import{S as Bcs,i as Ics,s as Ncs,e as a,k as l,w as v,t as o,M as $cs,c as n,d as t,m as i,a as s,x as M,h as r,b as d,G as e,g as b,y as T,q as F,o as E,B as y,v as jcs,L as W}from"../../chunks/vendor-hf-doc-builder.js";import{T as CZo}from"../../chunks/Tip-hf-doc-builder.js";import{D as G}from"../../chunks/Docstring-hf-doc-builder.js";import{C as P}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as te}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as V}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Pcs(x){let h,C,u,f,p,m,g,ze,Um,Og,Wt,Xm,Dm,cV,Qg,Qe,oo,qm,zs,fV,Hs,Ks,hV,Om,el,gV,Qm,Yg,In;return{c(){h=a("p"),C=o("If your "),u=a("code"),f=o("NewModelConfig"),p=o(" is a subclass of "),m=a("code"),g=o("~transformer.PretrainedConfig"),ze=o(`, make sure its
`),Um=a("code"),Og=o("model_type"),Wt=o(" attribute is set to the same key you use when registering the config (here "),Xm=a("code"),Dm=o('"new-model"'),cV=o(")."),Qg=l(),Qe=a("p"),oo=o("Likewise, if your "),qm=a("code"),zs=o("NewModel"),fV=o(" is a subclass of "),Hs=a("a"),Ks=o("PreTrainedModel"),hV=o(`, make sure its
`),Om=a("code"),el=o("config_class"),gV=o(` attribute is set to the same class you use when registering the model (here
`),Qm=a("code"),Yg=o("NewModelConfig"),In=o(")."),this.h()},l(ro){h=n(ro,"P",{});var Ie=s(h);C=r(Ie,"If your "),u=n(Ie,"CODE",{});var fY=s(u);f=r(fY,"NewModelConfig"),fY.forEach(t),p=r(Ie," is a subclass of "),m=n(Ie,"CODE",{});var Ym=s(m);g=r(Ym,"~transformer.PretrainedConfig"),Ym.forEach(t),ze=r(Ie,`, make sure its
`),Um=n(Ie,"CODE",{});var hY=s(Um);Og=r(hY,"model_type"),hY.forEach(t),Wt=r(Ie," attribute is set to the same key you use when registering the config (here "),Xm=n(Ie,"CODE",{});var gY=s(Xm);Dm=r(gY,'"new-model"'),gY.forEach(t),cV=r(Ie,")."),Ie.forEach(t),Qg=i(ro),Qe=n(ro,"P",{});var jo=s(Qe);oo=r(jo,"Likewise, if your "),qm=n(jo,"CODE",{});var Nn=s(qm);zs=r(Nn,"NewModel"),Nn.forEach(t),fV=r(jo," is a subclass of "),Hs=n(jo,"A",{href:!0});var uY=s(Hs);Ks=r(uY,"PreTrainedModel"),uY.forEach(t),hV=r(jo,`, make sure its
`),Om=n(jo,"CODE",{});var zg=s(Om);el=r(zg,"config_class"),zg.forEach(t),gV=r(jo,` attribute is set to the same class you use when registering the model (here
`),Qm=n(jo,"CODE",{});var pY=s(Qm);Yg=r(pY,"NewModelConfig"),pY.forEach(t),In=r(jo,")."),jo.forEach(t),this.h()},h(){d(Hs,"href","/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel")},m(ro,Ie){b(ro,h,Ie),e(h,C),e(h,u),e(u,f),e(h,p),e(h,m),e(m,g),e(h,ze),e(h,Um),e(Um,Og),e(h,Wt),e(h,Xm),e(Xm,Dm),e(h,cV),b(ro,Qg,Ie),b(ro,Qe,Ie),e(Qe,oo),e(Qe,qm),e(qm,zs),e(Qe,fV),e(Qe,Hs),e(Hs,Ks),e(Qe,hV),e(Qe,Om),e(Om,el),e(Qe,gV),e(Qe,Qm),e(Qm,Yg),e(Qe,In)},d(ro){ro&&t(h),ro&&t(Qg),ro&&t(Qe)}}}function Zcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If configuration file is in a directory (e.g., was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*).</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load a specific configuration file.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/my_configuration.json&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Change some config attributes when loading a pretrained config.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>config, unused_kwargs = AutoConfig.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>unused_kwargs
{<span class="hljs-string">&#x27;foo&#x27;</span>: <span class="hljs-literal">False</span>}`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Vcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># tokenizer = AutoTokenizer.from_pretrained(&quot;./test/bert_saved_model/&quot;)</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co and define model-specific arguments</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>, add_prefix_space=<span class="hljs-literal">True</span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Wcs(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(m,g){b(m,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(m){m&&t(h)}}}function Jcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download feature extractor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># feature_extractor = AutoFeatureExtractor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ucs(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(m,g){b(m,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(m){m&&t(h)}}}function Xcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download image processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224-in21k&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If image processor files are in a directory (e.g. image processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># image_processor = AutoImageProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Dcs(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(m,g){b(m,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(m){m&&t(h)}}}function qcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If processor files are in a directory (e.g. processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># processor = AutoProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ocs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Qcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ycs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbCUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function zcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbCUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Hcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbC5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Kcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function efs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JQcmVUcmFpbmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ofs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function rfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JQcmVUcmFpbmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function tfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function afs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function nfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function sfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function lfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ifs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function dfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function mfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function cfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ffs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function hfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function gfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ufs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function pfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function _fs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function bfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJ0NS1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxMlNlcUxNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function vfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/t5_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/t5_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Mfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMnQ1LWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Tfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ffs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIydDUtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Efs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function yfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Cfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function wfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Lfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Afs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Rfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function kfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Sfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function xfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Gfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Bfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ifs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UlMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Nfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function $fs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function jfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Pfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Zfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Vfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Wfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Jfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ufs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Xfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Dfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function qfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ofs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Qfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Yfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function zfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Hfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Kfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ehs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ohs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function rhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ths(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ahs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function nhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function shs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function lhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ihs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlkZW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function dhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function mhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkSW1hZ2VNb2RlbGluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function chs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function fhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function hhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ghs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function uhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function phs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckltYWdlU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function _hs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function bhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function vhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Mhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ths(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU2VtYW50aWNTZWdtZW50YXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Fhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbnN0YW5jZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckluc3RhbmNlU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ehs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function yhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Chs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function whs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Lhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ahs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yWmVyb1Nob3RJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Rhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function khs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdE9iamVjdERldGVjdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclplcm9TaG90T2JqZWN0RGV0ZWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Shs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function xhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQXVkaW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ghs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Bhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ihs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Nhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function $hs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function jhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDVEMlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JDVEMuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Phs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Zhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Vhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Whs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Jhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Uhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Xhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Dhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function qhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ohs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUYWJsZVF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGdGFwYXMtYmFzZS1maW5ldHVuZWQtd3RxJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVGFibGVRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Qhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/tapas_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/tapas_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Yhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZ0YXBhcy1iYXNlLWZpbmV0dW5lZC13dHElMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function zhs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/tapas_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/tapas_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Hhs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyaW1waXJhJTJGbGF5b3V0bG0tZG9jdW1lbnQtcWElMjIlMkMlMjByZXZpc2lvbiUzRCUyMjUyZTAxYjMlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Khs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/layoutlm_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/layoutlm_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function egs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckRvY3VtZW50UXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yRG9jdW1lbnRRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ogs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/layoutlm_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/layoutlm_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function rgs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXN1YWxRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmRhbmRlbGluJTJGdmlsdC1iMzItZmluZXR1bmVkLXZxYSUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclZpc3VhbFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function tgs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/vilt_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/vilt_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ags(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function ngs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function sgs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function lgs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function igs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVmlzaW9uMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JWaXNpb24yU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function dgs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:W,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function mgs(x){let 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m=new te({}),In=new P({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMik=",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),uV=new te({}),pV=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEFBdXRvQ29uZmlnLnJlZ2lzdGVyKCUyMm5ldy1tb2RlbCUyMiUyQyUyME5ld01vZGVsQ29uZmlnKSUwQUF1dG9Nb2RlbC5yZWdpc3RlcihOZXdNb2RlbENvbmZpZyUyQyUyME5ld01vZGVsKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

AutoConfig.register(<span class="hljs-string">&quot;new-model&quot;</span>, NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)`}}),Kg=new CZo({props:{warning:!0,$$slots:{default:[Pcs]},$$scope:{ctx:x}}}),_V=new te({}),bV=new G({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L899"}}),TV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoConfig.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoConfig.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing a configuration file saved using the
<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> method,
e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a saved configuration JSON <em>file</em>, e.g.,
<code>./my_model_directory/configuration.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoConfig.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoConfig.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoConfig.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoConfig.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoConfig.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoConfig.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final configuration object.</p>
<p>If <code>True</code>, then this functions returns a <code>Tuple(config, unused_kwargs)</code> where <em>unused_kwargs</em> is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of <code>kwargs</code> which has not been used to update <code>config</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoConfig.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoConfig.from_pretrained.kwargs(additional",description:`<strong>kwargs(additional</strong> keyword arguments, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs(additional"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L922"}}),X1=new V({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[Zcs]},$$scope:{ctx:x}}}),FV=new G({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoConfig.register.model_type",description:"<strong>model_type</strong> (<code>str</code>) &#x2014; The model type like &#x201C;bert&#x201D; or &#x201C;gpt&#x201D;.",name:"model_type"},{anchor:"transformers.AutoConfig.register.config",description:'<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L1057"}}),EV=new te({}),yV=new G({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L552"}}),LV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoTokenizer.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoTokenizer.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
single vocabulary file (like Bert or XLNet), e.g.: <code>./my_model_directory/vocab.txt</code>. (Not
applicable to all derived classes)</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoTokenizer.from_pretrained.inputs",description:`<strong>inputs</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the Tokenizer <code>__init__()</code> method.`,name:"inputs"},{anchor:"transformers.AutoTokenizer.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
The configuration object used to determine the tokenizer class to instantiate.`,name:"config"},{anchor:"transformers.AutoTokenizer.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoTokenizer.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoTokenizer.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.`,name:"subfolder"},{anchor:"transformers.AutoTokenizer.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Use a <a href="https://huggingface.co/docs/tokenizers/index" rel="nofollow">fast Rust-based tokenizer</a> if it is supported for
a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
is returned instead.`,name:"use_fast"},{anchor:"transformers.AutoTokenizer.from_pretrained.tokenizer_type",description:`<strong>tokenizer_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__()</code> for more details.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L566"}}),Wb=new V({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[Vcs]},$$scope:{ctx:x}}}),AV=new G({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoTokenizer.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizer</code>, <em>optional</em>) &#x2014;
The slow tokenizer to register.`,name:"slow_tokenizer_class"},{anchor:"transformers.AutoTokenizer.register.fast_tokenizer_class",description:`<strong>fast_tokenizer_class</strong> (<code>PretrainedTokenizerFast</code>, <em>optional</em>) &#x2014;
The fast tokenizer to register.`,name:"fast_tokenizer_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L776"}}),RV=new te({}),kV=new G({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L233"}}),GV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoFeatureExtractor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/main/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L247"}}),ev=new CZo({props:{$$slots:{default:[Wcs]},$$scope:{ctx:x}}}),ov=new V({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[Jcs]},$$scope:{ctx:x}}}),BV=new G({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoFeatureExtractor.register.feature_extractor_class",description:"<strong>feature_extractor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The feature extractor to register.",name:"feature_extractor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L381"}}),IV=new te({}),NV=new G({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L241"}}),PV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoImageProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/main/en/main_classes/image_processor#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoImageProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoImageProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.`,name:"force_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoImageProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoImageProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoImageProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>image_processor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoImageProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoImageProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> image processor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L255"}}),MM=new CZo({props:{$$slots:{default:[Ucs]},$$scope:{ctx:x}}}),TM=new V({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[Xcs]},$$scope:{ctx:x}}}),ZV=new G({props:{name:"register",anchor:"transformers.AutoImageProcessor.register",parameters:[{name:"config_class",val:""},{name:"image_processor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoImageProcessor.register.image_processor_class",description:'<strong>image_processor_class</strong> (<a href="/docs/transformers/main/en/main_classes/image_processor#transformers.ImageProcessingMixin">ImageProcessingMixin</a>) &#x2014; The image processor to register.',name:"image_processor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L407"}}),VV=new te({}),WV=new G({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L118"}}),XV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a processor files saved using the <code>save_pretrained()</code> method,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.AutoProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L132"}}),fT=new CZo({props:{$$slots:{default:[Dcs]},$$scope:{ctx:x}}}),hT=new V({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[qcs]},$$scope:{ctx:x}}}),DV=new G({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoProcessor.register.processor_class",description:"<strong>processor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The processor to register.",name:"processor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L321"}}),qV=new te({}),OV=new te({}),QV=new G({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1207"}}),zV=new G({props:{name:"from_config",anchor:"transformers.AutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bark#transformers.BarkModel">BarkModel</a> (Bark model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/main/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2Model">Dinov2Model</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encodec#transformers.EncodecConfig">EncodecConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a> (EnCodec model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconModel">FalconModel</a> (Falcon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetModel">FocalNetModel</a> (FocalNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsModel">IdeficsModel</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li>
<li><code>MaskFormerSwinConfig</code> configuration class: <code>MaskFormerSwinModel</code> (MaskFormerSwin model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaModel">MegaModel</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Model">MobileViTV2Model</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptModel">MptModel</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraModel">MraModel</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeModel">NllbMoeModel</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/persimmon#transformers.PersimmonModel">PersimmonModel</a> (Persimmon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtModel">PvtModel</a> (PVT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvModel">RwkvModel</a> (RWKV model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sam#transformers.SamModel">SamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerModel">SwiftFormerModel</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li>
<li><code>TimmBackboneConfig</code> configuration class: <code>TimmBackbone</code> (TimmBackbone model)</li>
<li><a href="/docs/transformers/main/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Model">UMT5Model</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vitdet#transformers.VitDetConfig">VitDetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vitdet#transformers.VitDetModel">VitDetModel</a> (VitDet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vits#transformers.VitsConfig">VitsConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vits#transformers.VitsModel">VitsModel</a> (VITS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),_T=new V({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[Ocs]},$$scope:{ctx:x}}}),HV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModel.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModel.