There are 4 repositories under reranking topic.
MTEB: Massive Text Embedding Benchmark
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.
Use late-interaction multi-modal models such as ColPali in just a few lines of code.
A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR).
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite
Is ChatGPT Good at Search? LLMs as Re-Ranking Agent [EMNLP 2023 Outstanding Paper Award]
Querying local documents, powered by LLM
Diffusion on manifolds for image retrieval
Code, datasets, and checkpoints for the paper "Improving Passage Retrieval with Zero-Shot Question Generation (EMNLP 2022)"
Code and resources for papers "Generation-Augmented Retrieval for Open-Domain Question Answering" and "Reader-Guided Passage Reranking for Open-Domain Question Answering", ACL 2021
Bag of Visual Feature with Hamming Enbedding, Reranking
rerank library for easy reranking of results
Energy-based modeling of chemical reactions
[ACL 2023] Few-shot Reranking for Multi-hop QA via Language Model Prompting
Simple script to re-rank images using OpenAI's CLIP https://github.com/openai/CLIP.
Explore from keyword search to dense retrieval and reranking, which injects the intelligence of LLMs into your search system, making it faster and more effective.
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (Findings of EMNLP 2022). Keywords: Bilingual Lexicon Induction, Word Translation, Cross-Lingual Word Embeddings.
Multilingual Semantic Search with Reranking on a prepared large vectorized dataset comprising 10 million Wikipedia documents. It supports dense retrieval, keyword search, and hybrid search.
Python-based toolkit for building and evaluating a transformer-based FAQ retrieval system
This is an official implementation for "Robust Graph Structure Learning over Images via Multiple Statistical Tests" accepted at NeurIPS 2022.
Who's that Pokemon (card)? Search over more than 10K Pokemon cards to find out the coolest one yet! ✨
Advanced RAG pipeline using Re-Ranking after initial retrieval
Implementation of Probabilistic Retrieval Query expansion and Relevance Model based Language Modelling aimed at improving the precision of results using pseudo-relevance feedback in Information Retrieval.
Alpaca, Bloom, DeciLM, Falcon, Vicuna, Llama2, Zephyr, Mistral(MoE), RAG, Reranking, Langchain, Langsmith..
Chroma DB vector database, with embedding and reranker models to implement a Retrieval Augmented Generation (RAG) system.