MTEB: Massive Text Embedding Benchmark
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Updated
Jun 18, 2024 - Python
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.
Advanced RAG pipeline using Re-Ranking after initial retrieval
Is ChatGPT Good at Search? LLMs as Re-Ranking Agent [EMNLP 2023 Outstanding Paper Award]
This repository showcases a comprehensive approach to information retrieval, document re-ranking, and language model integration. It incorporates techniques such as document chunking, embedding projection, and automatic query expansion to enhance the effectiveness of information retrieval systems.
Wikipedia Semantic Search w/ Embeddings
This is an official implementation for "Robust Graph Structure Learning over Images via Multiple Statistical Tests" accepted at NeurIPS 2022.
Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"
Information Retrieval using KoSentence-BERT
[ACL 2023] Few-shot Reranking for Multi-hop QA via Language Model Prompting
Multi-stage Retrieval using SPLADE or RM3 and T5.
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (Findings of EMNLP 2022). Keywords: Bilingual Lexicon Induction, Word Translation, Cross-Lingual Word Embeddings.
Energy-based modeling of chemical reactions
Code, datasets, and checkpoints for the paper "Improving Passage Retrieval with Zero-Shot Question Generation (EMNLP 2022)"
predicting a movie list with Two-sided Fairness-aware Recommendation Model (accotding to TFROM_A article) dataset : https://grouplens.org/datasets/movielens/100k/
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"
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
Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper: https://arxiv.org/abs/2104.06979.
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