import{s as se,o as te}from"../chunks/scheduler.37c15a92.js";import{S as ne,i as ae,g as M,s as p,r as c,A as pe,h as U,f as s,c as o,j as Pl,u,x as y,k as Ll,y as oe,a as n,v as m,t as r,b as Kl,d as J,w as T,p as le}from"../chunks/index.2bf4358c.js";import{C as w}from"../chunks/CodeBlock.4e987730.js";import{C as ee}from"../chunks/CourseFloatingBanner.9ff4c771.js";import{F as ie}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as Rl,E as re}from"../chunks/getInferenceSnippets.7bb25a7f.js";function Je(j){let a,i;return a=new ee({props:{chapter:2,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/zh-CN/chapter2/section6_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/zh-CN/chapter2/section6_tf.ipynb"}]}}),{c(){c(a.$$.fragment)},l(t){u(a.$$.fragment,t)},m(t,b){m(a,t,b),i=!0},i(t){i||(J(a.$$.fragment,t),i=!0)},o(t){r(a.$$.fragment,t),i=!1},d(t){T(a,t)}}}function ce(j){let a,i;return a=new ee({props:{chapter:2,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/zh-CN/chapter2/section6_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/zh-CN/chapter2/section6_pt.ipynb"}]}}),{c(){c(a.$$.fragment)},l(t){u(a.$$.fragment,t)},m(t,b){m(a,t,b),i=!0},i(t){i||(J(a.$$.fragment,t),i=!0)},o(t){r(a.$$.fragment,t),i=!1},d(t){T(a,t)}}}function ue(j){let a,i;return a=new w({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

tokens = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
output = model(**tokens)`,wrap:!1}}),{c(){c(a.$$.fragment)},l(t){u(a.$$.fragment,t)},m(t,b){m(a,t,b),i=!0},i(t){i||(J(a.$$.fragment,t),i=!0)},o(t){r(a.$$.fragment,t),i=!1},d(t){T(a,t)}}}function me(j){let a,i;return a=new w({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

tokens = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
output = model(**tokens)`,wrap:!1}}),{c(){c(a.$$.fragment)},l(t){u(a.$$.fragment,t)},m(t,b){m(a,t,b),i=!0},i(t){i||(J(a.$$.fragment,t),i=!0)},o(t){r(a.$$.fragment,t),i=!1},d(t){T(a,t)}}}function Te(j){let a,i,t,b,k,tl,g,nl,d,f,ll,V,Wl="在过去的几个章节中，我们已经尝试尽可能手动完成大部分工作。我们探索了 tokenizer 的运行机制，并且了解了分词、转换为 inputs ID、填充、截断以及注意力掩码的处理方式。",al,Z,Bl="然而，正如我们在第二节中看到的那样，🤗 Transformers API 能够通过一个高级函数为我们处理所有这些工作，接下来我们就要深入研究这个函数。当你直接在句子上调用你的 <code>tokenizer</code> 时，就可以得到转换后的可以直接放入模型的数据了：",pl,$,ol,N,Sl="这里， <code>model_inputs</code> 变量包含模型运行所需的所有数据。在 DistilBERT 中，它包括 inputs ID 和注意力掩码（attention mask）。其他接受额外输入的模型也会有对应的 tokenizer 可以将输入转化为模型所需要的输入。",il,E,zl="正如我们将在下面的一些例子中看到的，这个函数非常强大。首先，它可以对单个句子进行处理：",rl,Q,Jl,R,Cl="它还一次处理多个多个，并且 API 的用法完全一致：",cl,W,ul,B,_l="它可以使用多种不同的方法对目标进行填充：",ml,S,Tl,z,Gl="它还可以对序列进行截断：",Ml,C,Ul,_,Yl="<code>tokenizer</code> 对象可以处理指定框架张量的转换，然后可以直接发送到模型。例如，在下面的代码示例中，我们告诉 tokenizer 返回不同框架的张量 —— <code>&quot;pt&quot;</code> 返回 PyTorch 张量， <code>&quot;tf&quot;</code> 返回 TensorFlow 张量，而 <code>&quot;np&quot;</code> 则返回 NumPy 数组：",bl,G,yl,Y,wl,F,Fl="如果我们看一下 tokenizer 返回的 inputs ID，我们会发现它们与之前的略有不同：",jl,X,dl,x,fl,v,Xl="句子的开始和结束分别增加了一个 inputs ID。我们来解码上述的两个 ID 序列，看看是怎么回事：",hl,q,Il,H,kl,A,xl="tokenizer 在开头添加了特殊单词 <code>[CLS]</code> ，在结尾添加了特殊单词 <code>[SEP]</code> 。这是因为模型在预训练时使用了这些字词，所以为了得到相同的推断结果，我们也需要添加它们。请注意，有些模型不添加特殊单词，或者添加不同的特殊单词；模型也可能只在开头或结尾添加这些特殊单词。无论如何，tokenizer 知道哪些是必需的，并会为你处理这些问题。",gl,D,Vl,O,vl="现在我们已经看到 <code>tokenizer</code> 对象在处理文本时的所有步骤，让我们最后再看一次它如何通过 tokenizer  API 处理多个序列（填充！），非常长的序列（截断！）以及多种类型的张量：",Zl,h,I,el,P,$l,sl,Nl;k=new ie({props:{fw:j[0]}}),g=new Rl({props:{title:"综合应用",local:"综合应用",headingTag:"h1"}});const ql=[ce,Je],L=[];function Hl(l,e){return l[0]==="pt"?0:1}d=Hl(j),f=L[d]=ql[d](j),$=new w({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEFjaGVja3BvaW50JTIwJTNEJTIwJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQtZmluZXR1bmVkLXNzdC0yLWVuZ2xpc2glMjIlMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KSUwQSUwQXNlcXVlbmNlJTIwJTNEJTIwJTIySSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMEh1Z2dpbmdGYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIyJTBBJTBBbW9kZWxfaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKHNlcXVlbmNlKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

