import{s as Ja,o as Wa,n as I}from"../chunks/scheduler.9bc65507.js";import{S as Va,i as za,g as p,s as o,u as h,A as Ra,h as c,f as t,c as i,j as x,v as g,y as f,k,o as q,a,w as y,d as $,t as v,x as b}from"../chunks/index.78c82d43.js";import{T as wt}from"../chunks/Tip.87d55b76.js";import{I as le}from"../chunks/IconCopyLink.bedaa44d.js";import{C as J}from"../chunks/CodeBlock.48618ec2.js";import{F as xa,M as Js}from"../chunks/Markdown.993b48ef.js";function Ua(Z){let n,d='You can also save your configuration file as a dictionary or even just the difference between your custom configuration attributes and the default configuration attributes! See the <a href="main_classes/configuration">configuration</a> documentation for more details.';return{c(){n=p("p"),n.innerHTML=d},l(r){n=c(r,"P",{"data-svelte-h":!0}),f(n)!=="svelte-4asqog"&&(n.innerHTML=d)},m(r,m){a(r,n,m)},p:I,d(r){r&&t(n)}}}function Fa(Z){let n,d="Load your custom configuration attributes into the model:",r,m,_,j,R="This creates a model with random values instead of pretrained weights. You won’t be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.",C,T,W='Create a pretrained model with <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a>:',u,w,U,V,G="When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you’d like:",F,z,E;return m=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRNb2RlbCUwQSUwQW15X2NvbmZpZyUyMCUzRCUyMERpc3RpbEJlcnRDb25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZ5b3VyX21vZGVsX3NhdmVfcGF0aCUyRmNvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwRGlzdGlsQmVydE1vZGVsKG15X2NvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;./your_model_save_path/config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel(my_config)`}}),w=new J({props:{code:"bW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0TW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)'}}),z=new J({props:{code:"bW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0TW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyJTJDJTIwY29uZmlnJTNEbXlfY29uZmlnKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, config=my_config)'}}),{c(){n=p("p"),n.textContent=d,r=o(),h(m.$$.fragment),_=o(),j=p("p"),j.textContent=R,C=o(),T=p("p"),T.innerHTML=W,u=o(),h(w.$$.fragment),U=o(),V=p("p"),V.textContent=G,F=o(),h(z.$$.fragment)},l(l){n=c(l,"P",{"data-svelte-h":!0}),f(n)!=="svelte-16yp9sj"&&(n.textContent=d),r=i(l),g(m.$$.fragment,l),_=i(l),j=c(l,"P",{"data-svelte-h":!0}),f(j)!=="svelte-17byrht"&&(j.textContent=R),C=i(l),T=c(l,"P",{"data-svelte-h":!0}),f(T)!=="svelte-bav0i8"&&(T.innerHTML=W),u=i(l),g(w.$$.fragment,l),U=i(l),V=c(l,"P",{"data-svelte-h":!0}),f(V)!=="svelte-1dfgiok"&&(V.textContent=G),F=i(l),g(z.$$.fragment,l)},m(l,M){a(l,n,M),a(l,r,M),y(m,l,M),a(l,_,M),a(l,j,M),a(l,C,M),a(l,T,M),a(l,u,M),y(w,l,M),a(l,U,M),a(l,V,M),a(l,F,M),y(z,l,M),E=!0},p:I,i(l){E||($(m.$$.fragment,l),$(w.$$.fragment,l),$(z.$$.fragment,l),E=!0)},o(l){v(m.$$.fragment,l),v(w.$$.fragment,l),v(z.$$.fragment,l),E=!1},d(l){l&&(t(n),t(r),t(_),t(j),t(C),t(T),t(u),t(U),t(V),t(F)),b(m,l),b(w,l),b(z,l)}}}function Ea(Z){let n,d;return n=new Js({props:{$$slots:{default:[Fa]},$$scope:{ctx:Z}}}),{c(){h(n.$$.fragment)},l(r){g(n.$$.fragment,r)},m(r,m){y(n,r,m),d=!0},p(r,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:r}),n.$set(_)},i(r){d||($(n.$$.fragment,r),d=!0)},o(r){v(n.