import{s as St,o as Wt,n as Bt}from"../chunks/scheduler.37c15a92.js";import{S as Lt,i as Ft,g as f,s as c,r as J,A as Xt,h as T,f as n,c as M,j as Vt,u as U,x as b,k as Js,y as Yt,a,v as j,t as o,b as L,d as m,w,m as Pt,n as qt,p as F}from"../chunks/index.2bf4358c.js";import{T as Ht}from"../chunks/Tip.363c041f.js";import{Y as zt}from"../chunks/Youtube.1e50a667.js";import{C as h}from"../chunks/CodeBlock.4e987730.js";import{C as Rt}from"../chunks/CourseFloatingBanner.9ff4c771.js";import{F as Kt}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as fl,E as Ot}from"../chunks/getInferenceSnippets.7bb25a7f.js";function en(y){let s,r;return s=new Rt({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/section2_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/zh-CN/chapter2/section2_tf.ipynb"}]}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function ln(y){let s,r;return s=new Rt({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/section2_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/zh-CN/chapter2/section2_pt.ipynb"}]}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function sn(y){let s;return{c(){s=Pt("這是第一部分，根據您使用 PyTorch 或者 TensorFlow，內容略有不同。點擊標題上方的平臺，選一個您喜歡的吧！")},l(r){s=qt(r,"這是第一部分，根據您使用 PyTorch 或者 TensorFlow，內容略有不同。點擊標題上方的平臺，選一個您喜歡的吧！")},m(r,l){a(r,s,l)},d(r){r&&n(s)}}}function tn(y){let s,r;return s=new zt({props:{id:"wVN12smEvqg"}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function nn(y){let s,r;return s=new zt({props:{id:"1pedAIvTWXk"}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function an(y){let s,r;return s=new h({props:{code:"cmF3X2lucHV0cyUyMCUzRCUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMkkndmUlMjBiZWVuJTIwd2FpdGluZyUyMGZvciUyMGElMjBIdWdnaW5nRmFjZSUyMGNvdXJzZSUyMG15JTIwd2hvbGUlMjBsaWZlLiUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMkklMjBoYXRlJTIwdGhpcyUyMHNvJTIwbXVjaCElMjIlMkMlMEElNUQlMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIocmF3X2lucHV0cyUyQyUyMHBhZGRpbmclM0RUcnVlJTJDJTIwdHJ1bmNhdGlvbiUzRFRydWUlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnRmJTIyKSUwQXByaW50KGlucHV0cyk=",highlighted:`raw_inputs = [
    <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>,
    <span class="hljs-string">&quot;I hate this so much!&quot;</span>,
]
inputs = tokenizer(raw_inputs, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-built_in">print</span>(inputs)`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function rn(y){let s,r;return s=new h({props:{code:"cmF3X2lucHV0cyUyMCUzRCUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMkkndmUlMjBiZWVuJTIwd2FpdGluZyUyMGZvciUyMGElMjBIdWdnaW5nRmFjZSUyMGNvdXJzZSUyMG15JTIwd2hvbGUlMjBsaWZlLiUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMkklMjBoYXRlJTIwdGhpcyUyMHNvJTIwbXVjaCElMjIlMkMlMEElNUQlMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIocmF3X2lucHV0cyUyQyUyMHBhZGRpbmclM0RUcnVlJTJDJTIwdHJ1bmNhdGlvbiUzRFRydWUlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQXByaW50KGlucHV0cyk=",highlighted:`raw_inputs = [
