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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.
"""Indo-Aryan Language Identification Shared Task Dataset"""
from __future__ import absolute_import, division, print_function
import datasets
_CITATION = """\
@proceedings{ws-2018-nlp-similar,
title = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
editor = {Zampieri, Marcos and
Nakov, Preslav and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
Malmasi, Shervin and
Ali, Ahmed},
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-3900",
}
"""
_DESCRIPTION = """\
This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family –
Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri, and Magahi.
"""
_URL = "https://raw.githubusercontent.com/kmi-linguistics/vardial2018/master/dataset/{}.txt"
class Ilist(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"language_id": datasets.ClassLabel(names=["AWA", "BRA", "MAG", "BHO", "HIN"]),
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/kmi-linguistics/vardial2018",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
filepaths = dl_manager.download_and_extract(
{
"train": _URL.format("train"),
"test": _URL.format("gold"),
"dev": _URL.format("dev"),
}
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepaths["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepaths["test"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepaths["dev"],
},
),
]
def _generate_examples(self, filepath):
""" Yields examples. """
with open(filepath, "r", encoding="utf-8") as file:
for idx, row in enumerate(file):
row = row.strip("\n").split("\t")
if len(row) == 1:
continue
yield idx, {"language_id": row[1], "text": row[0]}
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