Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was intorduced 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.
The original code can be found here.
.. autoclass:: transformers.DPRConfig
:members:
.. autoclass:: transformers.DPRContextEncoderTokenizer
:members:
.. autoclass:: transformers.DPRContextEncoderTokenizerFast
:members:
.. autoclass:: transformers.DPRQuestionEncoderTokenizer
:members:
.. autoclass:: transformers.DPRQuestionEncoderTokenizerFast
:members:
.. autoclass:: transformers.DPRReaderTokenizer
:members:
.. autoclass:: transformers.DPRReaderTokenizerFast
:members:
.. autoclass:: transformers.modeling_dpr.DPRContextEncoderOutput
:members:
.. autoclass:: transformers.modeling_dpr.DPRQuestionEncoderOutput
:members:
.. autoclass:: transformers.modeling_dpr.DPRReaderOutput
:members:
.. autoclass:: transformers.DPRContextEncoder
:members: forward
.. autoclass:: transformers.DPRQuestionEncoder
:members: forward
.. autoclass:: transformers.DPRReader
:members: forward