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| <link rel="modulepreload" href="/docs/course/pr_1021/zh-CN/_app/immutable/chunks/getInferenceSnippets.ebf8be91.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"精通自然语言处理","local":"精通自然语言处理","sections":[],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="精通自然语言处理" 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="#精通自然语言处理"><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>精通自然语言处理</span></h1> <p data-svelte-h="svelte-1p04cvk">如果你在课程中做到了这一步,恭喜你——你现在拥有了用 🤗 Transformers 和 Hugging Face 生态系统解决(几乎)任何 NLP 任务所需的所有知识和工具!</p> <p data-svelte-h="svelte-ocwvo1">我们见过很多不同的数据整理器,所以我们制作了这个小视频来帮助你找到每个任务使用哪一个:</p> <iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/-RPeakdlHYo" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <p data-svelte-h="svelte-1qykogt">在完成核心 NLP 任务的快速入门后,你应该:</p> <ul data-svelte-h="svelte-1fcukwk"><li>了解哪种架构(编码器、解码器或编码器-解码器)最适合哪种任务</li> <li>了解预训练和微调语言模型之间的区别</li> <li>了解如何使用 <code>Trainer</code> API 和 🤗 Accelerate 或 TensorFlow 和 Keras 的分布式训练功能来训练 Transformer 模型,具体选择那一种方法取决于你所需要完成的任务。</li> <li>了解 ROUGE 和 BLEU 等指标在文本生成任务中的意义和局限性</li> <li>知道如何在 Hub 上和使用 🤗 Transformers 中的“管道”与你的微调模型进行交互</li></ul> <p data-svelte-h="svelte-1igubg2">尽管掌握了所有这些知识,但总有一天你会遇到代码中的困难错误,或者对如何解决特定的 NLP 问题有疑问。幸运的是,Hugging Face 社区随时为你提供帮助!在这部分课程的最后一章中,我们将探讨如何调试 Transformer 模型并有效地寻求帮助。</p> <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/zh-CN/chapter7/8.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
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