Datasets:
arxiv_dataset

	# 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."""


	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(
	"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('arxiv_dataset', data_dir=...)` that includes a file name {_FILENAME}. Manual download instructions: {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"],
	}