Datasets:
Tasks:
Text Classification
Sub-tasks:
text-scoring
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
toxicity-prediction
License:
File size: 7,782 Bytes
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# coding=utf-8
# Copyright 2021 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.
"""Jigsaw Unintended Bias in Toxicity Classification dataset"""
import os
import pandas as pd
import datasets
_DESCRIPTION = """\
A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity.
"""
_HOMEPAGE = "https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/"
_LICENSE = "CC0 (both the dataset and underlying text)"
class JigsawUnintendedBias(datasets.GeneratorBasedBuilder):
"""A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity."""
VERSION = datasets.Version("1.1.0")
@property
def manual_download_instructions(self):
return """\
To use jigsaw_unintended_bias you have to download it manually from Kaggle: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data
You can manually download the data from it's homepage or use the Kaggle CLI tool (follow the instructions here: https://www.kaggle.com/docs/api)
Please extract all files in one folder and then load the dataset with:
`datasets.load_dataset('jigsaw_unintended_bias', data_dir='/path/to/extracted/data/')`"""
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"target": datasets.Value("float32"),
"comment_text": datasets.Value("string"),
"severe_toxicity": datasets.Value("float32"),
"obscene": datasets.Value("float32"),
"identity_attack": datasets.Value("float32"),
"insult": datasets.Value("float32"),
"threat": datasets.Value("float32"),
"asian": datasets.Value("float32"),
"atheist": datasets.Value("float32"),
"bisexual": datasets.Value("float32"),
"black": datasets.Value("float32"),
"buddhist": datasets.Value("float32"),
"christian": datasets.Value("float32"),
"female": datasets.Value("float32"),
"heterosexual": datasets.Value("float32"),
"hindu": datasets.Value("float32"),
"homosexual_gay_or_lesbian": datasets.Value("float32"),
"intellectual_or_learning_disability": datasets.Value("float32"),
"jewish": datasets.Value("float32"),
"latino": datasets.Value("float32"),
"male": datasets.Value("float32"),
"muslim": datasets.Value("float32"),
"other_disability": datasets.Value("float32"),
"other_gender": datasets.Value("float32"),
"other_race_or_ethnicity": datasets.Value("float32"),
"other_religion": datasets.Value("float32"),
"other_sexual_orientation": datasets.Value("float32"),
"physical_disability": datasets.Value("float32"),
"psychiatric_or_mental_illness": datasets.Value("float32"),
"transgender": datasets.Value("float32"),
"white": datasets.Value("float32"),
"created_date": datasets.Value("string"),
"publication_id": datasets.Value("int32"),
"parent_id": datasets.Value("float"),
"article_id": datasets.Value("int32"),
"rating": datasets.ClassLabel(names=["rejected", "approved"]),
"funny": datasets.Value("int32"),
"wow": datasets.Value("int32"),
"sad": datasets.Value("int32"),
"likes": datasets.Value("int32"),
"disagree": datasets.Value("int32"),
"sexual_explicit": datasets.Value("float"),
"identity_annotator_count": datasets.Value("int32"),
"toxicity_annotator_count": datasets.Value("int32"),
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(data_dir):
raise FileNotFoundError(
f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('jigsaw_unintended_bias', data_dir=...)`. Manual download instructions: {self.manual_download_instructions}"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"path": os.path.join(data_dir, "train.csv"), "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split("test_private_leaderboard"),
# These kwargs will be passed to _generate_examples
gen_kwargs={"path": os.path.join(data_dir, "test_private_expanded.csv"), "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split("test_public_leaderboard"),
# These kwargs will be passed to _generate_examples
gen_kwargs={"path": os.path.join(data_dir, "test_public_expanded.csv"), "split": "test"},
),
]
def _generate_examples(self, split: str = "train", path: str = None):
"""Yields examples."""
# This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
# The key is not important, it's more here for legacy reason (legacy from tfds)
# Avoid loading everything into memory at once
all_data = pd.read_csv(path, chunksize=50000)
for data in all_data:
if split != "train":
data = data.rename(columns={"toxicity": "target"})
for _, row in data.iterrows():
example = row.to_dict()
ex_id = example.pop("id")
yield (ex_id, example)
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