  | 16000  | 80.4172     | 89.0081 | 0.5107          |
  | 0.5207        | 1.53  | 17000  | 80.6563     | 89.1881 | 0.5117          |
  | 0.4872        | 1.62  | 18000  | 80.5899     | 88.9920 | 0.5259          |
  | 0.5139        | 1.71  | 19000  | 80.4703     | 88.8763 | 0.5186          |
  | 0.5104        | 1.8   | 20000  | 80.2976     | 88.8165 | 0.5151          |
  | 0.4917        | 1.89  | 21000  | 80.6032     | 88.9459 | 0.5312          |
  | 0.5142        | 1.98  | 22000  | 80.6696     | 88.9736 | 0.5181          |
  | 0.4655        | 2.07  | 23000  | 80.9619     | 89.1750 | 0.5112          |
  | 0.4731        | 2.16  | 24000  | 81.0947     | 89.2199 | 0.4971          |
  | 0.4563        | 2.25  | 25000  | 81.0416     | 89.1501 | 0.5015          |
  | 0.4447        | 2.34  | 26000  | 81.1080     | 89.3572 | 0.5049          |
  | 0.4751        | 2.43  | 27000  | 81.1612     | 89.2913 | 0.5020          |
  | 0.4647        | 2.52  | 28000  | 81.2010     | 89.3781 | 0.4971          |
  | 0.4672        | 2.61  | 29000  | 80.9220     | 89.1665 | 0.5073          |
  | 0.4628        | 2.7   | 30000  | 81.2807     | 89.5747 | 0.5156          |
  | 0.4397        | 2.79  | 31000  | 81.4269     | 89.5239 | 0.5098          |
  | 0.4561        | 2.88  | 32000  | 81.5597     | 89.5456 | 0.4966          |
  | 0.4449        | 2.97  | 33000  | 81.5730     | 89.6600 | 0.4973          |
  | 0.435         | 3.06  | 34000  | 81.2143     | 89.4132 | 0.5020          |
  | 0.4271        | 3.15  | 35000  | 81.5464     | 89.6768 | 0.4956          |
  | 0.43          | 3.24  | 36000  | 81.5597     | 89.6178 | 0.5220          |
  | 0.4348        | 3.33  | 37000  | 81.3737     | 89.5019 | 0.5107          |
  | 0.4215        | 3.42  | 38000  | 81.5730     | 89.6766 | 0.4922          |
  | 0.4287        | 3.51  | 39000  | 81.6660     | 89.7750 | 0.5166          |
  | 0.4235        | 3.6   | 40000  | 81.5996     | 89.6392 | 0.4932          |
  | 0.4314        | 3.69  | 41000  | 81.5996     | 89.7113 | 0.4995          |
  | 0.4326        | 3.77  | 42000  | 81.5863     | 89.7004 | 0.4949          |
  | 0.4175        | 3.86  | 43000  | 81.7324     | 89.6672 | 0.4966          |
  | 0.4327        | 3.95  | 44000  | 81.5066     | 89.7201 | 0.4883          |
  | 0.4266        | 4.04  | 45000  | 81.6660     | 89.8173 | 0.5186          |
  | 0.4145        | 4.13  | 46000  | 81.2940     | 89.6006 | 0.4966          |
  | 0.3995        | 4.22  | 47000  | 81.7191     | 89.6950 | 0.4963          |
  | 0.3853        | 4.31  | 48000  | 81.8387  