readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - billsum
  metrics:
  - rouge
  model-index:
  - name: bart-large-cnn-small-billsum-3epochs
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: billsum
        type: billsum
        config: default
        split: train
        args: default
      metrics:
      - name: Rouge1
        type: rouge
        value: 0.5409
  ---
  
  <!-- 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-large-cnn-small-billsum-3epochs
  
  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the billsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7523
  - Rouge1: 0.5409
  - Rouge2: 0.3112
  - Rougel: 0.3929
  - Rougelsum: 0.4633
  
  ## 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: 2.5764683748161164e-05
  - train_batch_size: 4
  - eval_batch_size: 8
  - 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
  - lr_scheduler_warmup_steps: 16
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 2.7132        | 0.32  | 8    | 2.2000          | 0.4619 | 0.2328 | 0.3201 | 0.3939    |
  | 2.236         | 0.64  | 16   | 1.9705          | 0.499  | 0.2768 | 0.3651 | 0.4216    |
  | 2.1109        | 0.96  | 24   | 1.8845          | 0.5214 | 0.2974 | 0.3844 | 0.4417    |
  | 1.7663        | 1.28  | 32   | 1.8211          | 0.5226 | 0.2935 | 0.3718 | 0.4479    |
  | 1.7838        | 1.6   | 40   | 1.7981          | 0.5338 | 0.3001 | 0.383  | 0.4466    |
  | 1.5229        | 1.92  | 48   | 1.7625          | 0.5299 | 0.3012 | 0.3839 | 0.4494    |
  | 1.5221        | 2.24  | 56   | 1.7532          | 0.5384 | 0.3117 | 0.3939 | 0.4637    |
  | 1.2879        | 2.56  | 64   | 1.7560          | 0.5338 | 0.3075 | 0.3865 | 0.4584    |
  | 1.4046        | 2.88  | 72   | 1.7523          | 0.5409 | 0.3112 | 0.3929 | 0.4633    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  