Hire this AI Trainer
Sign in or create an account to invite AI Trainers to your job.
CVAT
Data Annotation Tech
Scale AINo subject matter listed
Image
Text
VideoWith a strong foundation in data labeling and AI training, I bring expertise in annotating diverse datasets across text, image, and video domains. My experience includes contributing to NLP projects by performing high-quality text annotation, sentiment analysis, and entity recognition, which have been integral to developing advanced language models. I’ve also worked in computer vision, labeling complex datasets for autonomous systems, including object detection, semantic segmentation, and behavioral analysis. My analytical skills, attention to detail, and ability to adapt to various annotation tools ensure precise and efficient data handling. I have a keen interest in understanding human behavior and psychology, which I leverage to add value to sentiment analysis and behavioral research projects. Combined with a commitment to quality and a collaborative approach to problem-solving, I am dedicated to producing impactful results in AI training and development.
Assisted in annotating datasets focused on behavioral patterns and emotional states, leveraging my understanding of human psychology to improve AI models used in healthcare and human-computer interaction
Labeled image and video data for object detection, classification, and behavior analysis, essential for self-driving car algorithms and safety systems
I worked on a big text labeling project. The goal was to create models that can classify feelings and recognize important information. We labeled customer feedback, reviews, and social media posts. We marked each piece with how people feel—positive, neutral, or negative. We also noted key things like products, services, and locations. We had over 50,000 text samples. It was important to label everything correctly for good training data. I used different tools to help with this. We followed strict rules to keep our work high-quality. There were checks to make sure our labeling was accurate, and we kept our accuracy above 95%. This way, our data was solid for building models that help understand customer opinions and automate processes.
Bachelor's in computer engineering, Computer Engineering
Psychology Content Writer & Editor
