Achieve Unmatched NER with Custom RoBERTa Resume ParserAchieve Unmatched NER with Custom RoBERTa Resume Parser
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Trashu Vashisth
• Mar 10
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.
RoBERTaAI Agent EngineerAI AutomationAI DevelopmentHugging FacePython
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