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: CastError
Message: Couldn't cast
level: string
leads: int64
frozen: double
blanket: double
guard: double
apply: double
harm: double
bharm: double
alpha: double
cpu_generated: bool
delta: double
fractions_percent: list<item: int64>
child 0, item: int64
per_fraction: struct<100: struct<alpha: double, delta: double, seeds: list<item: struct<seed: int64, frozen_metric (... 7728 chars omitted)
child 0, 100: struct<alpha: double, delta: double, seeds: list<item: struct<seed: int64, frozen_metric: double, gu (... 605 chars omitted)
child 0, alpha: double
child 1, delta: double
child 2, seeds: list<item: struct<seed: int64, frozen_metric: double, guard_without_certify_metric: double, guard_wi (... 256 chars omitted)
child 0, item: struct<seed: int64, frozen_metric: double, guard_without_certify_metric: double, guard_without_certi (... 244 chars omitted)
child 0, seed: int64
child 1, frozen_metric: double
child 2, guard_without_certify_metric: double
child 3, guard_without_certify_gain: double
child 4, guard_without_certify_joint_harm: double
child 5, guard_metric: double
child 6, guard_gain: double
child 7, joint_harm: double
child 8, apply_rate: double
child 9, selected_target: string
child 10, k: int64
child 11, space: string
child 12, weighting: string
child 13, temperature: double
...
d_wi (... 256 chars omitted)
child 0, item: struct<seed: int64, frozen_metric: double, guard_without_certify_metric: double, guard_without_certi (... 244 chars omitted)
child 0, seed: int64
child 1, frozen_metric: double
child 2, guard_without_certify_metric: double
child 3, guard_without_certify_gain: double
child 4, guard_without_certify_joint_harm: double
child 5, guard_metric: double
child 6, guard_gain: double
child 7, joint_harm: double
child 8, apply_rate: double
child 9, selected_target: string
child 10, k: int64
child 11, space: string
child 12, weighting: string
child 13, temperature: double
child 14, beta: double
child 3, mean: struct<frozen_metric: double, guard_without_certify_metric: double, guard_without_certify_gain: doub (... 173 chars omitted)
child 0, frozen_metric: double
child 1, guard_without_certify_metric: double
child 2, guard_without_certify_gain: double
child 3, guard_without_certify_joint_harm: double
child 4, guard_metric: double
child 5, guard_gain: double
child 6, joint_harm: double
child 7, apply_rate: double
child 8, k: double
child 9, temperature: double
child 10, beta: double
child 4, configuration: string
to
{'cpu_generated': Value('bool'), 'alpha': Value('float64'), 'delta': Value('float64'), 'fractions_percent': List(Value('int64')), 'per_fraction': {'100': {'alpha': Value('float64'), 'delta': Value('float64'), 'seeds': List({'seed': Value('int64'), 'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'selected_target': Value('string'), 'k': Value('int64'), 'space': Value('string'), 'weighting': Value('string'), 'temperature': Value('float64'), 'beta': Value('float64')}), 'mean': {'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'k': Value('float64'), 'temperature': Value('float64'), 'beta': Value('float64')}, 'configuration': Value('string')}, '80': {'alpha': Value('float64'), 'delta': Value('float64'), 'seeds': List({'seed': Value('int64'), 'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64
...
Value('float64')}), 'mean': {'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'k': Value('float64'), 'temperature': Value('float64'), 'beta': Value('float64')}, 'configuration': Value('string')}, '10': {'alpha': Value('float64'), 'delta': Value('float64'), 'seeds': List({'seed': Value('int64'), 'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'selected_target': Value('string'), 'k': Value('int64'), 'space': Value('string'), 'weighting': Value('string'), 'temperature': Value('float64'), 'beta': Value('float64')}), 'mean': {'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'k': Value('float64'), 'temperature': Value('float64'), 'beta': Value('float64')}, 'configuration': Value('string')}}}
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
level: string
leads: int64
frozen: double
blanket: double
guard: double
apply: double
harm: double
bharm: double
alpha: double
cpu_generated: bool
delta: double
fractions_percent: list<item: int64>
child 0, item: int64
per_fraction: struct<100: struct<alpha: double, delta: double, seeds: list<item: struct<seed: int64, frozen_metric (... 7728 chars omitted)
child 0, 100: struct<alpha: double, delta: double, seeds: list<item: struct<seed: int64, frozen_metric: double, gu (... 605 chars omitted)
child 0, alpha: double
child 1, delta: double
child 2, seeds: list<item: struct<seed: int64, frozen_metric: double, guard_without_certify_metric: double, guard_wi (... 256 chars omitted)
child 0, item: struct<seed: int64, frozen_metric: double, guard_without_certify_metric: double, guard_without_certi (... 244 chars omitted)
child 0, seed: int64
child 1, frozen_metric: double
child 2, guard_without_certify_metric: double
child 3, guard_without_certify_gain: double
child 4, guard_without_certify_joint_harm: double
child 5, guard_metric: double
child 6, guard_gain: double
child 7, joint_harm: double
child 8, apply_rate: double
child 9, selected_target: string
child 10, k: int64
child 11, space: string
child 12, weighting: string
child 13, temperature: double
...
