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Error code: DatasetGenerationCastError Exception: DatasetGenerationCastError Message: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 6 new columns ({'turns', 'dataset', 'dialogue_id', 'original_id', 'domains', 'data_split'}) and 3 missing columns ({'poi', 'poi_type', 'address'}). This happened while the json dataset builder was generating data using zip://data/dialogues.json::/tmp/hf-datasets-cache/medium/datasets/19788242969337-config-parquet-and-info-ConvLab-kvret-2856ced5/downloads/c4188ff8667fad5baca655e20bec4f12b340253be67138c6afeab69a3748aa24 Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast turns: list<item: struct<db_results: struct<navigate: list<item: struct<address: string, distance: string, poi: string, poi_type: string, traffic_info: string>>, schedule: list<item: struct<agenda: string, date: string, event: string, party: string, room: string, time: string>>, weather: list<item: struct<friday: string, location: string, monday: string, saturday: string, sunday: string, thursday: string, today: string, tuesday: string, wednesday: string>>>, dialogue_acts: struct<binary: list<item: struct<domain: string, intent: string, slot: string>>, categorical: list<item: null>, non-categorical: list<item: struct<domain: string, end: int64, intent: string, slot: string, start: int64, value: string>>>, speaker: string, state: struct<navigate: struct<address: string, distance: string, poi: string, poi_type: string, traffic_info: string>, schedule: struct<agenda: string, date: string, event: string, party: string, room: string, time: string>, weather: struct<date: string, location: string, weather_attribute: string>>, utt_idx: int64, utterance: string>> child 0, item: struct<db_results: struct<navigate: list<item: struct<address: string, distance: string, poi: string, poi_type: string, traffic_info: string>>, schedule: list<item: struct<agenda: string, date: string, event: string, party: string, room: string, time: string>>, weather: list<item: struct<friday: string, location: string, monday: string, saturday: string, sunday: string, thursday: string, today: string, tuesda ... child 4, start: int64 child 5, value: string child 2, speaker: string child 3, state: struct<navigate: struct<address: string, distance: string, poi: string, poi_type: string, traffic_info: string>, schedule: struct<agenda: string, date: string, event: string, party: string, room: string, time: string>, weather: struct<date: string, location: string, weather_attribute: string>> child 0, navigate: struct<address: string, distance: string, poi: string, poi_type: string, traffic_info: string> child 0, address: string child 1, distance: string child 2, poi: string child 3, poi_type: string child 4, traffic_info: string child 1, schedule: struct<agenda: string, date: string, event: string, party: string, room: string, time: string> child 0, agenda: string child 1, date: string child 2, event: string child 3, party: string child 4, room: string child 5, time: string child 2, weather: struct<date: string, location: string, weather_attribute: string> child 0, date: string child 1, location: string child 2, weather_attribute: string child 4, utt_idx: int64 child 5, utterance: string dataset: string dialogue_id: string original_id: string domains: list<item: string> child 0, item: string data_split: string to {'poi': Value(dtype='string', id=None), 'poi_type': Value(dtype='string', id=None), 'address': Value(dtype='string', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 6 new columns ({'turns', 'dataset', 'dialogue_id', 'original_id', 'domains', 'data_split'}) and 3 missing columns ({'poi', 'poi_type', 'address'}). This happened while the json dataset builder was generating data using zip://data/dialogues.json::/tmp/hf-datasets-cache/medium/datasets/19788242969337-config-parquet-and-info-ConvLab-kvret-2856ced5/downloads/c4188ff8667fad5baca655e20bec4f12b340253be67138c6afeab69a3748aa24 Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
poi_type
string | address
string | poi
string |
---|---|---|
chinese restaurant
|
593 Arrowhead Way
|
Chef Chu's
|
coffee or tea place
|
394 Van Ness Ave
|
Coupa
|
grocery store
|
408 University Ave
|
Trader Joes
|
pizza restaurant
|
113 Anton Ct
|
Round Table
|
grocery store
|
1313 Chester Ave
|
Hacienda Market
|
rest stop
|
465 Arcadia Pl
|
Four Seasons
|
chinese restaurant
|
830 Almanor Ln
|
tai pan
|
shopping center
|
773 Alger Dr
|
Stanford Shopping Center
|
gas station
|
53 University Av
|
Shell
|
rest stop
|
657 Ames Ave
|
The Clement Hotel
|
coffee or tea place
|
792 Bedoin Street
|
Starbucks
|
rest stop
|
