Buckets:

rtrm's picture
download
raw
6.61 kB
<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Modelos decodificadores&quot;,&quot;local&quot;:&quot;modelos-decodificadores&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}">
<link href="/docs/course/pr_1069/pt/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/entry/start.854f6ddb.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/scheduler.37c15a92.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/singletons.5a4db441.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/index.18351ede.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/paths.052a2e73.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/entry/app.99d5705b.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/index.2bf4358c.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/nodes/0.24f28b67.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/each.e59479a4.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/nodes/9.4f1d1a0b.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/CourseFloatingBanner.6add7356.js">
<link rel="modulepreload" href="/docs/course/pr_1069/pt/_app/immutable/chunks/getInferenceSnippets.24b50994.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Modelos decodificadores&quot;,&quot;local&quot;:&quot;modelos-decodificadores&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="modelos-decodificadores" 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="#modelos-decodificadores"><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>Modelos decodificadores</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> <p data-svelte-h="svelte-1cb7r38">Os modelos de decodificador usam apenas o decodificador de um modelo Transformer. Em cada etapa, para uma determinada palavra, as camadas de atenção só podem acessar as palavras posicionadas antes dela na frase. Esses modelos geralmente são chamados de <em>modelos auto-regressivos</em>.</p> <p data-svelte-h="svelte-fzoyvo">O pré-treinamento de modelos de decodificadores geralmente gira em torno de prever a próxima palavra na frase.</p> <p data-svelte-h="svelte-1knwfdy">Esses modelos são mais adequados para tarefas que envolvem geração de texto.</p> <p data-svelte-h="svelte-1x0lt16">Os representantes desta família de modelos incluem:</p> <ul data-svelte-h="svelte-1tiql5w"><li><a href="https://huggingface.co/transformers/model_doc/ctrl.html" rel="nofollow">CTRL</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/openai-gpt" rel="nofollow">GPT</a></li> <li><a href="https://huggingface.co/transformers/model_doc/gpt2.html" rel="nofollow">GPT-2</a></li> <li><a href="https://huggingface.co/transformers/model_doc/transfo-xl.html" rel="nofollow">Transformer XL</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/pt/chapter1/6.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_1ef170a = {
assets: "/docs/course/pr_1069/pt",
base: "/docs/course/pr_1069/pt",
env: {}
};
const element = document.currentScript.parentElement;
const data = [null,null];
Promise.all([
import("/docs/course/pr_1069/pt/_app/immutable/entry/start.854f6ddb.js"),
import("/docs/course/pr_1069/pt/_app/immutable/entry/app.99d5705b.js")
]).then(([kit, app]) => {
kit.start(app, element, {
node_ids: [0, 9],
data,
form: null,
error: null
});
});
}
</script>

Xet Storage Details

Size:
6.61 kB
·
Xet hash:
d83ded695d311e914147504a45b31290d6b90666c908bc9d21c370d9c936c895

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.