$$.fragment,r),d=!1},d(r){b(n,r)}}}function Ga(Z){let n,d="Load your custom configuration attributes into the model:",r,m,_,j,R="This creates a model with random values instead of pretrained weights. You won’t be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.",C,T,W='Create a pretrained model with <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a>:',u,w,U,V,G="When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you’d like:",F,z,E;return m=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGRGlzdGlsQmVydE1vZGVsJTBBJTBBbXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnlvdXJfbW9kZWxfc2F2ZV9wYXRoJTJGbXlfY29uZmlnLmpzb24lMjIpJTBBdGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRNb2RlbChteV9jb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;./your_model_save_path/my_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertModel(my_config)`}}),w=new J({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)'}}),z=new J({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIlMkMlMjBjb25maWclM0RteV9jb25maWcp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, config=my_config)'}}),{c(){n=p("p"),n.textContent=d,r=o(),h(m.$$.fragment),_=o(),j=p("p"),j.textContent=R,C=o(),T=p("p"),T.innerHTML=W,u=o(),h(w.$$.fragment),U=o(),V=p("p"),V.textContent=G,F=o(),h(z.$$.fragment)},l(l){n=c(l,"P",{"data-svelte-h":!0}),f(n)!=="svelte-16yp9sj"&&(n.textContent=d),r=i(l),g(m.$$.fragment,l),_=i(l),j=c(l,"P",{"data-svelte-h":!0}),f(j)!=="svelte-17byrht"&&(j.textContent=R),C=i(l),T=c(l,"P",{"data-svelte-h":!0}),f(T)!=="svelte-1lce61q"&&(T.innerHTML=W),u=i(l),g(w.$$.fragment,l),U=i(l),V=c(l,"P",{"data-svelte-h":!0}),f(V)!=="svelte-1dfgiok"&&(V.textContent=G),F=i(l),g(z.$$.fragment,l)},m(l,M){a(l,n,M),a(l,r,M),y(m,l,M),a(l,_,M),a(l,j,M),a(l,C,M),a(l,T,M),a(l,u,M),y(w,l,M),a(l,U,M),a(l,V,M),a(l,F,M),y(z,l,M),E=!0},p:I,i(l){E||($(m.$$.fragment,l),$(w.$$.fragment,l),$(z.$$.fragment,l),E=!0)},o(l){v(m.$$.fragment,l),v(w.$$.fragment,l),v(z.$$.fragment,l),E=!1},d(l){l&&(t(n),t(r),t(_),t(j),t(C),t(T),t(u),t(U),t(V),t(F)),b(m,l),b(w,l),b(z,l)}}}function Ha(Z){let n,d;return n=new Js({props:{$$slots:{default:[Ga]},$$scope:{ctx:Z}}}),{c(){h(n.$$.fragment)},l(r){g(n.$$.fragment,r)},m(r,m){y(n,r,m),d=!0},p(r,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:r}),n.$set(_)},i(r){d||($(n.$$.fragment,r),d=!0)},o(r){v(n.$$.fragment,r),d=!1},d(r){b(n,r)}}}function Ba(Z){let n,d='For example, <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.',r,m,_,j,R='Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.',C,T,W;return m=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBbW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0Rm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),T=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQW1vZGVsJTIwJTNEJTIwRGlzdGlsQmVydEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),{c(){n=p("p"),n.innerHTML=d,r=o(),h(m.$$.fragment),_=o(),j=p("p"),j.innerHTML=R,C=o(),h(T.$$.fragment)},l(u){n=c(u,"P",{"data-svelte-h":!0}),f(n)!=="svelte-10dq4mt"&&(n.innerHTML=d),r=i(u),g(m.$$.fragment,u),_=i(u),j=c(u,"P",{"data-svelte-h":!0}),f(j)!=="svelte-mjs6hn"&&(j.innerHTML=R),C=i(u),g(T.$$.fragment,u)},m(u,w){a(u,n,w),a(u,r,w),y(m,u,w),a(u,_,w),a(u,j,w),a(u,C,w),y(T,u,w),W=!0},p:I,i(u){W||($(m.$$.fragment,u),$(T.$$.fragment,u),W=!0)},o(u){v(m.$$.fragment,u),v(T.$$.fragment,u),W=!1},d(u){u&&(t(n),t(r),t(_),t(j),t(C)),b(m,u),b(T,u)}}}function Xa(Z){let n,d;return n=new Js({props:{$$slots:{default:[Ba]},$$scope:{ctx:Z}}}),{c(){h(n.$$.fragment)},l(r){g(n.$$.fragment,r)},m(r,m){y(n,r,m),d=!0},p(r,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:r}),n.$set(_)},i(r){d||($(n.$$.fragment,r),d=!0)},o(r){v(n.$$.fragment,r),d=!1},d(r){b(n,r)}}}function Ya(Z){let n,d='For example, <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.',r,m,_,j,R='Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.',C,T,W;return m=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGRGlzdGlsQmVydEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEF0Zl9tb2RlbCUyMCUzRCUyMFRGRGlzdGlsQmVydEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),T=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGRGlzdGlsQmVydEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBdGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),{c(){n=p("p"),n.innerHTML=d,r=o(),h(m.