Datasets:

google
/

jigsaw_unintended_bias

	# 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)