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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
id: string
group: string
question: string
source: string
task: string
type: string
variant: string
keys: list<item: string>
child 0, item: string
label: int64
control_id: null
pair_id: null
sibling: null
parent: string
p: list<item: double>
child 0, item: double
raw_probability_sum: double
zero_count: int64
report: struct<objective: double, paired_flip: null, unknowable: null, clean: struct<n: int64, nll: double, (... 5353 chars omitted)
child 0, objective: double
child 1, paired_flip: null
child 2, unknowable: null
child 3, clean: struct<n: int64, nll: double, acc: double, ece: double, brier: double, mean_conf: double, confident_ (... 576 chars omitted)
child 0, n: int64
child 1, nll: double
child 2, acc: double
child 3, ece: double
child 4, brier: double
child 5, mean_conf: double
child 6, confident_error_rate: double
child 7, coverage_at_0_9: double
child 8, accuracy_at_0_9: double
child 9, coverage_at_5pct_error: double
child 10, coverage_at_1pct_error: double
child 11, confidence_bias: double
child 12, top_bins: struct<0.9: struct<n: int64, errors: int64, error_rate: double>, 0.95: struct<n: int64, errors: int6 (... 82 chars omitted)
child 0, 0.9: struct<n: int64, errors: int64, error_rate: double>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: double
child 1, 0.95: struct<n: int64, errors: int64, error_ra
...
r: double
child 11, confidence_bias: double
child 12, top_bins: struct<0.9: struct<n: int64, errors: int64, error_rate: double>, 0.95: struct<n: int64, errors: int6 (... 80 chars omitted)
child 0, 0.9: struct<n: int64, errors: int64, error_rate: double>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: double
child 1, 0.95: struct<n: int64, errors: int64, error_rate: double>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: double
child 2, 0.99: struct<n: int64, errors: int64, error_rate: null>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: null
child 13, selective: struct<0.5: struct<coverage: double, accuracy: double, confidence_cutoff: double>, 0.8: struct<cover (... 58 chars omitted)
child 0, 0.5: struct<coverage: double, accuracy: double, confidence_cutoff: double>
child 0, coverage: double
child 1, accuracy: double
child 2, confidence_cutoff: double
child 1, 0.8: struct<coverage: double, accuracy: double, confidence_cutoff: double>
child 0, coverage: double
child 1, accuracy: double
child 2, confidence_cutoff: double
child 14, score_mae: double
child 15, ranked_probability_score: double
model: string
T_global: double
coverage_at_5pct_error: double
to
{'model': Value('string'), 'T_global': Value('float64'), 'per_task_T_metrics': {'typed_agent_trace_observability': {'n': Value('int64'), 'nll': Value('float64'), 'acc': Value('float64'), 'ece': Value('float64'), 'brier': Value('float64'), 'mean_conf': Value('float64'), 'confident_error_rate': Value('float64'), 'coverage_at_0_9': Value('float64'), 'accuracy_at_0_9': Value('float64'), 'coverage_at_5pct_error': Value('float64'), 'coverage_at_1pct_error': Value('float64'), 'confidence_bias': Value('float64'), 'top_bins': {'0.9': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.95': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.99': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}}, 'selective': {'0.5': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}, '0.8': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}}, 'score_mae': Value('float64'), 'ranked_probability_score': Value('float64')}, 'typed_customer_service': {'n': Value('int64'), 'nll': Value('float64'), 'acc': Value('float64'), 'ece': Value('float64'), 'brier': Value('float64'), 'mean_conf': Value('float64'), 'confident_error_rate': Value('float64'), 'coverage_at_0_9': Value('float64'), 'accuracy_at_0_9': Value('float64'), 'coverage_at_5pct_error': Value('float64'), 'coverage_at_1pct_error': Value('float64'), 'confidence_bia
...
oat64'), 'calibrated_clean': {'n': Value('int64'), 'nll': Value('float64'), 'acc': Value('float64'), 'ece': Value('float64'), 'brier': Value('float64'), 'mean_conf': Value('float64'), 'confident_error_rate': Value('float64'), 'coverage_at_0_9': Value('float64'), 'accuracy_at_0_9': Value('float64'), 'coverage_at_5pct_error': Value('float64'), 'coverage_at_1pct_error': Value('float64'), 'confidence_bias': Value('float64'), 'top_bins': {'0.9': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.95': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.99': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}}, 'selective': {'0.5': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}, '0.8': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}}, 'score_mae': Value('float64'), 'ranked_probability_score': Value('float64')}, 'metric_policy': {'nll_floor': Value('float64'), 'renormalize_returned_probabilities': Value('bool'), 'raw_sums_outside_1e_5': Value('int64'), 'returned_zeros': Value('int64')}, 'coverage': {'requested_records': Value('int64'), 'requested_questions': Value('int64'), 'evaluated_records': Value('int64'), 'evaluated_questions': Value('int64'), 'rejected_records': Value('int64'), 'truncated_records': Value('int64')}, 'latency_ms': {'median': Value('float64'), 'p95': Value('float64')}}}
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
id: string
group: string
question: string
source: string
task: string
type: string
variant: string
keys: list<item: string>
child 0, item: string
label: int64
control_id: null
pair_id: null
sibling: null
parent: string
p: list<item: double>
child 0, item: double
raw_probability_sum: double
zero_count: int64
report: struct<objective: double, paired_flip: null, unknowable: null, clean: struct<n: int64, nll: double, (... 5353 chars omitted)
child 0, objective: double
child 1, paired_flip: null
child 2, unknowable: null
child 3, clean: struct<n: int64, nll: double, acc: double, ece: double, brier: double, mean_conf: double, confident_ (... 576 chars omitted)
child 0, n: int64
child 1, nll: double
child 2, acc: double
child 3, ece: double
child 4, brier: double
child 5, mean_conf: double
child 6, confident_error_rate: double
child 7, coverage_at_0_9: double
child 8, accuracy_at_0_9: double
child 9, coverage_at_5pct_error: double
child 10, coverage_at_1pct_error: double
child 11, confidence_bias: double
child 12, top_bins: struct<0.9: struct<n: int64, errors: int64, error_rate: double>, 0.95: struct<n: int64, errors: int6 (... 82 chars omitted)
child 0, 0.9: struct<n: int64, errors: int64, error_rate: double>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: double
child 1, 0.95: struct<n: int64, errors: int64, error_ra
...
