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<h1 class="relative group"><a id="introduction" 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="#introduction"><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>Introduction
	</span></h1>

<p>In <a href="/course/chapter3">Chapter 3</a>, we looked at how to fine-tune a model on a given task. When we do that, we use the same tokenizer that the model was pretrained with — but what do we do when we want to train a model from scratch? In these cases, using a tokenizer that was pretrained on a corpus from another domain or language is typically suboptimal. For example, a tokenizer that’s trained on an English corpus will perform poorly on a corpus of Japanese texts because the use of spaces and punctuation is very different in the two languages.</p>
<p>In this chapter, you will learn how to train a brand new tokenizer on a corpus of texts, so it can then be used to pretrain a language model. This will all be done with the help of the <a href="https://github.com/huggingface/tokenizers" rel="nofollow">🤗 Tokenizers</a> library, which provides the “fast” tokenizers in the <a href="https://github.com/huggingface/transformers" rel="nofollow">🤗 Transformers</a> library. We’ll take a close look at the features that this library provides, and explore how the fast tokenizers differ from the “slow” versions.</p>
<p>Topics we will cover include:</p>
<ul><li>How to train a new tokenizer similar to the one used by a given checkpoint on a new corpus of texts</li>
<li>The special features of fast tokenizers</li>
<li>The differences between the three main subword tokenization algorithms used in NLP today</li>
<li>How to build a tokenizer from scratch with the 🤗 Tokenizers library and train it on some data</li></ul>
<p>The techniques introduced in this chapter will prepare you for the section in <a href="/course/chapter7/6">Chapter 7</a> where we look at creating a language model for Python source code. Let’s start by looking at what it means to “train” a tokenizer in the first place.</p>


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