Datasets:

itspublu
/

UniversalLabeler

license: apache-2.0
language:
  - en
  - zh
  - tl
  - es
  - hi
tags:
  - multilingual
  - translation
  - world-models
  - egocentric-video
  - robotics
  - annotations
  - json-schema
pretty_name: UniversalLabeler 1.0
size_categories:
  - n<1K

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}
}