    <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>,
    <span class="hljs-string">&quot;I hate this so much!&quot;</span>,
]
inputs = tokenizer(raw_inputs, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-built_in">print</span>(inputs)`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function pn(y){let s,r="以下是 TensorFlow 張量的結果：",l,i,u;return i=new h({props:{code:"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",highlighted:`{
    <span class="hljs-string">&#x27;input_ids&#x27;</span>: &lt;tf.Tensor: shape=(<span class="hljs-number">2</span>, <span class="hljs-number">16</span>), dtype=int32, numpy=
        array([
            [  <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">101</span>,  <span class="hljs-number">1045</span>,  <span class="hljs-number">5223</span>,  <span class="hljs-number">2023</span>,  <span class="hljs-number">2061</span>,  <span class="hljs-number">2172</span>,   <span class="hljs-number">999</span>,   <span class="hljs-number">102</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>]
        ], dtype=int32)&gt;, 
    <span class="hljs-string">&#x27;attention_mask&#x27;</span>: &lt;tf.Tensor: shape=(<span class="hljs-number">2</span>, <span class="hljs-number">16</span>), dtype=int32, numpy=
        array([
            [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
            [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]
        ], dtype=int32)&gt;
}`,wrap:!1}}),{c(){s=f("p"),s.textContent=r,l=c(),J(i.$$.fragment)},l(p){s=T(p,"P",{"data-svelte-h":!0}),b(s)!=="svelte-1f9p4nc"&&(s.textContent=r),l=M(p),U(i.$$.fragment,p)},m(p,d){a(p,s,d),a(p,l,d),j(i,p,d),u=!0},i(p){u||(m(i.$$.fragment,p),u=!0)},o(p){o(i.$$.fragment,p),u=!1},d(p){p&&(n(s),n(l)),w(i,p)}}}function cn(y){let s,r="以下是PyTorch張量的結果：",l,i,u;return i=new h({props:{code:"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",highlighted:`{
    <span class="hljs-string">&#x27;input_ids&#x27;</span>: tensor([
        [  <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">101</span>,  <span class="hljs-number">1045</span>,  <span class="hljs-number">5223</span>,  <span class="hljs-number">2023</span>,  <span class="hljs-number">2061</span>,  <span class="hljs-number">2172</span>,   <span class="hljs-number">999</span>,   <span class="hljs-number">102</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>,     <span class="hljs-number">0</span>]
    ]), 
    <span class="hljs-string">&#x27;attention_mask&#x27;</span>: tensor([
        [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
        [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]
    ])
