readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - xsum
  metrics:
  - rouge
  model-index:
  - name: bart-large-cnn-small-xsum-5epochs
    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: 0.2859
  ---
  
  <!-- 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-5epochs
  
  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: 2.7051
  - Rouge1: 0.2859
  - Rouge2: 0.0937
  - Rougel: 0.2033
  - Rougelsum: 0.2101
  
  ## 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: 2.045e-05
  - train_batch_size: 4
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 16
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 2.5007        | 0.32  | 16   | 2.0311          | 0.2393 | 0.0609 | 0.1618 | 0.1832    |
  | 2.0942        | 0.64  | 32   | 1.9169          | 0.2906 | 0.1053 | 0.2072 | 0.2166    |
  | 1.7543        | 0.96  | 48   | 1.9069          | 0.2904 | 0.0955 | 0.2058 | 0.2187    |
  | 1.2476        | 1.28  | 64   | 1.9614          | 0.2928 | 0.1043 | 0.2081 | 0.2257    |
  | 1.2318        | 1.6   | 80   | 1.9622          | 0.2892 | 0.0976 | 0.2099 | 0.2245    |
  | 1.0768        | 1.92  | 96   | 2.0244          | 0.2935 | 0.1008 | 0.2095 | 0.2209    |
  | 0.8845        | 2.24  | 112  | 2.0605          | 0.2886 | 0.0992 | 0.2039 | 0.2146    |
  | 0.5722        | 2.56  | 128  | 2.2340          | 0.2852 | 0.0946 | 0.1983 | 0.2146    |
  | 0.7132        | 2.88  | 144  | 2.1948          | 0.2838 | 0.0961 | 0.2047 | 0.2163    |
  | 0.4438        | 3.2   | 160  | 2.3758          | 0.2869 | 0.0906 | 0.1987 | 0.2102    |
  | 0.4194        | 3.52  | 176  | 2.5609          | 0.2882 | 0.0916 | 0.2022 | 0.2133    |
  | 0.3404        | 3.84  | 192  | 2.4988          | 0.2884 | 0.0907 | 0.2022 | 0.213     |
  | 0.2929        | 4.16  | 208  | 2.5802          | 0.2885 | 0.0967 | 0.2046 | 0.2141    |
  | 0.2466        | 4.48  | 224  | 2.6590          | 0.2823 | 0.094  | 0.1994 | 0.2119    |
  | 0.1889        | 4.8   | 240  | 2.7051          | 0.2859 | 0.0937 | 0.2033 | 0.2101    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  