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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: CastError
Message: Couldn't cast
name: string
phase: string
step: int64
total: int64
pct: double
elapsed_s: double
eta_s: null
rate_per_s: double
last_msg: string
updated_at: double
done: bool
hr: double
result_sha256: string
claim_1: struct<desc: string, measured_HR_pct: double, paper_HR_pct: double, measured_ISR_pct: double, paper_ (... 191 chars omitted)
child 0, desc: string
child 1, measured_HR_pct: double
child 2, paper_HR_pct: double
child 3, measured_ISR_pct: double
child 4, paper_ISR_pct: double
child 5, ISR_of_hits_pct: double
child 6, isr_tested: int64
child 7, target_model: string
child 8, n: int64
child 9, hits: int64
child 10, injections: int64
child 11, label: string
child 12, downstream_rag_injection_pct: double
child 13, rag_n: int64
paper_ref: struct<c1: struct<HR: double, ISR: double>, c2: struct<HR: double, ISR: double>, c4: struct<diag_min (... 110 chars omitted)
child 0, c1: struct<HR: double, ISR: double>
child 0, HR: double
child 1, ISR: double
child 1, c2: struct<HR: double, ISR: double>
child 0, HR: double
child 1, ISR: double
child 2, c4: struct<diag_min: double, off_min: double, off_max: double, abs_max: double>
child 0, diag_min: double
child 1, off_min: double
child 2, off_max: double
child 3, abs_max: double
child 3, c5: struct<best_dHR: double, best_dISR: double>
child 0, best_dHR: double
child 1, best_dISR: double
claim_4: struct<matrix: struct<MiniLM: struct<MiniLM: double, MPNet:
...
ng
child 1, victim_namespace: string
child 2, shared_cache_attack_HR_pct: double
child 3, isolated_cache_cross_user_attack_HR_pct: double
child 4, cross_user_attack_eliminated: bool
child 5, eff_legit_cross_user_reuse_shared_pct: double
child 6, eff_legit_cross_user_reuse_isolated_pct: double
child 7, cache_efficiency_cost_pp: double
child 8, n_legit_pairs: int64
child 9, note: string
child 7, desc: string
child 8, paper_best_dHR_pp: double
child 9, label: string
environment: struct<python: string, platform: string, torch: string, cuda: bool, device: string, gpu: string, see (... 359 chars omitted)
child 0, python: string
child 1, platform: string
child 2, torch: string
child 3, cuda: bool
child 4, device: string
child 5, gpu: string
child 6, seed: int64
child 7, tau: double
child 8, smoke: bool
child 9, emb_models: struct<MiniLM: string, MPNet: string, BGE: string, E5: string, GTE: string>
child 0, MiniLM: string
child 1, MPNet: string
child 2, BGE: string
child 3, E5: string
child 4, GTE: string
child 10, llm_id: string
child 11, n_victims: int64
child 12, steps: int64
child 13, cands: int64
child 14, suffix_len: int64
child 15, c4_steps: int64
child 16, c4_cands: int64
child 17, n_c4_victims: int64
child 18, n_pairs_c3: int64
child 19, max_new_tokens: int64
child 20, wall_s: double
child 21, data_source: string
child 22, llm_loaded: string
to
{'name': Value('string'), 'partial': Value('bool'), 'elapsed_s': Value('float64'), 'paper_ref': {'c1': {'HR': Value('float64'), 'ISR': Value('float64')}, 'c2': {'HR': Value('float64'), 'ISR': Value('float64')}, 'c4': {'diag_min': Value('float64'), 'off_min': Value('float64'), 'off_max': Value('float64'), 'abs_max': Value('float64')}, 'c5': {'best_dHR': Value('float64'), 'best_dISR': Value('float64')}}, 'environment': {'python': Value('string'), 'platform': Value('string'), 'torch': Value('string'), 'cuda': Value('bool'), 'device': Value('string'), 'gpu': Value('string'), 'seed': Value('int64'), 'tau': Value('float64'), 'smoke': Value('bool'), 'emb_models': {'MiniLM': Value('string'), 'MPNet': Value('string'), 'BGE': Value('string'), 'E5': Value('string'), 'GTE': Value('string')}, 'llm_id': Value('string'), 'n_victims': Value('int64'), 'steps': Value('int64'), 'cands': Value('int64'), 'suffix_len': Value('int64'), 'c4_steps': Value('int64'), 'c4_cands': Value('int64'), 'n_c4_victims': Value('int64'), 'n_pairs_c3': Value('int64'), 'max_new_tokens': Value('int64'), 'wall_s': Value('float64'), 'data_source': Value('string'), 'llm_loaded': Value('string')}, 'claim_1': {'desc': Value('string'), 'measured_HR_pct': Value('float64'), 'paper_HR_pct': Value('float64'), 'measured_ISR_pct': Value('float64'), 'paper_ISR_pct': Value('float64'), 'ISR_of_hits_pct': Value('float64'), 'isr_tested': Value('int64'), 'target_model': Value('string'), 'n': Value('int64'), 'hits': Value('int64'), 'in
...
