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			Pytorch
		</a><a class="flex justify-center flex-1 py-1.5 px-2.5 focus:outline-none !no-underline rounded-r text-gray-500 filter grayscale" href="?fw=tf"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
			TensorFlow
		</a></div>
<h1 class="relative group"><a id="gii-thiu" 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="#gii-thiu"><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>Giới thiệu
	</span></h1>



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<p>Trong <a href="/course/chapter2">Chương 2</a>, chúng ta đã khám phá cách sử dụng tokenizer và các mô hình huấn luyện trước để đưa ra dự đoán. Nhưng nếu bạn muốn tinh chỉnh một mô hình được huấn luyện trước cho tập dữ liệu của riêng mình thì sao? Đó là chủ đề của chương này! Bạn sẽ học:</p>
<ul><li>Cách chuẩn bị một tập dữ liệu lớn từ Hub</li>
<li>Cách sử dụng API <code>Trainer</code> cấp cao để tinh chỉnh mô hình</li>
<li>Cách sử dụng vòng lặp huấn luyện tùy chỉnh</li>
<li>Cách tận dụng thư viện 🤗 Accelerate để dễ dàng chạy vòng huấn luyện tùy chỉnh đó trên bất kỳ thiết lập phân tán nào</li></ul>
<p>Để tải các checkpoint được huấn luyện của bạn lên Hugging Face Hub, bạn sẽ cần có tài khoản huggingface.co: <a href="https://huggingface.co/join" rel="nofollow">tạo tài khoản</a></p>


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