text and data llabeling
The project involved large-scale data annotation for machine learning model training, specifically focusing on image and text-based datasets. The primary tasks included object detection, entity recognition, and sentiment analysis. To ensure high-quality annotations, a multi-step quality control process was implemented, including inter-annotator agreement checks, automated validation scripts, and expert review sessions. Annotators followed a detailed labeling guideline to maintain consistency and accuracy, with a targeted quality threshold of 95% accuracy.