# Cross-Encoder Architecture

A model specifically designed for Arabic reranking tasks, capable of accurately evaluating the relevance between queries and passages.

Ruri-Reranker is a Japanese text re-ranking model based on sentence-transformers, specifically designed to optimize the relevance ranking between queries and documents.

Ruri Reranker is a Japanese text reranking model based on Transformer architecture, specifically designed to optimize the ranking quality of retrieval results.

Ruri-Reranker is a reranking model specifically optimized for Japanese text, based on the sentence-transformers architecture, effectively improving the relevance ranking of search results.

The Ruri Reranker is a general-purpose Japanese reranking model specifically designed to improve the relevance ranking of Japanese text retrieval results. The small version maintains high performance while having a smaller parameter count.

French text classification model based on CamemBERT architecture, specifically designed for paragraph reordering tasks in the legal domain

A high-performance cross-encoder model optimized for Japanese text reranking tasks, featuring a 24-layer architecture with 1024 hidden units

MedCPT-Cross-Encoder is a sequence classification model for biomedical information retrieval, particularly suitable for relevance ranking of medical-related articles.

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

This is a French sentence similarity calculation model based on CamemBERT, used to predict the semantic similarity score between two sentences.