Datasets:
ohsumed

Task Categories: text-classification
Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: crowdsourced
Annotations Creators: expert-generated
Source Datasets: original
system commited on
Commit
06113a9
0 Parent(s):

Update files from the datasets library (from 1.2.0)

Browse files

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

.gitattributes ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ *.7z filter=lfs diff=lfs merge=lfs -text
2
+ *.arrow filter=lfs diff=lfs merge=lfs -text
3
+ *.bin filter=lfs diff=lfs merge=lfs -text
4
+ *.bin.* filter=lfs diff=lfs merge=lfs -text
5
+ *.bz2 filter=lfs diff=lfs merge=lfs -text
6
+ *.ftz filter=lfs diff=lfs merge=lfs -text
7
+ *.gz filter=lfs diff=lfs merge=lfs -text
8
+ *.h5 filter=lfs diff=lfs merge=lfs -text
9
+ *.joblib filter=lfs diff=lfs merge=lfs -text
10
+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
11
+ *.model filter=lfs diff=lfs merge=lfs -text
12
+ *.msgpack filter=lfs diff=lfs merge=lfs -text
13
+ *.onnx filter=lfs diff=lfs merge=lfs -text
14
+ *.ot filter=lfs diff=lfs merge=lfs -text
15
+ *.parquet filter=lfs diff=lfs merge=lfs -text
16
+ *.pb filter=lfs diff=lfs merge=lfs -text
17
+ *.pt filter=lfs diff=lfs merge=lfs -text
18
+ *.pth filter=lfs diff=lfs merge=lfs -text
19
+ *.rar filter=lfs diff=lfs merge=lfs -text
20
+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
21
+ *.tar.* filter=lfs diff=lfs merge=lfs -text
22
+ *.tflite filter=lfs diff=lfs merge=lfs -text
23
+ *.tgz filter=lfs diff=lfs merge=lfs -text
24
+ *.xz filter=lfs diff=lfs merge=lfs -text
25
+ *.zip filter=lfs diff=lfs merge=lfs -text
26
+ *.zstandard filter=lfs diff=lfs merge=lfs -text
27
+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,215 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - human-annotated
4
+ language_creators:
5
+ - crowdsourced
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - cc-by-nc-4-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 100k< n<500K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - text-classification
18
+ task_ids:
19
+ - multi-label-classification
20
+ ---
21
+
22
+ # Dataset Card for ohsumed
23
+
24
+ ## Table of Contents
25
+ - [Dataset Description](#dataset-description)
26
+ - [Dataset Summary](#dataset-summary)
27
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
28
+ - [Languages](#languages)
29
+ - [Dataset Structure](#dataset-structure)
30
+ - [Data Instances](#data-instances)
31
+ - [Data Fields](#data-fields)
32
+ - [Data Splits](#data-splits)
33
+ - [Dataset Creation](#dataset-creation)
34
+ - [Curation Rationale](#curation-rationale)
35
+ - [Source Data](#source-data)
36
+ - [Annotations](#annotations)
37
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
38
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
39
+ - [Social Impact of Dataset](#social-impact-of-dataset)
40
+ - [Discussion of Biases](#discussion-of-biases)
41
+ - [Other Known Limitations](#other-known-limitations)
42
+ - [Additional Information](#additional-information)
43
+ - [Dataset Curators](#dataset-curators)
44
+ - [Licensing Information](#licensing-information)
45
+ - [Citation Information](#citation-information)
46
+
47
+ ## Dataset Description
48
+
49
+ - **Homepage: http://davis.wpi.edu/xmdv/datasets/ohsumed.html**
50
+ - **Repository: https://trec.nist.gov/data/filtering/t9.filtering.tar.gz**
51
+ - **Paper: https://link.springer.com/chapter/10.1007/978-1-4471-2099-5_20**
52
+ - **Leaderboard:**
53
+ - **Point of Contact: [William Hersh](mailto:hersh@OHSU.EDU) [Aakash Gupta](mailto:aakashg80@gmail.com)**
54
+
55
+ ### Dataset Summary
56
+
57
+ The OHSUMED test collection is a set of 348,566 references from
58
+ MEDLINE, the on-line medical information database, consisting of
59
+ titles and/or abstracts from 270 medical journals over a five-year
60
+ period (1987-1991). The available fields are title, abstract, MeSH
61
+ indexing terms, author, source, and publication type. The National
62
+ Library of Medicine has agreed to make the MEDLINE references in the
63
+ test database available for experimentation, restricted to the
64
+ following conditions:
65
+
66
+ 1. The data will not be used in any non-experimental clinical,
67
+ library, or other setting.
