# Zero-shot Performance

OpenSearch's learned sparse retrieval model v2 mini version, encoding documents into sparse vectors to optimize search relevance and efficiency

The OpenSearch Neural Sparse Encoding Model v2 Distilled is an efficient learned sparse retrieval model designed for OpenSearch, capable of encoding queries and documents into 30,522-dimensional sparse vectors.

This is a multi-category image classification model trained using AutoTrain, capable of recognizing various object categories.