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Update files from the datasets library (from 1.2.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

Files changed (5) hide show
  1. .gitattributes +27 -0
  2. README.md +161 -0
  3. dataset_infos.json +1 -0
  4. dummy/1.0.0/dummy_data.zip +3 -0
  5. medal.py +146 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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1
+ ---
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+ annotations_creators:
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+ - expert-generated
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+ language_creators:
5
+ - expert-generated
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+ languages:
7
+ - en
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+ licenses:
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+ - unknown
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+ multilinguality:
11
+ - monolingual
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+ size_categories:
13
+ - n<1K
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+ source_datasets:
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+ - original
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+ task_categories:
17
+ - other
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+ task_ids:
19
+ - other-other-disambiguation
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+ ---
21
+ # Dataset Card Creation Guide
22
+
23
+ ## Table of Contents
24
+ - [Dataset Description](#dataset-description)
25
+ - [Dataset Summary](#dataset-summary)
26
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
27
+ - [Languages](#languages)
28
+ - [Dataset Structure](#dataset-structure)
29
+ - [Data Instances](#data-instances)
30
+ - [Data Fields](#data-instances)
31
+ - [Data Splits](#data-instances)
32
+ - [Dataset Creation](#dataset-creation)
33
+ - [Curation Rationale](#curation-rationale)
34
+ - [Source Data](#source-data)
35
+ - [Annotations](#annotations)
36
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
37
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
38
+ - [Social Impact of Dataset](#social-impact-of-dataset)
39
+ - [Discussion of Biases](#discussion-of-biases)
40
+ - [Other Known Limitations](#other-known-limitations)
41
+ - [Additional Information](#additional-information)
42
+ - [Dataset Curators](#dataset-curators)
43
+ - [Licensing Information](#licensing-information)
44
+ - [Citation Information](#citation-information)
45
+
46
+ ## Dataset Description
47
+
48
+ - **Homepage:** []()
49
+ - **Repository:** [https://github.com/BruceWen120/medal]()
50
+ - **Paper:** [https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/]()
51
+ - **Dataset (Kaggle):** [https://www.kaggle.com/xhlulu/medal-emnlp]()
52
+ - **Dataset (Zenodo):** [https://zenodo.org/record/4265632]()
53
+ - **Pretrained model:** [https://huggingface.co/xhlu/electra-medal]()
54
+ - **Leaderboard:** []()
55
+ - **Point of Contact:** []()
56
+
57
+ ### Dataset Summary
58
+
59
+ 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
60
+
61
+ ### Supported Tasks and Leaderboards
62
+
63
+ Medical abbreviation disambiguation
64
+
65
+ ### Languages
66
+
67
+ English (en)
68
+
69
+ ## Dataset Structure
70
+
71
+ [More Information Needed]
72
+
73
+ ### Data Instances
74
+
75
+ [More Information Needed]
76
+
77
+ ### Data Fields
78
+
79
+ [More Information Needed]
80
+
81
+ ### Data Splits
82
+
83
+ [More Information Needed]
84
+
85
+ ## Dataset Creation
86
+
87
+
88
+ ### Curation Rationale
89
+
90
+ [More Information Needed]
91
+
92
+ ### Source Data
93
+
94
+ [More Information Needed]
95
+
96
+ #### Initial Data Collection and Normalization
97
+
98
+ [More Information Needed]
99
+
100
+ #### Who are the source language producers?
101
+
102
+ [More Information Needed]
103
+
104
+ ### Annotations
105
+
106
+ [More Information Needed]
107
+
108
+ #### Annotation process
109
+
110
+ [More Information Needed]
111
+
112
+ #### Who are the annotators?
