Datasets:

ought
/

raft

	# 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.
	"""RAFT AI papers, test set."""

	import csv
	import json
	import os

	import datasets

	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {A great new dataset},
	author={huggingface, Inc.
	},
	year={2020}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	This dataset contains a corpus of AI papers. The first task is to determine\
	whether or not a datapoint is an AI safety paper. The second task is to\
	determine what type of paper it is.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = ""

	# 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)
	_URLs = {
	'TAISafety': {
	'train': "./data/TAISafety/train.csv",
	'test': "./data/TAISafety/test.csv"
	},
	'AIInitiatives': {
	'train': "./data/AIInitiatives/train.csv",
	'test': "./data/AIInitiatives/test.csv"
	}
	} # TODO: Generate these automatically.


	class Raft(datasets.GeneratorBasedBuilder):
	"""RAFT Dataset."""

	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 = [
	datasets.BuilderConfig(name="TAISafety-binary", version=VERSION,
	description="Decide whether the papers focus on AI safety methods."),
	datasets.BuilderConfig(name="TAISafety-multiclass", version=VERSION,
	description="If a paper has AI safety methods, determine if it is meta"
	"safety or technical safety."),
	datasets.BuilderConfig(name="AIInitiatives-multilabel", version=VERSION,
	description="For each initiative, decide which (if any) of Ethics, "
	"Governance, and Social Good apply to the initiative's AI goals")
	]

	DEFAULT_CONFIG_NAME = "TAISafety/binary" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	if self.config.name.startswith("TAISafety"):
	features = datasets.Features(
	{
	"title": datasets.Value("string"),
	"publication": datasets.Value("string"),
	"abstract": datasets.Value("string"),
	"answer": datasets.Value("string"),
	}
	)
	elif self.config.name.startswith("AIInitiatives"):
	features = datasets.Features(
	{
	"name": datasets.Value("string"),
	"organization": datasets.Value("string"),
	"description": datasets.Value("string"),
	"sector": datasets.Value("string"),
	"scope": datasets.Value("string"),
	"audience": datasets.Value("string"),
	"stage": datasets.Value("string"),
	"date": datasets.Value("string"),
	"country": datasets.Value("string"),
	"notes": datasets.Value("string"),
	"answer_ethics": datasets.Value("string"),
	"answer_governance": datasets.Value("string"),
	"answer_socialgood": 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,
	# 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(_URLs)
	dataset = self.config.name.split("-")[0]
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": data_dir[dataset]['train'],
	"split": "train"}),
	datasets.SplitGenerator(name=datasets.Split.TEST,
	gen_kwargs={"filepath": data_dir[dataset]['test'],
	"split": "test"})
	]

	def _generate_examples(
	self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	""" Yields examples as (key, example) tuples. """
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	dataset, config = self.config.name.split("-")

	with open(filepath, encoding="utf-8") as f:
	csv_reader = csv.reader(
	f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
	)
	for id_, row in enumerate(csv_reader):
	if id_ == 0: # First row is column names
	continue
	if dataset == "TAISafety":
	if split == "train":
	title, publication, abstract, category, binary = row
	answer = category if config == "multiclass" else binary
	elif split == "test":
	title, publication, abstract = row
	answer = ""
	yield id_, {"title": title,
	"publication": publication,
	"abstract": abstract,
	"answer": answer}
	if dataset == "AIInitiatives":
	name, organization, description, sector, scope, audience, \
	stage, date, country, notes, answer_ethics, \
	answer_governance, answer_socialgood = row
	if split == "test":
	answer = ""
	yield id_, {"name": name,
	"organization": organization,
	"description": description,
	"sector": sector,
	"scope": scope,
	"audience": audience,
	"stage": stage,
	"date": date,
	"country": country,
	"notes": notes,
	"answer_ethics": answer_ethics,
	"answer_governance": answer_governance,
	"answer_socialgood": answer_socialgood}