readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: pegasus-multi_news-NewsSummarization_BBC
    results: []
  ---
  
  # pegasus-multi_news-NewsSummarization_BBC
  
  This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on the None dataset.
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  I used this to improve my skillset. I thank all of authors of the different technologies and 
  dataset(s) for their contributions that have this possible. I am not too worried about getting 
  credit for my part, but make sure to properly cite the authors of the different technologies 
  and dataset(s) as they absolutely deserve credit for their contributions.
  
  ## Training and evaluation data
  
  Dataset Source: https://www.kaggle.com/datasets/pariza/bbc-news-summary
  
  ## Training procedure
  
  Here is the link to the script that I created to train this project: 
  
  https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/Text_Summarization_BBC_News-Pegasus.ipynb
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 50
  - num_epochs: 2
  
  ### Training results
  
  Unfortunately, I did not set the metrics to automatically upload here. They are as follows:
  
  | Training Loss | Epoch | Step |  rouge1  |  rouge2  |  rougeL  |  rougeLsum |
  |:-------------:|:-----:|:----:|:--------:|:--------:|:--------:|:----------:|
  |    6.41979    |  2.0  | 214  | 0.584474 | 0.463574 | 0.408729 |  0.408431  |
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1