readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - pub_med_summarization_dataset
  metrics:
  - rouge
  model-index:
  - name: distilbart-cnn-12-6-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: 40.0985
  ---
  
  <!-- 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. -->
  
  # distilbart-cnn-12-6-finetuned-pubmed
  
  This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the pub_med_summarization_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9895
  - Rouge1: 40.0985
  - Rouge2: 16.5016
  - Rougel: 24.8319
  - Rougelsum: 36.0775
  - Gen Len: 141.884
  
  ## 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  |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
  | 2.1709        | 1.0   | 4000  | 2.0257          | 38.1012 | 15.112  | 23.4064 | 33.9373   | 141.9195 |
  | 1.9495        | 2.0   | 8000  | 1.9593          | 39.529  | 16.1693 | 24.487  | 35.5238   | 141.9785 |
  | 1.756         | 3.0   | 12000 | 1.9488          | 39.9623 | 16.5799 | 24.949  | 35.9194   | 141.8855 |
  | 1.6032        | 4.0   | 16000 | 1.9732          | 39.672  | 16.1994 | 24.5996 | 35.7021   | 141.921  |
  | 1.4817        | 5.0   | 20000 | 1.9895          | 40.0985 | 16.5016 | 24.8319 | 36.0775   | 141.884  |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  