# High-dimensional vector embedding

This is a sentence embedding model optimized for Vietnamese, capable of converting text into 1024-dimensional vectors, suitable for semantic similarity and retrieval tasks.

A Hungarian sentence embedding model fine-tuned based on ModernBERT, used for semantic similarity calculation and text feature extraction

This is a sentence embedding model based on sentence-transformers, capable of converting text into 1024-dimensional dense vector representations, suitable for tasks like semantic search and text clustering.

This is a sentence transformer version based on the intfloat/e5-large-v2 model, capable of mapping sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks like clustering or semantic search.

Sentence embedding model based on MPNet architecture, mapping text to a 768-dimensional vector space, suitable for semantic search and sentence similarity calculation