}`,wrap:!1}}),{c(){s=f("p"),s.textContent=r,l=c(),J(i.$$.fragment)},l(p){s=T(p,"P",{"data-svelte-h":!0}),b(s)!=="svelte-1at5vza"&&(s.textContent=r),l=M(p),U(i.$$.fragment,p)},m(p,d){a(p,s,d),a(p,l,d),j(i,p,d),u=!0},i(p){u||(m(i.$$.fragment,p),u=!0)},o(p){o(i.$$.fragment,p),u=!1},d(p){p&&(n(s),n(l)),w(i,p)}}}function Mn(y){let s,r="我們可以像使用標記器一樣下載預訓練模型。🤗 Transformers提供了一個<code>TFAutoModel</code>類，該類還具有<code>from_pretrained()</code>方法：",l,i,u;return i=new h({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsJTBBJTBBY2hlY2twb2ludCUyMCUzRCUyMCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkLWZpbmV0dW5lZC1zc3QtMi1lbmdsaXNoJTIyJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCk=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
model = TFAutoModel.from_pretrained(checkpoint)`,wrap:!1}}),{c(){s=f("p"),s.innerHTML=r,l=c(),J(i.$$.fragment)},l(p){s=T(p,"P",{"data-svelte-h":!0}),b(s)!=="svelte-1l9rz52"&&(s.innerHTML=r),l=M(p),U(i.$$.fragment,p)},m(p,d){a(p,s,d),a(p,l,d),j(i,p,d),u=!0},i(p){u||(m(i.$$.fragment,p),u=!0)},o(p){o(i.$$.fragment,p),u=!1},d(p){p&&(n(s),n(l)),w(i,p)}}}function un(y){let s,r="我們可以像使用標記器一樣下載預訓練模型。🤗 Transformers提供了一個<code>AutoModel</code>類，該類還具有<code>from_pretrained()</code>方法：",l,i,u;return i=new h({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUwQSUwQWNoZWNrcG9pbnQlMjAlM0QlMjAlMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZC1maW5ldHVuZWQtc3N0LTItZW5nbGlzaCUyMiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
model = AutoModel.from_pretrained(checkpoint)`,wrap:!1}}),{c(){s=f("p"),s.innerHTML=r,l=c(),J(i.$$.fragment)},l(p){s=T(p,"P",{"data-svelte-h":!0}),b(s)!=="svelte-1rb1u70"&&(s.innerHTML=r),l=M(p),U(i.$$.fragment,p)},m(p,d){a(p,s,d),a(p,l,d),j(i,p,d),u=!0},i(p){u||(m(i.$$.fragment,p),u=!0)},o(p){o(i.$$.fragment,p),u=!1},d(p){p&&(n(s),n(l)),w(i,p)}}}function on(y){let s,r,l,i;return s=new h({props:{code:"b3V0cHV0cyUyMCUzRCUyMG1vZGVsKGlucHV0cyklMEFwcmludChvdXRwdXRzLmxhc3RfaGlkZGVuX3N0YXRlLnNoYXBlKQ==",highlighted:`outputs = model(inputs)
<span class="hljs-built_in">print</span>(outputs.last_hidden_state.shape)`,wrap:!1}}),l=new h({props:{code:"KDIlMkMlMjAxNiUyQyUyMDc2OCk=",highlighted:'(<span class="hljs-number">2</span>, <span class="hljs-number">16</span>, <span class="hljs-number">768</span>)',wrap:!1}}),{c(){J(s.$$.fragment),r=c(),J(l.$$.fragment)},l(u){U(s.$$.fragment,u),r=M(u),U(l.$$.fragment,u)},m(u,p){j(s,u,p),a(u,r,p),j(l,u,p),i=!0},i(u){i||(m(s.$$.fragment,u),m(l.$$.fragment,u),i=!0)},o(u){o(s.$$.fragment,u),o(l.$$.fragment,u),i=!1},d(u){u&&n(r),w(s,u),w(l,u)}}}function mn(y){let s,r,l,i;return s=new h({props:{code:"b3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQXByaW50KG91dHB1dHMubGFzdF9oaWRkZW5fc3RhdGUuc2hhcGUp",highlighted:`outputs = model(**inputs)
