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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.
"""Hate Speech Text Classification Dataset in Filipino."""
import csv
import os
import datasets
_DESCRIPTION = """\
Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections.
"""
_CITATION = """\
@article{Cabasag-2019-hate-speech,
title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.},
author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng},
journal={Philippine Computing Journal},
volume={XIV},
number={1},
month={August},
year={2019}
}
"""
_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
_URL = "https://huggingface.co/datasets/jcblaise/hatespeech_filipino/resolve/main/hatespeech_raw.zip"
class HateSpeechFilipino(datasets.GeneratorBasedBuilder):
"""Hate Speech Text Classification Dataset in Filipino."""
VERSION = datasets.Version("1.0.0")
def _info(self):
# Labels: 0="Non-hate Speech", 1="Hate Speech"
features = datasets.Features(
{"text": 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)
train_path = os.path.join(data_dir, "hatespeech", "train.csv")
test_path = os.path.join(data_dir, "hatespeech", "test.csv")
validation_path = os.path.join(data_dir, "hatespeech", "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):
try:
text, label = row
yield id_, {"text": text, "label": label}
except ValueError:
pass |