113
+
114
+ [More Information Needed]
115
+
116
+ ### Personal and Sensitive Information
117
+
118
+ [More Information Needed]
119
+
120
+ ## Considerations for Using the Data
121
+
122
+ ### Social Impact of Dataset
123
+
124
+ [More Information Needed]
125
+
126
+ ### Discussion of Biases
127
+
128
+ [More Information Needed]
129
+
130
+ ### Other Known Limitations
131
+
132
+ [More Information Needed]
133
+
134
+ ## Additional Information
135
+
136
+ ### Dataset Curators
137
+
138
+ [More Information Needed]
139
+
140
+ ### Licensing Information
141
+
142
+ [More Information Needed]
143
+
144
+ ### Citation Information
145
+
146
+ ```
147
+ @inproceedings{wen-etal-2020-medal,
148
+ title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
149
+ author = "Wen, Zhi and
150
+ Lu, Xing Han and
151
+ Reddy, Siva",
152
+ booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
153
+ month = nov,
154
+ year = "2020",
155
+ address = "Online",
156
+ publisher = "Association for Computational Linguistics",
157
+ url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
158
+ pages = "130--135",
159
+ 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.",
160
+ }
161
+ ```
dataset_infos.json ADDED
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+ {"default": {"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\n", "citation": "@inproceedings{wen-etal-2020-medal,\n title = \"{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining\",\n author = \"Wen, Zhi and\n Lu, Xing Han and\n Reddy, Siva\",\n booktitle = \"Proceedings of the 3rd Clinical Natural Language Processing Workshop\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.clinicalnlp-1.15\",\n pages = \"130--135\",\n 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.\",\n}", "homepage": "https://github.com/BruceWen120/medal", "license": "", "features": {"abstract_id": {"dtype": "int32", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "location": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "label": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "medal", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3573399948, "num_examples": 3000000, "dataset_name": "medal"}, "test": {"name": "test", "num_bytes": 1190766821, "num_examples": 1000000, "dataset_name": "medal"}, "validation": {"name": "validation", "num_bytes": 1191410723, "num_examples": 1000000, "dataset_name": "medal"}, "full": {"name": "full", "num_bytes": 15536883723, "num_examples": 14393619, "dataset_name": "medal"}}, "download_checksums": {"https://zenodo.org/record/4276178/files/train.csv": {"num_bytes": 3541556520, "checksum": "c5fef2feebd1ecd35b4fe7a0aec266b631c0ac511d4d6b685835328b1ffbf32d"}, "https://zenodo.org/record/4276178/files/test.csv": {"num_bytes": 1180152075, "checksum": "ad391a63449c2bbbdbdf8d1827da4c053607a8586f4162174ba4ccf13efd8f86"}, "https://zenodo.org/record/4276178/files/valid.csv": {"num_bytes": 1180795804, "checksum": "08a0a6c2ee40747744ec15675ab5dc1e2b04491ca951b14c15d8d7bf9d33694d"}, "https://zenodo.org/record/4276178/files/full_data.csv": {"num_bytes": 15158424679, "checksum": "70f1ad891bdf98a42395a8907b48284457ae36d17fcc5a0a9c65c0b6b45ecf8d"}}, "download_size": 21060929078, "post_processing_size": null, "dataset_size": 21492461215, "size_in_bytes": 42553390293}}
dummy/1.0.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5d4a921d222c4bbe5efd7ee2ce77bf13e0dbe7d5a848206327ff44d679109026
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+ size 3772
medal.py ADDED
@@ -0,0 +1,146 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2020 the HuggingFace Datasets Authors.
