Datasets:
Upload RiSAWOZ.py
Browse files- RiSAWOZ.py +489 -0
RiSAWOZ.py
ADDED
@@ -0,0 +1,489 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
"""RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling"""
|
16 |
+
|
17 |
+
|
18 |
+
import json
|
19 |
+
import os
|
20 |
+
from typing import Dict
|
21 |
+
|
22 |
+
import datasets
|
23 |
+
|
24 |
+
|
25 |
+
_CITATION = """\
|
26 |
+
@inproceedings{quan-etal-2020-risawoz,
|
27 |
+
title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
|
28 |
+
author = "Quan, Jun and
|
29 |
+
Zhang, Shian and
|
30 |
+
Cao, Qian and
|
31 |
+
Li, Zizhong and
|
32 |
+
Xiong, Deyi",
|
33 |
+
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
|
34 |
+
month = nov,
|
35 |
+
year = "2020",
|
36 |
+
address = "Online",
|
37 |
+
publisher = "Association for Computational Linguistics",
|
38 |
+
url = "https://www.aclweb.org/anthology/2020.emnlp-main.67",
|
39 |
+
pages = "930--940",
|
40 |
+
}
|
41 |
+
"""
|
42 |
+
|
43 |
+
|
44 |
+
_DESCRIPTION = """\
|
45 |
+
RiSAWOZ contains 11.2K human-to-human (H2H) multiturn semantically annotated dialogues, \
|
46 |
+
with more than 150K utterances spanning over 12 domains, \
|
47 |
+
which is larger than all previous annotated H2H conversational datasets.\
|
48 |
+
Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively.
|
49 |
+
"""
|
50 |
+
|
51 |
+
_HOMEPAGE = "https://github.com/terryqj0107/RiSAWOZ"
|
52 |
+
|
53 |
+
_LICENSE = "Attribution 4.0 International (CC BY 4.0) license."
|
54 |
+
|
55 |
+
|
56 |
+
class RiSAWOZ(datasets.GeneratorBasedBuilder):
|
57 |
+
"""RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling"""
|
58 |
+
|
59 |
+
VERSION = datasets.Version("1.1.0")
|
60 |
+
|
61 |
+
def _info(self):
|
62 |
+
features = datasets.Features(
|
63 |
+
{
|
64 |
+
"dialogue_id": datasets.Value("string"),
|
65 |
+
"goal": datasets.Value("string"),
|
66 |
+
"domains": datasets.Sequence(datasets.Value("string")),
|
67 |
+
"dialogue": [
|
68 |
+
{
|
69 |
+
"turn_id": datasets.Value("int32"),
|
70 |
+
"turn_domain": datasets.Sequence(datasets.Value("string")),
|
71 |
+
"user_utterance": datasets.Value("string"),
|
72 |
+
"system_utterance": datasets.Value("string"),
|
73 |
+
"belief_state": {
|
74 |
+
"inform slot-values": {
|
75 |
+
"旅游景点-名称": datasets.Value("string"),
|
76 |
+
"旅游景点-区域": datasets.Value("string"),
|
77 |
+
"旅游景点-景点类型": datasets.Value("string"),
|
78 |
+
"旅游景点-最适合人群": datasets.Value("string"),
|
79 |
+
"旅游景点-消费": datasets.Value("string"),
|
80 |
+
"旅游景点-是否地铁直达": datasets.Value("string"),
|
81 |
+
"旅游景点-门票价格": datasets.Value("string"),
|
82 |
+
"旅游景点-电话号码": datasets.Value("string"),
|
83 |
+
"旅游景点-地址": datasets.Value("string"),
|
84 |
+
"旅游景点-评分": datasets.Value("string"),
|
85 |
+
"旅游景点-开放时间": datasets.Value("string"),
|
86 |
+
"旅游景点-特点": datasets.Value("string"),
|
87 |
+
"餐厅-名称": datasets.Value("string"),
|
88 |
+
"餐厅-区域": datasets.Value("string"),
|
89 |
+
"餐厅-菜系": datasets.Value("string"),
