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Datasets:
Ever2after
/
3d-spatial-reasoning-2

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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 1 new columns ({'options'})

This happened while the json dataset builder was generating data using

zip://output/relative_distance.jsonl::hf://datasets/Ever2after/3d-spatial-reasoning-2@c33d82b10ddebbe8da73f522c43cd79468adde35/output.zip

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
              question: string
              answer: string
              options: list<item: string>
                child 0, item: string
              images: list<item: string>
                child 0, item: string
              scene_id: string
              region_idx: int64
              to
              {'scene_id': Value('string'), 'region_idx': Value('int64'), 'question': Value('string'), 'answer': Value('int64'), 'images': List(Value('string'))}
              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 1450, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 993, in stream_convert_to_parquet
                  builder._prepare_split(
                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 1 new columns ({'options'})
              
              This happened while the json dataset builder was generating data using
              
              zip://output/relative_distance.jsonl::hf://datasets/Ever2after/3d-spatial-reasoning-2@c33d82b10ddebbe8da73f522c43cd79468adde35/output.zip
              
              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.

scene_id
string
region_idx
int64
question
string
answer
int64
images
list
104348082_171512994
0
How many book are in the scene?
2
[ "view_006.png", "view_004.png", "view_007.png", "view_005.png", "view_009.png" ]
102344250
0
How many bin are in the scene?
1
[ "view_008.png", "view_017.png" ]
102344250
1
How many seat are in the scene?
6
[ "view_001.png", "view_011.png", "view_012.png", "view_005.png" ]
102344250
5
How many lamp are in the scene?
9
[ "view_005.png", "view_006.png" ]
105515286_173104287
0
How many car are in the scene?
2
[ "view_014.png", "view_016.png", "view_008.png", "view_001.png", "view_025.png" ]
105515286_173104287
5
How many table are in the scene?
2
[ "view_012.png", "view_000.png" ]
105515286_173104287
9
How many beam are in the scene?
3
[ "view_010.png", "view_007.png", "view_023.png", "view_022.png", "view_002.png" ]
105515286_173104287
13
How many seat are in the scene?
2
[ "view_003.png", "view_002.png", "view_008.png" ]
105515286_173104287
22
How many seat are in the scene?
2
[ "view_002.png", "view_010.png" ]
105515286_173104287
23
How many curtain are in the scene?
2
[ "view_011.png", "view_001.png", "view_008.png", "view_005.png" ]
107734056_175999839
8
How many cabinet are in the scene?
4
[ "view_007.png", "view_001.png", "view_006.png", "view_002.png" ]
107734056_175999839
13
How many car are in the scene?
1
[ "view_000.png", "view_004.png", "view_003.png" ]
103997940_171031257
0
How many rack are in the scene?
5
[ "view_010.png", "view_002.png" ]
103997940_171031257
1
How many car are in the scene?
2
[ "view_002.png", "view_022.png" ]
103997940_171031257
7
How many flowerpot are in the scene?
1
[ "view_013.png", "view_003.png", "view_010.png" ]
103997940_171031257
8
How many shelf are in the scene?
2
[ "view_014.png", "view_015.png", "view_021.png", "view_020.png" ]
103997940_171031257
12
How many picture are in the scene?
3
[ "view_009.png", "view_001.png", "view_007.png", "view_005.png" ]
103997940_171031257
13
How many cabinet are in the scene?
4
[ "view_000.png", "view_004.png", "view_001.png" ]
103997940_171031257
14
How many speaker are in the scene?
4
[ "view_010.png", "view_013.png", "view_001.png" ]
103997940_171031257
17
How many floor are in the scene?
6
[ "view_009.png", "view_010.png" ]
103997940_171031257
28
How many tap are in the scene?
2
[ "view_000.png", "view_003.png" ]
103997940_171031257
33
How many plant are in the scene?
2
[ "view_012.png", "view_002.png" ]
103997586_171030669
3
How many plant are in the scene?
5
[ "view_001.png", "view_000.png" ]
103997586_171030669
4
How many plant are in the scene?
4
[ "view_007.png", "view_003.png" ]
103997586_171030669
9
How many seat are in the scene?
1
[ "view_007.png", "view_002.png" ]
103997586_171030669
12
How many picture are in the scene?
2
[ "view_000.png", "view_008.png", "view_007.png" ]
103997586_171030669
13
How many lamp are in the scene?
5
[ "view_006.png", "view_003.png" ]
104348361_171513414
3
How many table are in the scene?
3
[ "view_015.png", "view_018.png", "view_005.png", "view_006.png" ]
104348361_171513414
7
How many kitchen lower cabinet are in the scene?
