readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: distilbart-cnn-12-6-eval-test-2
    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. -->
  
  # distilbart-cnn-12-6-eval-test-2
  
  This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.7250
  - Rouge1: 31.3552
  - Rouge2: 4.2825
  - Rougel: 15.1982
  - Rougelsum: 27.9577
  - Gen Len: 139.0
  
  ## 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: 1
  - eval_batch_size: 1
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 4.4419        | 1.0   | 80   | 4.2847          | 30.8184 | 4.024  | 15.5589 | 27.647    | 133.6   |
  | 3.5861        | 2.0   | 160  | 4.2721          | 30.7823 | 3.7736 | 14.992  | 28.0105   | 137.1   |
  | 2.9885        | 3.0   | 240  | 4.4295          | 30.4747 | 3.8971 | 15.6055 | 27.9916   | 135.5   |
  | 2.5254        | 4.0   | 320  | 4.5978          | 31.0505 | 4.1062 | 14.7292 | 27.9009   | 134.2   |
  | 2.2404        | 5.0   | 400  | 4.7250          | 31.3552 | 4.2825 | 15.1982 | 27.9577   | 139.0   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.11.0
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  