task_categories:question-answering size_categories:1M<n<10M language:ru license:apache-2.0 wikipedia wikiomnia squad QA arxiv:2204.08009 region:us
TBA
---
license: apache-2.0
dataset_info:
- config_name: wikiomnia_ruT5_raw
  features:
  - name: title
    dtype: string
  - name: categories
    dtype: string
  - name: summary
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: batch_id
    dtype: string
  splits:
  - name: dev
    num_bytes: 600356136
    num_examples: 266295
  - name: test
    num_bytes: 572651444
    num_examples: 267751
  download_size: 1204094848
  dataset_size: 1173007580
- config_name: wikiomnia_ruT5_filtered
  features:
  - name: title
    dtype: string
  - name: categories
    dtype: string
  - name: summary
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: batch_id
    dtype: string
  splits:
  - name: train
    num_bytes: 4157093224
    num_examples: 2088027
  download_size: 4278635364
  dataset_size: 4157093224
- config_name: wikiomnia_ruGPT3_filtered
  features:
  - name: title
    dtype: string
  - name: categories
    dtype: string
  - name: summary
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: batch_id
    dtype: string
  splits:
  - name: train
    num_bytes: 338607635
    num_examples: 173314
  download_size: 348694031
  dataset_size: 338607635
- config_name: wikiomnia_ruGPT3_raw
  features:
  - name: title
    dtype: string
  - name: categories
    dtype: string
  - name: summary
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: batch_id
    dtype: string
  splits:
  - name: train_batch1
    num_bytes: 553204785
    num_examples: 260808
  - name: train_batch2
    num_bytes: 542823205
    num_examples: 263599
  - name: train_batch3
    num_bytes: 582321994
    num_examples: 269736
  - name: train_batch4
    num_bytes: 543315355
    num_examples: 265948
  - name: train_batch5
    num_bytes: 513288049
    num_examples: 268466
  - name: train_batch6
    num_bytes: 943556173
    num_examples: 512147
  - name: train_batch7
    num_bytes: 929464509
    num_examples: 508149
  - name: train_batch8
    num_bytes: 915128725
    num_examples: 507559
  - name: train_batch9
    num_bytes: 926443048
    num_examples: 504292
  - name: train_batch10
    num_bytes: 834958539
    num_examples: 463812
  - name: train_batch11
    num_bytes: 509866027
    num_examples: 287770
  - name: train_batch12
    num_bytes: 478843738
    num_examples: 271410
  - name: train_batch13
    num_bytes: 757068702
    num_examples: 385730
  - name: train_batch14
    num_bytes: 575937629
    num_examples: 304110
  - name: train_batch15
    num_bytes: 517092031
    num_examples: 277507
  - name: train_batch16
    num_bytes: 759363156
    num_examples: 402203
  - name: train_batch17
    num_bytes: 860544388
    num_examples: 466572
  - name: train_batch18
    num_bytes: 935985528
    num_examples: 518348
  - name: train_batch19
    num_bytes: 936782197
    num_examples: 514307
  - name: train_batch20
    num_bytes: 874299949
    num_examples: 487238
  download_size: 14939875008
  dataset_size: 14490287727
- config_name: wikiomnia_ruT5_raw_train
  features:
  - name: title
    dtype: string
  - name: categories
    dtype: string
  - name: summary
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: batch_id
    dtype: string
  splits:
  - name: train_batch3
    num_bytes: 612693602
    num_examples: 271391
  - name: train_batch4
    num_bytes: 570286147
    num_examples: 265947
  - name: train_batch5
    num_bytes: 552502041
    num_examples: 274650
  - name: train_batch6
    num_bytes: 1017066184
    num_examples: 525224
  - name: train_batch7
    num_bytes: 972351430
    num_examples: 509615
  - name: train_batch8
    num_bytes: 973314180
    num_examples: 516828
  - name: train_batch9
    num_bytes: 981651841
    num_examples: 512709
  - name: train_batch10
    num_bytes: 880664685
    num_examples: 469512
  - name: train_batch11
    num_bytes: 543971388
    num_examples: 294631
  - name: train_batch12
    num_bytes: 503939060
    num_examples: 273526
  - name: train_batch13
    num_bytes: 794421530
    num_examples: 392021
  - name: train_batch14
    num_bytes: 610815879
    num_examples: 311452
  - name: train_batch15
    num_bytes: 540225492
    num_examples: 278677
  - name: train_batch16
    num_bytes: 804003566
    num_examples: 411192
  - name: train_batch17
    num_bytes: 903347135
    num_examples: 469871
  - name: train_batch18
    num_bytes: 995239085
    num_examples: 528301
  - name: train_batch19
    num_bytes: 1003402360
    num_examples: 522264
  - name: train_batch20
    num_bytes: 948137237
    num_examples: 499866
  download_size: 14634332336
  dataset_size: 14208032842
task_categories:
- question-answering
language:
- ru
tags:
- wikipedia
- wikiomnia
- squad
- QA
pretty_name: WikiOmnia
size_categories:
- 1M<n<10M
---

