Mbart Summarization Fanpage

🚀 mbart-summarization-fanpage

这个模型是 facebook/mbart-large-cc25 在Fanpage数据集上针对抽象文本摘要任务进行微调后的版本。它可以根据给定的文本生成简洁且包含关键信息的摘要，为处理意大利语的抽象文本摘要任务提供了有效的解决方案。

🚀 快速开始

代码示例

from transformers import MBartTokenizer, MBartForConditionalGeneration
tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-fanpage")
model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-fanpage")

✨ 主要特性

该模型在Fanpage数据集上微调后，在抽象文本摘要任务中取得了以下成果：

• 损失值（Loss）：2.1833
• Rouge1：36.5027
• Rouge2：17.4428
• Rougel：26.1734
• Rougelsum：30.2636
• 生成文本长度（Gen Len）：75.2413

💻 使用示例

基础用法

from transformers import MBartTokenizer, MBartForConditionalGeneration
tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-fanpage")
model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-fanpage")

📚 详细文档

训练超参数

训练过程中使用了以下超参数：

框架版本

• Transformers 4.15.0.dev0
• Pytorch 1.10.0+cu102
• Datasets 1.15.1
• Tokenizers 0.10.3

📄 引用

更多详细信息和结果请参考 已发表的论文

@Article{info13050228,
    AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
    TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
    JOURNAL = {Information},
    VOLUME = {13},
    YEAR = {2022},
    NUMBER = {5},
    ARTICLE-NUMBER = {228},
    URL = {https://www.mdpi.com/2078-2489/13/5/228},
    ISSN = {2078-2489},
    ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
    DOI = {10.3390/info13050228}
}