The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<category_type: string, anno_id: int64, order: int64, ignore: bool, poly: list<item: double>, line_with_spans: list<item: null>, attribute: struct<text_language: string, text_background: string, text_rotate: string>, text: string, perturbation: struct<level: string, type: string, subtype: string, severity: int64, target: string>>
to
{'category_type': Value('string'), 'anno_id': Value('int64'), 'order': Value('int64'), 'ignore': Value('bool'), 'poly': List(Value('float64')), 'line_with_spans': List(Value('null')), 'attribute': {'text_language': Value('string'), 'text_background': Value('string'), 'text_rotate': Value('string')}, 'text': Value('string'), 'merge_list': List({'category_type': Value('string'), 'anno_id': Value('int64'), 'order': Value('null'), 'ignore': Value('bool'), 'poly': List(Value('float64')), 'line_with_spans': List(Value('null')), 'attribute': {}, 'text': Value('string')})}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2255, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2061, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1806, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2101, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<category_type: string, anno_id: int64, order: int64, ignore: bool, poly: list<item: double>, line_with_spans: list<item: null>, attribute: struct<text_language: string, text_background: string, text_rotate: string>, text: string, perturbation: struct<level: string, type: string, subtype: string, severity: int64, target: string>>
to
{'category_type': Value('string'), 'anno_id': Value('int64'), 'order': Value('int64'), 'ignore': Value('bool'), 'poly': List(Value('float64')), 'line_with_spans': List(Value('null')), 'attribute': {'text_language': Value('string'), 'text_background': Value('string'), 'text_rotate': Value('string')}, 'text': Value('string'), 'merge_list': List({'category_type': Value('string'), 'anno_id': Value('int64'), 'order': Value('null'), 'ignore': Value('bool'), 'poly': List(Value('float64')), 'line_with_spans': List(Value('null')), 'attribute': {}, 'text': Value('string')})}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.
DocToR 5 documents Tracing of Robustness
Dataset Summary
We introduce the DOCTOR 5 benchmark to systematically test the visual and logical robustness of multimodal models. The dataset features approximately 13k+ document images and over 65000 visual question answering pairs. It covers 12 diverse domains including scientific papers financial reports and business presentations. This resource provides a standardized testbed for evaluating models against generation aware perturbations.
Taxonomy of Robustness
The benchmark categorizes cognitive and generative perturbations into five exclusive layers across 17 subcategories.
- L1 Perception focuses on font rendering noise character ghosting and physical artifacts like handwritten pen strokes and watermarks.
- L2 Language evaluates factual hallucinations semantic drift and key information removal.
- L3 Alignment targets structural layout mismatches and severe image text contradictions.
- L4 Reasoning injects multi hop logic failures fact inversions and numerical absurdities.
- L5 Agent traps models using double bind instructions and authority role mixing prompt injections.
Data Generation Pipeline
We employ a hybrid data generation pipeline. The pristine document pool is filtered dynamically using language models to ensure sufficient structural complexity. We combine programmatic pixel rendering for low level visual noise with automated language model injections for high level semantic errors. All textual interventions are strictly constrained to the original domain context. We do not apply uniform image resizing keeping the natural diversity of the original document resolutions.
Evaluation Metrics
We measure model performance using a set of standardized evaluation tools.
- Textual Tasks We utilize Exact Match and F1 scores for general question answering.
- Table Extraction We calculate tree structural similarity using the TEDS metric.
- Formula Extraction We evaluate the accuracy of generated formulas using the Edit Distance metric. We apply a standardized preprocessing and normalization pipeline to all predicted LaTeX code strings before calculating the edit distance. This standardization removes format variations and ensures objective evaluation.
Limitations and Broader Impacts
The synthetic noise generated in this benchmark may not perfectly capture all entirely random human errors occurring in the wild. The prompt injection techniques demonstrated in the Agent layer carry potential dual use risks. We release this dataset to accelerate community development of defensive mechanisms against adversarial document generation and to promote the creation of safer multimodal AI systems.
Usage
You can load the dataset directly using the Hugging Face datasets library.
from datasets import load_dataset
dataset = load_dataset("Doctor5benchmark/DocToR-5")
print(dataset)
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