{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Detoxify classifies text comments for toxic content using pre-trained transformer models, returning confidence scores across categories like toxicity, obscenity, threats, and identity-based attacks.","skillfed_tags":["content-moderation","nlp","transformer-models"],"use_cases":["Flag potentially harmful user comments in forums or social platforms for human review by moderators.","Rank or filter comments in a dataset by toxicity score to prioritize moderation efforts.","Build a content safety pipeline that rejects or quarantines submissions exceeding a toxicity threshold.","Analyze multilingual comment datasets across seven languages to identify toxic patterns by region or language.","Fine-tune the pre-trained models on domain-specific comment data (e.g., gaming, healthcare) to improve detection accuracy."],"what_it_does":"Detoxify is a Python library that wraps pre-trained transformer models to classify text comments for toxic content. It provides three model variants\u2014original, unbiased, and multilingual\u2014each trained on Jigsaw challenge datasets to detect different types of toxicity including threats, obscenity, insults, and identity-based attacks. The models return confidence scores for each toxicity category, allowing developers to programmatically flag harmful content.\n\nThe package is built on transformers and PyTorch Lightning, with low installation friction via a pure Python wheel. It's designed for research and content moderation workflows rather than as a production-grade system; the documentation explicitly acknowledges limitations around bias (e.g., flagging swearing regardless of intent) and recommends fine-tuning on carefully constructed datasets for real-world deployment. The multilingual model supports seven languages with documented per-language performance metrics.","worth_installing":"Yes, if you need to add toxicity classification to a Python application or research pipeline. The package is actively maintained, has no known vulnerabilities, and offers permissive licensing. However, treat it as a research tool or first-pass filter rather than a production content moderation system\u2014the documentation warns of bias limitations and recommends careful validation and fine-tuning before deployment in high-stakes scenarios."},"id":"detoxify","links":{"html":"https://skillfed.io/packages/detoxify","md":"https://skillfed.io/packages/detoxify.md","pypi":"https://pypi.org/project/detoxify/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-02-01","license_spdx":null,"license_treatment":"permissive","name":"detoxify","python_support":"supports_current","summary":"A python library for detecting toxic comments"},"popularity":{"monthly_downloads":110480,"position":12457,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.2"}