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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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