Mbart Summarization Fanpage

🚀 mbart-summarization-fanpage

這個模型是 facebook/mbart-large-cc25 在Fanpage數據集上針對抽象文本摘要任務進行微調後的版本。它可以根據給定的文本生成簡潔且包含關鍵信息的摘要，為處理意大利語的抽象文本摘要任務提供了有效的解決方案。

🚀 快速開始

代碼示例

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

該模型在Fanpage數據集上微調後，在抽象文本摘要任務中取得了以下成果：

• 損失值（Loss）：2.1833
• Rouge1：36.5027
• Rouge2：17.4428
• Rougel：26.1734
• Rougelsum：30.2636
• 生成文本長度（Gen Len）：75.2413

💻 使用示例

基礎用法

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

📚 詳細文檔

訓練超參數

訓練過程中使用了以下超參數：

框架版本

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

📄 引用

更多詳細信息和結果請參考 已發表的論文

@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}
}