readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: results
    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. -->
  
  # results
  
  This model is a fine-tuned version of [sshleifer/distill-pegasus-xsum-16-4](https://huggingface.co/sshleifer/distill-pegasus-xsum-16-4) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.4473
  
  ## 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: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 7.2378        | 0.51  | 100  | 7.1853          |
  | 7.2309        | 1.01  | 200  | 6.6342          |
  | 6.4796        | 1.52  | 300  | 6.3206          |
  | 6.2691        | 2.02  | 400  | 6.0184          |
  | 5.7382        | 2.53  | 500  | 5.5754          |
  | 4.9922        | 3.03  | 600  | 4.5178          |
  | 3.6031        | 3.54  | 700  | 2.8579          |
  | 2.5203        | 4.04  | 800  | 2.4718          |
  | 2.2563        | 4.55  | 900  | 2.4128          |
  | 2.1425        | 5.05  | 1000 | 2.3767          |
  | 2.004         | 5.56  | 1100 | 2.3982          |
  | 2.0437        | 6.06  | 1200 | 2.3787          |
  | 1.9407        | 6.57  | 1300 | 2.3952          |
  | 1.9194        | 7.07  | 1400 | 2.3964          |
  | 1.758         | 7.58  | 1500 | 2.4056          |
  | 1.918         | 8.08  | 1600 | 2.4101          |
  | 1.9162        | 8.59  | 1700 | 2.4085          |
  | 1.8983        | 9.09  | 1800 | 2.4058          |
  | 1.6939        | 9.6   | 1900 | 2.4050          |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.10.0+cu111
  - Datasets 1.15.1
  - Tokenizers 0.10.3
  