Asankhaya Sharma's picture

Asankhaya Sharma PRO

codelion

AI & ML interests

Creator of OptiLLM, OpenEvolve, Adaptive Classifier, and Ellora. Pioneering a new category in AI infrastructure: inference-time compute for LLMs.

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reacted to their post with 🤯 1 day ago
🚀 Adaptive Classifier v0.0.17 Released - Major Accuracy Improvements! We've just released a major update fixing critical bugs that were causing 40-50% accuracy drops in our enterprise classifiers! Key Fixes: • Fixed k-parameter prediction bug causing massive accuracy loss • Improved incremental learning for new classes • Enhanced weight preservation during model updates Dramatic Results: • fraud-detection: 43.9% → 92.7% (+48.8%) https://huggingface.co/adaptive-classifier/fraud-detection • business-sentiment: 88.9% → 98.8% (+9.9%) https://huggingface.co/adaptive-classifier/business-sentiment• expense-category: 26.7% → 84.2% (+57.5%) https://huggingface.co/adaptive-classifier/expense-category • language-detection: 98.8% → 100% (+1.2%) https://huggingface.co/adaptive-classifier/language-detection 15/17 enterprise classifiers now maintain ≤5% accuracy difference from original performance! Other High-Performing Models: • email-security (93.8%): https://huggingface.co/adaptive-classifier/email-security • content-moderation (100%): https://huggingface.co/adaptive-classifier/content-moderation • pii-detection (100%): https://huggingface.co/adaptive-classifier/pii-detection Quick Start: from adaptive_classifier import AdaptiveClassifier classifier = AdaptiveClassifier.load("adaptive-classifier/fraud-detection") predictions = classifier.predict("Suspicious transaction pattern", k=3) Install: pip install --upgrade adaptive-classifier==0.0.17 All models: https://huggingface.co/adaptive-classifier 🎯 Production-ready continuous learning for enterprise text classification! #MachineLearning #TextClassification #ContinualLearning #EnterpriseAI
reacted to their post with 🚀 1 day ago
🚀 Adaptive Classifier v0.0.17 Released - Major Accuracy Improvements! We've just released a major update fixing critical bugs that were causing 40-50% accuracy drops in our enterprise classifiers! Key Fixes: • Fixed k-parameter prediction bug causing massive accuracy loss • Improved incremental learning for new classes • Enhanced weight preservation during model updates Dramatic Results: • fraud-detection: 43.9% → 92.7% (+48.8%) https://huggingface.co/adaptive-classifier/fraud-detection • business-sentiment: 88.9% → 98.8% (+9.9%) https://huggingface.co/adaptive-classifier/business-sentiment• expense-category: 26.7% → 84.2% (+57.5%) https://huggingface.co/adaptive-classifier/expense-category • language-detection: 98.8% → 100% (+1.2%) https://huggingface.co/adaptive-classifier/language-detection 15/17 enterprise classifiers now maintain ≤5% accuracy difference from original performance! Other High-Performing Models: • email-security (93.8%): https://huggingface.co/adaptive-classifier/email-security • content-moderation (100%): https://huggingface.co/adaptive-classifier/content-moderation • pii-detection (100%): https://huggingface.co/adaptive-classifier/pii-detection Quick Start: from adaptive_classifier import AdaptiveClassifier classifier = AdaptiveClassifier.load("adaptive-classifier/fraud-detection") predictions = classifier.predict("Suspicious transaction pattern", k=3) Install: pip install --upgrade adaptive-classifier==0.0.17 All models: https://huggingface.co/adaptive-classifier 🎯 Production-ready continuous learning for enterprise text classification! #MachineLearning #TextClassification #ContinualLearning #EnterpriseAI
reacted to their post with 🔥 1 day ago
🚀 Adaptive Classifier v0.0.17 Released - Major Accuracy Improvements! We've just released a major update fixing critical bugs that were causing 40-50% accuracy drops in our enterprise classifiers! Key Fixes: • Fixed k-parameter prediction bug causing massive accuracy loss • Improved incremental learning for new classes • Enhanced weight preservation during model updates Dramatic Results: • fraud-detection: 43.9% → 92.7% (+48.8%) https://huggingface.co/adaptive-classifier/fraud-detection • business-sentiment: 88.9% → 98.8% (+9.9%) https://huggingface.co/adaptive-classifier/business-sentiment• expense-category: 26.7% → 84.2% (+57.5%) https://huggingface.co/adaptive-classifier/expense-category • language-detection: 98.8% → 100% (+1.2%) https://huggingface.co/adaptive-classifier/language-detection 15/17 enterprise classifiers now maintain ≤5% accuracy difference from original performance! Other High-Performing Models: • email-security (93.8%): https://huggingface.co/adaptive-classifier/email-security • content-moderation (100%): https://huggingface.co/adaptive-classifier/content-moderation • pii-detection (100%): https://huggingface.co/adaptive-classifier/pii-detection Quick Start: from adaptive_classifier import AdaptiveClassifier classifier = AdaptiveClassifier.load("adaptive-classifier/fraud-detection") predictions = classifier.predict("Suspicious transaction pattern", k=3) Install: pip install --upgrade adaptive-classifier==0.0.17 All models: https://huggingface.co/adaptive-classifier 🎯 Production-ready continuous learning for enterprise text classification! #MachineLearning #TextClassification #ContinualLearning #EnterpriseAI
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