It5 Summarization Mlsum

🚀 摘要生成模型summarization_mlsum

本模型是在MLSum - it數據集上針對抽象文本摘要任務對[gsarti/it5 - base](https://huggingface.co/gsarti/it5 - base)進行微調後的版本。它能夠對意大利語的文本進行摘要生成，為相關的文本處理任務提供了有效的解決方案。

🚀 快速開始

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

該模型在抽象文本摘要任務中取得了以下成果：

• 損失值（Loss）：2.0190
• Rouge1：19.3739
• Rouge2：5.9753
• Rougel：16.691
• Rougelsum：16.7862
• 生成長度（Gen Len）：32.5268

📚 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（learning_rate）：5e - 05
• 訓練批次大小（train_batch_size）：6
• 評估批次大小（eval_batch_size）：6
• 隨機種子（seed）：42
• 優化器（optimizer）：Adam，其中betas = (0.9, 0.999)，epsilon = 1e - 08
• 學習率調度器類型（lr_scheduler_type）：線性
• 訓練輪數（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

📄 許可證

文檔中未提及許可證相關信息。

📖 引用

更多詳細信息和結果請參考[已發表的論文](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}
}

信息表格