chore: import upstream snapshot with attribution
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<meta name="description" content="This is a PyTorch implementation/tutorial of the paper Improving language models by retrieving from trillions of tokens. It builds a key-value database of chunks of text and retrieves and uses them when making predictions."/>
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<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
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<meta name="twitter:title" content="Retrieval-Enhanced Transformer (Retro)"/>
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<meta name="twitter:description" content="This is a PyTorch implementation/tutorial of the paper Improving language models by retrieving from trillions of tokens. It builds a key-value database of chunks of text and retrieves and uses them when making predictions."/>
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<meta property="og:url" content="https://nn.labml.ai/transformers/retro/index.html"/>
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<meta property="og:title" content="Retrieval-Enhanced Transformer (Retro)"/>
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<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
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<meta property="og:site_name" content="Retrieval-Enhanced Transformer (Retro)"/>
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<meta property="og:type" content="object"/>
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<meta property="og:title" content="Retrieval-Enhanced Transformer (Retro)"/>
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<meta property="og:description" content="This is a PyTorch implementation/tutorial of the paper Improving language models by retrieving from trillions of tokens. It builds a key-value database of chunks of text and retrieves and uses them when making predictions."/>
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<title>Retrieval-Enhanced Transformer (Retro)</title>
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<link rel="stylesheet" href="../../pylit.css?v=1">
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<link rel="canonical" href="https://nn.labml.ai/transformers/retro/index.html"/>
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<p>
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<a class="parent" href="/">home</a>
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<a class="parent" href="../index.html">transformers</a>
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<a class="parent" href="index.html">retro</a>
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</p>
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<p>
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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations" target="_blank">
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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/transformers/retro/__init__.py" target="_blank">
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View code on Github</a>
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<a href='#section-0'>#</a>
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</div>
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<h1>Retrieval-Enhanced Transformer (Retro)</h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper <a href="https://arxiv.org/abs/2112.04426">Improving language models by retrieving from trillions of tokens</a>.</p>
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<p>It builds a database of chunks of text. It is a key-value database where the keys are indexed by the BERT embeddings of the chunks. They use a frozen pre-trained BERT model to calculate these embeddings. The values are the corresponding chunks and an equal length of text proceeding that chunk.</p>
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<p>Then the model retrieves text similar (nearest neighbors) to the input to the model from this database. These retrieved texts are used to predict the output.</p>
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<p>Since we use a frozen BERT model for retrieval we can pre-calculate all the nearest neighbors for the training dataset. This speeds up the training process.</p>
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<p>Components:</p>
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<ul><li><a href="bert_embeddings.html">BERT embeddings</a>: Code to get BERT embeddings of chunks of text. </li>
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<li><a href="database.html">Key-value database</a>: Build and retrieve chunks </li>
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<li><a href="model.html">Model</a> </li>
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<li><a href="dataset.html">Dataset</a>: Pre-calculate the nearest neighbors </li>
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<li><a href="train.html">Training code</a></li></ul>
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<div class='code'>
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<div class="highlight"><pre></pre></div>
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