MTEB: Massive Text Embedding Benchmark
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Updated
Jun 22, 2025 - Python
MTEB: Massive Text Embedding Benchmark
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL
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.
Is ChatGPT Good at Search? LLMs as Re-Ranking Agent [EMNLP 2023 Outstanding Paper Award]
🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥. Our toolkit integrates 40 pre-retrieved benchmark datasets and supports 7+ retrieval techniques, 24+ state-of-the-art Reranking models, and multiple RAG methods.
YT Navigator: AI-powered YouTube content explorer that lets you search and chat with channel videos using AI agents. Extract insights from hours of content in seconds with semantic search and precise timestamps.
A LLM RAG system runs on your laptop. 大模型检索增强生成系统,可以轻松部署在笔记本电脑上,实现本地知识库智能问答。
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
Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"
Bag of Visual Feature with Hamming Enbedding, Reranking
Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper: https://arxiv.org/abs/2104.06979.
Crispy reranking models by Mixedbread
Energy-based modeling of chemical reactions
[ACL 2023] Few-shot Reranking for Multi-hop QA via Language Model Prompting
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"
Neural Reranking for Named Entity Recognition, accepted as regular paper at RANLP 2017
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