The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
task_key: string
task_name: string
definition: string
instances: list<item: struct<full_prompt: string, input: string, output: string, instance_id: string>>
child 0, item: struct<full_prompt: string, input: string, output: string, instance_id: string>
child 0, full_prompt: string
child 1, input: string
child 2, output: string
child 3, instance_id: string
to
{'task_name': Value('string'), 'instances': List({'instance_id': Value('string'), 'full_prompt': Value('string'), 'output': Value('string')})}
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 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 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
task_key: string
task_name: string
definition: string
instances: list<item: struct<full_prompt: string, input: string, output: string, instance_id: string>>
child 0, item: struct<full_prompt: string, input: string, output: string, instance_id: string>
child 0, full_prompt: string
child 1, input: string
child 2, output: string
child 3, instance_id: string
to
{'task_name': Value('string'), 'instances': List({'instance_id': Value('string'), 'full_prompt': Value('string'), 'output': Value('string')})}
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.
SMoEA fixed 15-OOD benchmark data
The evaluation data for scripts/run_rejection_benchmark.py. Not needed for
interactive or batch mode — only the benchmark reads it.
Getting it
scripts/setup_workspace.sh downloads this for you into dataset/ood_data/,
which is what --benchmark-root defaults to. To fetch it on its own:
python scripts/fetch_benchmark.py --repo Tincan0325/smoea-15ood-benchmark
That verifies the per-group counts on arrival. The plain hf command works
too, but does no checking:
hf download Tincan0325/smoea-15ood-benchmark --repo-type dataset \
--local-dir dataset/ood_data
Layout — one flat directory
dataset/ood_data/
├── ni_task_descriptions.json
├── task149_test.json task476_test.json task933_test.json
├── task1622_test.json task1670_test.json 1722 instances
├── bbh_test.json 1187 instances
└── mmlu_pro_test.json 1250 instances
Same shape as dataset/train_data/, so both live under dataset/.
Earlier revisions of this repository also carried the three-level MoEA directory tree (
prompts/,ood/data/,dataset/natural_instructions/data/selected_10_tasks/test_data/). The files were byte-identical to the flat ones above.system/benchmark.pystill reads that layout if it finds it, so an existing local copy keeps working.
Usage
python scripts/run_rejection_benchmark.py \
--artifact ties_only \
--output-dir results/rejection-ties_only
Add --smoke to run one instance per family as a pipeline check. Without it,
the loader enforces the exact counts below and refuses to start if any differ.
--artifact takes any id declared in the registry, including base, arrow
and the four taskwise variants.
Contents
| group | datasets | instances |
|---|---|---|
ni |
task149, task476 (classification), task933, task1622, task1670 (generation) | 1722 |
bbh |
causal_judgement, dyck_languages, logical_deduction_five_objects, multistep_arithmetic_two, tracking_shuffled_objects_five_objects | 1187 |
mmlu_pro |
biology, chemistry, computer_science, economics, math | 1250 |
| total | 4159 |
Every instance is an answer-free full prompt: {instance_id, full_prompt, output}, with definition at the top level of each NI file. BBH and MMLU-Pro
instance ids carry the dataset in the second :: field
(bbh::causal_judgement::00000) — that is how the loader splits them apart.
Checksums
From the directory you downloaded into:
sha256sum -c SHA256SUMS
Sources
Preprocessed from BIG-Bench-Hard, MMLU-Pro and Natural Instructions: prompts were rendered from the task templates, sampled to the fixed counts above, and stripped of trailing answers. Redistribution follows each upstream dataset's own licence.
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