File size: 5,941 Bytes
297a502 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
"""Ethos dataset"""
import pandas as pd
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
@misc{mollas2020ethos,
title={ETHOS: an Online Hate Speech Detection Dataset},
author={Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and Grigorios Tsoumakas},
year={2020},
eprint={2006.08328},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """
ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech
detection on social media platforms, called Ethos. There are two variations of the dataset:
Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label
about hate speech presence or absence. 565 of them do not contain hate speech,
while the rest of them, 433, contain.
Ethos_Dataset_Multi_Label: which contains 8 labels for the 433 comments with hate speech content.
These labels are violence (if it incites (1) or not (0) violence), directed_vs_general (if it is
directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like,
gender, race, national_origin, disability, religion and sexual_orientation.
"""
_URL = "https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
class EthosConfig(datasets.BuilderConfig):
"""BuilderConfig for Ethos."""
def __init__(self, variation="binary", **kwargs):
"""Constructs an EthosDataset.
Args:
variation: can be binary or multilabel
**kwargs: keyword arguments forwarded to super.
"""
if variation.lower() == "binary":
self.variation = "binary"
elif variation.lower() == "multilabel":
self.variation = "multilabel"
else:
logger.warning("Wrong variation. Could be either 'binary' or 'multilabel', using 'binary' instead.")
self.variation = "binary"
super(EthosConfig, self).__init__(**kwargs)
class Ethos(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = EthosConfig
BUILDER_CONFIGS = [
EthosConfig(
name="binary",
version=datasets.Version("1.0.0", ""),
description="Ethos Binary",
variation="binary",
),
EthosConfig(
name="multilabel",
version=datasets.Version("1.0.0", ""),
description="Ethos Multi Label",
variation="multilabel",
),
]
def _info(self):
if self.config.variation == "binary":
f = datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["no_hate_speech", "hate_speech"]),
}
)
else:
f = datasets.Features(
{
"text": datasets.Value("string"),
"violence": datasets.ClassLabel(names=["not_violent", "violent"]),
"directed_vs_generalized": datasets.ClassLabel(names=["generalied", "directed"]),
"gender": datasets.ClassLabel(names=["false", "true"]),
"race": datasets.ClassLabel(names=["false", "true"]),
"national_origin": datasets.ClassLabel(names=["false", "true"]),
"disability": datasets.ClassLabel(names=["false", "true"]),
"religion": datasets.ClassLabel(names=["false", "true"]),
"sexual_orientation": datasets.ClassLabel(names=["false", "true"]),
}
)
return datasets.DatasetInfo(
features=f,
supervised_keys=None,
homepage="https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset/tree/masterethos/ethos_data",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.variation == "binary":
url = {
"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
"/master/ethos/ethos_data/Ethos_Dataset_Binary.csv"
}
else:
url = {
"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
"/master/ethos/ethos_data/Ethos_Dataset_Multi_Label.csv"
}
downloaded_files = dl_manager.download_and_extract(url)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})]
def _generate_examples(self, filepath):
"""Yields examples."""
data = pd.read_csv(filepath, delimiter=";")
if self.config.variation == "binary":
x = data["comment"].values
y = [1 if i >= 0.5 else 0 for i in data["isHate"].values]
class_names = ["no_hate_speech", "hate_speech"]
for i in range(len(x)):
_id = i
yield _id, {"text": x[i], "label": class_names[y[i]]}
else:
x = data["comment"].values
y_temp = data.loc[:, data.columns != "comment"].values
y = []
for yt in y_temp:
yi = []
for i in yt:
if i >= 0.5:
yi.append(int(1))
else:
yi.append(int(0))
y.append(yi)
for i in range(len(x)):
_id = i
yield _id, {
"text": x[i],
"violence": y[i][0],
"directed_vs_generalized": y[i][1],
"gender": y[i][2],
"race": y[i][3],
"national_origin": y[i][4],
"disability": y[i][5],
"religion": y[i][6],
"sexual_orientation": y[i][7],
}
|