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),Y0=new V({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[Qcs]},$$scope:{ctx:x}}}),KV=new te({}),eW=new G({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L528"}}),rW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.TFEfficientFormerModel">TFEfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sam#transformers.TFSamModel">TFSamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),H0=new V({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Ycs]},$$scope:{ctx:x}}}),tW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),i5=new V({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[zcs]},$$scope:{ctx:x}}}),aW=new te({}),nW=new G({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L274"}}),lW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.FlaxBloomModel">FlaxBloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.FlaxRegNetModel">FlaxRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.FlaxResNetModel">FlaxResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),m5=new V({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[Hcs]},$$scope:{ctx:x}}}),iW=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),U5=new V({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[Kcs]},$$scope:{ctx:x}}}),dW=new te({}),mW=new te({}),cW=new G({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1214"}}),hW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsForVisionText2Text">IdeficsForVisionText2Text</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),q5=new V({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[efs]},$$scope:{ctx:x}}}),gW=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),t9=new V({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[ofs]},$$scope:{ctx:x}}}),uW=new te({}),pW=new G({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L544"}}),bW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),n9=new V({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[rfs]},$$scope:{ctx:x}}}),vW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),S9=new V({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[tfs]},$$scope:{ctx:x}}}),MW=new te({}),TW=new G({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L281"}}),EW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),G9=new V({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[afs]},$$scope:{ctx:x}}}),yW=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),Q9=new V({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[nfs]},$$scope:{ctx:x}}}),CW=new te({}),wW=new te({}),LW=new G({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1229"}}),RW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconForCausalLM">FalconForCausalLM</a> (Falcon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForCausalLM">MegaForCausalLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/musicgen#transformers.MusicgenConfig">MusicgenConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/musicgen#transformers.MusicgenForCausalLM">MusicgenForCausalLM</a> (MusicGen model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/persimmon#transformers.PersimmonForCausalLM">PersimmonForCausalLM</a> (Persimmon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),H9=new V({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[sfs]},$$scope:{ctx:x}}}),kW=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),nC=new V({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[lfs]},$$scope:{ctx:x}}}),SW=new te({}),xW=new G({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L559"}}),BW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),lC=new V({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[ifs]},$$scope:{ctx:x}}}),IW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),CC=new V({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[dfs]},$$scope:{ctx:x}}}),NW=new te({}),$W=new G({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L288"}}),PW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.FlaxBloomForCausalLM">FlaxBloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),LC=new V({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[mfs]},$$scope:{ctx:x}}}),ZW=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),WC=new V({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[cfs]},$$scope:{ctx:x}}}),VW=new te({}),WW=new G({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1236"}}),UW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),UC=new V({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[ffs]},$$scope:{ctx:x}}}),XW=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),$w=new V({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[hfs]},$$scope:{ctx:x}}}),DW=new te({}),qW=new G({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L609"}}),QW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),Pw=new V({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[gfs]},$$scope:{ctx:x}}}),YW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),i6=new V({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[ufs]},$$scope:{ctx:x}}}),zW=new te({}),HW=new G({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L295"}}),eJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),m6=new V({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[pfs]},$$scope:{ctx:x}}}),oJ=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),F6=new V({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[_fs]},$$scope:{ctx:x}}}),rJ=new te({}),aJ=new G({props:{name:"class transformers.AutoModelForMaskGeneration",anchor:"transformers.AutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1199"}}),nJ=new te({}),lJ=new G({props:{name:"class transformers.TFAutoModelForMaskGeneration",anchor:"transformers.TFAutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L520"}}),iJ=new te({}),dJ=new G({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1243"}}),cJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5ForConditionalGeneration">UMT5ForConditionalGeneration</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),w6=new V({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[bfs]},$$scope:{ctx:x}}}),fJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),z6=new V({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[vfs]},$$scope:{ctx:x}}}),hJ=new te({}),gJ=new G({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L616"}}),pJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),K6=new V({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Mfs]},$$scope:{ctx:x}}}),_J=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),m8=new V({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Tfs]},$$scope:{ctx:x}}}),bJ=new te({}),vJ=new G({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L302"}}),TJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),f8=new V({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Ffs]},$$scope:{ctx:x}}}),FJ=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),E8=new V({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Efs]},$$scope:{ctx:x}}}),EJ=new te({}),yJ=new G({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1252"}}),wJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconForSequenceClassification">FalconForSequenceClassification</a> (Falcon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForSequenceClassification">GPTBigCodeForSequenceClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForSequenceClassification">GPTNeoXForSequenceClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5ForSequenceClassification">MT5ForSequenceClassification</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForSequenceClassification">MegaForSequenceClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForSequenceClassification">MptForSequenceClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForSequenceClassification">MraForSequenceClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/persimmon#transformers.PersimmonForSequenceClassification">PersimmonForSequenceClassification</a> (Persimmon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForSequenceClassification">T5ForSequenceClassification</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5ForSequenceClassification">UMT5ForSequenceClassification</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),C8=new V({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[yfs]},$$scope:{ctx:x}}}),LJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),O7=new V({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Cfs]},$$scope:{ctx:x}}}),AJ=new te({}),RJ=new G({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L625"}}),SJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),Y7=new V({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[wfs]},$$scope:{ctx:x}}}),xJ=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),AL=new V({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Lfs]},$$scope:{ctx:x}}}),GJ=new te({}),BJ=new G({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L311"}}),NJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),kL=new V({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[Afs]},$$scope:{ctx:x}}}),$J=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),WL=new V({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Rfs]},$$scope:{ctx:x}}}),jJ=new te({}),PJ=new G({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1308"}}),VJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForMultipleChoice">MegaForMultipleChoice</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForMultipleChoice">MraForMultipleChoice</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),UL=new V({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[kfs]},$$scope:{ctx:x}}}),WJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),kA=new V({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Sfs]},$$scope:{ctx:x}}}),JJ=new te({}),UJ=new G({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L672"}}),DJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMultipleChoice">TFDebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),xA=new V({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[xfs]},$$scope:{ctx:x}}}),qJ=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),HA=new V({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Gfs]},$$scope:{ctx:x}}}),OJ=new te({}),QJ=new G({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L336"}}),zJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),eR=new V({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[Bfs]},$$scope:{ctx:x}}}),HJ=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),mR=new V({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Ifs]},$$scope:{ctx:x}}}),KJ=new te({}),eU=new G({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1315"}}),rU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),fR=new V({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Nfs]},$$scope:{ctx:x}}}),tU=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),TR=new V({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[$fs]},$$scope:{ctx:x}}}),aU=new te({}),nU=new G({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L679"}}),lU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),ER=new V({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[jfs]},$$scope:{ctx:x}}}),iU=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),wR=new V({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Pfs]},$$scope:{ctx:x}}}),mU=new te({}),cU=new G({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L343"}}),hU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),AR=new V({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Zfs]},$$scope:{ctx:x}}}),gU=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),kR=new V({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Vfs]},$$scope:{ctx:x}}}),uU=new te({}),pU=new G({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1301"}}),bU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconForTokenClassification">FalconForTokenClassification</a> (Falcon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForTokenClassification">GPTBigCodeForTokenClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForTokenClassification">GPTNeoForTokenClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForTokenClassification">GPTNeoXForTokenClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForTokenClassification">MegaForTokenClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForTokenClassification">MptForTokenClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForTokenClassification">MraForTokenClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),xR=new V({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[Wfs]},$$scope:{ctx:x}}}),vU=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),Bk=new V({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Jfs]},$$scope:{ctx:x}}}),MU=new te({}),TU=new G({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L663"}}),EU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),Nk=new V({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[Ufs]},$$scope:{ctx:x}}}),yU=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),sS=new V({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Xfs]},$$scope:{ctx:x}}}),CU=new te({}),wU=new G({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L327"}}),AU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),iS=new V({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[Dfs]},$$scope:{ctx:x}}}),RU=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),bS=new V({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[qfs]},$$scope:{ctx:x}}}),kU=new te({}),SU=new G({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1261"}}),GU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/falcon#transformers.FalconForQuestionAnswering">FalconForQuestionAnswering</a> (Falcon model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForQuestionAnswering">GPT2ForQuestionAnswering</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForQuestionAnswering">GPTNeoForQuestionAnswering</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForQuestionAnswering">GPTNeoXForQuestionAnswering</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5ForQuestionAnswering">MT5ForQuestionAnswering</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForQuestionAnswering">MegaForQuestionAnswering</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForQuestionAnswering">MptForQuestionAnswering</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForQuestionAnswering">MraForQuestionAnswering</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForQuestionAnswering">T5ForQuestionAnswering</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5ForQuestionAnswering">UMT5ForQuestionAnswering</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),MS=new V({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Ofs]},$$scope:{ctx:x}}}),BU=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),kx=new V({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Qfs]},$$scope:{ctx:x}}}),IU=new te({}),NU=new G({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L634"}}),jU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),xx=new V({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Yfs]},$$scope:{ctx:x}}}),PU=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),oG=new V({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[zfs]},$$scope:{ctx:x}}}),ZU=new te({}),VU=new G({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L320"}}),JU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),tG=new V({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Hfs]},$$scope:{ctx:x}}}),UU=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),uG=new V({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Kfs]},$$scope:{ctx:x}}}),XU=new te({}),qU=new G({props:{name:"class transformers.AutoModelForTextEncoding",anchor:"transformers.AutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1203"}}),OU=new te({}),YU=new G({props:{name:"class transformers.TFAutoModelForTextEncoding",anchor:"transformers.TFAutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L524"}}),zU=new te({}),HU=new te({}),KU=new G({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1390"}}),oX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForDepthEstimation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),MG=new V({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[ehs]},$$scope:{ctx:x}}}),rX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDepthEstimation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),yG=new V({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[ohs]},$$scope:{ctx:x}}}),aX=new te({}),nX=new G({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1324"}}),lX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2ForImageClassification">Dinov2ForImageClassification</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetForImageClassification">FocalNetForImageClassification</a> (FocalNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification">MobileViTV2ForImageClassification</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtForImageClassification">PvtForImageClassification</a> (PVT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification">SwiftFormerForImageClassification</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),wG=new V({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[rhs]},$$scope:{ctx:x}}}),iX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),rB=new V({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[ths]},$$scope:{ctx:x}}}),dX=new te({}),mX=new G({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L575"}}),fX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassification">TFEfficientFormerForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassificationWithTeacher">TFEfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),aB=new V({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[ahs]},$$scope:{ctx:x}}}),hX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),gB=new V({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[nhs]},$$scope:{ctx:x}}}),gX=new te({}),uX=new G({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L352"}}),_X=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.FlaxRegNetForImageClassification">FlaxRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.FlaxResNetForImageClassification">FlaxResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),pB=new V({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[shs]},$$scope:{ctx:x}}}),bX=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),TB=new V({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[lhs]},$$scope:{ctx:x}}}),vX=new te({}),MX=new G({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1397"}}),FX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVideoClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),EB=new V({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[ihs]},$$scope:{ctx:x}}}),EX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVideoClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),AB=new V({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[dhs]},$$scope:{ctx:x}}}),yX=new te({}),CX=new G({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1462"}}),LX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetForMaskedImageModeling">FocalNetForMaskedImageModeling</a> (FocalNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),kB=new V({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[mhs]},$$scope:{ctx:x}}}),AX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),$B=new V({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[chs]},$$scope:{ctx:x}}}),RX=new te({}),kX=new G({props:{name:"class transformers.TFAutoModelForMaskedImageModeling",anchor:"transformers.TFAutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L566"}}),xX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTForMaskedImageModeling">TFDeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),PB=new V({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[fhs]},$$scope:{ctx:x}}}),GX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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<li><a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
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<ul>
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<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
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<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
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underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
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supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
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<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),tI=new V({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[_hs]},$$scope:{ctx:x}}}),XX=new te({}),DX=new G({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1347"}}),OX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2ForSemanticSegmentation">MobileViTV2ForSemanticSegmentation</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),nI=new V({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[bhs]},$$scope:{ctx:x}}}),QX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),uI=new V({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[vhs]},$$scope:{ctx:x}}}),YX=new te({}),zX=new G({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L593"}}),KX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),_I=new V({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[Mhs]},$$scope:{ctx:x}}}),eD=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),TI=new V({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[Ths]},$$scope:{ctx:x}}}),oD=new te({}),rD=new G({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1365"}}),aD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForInstanceSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),EI=new V({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[Fhs]},$$scope:{ctx:x}}}),nD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
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<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),AI=new V({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[yhs]},$$scope:{ctx:x}}}),mD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),BI=new V({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[Chs]},$$scope:{ctx:x}}}),cD=new te({}),fD=new G({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1331"}}),gD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),NI=new V({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[whs]},$$scope:{ctx:x}}}),uD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),UI=new V({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[Lhs]},$$scope:{ctx:x}}}),pD=new te({}),_D=new G({props:{name:"class transformers.TFAutoModelForZeroShotImageClassification",anchor:"transformers.TFAutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L584"}}),vD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),DI=new V({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[Ahs]},$$scope:{ctx:x}}}),MD=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),QI=new V({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[Rhs]},$$scope:{ctx:x}}}),FD=new te({}),ED=new G({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1381"}}),CD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),zI=new V({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[khs]},$$scope:{ctx:x}}}),wD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),eN=new V({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[Shs]},$$scope:{ctx:x}}}),LD=new te({}),AD=new te({}),RD=new G({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1411"}}),SD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),tN=new V({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[xhs]},$$scope:{ctx:x}}}),xD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),pN=new V({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[Ghs]},$$scope:{ctx:x}}}),GD=new te({}),BD=new G({props:{name:"class transformers.TFAutoModelForAudioClassification",anchor:"transformers.TFAutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L535"}}),ND=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.TFWav2Vec2ForSequenceClassification">TFWav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),bN=new V({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[Bhs]},$$scope:{ctx:x}}}),$D=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),MN=new V({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[Ihs]},$$scope:{ctx:x}}}),jD=new te({}),PD=new G({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1434"}}),VD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioFrameClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),FN=new V({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[Nhs]},$$scope:{ctx:x}}}),WD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),RN=new V({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[$hs]},$$scope:{ctx:x}}}),JD=new te({}),UD=new G({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1418"}}),DD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForCTC.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),SN=new V({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[jhs]},$$scope:{ctx:x}}}),qD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCTC.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCTC.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCTC.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCTC.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCTC.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCTC.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCTC.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCTC.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),JN=new V({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[Phs]},$$scope:{ctx:x}}}),OD=new te({}),QD=new G({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1425"}}),zD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/pop2piano#transformers.Pop2PianoConfig">Pop2PianoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pop2piano#transformers.Pop2PianoForConditionalGeneration">Pop2PianoForConditionalGeneration</a> (Pop2Piano model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),XN=new V({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Zhs]},$$scope:{ctx:x}}}),HD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),HN=new V({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Vhs]},$$scope:{ctx:x}}}),KD=new te({}),eq=new G({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L688"}}),rq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),e$=new V({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Whs]},$$scope:{ctx:x}}}),tq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),t$=new V({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Jhs]},$$scope:{ctx:x}}}),nq=new te({}),sq=new G({props:{name:"class transformers.FlaxAutoModelForSpeechSeq2Seq",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L368"}}),iq=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),n$=new V({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Uhs]},$$scope:{ctx:x}}}),dq=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),i$=new V({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Xhs]},$$scope:{ctx:x}}}),cq=new te({}),fq=new G({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1443"}}),gq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioXVector.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),m$=new V({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[Dhs]},$$scope:{ctx:x}}}),uq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioXVector.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),_$=new V({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[qhs]},$$scope:{ctx:x}}}),pq=new te({}),bq=new G({props:{name:"class transformers.AutoModelForTextToSpectrogram",anchor:"transformers.AutoModelForTextToSpectrogram",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1447"}}),vq=new te({}),Tq=new G({props:{name:"class transformers.AutoModelForTextToWaveform",anchor:"transformers.AutoModelForTextToWaveform",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1451"}}),Fq=new te({}),Eq=new te({}),yq=new G({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1268"}}),wq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),F$=new V({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Ohs]},$$scope:{ctx:x}}}),Lq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),L$=new V({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Yhs]},$$scope:{ctx:x}}}),xq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),R$=new V({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[zhs]},$$scope:{ctx:x}}}),Gq=new te({}),Bq=new G({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1290"}}),Nq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),S$=new V({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[Hhs]},$$scope:{ctx:x}}}),$q=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),N$=new V({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[Khs]},$$scope:{ctx:x}}}),jq=new te({}),Pq=new G({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L641"}}),Vq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),j$=new V({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[egs]},$$scope:{ctx:x}}}),Wq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),Z$=new V({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[ogs]},$$scope:{ctx:x}}}),Jq=new te({}),Uq=new G({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1279"}}),Dq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),W$=new V({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[rgs]},$$scope:{ctx:x}}}),qq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),D$=new V({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[tgs]},$$scope:{ctx:x}}}),Qq=new te({}),Yq=new G({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1404"}}),Hq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/instructblip#transformers.InstructBlipConfig">InstructBlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/instructblip#transformers.InstructBlipForConditionalGeneration">InstructBlipForConditionalGeneration</a> (InstructBLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig">Pix2StructConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration">Pix2StructForConditionalGeneration</a> (Pix2Struct model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),O$=new V({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[ags]},$$scope:{ctx:x}}}),Kq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L453"}}),rj=new V({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[ngs]},$$scope:{ctx:x}}}),eO=new te({}),oO=new G({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L602"}}),tO=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.TFBlipForConditionalGeneration">TFBlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L422"}}),aj=new V({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[sgs]},$$scope:{ctx:x}}}),aO=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
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method.`),nSr=l(),YV=a("p"),sSr=o("This class cannot be instantiated directly using "),pIe=a("code"),lSr=o("__init__()"),iSr=o(" (throws an error)."),dSr=l(),Jt=a("div"),v(zV.$$.fragment),mSr=l(),_Ie=a("p"),cSr=o("Instantiates one of the base model classes of the library from a configuration."),fSr=l(),ic=a("p"),hSr=o(`Note:
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model)"),Het=l(),Oy=a("li"),mZe=a("strong"),Ket=o("speech_to_text"),eot=o(" \u2014 "),Cfe=a("a"),oot=o("TFSpeech2TextModel"),rot=o(" (Speech2Text model)"),tot=l(),Qy=a("li"),cZe=a("strong"),aot=o("swin"),not=o(" \u2014 "),wfe=a("a"),sot=o("TFSwinModel"),lot=o(" (Swin Transformer model)"),iot=l(),Yy=a("li"),fZe=a("strong"),dot=o("t5"),mot=o(" \u2014 "),Lfe=a("a"),cot=o("TFT5Model"),fot=o(" (T5 model)"),hot=l(),zy=a("li"),hZe=a("strong"),got=o("tapas"),uot=o(" \u2014 "),Afe=a("a"),pot=o("TFTapasModel"),_ot=o(" (TAPAS model)"),bot=l(),Hy=a("li"),gZe=a("strong"),vot=o("transfo-xl"),Mot=o(" \u2014 "),Rfe=a("a"),Tot=o("TFTransfoXLModel"),Fot=o(" (Transformer-XL model)"),Eot=l(),Ky=a("li"),uZe=a("strong"),yot=o("vision-text-dual-encoder"),Cot=o(" \u2014 "),kfe=a("a"),wot=o("TFVisionTextDualEncoderModel"),Lot=o(" (VisionTextDualEncoder model)"),Aot=l(),e5=a("li"),pZe=a("strong"),Rot=o("vit"),kot=o(" \u2014 "),Sfe=a("a"),Sot=o("TFViTModel"),xot=o(" (ViT model)"),Got=l(),o5=a("li"),_Ze=a("strong"),Bot=o("vit_mae"),Iot=o(" \u2014 "),xfe=a("a"),Not=o("TFViTMAEModel"),$ot=o(" (ViTMAE model)"),jot=l(),r5=a("li"),bZe=a("strong"),Pot=o("wav2vec2"),Zot=o(" \u2014 "),Gfe=a("a"),Vot=o("TFWav2Vec2Model"),Wot=o(" (Wav2Vec2 model)"),Jot=l(),t5=a("li"),vZe=a("strong"),Uot=o("whisper"),Xot=o(" \u2014 "),Bfe=a("a"),Dot=o("TFWhisperModel"),qot=o(" (Whisper model)"),Oot=l(),a5=a("li"),MZe=a("strong"),Qot=o("xglm"),Yot=o(" \u2014 "),Ife=a("a"),zot=o("TFXGLMModel"),Hot=o(" (XGLM model)"),Kot=l(),n5=a("li"),TZe=a("strong"),ert=o("xlm"),ort=o(" \u2014 "),Nfe=a("a"),rrt=o("TFXLMModel"),trt=o(" (XLM model)"),art=l(),s5=a("li"),FZe=a("strong"),nrt=o("xlm-roberta"),srt=o(" \u2014 "),$fe=a("a"),lrt=o("TFXLMRobertaModel"),irt=o(" (XLM-RoBERTa model)"),drt=l(),l5=a("li"),EZe=a("strong"),mrt=o("xlnet"),crt=o(" \u2014 "),jfe=a("a"),frt=o("TFXLNetModel"),hrt=o(" (XLNet model)"),grt=l(),v(i5.$$.fragment),XBo=l(),fc=a("h3"),d5=a("a"),yZe=a("span"),v(aW.$$.fragment),urt=l(),CZe=a("span"),prt=o("FlaxAutoModel"),DBo=l(),Do=a("div"),v(nW.$$.fragment),_rt=l(),hc=a("p"),brt=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Pfe=a("a"),vrt=o("from_pretrained()"),Mrt=o(" class method or the "),Zfe=a("a"),Trt=o("from_config()"),Frt=o(` class
method.`),Ert=l(),sW=a("p"),yrt=o("This class cannot be instantiated directly using "),wZe=a("code"),Crt=o("__init__()"),wrt=o(" (throws an error)."),Lrt=l(),Xt=a("div"),v(lW.$$.fragment),Art=l(),LZe=a("p"),Rrt=o("Instantiates one of the base model classes of the library from a configuration."),krt=l(),gc=a("p"),Srt=o(`Note:
