Data Annotation (Training)
TextClassification
This project involves structured training in text data annotation and classification. The scope includes labeling text data according to predefined guidelines, ensuring consistency across categories, and accurately assigning labels based on content meaning.
Tasks performed include text classification, data categorization, and guideline-based annotation of structured datasets. Special attention is given to edge cases, ambiguous inputs, and maintaining uniformity across multiple samples.
Quality measures followed include strict adherence to annotation guidelines, consistency checks across labeled samples, and self-review of outputs to reduce errors and improve labeling accuracy.
This project involves structured training in text data annotation and classification. The scope includes labeling text data according to predefined guidelines, ensuring consistency across categories, and accurately assigning labels based on content meaning. Tasks performed include text classification, data categorization, and guideline-based annotation of structured datasets. Special attention is given to edge cases, ambiguous inputs, and maintaining uniformity across multiple samples. Quality measures followed include strict adherence to annotation guidelines, consistency checks across labeled samples, and self-review of outputs to reduce errors and improve labeling accuracy.
2026 - Present