readme: ---
  tags:
  - xsum_v1_last
  - generated_from_trainer
  datasets:
  - xsum
  metrics:
  - rouge
  model-index:
  - name: pegasus-large-finetune-xsum
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: xsum
        type: xsum
        args: default
      metrics:
      - name: Rouge1
        type: rouge
        value: 5.0462
  ---
  
  <!-- 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-finetune-xsum
  
  This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the xsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 10.0826
  - Rouge1: 5.0462
  - Rouge2: 0.6914
  - Rougel: 3.5071
  - Rougelsum: 3.9548
  
  ## 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: 5.6e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 11.4044       | 1.0   | 13   | 10.7501         | 5.5154 | 0.5561 | 3.8425 | 4.2435    |
  | 10.5741       | 2.0   | 26   | 10.2309         | 5.4282 | 0.7228 | 3.5759 | 4.0538    |
  | 10.0146       | 3.0   | 39   | 10.0826         | 5.0462 | 0.6914 | 3.5071 | 3.9548    |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.13.0
  - Datasets 2.6.1
  - Tokenizers 0.11.0
  