# Grammarly's Delicate Text Detection System: A Comprehensive LLMOps Case Study ## Project Overview Grammarly has developed an innovative system for detecting delicate text content, addressing a critical gap in content safety that goes beyond traditional toxicity detection. This case study demonstrates a complete MLOps lifecycle, from problem definition to production deployment, showcasing best practices in building and deploying NLP systems at scale. ## Problem Definition and Data Engineering - Identified a gap in existing content moderation systems that focus primarily on toxic content - Developed a broader definition of "delicate text" that includes emotionally charged or potentially triggering content - Created a comprehensive data collection and annotation pipeline: ## Dataset Creation and Management - Built DeTexD, a substantial benchmark dataset consisting of: - Implemented a two-stage annotation process: - Made the dataset publicly available through Hugging Face's dataset hub - Established clear documentation and usage guidelines ## Model Development and Training - Selected RoBERTa as the base architecture for the classification model - Implemented fine-tuning pipeline for delicate text detection - Conducted extensive experimentation to validate model performance - Compared against existing solutions including: ## Model Evaluation and Benchmarking - Developed comprehensive evaluation metrics: - Achieved superior performance compared to existing solutions: - Conducted comparative analysis against other content moderation systems - Identified specific strengths and weaknesses in different approaches ## Production Infrastructure - Released production-ready model artifacts: - Established clear usage protocols and ethical guidelines - Implemented necessary security and privacy measures ## Technical Implementation Details - Model Architecture: - Data Processing Pipeline: - Evaluation Framework: ## Deployment and Integration - Made the system available through multiple channels: - Provided clear guidelines for: ## Ethical Considerations and Safety Measures - Implemented comprehensive ethical guidelines: - Established content handling protocols: ## Results and Impact - Created a new standard for content safety systems - Demonstrated superior performance in delicate content detection - Provided public resources for further research and development - Established a framework for responsible AI deployment ## Lessons Learned and Best Practices - Importance of high-quality, domain-specific datasets - Value of expert annotation in sensitive content areas - Need for balanced evaluation metrics - Significance of ethical considerations in deployment - Importance of comprehensive documentation and guidelines ## Future Developments - Planned improvements: - Ongoing research into: