Dataset 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<additional_supports_per_transition: int64, dataset_version: string, depth: int64, edge_complexity: string, event_mechanisms: list<item: string>, graph_type: string, level_nodes: list<item: list<item: string>>, num_edges: int64, num_explicit_updates: int64, num_nodes: int64, num_query_events: int64, num_rule_retractions: int64, retraction_sites: list<item: struct<conclusion: string, level: string, outcome_mode: string, rule_id: string>>, revision_demands: list<item: int64>, revision_mechanism: string, seed: int64>
to
{'dataset_version': Value('string'), 'depth': Value('int64'), 'derived_target_count': Value('int64'), 'graph_type': Value('string'), 'level_nodes': List(List(Value('string'))), 'multi_support_clusters': List({'negative_support_rules': List(Value('null')), 'path_count': Value('int64'), 'positive_support_rules': List(Value('string')), 'target': Value('string')}), 'multi_support_ratio': Value('float64'), 'multi_support_slots': List(Value('int64')), 'multi_support_target_count': Value('int64'), 'num_edges': Value('int64'), 'num_explicit_updates': Value('int64'), 'num_nodes': Value('int64'), 'num_query_events': Value('int64'), 'path_edge_count': Value('int64'), 'revision_demands': List(Value('int64')), 'seed': Value('int64'), 'shared_support_rule_count': Value('int64'), 'support_level': Value('string')}
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 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<additional_supports_per_transition: int64, dataset_version: string, depth: int64, edge_complexity: string, event_mechanisms: list<item: string>, graph_type: string, level_nodes: list<item: list<item: string>>, num_edges: int64, num_explicit_updates: int64, num_nodes: int64, num_query_events: int64, num_rule_retractions: int64, retraction_sites: list<item: struct<conclusion: string, level: string, outcome_mode: string, rule_id: string>>, revision_demands: list<item: int64>, revision_mechanism: string, seed: int64>
to
{'dataset_version': Value('string'), 'depth': Value('int64'), 'derived_target_count': Value('int64'), 'graph_type': Value('string'), 'level_nodes': List(List(Value('string'))), 'multi_support_clusters': List({'negative_support_rules': List(Value('null')), 'path_count': Value('int64'), 'positive_support_rules': List(Value('string')), 'target': Value('string')}), 'multi_support_ratio': Value('float64'), 'multi_support_slots': List(Value('int64')), 'multi_support_target_count': Value('int64'), 'num_edges': Value('int64'), 'num_explicit_updates': Value('int64'), 'num_nodes': Value('int64'), 'num_query_events': Value('int64'), 'path_edge_count': Value('int64'), 'revision_demands': List(Value('int64')), 'seed': Value('int64'), 'shared_support_rule_count': Value('int64'), 'support_level': Value('string')}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.
NMR Dataset: Synthetic Retraction
This dataset contains 360 complete belief-revision episodes: 90 each for
monotonic, nmr_new_evidence, nmr_retraction, and nmr_mixed.
Each JSONL row is one episode, with a fixed dependency graph, an initial
belief base, three revisions, and complete gold T/F/U states at all four
checkpoints. It is intentionally not expanded into one row per proposition.
An evaluator can construct a query for every proposition at any checkpoint
from the episode history and score it against that checkpoint's gold_state.
For node retraction, a direct belief is replaced at its checkpoint. The old-literal route becomes inactive and the complementary rule for the new literal becomes active; rules themselves remain in the graph.
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