|
90 |
+
"餐厅-价位": datasets.Value("string"),
|
91 |
+
"餐厅-是否地铁直达": datasets.Value("string"),
|
92 |
+
"餐厅-人均消费": datasets.Value("string"),
|
93 |
+
"餐厅-地址": datasets.Value("string"),
|
94 |
+
"餐厅-电话号码": datasets.Value("string"),
|
95 |
+
"餐厅-评分": datasets.Value("string"),
|
96 |
+
"餐厅-营业时间": datasets.Value("string"),
|
97 |
+
"餐厅-推荐菜": datasets.Value("string"),
|
98 |
+
"酒店-名称": datasets.Value("string"),
|
99 |
+
"酒店-区域": datasets.Value("string"),
|
100 |
+
"酒店-星级": datasets.Value("string"),
|
101 |
+
"酒店-价位": datasets.Value("string"),
|
102 |
+
"酒店-酒店类型": datasets.Value("string"),
|
103 |
+
"酒店-房型": datasets.Value("string"),
|
104 |
+
"酒店-停车场": datasets.Value("string"),
|
105 |
+
"酒店-房费": datasets.Value("string"),
|
106 |
+
"酒店-地址": datasets.Value("string"),
|
107 |
+
"酒店-电话号码": datasets.Value("string"),
|
108 |
+
"酒店-评分": datasets.Value("string"),
|
109 |
+
"电脑-品牌": datasets.Value("string"),
|
110 |
+
"电脑-产品类别": datasets.Value("string"),
|
111 |
+
"电脑-分类": datasets.Value("string"),
|
112 |
+
"电脑-内存容量": datasets.Value("string"),
|
113 |
+
"电脑-屏幕尺寸": datasets.Value("string"),
|
114 |
+
"电脑-CPU": datasets.Value("string"),
|
115 |
+
"电脑-价格区间": datasets.Value("string"),
|
116 |
+
"电脑-系列": datasets.Value("string"),
|
117 |
+
"电脑-商品名称": datasets.Value("string"),
|
118 |
+
"电脑-系统": datasets.Value("string"),
|
119 |
+
"电脑-游戏性能": datasets.Value("string"),
|
120 |
+
"电脑-CPU型号": datasets.Value("string"),
|
121 |
+
"电脑-裸机重量": datasets.Value("string"),
|
122 |
+
"电脑-显卡类别": datasets.Value("string"),
|
123 |
+
"电脑-显卡型号": datasets.Value("string"),
|
124 |
+
"电脑-特性": datasets.Value("string"),
|
125 |
+
"电脑-色系": datasets.Value("string"),
|
126 |
+
"电脑-待机时长": datasets.Value("string"),
|
127 |
+
"电脑-硬盘容量": datasets.Value("string"),
|
128 |
+
"电脑-价格": datasets.Value("string"),
|
129 |
+
"火车-出发地": datasets.Value("string"),
|
130 |
+
"火车-目的地": datasets.Value("string"),
|
131 |
+
"火车-日期": datasets.Value("string"),
|
132 |
+
"火车-车型": datasets.Value("string"),
|
133 |
+
"火车-坐席": datasets.Value("string"),
|
134 |
+
"火车-车次信息": datasets.Value("string"),
|
135 |
+
"火车-时长": datasets.Value("string"),
|
136 |
+
"火车-出发时间": datasets.Value("string"),
|
137 |
+
"火车-到达时间": datasets.Value("string"),
|
138 |
+
"火车-票价": datasets.Value("string"),
|
139 |
+
"飞机-出发地": datasets.Value("string"),
|
140 |
+
"飞机-目的地": datasets.Value("string"),
|
141 |
+
"飞机-日期": datasets.Value("string"),
|
142 |
+
"飞机-舱位档次": datasets.Value("string"),
|
143 |
+
"飞机-航班信息": datasets.Value("string"),
|
144 |
+
"飞机-起飞时间": datasets.Value("string"),
|
145 |
+
"飞机-到达时间": datasets.Value("string"),
|
146 |
+
"飞机-票价": datasets.Value("string"),
|
147 |
+
"飞机-准点率": datasets.Value("string"),
|
148 |
+
"天气-城市": datasets.Value("string"),
|
149 |
+
"天气-日期": datasets.Value("string"),
|
150 |
+
"天气-天气": datasets.Value("string"),
|
151 |
+
"天气-温度": datasets.Value("string"),
|
152 |
+
"天气-风力风向": datasets.Value("string"),
|
153 |
+
"天气-紫外线强度": datasets.Value("string"),
|