d_wi (... 256 chars omitted)
child 0, item: struct<seed: int64, frozen_metric: double, guard_without_certify_metric: double, guard_without_certi (... 244 chars omitted)
child 0, seed: int64
child 1, frozen_metric: double
child 2, guard_without_certify_metric: double
child 3, guard_without_certify_gain: double
child 4, guard_without_certify_joint_harm: double
child 5, guard_metric: double
child 6, guard_gain: double
child 7, joint_harm: double
child 8, apply_rate: double
child 9, selected_target: string
child 10, k: int64
child 11, space: string
child 12, weighting: string
child 13, temperature: double
child 14, beta: double
child 3, mean: struct<frozen_metric: double, guard_without_certify_metric: double, guard_without_certify_gain: doub (... 173 chars omitted)
child 0, frozen_metric: double
child 1, guard_without_certify_metric: double
child 2, guard_without_certify_gain: double
child 3, guard_without_certify_joint_harm: double
child 4, guard_metric: double
child 5, guard_gain: double
child 6, joint_harm: double
child 7, apply_rate: double
child 8, k: double
child 9, temperature: double
child 10, beta: double
child 4, configuration: string
to
{'cpu_generated': Value('bool'), 'alpha': Value('float64'), 'delta': Value('float64'), 'fractions_percent': List(Value('int64')), 'per_fraction': {'100': {'alpha': Value('float64'), 'delta': Value('float64'), 'seeds': List({'seed': Value('int64'), 'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'selected_target': Value('string'), 'k': Value('int64'), 'space': Value('string'), 'weighting': Value('string'), 'temperature': Value('float64'), 'beta': Value('float64')}), 'mean': {'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'k': Value('float64'), 'temperature': Value('float64'), 'beta': Value('float64')}, 'configuration': Value('string')}, '80': {'alpha': Value('float64'), 'delta': Value('float64'), 'seeds': List({'seed': Value('int64'), 'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64
...
Value('float64')}), 'mean': {'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'k': Value('float64'), 'temperature': Value('float64'), 'beta': Value('float64')}, 'configuration': Value('string')}, '10': {'alpha': Value('float64'), 'delta': Value('float64'), 'seeds': List({'seed': Value('int64'), 'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'selected_target': Value('string'), 'k': Value('int64'), 'space': Value('string'), 'weighting': Value('string'), 'temperature': Value('float64'), 'beta': Value('float64')}), 'mean': {'frozen_metric': Value('float64'), 'guard_without_certify_metric': Value('float64'), 'guard_without_certify_gain': Value('float64'), 'guard_without_certify_joint_harm': Value('float64'), 'guard_metric': Value('float64'), 'guard_gain': Value('float64'), 'joint_harm': Value('float64'), 'apply_rate': Value('float64'), 'k': Value('float64'), 'temperature': Value('float64'), 'beta': Value('float64')}, 'configuration': 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.
GUARD: checkpoints and dumps for review
Companion artifact for the GUARD submission. The code lives at
github.com/anonymous221206/GUARD; this repository carries what is too large for git.
Checkpoints we trained
| Path | Contents |
|---|---|
| ninapro_cnn/ | 100 sEMG CNN checkpoints, 2 seeds x 10 subjects x 5 electrode counts |
| ninapro_specialist/ | 60 condition-specialist checkpoints, trained on masked input |
| opportunity_dcl_v2/ | 15 DeepConvLSTM checkpoints, full hosts and condition specialists |
| ptbxl_resnet1d_wang.pt | the frozen PTB-XL predictor |
Retraining the OPPORTUNITY hosts reproduced the stored outputs bitwise, so the code repository ships a training path rather than dumps alone.
Dumps for the no-dataset reproduction check
mosei_cmad/ holds the frozen CMAD outputs and retrieval features that
experiments/repro_check.py reads. With these two files and nothing else, that script
reproduces the CMU-MOSEI row of Table 1: 63.1 -> 68.6, 63.7 -> 68.3, 64.5 -> 70.3.
raw_features.npz is derived from CMU-MOSEI, distributed through CMU-MultimodalSDK;
student_preds.npz is our own frozen run of a published CMAD checkpoint.
Not mirrored here
Third-party model checkpoints (DrugBAN, CMAD, TMDC, MoMKE, GCNet, AV-att) stay with their authors, who release them themselves. The datasets these models were trained on are not redistributable: IEMOCAP in particular requires a signed agreement with USC. The code repository carries download scripts that fetch each dataset from its origin and, once the data is present, run the experiment end to end.
- Downloads last month
- 278