Data annotation Specialist
Data Annotation TechTextText GenerationObject Detection
The project focused on large-scale data labeling for AI model training in the domain of image and text classification. The scope included identifying, categorizing, and annotating data with high accuracy to ensure reliable machine learning outcomes. Over 50,000 data points were processed, covering various categories and edge cases to improve dataset diversity. Specific tasks included bounding box annotation, entity recognition, and sentiment labeling, depending on the dataset type. Quality assurance was maintained through multi-level review, cross-validation, and strict adherence to project guidelines, achieving a consistency rate above 98%.
The project focused on large-scale data labeling for AI model training in the domain of image and text classification. The scope included identifying, categorizing, and annotating data with high accuracy to ensure reliable machine learning outcomes. Over 50,000 data points were processed, covering various categories and edge cases to improve dataset diversity. Specific tasks included bounding box annotation, entity recognition, and sentiment labeling, depending on the dataset type. Quality assurance was maintained through multi-level review, cross-validation, and strict adherence to project guidelines, achieving a consistency rate above 98%.
2023 - 2025