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
first_sample_id: string
images: int64
job: string
last_sample_id: string
origin: string
phys4_recomputed: bool
precision: string
sha256: string
shard: string
source_path: string
tiles: int64
certified_at: string
kind: string
jobs: int64
unique_sample_ids: int64
shards: int64
status: string
sources: struct<colab20: struct<images: int64, tiles: int64, jobs: int64>, colab14: struct<images: int64, til (... 143 chars omitted)
child 0, colab20: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
child 1, colab14: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
child 2, screens: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
child 3, residual: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
manifest_sha256: string
gates: list<item: string>
child 0, item: string
ledger_sha256: string
to
{'status': Value('string'), 'kind': Value('string'), 'images': Value('int64'), 'unique_sample_ids': Value('int64'), 'shards': Value('int64'), 'jobs': Value('int64'), 'tiles': Value('int64'), 'sources': {'colab20': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}, 'colab14': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}, 'screens': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}, 'residual': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}}, 'manifest_sha256': Value('string'), 'ledger_sha256': Value('string'), 'certified_at': Value('string'), 'gates': List(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 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
first_sample_id: string
images: int64
job: string
last_sample_id: string
origin: string
phys4_recomputed: bool
precision: string
sha256: string
shard: string
source_path: string
tiles: int64
certified_at: string
kind: string
jobs: int64
unique_sample_ids: int64
shards: int64
status: string
sources: struct<colab20: struct<images: int64, tiles: int64, jobs: int64>, colab14: struct<images: int64, til (... 143 chars omitted)
child 0, colab20: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
child 1, colab14: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
child 2, screens: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
child 3, residual: struct<images: int64, tiles: int64, jobs: int64>
child 0, images: int64
child 1, tiles: int64
child 2, jobs: int64
manifest_sha256: string
gates: list<item: string>
child 0, item: string
ledger_sha256: string
to
{'status': Value('string'), 'kind': Value('string'), 'images': Value('int64'), 'unique_sample_ids': Value('int64'), 'shards': Value('int64'), 'jobs': Value('int64'), 'tiles': Value('int64'), 'sources': {'colab20': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}, 'colab14': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}, 'screens': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}, 'residual': {'images': Value('int64'), 'tiles': Value('int64'), 'jobs': Value('int64')}}, 'manifest_sha256': Value('string'), 'ledger_sha256': Value('string'), 'certified_at': Value('string'), 'gates': List(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.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
forensic86k_final
Certified learned-encoder feature cache for the strict 86,000-image ledger: 86000 images, 886 shards, 4296989 tiles (1,048-d tile features, 11,528-d image summaries, 18 label columns).
forensic86k-<job>-<NNNNN>.npz are hard links to the shards in the four source folders (no extra disk); ledger.jsonl lists origin, precision and phys4_recomputed for every shard. Point a trainer at this folder (it needs complete.json and globs *.npz).
- Downloads last month
- 37