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Label Studio
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Entity (NER) ClassificationAs a final-year Computer Science student, I approach AI data training with a strong foundational understanding of how high-quality data directly impacts machine learning models. I have practical experience working with industry-standard tools like Label Studio. My background includes handling Computer Vision tasks, where I am highly meticulous in creating accurate polygon annotations and bounding boxes for object detection and image segmentation workflows.
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Polygonxecuted high-precision polygon annotations on image datasets to train computer vision models for object detection and image segmentation. Utilized Label Studio to accurately outline and segment complex objects, ensuring high-quality training data delivery and strict adherence to project guidelines
Label StudioTextEntity (NER) ClassificationFreelance AI Data Annotator performed precision data labeling for NLP datasets in support of Named Entity Recognition (NER) tasks. The work included span annotation and custom multi-class XML labeling interface configuration to extract entity categories such as PERSON, ORGANIZATION, LOCATION, DATE, and MISC. The annotator also evaluated ground-truth logic and handled edge cases to reduce linguistic ambiguities while resolving XML/JSON syntax anomalies that affected parsing reliability. • Labeled entity spans for NER using Label Studio • Built custom XML labeling interfaces for multi-class extraction • Conducted edge-case evaluation to improve training data fidelity • Debugged XML/JSON structured errors during environment setup to ensure reliable parsing
Bachelor of Computer Science, Computer Science
Data Engineering & Analytical Sandbox