$$.fragment),_=o(),j=p("p"),j.innerHTML=R,C=o(),h(T.$$.fragment)},l(u){n=c(u,"P",{"data-svelte-h":!0}),f(n)!=="svelte-1qa2xrx"&&(n.innerHTML=d),r=i(u),g(m.$$.fragment,u),_=i(u),j=c(u,"P",{"data-svelte-h":!0}),f(j)!=="svelte-iibk5n"&&(j.innerHTML=R),C=i(u),g(T.$$.fragment,u)},m(u,w){a(u,n,w),a(u,r,w),y(m,u,w),a(u,_,w),a(u,j,w),a(u,C,w),y(T,u,w),W=!0},p:I,i(u){W||($(m.$$.fragment,u),$(T.$$.fragment,u),W=!0)},o(u){v(m.$$.fragment,u),v(T.$$.fragment,u),W=!1},d(u){u&&(t(n),t(r),t(_),t(j),t(C)),b(m,u),b(T,u)}}}function Pa(Z){let n,d;return n=new Js({props:{$$slots:{default:[Ya]},$$scope:{ctx:Z}}}),{c(){h(n.$$.fragment)},l(r){g(n.$$.fragment,r)},m(r,m){y(n,r,m),d=!0},p(r,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:r}),n.$set(_)},i(r){d||($(n.$$.fragment,r),d=!0)},o(r){v(n.$$.fragment,r),d=!1},d(r){b(n,r)}}}function La(Z){let n,d='Not every model supports a fast tokenizer. Take a look at this <a href="index#supported-frameworks">table</a> to check if a model has fast tokenizer support.';return{c(){n=p("p"),n.innerHTML=d},l(r){n=c(r,"P",{"data-svelte-h":!0}),f(n)!=="svelte-18y3slz"&&(n.innerHTML=d)},m(r,m){a(r,n,m)},p:I,d(r){r&&t(n)}}}function Ia(Z){let n,d='By default, <a href="/docs/transformers/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a> will try to load a fast tokenizer. You can disable this behavior by setting <code>use_fast=False</code> in <code>from_pretrained</code>.';return{c(){n=p("p"),n.innerHTML=d},l(r){n=c(r,"P",{"data-svelte-h":!0}),f(n)!=="svelte-1fpo2gs"&&(n.innerHTML=d)},m(r,m){a(r,n,m)},p:I,d(r){r&&t(n)}}}function Na(Z){let n,d="If you aren’t looking for any customization, just use the <code>from_pretrained</code> method to load a model’s default image processor parameters.";return{c(){n=p("p"),n.innerHTML=d},l(r){n=c(r,"P",{"data-svelte-h":!0}),f(n)!=="svelte-10var38"&&(n.innerHTML=d)},m(r,m){a(r,n,m)},p:I,d(r){r&&t(n)}}}function Qa(Z){let n,d="If you aren’t looking for any customization, just use the <code>from_pretrained</code> method to load a model’s default feature extractor parameters.";return{c(){n=p("p"),n.innerHTML=d},l(r){n=c(r,"P",{"data-svelte-h":!0}),f(n)!=="svelte-1e7p61z"&&(n.innerHTML=d)},m(r,m){a(r,n,m)},p:I,d(r){r&&t(n)}}}function Sa(Z){let n,d,r,m,_,j,R,C,T,W,u="Create a custom architecture",w,U,V='An <a href="model_doc/auto"><code>AutoClass</code></a> automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an <code>AutoClass</code> to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers model from just a few base classes. This could be particularly useful for anyone who is interested in studying, training or experimenting with a 🤗 Transformers model. In this guide, dive deeper into creating a custom model without an <code>AutoClass</code>. Learn how to:',G,F,z="<li>Load and customize a model configuration.</li> <li>Create a model architecture.</li> <li>Create a slow and fast tokenizer for text.</li> <li>Create an image processor for vision tasks.</li> <li>Create a feature extractor for audio tasks.</li> <li>Create a processor for multimodal tasks.</li>",E,l,M,mt,oe,Ws,nt,Gs="Configuration",Mt,ie,Hs='A <a href="main_classes/configuration">configuration</a> refers to a model’s specific attributes. Each model configuration has different attributes; for instance, all NLP models have the <code>hidden_size</code>, <code>num_attention_heads</code>, <code>num_hidden_layers</code> and <code>vocab_size</code> attributes in common. These attributes specify the number of attention heads or hidden layers to construct a model with.',Tt,pe,Bs='Get a closer look at <a href="model_doc/distilbert">DistilBERT</a> by accessing <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> to inspect it’s attributes:',kt,ce,qt,me,Xs='<a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> displays all the default attributes used to build a base <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a>. All attributes are customizable, creating space for experimentation. For example, you can customize a default model to:',Ct,ue,Ys="<li>Try a different activation function with the <code>activation</code> parameter.