Loading a model from its configuration file does `),AZe=a("strong"),xrt=o("not"),Grt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Vfe=a("a"),Brt=o("from_pretrained()"),Irt=o(" to load the model weights."),Nrt=l(),v(m5.$$.fragment),$rt=l(),at=a("div"),v(iW.$$.fragment),jrt=l(),RZe=a("p"),Prt=o("Instantiate one of the base model classes of the library from a pretrained model."),Zrt=l(),Wn=a("p"),Vrt=o("The model class to instantiate is selected based on the "),kZe=a("code"),Wrt=o("model_type"),Jrt=o(` property of the config object (either
passed as an argument or loaded from `),SZe=a("code"),Urt=o("pretrained_model_name_or_path"),Xrt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xZe=a("code"),Drt=o("pretrained_model_name_or_path"),qrt=o(":"),Ort=l(),se=a("ul"),c5=a("li"),GZe=a("strong"),Qrt=o("albert"),Yrt=o(" \u2014 "),Wfe=a("a"),zrt=o("FlaxAlbertModel"),Hrt=o(" (ALBERT model)"),Krt=l(),f5=a("li"),BZe=a("strong"),ett=o("bart"),ott=o(" \u2014 "),Jfe=a("a"),rtt=o("FlaxBartModel"),ttt=o(" (BART model)"),att=l(),h5=a("li"),IZe=a("strong"),ntt=o("beit"),stt=o(" \u2014 "),Ufe=a("a"),ltt=o("FlaxBeitModel"),itt=o(" (BEiT model)"),dtt=l(),g5=a("li"),NZe=a("strong"),mtt=o("bert"),ctt=o(" \u2014 "),Xfe=a("a"),ftt=o("FlaxBertModel"),htt=o(" (BERT model)"),gtt=l(),u5=a("li"),$Ze=a("strong"),utt=o("big_bird"),ptt=o(" \u2014 "),Dfe=a("a"),_tt=o("FlaxBigBirdModel"),btt=o(" (BigBird model)"),vtt=l(),p5=a("li"),jZe=a("strong"),Mtt=o("blenderbot"),Ttt=o(" \u2014 "),qfe=a("a"),Ftt=o("FlaxBlenderbotModel"),Ett=o(" (Blenderbot model)"),ytt=l(),_5=a("li"),PZe=a("strong"),Ctt=o("blenderbot-small"),wtt=o(" \u2014 "),Ofe=a("a"),Ltt=o("FlaxBlenderbotSmallModel"),Att=o(" (BlenderbotSmall model)"),Rtt=l(),b5=a("li"),ZZe=a("strong"),ktt=o("bloom"),Stt=o(" \u2014 "),Qfe=a("a"),xtt=o("FlaxBloomModel"),Gtt=o(" (BLOOM model)"),Btt=l(),v5=a("li"),VZe=a("strong"),Itt=o("clip"),Ntt=o(" \u2014 "),Yfe=a("a"),$tt=o("FlaxCLIPModel"),jtt=o(" (CLIP model)"),Ptt=l(),M5=a("li"),WZe=a("strong"),Ztt=o("distilbert"),Vtt=o(" \u2014 "),zfe=a("a"),Wtt=o("FlaxDistilBertModel"),Jtt=o(" (DistilBERT model)"),Utt=l(),T5=a("li"),JZe=a("strong"),Xtt=o("electra"),Dtt=o(" \u2014 "),Hfe=a("a"),qtt=o("FlaxElectraModel"),Ott=o(" (ELECTRA model)"),Qtt=l(),F5=a("li"),UZe=a("strong"),Ytt=o("gpt-sw3"),ztt=o(" \u2014 "),Kfe=a("a"),Htt=o("FlaxGPT2Model"),Ktt=o(" (GPT-Sw3 model)"),eat=l(),E5=a("li"),XZe=a("strong"),oat=o("gpt2"),rat=o(" \u2014 "),ehe=a("a"),tat=o("FlaxGPT2Model"),aat=o(" (OpenAI GPT-2 model)"),nat=l(),y5=a("li"),DZe=a("strong"),sat=o("gpt_neo"),lat=o(" \u2014 "),ohe=a("a"),iat=o("FlaxGPTNeoModel"),dat=o(" (GPT Neo model)"),mat=l(),C5=a("li"),qZe=a("strong"),cat=o("gptj"),fat=o(" \u2014 "),rhe=a("a"),hat=o("FlaxGPTJModel"),gat=o(" (GPT-J model)"),uat=l(),w5=a("li"),OZe=a("strong"),pat=o("longt5"),_at=o(" \u2014 "),the=a("a"),bat=o("FlaxLongT5Model"),vat=o(" (LongT5 model)"),Mat=l(),L5=a("li"),QZe=a("strong"),Tat=o("marian"),Fat=o(" \u2014 "),ahe=a("a"),Eat=o("FlaxMarianModel"),yat=o(" (Marian model)"),Cat=l(),A5=a("li"),YZe=a("strong"),wat=o("mbart"),Lat=o(" \u2014 "),nhe=a("a"),Aat=o("FlaxMBartModel"),Rat=o(" (mBART model)"),kat=l(),R5=a("li"),zZe=a("strong"),Sat=o("mt5"),xat=o(" \u2014 "),she=a("a"),Gat=o("FlaxMT5Model"),Bat=o(" (MT5 model)"),Iat=l(),k5=a("li"),HZe=a("strong"),Nat=o("opt"),$at=o(" \u2014 "),lhe=a("a"),jat=o("FlaxOPTModel"),Pat=o(" (OPT model)"),Zat=l(),S5=a("li"),KZe=a("strong"),Vat=o("pegasus"),Wat=o(" \u2014 "),ihe=a("a"),Jat=o("FlaxPegasusModel"),Uat=o(" (Pegasus model)"),Xat=l(),x5=a("li"),eVe=a("strong"),Dat=o("regnet"),qat=o(" \u2014 "),dhe=a("a"),Oat=o("FlaxRegNetModel"),Qat=o(" (RegNet model)"),Yat=l(),G5=a("li"),oVe=a("strong"),zat=o("resnet"),Hat=o(" \u2014 "),mhe=a("a"),Kat=o("FlaxResNetModel"),ent=o(" (ResNet model)"),ont=l(),B5=a("li"),rVe=a("strong"),rnt=o("roberta"),tnt=o(" \u2014 "),che=a("a"),ant=o("FlaxRobertaModel"),nnt=o(" (RoBERTa model)"),snt=l(),I5=a("li"),tVe=a("strong"),lnt=o("roberta-prelayernorm"),int=o(" \u2014 "),fhe=a("a"),dnt=o("FlaxRobertaPreLayerNormModel"),mnt=o(" (RoBERTa-PreLayerNorm model)"),cnt=l(),N5=a("li"),aVe=a("strong"),fnt=o("roformer"),hnt=o(" \u2014 "),hhe=a("a"),gnt=o("FlaxRoFormerModel"),unt=o(" (RoFormer model)"),pnt=l(),$5=a("li"),nVe=a("strong"),_nt=o("t5"),bnt=o(" \u2014 "),ghe=a("a"),vnt=o("FlaxT5Model"),Mnt=o(" (T5 model)"),Tnt=l(),j5=a("li"),sVe=a("strong"),Fnt=o("vision-text-dual-encoder"),Ent=o(" \u2014 "),uhe=a("a"),ynt=o("FlaxVisionTextDualEncoderModel"),Cnt=o(" (VisionTextDualEncoder model)"),wnt=l(),P5=a("li"),lVe=a("strong"),Lnt=o("vit"),Ant=o(" \u2014 "),phe=a("a"),Rnt=o("FlaxViTModel"),knt=o(" (ViT model)"),Snt=l(),Z5=a("li"),iVe=a("strong"),xnt=o("wav2vec2"),Gnt=o(" \u2014 "),_he=a("a"),Bnt=o("FlaxWav2Vec2Model"),Int=o(" (Wav2Vec2 model)"),Nnt=l(),V5=a("li"),dVe=a("strong"),$nt=o("whisper"),jnt=o(" \u2014 "),bhe=a("a"),Pnt=o("FlaxWhisperModel"),Znt=o(" (Whisper model)"),Vnt=l(),W5=a("li"),mVe=a("strong"),Wnt=o("xglm"),Jnt=o(" \u2014 "),vhe=a("a"),Unt=o("FlaxXGLMModel"),Xnt=o(" (XGLM model)"),Dnt=l(),J5=a("li"),cVe=a("strong"),qnt=o("xlm-roberta"),Ont=o(" \u2014 "),Mhe=a("a"),Qnt=o("FlaxXLMRobertaModel"),Ynt=o(" (XLM-RoBERTa model)"),znt=l(),v(U5.$$.fragment),qBo=l(),uc=a("h2"),X5=a("a"),fVe=a("span"),v(dW.$$.fragment),Hnt=l(),hVe=a("span"),Knt=o("Generic pretraining classes"),OBo=l(),The=a("p"),est=o("The following auto classes are available for instantiating a model with a pretraining head."),QBo=l(),pc=a("h3"),D5=a("a"),gVe=a("span"),v(mW.$$.fragment),ost=l(),uVe=a("span"),rst=o("AutoModelForPreTraining"),YBo=l(),qo=a("div"),v(cW.$$.fragment),tst=l(),_c=a("p"),ast=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Fhe=a("a"),nst=o("from_pretrained()"),sst=o(" class method or the "),Ehe=a("a"),lst=o("from_config()"),ist=o(` class
method.`),dst=l(),fW=a("p"),mst=o("This class cannot be instantiated directly using "),pVe=a("code"),cst=o("__init__()"),fst=o(" (throws an error)."),hst=l(),Dt=a("div"),v(hW.$$.fragment),gst=l(),_Ve=a("p"),ust=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),pst=l(),bc=a("p"),_st=o(`Note:
Loading a model from its configuration file does `),bVe=a("strong"),bst=o("not"),vst=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),yhe=a("a"),Mst=o("from_pretrained()"),Tst=o(" to load the model weights."),Fst=l(),v(q5.$$.fragment),Est=l(),lo=a("div"),v(gW.$$.fragment),yst=l(),vVe=a("p"),Cst=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),wst=l(),Jn=a("p"),Lst=o("The model class to instantiate is selected based on the "),MVe=a("code"),Ast=o("model_type"),Rst=o(` property of the config object (either
passed as an argument or loaded from `),TVe=a("code"),kst=o("pretrained_model_name_or_path"),Sst=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FVe=a("code"),xst=o("pretrained_model_name_or_path"),Gst=o(":"),Bst=l(),U=a("ul"),O5=a("li"),EVe=a("strong"),Ist=o("albert"),Nst=o(" \u2014 "),Che=a("a"),$st=o("AlbertForPreTraining"),jst=o(" (ALBERT model)"),Pst=l(),Q5=a("li"),yVe=a("strong"),Zst=o("bart"),Vst=o(" \u2014 "),whe=a("a"),Wst=o("BartForConditionalGeneration"),Jst=o(" (BART model)"),Ust=l(),Y5=a("li"),CVe=a("strong"),Xst=o("bert"),Dst=o(" \u2014 "),Lhe=a("a"),qst=o("BertForPreTraining"),Ost=o(" (BERT model)"),Qst=l(),z5=a("li"),wVe=a("strong"),Yst=o("big_bird"),zst=o(" \u2014 "),Ahe=a("a"),Hst=o("BigBirdForPreTraining"),Kst=o(" (BigBird model)"),elt=l(),H5=a("li"),LVe=a("strong"),olt=o("bloom"),rlt=o(" \u2014 "),Rhe=a("a"),tlt=o("BloomForCausalLM"),alt=o(" (BLOOM model)"),nlt=l(),K5=a("li"),AVe=a("strong"),slt=o("camembert"),llt=o(" \u2014 "),khe=a("a"),ilt=o("CamembertForMaskedLM"),dlt=o(" (CamemBERT model)"),mlt=l(),e3=a("li"),RVe=a("strong"),clt=o("ctrl"),flt=o(" \u2014 "),She=a("a"),hlt=o("CTRLLMHeadModel"),glt=o(" (CTRL model)"),ult=l(),o3=a("li"),kVe=a("strong"),plt=o("data2vec-text"),_lt=o(" \u2014 "),xhe=a("a"),blt=o("Data2VecTextForMaskedLM"),vlt=o(" (Data2VecText model)"),Mlt=l(),r3=a("li"),SVe=a("strong"),Tlt=o("deberta"),Flt=o(" \u2014 "),Ghe=a("a"),Elt=o("DebertaForMaskedLM"),ylt=o(" (DeBERTa model)"),Clt=l(),t3=a("li"),xVe=a("strong"),wlt=o("deberta-v2"),Llt=o(" \u2014 "),Bhe=a("a"),Alt=o("DebertaV2ForMaskedLM"),Rlt=o(" (DeBERTa-v2 model)"),klt=l(),a3=a("li"),GVe=a("strong"),Slt=o("distilbert"),xlt=o(" \u2014 "),Ihe=a("a"),Glt=o("DistilBertForMaskedLM"),Blt=o(" (DistilBERT model)"),Ilt=l(),n3=a("li"),BVe=a("strong"),Nlt=o("electra"),$lt=o(" \u2014 "),Nhe=a("a"),jlt=o("ElectraForPreTraining"),Plt=o(" (ELECTRA model)"),Zlt=l(),s3=a("li"),IVe=a("strong"),Vlt=o("ernie"),Wlt=o(" \u2014 "),$he=a("a"),Jlt=o("ErnieForPreTraining"),Ult=o(" (ERNIE model)"),Xlt=l(),l3=a("li"),NVe=a("strong"),Dlt=o("flaubert"),qlt=o(" \u2014 "),jhe=a("a"),Olt=o("FlaubertWithLMHeadModel"),Qlt=o(" (FlauBERT model)"),Ylt=l(),i3=a("li"),$Ve=a("strong"),zlt=o("flava"),Hlt=o(" \u2014 "),Phe=a("a"),Klt=o("FlavaForPreTraining"),eit=o(" (FLAVA model)"),oit=l(),d3=a("li"),jVe=a("strong"),rit=o("fnet"),tit=o(" \u2014 "),Zhe=a("a"),ait=o("FNetForPreTraining"),nit=o(" (FNet model)"),sit=l(),m3=a("li"),PVe=a("strong"),lit=o("fsmt"),iit=o(" \u2014 "),Vhe=a("a"),dit=o("FSMTForConditionalGeneration"),mit=o(" (FairSeq Machine-Translation model)"),cit=l(),c3=a("li"),ZVe=a("strong"),fit=o("funnel"),hit=o(" \u2014 "),Whe=a("a"),git=o("FunnelForPreTraining"),uit=o(" (Funnel Transformer model)"),pit=l(),f3=a("li"),VVe=a("strong"),_it=o("gpt-sw3"),bit=o(" \u2014 "),Jhe=a("a"),vit=o("GPT2LMHeadModel"),Mit=o(" (GPT-Sw3 model)"),Tit=l(),h3=a("li"),WVe=a("strong"),Fit=o("gpt2"),Eit=o(" \u2014 "),Uhe=a("a"),yit=o("GPT2LMHeadModel"),Cit=o(" (OpenAI GPT-2 model)"),wit=l(),g3=a("li"),JVe=a("strong"),Lit=o("gpt_bigcode"),Ait=o(" \u2014 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model\u2019s configuration. Use `),Aue=a("a"),D_t=o("from_pretrained()"),q_t=o(" to load the model weights."),O_t=l(),v(H9.$$.fragment),Q_t=l(),io=a("div"),v(kW.$$.fragment),Y_t=l(),UJe=a("p"),z_t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),H_t=l(),Dn=a("p"),K_t=o("The model class to instantiate is selected based on the "),XJe=a("code"),e1t=o("model_type"),o1t=o(` property of the config object (either
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falling back to using pattern matching on `),qJe=a("code"),a1t=o("pretrained_model_name_or_path"),n1t=o(":"),s1t=l(),Q=a("ul"),K9=a("li"),OJe=a("strong"),l1t=o("bart"),i1t=o(" \u2014 "),Rue=a("a"),d1t=o("BartForCausalLM"),m1t=o(" (BART model)"),c1t=l(),e4=a("li"),QJe=a("strong"),f1t=o("bert"),h1t=o(" \u2014 "),kue=a("a"),g1t=o("BertLMHeadModel"),u1t=o(" (BERT model)"),p1t=l(),o4=a("li"),YJe=a("strong"),_1t=o("bert-generation"),b1t=o(" \u2014 "),Sue=a("a"),v1t=o("BertGenerationDecoder"),M1t=o(" (Bert Generation model)"),T1t=l(),r4=a("li"),zJe=a("strong"),F1t=o("big_bird"),E1t=o(" \u2014 "),xue=a("a"),y1t=o("BigBirdForCausalLM"),C1t=o(" (BigBird model)"),w1t=l(),t4=a("li"),HJe=a("strong"),L1t=o("bigbird_pegasus"),A1t=o(" \u2014 "),Gue=a("a"),R1t=o("BigBirdPegasusForCausalLM"),k1t=o(" (BigBird-Pegasus model)"),S1t=l(),a4=a("li"),KJe=a("strong"),x1t=o("biogpt"),G1t=o(" \u2014 "),Bue=a("a"),B1t=o("BioGptForCausalLM"),I1t=o(" (BioGpt model)"),N1t=l(),n4=a("li"),eUe=a("strong"),$1t=o("blenderbot"),j1t=o(" \u2014 "),Iue=a("a"),P1t=o("BlenderbotForCausalLM"),Z1t=o(" (Blenderbot model)"),V1t=l(),s4=a("li"),oUe=a("strong"),W1t=o("blenderbot-small"),J1t=o(" \u2014 "),Nue=a("a"),U1t=o("BlenderbotSmallForCausalLM"),X1t=o(" (BlenderbotSmall model)"),D1t=l(),l4=a("li"),rUe=a("strong"),q1t=o("bloom"),O1t=o(" \u2014 "),$ue=a("a"),Q1t=o("BloomForCausalLM"),Y1t=o(" (BLOOM model)"),z1t=l(),i4=a("li"),tUe=a("strong"),H1t=o("camembert"),K1t=o(" \u2014 "),jue=a("a"),ebt=o("CamembertForCausalLM"),obt=o(" (CamemBERT model)"),rbt=l(),d4=a("li"),aUe=a("strong"),tbt=o("code_llama"),abt=o(" \u2014 "),Pue=a("a"),nbt=o("LlamaForCausalLM"),sbt=o(" (CodeLlama model)"),lbt=l(),m4=a("li"),nUe=a("strong"),ibt=o("codegen"),dbt=o(" \u2014 "),Zue=a("a"),mbt=o("CodeGenForCausalLM"),cbt=o(" (CodeGen model)"),fbt=l(),c4=a("li"),sUe=a("strong"),hbt=o("cpmant"),gbt=o(" \u2014 "),Vue=a("a"),ubt=o("CpmAntForCausalLM"),pbt=o(" (CPM-Ant model)"),_bt=l(),f4=a("li"),lUe=a("strong"),bbt=o("ctrl"),vbt=o(" \u2014 "),Wue=a("a"),Mbt=o("CTRLLMHeadModel"),Tbt=o(" (CTRL model)"),Fbt=l(),h4=a("li"),iUe=a("strong"),Ebt=o("data2vec-text"),ybt=o(" \u2014 "),Jue=a("a"),Cbt=o("Data2VecTextForCausalLM"),wbt=o(" (Data2VecText model)"),Lbt=l(),g4=a("li"),dUe=a("strong"),Abt=o("electra"),Rbt=o(" \u2014 "),Uue=a("a"),kbt=o("ElectraForCausalLM"),Sbt=o(" (ELECTRA model)"),xbt=l(),u4=a("li"),mUe=a("strong"),Gbt=o("ernie"),Bbt=o(" \u2014 "),Xue=a("a"),Ibt=o("ErnieForCausalLM"),Nbt=o(" (ERNIE model)"),$bt=l(),p4=a("li"),cUe=a("strong"),jbt=o("falcon"),Pbt=o(" \u2014 "),Due=a("a"),Zbt=o("FalconForCausalLM"),Vbt=o(" (Falcon model)"),Wbt=l(),_4=a("li"),fUe=a("strong"),Jbt=o("git"),Ubt=o(" \u2014 "),que=a("a"),Xbt=o("GitForCausalLM"),Dbt=o(" (GIT model)"),qbt=l(),b4=a("li"),hUe=a("strong"),Obt=o("gpt-sw3"),Qbt=o(" \u2014 "),Oue=a("a"),Ybt=o("GPT2LMHeadModel"),zbt=o(" (GPT-Sw3 model)"),Hbt=l(),v4=a("li"),gUe=a("strong"),Kbt=o("gpt2"),e2t=o(" \u2014 "),Que=a("a"),o2t=o("GPT2LMHeadModel"),r2t=o(" (OpenAI GPT-2 model)"),t2t=l(),M4=a("li"),uUe=a("strong"),a2t=o("gpt_bigcode"),n2t=o(" \u2014 "),Yue=a("a"),s2t=o("GPTBigCodeForCausalLM"),l2t=o(" (GPTBigCode model)"),i2t=l(),T4=a("li"),pUe=a("strong"),d2t=o("gpt_neo"),m2t=o(" \u2014 "),zue=a("a"),c2t=o("GPTNeoForCausalLM"),f2t=o(" (GPT Neo model)"),h2t=l(),F4=a("li"),_Ue=a("strong"),g2t=o("gpt_neox"),u2t=o(" \u2014 "),Hue=a("a"),p2t=o("GPTNeoXForCausalLM"),_2t=o(" (GPT NeoX model)"),b2t=l(),E4=a("li"),bUe=a("strong"),v2t=o("gpt_neox_japanese"),M2t=o(" \u2014 "),Kue=a("a"),T2t=o("GPTNeoXJapaneseForCausalLM"),F2t=o(" (GPT NeoX Japanese model)"),E2t=l(),y4=a("li"),vUe=a("strong"),y2t=o("gptj"),C2t=o(" \u2014 "),epe=a("a"),w2t=o("GPTJForCausalLM"),L2t=o(" (GPT-J model)"),A2t=l(),C4=a("li"),MUe=a("strong"),R2t=o("llama"),k2t=o(" \u2014 "),ope=a("a"),S2t=o("LlamaForCausalLM"),x2t=o(" (LLaMA model)"),G2t=l(),w4=a("li"),TUe=a("strong"),B2t=o("marian"),I2t=o(" \u2014 "),rpe=a("a"),N2t=o("MarianForCausalLM"),$2t=o(" (Marian model)"),j2t=l(),L4=a("li"),FUe=a("strong"),P2t=o("mbart"),Z2t=o(" \u2014 "),tpe=a("a"),V2t=o("MBartForCausalLM"),W2t=o(" (mBART model)"),J2t=l(),A4=a("li"),EUe=a("strong"),U2t=o("mega"),X2t=o(" \u2014 "),ape=a("a"),D2t=o("MegaForCausalLM"),q2t=o(" (MEGA model)"),O2t=l(),R4=a("li"),yUe=a("strong"),Q2t=o("megatron-bert"),Y2t=o(" \u2014 "),npe=a("a"),z2t=o("MegatronBertForCausalLM"),H2t=o(" (Megatron-BERT model)"),K2t=l(),k4=a("li"),CUe=a("strong"),evt=o("mpt"),ovt=o(" \u2014 "),spe=a("a"),rvt=o("MptForCausalLM"),tvt=o(" (MPT model)"),avt=l(),S4=a("li"),wUe=a("strong"),nvt=o("musicgen"),svt=o(" \u2014 "),lpe=a("a"),lvt=o("MusicgenForCausalLM"),ivt=o(" (MusicGen model)"),dvt=l(),x4=a("li"),LUe=a("strong"),mvt=o("mvp"),cvt=o(" \u2014 "),ipe=a("a"),fvt=o("MvpForCausalLM"),hvt=o(" (MVP model)"),gvt=l(),G4=a("li"),AUe=a("strong"),uvt=o("open-llama"),pvt=o(" \u2014 "),dpe=a("a"),_vt=o("OpenLlamaForCausalLM"),bvt=o(" (OpenLlama 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model)"),IMt=l(),O4=a("li"),JUe=a("strong"),NMt=o("speech_to_text_2"),$Mt=o(" \u2014 "),ype=a("a"),jMt=o("Speech2Text2ForCausalLM"),PMt=o(" (Speech2Text2 model)"),ZMt=l(),Q4=a("li"),UUe=a("strong"),VMt=o("transfo-xl"),WMt=o(" \u2014 "),Cpe=a("a"),JMt=o("TransfoXLLMHeadModel"),UMt=o(" (Transformer-XL model)"),XMt=l(),Y4=a("li"),XUe=a("strong"),DMt=o("trocr"),qMt=o(" \u2014 "),wpe=a("a"),OMt=o("TrOCRForCausalLM"),QMt=o(" (TrOCR model)"),YMt=l(),z4=a("li"),DUe=a("strong"),zMt=o("xglm"),HMt=o(" \u2014 "),Lpe=a("a"),KMt=o("XGLMForCausalLM"),eTt=o(" (XGLM model)"),oTt=l(),H4=a("li"),qUe=a("strong"),rTt=o("xlm"),tTt=o(" \u2014 "),Ape=a("a"),aTt=o("XLMWithLMHeadModel"),nTt=o(" (XLM model)"),sTt=l(),K4=a("li"),OUe=a("strong"),lTt=o("xlm-prophetnet"),iTt=o(" \u2014 "),Rpe=a("a"),dTt=o("XLMProphetNetForCausalLM"),mTt=o(" (XLM-ProphetNet model)"),cTt=l(),eC=a("li"),QUe=a("strong"),fTt=o("xlm-roberta"),hTt=o(" \u2014 "),kpe=a("a"),gTt=o("XLMRobertaForCausalLM"),uTt=o(" (XLM-RoBERTa 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method.`),XTt=l(),GW=a("p"),DTt=o("This class cannot be instantiated directly using "),tXe=a("code"),qTt=o("__init__()"),OTt=o(" (throws an error)."),QTt=l(),Yt=a("div"),v(BW.$$.fragment),YTt=l(),aXe=a("p"),zTt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),HTt=l(),Sc=a("p"),KTt=o(`Note:
Loading a model from its configuration file does `),nXe=a("strong"),eFt=o("not"),oFt=o(` load the model weights. It only affects the
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passed as an argument or loaded from `),iXe=a("code"),fFt=o("pretrained_model_name_or_path"),hFt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dXe=a("code"),gFt=o("pretrained_model_name_or_path"),uFt=o(":"),pFt=l(),ye=a("ul"),iC=a("li"),mXe=a("strong"),_Ft=o("bert"),bFt=o(" \u2014 "),$pe=a("a"),vFt=o("TFBertLMHeadModel"),MFt=o(" (BERT model)"),TFt=l(),dC=a("li"),cXe=a("strong"),FFt=o("camembert"),EFt=o(" \u2014 "),jpe=a("a"),yFt=o("TFCamembertForCausalLM"),CFt=o(" (CamemBERT model)"),wFt=l(),mC=a("li"),fXe=a("strong"),LFt=o("ctrl"),AFt=o(" \u2014 "),Ppe=a("a"),RFt=o("TFCTRLLMHeadModel"),kFt=o(" (CTRL model)"),SFt=l(),cC=a("li"),hXe=a("strong"),xFt=o("gpt-sw3"),GFt=o(" \u2014 "),Zpe=a("a"),BFt=o("TFGPT2LMHeadModel"),IFt=o(" (GPT-Sw3 model)"),NFt=l(),fC=a("li"),gXe=a("strong"),$Ft=o("gpt2"),jFt=o(" \u2014 "),Vpe=a("a"),PFt=o("TFGPT2LMHeadModel"),ZFt=o(" (OpenAI GPT-2 model)"),VFt=l(),hC=a("li"),uXe=a("strong"),WFt=o("gptj"),JFt=o(" \u2014 "),Wpe=a("a"),UFt=o("TFGPTJForCausalLM"),XFt=o(" (GPT-J model)"),DFt=l(),gC=a("li"),pXe=a("strong"),qFt=o("openai-gpt"),OFt=o(" \u2014 "),Jpe=a("a"),QFt=o("TFOpenAIGPTLMHeadModel"),YFt=o(" (OpenAI GPT model)"),zFt=l(),uC=a("li"),_Xe=a("strong"),HFt=o("opt"),KFt=o(" \u2014 "),Upe=a("a"),eEt=o("TFOPTForCausalLM"),oEt=o(" (OPT model)"),rEt=l(),pC=a("li"),bXe=a("strong"),tEt=o("rembert"),aEt=o(" \u2014 "),Xpe=a("a"),nEt=o("TFRemBertForCausalLM"),sEt=o(" (RemBERT model)"),lEt=l(),_C=a("li"),vXe=a("strong"),iEt=o("roberta"),dEt=o(" \u2014 "),Dpe=a("a"),mEt=o("TFRobertaForCausalLM"),cEt=o(" (RoBERTa model)"),fEt=l(),bC=a("li"),MXe=a("strong"),hEt=o("roberta-prelayernorm"),gEt=o(" \u2014 "),qpe=a("a"),uEt=o("TFRobertaPreLayerNormForCausalLM"),pEt=o(" (RoBERTa-PreLayerNorm model)"),_Et=l(),vC=a("li"),TXe=a("strong"),bEt=o("roformer"),vEt=o(" \u2014 "),Ope=a("a"),MEt=o("TFRoFormerForCausalLM"),TEt=o(" (RoFormer model)"),FEt=l(),MC=a("li"),FXe=a("strong"),EEt=o("transfo-xl"),yEt=o(" \u2014 "),Qpe=a("a"),CEt=o("TFTransfoXLLMHeadModel"),wEt=o(" (Transformer-XL model)"),LEt=l(),TC=a("li"),EXe=a("strong"),AEt=o("xglm"),REt=o(" \u2014 "),Ype=a("a"),kEt=o("TFXGLMForCausalLM"),SEt=o(" (XGLM model)"),xEt=l(),FC=a("li"),yXe=a("strong"),GEt=o("xlm"),BEt=o(" \u2014 "),zpe=a("a"),IEt=o("TFXLMWithLMHeadModel"),NEt=o(" (XLM model)"),$Et=l(),EC=a("li"),CXe=a("strong"),jEt=o("xlm-roberta"),PEt=o(" \u2014 "),Hpe=a("a"),ZEt=o("TFXLMRobertaForCausalLM"),VEt=o(" (XLM-RoBERTa model)"),WEt=l(),yC=a("li"),wXe=a("strong"),JEt=o("xlnet"),UEt=o(" \u2014 "),Kpe=a("a"),XEt=o("TFXLNetLMHeadModel"),DEt=o(" (XLNet model)"),qEt=l(),v(CC.$$.fragment),lIo=l(),xc=a("h3"),wC=a("a"),LXe=a("span"),v(NW.$$.fragment),OEt=l(),AXe=a("span"),QEt=o("FlaxAutoModelForCausalLM"),iIo=l(),Ho=a("div"),v($W.$$.fragment),YEt=l(),Gc=a("p"),zEt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
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Loading a model from its configuration file does `),SXe=a("strong"),c0t=o("not"),f0t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),r_e=a("a"),h0t=o("from_pretrained()"),g0t=o(" to load the model weights."),u0t=l(),v(LC.$$.fragment),p0t=l(),it=a("div"),v(ZW.$$.fragment),_0t=l(),xXe=a("p"),b0t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),v0t=l(),On=a("p"),M0t=o("The model class to instantiate is selected based on the "),GXe=a("code"),T0t=o("model_type"),F0t=o(` property of the config object (either
passed as an argument or loaded from `),BXe=a("code"),E0t=o("pretrained_model_name_or_path"),y0t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),IXe=a("code"),C0t=o("pretrained_model_name_or_path"),w0t=o(":"),L0t=l(),Le=a("ul"),AC=a("li"),NXe=a("strong"),A0t=o("bart"),R0t=o(" \u2014 "),t_e=a("a"),k0t=o("FlaxBartForCausalLM"),S0t=o(" (BART model)"),x0t=l(),RC=a("li"),$Xe=a("strong"),G0t=o("bert"),B0t=o(" \u2014 "),a_e=a("a"),I0t=o("FlaxBertForCausalLM"),N0t=o(" (BERT model)"),$0t=l(),kC=a("li"),jXe=a("strong"),j0t=o("big_bird"),P0t=o(" \u2014 "),n_e=a("a"),Z0t=o("FlaxBigBirdForCausalLM"),V0t=o(" (BigBird model)"),W0t=l(),SC=a("li"),PXe=a("strong"),J0t=o("bloom"),U0t=o(" \u2014 "),s_e=a("a"),X0t=o("FlaxBloomForCausalLM"),D0t=o(" (BLOOM model)"),q0t=l(),xC=a("li"),ZXe=a("strong"),O0t=o("electra"),Q0t=o(" \u2014 "),l_e=a("a"),Y0t=o("FlaxElectraForCausalLM"),z0t=o(" (ELECTRA model)"),H0t=l(),GC=a("li"),VXe=a("strong"),K0t=o("gpt-sw3"),eyt=o(" \u2014 "),i_e=a("a"),oyt=o("FlaxGPT2LMHeadModel"),ryt=o(" (GPT-Sw3 model)"),tyt=l(),BC=a("li"),WXe=a("strong"),ayt=o("gpt2"),nyt=o(" \u2014 "),d_e=a("a"),syt=o("FlaxGPT2LMHeadModel"),lyt=o(" (OpenAI GPT-2 model)"),iyt=l(),IC=a("li"),JXe=a("strong"),dyt=o("gpt_neo"),myt=o(" \u2014 "),m_e=a("a"),cyt=o("FlaxGPTNeoForCausalLM"),fyt=o(" (GPT Neo model)"),hyt=l(),NC=a("li"),UXe=a("strong"),gyt=o("gptj"),uyt=o(" \u2014 "),c_e=a("a"),pyt=o("FlaxGPTJForCausalLM"),_yt=o(" (GPT-J model)"),byt=l(),$C=a("li"),XXe=a("strong"),vyt=o("opt"),Myt=o(" \u2014 "),f_e=a("a"),Tyt=o("FlaxOPTForCausalLM"),Fyt=o(" (OPT model)"),Eyt=l(),jC=a("li"),DXe=a("strong"),yyt=o("roberta"),Cyt=o(" \u2014 "),h_e=a("a"),wyt=o("FlaxRobertaForCausalLM"),Lyt=o(" (RoBERTa model)"),Ayt=l(),PC=a("li"),qXe=a("strong"),Ryt=o("roberta-prelayernorm"),kyt=o(" \u2014 "),g_e=a("a"),Syt=o("FlaxRobertaPreLayerNormForCausalLM"),xyt=o(" (RoBERTa-PreLayerNorm model)"),Gyt=l(),ZC=a("li"),OXe=a("strong"),Byt=o("xglm"),Iyt=o(" \u2014 "),u_e=a("a"),Nyt=o("FlaxXGLMForCausalLM"),$yt=o(" (XGLM model)"),jyt=l(),VC=a("li"),QXe=a("strong"),Pyt=o("xlm-roberta"),Zyt=o(" \u2014 "),p_e=a("a"),Vyt=o("FlaxXLMRobertaForCausalLM"),Wyt=o(" (XLM-RoBERTa model)"),Jyt=l(),v(WC.$$.fragment),dIo=l(),Ic=a("h3"),JC=a("a"),YXe=a("span"),v(VW.$$.fragment),Uyt=l(),zXe=a("span"),Xyt=o("AutoModelForMaskedLM"),mIo=l(),Ko=a("div"),v(WW.$$.fragment),Dyt=l(),Nc=a("p"),qyt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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Loading a model from its configuration file does `),eDe=a("strong"),l5t=o("not"),i5t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),v_e=a("a"),d5t=o("from_pretrained()"),m5t=o(" to load the model weights."),c5t=l(),v(UC.$$.fragment),f5t=l(),mo=a("div"),v(XW.$$.fragment),h5t=l(),oDe=a("p"),g5t=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),u5t=l(),Qn=a("p"),p5t=o("The model class to instantiate is selected based on the "),rDe=a("code"),_5t=o("model_type"),b5t=o(` property of the config object (either
passed as an argument or loaded from `),tDe=a("code"),v5t=o("pretrained_model_name_or_path"),M5t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aDe=a("code"),T5t=o("pretrained_model_name_or_path"),F5t=o(":"),E5t=l(),K=a("ul"),XC=a("li"),nDe=a("strong"),y5t=o("albert"),C5t=o(" \u2014 "),M_e=a("a"),w5t=o("AlbertForMaskedLM"),L5t=o(" (ALBERT model)"),A5t=l(),DC=a("li"),sDe=a("strong"),R5t=o("bart"),k5t=o(" \u2014 "),T_e=a("a"),S5t=o("BartForConditionalGeneration"),x5t=o(" (BART model)"),G5t=l(),qC=a("li"),lDe=a("strong"),B5t=o("bert"),I5t=o(" \u2014 "),F_e=a("a"),N5t=o("BertForMaskedLM"),$5t=o(" (BERT model)"),j5t=l(),OC=a("li"),iDe=a("strong"),P5t=o("big_bird"),Z5t=o(" \u2014 "),E_e=a("a"),V5t=o("BigBirdForMaskedLM"),W5t=o(" (BigBird model)"),J5t=l(),QC=a("li"),dDe=a("strong"),U5t=o("camembert"),X5t=o(" \u2014 "),y_e=a("a"),D5t=o("CamembertForMaskedLM"),q5t=o(" (CamemBERT model)"),O5t=l(),YC=a("li"),mDe=a("strong"),Q5t=o("convbert"),Y5t=o(" \u2014 "),C_e=a("a"),z5t=o("ConvBertForMaskedLM"),H5t=o(" (ConvBERT model)"),K5t=l(),zC=a("li"),cDe=a("strong"),e3t=o("data2vec-text"),o3t=o(" 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with the `),I1e=a("a"),y8t=o("from_pretrained()"),C8t=o(" class method or the "),N1e=a("a"),w8t=o("from_config()"),L8t=o(` class
method.`),A8t=l(),KW=a("p"),R8t=o("This class cannot be instantiated directly using "),xqe=a("code"),k8t=o("__init__()"),S8t=o(" (throws an error)."),x8t=l(),ea=a("div"),v(eJ.$$.fragment),G8t=l(),Gqe=a("p"),B8t=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),I8t=l(),Jc=a("p"),N8t=o(`Note:
Loading a model from its configuration file does `),Bqe=a("strong"),$8t=o("not"),j8t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),$1e=a("a"),P8t=o("from_pretrained()"),Z8t=o(" to load the model weights."),V8t=l(),v(m6.$$.fragment),W8t=l(),mt=a("div"),v(oJ.$$.fragment),J8t=l(),Iqe=a("p"),U8t=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),X8t=l(),zn=a("p"),D8t=o("The model class to instantiate is selected based on the "),Nqe=a("code"),q8t=o("model_type"),O8t=o(` property of the config object (either
passed as an argument or loaded from `),$qe=a("code"),Q8t=o("pretrained_model_name_or_path"),Y8t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jqe=a("code"),z8t=o("pretrained_model_name_or_path"),H8t=o(":"),K8t=l(),ke=a("ul"),c6=a("li"),Pqe=a("strong"),e7t=o("albert"),o7t=o(" \u2014 "),j1e=a("a"),r7t=o("FlaxAlbertForMaskedLM"),t7t=o(" (ALBERT model)"),a7t=l(),f6=a("li"),Zqe=a("strong"),n7t=o("bart"),s7t=o(" \u2014 "),P1e=a("a"),l7t=o("FlaxBartForConditionalGeneration"),i7t=o(" (BART model)"),d7t=l(),h6=a("li"),Vqe=a("strong"),m7t=o("bert"),c7t=o(" \u2014 "),Z1e=a("a"),f7t=o("FlaxBertForMaskedLM"),h7t=o(" (BERT model)"),g7t=l(),g6=a("li"),Wqe=a("strong"),u7t=o("big_bird"),p7t=o(" \u2014 "),V1e=a("a"),_7t=o("FlaxBigBirdForMaskedLM"),b7t=o(" (BigBird model)"),v7t=l(),u6=a("li"),Jqe=a("strong"),M7t=o("distilbert"),T7t=o(" \u2014 "),W1e=a("a"),F7t=o("FlaxDistilBertForMaskedLM"),E7t=o(" (DistilBERT model)"),y7t=l(),p6=a("li"),Uqe=a("strong"),C7t=o("electra"),w7t=o(" \u2014 "),J1e=a("a"),L7t=o("FlaxElectraForMaskedLM"),A7t=o(" (ELECTRA model)"),R7t=l(),_6=a("li"),Xqe=a("strong"),k7t=o("mbart"),S7t=o(" \u2014 "),U1e=a("a"),x7t=o("FlaxMBartForConditionalGeneration"),G7t=o(" (mBART model)"),B7t=l(),b6=a("li"),Dqe=a("strong"),I7t=o("roberta"),N7t=o(" \u2014 "),X1e=a("a"),$7t=o("FlaxRobertaForMaskedLM"),j7t=o(" (RoBERTa model)"),P7t=l(),v6=a("li"),qqe=a("strong"),Z7t=o("roberta-prelayernorm"),V7t=o(" \u2014 "),D1e=a("a"),W7t=o("FlaxRobertaPreLayerNormForMaskedLM"),J7t=o(" (RoBERTa-PreLayerNorm model)"),U7t=l(),M6=a("li"),Oqe=a("strong"),X7t=o("roformer"),D7t=o(" \u2014 "),q1e=a("a"),q7t=o("FlaxRoFormerForMaskedLM"),O7t=o(" (RoFormer model)"),Q7t=l(),T6=a("li"),Qqe=a("strong"),Y7t=o("xlm-roberta"),z7t=o(" \u2014 "),O1e=a("a"),H7t=o("FlaxXLMRobertaForMaskedLM"),K7t=o(" (XLM-RoBERTa model)"),eLt=l(),v(F6.$$.fragment),uIo=l(),Uc=a("h3"),E6=a("a"),Yqe=a("span"),v(rJ.$$.fragment),oLt=l(),zqe=a("span"),rLt=o("AutoModelForMaskGeneration"),pIo=l(),tJ=a("div"),v(aJ.$$.fragment),_Io=l(),Xc=a("h3"),y6=a("a"),Hqe=a("span"),v(nJ.$$.fragment),tLt=l(),Kqe=a("span"),aLt=o("TFAutoModelForMaskGeneration"),bIo=l(),sJ=a("div"),v(lJ.$$.fragment),vIo=l(),Dc=a("h3"),C6=a("a"),eOe=a("span"),v(iJ.$$.fragment),nLt=l(),oOe=a("span"),sLt=o("AutoModelForSeq2SeqLM"),MIo=l(),rr=a("div"),v(dJ.$$.fragment),lLt=l(),qc=a("p"),iLt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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method.`),hLt=l(),mJ=a("p"),gLt=o("This class cannot be instantiated directly using "),rOe=a("code"),uLt=o("__init__()"),pLt=o(" (throws an error)."),_Lt=l(),oa=a("div"),v(cJ.$$.fragment),bLt=l(),tOe=a("p"),vLt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),MLt=l(),Oc=a("p"),TLt=o(`Note:
Loading a model from its configuration file does `),aOe=a("strong"),FLt=o("not"),ELt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),z1e=a("a"),yLt=o("from_pretrained()"),CLt=o(" to load the model weights."),wLt=l(),v(w6.$$.fragment),LLt=l(),co=a("div"),v(fJ.$$.fragment),ALt=l(),nOe=a("p"),RLt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),kLt=l(),Hn=a("p"),SLt=o("The model class to instantiate is selected based on the "),sOe=a("code"),xLt=o("model_type"),GLt=o(` property of the config object (either
passed as an argument or loaded from `),lOe=a("code"),BLt=o("pretrained_model_name_or_path"),ILt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iOe=a("code"),NLt=o("pretrained_model_name_or_path"),$Lt=o(":"),jLt=l(),ge=a("ul"),L6=a("li"),dOe=a("strong"),PLt=o("bart"),ZLt=o(" \u2014 "),H1e=a("a"),VLt=o("BartForConditionalGeneration"),WLt=o(" (BART model)"),JLt=l(),A6=a("li"),mOe=a("strong"),ULt=o("bigbird_pegasus"),XLt=o(" \u2014 "),K1e=a("a"),DLt=o("BigBirdPegasusForConditionalGeneration"),qLt=o(" (BigBird-Pegasus model)"),OLt=l(),R6=a("li"),cOe=a("strong"),QLt=o("blenderbot"),YLt=o(" \u2014 "),ebe=a("a"),zLt=o("BlenderbotForConditionalGeneration"),HLt=o(" (Blenderbot model)"),KLt=l(),k6=a("li"),fOe=a("strong"),eAt=o("blenderbot-small"),oAt=o(" \u2014 "),obe=a("a"),rAt=o("BlenderbotSmallForConditionalGeneration"),tAt=o(" (BlenderbotSmall model)"),aAt=l(),S6=a("li"),hOe=a("strong"),nAt=o("encoder-decoder"),sAt=o(" \u2014 "),rbe=a("a"),lAt=o("EncoderDecoderModel"),iAt=o(" (Encoder decoder model)"),dAt=l(),x6=a("li"),gOe=a("strong"),mAt=o("fsmt"),cAt=o(" \u2014 "),tbe=a("a"),fAt=o("FSMTForConditionalGeneration"),hAt=o(" (FairSeq Machine-Translation model)"),gAt=l(),G6=a("li"),uOe=a("strong"),uAt=o("gptsan-japanese"),pAt=o(" \u2014 "),abe=a("a"),_At=o("GPTSanJapaneseForConditionalGeneration"),bAt=o(" (GPTSAN-japanese model)"),vAt=l(),B6=a("li"),pOe=a("strong"),MAt=o("led"),TAt=o(" \u2014 "),nbe=a("a"),FAt=o("LEDForConditionalGeneration"),EAt=o(" (LED model)"),yAt=l(),I6=a("li"),_Oe=a("strong"),CAt=o("longt5"),wAt=o(" \u2014 "),sbe=a("a"),LAt=o("LongT5ForConditionalGeneration"),AAt=o(" (LongT5 model)"),RAt=l(),N6=a("li"),bOe=a("strong"),kAt=o("m2m_100"),SAt=o(" \u2014 "),lbe=a("a"),xAt=o("M2M100ForConditionalGeneration"),GAt=o(" (M2M100 model)"),BAt=l(),$6=a("li"),vOe=a("strong"),IAt=o("marian"),NAt=o(" \u2014 "),ibe=a("a"),$At=o("MarianMTModel"),jAt=o(" (Marian model)"),PAt=l(),j6=a("li"),MOe=a("strong"),ZAt=o("mbart"),VAt=o(" \u2014 "),dbe=a("a"),WAt=o("MBartForConditionalGeneration"),JAt=o(" (mBART model)"),UAt=l(),P6=a("li"),TOe=a("strong"),XAt=o("mt5"),DAt=o(" \u2014 "),mbe=a("a"),qAt=o("MT5ForConditionalGeneration"),OAt=o(" (MT5 model)"),QAt=l(),Z6=a("li"),FOe=a("strong"),YAt=o("mvp"),zAt=o(" \u2014 "),cbe=a("a"),HAt=o("MvpForConditionalGeneration"),KAt=o(" (MVP model)"),eRt=l(),V6=a("li"),EOe=a("strong"),oRt=o("nllb-moe"),rRt=o(" \u2014 "),fbe=a("a"),tRt=o("NllbMoeForConditionalGeneration"),aRt=o(" (NLLB-MOE model)"),nRt=l(),W6=a("li"),yOe=a("strong"),sRt=o("pegasus"),lRt=o(" \u2014 "),hbe=a("a"),iRt=o("PegasusForConditionalGeneration"),dRt=o(" (Pegasus model)"),mRt=l(),J6=a("li"),COe=a("strong"),cRt=o("pegasus_x"),fRt=o(" \u2014 "),gbe=a("a"),hRt=o("PegasusXForConditionalGeneration"),gRt=o(" (PEGASUS-X model)"),uRt=l(),U6=a("li"),wOe=a("strong"),pRt=o("plbart"),_Rt=o(" \u2014 "),ube=a("a"),bRt=o("PLBartForConditionalGeneration"),vRt=o(" (PLBart model)"),MRt=l(),X6=a("li"),LOe=a("strong"),TRt=o("prophetnet"),FRt=o(" \u2014 "),pbe=a("a"),ERt=o("ProphetNetForConditionalGeneration"),yRt=o(" (ProphetNet model)"),CRt=l(),D6=a("li"),AOe=a("strong"),wRt=o("switch_transformers"),LRt=o(" \u2014 "),_be=a("a"),ARt=o("SwitchTransformersForConditionalGeneration"),RRt=o(" (SwitchTransformers model)"),kRt=l(),q6=a("li"),ROe=a("strong"),SRt=o("t5"),xRt=o(" \u2014 "),bbe=a("a"),GRt=o("T5ForConditionalGeneration"),BRt=o(" (T5 model)"),IRt=l(),O6=a("li"),kOe=a("strong"),NRt=o("umt5"),$Rt=o(" \u2014 "),vbe=a("a"),jRt=o("UMT5ForConditionalGeneration"),PRt=o(" (UMT5 model)"),ZRt=l(),Q6=a("li"),SOe=a("strong"),VRt=o("xlm-prophetnet"),WRt=o(" \u2014 "),Mbe=a("a"),JRt=o("XLMProphetNetForConditionalGeneration"),URt=o(" (XLM-ProphetNet model)"),XRt=l(),Y6=a("p"),DRt=o("The model is set in evaluation mode by default using "),xOe=a("code"),qRt=o("model.eval()"),ORt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),GOe=a("code"),QRt=o("model.train()"),YRt=l(),v(z6.$$.fragment),TIo=l(),Qc=a("h3"),H6=a("a"),BOe=a("span"),v(hJ.$$.fragment),zRt=l(),IOe=a("span"),HRt=o("TFAutoModelForSeq2SeqLM"),FIo=l(),tr=a("div"),v(gJ.$$.fragment),KRt=l(),Yc=a("p"),ekt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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method.`),nkt=l(),uJ=a("p"),skt=o("This class cannot be instantiated directly using "),NOe=a("code"),lkt=o("__init__()"),ikt=o(" (throws an error)."),dkt=l(),ra=a("div"),v(pJ.$$.fragment),mkt=l(),$Oe=a("p"),ckt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),fkt=l(),zc=a("p"),hkt=o(`Note:
