Datasets:
File size: 5,611 Bytes
da99b7b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
# 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 """
from __future__ import absolute_import, division, print_function
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
|