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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0x90 in position 7: invalid start byte
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1855, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 188, in _generate_tables
csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 75, in wrapper
return function(*args, download_config=download_config, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1213, in xpandas_read_csv
return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
return _read(filepath_or_buffer, kwds)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 620, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
self._engine = self._make_engine(f, self.engine)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
return mapping[engine](f, **self.options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
File "parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x90 in position 7: invalid start byte
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, 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 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, 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 1742, 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 1898, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
age
int64 | workclass
string | functional_weight
int64 | education
string | education_num
int64 | marital_status
string | occupation
string | relationship
string | race
string | sex
string | capital_gain
int64 | capital_loss
int64 | hours_per_week
int64 | native_country
string | income_bracket
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
39
|
Private
| 297,847
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Wife
|
Black
|
Female
| 3,411
| 0
| 34
|
United-States
|
<=50K
|
77
|
Private
| 344,425
|
9th
| 5
|
Married-civ-spouse
|
Priv-house-serv
|
Wife
|
Black
|
Female
| 0
| 0
| 10
|
United-States
|
<=50K
|
38
|
Private
| 131,461
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Wife
|
Black
|
Female
| 0
| 0
| 24
|
Haiti
|
<=50K
|
28
|
Private
| 190,350
|
9th
| 5
|
Married-civ-spouse
|
Protective-serv
|
Wife
|
Black
|
Female
| 0
| 0
| 40
|
United-States
|
<=50K
|
37
|
Private
| 171,090
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Wife
|
Black
|
Female
| 0
| 0
| 48
|
United-States
|
<=50K
|
35
|
?
| 374,716
|
9th
| 5
|
Married-civ-spouse
|
?
|
Wife
|
White
|
Female
| 0
| 0
| 35
|
United-States
|
<=50K
|
45
|
Private
| 178,215
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Wife
|
White
|
Female
| 0
| 0
| 40
|
United-States
|
>50K
|
55
|
Private
| 176,012
|
9th
| 5
|
Married-civ-spouse
|
Tech-support
|
Wife
|
White
|
Female
| 0
| 0
| 23
|
United-States
|
<=50K
|
27
|
Private
| 109,611
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Wife
|
White
|
Female
| 0
| 0
| 37
|
Portugal
|
<=50K
|
31
|
Private
| 86,958
|
9th
| 5
|
Married-civ-spouse
|
Exec-managerial
|
Wife
|
White
|
Female
| 0
| 0
| 40
|
United-States
|
<=50K
|
30
|
Private
| 61,272
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Wife
|
White
|
Female
| 0
| 0
| 40
|
Portugal
|
<=50K
|
28
|
Private
| 209,801
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Wife
|
White
|
Female
| 0
| 0
| 40
|
United-States
|
<=50K
|
46
|
Private
| 184,883
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Wife
|
White
|
Female
| 0
| 0
| 40
|
United-States
|
<=50K
|
70
|
Private
| 216,390
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Wife
|
White
|
Female
| 2,653
| 0
| 40
|
United-States
|
<=50K
|
31
|
Private
| 399,052
|
9th
| 5
|
Married-civ-spouse
|
Farming-fishing
|
Wife
|
White
|
Female
| 0
| 0
| 42
|
United-States
|
<=50K
|
40
|
Local-gov
| 183,096
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Wife
|
White
|
Female
| 0
| 0
| 40
|
Yugoslavia
|
>50K
|
52
|
Local-gov
| 330,799
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Wife
|
White
|
Female
| 0
| 0
| 40
|
United-States
|
<=50K
|
46
|
Self-emp-inc
| 161,386
|
9th
| 5
|
Married-civ-spouse
|
Adm-clerical
|
Wife
|
White
|
Female
| 0
| 0
| 50
|
United-States
|
<=50K
|
41
|
Self-emp-inc
| 299,813
|
9th
| 5
|
Married-civ-spouse
|
Sales
|
Wife
|
White
|
Female
| 0
| 0
| 70
|
Dominican-Republic
|
<=50K
|
41
|
?
| 217,921
|
9th
| 5
|
Married-civ-spouse
|
?
|
Wife
|
Asian-Pac-Islander
|
Female
| 0
| 0
| 40
|
Hong
|
<=50K
|
72
|
Private
| 74,141
|
9th
| 5
|
Married-civ-spouse
|
Exec-managerial
|
Wife
|
Asian-Pac-Islander
|
Female
| 0
| 0
| 48
|
United-States
|
>50K
|
75
|
?
| 164,849
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
Black
|
Male
| 1,409
| 0
| 5
|
United-States
|
<=50K
|
77
|
?
| 232,894
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
66
|
?
