# Dense vector encoding

Armenian text embedding model optimized based on multilingual-e5-base, supporting semantic search and cross-lingual understanding

A sentence embedding extraction model specifically designed for Russian, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

MMLW (I Must Get Better Messages) is a neural text encoder for Polish, optimized for information retrieval tasks.

MMLW is a neural text encoder for Polish, optimized for information retrieval tasks, capable of converting queries and passages into 1024-dimensional vectors

MMLW (I Must Get Better Messages) is a neural text encoder for Polish, optimized for information retrieval tasks, capable of converting queries and passages into 384-dimensional vectors.

This is an Italian sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

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.

This is a multilingual sentence transformer model that supports English, Russian, Ukrainian, and Polish, mapping sentences into a 768-dimensional vector space for tasks such as semantic similarity calculation.

This is a Spanish-based sentence-transformers model specifically designed for question-answering scenarios, capable of mapping sentences and paragraphs into a 768-dimensional vector space, suitable for semantic search and clustering tasks.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a Thai sentence similarity model based on the RoBERTa architecture, capable of mapping sentences into a 768-dimensional vector space, suitable for tasks like semantic search.

A sentence embedding model based on sentence-transformers that maps text to a 1024-dimensional vector space, suitable for semantic search and text clustering tasks.

This is a multilingual sentence transformer model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering or semantic search.