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import datasets
_CITATION = """\
@inproceedings{siripragada-etal-2020-multilingual,
title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages",
author = "Siripragada, Shashank and
Philip, Jerin and
Namboodiri, Vinay P. and
Jawahar, C V",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.462",
pages = "3743--3751",
language = "English",
ISBN = "979-10-95546-34-4",
}
@article{2020,
title={Revisiting Low Resource Status of Indian Languages in Machine Translation},
url={http://dx.doi.org/10.1145/3430984.3431026},
DOI={10.1145/3430984.3431026},
journal={8th ACM IKDD CODS and 26th COMAD},
publisher={ACM},
author={Philip, Jerin and Siripragada, Shashank and Namboodiri, Vinay P. and Jawahar, C. V.},
year={2020},
month={Dec}
}
"""
_DESCRIPTION = """\
Sentence aligned parallel corpus between 11 Indian Languages, crawled and extracted from the press information bureau
website.
"""
_HOMEPAGE = "http://preon.iiit.ac.in/~jerin/bhasha/"
_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International"
_URL = {
"0.0.0": "http://preon.iiit.ac.in/~jerin/resources/datasets/pib-v0.tar",
"1.3.0": "http://preon.iiit.ac.in/~jerin/resources/datasets/pib_v1.3.tar.gz",
}
_ROOT_DIR = {
"0.0.0": "pib",
"1.3.0": "pib-v1.3",
}
_LanguagePairs = [
"or-ur",
"ml-or",
"bn-ta",
"gu-mr",
"hi-or",
"en-or",
"mr-ur",
"en-ta",
"hi-ta",
"bn-en",
"bn-or",
"ml-ta",
"gu-ur",
"bn-ml",
"ml-pa",
"en-pa",
"bn-hi",
"hi-pa",
"gu-te",
"pa-ta",
"hi-ml",
"or-te",
"en-ml",
"en-hi",
"bn-pa",
"mr-te",
"mr-pa",
"bn-te",
"gu-hi",
"ta-ur",
"te-ur",
"or-pa",
"gu-ml",
"gu-pa",
"hi-te",
"en-te",
"ml-te",
"pa-ur",
"hi-ur",
"mr-or",
"en-ur",
"ml-ur",
"bn-mr",
"gu-ta",
"pa-te",
"bn-gu",
"bn-ur",
"ml-mr",
"or-ta",
"ta-te",
"gu-or",
"en-gu",
"hi-mr",
"mr-ta",
"en-mr"
"as-or",
]
class PibConfig(datasets.BuilderConfig):
"""BuilderConfig for PIB"""
def __init__(self, language_pair, version=datasets.Version("1.3.0"), **kwargs):
super().__init__(version=version, **kwargs)
"""
Args:
language_pair: language pair, you want to load
**kwargs: keyword arguments forwarded to super.
"""
self.src, self.tgt = language_pair.split("-")
class Pib(datasets.GeneratorBasedBuilder):
"""This new dataset is the large scale sentence aligned corpus in 11 Indian languages, viz.
CVIT-PIB corpus that is the largest multilingual corpus available for Indian languages.
"""
BUILDER_CONFIG_CLASS = PibConfig
BUILDER_CONFIGS = [PibConfig(name=pair, description=_DESCRIPTION, language_pair=pair) for pair in _LanguagePairs]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{"translation": datasets.features.Translation(languages=[self.config.src, self.config.tgt])}
),
supervised_keys=(self.config.src, self.config.tgt),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(_URL[str(self.config.version)])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"archive": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, archive):
root_dir = _ROOT_DIR[str(self.config.version)]
data_dir = f"{root_dir}/{self.config.src}-{self.config.tgt}"
src = tgt = None
for path, file in archive:
if data_dir in path:
if f"{data_dir}/train.{self.config.src}" in path:
src = file.read().decode("utf-8").split("\n")[:-1]
if f"{data_dir}/train.{self.config.tgt}" in path:
tgt = file.read().decode("utf-8").split("\n")[:-1]
if src and tgt:
break
for idx, (s, t) in enumerate(zip(src, tgt)):
yield idx, {"translation": {self.config.src: s, self.config.tgt: t}} |