154 |
+
"电影-制片国家/地区": datasets.Value("string"),
|
155 |
+
"电影-类型": datasets.Value("string"),
|
156 |
+
"电影-年代": datasets.Value("string"),
|
157 |
+
"电影-主演": datasets.Value("string"),
|
158 |
+
"电影-导演": datasets.Value("string"),
|
159 |
+
"电影-片名": datasets.Value("string"),
|
160 |
+
"电影-主演名单": datasets.Value("string"),
|
161 |
+
"电影-具体上映时间": datasets.Value("string"),
|
162 |
+
"电影-片长": datasets.Value("string"),
|
163 |
+
"电影-豆瓣评分": datasets.Value("string"),
|
164 |
+
"电视剧-制片国家/地区": datasets.Value("string"),
|
165 |
+
"电视剧-类型": datasets.Value("string"),
|
166 |
+
"电视剧-年代": datasets.Value("string"),
|
167 |
+
"电视剧-主演": datasets.Value("string"),
|
168 |
+
"电视剧-导演": datasets.Value("string"),
|
169 |
+
"电视剧-片名": datasets.Value("string"),
|
170 |
+
"电视剧-主演名单": datasets.Value("string"),
|
171 |
+
"电视剧-首播时间": datasets.Value("string"),
|
172 |
+
"电视剧-集数": datasets.Value("string"),
|
173 |
+
"电视剧-单集片长": datasets.Value("string"),
|
174 |
+
"电视剧-豆瓣评分": datasets.Value("string"),
|
175 |
+
"辅导班-班号": datasets.Value("string"),
|
176 |
+
"辅导班-难度": datasets.Value("string"),
|
177 |
+
"辅导班-科目": datasets.Value("string"),
|
178 |
+
"辅导班-年级": datasets.Value("string"),
|
179 |
+
"辅导班-区域": datasets.Value("string"),
|
180 |
+
"辅导班-校区": datasets.Value("string"),
|
181 |
+
"辅导班-上课方式": datasets.Value("string"),
|
182 |
+
"辅导班-开始日期": datasets.Value("string"),
|
183 |
+
"辅导班-结束日期": datasets.Value("string"),
|
184 |
+
"辅导班-每周": datasets.Value("string"),
|
185 |
+
"辅导班-上课时间": datasets.Value("string"),
|
186 |
+
"辅导班-下课时间": datasets.Value("string"),
|
187 |
+
"辅导班-时段": datasets.Value("string"),
|
188 |
+
"辅导班-课次": datasets.Value("string"),
|
189 |
+
"辅导班-课时": datasets.Value("string"),
|
190 |
+
"辅导班-教室地点": datasets.Value("string"),
|
191 |
+
"辅导班-教师": datasets.Value("string"),
|
192 |
+
"辅导班-价格": datasets.Value("string"),
|
193 |
+
"辅导班-课程网址": datasets.Value("string"),
|
194 |
+
"辅导班-教师网址": datasets.Value("string"),
|
195 |
+
"汽车-名称": datasets.Value("string"),
|
196 |
+
"汽车-车型": datasets.Value("string"),
|
197 |
+
"汽车-级别": datasets.Value("string"),
|
198 |
+
"汽车-座位数": datasets.Value("string"),
|
199 |
+
"汽车-车身尺寸(mm)": datasets.Value("string"),
|
200 |
+
"汽车-厂商": datasets.Value("string"),
|
201 |
+
"汽车-能源类型": datasets.Value("string"),
|
202 |
+
"汽车-发动机排量(L)": datasets.Value("string"),
|
203 |
+
"汽车-发动机马力(Ps)": datasets.Value("string"),
|
204 |
+
"汽车-驱动方式": datasets.Value("string"),
|
205 |
+
"汽车-综合油耗(L/100km)": datasets.Value("string"),
|
206 |
+
"汽车-环保标准": datasets.Value("string"),
|
207 |
+
"汽车-驾驶辅助影像": datasets.Value("string"),
|
208 |
+
"汽车-巡航系统": datasets.Value("string"),
|
209 |
+
"汽车-价格(万元)": datasets.Value("string"),
|
210 |
+
"汽车-车系": datasets.Value("string"),
|
211 |
+
"汽车-动力水平": datasets.Value("string"),
|
212 |
+
"汽车-油耗水平": datasets.Value("string"),
|
213 |
+
"汽车-倒车影像": datasets.Value("string"),
|
214 |
+
"汽车-定速巡航": datasets.Value("string"),
|
215 |
+
"汽车-座椅加热": datasets.Value("string"),
|
216 |
+
