It5 Summarization Fanpage

🚀 摘要生成模型 summarization_fanpage128

本模型是 gsarti/it5-base 在 Fanpage 數據集上針對抽象摘要生成任務進行微調後的版本。它能夠有效處理意大利語的文本摘要生成任務，為相關領域提供了高效的解決方案。

🚀 快速開始

本模型是在 Fanpage 數據集上對 gsarti/it5-base 進行微調得到的，用於抽象摘要生成任務。

它取得了以下結果：

• 損失值：1.5348
• Rouge1：34.1882
• Rouge2：15.7866
• Rougel：25.141
• Rougelsum：28.4882
• 生成長度：69.3041

💻 使用示例

基礎用法

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage-128")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-fanpage-128")

📚 詳細文檔

訓練超參數

訓練過程中使用了以下超參數：

• 學習率：5e-05
• 訓練批次大小：3
• 評估批次大小：3
• 隨機種子：42
• 優化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
• 學習率調度器類型：線性
• 訓練輪數：4.0

框架版本

• Transformers 4.12.0.dev0
• Pytorch 1.9.1+cu102
• Datasets 1.12.1
• Tokenizers 0.10.3

📄 許可證

文檔中未提及相關許可證信息。

📖 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}
}

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