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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 8 new columns ({'PitchSatisfactionScore', 'ProdTaken', 'NumberOfPersonVisiting', 'CustomerID', 'Unnamed: 0', 'NumberOfFollowups', 'DurationOfPitch', 'NumberOfChildrenVisiting'}) and 1 missing columns ({'TotalVisiting'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mkrish2025/Tourism-Customer-Prediction/tourism.csv (at revision 871a443339237f510529846586dc425d960e9b4b), [/tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/tourism.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/ytrain.csv)]
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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Unnamed: 0: int64
CustomerID: int64
ProdTaken: int64
Age: double
TypeofContact: string
CityTier: int64
DurationOfPitch: double
Occupation: string
Gender: string
NumberOfPersonVisiting: int64
NumberOfFollowups: double
ProductPitched: string
PreferredPropertyStar: double
MaritalStatus: string
NumberOfTrips: double
Passport: int64
PitchSatisfactionScore: int64
OwnCar: int64
NumberOfChildrenVisiting: double
Designation: string
MonthlyIncome: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2881
to
{'Age': Value('float64'), 'TypeofContact': Value('string'), 'CityTier': Value('int64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'OwnCar': Value('int64'), 'Designation': Value('string'), 'MonthlyIncome': Value('float64'), 'TotalVisiting': Value('float64')}
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 1347, 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 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, 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 8 new columns ({'PitchSatisfactionScore', 'ProdTaken', 'NumberOfPersonVisiting', 'CustomerID', 'Unnamed: 0', 'NumberOfFollowups', 'DurationOfPitch', 'NumberOfChildrenVisiting'}) and 1 missing columns ({'TotalVisiting'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mkrish2025/Tourism-Customer-Prediction/tourism.csv (at revision 871a443339237f510529846586dc425d960e9b4b), [/tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/tourism.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/ytrain.csv)]
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.
Age float64 | TypeofContact string | CityTier int64 | Occupation string | Gender string | ProductPitched string | PreferredPropertyStar float64 | MaritalStatus string | NumberOfTrips float64 | Passport int64 | OwnCar int64 | Designation string | MonthlyIncome float64 | TotalVisiting float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34 | Company Invited | 1 | Salaried | Male | Basic | 3 | Married | 4 | 0 | 0 | Executive | 17,979 | 2 |
32 | Self Enquiry | 1 | Salaried | Male | Deluxe | 4 | Divorced | 2 | 0 | 0 | Manager | 21,220 | 3 |
30 | Self Enquiry | 3 | Salaried | Female | Standard | 3 | Divorced | 3 | 0 | 1 | Senior Manager | 24,419 | 3 |
39 | Self Enquiry | 3 | Small Business | Male | Standard | 4 | Unmarried | 2 | 0 | 1 | Senior Manager | 26,029 | 5 |
37 | Company Invited | 1 | Salaried | Female | Deluxe | 4 | Married | 2 | 0 | 1 | Manager | 24,352 | 5 |
34 | Self Enquiry | 1 | Salaried | Male | Basic | 3 | Single | 2 | 0 | 0 | Executive | 21,178 | 5 |
27 | Company Invited | 1 | Salaried | Female | Basic | 3 | Married | 5 | 0 | 1 | Executive | 23,042 | 7 |
