File size: 5,236 Bytes
4e12778 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
# coding=utf-8
# Copyright 2022 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.
"""
MayoSRS consists of 101 clinical term pairs whose relatedness was determined by
nine medical coders and three physicians from the Mayo Clinic.
"""
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from .bigbiohub import pairs_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
_LANGUAGES = ['English']
_PUBMED = False
_LOCAL = False
_CITATION = """\
@article{pedersen2007measures,
title={Measures of semantic similarity and relatedness in the biomedical domain},
author={Pedersen, Ted and Pakhomov, Serguei VS and Patwardhan, Siddharth and Chute, Christopher G},
journal={Journal of biomedical informatics},
volume={40},
number={3},
pages={288--299},
year={2007},
publisher={Elsevier}
}
"""
_DATASETNAME = "minimayosrs"
_DISPLAYNAME = "MiniMayoSRS"
_DESCRIPTION = """\
MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement was
achieved. The average correlation between physicians is 0.68. The average correlation between medical coders is 0.78.
"""
_HOMEPAGE = "https://conservancy.umn.edu/handle/11299/196265"
_LICENSE = 'Creative Commons Zero v1.0 Universal'
_URLS = {
_DATASETNAME: "https://conservancy.umn.edu/bitstream/handle/11299/196265/MiniMayoSRS.csv?sequence=2&isAllowed=y"
}
_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class MinimayosrsDataset(datasets.GeneratorBasedBuilder):
"""MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement
was achieved. The average correlation between physicians is 0.68. The average correlation between medical coders
is 0.78.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="minimayosrs_source",
version=SOURCE_VERSION,
description="MiniMayoSRS source schema",
schema="source",
subset_id="minimayosrs",
),
BigBioConfig(
name="minimayosrs_bigbio_pairs",
version=BIGBIO_VERSION,
description="MiniMayoSRS BigBio schema",
schema="bigbio_pairs",
subset_id="minimayosrs",
),
]
DEFAULT_CONFIG_NAME = "minimayosrs_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"text_1": datasets.Value("string"),
"text_2": datasets.Value("string"),
"code_1": datasets.Value("string"),
"code_2": datasets.Value("string"),
"label_physicians": datasets.Value("float32"),
"label_coders": datasets.Value("float32"),
}
)
elif self.config.schema == "bigbio_pairs":
features = pairs_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=str(_LICENSE),
citation=_CITATION,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
urls = _URLS[_DATASETNAME]
filepath = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": filepath},
)
]
def _generate_examples(self, filepath) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
data = pd.read_csv(
filepath,
sep=",",
header=0,
names=[
"label_physicians",
"label_coders",
"code_1",
"code_2",
"text_1",
"text_2",
],
)
if self.config.schema == "source":
for id_, row in data.iterrows():
yield id_, row.to_dict()
elif self.config.schema == "bigbio_pairs":
for id_, row in data.iterrows():
yield id_, {
"id": id_,
"document_id": id_,
"text_1": row["text_1"],
"text_2": row["text_2"],
"label": str((row["label_physicians"] + row["label_coders"]) / 2),
}
|