Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type struct<account_id: string, available_balance: int64, hold_amount: int64> to null
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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2016, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type struct<account_id: string, available_balance: int64, hold_amount: int64> to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
IndicBankBench dataset
IndicBankBench is a 799-case benchmark for evaluating tool-using language-model assistants in Indian retail banking. Each JSON case describes a synthetic, scripted, multi-turn interaction, the available mock tools, the expected tool behavior, and staged grading criteria. It contains no real customer records.
Anonymous-review locations:
- Dataset: https://huggingface.co/datasets/anonuser73268/IndicBankBench
- Dataset ID:
anonuser73268/IndicBankBench - Compatible code: https://github.com/anonuser73268/IndicBankBench
Files and split
The release has one unsplit case bank:
case_bank/<domain>/<tool>/<axis>.NNN.json
There are 799 JSON files and 799 unique case_id values. Cases are grouped by domain and
primary tool for navigation; those directories are not training, validation, or test splits.
File order is the lexicographic order of relative paths, which is also the order used by the
reference harness.
case_bank/: canonical case JSON filesSCHEMA.md: field and format documentationmeta.json: count, layout, compatibility, and aggregate checksum metadataVALIDATION.md: release validation results and known linter findingsLICENSE: CC BY 4.0 license notice and attribution guidanceDISCLAIMER.md: intended-use and warranty disclaimer
Load the cases
The files can be loaded with the Python standard library:
import json
from pathlib import Path
root = Path("case_bank")
cases = [json.loads(path.read_text(encoding="utf-8"))
for path in sorted(root.rglob("*.json"))]
assert len(cases) == 799
assert len({case["case_id"] for case in cases}) == 799
To use the reference harness from a sibling checkout:
cd ../code
export INDICBANKBENCH_DATA=../data
python -m unittest discover -s indicbankbench/tests
python indicbankbench/scripts/case_lint.py
Dataset characteristics
- Language: English
- Modality: scripted multi-turn text interactions plus structured mock-tool data
- Domains: five operational banking domains plus capability/refusal cases
- Tools: 32 mock banking tools
- Primary evaluation axes: 20
- Personal data: synthetic fixtures only
The dataset is intended for controlled research, testing, and benchmarking. It must not be used for live banking, customer, legal, regulatory, credit, fraud, or financial decisions.
Provenance
The cases and bank-specific fixture fields are synthetic. Names and selected demographic descriptors used when constructing the synthetic login contexts were derived from NVIDIA Nemotron-Personas-India, which is licensed under CC BY 4.0. Bank-specific identifiers, masked contact fields, addresses, dates, and relationship fields were generated synthetically.
License and citation
The case data and authored dataset documentation are licensed under Creative Commons Attribution 4.0 International; see LICENSE. Evaluation results and model or judge outputs are not part of this dataset release. During anonymous review, cite the accompanying anonymous paper rather than attempting to identify its authors. Final citation metadata will be added after review.
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