Datasets:
dream

	# coding=utf-8
	# Copyright 2020 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
	"""DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension"""


	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{sundream2018,
	title={{DREAM}: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension},
	author={Sun, Kai and Yu, Dian and Chen, Jianshu and Yu, Dong and Choi, Yejin and Cardie, Claire},
	journal={Transactions of the Association for Computational Linguistics},
	year={2019},
	url={https://arxiv.org/abs/1902.00164v1}
	}
	"""

	_DESCRIPTION = """\
	DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. \
	In contrast to existing reading comprehension datasets, DREAM is the first to focus on \
	in-depth multi-turn multi-party dialogue understanding.
	"""

	_URL = "https://raw.githubusercontent.com/nlpdata/dream/master/data/"
	_URLS = {
	"train": _URL + "train.json",
	"dev": _URL + "dev.json",
	"test": _URL + "test.json",
	}


	class DreamConfig(datasets.BuilderConfig):
	"""BuilderConfig for Dream."""

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

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(DreamConfig, self).__init__(**kwargs)


	class Dream(datasets.GeneratorBasedBuilder):
	"""DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	DreamConfig(
	name="plain_text",
	version=datasets.Version("1.0.0"),
	description="plain_text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"dialogue_id": datasets.Value("string"),
	"dialogue": datasets.Sequence(datasets.Value("string")),
	"question": datasets.Value("string"),
	"choice": datasets.features.Sequence(datasets.Value("string")),
	"answer": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://dataset.org/dream/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	dialogues = json.load(f)
	counter = 0
	for dialogue in dialogues:
	dialogue_text = dialogue[0]
	questions = dialogue[1]
	dialogue_id = dialogue[2]

	for que in questions:
	yield counter, {
	"id": counter,
	"dialogue_id": dialogue_id,
	"dialogue": dialogue_text,
	"question": que["question"],
	"choice": que["choice"],
	"answer": que["answer"],
	}
	counter += 1