readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - wsj_markets
  metrics:
  - rouge
  model_index:
  - name: bart-large-finetuned-xsum
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: wsj_markets
        type: wsj_markets
        args: default
      metric:
        name: Rouge1
        type: rouge
        value: 15.3934
  ---
  
  <!-- 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-finetuned-xsum
  
  This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the wsj_markets dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8497
  - Rouge1: 15.3934
  - Rouge2: 7.0378
  - Rougel: 13.9522
  - Rougelsum: 14.3541
  - Gen Len: 20.0
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 1.0964        | 1.0   | 1735 | 0.9365          | 18.703  | 12.7539 | 18.1293 | 18.5397   | 20.0    |
  | 0.95          | 2.0   | 3470 | 0.8871          | 19.5223 | 13.0938 | 18.9148 | 18.8363   | 20.0    |
  | 0.8687        | 3.0   | 5205 | 0.8587          | 15.0915 | 7.142   | 13.6693 | 14.5975   | 20.0    |
  | 0.7989        | 4.0   | 6940 | 0.8569          | 18.243  | 11.4495 | 17.4326 | 17.489    | 20.0    |
  | 0.7493        | 5.0   | 8675 | 0.8497          | 15.3934 | 7.0378  | 13.9522 | 14.3541   | 20.0    |
  
  
  ### Framework versions
  
  - Transformers 4.8.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.10.0
  - Tokenizers 0.10.3
  