Datasets:

bigbio
/

ask_a_patient

Languages:

English

Multilinguality:

monolingual

Tags:

License:

Dataset card Viewer Files Files and versions Community

ask_a_patient / ask_a_patient.py

gabrielaltay

Fix TOO MANY REQUESTS error (#2)

a64ba3e over 1 year ago

raw history blame contribute delete

No virus

5.9 kB

	# 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.

	import os

	import datasets

	from .bigbiohub import BigBioConfig, Tasks, kb_features

	_DATASETNAME = "ask_a_patient"
	_DISPLAYNAME = "AskAPatient"

	_LANGUAGES = ["English"]
	_PUBMED = True
	_LOCAL = False
	_CITATION = """
	@inproceedings{limsopatham-collier-2016-normalising,
	title = "Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation",
	author = "Limsopatham, Nut and
	Collier, Nigel",
	booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
	month = aug,
	year = "2016",
	address = "Berlin, Germany",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/P16-1096",
	doi = "10.18653/v1/P16-1096",
	pages = "1014--1023",
	}
	"""

	_DESCRIPTION = """
	The AskAPatient dataset contains medical concepts written on social media \
	mapped to how they are formally written in medical ontologies (SNOMED-CT and AMT).
	"""

	_HOMEPAGE = "https://zenodo.org/record/55013"

	_LICENSE = "Creative Commons Attribution 4.0 International"

	_URLs = "https://zenodo.org/record/55013/files/datasets.zip"

	_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
	_SOURCE_VERSION = "1.0.0"
	_BIGBIO_VERSION = "1.0.0"


	class AskAPatient(datasets.GeneratorBasedBuilder):
	"""AskAPatient: Dataset for Normalising Medical Concepts in Social Media Text."""

	DEFAULT_CONFIG_NAME = "ask_a_patient_source"
	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="ask_a_patient_source",
	version=SOURCE_VERSION,
	description="AskAPatient source schema",
	schema="source",
	subset_id="ask_a_patient",
	),
	BigBioConfig(
	name="ask_a_patient_bigbio_kb",
	version=BIGBIO_VERSION,
	description="AskAPatient simplified BigBio schema",
	schema="bigbio_kb",
	subset_id="ask_a_patient",
	),
	]

	def _info(self):
	if self.config.schema == "source":
	features = datasets.Features(
	{
	"cui": datasets.Value("string"),
	"medical_concept": datasets.Value("string"),
	"social_media_text": datasets.Value("string"),
	}
	)
	elif self.config.schema == "bigbio_kb":
	features = kb_features
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=str(_LICENSE),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	dl_dir = dl_manager.download_and_extract(_URLs)
	dataset_dir = os.path.join(dl_dir, "datasets", "AskAPatient")
	# dataset supports k-folds
	splits_names = ["train", "validation", "test"]
	fold_ids = range(10)
	return [
	datasets.SplitGenerator(
	name=f"{split_name}_{fold_id}",
	gen_kwargs={
	"filepath": os.path.join(dataset_dir, f"AskAPatient.fold-{fold_id}.{split_name}.txt"),
	"split_id": f"{split_name}_{fold_id}",
	},
	)
	for split_name in splits_names
	for fold_id in fold_ids
	]

	def _generate_examples(self, filepath, split_id):
	with open(filepath, "r", encoding="latin-1") as f:
	for i, line in enumerate(f):
	uid = f"{split_id}_{i}"
	cui, medical_concept, social_media_text = line.strip().split("\t")
	if self.config.schema == "source":
	yield uid, {
	"cui": cui,
	"medical_concept": medical_concept,
	"social_media_text": social_media_text,
	}
	elif self.config.schema == "bigbio_kb":
	text_type = "social_media_text"
	offset = (0, len(social_media_text))
	yield uid, {
	"id": uid,
	"document_id": uid,
	"passages": [
	{
	"id": f"{uid}_passage",
	"type": text_type,
	"text": [social_media_text],
	"offsets": [offset],
	}
	],
	"entities": [
	{
	"id": f"{uid}_entity",
	"type": text_type,
	"text": [social_media_text],
	"offsets": [offset],
	"normalized": [{"db_name": "SNOMED-CT\|AMT", "db_id": cui}],
	}
	],
	"events": [],
	"coreferences": [],
	"relations": [],
	}