Model overview
{ "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Gibberish Detector - High-Accuracy Text Classification Model", "url": "https://huggingface.co/madhurjindal/autonlp-Gibberish-Detector-492513457", "applicationCategory": "NaturalLanguageProcessing", "description": "State-of-the-art gibberish detection model using DistilBERT. Detect nonsensical text, spam, and incoherent input with 97.36% accuracy. Perfect for chatbots, content moderation, and text validation.", "keywords": "gibberish detector, gibberish detection, text classification, spam filter, content moderation, text validation, NLP model, DistilBERT, AutoNLP, text quality, input validation, chatbot filter", "creator": { "@type": "Person", "name": "Madhur Jindal" }, "datePublished": "2021-05-01", "softwareVersion": "1.0", "operatingSystem": "Cross-platform", "offers": { "@type": "Offer", "price": "0", "priceCurrency": "USD" } } **State-of-the-art gibberish detection model** that accurately identifies nonsensical text, spam, and incoherent input in English. Built with DistilBERT and AutoNLP, this model achieves **97.36% accuracy** in multi-class text classification, making it the ideal solution for content moderation, chatbot input validation, and text quality assurance. - **🎯 97.36% Accuracy**: Industry-leading performance in gibberish detection - **⚡ Fast Inference**: Optimized DistilBERT architecture for real-time applications - **🏷️ Multi-Class Detection**: Distinguishes between Noise, Word Salad, Mild Gibberish, and Clean text - **🔧 Easy Integration**: Simple API with transformers pipeline - **🌐 Production Ready**: Tested on diverse real-world datasets - **💚 Eco-Friendly**: Low carbon footprint (5.53g CO2 emissions)
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
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Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。