Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
expert-generated
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
File size: 4,790 Bytes
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# coding=utf-8
# Copyright 2020 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.
"""arXiv Dataset."""
from __future__ import absolute_import, division, print_function
import json
import os
import datasets
_CITATION = """\
@misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
"""
_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.
"""
_HOMEPAGE = "https://www.kaggle.com/Cornell-University/arxiv"
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
_ID = "id"
_SUBMITTER = "submitter"
_AUTHORS = "authors"
_TITLE = "title"
_COMMENTS = "comments"
_JOURNAL_REF = "journal-ref"
_DOI = "doi"
_REPORT_NO = "report-no"
_CATEGORIES = "categories"
_LICENSE = "license"
_ABSTRACT = "abstract"
_UPDATE_DATE = "update_date"
_FILENAME = "arxiv-metadata-oai-snapshot.json"
class ArxivDataset(datasets.GeneratorBasedBuilder):
"""arXiv Dataset: arXiv dataset and metadata of 1.7M+ scholarly papers across STEM"""
VERSION = datasets.Version("1.1.0")
@property
def manual_download_instructions(self):
return """\
You need to go to https://www.kaggle.com/Cornell-University/arxiv,
and manually download the dataset. Once it is completed,
a zip folder named archive.zip will be appeared in your Downloads folder
or whichever folder your browser chooses to save files to. Extract that folder
and you would get a arxiv-metadata-oai-snapshot.json file
You can then move that file under <path/to/folder>.
The <path/to/folder> can e.g. be "~/manual_data".
arxiv_dataset can then be loaded using the following command `datasets.load_dataset("arxiv_dataset", data_dir="<path/to/folder>")`.
"""
def _info(self):
feature_names = [
_ID,
_SUBMITTER,
_AUTHORS,
_TITLE,
_COMMENTS,
_JOURNAL_REF,
_DOI,
_REPORT_NO,
_CATEGORIES,
_LICENSE,
_ABSTRACT,
_UPDATE_DATE,
]
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({k: datasets.Value("string") for k in feature_names}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
path_to_manual_file = os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), _FILENAME)
if not os.path.exists(path_to_manual_file):
raise FileNotFoundError(
"{} 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(
path_to_manual_file, _FILENAME, self.manual_download_instructions
)
)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": path_to_manual_file})]
def _generate_examples(self, path=None, title_set=None):
""" Yields examples. """
with open(path, encoding="utf8") as f:
for i, entry in enumerate(f):
data = dict(json.loads(entry))
yield i, {
_ID: data["id"],
_SUBMITTER: data["submitter"],
_AUTHORS: data["authors"],
_TITLE: data["title"],
_COMMENTS: data["comments"],
_JOURNAL_REF: data["journal-ref"],
_DOI: data["doi"],
_REPORT_NO: data["report-no"],
_CATEGORIES: data["categories"],
_LICENSE: data["license"],
_ABSTRACT: data["abstract"],
_UPDATE_DATE: data["update_date"],
}
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