readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - samsum
  metrics:
  - rouge
  model-index:
  - name: pegasus-large-samsum
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: samsum
        type: samsum
        args: samsum
      metrics:
      - name: Rouge1
        type: rouge
        value: 48.0968
  ---
  
  <!-- 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-large-samsum
  
  This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the samsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4109
  - Rouge1: 48.0968
  - Rouge2: 24.6663
  - Rougel: 40.2569
  - Rougelsum: 44.0137
  
  ## 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
  - gradient_accumulation_steps: 64
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
  | No log        | 1.0   | 230  | 1.4646          | 45.0631 | 22.5567 | 38.0518 | 41.2694   |
  | No log        | 2.0   | 460  | 1.4203          | 47.4122 | 24.158  | 39.7414 | 43.3485   |
  | 1.699         | 3.0   | 690  | 1.4109          | 48.0968 | 24.6663 | 40.2569 | 44.0137   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  