It5 Summarization Mlsum

🚀 摘要生成模型summarization_mlsum

本模型是在MLSum - it数据集上针对抽象文本摘要任务对[gsarti/it5 - base](https://huggingface.co/gsarti/it5 - base)进行微调后的版本。它能够对意大利语的文本进行摘要生成，为相关的文本处理任务提供了有效的解决方案。

🚀 快速开始

基本使用

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

✨ 主要特性

该模型在抽象文本摘要任务中取得了以下成果：

• 损失值（Loss）：2.0190
• Rouge1：19.3739
• Rouge2：5.9753
• Rougel：16.691
• Rougelsum：16.7862
• 生成长度（Gen Len）：32.5268

📚 详细文档

训练超参数

训练过程中使用了以下超参数：

• 学习率（learning_rate）：5e - 05
• 训练批次大小（train_batch_size）：6
• 评估批次大小（eval_batch_size）：6
• 随机种子（seed）：42
• 优化器（optimizer）：Adam，其中betas = (0.9, 0.999)，epsilon = 1e - 08
• 学习率调度器类型（lr_scheduler_type）：线性
• 训练轮数（num_epochs）：4.0

框架版本

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

📄 许可证

文档中未提及许可证相关信息。

📖 引用

更多详细信息和结果请参考[已发表的论文](https://www.mdpi.com/2078 - 2489/13/5/228)

@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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