readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - pub_med_summarization_dataset
  metrics:
  - rouge
  model-index:
  - name: distilbart-xsum-12-1-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: 27.0012
  ---
  
  <!-- 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-xsum-12-1-finetuned-pubmed
  
  This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-1](https://huggingface.co/sshleifer/distilbart-xsum-12-1) on the pub_med_summarization_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.8236
  - Rouge1: 27.0012
  - Rouge2: 12.728
  - Rougel: 19.8685
  - Rougelsum: 25.0485
  - Gen Len: 59.969
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 3.3604        | 1.0   | 4000  | 3.1575          | 25.0078 | 11.5381 | 18.4246 | 23.1605   | 54.8935 |
  | 3.0697        | 2.0   | 8000  | 2.9478          | 26.4947 | 12.5411 | 19.4328 | 24.6123   | 57.948  |
  | 2.8638        | 3.0   | 12000 | 2.8672          | 26.8856 | 12.7568 | 19.8949 | 24.8745   | 59.6245 |
  | 2.7243        | 4.0   | 16000 | 2.8347          | 26.7347 | 12.5152 | 19.6516 | 24.7756   | 60.439  |
  | 2.6072        | 5.0   | 20000 | 2.8236          | 27.0012 | 12.728  | 19.8685 | 25.0485   | 59.969  |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  