<span class="hljs-built_in">print</span>(outputs.last_hidden_state.shape)`,wrap:!1}}),l=new h({props:{code:"dG9yY2guU2l6ZSglNUIyJTJDJTIwMTYlMkMlMjA3NjglNUQp",highlighted:'torch.Size([<span class="hljs-number">2</span>, <span class="hljs-number">16</span>, <span class="hljs-number">768</span>])',wrap:!1}}),{c(){J(s.$$.fragment),r=c(),J(l.$$.fragment)},l(u){U(s.$$.fragment,u),r=M(u),U(l.$$.fragment,u)},m(u,p){j(s,u,p),a(u,r,p),j(l,u,p),i=!0},i(u){i||(m(s.$$.fragment,u),m(l.$$.fragment,u),i=!0)},o(u){o(s.$$.fragment,u),o(l.$$.fragment,u),i=!1},d(u){u&&n(r),w(s,u),w(l,u)}}}function yn(y){let s,r="For our example, we will need a model with a sequence classification head (to be able to classify the sentences as positive or negative). So, we won’t actually use the <code>TFAutoModel</code> class, but <code>TFAutoModelForSequenceClassification</code>:",l,i,u;return i=new h({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQWNoZWNrcG9pbnQlMjAlM0QlMjAlMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZC1maW5ldHVuZWQtc3N0LTItZW5nbGlzaCUyMiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbChpbnB1dHMp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
outputs = model(inputs)`,wrap:!1}}),{c(){s=f("p"),s.innerHTML=r,l=c(),J(i.$$.fragment)},l(p){s=T(p,"P",{"data-svelte-h":!0}),b(s)!=="svelte-6up9xi"&&(s.innerHTML=r),l=M(p),U(i.$$.fragment,p)},m(p,d){a(p,s,d),a(p,l,d),j(i,p,d),u=!0},i(p){u||(m(i.$$.fragment,p),u=!0)},o(p){o(i.$$.fragment,p),u=!1},d(p){p&&(n(s),n(l)),w(i,p)}}}function fn(y){let s,r="對於我們的示例，我們需要一個帶有序列分類頭的模型（能夠將句子分類為肯定或否定）。因此，我們實際上不會使用<code>AutoModel</code>類，而是使用<code>AutoModelForSequenceClassification</code>：",l,i,u;return i=new h({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEFjaGVja3BvaW50JTIwJTNEJTIwJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQtZmluZXR1bmVkLXNzdC0yLWVuZ2xpc2glMjIlMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKGNoZWNrcG9pbnQpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
outputs = model(**inputs)`,wrap:!1}}),{c(){s=f("p"),s.innerHTML=r,l=c(),J(i.$$.fragment)},l(p){s=T(p,"P",{"data-svelte-h":!0}),b(s)!=="svelte-vhai1b"&&(s.innerHTML=r),l=M(p),U(i.$$.fragment,p)},m(p,d){a(p,s,d),a(p,l,d),j(i,p,d),u=!0},i(p){u||(m(i.$$.fragment,p),u=!0)},o(p){o(i.$$.fragment,p),u=!1},d(p){p&&(n(s),n(l)),w(i,p)}}}function Tn(y){let s,r;return s=new h({props:{code:"KDIlMkMlMjAyKQ==",highlighted:'(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>)',wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function bn(y){let s,r;return s=new h({props:{code:"dG9yY2guU2l6ZSglNUIyJTJDJTIwMiU1RCk=",highlighted:'torch.Size([<span class="hljs-number">2</span>, <span class="hljs-number">2</span>])',wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function Jn(y){let s,r;return s=new h({props:{code:"JTNDdGYuVGVuc29yJTNBJTIwc2hhcGUlM0QoMiUyQyUyMDIpJTJDJTIwZHR5cGUlM0RmbG9hdDMyJTJDJTIwbnVtcHklM0QlMEElMjAlMjAlMjAlMjBhcnJheSglNUIlNUItMS41NjA2OTkxJTJDJTIwJTIwMS42MTIyODQyJTVEJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTVCJTIwNC4xNjkyMzElMjAlMkMlMjAtMy4zNDY0NDcyJTVEJTVEJTJDJTIwZHR5cGUlM0RmbG9hdDMyKSUzRQ==",highlighted:`&lt;tf.Tensor: shape=(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>), dtype=float32, numpy=
