Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
hate-speech-detection
Languages:
English
Size:
100K - 1M
License:
File size: 4,547 Bytes
32a38db 5c6e5ca 2a16c9a 32a38db bb05a2f 5c05f44 32a38db 5c05f44 9bac24d 32a38db 9bac24d 32a38db 9bac24d 32a38db 9bac24d 32a38db 9bac24d 32a38db |
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 |
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""Jigsaw Toxic Comment Challenge dataset"""
import pandas as pd
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """"""
_DESCRIPTION = """\
Jigsaw Toxic Comment Challenge dataset. This dataset was the basis of a Kaggle competition run by Jigsaw
"""
_HOMEPAGE = "https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
_URLS = {
"train": "train.csv",
"validation": "validation.csv",
"test": "test.csv",
"balanced_train": "balanced_train.csv",
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class WikiToxic(datasets.GeneratorBasedBuilder):
"""Jigsaw Toxic Comment Challenge dataset."""
VERSION = datasets.Version("1.0.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"comment_text": datasets.Value("string"),
"label": datasets.ClassLabel(names=["non", "tox"])
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
features=features
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["validation"],
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test"
},
),
datasets.SplitGenerator(
name="balanced_train",
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["balanced_train"],
"split": "balanced_train"
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
df = pd.read_csv(filepath)
for index, row in df.iterrows():
yield index, {
"id": row["id"],
"comment_text": row["comment_text"],
"label": row["label"],
} |