TextLabels

Textlabels, also written as text labels, are textual identifiers used to annotate data samples and interface elements. They are strings that describe the category, topic, attribute, or role of an item. In machine learning, text labels serve as the target variables in supervised tasks: annotators assign a label such as "dog" or "finance" to each instance, enabling models to learn mappings from input features to labels. Labels can be single-label or multi-label, and they may be organized hierarchically or arranged in flat sets.

In natural language processing and data annotation, text labels also apply to token- or span-level annotations (for example, part-of-speech tags or named-entity types), which are textual tags used during training and evaluation. Textlabels thus function both as category identifiers and as descriptive tags that guide model learning and evaluation.

Textlabels appear outside pure ML contexts as well. They describe user interface elements and data visualizations: for example, a button labeled "Submit" or a bar in a chart labeled "Q1 revenue" helps users understand actions and values. In documentation and metadata, textlabels provide human-readable descriptions for items, categories, or attributes to improve searchability and interpretability.

Practical considerations include label quality and consistency, inter-annotator agreement, and label noise. In ML pipelines, labels must be encoded for model training, typically as integers or one-hot vectors; decisions about single-label versus multi-label arrangements and class imbalance can influence performance and evaluation.

See also: supervised learning, data annotation, labeling, dataset tagging.