readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: final_bart
    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. -->
  
  # final_bart
  
  This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.6848
  - Rouge1: 35.7722
  - Rouge2: 12.5127
  - Rougel: 23.3002
  - Rdass: 0.6248
  - Bleu1: 30.5261
  - Bleu2: 17.6264
  - Bleu3: 10.3974
  - Bleu4: 5.4348
  - Gen Len: 53.47
  
  ## 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: 3e-05
  - train_batch_size: 64
  - 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  | Rdass  | Bleu1   | Bleu2   | Bleu3   | Bleu4  | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:------:|:-------:|:-------:|:-------:|:------:|:-------:|
  | 2.1542        | 1.5   | 1000 | 2.7491          | 33.5554 | 11.2371 | 22.006  | 0.6093 | 27.9938 | 15.5354 | 8.2494  | 4.42   | 50.08   |
  | 2.0071        | 2.99  | 2000 | 2.6813          | 35.0501 | 12.2759 | 22.6669 | 0.6155 | 29.6866 | 17.1396 | 9.7016  | 5.3559 | 54.04   |
  | 1.8694        | 4.49  | 3000 | 2.6848          | 35.7722 | 12.5127 | 23.3002 | 0.6248 | 30.5261 | 17.6264 | 10.3974 | 5.4348 | 53.47   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  