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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""CVIT IIIT-H PIB Multilingual Corpus"""
import os
import datasets
_CITATION = """\
@InProceedings{cvit-pib:multilingual-corpus,
title = {Revisiting Low Resource Status of Indian Languages in Machine Translation},
authors={Jerin Philip, Shashank Siripragada, Vinay P. Namboodiri, C.V. Jawahar
},
year={2020}
}
"""
_DESCRIPTION = """\
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.
"""
_URL = "http://preon.iiit.ac.in/~jerin/resources/datasets/pib-v0.tar"
_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",
]
class PibConfig(datasets.BuilderConfig):
"""BuilderConfig for PIB"""
def __init__(self, language_pair, **kwargs):
super().__init__(**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):
# TODO: Specifies the datasets.DatasetInfo object
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="http://preon.iiit.ac.in/~jerin/bhasha/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_URL)
data_dir = os.path.join(dl_dir, f"pib/{self.config.src}-{self.config.tgt}")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, f"train.{self.config.src}"),
"labelpath": os.path.join(data_dir, f"train.{self.config.tgt}"),
},
),
]
def _generate_examples(self, filepath, labelpath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f1, open(labelpath, encoding="utf-8") as f2:
src = f1.read().split("\n")[:-1]
tgt = f2.read().split("\n")[:-1]
for idx, (s, t) in enumerate(zip(src, tgt)):
yield idx, {"translation": {self.config.src: s, self.config.tgt: t}}
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