It5 Summarization Fanpage

🚀 要約_fanpage128

このモデルは、要約生成タスクのためにFanpageデータセットでgsarti/it5-baseをファインチューニングしたバージョンです。

以下の結果を達成しています：

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

🚀 クイックスタート

💻 使用例

基本的な使用法

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

🔧 技術詳細

学習ハイパーパラメータ

学習時には以下のハイパーパラメータが使用されました：

• 学習率: 5e-05
• 学習バッチサイズ: 3
• 評価バッチサイズ: 3
• シード: 42
• オプティマイザ: Adam (betas=(0.9,0.999), epsilon=1e-08)
• 学習率スケジューラの種類: linear
• エポック数: 4.0

フレームワークのバージョン

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

📚 ドキュメント

引用

公開された研究成果に詳細と結果が記載されています。

@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}
}

📄 情報テーブル