Datasets:
taskmaster3

	# 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.
	"""Taskmaster-3: A goal oriented conversations dataset for movie ticketing domain """


	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-3 dataset consists of 23,757 movie ticketing dialogs. \
	By "movie ticketing" we mean conversations where the customer's goal is to purchase tickets after deciding \
	on theater, time, movie name, number of tickets, and date, or opt out of the transaction. This collection \
	was created using the "self-dialog" method. This means a single, crowd-sourced worker is \
	paid to create a conversation writing turns for both speakers, i.e. the customer and the ticketing agent.
	"""

	_HOMEPAGE = "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020"

	_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-3-2020/data"


	class Taskmaster3(datasets.GeneratorBasedBuilder):
	"""Taskmaster-3: A goal oriented conversations dataset for movie ticketing domain"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = {
	"conversation_id": datasets.Value("string"),
	"vertical": datasets.Value("string"),
	"instructions": datasets.Value("string"),
	"scenario": datasets.Value("string"),
	"utterances": [
	{
	"index": datasets.Value("int32"),
	"speaker": datasets.Value("string"),
	"text": datasets.Value("string"),
	"apis": [
	{
	"name": datasets.Value("string"),
	"index": datasets.Value("int32"),
	"args": [
	{
	"arg_name": datasets.Value("string"),
	"arg_value": datasets.Value("string"),
	}
	],
	"response": [
	{
	"response_name": datasets.Value("string"),
	"response_value": 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):
	urls = [f"{_BASE_URL}/data_{i:02}.json" for i in range(20)]
	dialog_files = dl_manager.download(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"dialog_files": dialog_files},
	),
	]

	def _generate_examples(self, dialog_files):
	for filepath in dialog_files:
	with open(filepath, encoding="utf-8") as f:
	dialogs = json.load(f)
	for dialog in dialogs:
	example = self._prepare_example(dialog)
	yield example["conversation_id"], example

	def _prepare_example(self, dialog):
	utterances = dialog["utterances"]
	for utterance in utterances:
	if "segments" not in utterance:
	utterance["segments"] = []

	if "apis" in utterance:
	utterance["apis"] = self._transform_apis(utterance["apis"])
	else:
	utterance["apis"] = []
	return dialog

	def _transform_apis(self, apis):
	for api in apis:
	if "args" in api:
	api["args"] = [{"arg_name": k, "arg_value": v} for k, v in api["args"].items()]
	else:
	api["args"] = []

	if "response" in api:
	api["response"] = [{"response_name": k, "response_value": v} for k, v in api["response"].items()]
	else:
	api["response"] = []

	return apis