30 | Self Enquiry | 3 | Salaried | Male | Deluxe | 5 | Married | 2 | 0 | 1 | Manager | 24,714 | 4 |
53 | Company Invited | 1 | Small Business | Female | Super Deluxe | 3 | Married | 5 | 0 | 0 | AVP | 32,504 | 5 |
55 | Company Invited | 1 | Salaried | Female | Standard | 3 | Married | 2 | 0 | 1 | Senior Manager | 29,180 | 5 |
46 | Company Invited | 1 | Small Business | Male | Standard | 5 | Divorced | 3 | 1 | 1 | Senior Manager | 25,673 | 3 |
39 | Company Invited | 1 | Salaried | Male | Deluxe | 5 | Married | 4 | 0 | 1 | Manager | 24,966 | 3 |
54 | Company Invited | 2 | Salaried | Female | Super Deluxe | 3 | Single | 3 | 1 | 1 | AVP | 32,328 | 1 |
42 | Self Enquiry | 1 | Small Business | Male | Deluxe | 5 | Married | 6 | 0 | 1 | Manager | 20,538 | 3 |
33 | Self Enquiry | 1 | Salaried | Female | Basic | 3 | Married | 5 | 0 | 1 | Executive | 21,990 | 5 |
35 | Self Enquiry | 1 | Small Business | Male | Basic | 3 | Single | 2 | 0 | 1 | Executive | 17,859 | 1 |
39 | Self Enquiry | 1 | Small Business | Male | Standard | 3 | Unmarried | 1 | 0 | 1 | Senior Manager | 28,464 | 3 |
29 | Self Enquiry | 1 | Salaried | Female | Deluxe | 3 | Unmarried | 5 | 0 | 1 | Manager | 22,338 | 5 |
23 | Company Invited | 1 | Large Business | Male | Basic | 3 | Unmarried | 7 | 0 | 1 | Executive | 22,572 | 4 |
37 | Company Invited | 1 | Small Business | Male | Basic | 3 | Divorced | 2 | 1 | 0 | Executive | 17,326 | 2 |
33 | Self Enquiry | 1 | Small Business | Female | Deluxe | 5 | Married | 3 | 0 | 1 | Manager | 25,403 | 5 |
33 | Self Enquiry | 1 | Salaried | Male | Basic | 5 | Unmarried | 3 | 0 | 0 | Executive | 21,634 | 6 |
50 | Company Invited | 1 | Salaried | Male | Deluxe | 3 | Married | 3 | 1 | 0 | Manager | 25,482 | 5 |
42 | Self Enquiry | 1 | Salaried | Female | Deluxe | 3 | Married | 1 | 1 | 0 | Manager | 21,062 | 2 |
43 | Company Invited | 1 | Small Business | Female | Standard | 5 | Married | 5 | 1 | 0 | Senior Manager | 31,869 | 4 |
36 | Company Invited | 1 | Salaried | Male | Basic | 4 | Married | 2 | 0 | 1 | Executive | 17,810 | 3 |
27 | Self Enquiry | 3 | Small Business | Female | Deluxe | 3 | Unmarried | 1 | 0 | 0 | Manager | 21,500 | 3 |
29 | Self Enquiry | 3 | Salaried | Male | Deluxe | 3 | Unmarried | 3 | 0 | 1 | Manager | 23,931 | 6 |
34 | Self Enquiry | 1 | Salaried | Female | Basic | 3 | Divorced | 3 | 0 | 0 | Executive | 21,589 | 7 |
41 | Self Enquiry | 3 | Salaried | Female | Deluxe | 5 | Married | 3 | 0 | 0 | Manager | 23,317 | 5 |
32 | Self Enquiry | 3 | Small Business | Male | Deluxe | 5 | Married | 7 | 1 | 1 | Manager | 20,980 | 5 |
50 | Company Invited | 2 | Small Business | Male | King | 4 | Married | 2 | 0 | 1 | VP | 33,200 | 5 |
24 | Company Invited | 3 | Small Business | Male | Basic | 3 | Married | 1 | 0 | 1 | Executive | 17,400 | 3 |
43 | Self Enquiry | 1 | Salaried | Female | Deluxe | 3 | Married | 2 | 1 | 0 | Manager | 24,740 | 4 |
39 | Self Enquiry | 1 | Small Business | Male | Deluxe | 5 | Married | 3 | 0 | 1 | Manager | 20,377 | 5 |
55 | Self Enquiry | 1 | Small Business | Male | King | 5 | Single | 1 | 1 | 1 | VP | 34,045 | 3 |
33 | Company Invited | 1 | Salaried | Female | Basic | 3 | Unmarried | 3 | 0 | 1 | Executive | 24,887 | 4 |
34 | Self Enquiry | 3 | Salaried | Female | Standard | 5 | Unmarried | 4 | 1 | 0 | Senior Manager | 27,242 | 5 |
