task_categories:question-answering task_ids:extractive-qa annotations_creators:auto_translation language_creators:auto_translation multilinguality:monolingual size_categories:10K<n<100K source_datasets:squad region:us
SStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. This Hebrew dataset is an automatic translation of the English SQuAD dataset.
---
pretty_name: Hebrew_Squad_v1
annotations_creators:
- auto_translation
language_creators:
- auto_translation
languages:
- Hebrew
- he
licenses:
- cc-by-4-0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- squad
task_categories:
- question-answering
task_ids:
- extractive-qa
---

# Dataset Card for "Hebrew_Squad_v1"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/TechnionTDK/hebwiki-qa/](https://github.com/TechnionTDK/hebwiki-qa/)
- **Size of train dataset files:** 62.3 MB
- **Size of validation dataset files:** 9.48 MB
- **Total amount of disk used:** 71.78 MB


### Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. This Hebrew dataset is an automatic translation of the English SQuAD dataset https://huggingface.co/datasets/squad.
### Supported Tasks and Leaderboards
Extractive Question-Answering

### Languages
Hebrew

## Dataset Structure
Follows the standars SQuAD format.


### Data Instances
#### plain_text
- **Size of train dataset files:** 62.3 MB
- **Size of validation dataset files:** 9.48 MB
- **Total amount of disk used:** 71.78 MB

An example of 'train' looks as follows.

```
{
            "id": "56be4db0acb8001400a502ee",
            "title": "Super_Bowl_50",
            "context": "סופרבול 50 היה משחק כדורגל אמריקאי כדי לקבוע את אלופת ליגת הפוטבול הלאומית (NFL) לעונת 2015. אלופת ועידת הכדורגל האמריקאית (AFC) דנבר ברונקוס ניצחה את אלופת ועידת הכדורגל הלאומית (NFC) קרולינה פנתרס 24–10 כדי לזכות בתואר הסופרבול השלישי שלה. המשחק נערך ב-7 בפברואר 2016 באצטדיון ליווי'ס באזור מפרץ סן פרנסיסקו בסנטה קלרה, קליפורניה. מכיוון שזה היה הסופרבול ה-50, הליגה הדגישה את יום השנה הזהב עם יוזמות שונות בנושא זהב, כמו גם השעיה זמנית את המסורת של שם כל משחק סופרבול עם ספרות רומיות (שתחתן המשחק היה ידוע בתור סופרבול L ), כך שהלוגו יוכל להציג באופן בולט את הספרות הערביות 50.",
            "question": "היכן התקיים סופרבול 50?",
            "answers": {
                "text": ["סנטה קלרה, קליפורניה", "אצטדיון ליווי"],
                "answer_start": [311, 271]
            }
}
```


### Data Fields
The data fields are the same among all splits.
#### Hebrew_Squad_v1
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.
  
### Data Splits

|   name   |train|validation|
|----------|----|---------|
|Hebrew_Squad_v1|52405|     7455|




### Contributions
Created by Matan Ben-chorin, May Flaster, Guided by Dr. Oren Mishali.
This is our final project as part of computer engineering B.Sc studies in the Faculty of Electrical Engineering combined with Computer Science at Technion, Israel Institute of Technology.
For more cooperation, please contact email: 
Matan Ben-chorin: matan.bh1@gmail.com
May Flaster: mayflaster96@gmail.com
