The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
MZC-Corpus — Model Zoo Cartography trained-MLP corpus
Public as of 2026-08-18. Code, instruments, tests, and the companion paper draft: Model_Zoo_Cartography.
A population of 1,569 trained MLPs across 69 families at the ARC
White-Box Estimation Challenge Phase-1 architecture (reference spec
depth-32/width-256; the corpus spans widths 64–512 and depths 8–64),
He-Gaussian N(0, 2/fan_in) init, bias-free, ReLU after every layer,
weights stored (in, out), forward x @ W — every net with full training
provenance. This is the "population of trained networks with shared
statistics" the challenge write-up's §8 names as the missing piece for
extending random-weight moment propagation to trained networks.
Layout
corpus/<run_id>/net_<seed>.npz init_w0..d (init weights), w0..d (trained),
head_w (readout head, when present)
corpus/<run_id>/net_<seed>.json provenance: architecture, task (family, C,
separation/projection seed, exact Bayes acc),
training (optimizer, lr, wd, steps, seeds),
outcome, val-accuracy trajectory, git commit
analysis/census/ per-net weight + activation censuses
(analytic & robust MP floors, anchored counts)
analysis/null_baseline/ analytic state trajectories (q-clock) per net
Task families: Gaussian mixtures analytically whitened so the aggregate input matches the null's mean and covariance exactly (as a mixture, its higher moments remain non-Gaussian — the residual is recorded per net); C ∈ {2..72} with separation and weight-decay sweeps, budgets 20k–200k steps, learning-rate arms, two readout modes, 16–32 seeds per configuration; plus whitened MNIST and Fashion-MNIST (784→d seeded Gaussian projection + ZCA).
Citation
Code + paper archive DOI: 10.5281/zenodo.22017454 (concept DOI — always resolves to the latest archived version).
Companion paper
Task rank is imprinted in the input layer — draft at paper/DRAFT.md in
the code repo; findings digest with instruments and data pointers in
FINDINGS.md there. The analysis/ JSONs here are the per-net data behind
the paper's committed summaries; train/corpus_io.py in the code repo
re-downloads pruned nets on demand.
- Downloads last month
- 143