('float64'), 'BGE': Value('float64'), 'E5': Value('float64'), 'GTE': Value('float64')}}, 'models': {'MiniLM': Value('string'), 'MPNet': Value('string'), 'BGE': Value('string'), 'E5': Value('string'), 'GTE': Value('string')}, 'diag_min_pct': Value('float64'), 'diag_mean_pct': Value('float64'), 'off_min_pct': Value('float64'), 'off_max_pct': Value('float64'), 'range_pct': List(Value('float64')), 'desc': Value('string'), 'paper_range_pct': List(Value('float64')), 'label': Value('string')}, 'claim_5': {'baseline_HR_pct': Value('float64'), 'salt_token_count': Value('int64'), 'salting': {'prefix': {'HR_pct': Value('float64'), 'dHR_pp': Value('float64')}, 'suffix': {'HR_pct': Value('float64'), 'dHR_pp': Value('float64')}, 'template': {'HR_pct': Value('float64'), 'dHR_pp': Value('float64')}}, 'best_salt_mode': Value('string'), 'best_dHR_pp': Value('float64'), 'salting_disclosure': Value('string'), 'isolation': {'attacker_namespace': Value('string'), 'victim_namespace': Value('string'), 'shared_cache_attack_HR_pct': Value('float64'), 'isolated_cache_cross_user_attack_HR_pct': Value('float64'), 'cross_user_attack_eliminated': Value('bool'), 'eff_legit_cross_user_reuse_shared_pct': Value('float64'), 'eff_legit_cross_user_reuse_isolated_pct': Value('float64'), 'cache_efficiency_cost_pp': Value('float64'), 'n_legit_pairs': Value('int64'), 'note': Value('string')}, 'desc': Value('string'), 'paper_best_dHR_pp': Value('float64'), 'label': Value('string')}, 'injection_model': Value('string')}
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
name: string
phase: string
step: int64
total: int64
pct: double
elapsed_s: double
eta_s: null
rate_per_s: double
last_msg: string
updated_at: double
done: bool
hr: double
result_sha256: string
claim_1: struct<desc: string, measured_HR_pct: double, paper_HR_pct: double, measured_ISR_pct: double, paper_ (... 191 chars omitted)
child 0, desc: string
child 1, measured_HR_pct: double
child 2, paper_HR_pct: double
child 3, measured_ISR_pct: double
child 4, paper_ISR_pct: double
child 5, ISR_of_hits_pct: double
child 6, isr_tested: int64
child 7, target_model: string
child 8, n: int64
child 9, hits: int64
child 10, injections: int64
child 11, label: string
child 12, downstream_rag_injection_pct: double
child 13, rag_n: int64
paper_ref: struct<c1: struct<HR: double, ISR: double>, c2: struct<HR: double, ISR: double>, c4: struct<diag_min (... 110 chars omitted)
child 0, c1: struct<HR: double, ISR: double>
child 0, HR: double
child 1, ISR: double
child 1, c2: struct<HR: double, ISR: double>
child 0, HR: double
child 1, ISR: double
child 2, c4: struct<diag_min: double, off_min: double, off_max: double, abs_max: double>
child 0, diag_min: double
child 1, off_min: double
child 2, off_max: double
child 3, abs_max: double
child 3, c5: struct<best_dHR: double, best_dISR: double>
child 0, best_dHR: double
child 1, best_dISR: double
claim_4: struct<matrix: struct<MiniLM: struct<MiniLM: double, MPNet:
...