Loading a model from its configuration file does `),jOe=a("strong"),gkt=o("not"),ukt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Ebe=a("a"),pkt=o("from_pretrained()"),_kt=o(" to load the model weights."),bkt=l(),v(K6.$$.fragment),vkt=l(),ct=a("div"),v(_J.$$.fragment),Mkt=l(),POe=a("p"),Tkt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Fkt=l(),Kn=a("p"),Ekt=o("The model class to instantiate is selected based on the "),ZOe=a("code"),ykt=o("model_type"),Ckt=o(` property of the config object (either
passed as an argument or loaded from `),VOe=a("code"),wkt=o("pretrained_model_name_or_path"),Lkt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WOe=a("code"),Akt=o("pretrained_model_name_or_path"),Rkt=o(":"),kkt=l(),Ne=a("ul"),e8=a("li"),JOe=a("strong"),Skt=o("bart"),xkt=o(" \u2014 "),ybe=a("a"),Gkt=o("TFBartForConditionalGeneration"),Bkt=o(" (BART model)"),Ikt=l(),o8=a("li"),UOe=a("strong"),Nkt=o("blenderbot"),$kt=o(" \u2014 "),Cbe=a("a"),jkt=o("TFBlenderbotForConditionalGeneration"),Pkt=o(" (Blenderbot model)"),Zkt=l(),r8=a("li"),XOe=a("strong"),Vkt=o("blenderbot-small"),Wkt=o(" \u2014 "),wbe=a("a"),Jkt=o("TFBlenderbotSmallForConditionalGeneration"),Ukt=o(" (BlenderbotSmall model)"),Xkt=l(),t8=a("li"),DOe=a("strong"),Dkt=o("encoder-decoder"),qkt=o(" \u2014 "),Lbe=a("a"),Okt=o("TFEncoderDecoderModel"),Qkt=o(" (Encoder decoder model)"),Ykt=l(),a8=a("li"),qOe=a("strong"),zkt=o("led"),Hkt=o(" \u2014 "),Abe=a("a"),Kkt=o("TFLEDForConditionalGeneration"),eSt=o(" (LED model)"),oSt=l(),n8=a("li"),OOe=a("strong"),rSt=o("marian"),tSt=o(" \u2014 "),Rbe=a("a"),aSt=o("TFMarianMTModel"),nSt=o(" (Marian model)"),sSt=l(),s8=a("li"),QOe=a("strong"),lSt=o("mbart"),iSt=o(" \u2014 "),kbe=a("a"),dSt=o("TFMBartForConditionalGeneration"),mSt=o(" (mBART model)"),cSt=l(),l8=a("li"),YOe=a("strong"),fSt=o("mt5"),hSt=o(" \u2014 "),Sbe=a("a"),gSt=o("TFMT5ForConditionalGeneration"),uSt=o(" (MT5 model)"),pSt=l(),i8=a("li"),zOe=a("strong"),_St=o("pegasus"),bSt=o(" \u2014 "),xbe=a("a"),vSt=o("TFPegasusForConditionalGeneration"),MSt=o(" (Pegasus model)"),TSt=l(),d8=a("li"),HOe=a("strong"),FSt=o("t5"),ESt=o(" \u2014 "),Gbe=a("a"),ySt=o("TFT5ForConditionalGeneration"),CSt=o(" (T5 model)"),wSt=l(),v(m8.$$.fragment),EIo=l(),Hc=a("h3"),c8=a("a"),KOe=a("span"),v(bJ.$$.fragment),LSt=l(),eQe=a("span"),ASt=o("FlaxAutoModelForSeq2SeqLM"),yIo=l(),ar=a("div"),v(vJ.$$.fragment),RSt=l(),Kc=a("p"),kSt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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method.`),ISt=l(),MJ=a("p"),NSt=o("This class cannot be instantiated directly using "),oQe=a("code"),$St=o("__init__()"),jSt=o(" (throws an error)."),PSt=l(),ta=a("div"),v(TJ.$$.fragment),ZSt=l(),rQe=a("p"),VSt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),WSt=l(),ef=a("p"),JSt=o(`Note:
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falling back to using pattern matching on `),nKe=a("code"),$Yt=o("pretrained_model_name_or_path"),jYt=o(":"),PYt=l(),Fe=a("ul"),GA=a("li"),sKe=a("strong"),ZYt=o("albert"),VYt=o(" \u2014 "),oTe=a("a"),WYt=o("TFAlbertForMultipleChoice"),JYt=o(" (ALBERT model)"),UYt=l(),BA=a("li"),lKe=a("strong"),XYt=o("bert"),DYt=o(" \u2014 "),rTe=a("a"),qYt=o("TFBertForMultipleChoice"),OYt=o(" (BERT model)"),QYt=l(),IA=a("li"),iKe=a("strong"),YYt=o("camembert"),zYt=o(" \u2014 "),tTe=a("a"),HYt=o("TFCamembertForMultipleChoice"),KYt=o(" (CamemBERT model)"),ezt=l(),NA=a("li"),dKe=a("strong"),ozt=o("convbert"),rzt=o(" \u2014 "),aTe=a("a"),tzt=o("TFConvBertForMultipleChoice"),azt=o(" (ConvBERT model)"),nzt=l(),$A=a("li"),mKe=a("strong"),szt=o("deberta-v2"),lzt=o(" \u2014 "),nTe=a("a"),izt=o("TFDebertaV2ForMultipleChoice"),dzt=o(" (DeBERTa-v2 model)"),mzt=l(),jA=a("li"),cKe=a("strong"),czt=o("distilbert"),fzt=o(" \u2014 "),sTe=a("a"),hzt=o("TFDistilBertForMultipleChoice"),gzt=o(" (DistilBERT model)"),uzt=l(),PA=a("li"),fKe=a("strong"),pzt=o("electra"),_zt=o(" \u2014 "),lTe=a("a"),bzt=o("TFElectraForMultipleChoice"),vzt=o(" (ELECTRA model)"),Mzt=l(),ZA=a("li"),hKe=a("strong"),Tzt=o("flaubert"),Fzt=o(" \u2014 "),iTe=a("a"),Ezt=o("TFFlaubertForMultipleChoice"),yzt=o(" (FlauBERT model)"),Czt=l(),VA=a("li"),gKe=a("strong"),wzt=o("funnel"),Lzt=o(" \u2014 "),dTe=a("a"),Azt=o("TFFunnelForMultipleChoice"),Rzt=o(" (Funnel Transformer model)"),kzt=l(),WA=a("li"),uKe=a("strong"),Szt=o("longformer"),xzt=o(" \u2014 "),mTe=a("a"),Gzt=o("TFLongformerForMultipleChoice"),Bzt=o(" (Longformer model)"),Izt=l(),JA=a("li"),pKe=a("strong"),Nzt=o("mobilebert"),$zt=o(" \u2014 "),cTe=a("a"),jzt=o("TFMobileBertForMultipleChoice"),Pzt=o(" (MobileBERT model)"),Zzt=l(),UA=a("li"),_Ke=a("strong"),Vzt=o("mpnet"),Wzt=o(" \u2014 "),fTe=a("a"),Jzt=o("TFMPNetForMultipleChoice"),Uzt=o(" (MPNet model)"),Xzt=l(),XA=a("li"),bKe=a("strong"),Dzt=o("rembert"),qzt=o(" \u2014 "),hTe=a("a"),Ozt=o("TFRemBertForMultipleChoice"),Qzt=o(" (RemBERT model)"),Yzt=l(),DA=a("li"),vKe=a("strong"),zzt=o("roberta"),Hzt=o(" \u2014 "),gTe=a("a"),Kzt=o("TFRobertaForMultipleChoice"),eHt=o(" (RoBERTa model)"),oHt=l(),qA=a("li"),MKe=a("strong"),rHt=o("roberta-prelayernorm"),tHt=o(" \u2014 "),uTe=a("a"),aHt=o("TFRobertaPreLayerNormForMultipleChoice"),nHt=o(" (RoBERTa-PreLayerNorm model)"),sHt=l(),OA=a("li"),TKe=a("strong"),lHt=o("roformer"),iHt=o(" \u2014 "),pTe=a("a"),dHt=o("TFRoFormerForMultipleChoice"),mHt=o(" (RoFormer model)"),cHt=l(),QA=a("li"),FKe=a("strong"),fHt=o("xlm"),hHt=o(" \u2014 "),_Te=a("a"),gHt=o("TFXLMForMultipleChoice"),uHt=o(" (XLM model)"),pHt=l(),YA=a("li"),EKe=a("strong"),_Ht=o("xlm-roberta"),bHt=o(" \u2014 "),bTe=a("a"),vHt=o("TFXLMRobertaForMultipleChoice"),MHt=o(" (XLM-RoBERTa model)"),THt=l(),zA=a("li"),yKe=a("strong"),FHt=o("xlnet"),EHt=o(" \u2014 "),vTe=a("a"),yHt=o("TFXLNetForMultipleChoice"),CHt=o(" (XLNet model)"),wHt=l(),v(HA.$$.fragment),IIo=l(),_f=a("h3"),KA=a("a"),CKe=a("span"),v(OJ.$$.fragment),LHt=l(),wKe=a("span"),AHt=o("FlaxAutoModelForMultipleChoice"),NIo=l(),mr=a("div"),v(QJ.$$.fragment),RHt=l(),bf=a("p"),kHt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
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Loading a model from its configuration file does `),RKe=a("strong"),UHt=o("not"),XHt=o(` load the model weights. It only affects the
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passed as an argument or loaded from `),xKe=a("code"),rKt=o("pretrained_model_name_or_path"),tKt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GKe=a("code"),aKt=o("pretrained_model_name_or_path"),nKt=o(":"),sKt=l(),Ue=a("ul"),oR=a("li"),BKe=a("strong"),lKt=o("albert"),iKt=o(" \u2014 "),ETe=a("a"),dKt=o("FlaxAlbertForMultipleChoice"),mKt=o(" (ALBERT model)"),cKt=l(),rR=a("li"),IKe=a("strong"),fKt=o("bert"),hKt=o(" \u2014 "),yTe=a("a"),gKt=o("FlaxBertForMultipleChoice"),uKt=o(" (BERT model)"),pKt=l(),tR=a("li"),NKe=a("strong"),_Kt=o("big_bird"),bKt=o(" \u2014 "),CTe=a("a"),vKt=o("FlaxBigBirdForMultipleChoice"),MKt=o(" (BigBird model)"),TKt=l(),aR=a("li"),$Ke=a("strong"),FKt=o("distilbert"),EKt=o(" \u2014 "),wTe=a("a"),yKt=o("FlaxDistilBertForMultipleChoice"),CKt=o(" (DistilBERT model)"),wKt=l(),nR=a("li"),jKe=a("strong"),LKt=o("electra"),AKt=o(" \u2014 "),LTe=a("a"),RKt=o("FlaxElectraForMultipleChoice"),kKt=o(" (ELECTRA model)"),SKt=l(),sR=a("li"),PKe=a("strong"),xKt=o("roberta"),GKt=o(" \u2014 "),ATe=a("a"),BKt=o("FlaxRobertaForMultipleChoice"),IKt=o(" (RoBERTa model)"),NKt=l(),lR=a("li"),ZKe=a("strong"),$Kt=o("roberta-prelayernorm"),jKt=o(" \u2014 "),RTe=a("a"),PKt=o("FlaxRobertaPreLayerNormForMultipleChoice"),ZKt=o(" (RoBERTa-PreLayerNorm model)"),VKt=l(),iR=a("li"),VKe=a("strong"),WKt=o("roformer"),JKt=o(" \u2014 "),kTe=a("a"),UKt=o("FlaxRoFormerForMultipleChoice"),XKt=o(" (RoFormer model)"),DKt=l(),dR=a("li"),WKe=a("strong"),qKt=o("xlm-roberta"),OKt=o(" \u2014 "),STe=a("a"),QKt=o("FlaxXLMRobertaForMultipleChoice"),YKt=o(" (XLM-RoBERTa model)"),zKt=l(),v(mR.$$.fragment),$Io=l(),Mf=a("h3"),cR=a("a"),JKe=a("span"),v(KJ.$$.fragment),HKt=l(),UKe=a("span"),KKt=o("AutoModelForNextSentencePrediction"),jIo=l(),cr=a("div"),v(eU.$$.fragment),eea=l(),Tf=a("p"),oea=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),xTe=a("a"),rea=o("from_pretrained()"),tea=o(" class method or the "),GTe=a("a"),aea=o("from_config()"),nea=o(` class
method.`),sea=l(),oU=a("p"),lea=o("This class cannot be instantiated directly using "),XKe=a("code"),iea=o("__init__()"),dea=o(" (throws an error)."),mea=l(),ma=a("div"),v(rU.$$.fragment),cea=l(),DKe=a("p"),fea=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),hea=l(),Ff=a("p"),gea=o(`Note:
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model\u2019s configuration. Use `),BTe=a("a"),_ea=o("from_pretrained()"),bea=o(" to load the model weights."),vea=l(),v(fR.$$.fragment),Mea=l(),go=a("div"),v(tU.$$.fragment),Tea=l(),OKe=a("p"),Fea=o("Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),Eea=l(),ls=a("p"),yea=o("The model class to instantiate is selected based on the "),QKe=a("code"),Cea=o("model_type"),wea=o(` property of the config object (either
passed as an argument or loaded from `),YKe=a("code"),Lea=o("pretrained_model_name_or_path"),Aea=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zKe=a("code"),Rea=o("pretrained_model_name_or_path"),kea=o(":"),Sea=l(),eo=a("ul"),hR=a("li"),HKe=a("strong"),xea=o("bert"),Gea=o(" \u2014 "),ITe=a("a"),Bea=o("BertForNextSentencePrediction"),Iea=o(" (BERT model)"),Nea=l(),gR=a("li"),KKe=a("strong"),$ea=o("ernie"),jea=o(" \u2014 "),NTe=a("a"),Pea=o("ErnieForNextSentencePrediction"),Zea=o(" (ERNIE model)"),Vea=l(),uR=a("li"),eeo=a("strong"),Wea=o("fnet"),Jea=o(" \u2014 "),$Te=a("a"),Uea=o("FNetForNextSentencePrediction"),Xea=o(" (FNet model)"),Dea=l(),pR=a("li"),oeo=a("strong"),qea=o("megatron-bert"),Oea=o(" \u2014 "),jTe=a("a"),Qea=o("MegatronBertForNextSentencePrediction"),Yea=o(" (Megatron-BERT model)"),zea=l(),_R=a("li"),reo=a("strong"),Hea=o("mobilebert"),Kea=o(" \u2014 "),PTe=a("a"),eoa=o("MobileBertForNextSentencePrediction"),ooa=o(" (MobileBERT model)"),roa=l(),bR=a("li"),teo=a("strong"),toa=o("nezha"),aoa=o(" \u2014 "),ZTe=a("a"),noa=o("NezhaForNextSentencePrediction"),soa=o(" (Nezha model)"),loa=l(),vR=a("li"),aeo=a("strong"),ioa=o("qdqbert"),doa=o(" \u2014 "),VTe=a("a"),moa=o("QDQBertForNextSentencePrediction"),coa=o(" (QDQBert model)"),foa=l(),MR=a("p"),hoa=o("The model is set in evaluation mode by default using "),neo=a("code"),goa=o("model.eval()"),uoa=o(` (so for instance, dropout modules are
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passed as an argument or loaded from `),geo=a("code"),qoa=o("pretrained_model_name_or_path"),Ooa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ueo=a("code"),Qoa=o("pretrained_model_name_or_path"),Yoa=o(":"),zoa=l(),dU=a("ul"),yR=a("li"),peo=a("strong"),Hoa=o("bert"),Koa=o(" \u2014 "),XTe=a("a"),era=o("TFBertForNextSentencePrediction"),ora=o(" (BERT model)"),rra=l(),CR=a("li"),_eo=a("strong"),tra=o("mobilebert"),ara=o(" \u2014 "),DTe=a("a"),nra=o("TFMobileBertForNextSentencePrediction"),sra=o(" (MobileBERT model)"),lra=l(),v(wR.$$.fragment),VIo=l(),wf=a("h3"),LR=a("a"),beo=a("span"),v(mU.$$.fragment),ira=l(),veo=a("span"),dra=o("FlaxAutoModelForNextSentencePrediction"),WIo=l(),hr=a("div"),v(cU.$$.fragment),mra=l(),Lf=a("p"),cra=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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passed as an argument or loaded from `),Ceo=a("code"),$ra=o("pretrained_model_name_or_path"),jra=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),weo=a("code"),Pra=o("pretrained_model_name_or_path"),Zra=o(":"),Vra=l(),Leo=a("ul"),RR=a("li"),Aeo=a("strong"),Wra=o("bert"),Jra=o(" \u2014 "),YTe=a("a"),Ura=o("FlaxBertForNextSentencePrediction"),Xra=o(" (BERT model)"),Dra=l(),v(kR.$$.fragment),JIo=l(),Rf=a("h3"),SR=a("a"),Reo=a("span"),v(uU.$$.fragment),qra=l(),keo=a("span"),Ora=o("AutoModelForTokenClassification"),UIo=l(),gr=a("div"),v(pU.$$.fragment),Qra=l(),kf=a("p"),Yra=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),ota=l(),_U=a("p"),rta=o("This class cannot be instantiated directly using "),Seo=a("code"),tta=o("__init__()"),ata=o(" (throws an error)."),nta=l(),ha=a("div"),v(bU.$$.fragment),sta=l(),xeo=a("p"),lta=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),ita=l(),Sf=a("p"),dta=o(`Note:
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passed as an argument or loaded from `),Neo=a("code"),Fta=o("pretrained_model_name_or_path"),Eta=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$eo=a("code"),yta=o("pretrained_model_name_or_path"),Cta=o(":"),wta=l(),z=a("ul"),GR=a("li"),jeo=a("strong"),Lta=o("albert"),Ata=o(" \u2014 "),eFe=a("a"),Rta=o("AlbertForTokenClassification"),kta=o(" (ALBERT model)"),Sta=l(),BR=a("li"),Peo=a("strong"),xta=o("bert"),Gta=o(" \u2014 "),oFe=a("a"),Bta=o("BertForTokenClassification"),Ita=o(" (BERT model)"),Nta=l(),IR=a("li"),Zeo=a("strong"),$ta=o("big_bird"),jta=o(" \u2014 "),rFe=a("a"),Pta=o("BigBirdForTokenClassification"),Zta=o(" (BigBird model)"),Vta=l(),NR=a("li"),Veo=a("strong"),Wta=o("biogpt"),Jta=o(" \u2014 "),tFe=a("a"),Uta=o("BioGptForTokenClassification"),Xta=o(" (BioGpt model)"),Dta=l(),$R=a("li"),Weo=a("strong"),qta=o("bloom"),Ota=o(" \u2014 "),aFe=a("a"),Qta=o("BloomForTokenClassification"),Yta=o(" (BLOOM model)"),zta=l(),jR=a("li"),Jeo=a("strong"),Hta=o("camembert"),Kta=o(" \u2014 "),nFe=a("a"),eaa=o("CamembertForTokenClassification"),oaa=o(" (CamemBERT 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model\u2019s configuration. Use `),Fye=a("a"),pMa=o("from_pretrained()"),_Ma=o(" to load the model weights."),bMa=l(),v(tG.$$.fragment),vMa=l(),Ft=a("div"),v(UU.$$.fragment),MMa=l(),$ao=a("p"),TMa=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),FMa=l(),us=a("p"),EMa=o("The model class to instantiate is selected based on the "),jao=a("code"),yMa=o("model_type"),CMa=o(` property of the config object (either
passed as an argument or loaded from `),Pao=a("code"),wMa=o("pretrained_model_name_or_path"),LMa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Zao=a("code"),AMa=o("pretrained_model_name_or_path"),RMa=o(":"),kMa=l(),xe=a("ul"),aG=a("li"),Vao=a("strong"),SMa=o("albert"),xMa=o(" \u2014 "),Eye=a("a"),GMa=o("FlaxAlbertForQuestionAnswering"),BMa=o(" (ALBERT model)"),IMa=l(),nG=a("li"),Wao=a("strong"),NMa=o("bart"),$Ma=o(" \u2014 "),yye=a("a"),jMa=o("FlaxBartForQuestionAnswering"),PMa=o(" (BART model)"),ZMa=l(),sG=a("li"),Jao=a("strong"),VMa=o("bert"),WMa=o(" \u2014 "),Cye=a("a"),JMa=o("FlaxBertForQuestionAnswering"),UMa=o(" (BERT model)"),XMa=l(),lG=a("li"),Uao=a("strong"),DMa=o("big_bird"),qMa=o(" \u2014 "),wye=a("a"),OMa=o("FlaxBigBirdForQuestionAnswering"),QMa=o(" (BigBird model)"),YMa=l(),iG=a("li"),Xao=a("strong"),zMa=o("distilbert"),HMa=o(" \u2014 "),Lye=a("a"),KMa=o("FlaxDistilBertForQuestionAnswering"),eTa=o(" (DistilBERT model)"),oTa=l(),dG=a("li"),Dao=a("strong"),rTa=o("electra"),tTa=o(" \u2014 "),Aye=a("a"),aTa=o("FlaxElectraForQuestionAnswering"),nTa=o(" (ELECTRA model)"),sTa=l(),mG=a("li"),qao=a("strong"),lTa=o("mbart"),iTa=o(" \u2014 "),Rye=a("a"),dTa=o("FlaxMBartForQuestionAnswering"),mTa=o(" (mBART model)"),cTa=l(),cG=a("li"),Oao=a("strong"),fTa=o("roberta"),hTa=o(" \u2014 "),kye=a("a"),gTa=o("FlaxRobertaForQuestionAnswering"),uTa=o(" (RoBERTa model)"),pTa=l(),fG=a("li"),Qao=a("strong"),_Ta=o("roberta-prelayernorm"),bTa=o(" \u2014 "),Sye=a("a"),vTa=o("FlaxRobertaPreLayerNormForQuestionAnswering"),MTa=o(" (RoBERTa-PreLayerNorm model)"),TTa=l(),hG=a("li"),Yao=a("strong"),FTa=o("roformer"),ETa=o(" \u2014 "),xye=a("a"),yTa=o("FlaxRoFormerForQuestionAnswering"),CTa=o(" (RoFormer model)"),wTa=l(),gG=a("li"),zao=a("strong"),LTa=o("xlm-roberta"),ATa=o(" \u2014 "),Gye=a("a"),RTa=o("FlaxXLMRobertaForQuestionAnswering"),kTa=o(" (XLM-RoBERTa model)"),STa=l(),v(uG.$$.fragment),oNo=l(),qf=a("h3"),pG=a("a"),Hao=a("span"),v(XU.$$.fragment),xTa=l(),Kao=a("span"),GTa=o("AutoModelForTextEncoding"),rNo=l(),DU=a("div"),v(qU.$$.fragment),tNo=l(),Of=a("h3"),_G=a("a"),eno=a("span"),v(OU.$$.fragment),BTa=l(),ono=a("span"),ITa=o("TFAutoModelForTextEncoding"),aNo=l(),QU=a("div"),v(YU.$$.fragment),nNo=l(),Qf=a("h2"),bG=a("a"),rno=a("span"),v(zU.$$.fragment),NTa=l(),tno=a("span"),$Ta=o("Computer vision"),sNo=l(),Bye=a("p"),jTa=o("The following auto classes are available for the following computer vision tasks."),lNo=l(),Yf=a("h3"),vG=a("a"),ano=a("span"),v(HU.$$.fragment),PTa=l(),nno=a("span"),ZTa=o("AutoModelForDepthEstimation"),iNo=l(),Mr=a("div"),v(KU.$$.fragment),VTa=l(),zf=a("p"),WTa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),Iye=a("a"),JTa=o("from_pretrained()"),UTa=o(" class method or the "),Nye=a("a"),XTa=o("from_config()"),DTa=o(` class
method.`),qTa=l(),eX=a("p"),OTa=o("This class cannot be instantiated directly using "),sno=a("code"),QTa=o("__init__()"),YTa=o(" (throws an error)."),zTa=l(),va=a("div"),v(oX.$$.fragment),HTa=l(),lno=a("p"),KTa=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),eFa=l(),Hf=a("p"),oFa=o(`Note:
Loading a model from its configuration file does `),ino=a("strong"),rFa=o("not"),tFa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),$ye=a("a"),aFa=o("from_pretrained()"),nFa=o(" to load the model weights."),sFa=l(),v(MG.$$.fragment),lFa=l(),_o=a("div"),v(rX.$$.fragment),iFa=l(),dno=a("p"),dFa=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),mFa=l(),ps=a("p"),cFa=o("The model class to instantiate is selected based on the "),mno=a("code"),fFa=o("model_type"),hFa=o(` property of the config object (either
passed as an argument or loaded from `),cno=a("code"),gFa=o("pretrained_model_name_or_path"),uFa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fno=a("code"),pFa=o("pretrained_model_name_or_path"),_Fa=o(":"),bFa=l(),tX=a("ul"),TG=a("li"),hno=a("strong"),vFa=o("dpt"),MFa=o(" \u2014 "),jye=a("a"),TFa=o("DPTForDepthEstimation"),FFa=o(" (DPT model)"),EFa=l(),FG=a("li"),gno=a("strong"),yFa=o("glpn"),CFa=o(" \u2014 "),Pye=a("a"),wFa=o("GLPNForDepthEstimation"),LFa=o(" (GLPN model)"),AFa=l(),EG=a("p"),RFa=o("The model is set in evaluation mode by default using "),uno=a("code"),kFa=o("model.eval()"),SFa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),pno=a("code"),xFa=o("model.train()"),GFa=l(),v(yG.$$.fragment),dNo=l(),Kf=a("h3"),CG=a("a"),_no=a("span"),v(aX.$$.fragment),BFa=l(),bno=a("span"),IFa=o("AutoModelForImageClassification"),mNo=l(),Tr=a("div"),v(nX.$$.fragment),NFa=l(),eh=a("p"),$Fa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),Zye=a("a"),jFa=o("from_pretrained()"),PFa=o(" class method or the "),Vye=a("a"),ZFa=o("from_config()"),VFa=o(` class
method.`),WFa=l(),sX=a("p"),JFa=o("This class cannot be instantiated directly using "),vno=a("code"),UFa=o("__init__()"),XFa=o(" (throws an error)."),DFa=l(),Ma=a("div"),v(lX.$$.fragment),qFa=l(),Mno=a("p"),OFa=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),QFa=l(),oh=a("p"),YFa=o(`Note:
Loading a model from its configuration file does `),Tno=a("strong"),zFa=o("not"),HFa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Wye=a("a"),KFa=o("from_pretrained()"),eEa=o(" to load the model weights."),oEa=l(),v(wG.$$.fragment),rEa=l(),bo=a("div"),v(iX.$$.fragment),tEa=l(),Fno=a("p"),aEa=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),nEa=l(),_s=a("p"),sEa=o("The model class to instantiate is selected based on the "),Eno=a("code"),lEa=o("model_type"),iEa=o(` property of the config object (either
passed as an argument or loaded from `),yno=a("code"),dEa=o("pretrained_model_name_or_path"),mEa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Cno=a("code"),cEa=o("pretrained_model_name_or_path"),fEa=o(":"),hEa=l(),le=a("ul"),LG=a("li"),wno=a("strong"),gEa=o("beit"),uEa=o(" \u2014 "),Jye=a("a"),pEa=o("BeitForImageClassification"),_Ea=o(" (BEiT model)"),bEa=l(),AG=a("li"),Lno=a("strong"),vEa=o("bit"),MEa=o(" \u2014 "),Uye=a("a"),TEa=o("BitForImageClassification"),FEa=o(" (BiT model)"),EEa=l(),RG=a("li"),Ano=a("strong"),yEa=o("convnext"),CEa=o(" \u2014 "),Xye=a("a"),wEa=o("ConvNextForImageClassification"),LEa=o(" (ConvNeXT model)"),AEa=l(),kG=a("li"),Rno=a("strong"),REa=o("convnextv2"),kEa=o(" \u2014 "),Dye=a("a"),SEa=o("ConvNextV2ForImageClassification"),xEa=o(" (ConvNeXTV2 model)"),GEa=l(),SG=a("li"),kno=a("strong"),BEa=o("cvt"),IEa=o(" \u2014 "),qye=a("a"),NEa=o("CvtForImageClassification"),$Ea=o(" (CvT model)"),jEa=l(),xG=a("li"),Sno=a("strong"),PEa=o("data2vec-vision"),ZEa=o(" \u2014 "),Oye=a("a"),VEa=o("Data2VecVisionForImageClassification"),WEa=o(" (Data2VecVision model)"),JEa=l(),Ui=a("li"),xno=a("strong"),UEa=o("deit"),XEa=o(" \u2014 "),Qye=a("a"),DEa=o("DeiTForImageClassification"),qEa=o(" or "),Yye=a("a"),OEa=o("DeiTForImageClassificationWithTeacher"),QEa=o(" (DeiT model)"),YEa=l(),GG=a("li"),Gno=a("strong"),zEa=o("dinat"),HEa=o(" \u2014 "),zye=a("a"),KEa=o("DinatForImageClassification"),e0a=o(" (DiNAT model)"),o0a=l(),BG=a("li"),Bno=a("strong"),r0a=o("dinov2"),t0a=o(" \u2014 "),Hye=a("a"),a0a=o("Dinov2ForImageClassification"),n0a=o(" (DINOv2 model)"),s0a=l(),Xi=a("li"),Ino=a("strong"),l0a=o("efficientformer"),i0a=o(" \u2014 "),Kye=a("a"),d0a=o("EfficientFormerForImageClassification"),m0a=o(" or "),e5e=a("a"),c0a=o("EfficientFormerForImageClassificationWithTeacher"),f0a=o(" (EfficientFormer model)"),h0a=l(),IG=a("li"),Nno=a("strong"),g0a=o("efficientnet"),u0a=o(" \u2014 "),o5e=a("a"),p0a=o("EfficientNetForImageClassification"),_0a=o(" (EfficientNet model)"),b0a=l(),NG=a("li"),$no=a("strong"),v0a=o("focalnet"),M0a=o(" \u2014 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model)"),kya=l(),DG=a("li"),Qno=a("strong"),Sya=o("resnet"),xya=o(" \u2014 "),_5e=a("a"),Gya=o("ResNetForImageClassification"),Bya=o(" (ResNet model)"),Iya=l(),qG=a("li"),Yno=a("strong"),Nya=o("segformer"),$ya=o(" \u2014 "),b5e=a("a"),jya=o("SegformerForImageClassification"),Pya=o(" (SegFormer model)"),Zya=l(),OG=a("li"),zno=a("strong"),Vya=o("swiftformer"),Wya=o(" \u2014 "),v5e=a("a"),Jya=o("SwiftFormerForImageClassification"),Uya=o(" (SwiftFormer model)"),Xya=l(),QG=a("li"),Hno=a("strong"),Dya=o("swin"),qya=o(" \u2014 "),M5e=a("a"),Oya=o("SwinForImageClassification"),Qya=o(" (Swin Transformer model)"),Yya=l(),YG=a("li"),Kno=a("strong"),zya=o("swinv2"),Hya=o(" \u2014 "),T5e=a("a"),Kya=o("Swinv2ForImageClassification"),e5a=o(" (Swin Transformer V2 model)"),o5a=l(),zG=a("li"),eso=a("strong"),r5a=o("van"),t5a=o(" \u2014 "),F5e=a("a"),a5a=o("VanForImageClassification"),n5a=o(" (VAN model)"),s5a=l(),HG=a("li"),oso=a("strong"),l5a=o("vit"),i5a=o(" \u2014 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deactivated). To train the model, you should first set it back in training mode with `),nso=a("code"),C5a=o("model.train()"),w5a=l(),v(rB.$$.fragment),cNo=l(),rh=a("h3"),tB=a("a"),sso=a("span"),v(dX.$$.fragment),L5a=l(),lso=a("span"),A5a=o("TFAutoModelForImageClassification"),fNo=l(),Fr=a("div"),v(mX.$$.fragment),R5a=l(),th=a("p"),k5a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),w5e=a("a"),S5a=o("from_pretrained()"),x5a=o(" class method or the "),L5e=a("a"),G5a=o("from_config()"),B5a=o(` class
method.`),I5a=l(),cX=a("p"),N5a=o("This class cannot be instantiated directly using "),iso=a("code"),$5a=o("__init__()"),j5a=o(" (throws an error)."),P5a=l(),Fa=a("div"),v(fX.$$.fragment),Z5a=l(),dso=a("p"),V5a=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),W5a=l(),ah=a("p"),J5a=o(`Note:
Loading a model from its configuration file does `),mso=a("strong"),U5a=o("not"),X5a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),A5e=a("a"),D5a=o("from_pretrained()"),q5a=o(" to load the model weights."),O5a=l(),v(aB.$$.fragment),Q5a=l(),Et=a("div"),v(hX.$$.fragment),Y5a=l(),cso=a("p"),z5a=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),H5a=l(),bs=a("p"),K5a=o("The model class to instantiate is selected based on the "),fso=a("code"),e3a=o("model_type"),o3a=o(` property of the config object (either
passed as an argument or loaded from `),hso=a("code"),r3a=o("pretrained_model_name_or_path"),t3a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gso=a("code"),a3a=o("pretrained_model_name_or_path"),n3a=o(":"),s3a=l(),Ge=a("ul"),nB=a("li"),uso=a("strong"),l3a=o("convnext"),i3a=o(" \u2014 "),R5e=a("a"),d3a=o("TFConvNextForImageClassification"),m3a=o(" (ConvNeXT model)"),c3a=l(),sB=a("li"),pso=a("strong"),f3a=o("cvt"),h3a=o(" \u2014 "),k5e=a("a"),g3a=o("TFCvtForImageClassification"),u3a=o(" (CvT model)"),p3a=l(),lB=a("li"),_so=a("strong"),_3a=o("data2vec-vision"),b3a=o(" \u2014 "),S5e=a("a"),v3a=o("TFData2VecVisionForImageClassification"),M3a=o(" (Data2VecVision model)"),T3a=l(),qi=a("li"),bso=a("strong"),F3a=o("deit"),E3a=o(" \u2014 "),x5e=a("a"),y3a=o("TFDeiTForImageClassification"),C3a=o(" or "),G5e=a("a"),w3a=o("TFDeiTForImageClassificationWithTeacher"),L3a=o(" (DeiT model)"),A3a=l(),Oi=a("li"),vso=a("strong"),R3a=o("efficientformer"),k3a=o(" \u2014 "),B5e=a("a"),S3a=o("TFEfficientFormerForImageClassification"),x3a=o(" or "),I5e=a("a"),G3a=o("TFEfficientFormerForImageClassificationWithTeacher"),B3a=o(" (EfficientFormer model)"),I3a=l(),iB=a("li"),Mso=a("strong"),N3a=o("mobilevit"),$3a=o(" \u2014 "),N5e=a("a"),j3a=o("TFMobileViTForImageClassification"),P3a=o(" (MobileViT model)"),Z3a=l(),dB=a("li"),Tso=a("strong"),V3a=o("regnet"),W3a=o(" \u2014 "),$5e=a("a"),J3a=o("TFRegNetForImageClassification"),U3a=o(" (RegNet model)"),X3a=l(),mB=a("li"),Fso=a("strong"),D3a=o("resnet"),q3a=o(" \u2014 "),j5e=a("a"),O3a=o("TFResNetForImageClassification"),Q3a=o(" (ResNet model)"),Y3a=l(),cB=a("li"),Eso=a("strong"),z3a=o("segformer"),H3a=o(" \u2014 "),P5e=a("a"),K3a=o("TFSegformerForImageClassification"),e9a=o(" (SegFormer model)"),o9a=l(),fB=a("li"),yso=a("strong"),r9a=o("swin"),t9a=o(" \u2014 "),Z5e=a("a"),a9a=o("TFSwinForImageClassification"),n9a=o(" (Swin Transformer model)"),s9a=l(),hB=a("li"),Cso=a("strong"),l9a=o("vit"),i9a=o(" \u2014 "),V5e=a("a"),d9a=o("TFViTForImageClassification"),m9a=o(" (ViT model)"),c9a=l(),v(gB.$$.fragment),hNo=l(),nh=a("h3"),uB=a("a"),wso=a("span"),v(gX.$$.fragment),f9a=l(),Lso=a("span"),h9a=o("FlaxAutoModelForImageClassification"),gNo=l(),Er=a("div"),v(uX.$$.fragment),g9a=l(),sh=a("p"),u9a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),W5e=a("a"),p9a=o("from_pretrained()"),_9a=o(" class method or the "),J5e=a("a"),b9a=o("from_config()"),v9a=o(` class
method.`),M9a=l(),pX=a("p"),T9a=o("This class cannot be instantiated directly using "),Aso=a("code"),F9a=o("__init__()"),E9a=o(" (throws an error)."),y9a=l(),Ea=a("div"),v(_X.$$.fragment),C9a=l(),Rso=a("p"),w9a=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),L9a=l(),lh=a("p"),A9a=o(`Note:
Loading a model from its configuration file does `),kso=a("strong"),R9a=o("not"),k9a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),U5e=a("a"),S9a=o("from_pretrained()"),x9a=o(" to load the model weights."),G9a=l(),v(pB.$$.fragment),B9a=l(),yt=a("div"),v(bX.$$.fragment),I9a=l(),Sso=a("p"),N9a=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),$9a=l(),vs=a("p"),j9a=o("The model class to instantiate is selected based on the "),xso=a("code"),P9a=o("model_type"),Z9a=o(` property of the config object (either
passed as an argument or loaded from `),Gso=a("code"),V9a=o("pretrained_model_name_or_path"),W9a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bso=a("code"),J9a=o("pretrained_model_name_or_path"),U9a=o(":"),X9a=l(),Ms=a("ul"),_B=a("li"),Iso=a("strong"),D9a=o("beit"),q9a=o(" \u2014 "),X5e=a("a"),O9a=o("FlaxBeitForImageClassification"),Q9a=o(" (BEiT model)"),Y9a=l(),bB=a("li"),Nso=a("strong"),z9a=o("regnet"),H9a=o(" \u2014 "),D5e=a("a"),K9a=o("FlaxRegNetForImageClassification"),e4a=o(" (RegNet model)"),o4a=l(),vB=a("li"),$so=a("strong"),r4a=o("resnet"),t4a=o(" \u2014 "),q5e=a("a"),a4a=o("FlaxResNetForImageClassification"),n4a=o(" (ResNet model)"),s4a=l(),MB=a("li"),jso=a("strong"),l4a=o("vit"),i4a=o(" \u2014 "),O5e=a("a"),d4a=o("FlaxViTForImageClassification"),m4a=o(" (ViT model)"),c4a=l(),v(TB.$$.fragment),uNo=l(),ih=a("h3"),FB=a("a"),Pso=a("span"),v(vX.$$.fragment),f4a=l(),Zso=a("span"),h4a=o("AutoModelForVideoClassification"),pNo=l(),yr=a("div"),v(MX.$$.fragment),g4a=l(),dh=a("p"),u4a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),Q5e=a("a"),p4a=o("from_pretrained()"),_4a=o(" class method or the "),Y5e=a("a"),b4a=o("from_config()"),v4a=o(` class
