Datasets:

nlp-thedeep
/

humset

	# 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
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	# 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.

	"""HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response"""

	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 = """\
	HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three languages of English, French, and Spanish, originally taken from publicly-available resources. For each document, analysts have identified informative snippets (entries) in respect to common humanitarian frameworks, and assigned one or many classes to each entry. See the our paper for details.
	"""


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

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


	_URLs = {
	"1.0.0": {
	"train": "data/train.jsonl",
	"dev": "data/validation.jsonl",
	"test": "data/test.jsonl",
	},
	"2.0.0": {
	"train": "data/train_1_1.jsonl",
	"dev": "data/validation_1_1.jsonl",
	"test": "data/test_1_1.jsonl"
	}
	}


	_SUPPORTED_VERSIONS = [
	# First version
	datasets.Version("1.0.0", "Only primary tags"),
	# Second version
	datasets.Version("2.0.0", "Extented data points including secondary tags and geolocations"),
	]


	"""
	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.
	"""


	FIRST_FEATURES = datasets.Features(
	{
	"entry_id": datasets.Value("string"),
	"lead_id": datasets.Value("string"),
	"project_id": datasets.Value("string"),
	"lang": datasets.Value("string"),
	"n_tokens": datasets.Value("int64"),
	"project_title": datasets.Value("string"),
	"created_at": datasets.Value("string"),
	"document": datasets.Value("string"),
	"excerpt": 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),
	}
	)

	SECOND_FEATURES = datasets.Features(
	{
	"entry_id": datasets.Value("string"),
	"lead_id": datasets.Value("string"),
	"lang": datasets.Value("string"),
	"n_tokens": datasets.Value("int64"),
	"project_title": datasets.Value("string"),
	"created_at": datasets.Value("string"),
	"document": datasets.Value("string"),
	"source_title": datasets.Value("string"),
	"author_title": datasets.Value("string"),
	"excerpt": datasets.Value("string"),
	"geo_location": datasets.Sequence(datasets.Value("string"), length=-1),
	"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),
	"displaced": datasets.Sequence(datasets.Value("string"), length=-1),
	"non_displaced": datasets.Sequence(datasets.Value("string"), length=-1),
	"affected": datasets.Sequence(datasets.Value("string"), length=-1),
	"severity": datasets.Sequence(datasets.Value("string"), length=-1),
	"age": datasets.Sequence(datasets.Value("string"), length=-1),
	"gender": datasets.Sequence(datasets.Value("string"), length=-1),
	"specific_needs_groups": datasets.Sequence(datasets.Value("string"), length=-1)
	}
	)

	class HumsetConfig(datasets.BuilderConfig):
	"""BuilderConfig for DuoRC SelfRC."""

	def __init__(self, **kwargs):
	"""BuilderConfig for DuoRC 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 = "2.0.0"

	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,
	# This defines the different columns of the dataset and their types
	features=FIRST_FEATURES,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)
	elif self.config.name == "2.0.0":
	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=SECOND_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"],
	},
	)]

	if self.config.name in ["1.0.0", "2.0.0"]:
	splits = splits + [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": downloaded_files["test"],
	},
	)
	]

	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 self.config.name == "1.0.0":
	yield idx, row
	idx+=1