readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: final_bart_prepro_fix
    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_prepro_fix
  
  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: 2.6100
  - Rouge1: 35.5593
  - Rouge2: 13.0497
  - Rougel: 23.5672
  - Bleu1: 29.5206
  - Bleu2: 17.3914
  - Bleu3: 10.5577
  - Bleu4: 6.1502
  - Rdass: 0.6449
  - Gen Len: 49.7389
  
  ## 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: 64
  - 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  | Rdass  | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:------:|:-------:|
  | 2.1622        | 1.51  | 1000 | 2.6687          | 35.4366 | 12.8631 | 23.1588 | 29.4018 | 17.2004 | 10.3744 | 6.052  | 0.6379 | 49.4266 |
  | 2.0114        | 3.02  | 2000 | 2.6090          | 35.1436 | 13.0347 | 23.4682 | 28.8917 | 17.0965 | 10.1873 | 5.896  | 0.6389 | 46.1096 |
  | 1.8758        | 4.53  | 3000 | 2.6100          | 35.5593 | 13.0497 | 23.5672 | 29.5206 | 17.3914 | 10.5577 | 6.1502 | 0.6449 | 49.7389 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  