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
backbone_model_id: string
comparison: struct<bf16_unequal_names: list<item: null>, fp32_unequal_count: int64, fp32_unequal_names_preview: (... 233 chars omitted)
child 0, bf16_unequal_names: list<item: null>
child 0, item: null
child 1, fp32_unequal_count: int64
child 2, fp32_unequal_names_preview: list<item: string>
child 0, item: string
child 3, max_bf16_abs_error: double
child 4, missing: list<item: null>
child 0, item: null
child 5, native_exact_bf16_roundtrip: bool
child 6, shape_mismatches: list<item: null>
child 0, item: null
child 7, standard_exact_bf16_roundtrip: bool
child 8, tensor_count: int64
child 9, unexpected: list<item: null>
child 0, item: null
model_id: string
native: struct<files: struct<config.json: string, model.safetensors: string>, repo_id: string, revision: str (... 4 chars omitted)
child 0, files: struct<config.json: string, model.safetensors: string>
child 0, config.json: string
child 1, model.safetensors: string
child 1, repo_id: string
child 2, revision: string
schema_version: int64
standard: struct<files: struct<config.json: string, model.safetensors: string>, repo_id: string, revision: str (... 4 chars omitted)
child 0, files: struct<config.json: string, model.safetensors: string>
child 0, config.json: string
child 1, model.safetensors: string
child 1, repo_id: string
child 2, revision: string
status: string
geometry: struct<ca_rmsd: double, lddt_ca: double>
...
z_mlp_input: struct<pooled_cosine_min: double, relative_l2: double, relative_q999: double, residue_cosine_p01: do (... 5 chars omitted)
child 0, pooled_cosine_min: double
child 1, relative_l2: double
child 2, relative_q999: double
child 3, residue_cosine_p01: double
failures: list<item: null>
child 0, item: null
exact_tensors: struct<feature__asym_id: string, feature__atom_attention_mask: string, feature__atom_to_token: strin (... 655 chars omitted)
child 0, feature__asym_id: string
child 1, feature__atom_attention_mask: string
child 2, feature__atom_to_token: string
child 3, feature__deletion_mean: string
child 4, feature__deletion_value: string
child 5, feature__distogram_atom_idx: string
child 6, feature__entity_id: string
child 7, feature__has_deletion: string
child 8, feature__input_ids: string
child 9, feature__mol_type: string
child 10, feature__msa: string
child 11, feature__msa_attention_mask: string
child 12, feature__ref_atom_name_chars: string
child 13, feature__ref_charge: string
child 14, feature__ref_element: string
child 15, feature__ref_pos: string
child 16, feature__ref_space_uid: string
child 17, feature__res_type: string
child 18, feature__residue_index: string
child 19, feature__sym_id: string
child 20, feature__token_attention_mask: string
child 21, feature__token_bonds: string
child 22, feature__token_index: string
child 23, noise__initial_standard_normal: string
reference: string
to
{'candidate': Value('string'), 'exact_tensors': {'feature__asym_id': Value('string'), 'feature__atom_attention_mask': Value('string'), 'feature__atom_to_token': Value('string'), 'feature__deletion_mean': Value('string'), 'feature__deletion_value': Value('string'), 'feature__distogram_atom_idx': Value('string'), 'feature__entity_id': Value('string'), 'feature__has_deletion': Value('string'), 'feature__input_ids': Value('string'), 'feature__mol_type': Value('string'), 'feature__msa': Value('string'), 'feature__msa_attention_mask': Value('string'), 'feature__ref_atom_name_chars': Value('string'), 'feature__ref_charge': Value('string'), 'feature__ref_element': Value('string'), 'feature__ref_pos': Value('string'), 'feature__ref_space_uid': Value('string'), 'feature__res_type': Value('string'), 'feature__residue_index': Value('string'), 'feature__sym_id': Value('string'), 'feature__token_attention_mask': Value('string'), 'feature__token_bonds': Value('string'), 'feature__token_index': Value('string'), 'noise__initial_standard_normal': Value('string')}, 'failures': List(Value('null')), 'geometry': {'ca_rmsd': Value('float64'), 'lddt_ca': Value('float64')}, 'numeric': {'hidden__lm': {'pooled_cosine_min': Value('float64'), 'relative_l2': Value('float64'), 'relative_q999': Value('float64'), 'residue_cosine_p01': Value('float64')}, 'output__atom_pad_mask': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__distogram_logits': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__entity_id': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__residue_index': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__sample_atom_coords': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'projection__base_z_mlp_input': {'pooled_cosine_min': Value('float64'), 'relative_l2': Value('float64'), 'relative_q999': Value('float64'), 'residue_cosine_p01': Value('float64')}}, 'reference': Value('string'), 'schema_version': Value('int64'), 'status': 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
backbone_model_id: string
comparison: struct<bf16_unequal_names: list<item: null>, fp32_unequal_count: int64, fp32_unequal_names_preview: (... 233 chars omitted)
child 0, bf16_unequal_names: list<item: null>
child 0, item: null
child 1, fp32_unequal_count: int64
child 2, fp32_unequal_names_preview: list<item: string>
child 0, item: string
child 3, max_bf16_abs_error: double
child 4, missing: list<item: null>
child 0, item: null
child 5, native_exact_bf16_roundtrip: bool
child 6, shape_mismatches: list<item: null>
child 0, item: null
child 7, standard_exact_bf16_roundtrip: bool
child 8, tensor_count: int64
child 9, unexpected: list<item: null>
child 0, item: null
model_id: string
native: struct<files: struct<config.json: string, model.safetensors: string>, repo_id: string, revision: str (... 4 chars omitted)
child 0, files: struct<config.json: string, model.safetensors: string>
child 0, config.json: string
child 1, model.safetensors: string
child 1, repo_id: string
child 2, revision: string
schema_version: int64
standard: struct<files: struct<config.json: string, model.safetensors: string>, repo_id: string, revision: str (... 4 chars omitted)
child 0, files: struct<config.json: string, model.safetensors: string>
child 0, config.json: string
child 1, model.safetensors: string
child 1, repo_id: string
child 2, revision: string
status: string
geometry: struct<ca_rmsd: double, lddt_ca: double>
...
