parsinlu_entailment / parsinlu_entailment.py
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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.
"""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 textual entailment task (deciding `sent1` entails `sent2`).
"""
_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"
_LICENSE = "CC BY-NC-SA 4.0"
_URL = "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/entailment/"
_URLs = {
"train": _URL + "train.csv",
"dev": _URL + "dev.csv",
"test": _URL + "test.csv",
}
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: query-paraphrasing"
),
]
def _info(self):
features = datasets.Features(
{
"sent1": datasets.Value("string"),
"sent2": datasets.Value("string"),
"category": datasets.Value("string"),
"label": 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)
with open(filepath, encoding="utf-8") as f:
reader = csv.reader(f)
for id_, row in enumerate(reader):
if id_ == 0:
continue
sent1 = row[1].replace("\t", "").replace("\n", "")
sent2 = row[2].replace("\t", "").replace("\n", "")
label = row[3].replace("\t", "").replace("\n", "")
cat = row[4].replace("\t", "").replace("\n", "")
yield id_, {
'sent1': sent1,
'sent2': sent2,
'label': label,
'category': cat,
}