readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: ainize-kobart-news-eb-finetuned-papers
    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-papers
  
  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: 0.3066
  - Rouge1: 14.5433
  - Rouge2: 5.2238
  - Rougel: 14.4731
  - Rougelsum: 14.5183
  - Gen Len: 19.9934
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 0.1918        | 1.0   | 7200  | 0.2403          | 14.6883 | 5.2427 | 14.6306 | 14.6489   | 19.9938 |
  | 0.1332        | 2.0   | 14400 | 0.2391          | 14.5165 | 5.2443 | 14.493  | 14.4908   | 19.9972 |
  | 0.0966        | 3.0   | 21600 | 0.2539          | 14.758  | 5.4976 | 14.6906 | 14.7188   | 19.9941 |
  | 0.0736        | 4.0   | 28800 | 0.2782          | 14.6267 | 5.3371 | 14.5578 | 14.6014   | 19.9934 |
  | 0.0547        | 5.0   | 36000 | 0.3066          | 14.5433 | 5.2238 | 14.4731 | 14.5183   | 19.9934 |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  