z_mlp_input: struct<pooled_cosine_min: double, relative_l2: double, relative_q999: double, residue_cosine_p01: do (... 5 chars omitted)
child 0, pooled_cosine_min: double
child 1, relative_l2: double
child 2, relative_q999: double
child 3, residue_cosine_p01: double
failures: list<item: null>
child 0, item: null
exact_tensors: struct<feature__asym_id: string, feature__atom_attention_mask: string, feature__atom_to_token: strin (... 655 chars omitted)
child 0, feature__asym_id: string
child 1, feature__atom_attention_mask: string
child 2, feature__atom_to_token: string
child 3, feature__deletion_mean: string
child 4, feature__deletion_value: string
child 5, feature__distogram_atom_idx: string
child 6, feature__entity_id: string
child 7, feature__has_deletion: string
child 8, feature__input_ids: string
child 9, feature__mol_type: string
child 10, feature__msa: string
child 11, feature__msa_attention_mask: string
child 12, feature__ref_atom_name_chars: string
child 13, feature__ref_charge: string
child 14, feature__ref_element: string
child 15, feature__ref_pos: string
child 16, feature__ref_space_uid: string
child 17, feature__res_type: string
child 18, feature__residue_index: string
child 19, feature__sym_id: string
child 20, feature__token_attention_mask: string
child 21, feature__token_bonds: string
child 22, feature__token_index: string
child 23, noise__initial_standard_normal: string
reference: string
to
{'candidate': Value('string'), 'exact_tensors': {'feature__asym_id': Value('string'), 'feature__atom_attention_mask': Value('string'), 'feature__atom_to_token': Value('string'), 'feature__deletion_mean': Value('string'), 'feature__deletion_value': Value('string'), 'feature__distogram_atom_idx': Value('string'), 'feature__entity_id': Value('string'), 'feature__has_deletion': Value('string'), 'feature__input_ids': Value('string'), 'feature__mol_type': Value('string'), 'feature__msa': Value('string'), 'feature__msa_attention_mask': Value('string'), 'feature__ref_atom_name_chars': Value('string'), 'feature__ref_charge': Value('string'), 'feature__ref_element': Value('string'), 'feature__ref_pos': Value('string'), 'feature__ref_space_uid': Value('string'), 'feature__res_type': Value('string'), 'feature__residue_index': Value('string'), 'feature__sym_id': Value('string'), 'feature__token_attention_mask': Value('string'), 'feature__token_bonds': Value('string'), 'feature__token_index': Value('string'), 'noise__initial_standard_normal': Value('string')}, 'failures': List(Value('null')), 'geometry': {'ca_rmsd': Value('float64'), 'lddt_ca': Value('float64')}, 'numeric': {'hidden__lm': {'pooled_cosine_min': Value('float64'), 'relative_l2': Value('float64'), 'relative_q999': Value('float64'), 'residue_cosine_p01': Value('float64')}, 'output__atom_pad_mask': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__distogram_logits': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__entity_id': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__residue_index': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'output__sample_atom_coords': {'max_abs': Value('float64'), 'mean_abs': Value('float64'), 'relative_l2': Value('float64')}, 'projection__base_z_mlp_input': {'pooled_cosine_min': Value('float64'), 'relative_l2': Value('float64'), 'relative_q999': Value('float64'), 'residue_cosine_p01': Value('float64')}}, 'reference': Value('string'), 'schema_version': Value('int64'), 'status': 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.
FastPLMs artifacts
This dataset holds the measured reports and golden regression tensors maintained by FastPLMs. It is not a training dataset or a set of model checkpoints.
The source repository keeps evidence.toml, which pins an immutable dataset revision and each payload's SHA-256 digest and size. Fetch explicitly with python -m tools.artifacts.evidence_store fetch before offline documentation, release, or parity checks.
Paths preserve the source workspace layout: docs/evidence, docs/validation, and tests/goldens. Runtime configuration, model manifests, dependency locks, and small synthetic fixtures remain in Git.
Evidence status
The v2 confidence reports preserve historical numeric results. Their metrics_review fields mark correlations, bootstrap intervals, and acceptance gates as requiring recomputation after the tied-rank correction. Raw per-target predictions were not recovered from the closed training workstation or W&B. These archived values must not be presented as corrected evaluation results. No v2 confidence weights are included.
Other artifacts retain their recorded source revisions, environments, and evidence boundaries. Presence in this dataset is not an assertion that all release gates pass.
Terms and provenance
Payloads originated in the public FastPLMs repository at PR #49 (1ae17f2b3955db28fe9c076b5c7e2394c6fc83ac), with review-status annotations added to the v2 confidence reports. Golden metadata identifies originating checkpoints and source revisions. Applicable model and source terms remain in effect; migration does not relicense them. Consult the source repository's LICENSES, model manifest, and licensing documentation. No blanket permissive license is asserted for the combined collection.
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