model_inputs = tokenizer(sequence)`,wrap:!1}}),Q=new w({props:{code:"c2VxdWVuY2UlMjAlM0QlMjAlMjJJJ3ZlJTIwYmVlbiUyMHdhaXRpbmclMjBmb3IlMjBhJTIwSHVnZ2luZ0ZhY2UlMjBjb3Vyc2UlMjBteSUyMHdob2xlJTIwbGlmZS4lMjIlMEElMEFtb2RlbF9pbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoc2VxdWVuY2Up",highlighted:`sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

model_inputs = tokenizer(sequence)`,wrap:!1}}),W=new w({props:{code:"c2VxdWVuY2VzJTIwJTNEJTIwJTVCJTIySSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMEh1Z2dpbmdGYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIyJTJDJTIwJTIyU28lMjBoYXZlJTIwSSElMjIlNUQlMEElMEFtb2RlbF9pbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoc2VxdWVuY2VzKQ==",highlighted:`sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

model_inputs = tokenizer(sequences)`,wrap:!1}}),S=new w({props:{code:"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",highlighted:`<span class="hljs-comment"># 将句子序列填充到最长句子的长度</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-string">&quot;longest&quot;</span>)

<span class="hljs-comment"># 将句子序列填充到模型的最大长度</span>
<span class="hljs-comment"># (512 for BERT or DistilBERT)</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-string">&quot;max_length&quot;</span>)

<span class="hljs-comment"># 将句子序列填充到指定的最大长度</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-string">&quot;max_length&quot;</span>, max_length=<span class="hljs-number">8</span>)`,wrap:!1}}),C=new w({props:{code:"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",highlighted:`sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

<span class="hljs-comment"># 将截断比模型最大长度长的句子序列</span>
<span class="hljs-comment"># (512 for BERT or DistilBERT)</span>
model_inputs = tokenizer(sequences, truncation=<span class="hljs-literal">True</span>)

<span class="hljs-comment"># 将截断长于指定最大长度的句子序列</span>
model_inputs = tokenizer(sequences, max_length=<span class="hljs-number">8</span>, truncation=<span class="hljs-literal">True</span>)`,wrap:!1}}),G=new w({props:{code:"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",highlighted:`sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

<span class="hljs-comment"># 返回 PyTorch tensors</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-comment"># 返回 TensorFlow tensors</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-comment"># 返回 NumPy arrays</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)`,wrap:!1}}),Y=new Rl({props:{title:"特殊的 tokens",local:"特殊的 tokens",headingTag:"h2"}}),X=new w({props:{code:"c2VxdWVuY2UlMjAlM0QlMjAlMjJJJ3ZlJTIwYmVlbiUyMHdhaXRpbmclMjBmb3IlMjBhJTIwSHVnZ2luZ0ZhY2UlMjBjb3Vyc2UlMjBteSUyMHdob2xlJTIwbGlmZS4lMjIlMEElMEFtb2RlbF9pbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoc2VxdWVuY2UpJTBBcHJpbnQobW9kZWxfaW5wdXRzJTVCJTIyaW5wdXRfaWRzJTIyJTVEKSUwQSUwQXRva2VucyUyMCUzRCUyMHRva2VuaXplci50b2tlbml6ZShzZXF1ZW5jZSklMEFpZHMlMjAlM0QlMjB0b2tlbml6ZXIuY29udmVydF90b2tlbnNfdG9faWRzKHRva2VucyklMEFwcmludChpZHMp",highlighted:`sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