    array([[-<span class="hljs-number">1.5606991</span>,  <span class="hljs-number">1.6122842</span>],
           [ <span class="hljs-number">4.169231</span> , -<span class="hljs-number">3.3464472</span>]], dtype=float32)&gt;`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function Un(y){let s,r;return s=new h({props:{code:"dGVuc29yKCU1QiU1Qi0xLjU2MDclMkMlMjAlMjAxLjYxMjMlNUQlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlNUIlMjA0LjE2OTIlMkMlMjAtMy4zNDY0JTVEJTVEJTJDJTIwZ3JhZF9mbiUzRCUzQ0FkZG1tQmFja3dhcmQlM0Up",highlighted:`tensor([[-<span class="hljs-number">1.5607</span>,  <span class="hljs-number">1.6123</span>],
        [ <span class="hljs-number">4.1692</span>, -<span class="hljs-number">3.3464</span>]], grad_fn=&lt;AddmmBackward&gt;)`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function jn(y){let s,r;return s=new h({props:{code:"aW1wb3J0JTIwdGVuc29yZmxvdyUyMGFzJTIwdGYlMEElMEFwcmVkaWN0aW9ucyUyMCUzRCUyMHRmLm1hdGguc29mdG1heChvdXRwdXRzLmxvZ2l0cyUyQyUyMGF4aXMlM0QtMSklMEFwcmludChwcmVkaWN0aW9ucyk=",highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

predictions = tf.math.softmax(outputs.logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-built_in">print</span>(predictions)`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function wn(y){let s,r;return s=new h({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEFwcmVkaWN0aW9ucyUyMCUzRCUyMHRvcmNoLm5uLmZ1bmN0aW9uYWwuc29mdG1heChvdXRwdXRzLmxvZ2l0cyUyQyUyMGRpbSUzRC0xKSUwQXByaW50KHByZWRpY3Rpb25zKQ==",highlighted:`<span class="hljs-keyword">import</span> torch

predictions = torch.nn.functional.softmax(outputs.logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-built_in">print</span>(predictions)`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function dn(y){let s,r;return s=new h({props:{code:"dGYuVGVuc29yKCUwQSU1QiU1QjQuMDE5NTE2NzFlLTAyJTIwOS41OTgwNDgzM2UtMDElNUQlMEElMjAlNUI5Ljk5NDU1ODdlLTAxJTIwNS40NDE4NDI0ZS0wNCU1RCU1RCUyQyUyMHNoYXBlJTNEKDIlMkMlMjAyKSUyQyUyMGR0eXBlJTNEZmxvYXQzMik=",highlighted:`tf.Tensor(
[[<span class="hljs-number">4.01951671e-02</span> <span class="hljs-number">9.59804833e-01</span>]
 [<span class="hljs-number">9.9945587e-01</span> <span class="hljs-number">5.4418424e-04</span>]], shape=(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>), dtype=float32)`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function hn(y){let s,r;return s=new h({props:{code:"dGVuc29yKCU1QiU1QjQuMDE5NWUtMDIlMkMlMjA5LjU5ODBlLTAxJTVEJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTVCOS45OTQ2ZS0wMSUyQyUyMDUuNDQxOGUtMDQlNUQlNUQlMkMlMjBncmFkX2ZuJTNEJTNDU29mdG1heEJhY2t3YXJkJTNFKQ==",highlighted:`tensor([[<span class="hljs-number">4.0195e-02</span>, <span class="hljs-number">9.5980e-01</span>],