25 | Self Enquiry | 1 | Salaried | Male | Basic | 3 | Married | 2 | 0 | 0 | Executive | 21,452 | 4 |
30 | Self Enquiry | 1 | Salaried | Female | Basic | 3 | Single | 2 | 0 | 1 | Executive | 17,632 | 5 |
32 | Company Invited | 3 | Small Business | Female | Basic | 4 | Married | 3 | 0 | 0 | Executive | 21,467 | 4 |
34 | Company Invited | 1 | Salaried | Female | Standard | 4 | Divorced | 8 | 0 | 1 | Senior Manager | 30,556 | 7 |
50 | Self Enquiry | 1 | Salaried | Male | Super Deluxe | 3 | Married | 4 | 1 | 1 | AVP | 28,973 | 5 |
33 | Self Enquiry | 1 | Salaried | Male | Basic | 5 | Single | 4 | 1 | 0 | Executive | 17,799 | 3 |
36 | Company Invited | 3 | Small Business | Male | Deluxe | 3 | Married | 3 | 0 | 0 | Manager | 23,646 | 4 |
50 | Company Invited | 1 | Salaried | Male | Deluxe | 3 | Married | 3 | 1 | 0 | Manager | 25,482 | 6 |
49 | Company Invited | 3 | Small Business | Female | Basic | 3 | Married | 4 | 1 | 1 | Executive | 21,333 | 6 |
37 | Company Invited | 3 | Small Business | Female | Deluxe | 5 | Divorced | 4 | 0 | 1 | Manager | 23,317 | 4 |
30 | Self Enquiry | 1 | Salaried | Female | Basic | 3 | Single | 2 | 0 | 1 | Executive | 17,632 | 3 |
23 | Self Enquiry | 1 | Salaried | Male | Basic | 3 | Unmarried | 2 | 0 | 0 | Executive | 22,053 | 7 |
34 | Self Enquiry | 1 | Small Business | Female | Basic | 4 | Single | 3 | 0 | 0 | Executive | 17,311 | 3 |
52 | Self Enquiry | 3 | Small Business | Male | Deluxe | 3 | Unmarried | 2 | 1 | 0 | Manager | 24,119 | 7 |
27 | Company Invited | 3 | Small Business | Male | Deluxe | 5 | Unmarried | 2 | 0 | 0 | Manager | 23,647 | 5 |
40 | Company Invited | 3 | Salaried | Female | Super Deluxe | 4 | Unmarried | 5 | 1 | 1 | AVP | 28,194 | 5 |
44 | Self Enquiry | 1 | Salaried | Female | Basic | 3 | Divorced | 2 | 0 | 1 | Executive | 17,011 | 3 |
27 | Company Invited | 1 | Salaried | Male | Basic | 5 | Married | 8 | 1 | 0 | Executive | 20,720 | 4 |
42 | Company Invited | 1 | Salaried | Male | Basic | 5 | Married | 8 | 0 | 1 | Executive | 20,785 | 5 |
28 | Self Enquiry | 3 | Small Business | Male | Basic | 5 | Married | 2 | 0 | 0 | Executive | 21,719 | 5 |
59 | Self Enquiry | 1 | Large Business | Female | Standard | 4 | Married | 4 | 1 | 1 | Senior Manager | 29,230 | 5 |
40 | Self Enquiry | 3 | Salaried | Male | Deluxe | 3 | Divorced | 5 | 1 | 0 | Manager | 24,798 | 5 |
29 | Company Invited | 2 | Salaried | Male | Basic | 3 | Married | 3 | 0 | 0 | Executive | 21,384 | 5 |
35 | Self Enquiry | 1 | Salaried | Female | Deluxe | 5 | Married | 5 | 0 | 1 | Manager | 23,799 | 4 |
34 | Self Enquiry | 2 | Large Business | Female | Basic | 3 | Divorced | 2 | 0 | 1 | Executive | 17,742 | 2 |
36 | Self Enquiry | 1 | Salaried | Male | Deluxe | 3 | Single | 2 | 0 | 1 | Manager | 20,810 | 3 |
41 | Company Invited | 1 | Salaried | Male | Super Deluxe | 5 | Married | 5 | 0 | 1 | AVP | 32,181 | 3 |
46 | Company Invited | 1 | Small Business | Male | Standard | 5 | Married | 3 | 1 | 1 | Senior Manager | 25,673 | 3 |
27 | Self Enquiry | 3 | Small Business | Male | Deluxe | 3 | Married | 7 | 0 | 1 | Manager | 22,984 | 4 |
32 | Company Invited | 3 | Salaried | Male | Basic | 3 | Married | 2 | 0 | 1 | Executive | 21,469 | 5 |
38 | Self Enquiry | 1 | Salaried | Male | Basic | 4 | Married | 6 | 0 | 0 | Executive | 21,700 | 6 |
