# Text Reordering

This is a cross-encoder model fine-tuned from BAAI/bge-reranker-base, designed for scoring text pairs, suitable for text reordering and semantic search tasks.

This is a cross-encoder model fine-tuned from ModernBERT-base, designed for calculating scores of text pairs, suitable for text reordering and semantic search tasks.

mxbai-rerank-large-v2 is a foundational model for text reordering, focused on improving the relevance and accuracy of search results.

This is a text ranking model used for reordering and scoring texts.

A 50-million-parameter text reranking model based on the Qwen2.5 architecture, suitable for information retrieval and document ranking tasks

A text reranking model based on the Qwen2.5 architecture with 0.5B parameters, suitable for reranking tasks.

A 0.5B parameter text reranking model based on Qwen2.5 architecture, efficiently trained using Unsloth and TRL libraries

This is a Transformer-based Reranker model primarily used for information retrieval and search result optimization tasks.

This model is converted from BAAI/bge-reranker-base into the GGUF format, primarily used for text reordering tasks.

This is a lightweight text ranking model based on TinyLlama, specifically designed for classifying and reordering context-question pairs.

Text ranking model based on Gemma 2B architecture, offering multiple quantization versions to suit different hardware needs

The ONNX O4 version of BGE-RERANKER-V2 is an optimized text reordering model that supports relevance scoring for multilingual text pairs.

A Transformer-based text ranking model for sorting sentences or paragraphs by relevance.

A re-ranking model based on the T5-base architecture, fine-tuned for 100,000 steps on the MS MARCO passage dataset, suitable for document re-ranking tasks in information retrieval.