readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - samsum
  model-index:
  - name: pegasus-samsum
    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. -->
  
  # pegasus-samsum
  
  This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4251
  
  ## 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
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 3.1284        | 0.01  | 10   | 2.5960          |
  | 3.122         | 0.02  | 20   | 2.5579          |
  | 3.0196        | 0.03  | 30   | 2.4983          |
  | 2.9803        | 0.04  | 40   | 2.4197          |
  | 2.8471        | 0.05  | 50   | 2.3258          |
  | 2.7692        | 0.07  | 60   | 2.2438          |
  | 2.682         | 0.08  | 70   | 2.1608          |
  | 2.3648        | 0.09  | 80   | 2.0838          |
  | 2.5696        | 0.1   | 90   | 2.0222          |
  | 2.3403        | 0.11  | 100  | 1.9713          |
  | 2.2036        | 0.12  | 110  | 1.9199          |
  | 2.1998        | 0.13  | 120  | 1.8750          |
  | 2.3006        | 0.14  | 130  | 1.8382          |
  | 2.1182        | 0.15  | 140  | 1.8050          |
  | 2.1493        | 0.16  | 150  | 1.7748          |
  | 2.0437        | 0.17  | 160  | 1.7494          |
  | 1.9236        | 0.18  | 170  | 1.7289          |
  | 2.0114        | 0.2   | 180  | 1.7106          |
  | 1.9939        | 0.21  | 190  | 1.6906          |
  | 1.928         | 0.22  | 200  | 1.6737          |
  | 1.9444        | 0.23  | 210  | 1.6603          |
  | 1.9071        | 0.24  | 220  | 1.6485          |
  | 1.8314        | 0.25  | 230  | 1.6369          |
  | 1.8085        | 0.26  | 240  | 1.6277          |
  | 1.7493        | 0.27  | 250  | 1.6203          |
  | 1.8539        | 0.28  | 260  | 1.6089          |
  | 1.7048        | 0.29  | 270  | 1.5999          |
  | 1.7486        | 0.3   | 280  | 1.5921          |
  | 1.795         | 0.31  | 290  | 1.5842          |
  | 1.6613        | 0.33  | 300  | 1.5815          |
  | 1.8163        | 0.34  | 310  | 1.5732          |
  | 1.6133        | 0.35  | 320  | 1.5621          |
  | 1.8           | 0.36  | 330  | 1.5542          |
  | 1.7159        | 0.37  | 340  | 1.5506          |
  | 1.8081        | 0.38  | 350  | 1.5483          |
  | 1.7365        | 0.39  | 360  | 1.5451          |
  | 1.7334        | 0.4   | 370  | 1.5405          |
  | 1.7329        | 0.41  | 380  | 1.5334          |
  | 1.6923        | 0.42  | 390  | 1.5259          |
  | 1.6868        | 0.43  | 400  | 1.5227          |
  | 1.7033        | 0.45  | 410  | 1.5163          |
  | 1.6805        | 0.46  | 420  | 1.5144          |
  | 1.6056        | 0.47  | 430  | 1.5126          |
  | 1.7317        | 0.48  | 440  | 1.5086          |
  | 1.6303        | 0.49  | 450  | 1.5015          |
  | 1.7136        | 0.5   | 460  | 1.4943          |
  | 1.534         | 0.51  | 470  | 1.4910          |
  | 1.6682        | 0.52  | 480  | 1.4917          |
  | 1.6234        | 0.53  | 490  | 1.4885          |
  | 1.7103        | 0.54  | 500  | 1.4857          |
  | 1.7673        | 0.55  | 510  | 1.4800          |
  | 1.6631        | 0.56  | 520  | 1.4776          |
  | 1.7073        | 0.58  | 530  | 1.4745          |
  | 1.6843        | 0.59  | 540  | 1.4698          |
  | 1.6849        | 0.6   | 550  | 1.4679          |
  | 1.6054        | 0.61  | 560  | 1.4642          |
  | 1.6073        | 0.62  | 570  | 1.4629          |
  | 1.5896        | 0.63  | 580  | 1.4591          |
  | 1.608         | 0.64  | 590  | 1.4580          |
  | 1.58          | 0.65  | 600  | 1.4548          |
  | 1.5722        | 0.66  | 610  | 1.4548          |
  | 1.5529        | 0.67  | 620  | 1.4542          |
  | 1.5948        | 0.68  | 630  | 1.4518          |
  | 1.5869        | 0.7   | 640  | 1.4489          |
  | 1.577         | 0.71  | 650  | 1.4488          |
  | 1.6517        | 0.72  | 660  | 1.4477          |
  | 1.5955        | 0.73  | 670  | 1.4436          |
  | 1.5678        | 0.74  | 680  | 1.4402          |
  | 1.6743        | 0.75  | 690  | 1.4384          |
  | 1.5791        | 0.76  | 700  | 1.4374          |
  | 1.6397        | 0.77  | 710  | 1.4380          |
  | 1.5637        | 0.78  | 720  | 1.4363          |
  | 1.5849        | 0.79  | 730  | 1.4356          |
  | 1.5815        | 0.8   | 740  | 1.4350          |
  | 1.5797        | 0.