1
[ "view_007.png", "view_002.png" ]
103997919_171031233
0
How many table are in the scene?
2
[ "view_010.png", "view_006.png", "view_009.png", "view_008.png", "view_004.png" ]
103997919_171031233
3
How many picture are in the scene?
4
[ "view_002.png", "view_008.png", "view_000.png", "view_003.png" ]
108294537_176710050
3
How many pillow are in the scene?
7
[ "view_002.png", "view_003.png", "view_012.png", "view_007.png" ]
108294537_176710050
4
How many candle are in the scene?
2
[ "view_018.png", "view_019.png", "view_004.png", "view_009.png" ]
108294537_176710050
5
How many carpet are in the scene?
1
[ "view_022.png", "view_010.png", "view_005.png" ]
108294537_176710050
6
How many towel are in the scene?
1
[ "view_001.png", "view_012.png", "view_013.png", "view_019.png", "view_005.png" ]
108294537_176710050
7
How many mirror are in the scene?
2
[ "view_008.png", "view_005.png" ]
108294537_176710050
10
How many shelf are in the scene?
1
[ "view_001.png", "view_015.png" ]
108294537_176710050
13
How many seat are in the scene?
3
[ "view_000.png", "view_012.png", "view_013.png", "view_006.png", "view_003.png" ]
102344529
0
How many plant are in the scene?
1
[ "view_006.png", "view_010.png" ]
102344529
2
How many lamp are in the scene?
2
[ "view_000.png", "view_006.png", "view_020.png", "view_018.png" ]
102344529
4
How many flowerpot are in the scene?
3
[ "view_003.png", "view_001.png" ]
102344529
7
How many lamp are in the scene?
2
[ "view_013.png", "view_004.png", "view_012.png" ]
104348082_171512994
0
Which object is closer to table?
A
[ "view_004.png", "view_008.png" ]
104348082_171512994
0
Which object is closer to the viewpoint of the 4th image?
A
[ "view_006.png", "view_005.png", "view_003.png", "view_007.png" ]
104348082_171512994
0
Which image's viewpoint is closer to vase?
B
[ "view_001.png", "view_003.png", "view_009.png", "view_008.png" ]
104348082_171512994
0
Which image's viewpoint is closer to the viewpoint of the 5th image?
B
[ "view_001.png", "view_008.png", "view_005.png", "view_003.png", "view_006.png" ]
102344250
0
Which object is closer to sink_cabinet?
B
[ "view_005.png", "view_006.png", "view_018.png", "view_001.png" ]
102344250
0
Which object is closer to the viewpoint of the 2nd image?
B
[ "view_015.png", "view_002.png", "view_017.png", "view_004.png" ]
102344250
0
Which image's viewpoint is closer to bin?
B
[ "view_016.png", "view_008.png", "view_018.png", "view_007.png", "view_013.png" ]
102344250
0
Which image's viewpoint is closer to the viewpoint of the 3rd image?
A
[ "view_008.png", "view_014.png", "view_015.png", "view_011.png", "view_001.png" ]
102344250
1
Which object is closer to clock?
A
[ "view_000.png", "view_011.png", "view_004.png" ]
102344250
1
Which object is closer to the viewpoint of the 2nd image?
B
[ "view_011.png", "view_003.png" ]
102344250
1
Which image's viewpoint is closer to clock?
B
[ "view_000.png", "view_001.png" ]
102344250
1
Which image's viewpoint is closer to the viewpoint of the 1st image?
B
[ "view_001.png", "view_005.png", "view_012.png", "view_000.png" ]
102344250
5
Which object is closer to wardrobe?
B
[ "view_009.png", "view_001.png", "view_004.png", "view_011.png", "view_003.png" ]
102344250
5
Which object is closer to the viewpoint of the 2nd image?
A
[ "view_010.png", "view_004.png" ]
102344250
5
Which image's viewpoint is closer to wardrobe?
B
[ "view_000.png", "view_009.png", "view_001.png", "view_006.png", "view_003.png" ]
102344250
5
Which image's viewpoint is closer to the viewpoint of the 2nd image?
A
[ "view_007.png", "view_009.png", "view_002.png", "view_003.png", "view_000.png" ]
105515286_173104287
0
Which object is closer to ventilation_hood?
A
[ "view_021.png", "view_005.png", "view_000.png", "view_015.png", "view_013.png" ]
105515286_173104287
0
Which object is closer to the viewpoint of the 3rd image?
A
[ "view_020.png", "view_011.png", "view_012.png", "view_021.png" ]
105515286_173104287
0
Which image's viewpoint is closer to ladder?
A
[ "view_010.png", "view_023.png" ]
105515286_173104287
0
Which image's viewpoint is closer to the viewpoint of the 4th image?
A
[ "view_020.png", "view_019.png", "view_004.png", "view_022.png", "view_009.png" ]
105515286_173104287
5
Which object is closer to fridge?
B
[ "view_005.png", "view_007.png", "view_016.png", "view_010.png" ]
105515286_173104287
5
Which object is closer to the viewpoint of the 4th image?
B
[ "view_006.png", "view_002.png", "view_004.png", "view_000.png", "view_017.png" ]
105515286_173104287
5
Which image's viewpoint is closer to stool?
A
[ "view_009.png", "view_008.png", "view_005.png" ]
105515286_173104287
5
Which image's viewpoint is closer to the viewpoint of the 4th image?
A
[ "view_002.png", "view_000.png", "view_008.png", "view_014.png" ]
