i have worked with Imerit annotation company
In my data labeling work, the scope of projects has typically involved preparing high-quality training datasets for machine learning models across both natural language processing (NLP) and computer vision domains. For NLP projects, I performed tasks such as sentiment annotation, named entity recognition (NER), intent classification, and content moderation labeling. On the computer vision side, I handled image classification, object detection (bounding boxes), and basic segmentation tasks. Each project required strict adherence to detailed annotation guidelines, including handling ambiguous cases, flagging uncertain data, and maintaining consistency across large datasets. The project sizes I worked on ranged from a few thousand samples in specialized pilot tasks to over 100,000+ data points in large-scale production workflows. To ensure quality, I followed multiple quality control measures such as double-blind labeling (where applicable), peer reviews, and periodic audits against gold-standard datasets. I also maintained high inter-annotator agreement scores by carefully aligning with guidelines and participating in calibration exercises. Additional quality measures included self-review before submission, tracking error patterns, and incorporating feedback from QA teams to continuously improve accuracy and consistency.