</li> <li>Use a higher dropout ratio for the attention probabilities with the <code>attention_dropout</code> parameter.</li>",Zt,fe,xt,de,Ps='Pretrained model attributes can be modified in the <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a> function:',Jt,he,Wt,ge,Ls='Once you are satisfied with your model configuration, you can save it with <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a>. Your configuration file is stored as a JSON file in the specified save directory:',Vt,ye,zt,$e,Is='To reuse the configuration file, load it with <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>:',Rt,ve,Ut,N,Ft,H,Q,ut,be,Vs,rt,Ns="Model",Et,_e,Qs='The next step is to create a <a href="main_classes/models">model</a>. The model - also loosely referred to as the architecture - defines what each layer is doing and what operations are happening. Attributes like <code>num_hidden_layers</code> from the configuration are used to define the architecture. Every model shares the base class <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> and a few common methods like resizing input embeddings and pruning self-attention heads. In addition, all models are also either a <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html" rel="nofollow"><code>torch.nn.Module</code></a>, <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow"><code>tf.keras.Model</code></a> or <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" rel="nofollow"><code>flax.linen.Module</code></a> subclass. This means models are compatible with each of their respective framework’s usage.',Gt,S,Ht,B,D,ft,je,zs,lt,Ss="Model heads",Bt,we,Ds="At this point, you have a base DistilBERT model which outputs the <em>hidden states</em>. The hidden states are passed as inputs to a model head to produce the final output. 🤗 Transformers provides a different model head for each task as long as a model supports the task (i.e., you can’t use DistilBERT for a sequence-to-sequence task like translation).",Xt,A,Yt,X,K,dt,Me,Rs,ot,As="Tokenizer",Pt,Te,Ks='The last base class you need before using a model for textual data is a <a href="main_classes/tokenizer">tokenizer</a> to convert raw text to tensors. There are two types of tokenizers you can use with 🤗 Transformers:',Lt,ke,Os='<li><a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>: a Python implementation of a tokenizer.</li> <li><a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>: a tokenizer from our Rust-based <a href="https://huggingface.co/docs/tokenizers/python/latest/" rel="nofollow">🤗 Tokenizer</a> library. This tokenizer type is significantly faster - especially during batch tokenization - due to its Rust implementation. The fast tokenizer also offers additional methods like <em>offset mapping</em> which maps tokens to their original words or characters.</li>',It,qe,ea="Both tokenizers support common methods such as encoding and decoding, adding new tokens, and managing special tokens.",Nt,O,Qt,Ce,ta="If you trained your own tokenizer, you can create one from your <em>vocabulary</em> file:",St,Ze,Dt,xe,sa='It is important to remember the vocabulary from a custom tokenizer will be different from the vocabulary generated by a pretrained model’s tokenizer. You need to use a pretrained model’s vocabulary if you are using a pretrained model, otherwise the inputs won’t make sense. Create a tokenizer with a pretrained model’s vocabulary with the <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertTokenizer">DistilBertTokenizer</a> class:',At,Je,Kt,We,aa='Create