readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - xlsum
  metrics:
  - rouge
  model-index:
  - name: bart-base-finetuned-xlsum-10-epoch
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: xlsum
        type: xlsum
        args: english
      metrics:
      - name: Rouge1
        type: rouge
        value: 38.5509
  ---
  
  <!-- 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-base-finetuned-xlsum-10-epoch
  
  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7506
  - Rouge1: 38.5509
  - Rouge2: 17.1804
  - Rougel: 31.6297
  - Rougelsum: 31.6993
  - Gen Len: 19.6701
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 2.0865        | 1.0   | 19158  | 1.8344          | 36.709  | 15.4309 | 29.8542 | 29.9152   | 19.5716 |
  | 1.9828        | 2.0   | 38316  | 1.7894          | 37.689  | 16.2171 | 30.7743 | 30.8346   | 19.6636 |
  | 1.8778        | 3.0   | 57474  | 1.7727          | 37.5849 | 16.3555 | 30.8276 | 30.8936   | 19.5898 |
  | 1.785         | 4.0   | 76632  | 1.7546          | 38.3036 | 16.911  | 31.4077 | 31.4608   | 19.5976 |
  | 1.7246        | 5.0   | 95790  | 1.7505          | 38.2107 | 16.929  | 31.3889 | 31.4485   | 19.6316 |
  | 1.6883        | 6.0   | 114948 | 1.7467          | 38.2416 | 17.0113 | 31.4098 | 31.4639   | 19.6048 |
  | 1.6301        | 7.0   | 134106 | 1.7475          | 38.4083 | 17.1098 | 31.5605 | 31.6322   | 19.6438 |
  | 1.6034        | 8.0   | 153264 | 1.7493          | 38.4812 | 17.1939 | 31.6284 | 31.6959   | 19.6574 |
  | 1.562         | 9.0   | 172422 | 1.7481          | 38.5011 | 17.1622 | 31.6031 | 31.6808   | 19.7056 |
  | 1.5496        | 10.0  | 191580 | 1.7506          | 38.5509 | 17.1804 | 31.6297 | 31.6993   | 19.6701 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.13.1+cpu
  - Datasets 2.8.0
  - Tokenizers 0.10.3
  