3
+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ # Lint as: python3
17
+ """MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""
18
+
19
+ from __future__ import absolute_import, division, print_function
20
+
21
+ import csv
22
+ import logging
23
+
24
+ import datasets
25
+
26
+
27
+ logger = logging.getLogger(__name__)
28
+
29
+
30
+ _CITATION = """\
31
+ @inproceedings{wen-etal-2020-medal,
32
+ title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
33
+ author = "Wen, Zhi and
34
+ Lu, Xing Han and
35
+ Reddy, Siva",
36
+ booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
37
+ month = nov,
38
+ year = "2020",
39
+ address = "Online",
40
+ publisher = "Association for Computational Linguistics",
41
+ url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
42
+ pages = "130--135",
43
+ 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.",
44
+ }"""
45
+
46
+ _DESCRIPTION = """\
47
+ 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
48
+ """
49
+
50
+ _URL = "https://zenodo.org/record/4276178/files/"
51
+ _URLS = {
52
+ "train": _URL + "train.csv",
53
+ "test": _URL + "test.csv",
54
+ "valid": _URL + "valid.csv",
55
+ "full": _URL + "full_data.csv",
56
+ }
57
+
58
+
59
+ class Medal(datasets.GeneratorBasedBuilder):
60
+ """Medal: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""
61
+
62
+ VERSION = datasets.Version("1.0.0")
63
+
64
+ def _info(self):
65
+ return datasets.DatasetInfo(
66
+ # This is the description that will appear on the datasets page.
67
+ description=_DESCRIPTION,
68
+ # datasets.features.FeatureConnectors
69
+ features=datasets.Features(
70
+ {
71
+ "abstract_id": datasets.Value("int32"),
72
+ "text": datasets.Value("string"),
73
+ "location": datasets.Sequence(datasets.Value("int32")),
74
+ "label": datasets.Sequence(datasets.Value("string")),
75
+ # These are the features of your dataset like images, labels ...
76
+ }
77
+ ),
78
+ # If there's a common (input, target) tuple from the features,
79
+ # specify them here. They'll be used if as_supervised=True in
80
+ # builder.as_dataset.
81
+ supervised_keys=None,
82
+ # Homepage of the dataset for documentation
83
+ homepage="https://github.com/BruceWen120/medal",
84
+ citation=_CITATION,
85
+ )
86
+
87
+ def _split_generators(self, dl_manager):
88
+ """Returns SplitGenerators."""
89
+ # dl_manager is a datasets.download.DownloadManager that can be used to
90
+ # download and extract URLs
91
+ urls_to_dl = _URLS
92
+ try:
93
+ dl_dir = dl_manager.download_and_extract(urls_to_dl)
94
+ except Exception:
95
+ logger.warning(
96
+ "This dataset is downloaded through Zenodo which is flaky. If this download failed try a few times before reporting an issue"
97
+ )
98
+ raise
99
+
100
+ return [
101
+ datasets.SplitGenerator(
102
+ name=datasets.Split.TRAIN,
103
+ # These kwargs will be passed to _generate_examples
104
+ gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
105
+ ),
106
+ datasets.SplitGenerator(
107
+ name=datasets.Split.TEST,
108
+ # These kwargs will be passed to _generate_examples
109
+ gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
110
+ ),
111
+ datasets.SplitGenerator(
112
+ name=datasets.Split.VALIDATION,
113
+ # These kwargs will be passed to _generate_examples
114
+ gen_kwargs={"filepath": dl_dir["valid"], "split": "val"},
115
+ ),
116
+ datasets.SplitGenerator(
117
+ name="full",
118
+ # These kwargs will be passed to _generate_examples
119
+ gen_kwargs={"filepath": dl_dir["full"], "split": "full"},
120
+ ),
121
+ ]
122
+
123
+ def _generate_examples(self, filepath, split):
124
+ """Yields examples."""
125
+ with open(filepath, encoding="utf-8") as f:
126
+ data = csv.reader(f)
127
+ # Skip header
128
+ next(data)
129
+ # print(split, filepath, next(data))
130
+ if split == "full":
131
+ id_ = 0
132
+ for id_, row in enumerate(data):
133
+ yield id_, {
134
+ "abstract_id": -1,
135
+ "text": row[0],
136
+ "location": [int(location) for location in row[1].split("|")],
137
+ "label": row[2].split("|"),
138
+ }
139
+ else:
140
+ for id_, row in enumerate(data):
141
+ yield id_, {
142
+ "abstract_id": int(row[0]),
143
+ "text": row[1],
144
+ "location": [int(row[2])],
145
+ "label": [row[3]],
146
+ }