AI training
I worked on data labeling projects focused on training and improving machine learning models, particularly in natural language processing (NLP) and content moderation. The scope involved annotating large datasets to help models better understand context, intent, and accuracy in real-world scenarios. Specific Data Labeling Tasks Performed: Labeled text data for sentiment analysis (positive, negative, neutral) Annotated conversational data for intent classification and entity recognition Reviewed and categorized user-generated content for policy compliance (e.g., spam, harmful content) Performed ranking tasks by comparing multiple AI-generated responses and selecting the most relevant or accurate one Carried out data validation and correction to improve annotation consistency Project Size: Worked on datasets ranging from 5,000 to 20,000+ data points per project Completed hundreds to thousands of annotations per week, depending on task complexity Collaborated within a distributed team where outputs contributed to large-scale model training pipelines Quality Measures Adhered To: Followed detailed annotation guidelines to ensure consistency and accuracy Maintained high agreement scores with benchmark/ground truth data