readme: ---
  license: apache-2.0
  tags:
  - summarization_t5_xsum
  - generated_from_trainer
  datasets:
  - xsum
  metrics:
  - rouge
  model-index:
  - name: bart-base-finetuned-xsum
    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: 34.6293
  ---
  
  <!-- 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-xsum
  
  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the xsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.1755
  - Rouge1: 34.6293
  - Rouge2: 13.4749
  - Rougel: 28.2616
  - Rougelsum: 28.2553
  
  ## 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: 5.6e-05
  - train_batch_size: 10
  - eval_batch_size: 10
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
  | 2.4765        | 1.0   | 1000 | 2.0873          | 33.9596 | 12.722  | 27.4135 | 27.4062   |
  | 1.9854        | 2.0   | 2000 | 2.0802          | 33.6802 | 12.8965 | 27.4061 | 27.4064   |
  | 1.6677        | 3.0   | 3000 | 2.0998          | 34.2038 | 13.1362 | 27.8808 | 27.8806   |
  | 1.4313        | 4.0   | 4000 | 2.1404          | 34.8491 | 13.4154 | 28.2768 | 28.2702   |
  | 1.275         | 5.0   | 5000 | 2.1755          | 34.6293 | 13.4749 | 28.2616 | 28.2553   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  