Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
model: string
content: list<item: struct<type: string, text: string>>
child 0, item: struct<type: string, text: string>
child 0, type: string
child 1, text: string
result: list<item: struct<type: string, box_2d: list<item: int64>, content: string>>
child 0, item: struct<type: string, box_2d: list<item: int64>, content: string>
child 0, type: string
child 1, box_2d: list<item: int64>
child 0, item: int64
child 2, content: string
usage_metadata: struct<prompt_token_count: int64, candidates_token_count: int64, total_token_count: int64>
child 0, prompt_token_count: int64
child 1, candidates_token_count: int64
child 2, total_token_count: int64
provider_usage: struct<prompt_tokens: int64, completion_tokens: int64, total_tokens: int64, pages_processed: int64>
child 0, prompt_tokens: int64
child 1, completion_tokens: int64
child 2, total_tokens: int64
child 3, pages_processed: int64
cost_estimate: struct<input_rate_per_1m: double, output_rate_per_1m: double, input_cost_usd: double, output_cost_us (... 34 chars omitted)
child 0, input_rate_per_1m: double
child 1, output_rate_per_1m: double
child 2, input_cost_usd: double
child 3, output_cost_usd: double
child 4, total_cost_usd: double
to
{'model': Value('string'), 'content': List({'type': Value('string'), 'text': Value('string')}), 'result': List({'type': Value('string'), 'box_2d': List(Value('int64')), 'content': Value('string')}), 'usage_metadata': {'prompt_token_count': Value('int64'), 'candidates_token_count': Value('int64'), 'total_token_count': Value('int64')}, 'provider_usage': {'prompt_tokens': Value('int64'), 'completion_tokens': Value('int64'), 'total_tokens': Value('int64'), 'pages_processed': Value('int64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model: string
content: list<item: struct<type: string, text: string>>
child 0, item: struct<type: string, text: string>
child 0, type: string
child 1, text: string
result: list<item: struct<type: string, box_2d: list<item: int64>, content: string>>
child 0, item: struct<type: string, box_2d: list<item: int64>, content: string>
child 0, type: string
child 1, box_2d: list<item: int64>
child 0, item: int64
child 2, content: string
usage_metadata: struct<prompt_token_count: int64, candidates_token_count: int64, total_token_count: int64>
child 0, prompt_token_count: int64
child 1, candidates_token_count: int64
child 2, total_token_count: int64
provider_usage: struct<prompt_tokens: int64, completion_tokens: int64, total_tokens: int64, pages_processed: int64>
child 0, prompt_tokens: int64
child 1, completion_tokens: int64
child 2, total_tokens: int64
child 3, pages_processed: int64
cost_estimate: struct<input_rate_per_1m: double, output_rate_per_1m: double, input_cost_usd: double, output_cost_us (... 34 chars omitted)
child 0, input_rate_per_1m: double
child 1, output_rate_per_1m: double
child 2, input_cost_usd: double
child 3, output_cost_usd: double
child 4, total_cost_usd: double
to
{'model': Value('string'), 'content': List({'type': Value('string'), 'text': Value('string')}), 'result': List({'type': Value('string'), 'box_2d': List(Value('int64')), 'content': Value('string')}), 'usage_metadata': {'prompt_token_count': Value('int64'), 'candidates_token_count': Value('int64'), 'total_token_count': Value('int64')}, 'provider_usage': {'prompt_tokens': Value('int64'), 'completion_tokens': Value('int64'), 'total_tokens': Value('int64'), 'pages_processed': Value('int64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
IndicGovBench-Doc: Comprehensive Benchmark for Indian Government Documents
IndicGovBench-Doc is an evaluation benchmark for document understanding, optical character recognition (OCR), visual layout analysis, and table extraction across Indian government administrative documents (Government Resolutions, Gazettes, Circulars, Certificates).
Dataset Summary
- Documents: 270 full-page official government records.
- Languages: Marathi (Mr), Hindi (Hi), English (En), and multi-lingual codeswitching.
- Annotations: High-density bounding box segmentation under RFC-style annotation protocol v0.9.
- Multi-column reading order.
- Complex nested and irregular table structures.
- Explicit scope boundaries:
target,excluded,uncertain. - Word-level spatial anchors aligned with Google Cloud Vision.
Dataset Structure
Each row in the dataset corresponds to a document page:
doc_id: Unique identifier (e.g.order_mr_archive_001).language: Primary language (mr,hi,en).doc_category: Document category (archive,current,gazette, etc.).width,height: Canvas pixel dimensions.content_bbox: Bounding box of all valid content[min_x, min_y, max_x, max_y].ground_truth_blocks: Structured text blocks with category, polygon, text, and reading order.ground_truth_tables: Table data with HTML, markdown, rows, cols, and cell grids.metadata: Outward number, date, department, and scan quality strata.
Citation
@dataset{indicgovbench2026,
author = {Orgpedia Foundation},
title = {IndicGovBench-Doc: Benchmark for Indian Government Document Processing},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/Orgpedia/indicGovBench-Doc}}
}
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