        [<span class="hljs-number">9.9946e-01</span>, <span class="hljs-number">5.4418e-04</span>]], grad_fn=&lt;SoftmaxBackward&gt;)`,wrap:!1}}),{c(){J(s.$$.fragment)},l(l){U(s.$$.fragment,l)},m(l,i){j(s,l,i),r=!0},i(l){r||(m(s.$$.fragment,l),r=!0)},o(l){o(s.$$.fragment,l),r=!1},d(l){w(s,l)}}}function _n(y){let s,r="✏️ <strong>試試看！</strong> 選擇兩個（或更多）你自己的文本並在管道中運行它們。然後自己複製在這裡看到的步驟，並檢查是否獲得相同的結果！";return{c(){s=f("p"),s.innerHTML=r},l(l){s=T(l,"P",{"data-svelte-h":!0}),b(s)!=="svelte-9lmtrv"&&(s.innerHTML=r)},m(l,i){a(l,s,i)},p:Bt,d(l){l&&n(s)}}}function $n(y){let s,r,l,i,u,p,d,bl,_,$,nl,X,Jl,C,k,al,K,As='讓我們從一個完整的示例開始，看看在<a href="/course/chapter1">Chapter 1</a>中執行以下代碼時在幕後發生了什麼',Ul,O,jl,ee,vs="獲得:",wl,le,dl,se,xs='正如我們在<a href="/course/chapter1">Chapter 1</a>中看到的，此管道將三個步驟組合在一起：預處理、通過模型傳遞輸入和後處理：',hl,Y,Qs='<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/full_nlp_pipeline.svg" alt="The full NLP pipeline: tokenization of text, conversion to IDs, and inference through the Transformer model and the model head."/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/full_nlp_pipeline-dark.svg" alt="The full NLP pipeline: tokenization of text, conversion to IDs, and inference through the Transformer model and the model head."/>',_l,te,Ns="讓我們快速瀏覽一下這些內容。",$l,ne,Cl,ae,Ds="與其他神經網絡一樣，Transformer 模型無法直接處理原始文本， 因此我們管道的第一步是將文本輸入轉換為模型能夠理解的數字。 為此，我們使用<em>tokenizer</em>(標記器)，負責：",kl,re,Es="<li>將輸入拆分為單詞、子單詞或符號（如標點符號），稱為標記(<em>token</em>)</li> <li>將每個標記(token)映射到一個整數</li> <li>添加可能對模型有用的其他輸入</li>",Il,pe,Gs='所有這些預處理都需要以與模型預訓練時完全相同的方式完成，因此我們首先需要從<a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>中下載這些信息。為此，我們使用<code>AutoTokenizer</code>類及其<code>from_pretrained()</code>方法。使用我們模型的檢查點名稱，它將自動獲取與模型的標記器相關聯的數據，並對其進行緩存（因此只有在您第一次運行下面的代碼時才會下載）。',gl,ie,Zs='因為<code>sentiment-analysis</code>（情緒分析）管道的默認檢查點是<code>distilbert-base-uncased-finetuned-sst-2-english</code>（你可以看到它的模型卡<a href="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english" rel="nofollow">here</a>)，我們運行以下程序：',Al,ce,vl,Me,Vs="一旦我們有了標記器，我們就可以直接將我們的句子傳遞給它，然後我們就會得到一本字典，它可以提供給我們的模型！剩下要做的唯一一件事就是將輸入ID列表轉換為張量。",xl,ue,Hs="您可以使用🤗 Transformers，而不必擔心哪個 ML 框架被用作後端；它可能是 PyTorch 或 TensorFlow，或 Flax。但是，Transformers型號只接受<em>張量</em>作為輸入。如果這是你第一次聽說張量，你可以把它們想象成NumPy數組。NumPy數組可以是標量（0D）、向量（1D）、矩陣（2D）或具有更多維度。它實際上是張量；其他 ML 框架的張量行為類似，通常與 NumPy 數組一樣易於實例化。",Ql,oe,zs="要指定要返回的張量類型（PyTorch、TensorFlow 或 plain NumPy），我們使用<code>return_tensors</code>參數：",Nl,I,g,rl,me,Rs="現在不要擔心填充和截斷；我們稍後會解釋這些。這裡要記住的主要事情是，您可以傳遞一個句子或一組句子，還可以指定要返回的張量類型（如果沒有傳遞類型，您將得到一組列表）。",Dl,A,v,pl,ye,Ss="輸出本身是一個包含兩個鍵的字典，<code>input_ids</code>和<code>attention_mask</code>。<code>input_ids</code>包含兩行整數（每個句子一行），它們是每個句子中標記的唯一標記（token）。