readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: kobart_8_6e-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_6e-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: 2.8935
  - Rouge1: 35.9396
  - Rouge2: 12.7251
  - Rougel: 23.4072
  - Bleu1: 29.8836
  - Bleu2: 17.3868
  - Bleu3: 10.1034
  - Bleu4: 5.6852
  - Gen Len: 50.5012
  
  ## 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: 6e-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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
  | 2.5006        | 0.19  | 1000  | 2.9748          | 31.9305 | 10.219  | 20.9486 | 25.9772 | 14.0989 | 7.5807  | 3.9049 | 46.8951 |
  | 2.3738        | 0.38  | 2000  | 2.8691          | 34.1196 | 11.4746 | 22.0999 | 28.4466 | 16.0082 | 8.9955  | 4.6276 | 52.7669 |
  | 2.3468        | 0.57  | 3000  | 2.8207          | 34.1168 | 11.3998 | 22.5175 | 28.3223 | 15.791  | 8.5992  | 4.6269 | 43.3869 |
  | 2.3217        | 0.76  | 4000  | 2.7748          | 33.0369 | 11.0712 | 22.1962 | 27.127  | 15.1147 | 8.3628  | 4.6229 | 43.7366 |
  | 2.2252        | 0.94  | 5000  | 2.7395          | 34.4044 | 12.5602 | 23.0083 | 28.3603 | 16.6789 | 9.7892  | 5.6717 | 47.5828 |
  | 1.9933        | 1.13  | 6000  | 2.7503          | 34.5083 | 11.7179 | 22.196  | 28.8115 | 16.4201 | 9.3595  | 4.9562 | 52.1865 |
  | 1.963         | 1.32  | 7000  | 2.7527          | 33.7739 | 11.3831 | 22.3692 | 27.633  | 15.5257 | 8.7664  | 4.8824 | 45.3497 |
  | 1.997         | 1.51  | 8000  | 2.7051          | 35.9943 | 12.9136 | 23.8678 | 30.0639 | 17.6209 | 10.5702 | 6.1691 | 46.5128 |
  | 1.9855        | 1.7   | 9000  | 2.6832          | 34.1919 | 11.6503 | 22.7604 | 27.9586 | 15.8212 | 8.7798  | 4.906  | 45.3566 |
  | 1.9522        | 1.89  | 10000 | 2.6502          | 35.5575 | 12.6492 | 23.1904 | 29.4797 | 17.1112 | 9.9781  | 5.7052 | 50.0559 |
  | 1.6341        | 2.08  | 11000 | 2.7328          | 34.6455 | 11.8656 | 22.9323 | 28.484  | 16.09   | 9.0409  | 5.0875 | 44.0932 |
  | 1.645         | 2.27  | 12000 | 2.7198          | 35.0304 | 12.3304 | 23.4026 | 28.7978 | 16.6707 | 9.6501  | 5.4396 | 45.3427 |
  | 1.6333        | 2.45  | 13000 | 2.7258          | 35.6562 | 12.7612 | 23.3402 | 29.9319 | 17.4185 | 10.2105 | 5.6995 | 51.2727 |
  | 1.6663        | 2.64  | 14000 | 2.7008          | 34.2188 | 11.7236 | 22.6835 | 28.2471 | 15.9416 | 9.0996  | 4.8797 | 45.1818 |
  | 1.6786        | 2.83  | 15000 | 2.7106          | 35.3961 | 12.1801 | 23.1129 | 29.6386 | 17.0003 | 9.7356  | 5.3716 | 49.1958 |
  | 1.3555        | 3.02  | 16000 | 2.8057          | 35.4698 | 12.4315 | 23.2317 | 29.5758 | 16.9988 | 9.8794  | 5.5261 | 49.8089 |
  | 1.3975        | 3.21  | 17000 | 2.8155          | 35.7874 | 13.1167 | 24.1395 | 29.7118 | 17.4772 | 10.4028 | 5.8877 | 47.1608 |
  | 1.3958        | 3.4   | 18000 | 2.8128          | 35.7796 | 12.7994 | 23.701  | 29.8194 | 17.3474 | 10.0427 | 5.3794 | 51.2005 |
  | 1.3929        | 3.59  | 19000 | 2.8084          | 35.7019 | 12.8359 | 23.4838 | 29.8411 | 17.506  | 10.2791 | 5.6268 | 50.5897 |
  | 1.4165        | 3.78  | 20000 | 2.8067          | 35.4685 | 12.3161 | 23.4552 | 29.8108 | 17.0718 | 9.636   | 5.4738 | 49.0769 |
  | 1.399         | 3.97  | 21000 | 2.8022          | 36.0382 | 13.0705 | 23.8823 | 30.0459 | 17.5222 | 10.2384 | 5.7993 | 50.0979 |
  | 1.1604        | 4.15  | 22000 | 2.9069          | 35.9586 | 12.9506 | 23.5262 | 30.2279 | 17.6621 | 10.4464 | 6.0544 | 53.4755 |
  | 1.14          | 4.34  | 23000 | 2.9020          | 35.6245 | 12.2182 | 23.4536 | 29.8692 | 17.0002 | 9.7911  | 5.5078 | 49.5944 |
  | 1.1943        | 4.53  | 24000 | 2.8960          | 35.9293 | 12.6219 | 23.4135 | 30.077  | 17.4198 | 10.1376 | 5.6971 | 53.9091 |
  | 1.1582        | 4.72  | 25000 | 2.8975          | 35.7625 | 12.7562 | 23.3171 | 29.7443 | 17.4017 | 10.1272 | 5.5476 | 51.5618 |
  | 1.1561        | 4.91  | 26000 | 2.8935          | 35.9396 | 12.7251 | 23.4072 | 29.8836 | 17.3868 | 10.1034 | 5.6852 | 50.5012 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.