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Browse files- .gitattributes +0 -37
- IndicQuestionGeneration.py +0 -112
- README.md +0 -213
- data/bn_QuestionGeneration_v1.0.zip → as/indic_question_generation-test.parquet +2 -2
- data/gu_QuestionGeneration_v1.0.zip → as/indic_question_generation-train.parquet +2 -2
- data/hi_QuestionGeneration_v1.0.zip → as/indic_question_generation-validation.parquet +2 -2
- data/as_QuestionGeneration_v1.0.zip → bn/indic_question_generation-test.parquet +2 -2
- bn/indic_question_generation-train.parquet +3 -0
- bn/indic_question_generation-validation.parquet +3 -0
- data/kn_QuestionGeneration_v1.0.zip +0 -3
- data/ml_QuestionGeneration_v1.0.zip +0 -3
- data/mr_QuestionGeneration_v1.0.zip +0 -3
- data/or_QuestionGeneration_v1.0.zip +0 -3
- data/pa_QuestionGeneration_v1.0.zip +0 -3
- data/ta_QuestionGeneration_v1.0.zip +0 -3
- data/te_QuestionGeneration_v1.0.zip +0 -3
- gu/indic_question_generation-test.parquet +3 -0
- gu/indic_question_generation-train.parquet +3 -0
- gu/indic_question_generation-validation.parquet +3 -0
- hi/indic_question_generation-test.parquet +3 -0
- hi/indic_question_generation-train.parquet +3 -0
- hi/indic_question_generation-validation.parquet +3 -0
- kn/indic_question_generation-test.parquet +3 -0
- kn/indic_question_generation-train.parquet +3 -0
- kn/indic_question_generation-validation.parquet +3 -0
- ml/indic_question_generation-test.parquet +3 -0
- ml/indic_question_generation-train.parquet +3 -0
- ml/indic_question_generation-validation.parquet +3 -0
- mr/indic_question_generation-test.parquet +3 -0
- mr/indic_question_generation-train.parquet +3 -0
- mr/indic_question_generation-validation.parquet +3 -0
- or/indic_question_generation-test.parquet +3 -0
- or/indic_question_generation-train.parquet +3 -0
- or/indic_question_generation-validation.parquet +3 -0
- pa/indic_question_generation-test.parquet +3 -0
- pa/indic_question_generation-train.parquet +3 -0
- pa/indic_question_generation-validation.parquet +3 -0
- ta/indic_question_generation-test.parquet +3 -0
- ta/indic_question_generation-train.parquet +3 -0
- ta/indic_question_generation-validation.parquet +3 -0
- te/indic_question_generation-test.parquet +3 -0
- te/indic_question_generation-train.parquet +3 -0
- te/indic_question_generation-validation.parquet +3 -0
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IndicQuestionGeneration.py
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import json
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import os
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import datasets
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_CITATION = """\
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@inproceedings{Kumar2022IndicNLGSM,
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title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
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author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
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year={2022},
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url = "https://arxiv.org/abs/2203.05437"
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}
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"""
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_DESCRIPTION = """\
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This is the Question Generation dataset released as part of IndicNLG Suite. Each
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example has five fields: id, squad_id, answer, context and question. We create this dataset in eleven
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languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. This is a translated data. The examples in each language are exactly similar but in different languages.
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The number of examples in each language is 98,027.
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"""
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_HOMEPAGE = "https://indicnlp.ai4bharat.org/indicnlg-suite"
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_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International Public License"
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_URL = "https://huggingface.co/datasets/ai4bharat/IndicQuestionGeneration/resolve/main/data/{}_QuestionGeneration_v{}.zip"
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_LANGUAGES = [
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"as",
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"bn",
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"gu",
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"hi",
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"kn",
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"ml",
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"mr",
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"or",
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"pa",
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"ta",
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"te"
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]
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class QuestionGeneration(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="{}".format(lang),
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version=datasets.Version("1.0.0")
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)
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for lang in _LANGUAGES
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"squad_id": datasets.Value("string"),
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"answer": datasets.Value("string"),
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"context": datasets.Value("string"),
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"question": datasets.Value("string")
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}
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),
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supervised_keys=None,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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license=_LICENSE,
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version=self.VERSION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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lang = str(self.config.name)
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url = _URL.format(lang, self.VERSION.version_str[:-2])
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data_dir = dl_manager.download_and_extract(url)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": os.path.join(data_dir, lang + "_train" + ".jsonl"),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepath": os.path.join(data_dir, lang + "_test" + ".jsonl"),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepath": os.path.join(data_dir, lang + "_val" + ".jsonl"),
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},
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),
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]
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def _generate_examples(self, filepath):
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"""Yields examples as (key, example) tuples."""
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with open(filepath, encoding="utf-8") as f:
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for idx_, row in enumerate(f):
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data = json.loads(row)
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yield idx_, {
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"id": data["id"],
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"squad_id": data["squad_id"],
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"answer": data["answer"],
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"context": data["context"],
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"question": data["question"]
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}
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README.md
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---
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annotations_creators:
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- no-annotation
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language_creators:
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- found
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language:
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- as
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- bn
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- gu
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- hi
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- kn
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- ml
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- mr
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- or
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- pa
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- ta
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- te
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license:
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- cc-by-nc-4.0
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multilinguality:
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- multilingual
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pretty_name: IndicQuestionGeneration
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size_categories:
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- 98K<n<98K
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source_datasets:
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- we start with the SQuAD question answering dataset repurposed to serve as a question generation dataset. We translate this dataset into different Indic languages.
