README.md

metadata

pretty_name: SberQuAD
annotations_creators:
  - crowdsourced
language_creators:
  - found
  - crowdsourced
language:
  - ru
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - extractive-qa
paperswithcode_id: sberquad
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: title
      dtype: string
    - name: context
      dtype: string
    - name: question
      dtype: string
    - name: answers
      sequence:
        - name: text
          dtype: string
        - name: answer_start
          dtype: int32
  config_name: sberquad
  splits:
    - name: test
      num_bytes: 36397848
      num_examples: 23936
    - name: train
      num_bytes: 71631661
      num_examples: 45328
    - name: validation
      num_bytes: 7972977
      num_examples: 5036
  download_size: 66047276
  dataset_size: 116002486

Dataset Card for sberquad

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: [Needs More Information]
• Repository: https://github.com/sberbank-ai/data-science-journey-2017
• Paper: https://arxiv.org/abs/1912.09723
• Leaderboard: [Needs More Information]
• Point of Contact: [Needs More Information]

Dataset Summary

Sber Question Answering Dataset (SberQuAD) 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. Russian original analogue presented in Sberbank Data Science Journey 2017.

Supported Tasks and Leaderboards

[Needs More Information]

Languages

Russian

Dataset Structure

Data Instances

{
    "context": "Первые упоминания о строении человеческого тела встречаются в Древнем Египте...",
    "id": 14754,
    "qas": [
        {
            "id": 60544,
            "question": "Где встречаются первые упоминания о строении человеческого тела?",
            "answers": [{"answer_start": 60, "text": "в Древнем Египте"}],
        }
    ]
}

Data Fields

• id: a int32 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	test
plain_text	45328	5036	23936

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

@article{DBLP:journals/corr/abs-1912-09723,
  author    = {Pavel Efimov and
               Leonid Boytsov and
               Pavel Braslavski},
  title     = {SberQuAD - Russian Reading Comprehension Dataset: Description and
               Analysis},
  journal   = {CoRR},
  volume    = {abs/1912.09723},
  year      = {2019},
  url       = {http://arxiv.org/abs/1912.09723},
  eprinttype = {arXiv},
  eprint    = {1912.09723},
  timestamp = {Fri, 03 Jan 2020 16:10:45 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1912-09723.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

Thanks to @alenusch for adding this dataset.