Projects using RoBERTa

Projects using RoBERTaProjects using RoBERTa

Cover image for Project Title: Sentix AI: Global

Project Title: Sentix AI: Global Sentiment Intelligence Results & Impact: 1. Achieved a 92% F1-score by implementing domain-specific fine-tuning on diverse consumer datasets, ensuring high reliability in classification. 2. Engineered an Aspect-Based Sentiment Analysis (ABSA) module that decomposed reviews into granular attributes (performance, pricing, reliability), making trend tracking 15% more precise. 3. Optimized inference performance by 25% through model distillation and Docker-based containerization, enabling the system to handle thousands of concurrent requests. 4. Mitigated data bias by designing a robust pre-processing workflow including lemmatization and custom noise reduction, critical for handling global marketplace jargon.

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Cover image for Sentiment Analysis with RoBERTa

Sentiment Analysis with RoBERTa

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Cover image for 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.

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Cover image for ISE Omnichannel experiences analysis and implementation

ISE Omnichannel experiences analysis and implementation

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