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Datasets:
zhangzicheng
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qbench_tsv

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Failed to parse string: 'D' as a scalar of type double
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 2261, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature
                  return array_cast(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1963, in array_cast
                  return array.cast(pa_type)
                File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
                  return call_function("cast", [arr], options, memory_pool)
                File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
                File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Failed to parse string: 'D' as a scalar of type double
              
              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 1529, 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 1154, 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 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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index
string
image
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question
string
A
string
B
string
C
string
D
string
answer
null
split
string
category
string
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
How is the image clarity of the building?
Blurry
Clear
Moderate
null
null
test
type_2_concern_2
2415943373.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
Which part of the human is cropped out of the image?
His hand
His head
His leg
null
null
test
type_1_concern_1
291.bmp
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
What problems exist in this image?
Underexposure
Overexposure
Motion blur
Compression artifacts
null
test
type_1_concern_0
141.bmp
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
Is the image blurred due to movement?
Yes
No
null
null
null
test
type_0_concern_0
3468424403.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
Is the feet of the bird blurred?
No
Yes
null
null
null
test
type_0_concern_2
17.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
Is this imag clear?
Yes
No
null
null
null
test
type_0_concern_0
07539.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
What level of blurriness does the background skyscrapers of this image have?
Slight
Severe
Moderate
null
null
test
type_2_concern_2
67.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
What kind of distortion occurs in the image?
Underexposure
Motion Blur
Overexposure
Out of Focus
null
test
type_1_concern_0
AVA__303546.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
Is the electric pole clear in the image?
No
Yes
null
null
null
test
type_0_concern_2
6643514423.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED)
Is there any problem with image compression distortion?
No
Yes
null
null
null
test
type_0_concern_0
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