MTEB: Massive Text Embedding Benchmark
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Updated
Dec 28, 2024 - Jupyter Notebook
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.
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
Rust library for generating vector embeddings, reranking locally
Diffusion on manifolds for image retrieval
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。
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"
An Unsupervised Distance Learning Framework for Multimedia Retrieval
Bag of Visual Feature with Hamming Enbedding, Reranking
rerank library for easy reranking of results
Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper: https://arxiv.org/abs/2104.06979.
Energy-based modeling of chemical reactions
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