Datasets:

TurkuNLP
/

jigsaw_toxicity_pred_fi

	"""Comments from Jigsaw Toxic Comment Classification Kaggle Competition """

	import json

	import pandas as pd

	import datasets


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

	_HOMEPAGE = "https://turkunlp.org/"

	_URLS = {
	"train": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/train_fi_deepl.jsonl.bz2",
	"test": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/test_fi_deepl.jsonl.bz2"
	}


	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")


	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(
	{
	"text": datasets.Value("string"),
	"label_toxicity": datasets.ClassLabel(names=["false", "true"]),
	"label_severe_toxicity": datasets.ClassLabel(names=["false", "true"]),
	"label_obscene": datasets.ClassLabel(names=["false", "true"]),
	"label_threat": datasets.ClassLabel(names=["false", "true"]),
	"label_insult": datasets.ClassLabel(names=["false", "true"]),
	"label_identity_attack": 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
	)

	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

	urls_to_download = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": downloaded_files["train"]}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files["test"],
	},
	),
	]

	def _generate_examples(self, filepath):
	"""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)

	# read the json into dictionaries
	with open(filepath, 'r') as json_file:
	json_list = list(json_file)
	lines = [json.loads(jline) for jline in json_list]

	for data in lines:
	example = {}
	example["text"] = data["text"]

	for label in ["label_toxicity", "label_severe_toxicity", "label_obscene", "label_threat", "label_insult", "label_identity_attack"]:
	example[label] = int(data[label])

	yield (data["id"], example)