NER Model Builder and Data Annotator
I developed a Named Entity Recognition (NER) model to identify and classify entities such as Person, Organization, Location, and Date in unstructured text. My work involved preprocessing a large Kaggle NER dataset, structuring data for accurate labeling, and training a deep learning model for entity classification. The process included systematic data cleaning, manual review of label categories, and performance evaluation using several metrics. • Labeled and classified text data into standard NER categories (Person, Organization, Location, Date). • Performed thorough data cleaning and annotation validation prior to model training. • Used tokenization, lemmatization, and POS tagging for data preprocessing and label accuracy. • Evaluated model performance using Precision, Recall, and F1-Score metrics.