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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
$schema: string
$id: string
title: string
description: string
$defs: struct<identifier: struct<type: string, pattern: string, maxLength: int64>, sha256: struct<type: str (... 12638 chars omitted)
child 0, identifier: struct<type: string, pattern: string, maxLength: int64>
child 0, type: string
child 1, pattern: string
child 2, maxLength: int64
child 1, sha256: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 2, artifactId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 3, tasksetId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 4, taskId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 5, evidenceBundleId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 6, submissionId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 7, resultId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 8, gradeId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 9, traceId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 10, nonNegativeNanoseconds: struct<type: string, minimu
...
ef: string
child 43, prediction: struct<type: string, additionalProperties: bool, required: list<item: string>, properties: struct<ta (... 208 chars omitted)
child 0, type: string
child 1, additionalProperties: bool
child 2, required: list<item: string>
child 0, item: string
child 3, properties: struct<task_id: struct<$ref: string>, attempt_index: struct<type: string, minimum: int64, maximum: i (... 116 chars omitted)
child 0, task_id: struct<$ref: string>
child 0, $ref: string
child 1, attempt_index: struct<type: string, minimum: int64, maximum: int64>
child 0, type: string
child 1, minimum: int64
child 2, maximum: int64
child 2, outputs: struct<type: string, maxItems: int64, items: struct<$ref: string>>
child 0, type: string
child 1, maxItems: int64
child 2, items: struct<$ref: string>
child 0, $ref: string
child 3, trace_ref: struct<$ref: string>
child 0, $ref: string
validation_date: timestamp[s]
public_schemas: int64
uap_claims: int64
release_id: string
private_runtime_included: bool
label_language_profiles: int64
world_model_dimensions: int64
controlled_surfaces: int64
checks: list<item: string>
child 0, item: string
controlled_human_languages: int64
result: string
schema_version: string
profile_bundle_digest: string
typed_label_kinds: int64
controlled_frames: int64
to
{'checks': List(Value('string')), 'controlled_frames': Value('int64'), 'controlled_human_languages': Value('int64'), 'controlled_surfaces': Value('int64'), 'label_language_profiles': Value('int64'), 'private_runtime_included': Value('bool'), 'profile_bundle_digest': Value('string'), 'public_schemas': Value('int64'), 'release_id': Value('string'), 'result': Value('string'), 'schema_version': Value('string'), 'typed_label_kinds': Value('int64'), 'uap_claims': Value('int64'), 'validation_date': Value('timestamp[s]'), 'world_model_dimensions': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, 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 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
$schema: string
$id: string
title: string
description: string
$defs: struct<identifier: struct<type: string, pattern: string, maxLength: int64>, sha256: struct<type: str (... 12638 chars omitted)
child 0, identifier: struct<type: string, pattern: string, maxLength: int64>
child 0, type: string
child 1, pattern: string
child 2, maxLength: int64
child 1, sha256: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 2, artifactId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 3, tasksetId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 4, taskId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 5, evidenceBundleId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 6, submissionId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 7, resultId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 8, gradeId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 9, traceId: struct<type: string, pattern: string>
child 0, type: string
child 1, pattern: string
child 10, nonNegativeNanoseconds: struct<type: string, minimu
...
ef: string
child 43, prediction: struct<type: string, additionalProperties: bool, required: list<item: string>, properties: struct<ta (... 208 chars omitted)
child 0, type: string
child 1, additionalProperties: bool
child 2, required: list<item: string>
child 0, item: string
child 3, properties: struct<task_id: struct<$ref: string>, attempt_index: struct<type: string, minimum: int64, maximum: i (... 116 chars omitted)
child 0, task_id: struct<$ref: string>
child 0, $ref: string
child 1, attempt_index: struct<type: string, minimum: int64, maximum: int64>
child 0, type: string
child 1, minimum: int64
child 2, maximum: int64
child 2, outputs: struct<type: string, maxItems: int64, items: struct<$ref: string>>
child 0, type: string
child 1, maxItems: int64
child 2, items: struct<$ref: string>
child 0, $ref: string
child 3, trace_ref: struct<$ref: string>
child 0, $ref: string
validation_date: timestamp[s]
public_schemas: int64
uap_claims: int64
release_id: string
private_runtime_included: bool
label_language_profiles: int64
world_model_dimensions: int64
controlled_surfaces: int64
checks: list<item: string>
child 0, item: string
controlled_human_languages: int64
result: string
schema_version: string
profile_bundle_digest: string
typed_label_kinds: int64
controlled_frames: int64
to
{'checks': List(Value('string')), 'controlled_frames': Value('int64'), 'controlled_human_languages': Value('int64'), 'controlled_surfaces': Value('int64'), 'label_language_profiles': Value('int64'), 'private_runtime_included': Value('bool'), 'profile_bundle_digest': Value('string'), 'public_schemas': Value('int64'), 'release_id': Value('string'), 'result': Value('string'), 'schema_version': Value('string'), 'typed_label_kinds': Value('int64'), 'uap_claims': Value('int64'), 'validation_date': Value('timestamp[s]'), 'world_model_dimensions': Value('int64')}
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 1694, 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 1880, 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.