68
+ 2. Any human users of the data will explicitly be told that the data
69
+ is incomplete and out-of-date.
70
+
71
+ Please check this [readme](https://trec.nist.gov/data/filtering/README.t9.filtering) for more details
72
+
73
+
74
+ ### Supported Tasks and Leaderboards
75
+
76
+ [Text Classification](https://paperswithcode.com/sota/text-classification-on-ohsumed)
77
+
78
+ ### Languages
79
+
80
+ The text is primarily in English. The BCP 47 code is `en`
81
+
82
+ ## Dataset Structure
83
+
84
+ ### Data Instances
85
+
86
+ ```
87
+ {'seq_id': 7770,
88
+ 'medline_ui': 87120420,
89
+ 'mesh_terms': 'Adult; Aged; Aneurysm/CO; Arteriovenous Fistula/*TH; Carotid Arteries; Case Report; Female; Human; Jugular Veins; Male; Methods; Middle Age; Neck/*BS; Vertebral Artery.',
90
+ 'title': 'Arteriovenous fistulas of the large vessels of the neck: nonsurgical percutaneous occlusion.',
91
+ 'publication_type': 'JOURNAL ARTICLE.',
92
+ 'abstract': 'We describe the nonsurgical treatment of arteriovenous fistulas of the large vessels in the neck using three different means of endovascular occlusion of these large lesions, which are surgically difficult to approach and treat.',
93
+ 'author': 'Vitek JJ; Keller FS.',
94
+ 'source': 'South Med J 8705; 80(2):196-200'}
95
+
96
+ ```
97
+
98
+
99
+ ### Data Fields
100
+
101
+ Here are the field definitions:
102
+
103
+ - seg_id: sequential identifier
104
+ (important note: documents should be processed in this order)
105
+ - medline_ui: MEDLINE identifier (UI)
106
+ (<DOCNO> used for relevance judgements)
107
+ - mesh_terms: Human-assigned MeSH terms (MH)
108
+ - title: Title (TI)
109
+ - publication_type : Publication type (PT)
110
+ - abstract: Abstract (AB)
111
+ - author: Author (AU)
112
+ - source: Source (SO)
113
+
114
+ Note: some abstracts are truncated at 250 words and some references
115
+ have no abstracts at all (titles only). We do not have access to the
116
+ full text of the documents.
117
+
118
+ ### Data Splits
119
+
120
+ The files are Train/ Test. Where the training has files from 1987 while the test files has abstracts from 1988-91
121
+
122
+ Total number of files:
123
+ Train: 54710
124
+ Test: 348567
125
+
126
+
127
+ ## Dataset Creation
128
+
129
+ ### Curation Rationale
130
+
131
+ The OHSUMED document collection was obtained by William Hersh
132
+ (hersh@OHSU.EDU) and colleagues for the experiments described in the
133
+ papers below. [Check citation](#citation-information)
134
+
135
+ ### Source Data
136
+
137
+ #### Initial Data Collection and Normalization
138
+
139
+ [More Information Needed]
140
+
141
+ #### Who are the source language producers?
142
+
143
+ The test collection was built as part of a study assessing the use of
144
+ MEDLINE by physicians in a clinical setting (Hersh and Hickam, above).
145
+ Novice physicians using MEDLINE generated 106 queries. Only a subset
146
+ of these queries were used in the TREC-9 Filtering Track. Before
147
+ they searched, they were asked to provide a statement of information
148
+ about their patient as well as their information need.
149
+ The data was collected by William Hersh & colleagues
150
+
151
+ ### Annotations
152
+
153
+ #### Annotation process
154
+
155
+ The existing OHSUMED topics describe actual information needs, but the
156
+ relevance judgements probably do not have the same coverage provided
157
+ by the TREC pooling process. The MeSH terms do not directly represent
158
+ information needs, rather they are controlled indexing terms. However,
159
+ the assessment should be more or less complete and there are a lot of
160
+ them, so this provides an unusual opportunity to work with a very
161
+ large topic sample.