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task_categories:
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- conditional-text-generation
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task_ids:
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- conditional-text-generation-other-question-generation
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---
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# Dataset Card for "IndicQuestionGeneration"
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## Table of Contents
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- [Dataset Card Creation Guide](#dataset-card-creation-guide)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
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- [Who are the source language producers?](#who-are-the-source-language-producers)
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- [Annotations](#annotations)
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- [Annotation process](#annotation-process)
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- [Who are the annotators?](#who-are-the-annotators)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** https://indicnlp.ai4bharat.org/indicnlg-suite
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- **Paper:** [IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://arxiv.org/abs/2203.05437)
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- **Point of Contact:**
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### Dataset Summary
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IndicQuestionGeneration is the question generation dataset released as part of IndicNLG Suite. Each
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example has five fields: id, squad_id, answer, context and question. We create this dataset in eleven
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languages, including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. This is translated data. The examples in each language are exactly similar but in different languages.
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The number of examples in each language is 98,027.
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### Supported Tasks and Leaderboards
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**Tasks:** Question Generation
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**Leaderboards:** Currently there is no Leaderboard for this dataset.
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### Languages
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- `Assamese (as)`
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- `Bengali (bn)`
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- `Gujarati (gu)`
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- `Kannada (kn)`
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- `Hindi (hi)`
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- `Malayalam (ml)`
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- `Marathi (mr)`
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- `Oriya (or)`
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- `Punjabi (pa)`
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- `Tamil (ta)`
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- `Telugu (te)`
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## Dataset Structure
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### Data Instances
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One random example from the `hi` dataset is given below in JSON format.
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```
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{
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"id": 8,
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"squad_id": "56be8e613aeaaa14008c90d3",
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"answer": "अमेरिकी फुटबॉल सम्मेलन",
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"context": "अमेरिकी फुटबॉल सम्मेलन (एएफसी) के चैंपियन डेनवर ब्रोंकोस ने नेशनल फुटबॉल कांफ्रेंस (एनएफसी) की चैंपियन कैरोलिना पैंथर्स को 24-10 से हराकर अपना तीसरा सुपर बाउल खिताब जीता।",
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"question": "एएफसी का मतलब क्या है?"
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}
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```
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### Data Fields
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- `id (string)`: Unique identifier.
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- `squad_id (string)`: Unique identifier in Squad dataset.
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- `answer (strings)`: Answer as one of the two inputs.
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- `context (string)`: Context, the other input.
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- `question (string)`: Question, the output.
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### Data Splits
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Here is the number of samples in each split for all the languages.
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Language | ISO 639-1 Code | Train | Dev | Test |
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---------- | ---------- | ---------- | ---------- | ---------- |
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Assamese | as | 69,979 | 17,495 | 10,553 |
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Bengali | bn | 69,979 | 17,495 | 10,553 |
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Gujarati | gu | 69,979 | 17,495 | 10,553 |
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Hindi | hi | 69,979 | 17,495 | 10,553 |
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Kannada | kn | 69,979 | 17,495 | 10,553 |
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Malayalam | ml | 69,979 | 17,495 | 10,553 |
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Marathi | mr | 69,979 | 17,495 | 10,553 |
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Oriya | or | 69,979 | 17,495 | 10,553 |
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Punjabi | pa | 69,979 | 17,495 | 10,553 |
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Tamil | ta | 69,979 | 17,495 | 10,553 |
|
140 |
-
Telugu | te | 69,979 | 17,495 | 10,553 |
|
141 |
-
|
142 |
-
|
143 |
-
## Dataset Creation
|
144 |
-
|
145 |
-
### Curation Rationale
|
146 |
-
|
147 |
-
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
|
148 |
-
|
149 |
-
### Source Data
|
150 |
-
|
151 |
-
Squad Dataset(https://rajpurkar.github.io/SQuAD-explorer/)
|
152 |
-
|
153 |
-
#### Initial Data Collection and Normalization
|
154 |
-
|
155 |
-
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
|
156 |
-
|
157 |
-
|
158 |
-
#### Who are the source language producers?
|
159 |
-
|
160 |
-
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
|
161 |
-
|
162 |
-
|
163 |
-
### Annotations
|
164 |
-
[More information needed]
|
165 |
-
#### Annotation process
|
166 |
-
[More information needed]
|
167 |
-
|
168 |
-
#### Who are the annotators?
|
169 |
-
|
170 |
-
[More information needed]
|
171 |
-
|
172 |
-
### Personal and Sensitive Information
|
173 |
-
|
174 |
-
[More information needed]
|
175 |
-
|
176 |
-
## Considerations for Using the Data
|
177 |
-
|
178 |
-
### Social Impact of Dataset
|
179 |
-
|
180 |
-
[More information needed]
|
181 |
-
|
182 |
-
### Discussion of Biases
|
183 |
-
|
184 |
-
[More information needed]
|
185 |
-
|
186 |
-
### Other Known Limitations
|
187 |
-
|
188 |
-
[More information needed]
|
189 |
-
|
190 |
-
## Additional Information
|
191 |
-
|
192 |
-
### Dataset Curators
|
193 |
-
|
194 |
-
[More information needed]
|
195 |
-
|
196 |
-
### Licensing Information
|
197 |
-
|
198 |
-
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
|
199 |
-
### Citation Information
|
200 |
-
|
201 |
-
If you use any of the datasets, models or code modules, please cite the following paper:
|
202 |
-
```
|
203 |
-
@inproceedings{Kumar2022IndicNLGSM,
|
204 |
-
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
|
205 |
-
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
|
206 |
-
year={2022},
|
207 |
-
url = "https://arxiv.org/abs/2203.05437",
|
208 |
-
```
|
209 |
-
|
210 |
-
|
211 |
-
### Contributions
|
212 |
-
|
213 |
-
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
|
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data/bn_QuestionGeneration_v1.0.zip → as/indic_question_generation-test.parquet
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