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README.md

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----
-license: cc-by-4.0
-pretty_name: SberQuAD for question generation
-language: ru
-multilinguality: monolingual
-size_categories: 10K<n<100K
-source_datasets: deepset/germanquad
-task_categories:
-- text-generation
-task_ids:
-- language-modeling
-tags:
-- question-generation
----
-
-# Dataset Card for "lmqg/qg_ruquad"
-
-
-## Dataset Description
-- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
-- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
-- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
-
-### Dataset Summary
-This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
- ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
-This is a modified version of [SberQuaD](https://huggingface.co/datasets/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 is assumed to be used to train a model for question generation.
-  Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
-
-### Languages
-Russian (ru)
-
-## Dataset Structure
-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..."  
-}
-```
-The data fields are the same among all splits.
-- `question`: a `string` feature. 
-- `paragraph`: a `string` feature.
-- `answer`: a `string` feature.
-- `sentence`: a `string` feature.
-- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
-- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
-- `sentence_answer`: a `string` 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
-
-|train|validation|test |
-|----:|---------:|----:|
-| 45327 |   5036    |23936  |
-
-
-## Citation Information
-
-```
-@inproceedings{ushio-etal-2022-generative,
-    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
-    author = "Ushio, Asahi  and
-        Alva-Manchego, Fernando  and
-        Camacho-Collados, Jose",
-    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
-    month = dec,
-    year = "2022",
-    address = "Abu Dhabi, U.A.E.",
-    publisher = "Association for Computational Linguistics",
-}
-```