329 El Camino Real
|
The Westin
|
shopping center
|
383 University Ave
|
Town and Country
|
grocery store
|
452 Arcadia Pl
|
Safeway
|
shopping center
|
171 Oak Rd
|
Topanga Mall
|
chinese restaurant
|
842 Arrowhead Way
|
Panda Express
|
pizza restaurant
|
704 El Camino Real
|
Pizza Hut
|
hospital
|
899 Ames Ct
|
Stanford Childrens Health
|
certain address
|
5677 southwest 4th street
|
5677 southwest 4th street
|
certain address
|
5672 barringer street
|
5672 barringer street
|
hospital
|
214 El Camino Real
|
Stanford Express Care
|
grocery store
|
638 Amherst St
|
Sigona Farmers Market
|
hospital
|
611 Ames Ave
|
Palo Alto Medical Foundation
|
shopping center
|
434 Arastradero Rd
|
Ravenswood Shopping Center
|
shopping center
|
338 Alester Ave
|
Midtown Shopping Center
|
chinese restaurant
|
271 Springer Street
|
Mandarin Roots
|
rest stop
|
753 University Ave
|
Comfort Inn
|
chinese restaurant
|
669 El Camino Real
|
P.F. Changs
|
pizza restaurant
|
915 Arbol Dr
|
Pizza Chicago
|
rest stop
|
333 Arbol Dr
|
Travelers Lodge
|
coffee or tea place
|
436 Alger Dr
|
Palo Alto Cafe
|
certain address
|
5677 springer street
|
5677 springer street
|
chinese restaurant
|
113 Arbol Dr
|
Jing Jing
|
grocery store
|
409 Bollard St
|
Willows Market
|
pizza restaurant
|
776 Arastradero Rd
|
Dominos
|
coffee or tea place
|
269 Alger Dr
|
Cafe Venetia
|
pizza restaurant
|
110 Arastradero Rd
|
Papa Johns
|
parking garage
|
550 Alester Ave
|
Dish Parking
|
rest stop
|
578 Arbol Dr
|
Hotel Keen
|
coffee or tea place
|
9981 Archuleta Ave
|
Peets Coffee
|
gas station
|
200 Alester Ave
|
Valero
|
grocery store
|
819 Alma St
|
Whole Foods
|
gas station
|
91 El Camino Real
|
76
|
coffee or tea place
|
583 Alester Ave
|
Philz
|
parking garage
|
270 Altaire Walk
|
Civic Center Garage
|
parking garage
|
610 Amarillo Ave
|
Stanford Oval Parking
|
friends house
|
347 Alta Mesa Ave
|
jills house
|
parking garage
|
880 Ames Ct
|
Webster Garage
|
friends house
|
864 Almanor Ln
|
jacks house
|
home
|
56 cadwell street
|
home_2
|
home
|
5671 barringer street
|
home_3
|
pizza restaurant
|
528 Anton Ct
|
Pizza My Heart
|
home
|
10 ames street
|
home_1
|
friends house
|
580 Van Ness Ave
|
toms house
|
parking garage
|
481 Amaranta Ave
|
Palo Alto Garage R
|
gas station
|
783 Arcadia Pl
|
Chevron
|
coffee or tea place
|
145 Amherst St
|
Teavana
|
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Dataset Card for KVRET
- Repository: https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/
- Paper: https://arxiv.org/pdf/1705.05414.pdf
- Leaderboard: None
- Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install ConvLab-3 platform first. Then you can load the dataset via:
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('kvret')
ontology = load_ontology('kvret')
database = load_database('kvret')
For more usage please refer to here.
Dataset Summary
In an effort to help alleviate this problem, we release a corpus of 3,031 multi-turn dialogues in three distinct domains appropriate for an in-car assistant: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our dialogues are grounded through knowledge bases ensuring that they are versatile in their natural language without being completely free form.
- How to get the transformed data from original data:
- Run
python preprocess.py
in the current directory.
- Run
- Main changes of the transformation:
- Create user
dialogue acts
andstate
according to original annotation. - Put dialogue level kb into system side
db_results
. - Skip repeated turns and empty dialogue.
- Create user
- Annotations:
- user dialogue acts, state, db_results.
Supported Tasks and Leaderboards
NLU, DST, Context-to-response
Languages
English
Data Splits
split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
---|---|---|---|---|---|---|---|---|---|
train | 2424 | 12720 | 5.25 | 8.02 | 1 | - | - | - | 98.07 |
validation | 302 | 1566 | 5.19 | 7.93 | 1 | - | - | - | 97.62 |
test | 304 | 1627 | 5.35 | 7.7 | 1 | - | - | - | 97.72 |
all | 3030 | 15913 | 5.25 | 7.98 | 1 | - | - | - | 97.99 |
3 domains: ['schedule', 'weather', 'navigate']
- cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
- non-cat slot span: how many values of non-categorical slots have span annotation in percentage.
Citation
@inproceedings{eric-etal-2017-key,
title = "Key-Value Retrieval Networks for Task-Oriented Dialogue",
author = "Eric, Mihail and
Krishnan, Lakshmi and
Charette, Francois and
Manning, Christopher D.",
booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
year = "2017",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5506",
}
Licensing Information
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