readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: ainize-kobart-news-eb-finetuned-xsum
    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. -->
  
  # ainize-kobart-news-eb-finetuned-xsum
  
  This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2147
  - Rouge1: 60.732
  - Rouge2: 39.1933
  - Rougel: 60.6507
  - Rougelsum: 60.6712
  - Gen Len: 19.3417
  
  ## 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: 2e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 1.0649        | 1.0   | 749  | 0.5502          | 56.6571 | 36.5992 | 56.6185 | 56.6364   | 19.2929 |
  | 0.7103        | 2.0   | 1498 | 0.3904          | 59.1212 | 38.3611 | 59.093  | 59.1191   | 19.31   |
  | 0.4723        | 3.0   | 2247 | 0.2922          | 60.1133 | 38.7819 | 60.0439 | 60.0572   | 19.2659 |
  | 0.3841        | 4.0   | 2996 | 0.2367          | 60.4405 | 39.0176 | 60.366  | 60.4057   | 19.3397 |
  | 0.3091        | 5.0   | 3745 | 0.2147          | 60.732  | 39.1933 | 60.6507 | 60.6712   | 19.3417 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  