| 108,185
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
45
|
Private
| 186,272
|
9th
| 5
|
Married-civ-spouse
|
Adm-clerical
|
Husband
|
Black
|
Male
| 5,178
| 0
| 40
|
United-States
|
>50K
|
57
|
Private
| 136,107
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
>50K
|
57
|
Private
| 342,906
|
9th
| 5
|
Married-civ-spouse
|
Sales
|
Husband
|
Black
|
Male
| 0
| 0
| 55
|
United-States
|
>50K
|
47
|
Private
| 209,212
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
Black
|
Male
| 0
| 0
| 56
|
?
|
<=50K
|
61
|
Private
| 355,645
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
Black
|
Male
| 0
| 0
| 20
|
Trinadad&Tobago
|
<=50K
|
63
|
Private
| 201,631
|
9th
| 5
|
Married-civ-spouse
|
Farming-fishing
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
32
|
Private
| 124,187
|
9th
| 5
|
Married-civ-spouse
|
Farming-fishing
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
56
|
Private
| 229,525
|
9th
| 5
|
Married-civ-spouse
|
Transport-moving
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
38
|
Private
| 257,416
|
9th
| 5
|
Married-civ-spouse
|
Transport-moving
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
58
|
Private
| 298,601
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
Black
|
Male
| 3,781
| 0
| 40
|
United-States
|
<=50K
|
44
|
Private
| 123,572
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
53
|
Private
| 347,446
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
44
|
Private
| 118,536
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
62
|
Private
| 271,431
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Husband
|
Black
|
Male
| 0
| 0
| 42
|
United-States
|
<=50K
|
68
|
Private
| 148,874
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
Black
|
Male
| 0
| 0
| 44
|
United-States
|
<=50K
|
31
|
Private
| 393,357
|
9th
| 5
|
Married-civ-spouse
|
Handlers-cleaners
|
Husband
|
Black
|
Male
| 0
| 0
| 48
|
United-States
|
<=50K
|
58
|
Private
| 104,945
|
9th
| 5
|
Married-civ-spouse
|
Handlers-cleaners
|
Husband
|
Black
|
Male
| 0
| 0
| 60
|
United-States
|
<=50K
|
28
|
Local-gov
| 154,863
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
Trinadad&Tobago
|
>50K
|
51
|
Local-gov
| 146,181
|
9th
| 5
|
Married-civ-spouse
|
Transport-moving
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
35
|
Federal-gov
| 76,845
|
9th
| 5
|
Married-civ-spouse
|
Farming-fishing
|
Husband
|
Black
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
35
|
Private
| 255,635
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
Other
|
Male
| 0
| 0
| 40
|
Mexico
|
<=50K
|
30
|
Private
| 348,618
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
Other
|
Male
| 0
| 0
| 40
|
Mexico
|
<=50K
|
63
|
?
| 310,396
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 5,178
| 0
| 40
|
United-States
|
>50K
|
68
|
?
| 141,181
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 2
|
United-States
|
<=50K
|
67
|
?
| 243,256
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 15
|
United-States
|
<=50K
|
69
|
?
| 111,238
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 20
|
United-States
|
<=50K
|
74
|
?
| 340,939
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 3,471
| 0
| 40
|
United-States
|
<=50K
|
60
|
?
| 163,946
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
66
|
?
| 175,891
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
66
|
?
| 68,219
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
64
|
?
| 45,817
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 50
|
United-States
|
<=50K
|
50
|
?
| 257,117
|
9th
| 5
|
Married-civ-spouse
|
?
