Datasets:
Tasks:
Text Classification
Modalities:
Text
Languages:
Persian
Size:
10K<n<100K
ArXiv:
Tags:
License:
File size: 2,987 Bytes
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import json
import csv
import datasets
_CITATION = """\\
@article{amirkhani2020farstail,
title={FarsTail: A Persian Natural Language Inference Dataset},
author={Hossein Amirkhani, Mohammad Azari Jafari, Azadeh Amirak, Zohreh Pourjafari, Soroush Faridan Jahromi, and Zeinab Kouhkan},
journal={arXiv preprint arXiv:2009.08820},
year={2020}
}
"""
_DESCRIPTION = """\\\\\\\\
A Persian Natural Language Inference Dataset
"""
_URL = "https://raw.githubusercontent.com/dml-qom/FarsTail/master/data/"
_URLS = {
"train": _URL + "Train-word.csv",
"test": _URL + "Test-word.csv",
"validation": _URL + "Val-word.csv"
}
class FarsTailConfig(datasets.BuilderConfig):
"""BuilderConfig for FarsTail."""
def __init__(self, **kwargs):
"""BuilderConfig for FarsTail.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(FarsTailConfig, self).__init__(**kwargs)
class FarsTail(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
FarsTailConfig(name="FarsTail", version=datasets.Version("1.0.0"), description="persian NLI dataset"),
]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.Value("string")
}
),
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/dml-qom/FarsTail",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}),
]
def _generate_examples(self, filepath):
try:
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for idx, row in enumerate(reader):
yield idx, {
"premise": row["premise"],
"hypothesis": row["hypothesis"],
"label": row["label"],
}
except Exception as e:
print(e)
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