readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - xsum
  metrics:
  - rouge
  model-index:
  - name: bart-large-cnn-small-xsum-3epochs
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: xsum
        type: xsum
        config: default
        split: train
        args: default
      metrics:
      - name: Rouge1
        type: rouge
        value: 0.2948
  ---
  
  <!-- 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-xsum-3epochs
  
  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the xsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9215
  - Rouge1: 0.2948
  - Rouge2: 0.102
  - Rougel: 0.2136
  - Rougelsum: 0.2237
  
  ## 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: 1.4899457508828423e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 2.6493        | 0.64  | 8    | 2.3018          | 0.2072 | 0.038  | 0.1369 | 0.1665    |
  | 2.1539        | 1.32  | 16   | 2.0677          | 0.2403 | 0.0689 | 0.165  | 0.1833    |
  | 1.9295        | 1.96  | 24   | 1.9494          | 0.2779 | 0.0907 | 0.1998 | 0.209     |
  | 1.7358        | 2.64  | 32   | 1.9215          | 0.2948 | 0.102  | 0.2136 | 0.2237    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  