readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: kobart_8_4e-5_datav2_min30_lp5.0_temperature1.0
    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. -->
  
  # kobart_8_4e-5_datav2_min30_lp5.0_temperature1.0
  
  This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.7690
  - Rouge1: 35.7198
  - Rouge2: 12.6777
  - Rougel: 23.5157
  - Bleu1: 29.7798
  - Bleu2: 17.2442
  - Bleu3: 10.1198
  - Bleu4: 5.5845
  - Gen Len: 50.2914
  
  ## 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: 4e-05
  - train_batch_size: 8
  - eval_batch_size: 128
  - 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: 5.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Bleu1   | Bleu2   | Bleu3   | Bleu4  | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
  | 2.5571        | 0.19  | 1000  | 3.0256          | 30.6752 | 9.655   | 20.3793 | 24.9545 | 13.4562 | 7.0852  | 3.6167 | 47.2378 |
  | 2.3748        | 0.38  | 2000  | 2.8633          | 33.6862 | 11.3467 | 21.6442 | 27.6602 | 15.5034 | 8.564   | 4.7708 | 52.5921 |
  | 2.3327        | 0.57  | 3000  | 2.7965          | 34.1286 | 11.5936 | 22.3078 | 28.2895 | 15.9539 | 9.0344  | 5.0261 | 46.4336 |
  | 2.2987        | 0.76  | 4000  | 2.7423          | 33.7844 | 11.4184 | 22.2715 | 27.9016 | 15.7678 | 8.887   | 4.9817 | 44.1305 |
  | 2.2137        | 0.94  | 5000  | 2.6925          | 34.4899 | 12.4798 | 23.0933 | 28.5676 | 16.7234 | 9.854   | 5.4929 | 46.5431 |
  | 2.0205        | 1.13  | 6000  | 2.6899          | 35.1651 | 12.2364 | 22.6918 | 29.561  | 16.9967 | 9.5871  | 5.4011 | 51.4126 |
  | 1.9818        | 1.32  | 7000  | 2.7037          | 34.1708 | 12.01   | 22.3273 | 28.597  | 16.3676 | 9.6473  | 5.2881 | 48.0979 |
  | 2.0085        | 1.51  | 8000  | 2.6568          | 35.1423 | 12.6615 | 23.3564 | 29.0896 | 16.9543 | 10.0793 | 5.8229 | 47.014  |
  | 1.9972        | 1.7   | 9000  | 2.6399          | 35.3604 | 12.6992 | 23.3829 | 29.2344 | 17.0287 | 9.9469  | 5.5226 | 46.4336 |
  | 1.963         | 1.89  | 10000 | 2.6225          | 34.992  | 12.3573 | 23.0134 | 29.0142 | 16.8063 | 9.6906  | 5.5045 | 51.4452 |
  | 1.718         | 2.08  | 11000 | 2.6629          | 34.8932 | 12.2868 | 23.2794 | 28.7742 | 16.5584 | 9.6199  | 5.4499 | 47.5804 |
  | 1.7171        | 2.27  | 12000 | 2.6648          | 35.4343 | 12.7376 | 23.4355 | 29.4051 | 17.1878 | 10.2903 | 5.824  | 46.4359 |
  | 1.695         | 2.45  | 13000 | 2.6578          | 35.0225 | 12.1733 | 22.9686 | 28.8901 | 16.5961 | 9.3781  | 5.2049 | 49.0443 |
  | 1.7282        | 2.64  | 14000 | 2.6435          | 33.9569 | 11.9783 | 22.9137 | 27.9425 | 16.0888 | 9.3867  | 5.3915 | 46.0886 |
  | 1.7541        | 2.83  | 15000 | 2.6469          | 34.6347 | 12.1309 | 22.7496 | 28.9934 | 16.6886 | 9.7165  | 5.2098 | 49.62   |
  | 1.4855        | 3.02  | 16000 | 2.7137          | 35.3936 | 12.7873 | 23.3762 | 29.4388 | 17.1262 | 10.0549 | 5.9223 | 50.0256 |
  | 1.5382        | 3.21  | 17000 | 2.7161          | 35.211  | 12.7758 | 23.8604 | 29.1727 | 17.007  | 10.1639 | 6.0141 | 46.8159 |
  | 1.5243        | 3.4   | 18000 | 2.7222          | 35.6339 | 12.683  | 23.5104 | 29.8071 | 17.3418 | 10.178  | 5.5185 | 49.5944 |
  | 1.5265        | 3.59  | 19000 | 2.7210          | 35.4469 | 12.5754 | 23.3784 | 29.5035 | 17.1414 | 9.8427  | 5.5385 | 50.7762 |
  | 1.5394        | 3.78  | 20000 | 2.7193          | 35.9595 | 12.9418 | 23.5227 | 30.0655 | 17.5487 | 10.115  | 5.6725 | 50.3357 |
  | 1.5364        | 3.97  | 21000 | 2.7000          | 35.6398 | 12.9591 | 23.8267 | 29.9125 | 17.587  | 10.4197 | 5.985  | 48.4476 |
  | 1.343         | 4.15  | 22000 | 2.7756          | 35.8172 | 12.7519 | 23.5584 | 29.7877 | 17.2715 | 10.219  | 5.9187 | 49.2984 |
  | 1.3182        | 4.34  | 23000 | 2.7813          | 35.2382 | 12.7271 | 23.3914 | 29.5501 | 17.3306 | 10.3873 | 6.1428 | 50.8228 |
  | 1.3771        | 4.53  | 24000 | 2.7716          | 35.4267 | 12.6279 | 23.3564 | 29.6336 | 17.245  | 10.2511 | 5.9128 | 51.8695 |
  | 1.3522        | 4.72  | 25000 | 2.7700          | 35.8057 | 12.9656 | 23.6143 | 29.8501 | 17.475  | 10.2721 | 5.7671 | 50.6946 |
  | 1.3508        | 4.91  | 26000 | 2.7690          | 35.7198 | 12.6777 | 23.5157 | 29.7798 | 17.2442 | 10.1198 | 5.5845 | 50.2914 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.