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)