"汽车-座椅通风": datasets.Value("string"),
|
217 |
+
"汽车-所属价格区间": datasets.Value("string"),
|
218 |
+
"医院-名称": datasets.Value("string"),
|
219 |
+
"医院-等级": datasets.Value("string"),
|
220 |
+
"医院-类别": datasets.Value("string"),
|
221 |
+
"医院-性质": datasets.Value("string"),
|
222 |
+
"医院-区域": datasets.Value("string"),
|
223 |
+
"医院-地址": datasets.Value("string"),
|
224 |
+
"医院-电话": datasets.Value("string"),
|
225 |
+
"医院-挂号时间": datasets.Value("string"),
|
226 |
+
"医院-门诊时间": datasets.Value("string"),
|
227 |
+
"医院-公交线路": datasets.Value("string"),
|
228 |
+
"医院-地铁可达": datasets.Value("string"),
|
229 |
+
"医院-地铁线路": datasets.Value("string"),
|
230 |
+
"医院-重点科室": datasets.Value("string"),
|
231 |
+
"医院-CT": datasets.Value("string"),
|
232 |
+
"医院-3.0T MRI": datasets.Value("string"),
|
233 |
+
"医院-DSA": datasets.Value("string"),
|
234 |
+
},
|
235 |
+
# "inform slot-values": datasets.Value("string"),
|
236 |
+
"turn_inform": {
|
237 |
+
"旅游景点-名称": datasets.Value("string"),
|
238 |
+
"旅游景点-区域": datasets.Value("string"),
|
239 |
+
"旅游景点-景点类型": datasets.Value("string"),
|
240 |
+
"旅游景点-最适合人群": datasets.Value("string"),
|
241 |
+
"旅游景点-消费": datasets.Value("string"),
|
242 |
+
"旅游景点-是否地铁直达": datasets.Value("string"),
|
243 |
+
"旅游景点-门票价格": datasets.Value("string"),
|
244 |
+
"旅游景点-电话号码": datasets.Value("string"),
|
245 |
+
"旅游景点-地址": datasets.Value("string"),
|
246 |
+
"旅游景点-评分": datasets.Value("string"),
|
247 |
+
"旅游景点-开放时间": datasets.Value("string"),
|
248 |
+
"旅游景点-特点": datasets.Value("string"),
|
249 |
+
"餐厅-名称": datasets.Value("string"),
|
250 |
+
"餐厅-区域": datasets.Value("string"),
|
251 |
+
"餐厅-菜系": datasets.Value("string"),
|
252 |
+
"餐厅-价位": datasets.Value("string"),
|
253 |
+
"餐厅-是否地铁直达": datasets.Value("string"),
|
254 |
+
"餐厅-人均消费": datasets.Value("string"),
|
255 |
+
"餐厅-地址": datasets.Value("string"),
|
256 |
+
"餐厅-电话号码": datasets.Value("string"),
|
257 |
+
"餐厅-评分": datasets.Value("string"),
|
258 |
+
"餐厅-营业时间": datasets.Value("string"),
|
259 |
+
"餐厅-推荐菜": datasets.Value("string"),
|
260 |
+
"酒店-名称": datasets.Value("string"),
|
261 |
+
"酒店-区域": datasets.Value("string"),
|
262 |
+
"酒店-星级": datasets.Value("string"),
|
263 |
+
"酒店-价位": datasets.Value("string"),
|
264 |
+
"酒店-酒店类型": datasets.Value("string"),
|
265 |
+
"酒店-房型": datasets.Value("string"),
|
266 |
+
"酒店-停车场": datasets.Value("string"),
|
267 |
+
"酒店-房费": datasets.Value("string"),
|
268 |
+
"酒店-地址": datasets.Value("string"),
|
269 |
+
"酒店-电话号码": datasets.Value("string"),
|
270 |
+
"酒店-评分": datasets.Value("string"),
|
271 |
+
"电脑-品牌": datasets.Value("string"),
|
272 |
+
"电脑-产品类别": datasets.Value("string"),
|
273 |
+
"电脑-分类": datasets.Value("string"),
|
274 |
+
"电脑-内存容量": datasets.Value("string"),
|
275 |
+
"电脑-屏幕尺寸": datasets.Value("string"),
|
276 |
+
"电脑-CPU": datasets.Value("string"),
|
277 |
+
"电脑-价格区间": datasets.Value("string"),
|
278 |
+
"电脑-系列": datasets.Value("string"),
|
279 |
+
"电脑-商品名称": datasets.Value("string"),
|
280 |
+
"电脑-系统": datasets.Value("string"),
