course/pr_1069/ja/chapter1/7.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Sequence-to-sequence モデル&quot;,&quot;local&quot;:&quot;sequence-to-sequence-モデル&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}">
		<link href="/docs/course/pr_1069/ja/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/entry/start.e837503d.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/scheduler.37c15a92.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/singletons.29b81cfd.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/index.18351ede.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/paths.a1ea15c2.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/entry/app.2ea0d654.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/index.7cb9c9b8.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/nodes/0.dbe7c785.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/each.e59479a4.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/nodes/10.1408a089.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/Youtube.8666c400.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/CourseFloatingBanner.df82c153.js">
		<link rel="modulepreload" href="/docs/course/pr_1069/ja/_app/immutable/chunks/getInferenceSnippets.f9350a3f.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Sequence-to-sequence モデル&quot;,&quot;local&quot;:&quot;sequence-to-sequence-モデル&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="sequence-to-sequence-モデル" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#sequence-to-sequence-モデル"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Sequence-to-sequence モデル</span></h1>  <div class="flex space-x-1 absolute z-10 right-0 top-0"><a href="https://discuss.huggingface.co/t/chapter-1-questions" target="_blank"><img alt="Ask a Question" class="!m-0" src="https://img.shields.io/badge/Ask%20a%20question-ffcb4c.svg?logo=data:image/svg+xml;base64,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"></a>  </div> <iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/0_4KEb08xrE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <p data-svelte-h="svelte-lyvf0o">Encoder-decoderモデル（<em>sequence-to-sequence models</em>とも呼ばれる）はTransformerアーキテクチャのエンコーダーとデコーダーの両方を使用します。
それぞれのステージにおいて、エンコーダーのアテンション層は入力文のすべての単語にアクセスできるのに対して、デコーダーのアテンション層は入力中のある単語の前に位置する単語にのみアクセスできます。</p> <p data-svelte-h="svelte-16xwvvq">これらのモデルの事前学習は、エンコーダー、またはデコーダーの学習と同じように行われますが、通常はより複雑な方法を含みます。
例えば、<a href="https://huggingface.co/t5-base" rel="nofollow">T5</a> は、特殊な単語で文中のスパン（複数の単語を含むことができる）をランダムにマスクしたときに、そのマスクされた文を予測する事を目的として事前学習されています。</p> <p data-svelte-h="svelte-1bxv4ew">Sequence-to-sequenceモデルは、要約、翻訳、質問応答生成などのように、与えられた入力文に対して新しい文を生成するタスクにとても適しています。</p> <p data-svelte-h="svelte-gvyoa7">これらの系統のモデルの代表は次のとおりです:</p> <ul data-svelte-h="svelte-1lyffe"><li><a href="https://huggingface.co/transformers/model_doc/bart.html" rel="nofollow">BART</a></li> <li><a href="https://huggingface.co/transformers/model_doc/mbart.html" rel="nofollow">mBART</a></li> <li><a href="https://huggingface.co/transformers/model_doc/marian.html" rel="nofollow">Marian</a></li> <li><a href="https://huggingface.co/transformers/model_doc/t5.html" rel="nofollow">T5</a></li></ul> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/course/blob/main/chapters/ja/chapter1/7.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
			<script>
				{
					__sveltekit_1tghmjc = {
						assets: "/docs/course/pr_1069/ja",
						base: "/docs/course/pr_1069/ja",
						env: {}
					};

					const element = document.currentScript.parentElement;

					const data = [null,null];

					Promise.all([
						import("/docs/course/pr_1069/ja/_app/immutable/entry/start.e837503d.js"),
						import("/docs/course/pr_1069/ja/_app/immutable/entry/app.2ea0d654.js")
					]).then(([kit, app]) => {
						kit.start(app, element, {
							node_ids: [0, 10],
							data,
							form: null,
							error: null
						});
					});
				}
			</script>