Datasets:

TurkuNLP
/

Suomi24-toxicity-annotated

	import json
	import pandas as pd
	import datasets


	_DESCRIPTION = """\
	This dataset consists of Suomi24 comments which have been labeled by human raters for toxic behavior.
	"""

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

	_URLS = {
	"test": "https://huggingface.co/datasets/TurkuNLP/Suomi24-toxicity-annotated/resolve/main/all_annotations.tsv"
	}


	class Suomi24ToxicityPred(datasets.GeneratorBasedBuilder):
	"""This is a dataset of comments sampled from Suomi24 and annotated using jigsaw toxicity labels."""

	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": datasets.Value("string") # we only have one label for each text
	}
	),
	# 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="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 tsv file
	with open(filepath, "r") as f:
	data = f.readlines()
	data = data[1:]
	for i in range(len(data)):
	data[i] = data[i].replace("\n", "")
	data[i] = data[i].split("\t")
	assert len(data[i]) == 3

	from collections import Counter
	from itertools import count

	ids = [one[0] for one in data]
	c = Counter(ids)

	iters = {k: count(1) for k, v in c.items() if v > 1}
	output_list = [x+str(next(iters[x])) if x in iters else x for x in ids]

	count = 0
	# here make the data into a proper thing
	for one in data:
	example = {}
	text_id = output_list[count] # change this somehow
	count = count + 1
	example["text"] = one[2]
	example["label"] = one[1]

	yield (text_id, example)