# 768-dimensional dense vectors

This is a fine-tuned sentence transformer model based on xlm-roberta-base, supporting English and Ukrainian, suitable for tasks like semantic textual similarity and semantic search.

A sentence embedding model optimized for Ukrainian, based on the multilingual MPNet architecture, suitable for semantic similarity and feature extraction tasks

This is a sentence embedding model based on the Turkish BERT model, suitable for sentence similarity calculation and semantic search tasks.

This is a sentence-transformers-based model capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

A sentence-transformers model based on XLM-R, trained using the SimCSE method, which maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A multilingual sentence embedding model based on sentence-transformers, supporting Catalan, capable of mapping text to a 768-dimensional vector space

Multilingual sentence embedding model that maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks

This is a case-insensitive Italian BERT model based on sentence-transformers, designed to generate 768-dimensional dense vector representations for sentences and paragraphs.

German sentence embedding model based on XLM-RoBERTa architecture, mapping text to 768-dimensional vector space, suitable for semantic search and clustering tasks

Italian sentence embedding model based on sentence-transformers, mapping text to a 768-dimensional vector space

This is a BERT-based sentence transformer model specifically optimized for Hinglish (Hindi-English mixed language), capable of mapping sentences into a 768-dimensional vector space

A Thai sentence embedding model based on mBERT, trained using the SimCSE method on Thai Wikipedia data, capable of mapping text to 768-dimensional vectors

Hybrid model based on deep NLP, using enhanced SBERT for patent distance calculation and classification