Bart Large Gigaword

🚀 BART for Gigaword

This project presents a fine - tuned BART model on the Gigaword dataset. It leverages the pre - trained weights of facebook/bart - large - cnn from HuggingFace, aiming to provide high - quality text summarization.

🚀 Quick Start

Model Creation

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.

Tokenizer Loading

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")

✨ Features

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)

📄 License

This project is licensed under the MIT license.

📦 Additional Information