readme: ---
  tags:
  - summarization
  datasets:
  - gigaword
  license: mit
  thumbnail: https://en.wikipedia.org/wiki/Bart_Simpson#/media/File:Bart_Simpson_200px.png
  ---
  # BART for Gigaword
   - This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept.
   - The BART Tokenizer for CNN-Dailymail was used in the fine-tuning process and that is the tokenizer that will be loaded automatically when doing:
  ```
  from transformers import AutoTokenizer
  tokenizer = AutoTokenizer.from_pretrained("a1noack/bart-large-gigaword")
  ```
  # Summary generation
   - This model achieves ROUGE-1 / ROUGE-2 / ROUGE-L of 37.28 / 18.58 / 34.53 on the Gigaword test set; this is pretty good when compared to PEGASUS, `google/pegasus-gigaword`, which achieves 39.12 / 19.86 / 36.24.
   - To achieve these results, generate text using the code below. `text_list` is a list of input text string.
   ```
  input_ids_list = tokenizer(text_list, truncation=True, max_length=128, 
          return_tensors='pt', padding=True)['input_ids']
  output_ids_list = model.generate(input_ids_list, min_length=0)
  outputs_list = tokenizer.batch_decode(output_ids_list, skip_special_tokens=True, 
          clean_up_tokenization_spaces=False)
  ```