Mostafa3zazi/Arabic_SQuAD

---
dataset_info:
features:
- name: index
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: text
dtype: string
- name: answer_start
dtype: int64
- name: c_id
dtype: int64
splits:
- name: train
num_bytes: 61868003
num_examples: 48344
download_size: 10512179
dataset_size: 61868003
---
# Dataset Card for "Arabic_SQuAD"

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

---
# Citation
```
@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.",
}
```
---

原始信息汇总

数据集概述

数据集名称

• 名称: Arabic_SQuAD

数据集特征

• 特征列表:
  • index: 数据类型为 string
  • question: 数据类型为 string
  • context: 数据类型为 string
  • text: 数据类型为 string
  • answer_start: 数据类型为 int64
  • c_id: 数据类型为 int64

数据集分割

• 训练集:
  • 样本数量: 48344
  • 数据大小: 61868003 字节

数据集大小

• 下载大小: 10512179 字节
• 总数据大小: 61868003 字节