Researchers developed a new method for out-of-scope intent detection using MiniLM embeddings, treating it as a one-class classification task with multi-cluster boundary learning. The approach outperfo…
Researchers propose a multi-cluster boundary learning method for out-of-scope intent detection using MiniLM embedding, achieving state-of-the-art performance on CLINC150, StackOverflow, and Banking77 …
The SEMQ Group, led by CEO Andrés Mac Allister, introduced Symbolic Embedding Multi-Quantization (SEMQ), a new method for AI model efficiency that separates semantics from numeric representation. Init…
Researchers at The SEMQ Group have developed a new method called Symbolic Embedding Multi-Quantization (SEMQ) that reduces the memory and storage footprint of AI models by decoupling semantic meaning …
A developer fully fine-tuned a 270M-parameter Gemma 3 model on a laptop using the Banking77 dataset, achieving ~96% accuracy on intent classification. The project used full fine-tuning with loss maski…