readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - pub_med_summarization_dataset
  metrics:
  - rouge
  model-index:
  - name: pegasus-cnn_dailymail-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: 37.2569
  ---
  
  <!-- 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. -->
  
  # pegasus-cnn_dailymail-finetuned-pubmed
  
  This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the pub_med_summarization_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.8050
  - Rouge1: 37.2569
  - Rouge2: 15.8205
  - Rougel: 24.1969
  - Rougelsum: 34.0331
  - Gen Len: 125.892
  
  ## 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
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 2.2449        | 1.0   | 1000 | 1.8942          | 36.4494 | 14.9948 | 23.8279 | 33.3081   | 124.482 |
  | 2.0803        | 2.0   | 2000 | 1.8440          | 36.998  | 15.4992 | 24.091  | 33.6614   | 125.678 |
  | 2.0166        | 3.0   | 3000 | 1.8176          | 37.4703 | 16.0358 | 24.5735 | 34.1789   | 125.094 |
  | 1.9911        | 4.0   | 4000 | 1.8055          | 37.1338 | 15.7921 | 24.1412 | 33.8293   | 125.874 |
  | 1.9419        | 5.0   | 5000 | 1.8050          | 37.2569 | 15.8205 | 24.1969 | 34.0331   | 125.892 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.9.1
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  