162
+
163
+ The topic statements are provided in the standard TREC format
164
+
165
+ #### Who are the annotators?
166
+
167
+ Each query was replicated by four searchers, two physicians
168
+ experienced in searching and two medical librarians. The results were
169
+ assessed for relevance by a different group of physicians, using a
170
+ three point scale: definitely, possibly, or not relevant. The list of
171
+ documents explicitly judged to be not relevant is not provided here.
172
+ Over 10% of the query-document pairs were judged in duplicate to
173
+ assess inter-observer reliability. For evaluation, all documents
174
+ judged here as either possibly or definitely relevant were
175
+ considered relevant. TREC-9 systems were allowed to distinguish
176
+ between these two categories during the learning process if desired.
177
+
178
+ ### Personal and Sensitive Information
179
+
180
+ No PII data is present in the train, test or query files.
181
+
182
+ ## Considerations for Using the Data
183
+
184
+ ### Social Impact of Dataset
185
+
186
+ [More Information Needed]
187
+
188
+ ### Discussion of Biases
189
+
190
+ [More Information Needed]
191
+
192
+ ### Other Known Limitations
193
+
194
+ [More Information Needed]
195
+
196
+ ## Additional Information
197
+
198
+ ### Dataset Curators
199
+
200
+ [Aakash Gupta](mailto:aakashg80@gmail.com)
201
+ *Th!nkEvolve Consulting* and Researcher at CoronaWhy
202
+
203
+ ### Licensing Information
204
+
205
+ CC BY-NC 4.0
206
+
207
+ ### Citation Information
208
+
209
+ Hersh WR, Buckley C, Leone TJ, Hickam DH, OHSUMED: An interactive
210
+ retrieval evaluation and new large test collection for research,
211
+ Proceedings of the 17th Annual ACM SIGIR Conference, 1994, 192-201.
212
+
213
+ Hersh WR, Hickam DH, Use of a multi-application computer workstation
214
+ in a clinical setting, Bulletin of the Medical Library Association,
215
+ 1994, 82: 382-389.
dataset_infos.json ADDED
@@ -0,0 +1 @@
 
1
+ {"ohsumed": {"description": "The OHSUMED test collection is a set of 348,566 references from\nMEDLINE, the on-line medical information database, consisting of\ntitles and/or abstracts from 270 medical journals over a five-year\nperiod (1987-1991). The available fields are title, abstract, MeSH\nindexing terms, author, source, and publication type.\n", "citation": "@InProceedings{10.1007/978-1-4471-2099-5_20,\nauthor=\"Hersh, William\nand Buckley, Chris\nand Leone, T. J.\nand Hickam, David\",\neditor=\"Croft, Bruce W.\nand van Rijsbergen, C. J.\",\ntitle=\"OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research\",\nbooktitle=\"SIGIR '94\",\nyear=\"1994\",\npublisher=\"Springer London\",\naddress=\"London\",\npages=\"192--201\",\nabstract=\"A series of information retrieval experiments was carried out with a computer installed in a medical practice setting for relatively inexperienced physician end-users. Using a commercial MEDLINE product based on the vector space model, these physicians searched just as effectively as more experienced searchers using Boolean searching. The results of this experiment were subsequently used to create a new large medical test collection, which was used in experiments with the SMART retrieval system to obtain baseline performance data as well as compare SMART with the other searchers.\",\nisbn=\"978-1-4471-2099-5\"\n}\n", "homepage": "http://davis.wpi.edu/xmdv/datasets/ohsumed.html", "license": "CC BY-NC 4.0", "features": {"seq_id": {"dtype": "int64", "id": null, "_type": "Value"}, "medline_ui": {"dtype": "int64", "id": null, "_type": "Value"}, "mesh_terms": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "publication_type": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "author": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ohsumed", "config_name": "ohsumed", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60117860, "num_examples": 54709, "dataset_name": "ohsumed"}, "test": {"name": "test", "num_bytes": 338533901, "num_examples": 293855, "dataset_name": "ohsumed"}}, "download_checksums": {"https://trec.nist.gov/data/filtering/t9.filtering.tar.gz": {"num_bytes": 139454017, "checksum": "39184391aab6d080699882dbfd87de4cbcb24cce8a0cffd611debf18914481b0"}}, "download_size": 139454017, "post_processing_size": null, "dataset_size": 398651761, "size_in_bytes": 538105778}}
dummy/ohsumed/1.1.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fc43b3b89ff1bf945681b92833b312f10f0e734d52f83dbaeddf1347b5d7585f
3
+ size 7432
ohsumed.py ADDED
@@ -0,0 +1,223 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
3
+ #
4
+ # 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
+ """OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research."""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import os
20
+
21
+ import datasets
22
+
23
+
24
+ # TODO: Add BibTeX citation
25
+ # Find for instance the citation on arxiv or on the dataset repo/website
26
+ _CITATION = """\
27
+ @InProceedings{10.1007/978-1-4471-2099-5_20,
28
+ author="Hersh, William
29
+ and Buckley, Chris
30
+ and Leone, T. J.
