NLP Text Classification & Entity Recognition Training Project
I completed a structured, self-directed AI data labeling training project focused on both text classification and named entity recognition (NER). The project involved annotating over 3,000 short text samples covering topics such as sentiment, intent, and general subject classification. I also performed NER tagging for people, locations, and organizations while adhering to clear labeling rules and entity boundary guidelines. Throughout the project, I followed detailed annotation instructions, documented edge cases, and performed periodic quality checks to ensure consistent and accurate labeling. I used platform features such as taxonomy setup, span selection, and label validation tools to maintain a high-quality dataset suitable for LLM training and evaluation. This project strengthened my ability to interpret guidelines, manage annotation workflow efficiently, and deliver high-quality, reliable training data.