|
281 |
+
"电脑-游戏性能": datasets.Value("string"),
|
282 |
+
"电脑-CPU型号": datasets.Value("string"),
|
283 |
+
"电脑-裸机重量": datasets.Value("string"),
|
284 |
+
"电脑-显卡类别": datasets.Value("string"),
|
285 |
+
"电脑-显卡型号": datasets.Value("string"),
|
286 |
+
"电脑-特性": datasets.Value("string"),
|
287 |
+
"电脑-色系": datasets.Value("string"),
|
288 |
+
"电脑-待机时长": datasets.Value("string"),
|
289 |
+
"电脑-硬盘容量": datasets.Value("string"),
|
290 |
+
"电脑-价格": datasets.Value("string"),
|
291 |
+
"火车-出发地": datasets.Value("string"),
|
292 |
+
"火车-目的地": datasets.Value("string"),
|
293 |
+
"火车-日期": datasets.Value("string"),
|
294 |
+
"火车-车型": datasets.Value("string"),
|
295 |
+
"火车-坐席": datasets.Value("string"),
|
296 |
+
"火车-车次信息": datasets.Value("string"),
|
297 |
+
"火车-时长": datasets.Value("string"),
|
298 |
+
"火车-出发时间": datasets.Value("string"),
|
299 |
+
"火车-到达时间": datasets.Value("string"),
|
300 |
+
"火车-票价": datasets.Value("string"),
|
301 |
+
"飞机-出发地": datasets.Value("string"),
|
302 |
+
"飞机-目的地": datasets.Value("string"),
|
303 |
+
"飞机-日期": datasets.Value("string"),
|
304 |
+
"飞机-舱位档次": datasets.Value("string"),
|
305 |
+
"飞机-航班信息": datasets.Value("string"),
|
306 |
+
"飞机-起飞时间": datasets.Value("string"),
|
307 |
+
"飞机-到达时间": datasets.Value("string"),
|
308 |
+
"飞机-票价": datasets.Value("string"),
|
309 |
+
"飞机-准点率": datasets.Value("string"),
|
310 |
+
"天气-城市": datasets.Value("string"),
|
311 |
+
"天气-日期": datasets.Value("string"),
|
312 |
+
"天气-天气": datasets.Value("string"),
|
313 |
+
"天气-温度": datasets.Value("string"),
|
314 |
+
"天气-风力风向": datasets.Value("string"),
|
315 |
+
"天气-紫外线强度": datasets.Value("string"),
|
316 |
+
"电影-制片国家/地区": datasets.Value("string"),
|
317 |
+
"电影-类型": datasets.Value("string"),
|
318 |
+
"电影-年代": datasets.Value("string"),
|
319 |
+
"电影-主演": datasets.Value("string"),
|
320 |
+
"电影-导演": datasets.Value("string"),
|
321 |
+
"电影-片名": datasets.Value("string"),
|
322 |
+
"电影-主演名单": datasets.Value("string"),
|
323 |
+
"电影-具体上映时间": datasets.Value("string"),
|
324 |
+
"电影-片长": datasets.Value("string"),
|
325 |
+
"电影-豆瓣评分": datasets.Value("string"),
|
326 |
+
"电视剧-制片国家/地区": datasets.Value("string"),
|
327 |
+
"电视剧-类型": datasets.Value("string"),
|
328 |
+
"电视剧-年代": datasets.Value("string"),
|
329 |
+
"电视剧-主演": datasets.Value("string"),
|
330 |
+
"电视剧-导演": datasets.Value("string"),
|
331 |
+
"电视剧-片名": datasets.Value("string"),
|
332 |
+
"电视剧-主演名单": datasets.Value("string"),
|
333 |
+
"电视剧-首播时间": datasets.Value("string"),
|
334 |
+
"电视剧-集数": datasets.Value("string"),
|
335 |
+
"电视剧-单集片长": datasets.Value("string"),
|
336 |
+
"电视剧-豆瓣评分": datasets.Value("string"),
|
337 |
+
"辅导班-班号": datasets.Value("string"),
|
338 |
+
"辅导班-难度": datasets.Value("string"),
|
339 |
+
"辅导班-科目": datasets.Value("string"),
|
340 |
+
"辅导班-年级": datasets.Value("string"),
|
341 |
+
"辅导班-区域": datasets.Value("string"),
|
342 |
+
"辅导班-校区": datasets.Value("string"),
|
343 |
+
"辅导班-上课方式": datasets.Value("string"),
|
344 |
