Dataset Viewer
text
stringlengths 2
189
| label
int64 0
59
|
|---|---|
wake me up at nine am on friday
| 55
|
set an alarm for two hours from now
| 55
|
olly quiet
| 7
|
stop
| 7
|
olly pause for ten seconds
| 7
|
pause for ten seconds
| 7
|
make the lighting bit more warm here
| 49
|
please set the lighting suitable for reading
| 49
|
time to sleep
| 57
|
time to sleep olly
| 57
|
turn off the light in the bathroom
| 57
|
olly dim the lights in the hall
| 16
|
turn the lights off in the bedroom
| 57
|
set lights to twenty percent
| 49
|
olly set lights to twenty percent
| 49
|
dim the lights in the kitchen olly
| 16
|
dim the lights in the kitchen
| 16
|
olly clean the flat
| 54
|
vacuum the house
| 54
|
vacuum the house olly
| 54
|
hoover the carpets around
| 54
|
check when the show starts
| 27
|
i want to listen arijit singh song once again
| 1
|
i want to play that music one again
| 1
|
check my car is ready
| 42
|
check my laptop is working
| 42
|
is the brightness of my screen running low
| 42
|
i need to have location services on can you check
| 42
|
check the status of my power usage
| 42
|
i am not tired i am actually happy
| 42
|
olly i am not tired i am actually happy
| 42
|
what's up
| 3
|
tell me the time in moscow
| 46
|
tell me the time in g. m. t. plus five
| 56
|
olly list most rated delivery options for chinese food
| 12
|
most rated delivery options for chinese food
| 12
|
olly most rated delivery options for chinese food
| 12
|
i want some curry to go any recommendations
| 12
|
i want some curry to go any recommendations olly
| 12
|
find my thai takeaways around grassmarket
| 12
|
stop seven am alarm
| 21
|
please list active alarms
| 40
|
what's happening in football today
| 20
|
please play yesterday from beatles
| 1
|
i like rock music
| 43
|
my favorite music band is queen
| 43
|
start playing music from favorites
| 1
|
please play my best music
| 1
|
who's current music's author
| 26
|
what's that the album is current music from
| 26
|
olly i'm really enjoying this song
| 43
|
the song you are playing is amazing
| 43
|
this is one of the best songs for me
| 43
|
make lights brightener
| 9
|
please raise the lights to max
| 9
|
hey start vacuum cleaner robot
| 54
|
turn cleaner robot on
| 54
|
please order some sushi for dinner
| 25
|
hey i'd like you to order burger
| 25
|
can i order takeaway dinner from byron's
| 25
|
does byron's supports takeaways
| 12
|
set an alarm for twelve
| 55
|
set an alarm forty minutes from now
| 55
|
set alarm for eight every weekday
| 55
|
is it raining
| 19
|
is it going to rain
| 19
|
is it currently snowing
| 19
|
what's this weeks weather
| 19
|
tell me b. b. c. news
| 20
|
what's the news on b. b. c. news
| 20
|
what is the b. b. c.'s latest news
| 20
|
play a song i like
| 1
|
play daft punk
| 1
|
put on some coldplay
| 1
|
shuffle this playlist
| 18
|
what's playing
| 26
|
what music is this
| 26
|
tell me the artist of this song
| 26
|
make me laugh
| 31
|
olly make me laugh
| 31
|
tell me a good joke
| 31
|
tell me a joke
| 31
|
alexa tell me a joke
| 31
|
cheer me up
| 31
|
tell me about today
| 42
|
order a pizza
| 25
|
order me a byron from deliveroo
| 25
|
when is my order arriving
| 12
|
how long until my takeaway
| 12
|
domino's delivery status
| 12
|
what's playing
| 26
|
tell me the name of the song
| 26
|
play my jazz playlist
| 1
|
start my jazz playlist
| 1
|
play my favorite playlist
| 1
|
that's a good song
| 43
|
i don't like it
| 59
|
i like it
| 43
|
i like jazz
| 43
|
can you play some jazz
| 1
|
End of preview. Expand
in Data Studio
Data Preprocessing AutoML Benchmarks
This repository contains text classification datasets with known data quality issues for preprocessing research in AutoML.
Usage
Load a specific dataset configuration like this:
from datasets import load_dataset
# Example for loading the TREC dataset
dataset = load_dataset("MothMalone/data-preprocessing-automl-benchmarks", "trec")
Available Datasets
Below are the details for each dataset configuration available in this repository.
Of course. Here are the completed descriptions for your dataset card.
imdb
- Description: A large movie review dataset for binary sentiment classification, containing 25,000 highly polarized movie reviews for training and 25,000 for testing.
- Data Quality Issue: N/A
- Classes: 2
- Training Samples: 18750
- Validation Samples: 6250
- Test Samples: 25000
twenty_newsgroups
- Description: A collection of approximately 20,000 newsgroup documents, partitioned evenly across 20 different newsgroups, making it a classic benchmark for text classification.
- Data Quality Issue: N/A
- Classes: 20
- Training Samples: 8485
- Validation Samples: 2829
- Test Samples: 7532
banking77
- Description: A fine-grained dataset of 13,083 customer service queries from the banking domain, annotated with 77 distinct intents.
- Data Quality Issue: N/A
- Classes: 77
- Training Samples: 7502
- Validation Samples: 2501
- Test Samples: 3080
trec
- Description: The Text REtrieval Conference (TREC) question classification dataset, containing questions categorized by their answer type (e.g., Person, Location, Number).
- Data Quality Issue: N/A
- Classes: 6
- Training Samples: 4089
- Validation Samples: 1363
- Test Samples: 500
financial_phrasebank
- Description: A collection of sentences from English financial news, annotated for sentiment (positive, negative, or neutral) by financial experts.
- Data Quality Issue: N/A
- Classes: 3
- Training Samples: 1358
- Validation Samples: 453
- Test Samples: 453
MASSIVE
- Description: A multilingual dataset of 1 million utterances for intent classification and slot filling, covering 52 languages. The en-US configuration is used here.
- Data Quality Issue: N/A
- Classes: 60
- Training Samples: 11514
- Validation Samples: 2033
- Test Samples: 2974
- Downloads last month
- 99