Datasets:
taskmaster2

	# 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.
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	# limitations under the License.
	"""Taskmaster: A dataset for goal oriented conversations."""


	import json

	import datasets


	_CITATION = """\
	@inproceedings{48484,
	title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
	author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
	year = {2019}
	}
	"""

	_DESCRIPTION = """\
	Taskmaster is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs \
	in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. \
	Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, \
	Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is \
	almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. \
	All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced \
	workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. \
	In this way, users were led to believe they were interacting with an automated system that “spoke” \
	using text-to-speech (TTS) even though it was in fact a human behind the scenes. \
	As a result, users could express themselves however they chose in the context of an automated interface.
	"""

	_HOMEPAGE = "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020"

	_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data"


	class Taskmaster2(datasets.GeneratorBasedBuilder):
	"""Taskmaster: A dataset for goal oriented conversations."""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="flights", version=datasets.Version("1.0.0"), description="Taskmaster-2 flights domain."
	),
	datasets.BuilderConfig(
	name="food-ordering", version=datasets.Version("1.0.0"), description="Taskmaster-2 food-ordering domain"
	),
	datasets.BuilderConfig(
	name="hotels", version=datasets.Version("1.0.0"), description="Taskmaster-2 hotel domain"
	),
	datasets.BuilderConfig(
	name="movies", version=datasets.Version("1.0.0"), description="Taskmaster-2 movies domain"
	),
	datasets.BuilderConfig(
	name="music", version=datasets.Version("1.0.0"), description="Taskmaster-2 music domain"
	),
	datasets.BuilderConfig(
	name="restaurant-search",
	version=datasets.Version("1.0.0"),
	description="Taskmaster-2 restaurant-search domain",
	),
	datasets.BuilderConfig(
	name="sports", version=datasets.Version("1.0.0"), description="Taskmaster-2 sports domain"
	),
	]

	def _info(self):
	features = {
	"conversation_id": datasets.Value("string"),
	"instruction_id": datasets.Value("string"),
	"utterances": [
	{
	"index": datasets.Value("int32"),
	"speaker": datasets.Value("string"),
	"text": datasets.Value("string"),
	"segments": [
	{
	"start_index": datasets.Value("int32"),
	"end_index": datasets.Value("int32"),
	"text": datasets.Value("string"),
	"annotations": [{"name": datasets.Value("string")}],
	}
	],
	}
	],
	}
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	url = f"{_BASE_URL}/{self.config.name}.json"
	dialogs_file = dl_manager.download(url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": dialogs_file},
	),
	]

	def _generate_examples(self, filepath):
	key = 0
	with open(filepath, encoding="utf-8") as f:
	dialogs = json.load(f)
	for dialog in dialogs:
	utterances = dialog["utterances"]
	for utterance in utterances:
	if "segments" not in utterance:
	utterance["segments"] = []
	yield key, dialog
	key += 1