+
"辅导班-开始日期": datasets.Value("string"),
|
345 |
+
"辅导班-结束日期": datasets.Value("string"),
|
346 |
+
"辅导班-每周": datasets.Value("string"),
|
347 |
+
"辅导班-上课时间": datasets.Value("string"),
|
348 |
+
"辅导班-下课时间": datasets.Value("string"),
|
349 |
+
"辅导班-时段": datasets.Value("string"),
|
350 |
+
"辅导班-课次": datasets.Value("string"),
|
351 |
+
"辅导班-课时": datasets.Value("string"),
|
352 |
+
"辅导班-教室地点": datasets.Value("string"),
|
353 |
+
"辅导班-教师": datasets.Value("string"),
|
354 |
+
"辅导班-价格": datasets.Value("string"),
|
355 |
+
"辅导班-课程网址": datasets.Value("string"),
|
356 |
+
"辅导班-教师网址": datasets.Value("string"),
|
357 |
+
"汽车-名称": datasets.Value("string"),
|
358 |
+
"汽车-车型": datasets.Value("string"),
|
359 |
+
"汽车-级别": datasets.Value("string"),
|
360 |
+
"汽车-座位数": datasets.Value("string"),
|
361 |
+
"汽车-车身尺寸(mm)": datasets.Value("string"),
|
362 |
+
"汽车-厂商": datasets.Value("string"),
|
363 |
+
"汽车-能源类型": datasets.Value("string"),
|
364 |
+
"汽车-发动机排量(L)": datasets.Value("string"),
|
365 |
+
"汽车-发动机马力(Ps)": datasets.Value("string"),
|
366 |
+
"汽车-驱动方式": datasets.Value("string"),
|
367 |
+
"汽车-综合油耗(L/100km)": datasets.Value("string"),
|
368 |
+
"汽车-环保标准": datasets.Value("string"),
|
369 |
+
"汽车-驾驶辅助影像": datasets.Value("string"),
|
370 |
+
"汽车-巡航系统": datasets.Value("string"),
|
371 |
+
"汽车-价格(万元)": datasets.Value("string"),
|
372 |
+
"汽车-车系": datasets.Value("string"),
|
373 |
+
"汽车-动力水平": datasets.Value("string"),
|
374 |
+
"汽车-油耗水平": datasets.Value("string"),
|
375 |
+
"汽车-倒车影像": datasets.Value("string"),
|
376 |
+
"汽车-定速巡航": datasets.Value("string"),
|
377 |
+
"汽车-座椅加热": datasets.Value("string"),
|
378 |
+
"汽车-座椅通风": datasets.Value("string"),
|
379 |
+
"汽车-所属价格区间": datasets.Value("string"),
|
380 |
+
"医院-名称": datasets.Value("string"),
|
381 |
+
"医院-等级": datasets.Value("string"),
|
382 |
+
"医院-类别": datasets.Value("string"),
|
383 |
+
"医院-性质": datasets.Value("string"),
|
384 |
+
"医院-区域": datasets.Value("string"),
|
385 |
+
"医院-地址": datasets.Value("string"),
|
386 |
+
"医院-电话": datasets.Value("string"),
|
387 |
+
"医院-挂号时间": datasets.Value("string"),
|
388 |
+
"医院-门诊时间": datasets.Value("string"),
|
389 |
+
"医院-公交线路": datasets.Value("string"),
|
390 |
+
"医院-地铁可达": datasets.Value("string"),
|
391 |
+
"医院-地铁线路": datasets.Value("string"),
|
392 |
+
"医院-重点科室": datasets.Value("string"),
|
393 |
+
"医院-CT": datasets.Value("string"),
|
394 |
+
"医院-3.0T MRI": datasets.Value("string"),
|
395 |
+
"医院-DSA": datasets.Value("string"),
|
396 |
+
},
|
397 |
+
"turn request": datasets.Sequence(datasets.Value("string"))
|
398 |
+
},
|
399 |
+
"user_actions": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
|
400 |
+
"system_actions": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
|
401 |
+
"db_results": datasets.Sequence(datasets.Value("string")),
|
402 |
+
"segmented_user_utterance": datasets.Value("string"),
|
403 |
+
"segmented_system_utterance": datasets.Value("string")
|
404 |
+
}
|
405 |
+
]
|
406 |
+
}
|
407 |
+
)
