readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: k40-B128-klue-roberta-large-finetuned-train_dataset
    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. -->
  
  # k40-B128-klue-roberta-large-finetuned-train_dataset
  
  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: 59.5833
  - F1: 67.7399
  - Loss: 2.0625
  
  ## 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
  - gradient_accumulation_steps: 16
  - 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: 30
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Exact Match | F1      | Validation Loss |
  |:-------------:|:-----:|:----:|:-----------:|:-------:|:---------------:|
  | 5.3337        | 0.42  | 20   | 0.8333      | 3.3629  | 4.3672          |
  | 3.4151        | 0.83  | 40   | 54.5833     | 63.9656 | 2.3438          |
  | 1.6499        | 1.25  | 60   | 59.5833     | 69.6270 | 1.4004          |
  | 1.2309        | 1.67  | 80   | 64.5833     | 75.2648 | 1.1523          |
  | 0.9588        | 2.08  | 100  | 62.9167     | 75.0527 | 1.2793          |
  | 0.7368        | 2.5   | 120  | 65.8333     | 75.2080 | 1.0957          |
  | 0.7607        | 2.92  | 140  | 62.9167     | 72.4981 | 1.2559          |
  | 0.6658        | 3.33  | 160  | 61.25       | 69.8265 | 1.3887          |
  | 0.7594        | 3.75  | 180  | 64.5833     | 74.1011 | 1.2891          |
  | 0.6627        | 4.17  | 200  | 60.8333     | 69.9682 | 1.2480          |
  | 0.5145        | 4.58  | 220  | 62.5        | 72.4746 | 1.3770          |
  | 0.4045        | 5.0   | 240  | 67.0833     | 76.2119 | 1.1836          |
  | 0.3877        | 5.42  | 260  | 62.5        | 73.4728 | 1.3740          |
  | 0.265         | 5.83  | 280  | 65.4167     | 75.0515 | 1.2695          |
  | 0.1806        | 6.25  | 300  | 66.6667     | 76.6871 | 1.2969          |
  | 0.1215        | 6.67  | 320  | 67.5        | 77.1973 | 1.4023          |
  | 0.1149        | 7.08  | 340  | 62.9167     | 73.2902 | 1.5371          |
  | 0.1462        | 7.5   | 360  | 64.5833     | 74.8781 | 1.5             |
  | 0.2145        | 7.92  | 380  | 62.9167     | 72.5644 | 1.5156          |
  | 0.2666        | 8.33  | 400  | 64.1667     | 73.7049 | 1.2617          |
  | 0.2852        | 8.75  | 420  | 62.9167     | 72.7485 | 1.5898          |
  | 0.4549        | 9.17  | 440  | 64.1667     | 73.7479 | 1.3828          |
  | 0.4197        | 9.58  | 460  | 60.4167     | 70.6652 | 1.5967          |
  | 0.4102        | 10.0  | 480  | 60.0        | 68.9411 | 1.4824          |
  | 0.3265        | 10.42 | 500  | 58.3333     | 68.6892 | 1.5420          |
  | 0.282         | 10.83 | 520  | 64.1667     | 73.6281 | 1.4746          |
  | 0.1944        | 11.25 | 540  | 59.5833     | 69.5715 | 1.8154          |
  | 0.1885        | 11.67 | 560  | 66.6667     | 75.0492 | 1.5430          |
  | 0.1429        | 12.08 | 580  | 67.9167     | 75.9797 | 1.5938          |
  | 0.055         | 12.5  | 600  | 66.25       | 74.7943 | 1.8848          |
  | 0.0699        | 12.92 | 620  | 68.3333     | 75.8125 | 1.7393          |
  | 0.049         | 13.33 | 640  | 65.4167     | 74.6337 | 1.9912          |
  | 0.0892        | 13.75 | 660  | 65.0        | 73.7429 | 1.9355          |
  | 0.0951        | 14.17 | 680  | 65.4167     | 72.9353 | 1.7695          |
  | 0.1836        | 14.58 | 700  | 60.4167     | 67.7400 | 1.8242          |
  | 0.1577        | 15.0  | 720  | 62.9167     | 73.3646 | 2.0352          |
  | 0.1808        | 15.42 | 740  | 58.3333     | 68.1992 | 1.9990          |
  | 0.2799        | 15.83 | 760  | 58.75       | 68.8545 | 1.7031          |
  | 0.1927        | 16.25 | 780  | 63.3333     | 71.2111 | 1.8945          |
  | 0.217         | 16.67 | 800  | 63.3333     | 72.2130 | 1.5957          |
  | 0.1768        | 17.08 | 820  | 62.9167     | 72.2659 | 1.7617          |
  | 0.122         | 17.5  | 840  | 60.0        | 68.5236 | 1.9043          |
  | 0.1132        | 17.92 | 860  | 62.0833     | 72.1359 | 1.7256          |
  | 0.0574        | 18.33 | 880  | 62.5        | 72.6974 | 1.7656          |
  | 0.0516        | 18.75 | 900  | 64.1667     | 74.5338 | 1.7256          |
  | 0.0302        | 19.17 | 920  | 68.75       | 77.9872 | 1.8203          |
  | 0.023         | 19.58 | 940  | 64.1667     | 73.2770 | 2.0469          |
  | 0.0567        | 20.0  | 960  | 66.25       | 73.3724 | 2.0586   