# Dataset Card for "Wikiomnia"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)
 
## Dataset Description

- **Repository:** [https://github.com/RussianNLP](https://github.com/RussianNLP)
- **Paper:** [WikiOmnia: filtration and evaluation of the generated QA corpus on the whole Russian Wikipedia](https://arxiv.org/abs/2204.08009)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Dataset Summary
We present the WikiOmnia dataset, a new publicly available set of QA-pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generative pipeline. The dataset includes every available article from Wikipedia for the Russian language. The WikiOmnia pipeline is available open-source and is also tested for creating SQuAD-formatted QA on other domains, like news texts, fiction, and social media. The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).

WikiOmnia consists of 2 parts:
1. the voluminous, automatically generated part: 15,9 million triplets consisting of the original article summary, a corresponding generated question and a generated answer;
2. the filtered part: the subsample of 3,5 million triplets, fully verified with automatic means

Wikiomnia adheres to a standard SQuAD format problem, resulting in triplets "text paragraph - question based on paragraph - answer from the paragraph", see the following example:

**Original Wikipedia paragraph**: Коити Масимо (яп. Масимо Ко:ити) — известный режиссёр аниме и основатель японской анимационной студии Bee Train. С
момента основания студии он руководит производством почти всех её картин, а также время от времени принимает участие в работе над анимацией и музыкой.

**English translation**: Koichi Mashimo is a famous anime director and the founder of the Japanese animation studio Bee Train. Since the creation of the studio, he directed almost all studio’s works, and he
also sometimes participates in art and sound tasks. 

**Generated question (ruT5)**: Кто является основателем японской анимационной студии Bee Train?

**Generated answer (ruT5)**: Коити Масимо 

**English QA translation**: Who is the founder of the Japanese animation studio Bee Train? Koichi Mashimo 

## Dataset Creation

Models used for dataset generation:
 - [ruT5](https://huggingface.co/sberbank-ai/ruT5-large) large fine-tuned on SberQuaD
 - [ruGPT-3](https://huggingface.co/sberbank-ai/rugpt3xl) XL fine-tuned on SberQuaD
 - [ruBERT](http://docs.deeppavlov.ai/en/master/features/models/squad.html) DeepPavlov tuned for QA tasks

Source: Wikipedia version March 2021

Special tokens: <[TEXT]>, <[QUESTION]>, <[ANSWER]>

The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-
large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).

![](https://raw.githubusercontent.com/RussianNLP/WikiOmnia/main/wikomnia_pipeline.png)

## Additional Information

### Licensing Information

[Apache 2.0 license](https://github.com/RussianNLP/WikiOmnia/blob/main/LICENSE)

### Citation Information

```
@inproceedings{pisarevskaya-shavrina-2022-wikiomnia,
    title = "{W}iki{O}mnia: filtration and evaluation of the generated {QA} corpus on the whole {R}ussian {W}ikipedia",
    author = "Pisarevskaya, Dina  and
      Shavrina, Tatiana",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.gem-1.10",
    pages = "125--135",
    abstract = "The General QA field has been developing the methodology referencing the Stanford Question answering dataset (SQuAD) as the significant benchmark. Compiling factual questions datasets requires manual annotations, limiting the training data{'}s potential size. We present the WikiOmnia dataset, a new publicly available set of QA pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generation and filtration pipeline. To ensure high quality of generated QA pairs, diverse manual and automated evaluation techniques were applied. The WikiOmnia pipeline is available open-source and is also tested for creating SQuAD-formatted QA on other domains, like news texts, fiction, and social media. The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).",
}

```
### Contributions

Thanks to [@Deenochka](https://github.com/deenochka), [@TatianaShavrina](https://github.com/TatianaShavrina)