a fast tokenizer with the <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertTokenizerFast">DistilBertTokenizerFast</a> class:',Ot,Ve,es,ee,ts,Y,te,ht,ze,Us,it,na="Image Processor",ss,Re,ra='An image processor processes vision inputs. It inherits from the base <a href="/docs/transformers/v4.33.3/en/main_classes/image_processor#transformers.ImageProcessingMixin">ImageProcessingMixin</a> class.',as,Ue,la='To use, create an image processor associated with the model you’re using. For example, create a default <a href="/docs/transformers/v4.33.3/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> if you are using <a href="model_doc/vit">ViT</a> for image classification:',ns,Fe,rs,se,ls,Ee,oa='Modify any of the <a href="/docs/transformers/v4.33.3/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> parameters to create your custom image processor:',os,Ge,is,P,ae,gt,He,Fs,pt,ia="Feature Extractor",ps,Be,pa='A feature extractor processes audio inputs. It inherits from the base <a href="/docs/transformers/v4.33.3/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin">FeatureExtractionMixin</a> class, and may also inherit from the <a href="/docs/transformers/v4.33.3/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor">SequenceFeatureExtractor</a> class for processing audio inputs.',cs,Xe,ca='To use, create a feature extractor associated with the model you’re using. For example, create a default <a href="/docs/transformers/v4.33.3/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> if you are using <a href="model_doc/wav2vec2">Wav2Vec2</a> for audio classification:',ms,Ye,us,ne,fs,Pe,ma='Modify any of the <a href="/docs/transformers/v4.33.3/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> parameters to create your custom feature extractor:',ds,Le,hs,L,re,yt,Ie,Es,ct,ua="Processor",gs,Ne,fa='For models that support multimodal tasks, 🤗 Transformers offers a processor class that conveniently wraps processing classes such as a feature extractor and a tokenizer into a single object. For example, let’s use the <a href="/docs/transformers/v4.33.3/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> for an automatic speech recognition task (ASR). ASR transcribes audio to text, so you will need a feature extractor and a tokenizer.',ys,Qe,da="Create a feature extractor to handle the audio inputs:",$s,Se,vs,De,ha="Create a tokenizer to handle the text inputs:",bs,Ae,_s,Ke,ga='Combine the feature extractor and tokenizer in <a href="/docs/transformers/v4.33.3/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a>:',js,Oe,ws,et,ya="With two basic classes - configuration and model - and an additional preprocessing class (tokenizer, image processor, feature extractor, or processor), you can create any of the models supported by 🤗 Transformers. Each of these base classes are configurable, allowing you to use the specific attributes you want. You can easily setup a model for training or modify an existing pretrained model to fine-tune.",Ms,$t,Ts;return C=new le({}),oe=new le({}),ce=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRDb25maWclMEElMEFjb25maWclMjAlM0QlMjBEaXN0aWxCZXJ0Q29uZmlnKCklMEFwcmludChjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertConfig

<span class="hljs-meta">&gt;&gt;&gt; </span>config = DistilBertConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(config)
DistilBertConfig {
  <span class="hljs-string">&quot;activation&quot;</span>: <span class="hljs-string">&quot;gelu&quot;</span>,
  <span class="hljs-string">&quot;attention_dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;dim&quot;</span>: <span class="hljs-number">768</span>,
  <span class="hljs-string">&quot;dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;hidden_dim&quot;</span>: <span class="hljs-number">3072</span>,
