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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
primitive: string
p_yes: double
answer: string
model: string
probabilities: struct<A: double, B: double, C: double, D: double, 1: double, 2: double, 3: double, 4: double, 0: do (... 4139 chars omitted)
child 0, A: double
child 1, B: double
child 2, C: double
child 3, D: double
child 4, 1: double
child 5, 2: double
child 6, 3: double
child 7, 4: double
child 8, 0: double
child 9, entailment: double
child 10, neutral: double
child 11, contradiction: double
child 12, E: double
child 13, admiration: double
child 14, amusement: double
child 15, anger: double
child 16, annoyance: double
child 17, approval: double
child 18, caring: double
child 19, confusion: double
child 20, curiosity: double
child 21, desire: double
child 22, disappointment: double
child 23, disapproval: double
child 24, disgust: double
child 25, embarrassment: double
child 26, excitement: double
child 27, fear: double
child 28, gratitude: double
child 29, grief: double
child 30, joy: double
child 31, love: double
child 32, nervousness: double
child 33, optimism: double
child 34, pride: double
child 35, realization: double
child 36, relief: double
child 37, remorse: double
child 38, sadness: double
child 39, surprise: double
child 40, 5: double
child 41, Adjustments: double
child 42, Agreements: double
child 43, Amendments: double
child 44, Anti-Corruption Laws: double
child 45, Applicable Laws: double
child 46
...
, within_one: null
child 12, auroc: null
child 13, tvd_to_human: null
child 14, kl_human_model: null
child 15, mean_confidence: double
child 16, reliability: list<item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>>
child 0, item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>
child 0, lo: double
child 1, hi: double
child 2, count: int64
child 3, accuracy: double
child 4, confidence: double
yelp5: struct<n: int64, accuracy: double, nll: double, brier: double, ece: double, mce: double, selective_a (... 295 chars omitted)
child 0, n: int64
child 1, accuracy: double
child 2, nll: double
child 3, brier: double
child 4, ece: double
child 5, mce: double
child 6, selective_acc_at_90: double
child 7, selective_acc_at_50: double
child 8, aurc: double
child 9, rps: double
child 10, mae: double
child 11, within_one: double
child 12, auroc: null
child 13, tvd_to_human: null
child 14, kl_human_model: null
child 15, mean_confidence: double
child 16, reliability: list<item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>>
child 0, item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>
child 0, lo: double
child 1, hi: double
child 2, count: int64
child 3, accuracy: double
child 4, confidence: double
to
{'arc_challenge': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('null'), 'mae': Value('null'), 'within_one': Value('null'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'banking77': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('null'), 'mae': Value('null'), 'within_one': Value('null'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'boolq': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Valu
...
kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'stsb': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('float64'), 'mae': Value('float64'), 'within_one': Value('float64'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'yelp5': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('float64'), 'mae': Value('float64'), 'within_one': Value('float64'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': 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 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
id: string
primitive: string
p_yes: double
answer: string
model: string
probabilities: struct<A: double, B: double, C: double, D: double, 1: double, 2: double, 3: double, 4: double, 0: do (... 4139 chars omitted)
child 0, A: double
child 1, B: double
child 2, C: double
child 3, D: double
child 4, 1: double
child 5, 2: double
child 6, 3: double
child 7, 4: double
child 8, 0: double
child 9, entailment: double
child 10, neutral: double
child 11, contradiction: double
child 12, E: double
child 13, admiration: double
child 14, amusement: double
child 15, anger: double
child 16, annoyance: double
child 17, approval: double
child 18, caring: double
child 19, confusion: double
child 20, curiosity: double
child 21, desire: double
child 22, disappointment: double
child 23, disapproval: double
child 24, disgust: double
child 25, embarrassment: double
child 26, excitement: double
child 27, fear: double
child 28, gratitude: double
child 29, grief: double
child 30, joy: double
child 31, love: double
child 32, nervousness: double
child 33, optimism: double
child 34, pride: double
child 35, realization: double
child 36, relief: double
child 37, remorse: double
child 38, sadness: double
child 39, surprise: double
child 40, 5: double
child 41, Adjustments: double
child 42, Agreements: double
child 43, Amendments: double
child 44, Anti-Corruption Laws: double
child 45, Applicable Laws: double
child 46
...
, within_one: null
child 12, auroc: null
child 13, tvd_to_human: null
child 14, kl_human_model: null
child 15, mean_confidence: double
child 16, reliability: list<item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>>
child 0, item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>
child 0, lo: double
child 1, hi: double
child 2, count: int64
child 3, accuracy: double
child 4, confidence: double
yelp5: struct<n: int64, accuracy: double, nll: double, brier: double, ece: double, mce: double, selective_a (... 295 chars omitted)
child 0, n: int64
child 1, accuracy: double
child 2, nll: double
child 3, brier: double
child 4, ece: double
child 5, mce: double
child 6, selective_acc_at_90: double
child 7, selective_acc_at_50: double
child 8, aurc: double
child 9, rps: double
child 10, mae: double
child 11, within_one: double
child 12, auroc: null
child 13, tvd_to_human: null
child 14, kl_human_model: null
child 15, mean_confidence: double
child 16, reliability: list<item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>>
child 0, item: struct<lo: double, hi: double, count: int64, accuracy: double, confidence: double>
child 0, lo: double
child 1, hi: double
child 2, count: int64
child 3, accuracy: double
child 4, confidence: double
to
{'arc_challenge': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('null'), 'mae': Value('null'), 'within_one': Value('null'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'banking77': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('null'), 'mae': Value('null'), 'within_one': Value('null'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'boolq': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Valu
...
kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'stsb': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('float64'), 'mae': Value('float64'), 'within_one': Value('float64'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': Value('float64')})}, 'yelp5': {'n': Value('int64'), 'accuracy': Value('float64'), 'nll': Value('float64'), 'brier': Value('float64'), 'ece': Value('float64'), 'mce': Value('float64'), 'selective_acc_at_90': Value('float64'), 'selective_acc_at_50': Value('float64'), 'aurc': Value('float64'), 'rps': Value('float64'), 'mae': Value('float64'), 'within_one': Value('float64'), 'auroc': Value('null'), 'tvd_to_human': Value('null'), 'kl_human_model': Value('null'), 'mean_confidence': Value('float64'), 'reliability': List({'lo': Value('float64'), 'hi': Value('float64'), 'count': Value('int64'), 'accuracy': Value('float64'), 'confidence': 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.
Herbert results
runs/vision-herbert-v0.5/: Herbert v0.5 on the Decision Index 0.3
public vision suite with the kit at f5a6e4f: results.jsonl (12,210 rows, all ok), vision-scores.json
(public vision index 75.69, "complete": true), environment.json, status.json.
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