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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
pair_id: string
pair_type: string
margin: double
margin_cat: string
winner: struct<uuid: string, category: string, tier: string, population: string, h0: double, h0_norm: double (... 54 chars omitted)
child 0, uuid: string
child 1, category: string
child 2, tier: string
child 3, population: string
child 4, h0: double
child 5, h0_norm: double
child 6, prompt: string
child 7, label: string
child 8, ranking_text: string
loser: struct<uuid: string, category: string, tier: string, population: string, h0: double, h0_norm: double (... 54 chars omitted)
child 0, uuid: string
child 1, category: string
child 2, tier: string
child 3, population: string
child 4, h0: double
child 5, h0_norm: double
child 6, prompt: string
child 7, label: string
child 8, ranking_text: string
tier_final: string
tier: string
training_label: string
h0_norm: double
sha256: string
source: string
population: string
uuid: string
features: struct<valid: bool, file_size: int64, vertex_count: int64, triangle_count: int64, mesh_count: int64, (... 257 chars omitted)
child 0, valid: bool
child 1, file_size: int64
child 2, vertex_count: int64
child 3, triangle_count: int64
child 4, mesh_count: int64
child 5, material_count: int64
child 6, texture_count: int64
child 7, has_normals: bool
child 8, has_uvs: bool
child 9, has_tangents: bool
child 10, accessor_count: int64
child 11, buffer_view_count: int64
child 12, node_count: int64
child 13, animation_count: int64
child 14, skin_count: int64
child 15, json_chunk_size: int64
child 16, bin_chunk_size: int64
capped: bool
h0_raw: double
completeness: int64
tier_calibrated: string
h0: double
to
{'uuid': Value('string'), 'source': Value('string'), 'sha256': Value('string'), 'h0': Value('float64'), 'tier': Value('string'), 'features': {'valid': Value('bool'), 'file_size': Value('int64'), 'vertex_count': Value('int64'), 'triangle_count': Value('int64'), 'mesh_count': Value('int64'), 'material_count': Value('int64'), 'texture_count': Value('int64'), 'has_normals': Value('bool'), 'has_uvs': Value('bool'), 'has_tangents': Value('bool'), 'accessor_count': Value('int64'), 'buffer_view_count': Value('int64'), 'node_count': Value('int64'), 'animation_count': Value('int64'), 'skin_count': Value('int64'), 'json_chunk_size': Value('int64'), 'bin_chunk_size': Value('int64')}, 'completeness': Value('int64'), 'tier_calibrated': Value('string'), 'capped': Value('bool'), 'h0_norm': Value('float64'), 'tier_final': Value('string'), 'population': Value('string'), 'training_label': Value('string'), 'h0_raw': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
pair_id: string
pair_type: string
margin: double
margin_cat: string
winner: struct<uuid: string, category: string, tier: string, population: string, h0: double, h0_norm: double (... 54 chars omitted)
child 0, uuid: string
child 1, category: string
child 2, tier: string
child 3, population: string
child 4, h0: double
child 5, h0_norm: double
child 6, prompt: string
child 7, label: string
child 8, ranking_text: string
loser: struct<uuid: string, category: string, tier: string, population: string, h0: double, h0_norm: double (... 54 chars omitted)
child 0, uuid: string
child 1, category: string
child 2, tier: string
child 3, population: string
child 4, h0: double
child 5, h0_norm: double
child 6, prompt: string
child 7, label: string
child 8, ranking_text: string
tier_final: string
tier: string
training_label: string
h0_norm: double
sha256: string
source: string
population: string
uuid: string
features: struct<valid: bool, file_size: int64, vertex_count: int64, triangle_count: int64, mesh_count: int64, (... 257 chars omitted)
child 0, valid: bool
child 1, file_size: int64
child 2, vertex_count: int64
child 3, triangle_count: int64
