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Chris

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reacted to codelion's post with 🤗 5 days ago

Adaptive Classifier: Dynamic Text Classification with Strategic Learning New text classification system that learns continuously without catastrophic forgetting. Achieved 22.2% robustness improvement on adversarial datasets while maintaining clean data performance. 🎯 THE PROBLEM Traditional classifiers require complete retraining when adding new classes. Expensive and time-consuming, especially with adversarial users trying to game the system. 🚀 KEY INNOVATIONS • Hybrid memory-neural architecture (prototype-based + neural adaptation) • Strategic classification using game theory to predict and defend against manipulation • Elastic Weight Consolidation prevents catastrophic forgetting 📊 RESULTS Tested on AI-Secure/adv_glue dataset: • Clean data: 80.0% → 82.2% (+2.2%) • Manipulated data: 60.0% → 82.2% (+22.2%) • Zero performance drop under adversarial attacks 🔬 APPLICATIONS • Hallucination detection: 80.7% recall for RAG safety • LLM routing: 26.6% cost optimization improvement • Content moderation: Robust against gaming attempts ⚙️ USAGE pip install adaptive-classifier from adaptive_classifier import AdaptiveClassifier classifier = AdaptiveClassifier("bert-base-uncased") classifier.add_examples(texts, labels) predictions = classifier.predict("New text") 🔗 RESOURCES Blog: https://huggingface.co/blog/codelion/adaptive-classifier Code: https://github.com/codelion/adaptive-classifier Models: https://huggingface.co/adaptive-classifier Available models: llm-hallucination-detector, llm-config-optimizer, llm-router Works with any HuggingFace transformer. Fully open source and production-ready!

new activity 2 months ago

mradermacher/model_requests:Model request GLM-4-0414 series model

updated a model 2 months ago

WesPro/Broken-Tutu-24B-Q6_K-GGUF

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