34 | Company Invited | 3 | Small Business | Male | Deluxe | 4 | Married | 2 | 0 | 0 | Manager | 24,824 | 5 |
51 | Self Enquiry | 2 | Salaried | Male | Super Deluxe | 4 | Married | 2 | 1 | 1 | AVP | 29,026 | 3 |
40 | Self Enquiry | 1 | Small Business | Female | Basic | 3 | Single | 1 | 1 | 1 | Executive | 17,342 | 3 |
49 | Self Enquiry | 1 | Salaried | Male | Standard | 3 | Unmarried | 1 | 0 | 1 | Senior Manager | 25,965 | 2 |
48 | Self Enquiry | 1 | Salaried | Female | Basic | 3 | Single | 6 | 0 | 1 | Executive | 20,783 | 5 |
29 | Self Enquiry | 3 | Small Business | Male | Deluxe | 3 | Married | 3 | 0 | 1 | Manager | 21,931 | 2 |
25 | Company Invited | 3 | Small Business | Male | Basic | 3 | Married | 2 | 0 | 1 | Executive | 21,078 | 5 |
35 | Self Enquiry | 3 | Salaried | Male | Deluxe | 5 | Married | 4 | 1 | 0 | Manager | 23,966 | 5 |
30 | Self Enquiry | 3 | Small Business | Female | Deluxe | 4 | Married | 3 | 1 | 1 | Manager | 26,946 | 4 |
35 | Self Enquiry | 1 | Salaried | Male | Deluxe | 3 | Married | 4 | 1 | 1 | Manager | 20,916 | 2 |
36 | Self Enquiry | 1 | Salaried | Female | Basic | 3 | Married | 5 | 0 | 1 | Executive | 17,543 | 3 |
50 | Self Enquiry | 3 | Small Business | Male | King | 3 | Married | 5 | 1 | 0 | VP | 34,331 | 3 |
44 | Self Enquiry | 3 | Small Business | Male | Standard | 3 | Married | 7 | 0 | 1 | Senior Manager | 29,476 | 6 |
38 | Self Enquiry | 3 | Small Business | Male | Standard | 4 | Unmarried | 1 | 0 | 1 | Senior Manager | 22,351 | 2 |
37 | Self Enquiry | 1 | Salaried | Male | Basic | 4 | Single | 4 | 0 | 0 | Executive | 20,691 | 7 |
32 | Self Enquiry | 2 | Salaried | Male | Deluxe | 5 | Divorced | 5 | 0 | 0 | Manager | 25,088 | 6 |
42 | Company Invited | 3 | Salaried | Male | Deluxe | 3 | Unmarried | 2 | 0 | 0 | Manager | 24,908 | 5 |
50 | Self Enquiry | 1 | Small Business | Male | Basic | 3 | Divorced | 2 | 1 | 1 | Executive | 18,221 | 5 |
25 | Company Invited | 1 | Salaried | Female | Basic | 3 | Married | 3 | 1 | 0 | Executive | 21,564 | 4 |
19 | Self Enquiry | 1 | Salaried | Male | Basic | 5 | Single | 2 | 0 | 0 | Executive | 17,552 | 2 |
41 | Self Enquiry | 3 | Small Business | Male | Standard | 4 | Married | 4 | 0 | 0 | Senior Manager | 28,383 | 5 |
47 | Company Invited | 1 | Small Business | Female | Standard | 3 | Divorced | 7 | 0 | 1 | Senior Manager | 29,205 | 4 |
32 | Company Invited | 3 | Small Business | Female | Deluxe | 3 | Divorced | 3 | 0 | 1 | Manager | 25,610 | 4 |
44 | Self Enquiry | 3 | Small Business | Female | Super Deluxe | 3 | Divorced | 4 | 1 | 1 | AVP | 28,320 | 3 |
51 | Self Enquiry | 3 | Small Business | Male | Basic | 4 | Divorced | 2 | 0 | 1 | Executive | 22,553 | 4 |
37 | Self Enquiry | 1 | Salaried | Female | Deluxe | 3 | Married | 2 | 0 | 0 | Manager | 21,474 | 2 |
36 | Self Enquiry | 1 | Small Business | Male | Basic | 5 | Single | 3 | 0 | 0 | Executive | 21,128 | 7 |
30 | Self Enquiry | 1 | Salaried | Male | Basic | 5 | Divorced | 3 | 1 | 1 | Executive | 20,797 | 6 |
43 | Self Enquiry | 3 | Small Business | Female | Deluxe | 3 | Unmarried | 2 | 0 | 1 | Manager | 24,922 | 5 |
28 | Self Enquiry | 3 | Salaried | Male | Deluxe | 3 | Unmarried | 3 | 1 | 0 | Manager | 23,156 | 6 |
33 | Self Enquiry | 1 | Large Business | Male | Deluxe | 5 | Single | 6 | 0 | 0 | Manager | 20,854 | 5 |
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