readme: ---
  language: 
  - English
  -
  thumbnail: 
  tags:
  - 
  -
  - 
  license: 
  datasets:
  - XSUM 
  - Gigaword
  metrics:
  - Rouge
  -
  ---
  
  # Pegasus XSUM Gigaword
  
  ## Model description
  
  Pegasus XSUM model finetuned to Gigaword Summarization task, significantly better performance than pegasus gigaword, but still doesn't match model paper performance.  
  
  ## Intended uses & limitations
  Produces short summaries with the coherence of the XSUM Model
  #### How to use
  
  ```python
  # You can include sample code which will be formatted
  ```
  
  #### Limitations and bias
  
  Still has all the biases of any of the abstractive models, but seems a little less prone to hallucination. 
  ## Training data
  
  Initialized with pegasus-XSUM
  
  ## Training procedure
  
  Trained for 11500 iterations on Gigaword corpus using OOB seq2seq (from hugging face using the default parameters)
  
  ## Eval results
  Evaluated on Gigaword test set (from hugging face using the default parameters)
  run_summarization.py     --model_name_or_path pegasus-xsum/checkpoint-11500/   --do_predict   --dataset_name gigaword   --dataset_config "3.0.0"     --source_prefix "summarize: "     --output_dir  pegasus-xsum    --per_device_train_batch_size=8     --per_device_eval_batch_size=8     --overwrite_output_dir     --predict_with_generate
  
  | Metric      | Score |
  | ----------- | ----------- |
  | eval_rouge1 | 34.1958   |
  | eval_rouge2 | 15.4033   |
  | eval_rougeL | 31.4488  |
  
  
  run_summarization.py     --model_name_or_path google/pegasus-gigaword   --do_predict   --dataset_name gigaword   --dataset_config "3.0.0"     --source_prefix "summarize: "     --output_dir  pegasus-xsum    --per_device_train_batch_size=8     --per_device_eval_batch_size=8     --overwrite_output_dir     --predict_with_generate
  
  | Metric      | Score |
  | ----------- | ----------- |
  | eval_rouge1 | 20.8111   |
  | eval_rouge2 | 8.766   |
  | eval_rougeL | 18.4431  |
  
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{...,
    year={2020}
  }
  ```
  