DPR

Overview
Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
introduced in Dense Passage Retrieval for Open-Domain Question Answering by
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.
The abstract from the paper is the following:
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 learned from a small number of
questions and passages by a simple dual-encoder framework. 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.
This model was contributed by lhoestq. The original code can be found here.
Usage tips

DPR consists in three models:

Question encoder: encode questions as vectors
Context encoder: encode contexts as vectors
Reader: extract the answer of the questions inside retrieved contexts, along with a relevance score (high if the inferred span actually answers the question).

DPRConfig
[[autodoc]] DPRConfig
DPRContextEncoderTokenizer
[[autodoc]] DPRContextEncoderTokenizer
DPRContextEncoderTokenizerFast
[[autodoc]] DPRContextEncoderTokenizerFast
DPRQuestionEncoderTokenizer
[[autodoc]] DPRQuestionEncoderTokenizer
DPRQuestionEncoderTokenizerFast
[[autodoc]] DPRQuestionEncoderTokenizerFast
DPRReaderTokenizer
[[autodoc]] DPRReaderTokenizer
DPRReaderTokenizerFast
[[autodoc]] DPRReaderTokenizerFast
DPR specific outputs
[[autodoc]] models.dpr.modeling_dpr.DPRContextEncoderOutput
[[autodoc]] models.dpr.modeling_dpr.DPRQuestionEncoderOutput
[[autodoc]] models.dpr.modeling_dpr.DPRReaderOutput

DPRContextEncoder
[[autodoc]] DPRContextEncoder
    - forward
DPRQuestionEncoder
[[autodoc]] DPRQuestionEncoder
    - forward
DPRReader
[[autodoc]] DPRReader
    - forward

TFDPRContextEncoder
[[autodoc]] TFDPRContextEncoder
    - call
TFDPRQuestionEncoder
[[autodoc]] TFDPRQuestionEncoder
    - call
TFDPRReader
[[autodoc]] TFDPRReader
    - call