|
Husband
|
White
|
Male
| 0
| 0
| 50
|
United-States
|
<=50K
|
45
|
Private
| 223,999
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Husband
|
White
|
Male
| 0
| 1,848
| 40
|
United-States
|
>50K
|
54
|
Private
| 174,865
|
9th
| 5
|
Married-civ-spouse
|
Exec-managerial
|
Husband
|
White
|
Male
| 0
| 0
| 45
|
United-States
|
>50K
|
51
|
Private
| 199,995
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 50
|
United-States
|
>50K
|
58
|
Private
| 214,502
|
9th
| 5
|
Married-civ-spouse
|
Handlers-cleaners
|
Husband
|
White
|
Male
| 0
| 0
| 50
|
United-States
|
>50K
|
37
|
Private
| 147,258
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
White
|
Male
| 0
| 0
| 50
|
United-States
|
>50K
|
59
|
Private
| 43,221
|
9th
| 5
|
Married-civ-spouse
|
Transport-moving
|
Husband
|
White
|
Male
| 0
| 0
| 60
|
United-States
|
>50K
|
31
|
Private
| 373,432
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 43
|
United-States
|
<=50K
|
33
|
Private
| 233,107
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 33
|
Mexico
|
<=50K
|
30
|
Private
| 229,051
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Husband
|
White
|
Male
| 0
| 0
| 37
|
United-States
|
<=50K
|
38
|
Private
| 430,035
|
9th
| 5
|
Married-civ-spouse
|
Farming-fishing
|
Husband
|
White
|
Male
| 0
| 0
| 54
|
Mexico
|
<=50K
|
76
|
Private
| 199,949
|
9th
| 5
|
Married-civ-spouse
|
Protective-serv
|
Husband
|
White
|
Male
| 0
| 0
| 13
|
United-States
|
<=50K
|
35
|
Private
| 186,489
|
9th
| 5
|
Married-civ-spouse
|
Handlers-cleaners
|
Husband
|
White
|
Male
| 0
| 0
| 46
|
United-States
|
<=50K
|
39
|
Private
| 347,434
|
9th
| 5
|
Married-civ-spouse
|
Handlers-cleaners
|
Husband
|
White
|
Male
| 0
| 0
| 43
|
Mexico
|
<=50K
|
31
|
Private
| 507,875
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
White
|
Male
| 0
| 0
| 43
|
United-States
|
<=50K
|
60
|
Private
| 39,952
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
White
|
Male
| 2,228
| 0
| 37
|
United-States
|
<=50K
|
46
|
Private
| 72,896
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
White
|
Male
| 0
| 0
| 43
|
United-States
|
<=50K
|
60
|
Private
| 71,683
|
9th
| 5
|
Married-civ-spouse
|
Machine-op-inspct
|
Husband
|
White
|
Male
| 0
| 0
| 49
|
United-States
|
<=50K
|
63
|
Private
| 66,634
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 16
|
United-States
|
<=50K
|
26
|
Private
| 105,059
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 20
|
United-States
|
<=50K
|
39
|
Private
| 188,069
|
9th
| 5
|
Married-civ-spouse
|
Transport-moving
|
Husband
|
White
|
Male
| 0
| 0
| 25
|
United-States
|
<=50K
|
59
|
Private
| 366,618
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Husband
|
White
|
Male
| 0
| 0
| 30
|
United-States
|
<=50K
|
27
|
Private
| 116,207
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 32
|
United-States
|
<=50K
|
26
|
Private
| 229,977
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 35
|
United-States
|
<=50K
|
36
|
Private
| 219,814
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 35
|
Guatemala
|
<=50K
|
69
|
Private
| 88,566
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Husband
|
White
|
Male
| 1,424
| 0
| 35
|
United-States
|
<=50K
|
62
|
Private
| 84,756
|
9th
| 5
|
Married-civ-spouse
|
Other-service
|
Husband
|
White
|
Male
| 0
| 0
| 35
|
United-States
|
<=50K
|
41
|
Private
| 294,270
|
9th
| 5
|
Married-civ-spouse
|
Transport-moving
|
Husband
|
White
|
Male
| 0
| 0
| 35
|
United-States
|
<=50K
|
60
|
Private
| 81,578
|
9th
| 5
|
Married-civ-spouse
|
Sales
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
28
|
Private
| 163,265
|
9th
| 5
|
Married-civ-spouse
|
Sales
|
Husband
|
White
|
Male
| 4,508
| 0
| 40
|
United-States
|
<=50K
|
51
|
Private
| 173,987
|
9th
| 5
|
Married-civ-spouse
|
Sales
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
56
|
Private
| 437,727
|
9th
| 5
|
Married-civ-spouse
|
Sales
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
38
|
Private
| 31,964
|
9th
| 5
|
Married-civ-spouse
|
Adm-clerical
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
61
|
Private
| 197,286
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
38
|
Private
| 103,751
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
30
|
Private
| 151,868
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
34
|
Private
| 314,646
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
37
|
Private
| 203,828
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
42
|
Private
| 445,940
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
Mexico
|
<=50K
|
32
|
Private
| 182,323
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
29
|
Private
| 309,463
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
27
|
Private
| 114,967
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
60
|
Private
| 117,509
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
49
|
Private
| 39,986
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 0
| 0
| 40
|
United-States
|
<=50K
|
30
|
Private
| 326,199
|
9th
| 5
|
Married-civ-spouse
|
Craft-repair
|
Husband
|
White
|
Male
| 2,580
| 0
| 40
|
United-States
|
<=50K
|
End of preview.
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
What You Can Do With This Data:
Test for algorithmic bias - Compare model performance across demographic groups
Evaluate name-based biases - Test if your systems treat names differently based on gender or cultural origin
Develop fair ML models - Use the Adult Income dataset with its protected attributes
Benchmark against baselines - Compare your fairness metrics against the provided calculations
This approach gives you a more useful fairness benchmark dataset than simply pulling one large table from BigQuery, as it provides complementary data types specifically selected for fairness testing.
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