105515286_173104287
9
Which object is closer to table?
A
[ "view_003.png", "view_004.png", "view_008.png", "view_000.png" ]
105515286_173104287
9
Which object is closer to the viewpoint of the 4th image?
A
[ "view_018.png", "view_001.png", "view_023.png", "view_022.png" ]
105515286_173104287
9
Which image's viewpoint is closer to firewood_holder?
B
[ "view_011.png", "view_017.png", "view_005.png", "view_006.png", "view_012.png" ]
105515286_173104287
9
Which image's viewpoint is closer to the viewpoint of the 2nd image?
A
[ "view_004.png", "view_008.png", "view_010.png" ]
105515286_173104287
13
Which object is closer to record_player?
B
[ "view_005.png", "view_006.png", "view_008.png", "view_002.png" ]
105515286_173104287
13
Which object is closer to the viewpoint of the 3rd image?
B
[ "view_001.png", "view_007.png", "view_003.png", "view_000.png" ]
105515286_173104287
13
Which image's viewpoint is closer to plant?
A
[ "view_005.png", "view_011.png", "view_001.png", "view_007.png", "view_009.png" ]
105515286_173104287
13
Which image's viewpoint is closer to the viewpoint of the 1st image?
A
[ "view_004.png", "view_005.png", "view_002.png" ]
107734056_175999839
8
Which object is closer to couch?
B
[ "view_001.png", "view_006.png" ]
107734056_175999839
8
Which object is closer to the viewpoint of the 3rd image?
A
[ "view_007.png", "view_006.png", "view_005.png" ]
107734056_175999839
8
Which image's viewpoint is closer to carpet?
A
[ "view_002.png", "view_006.png", "view_008.png", "view_004.png", "view_003.png" ]
107734056_175999839
8
Which image's viewpoint is closer to the viewpoint of the 3rd image?
B
[ "view_003.png", "view_008.png", "view_009.png", "view_000.png" ]
107734056_175999839
13
Which object is closer to washing_machine_and_dryer?
B
[ "view_004.png", "view_002.png", "view_006.png" ]
107734056_175999839
13
Which object is closer to the viewpoint of the 1st image?
B
[ "view_004.png", "view_007.png", "view_000.png", "view_006.png", "view_008.png" ]
107734056_175999839
13
Which image's viewpoint is closer to car?
B
[ "view_004.png", "view_000.png" ]
107734056_175999839
13
Which image's viewpoint is closer to the viewpoint of the 3rd image?
A
[ "view_001.png", "view_008.png", "view_005.png", "view_003.png", "view_007.png" ]
103997586_171030669
3
Which object is closer to painting?
A
[ "view_000.png", "view_005.png" ]
103997586_171030669
3
Which object is closer to the viewpoint of the 4th image?
A
[ "view_016.png", "view_008.png", "view_005.png", "view_001.png" ]
103997586_171030669
3
Which image's viewpoint is closer to painting?
B
[ "view_012.png", "view_001.png", "view_002.png", "view_010.png", "view_014.png" ]
103997586_171030669
3
Which image's viewpoint is closer to the viewpoint of the 2nd image?
A
[ "view_010.png", "view_001.png", "view_000.png", "view_007.png", "view_006.png" ]
103997586_171030669
4
Which object is closer to book?
B
[ "view_005.png", "view_006.png", "view_000.png" ]
103997586_171030669
4
Which object is closer to the viewpoint of the 1st image?
B
[ "view_005.png", "view_001.png", "view_009.png" ]
103997586_171030669
4
Which image's viewpoint is closer to couch?
A
[ "view_009.png", "view_007.png" ]
103997586_171030669
4
Which image's viewpoint is closer to the viewpoint of the 1st image?
A
[ "view_009.png", "view_004.png", "view_002.png", "view_003.png", "view_007.png" ]
103997586_171030669
9
Which object is closer to decoration?
A
[ "view_014.png", "view_011.png", "view_025.png" ]
103997586_171030669
9
Which object is closer to the viewpoint of the 1st image?
A
[ "view_001.png", "view_028.png" ]
103997586_171030669
9
Which image's viewpoint is closer to seat?
A
[ "view_004.png", "view_013.png" ]
103997586_171030669
9
Which image's viewpoint is closer to the viewpoint of the 2nd image?
B
[ "view_013.png", "view_002.png", "view_003.png" ]
103997586_171030669
12
Which object is closer to flowerpot?
B
[ "view_007.png", "view_005.png" ]
103997586_171030669
12
Which object is closer to the viewpoint of the 3rd image?
B
[ "view_001.png", "view_005.png", "view_013.png", "view_011.png" ]
103997586_171030669
12
Which image's viewpoint is closer to shelf?
A
[ "view_000.png", "view_003.png", "view_010.png" ]
103997586_171030669
12
Which image's viewpoint is closer to the viewpoint of the 2nd image?
A
[ "view_000.png", "view_004.png", "view_005.png", "view_013.png", "view_009.png" ]
103997586_171030669
13
Which object is closer to couch?
A
[ "view_004.png", "view_002.png", "view_000.png", "view_001.png" ]
103997586_171030669
13
Which object is closer to the viewpoint of the 1st image?
A
[ "view_003.png", "view_005.png", "view_009.png" ]
End of preview.

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