readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - pub_med_summarization_dataset
  metrics:
  - rouge
  model-index:
  - name: bart-large-cnn-finetuned-pubmed
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: pub_med_summarization_dataset
        type: pub_med_summarization_dataset
        args: document
      metrics:
      - name: Rouge1
        type: rouge
        value: 40.4866
  ---
  
  <!-- 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-finetuned-pubmed
  
  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the pub_med_summarization_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.8416
  - Rouge1: 40.4866
  - Rouge2: 16.7472
  - Rougel: 24.9831
  - Rougelsum: 36.4002
  - Gen Len: 142.0
  
  ## 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  |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
  | 1.932         | 1.0   | 4000  | 1.8110          | 38.1151 | 15.2255 | 23.4286 | 34.2521   | 141.8905 |
  | 1.7001        | 2.0   | 8000  | 1.7790          | 39.8217 | 16.3042 | 24.649  | 35.831    | 142.0    |
  | 1.5           | 3.0   | 12000 | 1.7971          | 40.6108 | 17.0446 | 25.1977 | 36.5556   | 141.9865 |
  | 1.3316        | 4.0   | 16000 | 1.8106          | 40.0466 | 16.4851 | 24.7094 | 36.0998   | 141.9335 |
  | 1.1996        | 5.0   | 20000 | 1.8416          | 40.4866 | 16.7472 | 24.9831 | 36.4002   | 142.0    |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  