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ConvLab
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kvret

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The dataset generation failed because of a cast error
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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End of preview.
YAML Metadata Warning: The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

Dataset Card for KVRET

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.
  • Main changes of the transformation:
    • Create user dialogue acts and state according to original annotation.
    • Put dialogue level kb into system side db_results.
    • Skip repeated turns and empty dialogue.
  • 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

TODO

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