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Passage Retrieval

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보존된 평가 기록: 38

Passage retrieval is a specialized type of IR application that retrieves relevant passages (or pieces of text) rather than an entire ranked set of documents.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/webis-de/acl-22

공식 코드

Clickbait Spoiling via Question Answering and Passage Retrieval

https://github.com/wuning0929/ccp_ijcai22

공식 코드

Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval

https://github.com/xhluca/bm25s

공식 코드

BM25S: Orders of magnitude faster lexical search via eager sparse scoring

https://github.com/yifan-gao/multilingual_keyphrase_generation

공식 코드

Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training

https://github.com/ypapanik/biomedical-slot-filling

공식 코드

Slot Filling for Biomedical Information Extraction

https://github.com/zaaachary/csqa-kepr

공식 코드

KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering

https://github.com/zmzhang2000/hybrank

공식 코드

Hybrid and Collaborative Passage Reranking

https://github.com/AkariAsai/XORQA

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/Ankur3107/dpr-tf

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/FenQQQ/Fusion-in-decoder

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

https://github.com/Georgetown-IR-Lab/curricula-neural-ir

ANTIQUE: A Non-Factoid Question Answering Benchmark

https://github.com/Hannibal046/nanoDPR

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/IBM/retrieve-write-slot-filling

Robust Retrieval Augmented Generation for Zero-shot Slot Filling

https://github.com/MindSpore-scientific/code-7/tree/main/DPT

DPTDR: Deep Prompt Tuning for Dense Passage Retrieval

https://github.com/VietHoang1512/khmer-nltk

ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System

https://github.com/VietHoang1710/khmer-nltk

ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System

https://github.com/ZIZUN/MAFiD

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

https://github.com/airKlizz/MsMarco

Passage Re-ranking with BERT

https://github.com/alexlimh/DPR_MUF

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/amzn/refuel-open-domain-qa

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/beir-cellar/beir

BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

https://github.com/bmitra-msft/TREC-Deep-Learning-Quick-Start

Overview of the TREC 2019 deep learning track

https://github.com/castorini/pygaggle

Passage Re-ranking with BERT

https://github.com/conda-forge/bm25s-feedstock

BM25S: Orders of magnitude faster lexical search via eager sparse scoring

https://github.com/deepset-ai/haystack

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/efficientqa/retrieval-based-baselines

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/emory-irlab/pyterrier_genrank

Fine-Tuning LLaMA for Multi-Stage Text Retrieval

https://github.com/facebookresearch/FiD

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

https://github.com/google-research/t5x_retrieval

Large Dual Encoders Are Generalizable Retrievers

https://github.com/grill-lab/codec

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

https://github.com/hongyuntw/DPR

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/hongyuntw/DPR_BESS

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/huggingface/transformers

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/jhyuklee/DensePhrases

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

https://github.com/jingtaozhan/RepBERT-Index

Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently

https://github.com/jongwooko/distillm

MiniLLM: Knowledge Distillation of Large Language Models

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