|
408 |
+
|
409 |
+
return datasets.DatasetInfo(
|
410 |
+
# This is the description that will appear on the datasets page.
|
411 |
+
description=_DESCRIPTION,
|
412 |
+
# This defines the different columns of the dataset and their types
|
413 |
+
features=features, # Here we define them above because they are different between the two configurations
|
414 |
+
# If there's a common (input, target) tuple from the features,
|
415 |
+
# specify them here. They'll be used if as_supervised=True in
|
416 |
+
# builder.as_dataset.
|
417 |
+
supervised_keys=None,
|
418 |
+
# Homepage of the dataset for documentation
|
419 |
+
homepage=_HOMEPAGE,
|
420 |
+
# License for the dataset if available
|
421 |
+
license=_LICENSE,
|
422 |
+
# Citation for the dataset
|
423 |
+
citation=_CITATION,
|
424 |
+
)
|
425 |
+
|
426 |
+
def _split_generators(self, dl_manager):
|
427 |
+
"""Returns SplitGenerators."""
|
428 |
+
|
429 |
+
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
|
430 |
+
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
|
431 |
+
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
432 |
+
_URL = {"train": "train.json", "test": "test.json", "dev": "dev.json"}
|
433 |
+
|
434 |
+
data_dir2 = dl_manager.download_and_extract(_URL)
|
435 |
+
|
436 |
+
return [
|
437 |
+
datasets.SplitGenerator(
|
438 |
+
name=datasets.Split.TRAIN,
|
439 |
+
# These kwargs will be passed to _generate_examples
|
440 |
+
gen_kwargs={
|
441 |
+
# "filepath": os.path.join(data_dir, "train.json"),
|
442 |
+
"filepath": data_dir2["train"],
|
443 |
+
"split": "train",
|
444 |
+
},
|
445 |
+
),
|
446 |
+
datasets.SplitGenerator(
|
447 |
+
name=datasets.Split.TEST,
|
448 |
+
# These kwargs will be passed to _generate_examples
|
449 |
+
gen_kwargs={
|
450 |
+
"filepath": data_dir2["test"],
|
451 |
+
"split": "test"
|
452 |
+
},
|
453 |
+
),
|
454 |
+
datasets.SplitGenerator(
|
455 |
+
name=datasets.Split.VALIDATION,
|
456 |
+
# These kwargs will be passed to _generate_examples
|
457 |
+
gen_kwargs={
|
458 |
+
"filepath": data_dir2["dev"],
|
459 |
+
"split": "dev",
|
460 |
+
},
|
461 |
+
),
|
462 |
+
]
|
463 |
+
|
464 |
+
def _generate_examples(
|
465 |
+
self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
466 |
+
):
|
467 |
+
""" Yields examples as (key, example) tuples. """
