readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: distill-pegasus-cnn-16-4-sec
    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. -->
  
  # distill-pegasus-cnn-16-4-sec
  
  This model is a fine-tuned version of [sshleifer/distill-pegasus-cnn-16-4](https://huggingface.co/sshleifer/distill-pegasus-cnn-16-4) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0146
  - Rouge1: 48.3239
  - Rouge2: 34.4713
  - Rougel: 43.5113
  - Rougelsum: 46.371
  - Gen Len: 106.98
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 12
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | No log        | 1.0   | 99   | 3.0918          | 20.297  | 6.5201  | 16.1329 | 18.0062   | 64.38   |
  | No log        | 2.0   | 198  | 2.4999          | 23.2475 | 10.4548 | 19.4955 | 21.3927   | 73.92   |
  | No log        | 3.0   | 297  | 2.0991          | 25.1919 | 13.2866 | 22.1497 | 23.7988   | 80.5    |
  | No log        | 4.0   | 396  | 1.7855          | 29.3799 | 17.4892 | 26.0768 | 27.3547   | 84.08   |
  | No log        | 5.0   | 495  | 1.5388          | 34.3057 | 21.5888 | 30.043  | 32.1758   | 98.26   |
  | 2.7981        | 6.0   | 594  | 1.3553          | 36.5817 | 22.9587 | 32.0113 | 34.3963   | 95.02   |
  | 2.7981        | 7.0   | 693  | 1.2281          | 37.9149 | 24.4547 | 33.9621 | 35.7424   | 90.04   |
  | 2.7981        | 8.0   | 792  | 1.1430          | 40.9219 | 27.4248 | 36.1746 | 38.8887   | 96.56   |
  | 2.7981        | 9.0   | 891  | 1.0844          | 43.935  | 29.7536 | 38.63   | 41.6618   | 98.7    |
  | 2.7981        | 10.0  | 990  | 1.0472          | 45.3353 | 32.042  | 40.8945 | 43.3416   | 106.22  |
  | 1.5684        | 11.0  | 1089 | 1.0254          | 47.6564 | 34.3221 | 43.1757 | 45.7094   | 107.88  |
  | 1.5684        | 12.0  | 1188 | 1.0146          | 48.3239 | 34.4713 | 43.5113 | 46.371    | 106.98  |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  