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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 11 new columns ({'country', 'rank', 'category', 'city', 'title', 'finalWorth', 'source', 'gender', 'philanthropyScore', 'selfMade', 'age'}) and 6 missing columns ({'sample_answer', 'question', 'answer', 'columns_used', 'column_types', 'type'}).
This happened while the csv dataset builder was generating data using
hf://datasets/cardiffnlp/databench/data/001_Forbes/sample.csv (at revision 72fe9175583ae2a32b1bc371914b310b194deb26)
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
selfMade: bool
finalWorth: int64
city: string
title: string
gender: string
age: double
rank: int64
philanthropyScore: double
category: string
source: string
country: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1507
to
{'question': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'columns_used': Value(dtype='string', id=None), 'column_types': Value(dtype='string', id=None), 'sample_answer': 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 11 new columns ({'country', 'rank', 'category', 'city', 'title', 'finalWorth', 'source', 'gender', 'philanthropyScore', 'selfMade', 'age'}) and 6 missing columns ({'sample_answer', 'question', 'answer', 'columns_used', 'column_types', 'type'}).
This happened while the csv dataset builder was generating data using
hf://datasets/cardiffnlp/databench/data/001_Forbes/sample.csv (at revision 72fe9175583ae2a32b1bc371914b310b194deb26)
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? Open a discussion for direct support.
question
string | answer
string | type
string | columns_used
string | column_types
string | sample_answer
string |
|---|---|---|---|---|---|
Is the person with the highest net worth self-made? | True | boolean | ['finalWorth', 'selfMade'] | ['number[uint32]', 'boolean'] | False |
Does the youngest billionaire identify as male? | True | boolean | ['age', 'gender'] | ['number[UInt8]', 'category'] | True |
Is the city with the most billionaires in the United States? | True | boolean | ['city', 'country'] | ['category', 'category'] | True |
Is there a non-self-made billionaire in the top 5 ranks? | True | boolean | ['rank', 'selfMade'] | ['number[uint16]', 'boolean'] | False |
Does the oldest billionaire have a philanthropy score of 5? | False | boolean | ['age', 'philanthropyScore'] | ['number[UInt8]', 'number[UInt8]'] | False |
What is the age of the youngest billionaire? | 19.0 | number | ['age'] | ['number[UInt8]'] | 32.0 |
How many billionaires are there from the 'Technology' category? | 343 | number | ['category'] | ['category'] | 0 |
What's the total worth of billionaires in the 'Automotive' category? | 583600 | number | ['category', 'finalWorth'] | ['category', 'number[uint32]'] | 0 |
How many billionaires have a philanthropy score above 3? | 25 | number | ['philanthropyScore'] | ['number[UInt8]'] | 0 |
What's the rank of the wealthiest non-self-made billionaire? | 3 | number | ['selfMade', 'rank'] | ['boolean', 'number[uint16]'] | 288 |
Which category does the richest billionaire belong to? | Automotive | category | ['finalWorth', 'category'] | ['number[uint32]', 'category'] | Food & Beverage |
What's the country of origin of the oldest billionaire? | United States | category | ['age', 'country'] | ['number[UInt8]', 'category'] | United Kingdom |
What's the gender of the billionaire with the highest philanthropy score? | M | category | ['philanthropyScore', 'gender'] | ['number[UInt8]', 'category'] | M |
What's the source of wealth for the youngest billionaire? | drugstores | category | ['age', 'source'] | ['number[UInt8]', 'category'] | fintech |
What is the title of the billionaire with the lowest rank? | null | category | ['rank', 'title'] | ['number[uint16]', 'category'] | null |
List the top 3 countries with the most billionaires. | ['United States', 'China', 'India'] | list[category] | ['country'] | ['category'] | ['United States', 'China', 'Brazil'] |
List the top 5 sources of wealth for billionaires. | ['real estate', 'investments', 'pharmaceuticals', 'diversified', 'software'] | list[category] | ['source'] | ['category'] | ['diversified', 'media, automotive', 'Semiconductor materials', 'WeWork', 'beverages'] |
List the top 4 cities where the youngest billionaires live. | [nan, 'Los Angeles', 'Jiaozuo', 'Oslo'] | list[category] | ['age', 'city'] | ['number[UInt8]', 'category'] | ['San Francisco', 'New York', 'Wuhan', 'Bangalore'] |
List the bottom 3 categories with the fewest billionaires. | ['Logistics', 'Sports', 'Gambling & Casinos'] | list[category] | ['category'] | ['category'] | ['Service', 'Fashion & Retail', 'Manufacturing'] |
List the bottom 2 countries with the least number of billionaires. | ['Colombia', 'Andorra'] | list[category] | ['country'] | ['category'] | ['Canada', 'Egypt'] |
List the top 5 ranks of billionaires who are not self-made. | [3, 10, 14, 16, 18] | list[number] | ['selfMade', 'rank'] | ['boolean', 'number[uint16]'] | [288, 296, 509, 523, 601] |
List the bottom 3 ages of billionaires who have a philanthropy score of 5. | [48.0, 83.0, 83.0] | list[number] | ['philanthropyScore', 'age'] | ['number[UInt8]', 'number[UInt8]'] | [] |
List the top 6 final worth values of billionaires in the 'Technology' category. | [171000, 129000, 111000, 107000, 106000, 91400] | list[number] | ['category', 'finalWorth'] | ['category', 'number[uint32]'] | [] |
List the bottom 4 ranks of female billionaires. | [14, 18, 21, 30] | list[number] | ['gender', 'rank'] | ['category', 'number[uint16]'] | [] |
