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Dataset Card for "Hebrew_Squad_v1"

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

Supported Tasks and Leaderboards

Extractive Question-Answering



Dataset Structure

Follows the standars SQuAD format.

Data Instances


  • 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": "住讜驻专讘讜诇 50 讛讬讛 诪砖讞拽 讻讚讜专讙诇 讗诪专讬拽讗讬 讻讚讬 诇拽讘讜注 讗转 讗诇讜驻转 诇讬讙转 讛驻讜讟讘讜诇 讛诇讗讜诪讬转 (NFL) 诇注讜谞转 2015. 讗诇讜驻转 讜注讬讚转 讛讻讚讜专讙诇 讛讗诪专讬拽讗讬转 (AFC) 讚谞讘专 讘专讜谞拽讜住 谞讬爪讞讛 讗转 讗诇讜驻转 讜注讬讚转 讛讻讚讜专讙诇 讛诇讗讜诪讬转 (NFC) 拽专讜诇讬谞讛 驻谞转专住 24鈥10 讻讚讬 诇讝讻讜转 讘转讜讗专 讛住讜驻专讘讜诇 讛砖诇讬砖讬 砖诇讛. 讛诪砖讞拽 谞注专讱 讘-7 讘驻讘专讜讗专 2016 讘讗爪讟讚讬讜谉 诇讬讜讜讬'住 讘讗讝讜专 诪驻专抓 住谉 驻专谞住讬住拽讜 讘住谞讟讛 拽诇专讛, 拽诇讬驻讜专谞讬讛. 诪讻讬讜讜谉 砖讝讛 讛讬讛 讛住讜驻专讘讜诇 讛-50, 讛诇讬讙讛 讛讚讙讬砖讛 讗转 讬讜诐 讛砖谞讛 讛讝讛讘 注诐 讬讜讝诪讜转 砖讜谞讜转 讘谞讜砖讗 讝讛讘, 讻诪讜 讙诐 讛砖注讬讛 讝诪谞讬转 讗转 讛诪住讜专转 砖诇 砖诐 讻诇 诪砖讞拽 住讜驻专讘讜诇 注诐 住驻专讜转 专讜诪讬讜转 (砖转讞转谉 讛诪砖讞拽 讛讬讛 讬讚讜注 讘转讜专 住讜驻专讘讜诇 L ), 讻讱 砖讛诇讜讙讜 讬讜讻诇 诇讛爪讬讙 讘讗讜驻谉 讘讜诇讟 讗转 讛住驻专讜转 讛注专讘讬讜转 50.",
            "question": "讛讬讻谉 讛转拽讬讬诐 住讜驻专讘讜诇 50?",
            "answers": {
                "text": ["住谞讟讛 拽诇专讛, 拽诇讬驻讜专谞讬讛", "讗爪讟讚讬讜谉 诇讬讜讜讬"],
                "answer_start": [311, 271]

Data Fields

The data fields are the same among all splits.


  • 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


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: May Flaster:

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Models trained or fine-tuned on tdklab/Hebrew_Squad_v1