🔤 TitleCaser
A powerful utility for transforming text to title case with support for multiple style guides and extensive customization options, especially valuable for job title formatting and dataset cleanup.
Achievements
Core Maintainer
Established role as a core maintainer of the open-source project, contributing significantly to its architecture and features.
𝗦𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻
There was no reliable way to manage title casing properly for job titles, especially when working with the ESCO dataset that needed cleanup for ML and Gen AI Agents pipelines. Existing solutions lacked flexibility and comprehensive style guide support.
𝗧𝗮𝘀𝗸
Create a robust library for text transformation to title case that supports multiple style guides (AP, Chicago, APA, NYT, Wikipedia), handles complex use cases, and provides extensive customization options.
𝗔𝗰𝘁𝗶𝗼𝗻
Collaborated on the development of TitleCaser as a core maintainer, contributing to its design and implementation.
Implemented support for various style guides with proper handling of articles, prepositions, conjunctions, and other special cases.
Added features for custom term replacements, exact phrase replacements, and handling of hyphenated words, apostrophes, and acronyms.
Created disambiguation capabilities for complex cases like country codes vs. pronouns.
Developed extensive testing to ensure proper handling of edge cases across different style guides.
𝗥𝗲𝘀𝘂𝗹𝘁
The TitleCaser library now serves as a powerful tool for text transformation with applications in data cleaning, content formatting, and preparing datasets for machine learning and AI agent pipelines.
Successfully solved the specific challenge of formatting job titles consistently, particularly for the ESCO dataset, enabling more effective data processing for ML applications.