  <span class="hljs-string">&quot;initializer_range&quot;</span>: <span class="hljs-number">0.02</span>,
  <span class="hljs-string">&quot;max_position_embeddings&quot;</span>: <span class="hljs-number">512</span>,
  <span class="hljs-string">&quot;model_type&quot;</span>: <span class="hljs-string">&quot;distilbert&quot;</span>,
  <span class="hljs-string">&quot;n_heads&quot;</span>: <span class="hljs-number">12</span>,
  <span class="hljs-string">&quot;n_layers&quot;</span>: <span class="hljs-number">6</span>,
  <span class="hljs-string">&quot;pad_token_id&quot;</span>: <span class="hljs-number">0</span>,
  <span class="hljs-string">&quot;qa_dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;seq_classif_dropout&quot;</span>: <span class="hljs-number">0.2</span>,
  <span class="hljs-string">&quot;sinusoidal_pos_embds&quot;</span>: false,
  <span class="hljs-string">&quot;transformers_version&quot;</span>: <span class="hljs-string">&quot;4.16.2&quot;</span>,
  <span class="hljs-string">&quot;vocab_size&quot;</span>: <span class="hljs-number">30522</span>
}`}}),fe=new J({props:{code:"bXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZyhhY3RpdmF0aW9uJTNEJTIycmVsdSUyMiUyQyUyMGF0dGVudGlvbl9kcm9wb3V0JTNEMC40KSUwQXByaW50KG15X2NvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig(activation=<span class="hljs-string">&quot;relu&quot;</span>, attention_dropout=<span class="hljs-number">0.4</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(my_config)
DistilBertConfig {
  <span class="hljs-string">&quot;activation&quot;</span>: <span class="hljs-string">&quot;relu&quot;</span>,
  <span class="hljs-string">&quot;attention_dropout&quot;</span>: <span class="hljs-number">0.4</span>,
  <span class="hljs-string">&quot;dim&quot;</span>: <span class="hljs-number">768</span>,
  <span class="hljs-string">&quot;dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;hidden_dim&quot;</span>: <span class="hljs-number">3072</span>,
  <span class="hljs-string">&quot;initializer_range&quot;</span>: <span class="hljs-number">0.02</span>,
  <span class="hljs-string">&quot;max_position_embeddings&quot;</span>: <span class="hljs-number">512</span>,
  <span class="hljs-string">&quot;model_type&quot;</span>: <span class="hljs-string">&quot;distilbert&quot;</span>,
  <span class="hljs-string">&quot;n_heads&quot;</span>: <span class="hljs-number">12</span>,
  <span class="hljs-string">&quot;n_layers&quot;</span>: <span class="hljs-number">6</span>,
  <span class="hljs-string">&quot;pad_token_id&quot;</span>: <span class="hljs-number">0</span>,
  <span class="hljs-string">&quot;qa_dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;seq_classif_dropout&quot;</span>: <span class="hljs-number">0.2</span>,
  <span class="hljs-string">&quot;sinusoidal_pos_embds&quot;</span>: false,
  <span class="hljs-string">&quot;transformers_version&quot;</span>: <span class="hljs-string">&quot;4.16.2&quot;</span>,
  <span class="hljs-string">&quot;vocab_size&quot;</span>: <span class="hljs-number">30522</span>
}`}}),he=new J({props:{code:"bXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIlMkMlMjBhY3RpdmF0aW9uJTNEJTIycmVsdSUyMiUyQyUyMGF0dGVudGlvbl9kcm9wb3V0JTNEMC40KQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, activation=<span class="hljs-string">&quot;relu&quot;</span>, attention_dropout=<span class="hljs-number">0.4</span>)'}}),ye=new J({props:{code:"bXlfY29uZmlnLnNhdmVfcHJldHJhaW5lZChzYXZlX2RpcmVjdG9yeSUzRCUyMi4lMkZ5b3VyX21vZGVsX3NhdmVfcGF0aCUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>my_config.save_pretrained(save_directory=<span class="hljs-string">&quot;./your_model_save_path&quot;</span>)'}}),ve=new J({props:{code:"bXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnlvdXJfbW9kZWxfc2F2ZV9wYXRoJTJGY29uZmlnLmpzb24lMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;./your_model_save_path/config.json&quot;</span>)'}}),N=new