Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
Russian
Size:
10K<n<100K
ArXiv:
License:
metadata
license: cc-by-4-0
pretty_name: SberQuAD for question generation
languages: ru
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: deepset/germanquad
task_categories: question-generation
task_ids: question-generation
Dataset Card for "qg_ruquad"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Repository: https://github.com/asahi417/lm-question-generation
- Paper: More Information Needed
- Point of Contact: Asahi Ushio
Dataset Summary
Modified version of SberQuaD for question generation (QG) task. Since the original dataset only contains training/validation set, we manually sample test set from training set, which has no overlap in terms of the paragraph with the training set.
Supported Tasks and Leaderboards
question-generation
: The dataset can be used to train a model for question generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L score.
Languages
Russian (ru)
Dataset Structure
Data Instances
plain_text
An example of 'train' looks as follows.
{
'answer': 'известковыми выделениями сине-зелёных водорослей',
'question': 'чем представлены органические остатки?',
'sentence': 'Они представлены известковыми выделениями сине-зелёных водорослей , ходами червей, остатками кишечнополостных.'
'paragraph': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. Они представлены..."
'sentence_answer': "Они представлены <hl> известковыми выделениями сине-зелёных водорослей <hl> , ход...",
'paragraph_answer': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. Они представлены <hl> известковыми выделениям...",
'paragraph_sentence': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. <hl> Они представлены известковыми выделениями сине-зелёных водорослей , ходами червей, остатками кишечнополостных. <hl> Кроме..."
}
Data Fields
The data fields are the same among all splits.
plain_text
question
: astring
feature.paragraph
: astring
feature.answer
: astring
feature.sentence
: astring
feature.paragraph_answer
: astring
feature, which is same as the paragraph but the answer is highlighted by a special token<hl>
.paragraph_sentence
: astring
feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token<hl>
.sentence_answer
: astring
feature, which is same as the sentence but the answer is highlighted by a special token<hl>
.
Each of paragraph_answer
, paragraph_sentence
, and sentence_answer
feature is assumed to be used to train a question generation model,
but with different information. The paragraph_answer
and sentence_answer
features are for answer-aware question generation and
paragraph_sentence
feature is for sentence-aware question generation.
Data Splits
name | train | validation | test |
---|---|---|---|
plain_text | 45327 | 5036 | 23936 |