Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Sub-tasks:
entity-linking-retrieval
Size:
10M - 100M
ArXiv:
License:
File size: 5,038 Bytes
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# 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"],
}
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