US-DATAData Annotation
Cabinet
NER annotation helps NLP models find meaningful entities in text, identify their boundaries and assign defined categories.
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Named Entity Recognition labels words, phrases and text spans as named entities. It turns unstructured text into data for search, analytics and information extraction.
PER, ORG, LOC, DATE, MONEY and other common entities.
Custom classes for healthcare, law, finance and other domains.
One entity is wholly or partly inside another.
One span belongs to multiple categories.
A hierarchy of classes and subclasses.
Annotation covers exact boundaries, class, attributes, normalization, linked mentions and nested or overlapping entities. Unified rules preserve consistency in every context.
We prepare BIO, BIOES, span, JSON and custom formats compatible with your NLP framework and infrastructure.
We define classes, meanings, boundaries, abbreviations, spelling variants, edge cases, exclusions and examples. A pilot phase refines the rules before scale-up.
We label documents, dialogues, news, contracts, medical records, financial reports and knowledge bases; create taxonomies and guidelines; provide pilots, large-scale annotation, normalization, QA and exports.
Inconsistent labels reduce accuracy, introduce bias and can make production behavior unreliable.
We create task-specific guidelines and validate every stage so the dataset matches the model architecture and business objective.
Examples of data annotation for machine learning
Full data preparation cycle from raw data to model-ready output
How does US-DATA deliver the results business needs?
We pay close attention to quality. Even the most accurate model will not perform well if the data is labeled with errors.
Our team works under a unified annotation rule system based on multi-level review and consistency control. Every process is adapted to client needs and the specifics of the ML model. The result is a clean dataset ready for training without additional rework.
We understand how data quality affects model training.
Annotation tailored to architecture and project goals.
From pilots to large-scale data volumes.
Control at every stage and transparent metrics.
We handle non-standard and challenging scenarios.
Data exported in the format you need.
Faster model training
Higher accuracy and robustness
Lower retraining costs
Production-ready datasets
Data security and compliance
Expandable sections with indicative cost tables.
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* This estimate is not a public offer. Final cost is determined after technical analysis and data review.
Latest materials on data annotation and machine learning
Leave a request — we will assess the task and propose the right annotation approach and delivery format.
NER annotation identifies people, organizations, locations, dates, monetary values and domain-specific entities in unstructured text. US-DATA prepares consistent datasets in BIO, BIOES, span, JSON and other formats.
We develop annotation guidelines, run pilot labeling and multi-level quality control, then deliver a consistent dataset in the format required by your ML pipeline.
The result is data that is ready to train, validate and deploy machine learning models in real operating conditions.
