Datasets:
newsph_nli

Task Categories: text-classification
Languages: Tagalog
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: found
Annotations Creators: machine-generated
Source Datasets: original
Licenses: unknown
newsph_nli / newsph_nli.py
# 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.
"""NewsPH-NLI Sentence Entailment Dataset in Filipino"""
import csv
import os
import datasets
_DESCRIPTION = """\
First benchmark dataset for sentence entailment in the low-resource Filipino language.
Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs,
in 70-15-15 split for training, validation, and testing.
"""
_CITATION = """\
@article{cruz2020investigating,
title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
journal={arXiv preprint arXiv:2010.11574},
year={2020}
}
"""
_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "Filipino-Text-Benchmarks is licensed under the GNU General Public License v3.0"
_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/newsph/newsph-nli.zip"
class NewsphNli(datasets.GeneratorBasedBuilder):
"""NewsPH-NLI Sentence Entailment Dataset in Filipino"""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["0", "1"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL)
download_path = os.path.join(data_dir, "newsph-nli")
train_path = os.path.join(download_path, "train.csv")
test_path = os.path.join(download_path, "test.csv")
validation_path = os.path.join(download_path, "valid.csv")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_path,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_path,
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": validation_path,
"split": "dev",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
)
next(csv_reader)
for id_, row in enumerate(csv_reader):
premise, hypothesis, label = row
yield id_, {"premise": premise, "hypothesis": hypothesis, "label": label}