Datasets:

bigIR
/

ar_cov19

	# 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.
	"""TODO: Add a description here."""


	import glob
	import os

	import pandas as pd

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{haouari2020arcov19,
	title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},
	author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed},
	journal={arXiv preprint arXiv:2004.05861},
	year={2020}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://gitlab.com/bigirqu/ArCOV-19"

	# TODO: Add the licence for the dataset here if you can find it
	# _LICENSE = ""

	# TODO: Add link to the official dataset URLs here
	# 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)
	_URL = "https://gitlab.com/bigirqu/ArCOV-19/-/archive/master/ArCOV-19-master.zip"
	# _URL="https://gitlab.com/bigirqu/ArCOV-19/-/archive/master/ArCOV-19-master.zip?path=dataset/all_tweets"


	class ArCov19Config(datasets.BuilderConfig):
	"""BuilderConfig for ArCOV19."""

	def __init__(self, **kwargs):
	"""BuilderConfig for ArCOV19.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(ArCov19Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class ArCov19(datasets.GeneratorBasedBuilder):
	"""ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others"""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	ArCov19Config(
	name="ar_cov19",
	description="Plain text",
	)
	]

	def _info(self):

	features = {}

	features["tweetID"] = datasets.Value("int64")

	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
	), # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# 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):
	"""Returns SplitGenerators."""
	# 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)
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir})]

	def _generate_examples(self, data_dir):
	"""Yields examples."""
	# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
	# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
	# The key is not important, it's more here for legacy reason (legacy from tfds)
	for fname in sorted(glob.glob(os.path.join(data_dir, "ArCOV-19-master/dataset/all_tweets/2020-*"))):

	df = pd.read_csv(fname, names=["tweetID"])
	for id_, record in df.iterrows():

	tweetID = record["tweetID"]

	yield str(id_), {"tweetID": tweetID}