readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: 8_koelectra-base-v3-finetuned-korquad_augment_korquad-1_2_aihub-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_koelectra-base-v3-finetuned-korquad_augment_korquad-1_2_aihub-final
  
  This model is a fine-tuned version of [monologg/koelectra-base-v3-finetuned-korquad](https://huggingface.co/monologg/koelectra-base-v3-finetuned-korquad) on the None dataset.
  It achieves the following results on the evaluation set:
  - Exact Match: 68.4291
  - F1: 83.2037
  - Loss: 1.0459
  
  ## 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 |
  |:-------------:|:-----:|:------:|:-----------:|:-------:|:---------------:|
  | 1.7364        | 0.16  | 6000   | 61.8627     | 78.1626 | 1.2363          |
  | 1.129         | 0.33  | 12000  | 64.0978     | 79.8857 | 1.1445          |
  | 1.0497        | 0.49  | 18000  | 64.9327     | 80.4719 | 1.1084          |
  | 1.0207        | 0.65  | 24000  | 65.2334     | 80.6901 | 1.0947          |
  | 1.0206        | 0.82  | 30000  | 65.4443     | 80.9532 | 1.0732          |
  | 1.0106        | 0.98  | 36000  | 65.7271     | 81.1378 | 1.0879          |
  | 0.9736        | 1.15  | 42000  | 66.0189     | 81.3277 | 1.0820          |
  | 0.9575        | 1.31  | 48000  | 66.1804     | 81.5178 | 1.0547          |
  | 0.9448        | 1.47  | 54000  | 66.3330     | 81.6589 | 1.0430          |
  | 0.9513        | 1.64  | 60000  | 66.5485     | 81.5404 | 1.0752          |
  | 0.9463        | 1.8   | 66000  | 66.6607     | 81.7489 | 1.0430          |
  | 0.9334        | 1.96  | 72000  | 66.9255     | 81.9937 | 1.0244          |
  | 0.906         | 2.13  | 78000  | 66.9120     | 81.9072 | 1.0225          |
  | 0.9177        | 2.29  | 84000  | 66.8671     | 82.0070 | 1.0342          |
  | 0.9157        | 2.45  | 90000  | 67.0422     | 82.0179 | 1.0234          |
  | 0.8941        | 2.62  | 96000  | 67.3250     | 82.2431 | 1.0176          |
  | 0.896         | 2.78  | 102000 | 67.0781     | 82.0828 | 1.0195          |
  | 0.8949        | 2.95  | 108000 | 67.3205     | 82.3027 | 1.0039          |
  | 0.878         | 3.11  | 114000 | 67.3160     | 82.3084 | 1.0088          |
  | 0.8676        | 3.27  | 120000 | 67.5224     | 82.3786 | 1.0137          |
  | 0.8743        | 3.44  | 126000 | 67.5583     | 82.3468 | 1.0049          |
  | 0.8779        | 3.6   | 132000 | 67.3968     | 82.3132 | 1.0146          |
  | 0.872         | 3.76  | 138000 | 67.6212     | 82.4477 | 1.0098          |
  | 0.868         | 3.93  | 144000 | 67.7244     | 82.5110 | 1.0068          |
  | 0.8539        | 4.09  | 150000 | 67.7154     | 82.4795 | 1.0195          |
  | 0.8434        | 4.25  | 156000 | 67.5718     | 82.4538 | 1.0166          |
  | 0.8552        | 4.42  | 162000 | 67.9039     | 82.6227 | 1.0293          |
  | 0.8443        | 4.58  | 168000 | 67.7513     | 82.6146 | 1.0039          |
  | 0.8452        | 4.75  | 174000 | 67.8411     | 82.6710 | 1.0156          |
  | 0.856         | 4.91  | 180000 | 67.8366     | 82.7559 | 0.9932          |
  | 0.8402        | 5.07  | 186000 | 67.7558     | 82.5685 | 1.0146          |
  | 0.8191        | 5.24  | 192000 | 67.9174     | 82.7676 | 1.0059          |
  | 0.8216        | 5.4   | 198000 | 67.7917     | 82.6217 | 1.0186          |
  | 0.8366        | 5.56  | 204000 | 67.7154     | 82.6135 | 1.0059          |
  | 0.8341        | 5.73  | 210000 | 68.0341     | 82.7362 | 0.9985          |
  | 0.8191        | 5.89  | 216000 | 67.9847     | 82.7773 | 1.0059          |
  | 0.8114        | 6.05  | 222000 | 67.9578     | 82.7099 | 1.0             |
  | 0.8117        | 6.22  | 228000 | 67.7469     | 82.6885 | 1.0020          |
  | 0.8174        | 6.38  | 234000 | 68.0745     | 82.9214 | 1.0146          |
  | 0.8094        | 6.55  | 240000 | 68.0296     | 82.9424 | 1.0107          |
  | 0.7991        | 6.71  | 246000 | 67.9578     | 82.8998 | 1.0010          |
  | 0.8193        | 6.87  | 252000 | 67.9803     | 82.9003 | 0.9951          |
  | 0.8075        | 7.04  | 258000 | 68.0566     | 82.8525 | 0.9961          |
  | 0.7876        | 7.2   | 264000 | 68.0835     | 82.8723 | 1.0098          |
  | 0.7874        | 7.36  | 270000 | 68.1822     | 82.8987 | 1.0195          |
  | 0.7941        | 7.53  | 276000 | 68.1912     | 82.9071 | 1.0107          |
  | 0.8024        | 7.6