FRENK-hate-sl / FRENK-hate-sl.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,
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# See the License for the specific language governing permissions and
# limitations under the License.
"""An annotated dataset for classifying offensive or acceptable speech."""
import os
import csv
import datasets
_CITATION = """\
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
"""
_DESCRIPTION = """\
The FRENK Datasets of Socially Unacceptable Discourse in Slovene.
"""
_HOMEPAGE = "https://www.clarin.si/repository/xmlui/handle/11356/1433"
_LICENSE = "CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0"
_URL = "https://huggingface.co/datasets/classla/FRENK-hate-sl/resolve/main/data.zip"
_CLASS_MAP_MULTICLASS = {
'Acceptable speech': 0,
'Inappropriate': 1,
'Background offensive': 2,
'Other offensive': 3,
'Background violence': 4,
'Other violence': 5,
}
_CLASS_MAP_BINARY = {
'Acceptable': 0,
'Offensive': 1,
}
class FRENKHateSpeechSL(datasets.GeneratorBasedBuilder):
"""The FRENK Datasets of Socially Unacceptable Discourse in Slovene"""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="binary", version=VERSION,
description="Labels are either 'Offensive' or 'Acceptable'."),
datasets.BuilderConfig(name="multiclass", version=VERSION,
description="Labels are 'Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence'"),
]
DEFAULT_CONFIG_NAME = "binary"
def _info(self):
feature_dict = {
"text": datasets.Value("string"),
"target": datasets.Value("string"),
"topic": datasets.Value("string"),
}
if self.config.name == "binary":
features = datasets.Features(
{
**feature_dict,
"label": datasets.ClassLabel(names=["Acceptable", "Offensive"]),
}
)
else:
features = datasets.Features(
{
**feature_dict,
"label": datasets.ClassLabel(names=['Acceptable speech', 'Other offensive', 'Background offensive', 'Inappropriate', 'Other violence', 'Background violence']),
}
)
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_file = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={
'filepath': os.path.join(data_file, "train.tsv"),
}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={
'filepath': os.path.join(data_file, "dev.tsv"),
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={
'filepath': os.path.join(data_file, "test.tsv"),
}
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.reader(f, delimiter="\t")
for id_, row in enumerate(reader):
if id_ == 0:
continue
to_return_dict = {
"text": row[1],
"target": row[4] ,
"topic": row[5]
}
yield id_, {
**to_return_dict,
**{"label": _CLASS_MAP_BINARY[row[3]] if self.config.name == "binary" else _CLASS_MAP_MULTICLASS[row[2]]}
}