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Retrieval and Retrieval-augmented LLMs
Run Effective Large Batch Contrastive Learning Beyond GPU/TPU Memory Constraint
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
efficient query encoding for dense retrieval
A Python Interface to Reproducibility Measures of System-Oriented IR Experiments
llama3 implementation one matrix multiplication at a time
Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
A large-scale information-rich web dataset, featuring millions of real clicked query-document labels
This is the official implementation of SpaDE. (CIKM'22)
An open source trusted cloud native registry project that stores, signs, and scans content.
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers https://arxiv.org/abs/2109.08535
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
Survey of Surveys for Natural Language Processing (SOS4NLP)
This is the official code for the WSDM 2022 paper: 'S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks'.
Interpreting Bidirectional Encoder Representations from Transformers (BERT)
Provides a common interface to many IR ranking datasets.
Anserini is a Lucene toolkit for reproducible information retrieval research
A Python framework for performing information retrieval experiments, building on http:https://terrier.org/
A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
Multi-stage passage ranking: monoBERT + duoBERT
Open-Source Information Retrieval Courses @ TU Wien
ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)