我們將在本章後面解釋什麼是<code>attention_mask</code>。",El,fe,Gl,x,Q,il,Te,Ws="在這個代碼片段中，我們下載了之前在管道中使用的相同檢查點（它實際上應該已經被緩存），並用它實例化了一個模型。",Zl,be,Bs="這個架構只包含基本轉換器模塊：給定一些輸入，它輸出我們將調用的內容<em>隱藏狀態（hidden states）</em>，亦稱<em>特徵（features）</em>。對於每個模型輸入，我們將檢索一個高維向量，表示<strong>Transformer模型對該輸入的上下文理解</strong>。",Vl,Je,Ls="如果這不合理，不要擔心。我們以後再解釋。",Hl,Ue,Fs='雖然這些隱藏狀態本身可能很有用，但它們通常是模型另一部分（稱為<em>頭部(head)</em>）的輸入。 在<a href="/course/chapter1">Chapter 1</a>中，可以使用相同的體系結構執行不同的任務，但這些任務中的每個任務都有一個與之關聯的不同頭。',zl,je,Rl,we,Xs="Transformers 模塊的向量輸出通常較大。它通常有三個維度：",Sl,de,Ys="<li><strong>Batch size</strong>: 一次處理的序列數（在我們的示例中為2）。</li> <li><strong>Sequence length</strong>: 序列的數值表示的長度（在我們的示例中為16）。</li> <li><strong>Hidden size</strong>: 每個模型輸入的向量維度。</li>",Wl,he,Ps="由於最後一個值，它被稱為「高維」。隱藏的大小可能非常大（768通常用於較小的型號，而在較大的型號中，這可能達到3072或更大）。",Bl,_e,qs="如果我們將預處理的輸入輸入到模型中，我們可以看到這一點：",Ll,N,D,cl,$e,Ks="注意🤗 Transformers 模型的輸出與<code>namedtuple</code>或詞典相似。您可以通過屬性（就像我們所做的那樣）或鍵（<code>輸出[&quot;last_hidden_state&quot;]</code>）訪問元素，甚至可以通過索引訪問元素，前提是您確切知道要查找的內容在哪裡（<code>outputs[0]</code>）。",Fl,Ce,Xl,ke,Os="模型頭將隱藏狀態的高維向量作為輸入，並將其投影到不同的維度。它們通常由一個或幾個線性層組成：",Yl,P,et='<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/transformer_and_head.svg" alt="A Transformer network alongside its head."/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/transformer_and_head-dark.svg" alt="A Transformer network alongside its head."/>',Pl,Ie,lt="Transformers 模型的輸出直接發送到模型頭進行處理。",ql,ge,st="在此圖中，模型由其嵌入層和後續層表示。嵌入層將標記化輸入中的每個輸入ID轉換為表示關聯標記(token)的向量。後續層使用注意機制操縱這些向量，以生成句子的最終表示。",Kl,Ae,tt="🤗 Transformers中有許多不同的體系結構，每種體系結構都是圍繞處理特定任務而設計的。以下是一個非詳盡的列表：",Ol,ve,nt="<li><code>*Model</code> (retrieve the hidden states)</li> <li><code>*ForCausalLM</code></li> <li><code>*ForMaskedLM</code></li> <li><code>*ForMultipleChoice</code></li> <li><code>*ForQuestionAnswering</code></li> <li><code>*ForSequenceClassification</code></li> <li><code>*ForTokenClassification</code></li> <li>以及其他 🤗</li>",es,E,G,Ml,xe,at="現在，如果我們觀察輸入的形狀，維度將低得多：模型頭將我們之前看到的高維向量作為輸入，並輸出包含兩個值的向量（每個標籤一個）：",ls,Qe,ss,Z,V,ul,Ne,rt="因為我們只有兩個句子和兩個標籤，所以我們從模型中得到的結果是2 x 2的形狀。",ts,De,ns,Ee,pt="我們從模型中得到的輸出值本身並不一定有意義。我們來看看,",as,Ge,rs,H,z,ol,Ze,it='我們的模型預測第一句為<code>[-1.5607, 1.6123]</code>，第二句為<code>[ 4.1692, -3.3464]</code>。這些不是概率，而是<em>logits</em>，即模型最後一層輸出的原始非標準化分數。要轉換為概率，它們需要經過<a href="https://en.wikipedia.org/wiki/Softmax_function" rel="nofollow">SoftMax</a>層（所有🤗Transformers模型輸出logits，因為用於訓練的損耗函數通常會將最後的激活函數（如SoftMax）與實際損耗函數（如交叉熵）融合）：',ps,R,S,ml,W,B,yl,Ve,ct="現在我們可以看到，模型預測第一句為<code>[0.0402, 0.9598]</code>，第二句為<code>[0.9995, 0.0005]</code>。這些是可識別的概率分數。",is,He,Mt="為了獲得每個位置對應的標籤，我們可以檢查模型配置的<code>id2label</code>屬性（下一節將對此進行詳細介紹）：",cs,ze,Ms,Re,us,Se,ut="現在我們可以得出結論，該模型預測了以下幾點：",os,We,ot="<li>第一句：否定：0.0402，肯定：0.9598</li> <li>第二句：否定：0.9995，肯定：0.0005</li>",ms,Be,mt="我們已經成功地複製了管道的三個步驟：使用標記化器進行預處理、通過模型傳遞輸入以及後處理！