Datasets:
Tasks:
Translation
Languages:
Persian
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
extended
ArXiv:
Tags:
License:
# 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. | |
"""ParsiNLU Persian reading comprehension task""" | |
from __future__ import absolute_import, division, print_function | |
import csv | |
import json | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """\ | |
@article{huggingface:dataset, | |
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian}, | |
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others}, | |
year={2020} | |
journal = {arXiv e-prints}, | |
eprint = {2012.06154}, | |
} | |
""" | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
A Persian translation dataset (English -> Persian). | |
""" | |
_HOMEPAGE = "https://github.com/persiannlp/parsinlu/" | |
_LICENSE = "CC BY-NC-SA 4.0" | |
_URL = "https://media.githubusercontent.com/media/persiannlp/parsinlu/master/data/translation/translation_combined_en_fa/" | |
_URLs = { | |
"train": _URL + "train.tsv", | |
"dev": _URL + "dev.tsv", | |
"test": _URL + "test.tsv", | |
} | |
class ParsinluReadingComprehension(datasets.GeneratorBasedBuilder): | |
"""ParsiNLU Persian reading comprehension task.""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="parsinlu-repo", version=VERSION, description="ParsiNLU repository: translation" | |
), | |
] | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"source": datasets.Value("string"), | |
"targets": datasets.features.Sequence( | |
datasets.Value("string") | |
), | |
"category": datasets.Value("string"), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, | |
# specify them here. They'll be used if as_supervised=True in | |
# builder.as_dataset. | |
supervised_keys=None, | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_dir = dl_manager.download_and_extract(_URLs) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": data_dir["train"], | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": data_dir["test"], "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": data_dir["dev"], | |
"split": "dev", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
logger.info("generating examples from = %s", filepath) | |
print(filepath) | |
with open(filepath) as f: | |
for id_, row in enumerate(f.readlines()): | |
try: | |
if id_ == 0: | |
continue | |
row = row.split("\t") | |
if len(row) < 3: | |
print(f"* Ignoring the following line since it doesn't have three columns: {row}") | |
continue | |
source = row[0].replace("\t", "").replace("\n", "") | |
targets = row[1].replace("\t", "").replace("\n", "").split('///') | |
category = row[2].replace("\t", "").replace("\n", "") | |
yield id_, { | |
'source': source, | |
'targets': targets, | |
'category': category, | |
} | |
except: | |
print(" * skipping . . . ") | |