# Dense Vector Retrieval

A semantic text similarity and embedding model specifically designed for Persian, capable of mapping sentences into a dense vector space where semantically similar texts are positioned close to each other.

DRAMA-large (0.3B) is a dense retrieval model built upon a pruned large language model architecture, optimized for efficient and generalizable multilingual text retrieval tasks.

This is a PyLate model fine-tuned from answerdotai/ModernBERT-base on the MS-MARCO dataset, designed for sentence similarity calculation and document retrieval.

GTE Multilingual Base is a multilingual sentence embedding model supporting over 50 languages, suitable for tasks like sentence similarity calculation.

This is a semantic search model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional vector space, suitable for tasks such as semantic search and clustering.

A sentence transformer-based model that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

This is a version of the Contriever MSMARCO model adapted for the Sentence Transformer framework, capable of mapping text to a 768-dimensional dense vector space, suitable for semantic search and clustering tasks.

This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

A sentence transformer model optimized based on PubMedBert, specifically designed for medical literature and scientific fact-checking tasks