readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: dialogue_sumarization
    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. -->
  
  # dialogue_sumarization
  
  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: 1.9575
  - Rouge1: 9.6031
  - Rouge2: 1.8433
  - Rougel: 9.5081
  - Rougelsum: 9.5143
  - Gen Len: 19.8374
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.01
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
  | 2.3507        | 0.34  | 1500  | 2.2700          | 8.4697 | 1.5484 | 8.4046 | 8.4123    | 19.3838 |
  | 2.2652        | 0.69  | 3000  | 2.1763          | 8.7686 | 1.7278 | 8.695  | 8.7087    | 19.8848 |
  | 2.0005        | 1.03  | 4500  | 2.1184          | 8.8569 | 1.6434 | 8.7868 | 8.7948    | 19.8100 |
  | 2.0172        | 1.37  | 6000  | 2.0984          | 9.0878 | 1.7367 | 9.0184 | 9.022     | 19.7954 |
  | 1.9347        | 1.71  | 7500  | 1.9834          | 9.3386 | 1.7695 | 9.2666 | 9.2663    | 19.8798 |
  | 1.749         | 2.06  | 9000  | 1.9862          | 9.4569 | 1.7535 | 9.362  | 9.3818    | 19.8214 |
  | 1.7747        | 2.4   | 10500 | 1.9708          | 9.6823 | 1.8402 | 9.5739 | 9.5859    | 19.7747 |
  | 1.7708        | 2.74  | 12000 | 1.9591          | 9.5359 | 1.8083 | 9.4504 | 9.4527    | 19.7388 |
  | 1.6303        | 3.09  | 13500 | 1.9755          | 9.6598 | 1.9273 | 9.5589 | 9.5614    | 19.8874 |
  | 1.6443        | 3.43  | 15000 | 1.9666          | 9.435  | 1.8141 | 9.3569 | 9.3475    | 19.8244 |
  | 1.6551        | 3.77  | 16500 | 1.9575          | 9.6031 | 1.8433 | 9.5081 | 9.5143    | 19.8374 |
  | 1.5406        | 4.11  | 18000 | 1.9709          | 9.6326 | 1.8698 | 9.5411 | 9.5491    | 19.8098 |
  | 1.5493        | 4.46  | 19500 | 1.9695          | 9.7708 | 1.9322 | 9.6869 | 9.6834    | 19.8352 |
  | 1.5553        | 4.8   | 21000 | 1.9636          | 9.6944 | 1.8836 | 9.5967 | 9.6145    | 19.8308 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.7.1
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  