Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""Elsevier OA CC-By Corpus Dataset.""" | |
import json | |
import glob | |
import os | |
import datasets | |
_CITATION = """ | |
@article{Kershaw2020ElsevierOC, | |
title = {Elsevier OA CC-By Corpus}, | |
author = {Daniel James Kershaw and R. Koeling}, | |
journal = {ArXiv}, | |
year = {2020}, | |
volume = {abs/2008.00774}, | |
doi = {https://doi.org/10.48550/arXiv.2008.00774}, | |
url = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs}, | |
keywords = {Science, Natural Language Processing, Machine Learning, Open Dataset}, | |
abstract = {We introduce the Elsevier OA CC-BY corpus. This is the first open | |
corpus of Scientific Research papers which has a representative sample | |
from across scientific disciplines. This corpus not only includes the | |
full text of the article, but also the metadata of the documents, | |
along with the bibliographic information for each reference.} | |
} | |
""" | |
_DESCRIPTION = """ | |
Elsevier OA CC-By is a corpus of 40k (40, 091) open access (OA) CC-BY articles | |
from across Elsevier’s journals and include the full text of the article, the metadata, | |
the bibliographic information for each reference, and author highlights. | |
""" | |
_HOMEPAGE = "https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs/3" | |
_LICENSE = "CC-BY-4.0" | |
_URL = "https://data.mendeley.com/public-files/datasets/zm33cdndxs/files/4e03ae48-04a7-44d4-b103-ce73e548679c/file_downloaded" | |
class ElsevierOaCcBy(datasets.GeneratorBasedBuilder): | |
"""Elsevier OA CC-By Dataset.""" | |
VERSION = datasets.Version("1.0.1") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name="all", version=VERSION, description="Official Mendeley dataset for Elsevier OA CC-By Corpus"), | |
] | |
DEFAULT_CONFIG_NAME = "all" | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"title": datasets.Value("string"), | |
"abstract": datasets.Value("string"), | |
"subjareas": datasets.Sequence(datasets.Value("string")), | |
"keywords": datasets.Sequence(datasets.Value("string")), | |
"asjc": datasets.Sequence(datasets.Value("string")), | |
"body_text": datasets.Sequence(datasets.Value("string")), | |
"author_highlights": datasets.Sequence(datasets.Value("string")), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
# specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
# supervised_keys=("sentence", "label"), | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
# 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. | |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
data_dir = dl_manager.download_and_extract(_URL) | |
corpus_path = os.path.join(data_dir, "json") | |
doc_count = len(glob.glob(f"{corpus_path}/*.json")) | |
train_split = [0, doc_count*80//100] | |
test_split = [doc_count*80//100+1, doc_count*90//100] | |
validation_split = [doc_count*90//100+1, doc_count] | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": corpus_path, | |
"split": "train", | |
"split_range": train_split | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": corpus_path, | |
"split": "test", | |
"split_range": test_split | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": corpus_path, | |
"split": "validation", | |
"split_range": validation_split | |
}, | |
), | |
] | |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
def _generate_examples(self, filepath, split, split_range): | |
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
json_files = glob.glob(f"{filepath}/*.json") | |
for doc in json_files[split_range[0]:split_range[1]]: | |
with open(doc) as f: | |
paper = json.loads(f.read()) | |
# Yields examples as (key, example) tuples | |
yield paper['docId'], { | |
'title': paper['metadata']['title'], | |
'subjareas': paper['metadata']['subjareas'] if 'subjareas' in paper['metadata'] else [], | |
'keywords': paper['metadata']['keywords'] if 'keywords' in paper['metadata'] else [], | |
'asjc': paper['metadata']['asjc'] if 'asjc' in paper['metadata'] else [], | |
'abstract': paper['abstract'] if 'abstract' in paper else "", | |
"body_text": [s['sentence'] for s in sorted(paper['body_text'], key = lambda i: (i['secId'], i['startOffset']))], | |
"author_highlights": [s['sentence'] for s in sorted(paper['author_highlights'], key = lambda i: i['startOffset'])] if 'author_highlights' in paper else [], | |
} | |