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<h1 id="">“编码器”模型</h1>


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<p>“编码器”模型指仅使用编码器的Transformer模型。在每个阶段，注意力层都可以获取初始句子中的所有单词。这些模型通常具有“双向”注意力，被称为自编码模型。</p>
<p>这些模型的预训练通常围绕着以某种方式破坏给定的句子（例如：通过随机遮盖其中的单词），并让模型寻找或重建给定的句子。</p>
<p>“编码器”模型最适合于需要理解完整句子的任务，例如：句子分类、命名实体识别（以及更普遍的单词分类）和阅读理解后回答问题。</p>
<p>该系列模型的典型代表有：</p>
<ul><li><a href="https://huggingface.co/transformers/model_doc/albert.html" rel="nofollow">ALBERT</a></li>
<li><a href="https://huggingface.co/transformers/model_doc/bert.html" rel="nofollow">BERT</a></li>
<li><a href="https://huggingface.co/transformers/model_doc/distilbert.html" rel="nofollow">DistilBERT</a></li>
<li><a href="https://huggingface.co/transformers/model_doc/electra.html" rel="nofollow">ELECTRA</a></li>
<li><a href="https://huggingface.co/transformers/model_doc/roberta.html" rel="nofollow">RoBERTa</a></li></ul>


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