wt({props:{$$slots:{default:[Ua]},$$scope:{ctx:Z}}}),be=new le({}),S=new xa({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Ha],pytorch:[Ea]},$$scope:{ctx:Z}}}),je=new le({}),A=new xa({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Pa],pytorch:[Xa]},$$scope:{ctx:Z}}}),Me=new le({}),O=new wt({props:{warning:!0,$$slots:{default:[La]},$$scope:{ctx:Z}}}),Ze=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRUb2tlbml6ZXIlMEElMEFteV90b2tlbml6ZXIlMjAlM0QlMjBEaXN0aWxCZXJ0VG9rZW5pemVyKHZvY2FiX2ZpbGUlM0QlMjJteV92b2NhYl9maWxlLnR4dCUyMiUyQyUyMGRvX2xvd2VyX2Nhc2UlM0RGYWxzZSUyQyUyMHBhZGRpbmdfc2lkZSUzRCUyMmxlZnQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>my_tokenizer = DistilBertTokenizer(vocab_file=<span class="hljs-string">&quot;my_vocab_file.txt&quot;</span>, do_lower_case=<span class="hljs-literal">False</span>, padding_side=<span class="hljs-string">&quot;left&quot;</span>)`}}),Je=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRUb2tlbml6ZXIlMEElMEFzbG93X3Rva2VuaXplciUyMCUzRCUyMERpc3RpbEJlcnRUb2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>slow_tokenizer = DistilBertTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),Ve=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRUb2tlbml6ZXJGYXN0JTBBJTBBZmFzdF90b2tlbml6ZXIlMjAlM0QlMjBEaXN0aWxCZXJ0VG9rZW5pemVyRmFzdC5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizerFast

<span class="hljs-meta">&gt;&gt;&gt; </span>fast_tokenizer = DistilBertTokenizerFast.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),ee=new wt({props:{$$slots:{default:[Ia]},$$scope:{ctx:Z}}}),ze=new le({}),Fe=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpVEltYWdlUHJvY2Vzc29yJTBBJTBBdml0X2V4dHJhY3RvciUyMCUzRCUyMFZpVEltYWdlUHJvY2Vzc29yKCklMEFwcmludCh2aXRfZXh0cmFjdG9yKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>vit_extractor = ViTImageProcessor()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(vit_extractor)
ViTImageProcessor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: true,
  <span class="hljs-string">&quot;do_resize&quot;</span>: true,
  <span class="hljs-string">&quot;image_processor_type&quot;</span>: <span class="hljs-string">&quot;ViTImageProcessor&quot;</span>,
  <span class="hljs-string">&quot;image_mean&quot;</span>: [
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>
  ],
  <span class="hljs-string">&quot;image_std&quot;</span>: [
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>
  ],
  <span class="hljs-string">&quot;resample&quot;</span>: <span class="hljs-number">2</span>,
  <span class="hljs-string">&quot;size&quot;</span>: <span class="hljs-number">224</span>
}`}}),se=new wt({props:{$$slots:{default:[Na]},$$scope:{ctx:Z}}}),Ge=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpVEltYWdlUHJvY2Vzc29yJTBBJTBBbXlfdml0X2V4dHJhY3RvciUyMCUzRCUyMFZpVEltYWdlUHJvY2Vzc29yKHJlc2FtcGxlJTNEJTIyUElMLkltYWdlLkJPWCUyMiUyQyUyMGRvX25vcm1hbGl6ZSUzREZhbHNlJTJDJTIwaW1hZ2VfbWVhbiUzRCU1QjAuMyUyQyUyMDAuMyUyQyUyMDAuMyU1RCklMEFwcmludChteV92aXRfZXh0cmFjdG9yKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>my_vit_extractor = ViTImageProcessor(resample=<span class="hljs-string">&quot;PIL.Image.BOX&quot;</span>, do_normalize=<span class="hljs-literal">False</span>, image_mean=[<span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(my_vit_extractor)
ViTImageProcessor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: false,
  <span class="hljs-string">&quot;do_resize&quot;</span>: true,
  <span class="hljs-string">&quot;image_processor_type&quot;</span>: <span class="hljs-string">&quot;ViTImageProcessor&quot;</span>,
  <span class="hljs-string">&quot;image_mean&quot;</span>: [
    <span class="hljs-number">0.3</span>,
    <span class="hljs-number">0.3</span>,
    <span class="hljs-number">0.3</span>
  ],
