89 lines
3.3 KiB
Markdown
89 lines
3.3 KiB
Markdown
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# REALM
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## Overview
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The REALM model was proposed in [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. It's a
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retrieval-augmented language model that firstly retrieves documents from a textual knowledge corpus and then
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utilizes retrieved documents to process question answering tasks.
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The abstract from the paper is the following:
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*Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks
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such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network,
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requiring ever-larger networks to cover more facts. To capture knowledge in a more modular and interpretable way, we
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augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend
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over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. For the
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first time, we show how to pre-train such a knowledge retriever in an unsupervised manner, using masked language
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modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents. We
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demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the
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challenging task of Open-domain Question Answering (Open-QA). We compare against state-of-the-art models for both
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explicit and implicit knowledge storage on three popular Open-QA benchmarks, and find that we outperform all previous
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methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as
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interpretability and modularity.*
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This model was contributed by [qqaatw](https://huggingface.co/qqaatw). The original code can be found
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[here](https://github.com/google-research/language/tree/master/language/realm).
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## RealmConfig
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[[autodoc]] RealmConfig
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## RealmTokenizer
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[[autodoc]] RealmTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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- batch_encode_candidates
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## RealmTokenizerFast
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[[autodoc]] RealmTokenizerFast
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- batch_encode_candidates
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## RealmRetriever
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[[autodoc]] RealmRetriever
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## RealmEmbedder
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[[autodoc]] RealmEmbedder
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- forward
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## RealmScorer
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[[autodoc]] RealmScorer
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- forward
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## RealmKnowledgeAugEncoder
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[[autodoc]] RealmKnowledgeAugEncoder
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- forward
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## RealmReader
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[[autodoc]] RealmReader
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- forward
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## RealmForOpenQA
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[[autodoc]] RealmForOpenQA
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- block_embedding_to
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- forward |