31
+ and Hickam, David",
32
+ editor="Croft, Bruce W.
33
+ and van Rijsbergen, C. J.",
34
+ title="OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research",
35
+ booktitle="SIGIR '94",
36
+ year="1994",
37
+ publisher="Springer London",
38
+ address="London",
39
+ pages="192--201",
40
+ abstract="A series of information retrieval experiments was carried out with a computer installed in a medical practice setting for relatively inexperienced physician end-users. Using a commercial MEDLINE product based on the vector space model, these physicians searched just as effectively as more experienced searchers using Boolean searching. The results of this experiment were subsequently used to create a new large medical test collection, which was used in experiments with the SMART retrieval system to obtain baseline performance data as well as compare SMART with the other searchers.",
41
+ isbn="978-1-4471-2099-5"
42
+ }
43
+ """
44
+
45
+ # TODO: Add description of the dataset here
46
+ # You can copy an official description
47
+ _DESCRIPTION = """\
48
+ The OHSUMED test collection is a set of 348,566 references from
49
+ MEDLINE, the on-line medical information database, consisting of
50
+ titles and/or abstracts from 270 medical journals over a five-year
51
+ period (1987-1991). The available fields are title, abstract, MeSH
52
+ indexing terms, author, source, and publication type.
53
+ """
54
+
55
+ # TODO: Add a link to an official homepage for the dataset here
56
+ _HOMEPAGE = "http://davis.wpi.edu/xmdv/datasets/ohsumed.html"
57
+
58
+ # TODO: Add the licence for the dataset here if you can find it
59
+ _LICENSE = "CC BY-NC 4.0"
60
+
61
+ # TODO: Add link to the official dataset URLs here
62
+ # The HuggingFace dataset library don't host the datasets but only point to the original files
63
+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
64
+ _URLs = {"ohsumed": "https://trec.nist.gov/data/filtering/t9.filtering.tar.gz"}
65
+
66
+
67
+ # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
68
+ class Ohsumed(datasets.GeneratorBasedBuilder):
69
+ """OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research."""
70
+
71
+ VERSION = datasets.Version("1.1.0")
72
+
73
+ # This is an example of a dataset with multiple configurations.
74
+ # If you don't want/need to define several sub-sets in your dataset,
75
+ # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
76
+
77
+ # If you need to make complex sub-parts in the datasets with configurable options
78
+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
79
+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
80
+
81
+ # You will be able to load one or the other configurations in the following list with
82
+ # data = datasets.load_dataset('my_dataset', 'first_domain')
83
+ # data = datasets.load_dataset('my_dataset', 'second_domain')
84
+ BUILDER_CONFIGS = [
85
+ datasets.BuilderConfig(
86
+ name="ohsumed",
87
+ version=VERSION,
88
+ description="Config for the entire ohsumed dataset. An Interactive Retrieval Evaluation and New Large Test Collection for Research",
89
+ )
90
+ ]
91
+
92
+ def _info(self):
93
+ # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
94
+ features = datasets.Features(
95
+ {
96
+ "seq_id": datasets.Value("int64"),
97
+ "medline_ui": datasets.Value("int64"),
98
+ "mesh_terms": datasets.Value("string"),
99
+ "title": datasets.Value("string"),
100
+ "publication_type": datasets.Value("string"),
101
+ "abstract": datasets.Value("string"),
102
+ "author": datasets.Value("string"),
103
+ "source": datasets.Value("string"),
104
+ }
105
+ )
106
+ return datasets.DatasetInfo(
107
+ # This is the description that will appear on the datasets page.
