allen-li1231
/

treehop-rag

@@ -1,21 +1,22 @@
 ---
 library_name: treehop-rag
 license: mit
 tags:
 - Information Retrieval
 - Retrieval-Augmented Generation
 - model_hub_mixin
 - multi-hop question answering
 - pytorch_model_hub_mixin
-base_model:
-- BAAI/bge-m3
 ---
 # TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering
 [![arXiv](https://img.shields.io/badge/arXiv-2504.20114-b31b1b.svg?style=flat)](https://arxiv.org/abs/2504.20114)
 [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/badge/license-MIT-blue)
 [![Python 3.9+](https://img.shields.io/badge/Python-3.9+-green.svg)](https://www.python.org/downloads/)
@@ -36,6 +37,7 @@ base_model:
 ## Introduction
 TreeHop is a lightweight, embedding-level framework designed to address the computational inefficiencies of traditional recursive retrieval paradigm in the realm of Retrieval-Augmented Generation (RAG). By eliminating the need for iterative LLM-based query rewriting, TreeHop significantly reduces latency while maintaining state-of-the-art performance. It achieves this through dynamic query embedding updates and pruning strategies, enabling a streamlined "Retrieve-Embed-Retrieve" workflow.
 ## Why TreeHop for Multi-hop Retrieval?
 - **Handle Complex Queries**: Real-world questions often require multiple hops to retrieve relevant information, which traditional retrieval methods struggle with.
@@ -43,6 +45,7 @@ TreeHop is a lightweight, embedding-level framework designed to address the comp
 - **Speed**: 99% faster inference compared to iterative LLM approaches, ideal for industrial applications where response speed is crucial.
 - **Performant**: Maintains high recall with controlled number of retrieved passages, ensuring relevance without overwhelming the system.
 ## System Requirement

 ---
+base_model:
+- BAAI/bge-m3
 library_name: treehop-rag
 license: mit
+pipeline_tag: text-ranking
 tags:
 - Information Retrieval
 - Retrieval-Augmented Generation
 - model_hub_mixin
 - multi-hop question answering
 - pytorch_model_hub_mixin
 ---
 # TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering
 [![arXiv](https://img.shields.io/badge/arXiv-2504.20114-b31b1b.svg?style=flat)](https://arxiv.org/abs/2504.20114)
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-Model-blue.svg)](https://huggingface.co/allen-li1231/treehop-rag)
 [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/badge/license-MIT-blue)
 [![Python 3.9+](https://img.shields.io/badge/Python-3.9+-green.svg)](https://www.python.org/downloads/)
 ## Introduction
 TreeHop is a lightweight, embedding-level framework designed to address the computational inefficiencies of traditional recursive retrieval paradigm in the realm of Retrieval-Augmented Generation (RAG). By eliminating the need for iterative LLM-based query rewriting, TreeHop significantly reduces latency while maintaining state-of-the-art performance. It achieves this through dynamic query embedding updates and pruning strategies, enabling a streamlined "Retrieve-Embed-Retrieve" workflow.
+![Simplified Iteration Enabled by TreeHop in RAG system](pics/TreeHop_iteration.png)
 ## Why TreeHop for Multi-hop Retrieval?
 - **Handle Complex Queries**: Real-world questions often require multiple hops to retrieve relevant information, which traditional retrieval methods struggle with.
 - **Speed**: 99% faster inference compared to iterative LLM approaches, ideal for industrial applications where response speed is crucial.
 - **Performant**: Maintains high recall with controlled number of retrieved passages, ensuring relevance without overwhelming the system.
+![Main Experiment](pics/main_experiment.png)
 ## System Requirement