MTEB: Massive Text Embedding Benchmark
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Updated
Oct 3, 2024 - Jupyter Notebook
MTEB: Massive Text Embedding Benchmark
A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR).
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]
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
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
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
rerank library for easy reranking of results
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"
[ACL 2023] Few-shot Reranking for Multi-hop QA via Language Model Prompting
Neural Reranking for Named Entity Recognition, accepted as regular paper at RANLP 2017
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