108
+ description=_DESCRIPTION,
109
+ # This defines the different columns of the dataset and their types
110
+ features=features, # Here we define them above because they are different between the two configurations
111
+ # If there's a common (input, target) tuple from the features,
112
+ # specify them here. They'll be used if as_supervised=True in
113
+ # builder.as_dataset.
114
+ supervised_keys=None,
115
+ # Homepage of the dataset for documentation
116
+ homepage=_HOMEPAGE,
117
+ # License for the dataset if available
118
+ license=_LICENSE,
119
+ # Citation for the dataset
120
+ citation=_CITATION,
121
+ )
122
+
123
+ def _split_generators(self, dl_manager):
124
+ """Returns SplitGenerators."""
125
+ # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
126
+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
127
+
128
+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
129
+ # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
130
+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
131
+ my_urls = _URLs[self.config.name]
132
+ data_dir = dl_manager.download_and_extract(my_urls)
133
+ return [
134
+ datasets.SplitGenerator(
135
+ name=datasets.Split.TRAIN,
136
+ # These kwargs will be passed to _generate_examples
137
+ gen_kwargs={
138
+ "filepath": os.path.join(data_dir, "ohsu-trec/trec9-train/ohsumed.87"),
139
+ "split": "train",
140
+ },
141
+ ),
142
+ datasets.SplitGenerator(
143
+ name=datasets.Split.TEST,
144
+ # These kwargs will be passed to _generate_examples
145
+ gen_kwargs={"filepath": os.path.join(data_dir, "ohsu-trec/trec9-test/ohsumed.88-91"), "split": "test"},
146
+ ),
147
+ ]
148
+
149
+ def _generate_examples(self, filepath, split):
150
+ """ Yields examples. """
151
+ # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
152
+ # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
153
+ # The key is not important, it's more here for legacy reason (legacy from tfds)
154
+
155
+ def ohsumed_dict():
156
+ """Returns a dict."""
157
+
158
+ data = {
159
+ "seq_id": -1,
160
+ "medline_ui": -1,
161
+ "mesh_terms": "",
162
+ "title": "",
163
+ "publication_type": "",
164
+ "abstract": "",
165
+ "author": "",
166
+ "source": "",
167
+ }
168
+
169
+ return data
170
+
171
+ tag = ""
172
+ column_map = {
173
+ ".I": "seq_id",
174
+ ".U": "medline_ui",
175
+ ".M": "mesh_terms",
176
+ ".T": "title",
177
+ ".P": "publication_type",
178
+ ".W": "abstract",
179
+ ".A": "author",
180
+ ".S": "source",
181
+ }
182
+
183
+ with open(filepath, encoding="utf-8") as f:
184
+ data = ohsumed_dict()
185
+
186
+ for line in f.readlines():
187
+ line = line.strip()
188
+
189
+ if line.startswith(".I"):
190
+ tag = ".I"
191
+ if data["medline_ui"] != -1:
192
+ id_ = data["seq_id"]
193
+ yield id_, {
194
+ "seq_id": data["seq_id"],
195
+ "medline_ui": data["medline_ui"],
196
+ "mesh_terms": str(data["mesh_terms"]),
197
+ "title": str(data["title"]),
198
+ "publication_type": str(data["publication_type"]),
199
+ "abstract": str(data["abstract"]),
200
+ "author": str(data["author"]),
201
+ "source": str(data["source"]),
202
+ }
203
+ else:
204
+ data = ohsumed_dict()
205
+ line = line.replace(".I", "").strip()
206
+ data["seq_id"] = line
207
+ elif tag and not line.startswith("."):
208
+ key = column_map[tag]
209
+ data[key] = line
210
+ elif ".U" in line:
211
+ tag = ".U"
212
+ elif ".M" in line:
213
+ tag = ".M"
214
+ elif ".T" in line:
215
+ tag = ".T"
216
+ elif ".P" in line:
217
+ tag = ".P"
218
+ elif ".W" in line:
219
+ tag = ".W"
220
+ elif ".A" in line:
221
+ tag = ".A"
222
+ elif ".S" in line:
223
+ tag = ".S"