Bekko System One: building ultra-small decision models that run in a browser
hotchpotch
• • 2Dataset 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: TypeError
Message: Couldn't cast array of type
struct<target_mass_at_prediction: double, uniform_accuracy_baseline: double, fixed_answer_accuracy_baseline: double, baseline_adjusted_skill: double, baseline_adjusted_score: double>
to
{'binary_brier': Value('float64'), 'accuracy': Value('float64'), 'positive_recall': Value('float64'), 'specificity': Value('float64'), 'precision': Value('float64'), 'balanced_accuracy': Value('float64'), 'f1': Value('float64'), 'true_positive': Value('float64'), 'false_positive': Value('float64'), 'false_negative': Value('float64'), 'true_negative': Value('float64'), 'positive_prevalence': Value('float64'), 'majority_accuracy_baseline': Value('float64'), 'constant_brier_baseline': Value('float64'), 'brier_skill': Value('float64'), 'baseline_adjusted_skill': Value('float64'), 'baseline_adjusted_score': Value('float64')}
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 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 2158, 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<target_mass_at_prediction: double, uniform_accuracy_baseline: double, fixed_answer_accuracy_baseline: double, baseline_adjusted_skill: double, baseline_adjusted_score: double>
to
{'binary_brier': Value('float64'), 'accuracy': Value('float64'), 'positive_recall': Value('float64'), 'specificity': Value('float64'), 'precision': Value('float64'), 'balanced_accuracy': Value('float64'), 'f1': Value('float64'), 'true_positive': Value('float64'), 'false_positive': Value('float64'), 'false_negative': Value('float64'), 'true_negative': Value('float64'), 'positive_prevalence': Value('float64'), 'majority_accuracy_baseline': Value('float64'), 'constant_brier_baseline': Value('float64'), 'brier_skill': Value('float64'), 'baseline_adjusted_skill': Value('float64'), 'baseline_adjusted_score': Value('float64')}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.
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S1MB evaluation results
Model evaluation results for System One Mosaic Benchmark (S1MB), combining specialized Choice, Noul, and Score tasks.
Each <organization-or-user>__<model-id>/ folder represents one leaderboard entry
and contains metadata.json plus per-benchmark <benchmark-id>.json.xz files.
Results retain their evaluation settings and model, dataset, and evaluator provenance.
To add or update results, follow the submission guide for evaluation, export, validation, and opening a pull request to this dataset.
See the S1MB project for code and documentation.
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