Datasets:

BNNT
/

IPQuiz

	# 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: Address all TODOs and remove all explanatory comments
	"""TODO: Add a description here."""


	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}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
	"""

	# 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 = ""

	_URLS = {
	"zh": "https://huggingface.co/datasets/BNNT/IPQuiz/raw/main/zh.json",
	"en": "https://huggingface.co/datasets/BNNT/IPQuiz/raw/main/en.json",
	"xl": "https://huggingface.co/datasets/BNNT/IPQuiz/raw/main/xl.json",
	}


	# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
	class IPQuizDataset(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my 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="zh", version=VERSION, description="This part of my dataset covers a first domain"),
	datasets.BuilderConfig(name="en", version=VERSION, description="This part of my dataset covers a first domain"),
	datasets.BuilderConfig(name="xl", version=VERSION, description="This part of my dataset covers a second domain"),
	]

	DEFAULT_CONFIG_NAME = "zh" # 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
	features = datasets.Features(
	{
	"language": datasets.Value("string"),
	"problem": datasets.Value("string"),
	# "candidates": datasets.Sequence(
	# {
	# "texts": datasets.Value("string"),
	# "options": datasets.Value("string"),
	# }
	# ),
	"candidates": datasets.Value("string"),
	"answer": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	)
	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
	downloaded_files = dl_manager.download(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files[self.config.name],
	"split": 'train'
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for key, row in enumerate(data):
	# Yields examples as (key, example) tuples
	yield key, {
	"language": row["language"],
	"problem": row["problem"],
	#"candidates": {
	# "texts": [v for k, v in row["candidates"].items()],
	# "options": [k for k, v in row["candidates"].items()],
	#},
	"candidates": json.dumps(row["candidates"]),
	"answer": row['answer'],
	}