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fbd8792195
* beginning of dpr modeling * wip * implement forward * remove biencoder + better init weights * export dpr model to embed model for nlp lib * add new api * remove old code * make style * fix dumb typo * don't load bert weights * docs * docs * style * move the `k` parameter * fix init_weights * add pretrained configs * minor * update config names * style * better config * style * clean code based on PR comments * change Dpr to DPR * fix config * switch encoder config to a dict * style * inheritance -> composition * add messages in assert startements * add dpr reader tokenizer * one tokenizer per model * fix base_model_prefix * fix imports * typo * add convert script * docs * change tokenizers conf names * style * change tokenizers conf names * minor * minor * fix wrong names * minor * remove unused convert functions * rename convert script * use return_tensors in tokenizers * remove n_questions dim * move generate logic to tokenizer * style * add docs * docs * quality * docs * add tests * style * add tokenization tests * DPR full tests * Stay true to the attention mask building * update docs * missing param in bert input docs * docs * style Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
90 lines
2.4 KiB
ReStructuredText
90 lines
2.4 KiB
ReStructuredText
DPR
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----------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~
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Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research.
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It is based on the following paper:
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Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, Dense Passage Retrieval for Open-Domain Question Answering.
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The abstract from the paper is the following:
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*Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional
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sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can
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be practically implemented using dense representations alone, where embeddings are learned from a small number of
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questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets,
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our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage
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retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
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benchmarks.*
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The original code can be found `here <https://github.com/facebookresearch/DPR>`_.
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DPRConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRConfig
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:members:
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DPRContextEncoderTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRContextEncoderTokenizer
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:members:
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DPRContextEncoderTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRContextEncoderTokenizerFast
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:members:
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DPRQuestionEncoderTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRQuestionEncoderTokenizer
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:members:
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DPRQuestionEncoderTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRQuestionEncoderTokenizerFast
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:members:
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DPRReaderTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRReaderTokenizer
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:members:
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DPRReaderTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRReaderTokenizerFast
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:members:
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DPRContextEncoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRContextEncoder
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:members:
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DPRQuestionEncoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRQuestionEncoder
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:members:
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DPRReader
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DPRReader
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:members:
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