Datasets:
curiosity_dialogs

	# 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.
	"""Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity"""


	import json
	import os

	import datasets


	_CITATION = """\
	@inproceedings{rodriguez2020curiosity,
	title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},
	author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},
	year = 2020,
	booktitle = {Empirical Methods in Natural Language Processing}
	}
	"""


	_DESCRIPTION = """\
	This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like
	geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog
	acts, grounding to Wikipedia, and user reactions to messages.
	"""

	_HOMEPAGE = "https://www.pedro.ai/curiosity"

	_LICENSE = "https://github.com/facebookresearch/curiosity/blob/master/LICENSE"

	_URL = "https://obj.umiacs.umd.edu/curiosity/"
	_URLs = {
	"train": _URL + "curiosity_dialogs.train.json",
	"val": _URL + "curiosity_dialogs.val.json",
	"test": _URL + "curiosity_dialogs.test.json",
	"test_zero": _URL + "curiosity_dialogs.test_zero.json",
	}


	class CuriosityDialogsConfig(datasets.BuilderConfig):
	"""BuilderConfig for Curiosity Dialogs dataset"""

	def __init__(self, **kwargs):
	"""BuilderConfig for Curiosity Dialogs dataset.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(CuriosityDialogsConfig, self).__init__(**kwargs)


	class CuriosityDialogs(datasets.GeneratorBasedBuilder):
	"""Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	CuriosityDialogsConfig(
	name="curiosity_dialogs",
	version=datasets.Version("1.1.0"),
	description="Curiosity Dialog: A Dataset for Conversational Curiosity",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"messages": datasets.Sequence(
	{
	"message": datasets.Value("string"),
	"liked": datasets.ClassLabel(names=["False", "True"]),
	"sender": datasets.ClassLabel(names=["user", "assistant"]),
	"facts": datasets.Sequence(
	{
	"fid": datasets.Value("int32"),
	"used": datasets.ClassLabel(names=["False", "True"]),
	"source": datasets.ClassLabel(names=["section", "known", "random"]),
	}
	),
	"message_id": datasets.Value("string"),
	"dialog_acts": datasets.Sequence(datasets.Value("string")),
	}
	),
	"known_entities": datasets.Sequence(datasets.Value("string")),
	"focus_entity": datasets.Value("string"),
	"dialog_id": datasets.Value("int32"),
	"inferred_steps": datasets.ClassLabel(names=["False", "True"]),
	"created_time": datasets.Value("int64"),
	"aspects": datasets.Sequence(datasets.Value("string")),
	"first_aspect": datasets.Value("string"),
	"second_aspect": datasets.Value("string"),
	"shuffle_facts": datasets.ClassLabel(names=["False", "True"]),
	"related_entities": datasets.Sequence(datasets.Value("string")),
	"tag": datasets.Value("string"),
	"user_id": datasets.Value("int32"),
	"assistant_id": datasets.Value("int32"),
	"is_annotated": datasets.ClassLabel(names=["False", "True"]),
	"user_dialog_rating": datasets.Value("int32"),
	"user_other_agent_rating": datasets.Value("int32"),
	"assistant_dialog_rating": datasets.Value("int32"),
	"assistant_other_agent_rating": datasets.Value("int32"),
	"reported": datasets.ClassLabel(names=["False", "True"]),
	"annotated": datasets.ClassLabel(names=["False", "True"]),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	data_dir = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name="train",
	gen_kwargs={
	"filepath": os.path.join(data_dir["train"]),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name="val",
	gen_kwargs={"filepath": os.path.join(data_dir["val"]), "split": "val"},
	),
	datasets.SplitGenerator(
	name="test",
	gen_kwargs={
	"filepath": os.path.join(data_dir["test"]),
	"split": "test_zero",
	},
	),
	datasets.SplitGenerator(
	name="test_zero",
	gen_kwargs={
	"filepath": os.path.join(data_dir["test_zero"]),
	"split": "test_zero",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""

	# Bool entries are converted to string entries because of PyArrow error
	with open(filepath, encoding="utf-8") as f:
	dataset = json.load(f)
	dialogs = dataset["dialogs"]

	for id_, data in enumerate(dialogs):
	messages = data["messages"]

	for message in messages:
	message["liked"] = str(message["liked"])
	facts = message["facts"]

	for fact in facts:
	fact["used"] = str(fact["used"])

	known_entities = data["known_entities"]
	focus_entity = data["focus_entity"]
	dialog_id = data["dialog_id"]
	inferred_steps = str(data["inferred_steps"])
	created_time = data["created_time"]
	aspects = data["aspects"]
	first_aspect = data["first_aspect"]
	second_aspect = data["second_aspect"]
	shuffle_facts = str(data["shuffle_facts"])
	related_entities = data["related_entities"]
	tag = data["tag"]
	user_id = data["user_id"]
	assistant_id = data["assistant_id"]
	is_annotated = str(data["is_annotated"])
	user_dialog_rating = data["user_dialog_rating"]
	user_other_agent_rating = data["user_other_agent_rating"]
	assistant_dialog_rating = data["assistant_dialog_rating"]
	assistant_other_agent_rating = data["assistant_other_agent_rating"]
	reported = str(data["reported"])
	annotated = str(data["annotated"])

	yield id_, {
	"messages": messages,
	"known_entities": known_entities,
	"focus_entity": focus_entity,
	"dialog_id": dialog_id,
	"inferred_steps": inferred_steps,
	"created_time": created_time,
	"aspects": aspects,
	"first_aspect": first_aspect,
	"second_aspect": second_aspect,
	"shuffle_facts": shuffle_facts,
	"related_entities": related_entities,
	"tag": tag,
	"user_id": user_id,
	"assistant_id": assistant_id,
	"is_annotated": is_annotated,
	"user_dialog_rating": user_dialog_rating,
	"user_other_agent_rating": user_other_agent_rating,
	"assistant_dialog_rating": assistant_dialog_rating,
	"assistant_other_agent_rating": assistant_other_agent_rating,
	"reported": reported,
	"annotated": annotated,
	}