method.`),M4a=l(),TX=a("p"),T4a=o("This class cannot be instantiated directly using "),Vso=a("code"),F4a=o("__init__()"),E4a=o(" (throws an error)."),y4a=l(),ya=a("div"),v(FX.$$.fragment),C4a=l(),Wso=a("p"),w4a=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),L4a=l(),mh=a("p"),A4a=o(`Note:
Loading a model from its configuration file does `),Jso=a("strong"),R4a=o("not"),k4a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),z5e=a("a"),S4a=o("from_pretrained()"),x4a=o(" to load the model weights."),G4a=l(),v(EB.$$.fragment),B4a=l(),vo=a("div"),v(EX.$$.fragment),I4a=l(),Uso=a("p"),N4a=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),$4a=l(),Ts=a("p"),j4a=o("The model class to instantiate is selected based on the "),Xso=a("code"),P4a=o("model_type"),Z4a=o(` property of the config object (either
passed as an argument or loaded from `),Dso=a("code"),V4a=o("pretrained_model_name_or_path"),W4a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qso=a("code"),J4a=o("pretrained_model_name_or_path"),U4a=o(":"),X4a=l(),ch=a("ul"),yB=a("li"),Oso=a("strong"),D4a=o("timesformer"),q4a=o(" \u2014 "),H5e=a("a"),O4a=o("TimesformerForVideoClassification"),Q4a=o(" (TimeSformer model)"),Y4a=l(),CB=a("li"),Qso=a("strong"),z4a=o("videomae"),H4a=o(" \u2014 "),K5e=a("a"),K4a=o("VideoMAEForVideoClassification"),eCa=o(" (VideoMAE model)"),oCa=l(),wB=a("li"),Yso=a("strong"),rCa=o("vivit"),tCa=o(" \u2014 "),e3e=a("a"),aCa=o("VivitForVideoClassification"),nCa=o(" (ViViT model)"),sCa=l(),LB=a("p"),lCa=o("The model is set in evaluation mode by default using "),zso=a("code"),iCa=o("model.eval()"),dCa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Hso=a("code"),mCa=o("model.train()"),cCa=l(),v(AB.$$.fragment),_No=l(),fh=a("h3"),RB=a("a"),Kso=a("span"),v(yX.$$.fragment),fCa=l(),elo=a("span"),hCa=o("AutoModelForMaskedImageModeling"),bNo=l(),Cr=a("div"),v(CX.$$.fragment),gCa=l(),hh=a("p"),uCa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),o3e=a("a"),pCa=o("from_pretrained()"),_Ca=o(" class method or the "),r3e=a("a"),bCa=o("from_config()"),vCa=o(` class
method.`),MCa=l(),wX=a("p"),TCa=o("This class cannot be instantiated directly using "),olo=a("code"),FCa=o("__init__()"),ECa=o(" (throws an error)."),yCa=l(),Ca=a("div"),v(LX.$$.fragment),CCa=l(),rlo=a("p"),wCa=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),LCa=l(),gh=a("p"),ACa=o(`Note:
Loading a model from its configuration file does `),tlo=a("strong"),RCa=o("not"),kCa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),t3e=a("a"),SCa=o("from_pretrained()"),xCa=o(" to load the model weights."),GCa=l(),v(kB.$$.fragment),BCa=l(),Mo=a("div"),v(AX.$$.fragment),ICa=l(),alo=a("p"),NCa=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),$Ca=l(),Fs=a("p"),jCa=o("The model class to instantiate is selected based on the "),nlo=a("code"),PCa=o("model_type"),ZCa=o(` property of the config object (either
passed as an argument or loaded from `),slo=a("code"),VCa=o("pretrained_model_name_or_path"),WCa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),llo=a("code"),JCa=o("pretrained_model_name_or_path"),UCa=o(":"),XCa=l(),Nt=a("ul"),SB=a("li"),ilo=a("strong"),DCa=o("deit"),qCa=o(" \u2014 "),a3e=a("a"),OCa=o("DeiTForMaskedImageModeling"),QCa=o(" (DeiT model)"),YCa=l(),xB=a("li"),dlo=a("strong"),zCa=o("focalnet"),HCa=o(" \u2014 "),n3e=a("a"),KCa=o("FocalNetForMaskedImageModeling"),ewa=o(" (FocalNet model)"),owa=l(),GB=a("li"),mlo=a("strong"),rwa=o("swin"),twa=o(" \u2014 "),s3e=a("a"),awa=o("SwinForMaskedImageModeling"),nwa=o(" (Swin Transformer model)"),swa=l(),BB=a("li"),clo=a("strong"),lwa=o("swinv2"),iwa=o(" \u2014 "),l3e=a("a"),dwa=o("Swinv2ForMaskedImageModeling"),mwa=o(" (Swin Transformer V2 model)"),cwa=l(),IB=a("li"),flo=a("strong"),fwa=o("vit"),hwa=o(" \u2014 "),i3e=a("a"),gwa=o("ViTForMaskedImageModeling"),uwa=o(" (ViT model)"),pwa=l(),NB=a("p"),_wa=o("The model is set in evaluation mode by default using "),hlo=a("code"),bwa=o("model.eval()"),vwa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),glo=a("code"),Mwa=o("model.train()"),Twa=l(),v($B.$$.fragment),vNo=l(),uh=a("h3"),jB=a("a"),ulo=a("span"),v(RX.$$.fragment),Fwa=l(),plo=a("span"),Ewa=o("TFAutoModelForMaskedImageModeling"),MNo=l(),wr=a("div"),v(kX.$$.fragment),ywa=l(),ph=a("p"),Cwa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),d3e=a("a"),wwa=o("from_pretrained()"),Lwa=o(" class method or the "),m3e=a("a"),Awa=o("from_config()"),Rwa=o(` class
method.`),kwa=l(),SX=a("p"),Swa=o("This class cannot be instantiated directly using "),_lo=a("code"),xwa=o("__init__()"),Gwa=o(" (throws an error)."),Bwa=l(),wa=a("div"),v(xX.$$.fragment),Iwa=l(),blo=a("p"),Nwa=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),$wa=l(),_h=a("p"),jwa=o(`Note:
Loading a model from its configuration file does `),vlo=a("strong"),Pwa=o("not"),Zwa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),c3e=a("a"),Vwa=o("from_pretrained()"),Wwa=o(" to load the model weights."),Jwa=l(),v(PB.$$.fragment),Uwa=l(),Ct=a("div"),v(GX.$$.fragment),Xwa=l(),Mlo=a("p"),Dwa=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),qwa=l(),Es=a("p"),Owa=o("The model class to instantiate is selected based on the "),Tlo=a("code"),Qwa=o("model_type"),Ywa=o(` property of the config object (either
passed as an argument or loaded from `),Flo=a("code"),zwa=o("pretrained_model_name_or_path"),Hwa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Elo=a("code"),Kwa=o("pretrained_model_name_or_path"),e6a=o(":"),o6a=l(),BX=a("ul"),ZB=a("li"),ylo=a("strong"),r6a=o("deit"),t6a=o(" \u2014 "),f3e=a("a"),a6a=o("TFDeiTForMaskedImageModeling"),n6a=o(" (DeiT model)"),s6a=l(),VB=a("li"),Clo=a("strong"),l6a=o("swin"),i6a=o(" \u2014 "),h3e=a("a"),d6a=o("TFSwinForMaskedImageModeling"),m6a=o(" (Swin Transformer model)"),c6a=l(),v(WB.$$.fragment),TNo=l(),bh=a("h3"),JB=a("a"),wlo=a("span"),v(IX.$$.fragment),f6a=l(),Llo=a("span"),h6a=o("AutoModelForObjectDetection"),FNo=l(),Lr=a("div"),v(NX.$$.fragment),g6a=l(),vh=a("p"),u6a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),g3e=a("a"),p6a=o("from_pretrained()"),_6a=o(" class method or the "),u3e=a("a"),b6a=o("from_config()"),v6a=o(` class
method.`),M6a=l(),$X=a("p"),T6a=o("This class cannot be instantiated directly using "),Alo=a("code"),F6a=o("__init__()"),E6a=o(" (throws an error)."),y6a=l(),La=a("div"),v(jX.$$.fragment),C6a=l(),Rlo=a("p"),w6a=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),L6a=l(),Mh=a("p"),A6a=o(`Note:
Loading a model from its configuration file does `),klo=a("strong"),R6a=o("not"),k6a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),p3e=a("a"),S6a=o("from_pretrained()"),x6a=o(" to load the model weights."),G6a=l(),v(UB.$$.fragment),B6a=l(),To=a("div"),v(PX.$$.fragment),I6a=l(),Slo=a("p"),N6a=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),$6a=l(),ys=a("p"),j6a=o("The model class to instantiate is selected based on the "),xlo=a("code"),P6a=o("model_type"),Z6a=o(` property of the config object (either
passed as an argument or loaded from `),Glo=a("code"),V6a=o("pretrained_model_name_or_path"),W6a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Blo=a("code"),J6a=o("pretrained_model_name_or_path"),U6a=o(":"),X6a=l(),Ar=a("ul"),XB=a("li"),Ilo=a("strong"),D6a=o("conditional_detr"),q6a=o(" \u2014 "),_3e=a("a"),O6a=o("ConditionalDetrForObjectDetection"),Q6a=o(" (Conditional DETR model)"),Y6a=l(),DB=a("li"),Nlo=a("strong"),z6a=o("deformable_detr"),H6a=o(" \u2014 "),b3e=a("a"),K6a=o("DeformableDetrForObjectDetection"),e8a=o(" (Deformable DETR model)"),o8a=l(),qB=a("li"),$lo=a("strong"),r8a=o("deta"),t8a=o(" \u2014 "),v3e=a("a"),a8a=o("DetaForObjectDetection"),n8a=o(" (DETA model)"),s8a=l(),OB=a("li"),jlo=a("strong"),l8a=o("detr"),i8a=o(" \u2014 "),M3e=a("a"),d8a=o("DetrForObjectDetection"),m8a=o(" (DETR model)"),c8a=l(),QB=a("li"),Plo=a("strong"),f8a=o("table-transformer"),h8a=o(" \u2014 "),T3e=a("a"),g8a=o("TableTransformerForObjectDetection"),u8a=o(" (Table Transformer model)"),p8a=l(),YB=a("li"),Zlo=a("strong"),_8a=o("yolos"),b8a=o(" \u2014 "),F3e=a("a"),v8a=o("YolosForObjectDetection"),M8a=o(" (YOLOS model)"),T8a=l(),zB=a("p"),F8a=o("The model is set in evaluation mode by default using "),Vlo=a("code"),E8a=o("model.eval()"),y8a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Wlo=a("code"),C8a=o("model.train()"),w8a=l(),v(HB.$$.fragment),ENo=l(),Th=a("h3"),KB=a("a"),Jlo=a("span"),v(ZX.$$.fragment),L8a=l(),Ulo=a("span"),A8a=o("AutoModelForImageSegmentation"),yNo=l(),Rr=a("div"),v(VX.$$.fragment),R8a=l(),Fh=a("p"),k8a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),E3e=a("a"),S8a=o("from_pretrained()"),x8a=o(" class method or the "),y3e=a("a"),G8a=o("from_config()"),B8a=o(` class
method.`),I8a=l(),WX=a("p"),N8a=o("This class cannot be instantiated directly using "),Xlo=a("code"),$8a=o("__init__()"),j8a=o(" (throws an error)."),P8a=l(),Aa=a("div"),v(JX.$$.fragment),Z8a=l(),Dlo=a("p"),V8a=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),W8a=l(),Eh=a("p"),J8a=o(`Note:
Loading a model from its configuration file does `),qlo=a("strong"),U8a=o("not"),X8a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),C3e=a("a"),D8a=o("from_pretrained()"),q8a=o(" to load the model weights."),O8a=l(),v(eI.$$.fragment),Q8a=l(),Fo=a("div"),v(UX.$$.fragment),Y8a=l(),Olo=a("p"),z8a=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),H8a=l(),Cs=a("p"),K8a=o("The model class to instantiate is selected based on the "),Qlo=a("code"),e7a=o("model_type"),o7a=o(` property of the config object (either
passed as an argument or loaded from `),Ylo=a("code"),r7a=o("pretrained_model_name_or_path"),t7a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zlo=a("code"),a7a=o("pretrained_model_name_or_path"),n7a=o(":"),s7a=l(),Hlo=a("ul"),oI=a("li"),Klo=a("strong"),l7a=o("detr"),i7a=o(" \u2014 "),w3e=a("a"),d7a=o("DetrForSegmentation"),m7a=o(" (DETR model)"),c7a=l(),rI=a("p"),f7a=o("The model is set in evaluation mode by default using "),eio=a("code"),h7a=o("model.eval()"),g7a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oio=a("code"),u7a=o("model.train()"),p7a=l(),v(tI.$$.fragment),CNo=l(),yh=a("h3"),aI=a("a"),rio=a("span"),v(XX.$$.fragment),_7a=l(),tio=a("span"),b7a=o("AutoModelForSemanticSegmentation"),wNo=l(),kr=a("div"),v(DX.$$.fragment),v7a=l(),Ch=a("p"),M7a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),L3e=a("a"),T7a=o("from_pretrained()"),F7a=o(" class method or the "),A3e=a("a"),E7a=o("from_config()"),y7a=o(` class
method.`),C7a=l(),qX=a("p"),w7a=o("This class cannot be instantiated directly using "),aio=a("code"),L7a=o("__init__()"),A7a=o(" (throws an error)."),R7a=l(),Ra=a("div"),v(OX.$$.fragment),k7a=l(),nio=a("p"),S7a=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),x7a=l(),wh=a("p"),G7a=o(`Note:
Loading a model from its configuration file does `),sio=a("strong"),B7a=o("not"),I7a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),R3e=a("a"),N7a=o("from_pretrained()"),$7a=o(" to load the model weights."),j7a=l(),v(nI.$$.fragment),P7a=l(),Eo=a("div"),v(QX.$$.fragment),Z7a=l(),lio=a("p"),V7a=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),W7a=l(),ws=a("p"),J7a=o("The model class to instantiate is selected based on the "),iio=a("code"),U7a=o("model_type"),X7a=o(` property of the config object (either
passed as an argument or loaded from `),dio=a("code"),D7a=o("pretrained_model_name_or_path"),q7a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mio=a("code"),O7a=o("pretrained_model_name_or_path"),Q7a=o(":"),Y7a=l(),Ye=a("ul"),sI=a("li"),cio=a("strong"),z7a=o("beit"),H7a=o(" \u2014 "),k3e=a("a"),K7a=o("BeitForSemanticSegmentation"),eLa=o(" (BEiT model)"),oLa=l(),lI=a("li"),fio=a("strong"),rLa=o("data2vec-vision"),tLa=o(" \u2014 "),S3e=a("a"),aLa=o("Data2VecVisionForSemanticSegmentation"),nLa=o(" (Data2VecVision model)"),sLa=l(),iI=a("li"),hio=a("strong"),lLa=o("dpt"),iLa=o(" \u2014 "),x3e=a("a"),dLa=o("DPTForSemanticSegmentation"),mLa=o(" (DPT model)"),cLa=l(),dI=a("li"),gio=a("strong"),fLa=o("mobilenet_v2"),hLa=o(" \u2014 "),G3e=a("a"),gLa=o("MobileNetV2ForSemanticSegmentation"),uLa=o(" (MobileNetV2 model)"),pLa=l(),mI=a("li"),uio=a("strong"),_La=o("mobilevit"),bLa=o(" \u2014 "),B3e=a("a"),vLa=o("MobileViTForSemanticSegmentation"),MLa=o(" (MobileViT model)"),TLa=l(),cI=a("li"),pio=a("strong"),FLa=o("mobilevitv2"),ELa=o(" \u2014 "),I3e=a("a"),yLa=o("MobileViTV2ForSemanticSegmentation"),CLa=o(" (MobileViTV2 model)"),wLa=l(),fI=a("li"),_io=a("strong"),LLa=o("segformer"),ALa=o(" \u2014 "),N3e=a("a"),RLa=o("SegformerForSemanticSegmentation"),kLa=o(" (SegFormer model)"),SLa=l(),hI=a("li"),bio=a("strong"),xLa=o("upernet"),GLa=o(" \u2014 "),$3e=a("a"),BLa=o("UperNetForSemanticSegmentation"),ILa=o(" (UPerNet model)"),NLa=l(),gI=a("p"),$La=o("The model is set in evaluation mode by default using "),vio=a("code"),jLa=o("model.eval()"),PLa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Mio=a("code"),ZLa=o("model.train()"),VLa=l(),v(uI.$$.fragment),LNo=l(),Lh=a("h3"),pI=a("a"),Tio=a("span"),v(YX.$$.fragment),WLa=l(),Fio=a("span"),JLa=o("TFAutoModelForSemanticSegmentation"),ANo=l(),Sr=a("div"),v(zX.$$.fragment),ULa=l(),Ah=a("p"),XLa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
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method.`),YLa=l(),HX=a("p"),zLa=o("This class cannot be instantiated directly using "),Eio=a("code"),HLa=o("__init__()"),KLa=o(" (throws an error)."),eAa=l(),ka=a("div"),v(KX.$$.fragment),oAa=l(),yio=a("p"),rAa=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),tAa=l(),Rh=a("p"),aAa=o(`Note:
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passed as an argument or loaded from `),Aio=a("code"),_Aa=o("pretrained_model_name_or_path"),bAa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Rio=a("code"),vAa=o("pretrained_model_name_or_path"),MAa=o(":"),TAa=l(),kh=a("ul"),bI=a("li"),kio=a("strong"),FAa=o("data2vec-vision"),EAa=o(" \u2014 "),V3e=a("a"),yAa=o("TFData2VecVisionForSemanticSegmentation"),CAa=o(" (Data2VecVision model)"),wAa=l(),vI=a("li"),Sio=a("strong"),LAa=o("mobilevit"),AAa=o(" \u2014 "),W3e=a("a"),RAa=o("TFMobileViTForSemanticSegmentation"),kAa=o(" (MobileViT model)"),SAa=l(),MI=a("li"),xio=a("strong"),xAa=o("segformer"),GAa=o(" \u2014 "),J3e=a("a"),BAa=o("TFSegformerForSemanticSegmentation"),IAa=o(" (SegFormer model)"),NAa=l(),v(TI.$$.fragment),RNo=l(),Sh=a("h3"),FI=a("a"),Gio=a("span"),v(oD.$$.fragment),$Aa=l(),Bio=a("span"),jAa=o("AutoModelForInstanceSegmentation"),kNo=l(),xr=a("div"),v(rD.$$.fragment),PAa=l(),xh=a("p"),ZAa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),U3e=a("a"),VAa=o("from_pretrained()"),WAa=o(" class method or the "),X3e=a("a"),JAa=o("from_config()"),UAa=o(` class
method.`),XAa=l(),tD=a("p"),DAa=o("This class cannot be instantiated directly using "),Iio=a("code"),qAa=o("__init__()"),OAa=o(" (throws an error)."),QAa=l(),Sa=a("div"),v(aD.$$.fragment),YAa=l(),Nio=a("p"),zAa=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),HAa=l(),Gh=a("p"),KAa=o(`Note:
Loading a model from its configuration file does `),$io=a("strong"),eRa=o("not"),oRa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),D3e=a("a"),rRa=o("from_pretrained()"),tRa=o(" to load the model weights."),aRa=l(),v(EI.$$.fragment),nRa=l(),yo=a("div"),v(nD.$$.fragment),sRa=l(),jio=a("p"),lRa=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),iRa=l(),As=a("p"),dRa=o("The model class to instantiate is selected based on the "),Pio=a("code"),mRa=o("model_type"),cRa=o(` property of the config object (either
passed as an argument or loaded from `),Zio=a("code"),fRa=o("pretrained_model_name_or_path"),hRa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vio=a("code"),gRa=o("pretrained_model_name_or_path"),uRa=o(":"),pRa=l(),Wio=a("ul"),yI=a("li"),Jio=a("strong"),_Ra=o("maskformer"),bRa=o(" \u2014 "),q3e=a("a"),vRa=o("MaskFormerForInstanceSegmentation"),MRa=o(" (MaskFormer model)"),TRa=l(),CI=a("p"),FRa=o("The model is set in evaluation mode by default using "),Uio=a("code"),ERa=o("model.eval()"),yRa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xio=a("code"),CRa=o("model.train()"),wRa=l(),v(wI.$$.fragment),SNo=l(),Bh=a("h3"),LI=a("a"),Dio=a("span"),v(sD.$$.fragment),LRa=l(),qio=a("span"),ARa=o("AutoModelForUniversalSegmentation"),xNo=l(),Gr=a("div"),v(lD.$$.fragment),RRa=l(),Ih=a("p"),kRa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
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method.`),IRa=l(),iD=a("p"),NRa=o("This class cannot be instantiated directly using "),Oio=a("code"),$Ra=o("__init__()"),jRa=o(" (throws an error)."),PRa=l(),xa=a("div"),v(dD.$$.fragment),ZRa=l(),Qio=a("p"),VRa=o("Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),WRa=l(),Nh=a("p"),JRa=o(`Note:
Loading a model from its configuration file does `),Yio=a("strong"),URa=o("not"),XRa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Y3e=a("a"),DRa=o("from_pretrained()"),qRa=o(" to load the model weights."),ORa=l(),v(AI.$$.fragment),QRa=l(),Co=a("div"),v(mD.$$.fragment),YRa=l(),zio=a("p"),zRa=o("Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),HRa=l(),Rs=a("p"),KRa=o("The model class to instantiate is selected based on the "),Hio=a("code"),eka=o("model_type"),oka=o(` property of the config object (either
passed as an argument or loaded from `),Kio=a("code"),rka=o("pretrained_model_name_or_path"),tka=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),edo=a("code"),aka=o("pretrained_model_name_or_path"),nka=o(":"),ska=l(),ks=a("ul"),RI=a("li"),odo=a("strong"),lka=o("detr"),ika=o(" \u2014 "),z3e=a("a"),dka=o("DetrForSegmentation"),mka=o(" (DETR model)"),cka=l(),kI=a("li"),rdo=a("strong"),fka=o("mask2former"),hka=o(" \u2014 "),H3e=a("a"),gka=o("Mask2FormerForUniversalSegmentation"),uka=o(" (Mask2Former model)"),pka=l(),SI=a("li"),tdo=a("strong"),_ka=o("maskformer"),bka=o(" \u2014 "),K3e=a("a"),vka=o("MaskFormerForInstanceSegmentation"),Mka=o(" (MaskFormer model)"),Tka=l(),xI=a("li"),ado=a("strong"),Fka=o("oneformer"),Eka=o(" \u2014 "),e9e=a("a"),yka=o("OneFormerForUniversalSegmentation"),Cka=o(" (OneFormer model)"),wka=l(),GI=a("p"),Lka=o("The model is set in evaluation mode by default using "),ndo=a("code"),Aka=o("model.eval()"),Rka=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),sdo=a("code"),kka=o("model.train()"),Ska=l(),v(BI.$$.fragment),GNo=l(),$h=a("h3"),II=a("a"),ldo=a("span"),v(cD.$$.fragment),xka=l(),ido=a("span"),Gka=o("AutoModelForZeroShotImageClassification"),BNo=l(),Br=a("div"),v(fD.$$.fragment),Bka=l(),jh=a("p"),Ika=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),o9e=a("a"),Nka=o("from_pretrained()"),$ka=o(" class method or the "),r9e=a("a"),jka=o("from_config()"),Pka=o(` class
method.`),Zka=l(),hD=a("p"),Vka=o("This class cannot be instantiated directly using "),ddo=a("code"),Wka=o("__init__()"),Jka=o(" (throws an error)."),Uka=l(),Ga=a("div"),v(gD.$$.fragment),Xka=l(),mdo=a("p"),Dka=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),qka=l(),Ph=a("p"),Oka=o(`Note:
Loading a model from its configuration file does `),cdo=a("strong"),Qka=o("not"),Yka=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),t9e=a("a"),zka=o("from_pretrained()"),Hka=o(" to load the model weights."),Kka=l(),v(NI.$$.fragment),eSa=l(),wo=a("div"),v(uD.$$.fragment),oSa=l(),fdo=a("p"),rSa=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),tSa=l(),Ss=a("p"),aSa=o("The model class to instantiate is selected based on the "),hdo=a("code"),nSa=o("model_type"),sSa=o(` property of the config object (either
passed as an argument or loaded from `),gdo=a("code"),lSa=o("pretrained_model_name_or_path"),iSa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),udo=a("code"),dSa=o("pretrained_model_name_or_path"),mSa=o(":"),cSa=l(),Ir=a("ul"),$I=a("li"),pdo=a("strong"),fSa=o("align"),hSa=o(" \u2014 "),a9e=a("a"),gSa=o("AlignModel"),uSa=o(" (ALIGN model)"),pSa=l(),jI=a("li"),_do=a("strong"),_Sa=o("altclip"),bSa=o(" \u2014 "),n9e=a("a"),vSa=o("AltCLIPModel"),MSa=o(" (AltCLIP model)"),TSa=l(),PI=a("li"),bdo=a("strong"),FSa=o("blip"),ESa=o(" \u2014 "),s9e=a("a"),ySa=o("BlipModel"),CSa=o(" (BLIP model)"),wSa=l(),ZI=a("li"),vdo=a("strong"),LSa=o("chinese_clip"),ASa=o(" \u2014 "),l9e=a("a"),RSa=o("ChineseCLIPModel"),kSa=o(" (Chinese-CLIP model)"),SSa=l(),VI=a("li"),Mdo=a("strong"),xSa=o("clip"),GSa=o(" \u2014 "),i9e=a("a"),BSa=o("CLIPModel"),ISa=o(" (CLIP model)"),NSa=l(),WI=a("li"),Tdo=a("strong"),$Sa=o("clipseg"),jSa=o(" \u2014 "),d9e=a("a"),PSa=o("CLIPSegModel"),ZSa=o(" (CLIPSeg model)"),VSa=l(),JI=a("p"),WSa=o("The model is set in evaluation mode by default using "),Fdo=a("code"),JSa=o("model.eval()"),USa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Edo=a("code"),XSa=o("model.train()"),DSa=l(),v(UI.$$.fragment),INo=l(),Zh=a("h3"),XI=a("a"),ydo=a("span"),v(pD.$$.fragment),qSa=l(),Cdo=a("span"),OSa=o("TFAutoModelForZeroShotImageClassification"),NNo=l(),Nr=a("div"),v(_D.$$.fragment),QSa=l(),Vh=a("p"),YSa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
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method.`),oxa=l(),bD=a("p"),rxa=o("This class cannot be instantiated directly using "),wdo=a("code"),txa=o("__init__()"),axa=o(" (throws an error)."),nxa=l(),Ba=a("div"),v(vD.$$.fragment),sxa=l(),Ldo=a("p"),lxa=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),ixa=l(),Wh=a("p"),dxa=o(`Note:
Loading a model from its configuration file does `),Ado=a("strong"),mxa=o("not"),cxa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),f9e=a("a"),fxa=o("from_pretrained()"),hxa=o(" to load the model weights."),gxa=l(),v(DI.$$.fragment),uxa=l(),Lt=a("div"),v(MD.$$.fragment),pxa=l(),Rdo=a("p"),_xa=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),bxa=l(),xs=a("p"),vxa=o("The model class to instantiate is selected based on the "),kdo=a("code"),Mxa=o("model_type"),Txa=o(` property of the config object (either
passed as an argument or loaded from `),Sdo=a("code"),Fxa=o("pretrained_model_name_or_path"),Exa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xdo=a("code"),yxa=o("pretrained_model_name_or_path"),Cxa=o(":"),wxa=l(),TD=a("ul"),qI=a("li"),Gdo=a("strong"),Lxa=o("blip"),Axa=o(" \u2014 "),h9e=a("a"),Rxa=o("TFBlipModel"),kxa=o(" (BLIP model)"),Sxa=l(),OI=a("li"),Bdo=a("strong"),xxa=o("clip"),Gxa=o(" \u2014 "),g9e=a("a"),Bxa=o("TFCLIPModel"),Ixa=o(" (CLIP model)"),Nxa=l(),v(QI.$$.fragment),$No=l(),Jh=a("h3"),YI=a("a"),Ido=a("span"),v(FD.$$.fragment),$xa=l(),Ndo=a("span"),jxa=o("AutoModelForZeroShotObjectDetection"),jNo=l(),$r=a("div"),v(ED.$$.fragment),Pxa=l(),Uh=a("p"),Zxa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),u9e=a("a"),Vxa=o("from_pretrained()"),Wxa=o(" class method or the "),p9e=a("a"),Jxa=o("from_config()"),Uxa=o(` class
method.`),Xxa=l(),yD=a("p"),Dxa=o("This class cannot be instantiated directly using "),$do=a("code"),qxa=o("__init__()"),Oxa=o(" (throws an error)."),Qxa=l(),Ia=a("div"),v(CD.$$.fragment),Yxa=l(),jdo=a("p"),zxa=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),Hxa=l(),Xh=a("p"),Kxa=o(`Note:
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model\u2019s configuration. Use `),_9e=a("a"),rGa=o("from_pretrained()"),tGa=o(" to load the model weights."),aGa=l(),v(zI.$$.fragment),nGa=l(),Lo=a("div"),v(wD.$$.fragment),sGa=l(),Zdo=a("p"),lGa=o("Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),iGa=l(),Gs=a("p"),dGa=o("The model class to instantiate is selected based on the "),Vdo=a("code"),mGa=o("model_type"),cGa=o(` property of the config object (either
passed as an argument or loaded from `),Wdo=a("code"),fGa=o("pretrained_model_name_or_path"),hGa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jdo=a("code"),gGa=o("pretrained_model_name_or_path"),uGa=o(":"),pGa=l(),Udo=a("ul"),HI=a("li"),Xdo=a("strong"),_Ga=o("owlvit"),bGa=o(" \u2014 "),b9e=a("a"),vGa=o("OwlViTForObjectDetection"),MGa=o(" (OWL-ViT model)"),TGa=l(),KI=a("p"),FGa=o("The model is set in evaluation mode by default using "),Ddo=a("code"),EGa=o("model.eval()"),yGa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),qdo=a("code"),CGa=o("model.train()"),wGa=l(),v(eN.$$.fragment),PNo=l(),Dh=a("h2"),oN=a("a"),Odo=a("span"),v(LD.$$.fragment),LGa=l(),Qdo=a("span"),AGa=o("Audio"),ZNo=l(),v9e=a("p"),RGa=o("The following auto classes are available for the following audio tasks."),VNo=l(),qh=a("h3"),rN=a("a"),Ydo=a("span"),v(AD.$$.fragment),kGa=l(),zdo=a("span"),SGa=o("AutoModelForAudioClassification"),WNo=l(),jr=a("div"),v(RD.$$.fragment),xGa=l(),Oh=a("p"),GGa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
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method.`),jGa=l(),kD=a("p"),PGa=o("This class cannot be instantiated directly using "),Hdo=a("code"),ZGa=o("__init__()"),VGa=o(" (throws an error)."),WGa=l(),Na=a("div"),v(SD.$$.fragment),JGa=l(),Kdo=a("p"),UGa=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),XGa=l(),Qh=a("p"),DGa=o(`Note:
Loading a model from its configuration file does `),emo=a("strong"),qGa=o("not"),OGa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),F9e=a("a"),QGa=o("from_pretrained()"),YGa=o(" to load the model weights."),zGa=l(),v(tN.$$.fragment),HGa=l(),Ao=a("div"),v(xD.$$.fragment),KGa=l(),omo=a("p"),eBa=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),oBa=l(),Bs=a("p"),rBa=o("The model class to instantiate is selected based on the "),rmo=a("code"),tBa=o("model_type"),aBa=o(` property of the config object (either
passed as an argument or loaded from `),tmo=a("code"),nBa=o("pretrained_model_name_or_path"),sBa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),amo=a("code"),lBa=o("pretrained_model_name_or_path"),iBa=o(":"),dBa=l(),Be=a("ul"),aN=a("li"),nmo=a("strong"),mBa=o("audio-spectrogram-transformer"),cBa=o(" \u2014 "),E9e=a("a"),fBa=o("ASTForAudioClassification"),hBa=o(" (Audio Spectrogram Transformer model)"),gBa=l(),nN=a("li"),smo=a("strong"),uBa=o("data2vec-audio"),pBa=o(" \u2014 "),y9e=a("a"),_Ba=o("Data2VecAudioForSequenceClassification"),bBa=o(" (Data2VecAudio model)"),vBa=l(),sN=a("li"),lmo=a("strong"),MBa=o("hubert"),TBa=o(" \u2014 "),C9e=a("a"),FBa=o("HubertForSequenceClassification"),EBa=o(" (Hubert model)"),yBa=l(),lN=a("li"),imo=a("strong"),CBa=o("sew"),wBa=o(" \u2014 "),w9e=a("a"),LBa=o("SEWForSequenceClassification"),ABa=o(" (SEW model)"),RBa=l(),iN=a("li"),dmo=a("strong"),kBa=o("sew-d"),SBa=o(" \u2014 "),L9e=a("a"),xBa=o("SEWDForSequenceClassification"),GBa=o(" (SEW-D model)"),BBa=l(),dN=a("li"),mmo=a("strong"),IBa=o("unispeech"),NBa=o(" \u2014 "),A9e=a("a"),$Ba=o("UniSpeechForSequenceClassification"),jBa=o(" (UniSpeech model)"),PBa=l(),mN=a("li"),cmo=a("strong"),ZBa=o("unispeech-sat"),VBa=o(" \u2014 "),R9e=a("a"),WBa=o("UniSpeechSatForSequenceClassification"),JBa=o(" (UniSpeechSat model)"),UBa=l(),cN=a("li"),fmo=a("strong"),XBa=o("wav2vec2"),DBa=o(" \u2014 "),k9e=a("a"),qBa=o("Wav2Vec2ForSequenceClassification"),OBa=o(" (Wav2Vec2 model)"),QBa=l(),fN=a("li"),hmo=a("strong"),YBa=o("wav2vec2-conformer"),zBa=o(" \u2014 "),S9e=a("a"),HBa=o("Wav2Vec2ConformerForSequenceClassification"),KBa=o(" (Wav2Vec2-Conformer model)"),eIa=l(),hN=a("li"),gmo=a("strong"),oIa=o("wavlm"),rIa=o(" \u2014 "),x9e=a("a"),tIa=o("WavLMForSequenceClassification"),aIa=o(" (WavLM model)"),nIa=l(),gN=a("li"),umo=a("strong"),sIa=o("whisper"),lIa=o(" \u2014 "),G9e=a("a"),iIa=o("WhisperForAudioClassification"),dIa=o(" (Whisper model)"),mIa=l(),uN=a("p"),cIa=o("The model is set in evaluation mode by default using "),pmo=a("code"),fIa=o("model.eval()"),hIa=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),$mo=a("code"),xNa=o("pretrained_model_name_or_path"),GNa=o(":"),BNa=l(),$t=a("ul"),EN=a("li"),jmo=a("strong"),INa=o("data2vec-audio"),NNa=o(" \u2014 "),V9e=a("a"),$Na=o("Data2VecAudioForAudioFrameClassification"),jNa=o(" (Data2VecAudio model)"),PNa=l(),yN=a("li"),Pmo=a("strong"),ZNa=o("unispeech-sat"),VNa=o(" \u2014 "),W9e=a("a"),WNa=o("UniSpeechSatForAudioFrameClassification"),JNa=o(" (UniSpeechSat model)"),UNa=l(),CN=a("li"),Zmo=a("strong"),XNa=o("wav2vec2"),DNa=o(" \u2014 "),J9e=a("a"),qNa=o("Wav2Vec2ForAudioFrameClassification"),ONa=o(" (Wav2Vec2 model)"),QNa=l(),wN=a("li"),Vmo=a("strong"),YNa=o("wav2vec2-conformer"),zNa=o(" \u2014 "),U9e=a("a"),HNa=o("Wav2Vec2ConformerForAudioFrameClassification"),KNa=o(" (Wav2Vec2-Conformer model)"),e$a=l(),LN=a("li"),Wmo=a("strong"),o$a=o("wavlm"),r$a=o(" \u2014 "),X9e=a("a"),t$a=o("WavLMForAudioFrameClassification"),a$a=o(" (WavLM model)"),n$a=l(),AN=a("p"),s$a=o("The model is set in evaluation mode by default using "),Jmo=a("code"),l$a=o("model.eval()"),i$a=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),Tho=a("code"),UDa=o("pretrained_model_name_or_path"),XDa=o(":"),DDa=l(),Oq=a("ul"),J$=a("li"),Fho=a("strong"),qDa=o("blip-2"),ODa=o(" \u2014 "),K4e=a("a"),QDa=o("Blip2ForConditionalGeneration"),YDa=o(" (BLIP-2 model)"),zDa=l(),U$=a("li"),Eho=a("strong"),HDa=o("vilt"),KDa=o(" \u2014 "),eCe=a("a"),eqa=o("ViltForQuestionAnswering"),oqa=o(" (ViLT model)"),rqa=l(),X$=a("p"),tqa=o("The model is set in evaluation mode by default using "),yho=a("code"),aqa=o("model.eval()"),nqa=o(` (so for instance, dropout modules are
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library when created with the `),jne=n(N$o,"A",{href:!0});var mfn=s(jne);N3r=r(mfn,"AutoImageProcessor.from_pretrained()"),mfn.forEach(t),$3r=r(N$o," class method."),N$o.forEach(t),j3r=i(Hi),jV=n(Hi,"P",{});var $$o=s(jV);P3r=r($$o,"This class cannot be instantiated directly using "),Qxe=n($$o,"CODE",{});var cfn=s(Qxe);Z3r=r(cfn,"__init__()"),cfn.forEach(t),V3r=r($$o," (throws an error)."),$$o.forEach(t),W3r=i(Hi),ao=n(Hi,"DIV",{class:!0});var Ka=s(ao);M(PV.$$.fragment,Ka),J3r=i(Ka),Yxe=n(Ka,"P",{});var ffn=s(Yxe);U3r=r(ffn,"Instantiate one of the image processor classes of the library from a pretrained model vocabulary."),ffn.forEach(t),X3r=i(Ka),Pn=n(Ka,"P",{});var Dj=s(Pn);D3r=r(Dj,"The image processor class to instantiate is selected based on the "),zxe=n(Dj,"CODE",{});var hfn=s(zxe);q3r=r(hfn,"model_type"),hfn.forEach(t),O3r=r(Dj,` property of the config object
