π« Industrial-strength Natural Language Processing (NLP) in Python
MTEB: State-of-the-art evaluation of embeddings across languages and modalities
A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. π26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. π Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
π¦ Integrating LLMs into structured NLP pipelines
NucliaDB, The AI Search database for RAG
A cautious, explainable AI text detector for local workflows and coding agents. Need production detection in your app? Try the Lynote AI Detector API β lynote.ai/ai-detector-api
Active Learning for Text Classification in Python
A flexible, adaptive classification system for dynamic text classification
Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)
Turn your documents into a Markdown wiki you can read, that answers with citations. Works with any LLM provider, Ollama included. CROW classifier routing: 89% right note vs 62%, 2.1Γ faster ingest, 68% fewer tokens.
ζζ΅·θ±Ή β Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions. ε°ζ΅·θ±ΉοΌε€§ε³ζγ
Small decision models (jev style) for latency critical scenarios
Open reproduction of TypeSafe Jev: a 150M typed decision engine (noul/choice/score in one non-autoregressive pass, calibrated confidence). 0.697 vs Jev's 0.727, 2.5x better calibrated, 4x faster, free. Trains on a Colab T4 in 30 min.
AI-powered fake news detection system with multiple ML models and explainability.