Datasets:

mvarma
/

medwiki

	# 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.
	"""MedWiki is a large-scale sentence dataset collected from Wikipedia with medical entity (UMLS) annotations. This dataset is intended for pretraining"""

	import csv
	import json
	import os

	import datasets

	_CITATION = """\
	@inproceedings{medwiki,
	title={Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text},
	author={Maya Varma and Laurel Orr and Sen Wu and Megan Leszczynski and Xiao Ling and Christopher Ré},
	year={2021},
	booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021}
	}
	"""

	_DESCRIPTION = """\
	MedWiki is a large-scale sentence dataset collected from Wikipedia with medical entity (UMLS) annotations. This dataset is intended for pretraining.
	"""

	_HOMEPAGE = ""

	_LICENSE = ""

	_URLs = {"medwiki_full": "medwiki_full.zip", "medwiki_hq": "medwiki_hq.zip"}


	class MedWiki(datasets.GeneratorBasedBuilder):
	"""MedWiki: A Large-Scale Sentence Dataset with Medical Entity (UMLS) Annotations"""

	VERSION = datasets.Version("1.1.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="medwiki",
	version=VERSION,
	description="MedWiki: A Large-Scale Sentence Dataset with Medical Entity (UMLS) Annotations"),
	]
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="medwiki_full", version=VERSION, description="MedWiki (Full): A Large-Scale Sentence Dataset with Medical Entity (UMLS) Annotations."),
	datasets.BuilderConfig(name="medwiki_hq", version=VERSION, description="MedWiki (HQ): A Large-Scale Sentence Dataset with Medical Entity (UMLS) Annotations. The HQ (high quality) subset of MedWiki includes a portion of the dataset with higher-quality entity annotations."),
	]


	def _info(self):
	features = datasets.Features(
	{
	"mentions": datasets.Sequence(datasets.Value("string")),
	"entities": datasets.Sequence(datasets.Value("string")),
	"entity_titles": datasets.Sequence(datasets.Value("string")),
	"types": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	"spans": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
	"sentence": datasets.Value("string"),
	"sent_idx_unq": datasets.Value("int32"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)

	#Adjust filenames for medwiki_hq subset
	ext = ""
	if self.config.name == "medwiki_hq": ext = "_hq"

	#Load splits
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, f"train{ext}.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, f"test{ext}.jsonl"),
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir, f"dev{ext}.jsonl"),
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split ):
	""" Yields examples as (key, example) tuples. """

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	yield id_, {
	"mentions": data["mentions"],
	"entities": data["entities"],
	"entity_titles": data['entity_titles'],
	"types": data["types"],
	"spans": data["spans"],
	"sentence": data["sentence"],
	"sent_idx_unq": data["sent_idx_unq"],
	}