Dataset Preview
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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 ({'__index_level_3__', '__index_level_0__', 'nconst\tprimaryName\tgender\tgenderProbability\tbirthYear\tdeathYear\tprimaryProfession\tknownForTitles', '__index_level_4__', '__index_level_1__', '__index_level_2__'}) and 1 missing columns ({'teamsvecs-females-col-idx'}).
This happened while the csv dataset builder was generating data using
hf://datasets/fani-lab/OpeNTF/output/imdb/title.basics.tsv/name.basics.tsv.gender.tsv (at revision c84ada3b47dad0352cd1129385f4a77766b00620)
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 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, 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
nconst primaryName gender genderProbability birthYear deathYear primaryProfession knownForTitles: string
__index_level_0__: string
__index_level_1__: string
__index_level_2__: string
__index_level_3__: string
__index_level_4__: string
-- schema metadata --
pandas: '{"index_columns": ["__index_level_0__", "__index_level_1__", "__' + 1198
to
{'teamsvecs-females-col-idx': Value('int64')}
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 1339, 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 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, 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 1833, 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 ({'__index_level_3__', '__index_level_0__', 'nconst\tprimaryName\tgender\tgenderProbability\tbirthYear\tdeathYear\tprimaryProfession\tknownForTitles', '__index_level_4__', '__index_level_1__', '__index_level_2__'}) and 1 missing columns ({'teamsvecs-females-col-idx'}).
This happened while the csv dataset builder was generating data using
hf://datasets/fani-lab/OpeNTF/output/imdb/title.basics.tsv/name.basics.tsv.gender.tsv (at revision c84ada3b47dad0352cd1129385f4a77766b00620)
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.
teamsvecs-females-col-idx
int64 |
|---|
12
|
21
|
30
|
82
|
89
|
111
|
116
|
123
|
150
|
159
|
178
|
179
|
193
|
221
|
223
|
225
|
226
|
243
|
248
|
279
|
296
|
312
|
320
|
342
|
354
|
355
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361
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363
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373
|
379
|
386
|
404
|
409
|
412
|
449
|
455
|
468
|
495
|
497
|
567
|
571
|
592
|
619
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620
|
630
|
637
|
641
|
649
|
653
|
666
|
676
|
685
|
724
|
727
|
739
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752
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762
|
787
|
789
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817
|
855
|
905
|
907
|
918
|
926
|
937
|
938
|
952
|
971
|
1,010
|
1,023
|
1,036
|
1,093
|
1,102
|
1,169
|
1,170
|
1,183
|
1,187
|
1,226
|
1,228
|
1,232
|
1,251
|
1,295
|
1,315
|
1,352
|
1,354
|
1,424
|
1,428
|
1,439
|
1,441
|
1,444
|
1,498
|
1,554
|
1,559
|
1,560
|
1,572
|
1,604
|
1,638
|
1,640
|
1,676
|
End of preview.
File Structure
- each training set will have it's own
teamsvecs.pklfile that can be accessed through the following file path: - the file structure is identical to that in the OpeNTF github repository
\---output
| \---{domain} # e.g. dblp
| \---{training dataset} # e.g. toy.dblp.v12.json
| teamsvecs.pkl
Teamsvecs Structure
Teamsvecs.pkl
{
'skill': {spare matrix of n_teams x n_skills},
'member': {sparse matrix of n_teams x n_members},
'loc': {sparse matrix of n_teams x n_locs} // optional
}
skill
- every team in the entire dataset is represented as a row
- every skill in the entire dataset is represented as a column
- each row represents the skills that everyone on the team possesses
member
- every team in the entire dataset is represented as a row
- every member in the entire dataset is represented as a column
- each row represents all the members for a the given team
loc
- every geolocation in the entire dataset is represented as a row
- every skill in the entire dataset is represented as a column
- each row represents all the geolocations for a given team
- NOTE: will be set to
Noneif the dataset does not include geolocation
Full Sparse Matrix
- the three sparse matrices from above can be aligned side by side to form a sparse matrix of
n_teams x (n_skills + n_members + n_locs)
Example
- team 1 includes members m1 and m2, skills of s2 and s4, and has the geolocation of l2.
- team 2 includes members m2 and m3, skills of s1 and s2, and has the geolocation of l1.
| s1 | s2 | s3 | s4 | m1 | m2 | m3 | l1 | l2 |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 |
| 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 |
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