File size: 5,933 Bytes
4aab736
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1b7eb6a
4aab736
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1b7eb6a
 
 
 
 
 
 
 
4aab736
 
 
 
 
 
8ab81e7
4aab736
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1b7eb6a
4aab736
 
 
 
 
1b7eb6a
4aab736
 
 
 
1b7eb6a
4aab736
 
 
 
1b7eb6a
4aab736
 
 
 
1b7eb6a
4aab736
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# coding=utf-8
# Copyright 2020 the HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""


import csv
import os.path

import datasets


_CITATION = """\
@inproceedings{wen-etal-2020-medal,
    title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
    author = "Wen, Zhi  and
      Lu, Xing Han  and
      Reddy, Siva",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
    pages = "130--135",
    abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
}"""

_DESCRIPTION = """\
A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate
"""

_URLS = {
    "pretrain": "data/pretrain_subset.zip",
    "full": "data/full_data.csv.zip"
}
_FILENAMES = {
    "train": "train.csv",
    "test": "test.csv",
    "valid": "valid.csv",
    "full": "full_data.csv",
}


class Medal(datasets.GeneratorBasedBuilder):
    """Medal: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""

    VERSION = datasets.Version("4.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # datasets.features.FeatureConnectors
            features=datasets.Features(
                {
                    "abstract_id": datasets.Value("int32"),
                    "text": datasets.Value("string"),
                    "location": datasets.Sequence(datasets.Value("int32")),
                    "label": datasets.Sequence(datasets.Value("string")),
                    # These are the features of your dataset like images, labels ...
                }
            ),
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage="https://github.com/BruceWen120/medal",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # dl_manager is a datasets.download.DownloadManager that can be used to
        # download and extract URLs
        dl_dir = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["train"]), "split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["test"]), "split": "test"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["valid"]), "split": "val"},
            ),
            datasets.SplitGenerator(
                name="full",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": os.path.join(dl_dir["full"], _FILENAMES["full"]), "split": "full"},
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            data = csv.reader(f)
            # Skip header
            next(data)
            if split == "full":
                id_ = 0
                for id_, row in enumerate(data):
                    yield id_, {
                        "abstract_id": -1,
                        "text": row[0],
                        "location": [int(location) for location in row[1].split("|")],
                        "label": row[2].split("|"),
                    }
            else:
                for id_, row in enumerate(data):
                    yield id_, {
                        "abstract_id": int(row[0]),
                        "text": row[1],
                        "location": [int(row[2])],
                        "label": [row[3]],
                    }