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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 +183 -0
  3. arxiv_dataset.py +130 -0
  4. dataset_infos.json +1 -0
  5. dummy/1.1.0/dummy_data.zip +3 -0
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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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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - no-annotation
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+ language_creators:
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+ - expert-generated
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+ languages:
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+ - en
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+ licenses:
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+ - cc0-1-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - n>1M
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - conditional-text-generation
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+ - text-retrieval
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+ task_ids:
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+ - document-retrieval
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+ - entity-linking-retrieval
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+ - explanation-generation
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+ - fact-checking-retrieval
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+ - machine-translation
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+ - summarization
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+ - text-simplification
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+ ---
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+
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+ # Dataset Card For arXiv Dataset
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-instances)
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+ - [Data Splits](#data-instances)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** [Kaggle arXiv Dataset Homepage](https://www.kaggle.com/Cornell-University/arxiv)
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+ - **Repository:**
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+ - **Paper:** [On the Use of ArXiv as a Dataset](https://arxiv.org/abs/1905.00075)
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+ - **Leaderboard:**
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+ - **Point of Contact:** [Matt Bierbaum](mailto:matt.bierbaum@gmail.com)
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+
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+ ### Dataset Summary
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+
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+ A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ [More Information Needed]
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+
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+ ### Languages
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+
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+ The language supported is English
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ This dataset is a mirror of the original ArXiv data. Because the full dataset is rather large (1.1TB and growing), this dataset provides only a metadata file in the json format. An example is given below
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+
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+ ```
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+ {'id': '0704.0002',
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+ 'submitter': 'Louis Theran',
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+ 'authors': 'Ileana Streinu and Louis Theran',
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+ 'title': 'Sparsity-certifying Graph Decompositions',
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+ 'comments': 'To appear in Graphs and Combinatorics',
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+ 'journal-ref': None,
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+ 'doi': None,
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+ 'report-no': None,
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+ 'categories': 'math.CO cs.CG',
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+ 'license': 'http://arxiv.org/licenses/nonexclusive-distrib/1.0/',
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+ 'abstract': ' We describe a new algorithm, the $(k,\\ell)$-pebble game with colors, and use\nit obtain a characterization of the family of $(k,\\ell)$-sparse graphs and\nalgorithmic solutions to a family of problems concerning tree decompositions of\ngraphs. Special instances of sparse graphs appear in rigidity theory and have\nreceived increased attention in recent years. In particular, our colored\npebbles generalize and strengthen the previous results of Lee and Streinu and\ngive a new proof of the Tutte-Nash-Williams characterization of arboricity. We\nalso present a new decomposition that certifies sparsity based on the\n$(k,\\ell)$-pebble game with colors. Our work also exposes connections between\npebble game algorithms and previous sparse graph algorithms by Gabow, Gabow and\nWestermann and Hendrickson.\n',
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+ 'update_date': '2008-12-13'}
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+ ```
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+
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+ ### Data Fields
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+
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+ - `id`: ArXiv ID (can be used to access the paper)
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+ - `submitter`: Who submitted the paper
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+ - `authors`: Authors of the paper
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+ - `title`: Title of the paper
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+ - `comments`: Additional info, such as number of pages and figures
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+ - `journal-ref`: Information about the journal the paper was published in
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+ - `doi`: [Digital Object Identifier](https://www.doi.org)
104
+ - `report-no`: Report Number
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+ - `abstract`: The abstract of the paper
106
+ - `categories`: Categories / tags in the ArXiv system
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+
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+
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+ ### Data Splits
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+
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+ The data was not splited.
112
+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming depth. In these times of unique global challenges, efficient extraction of insights from data is essential. To help make the arXiv more accessible, a free, open pipeline on Kaggle to the machine-readable arXiv dataset: a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more is presented to empower new use cases that can lead to the exploration of richer machine learning techniques that combine multi-modal features towards applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.
118
+
119
+ ### Source Data
120
+
121
+ This data is based on arXiv papers.
122
+ [More Information Needed]
123
+
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+ #### Initial Data Collection and Normalization
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+
126
+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
130
+ [More Information Needed]
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+
132
+ ### Annotations
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+
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+ This dataset contains no annotations.
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+
136
+ #### Annotation process
137
+
138
+ [More Information Needed]
139
+
140
+ #### Who are the annotators?
141
+
142
+ [More Information Needed]
143
+
144
+ ### Personal and Sensitive Information
145
+
146
+ [More Information Needed]
147
+
148
+ ## Considerations for Using the Data
149
+
150
+ ### Social Impact of Dataset
151
+
152
+ [More Information Needed]
153
+
154
+ ### Discussion of Biases
155
+
156
+ [More Information Needed]
157
+
158
+ ### Other Known Limitations
159
+
160
+ [More Information Needed]
161
+
162
+ ## Additional Information
163
+
164
+ ### Dataset Curators
165
+
166
+ The original data is maintained by [ArXiv](https://arxiv.org/)
167
+
168
+ ### Licensing Information
169
+
170
+ The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/)
171
+
172
+ ### Citation Information
173
+
174
+ ```
175
+ @misc{clement2019arxiv,
176
+ title={On the Use of ArXiv as a Dataset},
177
+ author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
178
+ year={2019},
179
+ eprint={1905.00075},
180
+ archivePrefix={arXiv},
181
+ primaryClass={cs.IR}
182
+ }
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+ ```
arxiv_dataset.py ADDED
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1
+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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.
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+
16
+ """arXiv Dataset."""