  <span class="hljs-string">&quot;image_std&quot;</span>: [
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>
  ],
  <span class="hljs-string">&quot;resample&quot;</span>: <span class="hljs-string">&quot;PIL.Image.BOX&quot;</span>,
  <span class="hljs-string">&quot;size&quot;</span>: <span class="hljs-number">224</span>
}`}}),He=new le({}),Ye=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyRmVhdHVyZUV4dHJhY3RvciUwQSUwQXcydjJfZXh0cmFjdG9yJTIwJTNEJTIwV2F2MlZlYzJGZWF0dXJlRXh0cmFjdG9yKCklMEFwcmludCh3MnYyX2V4dHJhY3Rvcik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>w2v2_extractor = Wav2Vec2FeatureExtractor()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(w2v2_extractor)
Wav2Vec2FeatureExtractor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: true,
  <span class="hljs-string">&quot;feature_extractor_type&quot;</span>: <span class="hljs-string">&quot;Wav2Vec2FeatureExtractor&quot;</span>,
  <span class="hljs-string">&quot;feature_size&quot;</span>: <span class="hljs-number">1</span>,
  <span class="hljs-string">&quot;padding_side&quot;</span>: <span class="hljs-string">&quot;right&quot;</span>,
  <span class="hljs-string">&quot;padding_value&quot;</span>: <span class="hljs-number">0.0</span>,
  <span class="hljs-string">&quot;return_attention_mask&quot;</span>: false,
  <span class="hljs-string">&quot;sampling_rate&quot;</span>: <span class="hljs-number">16000</span>
}`}}),ne=new wt({props:{$$slots:{default:[Qa]},$$scope:{ctx:Z}}}),Le=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyRmVhdHVyZUV4dHJhY3RvciUwQSUwQXcydjJfZXh0cmFjdG9yJTIwJTNEJTIwV2F2MlZlYzJGZWF0dXJlRXh0cmFjdG9yKHNhbXBsaW5nX3JhdGUlM0Q4MDAwJTJDJTIwZG9fbm9ybWFsaXplJTNERmFsc2UpJTBBcHJpbnQodzJ2Ml9leHRyYWN0b3Ip",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>w2v2_extractor = Wav2Vec2FeatureExtractor(sampling_rate=<span class="hljs-number">8000</span>, do_normalize=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(w2v2_extractor)
Wav2Vec2FeatureExtractor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: false,
  <span class="hljs-string">&quot;feature_extractor_type&quot;</span>: <span class="hljs-string">&quot;Wav2Vec2FeatureExtractor&quot;</span>,
  <span class="hljs-string">&quot;feature_size&quot;</span>: <span class="hljs-number">1</span>,
  <span class="hljs-string">&quot;padding_side&quot;</span>: <span class="hljs-string">&quot;right&quot;</span>,
  <span class="hljs-string">&quot;padding_value&quot;</span>: <span class="hljs-number">0.0</span>,
  <span class="hljs-string">&quot;return_attention_mask&quot;</span>: false,
  <span class="hljs-string">&quot;sampling_rate&quot;</span>: <span class="hljs-number">8000</span>
}`}}),Ie=new le({}),Se=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyRmVhdHVyZUV4dHJhY3RvciUwQSUwQWZlYXR1cmVfZXh0cmFjdG9yJTIwJTNEJTIwV2F2MlZlYzJGZWF0dXJlRXh0cmFjdG9yKHBhZGRpbmdfdmFsdWUlM0QxLjAlMkMlMjBkb19ub3JtYWxpemUlM0RUcnVlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = Wav2Vec2FeatureExtractor(padding_value=<span class="hljs-number">1.0</span>, do_normalize=<span class="hljs-literal">True</span>)`}}),Ae=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyQ1RDVG9rZW5pemVyJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwV2F2MlZlYzJDVENUb2tlbml6ZXIodm9jYWJfZmlsZSUzRCUyMm15X3ZvY2FiX2ZpbGUudHh0JTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2CTCTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = Wav2Vec2CTCTokenizer(vocab_file=<span class="hljs-string">&quot;my_vocab_file.txt&quot;</span>)`}}),Oe=new J({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyUHJvY2Vzc29yJTBBJTBBcHJvY2Vzc29yJTIwJTNEJTIwV2F2MlZlYzJQcm9jZXNzb3IoZmVhdHVyZV9leHRyYWN0b3IlM0RmZWF0dXJlX2V4dHJhY3RvciUyQyUyMHRva2VuaXplciUzRHRva2VuaXplcik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2Processor

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