# Sentence similarity

A lightweight and fast modified version of the FRIDA model for computing embedding vectors of Russian and English sentences

CareerBERT-JG is a sentence transformer model fine-tuned based on the ESCO classification method, specifically designed for occupation consultation and job search recommendation scenarios.

A high-performance Arabic information retrieval model built on the sentence-transformers framework, optimized for the richness and complexity of the Arabic language.

E5-RoPE-Base is an embedding model based on rotary position embedding (RoPE), designed to support long-context retrieval tasks.

This is a sentence embedding model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

A lightweight BERT model for the Italian legal domain built using knowledge distillation technology, featuring only 4 Transformer layers

Portuguese legal domain sentence similarity model based on BERTimbau large model, supporting 1024-dimensional vector representation

This is a sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space and is suitable for semantic search and clustering tasks.

This is a multilingual Roberta model that can generate embedding vectors for Bulgarian sentences. It is trained based on the Sentence-BERT concept and supports both Bulgarian and English.

A siamese network model specifically trained for zero-shot and few-shot text classification, based on the mpnet-base architecture and trained using SNLI and MNLI datasets.

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

A chemical domain BERT model trained based on TSDAE (Transformer-based Sequential Denoising Auto-Encoder), focusing on sentence similarity tasks