Datasets:
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# 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
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