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ZIZOU/Arabic_Squad

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Hugging Face2022-11-26 更新2024-03-04 收录
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https://hf-mirror.com/datasets/ZIZOU/Arabic_Squad
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资源简介:
Arabic_SQuAD数据集是一个阿拉伯语的问答数据集,包含了从维基百科文章中提取的问题和答案。数据集的特征包括索引、问题、上下文、文本、答案起始位置和上下文ID。训练集包含48,344个例子,文件大小为61,868,003字节。该数据集是为解决阿拉伯语开放领域问答问题而创建的,使用了维基百科作为知识源,并包含了一个基于BERT的神经阅读理解模型。
提供机构:
ZIZOU
原始信息汇总

数据集概述

数据集名称

  • 名称: Arabic_SQuAD

数据集创建者

  • 创建者: Mostafa3zazi

数据集内容

  • 数据结构:

    • 特征:
      • index: 字符串类型
      • question: 字符串类型
      • context: 字符串类型
      • text: 字符串类型
      • answer_start: 整数类型
      • c_id: 整数类型
  • 数据分割:

    • 训练集:
      • 示例数量: 48344
      • 数据大小: 61868003字节
      • 下载大小: 10512179字节

引用信息

@inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", }

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