List the top 2 final worth values of billionaires in the 'Automotive' category. | [219000, 44800] | list[number] | ['category', 'finalWorth'] | ['category', 'number[uint32]'] | [] |
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Did any children below the age of 18 survive? | True | boolean | [Age, Survived] | ['number[UInt8]', 'boolean'] | True |
Were there any passengers who paid a fare of more than $500? | True | boolean | [Fare] | ['number[double]'] | False |
Is every passenger's name unique? | True | boolean | [Name] | ['text'] | True |
Were there any female passengers in the 3rd class who survived? | True | boolean | [Sex, Pclass, Survived] | ['category', 'number[uint8]', 'boolean'] | True |
How many unique passenger classes are present in the dataset? | 3 | number | [Pclass] | ['number[uint8]'] | 3 |
What's the maximum age of the passengers? | 80.0 | number | [Age] | ['number[UInt8]'] | 69.0 |
How many passengers boarded without any siblings or spouses? | 604 | number | [Siblings_Spouses Aboard] | ['number[uint8]'] | 12 |
On average, how much fare did the passengers pay? | 32.31 | number | [Fare] | ['number[double]'] | 23.096459999999997 |
Which passenger class has the highest number of survivors? | 1 | category | [Pclass, Survived] | ['number[uint8]', 'boolean'] | 3 |
What's the most common gender among the survivors? | female | category | [Sex, Survived] | ['category', 'boolean'] | female |
Among those who survived, which fare range was the most common: (0-50, 50-100, 100-150, 150+)? | 0-50 | category | [Fare, Survived] | ['number[double]', 'boolean'] | 0-50 |
What's the most common age range among passengers: (0-18, 18-30, 30-50, 50+)? | 18-30 | category | [Age] | ['number[UInt8]'] | 18-30 |
Name the top 3 passenger classes by survival rate. | [1, 2, 3] | list[category] | [Pclass, Survived] | ['number[uint8]', 'boolean'] | [1, 3, 2] |
Could you list the bottom 3 fare ranges by number of survivors: (0-50, 50-100, 100-150, 150+)? | ['50-100', '150+', '100-150'] | list[category] | [Fare, Survived] | ['number[double]', 'boolean'] | [50-100, 150+, 100-150] |
What is the top 4 age ranges('30-50', '18-30', '0-18', '50+') with the highest number of survivors? | ['30-50', '18-30', '0-18', '50+'] | list[category] | [Age, Survived] | ['number[UInt8]', 'boolean'] | [30-50, 18-30, 0-18, 50+] |
What are the top 2 genders by average fare paid? | ['female', 'male'] | list[category] | [Sex, Fare] | ['category', 'number[double]'] | [female, male] |
What are the oldest 3 ages among the survivors? | [24.0, 22.0, 27.0] | list[number] | [Age, Survived] | ['number[UInt8]', 'boolean'] | [56.0, 47.0, 42.0] |
Which are the top 4 fares paid by survivors? | [13.0, 26.0, 7.75, 10.5] | list[number] | [Fare, Survived] | ['number[double]', 'boolean'] | [133.65, 39.0, 35.5, 30.5] |
Could you list the youngest 3 ages among the survivors? | [53.0, 55.0, 11.0] | list[number] | [Age, Survived] | ['number[UInt8]', 'boolean'] | [14.0, 24.0, 28.0] |
Which are the bottom 4 fares among those who didn't survive? | [90.0, 12.275, 9.35, 10.5167] | list[number] | [Fare, Survived] | ['number[double]', 'boolean'] | [13.0, 7.75, 11.5, 10.1708] |
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Is the average age of the respondents above 30? | True | boolean | ['What is your age? 👶🏻👵🏻'] | ['number[uint8]'] | True |
Are there more single individuals than married ones in the dataset? | True | boolean | ['What is your civil status? 💍'] | ['category'] | False |
Do the majority of respondents have a height greater than 170 cm? | True | boolean | [What's your height? in cm 📏] | ['number[uint8]'] | True |
Is the most frequent hair color black? | False | boolean | ['What is your hair color? 👩🦰👱🏽'] | ['category'] | False |
How many unique nationalities are present in the dataset? | 13 | number | [What's your nationality?"]" | ['category'] | 1 |
What is the average gross annual salary? | 56332.81720430108 | number | ['Gross annual salary (in euros) 💸'] | ['number[UInt32]'] | 62710.0 |
How many respondents wear glasses all the time? | 0 | number | ['How often do you wear glasses? 👓'] | ['category'] | 0 |
What's the median age of the respondents? | 33.0 | number | ['What is your age? 👶🏻👵🏻'] | ['number[uint8]'] | 32.5 |
What is the most common level of studies achieved? | Master | category | ['What is the maximum level of studies you have achieved? 🎓'] | ['category'] | Master |
Which body complexity has the least number of respondents? | Very thin | category | ['What is your body complexity? 🏋️'] | ['category'] | Obese |
What's the most frequent eye color? | Brown | category | ['What is your eye color? 👁️'] | ['category'] | Brown |
Which sexual orientation has the highest representation? | Heterosexual | category | [What's your sexual orientation?"]" | ['category'] | Heterosexual |
List the top 3 most common areas of knowledge. | ['[Computer Science]', '[Business]', '[Enginering & Architecture]'] | list[category] | ['What area of knowledge is closer to you?'] | ['list[category]'] | ['[Computer Science]', '[Business]', '[Enginering & Architecture]'] |
List the bottom 3 hair lengths in terms of frequency. | ['Medium', 'Long', 'Bald'] | list[category] | ['How long is your hair? 💇🏻♀️💇🏽♂️'] | ['category'] | ['Short', 'Medium', 'Long'] |
Name the top 5 civil statuses represented in the dataset. | ['Single', 'Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Divorced'] | list[category] | ['What is your civil status? 💍'] | ['category'] | ['Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Single', 'Divorced'] |
End of preview.