Developed a high-precision Resume Parser using a

Developed a high-precision Resume Parser using a custom-trained RoBERTa model, specifically fine-tuned for Named Entity Recognition (NER) tasks. This tool automates the extraction of critical information from unstructured resumes with deep learning accuracy.

Key Features:

NER-Based Extraction: Accurately identifies entities like Name, Experience, Skills, Education, and Contact Info.

RoBERTa Architecture: Leverages Transformer-based embeddings for superior contextual understanding compared to traditional parsers.

JSON Output: Seamlessly converts complex resume layouts into structured JSON format for easy database integration and ATS (Applicant Tracking System) workflows.

High Accuracy: Trained to handle diverse formatting and professional jargon.