model_inputs = tokenizer(sequence)
<span class="hljs-built_in">print</span>(model_inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
<span class="hljs-built_in">print</span>(ids)`,wrap:!1}}),x=new w({props:{code:"JTVCMTAxJTJDJTIwMTA0NSUyQyUyMDEwMDUlMkMlMjAyMzEwJTJDJTIwMjA0MiUyQyUyMDM0MDMlMkMlMjAyMDA1JTJDJTIwMTAzNyUyQyUyMDE3NjYyJTJDJTIwMTIxNzIlMkMlMjAyNjA3JTJDJTIwMjAyNiUyQyUyMDI4NzglMkMlMjAyMTY2JTJDJTIwMTAxMiUyQyUyMDEwMiU1RCUwQSU1QjEwNDUlMkMlMjAxMDA1JTJDJTIwMjMxMCUyQyUyMDIwNDIlMkMlMjAzNDAzJTJDJTIwMjAwNSUyQyUyMDEwMzclMkMlMjAxNzY2MiUyQyUyMDEyMTcyJTJDJTIwMjYwNyUyQyUyMDIwMjYlMkMlMjAyODc4JTJDJTIwMjE2NiUyQyUyMDEwMTIlNUQ=",highlighted:`[<span class="hljs-number">101</span>, <span class="hljs-number">1045</span>, <span class="hljs-number">1005</span>, <span class="hljs-number">2310</span>, <span class="hljs-number">2042</span>, <span class="hljs-number">3403</span>, <span class="hljs-number">2005</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">17662</span>, <span class="hljs-number">12172</span>, <span class="hljs-number">2607</span>, <span class="hljs-number">2026</span>, <span class="hljs-number">2878</span>, <span class="hljs-number">2166</span>, <span class="hljs-number">1012</span>, <span class="hljs-number">102</span>]
[<span class="hljs-number">1045</span>, <span class="hljs-number">1005</span>, <span class="hljs-number">2310</span>, <span class="hljs-number">2042</span>, <span class="hljs-number">3403</span>, <span class="hljs-number">2005</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">17662</span>, <span class="hljs-number">12172</span>, <span class="hljs-number">2607</span>, <span class="hljs-number">2026</span>, <span class="hljs-number">2878</span>, <span class="hljs-number">2166</span>, <span class="hljs-number">1012</span>]`,wrap:!1}}),q=new w({props:{code:"cHJpbnQodG9rZW5pemVyLmRlY29kZShtb2RlbF9pbnB1dHMlNUIlMjJpbnB1dF9pZHMlMjIlNUQpKSUwQXByaW50KHRva2VuaXplci5kZWNvZGUoaWRzKSk=",highlighted:`<span class="hljs-built_in">print</span>(tokenizer.decode(model_inputs[<span class="hljs-string">&quot;input_ids&quot;</span>]))
<span class="hljs-built_in">print</span>(tokenizer.decode(ids))`,wrap:!1}}),H=new w({props:{code:"JTIyJTVCQ0xTJTVEJTIwaSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMGh1Z2dpbmdmYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIwJTVCU0VQJTVEJTIyJTBBJTIyaSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMGh1Z2dpbmdmYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIy",highlighted:`<span class="hljs-string">&quot;[CLS] i&#x27;ve been waiting for a huggingface course my whole life. [SEP]&quot;</span>
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Me='{"title":"综合应用","local":"综合应用","sections":[{"title":"特殊的 tokens","local":"特殊的 tokens","sections":[],"depth":2},{"title":"小结：从 tokenizer 到模型","local":"小结：从 tokenizer 到模型","sections":[],"depth":2}],"depth":1}';function Ue(j,a,i){let t="pt";return te(()=>{const b=new URLSearchParams(window.location.search);i(0,t=b.get("fw")||"pt")}),[t]}class he extends ne{constructor(a){super(),ae(this,a,Ue,Te,se,{})}}export{he as component};
