# Cross-Encoder

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.

The pre-ranker is a cross-encoder-based text ranking model designed to optimize the function call process of large language models by narrowing down the corpus of available tools for improved efficiency.

Set-Encoder is a cross-encoder architecture specifically designed for efficient and permutation-invariant paragraph reordering, particularly suitable for novelty-aware re-ranking tasks.

A cross-encoder model fine-tuned based on sentence-transformers/all-mpnet-base-v2, used to evaluate the coherence and relevance of dialogue responses.

General-purpose Japanese reranking model for improving relevance ranking in Japanese text retrieval

Set-Encoder is a cross-encoder architecture specifically designed for efficient and permutation-invariant paragraph reordering.

lightning-ir is a cross-encoder model based on the ELECTRA architecture, specifically designed for text ranking tasks. The model optimizes paragraph reordering performance through large language model distillation techniques.

This is a Japanese-trained Reranker (CrossEncoder) model for text relevance ranking tasks.

This is a Japanese Reranker (Cross-Encoder) model for text ranking tasks, featuring 24 layers and a hidden layer size of 1024.

This is a Japanese-trained Reranker (Cross-Encoder) model for text ranking tasks.

This is a Japanese-trained Reranker (Cross-Encoder) model for text ranking tasks.

A Korean cross-encoder model fine-tuned from albert-small-kor-v1 for sentence similarity calculation