checks list | controlled_frames int64 | controlled_human_languages int64 | controlled_surfaces int64 | label_language_profiles int64 | private_runtime_included bool | profile_bundle_digest string | public_schemas int64 | release_id string | result string | schema_version string | typed_label_kinds int64 | uap_claims int64 | validation_date timestamp[s] | world_model_dimensions int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"json_schema_meta_validation",
"profile_bundle_validation",
"controlled_frame_semantic_equivalence",
"uap_content_address_and_ontology_validation",
"adapter_validation",
"world_model_audit_validation",
"private_marker_scan",
"byte_identical_double_build"
] | 6 | 5 | 30 | 19 | false | sha256:43f02476a7e74716b425b9536abd5def4444c86d7861fdcac484ac1dafe9c40e | 12 | universal-labeler-v1.0.5 | pass | 1.0.0 | 13 | 10 | 2026-08-25T00:00:00 | 22 |
UniversalLabeler
A loss-audited interchange format for multilingual world-model annotations.
UniversalLabeler separates what happened from how a language describes it. A source annotation is represented as small, evidence-linked claims—action, participants, hand roles, objects, state change, place, time and outcome. Human language captions and dataset-native labels are projections of the same packet, with omissions recorded rather than hidden.
This is a public data and schema release. It does not include a translation service, model prompts or private processing infrastructure.
Release 1.0.5
| Label-language profiles | 19 |
| Human languages | 5 |
| World-model dimensions | 22 |
| Typed annotation kinds | 13 |
| Controlled conformance frames | 6 |
| Language surfaces | 30 |
| License | Apache-2.0 |
Human-language profiles currently cover English, Simplified Chinese, Tagalog, Spanish and experimental Hindi. The controlled examples are test fixtures, not a general-purpose translation benchmark.
Data model
native record + evidence
│
▼
┌─────────────────────────────────────────────┐
│ Universal Annotation Packet │
│ action · roles · hands · objects · state │
│ place · time · outcome · uncertainty │
│ evidence selectors · provenance │
└─────────────────────────────────────────────┘
│
├── dataset-native label
├── English
├── 简体中文
├── Tagalog
├── Español
└── हिन्दी
The packet is compositional rather than a fixed dictionary of every possible verb and noun. Concepts receive stable identifiers; semantic roles describe their relationship to an event; evidence and source-clock selectors bind each claim to the underlying record. Language-specific grammar remains in the projection layer.
Repository contents
| Path | Purpose |
|---|---|
ontology/universal-annotation-core-v1.json |
Node types, predicates and availability dimensions |
data/label-languages-v1.json |
Dataset and human-language profiles |
data/world-model-label-audit-v1.json |
Cross-dataset supervision audit |
data/controlled-action-matrix.jsonl |
Five-language conformance fixtures |
schemas/ |
Strict JSON Schemas for packets, projections and audits |
examples/fold-towel.uap.json |
Grounded event with hands, state, time and provenance |
examples/*.adapter.json |
Loss-audited dataset views |
rubrics/ |
Bilingual review and evaluation protocol |
metadata/ |
Reproducibility and validation records |
An interactive explanation is included at demo/index.html. Each of its four
first-person event images is a generated, synthetic visual preview—not upstream
evidence or a source-dataset frame.
Recommended workflow
- Pin the source dataset, schema revision and native record.
- Preserve the native record and content digest.
- Encode only evidence-supported atomic claims. Mark missing dimensions as not collected, not applicable or unknown.
- Render every target language directly from the same claim packet; do not use one translated language as the source for the next.
- Record represented and omitted claim IDs for every projection.
- Validate against the included schemas.
- Require independent bilingual and evidence-grounded review before accepting generated language as dataset annotation.
Evaluation boundary
Release validation checks schema correctness, content addressing, ontology
references, semantic equivalence across the controlled matrix, private-data
markers and byte-identical rebuilds. Exact results and hashes are in
metadata/validation.json and SHA256SUMS.
These checks establish format conformance. They do not establish open-vocabulary
translation quality. Round trips can reproduce the same mistake twice;
production releases still require bilingual review against video or other
source evidence. The proposed acceptance thresholds and adversarial strata are
specified in rubrics/EVALUATION_PROTOCOL.md.
Known limits
- The six controlled frames cover predicate–patient directives only.
- Chinese aspect and classifiers, Tagalog voice/pivot, Spanish morphology, Hindi agreement, code switching, negation and quantifier scope need broader reviewed data.
- The supervision audit identifies common world-model dimensions; it does not claim complete adapters for every upstream dataset.
- Synthetic receipt identifiers are not evidence of human review.
License
UniversalLabeler's original schemas, profiles, ontology and synthetic fixtures are released under Apache-2.0. Upstream datasets, media and annotations retain their own licenses and access conditions and are not redistributed here.
Citation
@dataset{universal_labeler_2026,
author = {Pablo and contributors},
title = {UniversalLabeler 1.0: Loss-Resistant Translation Contracts for World-Model Labels},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/itspublu/UniversalLabeler}
}
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