{"repo":"PrithivirajDamodaran/FlashRank","free":true,"listed":false,"github":"https://github.com/PrithivirajDamodaran/FlashRank","clone":"git clone https://github.com/PrithivirajDamodaran/FlashRank.git","description":"Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.","language":"Python","stars":1002,"topics":["cross-encoder","full-text-search","hybrid-search","lexical-search","rag","ranking","reranking","retrieval-augmented-generation","semantic-search","vector-database"],"license":"Apache-2.0","category":"ai-agents","readme_excerpt":"Re-rank your search results with SoTA Pairwise or Listwise rerankers before feeding into your LLMs Ultra-lite & Super-fast Python library to add re-ranking to your existing search & retrieval pipelines. It is based on SoTA LLMs and cross-encoders, with gratitude to all the model owners. Supports: - Pairwise / Pointwise rerankers. (Cross encoder based, i.e. ) - Listwise LLM based rerankers. (LLM based, i.e. ) - See below for full list of supported models. Table of Contents 1. Features 2. Installation 3. Making ranking faster 4. Getting started 5. Deployment patterns 6. How to Cite? 7. Papers citing flashrank Features 1. ⚡ Ultra-lite : - No Torch or Transformers needed. Runs on CPU. - Boasts the tiniest reranking model in the world, 4MB . 2. ⏱️ Super-fast : - Rerank speed is a function of # of tokens in passages, query + model depth (layers) - To give an idea, Time taken by the example (in code) using the default model is below. - - Detailed benchmarking, TBD 3. 💸 $ concious : - Lowest $ per invocation: Serverless deployments like Lambda are charged by memory & time per invocation - Smaller package size = shorter cold start times, quicker re-deployments for Serverless. 4. 🎯 Based on SoTA Cross-encoders and other models : - \"How good are Zero-shot rerankers?\" - look at the reference section. Model Name Description Size Notes ------------ ------------- ------ ------- ms-marco-TinyBERT-L-2-v2 Default model 4MB Model card ms-marco-MiniLM-L-12-v2 Best Cross-encoder reranke","default_branch":null,"files":null,"tree":[],"storefront":"/r/PrithivirajDamodaran","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/PrithivirajDamodaran/FlashRank/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}