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uvo=s(gv);mGe=n(uvo,"STRONG",{});var Nfn=s(mGe);Y9r=r(Nfn,"convnext"),Nfn.forEach(t),z9r=r(uvo," \u2014 "),Qne=n(uvo,"A",{href:!0});var $fn=s(Qne);H9r=r($fn,"ConvNextImageProcessor"),$fn.forEach(t),K9r=r(uvo," (ConvNeXT model)"),uvo.forEach(t),e4r=i($),uv=n($,"LI",{});var pvo=s(uv);cGe=n(pvo,"STRONG",{});var jfn=s(cGe);o4r=r(jfn,"convnextv2"),jfn.forEach(t),r4r=r(pvo," \u2014 "),Yne=n(pvo,"A",{href:!0});var Pfn=s(Yne);t4r=r(Pfn,"ConvNextImageProcessor"),Pfn.forEach(t),a4r=r(pvo," (ConvNeXTV2 model)"),pvo.forEach(t),n4r=i($),pv=n($,"LI",{});var _vo=s(pv);fGe=n(_vo,"STRONG",{});var Zfn=s(fGe);s4r=r(Zfn,"cvt"),Zfn.forEach(t),l4r=r(_vo," \u2014 "),zne=n(_vo,"A",{href:!0});var Vfn=s(zne);i4r=r(Vfn,"ConvNextImageProcessor"),Vfn.forEach(t),d4r=r(_vo," (CvT model)"),_vo.forEach(t),m4r=i($),_v=n($,"LI",{});var bvo=s(_v);hGe=n(bvo,"STRONG",{});var Wfn=s(hGe);c4r=r(Wfn,"data2vec-vision"),Wfn.forEach(t),f4r=r(bvo," \u2014 "),Hne=n(bvo,"A",{href:!0});var Jfn=s(Hne);h4r=r(Jfn,"BeitImageProcessor"),Jfn.forEach(t),g4r=r(bvo," (Data2VecVision model)"),bvo.forEach(t),u4r=i($),bv=n($,"LI",{});var vvo=s(bv);gGe=n(vvo,"STRONG",{});var Ufn=s(gGe);p4r=r(Ufn,"deformable_detr"),Ufn.forEach(t),_4r=r(vvo," \u2014 "),Kne=n(vvo,"A",{href:!0});var Xfn=s(Kne);b4r=r(Xfn,"DeformableDetrImageProcessor"),Xfn.forEach(t),v4r=r(vvo," (Deformable DETR model)"),vvo.forEach(t),M4r=i($),vv=n($,"LI",{});var Mvo=s(vv);uGe=n(Mvo,"STRONG",{});var Dfn=s(uGe);T4r=r(Dfn,"deit"),Dfn.forEach(t),F4r=r(Mvo," \u2014 "),ese=n(Mvo,"A",{href:!0});var qfn=s(ese);E4r=r(qfn,"DeiTImageProcessor"),qfn.forEach(t),y4r=r(Mvo," (DeiT model)"),Mvo.forEach(t),C4r=i($),Mv=n($,"LI",{});var Tvo=s(Mv);pGe=n(Tvo,"STRONG",{});var Ofn=s(pGe);w4r=r(Ofn,"deta"),Ofn.forEach(t),L4r=r(Tvo," \u2014 "),ose=n(Tvo,"A",{href:!0});var Qfn=s(ose);A4r=r(Qfn,"DetaImageProcessor"),Qfn.forEach(t),R4r=r(Tvo," (DETA model)"),Tvo.forEach(t),k4r=i($),Tv=n($,"LI",{});var Fvo=s(Tv);_Ge=n(Fvo,"STRONG",{});var 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zvo=s(Yv);DGe=n(zvo,"STRONG",{});var egn=s(DGe);g6r=r(egn,"pix2struct"),egn.forEach(t),u6r=r(zvo," \u2014 "),Sse=n(zvo,"A",{href:!0});var ogn=s(Sse);p6r=r(ogn,"Pix2StructImageProcessor"),ogn.forEach(t),_6r=r(zvo," (Pix2Struct model)"),zvo.forEach(t),b6r=i($),zv=n($,"LI",{});var Hvo=s(zv);qGe=n(Hvo,"STRONG",{});var rgn=s(qGe);v6r=r(rgn,"poolformer"),rgn.forEach(t),M6r=r(Hvo," \u2014 "),xse=n(Hvo,"A",{href:!0});var tgn=s(xse);T6r=r(tgn,"PoolFormerImageProcessor"),tgn.forEach(t),F6r=r(Hvo," (PoolFormer model)"),Hvo.forEach(t),E6r=i($),Hv=n($,"LI",{});var Kvo=s(Hv);OGe=n(Kvo,"STRONG",{});var agn=s(OGe);y6r=r(agn,"pvt"),agn.forEach(t),C6r=r(Kvo," \u2014 "),Gse=n(Kvo,"A",{href:!0});var ngn=s(Gse);w6r=r(ngn,"PvtImageProcessor"),ngn.forEach(t),L6r=r(Kvo," (PVT model)"),Kvo.forEach(t),A6r=i($),Kv=n($,"LI",{});var eMo=s(Kv);QGe=n(eMo,"STRONG",{});var sgn=s(QGe);R6r=r(sgn,"regnet"),sgn.forEach(t),k6r=r(eMo," \u2014 "),Bse=n(eMo,"A",{href:!0});var lgn=s(Bse);S6r=r(lgn,"ConvNextImageProcessor"),lgn.forEach(t),x6r=r(eMo," (RegNet model)"),eMo.forEach(t),G6r=i($),eM=n($,"LI",{});var oMo=s(eM);YGe=n(oMo,"STRONG",{});var ign=s(YGe);B6r=r(ign,"resnet"),ign.forEach(t),I6r=r(oMo," \u2014 "),Ise=n(oMo,"A",{href:!0});var dgn=s(Ise);N6r=r(dgn,"ConvNextImageProcessor"),dgn.forEach(t),$6r=r(oMo," (ResNet model)"),oMo.forEach(t),j6r=i($),oM=n($,"LI",{});var rMo=s(oM);zGe=n(rMo,"STRONG",{});var mgn=s(zGe);P6r=r(mgn,"sam"),mgn.forEach(t),Z6r=r(rMo," \u2014 "),Nse=n(rMo,"A",{href:!0});var cgn=s(Nse);V6r=r(cgn,"SamImageProcessor"),cgn.forEach(t),W6r=r(rMo," (SAM model)"),rMo.forEach(t),J6r=i($),rM=n($,"LI",{});var tMo=s(rM);HGe=n(tMo,"STRONG",{});var fgn=s(HGe);U6r=r(fgn,"segformer"),fgn.forEach(t),X6r=r(tMo," \u2014 "),$se=n(tMo,"A",{href:!0});var hgn=s($se);D6r=r(hgn,"SegformerImageProcessor"),hgn.forEach(t),q6r=r(tMo," (SegFormer model)"),tMo.forEach(t),O6r=i($),tM=n($,"LI",{});var aMo=s(tM);KGe=n(aMo,"STRONG",{});var 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created with the `),ole=n(Z$o,"A",{href:!0});var Hgn=s(ole);B7r=r(Hgn,"AutoProcessor.from_pretrained()"),Hgn.forEach(t),I7r=r(Z$o," class method."),Z$o.forEach(t),N7r=i(Ki),UV=n(Ki,"P",{});var V$o=s(UV);$7r=r(V$o,"This class cannot be instantiated directly using "),vBe=n(V$o,"CODE",{});var Kgn=s(vBe);j7r=r(Kgn,"__init__()"),Kgn.forEach(t),P7r=r(V$o," (throws an error)."),V$o.forEach(t),Z7r=i(Ki),no=n(Ki,"DIV",{class:!0});var en=s(no);M(XV.$$.fragment,en),V7r=i(en),MBe=n(en,"P",{});var eun=s(MBe);W7r=r(eun,"Instantiate one of the processor classes of the library from a pretrained model vocabulary."),eun.forEach(t),J7r=i(en),ac=n(en,"P",{});var vCe=s(ac);U7r=r(vCe,"The processor class to instantiate is selected based on the "),TBe=n(vCe,"CODE",{});var oun=s(TBe);X7r=r(oun,"model_type"),oun.forEach(t),D7r=r(vCe,` property of the config object (either
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pun=s(ile);ALr=r(pun,"ChineseCLIPProcessor"),pun.forEach(t),RLr=r(LMo," (Chinese-CLIP model)"),LMo.forEach(t),kLr=i(re),SM=n(re,"LI",{});var AMo=s(SM);kBe=n(AMo,"STRONG",{});var _un=s(kBe);SLr=r(_un,"clap"),_un.forEach(t),xLr=r(AMo," \u2014 "),dle=n(AMo,"A",{href:!0});var bun=s(dle);GLr=r(bun,"ClapProcessor"),bun.forEach(t),BLr=r(AMo," (CLAP model)"),AMo.forEach(t),ILr=i(re),xM=n(re,"LI",{});var RMo=s(xM);SBe=n(RMo,"STRONG",{});var vun=s(SBe);NLr=r(vun,"clip"),vun.forEach(t),$Lr=r(RMo," \u2014 "),mle=n(RMo,"A",{href:!0});var Mun=s(mle);jLr=r(Mun,"CLIPProcessor"),Mun.forEach(t),PLr=r(RMo," (CLIP model)"),RMo.forEach(t),ZLr=i(re),GM=n(re,"LI",{});var kMo=s(GM);xBe=n(kMo,"STRONG",{});var Tun=s(xBe);VLr=r(Tun,"clipseg"),Tun.forEach(t),WLr=r(kMo," \u2014 "),cle=n(kMo,"A",{href:!0});var Fun=s(cle);JLr=r(Fun,"CLIPSegProcessor"),Fun.forEach(t),ULr=r(kMo," (CLIPSeg model)"),kMo.forEach(t),XLr=i(re),BM=n(re,"LI",{});var SMo=s(BM);GBe=n(SMo,"STRONG",{});var 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WMo=s(XM);UBe=n(WMo,"STRONG",{});var Xun=s(UBe);qAr=r(Xun,"oneformer"),Xun.forEach(t),OAr=r(WMo," \u2014 "),Ele=n(WMo,"A",{href:!0});var Dun=s(Ele);QAr=r(Dun,"OneFormerProcessor"),Dun.forEach(t),YAr=r(WMo," (OneFormer model)"),WMo.forEach(t),zAr=i(re),DM=n(re,"LI",{});var JMo=s(DM);XBe=n(JMo,"STRONG",{});var qun=s(XBe);HAr=r(qun,"owlvit"),qun.forEach(t),KAr=r(JMo," \u2014 "),yle=n(JMo,"A",{href:!0});var Oun=s(yle);eRr=r(Oun,"OwlViTProcessor"),Oun.forEach(t),oRr=r(JMo," (OWL-ViT model)"),JMo.forEach(t),rRr=i(re),qM=n(re,"LI",{});var UMo=s(qM);DBe=n(UMo,"STRONG",{});var Qun=s(DBe);tRr=r(Qun,"pix2struct"),Qun.forEach(t),aRr=r(UMo," \u2014 "),Cle=n(UMo,"A",{href:!0});var Yun=s(Cle);nRr=r(Yun,"Pix2StructProcessor"),Yun.forEach(t),sRr=r(UMo," (Pix2Struct model)"),UMo.forEach(t),lRr=i(re),OM=n(re,"LI",{});var XMo=s(OM);qBe=n(XMo,"STRONG",{});var zun=s(qBe);iRr=r(zun,"pop2piano"),zun.forEach(t),dRr=r(XMo," \u2014 "),wle=n(XMo,"A",{href:!0});var Hun=s(wle);mRr=r(Hun,"Pop2PianoProcessor"),Hun.forEach(t),cRr=r(XMo," (Pop2Piano model)"),XMo.forEach(t),fRr=i(re),QM=n(re,"LI",{});var DMo=s(QM);OBe=n(DMo,"STRONG",{});var Kun=s(OBe);hRr=r(Kun,"sam"),Kun.forEach(t),gRr=r(DMo," \u2014 "),Lle=n(DMo,"A",{href:!0});var epn=s(Lle);uRr=r(epn,"SamProcessor"),epn.forEach(t),pRr=r(DMo," (SAM model)"),DMo.forEach(t),_Rr=i(re),YM=n(re,"LI",{});var qMo=s(YM);QBe=n(qMo,"STRONG",{});var opn=s(QBe);bRr=r(opn,"sew"),opn.forEach(t),vRr=r(qMo," \u2014 "),Ale=n(qMo,"A",{href:!0});var rpn=s(Ale);MRr=r(rpn,"Wav2Vec2Processor"),rpn.forEach(t),TRr=r(qMo," (SEW model)"),qMo.forEach(t),FRr=i(re),zM=n(re,"LI",{});var OMo=s(zM);YBe=n(OMo,"STRONG",{});var tpn=s(YBe);ERr=r(tpn,"sew-d"),tpn.forEach(t),yRr=r(OMo," \u2014 "),Rle=n(OMo,"A",{href:!0});var apn=s(Rle);CRr=r(apn,"Wav2Vec2Processor"),apn.forEach(t),wRr=r(OMo," (SEW-D model)"),OMo.forEach(t),LRr=i(re),HM=n(re,"LI",{});var QMo=s(HM);zBe=n(QMo,"STRONG",{});var npn=s(zBe);ARr=r(npn,"speech_to_text"),npn.forEach(t),RRr=r(QMo," \u2014 "),kle=n(QMo,"A",{href:!0});var spn=s(kle);kRr=r(spn,"Speech2TextProcessor"),spn.forEach(t),SRr=r(QMo," (Speech2Text model)"),QMo.forEach(t),xRr=i(re),KM=n(re,"LI",{});var YMo=s(KM);HBe=n(YMo,"STRONG",{});var lpn=s(HBe);GRr=r(lpn,"speech_to_text_2"),lpn.forEach(t),BRr=r(YMo," \u2014 "),Sle=n(YMo,"A",{href:!0});var ipn=s(Sle);IRr=r(ipn,"Speech2Text2Processor"),ipn.forEach(t),NRr=r(YMo," (Speech2Text2 model)"),YMo.forEach(t),$Rr=i(re),eT=n(re,"LI",{});var zMo=s(eT);KBe=n(zMo,"STRONG",{});var dpn=s(KBe);jRr=r(dpn,"speecht5"),dpn.forEach(t),PRr=r(zMo," \u2014 "),xle=n(zMo,"A",{href:!0});var mpn=s(xle);ZRr=r(mpn,"SpeechT5Processor"),mpn.forEach(t),VRr=r(zMo," (SpeechT5 model)"),zMo.forEach(t),WRr=i(re),oT=n(re,"LI",{});var HMo=s(oT);eIe=n(HMo,"STRONG",{});var cpn=s(eIe);JRr=r(cpn,"trocr"),cpn.forEach(t),URr=r(HMo," \u2014 "),Gle=n(HMo,"A",{href:!0});var fpn=s(Gle);XRr=r(fpn,"TrOCRProcessor"),fpn.forEach(t),DRr=r(HMo," (TrOCR model)"),HMo.forEach(t),qRr=i(re),rT=n(re,"LI",{});var KMo=s(rT);oIe=n(KMo,"STRONG",{});var hpn=s(oIe);ORr=r(hpn,"tvlt"),hpn.forEach(t),QRr=r(KMo," \u2014 "),Ble=n(KMo,"A",{href:!0});var gpn=s(Ble);YRr=r(gpn,"TvltProcessor"),gpn.forEach(t),zRr=r(KMo," (TVLT model)"),KMo.forEach(t),HRr=i(re),tT=n(re,"LI",{});var eTo=s(tT);rIe=n(eTo,"STRONG",{});var upn=s(rIe);KRr=r(upn,"unispeech"),upn.forEach(t),ekr=r(eTo," \u2014 "),Ile=n(eTo,"A",{href:!0});var ppn=s(Ile);okr=r(ppn,"Wav2Vec2Processor"),ppn.forEach(t),rkr=r(eTo," (UniSpeech model)"),eTo.forEach(t),tkr=i(re),aT=n(re,"LI",{});var oTo=s(aT);tIe=n(oTo,"STRONG",{});var _pn=s(tIe);akr=r(_pn,"unispeech-sat"),_pn.forEach(t),nkr=r(oTo," \u2014 "),Nle=n(oTo,"A",{href:!0});var bpn=s(Nle);skr=r(bpn,"Wav2Vec2Processor"),bpn.forEach(t),lkr=r(oTo," (UniSpeechSat model)"),oTo.forEach(t),ikr=i(re),nT=n(re,"LI",{});var rTo=s(nT);aIe=n(rTo,"STRONG",{});var vpn=s(aIe);dkr=r(vpn,"vilt"),vpn.forEach(t),mkr=r(rTo," \u2014 "),$le=n(rTo,"A",{href:!0});var Mpn=s($le);ckr=r(Mpn,"ViltProcessor"),Mpn.forEach(t),fkr=r(rTo," (ViLT model)"),rTo.forEach(t),hkr=i(re),sT=n(re,"LI",{});var tTo=s(sT);nIe=n(tTo,"STRONG",{});var Tpn=s(nIe);gkr=r(Tpn,"vision-text-dual-encoder"),Tpn.forEach(t),ukr=r(tTo," \u2014 "),jle=n(tTo,"A",{href:!0});var Fpn=s(jle);pkr=r(Fpn,"VisionTextDualEncoderProcessor"),Fpn.forEach(t),_kr=r(tTo," (VisionTextDualEncoder model)"),tTo.forEach(t),bkr=i(re),lT=n(re,"LI",{});var aTo=s(lT);sIe=n(aTo,"STRONG",{});var Epn=s(sIe);vkr=r(Epn,"wav2vec2"),Epn.forEach(t),Mkr=r(aTo," \u2014 "),Ple=n(aTo,"A",{href:!0});var ypn=s(Ple);Tkr=r(ypn,"Wav2Vec2Processor"),ypn.forEach(t),Fkr=r(aTo," (Wav2Vec2 model)"),aTo.forEach(t),Ekr=i(re),iT=n(re,"LI",{});var nTo=s(iT);lIe=n(nTo,"STRONG",{});var Cpn=s(lIe);ykr=r(Cpn,"wav2vec2-conformer"),Cpn.forEach(t),Ckr=r(nTo," \u2014 "),Zle=n(nTo,"A",{href:!0});var wpn=s(Zle);wkr=r(wpn,"Wav2Vec2Processor"),wpn.forEach(t),Lkr=r(nTo," (Wav2Vec2-Conformer model)"),nTo.forEach(t),Akr=i(re),dT=n(re,"LI",{});var sTo=s(dT);iIe=n(sTo,"STRONG",{});var Lpn=s(iIe);Rkr=r(Lpn,"wavlm"),Lpn.forEach(t),kkr=r(sTo," \u2014 "),Vle=n(sTo,"A",{href:!0});var Apn=s(Vle);Skr=r(Apn,"Wav2Vec2Processor"),Apn.forEach(t),xkr=r(sTo," (WavLM model)"),sTo.forEach(t),Gkr=i(re),mT=n(re,"LI",{});var lTo=s(mT);dIe=n(lTo,"STRONG",{});var Rpn=s(dIe);Bkr=r(Rpn,"whisper"),Rpn.forEach(t),Ikr=r(lTo," \u2014 "),Wle=n(lTo,"A",{href:!0});var kpn=s(Wle);Nkr=r(kpn,"WhisperProcessor"),kpn.forEach(t),$kr=r(lTo," (Whisper model)"),lTo.forEach(t),jkr=i(re),cT=n(re,"LI",{});var iTo=s(cT);mIe=n(iTo,"STRONG",{});var Spn=s(mIe);Pkr=r(Spn,"xclip"),Spn.forEach(t),Zkr=r(iTo," \u2014 "),Jle=n(iTo,"A",{href:!0});var xpn=s(Jle);Vkr=r(xpn,"XCLIPProcessor"),xpn.forEach(t),Wkr=r(iTo," (X-CLIP model)"),iTo.forEach(t),re.forEach(t),Jkr=i(en),M(fT.$$.fragment,en),Ukr=i(en),M(hT.$$.fragment,en),en.forEach(t),Xkr=i(Ki),gT=n(Ki,"DIV",{class:!0});var W$o=s(gT);M(DV.$$.fragment,W$o),Dkr=i(W$o),cIe=n(W$o,"P",{});var Gpn=s(cIe);qkr=r(Gpn,"Register a new processor for this class."),Gpn.forEach(t),W$o.forEach(t),Ki.forEach(t),PBo=i(c),nc=n(c,"H2",{class:!0});var J$o=s(nc);uT=n(J$o,"A",{id:!0,class:!0,href:!0});var Bpn=s(uT);fIe=n(Bpn,"SPAN",{});var Ipn=s(fIe);M(qV.$$.fragment,Ipn),Ipn.forEach(t),Bpn.forEach(t),Okr=i(J$o),hIe=n(J$o,"SPAN",{});var Npn=s(hIe);Qkr=r(Npn,"Generic model classes"),Npn.forEach(t),J$o.forEach(t),ZBo=i(c),Ule=n(c,"P",{});var $pn=s(Ule);Ykr=r($pn,"The following auto classes are available for instantiating a base model class without a specific head."),$pn.forEach(t),VBo=i(c),sc=n(c,"H3",{class:!0});var U$o=s(sc);pT=n(U$o,"A",{id:!0,class:!0,href:!0});var jpn=s(pT);gIe=n(jpn,"SPAN",{});var Ppn=s(gIe);M(OV.$$.fragment,Ppn),Ppn.forEach(t),jpn.forEach(t),zkr=i(U$o),uIe=n(U$o,"SPAN",{});var Zpn=s(uIe);Hkr=r(Zpn,"AutoModel"),Zpn.forEach(t),U$o.forEach(t),WBo=i(c),Uo=n(c,"DIV",{class:!0});var ed=s(Uo);M(QV.$$.fragment,ed),Kkr=i(ed),lc=n(ed,"P",{});var MCe=s(lc);eSr=r(MCe,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Xle=n(MCe,"A",{href:!0});var Vpn=s(Xle);oSr=r(Vpn,"from_pretrained()"),Vpn.forEach(t),rSr=r(MCe," class method or the "),Dle=n(MCe,"A",{href:!0});var Wpn=s(Dle);tSr=r(Wpn,"from_config()"),Wpn.forEach(t),aSr=r(MCe,` class
method.`),MCe.forEach(t),nSr=i(ed),YV=n(ed,"P",{});var X$o=s(YV);sSr=r(X$o,"This class cannot be instantiated directly using "),pIe=n(X$o,"CODE",{});var Jpn=s(pIe);lSr=r(Jpn,"__init__()"),Jpn.forEach(t),iSr=r(X$o," (throws an error)."),X$o.forEach(t),dSr=i(ed),Jt=n(ed,"DIV",{class:!0});var qj=s(Jt);M(zV.$$.fragment,qj),mSr=i(qj),_Ie=n(qj,"P",{});var Upn=s(_Ie);cSr=r(Upn,"Instantiates one of the base model classes of the library from a configuration."),Upn.forEach(t),fSr=i(qj),ic=n(qj,"P",{});var TCe=s(ic);hSr=r(TCe,`Note:
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model\u2019s configuration. Use `),qle=n(TCe,"A",{href:!0});var Dpn=s(qle);pSr=r(Dpn,"from_pretrained()"),Dpn.forEach(t),_Sr=r(TCe," to load the model weights."),TCe.forEach(t),bSr=i(qj),M(_T.$$.fragment,qj),qj.forEach(t),vSr=i(ed),so=n(ed,"DIV",{class:!0});var on=s(so);M(HV.$$.fragment,on),MSr=i(on),vIe=n(on,"P",{});var qpn=s(vIe);TSr=r(qpn,"Instantiate one of the base model classes of the library from a pretrained model."),qpn.forEach(t),FSr=i(on),Zn=n(on,"P",{});var Oj=s(Zn);ESr=r(Oj,"The model class to instantiate is selected based on the "),MIe=n(Oj,"CODE",{});var Opn=s(MIe);ySr=r(Opn,"model_type"),Opn.forEach(t),CSr=r(Oj,` property of the config object (either
passed as an argument or loaded from `),TIe=n(Oj,"CODE",{});var Qpn=s(TIe);wSr=r(Qpn,"pretrained_model_name_or_path"),Qpn.forEach(t),LSr=r(Oj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FIe=n(Oj,"CODE",{});var Ypn=s(FIe);ASr=r(Ypn,"pretrained_model_name_or_path"),Ypn.forEach(t),RSr=r(Oj,":"),Oj.forEach(t),kSr=i(on),A=n(on,"UL",{});var R=s(A);bT=n(R,"LI",{});var dTo=s(bT);EIe=n(dTo,"STRONG",{});var zpn=s(EIe);SSr=r(zpn,"albert"),zpn.forEach(t),xSr=r(dTo," \u2014 "),Ole=n(dTo,"A",{href:!0});var Hpn=s(Ole);GSr=r(Hpn,"AlbertModel"),Hpn.forEach(t),BSr=r(dTo," (ALBERT model)"),dTo.forEach(t),ISr=i(R),vT=n(R,"LI",{});var mTo=s(vT);yIe=n(mTo,"STRONG",{});var Kpn=s(yIe);NSr=r(Kpn,"align"),Kpn.forEach(t),$Sr=r(mTo," \u2014 "),Qle=n(mTo,"A",{href:!0});var e_n=s(Qle);jSr=r(e_n,"AlignModel"),e_n.forEach(t),PSr=r(mTo," (ALIGN model)"),mTo.forEach(t),ZSr=i(R),MT=n(R,"LI",{});var cTo=s(MT);CIe=n(cTo,"STRONG",{});var o_n=s(CIe);VSr=r(o_n,"altclip"),o_n.forEach(t),WSr=r(cTo," \u2014 "),Yle=n(cTo,"A",{href:!0});var r_n=s(Yle);JSr=r(r_n,"AltCLIPModel"),r_n.forEach(t),USr=r(cTo," (AltCLIP model)"),cTo.forEach(t),XSr=i(R),TT=n(R,"LI",{});var fTo=s(TT);wIe=n(fTo,"STRONG",{});var t_n=s(wIe);DSr=r(t_n,"audio-spectrogram-transformer"),t_n.forEach(t),qSr=r(fTo," \u2014 "),zle=n(fTo,"A",{href:!0});var a_n=s(zle);OSr=r(a_n,"ASTModel"),a_n.forEach(t),QSr=r(fTo," (Audio Spectrogram Transformer model)"),fTo.forEach(t),YSr=i(R),FT=n(R,"LI",{});var hTo=s(FT);LIe=n(hTo,"STRONG",{});var n_n=s(LIe);zSr=r(n_n,"autoformer"),n_n.forEach(t),HSr=r(hTo," \u2014 "),Hle=n(hTo,"A",{href:!0});var s_n=s(Hle);KSr=r(s_n,"AutoformerModel"),s_n.forEach(t),exr=r(hTo," (Autoformer model)"),hTo.forEach(t),oxr=i(R),ET=n(R,"LI",{});var gTo=s(ET);AIe=n(gTo,"STRONG",{});var l_n=s(AIe);rxr=r(l_n,"bark"),l_n.forEach(t),txr=r(gTo," \u2014 "),Kle=n(gTo,"A",{href:!0});var i_n=s(Kle);axr=r(i_n,"BarkModel"),i_n.forEach(t),nxr=r(gTo," (Bark model)"),gTo.forEach(t),sxr=i(R),yT=n(R,"LI",{});var uTo=s(yT);RIe=n(uTo,"STRONG",{});var d_n=s(RIe);lxr=r(d_n,"bart"),d_n.forEach(t),ixr=r(uTo," \u2014 "),eie=n(uTo,"A",{href:!0});var 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h5n=s(cVe);qnt=r(h5n,"xlm-roberta"),h5n.forEach(t),Ont=r(A5o," \u2014 "),Mhe=n(A5o,"A",{href:!0});var g5n=s(Mhe);Qnt=r(g5n,"FlaxXLMRobertaModel"),g5n.forEach(t),Ynt=r(A5o," (XLM-RoBERTa model)"),A5o.forEach(t),ie.forEach(t),znt=i(ad),M(U5.$$.fragment,ad),ad.forEach(t),td.forEach(t),qBo=i(c),uc=n(c,"H2",{class:!0});var Y$o=s(uc);X5=n(Y$o,"A",{id:!0,class:!0,href:!0});var u5n=s(X5);fVe=n(u5n,"SPAN",{});var p5n=s(fVe);M(dW.$$.fragment,p5n),p5n.forEach(t),u5n.forEach(t),Hnt=i(Y$o),hVe=n(Y$o,"SPAN",{});var _5n=s(hVe);Knt=r(_5n,"Generic pretraining classes"),_5n.forEach(t),Y$o.forEach(t),OBo=i(c),The=n(c,"P",{});var b5n=s(The);est=r(b5n,"The following auto classes are available for instantiating a model with a pretraining head."),b5n.forEach(t),QBo=i(c),pc=n(c,"H3",{class:!0});var z$o=s(pc);D5=n(z$o,"A",{id:!0,class:!0,href:!0});var v5n=s(D5);gVe=n(v5n,"SPAN",{});var M5n=s(gVe);M(mW.$$.fragment,M5n),M5n.forEach(t),v5n.forEach(t),ost=i(z$o),uVe=n(z$o,"SPAN",{});var 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method.`),wCe.forEach(t),dst=i(nd),fW=n(nd,"P",{});var H$o=s(fW);mst=r(H$o,"This class cannot be instantiated directly using "),pVe=n(H$o,"CODE",{});var y5n=s(pVe);cst=r(y5n,"__init__()"),y5n.forEach(t),fst=r(H$o," (throws an error)."),H$o.forEach(t),hst=i(nd),Dt=n(nd,"DIV",{class:!0});var Kj=s(Dt);M(hW.$$.fragment,Kj),gst=i(Kj),_Ve=n(Kj,"P",{});var C5n=s(_Ve);ust=r(C5n,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),C5n.forEach(t),pst=i(Kj),bc=n(Kj,"P",{});var LCe=s(bc);_st=r(LCe,`Note:
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model\u2019s configuration. Use `),yhe=n(LCe,"A",{href:!0});var L5n=s(yhe);Mst=r(L5n,"from_pretrained()"),L5n.forEach(t),Tst=r(LCe," to load the model weights."),LCe.forEach(t),Fst=i(Kj),M(q5.$$.fragment,Kj),Kj.forEach(t),Est=i(nd),lo=n(nd,"DIV",{class:!0});var rn=s(lo);M(gW.$$.fragment,rn),yst=i(rn),vVe=n(rn,"P",{});var A5n=s(vVe);Cst=r(A5n,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),A5n.forEach(t),wst=i(rn),Jn=n(rn,"P",{});var eP=s(Jn);Lst=r(eP,"The model class to instantiate is selected based on the "),MVe=n(eP,"CODE",{});var R5n=s(MVe);Ast=r(R5n,"model_type"),R5n.forEach(t),Rst=r(eP,` property of the config object (either
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J4n=s(nJe);$gt=r(J4n,"roberta-prelayernorm"),J4n.forEach(t),jgt=r(o9o," \u2014 "),Kge=n(o9o,"A",{href:!0});var U4n=s(Kge);Pgt=r(U4n,"TFRobertaPreLayerNormForMaskedLM"),U4n.forEach(t),Zgt=r(o9o," (RoBERTa-PreLayerNorm model)"),o9o.forEach(t),Vgt=i(he),y9=n(he,"LI",{});var r9o=s(y9);sJe=n(r9o,"STRONG",{});var X4n=s(sJe);Wgt=r(X4n,"t5"),X4n.forEach(t),Jgt=r(r9o," \u2014 "),eue=n(r9o,"A",{href:!0});var D4n=s(eue);Ugt=r(D4n,"TFT5ForConditionalGeneration"),D4n.forEach(t),Xgt=r(r9o," (T5 model)"),r9o.forEach(t),Dgt=i(he),C9=n(he,"LI",{});var t9o=s(C9);lJe=n(t9o,"STRONG",{});var q4n=s(lJe);qgt=r(q4n,"tapas"),q4n.forEach(t),Ogt=r(t9o," \u2014 "),oue=n(t9o,"A",{href:!0});var O4n=s(oue);Qgt=r(O4n,"TFTapasForMaskedLM"),O4n.forEach(t),Ygt=r(t9o," (TAPAS model)"),t9o.forEach(t),zgt=i(he),w9=n(he,"LI",{});var a9o=s(w9);iJe=n(a9o,"STRONG",{});var Q4n=s(iJe);Hgt=r(Q4n,"transfo-xl"),Q4n.forEach(t),Kgt=r(a9o," \u2014 "),rue=n(a9o,"A",{href:!0});var Y4n=s(rue);eut=r(Y4n,"TFTransfoXLLMHeadModel"),Y4n.forEach(t),out=r(a9o," (Transformer-XL model)"),a9o.forEach(t),rut=i(he),L9=n(he,"LI",{});var n9o=s(L9);dJe=n(n9o,"STRONG",{});var z4n=s(dJe);tut=r(z4n,"vit_mae"),z4n.forEach(t),aut=r(n9o," \u2014 "),tue=n(n9o,"A",{href:!0});var H4n=s(tue);nut=r(H4n,"TFViTMAEForPreTraining"),H4n.forEach(t),sut=r(n9o," (ViTMAE model)"),n9o.forEach(t),lut=i(he),A9=n(he,"LI",{});var s9o=s(A9);mJe=n(s9o,"STRONG",{});var K4n=s(mJe);iut=r(K4n,"xlm"),K4n.forEach(t),dut=r(s9o," \u2014 "),aue=n(s9o,"A",{href:!0});var eCn=s(aue);mut=r(eCn,"TFXLMWithLMHeadModel"),eCn.forEach(t),cut=r(s9o," (XLM model)"),s9o.forEach(t),fut=i(he),R9=n(he,"LI",{});var l9o=s(R9);cJe=n(l9o,"STRONG",{});var oCn=s(cJe);hut=r(oCn,"xlm-roberta"),oCn.forEach(t),gut=r(l9o," \u2014 "),nue=n(l9o,"A",{href:!0});var rCn=s(nue);uut=r(rCn,"TFXLMRobertaForMaskedLM"),rCn.forEach(t),put=r(l9o," (XLM-RoBERTa model)"),l9o.forEach(t),_ut=i(he),k9=n(he,"LI",{});var i9o=s(k9);fJe=n(i9o,"STRONG",{});var tCn=s(fJe);but=r(tCn,"xlnet"),tCn.forEach(t),vut=r(i9o," \u2014 "),sue=n(i9o,"A",{href:!0});var aCn=s(sue);Mut=r(aCn,"TFXLNetLMHeadModel"),aCn.forEach(t),Tut=r(i9o," (XLNet model)"),i9o.forEach(t),he.forEach(t),Fut=i(ld),M(S9.$$.fragment,ld),ld.forEach(t),sd.forEach(t),KBo=i(c),Fc=n(c,"H3",{class:!0});var ojo=s(Fc);x9=n(ojo,"A",{id:!0,class:!0,href:!0});var nCn=s(x9);hJe=n(nCn,"SPAN",{});var sCn=s(hJe);M(MW.$$.fragment,sCn),sCn.forEach(t),nCn.forEach(t),Eut=i(ojo),gJe=n(ojo,"SPAN",{});var lCn=s(gJe);yut=r(lCn,"FlaxAutoModelForPreTraining"),lCn.forEach(t),ojo.forEach(t),eIo=i(c),Qo=n(c,"DIV",{class:!0});var id=s(Qo);M(TW.$$.fragment,id),Cut=i(id),Ec=n(id,"P",{});var kCe=s(Ec);wut=r(kCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),lue=n(kCe,"A",{href:!0});var iCn=s(lue);Lut=r(iCn,"from_pretrained()"),iCn.forEach(t),Aut=r(kCe," class method or the "),iue=n(kCe,"A",{href:!0});var dCn=s(iue);Rut=r(dCn,"from_config()"),dCn.forEach(t),kut=r(kCe,` class
method.`),kCe.forEach(t),Sut=i(id),FW=n(id,"P",{});var rjo=s(FW);xut=r(rjo,"This class cannot be instantiated directly using "),uJe=n(rjo,"CODE",{});var mCn=s(uJe);Gut=r(mCn,"__init__()"),mCn.forEach(t),But=r(rjo," (throws an error)."),rjo.forEach(t),Iut=i(id),Ot=n(id,"DIV",{class:!0});var tP=s(Ot);M(EW.$$.fragment,tP),Nut=i(tP),pJe=n(tP,"P",{});var cCn=s(pJe);$ut=r(cCn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),cCn.forEach(t),jut=i(tP),yc=n(tP,"P",{});var SCe=s(yc);Put=r(SCe,`Note:
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model\u2019s configuration. Use `),due=n(SCe,"A",{href:!0});var hCn=s(due);Wut=r(hCn,"from_pretrained()"),hCn.forEach(t),Jut=r(SCe," to load the model weights."),SCe.forEach(t),Uut=i(tP),M(G9.$$.fragment,tP),tP.forEach(t),Xut=i(id),st=n(id,"DIV",{class:!0});var dd=s(st);M(yW.$$.fragment,dd),Dut=i(dd),bJe=n(dd,"P",{});var gCn=s(bJe);qut=r(gCn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),gCn.forEach(t),Out=i(dd),Xn=n(dd,"P",{});var aP=s(Xn);Qut=r(aP,"The model class to instantiate is selected based on the "),vJe=n(aP,"CODE",{});var uCn=s(vJe);Yut=r(uCn,"model_type"),uCn.forEach(t),zut=r(aP,` property of the config object (either
passed as an argument or loaded from `),MJe=n(aP,"CODE",{});var pCn=s(MJe);Hut=r(pCn,"pretrained_model_name_or_path"),pCn.forEach(t),Kut=r(aP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),TJe=n(aP,"CODE",{});var _Cn=s(TJe);ept=r(_Cn,"pretrained_model_name_or_path"),_Cn.forEach(t),opt=r(aP,":"),aP.forEach(t),rpt=i(dd),we=n(dd,"UL",{});var Ae=s(we);B9=n(Ae,"LI",{});var d9o=s(B9);FJe=n(d9o,"STRONG",{});var bCn=s(FJe);tpt=r(bCn,"albert"),bCn.forEach(t),apt=r(d9o," \u2014 "),mue=n(d9o,"A",{href:!0});var vCn=s(mue);npt=r(vCn,"FlaxAlbertForPreTraining"),vCn.forEach(t),spt=r(d9o," (ALBERT model)"),d9o.forEach(t),lpt=i(Ae),I9=n(Ae,"LI",{});var m9o=s(I9);EJe=n(m9o,"STRONG",{});var MCn=s(EJe);ipt=r(MCn,"bart"),MCn.forEach(t),dpt=r(m9o," \u2014 "),cue=n(m9o,"A",{href:!0});var TCn=s(cue);mpt=r(TCn,"FlaxBartForConditionalGeneration"),TCn.forEach(t),cpt=r(m9o," (BART model)"),m9o.forEach(t),fpt=i(Ae),N9=n(Ae,"LI",{});var c9o=s(N9);yJe=n(c9o,"STRONG",{});var FCn=s(yJe);hpt=r(FCn,"bert"),FCn.forEach(t),gpt=r(c9o," \u2014 "),fue=n(c9o,"A",{href:!0});var ECn=s(fue);upt=r(ECn,"FlaxBertForPreTraining"),ECn.forEach(t),ppt=r(c9o," (BERT 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with the `),wue=n(xCe,"A",{href:!0});var own=s(wue);S_t=r(own,"from_pretrained()"),own.forEach(t),x_t=r(xCe," class method or the "),Lue=n(xCe,"A",{href:!0});var rwn=s(Lue);G_t=r(rwn,"from_config()"),rwn.forEach(t),B_t=r(xCe,` class
method.`),xCe.forEach(t),I_t=i(md),AW=n(md,"P",{});var njo=s(AW);N_t=r(njo,"This class cannot be instantiated directly using "),VJe=n(njo,"CODE",{});var twn=s(VJe);$_t=r(twn,"__init__()"),twn.forEach(t),j_t=r(njo," (throws an error)."),njo.forEach(t),P_t=i(md),Qt=n(md,"DIV",{class:!0});var nP=s(Qt);M(RW.$$.fragment,nP),Z_t=i(nP),WJe=n(nP,"P",{});var awn=s(WJe);V_t=r(awn,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),awn.forEach(t),W_t=i(nP),Ac=n(nP,"P",{});var GCe=s(Ac);J_t=r(GCe,`Note:
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model\u2019s configuration. Use `),Aue=n(GCe,"A",{href:!0});var swn=s(Aue);D_t=r(swn,"from_pretrained()"),swn.forEach(t),q_t=r(GCe," to load the model weights."),GCe.forEach(t),O_t=i(nP),M(H9.$$.fragment,nP),nP.forEach(t),Q_t=i(md),io=n(md,"DIV",{class:!0});var tn=s(io);M(kW.$$.fragment,tn),Y_t=i(tn),UJe=n(tn,"P",{});var lwn=s(UJe);z_t=r(lwn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),lwn.forEach(t),H_t=i(tn),Dn=n(tn,"P",{});var sP=s(Dn);K_t=r(sP,"The model class to instantiate is selected based on the "),XJe=n(sP,"CODE",{});var iwn=s(XJe);e1t=r(iwn,"model_type"),iwn.forEach(t),o1t=r(sP,` property of the config object (either
passed as an argument or loaded from `),DJe=n(sP,"CODE",{});var dwn=s(DJe);r1t=r(dwn,"pretrained_model_name_or_path"),dwn.forEach(t),t1t=r(sP,` if possible), or when it\u2019s missing, by
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Y6n=s(Tpe);EMt=r(Y6n,"RoCBertForCausalLM"),Y6n.forEach(t),yMt=r(p4o," (RoCBert model)"),p4o.forEach(t),CMt=i(Y),D4=n(Y,"LI",{});var _4o=s(D4);VUe=n(_4o,"STRONG",{});var z6n=s(VUe);wMt=r(z6n,"roformer"),z6n.forEach(t),LMt=r(_4o," \u2014 "),Fpe=n(_4o,"A",{href:!0});var H6n=s(Fpe);AMt=r(H6n,"RoFormerForCausalLM"),H6n.forEach(t),RMt=r(_4o," (RoFormer model)"),_4o.forEach(t),kMt=i(Y),q4=n(Y,"LI",{});var b4o=s(q4);WUe=n(b4o,"STRONG",{});var K6n=s(WUe);SMt=r(K6n,"rwkv"),K6n.forEach(t),xMt=r(b4o," \u2014 "),Epe=n(b4o,"A",{href:!0});var e8n=s(Epe);GMt=r(e8n,"RwkvForCausalLM"),e8n.forEach(t),BMt=r(b4o," (RWKV model)"),b4o.forEach(t),IMt=i(Y),O4=n(Y,"LI",{});var v4o=s(O4);JUe=n(v4o,"STRONG",{});var o8n=s(JUe);NMt=r(o8n,"speech_to_text_2"),o8n.forEach(t),$Mt=r(v4o," \u2014 "),ype=n(v4o,"A",{href:!0});var r8n=s(ype);jMt=r(r8n,"Speech2Text2ForCausalLM"),r8n.forEach(t),PMt=r(v4o," (Speech2Text2 model)"),v4o.forEach(t),ZMt=i(Y),Q4=n(Y,"LI",{});var M4o=s(Q4);UUe=n(M4o,"STRONG",{});var t8n=s(UUe);VMt=r(t8n,"transfo-xl"),t8n.forEach(t),WMt=r(M4o," \u2014 "),Cpe=n(M4o,"A",{href:!0});var a8n=s(Cpe);JMt=r(a8n,"TransfoXLLMHeadModel"),a8n.forEach(t),UMt=r(M4o," (Transformer-XL model)"),M4o.forEach(t),XMt=i(Y),Y4=n(Y,"LI",{});var T4o=s(Y4);XUe=n(T4o,"STRONG",{});var n8n=s(XUe);DMt=r(n8n,"trocr"),n8n.forEach(t),qMt=r(T4o," \u2014 "),wpe=n(T4o,"A",{href:!0});var s8n=s(wpe);OMt=r(s8n,"TrOCRForCausalLM"),s8n.forEach(t),QMt=r(T4o," (TrOCR model)"),T4o.forEach(t),YMt=i(Y),z4=n(Y,"LI",{});var F4o=s(z4);DUe=n(F4o,"STRONG",{});var l8n=s(DUe);zMt=r(l8n,"xglm"),l8n.forEach(t),HMt=r(F4o," \u2014 "),Lpe=n(F4o,"A",{href:!0});var i8n=s(Lpe);KMt=r(i8n,"XGLMForCausalLM"),i8n.forEach(t),eTt=r(F4o," (XGLM model)"),F4o.forEach(t),oTt=i(Y),H4=n(Y,"LI",{});var E4o=s(H4);qUe=n(E4o,"STRONG",{});var d8n=s(qUe);rTt=r(d8n,"xlm"),d8n.forEach(t),tTt=r(E4o," \u2014 "),Ape=n(E4o,"A",{href:!0});var m8n=s(Ape);aTt=r(m8n,"XLMWithLMHeadModel"),m8n.forEach(t),nTt=r(E4o," (XLM 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deactivated). To train the model, you should first set it back in training mode with `),eXe=n(R4o,"CODE",{});var F8n=s(eXe);ITt=r(F8n,"model.train()"),F8n.forEach(t),R4o.forEach(t),NTt=i(tn),M(nC.$$.fragment,tn),tn.forEach(t),md.forEach(t),nIo=i(c),Rc=n(c,"H3",{class:!0});var sjo=s(Rc);sC=n(sjo,"A",{id:!0,class:!0,href:!0});var E8n=s(sC);oXe=n(E8n,"SPAN",{});var y8n=s(oXe);M(SW.$$.fragment,y8n),y8n.forEach(t),E8n.forEach(t),$Tt=i(sjo),rXe=n(sjo,"SPAN",{});var C8n=s(rXe);jTt=r(C8n,"TFAutoModelForCausalLM"),C8n.forEach(t),sjo.forEach(t),sIo=i(c),zo=n(c,"DIV",{class:!0});var cd=s(zo);M(xW.$$.fragment,cd),PTt=i(cd),kc=n(cd,"P",{});var BCe=s(kc);ZTt=r(BCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Bpe=n(BCe,"A",{href:!0});var w8n=s(Bpe);VTt=r(w8n,"from_pretrained()"),w8n.forEach(t),WTt=r(BCe," class method or the "),Ipe=n(BCe,"A",{href:!0});var L8n=s(Ipe);JTt=r(L8n,"from_config()"),L8n.forEach(t),UTt=r(BCe,` class
method.`),BCe.forEach(t),XTt=i(cd),GW=n(cd,"P",{});var ljo=s(GW);DTt=r(ljo,"This class cannot be instantiated directly using "),tXe=n(ljo,"CODE",{});var A8n=s(tXe);qTt=r(A8n,"__init__()"),A8n.forEach(t),OTt=r(ljo," (throws an error)."),ljo.forEach(t),QTt=i(cd),Yt=n(cd,"DIV",{class:!0});var lP=s(Yt);M(BW.$$.fragment,lP),YTt=i(lP),aXe=n(lP,"P",{});var R8n=s(aXe);zTt=r(R8n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),R8n.forEach(t),HTt=i(lP),Sc=n(lP,"P",{});var ICe=s(Sc);KTt=r(ICe,`Note:
Loading a model from its configuration file does `),nXe=n(ICe,"STRONG",{});var k8n=s(nXe);eFt=r(k8n,"not"),k8n.forEach(t),oFt=r(ICe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Npe=n(ICe,"A",{href:!0});var S8n=s(Npe);rFt=r(S8n,"from_pretrained()"),S8n.forEach(t),tFt=r(ICe," to load the model weights."),ICe.forEach(t),aFt=i(lP),M(lC.$$.fragment,lP),lP.forEach(t),nFt=i(cd),lt=n(cd,"DIV",{class:!0});var fd=s(lt);M(IW.$$.fragment,fd),sFt=i(fd),sXe=n(fd,"P",{});var x8n=s(sXe);lFt=r(x8n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),x8n.forEach(t),iFt=i(fd),qn=n(fd,"P",{});var iP=s(qn);dFt=r(iP,"The model class to instantiate is selected based on the "),lXe=n(iP,"CODE",{});var G8n=s(lXe);mFt=r(G8n,"model_type"),G8n.forEach(t),cFt=r(iP,` property of the config object (either
