readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: kobart_8_1e-4_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_1e-4_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: 3.0961
  - Rouge1: 35.8883
  - Rouge2: 12.7003
  - Rougel: 23.3874
  - Bleu1: 30.2528
  - Bleu2: 17.5183
  - Bleu3: 10.2094
  - Bleu4: 5.6021
  - Gen Len: 50.1562
  
  ## 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: 0.0001
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
  | 2.4648        | 0.19  | 1000  | 2.9491          | 32.241  | 10.5261 | 21.21   | 26.5995 | 14.7371 | 7.8411  | 4.1361 | 48.303  |
  | 2.4028        | 0.38  | 2000  | 2.9226          | 33.8957 | 11.6309 | 22.4654 | 28.1592 | 15.9817 | 9.163   | 5.0564 | 49.5175 |
  | 2.4109        | 0.57  | 3000  | 2.9092          | 33.9997 | 11.4619 | 22.2822 | 28.0021 | 15.7774 | 8.7258  | 4.5887 | 44.6807 |
  | 2.3846        | 0.76  | 4000  | 2.8763          | 31.8881 | 10.1122 | 21.1754 | 25.4518 | 13.7126 | 7.4549  | 3.9979 | 40.9161 |
  | 2.2972        | 0.94  | 5000  | 2.8441          | 33.4146 | 11.8371 | 22.7219 | 27.1678 | 15.4977 | 9.1783  | 5.3303 | 43.8765 |
  | 2.0162        | 1.13  | 6000  | 2.8372          | 34.9461 | 11.8978 | 22.7877 | 28.9743 | 16.3778 | 9.2932  | 5.0534 | 47.1585 |
  | 1.9816        | 1.32  | 7000  | 2.8630          | 33.1249 | 10.8834 | 22.0846 | 27.0042 | 14.9508 | 8.3482  | 4.5422 | 44.676  |
  | 2.0172        | 1.51  | 8000  | 2.7998          | 34.1663 | 11.5471 | 22.8156 | 28.0367 | 15.7969 | 8.6235  | 4.5914 | 44.9254 |
  | 2.017         | 1.7   | 9000  | 2.7865          | 33.3775 | 11.194  | 22.6083 | 26.7485 | 14.9797 | 8.2559  | 4.279  | 41.5828 |
  | 1.9734        | 1.89  | 10000 | 2.7532          | 34.7147 | 12.353  | 23.0917 | 28.8012 | 16.7472 | 9.7079  | 5.5416 | 47.9883 |
  | 1.5937        | 2.08  | 11000 | 2.8433          | 34.9402 | 12.2318 | 23.2483 | 28.8006 | 16.5212 | 9.6008  | 5.3947 | 45.2401 |
  | 1.6112        | 2.27  | 12000 | 2.8377          | 34.9291 | 12.2349 | 23.278  | 28.8423 | 16.539  | 9.7674  | 5.4267 | 44.7599 |
  | 1.603         | 2.45  | 13000 | 2.8223          | 35.3837 | 12.5491 | 23.5272 | 29.3683 | 16.9828 | 9.6955  | 5.3166 | 47.6037 |
  | 1.6274        | 2.64  | 14000 | 2.8220          | 34.0515 | 11.7884 | 22.829  | 27.6635 | 15.8021 | 8.9724  | 4.9314 | 44.1235 |
  | 1.6435        | 2.83  | 15000 | 2.8139          | 34.9239 | 12.2122 | 22.9939 | 29.1796 | 16.763  | 9.5513  | 5.174  | 46.7832 |
  | 1.238         | 3.02  | 16000 | 2.9615          | 35.456  | 12.3012 | 23.3111 | 29.8676 | 17.0768 | 9.8694  | 5.4376 | 51.1935 |
  | 1.2767        | 3.21  | 17000 | 2.9781          | 35.2632 | 12.1441 | 23.2537 | 29.1438 | 16.6216 | 9.353   | 5.1593 | 46.0793 |
  | 1.2868        | 3.4   | 18000 | 2.9723          | 34.6808 | 11.9638 | 22.9058 | 28.9988 | 16.4994 | 9.3619  | 5.1178 | 47.4732 |
  | 1.2842        | 3.59  | 19000 | 2.9688          | 35.3792 | 12.5174 | 23.2012 | 29.6403 | 17.1517 | 9.9507  | 5.5561 | 49.1515 |
  | 1.2931        | 3.78  | 20000 | 2.9694          | 35.7525 | 12.8025 | 23.5228 | 29.8102 | 17.3544 | 10.239  | 5.6637 | 49.1189 |
  | 1.2733        | 3.97  | 21000 | 2.9618          | 35.8931 | 12.627  | 23.5571 | 30.0482 | 17.2582 | 9.8412  | 5.4747 | 48.5082 |
  | 0.963         | 4.15  | 22000 | 3.1113          | 35.7523 | 12.7633 | 23.3127 | 30.0193 | 17.4211 | 10.2596 | 5.853  | 51.6993 |
  | 0.9563        | 4.34  | 23000 | 3.1031          | 35.8437 | 12.6323 | 23.6011 | 30.0923 | 17.4089 | 9.9831  | 5.5993 | 48.7646 |
  | 0.992         | 4.53  | 24000 | 3.1016          | 36.1067 | 13.3428 | 24.0267 | 30.0275 | 17.8733 | 10.6929 | 6.2491 | 52.0373 |
  | 0.9722        | 4.72  | 25000 | 3.0956          | 35.4406 | 12.4799 | 23.3418 | 29.5123 | 17.0292 | 9.7401  | 5.3586 | 48.8974 |
  | 0.9519        | 4.91  | 26000 | 3.0961          | 35.8883 | 12.7003 | 23.3874 | 30.2528 | 17.5183 | 10.2094 | 5.6021 | 50.1562 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2