ng
child 1, victim_namespace: string
child 2, shared_cache_attack_HR_pct: double
child 3, isolated_cache_cross_user_attack_HR_pct: double
child 4, cross_user_attack_eliminated: bool
child 5, eff_legit_cross_user_reuse_shared_pct: double
child 6, eff_legit_cross_user_reuse_isolated_pct: double
child 7, cache_efficiency_cost_pp: double
child 8, n_legit_pairs: int64
child 9, note: string
child 7, desc: string
child 8, paper_best_dHR_pp: double
child 9, label: string
environment: struct<python: string, platform: string, torch: string, cuda: bool, device: string, gpu: string, see (... 359 chars omitted)
child 0, python: string
child 1, platform: string
child 2, torch: string
child 3, cuda: bool
child 4, device: string
child 5, gpu: string
child 6, seed: int64
child 7, tau: double
child 8, smoke: bool
child 9, emb_models: struct<MiniLM: string, MPNet: string, BGE: string, E5: string, GTE: string>
child 0, MiniLM: string
child 1, MPNet: string
child 2, BGE: string
child 3, E5: string
child 4, GTE: string
child 10, llm_id: string
child 11, n_victims: int64
child 12, steps: int64
child 13, cands: int64
child 14, suffix_len: int64
child 15, c4_steps: int64
child 16, c4_cands: int64
child 17, n_c4_victims: int64
child 18, n_pairs_c3: int64
child 19, max_new_tokens: int64
child 20, wall_s: double
child 21, data_source: string
child 22, llm_loaded: string
to
{'name': Value('string'), 'partial': Value('bool'), 'elapsed_s': Value('float64'), 'paper_ref': {'c1': {'HR': Value('float64'), 'ISR': Value('float64')}, 'c2': {'HR': Value('float64'), 'ISR': Value('float64')}, 'c4': {'diag_min': Value('float64'), 'off_min': Value('float64'), 'off_max': Value('float64'), 'abs_max': Value('float64')}, 'c5': {'best_dHR': Value('float64'), 'best_dISR': Value('float64')}}, 'environment': {'python': Value('string'), 'platform': Value('string'), 'torch': Value('string'), 'cuda': Value('bool'), 'device': Value('string'), 'gpu': Value('string'), 'seed': Value('int64'), 'tau': Value('float64'), 'smoke': Value('bool'), 'emb_models': {'MiniLM': Value('string'), 'MPNet': Value('string'), 'BGE': Value('string'), 'E5': Value('string'), 'GTE': Value('string')}, 'llm_id': Value('string'), 'n_victims': Value('int64'), 'steps': Value('int64'), 'cands': Value('int64'), 'suffix_len': Value('int64'), 'c4_steps': Value('int64'), 'c4_cands': Value('int64'), 'n_c4_victims': Value('int64'), 'n_pairs_c3': Value('int64'), 'max_new_tokens': Value('int64'), 'wall_s': Value('float64'), 'data_source': Value('string'), 'llm_loaded': Value('string')}, 'claim_1': {'desc': Value('string'), 'measured_HR_pct': Value('float64'), 'paper_HR_pct': Value('float64'), 'measured_ISR_pct': Value('float64'), 'paper_ISR_pct': Value('float64'), 'ISR_of_hits_pct': Value('float64'), 'isr_tested': Value('int64'), 'target_model': Value('string'), 'n': Value('int64'), 'hits': Value('int64'), 'in
...
('float64'), 'BGE': Value('float64'), 'E5': Value('float64'), 'GTE': Value('float64')}}, 'models': {'MiniLM': Value('string'), 'MPNet': Value('string'), 'BGE': Value('string'), 'E5': Value('string'), 'GTE': Value('string')}, 'diag_min_pct': Value('float64'), 'diag_mean_pct': Value('float64'), 'off_min_pct': Value('float64'), 'off_max_pct': Value('float64'), 'range_pct': List(Value('float64')), 'desc': Value('string'), 'paper_range_pct': List(Value('float64')), 'label': Value('string')}, 'claim_5': {'baseline_HR_pct': Value('float64'), 'salt_token_count': Value('int64'), 'salting': {'prefix': {'HR_pct': Value('float64'), 'dHR_pp': Value('float64')}, 'suffix': {'HR_pct': Value('float64'), 'dHR_pp': Value('float64')}, 'template': {'HR_pct': Value('float64'), 'dHR_pp': Value('float64')}}, 'best_salt_mode': Value('string'), 'best_dHR_pp': Value('float64'), 'salting_disclosure': Value('string'), 'isolation': {'attacker_namespace': Value('string'), 'victim_namespace': Value('string'), 'shared_cache_attack_HR_pct': Value('float64'), 'isolated_cache_cross_user_attack_HR_pct': Value('float64'), 'cross_user_attack_eliminated': Value('bool'), 'eff_legit_cross_user_reuse_shared_pct': Value('float64'), 'eff_legit_cross_user_reuse_isolated_pct': Value('float64'), 'cache_efficiency_cost_pp': Value('float64'), 'n_legit_pairs': Value('int64'), 'note': Value('string')}, 'desc': Value('string'), 'paper_best_dHR_pp': Value('float64'), 'label': Value('string')}, 'injection_model': Value('string')}
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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