Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
# 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. | |
""" | |
CORD-19 dataset implementation initiated by @ggdupont | |
""" | |
import csv | |
import json | |
import os | |
import datasets | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
@article{Wang2020CORD19TC, | |
title={CORD-19: The Covid-19 Open Research Dataset}, | |
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and | |
K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and | |
Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and | |
D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier}, | |
journal={ArXiv}, | |
year={2020} | |
} | |
""" | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related | |
historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information | |
retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 | |
has been downloaded over 75K times and has served as the basis of many Covid-19 text mining and discovery systems. | |
The dataset itself isn't defining a specific task, but there is a Kaggle challenge that define 17 open research | |
questions to be solved with the dataset: https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge/tasks | |
""" | |
# The HuggingFace dataset library don't host the datasets but only point to the original files | |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
CORD19_DATASET_DATE = "2020-11-29" | |
_URL = ( | |
"https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases/cord-19_" | |
+ CORD19_DATASET_DATE | |
+ ".tar.gz" | |
) | |
class Cord19(datasets.GeneratorBasedBuilder): | |
"""The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19.""" | |
VERSION = datasets.Version("0.0.1") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="metadata", | |
description="The set of documents but loading some metadata like title and " "abstract for each article.", | |
), | |
datasets.BuilderConfig( | |
name="fulltext", | |
description="The set of documents loading some metadata like title and " | |
"abstract and full text for each article.", | |
), | |
datasets.BuilderConfig( | |
name="embeddings", | |
description="The set of documents loading some metadata like title and " | |
"abstract and document embeddings for each article.", | |
), | |
] | |
def _info(self): | |
# default metadata only | |
features_dict = { | |
"cord_uid": datasets.Value("string"), | |
"sha": datasets.Value("string"), | |
"source_x": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"doi": datasets.Value("string"), | |
"abstract": datasets.Value("string"), | |
"publish_time": datasets.Value("string"), | |
"authors": datasets.Value("string"), | |
"journal": datasets.Value("string"), | |
"url": datasets.Value("string"), | |
} | |
if "fulltext" in self.config.name: | |
# adding full_text | |
features_dict["fulltext"] = datasets.Value("string") | |
if "embeddings" in self.config.name: | |
# adding embeddings | |
features_dict["doc_embeddings"] = datasets.Sequence(datasets.Value("float64")) | |
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=datasets.Features(features_dict), | |
supervised_keys=None, | |
homepage="https://www.semanticscholar.org/cord19/download", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
my_urls = _URL | |
data_dir = dl_manager.download_and_extract(my_urls) | |
files = dict() | |
files["metadata"] = os.path.join(data_dir, CORD19_DATASET_DATE, "metadata.csv") | |
if "fulltext" in self.config.name: | |
fulltext_dir_path = dl_manager.extract( | |
os.path.join(data_dir, CORD19_DATASET_DATE, "document_parses.tar.gz") | |
) | |
files["fulltext"] = fulltext_dir_path | |
if "embeddings" in self.config.name: | |
embeddings_dir_path = dl_manager.extract( | |
os.path.join(data_dir, CORD19_DATASET_DATE, "cord_19_embeddings.tar.gz") | |
) | |
files["embeddings"] = os.path.join( | |
embeddings_dir_path, "cord_19_embeddings_" + CORD19_DATASET_DATE + ".csv" | |
) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": files, | |
"split": "train", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""Yields examples.""" | |
metadata_filepath = filepath["metadata"] | |
if "fulltext" in self.config.name: | |
fulltext_dir_path = filepath["fulltext"] | |
fh = None | |
if "embeddings" in self.config.name: | |
embeddings_filepath = filepath["embeddings"] | |
fh = open(embeddings_filepath, mode="r", encoding="utf-8") | |
with open(metadata_filepath, mode="r", encoding="utf-8") as f: | |
reader = csv.reader(f, delimiter=",") | |
# skip headers | |
next(reader, None) | |
for i, line in enumerate(reader): | |
doc_fields = { | |
"cord_uid": line[0], | |
"sha": line[1], | |
"source_x": line[2], | |
"title": line[3], | |
"doi": line[4], | |
"abstract": line[8], | |
"publish_time": line[9], | |
"authors": line[10], | |
"journal": line[11], | |
"url": line[17], | |
} | |
if "fulltext" in self.config.name: | |
doc_fields["fulltext"] = "" | |
json_filepath = line[15] | |
# some entry do not have pdf transcript | |
if len(json_filepath) > 0: | |
# possibly multiple json (matching multiple pdf) then take the first one arbitrarily | |
if ";" in json_filepath: | |
json_filepath = json_filepath.split(";")[0] | |
# load json file | |
with open( | |
os.path.join(fulltext_dir_path, json_filepath), mode="r", encoding="utf-8" | |
) as json_file: | |
data = json.load(json_file) | |
doc_fields["fulltext"] = "\n".join(text_block["text"] for text_block in data["body_text"]) | |
if "embeddings" in self.config.name: | |
# synchronized reading of embeddings csv | |
data = fh.readline().split(",") | |
doc_id = data[0] | |
doc_fields["doc_embeddings"] = [] | |
if doc_id == doc_fields["cord_uid"]: | |
doc_fields["doc_embeddings"] = [float(v) for v in data[1:-1]] | |
yield i, doc_fields | |
if "embeddings" in self.config.name and fh is not None: | |
fh.close() | |