Datasets:

google
/

jigsaw_toxicity_pred

	# 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,
	# 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.
	"""Comments from Jigsaw Toxic Comment Classification Kaggle Competition """


	import os

	import pandas as pd

	import datasets


	_DESCRIPTION = """\
	This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.
	"""

	_HOMEPAGE = "https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data"

	_LICENSE = 'The "Toxic Comment Classification" dataset is released under CC0, with the underlying comment text being governed by Wikipedia\'s CC-SA-3.0.'


	class JigsawToxicityPred(datasets.GeneratorBasedBuilder):
	"""This is a dataset of comments from Wikipedia’s talk page edits which have been labeled by human raters for toxic behavior."""

	VERSION = datasets.Version("1.1.0")

	@property
	def manual_download_instructions(self):
	return """\
	To use jigsaw_toxicity_pred you have to download it manually from Kaggle: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/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_toxicity_pred', 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(
	{
	"comment_text": datasets.Value("string"),
	"toxic": datasets.ClassLabel(names=["false", "true"]),
	"severe_toxic": datasets.ClassLabel(names=["false", "true"]),
	"obscene": datasets.ClassLabel(names=["false", "true"]),
	"threat": datasets.ClassLabel(names=["false", "true"]),
	"insult": datasets.ClassLabel(names=["false", "true"]),
	"identity_hate": datasets.ClassLabel(names=["false", "true"]),
	}
	),
	# 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_toxicity_pred', 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={"train_path": os.path.join(data_dir, "train.csv"), "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"test_text_path": os.path.join(data_dir, "test.csv"),
	"test_labels_path": os.path.join(data_dir, "test_labels.csv"),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, split="train", train_path=None, test_text_path=None, test_labels_path=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)

	if split == "test":
	df1 = pd.read_csv(test_text_path)
	df2 = pd.read_csv(test_labels_path)
	df3 = df1.merge(df2)
	df4 = df3[df3["toxic"] != -1]

	elif split == "train":
	df4 = pd.read_csv(train_path)

	for _, row in df4.iterrows():
	example = {}
	example["comment_text"] = row["comment_text"]

	for label in ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]:
	if row[label] != -1:
	example[label] = int(row[label])
	yield (row["id"], example)