# Semantic Relevance Scoring

This is a fine-tuned cross-encoder model based on cross-encoder/ms-marco-MiniLM-L6-v2, primarily used for text re-ranking and semantic search tasks.

This is a cross-encoder based climate science text reranking model, specifically designed for semantic search and text relevance ranking in the field of climate science.

A revolutionary open-source model capable of reordering long Arabic texts with exceptional efficiency and accuracy.

A sentence-transformers model based on the DeepPavlov/rubert-base-cased pre-trained model and fine-tuned on the MS-MARCO Russian passage ranking dataset, designed for Russian information retrieval tasks.

A bilingual reranking model based on Bloomz-560m for measuring semantic relevance between queries and contexts, supporting both French and English

A cross-lingual reranking model based on Bloomz-3b, designed to measure semantic relevance between queries and contexts, supporting French and English.

A Reranker model fine-tuned on Korean data based on BAAI/bge-reranker-large, designed to enhance Korean Retrieval-Augmented Generation (RAG) performance

This model is trained using the Cross-Encoder class from SentenceTransformers to predict the semantic similarity score between two sentences.