r: double
child 11, confidence_bias: double
child 12, top_bins: struct<0.9: struct<n: int64, errors: int64, error_rate: double>, 0.95: struct<n: int64, errors: int6 (... 80 chars omitted)
child 0, 0.9: struct<n: int64, errors: int64, error_rate: double>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: double
child 1, 0.95: struct<n: int64, errors: int64, error_rate: double>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: double
child 2, 0.99: struct<n: int64, errors: int64, error_rate: null>
child 0, n: int64
child 1, errors: int64
child 2, error_rate: null
child 13, selective: struct<0.5: struct<coverage: double, accuracy: double, confidence_cutoff: double>, 0.8: struct<cover (... 58 chars omitted)
child 0, 0.5: struct<coverage: double, accuracy: double, confidence_cutoff: double>
child 0, coverage: double
child 1, accuracy: double
child 2, confidence_cutoff: double
child 1, 0.8: struct<coverage: double, accuracy: double, confidence_cutoff: double>
child 0, coverage: double
child 1, accuracy: double
child 2, confidence_cutoff: double
child 14, score_mae: double
child 15, ranked_probability_score: double
model: string
T_global: double
coverage_at_5pct_error: double
to
{'model': Value('string'), 'T_global': Value('float64'), 'per_task_T_metrics': {'typed_agent_trace_observability': {'n': Value('int64'), 'nll': Value('float64'), 'acc': Value('float64'), 'ece': Value('float64'), 'brier': Value('float64'), 'mean_conf': Value('float64'), 'confident_error_rate': Value('float64'), 'coverage_at_0_9': Value('float64'), 'accuracy_at_0_9': Value('float64'), 'coverage_at_5pct_error': Value('float64'), 'coverage_at_1pct_error': Value('float64'), 'confidence_bias': Value('float64'), 'top_bins': {'0.9': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.95': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.99': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}}, 'selective': {'0.5': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}, '0.8': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}}, 'score_mae': Value('float64'), 'ranked_probability_score': Value('float64')}, 'typed_customer_service': {'n': Value('int64'), 'nll': Value('float64'), 'acc': Value('float64'), 'ece': Value('float64'), 'brier': Value('float64'), 'mean_conf': Value('float64'), 'confident_error_rate': Value('float64'), 'coverage_at_0_9': Value('float64'), 'accuracy_at_0_9': Value('float64'), 'coverage_at_5pct_error': Value('float64'), 'coverage_at_1pct_error': Value('float64'), 'confidence_bia
...
oat64'), 'calibrated_clean': {'n': Value('int64'), 'nll': Value('float64'), 'acc': Value('float64'), 'ece': Value('float64'), 'brier': Value('float64'), 'mean_conf': Value('float64'), 'confident_error_rate': Value('float64'), 'coverage_at_0_9': Value('float64'), 'accuracy_at_0_9': Value('float64'), 'coverage_at_5pct_error': Value('float64'), 'coverage_at_1pct_error': Value('float64'), 'confidence_bias': Value('float64'), 'top_bins': {'0.9': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.95': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}, '0.99': {'n': Value('int64'), 'errors': Value('int64'), 'error_rate': Value('float64')}}, 'selective': {'0.5': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}, '0.8': {'coverage': Value('float64'), 'accuracy': Value('float64'), 'confidence_cutoff': Value('float64')}}, 'score_mae': Value('float64'), 'ranked_probability_score': Value('float64')}, 'metric_policy': {'nll_floor': Value('float64'), 'renormalize_returned_probabilities': Value('bool'), 'raw_sums_outside_1e_5': Value('int64'), 'returned_zeros': Value('int64')}, 'coverage': {'requested_records': Value('int64'), 'requested_questions': Value('int64'), 'evaluated_records': Value('int64'), 'evaluated_questions': Value('int64'), 'rejected_records': Value('int64'), 'truncated_records': Value('int64')}, 'latency_ms': {'median': Value('float64'), 'p95': Value('float64')}}}
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.
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