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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
func_id: string
func_seq: int64
window_idx: int64
n_windows: int64
code: string
ast_header: string
model_input: string
label: int64
cwe: string
language: string
source: string
func_n_tokens: int64
window_n_tokens: int64
ast: struct<buffers: list<item: string>, sinks: list<item: string>, tainted_args: list<item: string>>
child 0, buffers: list<item: string>
child 0, item: string
child 1, sinks: list<item: string>
child 0, item: string
child 2, tainted_args: list<item: string>
child 0, item: string
contains_sink: bool
train_data: string
eval_n: int64
metrics: struct<primevul: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double (... 470 chars omitted)
child 0, primevul: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double, flag_rate: doub (... 76 chars omitted)
child 0, precision: double
child 1, recall: double
child 2, f1: double
child 3, accuracy: double
child 4, fpr: double
child 5, flag_rate: double
child 6, n: int64
child 7, tp: int64
child 8, fp: int64
child 9, tn: int64
child 10, fn: int64
child 11, vuln_index: int64
child 1, diversevul: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double, flag_rate: doub (... 76 chars omitted)
child 0, precision: double
child 1, recall: double
child 2, f1: double
child 3, accuracy: double
child 4, fpr: double
child 5, flag_rate: double
child 6, n: int64
child 7, tp: int64
child 8, fp: int64
child 9, tn: int64
child 10, fn: int64
child 11, vuln_index: int64
child 2, juliet: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double, flag_rate: doub (... 76 chars omitted)
child 0, precision: double
child 1, recall: double
child 2, f1: double
child 3, accuracy: double
child 4, fpr: double
child 5, flag_rate: double
child 6, n: int64
child 7, tp: int64
child 8, fp: int64
child 9, tn: int64
child 10, fn: int64
child 11, vuln_index: int64
hyperparams: struct<r: int64, alpha: int64, dropout: double, epochs: int64, lr: double, batch: int64, max_len: in (... 4 chars omitted)
child 0, r: int64
child 1, alpha: int64
child 2, dropout: double
child 3, epochs: int64
child 4, lr: double
child 5, batch: int64
child 6, max_len: int64
base: string
adapter: string
run: string
to
{'run': Value('string'), 'base': Value('string'), 'train_data': Value('string'), 'adapter': Value('string'), 'hyperparams': {'r': Value('int64'), 'alpha': Value('int64'), 'dropout': Value('float64'), 'epochs': Value('int64'), 'lr': Value('float64'), 'batch': Value('int64'), 'max_len': Value('int64')}, 'eval_n': Value('int64'), 'metrics': {'primevul': {'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'accuracy': Value('float64'), 'fpr': Value('float64'), 'flag_rate': Value('float64'), 'n': Value('int64'), 'tp': Value('int64'), 'fp': Value('int64'), 'tn': Value('int64'), 'fn': Value('int64'), 'vuln_index': Value('int64')}, 'diversevul': {'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'accuracy': Value('float64'), 'fpr': Value('float64'), 'flag_rate': Value('float64'), 'n': Value('int64'), 'tp': Value('int64'), 'fp': Value('int64'), 'tn': Value('int64'), 'fn': Value('int64'), 'vuln_index': Value('int64')}, 'juliet': {'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'accuracy': Value('float64'), 'fpr': Value('float64'), 'flag_rate': Value('float64'), 'n': Value('int64'), 'tp': Value('int64'), 'fp': Value('int64'), 'tn': Value('int64'), 'fn': Value('int64'), 'vuln_index': Value('int64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
id: string
func_id: string
func_seq: int64
window_idx: int64
n_windows: int64
code: string
ast_header: string
model_input: string
label: int64
cwe: string
language: string
source: string
func_n_tokens: int64
window_n_tokens: int64
ast: struct<buffers: list<item: string>, sinks: list<item: string>, tainted_args: list<item: string>>
child 0, buffers: list<item: string>
child 0, item: string
child 1, sinks: list<item: string>
child 0, item: string
child 2, tainted_args: list<item: string>
child 0, item: string
contains_sink: bool
train_data: string
eval_n: int64
metrics: struct<primevul: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double (... 470 chars omitted)
child 0, primevul: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double, flag_rate: doub (... 76 chars omitted)
child 0, precision: double
child 1, recall: double
child 2, f1: double
child 3, accuracy: double
child 4, fpr: double
child 5, flag_rate: double
child 6, n: int64
child 7, tp: int64
child 8, fp: int64
child 9, tn: int64
child 10, fn: int64
child 11, vuln_index: int64
child 1, diversevul: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double, flag_rate: doub (... 76 chars omitted)
child 0, precision: double
child 1, recall: double
child 2, f1: double
child 3, accuracy: double
child 4, fpr: double
child 5, flag_rate: double
child 6, n: int64
child 7, tp: int64
child 8, fp: int64
child 9, tn: int64
child 10, fn: int64
child 11, vuln_index: int64
child 2, juliet: struct<precision: double, recall: double, f1: double, accuracy: double, fpr: double, flag_rate: doub (... 76 chars omitted)
child 0, precision: double
child 1, recall: double
child 2, f1: double
child 3, accuracy: double
child 4, fpr: double
child 5, flag_rate: double
child 6, n: int64
child 7, tp: int64
child 8, fp: int64
child 9, tn: int64
child 10, fn: int64
child 11, vuln_index: int64
hyperparams: struct<r: int64, alpha: int64, dropout: double, epochs: int64, lr: double, batch: int64, max_len: in (... 4 chars omitted)
child 0, r: int64
child 1, alpha: int64
child 2, dropout: double
child 3, epochs: int64
child 4, lr: double
child 5, batch: int64
child 6, max_len: int64
base: string
adapter: string
run: string
to
{'run': Value('string'), 'base': Value('string'), 'train_data': Value('string'), 'adapter': Value('string'), 'hyperparams': {'r': Value('int64'), 'alpha': Value('int64'), 'dropout': Value('float64'), 'epochs': Value('int64'), 'lr': Value('float64'), 'batch': Value('int64'), 'max_len': Value('int64')}, 'eval_n': Value('int64'), 'metrics': {'primevul': {'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'accuracy': Value('float64'), 'fpr': Value('float64'), 'flag_rate': Value('float64'), 'n': Value('int64'), 'tp': Value('int64'), 'fp': Value('int64'), 'tn': Value('int64'), 'fn': Value('int64'), 'vuln_index': Value('int64')}, 'diversevul': {'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'accuracy': Value('float64'), 'fpr': Value('float64'), 'flag_rate': Value('float64'), 'n': Value('int64'), 'tp': Value('int64'), 'fp': Value('int64'), 'tn': Value('int64'), 'fn': Value('int64'), 'vuln_index': Value('int64')}, 'juliet': {'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'accuracy': Value('float64'), 'fpr': Value('float64'), 'flag_rate': Value('float64'), 'n': Value('int64'), 'tp': Value('int64'), 'fp': Value('int64'), 'tn': Value('int64'), 'fn': Value('int64'), 'vuln_index': Value('int64')}}}
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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