dense passage retrieval | Dense Passage Retrieval for Open-Domain Ques

Keyword	CPC	PCC	Volume	Score	Length of keyword
dense passage retrieval	1.88	0.7	6946	56	23
dense	1.34	0.2	9542	4	5
passage	0.46	0.1	5537	89	7
retrieval	0.25	0.8	1194	5	9

Keyword	CPC	PCC	Volume	Score
dense passage retrieval	0.97	0.1	2251	68
dense passage retrieval dpr	1.27	0.9	9607	40
dense passage retrieval github	1.79	0.1	6920	76
dense passage retrieval paper	1.92	0.9	6341	19
dense passage retrieval huggingface	1.49	0.6	9629	60
dense passage retrieval tutorial	0.83	0.2	5074	34
dense passage retrieval for open-domain	0.8	0.4	4972	43
dense passage retrieval python	1.95	1	7447	35
haystack dense passage retrieval	1.54	0.1	9313	27
dense passage retriever dpr	1.21	0.8	1941	71

Search Results related to dense passage retrieval on Search Engine

Dense Passage Retrieval for Open-Domain Question Answering
arxiv.org

https://arxiv.org/abs/2004.04906

WebApr 10, 2020 · When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.

DA: 56 PA: 60 MOZ Rank: 34

facebookresearch/DPR: Dense Passage Retriever - GitHub
github.com

https://github.com/facebookresearch/DPR

WebDense Passage Retrieval ( DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. It is based on the following paper: Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering.

DA: 31 PA: 18 MOZ Rank: 1

DPR - Hugging Face
huggingface.co

https://huggingface.co/docs/transformers/model_doc/dpr

WebWhen evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.

DA: 22 PA: 67 MOZ Rank: 75

Dense Passage Retrieval for Open-Domain Question Answering
aclanthology.org

https://aclanthology.org/2020.emnlp-main.550/

WebApr 18, 2024 · Abstract. Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are …

DA: 48 PA: 92 MOZ Rank: 20

Understanding Dense Passage Retrieval (DPR) System
towardsdatascience.com

https://towardsdatascience.com/understanding-dense-passage-retrieval-dpr-system-bce5aee4fd40

WebSep 19, 2022 · A dense passage retriever (DPR) is responsible for fetching relevant passages w.r.t to the question asked based on the similarity between the high-quality low-dimensional continuous representation of passages and questions.

DA: 10 PA: 38 MOZ Rank: 37

Dense Passage Retrieval for Open-Domain Question …
arxiv.org

https://arxiv.org/pdf/2004.04906.pdf

WebWhen evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene- BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.1. 1 Introduction.

DA: 95 PA: 45 MOZ Rank: 94

DPR/README.md at main · facebookresearch/DPR · GitHub
github.com

https://github.com/facebookresearch/DPR/blob/main/README.md

WebDense Passage Retrieval ( DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. It is based on the following paper: Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering.

DA: 49 PA: 22 MOZ Rank: 46

Dense Passage Retrieval for Open-Domain Question …
arxiv.org

https://arxiv.org/pdf/2004.04906v1

WebOur dense passage retriever (DPR) uses a dense encoder E P() which maps any text passage to a d-dimensional real-valued vectors and build an index for all the Mpassages we will use for retrieval. At run-time, DPR applies a different encoder E Q() that maps the input question to a d-dimensional vector, and retrieves kpassages of which vectors

DA: 24 PA: 83 MOZ Rank: 30

Dense Passage Retrieval: Architectures and Augmentation Methods
acm.org

https://dl.acm.org/doi/epdf/10.1145/3539618.3591796

WebABSTRACT. The dual-encoder model is a dense retrieval architecture, consisting of two encoder models, that has surpassed traditional sparse retrieval methods for open-domain retrieval [1]. But, room exists for improvement, particularly when dense retrievers are exposed to unseen passages or queries.

DA: 75 PA: 82 MOZ Rank: 15

Brief Review — DPR: Dense Passage Retrieval for Open-Domain …
medium.com

https://sh-tsang.medium.com/brief-review-dpr-dense-passage-retrieval-for-open-domain-question-answering-9323cd8f85c4

WebDec 20, 2023 · Dense passage retriever (DPR) uses a dense encoder EP(.) which maps any text passage to a d-dimensional real-valued vectors and builds an index for all the M passages. At run-time, DPR applies a different encoder EQ(.) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to …

DA: 75 PA: 56 MOZ Rank: 53