Sylver-Icy/nsfw-classifier
Modular NSFW Text Classification Pipeline A fully modular text classification system built with Python and scikit-learn. Designed to scrape, clean, label, train, and evaluate NSFW/SFW text datasets using TF-IDF and Logistic Regression. Built as a learning project with real-world structure and scalability in mind.
Automatically identify and filter inappropriate user-generated text, such as comments or forum posts. It takes raw text from sources like Reddit, cleans it up, and then classifies it as either safe-for-work (SFW) or not-safe-for-work (NSFW). This tool is ideal for content moderators, community managers, or social media analysts who need to maintain a clean online environment.
Use this if you need a flexible system to scrape, clean, and automatically categorize text content to flag potentially offensive or explicit language.
Not ideal if you need a highly accurate, production-ready content moderation system for extremely high-stakes environments, as its initial model is for learning and experimentation.
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Python
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Last pushed
Dec 24, 2025
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