🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models.
-
Updated
Dec 19, 2023 - Python
🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models.
Tevatron - A flexible toolkit for neural retrieval research and development.
A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR).
Train Models Contrastively in Pytorch
Train and Infer Powerful Sentence Embeddings with AnglE | 🔥 SOTA on STS and MTEB Leaderboard
A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).
HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels
EMNLP 2021 - Pre-training architectures for dense retrieval
Train Dense Passage Retriever (DPR) with a single GPU
[SIGIR 2022] Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval
A Python Search Engine for Humans 🥸
WSDM'22 Best Paper: Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
CIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.
SimXNS is a research project for information retrieval. This repo contains official implementations by MSRA NLC team.
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.
🔗 A graph-augmented dense statute retriever. (EACL 2023)
SIGIR 2021: Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
Code for COLING22 paper, DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
An easy-to-use python toolkit for flexibly adapting various neural ranking models to any target domain.
Code and data for reproducing baselines for TopiOCQA, an open-domain conversational question-answering dataset
Add a description, image, and links to the dense-retrieval topic page so that developers can more easily learn about it.
To associate your repository with the dense-retrieval topic, visit your repo's landing page and select "manage topics."