data labelling
Data labeling tasks I have performed include annotating text, images, and occasionally audio data to support machine learning model training. For text data, I have handled tasks such as sentiment analysis, intent classification, and entity recognition by carefully tagging words or phrases based on predefined guidelines. For image annotation, I have worked on bounding boxes, object classification, and image categorization to help models identify and differentiate objects accurately. In terms of project size, I have contributed to both small and large-scale datasets, ranging from a few hundred data points to thousands of annotations per project. I am comfortable working under tight deadlines while maintaining consistency across large volumes of data. I also adapt quickly to new guidelines when switching between different projects or domains. To ensure quality, I strictly follow annotation guidelines and pay close attention to detail. I regularly perform self-review before submission to minimize errors. I also maintain consistency in labeling decisions and flag ambiguous cases for clarification. Additionally, I focus on accuracy, completeness, and consistency, which are key metrics in data labeling. When available, I incorporate feedback from quality assurance teams to continuously improve my performance.