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
run: string
group: string
functions: int64
docs: int64
seed: int64
n_train: int64
damping: double
mask_padding: string
seconds: int64
methods: struct<logra: struct<auc: double, recall_at_r: double, functions: int64, queries: int64, per_functio (... 4912 chars omitted)
child 0, logra: struct<auc: double, recall_at_r: double, functions: int64, queries: int64, per_function: struct<<B01 (... 4897 chars omitted)
child 0, auc: double
child 1, recall_at_r: double
child 2, functions: int64
child 3, queries: int64
child 4, per_function: struct<<B01>: struct<auc: double, recall_at_r: double>, <B02>: struct<auc: double, recall_at_r: doub (... 4807 chars omitted)
child 0, <B01>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 1, <B02>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 2, <B03>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 3, <B04>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 4, <B05>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 5, <B06>: struct<auc: double, recall_at_r: double>
ch
...
, <B90>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 91, <B91>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 92, <B92>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 93, <B93>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 94, <B94>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 95, <B95>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 96, <B96>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 97, <B97>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 98, <B98>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 99, <B99>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
worker: string
host: string
claimed_at: string
job: string
to
{'job': Value('string'), 'worker': Value('string'), 'host': Value('string'), 'claimed_at': 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
run: string
group: string
functions: int64
docs: int64
seed: int64
n_train: int64
damping: double
mask_padding: string
seconds: int64
methods: struct<logra: struct<auc: double, recall_at_r: double, functions: int64, queries: int64, per_functio (... 4912 chars omitted)
child 0, logra: struct<auc: double, recall_at_r: double, functions: int64, queries: int64, per_function: struct<<B01 (... 4897 chars omitted)
child 0, auc: double
child 1, recall_at_r: double
child 2, functions: int64
child 3, queries: int64
child 4, per_function: struct<<B01>: struct<auc: double, recall_at_r: double>, <B02>: struct<auc: double, recall_at_r: doub (... 4807 chars omitted)
child 0, <B01>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 1, <B02>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 2, <B03>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 3, <B04>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 4, <B05>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 5, <B06>: struct<auc: double, recall_at_r: double>
ch
...
, <B90>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 91, <B91>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 92, <B92>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 93, <B93>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 94, <B94>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 95, <B95>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 96, <B96>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 97, <B97>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 98, <B98>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
child 99, <B99>: struct<auc: double, recall_at_r: double>
child 0, auc: double
child 1, recall_at_r: double
worker: string
host: string
claimed_at: string
job: string
to
{'job': Value('string'), 'worker': Value('string'), 'host': Value('string'), 'claimed_at': 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.
vtok101 attribution baselines
Data-attribution scores over lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-lora-seeds: 3 function counts x 7 document counts x 4 seeds, scored by 5 methods.
Each training document defines one synthetic constant function, and each query asks for one function's value. The ground truth for a query is the set of documents describing its function, so a method is measured by how far up its ranking those documents come.
Layout
{run}/{group}/scores.npz the primary method's [query, train] matrix
{run}/{group}/scores.{method}.npz the group's other methods
{run}/{group}/metrics.json AUC and Recall@R, overall and per function
{run}/{group}/config.json every argument the ranker was given
run is f{functions}_{docs}d_sd{seed}, matching the adapter repo's
subfolders. group is one of ekfac, trak, logra.
What is in a scores.npz
scores float16 [n_query, n_train]
train_uids corpus uid of each column
train_func the function each training document describes
query_uids query uid of each row
query_func the function each query asks about
query_correct whether the model answers that query correctly
float16 because these are read to be ranked, and the gap between adjacent scores is far wider than the precision.
Methods
ekfacgroup: if-ekfac (EK-FAC influence functions), grad-dot (no curvature) and grad-sim (the cosine). One pass produces all three, since they differ only in what happens to the representation after it is computed.trakgroup: trak, in the dual form over factorised random projections.logragroup: logra, projected gradients with a block-diagonal Fisher.
Padding is excluded from the Fisher, and attention runs under sdpa. Both
choices, and why they matter more than they look, are in filter/DATTRI_PARITY.md
of the source repository.
Contributing
Anyone in the lamsheeper-data-attribution organization can fill in gaps:
git clone <repo> && cd influence-benchmarking-hops && uv sync
hf auth login
.venv/bin/python filter/baselines/selftest.py
filter/baselines/launch.sh 0 1
Workers coordinate through this repository's file listing, so they do not need to know about each other.
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