Datasets:
Multilinguality:
multilingual
Size Categories:
5K<n<112K
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original for Hindi, and modified [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) for other languages.
ArXiv:
Tags:
License:
File size: 3,446 Bytes
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import json
import os
import datasets
_CITATION = """\
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
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},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
"""
_DESCRIPTION = """\
This is the sentence summarization dataset released as part of IndicNLG Suite. Each
input sentence is paired with an output summary. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta and te. The total
size of the dataset is 431K.
"""
_HOMEPAGE = "https://indicnlp.ai4bharat.org/indicnlg-suite"
_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International Public License"
_URL = "https://huggingface.co/datasets/ai4bharat/IndicSentenceSummarization/resolve/main/data/{}_IndicSentenceSummarization_v{}.zip"
_LANGUAGES = [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
]
class IndicSentenceSummarization(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="{}".format(lang),
version=datasets.Version("1.0.0")
)
for lang in _LANGUAGES
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id":datasets.Value("string"),
"input": datasets.Value("string"),
"target": datasets.Value("string"),
"url":datasets.Value("string")
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
version=self.VERSION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
lang = str(self.config.name)
url = _URL.format(lang, self.VERSION.version_str[:-2])
data_dir = dl_manager.download_and_extract(url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir,"content",lang + "_train.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir,"content",lang + "_test.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir,"content",lang + "_dev.jsonl"),
},
),
]
def _generate_examples(self, filepath):
"""Yields examples as (key, example) tuples."""
with open(filepath, encoding="utf-8") as f:
for idx_, row in enumerate(f):
data = json.loads(row)
yield idx_, {
"id":data["id"],
"input": data["Sentence"],
"target": data["Summary"],
"url":data["URL"]
} |