readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: distilbart-cnn-arxiv-pubmed-v3-e4
    results: []
  ---
  
  <!-- 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-arxiv-pubmed-v3-e4
  
  This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8874
  - Rouge1: 53.8193
  - Rouge2: 34.9325
  - Rougel: 37.7425
  - Rougelsum: 51.3935
  - 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: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 1.5003        | 1.0   | 795  | 1.0794          | 51.738  | 31.9115 | 34.8247 | 49.603    | 142.0   |
  | 0.8923        | 2.0   | 1590 | 0.9549          | 53.7436 | 35.1983 | 37.8041 | 51.8837   | 142.0   |
  | 0.7274        | 3.0   | 2385 | 0.9023          | 54.2052 | 35.8112 | 38.4288 | 52.1851   | 142.0   |
  | 0.5554        | 4.0   | 3180 | 0.8874          | 53.8193 | 34.9325 | 37.7425 | 51.3935   | 142.0   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  