huggingface--transformers/docs/source/en/model_doc/dpr.md

This model was published in HF papers on 2020-04-10 and contributed to Hugging Face Transformers on 2020-11-16.

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