readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - samsum
  metrics:
  - rouge
  model-index:
  - name: pegasus-xsum-finetuned-samsum-v2
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: samsum
        type: samsum
        config: samsum
        split: train
        args: samsum
      metrics:
      - name: Rouge1
        type: rouge
        value: 48.6788
  ---
  
  <!-- 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-xsum-finetuned-samsum-v2
  
  This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the samsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5349
  - Rouge1: 48.6788
  - Rouge2: 24.4294
  - Rougel: 40.7392
  - Rougelsum: 44.5412
  - Gen Len: 17.4976
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 1.7107        | 1.0   | 1841 | 1.5349          | 48.6788 | 24.4294 | 40.7392 | 44.5412   | 17.4976 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  