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)
    
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Summarization
This model can be loaded on the Inference API on-demand.