Datasets:

nlp-thedeep
/

humsetbias

	# 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.

	"""HumSetBias"""

	import json
	import datasets


	_CITATION = """"""
	#@misc{https://doi.org/10.48550/arxiv.2210.04573,
	# doi = {10.48550/ARXIV.2210.04573},
	# url = {https://arxiv.org/abs/2210.04573},
	# author = {Fekih, Selim and Tamagnone, Nicolò and Minixhofer, Benjamin and Shrestha, Ranjan and Contla, Ximena and Oglethorpe, Ewan and Rekabsaz, Navid},
	# keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
	# title = {HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response},
	# publisher = {arXiv},
	# year = {2022},
	# copyright = {arXiv.org perpetual, non-exclusive license}
	#}
	#"""


	_DESCRIPTION_BIAS = """\
	HUMSETBIAS is a subset of the English part of the HumSet dataset, created by searching for specific sensitive English keywords related to genders and countries within the annotated text. In addition, we extended this
	subset by incorporating targeted counterfactual samples, generated by modifying the original entries in order to create the altered versions of each text with gender/country information. The purpose of HUMSETBIAS is to provide a more targeted resource for analyzing and addressing potential biases in humanitarian data and to enable the development of accurate and bias-aware NLP applications in the humanitarian sector.
	"""

	_HOMEPAGE = "https://huggingface.co/datasets/nlp-thedeep/humsetbias"

	_LICENSE = "The GitHub repository which houses this dataset has an Apache License 2.0."


	_URLs = {
	"1.0.0": {
	"train": "data/humset_bias_train.jsonl",
	"dev": "data/humset_bias_val.jsonl",
	#"gender": "data/test_gender.jsonl",
	#"country": "data/test_country.jsonl"
	}
	}


	_SUPPORTED_VERSIONS = [
	# First version
	datasets.Version("1.0.0", "Gender and Country bias extension of HumSet")
	]


	"""
	from: https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.Sequence
	a python list or a Sequence specifies that the field contains a list of objects.
	The python list or Sequence should be provided with a single sub-feature as an example of the feature type hosted in this list.
	"""


	HUMSETBIAS_FEATURES = datasets.Features(
	{
	"entry_id": datasets.Value("string"),
	"excerpt": datasets.Value("string"),
	"lang": datasets.Value("string"),
	"keywords": datasets.Sequence(datasets.Value("string"), length=-1),
	"kword_type": datasets.Sequence(datasets.Value("string"), length=-1),
	"gender_keywords": datasets.Sequence(datasets.Value("string"), length=-1),
	"country_keywords": datasets.Sequence(datasets.Value("string"), length=-1),
	"gender_kword_type": datasets.Sequence(datasets.Value("string"), length=-1),
	"country_kword_type": datasets.Sequence(datasets.Value("string"), length=-1),
	"gender_context_falsing_kw": datasets.Sequence(datasets.Value("string"), length=-1),
	"country_context_falsing_kw": datasets.Sequence(datasets.Value("string"), length=-1),
	"excerpt_type": datasets.Value("string"),
	"sectors": datasets.Sequence(datasets.Value("string"), length=-1),
	"pillars_1d": datasets.Sequence(datasets.Value("string"), length=-1),
	"pillars_2d": datasets.Sequence(datasets.Value("string"), length=-1),
	"subpillars_1d": datasets.Sequence(datasets.Value("string"), length=-1),
	"subpillars_2d": datasets.Sequence(datasets.Value("string"), length=-1),
	}
	)

	""""
	HUMSETBIAS_GENERAL = [
	'entry_id',
	'excerpt',
	"lang",
	'excerpt_type',
	"gender_keywords",
	"country_keywords",
	"gender_kword_type",
	"country_kword_type",
	'gender_context_falsing_kw',
	"country_context_falsing_kw",
	"sectors",
	"pillars_1d",
	"pillars_2d",
	"subpillars_1d",
	"subpillars_2d"
	]

	HUMSETBIAS_FEATURES_GENDER = [
	'entry_id',
	'excerpt',
	"lang",
	'keywords',
	'kword_type',
	'excerpt_type',
	'gender_context_falsing_kw',
	"sectors",
	"pillars_1d",
	"pillars_2d",
	"subpillars_1d",
	"subpillars_2d"
	]

	HUMSETBIAS_FEATURES_COUNTRY = [
	'entry_id',
	'excerpt',
	"lang",
	'keywords',
	'kword_type',
	'excerpt_type',
	'country_context_falsing_kw',
	"sectors",
	"pillars_1d",
	"pillars_2d",
	"subpillars_1d",
	"subpillars_2d"
	]
	"""


	class HumsetConfig(datasets.BuilderConfig):
	"""BuilderConfig for HumsetBias."""

	def __init__(self, **kwargs):
	"""BuilderConfig for HumsetBias SelfRC.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(HumsetConfig, self).__init__(**kwargs)


	class Humset(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	HumsetConfig(
	name=str(version),
	description=f"version {str(version)}",
	version=version
	)
	for version in _SUPPORTED_VERSIONS
	]

	DEFAULT_CONFIG_NAME = "1.0.0"

	def _info(self):

	if self.config.name == "1.0.0":
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION_BIAS,
	# This defines the different columns of the dataset and their types
	features=HUMSETBIAS_FEATURES,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):

	"""Returns SplitGenerators."""

	my_urls = _URLs[self.config.name]
	downloaded_files = dl_manager.download_and_extract(my_urls)
	splits = [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": downloaded_files["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": downloaded_files["dev"]
	},
	)
	]
	#datasets.SplitGenerator(
	# name="test_gender",
	# gen_kwargs={
	# "filepath": downloaded_files["gender"]
	# },
	#),
	#datasets.SplitGenerator(
	# name="test_country",
	# gen_kwargs={
	# "filepath": downloaded_files["country"]
	# }
	#),
	#]

	return splits

	def _generate_examples(self, filepath):

	"""This function returns the examples in the raw (text) form."""
	with open(filepath, encoding="utf-8") as f:
	data = list(f)
	idx = 0
	for line in data:
	row = json.loads(line)
	#if "train" in filepath:
	# row = {k: v for k, v in row.items() if k in HUMSETBIAS_GENERAL}
	#elif "gender" in filepath:
	# row = {k: v for k, v in row.items() if k in HUMSETBIAS_FEATURES_GENDER}
	#elif "country" in filepath:
	# row = {k: v for k, v in row.items() if k in HUMSETBIAS_FEATURES_COUNTRY}
	#if self.config.name == "1.0.0":
	yield idx, row
	idx+=1