Freelance Data Labeling Tasks (Remote Practice Projects)
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ClassificationWorked on structured data labeling tasks involving image, text, and conversational datasets used for machine learning model training and evaluation. The scope included handling multi-type annotation workflows across classification, detection, and evaluation-based tasks under defined labeling guidelines. For image data, performed object detection using bounding boxes to identify and label multiple real-world objects such as pedestrians, vehicles, street elements, and household items. Also completed segmentation-style annotations by accurately outlining object boundaries where required, and applied keypoint and polyline labeling for structured spatial data representation. For text datasets, carried out classification of short-form user-generated content into intent-based categories (questions, requests, complaints, greetings, spam, and irrelevant content). Performed sentiment analysis labeling based on contextual tone, and completed transcription and cleanup of short text inputs for dataset structuring. Additionally, contributed to evaluation and quality rating tasks by assessing AI-generated responses based on relevance, accuracy, and coherence. Participated in prompt-response (SFT-style) labeling tasks where responses were selected or refined according to instruction adherence and contextual correctness. Ensured high-quality output through strict adherence to annotation guidelines, consistent labeling decisions across similar data samples, and systematic review of edge cases to minimize inconsistencies and improve dataset reliability.