readme: ---
  tags:
  - paraphrasing
  - generated_from_trainer
  datasets:
  - paws
  metrics:
  - rouge
  model-index:
  - name: pegasus-xsum-finetuned-paws
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: paws
        type: paws
        args: labeled_final
      metrics:
      - name: Rouge1
        type: rouge
        value: 92.4371
  ---
  
  <!-- 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-paws
  
  This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the paws dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.1199
  - Rouge1: 92.4371
  - Rouge2: 75.4061
  - Rougel: 84.1519
  - Rougelsum: 84.1958
  
  ## 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: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
  | 2.1481        | 1.46  | 1000 | 2.0112          | 93.7727 | 73.3021 | 84.2963 | 84.2506   |
  | 2.0113        | 2.93  | 2000 | 2.0579          | 93.813  | 73.4119 | 84.3674 | 84.2693   |
  | 2.054         | 4.39  | 3000 | 2.0890          | 93.3926 | 73.3727 | 84.2814 | 84.1649   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  