readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - cnn_dailymail
  metrics:
  - rouge
  model-index:
  - name: bart-finetuned-cnn-3
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: cnn_dailymail
        type: cnn_dailymail
        args: 3.0.0
      metrics:
      - name: Rouge1
        type: rouge
        value: 40.201
  ---
  
  <!-- 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. -->
  
  # bart-finetuned-cnn-3
  
  This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-3](https://huggingface.co/sshleifer/distilbart-xsum-12-3) on the cnn_dailymail dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.0751
  - Rouge1: 40.201
  - Rouge2: 18.8482
  - Rougel: 29.4439
  - Rougelsum: 37.416
  - Gen Len: 56.7545
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 2.276         | 1.0   | 8883  | 2.1762          | 39.6581 | 18.3333 | 28.7765 | 36.7688   | 58.5386 |
  | 2.0806        | 2.0   | 17766 | 2.0909          | 40.0328 | 18.8026 | 29.417  | 37.3508   | 56.6804 |
  | 1.9615        | 3.0   | 26649 | 2.0751          | 40.201  | 18.8482 | 29.4439 | 37.416    | 56.7545 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  