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The dataset viewer is not available for this split.
Cannot load the dataset split (in normal download mode) to extract the first rows.
Error code: NormalRowsError
Exception: DatasetGenerationError
Message: An error occurred while generating the dataset
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 323, in compute
compute_first_rows_from_parquet_response(
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
rows_index = indexer.get_rows_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 631, in get_rows_index
return RowsIndex(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 512, in __init__
self.parquet_index = self._init_parquet_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 529, in _init_parquet_index
response = get_previous_step_or_raise(
File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise
raise CachedArtifactError(
libcommon.simple_cache.CachedArtifactError: The previous step failed.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 92, in get_rows_or_raise
return get_rows(
File "/src/libs/libcommon/src/libcommon/utils.py", line 183, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 69, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 86, in _generate_tables
parquet_file = pq.ParquetFile(f)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 341, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1262, in pyarrow._parquet.ParquetReader.open
File "pyarrow/types.pxi", line 88, in pyarrow.lib._datatype_to_pep3118
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow_hotfix/__init__.py", line 47, in __arrow_ext_deserialize__
raise RuntimeError(
RuntimeError: Disallowed deserialization of 'arrow.py_extension_type':
storage_type = struct<bytes: binary, path: string>
serialized = b'\x80\x04\x955\x00\x00\x00\x00\x00\x00\x00\x8c\x17datasets.features.image\x94\x8c\x12ImageExtensionType\x94\x93\x94)R\x94.'
pickle disassembly:
0: \x80 PROTO 4
2: \x95 FRAME 53
11: \x8c SHORT_BINUNICODE 'datasets.features.image'
36: \x94 MEMOIZE (as 0)
37: \x8c SHORT_BINUNICODE 'ImageExtensionType'
57: \x94 MEMOIZE (as 1)
58: \x93 STACK_GLOBAL
59: \x94 MEMOIZE (as 2)
60: ) EMPTY_TUPLE
61: R REDUCE
62: \x94 MEMOIZE (as 3)
63: . STOP
highest protocol among opcodes = 4
Reading of untrusted Parquet or Feather files with a PyExtensionType column
allows arbitrary code execution.
If you trust this file, you can enable reading the extension type by one of:
- upgrading to pyarrow >= 14.0.1, and call `pa.PyExtensionType.set_auto_load(True)`
- disable this error by running `import pyarrow_hotfix; pyarrow_hotfix.uninstall()`
We strongly recommend updating your Parquet/Feather files to use extension types
derived from `pyarrow.ExtensionType` instead, and register this type explicitly.
See https://arrow.apache.org/docs/dev/python/extending_types.html#defining-extension-types-user-defined-types
for more details.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 86, in _generate_tables
parquet_file = pq.ParquetFile(f)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 341, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1262, in pyarrow._parquet.ParquetReader.open
File "pyarrow/types.pxi", line 88, in pyarrow.lib._datatype_to_pep3118
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow_hotfix/__init__.py", line 47, in __arrow_ext_deserialize__
raise RuntimeError(
RuntimeError: Disallowed deserialization of 'arrow.py_extension_type':
storage_type = struct<bytes: binary, path: string>
serialized = b'\x80\x04\x955\x00\x00\x00\x00\x00\x00\x00\x8c\x17datasets.features.image\x94\x8c\x12ImageExtensionType\x94\x93\x94)R\x94.'
pickle disassembly:
0: \x80 PROTO 4
2: \x95 FRAME 53
11: \x8c SHORT_BINUNICODE 'datasets.features.image'
36: \x94 MEMOIZE (as 0)
37: \x8c SHORT_BINUNICODE 'ImageExtensionType'
57: \x94 MEMOIZE (as 1)
58: \x93 STACK_GLOBAL
59: \x94 MEMOIZE (as 2)
60: ) EMPTY_TUPLE
61: R REDUCE
62: \x94 MEMOIZE (as 3)
63: . STOP
highest protocol among opcodes = 4
Reading of untrusted Parquet or Feather files with a PyExtensionType column
allows arbitrary code execution.
If you trust this file, you can enable reading the extension type by one of:
- upgrading to pyarrow >= 14.0.1, and call `pa.PyExtensionType.set_auto_load(True)`
- disable this error by running `import pyarrow_hotfix; pyarrow_hotfix.uninstall()`
We strongly recommend updating your Parquet/Feather files to use extension types
derived from `pyarrow.ExtensionType` instead, and register this type explicitly.
See https://arrow.apache.org/docs/dev/python/extending_types.html#defining-extension-types-user-defined-types
for more details.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 120, in get_rows_or_raise
return get_rows(
File "/src/libs/libcommon/src/libcommon/utils.py", line 183, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 53, in get_rows
ds = load_dataset(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2609, in load_dataset
builder_instance.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 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.
Dataset Card for severo/embellishments
Test: link to a space:
https://huggingface.co/spaces/severo/voronoi-cloth
https://severo-voronoi-cloth.hf.space
Dataset Summary
This small dataset contains the thumbnails of the first 100 entries of Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG. It has been uploaded to the Hub to reproduce the tutorial by Daniel van Strien: Using 🤗 datasets for image search.
Dataset Structure
Data Instances
A typical row contains an image thumbnail, its filename, and the year of publication of the book it was extracted from.
An example looks as follows:
{
'fname': '000811462_05_000205_1_The Pictorial History of England being a history of the people as well as a hi_1855.jpg',
'year': '1855',
'path': 'embellishments/1855/000811462_05_000205_1_The Pictorial History of England being a history of the people as well as a hi_1855.jpg',
'img': ...
}
Data Fields
fname: the image filename.year: a string with the year of publication of the book from which the image has been extractedpath: local path to the imageimg: a thumbnail of the image with a max height and width of 224 pixels
Data Splits
The dataset only contains 100 rows, in a single 'train' split.
Dataset Creation
Curation Rationale
This dataset was chosen by Daniel van Strien for his tutorial Using 🤗 datasets for image search, which includes the code in Python to do it.
Source Data
Initial Data Collection and Normalization
As stated on the British Library webpage:
The images were algorithmically gathered from 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900. The books cover a wide range of subject areas including philosophy, history, poetry and literature. The images are in .JPEG format.d BCP-47 code is
en.
Who are the source data producers?
British Library, British Library Labs, Adrian Edwards (Curator), Neil Fitzgerald (Contributor ORCID)
Annotations
The dataset does not contain any additional annotations.
Annotation process
[N/A]
Who are the annotators?
[N/A]
Personal and Sensitive Information
[N/A]
Considerations for Using the Data
Social Impact of Dataset
[N/A]
Discussion of Biases
[N/A]
Other Known Limitations
This is a toy dataset that aims at:
- validating the process described in the tutorial Using 🤗 datasets for image search by Daniel van Strien,
- showing the dataset viewer on an image dataset.
Additional Information
Dataset Curators
The dataset was created by Sylvain Lesage at Hugging Face, to replicate the tutorial Using 🤗 datasets for image search by Daniel van Strien.
Licensing Information
CC0 1.0 Universal Public Domain
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