passed as an argument or loaded from `),iXe=n(iP,"CODE",{});var B8n=s(iXe);fFt=r(B8n,"pretrained_model_name_or_path"),B8n.forEach(t),hFt=r(iP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dXe=n(iP,"CODE",{});var I8n=s(dXe);gFt=r(I8n,"pretrained_model_name_or_path"),I8n.forEach(t),uFt=r(iP,":"),iP.forEach(t),pFt=i(fd),ye=n(fd,"UL",{});var Ce=s(ye);iC=n(Ce,"LI",{});var k4o=s(iC);mXe=n(k4o,"STRONG",{});var N8n=s(mXe);_Ft=r(N8n,"bert"),N8n.forEach(t),bFt=r(k4o," \u2014 "),$pe=n(k4o,"A",{href:!0});var $8n=s($pe);vFt=r($8n,"TFBertLMHeadModel"),$8n.forEach(t),MFt=r(k4o," (BERT model)"),k4o.forEach(t),TFt=i(Ce),dC=n(Ce,"LI",{});var S4o=s(dC);cXe=n(S4o,"STRONG",{});var j8n=s(cXe);FFt=r(j8n,"camembert"),j8n.forEach(t),EFt=r(S4o," \u2014 "),jpe=n(S4o,"A",{href:!0});var P8n=s(jpe);yFt=r(P8n,"TFCamembertForCausalLM"),P8n.forEach(t),CFt=r(S4o," (CamemBERT model)"),S4o.forEach(t),wFt=i(Ce),mC=n(Ce,"LI",{});var x4o=s(mC);fXe=n(x4o,"STRONG",{});var Z8n=s(fXe);LFt=r(Z8n,"ctrl"),Z8n.forEach(t),AFt=r(x4o," \u2014 "),Ppe=n(x4o,"A",{href:!0});var V8n=s(Ppe);RFt=r(V8n,"TFCTRLLMHeadModel"),V8n.forEach(t),kFt=r(x4o," (CTRL 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Q8n=s(Jpe);QFt=r(Q8n,"TFOpenAIGPTLMHeadModel"),Q8n.forEach(t),YFt=r(N4o," (OpenAI GPT model)"),N4o.forEach(t),zFt=i(Ce),uC=n(Ce,"LI",{});var $4o=s(uC);_Xe=n($4o,"STRONG",{});var Y8n=s(_Xe);HFt=r(Y8n,"opt"),Y8n.forEach(t),KFt=r($4o," \u2014 "),Upe=n($4o,"A",{href:!0});var z8n=s(Upe);eEt=r(z8n,"TFOPTForCausalLM"),z8n.forEach(t),oEt=r($4o," (OPT model)"),$4o.forEach(t),rEt=i(Ce),pC=n(Ce,"LI",{});var j4o=s(pC);bXe=n(j4o,"STRONG",{});var H8n=s(bXe);tEt=r(H8n,"rembert"),H8n.forEach(t),aEt=r(j4o," \u2014 "),Xpe=n(j4o,"A",{href:!0});var K8n=s(Xpe);nEt=r(K8n,"TFRemBertForCausalLM"),K8n.forEach(t),sEt=r(j4o," (RemBERT model)"),j4o.forEach(t),lEt=i(Ce),_C=n(Ce,"LI",{});var P4o=s(_C);vXe=n(P4o,"STRONG",{});var e7n=s(vXe);iEt=r(e7n,"roberta"),e7n.forEach(t),dEt=r(P4o," \u2014 "),Dpe=n(P4o,"A",{href:!0});var o7n=s(Dpe);mEt=r(o7n,"TFRobertaForCausalLM"),o7n.forEach(t),cEt=r(P4o," (RoBERTa model)"),P4o.forEach(t),fEt=i(Ce),bC=n(Ce,"LI",{});var Z4o=s(bC);MXe=n(Z4o,"STRONG",{});var 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d7n=s(Ype);kEt=r(d7n,"TFXGLMForCausalLM"),d7n.forEach(t),SEt=r(J4o," (XGLM model)"),J4o.forEach(t),xEt=i(Ce),FC=n(Ce,"LI",{});var U4o=s(FC);yXe=n(U4o,"STRONG",{});var m7n=s(yXe);GEt=r(m7n,"xlm"),m7n.forEach(t),BEt=r(U4o," \u2014 "),zpe=n(U4o,"A",{href:!0});var c7n=s(zpe);IEt=r(c7n,"TFXLMWithLMHeadModel"),c7n.forEach(t),NEt=r(U4o," (XLM model)"),U4o.forEach(t),$Et=i(Ce),EC=n(Ce,"LI",{});var X4o=s(EC);CXe=n(X4o,"STRONG",{});var f7n=s(CXe);jEt=r(f7n,"xlm-roberta"),f7n.forEach(t),PEt=r(X4o," \u2014 "),Hpe=n(X4o,"A",{href:!0});var h7n=s(Hpe);ZEt=r(h7n,"TFXLMRobertaForCausalLM"),h7n.forEach(t),VEt=r(X4o," (XLM-RoBERTa model)"),X4o.forEach(t),WEt=i(Ce),yC=n(Ce,"LI",{});var D4o=s(yC);wXe=n(D4o,"STRONG",{});var g7n=s(wXe);JEt=r(g7n,"xlnet"),g7n.forEach(t),UEt=r(D4o," \u2014 "),Kpe=n(D4o,"A",{href:!0});var u7n=s(Kpe);XEt=r(u7n,"TFXLNetLMHeadModel"),u7n.forEach(t),DEt=r(D4o," (XLNet model)"),D4o.forEach(t),Ce.forEach(t),qEt=i(fd),M(CC.$$.fragment,fd),fd.forEach(t),cd.forEach(t),lIo=i(c),xc=n(c,"H3",{class:!0});var ijo=s(xc);wC=n(ijo,"A",{id:!0,class:!0,href:!0});var p7n=s(wC);LXe=n(p7n,"SPAN",{});var _7n=s(LXe);M(NW.$$.fragment,_7n),_7n.forEach(t),p7n.forEach(t),OEt=i(ijo),AXe=n(ijo,"SPAN",{});var b7n=s(AXe);QEt=r(b7n,"FlaxAutoModelForCausalLM"),b7n.forEach(t),ijo.forEach(t),iIo=i(c),Ho=n(c,"DIV",{class:!0});var hd=s(Ho);M($W.$$.fragment,hd),YEt=i(hd),Gc=n(hd,"P",{});var NCe=s(Gc);zEt=r(NCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),e_e=n(NCe,"A",{href:!0});var v7n=s(e_e);HEt=r(v7n,"from_pretrained()"),v7n.forEach(t),KEt=r(NCe," class method or the "),o_e=n(NCe,"A",{href:!0});var M7n=s(o_e);e0t=r(M7n,"from_config()"),M7n.forEach(t),o0t=r(NCe,` class
method.`),NCe.forEach(t),r0t=i(hd),jW=n(hd,"P",{});var djo=s(jW);t0t=r(djo,"This class cannot be instantiated directly using "),RXe=n(djo,"CODE",{});var T7n=s(RXe);a0t=r(T7n,"__init__()"),T7n.forEach(t),n0t=r(djo," (throws an error)."),djo.forEach(t),s0t=i(hd),zt=n(hd,"DIV",{class:!0});var dP=s(zt);M(PW.$$.fragment,dP),l0t=i(dP),kXe=n(dP,"P",{});var F7n=s(kXe);i0t=r(F7n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),F7n.forEach(t),d0t=i(dP),Bc=n(dP,"P",{});var $Ce=s(Bc);m0t=r($Ce,`Note:
Loading a model from its configuration file does `),SXe=n($Ce,"STRONG",{});var E7n=s(SXe);c0t=r(E7n,"not"),E7n.forEach(t),f0t=r($Ce,` load the model weights. It only affects the
model\u2019s configuration. Use `),r_e=n($Ce,"A",{href:!0});var y7n=s(r_e);h0t=r(y7n,"from_pretrained()"),y7n.forEach(t),g0t=r($Ce," to load the model weights."),$Ce.forEach(t),u0t=i(dP),M(LC.$$.fragment,dP),dP.forEach(t),p0t=i(hd),it=n(hd,"DIV",{class:!0});var gd=s(it);M(ZW.$$.fragment,gd),_0t=i(gd),xXe=n(gd,"P",{});var C7n=s(xXe);b0t=r(C7n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),C7n.forEach(t),v0t=i(gd),On=n(gd,"P",{});var mP=s(On);M0t=r(mP,"The model class to instantiate is selected based on the "),GXe=n(mP,"CODE",{});var w7n=s(GXe);T0t=r(w7n,"model_type"),w7n.forEach(t),F0t=r(mP,` property of the config object (either
passed as an argument or loaded from `),BXe=n(mP,"CODE",{});var L7n=s(BXe);E0t=r(L7n,"pretrained_model_name_or_path"),L7n.forEach(t),y0t=r(mP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),IXe=n(mP,"CODE",{});var A7n=s(IXe);C0t=r(A7n,"pretrained_model_name_or_path"),A7n.forEach(t),w0t=r(mP,":"),mP.forEach(t),L0t=i(gd),Le=n(gd,"UL",{});var Re=s(Le);AC=n(Re,"LI",{});var q4o=s(AC);NXe=n(q4o,"STRONG",{});var R7n=s(NXe);A0t=r(R7n,"bart"),R7n.forEach(t),R0t=r(q4o," \u2014 "),t_e=n(q4o,"A",{href:!0});var k7n=s(t_e);k0t=r(k7n,"FlaxBartForCausalLM"),k7n.forEach(t),S0t=r(q4o," (BART model)"),q4o.forEach(t),x0t=i(Re),RC=n(Re,"LI",{});var O4o=s(RC);$Xe=n(O4o,"STRONG",{});var S7n=s($Xe);G0t=r(S7n,"bert"),S7n.forEach(t),B0t=r(O4o," \u2014 "),a_e=n(O4o,"A",{href:!0});var x7n=s(a_e);I0t=r(x7n,"FlaxBertForCausalLM"),x7n.forEach(t),N0t=r(O4o," (BERT model)"),O4o.forEach(t),$0t=i(Re),kC=n(Re,"LI",{});var Q4o=s(kC);jXe=n(Q4o,"STRONG",{});var G7n=s(jXe);j0t=r(G7n,"big_bird"),G7n.forEach(t),P0t=r(Q4o," \u2014 "),n_e=n(Q4o,"A",{href:!0});var B7n=s(n_e);Z0t=r(B7n,"FlaxBigBirdForCausalLM"),B7n.forEach(t),V0t=r(Q4o," (BigBird model)"),Q4o.forEach(t),W0t=i(Re),SC=n(Re,"LI",{});var Y4o=s(SC);PXe=n(Y4o,"STRONG",{});var I7n=s(PXe);J0t=r(I7n,"bloom"),I7n.forEach(t),U0t=r(Y4o," \u2014 "),s_e=n(Y4o,"A",{href:!0});var N7n=s(s_e);X0t=r(N7n,"FlaxBloomForCausalLM"),N7n.forEach(t),D0t=r(Y4o," (BLOOM model)"),Y4o.forEach(t),q0t=i(Re),xC=n(Re,"LI",{});var z4o=s(xC);ZXe=n(z4o,"STRONG",{});var $7n=s(ZXe);O0t=r($7n,"electra"),$7n.forEach(t),Q0t=r(z4o," \u2014 "),l_e=n(z4o,"A",{href:!0});var j7n=s(l_e);Y0t=r(j7n,"FlaxElectraForCausalLM"),j7n.forEach(t),z0t=r(z4o," (ELECTRA model)"),z4o.forEach(t),H0t=i(Re),GC=n(Re,"LI",{});var H4o=s(GC);VXe=n(H4o,"STRONG",{});var P7n=s(VXe);K0t=r(P7n,"gpt-sw3"),P7n.forEach(t),eyt=r(H4o," \u2014 "),i_e=n(H4o,"A",{href:!0});var Z7n=s(i_e);oyt=r(Z7n,"FlaxGPT2LMHeadModel"),Z7n.forEach(t),ryt=r(H4o," (GPT-Sw3 model)"),H4o.forEach(t),tyt=i(Re),BC=n(Re,"LI",{});var K4o=s(BC);WXe=n(K4o,"STRONG",{});var V7n=s(WXe);ayt=r(V7n,"gpt2"),V7n.forEach(t),nyt=r(K4o," \u2014 "),d_e=n(K4o,"A",{href:!0});var W7n=s(d_e);syt=r(W7n,"FlaxGPT2LMHeadModel"),W7n.forEach(t),lyt=r(K4o," (OpenAI GPT-2 model)"),K4o.forEach(t),iyt=i(Re),IC=n(Re,"LI",{});var eCo=s(IC);JXe=n(eCo,"STRONG",{});var J7n=s(JXe);dyt=r(J7n,"gpt_neo"),J7n.forEach(t),myt=r(eCo," \u2014 "),m_e=n(eCo,"A",{href:!0});var U7n=s(m_e);cyt=r(U7n,"FlaxGPTNeoForCausalLM"),U7n.forEach(t),fyt=r(eCo," (GPT Neo model)"),eCo.forEach(t),hyt=i(Re),NC=n(Re,"LI",{});var oCo=s(NC);UXe=n(oCo,"STRONG",{});var X7n=s(UXe);gyt=r(X7n,"gptj"),X7n.forEach(t),uyt=r(oCo," \u2014 "),c_e=n(oCo,"A",{href:!0});var D7n=s(c_e);pyt=r(D7n,"FlaxGPTJForCausalLM"),D7n.forEach(t),_yt=r(oCo," (GPT-J model)"),oCo.forEach(t),byt=i(Re),$C=n(Re,"LI",{});var rCo=s($C);XXe=n(rCo,"STRONG",{});var q7n=s(XXe);vyt=r(q7n,"opt"),q7n.forEach(t),Myt=r(rCo," \u2014 "),f_e=n(rCo,"A",{href:!0});var O7n=s(f_e);Tyt=r(O7n,"FlaxOPTForCausalLM"),O7n.forEach(t),Fyt=r(rCo," (OPT model)"),rCo.forEach(t),Eyt=i(Re),jC=n(Re,"LI",{});var tCo=s(jC);DXe=n(tCo,"STRONG",{});var Q7n=s(DXe);yyt=r(Q7n,"roberta"),Q7n.forEach(t),Cyt=r(tCo," \u2014 "),h_e=n(tCo,"A",{href:!0});var Y7n=s(h_e);wyt=r(Y7n,"FlaxRobertaForCausalLM"),Y7n.forEach(t),Lyt=r(tCo," (RoBERTa model)"),tCo.forEach(t),Ayt=i(Re),PC=n(Re,"LI",{});var aCo=s(PC);qXe=n(aCo,"STRONG",{});var z7n=s(qXe);Ryt=r(z7n,"roberta-prelayernorm"),z7n.forEach(t),kyt=r(aCo," \u2014 "),g_e=n(aCo,"A",{href:!0});var H7n=s(g_e);Syt=r(H7n,"FlaxRobertaPreLayerNormForCausalLM"),H7n.forEach(t),xyt=r(aCo," (RoBERTa-PreLayerNorm model)"),aCo.forEach(t),Gyt=i(Re),ZC=n(Re,"LI",{});var nCo=s(ZC);OXe=n(nCo,"STRONG",{});var K7n=s(OXe);Byt=r(K7n,"xglm"),K7n.forEach(t),Iyt=r(nCo," \u2014 "),u_e=n(nCo,"A",{href:!0});var eLn=s(u_e);Nyt=r(eLn,"FlaxXGLMForCausalLM"),eLn.forEach(t),$yt=r(nCo," (XGLM model)"),nCo.forEach(t),jyt=i(Re),VC=n(Re,"LI",{});var sCo=s(VC);QXe=n(sCo,"STRONG",{});var oLn=s(QXe);Pyt=r(oLn,"xlm-roberta"),oLn.forEach(t),Zyt=r(sCo," \u2014 "),p_e=n(sCo,"A",{href:!0});var rLn=s(p_e);Vyt=r(rLn,"FlaxXLMRobertaForCausalLM"),rLn.forEach(t),Wyt=r(sCo," (XLM-RoBERTa model)"),sCo.forEach(t),Re.forEach(t),Jyt=i(gd),M(WC.$$.fragment,gd),gd.forEach(t),hd.forEach(t),dIo=i(c),Ic=n(c,"H3",{class:!0});var mjo=s(Ic);JC=n(mjo,"A",{id:!0,class:!0,href:!0});var tLn=s(JC);YXe=n(tLn,"SPAN",{});var aLn=s(YXe);M(VW.$$.fragment,aLn),aLn.forEach(t),tLn.forEach(t),Uyt=i(mjo),zXe=n(mjo,"SPAN",{});var nLn=s(zXe);Xyt=r(nLn,"AutoModelForMaskedLM"),nLn.forEach(t),mjo.forEach(t),mIo=i(c),Ko=n(c,"DIV",{class:!0});var ud=s(Ko);M(WW.$$.fragment,ud),Dyt=i(ud),Nc=n(ud,"P",{});var jCe=s(Nc);qyt=r(jCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),__e=n(jCe,"A",{href:!0});var sLn=s(__e);Oyt=r(sLn,"from_pretrained()"),sLn.forEach(t),Qyt=r(jCe," class method or the "),b_e=n(jCe,"A",{href:!0});var lLn=s(b_e);Yyt=r(lLn,"from_config()"),lLn.forEach(t),zyt=r(jCe,` class
method.`),jCe.forEach(t),Hyt=i(ud),JW=n(ud,"P",{});var cjo=s(JW);Kyt=r(cjo,"This class cannot be instantiated directly using "),HXe=n(cjo,"CODE",{});var iLn=s(HXe);e5t=r(iLn,"__init__()"),iLn.forEach(t),o5t=r(cjo," (throws an error)."),cjo.forEach(t),r5t=i(ud),Ht=n(ud,"DIV",{class:!0});var cP=s(Ht);M(UW.$$.fragment,cP),t5t=i(cP),KXe=n(cP,"P",{});var dLn=s(KXe);a5t=r(dLn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),dLn.forEach(t),n5t=i(cP),$c=n(cP,"P",{});var PCe=s($c);s5t=r(PCe,`Note:
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model\u2019s configuration. Use `),v_e=n(PCe,"A",{href:!0});var cLn=s(v_e);d5t=r(cLn,"from_pretrained()"),cLn.forEach(t),m5t=r(PCe," to load the model weights."),PCe.forEach(t),c5t=i(cP),M(UC.$$.fragment,cP),cP.forEach(t),f5t=i(ud),mo=n(ud,"DIV",{class:!0});var an=s(mo);M(XW.$$.fragment,an),h5t=i(an),oDe=n(an,"P",{});var fLn=s(oDe);g5t=r(fLn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),fLn.forEach(t),u5t=i(an),Qn=n(an,"P",{});var fP=s(Qn);p5t=r(fP,"The model class to instantiate is selected based on the "),rDe=n(fP,"CODE",{});var hLn=s(rDe);_5t=r(hLn,"model_type"),hLn.forEach(t),b5t=r(fP,` property of the config object (either
passed as an argument or loaded from `),tDe=n(fP,"CODE",{});var gLn=s(tDe);v5t=r(gLn,"pretrained_model_name_or_path"),gLn.forEach(t),M5t=r(fP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aDe=n(fP,"CODE",{});var uLn=s(aDe);T5t=r(uLn,"pretrained_model_name_or_path"),uLn.forEach(t),F5t=r(fP,":"),fP.forEach(t),E5t=i(an),K=n(an,"UL",{});var ee=s(K);XC=n(ee,"LI",{});var lCo=s(XC);nDe=n(lCo,"STRONG",{});var pLn=s(nDe);y5t=r(pLn,"albert"),pLn.forEach(t),C5t=r(lCo," \u2014 "),M_e=n(lCo,"A",{href:!0});var _Ln=s(M_e);w5t=r(_Ln,"AlbertForMaskedLM"),_Ln.forEach(t),L5t=r(lCo," (ALBERT model)"),lCo.forEach(t),A5t=i(ee),DC=n(ee,"LI",{});var iCo=s(DC);sDe=n(iCo,"STRONG",{});var bLn=s(sDe);R5t=r(bLn,"bart"),bLn.forEach(t),k5t=r(iCo," \u2014 "),T_e=n(iCo,"A",{href:!0});var vLn=s(T_e);S5t=r(vLn,"BartForConditionalGeneration"),vLn.forEach(t),x5t=r(iCo," (BART model)"),iCo.forEach(t),G5t=i(ee),qC=n(ee,"LI",{});var dCo=s(qC);lDe=n(dCo,"STRONG",{});var MLn=s(lDe);B5t=r(MLn,"bert"),MLn.forEach(t),I5t=r(dCo," \u2014 "),F_e=n(dCo,"A",{href:!0});var TLn=s(F_e);N5t=r(TLn,"BertForMaskedLM"),TLn.forEach(t),$5t=r(dCo," (BERT model)"),dCo.forEach(t),j5t=i(ee),OC=n(ee,"LI",{});var mCo=s(OC);iDe=n(mCo,"STRONG",{});var FLn=s(iDe);P5t=r(FLn,"big_bird"),FLn.forEach(t),Z5t=r(mCo," \u2014 "),E_e=n(mCo,"A",{href:!0});var ELn=s(E_e);V5t=r(ELn,"BigBirdForMaskedLM"),ELn.forEach(t),W5t=r(mCo," (BigBird model)"),mCo.forEach(t),J5t=i(ee),QC=n(ee,"LI",{});var cCo=s(QC);dDe=n(cCo,"STRONG",{});var yLn=s(dDe);U5t=r(yLn,"camembert"),yLn.forEach(t),X5t=r(cCo," \u2014 "),y_e=n(cCo,"A",{href:!0});var CLn=s(y_e);D5t=r(CLn,"CamembertForMaskedLM"),CLn.forEach(t),q5t=r(cCo," (CamemBERT model)"),cCo.forEach(t),O5t=i(ee),YC=n(ee,"LI",{});var fCo=s(YC);mDe=n(fCo,"STRONG",{});var wLn=s(mDe);Q5t=r(wLn,"convbert"),wLn.forEach(t),Y5t=r(fCo," \u2014 "),C_e=n(fCo,"A",{href:!0});var LLn=s(C_e);z5t=r(LLn,"ConvBertForMaskedLM"),LLn.forEach(t),H5t=r(fCo," (ConvBERT model)"),fCo.forEach(t),K5t=i(ee),zC=n(ee,"LI",{});var hCo=s(zC);cDe=n(hCo,"STRONG",{});var ALn=s(cDe);e3t=r(ALn,"data2vec-text"),ALn.forEach(t),o3t=r(hCo," \u2014 "),w_e=n(hCo,"A",{href:!0});var RLn=s(w_e);r3t=r(RLn,"Data2VecTextForMaskedLM"),RLn.forEach(t),t3t=r(hCo," (Data2VecText model)"),hCo.forEach(t),a3t=i(ee),HC=n(ee,"LI",{});var gCo=s(HC);fDe=n(gCo,"STRONG",{});var kLn=s(fDe);n3t=r(kLn,"deberta"),kLn.forEach(t),s3t=r(gCo," \u2014 "),L_e=n(gCo,"A",{href:!0});var SLn=s(L_e);l3t=r(SLn,"DebertaForMaskedLM"),SLn.forEach(t),i3t=r(gCo," (DeBERTa model)"),gCo.forEach(t),d3t=i(ee),KC=n(ee,"LI",{});var uCo=s(KC);hDe=n(uCo,"STRONG",{});var xLn=s(hDe);m3t=r(xLn,"deberta-v2"),xLn.forEach(t),c3t=r(uCo," \u2014 "),A_e=n(uCo,"A",{href:!0});var GLn=s(A_e);f3t=r(GLn,"DebertaV2ForMaskedLM"),GLn.forEach(t),h3t=r(uCo," (DeBERTa-v2 model)"),uCo.forEach(t),g3t=i(ee),ew=n(ee,"LI",{});var pCo=s(ew);gDe=n(pCo,"STRONG",{});var BLn=s(gDe);u3t=r(BLn,"distilbert"),BLn.forEach(t),p3t=r(pCo," \u2014 "),R_e=n(pCo,"A",{href:!0});var ILn=s(R_e);_3t=r(ILn,"DistilBertForMaskedLM"),ILn.forEach(t),b3t=r(pCo," (DistilBERT model)"),pCo.forEach(t),v3t=i(ee),ow=n(ee,"LI",{});var 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DAn=s(ODe);bCt=r(DAn,"xlm-roberta-xl"),DAn.forEach(t),vCt=r(QCo," \u2014 "),l1e=n(QCo,"A",{href:!0});var qAn=s(l1e);MCt=r(qAn,"XLMRobertaXLForMaskedLM"),qAn.forEach(t),TCt=r(QCo," (XLM-RoBERTa-XL model)"),QCo.forEach(t),FCt=i(ee),Bw=n(ee,"LI",{});var YCo=s(Bw);QDe=n(YCo,"STRONG",{});var OAn=s(QDe);ECt=r(OAn,"xmod"),OAn.forEach(t),yCt=r(YCo," \u2014 "),i1e=n(YCo,"A",{href:!0});var QAn=s(i1e);CCt=r(QAn,"XmodForMaskedLM"),QAn.forEach(t),wCt=r(YCo," (X-MOD model)"),YCo.forEach(t),LCt=i(ee),Iw=n(ee,"LI",{});var zCo=s(Iw);YDe=n(zCo,"STRONG",{});var YAn=s(YDe);ACt=r(YAn,"yoso"),YAn.forEach(t),RCt=r(zCo," \u2014 "),d1e=n(zCo,"A",{href:!0});var zAn=s(d1e);kCt=r(zAn,"YosoForMaskedLM"),zAn.forEach(t),SCt=r(zCo," (YOSO model)"),zCo.forEach(t),ee.forEach(t),xCt=i(an),Nw=n(an,"P",{});var HCo=s(Nw);GCt=r(HCo,"The model is set in evaluation mode by default using "),zDe=n(HCo,"CODE",{});var HAn=s(zDe);BCt=r(HAn,"model.eval()"),HAn.forEach(t),ICt=r(HCo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),HDe=n(HCo,"CODE",{});var KAn=s(HDe);NCt=r(KAn,"model.train()"),KAn.forEach(t),HCo.forEach(t),$Ct=i(an),M($w.$$.fragment,an),an.forEach(t),ud.forEach(t),cIo=i(c),jc=n(c,"H3",{class:!0});var fjo=s(jc);jw=n(fjo,"A",{id:!0,class:!0,href:!0});var eRn=s(jw);KDe=n(eRn,"SPAN",{});var oRn=s(KDe);M(DW.$$.fragment,oRn),oRn.forEach(t),eRn.forEach(t),jCt=i(fjo),eqe=n(fjo,"SPAN",{});var rRn=s(eqe);PCt=r(rRn,"TFAutoModelForMaskedLM"),rRn.forEach(t),fjo.forEach(t),fIo=i(c),er=n(c,"DIV",{class:!0});var pd=s(er);M(qW.$$.fragment,pd),ZCt=i(pd),Pc=n(pd,"P",{});var ZCe=s(Pc);VCt=r(ZCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),m1e=n(ZCe,"A",{href:!0});var tRn=s(m1e);WCt=r(tRn,"from_pretrained()"),tRn.forEach(t),JCt=r(ZCe," class method or the "),c1e=n(ZCe,"A",{href:!0});var aRn=s(c1e);UCt=r(aRn,"from_config()"),aRn.forEach(t),XCt=r(ZCe,` class
method.`),ZCe.forEach(t),DCt=i(pd),OW=n(pd,"P",{});var hjo=s(OW);qCt=r(hjo,"This class cannot be instantiated directly using "),oqe=n(hjo,"CODE",{});var nRn=s(oqe);OCt=r(nRn,"__init__()"),nRn.forEach(t),QCt=r(hjo," (throws an error)."),hjo.forEach(t),YCt=i(pd),Kt=n(pd,"DIV",{class:!0});var hP=s(Kt);M(QW.$$.fragment,hP),zCt=i(hP),rqe=n(hP,"P",{});var sRn=s(rqe);HCt=r(sRn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),sRn.forEach(t),KCt=i(hP),Zc=n(hP,"P",{});var VCe=s(Zc);ewt=r(VCe,`Note:
Loading a model from its configuration file does `),tqe=n(VCe,"STRONG",{});var lRn=s(tqe);owt=r(lRn,"not"),lRn.forEach(t),rwt=r(VCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),f1e=n(VCe,"A",{href:!0});var iRn=s(f1e);twt=r(iRn,"from_pretrained()"),iRn.forEach(t),awt=r(VCe," to load the model weights."),VCe.forEach(t),nwt=i(hP),M(Pw.$$.fragment,hP),hP.forEach(t),swt=i(pd),dt=n(pd,"DIV",{class:!0});var _d=s(dt);M(YW.$$.fragment,_d),lwt=i(_d),aqe=n(_d,"P",{});var dRn=s(aqe);iwt=r(dRn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),dRn.forEach(t),dwt=i(_d),Yn=n(_d,"P",{});var gP=s(Yn);mwt=r(gP,"The model class to instantiate is selected based on the "),nqe=n(gP,"CODE",{});var mRn=s(nqe);cwt=r(mRn,"model_type"),mRn.forEach(t),fwt=r(gP,` property of the config object (either
passed as an argument or loaded from `),sqe=n(gP,"CODE",{});var cRn=s(sqe);hwt=r(cRn,"pretrained_model_name_or_path"),cRn.forEach(t),gwt=r(gP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lqe=n(gP,"CODE",{});var fRn=s(lqe);uwt=r(fRn,"pretrained_model_name_or_path"),fRn.forEach(t),pwt=r(gP,":"),gP.forEach(t),_wt=i(_d),pe=n(_d,"UL",{});var Me=s(pe);Zw=n(Me,"LI",{});var KCo=s(Zw);iqe=n(KCo,"STRONG",{});var hRn=s(iqe);bwt=r(hRn,"albert"),hRn.forEach(t),vwt=r(KCo," \u2014 "),h1e=n(KCo,"A",{href:!0});var gRn=s(h1e);Mwt=r(gRn,"TFAlbertForMaskedLM"),gRn.forEach(t),Twt=r(KCo," (ALBERT model)"),KCo.forEach(t),Fwt=i(Me),Vw=n(Me,"LI",{});var ewo=s(Vw);dqe=n(ewo,"STRONG",{});var uRn=s(dqe);Ewt=r(uRn,"bert"),uRn.forEach(t),ywt=r(ewo," \u2014 "),g1e=n(ewo,"A",{href:!0});var pRn=s(g1e);Cwt=r(pRn,"TFBertForMaskedLM"),pRn.forEach(t),wwt=r(ewo," (BERT model)"),ewo.forEach(t),Lwt=i(Me),Ww=n(Me,"LI",{});var owo=s(Ww);mqe=n(owo,"STRONG",{});var _Rn=s(mqe);Awt=r(_Rn,"camembert"),_Rn.forEach(t),Rwt=r(owo," \u2014 "),u1e=n(owo,"A",{href:!0});var bRn=s(u1e);kwt=r(bRn,"TFCamembertForMaskedLM"),bRn.forEach(t),Swt=r(owo," (CamemBERT 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dwo=s(Yw);bqe=n(dwo,"STRONG",{});var GRn=s(bqe);g6t=r(GRn,"funnel"),GRn.forEach(t),u6t=r(dwo," \u2014 "),E1e=n(dwo,"A",{href:!0});var BRn=s(E1e);p6t=r(BRn,"TFFunnelForMaskedLM"),BRn.forEach(t),_6t=r(dwo," (Funnel Transformer model)"),dwo.forEach(t),b6t=i(Me),zw=n(Me,"LI",{});var mwo=s(zw);vqe=n(mwo,"STRONG",{});var IRn=s(vqe);v6t=r(IRn,"layoutlm"),IRn.forEach(t),M6t=r(mwo," \u2014 "),y1e=n(mwo,"A",{href:!0});var NRn=s(y1e);T6t=r(NRn,"TFLayoutLMForMaskedLM"),NRn.forEach(t),F6t=r(mwo," (LayoutLM model)"),mwo.forEach(t),E6t=i(Me),Hw=n(Me,"LI",{});var cwo=s(Hw);Mqe=n(cwo,"STRONG",{});var $Rn=s(Mqe);y6t=r($Rn,"longformer"),$Rn.forEach(t),C6t=r(cwo," \u2014 "),C1e=n(cwo,"A",{href:!0});var jRn=s(C1e);w6t=r(jRn,"TFLongformerForMaskedLM"),jRn.forEach(t),L6t=r(cwo," (Longformer model)"),cwo.forEach(t),A6t=i(Me),Kw=n(Me,"LI",{});var fwo=s(Kw);Tqe=n(fwo,"STRONG",{});var PRn=s(Tqe);R6t=r(PRn,"mobilebert"),PRn.forEach(t),k6t=r(fwo," \u2014 "),w1e=n(fwo,"A",{href:!0});var ZRn=s(w1e);S6t=r(ZRn,"TFMobileBertForMaskedLM"),ZRn.forEach(t),x6t=r(fwo," (MobileBERT model)"),fwo.forEach(t),G6t=i(Me),e6=n(Me,"LI",{});var hwo=s(e6);Fqe=n(hwo,"STRONG",{});var VRn=s(Fqe);B6t=r(VRn,"mpnet"),VRn.forEach(t),I6t=r(hwo," \u2014 "),L1e=n(hwo,"A",{href:!0});var WRn=s(L1e);N6t=r(WRn,"TFMPNetForMaskedLM"),WRn.forEach(t),$6t=r(hwo," (MPNet model)"),hwo.forEach(t),j6t=i(Me),o6=n(Me,"LI",{});var gwo=s(o6);Eqe=n(gwo,"STRONG",{});var JRn=s(Eqe);P6t=r(JRn,"rembert"),JRn.forEach(t),Z6t=r(gwo," \u2014 "),A1e=n(gwo,"A",{href:!0});var URn=s(A1e);V6t=r(URn,"TFRemBertForMaskedLM"),URn.forEach(t),W6t=r(gwo," (RemBERT model)"),gwo.forEach(t),J6t=i(Me),r6=n(Me,"LI",{});var uwo=s(r6);yqe=n(uwo,"STRONG",{});var XRn=s(yqe);U6t=r(XRn,"roberta"),XRn.forEach(t),X6t=r(uwo," \u2014 "),R1e=n(uwo,"A",{href:!0});var DRn=s(R1e);D6t=r(DRn,"TFRobertaForMaskedLM"),DRn.forEach(t),q6t=r(uwo," (RoBERTa model)"),uwo.forEach(t),O6t=i(Me),t6=n(Me,"LI",{});var pwo=s(t6);Cqe=n(pwo,"STRONG",{});var qRn=s(Cqe);Q6t=r(qRn,"roberta-prelayernorm"),qRn.forEach(t),Y6t=r(pwo," \u2014 "),k1e=n(pwo,"A",{href:!0});var ORn=s(k1e);z6t=r(ORn,"TFRobertaPreLayerNormForMaskedLM"),ORn.forEach(t),H6t=r(pwo," (RoBERTa-PreLayerNorm model)"),pwo.forEach(t),K6t=i(Me),a6=n(Me,"LI",{});var _wo=s(a6);wqe=n(_wo,"STRONG",{});var QRn=s(wqe);e8t=r(QRn,"roformer"),QRn.forEach(t),o8t=r(_wo," \u2014 "),S1e=n(_wo,"A",{href:!0});var YRn=s(S1e);r8t=r(YRn,"TFRoFormerForMaskedLM"),YRn.forEach(t),t8t=r(_wo," (RoFormer model)"),_wo.forEach(t),a8t=i(Me),n6=n(Me,"LI",{});var bwo=s(n6);Lqe=n(bwo,"STRONG",{});var zRn=s(Lqe);n8t=r(zRn,"tapas"),zRn.forEach(t),s8t=r(bwo," \u2014 "),x1e=n(bwo,"A",{href:!0});var HRn=s(x1e);l8t=r(HRn,"TFTapasForMaskedLM"),HRn.forEach(t),i8t=r(bwo," (TAPAS model)"),bwo.forEach(t),d8t=i(Me),s6=n(Me,"LI",{});var vwo=s(s6);Aqe=n(vwo,"STRONG",{});var KRn=s(Aqe);m8t=r(KRn,"xlm"),KRn.forEach(t),c8t=r(vwo," \u2014 "),G1e=n(vwo,"A",{href:!0});var ekn=s(G1e);f8t=r(ekn,"TFXLMWithLMHeadModel"),ekn.forEach(t),h8t=r(vwo," (XLM model)"),vwo.forEach(t),g8t=i(Me),l6=n(Me,"LI",{});var Mwo=s(l6);Rqe=n(Mwo,"STRONG",{});var okn=s(Rqe);u8t=r(okn,"xlm-roberta"),okn.forEach(t),p8t=r(Mwo," \u2014 "),B1e=n(Mwo,"A",{href:!0});var rkn=s(B1e);_8t=r(rkn,"TFXLMRobertaForMaskedLM"),rkn.forEach(t),b8t=r(Mwo," (XLM-RoBERTa model)"),Mwo.forEach(t),Me.forEach(t),v8t=i(_d),M(i6.$$.fragment,_d),_d.forEach(t),pd.forEach(t),hIo=i(c),Vc=n(c,"H3",{class:!0});var gjo=s(Vc);d6=n(gjo,"A",{id:!0,class:!0,href:!0});var tkn=s(d6);kqe=n(tkn,"SPAN",{});var akn=s(kqe);M(zW.$$.fragment,akn),akn.forEach(t),tkn.forEach(t),M8t=i(gjo),Sqe=n(gjo,"SPAN",{});var nkn=s(Sqe);T8t=r(nkn,"FlaxAutoModelForMaskedLM"),nkn.forEach(t),gjo.forEach(t),gIo=i(c),or=n(c,"DIV",{class:!0});var bd=s(or);M(HW.$$.fragment,bd),F8t=i(bd),Wc=n(bd,"P",{});var WCe=s(Wc);E8t=r(WCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language 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with the `),I1e=n(WCe,"A",{href:!0});var skn=s(I1e);y8t=r(skn,"from_pretrained()"),skn.forEach(t),C8t=r(WCe," class method or the "),N1e=n(WCe,"A",{href:!0});var lkn=s(N1e);w8t=r(lkn,"from_config()"),lkn.forEach(t),L8t=r(WCe,` class
method.`),WCe.forEach(t),A8t=i(bd),KW=n(bd,"P",{});var ujo=s(KW);R8t=r(ujo,"This class cannot be instantiated directly using "),xqe=n(ujo,"CODE",{});var ikn=s(xqe);k8t=r(ikn,"__init__()"),ikn.forEach(t),S8t=r(ujo," (throws an error)."),ujo.forEach(t),x8t=i(bd),ea=n(bd,"DIV",{class:!0});var uP=s(ea);M(eJ.$$.fragment,uP),G8t=i(uP),Gqe=n(uP,"P",{});var dkn=s(Gqe);B8t=r(dkn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),dkn.forEach(t),I8t=i(uP),Jc=n(uP,"P",{});var JCe=s(Jc);N8t=r(JCe,`Note:
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model\u2019s configuration. Use `),$1e=n(JCe,"A",{href:!0});var ckn=s($1e);P8t=r(ckn,"from_pretrained()"),ckn.forEach(t),Z8t=r(JCe," to load the model weights."),JCe.forEach(t),V8t=i(uP),M(m6.$$.fragment,uP),uP.forEach(t),W8t=i(bd),mt=n(bd,"DIV",{class:!0});var vd=s(mt);M(oJ.$$.fragment,vd),J8t=i(vd),Iqe=n(vd,"P",{});var fkn=s(Iqe);U8t=r(fkn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),fkn.forEach(t),X8t=i(vd),zn=n(vd,"P",{});var pP=s(zn);D8t=r(pP,"The model class to instantiate is selected based on the "),Nqe=n(pP,"CODE",{});var hkn=s(Nqe);q8t=r(hkn,"model_type"),hkn.forEach(t),O8t=r(pP,` property of the config object (either
passed as an argument or loaded from `),$qe=n(pP,"CODE",{});var gkn=s($qe);Q8t=r(gkn,"pretrained_model_name_or_path"),gkn.forEach(t),Y8t=r(pP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jqe=n(pP,"CODE",{});var ukn=s(jqe);z8t=r(ukn,"pretrained_model_name_or_path"),ukn.forEach(t),H8t=r(pP,":"),pP.forEach(t),K8t=i(vd),ke=n(vd,"UL",{});var Pe=s(ke);c6=n(Pe,"LI",{});var Two=s(c6);Pqe=n(Two,"STRONG",{});var pkn=s(Pqe);e7t=r(pkn,"albert"),pkn.forEach(t),o7t=r(Two," \u2014 "),j1e=n(Two,"A",{href:!0});var _kn=s(j1e);r7t=r(_kn,"FlaxAlbertForMaskedLM"),_kn.forEach(t),t7t=r(Two," (ALBERT model)"),Two.forEach(t),a7t=i(Pe),f6=n(Pe,"LI",{});var Fwo=s(f6);Zqe=n(Fwo,"STRONG",{});var bkn=s(Zqe);n7t=r(bkn,"bart"),bkn.forEach(t),s7t=r(Fwo," \u2014 "),P1e=n(Fwo,"A",{href:!0});var vkn=s(P1e);l7t=r(vkn,"FlaxBartForConditionalGeneration"),vkn.forEach(t),i7t=r(Fwo," (BART model)"),Fwo.forEach(t),d7t=i(Pe),h6=n(Pe,"LI",{});var Ewo=s(h6);Vqe=n(Ewo,"STRONG",{});var Mkn=s(Vqe);m7t=r(Mkn,"bert"),Mkn.forEach(t),c7t=r(Ewo," \u2014 "),Z1e=n(Ewo,"A",{href:!0});var Tkn=s(Z1e);f7t=r(Tkn,"FlaxBertForMaskedLM"),Tkn.forEach(t),h7t=r(Ewo," (BERT model)"),Ewo.forEach(t),g7t=i(Pe),g6=n(Pe,"LI",{});var ywo=s(g6);Wqe=n(ywo,"STRONG",{});var Fkn=s(Wqe);u7t=r(Fkn,"big_bird"),Fkn.forEach(t),p7t=r(ywo," \u2014 "),V1e=n(ywo,"A",{href:!0});var Ekn=s(V1e);_7t=r(Ekn,"FlaxBigBirdForMaskedLM"),Ekn.forEach(t),b7t=r(ywo," (BigBird model)"),ywo.forEach(t),v7t=i(Pe),u6=n(Pe,"LI",{});var Cwo=s(u6);Jqe=n(Cwo,"STRONG",{});var ykn=s(Jqe);M7t=r(ykn,"distilbert"),ykn.forEach(t),T7t=r(Cwo," \u2014 "),W1e=n(Cwo,"A",{href:!0});var Ckn=s(W1e);F7t=r(Ckn,"FlaxDistilBertForMaskedLM"),Ckn.forEach(t),E7t=r(Cwo," (DistilBERT model)"),Cwo.forEach(t),y7t=i(Pe),p6=n(Pe,"LI",{});var wwo=s(p6);Uqe=n(wwo,"STRONG",{});var wkn=s(Uqe);C7t=r(wkn,"electra"),wkn.forEach(t),w7t=r(wwo," \u2014 "),J1e=n(wwo,"A",{href:!0});var Lkn=s(J1e);L7t=r(Lkn,"FlaxElectraForMaskedLM"),Lkn.forEach(t),A7t=r(wwo," (ELECTRA model)"),wwo.forEach(t),R7t=i(Pe),_6=n(Pe,"LI",{});var Lwo=s(_6);Xqe=n(Lwo,"STRONG",{});var Akn=s(Xqe);k7t=r(Akn,"mbart"),Akn.forEach(t),S7t=r(Lwo," \u2014 "),U1e=n(Lwo,"A",{href:!0});var Rkn=s(U1e);x7t=r(Rkn,"FlaxMBartForConditionalGeneration"),Rkn.forEach(t),G7t=r(Lwo," (mBART model)"),Lwo.forEach(t),B7t=i(Pe),b6=n(Pe,"LI",{});var Awo=s(b6);Dqe=n(Awo,"STRONG",{});var kkn=s(Dqe);I7t=r(kkn,"roberta"),kkn.forEach(t),N7t=r(Awo," \u2014 "),X1e=n(Awo,"A",{href:!0});var Skn=s(X1e);$7t=r(Skn,"FlaxRobertaForMaskedLM"),Skn.forEach(t),j7t=r(Awo," (RoBERTa model)"),Awo.forEach(t),P7t=i(Pe),v6=n(Pe,"LI",{});var Rwo=s(v6);qqe=n(Rwo,"STRONG",{});var xkn=s(qqe);Z7t=r(xkn,"roberta-prelayernorm"),xkn.forEach(t),V7t=r(Rwo," \u2014 "),D1e=n(Rwo,"A",{href:!0});var Gkn=s(D1e);W7t=r(Gkn,"FlaxRobertaPreLayerNormForMaskedLM"),Gkn.forEach(t),J7t=r(Rwo," (RoBERTa-PreLayerNorm model)"),Rwo.forEach(t),U7t=i(Pe),M6=n(Pe,"LI",{});var kwo=s(M6);Oqe=n(kwo,"STRONG",{});var Bkn=s(Oqe);X7t=r(Bkn,"roformer"),Bkn.forEach(t),D7t=r(kwo," \u2014 "),q1e=n(kwo,"A",{href:!0});var Ikn=s(q1e);q7t=r(Ikn,"FlaxRoFormerForMaskedLM"),Ikn.forEach(t),O7t=r(kwo," (RoFormer model)"),kwo.forEach(t),Q7t=i(Pe),T6=n(Pe,"LI",{});var Swo=s(T6);Qqe=n(Swo,"STRONG",{});var Nkn=s(Qqe);Y7t=r(Nkn,"xlm-roberta"),Nkn.forEach(t),z7t=r(Swo," \u2014 "),O1e=n(Swo,"A",{href:!0});var $kn=s(O1e);H7t=r($kn,"FlaxXLMRobertaForMaskedLM"),$kn.forEach(t),K7t=r(Swo," (XLM-RoBERTa model)"),Swo.forEach(t),Pe.forEach(t),eLt=i(vd),M(F6.$$.fragment,vd),vd.forEach(t),bd.forEach(t),uIo=i(c),Uc=n(c,"H3",{class:!0});var pjo=s(Uc);E6=n(pjo,"A",{id:!0,class:!0,href:!0});var jkn=s(E6);Yqe=n(jkn,"SPAN",{});var Pkn=s(Yqe);M(rJ.$$.fragment,Pkn),Pkn.forEach(t),jkn.forEach(t),oLt=i(pjo),zqe=n(pjo,"SPAN",{});var Zkn=s(zqe);rLt=r(Zkn,"AutoModelForMaskGeneration"),Zkn.forEach(t),pjo.forEach(t),pIo=i(c),tJ=n(c,"DIV",{class:!0});var Vkn=s(tJ);M(aJ.$$.fragment,Vkn),Vkn.forEach(t),_Io=i(c),Xc=n(c,"H3",{class:!0});var _jo=s(Xc);y6=n(_jo,"A",{id:!0,class:!0,href:!0});var Wkn=s(y6);Hqe=n(Wkn,"SPAN",{});var Jkn=s(Hqe);M(nJ.$$.fragment,Jkn),Jkn.forEach(t),Wkn.forEach(t),tLt=i(_jo),Kqe=n(_jo,"SPAN",{});var Ukn=s(Kqe);aLt=r(Ukn,"TFAutoModelForMaskGeneration"),Ukn.forEach(t),_jo.forEach(t),bIo=i(c),sJ=n(c,"DIV",{class:!0});var Xkn=s(sJ);M(lJ.$$.fragment,Xkn),Xkn.forEach(t),vIo=i(c),Dc=n(c,"H3",{class:!0});var bjo=s(Dc);C6=n(bjo,"A",{id:!0,class:!0,href:!0});var Dkn=s(C6);eOe=n(Dkn,"SPAN",{});var qkn=s(eOe);M(iJ.$$.fragment,qkn),qkn.forEach(t),Dkn.forEach(t),nLt=i(bjo),oOe=n(bjo,"SPAN",{});var Okn=s(oOe);sLt=r(Okn,"AutoModelForSeq2SeqLM"),Okn.forEach(t),bjo.forEach(t),MIo=i(c),rr=n(c,"DIV",{class:!0});var Md=s(rr);M(dJ.$$.fragment,Md),lLt=i(Md),qc=n(Md,"P",{});var UCe=s(qc);iLt=r(UCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Q1e=n(UCe,"A",{href:!0});var Qkn=s(Q1e);dLt=r(Qkn,"from_pretrained()"),Qkn.forEach(t),mLt=r(UCe," class method or the "),Y1e=n(UCe,"A",{href:!0});var Ykn=s(Y1e);cLt=r(Ykn,"from_config()"),Ykn.forEach(t),fLt=r(UCe,` class
method.`),UCe.forEach(t),hLt=i(Md),mJ=n(Md,"P",{});var vjo=s(mJ);gLt=r(vjo,"This class cannot be instantiated directly using "),rOe=n(vjo,"CODE",{});var zkn=s(rOe);uLt=r(zkn,"__init__()"),zkn.forEach(t),pLt=r(vjo," (throws an error)."),vjo.forEach(t),_Lt=i(Md),oa=n(Md,"DIV",{class:!0});var _P=s(oa);M(cJ.$$.fragment,_P),bLt=i(_P),tOe=n(_P,"P",{});var Hkn=s(tOe);vLt=r(Hkn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Hkn.forEach(t),MLt=i(_P),Oc=n(_P,"P",{});var XCe=s(Oc);TLt=r(XCe,`Note:
Loading a model from its configuration file does `),aOe=n(XCe,"STRONG",{});var Kkn=s(aOe);FLt=r(Kkn,"not"),Kkn.forEach(t),ELt=r(XCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),z1e=n(XCe,"A",{href:!0});var eSn=s(z1e);yLt=r(eSn,"from_pretrained()"),eSn.forEach(t),CLt=r(XCe," to load the model weights."),XCe.forEach(t),wLt=i(_P),M(w6.$$.fragment,_P),_P.forEach(t),LLt=i(Md),co=n(Md,"DIV",{class:!0});var nn=s(co);M(fJ.$$.fragment,nn),ALt=i(nn),nOe=n(nn,"P",{});var oSn=s(nOe);RLt=r(oSn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),oSn.forEach(t),kLt=i(nn),Hn=n(nn,"P",{});var bP=s(Hn);SLt=r(bP,"The model class to instantiate is selected based on the "),sOe=n(bP,"CODE",{});var rSn=s(sOe);xLt=r(rSn,"model_type"),rSn.forEach(t),GLt=r(bP,` property of the config object (either
