Datasets:

coastalcph
/

medical-bios

	# 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.
	"""Medical BIOS"""

	import json
	import os
	import textwrap

	import datasets


	MAIN_CITATION = """https://aclanthology.org/2023.emnlp-main.427/"""
	_DESCRIPTION = """NA"""
	MAIN_PATH = 'https://huggingface.co/datasets/coastalcph/medical-bios/resolve/main'


	class MedicalBIOSConfig(datasets.BuilderConfig):
	"""BuilderConfig for Medical BIOS."""

	def __init__(
	self,
	label_classes,
	url,
	data_url,
	citation,
	**kwargs,
	):
	"""BuilderConfig for Medical BIOS.
	Args:
	label_classes: `list`, list of label classes
	url: `string`, url for the original project
	data_url: `string`, url to download the zip file from
	data_file: `string`, filename for data set
	citation: `string`, citation for the data set
	url: `string`, url for information about the data set
	**kwargs: keyword arguments forwarded to super.
	"""
	super(MedicalBIOSConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.label_classes = label_classes
	self.url = url
	self.data_url = data_url
	self.citation = citation


	class XAIFairness(datasets.GeneratorBasedBuilder):
	"""Fairlex: A multilingual benchmark for evaluating fairness in legal text processing. Version 1.0"""

	BUILDER_CONFIGS = [
	MedicalBIOSConfig(
	name="standard",
	description=textwrap.dedent(
	"""\
	The dataset is based on the Common Crawl. Specifically, De-Arteaga et al. identified online
	biographies, written in English, by filtering for lines that began
	with a name-like pattern (i.e., a sequence of two capitalized words)
	followed by the string “is a(n) (xxx) title,” where title is
	an occupation from the BLS Standard Occupation Classification system.
	This version of the dataset comprises English biographies labeled with occupations.
	We also include a subset of biographies labeled with human rationales.
	"""
	),
	label_classes=['psychologist', 'surgeon', 'nurse', 'dentist', 'physician'],
	data_url=os.path.join(MAIN_PATH, "bios.zip"),
	url="https://github.com/microsoft/biosbias",
	citation=textwrap.dedent(
	"""\
	@inproceedings{eberle-etal-2023-rather,
	title = "Rather a Nurse than a Physician - Contrastive Explanations under Investigation",
	author = "Eberle, Oliver and
	Chalkidis, Ilias and
	Cabello, Laura and
	Brandl, Stephanie",
	booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
	year = "2023",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2023.emnlp-main.427",
	}"""
	),
	),
	MedicalBIOSConfig(
	name="rationales",
	description=textwrap.dedent(
	"""\
	The dataset is based on the Common Crawl. Specifically, De-Arteaga et al. identified online
	biographies, written in English, by filtering for lines that began
	with a name-like pattern (i.e., a sequence of two capitalized words)
	followed by the string “is a(n) (xxx) title,” where title is
	an occupation from the BLS Standard Occupation Classification system.
	This version of the dataset comprises English biographies labeled with occupations.
	We also include a subset of biographies labeled with human rationales.
	"""
	),
	label_classes=['psychologist', 'surgeon', 'nurse', 'dentist', 'physician'],
	data_url=os.path.join(MAIN_PATH, "bios.zip"),
	url="https://github.com/microsoft/biosbias",
	citation=textwrap.dedent(
	"""\
	@inproceedings{eberle-etal-2023-rather,
	title = "Rather a Nurse than a Physician - Contrastive Explanations under Investigation",
	author = "Eberle, Oliver and
	Chalkidis, Ilias and
	Cabello, Laura and
	Brandl, Stephanie",
	booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
	year = "2023",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2023.emnlp-main.427",
	}"""
	),
	),
	]

	def _info(self):
	if self.config.name == "standard":
	features = {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=self.config.label_classes)}
	else:
	features = {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=self.config.label_classes),
	"foil": datasets.ClassLabel(names=self.config.label_classes),
	"words": datasets.Sequence(datasets.Value("string")),
	"rationales": datasets.Sequence(datasets.Value("int8")),
	"contrastive_rationales": datasets.Sequence(datasets.Value("int8")),
	"annotations": datasets.Sequence(datasets.Sequence(datasets.Value("int8"))),
	"contrastive_annotations": datasets.Sequence(datasets.Sequence(datasets.Value("int8")))}
	return datasets.DatasetInfo(
	description=self.config.description,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=self.config.citation + "\n" + MAIN_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(self.config.data_url)
	if self.config.name == 'standard':
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, f"train.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "test.jsonl"),
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, f"validation.jsonl"),
	"split": "val",
	},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "test_rationales.jsonl"),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""This function returns the examples in the raw (text) form."""
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	example = {
	"text": data["text"],
	"label": data["title"]
	}
	if self.config.name == "rationales":
	example["foil"] = data["foil"]
	example["words"] = data["words"]
	example["rationales"] = data["rationales"]
	example["contrastive_rationales"] = data["contrastive_rationales"]
	example["annotations"] = data["annotations"]
	example["contrastive_annotations"] = data["contrastive_annotations"]
	yield id_, example