child 4, mesh_count: int64
child 5, material_count: int64
child 6, texture_count: int64
child 7, has_normals: bool
child 8, has_uvs: bool
child 9, has_tangents: bool
child 10, accessor_count: int64
child 11, buffer_view_count: int64
child 12, node_count: int64
child 13, animation_count: int64
child 14, skin_count: int64
child 15, json_chunk_size: int64
child 16, bin_chunk_size: int64
capped: bool
h0_raw: double
completeness: int64
tier_calibrated: string
h0: double
to
{'uuid': Value('string'), 'source': Value('string'), 'sha256': Value('string'), 'h0': Value('float64'), 'tier': Value('string'), 'features': {'valid': Value('bool'), 'file_size': Value('int64'), 'vertex_count': Value('int64'), 'triangle_count': Value('int64'), 'mesh_count': Value('int64'), 'material_count': Value('int64'), 'texture_count': Value('int64'), 'has_normals': Value('bool'), 'has_uvs': Value('bool'), 'has_tangents': Value('bool'), 'accessor_count': Value('int64'), 'buffer_view_count': Value('int64'), 'node_count': Value('int64'), 'animation_count': Value('int64'), 'skin_count': Value('int64'), 'json_chunk_size': Value('int64'), 'bin_chunk_size': Value('int64')}, 'completeness': Value('int64'), 'tier_calibrated': Value('string'), 'capped': Value('bool'), 'h0_norm': Value('float64'), 'tier_final': Value('string'), 'population': Value('string'), 'training_label': Value('string'), 'h0_raw': Value('float64')}
because column names don't match
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/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
uuid string | source string | sha256 string | h0 float64 | tier string | features dict | completeness int64 | tier_calibrated string | capped bool | h0_norm float64 | tier_final string | population string | training_label string | h0_raw float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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"animation_count":... | 0 | EPIC | false | 0.347982 | EPIC | BARE_GEO | tier=EPIC pop=BARE_GEO verts=601,254 tris=1,202,832 tex=0 h0=5054730 h0_norm=0.3480 | null |
019eda2f-a5cf-7287-a512-cee2dc8e0f4f | Meshy_AI_assets_20260624_021525.zip | 01475ab85e8915abb5a5002e401f86aaa5920646310b40e8457eddbf2b18898a | 6,933,929.5 | LEGENDARY | {
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End of preview.
ZKAEDI PRIME — Meshy 3D Asset H0 Dataset v1
460 Meshy AI-generated 3D assets scored with PRIME Hamiltonian energy (H0), semantically classified via generation prompts, and packaged as contrastive training pairs.
Stats
| Total assets | 460 |
| Contrastive pairs | 386 |
| PBR assets | 305 |
| Bare geometry | 155 |
Tiers
| Tier | Count | % |
|---|---|---|
| LEGENDARY | 70 | 15.2% |
| EPIC | 115 | 25.0% |
| RARE | 138 | 30.0% |
| COMMON | 137 | 29.8% |
Categories
| Category | Count |
|---|---|
| PROP | 161 |
| CREATURE | 92 |
| ENVIRONMENT | 68 |
| CHARACTER | 43 |
| SYMBOL | 40 |
| TOWER_PROP | 30 |
| VEHICLE | 17 |
| SMALL_PROP | 9 |
H0 Statistics
| Stat | Value |
|---|---|
| Min | 136,629 |
| Max | 14,269,820 |
| Mean | 4,089,453 |
| Median | 3,575,336 |
PRIME H0 Formula
H_t = H_0 + eta * H_{t-1} * sigmoid(gamma * H_{t-1}) + epsilon * N(0, 1 + beta * |H_{t-1}|)
Parameters: eta=0.4, gamma=0.3, beta=0.1, epsilon=0.05
Files
| File | Description |
|---|---|
| meshy_prime_v7.jsonl | Full labeled dataset |
| meshy_prime_pairs_v2.jsonl | 386 contrastive pairs |
| meshy_prime_pbr.jsonl | PBR subset |
| meshy_prime_geo.jsonl | Bare geometry subset |
| dataset_summary.txt | Summary report |
Notes
- Categories derived from Meshy generation prompts via Claude classification
- 279/305 PBR assets have pbr_default=true (Meshy default roughness/metallic=1.0)
- All assets single-mesh, static, no animations
- 2 outlier assets capped at H0=14,269,820
- Source: Meshy AI. Scored by ZKAEDI PRIME pipeline.
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