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/utils.py", line 126, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 64, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 103, 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 1384, 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 85, 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 1261, 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 1973, 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 85, 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 1261, 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 158, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 64, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 87, in get_rows
                  ds = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2549, in load_dataset
                  builder_instance.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1005, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1100, 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 1860, 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 2016, 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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Dataset Card for severo/embellishments

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 extracted
  • path: local path to the image
  • img: 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:

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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