autonlp-Gibberish-Detector-492513457
| Property | Value |
|---|---|
| Parameter Count | 67M |
| Model Type | Text Classification |
| Architecture | DistilBERT |
| License | MIT |
| Accuracy | 97.36% |
| CO2 Emissions | 5.53g |
What is autonlp-Gibberish-Detector-492513457?
This is a sophisticated text classification model designed to detect and categorize gibberish content in English text. Built using AutoNLP and based on the DistilBERT architecture, it classifies text into four distinct categories: Noise, Word Salad, Mild gibberish, and Clean content. The model demonstrates exceptional performance with 97.36% accuracy and is particularly useful for chatbots, content moderation, and text processing systems.
Implementation Details
The model utilizes a DistilBERT-based architecture with 67M parameters, implemented using PyTorch and compatible with ONNX runtime. It features F32 tensor type precision and includes Safetensors support. The model was trained using AutoTrain technology, focusing on multi-class classification across four distinct gibberish levels.
- Achieves 97.36% accuracy and macro F1 score
- Supports both REST API and Python implementation
- Environmentally conscious with only 5.53g CO2 emissions during training
- Includes inference endpoints for production deployment
Core Capabilities
- Zero-level noise detection for completely meaningless text
- Word salad identification for semantically disconnected content
- Mild gibberish detection for grammatically incorrect but partially meaningful text
- Clean text validation for proper, meaningful content
- Real-time classification with high precision (97.38%)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its granular classification of gibberish content across four distinct levels, high accuracy (97.36%), and lightweight architecture based on DistilBERT. It's particularly valuable for its ability to distinguish between different types of nonsensical content while maintaining high precision.
Q: What are the recommended use cases?
The model is ideal for chatbot input validation, content moderation systems, spam detection, and quality assurance in text generation. It's particularly useful in scenarios requiring real-time analysis of user-generated content or automated text processing systems.
