Annotated and labeled data

Annotated and labelled data is content that people have tagged according to a defined schema so that models can learn from it or be evaluated against it. It covers text classification, named entity recognition, sentiment, image boxes and masks, video tracks, audio transcripts, document extraction and human preference ratings for language models. Buyers include machine learning teams, AI product companies and researchers who need ground truth they can trust.

Typical fields

• Item ID and reference to the source content
• Label or labels according to the schema
• Pseudonymous annotator ID and timestamp
• Confidence or agreement score
• Review status and adjudication outcome
• Guideline version used

Sources and methods

Providers either label the buyer's own data as a service or sell pre-labelled catalogue datasets. Quality comes from clear guidelines, trained or domain-expert annotators, gold-standard test questions, multiple labels per item and expert adjudication of disagreements. Model-assisted pre-labelling speeds work up but still needs human review.

Delivery and pricing

Labels are delivered as JSON, CSV or tool-specific formats through cloud storage or labelling platforms. Pricing is usually per item, per object or per annotator hour, depending on complexity and the expertise required. Medical, legal or rare-language annotation costs more.

Before you buy

• Read the labelling guidelines and edge-case rules.
• Ask for inter-annotator agreement and gold-question accuracy.
• Check annotator qualifications for specialist domains.
• Confirm data security when your own content is being labelled.
• Clarify ownership of labels and derived datasets.