現在，讓我們花一些時間深入瞭解這些步驟中的每一步。",ys,q,fs,Le,Ts,Tl,bs;u=new Kt({props:{fw:y[0]}}),d=new fl({props:{title:"管道的內部",local:"管道的內部",headingTag:"h1"}});const yt=[ln,en],Fe=[];function ft(e,t){return e[0]==="pt"?0:1}_=ft(y),$=Fe[_]=yt[_](y),X=new Ht({props:{$$slots:{default:[sn]},$$scope:{ctx:y}}});const Tt=[nn,tn],Xe=[];function bt(e,t){return e[0]==="pt"?0:1}C=bt(y),k=Xe[C]=Tt[C](y),O=new h({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBY2xhc3NpZmllciUyMCUzRCUyMHBpcGVsaW5lKCUyMnNlbnRpbWVudC1hbmFseXNpcyUyMiklMEFjbGFzc2lmaWVyKCUwQSUyMCUyMCUyMCUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMkkndmUlMjBiZWVuJTIwd2FpdGluZyUyMGZvciUyMGElMjBIdWdnaW5nRmFjZSUyMGNvdXJzZSUyMG15JTIwd2hvbGUlMjBsaWZlLiUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMkklMjBoYXRlJTIwdGhpcyUyMHNvJTIwbXVjaCElMjIlMkMlMEElMjAlMjAlMjAlMjAlNUQlMEEp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

classifier = pipeline(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>)
classifier(
    [
        <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>,
        <span class="hljs-string">&quot;I hate this so much!&quot;</span>,
    ]
)`,wrap:!1}}),le=new h({props:{code:"JTVCJTdCJ2xhYmVsJyUzQSUyMCdQT1NJVElWRSclMkMlMjAnc2NvcmUnJTNBJTIwMC45NTk4MDQ3MTM3MjYwNDM3JTdEJTJDJTBBJTIwJTdCJ2xhYmVsJyUzQSUyMCdORUdBVElWRSclMkMlMjAnc2NvcmUnJTNBJTIwMC45OTk0NTU4MDk1OTMyMDA3JTdEJTVE",highlighted:`[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9598047137260437</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;NEGATIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9994558095932007</span>}]`,wrap:!1}}),ne=new fl({props:{title:"使用分詞器進行預處理",local:"使用分詞器進行預處理",headingTag:"h2"}}),ce=new h({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEFjaGVja3BvaW50JTIwJTNEJTIwJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQtZmluZXR1bmVkLXNzdC0yLWVuZ2xpc2glMjIlMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KQ==",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)`,wrap:!1}});const Jt=[rn,an],Ye=[];function Ut(e,t){return e[0]==="pt"?0:1}I=Ut(y),g=Ye[I]=Jt[I](y);const jt=[cn,pn],Pe=[];function wt(e,t){return e[0]==="pt"?0:1}A=wt(y),v=Pe[A]=jt[A](y),fe=new fl({props:{title:"瀏覽模型",local:"瀏覽模型",headingTag:"h2"}});const dt=[un,Mn],qe=[];function ht(e,t){return e[0]==="pt"?0:1}x=ht(y),Q=qe[x]=dt[x](y),je=new fl({props:{title:"高維向量？",local:"高維向量",headingTag:"h3"}});const _t=[mn,on],Ke=[];function $t(e,t){return e[0]==="pt"?0:1}N=$t(y),D=Ke[N]=_t[N](y),Ce=new fl({props:{title:"模型頭：數字的意義",local:"模型頭數字的意義",headingTag:"h3"}});const Ct=[fn,yn],Oe=[];function kt(e,t){return e[0]==="pt"?0:1}E=kt(y),G=Oe[E]=Ct[E](y),Qe=new h({props:{code:"cHJpbnQob3V0cHV0cy5sb2dpdHMuc2hhcGUp",highlighted:'<span class="hljs-built_in">print</span>(outputs.logits.shape)',wrap:!1}});const It=[bn,Tn],el=[];function gt(e,t){return e[0]==="pt"?0:1}Z=gt(y),V=el[Z]=It[Z](y),De=new 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