|
468 |
+
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
|
469 |
+
# The `key` is here for legacy reason (tfds) and is not important in itself.
|
470 |
+
empty_belief_state = ["旅游景点-名称", "旅游景点-区域", "旅游景点-景点类型", "旅游景点-最适合人群", "旅游景点-消费", "旅游景点-是否地铁直达", "旅游景点-门票价格", "旅游景点-电话号码", "旅游景点-地址", "旅游景点-评分", "旅游景点-开放时间", "旅游景点-特点", "餐厅-名称", "餐厅-区域", "餐厅-菜系", "餐厅-价位", "餐厅-是否地铁直达", "餐厅-人均消费", "餐厅-地址", "餐厅-电话号码", "餐厅-评分", "餐厅-营业时间", "餐厅-推荐菜", "酒店-名称", "酒店-区域", "酒店-星级", "酒店-价位", "酒店-酒店类型", "酒店-房型", "酒店-停车场", "酒店-房费", "酒店-地址", "酒店-电话号码", "酒店-评分", "电脑-品牌", "电脑-产品类别", "电脑-分类", "电脑-内存容量", "电脑-屏幕尺寸", "电脑-CPU", "电脑-价格区间", "电脑-系列", "电脑-商品名称", "电脑-系统", "电脑-游戏性能", "电脑-CPU型号", "电脑-裸机重量", "电脑-显卡类别", "电脑-显卡型号", "电脑-特性", "电脑-色系", "电脑-待机时长", "电脑-硬盘容量", "电脑-价格", "火车-出发地", "火车-目的地", "火车-日期", "火车-车型", "火车-坐席", "火车-车次信息", "火车-时长", "火车-出发时间", "火车-到达时间", "火车-票价", "飞机-出发地", "飞机-目的地", "飞机-日期", "飞机-舱位档次", "飞机-航班信息", "飞机-起飞时间", "飞机-到达时间", "飞机-票价", "飞机-准点率", "天气-城市", "天气-日期", "天气-天气", "天气-温度", "天气-风力风向", "天气-紫外线强度", "电影-制片国家/地区", "电影-类型", "电影-年代", "电影-主演", "电影-导演", "电影-片名", "电影-主演名单", "电影-具体上映时间", "电影-片长", "电影-豆瓣评分", "电视剧-制片国家/地区", "电视剧-类型", "电视剧-年代", "电视剧-主演", "电视剧-导演", "电视剧-片名", "电视剧-主演名单", "电视剧-首播时间", "电视剧-集数", "电视剧-单集片长", "电视剧-豆瓣评分", "辅导班-班号", "辅导班-难度", "辅导班-科目", "辅导班-年级", "辅导班-区域", "辅导班-校区", "辅导班-上课方式", "辅导班-开始日期", "辅导班-结束日期", "辅导班-每周", "辅导班-上课时间", "辅导班-下课时间", "辅导班-时段", "辅导班-课次", "辅导班-课时", "辅导班-教室地点", "辅导班-教师", "辅导班-价格", "辅导班-课程网址", "辅导班-教师网址", "汽车-名称", "汽车-车型", "汽车-级别", "汽车-座位数", "汽车-车身尺寸(mm)", "汽车-厂商", "汽车-能源类型", "汽车-发动机排量(L)", "汽车-发动机马力(Ps)", "汽车-驱动方式", "汽车-综合油耗(L/100km)", "汽车-环保标准", "汽车-驾驶辅助影像", "汽车-巡航系统", "汽车-价格(万元)", "汽车-车系", "汽车-动力水平", "汽车-油耗水平", "汽车-倒车影像", "汽车-定速巡航", "汽车-座椅加热", "汽车-座椅通风", "汽车-所属价格区间", "医院-名称", "医院-等级", "医院-类别", "医院-性质", "医院-区域", "医院-地址", "医院-电话", "医院-挂号时间", "医院-门诊时间", "医院-公交线路", "医院-地铁可达", "医院-地铁线路", "医院-重点科室", "医院-CT", "医院-3.0T MRI", "医院-DSA"]
|
471 |
+
|
472 |
+
with open(filepath, encoding="utf-8") as f:
|
473 |
+
all_data = json.load(f)
|
474 |
+
id_ = 0
|
475 |
+
for data in all_data:
|
476 |
+
for slot in empty_belief_state:
|
477 |
+
for dia in data["dialogue"]:
|
478 |
+
if slot not in dia["belief_state"]["inform slot-values"]:
|
479 |
+
dia["belief_state"]["inform slot-values"][slot] = ""
|
480 |
+
if slot not in dia["belief_state"]["turn_inform"]:
|
481 |
+
dia["belief_state"]["turn_inform"][slot] = ""
|
482 |
+
|
483 |
+
yield id_, {
|
484 |
+
"dialogue_id": data["dialogue_id"],
|
485 |
+
"goal": data["goal"],
|
486 |
+
"domains": data["domains"],
|
487 |
+
"dialogue": data["dialogue"]
|
488 |
+
}
|
489 |
+
id_ += 1
|