17
+
18
+ from __future__ import absolute_import, division, print_function
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+
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+ import json
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+ import os
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+
23
+ import datasets
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+
25
+
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+ _CITATION = """\
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+ @misc{clement2019arxiv,
28
+ title={On the Use of ArXiv as a Dataset},
29
+ author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
30
+ year={2019},
31
+ eprint={1905.00075},
32
+ archivePrefix={arXiv},
33
+ primaryClass={cs.IR}
34
+ }
35
+ """
36
+
37
+ _DESCRIPTION = """\
38
+ A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.
39
+ """
40
+
41
+ _HOMEPAGE = "https://www.kaggle.com/Cornell-University/arxiv"
42
+ _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
43
+
44
+ _ID = "id"
45
+ _SUBMITTER = "submitter"
46
+ _AUTHORS = "authors"
47
+ _TITLE = "title"
48
+ _COMMENTS = "comments"
49
+ _JOURNAL_REF = "journal-ref"
50
+ _DOI = "doi"
51
+ _REPORT_NO = "report-no"
52
+ _CATEGORIES = "categories"
53
+ _LICENSE = "license"
54
+ _ABSTRACT = "abstract"
55
+ _UPDATE_DATE = "update_date"
56
+
57
+ _FILENAME = "arxiv-metadata-oai-snapshot.json"
58
+
59
+
60
+ class ArxivDataset(datasets.GeneratorBasedBuilder):
61
+ """arXiv Dataset: arXiv dataset and metadata of 1.7M+ scholarly papers across STEM"""
62
+
63
+ VERSION = datasets.Version("1.1.0")
64
+
65
+ @property
66
+ def manual_download_instructions(self):
67
+ return """\
68
+ You need to go to https://www.kaggle.com/Cornell-University/arxiv,
69
+ and manually download the dataset. Once it is completed,
70
+ a zip folder named archive.zip will be appeared in your Downloads folder
71
+ or whichever folder your browser chooses to save files to. Extract that folder
72
+ and you would get a arxiv-metadata-oai-snapshot.json file
73
+ You can then move that file under <path/to/folder>.
74
+ The <path/to/folder> can e.g. be "~/manual_data".
75
+ arxiv_dataset can then be loaded using the following command `datasets.load_dataset("arxiv_dataset", data_dir="<path/to/folder>")`.
76
+ """
77
+
78
+ def _info(self):
79
+ feature_names = [
80
+ _ID,
81
+ _SUBMITTER,
82
+ _AUTHORS,
83
+ _TITLE,
84
+ _COMMENTS,
85
+ _JOURNAL_REF,
86
+ _DOI,
87
+ _REPORT_NO,
88
+ _CATEGORIES,
89
+ _LICENSE,
90
+ _ABSTRACT,
91
+ _UPDATE_DATE,
92
+ ]
93
+ return datasets.DatasetInfo(
94
+ description=_DESCRIPTION,
95
+ features=datasets.Features({k: datasets.Value("string") for k in feature_names}),
96
+ supervised_keys=None,
97
+ homepage=_HOMEPAGE,
98
+ citation=_CITATION,
99
+ )
100
+
101
+ def _split_generators(self, dl_manager):
102
+ """Returns SplitGenerators."""
103
+ path_to_manual_file = os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), _FILENAME)
104
+ if not os.path.exists(path_to_manual_file):
105
+ raise FileNotFoundError(
106
+ "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('arxiv_dataset', data_dir=...)` that includes a file name {}. Manual download instructions: {})".format(
107
+ path_to_manual_file, _FILENAME, self.manual_download_instructions
108
+ )
109
+ )
110
+ return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": path_to_manual_file})]
111
+
112
+ def _generate_examples(self, path=None, title_set=None):
113
+ """ Yields examples. """
114
+ with open(path, encoding="utf8") as f:
115
+ for i, entry in enumerate(f):
116
+ data = dict(json.loads(entry))
117
+ yield i, {
118
+ _ID: data["id"],
119
+ _SUBMITTER: data["submitter"],
120
+ _AUTHORS: data["authors"],
121
+ _TITLE: data["title"],
122
+ _COMMENTS: data["comments"],
123
+ _JOURNAL_REF: data["journal-ref"],
124
+ _DOI: data["doi"],
125
+ _REPORT_NO: data["report-no"],
126
+ _CATEGORIES: data["categories"],
127
+ _LICENSE: data["license"],
128
+ _ABSTRACT: data["abstract"],
129
+ _UPDATE_DATE: data["update_date"],
130
+ }
dataset_infos.json ADDED
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+ {"default": {"description": "A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.\n", "citation": "@misc{clement2019arxiv,\n title={On the Use of ArXiv as a Dataset},\n author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},\n year={2019},\n eprint={1905.00075},\n archivePrefix={arXiv},\n primaryClass={cs.IR}\n}\n", "homepage": "https://www.kaggle.com/Cornell-University/arxiv", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "submitter": {"dtype": "string", "id": null, "_type": "Value"}, "authors": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "comments": {"dtype": "string", "id": null, "_type": "Value"}, "journal-ref": {"dtype": "string", "id": null, "_type": "Value"}, "doi": {"dtype": "string", "id": null, "_type": "Value"}, "report-no": {"dtype": "string", "id": null, "_type": "Value"}, "categories": {"dtype": "string", "id": null, "_type": "Value"}, "license": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "update_date": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "arxiv_dataset", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2246545603, "num_examples": 1796911, "dataset_name": "arxiv_dataset"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 2246545603, "size_in_bytes": 2246545603}}
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