passed as an argument or loaded from `),lOe=n(bP,"CODE",{});var tSn=s(lOe);BLt=r(tSn,"pretrained_model_name_or_path"),tSn.forEach(t),ILt=r(bP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iOe=n(bP,"CODE",{});var aSn=s(iOe);NLt=r(aSn,"pretrained_model_name_or_path"),aSn.forEach(t),$Lt=r(bP,":"),bP.forEach(t),jLt=i(nn),ge=n(nn,"UL",{});var be=s(ge);L6=n(be,"LI",{});var xwo=s(L6);dOe=n(xwo,"STRONG",{});var nSn=s(dOe);PLt=r(nSn,"bart"),nSn.forEach(t),ZLt=r(xwo," \u2014 "),H1e=n(xwo,"A",{href:!0});var sSn=s(H1e);VLt=r(sSn,"BartForConditionalGeneration"),sSn.forEach(t),WLt=r(xwo," (BART model)"),xwo.forEach(t),JLt=i(be),A6=n(be,"LI",{});var Gwo=s(A6);mOe=n(Gwo,"STRONG",{});var lSn=s(mOe);ULt=r(lSn,"bigbird_pegasus"),lSn.forEach(t),XLt=r(Gwo," \u2014 "),K1e=n(Gwo,"A",{href:!0});var iSn=s(K1e);DLt=r(iSn,"BigBirdPegasusForConditionalGeneration"),iSn.forEach(t),qLt=r(Gwo," (BigBird-Pegasus model)"),Gwo.forEach(t),OLt=i(be),R6=n(be,"LI",{});var Bwo=s(R6);cOe=n(Bwo,"STRONG",{});var dSn=s(cOe);QLt=r(dSn,"blenderbot"),dSn.forEach(t),YLt=r(Bwo," \u2014 "),ebe=n(Bwo,"A",{href:!0});var mSn=s(ebe);zLt=r(mSn,"BlenderbotForConditionalGeneration"),mSn.forEach(t),HLt=r(Bwo," (Blenderbot model)"),Bwo.forEach(t),KLt=i(be),k6=n(be,"LI",{});var Iwo=s(k6);fOe=n(Iwo,"STRONG",{});var cSn=s(fOe);eAt=r(cSn,"blenderbot-small"),cSn.forEach(t),oAt=r(Iwo," \u2014 "),obe=n(Iwo,"A",{href:!0});var fSn=s(obe);rAt=r(fSn,"BlenderbotSmallForConditionalGeneration"),fSn.forEach(t),tAt=r(Iwo," (BlenderbotSmall model)"),Iwo.forEach(t),aAt=i(be),S6=n(be,"LI",{});var Nwo=s(S6);hOe=n(Nwo,"STRONG",{});var hSn=s(hOe);nAt=r(hSn,"encoder-decoder"),hSn.forEach(t),sAt=r(Nwo," \u2014 "),rbe=n(Nwo,"A",{href:!0});var gSn=s(rbe);lAt=r(gSn,"EncoderDecoderModel"),gSn.forEach(t),iAt=r(Nwo," (Encoder decoder model)"),Nwo.forEach(t),dAt=i(be),x6=n(be,"LI",{});var $wo=s(x6);gOe=n($wo,"STRONG",{});var uSn=s(gOe);mAt=r(uSn,"fsmt"),uSn.forEach(t),cAt=r($wo," \u2014 "),tbe=n($wo,"A",{href:!0});var pSn=s(tbe);fAt=r(pSn,"FSMTForConditionalGeneration"),pSn.forEach(t),hAt=r($wo," (FairSeq Machine-Translation model)"),$wo.forEach(t),gAt=i(be),G6=n(be,"LI",{});var jwo=s(G6);uOe=n(jwo,"STRONG",{});var _Sn=s(uOe);uAt=r(_Sn,"gptsan-japanese"),_Sn.forEach(t),pAt=r(jwo," \u2014 "),abe=n(jwo,"A",{href:!0});var bSn=s(abe);_At=r(bSn,"GPTSanJapaneseForConditionalGeneration"),bSn.forEach(t),bAt=r(jwo," (GPTSAN-japanese model)"),jwo.forEach(t),vAt=i(be),B6=n(be,"LI",{});var Pwo=s(B6);pOe=n(Pwo,"STRONG",{});var vSn=s(pOe);MAt=r(vSn,"led"),vSn.forEach(t),TAt=r(Pwo," \u2014 "),nbe=n(Pwo,"A",{href:!0});var MSn=s(nbe);FAt=r(MSn,"LEDForConditionalGeneration"),MSn.forEach(t),EAt=r(Pwo," (LED model)"),Pwo.forEach(t),yAt=i(be),I6=n(be,"LI",{});var Zwo=s(I6);_Oe=n(Zwo,"STRONG",{});var TSn=s(_Oe);CAt=r(TSn,"longt5"),TSn.forEach(t),wAt=r(Zwo," \u2014 "),sbe=n(Zwo,"A",{href:!0});var FSn=s(sbe);LAt=r(FSn,"LongT5ForConditionalGeneration"),FSn.forEach(t),AAt=r(Zwo," (LongT5 model)"),Zwo.forEach(t),RAt=i(be),N6=n(be,"LI",{});var Vwo=s(N6);bOe=n(Vwo,"STRONG",{});var ESn=s(bOe);kAt=r(ESn,"m2m_100"),ESn.forEach(t),SAt=r(Vwo," \u2014 "),lbe=n(Vwo,"A",{href:!0});var ySn=s(lbe);xAt=r(ySn,"M2M100ForConditionalGeneration"),ySn.forEach(t),GAt=r(Vwo," (M2M100 model)"),Vwo.forEach(t),BAt=i(be),$6=n(be,"LI",{});var Wwo=s($6);vOe=n(Wwo,"STRONG",{});var CSn=s(vOe);IAt=r(CSn,"marian"),CSn.forEach(t),NAt=r(Wwo," \u2014 "),ibe=n(Wwo,"A",{href:!0});var wSn=s(ibe);$At=r(wSn,"MarianMTModel"),wSn.forEach(t),jAt=r(Wwo," (Marian model)"),Wwo.forEach(t),PAt=i(be),j6=n(be,"LI",{});var Jwo=s(j6);MOe=n(Jwo,"STRONG",{});var LSn=s(MOe);ZAt=r(LSn,"mbart"),LSn.forEach(t),VAt=r(Jwo," \u2014 "),dbe=n(Jwo,"A",{href:!0});var ASn=s(dbe);WAt=r(ASn,"MBartForConditionalGeneration"),ASn.forEach(t),JAt=r(Jwo," (mBART model)"),Jwo.forEach(t),UAt=i(be),P6=n(be,"LI",{});var Uwo=s(P6);TOe=n(Uwo,"STRONG",{});var RSn=s(TOe);XAt=r(RSn,"mt5"),RSn.forEach(t),DAt=r(Uwo," \u2014 "),mbe=n(Uwo,"A",{href:!0});var kSn=s(mbe);qAt=r(kSn,"MT5ForConditionalGeneration"),kSn.forEach(t),OAt=r(Uwo," (MT5 model)"),Uwo.forEach(t),QAt=i(be),Z6=n(be,"LI",{});var Xwo=s(Z6);FOe=n(Xwo,"STRONG",{});var SSn=s(FOe);YAt=r(SSn,"mvp"),SSn.forEach(t),zAt=r(Xwo," \u2014 "),cbe=n(Xwo,"A",{href:!0});var xSn=s(cbe);HAt=r(xSn,"MvpForConditionalGeneration"),xSn.forEach(t),KAt=r(Xwo," (MVP model)"),Xwo.forEach(t),eRt=i(be),V6=n(be,"LI",{});var Dwo=s(V6);EOe=n(Dwo,"STRONG",{});var GSn=s(EOe);oRt=r(GSn,"nllb-moe"),GSn.forEach(t),rRt=r(Dwo," \u2014 "),fbe=n(Dwo,"A",{href:!0});var BSn=s(fbe);tRt=r(BSn,"NllbMoeForConditionalGeneration"),BSn.forEach(t),aRt=r(Dwo," (NLLB-MOE model)"),Dwo.forEach(t),nRt=i(be),W6=n(be,"LI",{});var qwo=s(W6);yOe=n(qwo,"STRONG",{});var ISn=s(yOe);sRt=r(ISn,"pegasus"),ISn.forEach(t),lRt=r(qwo," \u2014 "),hbe=n(qwo,"A",{href:!0});var NSn=s(hbe);iRt=r(NSn,"PegasusForConditionalGeneration"),NSn.forEach(t),dRt=r(qwo," (Pegasus model)"),qwo.forEach(t),mRt=i(be),J6=n(be,"LI",{});var Owo=s(J6);COe=n(Owo,"STRONG",{});var $Sn=s(COe);cRt=r($Sn,"pegasus_x"),$Sn.forEach(t),fRt=r(Owo," \u2014 "),gbe=n(Owo,"A",{href:!0});var jSn=s(gbe);hRt=r(jSn,"PegasusXForConditionalGeneration"),jSn.forEach(t),gRt=r(Owo," (PEGASUS-X model)"),Owo.forEach(t),uRt=i(be),U6=n(be,"LI",{});var Qwo=s(U6);wOe=n(Qwo,"STRONG",{});var PSn=s(wOe);pRt=r(PSn,"plbart"),PSn.forEach(t),_Rt=r(Qwo," \u2014 "),ube=n(Qwo,"A",{href:!0});var ZSn=s(ube);bRt=r(ZSn,"PLBartForConditionalGeneration"),ZSn.forEach(t),vRt=r(Qwo," (PLBart model)"),Qwo.forEach(t),MRt=i(be),X6=n(be,"LI",{});var Ywo=s(X6);LOe=n(Ywo,"STRONG",{});var VSn=s(LOe);TRt=r(VSn,"prophetnet"),VSn.forEach(t),FRt=r(Ywo," \u2014 "),pbe=n(Ywo,"A",{href:!0});var WSn=s(pbe);ERt=r(WSn,"ProphetNetForConditionalGeneration"),WSn.forEach(t),yRt=r(Ywo," (ProphetNet model)"),Ywo.forEach(t),CRt=i(be),D6=n(be,"LI",{});var zwo=s(D6);AOe=n(zwo,"STRONG",{});var JSn=s(AOe);wRt=r(JSn,"switch_transformers"),JSn.forEach(t),LRt=r(zwo," \u2014 "),_be=n(zwo,"A",{href:!0});var USn=s(_be);ARt=r(USn,"SwitchTransformersForConditionalGeneration"),USn.forEach(t),RRt=r(zwo," (SwitchTransformers model)"),zwo.forEach(t),kRt=i(be),q6=n(be,"LI",{});var Hwo=s(q6);ROe=n(Hwo,"STRONG",{});var XSn=s(ROe);SRt=r(XSn,"t5"),XSn.forEach(t),xRt=r(Hwo," \u2014 "),bbe=n(Hwo,"A",{href:!0});var DSn=s(bbe);GRt=r(DSn,"T5ForConditionalGeneration"),DSn.forEach(t),BRt=r(Hwo," (T5 model)"),Hwo.forEach(t),IRt=i(be),O6=n(be,"LI",{});var Kwo=s(O6);kOe=n(Kwo,"STRONG",{});var qSn=s(kOe);NRt=r(qSn,"umt5"),qSn.forEach(t),$Rt=r(Kwo," \u2014 "),vbe=n(Kwo,"A",{href:!0});var OSn=s(vbe);jRt=r(OSn,"UMT5ForConditionalGeneration"),OSn.forEach(t),PRt=r(Kwo," (UMT5 model)"),Kwo.forEach(t),ZRt=i(be),Q6=n(be,"LI",{});var e6o=s(Q6);SOe=n(e6o,"STRONG",{});var QSn=s(SOe);VRt=r(QSn,"xlm-prophetnet"),QSn.forEach(t),WRt=r(e6o," \u2014 "),Mbe=n(e6o,"A",{href:!0});var YSn=s(Mbe);JRt=r(YSn,"XLMProphetNetForConditionalGeneration"),YSn.forEach(t),URt=r(e6o," (XLM-ProphetNet model)"),e6o.forEach(t),be.forEach(t),XRt=i(nn),Y6=n(nn,"P",{});var o6o=s(Y6);DRt=r(o6o,"The model is set in evaluation mode by default using "),xOe=n(o6o,"CODE",{});var zSn=s(xOe);qRt=r(zSn,"model.eval()"),zSn.forEach(t),ORt=r(o6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),GOe=n(o6o,"CODE",{});var HSn=s(GOe);QRt=r(HSn,"model.train()"),HSn.forEach(t),o6o.forEach(t),YRt=i(nn),M(z6.$$.fragment,nn),nn.forEach(t),Md.forEach(t),TIo=i(c),Qc=n(c,"H3",{class:!0});var Mjo=s(Qc);H6=n(Mjo,"A",{id:!0,class:!0,href:!0});var KSn=s(H6);BOe=n(KSn,"SPAN",{});var exn=s(BOe);M(hJ.$$.fragment,exn),exn.forEach(t),KSn.forEach(t),zRt=i(Mjo),IOe=n(Mjo,"SPAN",{});var oxn=s(IOe);HRt=r(oxn,"TFAutoModelForSeq2SeqLM"),oxn.forEach(t),Mjo.forEach(t),FIo=i(c),tr=n(c,"DIV",{class:!0});var Td=s(tr);M(gJ.$$.fragment,Td),KRt=i(Td),Yc=n(Td,"P",{});var DCe=s(Yc);ekt=r(DCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Tbe=n(DCe,"A",{href:!0});var rxn=s(Tbe);okt=r(rxn,"from_pretrained()"),rxn.forEach(t),rkt=r(DCe," class method or the "),Fbe=n(DCe,"A",{href:!0});var txn=s(Fbe);tkt=r(txn,"from_config()"),txn.forEach(t),akt=r(DCe,` class
method.`),DCe.forEach(t),nkt=i(Td),uJ=n(Td,"P",{});var Tjo=s(uJ);skt=r(Tjo,"This class cannot be instantiated directly using "),NOe=n(Tjo,"CODE",{});var axn=s(NOe);lkt=r(axn,"__init__()"),axn.forEach(t),ikt=r(Tjo," (throws an error)."),Tjo.forEach(t),dkt=i(Td),ra=n(Td,"DIV",{class:!0});var vP=s(ra);M(pJ.$$.fragment,vP),mkt=i(vP),$Oe=n(vP,"P",{});var nxn=s($Oe);ckt=r(nxn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),nxn.forEach(t),fkt=i(vP),zc=n(vP,"P",{});var qCe=s(zc);hkt=r(qCe,`Note:
Loading a model from its configuration file does `),jOe=n(qCe,"STRONG",{});var sxn=s(jOe);gkt=r(sxn,"not"),sxn.forEach(t),ukt=r(qCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ebe=n(qCe,"A",{href:!0});var lxn=s(Ebe);pkt=r(lxn,"from_pretrained()"),lxn.forEach(t),_kt=r(qCe," to load the model weights."),qCe.forEach(t),bkt=i(vP),M(K6.$$.fragment,vP),vP.forEach(t),vkt=i(Td),ct=n(Td,"DIV",{class:!0});var Fd=s(ct);M(_J.$$.fragment,Fd),Mkt=i(Fd),POe=n(Fd,"P",{});var ixn=s(POe);Tkt=r(ixn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),ixn.forEach(t),Fkt=i(Fd),Kn=n(Fd,"P",{});var MP=s(Kn);Ekt=r(MP,"The model class to instantiate is selected based on the "),ZOe=n(MP,"CODE",{});var dxn=s(ZOe);ykt=r(dxn,"model_type"),dxn.forEach(t),Ckt=r(MP,` property of the config object (either
passed as an argument or loaded from `),VOe=n(MP,"CODE",{});var mxn=s(VOe);wkt=r(mxn,"pretrained_model_name_or_path"),mxn.forEach(t),Lkt=r(MP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WOe=n(MP,"CODE",{});var cxn=s(WOe);Akt=r(cxn,"pretrained_model_name_or_path"),cxn.forEach(t),Rkt=r(MP,":"),MP.forEach(t),kkt=i(Fd),Ne=n(Fd,"UL",{});var De=s(Ne);e8=n(De,"LI",{});var r6o=s(e8);JOe=n(r6o,"STRONG",{});var fxn=s(JOe);Skt=r(fxn,"bart"),fxn.forEach(t),xkt=r(r6o," \u2014 "),ybe=n(r6o,"A",{href:!0});var hxn=s(ybe);Gkt=r(hxn,"TFBartForConditionalGeneration"),hxn.forEach(t),Bkt=r(r6o," (BART model)"),r6o.forEach(t),Ikt=i(De),o8=n(De,"LI",{});var t6o=s(o8);UOe=n(t6o,"STRONG",{});var gxn=s(UOe);Nkt=r(gxn,"blenderbot"),gxn.forEach(t),$kt=r(t6o," \u2014 "),Cbe=n(t6o,"A",{href:!0});var uxn=s(Cbe);jkt=r(uxn,"TFBlenderbotForConditionalGeneration"),uxn.forEach(t),Pkt=r(t6o," (Blenderbot model)"),t6o.forEach(t),Zkt=i(De),r8=n(De,"LI",{});var a6o=s(r8);XOe=n(a6o,"STRONG",{});var pxn=s(XOe);Vkt=r(pxn,"blenderbot-small"),pxn.forEach(t),Wkt=r(a6o," \u2014 "),wbe=n(a6o,"A",{href:!0});var _xn=s(wbe);Jkt=r(_xn,"TFBlenderbotSmallForConditionalGeneration"),_xn.forEach(t),Ukt=r(a6o," (BlenderbotSmall model)"),a6o.forEach(t),Xkt=i(De),t8=n(De,"LI",{});var n6o=s(t8);DOe=n(n6o,"STRONG",{});var bxn=s(DOe);Dkt=r(bxn,"encoder-decoder"),bxn.forEach(t),qkt=r(n6o," \u2014 "),Lbe=n(n6o,"A",{href:!0});var vxn=s(Lbe);Okt=r(vxn,"TFEncoderDecoderModel"),vxn.forEach(t),Qkt=r(n6o," (Encoder decoder model)"),n6o.forEach(t),Ykt=i(De),a8=n(De,"LI",{});var s6o=s(a8);qOe=n(s6o,"STRONG",{});var Mxn=s(qOe);zkt=r(Mxn,"led"),Mxn.forEach(t),Hkt=r(s6o," \u2014 "),Abe=n(s6o,"A",{href:!0});var Txn=s(Abe);Kkt=r(Txn,"TFLEDForConditionalGeneration"),Txn.forEach(t),eSt=r(s6o," (LED model)"),s6o.forEach(t),oSt=i(De),n8=n(De,"LI",{});var l6o=s(n8);OOe=n(l6o,"STRONG",{});var Fxn=s(OOe);rSt=r(Fxn,"marian"),Fxn.forEach(t),tSt=r(l6o," \u2014 "),Rbe=n(l6o,"A",{href:!0});var Exn=s(Rbe);aSt=r(Exn,"TFMarianMTModel"),Exn.forEach(t),nSt=r(l6o," (Marian model)"),l6o.forEach(t),sSt=i(De),s8=n(De,"LI",{});var i6o=s(s8);QOe=n(i6o,"STRONG",{});var yxn=s(QOe);lSt=r(yxn,"mbart"),yxn.forEach(t),iSt=r(i6o," \u2014 "),kbe=n(i6o,"A",{href:!0});var Cxn=s(kbe);dSt=r(Cxn,"TFMBartForConditionalGeneration"),Cxn.forEach(t),mSt=r(i6o," (mBART model)"),i6o.forEach(t),cSt=i(De),l8=n(De,"LI",{});var d6o=s(l8);YOe=n(d6o,"STRONG",{});var wxn=s(YOe);fSt=r(wxn,"mt5"),wxn.forEach(t),hSt=r(d6o," \u2014 "),Sbe=n(d6o,"A",{href:!0});var Lxn=s(Sbe);gSt=r(Lxn,"TFMT5ForConditionalGeneration"),Lxn.forEach(t),uSt=r(d6o," (MT5 model)"),d6o.forEach(t),pSt=i(De),i8=n(De,"LI",{});var m6o=s(i8);zOe=n(m6o,"STRONG",{});var Axn=s(zOe);_St=r(Axn,"pegasus"),Axn.forEach(t),bSt=r(m6o," \u2014 "),xbe=n(m6o,"A",{href:!0});var Rxn=s(xbe);vSt=r(Rxn,"TFPegasusForConditionalGeneration"),Rxn.forEach(t),MSt=r(m6o," (Pegasus model)"),m6o.forEach(t),TSt=i(De),d8=n(De,"LI",{});var c6o=s(d8);HOe=n(c6o,"STRONG",{});var kxn=s(HOe);FSt=r(kxn,"t5"),kxn.forEach(t),ESt=r(c6o," \u2014 "),Gbe=n(c6o,"A",{href:!0});var Sxn=s(Gbe);ySt=r(Sxn,"TFT5ForConditionalGeneration"),Sxn.forEach(t),CSt=r(c6o," (T5 model)"),c6o.forEach(t),De.forEach(t),wSt=i(Fd),M(m8.$$.fragment,Fd),Fd.forEach(t),Td.forEach(t),EIo=i(c),Hc=n(c,"H3",{class:!0});var Fjo=s(Hc);c8=n(Fjo,"A",{id:!0,class:!0,href:!0});var xxn=s(c8);KOe=n(xxn,"SPAN",{});var Gxn=s(KOe);M(bJ.$$.fragment,Gxn),Gxn.forEach(t),xxn.forEach(t),LSt=i(Fjo),eQe=n(Fjo,"SPAN",{});var Bxn=s(eQe);ASt=r(Bxn,"FlaxAutoModelForSeq2SeqLM"),Bxn.forEach(t),Fjo.forEach(t),yIo=i(c),ar=n(c,"DIV",{class:!0});var Ed=s(ar);M(vJ.$$.fragment,Ed),RSt=i(Ed),Kc=n(Ed,"P",{});var OCe=s(Kc);kSt=r(OCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Bbe=n(OCe,"A",{href:!0});var Ixn=s(Bbe);SSt=r(Ixn,"from_pretrained()"),Ixn.forEach(t),xSt=r(OCe," class method or the "),Ibe=n(OCe,"A",{href:!0});var Nxn=s(Ibe);GSt=r(Nxn,"from_config()"),Nxn.forEach(t),BSt=r(OCe,` class
method.`),OCe.forEach(t),ISt=i(Ed),MJ=n(Ed,"P",{});var Ejo=s(MJ);NSt=r(Ejo,"This class cannot be instantiated directly using "),oQe=n(Ejo,"CODE",{});var $xn=s(oQe);$St=r($xn,"__init__()"),$xn.forEach(t),jSt=r(Ejo," (throws an error)."),Ejo.forEach(t),PSt=i(Ed),ta=n(Ed,"DIV",{class:!0});var TP=s(ta);M(TJ.$$.fragment,TP),ZSt=i(TP),rQe=n(TP,"P",{});var jxn=s(rQe);VSt=r(jxn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),jxn.forEach(t),WSt=i(TP),ef=n(TP,"P",{});var QCe=s(ef);JSt=r(QCe,`Note:
Loading a model from its configuration file does `),tQe=n(QCe,"STRONG",{});var Pxn=s(tQe);USt=r(Pxn,"not"),Pxn.forEach(t),XSt=r(QCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Nbe=n(QCe,"A",{href:!0});var Zxn=s(Nbe);DSt=r(Zxn,"from_pretrained()"),Zxn.forEach(t),qSt=r(QCe," to load the model weights."),QCe.forEach(t),OSt=i(TP),M(f8.$$.fragment,TP),TP.forEach(t),QSt=i(Ed),ft=n(Ed,"DIV",{class:!0});var yd=s(ft);M(FJ.$$.fragment,yd),YSt=i(yd),aQe=n(yd,"P",{});var Vxn=s(aQe);zSt=r(Vxn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Vxn.forEach(t),HSt=i(yd),es=n(yd,"P",{});var FP=s(es);KSt=r(FP,"The model class to instantiate is selected based on the "),nQe=n(FP,"CODE",{});var Wxn=s(nQe);ext=r(Wxn,"model_type"),Wxn.forEach(t),oxt=r(FP,` property of the config object (either
passed as an argument or loaded from `),sQe=n(FP,"CODE",{});var Jxn=s(sQe);rxt=r(Jxn,"pretrained_model_name_or_path"),Jxn.forEach(t),txt=r(FP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lQe=n(FP,"CODE",{});var Uxn=s(lQe);axt=r(Uxn,"pretrained_model_name_or_path"),Uxn.forEach(t),nxt=r(FP,":"),FP.forEach(t),sxt=i(yd),$e=n(yd,"UL",{});var qe=s($e);h8=n(qe,"LI",{});var f6o=s(h8);iQe=n(f6o,"STRONG",{});var Xxn=s(iQe);lxt=r(Xxn,"bart"),Xxn.forEach(t),ixt=r(f6o," \u2014 "),$be=n(f6o,"A",{href:!0});var Dxn=s($be);dxt=r(Dxn,"FlaxBartForConditionalGeneration"),Dxn.forEach(t),mxt=r(f6o," (BART model)"),f6o.forEach(t),cxt=i(qe),g8=n(qe,"LI",{});var h6o=s(g8);dQe=n(h6o,"STRONG",{});var qxn=s(dQe);fxt=r(qxn,"blenderbot"),qxn.forEach(t),hxt=r(h6o," \u2014 "),jbe=n(h6o,"A",{href:!0});var Oxn=s(jbe);gxt=r(Oxn,"FlaxBlenderbotForConditionalGeneration"),Oxn.forEach(t),uxt=r(h6o," (Blenderbot model)"),h6o.forEach(t),pxt=i(qe),u8=n(qe,"LI",{});var g6o=s(u8);mQe=n(g6o,"STRONG",{});var Qxn=s(mQe);_xt=r(Qxn,"blenderbot-small"),Qxn.forEach(t),bxt=r(g6o," \u2014 "),Pbe=n(g6o,"A",{href:!0});var Yxn=s(Pbe);vxt=r(Yxn,"FlaxBlenderbotSmallForConditionalGeneration"),Yxn.forEach(t),Mxt=r(g6o," (BlenderbotSmall model)"),g6o.forEach(t),Txt=i(qe),p8=n(qe,"LI",{});var u6o=s(p8);cQe=n(u6o,"STRONG",{});var zxn=s(cQe);Fxt=r(zxn,"encoder-decoder"),zxn.forEach(t),Ext=r(u6o," \u2014 "),Zbe=n(u6o,"A",{href:!0});var Hxn=s(Zbe);yxt=r(Hxn,"FlaxEncoderDecoderModel"),Hxn.forEach(t),Cxt=r(u6o," (Encoder decoder model)"),u6o.forEach(t),wxt=i(qe),_8=n(qe,"LI",{});var p6o=s(_8);fQe=n(p6o,"STRONG",{});var Kxn=s(fQe);Lxt=r(Kxn,"longt5"),Kxn.forEach(t),Axt=r(p6o," \u2014 "),Vbe=n(p6o,"A",{href:!0});var eGn=s(Vbe);Rxt=r(eGn,"FlaxLongT5ForConditionalGeneration"),eGn.forEach(t),kxt=r(p6o," (LongT5 model)"),p6o.forEach(t),Sxt=i(qe),b8=n(qe,"LI",{});var _6o=s(b8);hQe=n(_6o,"STRONG",{});var oGn=s(hQe);xxt=r(oGn,"marian"),oGn.forEach(t),Gxt=r(_6o," \u2014 "),Wbe=n(_6o,"A",{href:!0});var rGn=s(Wbe);Bxt=r(rGn,"FlaxMarianMTModel"),rGn.forEach(t),Ixt=r(_6o," (Marian model)"),_6o.forEach(t),Nxt=i(qe),v8=n(qe,"LI",{});var b6o=s(v8);gQe=n(b6o,"STRONG",{});var tGn=s(gQe);$xt=r(tGn,"mbart"),tGn.forEach(t),jxt=r(b6o," \u2014 "),Jbe=n(b6o,"A",{href:!0});var aGn=s(Jbe);Pxt=r(aGn,"FlaxMBartForConditionalGeneration"),aGn.forEach(t),Zxt=r(b6o," (mBART model)"),b6o.forEach(t),Vxt=i(qe),M8=n(qe,"LI",{});var v6o=s(M8);uQe=n(v6o,"STRONG",{});var nGn=s(uQe);Wxt=r(nGn,"mt5"),nGn.forEach(t),Jxt=r(v6o," \u2014 "),Ube=n(v6o,"A",{href:!0});var sGn=s(Ube);Uxt=r(sGn,"FlaxMT5ForConditionalGeneration"),sGn.forEach(t),Xxt=r(v6o," (MT5 model)"),v6o.forEach(t),Dxt=i(qe),T8=n(qe,"LI",{});var M6o=s(T8);pQe=n(M6o,"STRONG",{});var lGn=s(pQe);qxt=r(lGn,"pegasus"),lGn.forEach(t),Oxt=r(M6o," \u2014 "),Xbe=n(M6o,"A",{href:!0});var iGn=s(Xbe);Qxt=r(iGn,"FlaxPegasusForConditionalGeneration"),iGn.forEach(t),Yxt=r(M6o," (Pegasus model)"),M6o.forEach(t),zxt=i(qe),F8=n(qe,"LI",{});var T6o=s(F8);_Qe=n(T6o,"STRONG",{});var dGn=s(_Qe);Hxt=r(dGn,"t5"),dGn.forEach(t),Kxt=r(T6o," \u2014 "),Dbe=n(T6o,"A",{href:!0});var mGn=s(Dbe);eGt=r(mGn,"FlaxT5ForConditionalGeneration"),mGn.forEach(t),oGt=r(T6o," (T5 model)"),T6o.forEach(t),qe.forEach(t),rGt=i(yd),M(E8.$$.fragment,yd),yd.forEach(t),Ed.forEach(t),CIo=i(c),of=n(c,"H3",{class:!0});var yjo=s(of);y8=n(yjo,"A",{id:!0,class:!0,href:!0});var cGn=s(y8);bQe=n(cGn,"SPAN",{});var fGn=s(bQe);M(EJ.$$.fragment,fGn),fGn.forEach(t),cGn.forEach(t),tGt=i(yjo),vQe=n(yjo,"SPAN",{});var hGn=s(vQe);aGt=r(hGn,"AutoModelForSequenceClassification"),hGn.forEach(t),yjo.forEach(t),wIo=i(c),nr=n(c,"DIV",{class:!0});var Cd=s(nr);M(yJ.$$.fragment,Cd),nGt=i(Cd),rf=n(Cd,"P",{});var YCe=s(rf);sGt=r(YCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),qbe=n(YCe,"A",{href:!0});var gGn=s(qbe);lGt=r(gGn,"from_pretrained()"),gGn.forEach(t),iGt=r(YCe," class method or the "),Obe=n(YCe,"A",{href:!0});var uGn=s(Obe);dGt=r(uGn,"from_config()"),uGn.forEach(t),mGt=r(YCe,` class
method.`),YCe.forEach(t),cGt=i(Cd),CJ=n(Cd,"P",{});var Cjo=s(CJ);fGt=r(Cjo,"This class cannot be instantiated directly using "),MQe=n(Cjo,"CODE",{});var pGn=s(MQe);hGt=r(pGn,"__init__()"),pGn.forEach(t),gGt=r(Cjo," (throws an error)."),Cjo.forEach(t),uGt=i(Cd),aa=n(Cd,"DIV",{class:!0});var EP=s(aa);M(wJ.$$.fragment,EP),pGt=i(EP),TQe=n(EP,"P",{});var _Gn=s(TQe);_Gt=r(_Gn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),_Gn.forEach(t),bGt=i(EP),tf=n(EP,"P",{});var zCe=s(tf);vGt=r(zCe,`Note:
Loading a model from its configuration file does `),FQe=n(zCe,"STRONG",{});var bGn=s(FQe);MGt=r(bGn,"not"),bGn.forEach(t),TGt=r(zCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Qbe=n(zCe,"A",{href:!0});var vGn=s(Qbe);FGt=r(vGn,"from_pretrained()"),vGn.forEach(t),EGt=r(zCe," to load the model weights."),zCe.forEach(t),yGt=i(EP),M(C8.$$.fragment,EP),EP.forEach(t),CGt=i(Cd),fo=n(Cd,"DIV",{class:!0});var sn=s(fo);M(LJ.$$.fragment,sn),wGt=i(sn),EQe=n(sn,"P",{});var MGn=s(EQe);LGt=r(MGn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),MGn.forEach(t),AGt=i(sn),os=n(sn,"P",{});var yP=s(os);RGt=r(yP,"The model class to instantiate is selected based on the "),yQe=n(yP,"CODE",{});var TGn=s(yQe);kGt=r(TGn,"model_type"),TGn.forEach(t),SGt=r(yP,` property of the config object (either
passed as an argument or loaded from `),CQe=n(yP,"CODE",{});var FGn=s(CQe);xGt=r(FGn,"pretrained_model_name_or_path"),FGn.forEach(t),GGt=r(yP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wQe=n(yP,"CODE",{});var EGn=s(wQe);BGt=r(EGn,"pretrained_model_name_or_path"),EGn.forEach(t),IGt=r(yP,":"),yP.forEach(t),NGt=i(sn),B=n(sn,"UL",{});var I=s(B);w8=n(I,"LI",{});var F6o=s(w8);LQe=n(F6o,"STRONG",{});var yGn=s(LQe);$Gt=r(yGn,"albert"),yGn.forEach(t),jGt=r(F6o," \u2014 "),Ybe=n(F6o,"A",{href:!0});var CGn=s(Ybe);PGt=r(CGn,"AlbertForSequenceClassification"),CGn.forEach(t),ZGt=r(F6o," (ALBERT model)"),F6o.forEach(t),VGt=i(I),L8=n(I,"LI",{});var E6o=s(L8);AQe=n(E6o,"STRONG",{});var wGn=s(AQe);WGt=r(wGn,"bart"),wGn.forEach(t),JGt=r(E6o," \u2014 "),zbe=n(E6o,"A",{href:!0});var LGn=s(zbe);UGt=r(LGn,"BartForSequenceClassification"),LGn.forEach(t),XGt=r(E6o," (BART model)"),E6o.forEach(t),DGt=i(I),A8=n(I,"LI",{});var y6o=s(A8);RQe=n(y6o,"STRONG",{});var AGn=s(RQe);qGt=r(AGn,"bert"),AGn.forEach(t),OGt=r(y6o," \u2014 "),Hbe=n(y6o,"A",{href:!0});var RGn=s(Hbe);QGt=r(RGn,"BertForSequenceClassification"),RGn.forEach(t),YGt=r(y6o," (BERT model)"),y6o.forEach(t),zGt=i(I),R8=n(I,"LI",{});var C6o=s(R8);kQe=n(C6o,"STRONG",{});var kGn=s(kQe);HGt=r(kGn,"big_bird"),kGn.forEach(t),KGt=r(C6o," \u2014 "),Kbe=n(C6o,"A",{href:!0});var SGn=s(Kbe);eBt=r(SGn,"BigBirdForSequenceClassification"),SGn.forEach(t),oBt=r(C6o," (BigBird model)"),C6o.forEach(t),rBt=i(I),k8=n(I,"LI",{});var w6o=s(k8);SQe=n(w6o,"STRONG",{});var xGn=s(SQe);tBt=r(xGn,"bigbird_pegasus"),xGn.forEach(t),aBt=r(w6o," \u2014 "),e2e=n(w6o,"A",{href:!0});var GGn=s(e2e);nBt=r(GGn,"BigBirdPegasusForSequenceClassification"),GGn.forEach(t),sBt=r(w6o," (BigBird-Pegasus model)"),w6o.forEach(t),lBt=i(I),S8=n(I,"LI",{});var L6o=s(S8);xQe=n(L6o,"STRONG",{});var BGn=s(xQe);iBt=r(BGn,"biogpt"),BGn.forEach(t),dBt=r(L6o," \u2014 "),o2e=n(L6o,"A",{href:!0});var IGn=s(o2e);mBt=r(IGn,"BioGptForSequenceClassification"),IGn.forEach(t),cBt=r(L6o," (BioGpt model)"),L6o.forEach(t),fBt=i(I),x8=n(I,"LI",{});var A6o=s(x8);GQe=n(A6o,"STRONG",{});var 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f$n=s(Pve);iJt=r(f$n,"TFMobileBertForSequenceClassification"),f$n.forEach(t),dJt=r(d7o," (MobileBERT model)"),d7o.forEach(t),mJt=i(ce),_L=n(ce,"LI",{});var m7o=s(_L);Cze=n(m7o,"STRONG",{});var h$n=s(Cze);cJt=r(h$n,"mpnet"),h$n.forEach(t),fJt=r(m7o," \u2014 "),Zve=n(m7o,"A",{href:!0});var g$n=s(Zve);hJt=r(g$n,"TFMPNetForSequenceClassification"),g$n.forEach(t),gJt=r(m7o," (MPNet model)"),m7o.forEach(t),uJt=i(ce),bL=n(ce,"LI",{});var c7o=s(bL);wze=n(c7o,"STRONG",{});var u$n=s(wze);pJt=r(u$n,"openai-gpt"),u$n.forEach(t),_Jt=r(c7o," \u2014 "),Vve=n(c7o,"A",{href:!0});var p$n=s(Vve);bJt=r(p$n,"TFOpenAIGPTForSequenceClassification"),p$n.forEach(t),vJt=r(c7o," (OpenAI GPT model)"),c7o.forEach(t),MJt=i(ce),vL=n(ce,"LI",{});var f7o=s(vL);Lze=n(f7o,"STRONG",{});var _$n=s(Lze);TJt=r(_$n,"rembert"),_$n.forEach(t),FJt=r(f7o," \u2014 "),Wve=n(f7o,"A",{href:!0});var b$n=s(Wve);EJt=r(b$n,"TFRemBertForSequenceClassification"),b$n.forEach(t),yJt=r(f7o," (RemBERT 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C$n=s(Sze);VJt=r(C$n,"tapas"),C$n.forEach(t),WJt=r(p7o," \u2014 "),Dve=n(p7o,"A",{href:!0});var w$n=s(Dve);JJt=r(w$n,"TFTapasForSequenceClassification"),w$n.forEach(t),UJt=r(p7o," (TAPAS model)"),p7o.forEach(t),XJt=i(ce),yL=n(ce,"LI",{});var _7o=s(yL);xze=n(_7o,"STRONG",{});var L$n=s(xze);DJt=r(L$n,"transfo-xl"),L$n.forEach(t),qJt=r(_7o," \u2014 "),qve=n(_7o,"A",{href:!0});var A$n=s(qve);OJt=r(A$n,"TFTransfoXLForSequenceClassification"),A$n.forEach(t),QJt=r(_7o," (Transformer-XL model)"),_7o.forEach(t),YJt=i(ce),CL=n(ce,"LI",{});var b7o=s(CL);Gze=n(b7o,"STRONG",{});var R$n=s(Gze);zJt=r(R$n,"xlm"),R$n.forEach(t),HJt=r(b7o," \u2014 "),Ove=n(b7o,"A",{href:!0});var k$n=s(Ove);KJt=r(k$n,"TFXLMForSequenceClassification"),k$n.forEach(t),eUt=r(b7o," (XLM model)"),b7o.forEach(t),oUt=i(ce),wL=n(ce,"LI",{});var v7o=s(wL);Bze=n(v7o,"STRONG",{});var S$n=s(Bze);rUt=r(S$n,"xlm-roberta"),S$n.forEach(t),tUt=r(v7o," \u2014 "),Qve=n(v7o,"A",{href:!0});var x$n=s(Qve);aUt=r(x$n,"TFXLMRobertaForSequenceClassification"),x$n.forEach(t),nUt=r(v7o," (XLM-RoBERTa model)"),v7o.forEach(t),sUt=i(ce),LL=n(ce,"LI",{});var M7o=s(LL);Ize=n(M7o,"STRONG",{});var G$n=s(Ize);lUt=r(G$n,"xlnet"),G$n.forEach(t),iUt=r(M7o," \u2014 "),Yve=n(M7o,"A",{href:!0});var B$n=s(Yve);dUt=r(B$n,"TFXLNetForSequenceClassification"),B$n.forEach(t),mUt=r(M7o," (XLNet model)"),M7o.forEach(t),ce.forEach(t),cUt=i(Ld),M(AL.$$.fragment,Ld),Ld.forEach(t),wd.forEach(t),RIo=i(c),lf=n(c,"H3",{class:!0});var Ajo=s(lf);RL=n(Ajo,"A",{id:!0,class:!0,href:!0});var I$n=s(RL);Nze=n(I$n,"SPAN",{});var N$n=s(Nze);M(GJ.$$.fragment,N$n),N$n.forEach(t),I$n.forEach(t),fUt=i(Ajo),$ze=n(Ajo,"SPAN",{});var $$n=s($ze);hUt=r($$n,"FlaxAutoModelForSequenceClassification"),$$n.forEach(t),Ajo.forEach(t),kIo=i(c),lr=n(c,"DIV",{class:!0});var Ad=s(lr);M(BJ.$$.fragment,Ad),gUt=i(Ad),df=n(Ad,"P",{});var ewe=s(df);uUt=r(ewe,`This is a generic model class that will be instantiated as one of the model classes of 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with the `),zve=n(ewe,"A",{href:!0});var j$n=s(zve);pUt=r(j$n,"from_pretrained()"),j$n.forEach(t),_Ut=r(ewe," class method or the "),Hve=n(ewe,"A",{href:!0});var P$n=s(Hve);bUt=r(P$n,"from_config()"),P$n.forEach(t),vUt=r(ewe,` class
method.`),ewe.forEach(t),MUt=i(Ad),IJ=n(Ad,"P",{});var Rjo=s(IJ);TUt=r(Rjo,"This class cannot be instantiated directly using "),jze=n(Rjo,"CODE",{});var Z$n=s(jze);FUt=r(Z$n,"__init__()"),Z$n.forEach(t),EUt=r(Rjo," (throws an error)."),Rjo.forEach(t),yUt=i(Ad),sa=n(Ad,"DIV",{class:!0});var LP=s(sa);M(NJ.$$.fragment,LP),CUt=i(LP),Pze=n(LP,"P",{});var V$n=s(Pze);wUt=r(V$n,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),V$n.forEach(t),LUt=i(LP),mf=n(LP,"P",{});var owe=s(mf);AUt=r(owe,`Note:
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model\u2019s configuration. Use `),Kve=n(owe,"A",{href:!0});var J$n=s(Kve);SUt=r(J$n,"from_pretrained()"),J$n.forEach(t),xUt=r(owe," to load the model weights."),owe.forEach(t),GUt=i(LP),M(kL.$$.fragment,LP),LP.forEach(t),BUt=i(Ad),gt=n(Ad,"DIV",{class:!0});var Rd=s(gt);M($J.$$.fragment,Rd),IUt=i(Rd),Vze=n(Rd,"P",{});var U$n=s(Vze);NUt=r(U$n,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),U$n.forEach(t),$Ut=i(Rd),ts=n(Rd,"P",{});var AP=s(ts);jUt=r(AP,"The model class to instantiate is selected based on the "),Wze=n(AP,"CODE",{});var X$n=s(Wze);PUt=r(X$n,"model_type"),X$n.forEach(t),ZUt=r(AP,` property of the config object (either
passed as an argument or loaded from `),Jze=n(AP,"CODE",{});var D$n=s(Jze);VUt=r(D$n,"pretrained_model_name_or_path"),D$n.forEach(t),WUt=r(AP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Uze=n(AP,"CODE",{});var q$n=s(Uze);JUt=r(q$n,"pretrained_model_name_or_path"),q$n.forEach(t),UUt=r(AP,":"),AP.forEach(t),XUt=i(Rd),Se=n(Rd,"UL",{});var Ze=s(Se);SL=n(Ze,"LI",{});var T7o=s(SL);Xze=n(T7o,"STRONG",{});var O$n=s(Xze);DUt=r(O$n,"albert"),O$n.forEach(t),qUt=r(T7o," \u2014 "),eMe=n(T7o,"A",{href:!0});var Q$n=s(eMe);OUt=r(Q$n,"FlaxAlbertForSequenceClassification"),Q$n.forEach(t),QUt=r(T7o," (ALBERT model)"),T7o.forEach(t),YUt=i(Ze),xL=n(Ze,"LI",{});var F7o=s(xL);Dze=n(F7o,"STRONG",{});var Y$n=s(Dze);zUt=r(Y$n,"bart"),Y$n.forEach(t),HUt=r(F7o," \u2014 "),oMe=n(F7o,"A",{href:!0});var z$n=s(oMe);KUt=r(z$n,"FlaxBartForSequenceClassification"),z$n.forEach(t),eXt=r(F7o," (BART model)"),F7o.forEach(t),oXt=i(Ze),GL=n(Ze,"LI",{});var E7o=s(GL);qze=n(E7o,"STRONG",{});var H$n=s(qze);rXt=r(H$n,"bert"),H$n.forEach(t),tXt=r(E7o," \u2014 "),rMe=n(E7o,"A",{href:!0});var K$n=s(rMe);aXt=r(K$n,"FlaxBertForSequenceClassification"),K$n.forEach(t),nXt=r(E7o," (BERT model)"),E7o.forEach(t),sXt=i(Ze),BL=n(Ze,"LI",{});var y7o=s(BL);Oze=n(y7o,"STRONG",{});var ejn=s(Oze);lXt=r(ejn,"big_bird"),ejn.forEach(t),iXt=r(y7o," \u2014 "),tMe=n(y7o,"A",{href:!0});var ojn=s(tMe);dXt=r(ojn,"FlaxBigBirdForSequenceClassification"),ojn.forEach(t),mXt=r(y7o," (BigBird model)"),y7o.forEach(t),cXt=i(Ze),IL=n(Ze,"LI",{});var C7o=s(IL);Qze=n(C7o,"STRONG",{});var rjn=s(Qze);fXt=r(rjn,"distilbert"),rjn.forEach(t),hXt=r(C7o," \u2014 "),aMe=n(C7o,"A",{href:!0});var tjn=s(aMe);gXt=r(tjn,"FlaxDistilBertForSequenceClassification"),tjn.forEach(t),uXt=r(C7o," (DistilBERT model)"),C7o.forEach(t),pXt=i(Ze),NL=n(Ze,"LI",{});var w7o=s(NL);Yze=n(w7o,"STRONG",{});var ajn=s(Yze);_Xt=r(ajn,"electra"),ajn.forEach(t),bXt=r(w7o," \u2014 "),nMe=n(w7o,"A",{href:!0});var njn=s(nMe);vXt=r(njn,"FlaxElectraForSequenceClassification"),njn.forEach(t),MXt=r(w7o," (ELECTRA model)"),w7o.forEach(t),TXt=i(Ze),$L=n(Ze,"LI",{});var L7o=s($L);zze=n(L7o,"STRONG",{});var sjn=s(zze);FXt=r(sjn,"mbart"),sjn.forEach(t),EXt=r(L7o," \u2014 "),sMe=n(L7o,"A",{href:!0});var ljn=s(sMe);yXt=r(ljn,"FlaxMBartForSequenceClassification"),ljn.forEach(t),CXt=r(L7o," (mBART model)"),L7o.forEach(t),wXt=i(Ze),jL=n(Ze,"LI",{});var A7o=s(jL);Hze=n(A7o,"STRONG",{});var ijn=s(Hze);LXt=r(ijn,"roberta"),ijn.forEach(t),AXt=r(A7o," \u2014 "),lMe=n(A7o,"A",{href:!0});var djn=s(lMe);RXt=r(djn,"FlaxRobertaForSequenceClassification"),djn.forEach(t),kXt=r(A7o," (RoBERTa model)"),A7o.forEach(t),SXt=i(Ze),PL=n(Ze,"LI",{});var R7o=s(PL);Kze=n(R7o,"STRONG",{});var mjn=s(Kze);xXt=r(mjn,"roberta-prelayernorm"),mjn.forEach(t),GXt=r(R7o," \u2014 "),iMe=n(R7o,"A",{href:!0});var cjn=s(iMe);BXt=r(cjn,"FlaxRobertaPreLayerNormForSequenceClassification"),cjn.forEach(t),IXt=r(R7o," (RoBERTa-PreLayerNorm model)"),R7o.forEach(t),NXt=i(Ze),ZL=n(Ze,"LI",{});var k7o=s(ZL);eHe=n(k7o,"STRONG",{});var fjn=s(eHe);$Xt=r(fjn,"roformer"),fjn.forEach(t),jXt=r(k7o," \u2014 "),dMe=n(k7o,"A",{href:!0});var hjn=s(dMe);PXt=r(hjn,"FlaxRoFormerForSequenceClassification"),hjn.forEach(t),ZXt=r(k7o," (RoFormer model)"),k7o.forEach(t),VXt=i(Ze),VL=n(Ze,"LI",{});var S7o=s(VL);oHe=n(S7o,"STRONG",{});var gjn=s(oHe);WXt=r(gjn,"xlm-roberta"),gjn.forEach(t),JXt=r(S7o," \u2014 "),mMe=n(S7o,"A",{href:!0});var ujn=s(mMe);UXt=r(ujn,"FlaxXLMRobertaForSequenceClassification"),ujn.forEach(t),XXt=r(S7o," (XLM-RoBERTa model)"),S7o.forEach(t),Ze.forEach(t),DXt=i(Rd),M(WL.$$.fragment,Rd),Rd.forEach(t),Ad.forEach(t),SIo=i(c),cf=n(c,"H3",{class:!0});var kjo=s(cf);JL=n(kjo,"A",{id:!0,class:!0,href:!0});var pjn=s(JL);rHe=n(pjn,"SPAN",{});var _jn=s(rHe);M(jJ.$$.fragment,_jn),_jn.forEach(t),pjn.forEach(t),qXt=i(kjo),tHe=n(kjo,"SPAN",{});var bjn=s(tHe);OXt=r(bjn,"AutoModelForMultipleChoice"),bjn.forEach(t),kjo.forEach(t),xIo=i(c),ir=n(c,"DIV",{class:!0});var kd=s(ir);M(PJ.$$.fragment,kd),QXt=i(kd),ff=n(kd,"P",{});var rwe=s(ff);YXt=r(rwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
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method.`),rwe.forEach(t),oDt=i(kd),ZJ=n(kd,"P",{});var Sjo=s(ZJ);rDt=r(Sjo,"This class cannot be instantiated directly using "),aHe=n(Sjo,"CODE",{});var Tjn=s(aHe);tDt=r(Tjn,"__init__()"),Tjn.forEach(t),aDt=r(Sjo," (throws an error)."),Sjo.forEach(t),nDt=i(kd),la=n(kd,"DIV",{class:!0});var RP=s(la);M(VJ.$$.fragment,RP),sDt=i(RP),nHe=n(RP,"P",{});var Fjn=s(nHe);lDt=r(Fjn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Fjn.forEach(t),iDt=i(RP),hf=n(RP,"P",{});var twe=s(hf);dDt=r(twe,`Note:
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model\u2019s configuration. Use `),hMe=n(twe,"A",{href:!0});var yjn=s(hMe);fDt=r(yjn,"from_pretrained()"),yjn.forEach(t),hDt=r(twe," to load the model weights."),twe.forEach(t),gDt=i(RP),M(UL.$$.fragment,RP),RP.forEach(t),uDt=i(kd),ho=n(kd,"DIV",{class:!0});var ln=s(ho);M(WJ.$$.fragment,ln),pDt=i(ln),lHe=n(ln,"P",{});var Cjn=s(lHe);_Dt=r(Cjn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),Cjn.forEach(t),bDt=i(ln),as=n(ln,"P",{});var kP=s(as);vDt=r(kP,"The model class to instantiate is selected based on the "),iHe=n(kP,"CODE",{});var wjn=s(iHe);MDt=r(wjn,"model_type"),wjn.forEach(t),TDt=r(kP,` property of the config object (either
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falling back to using pattern matching on `),mHe=n(kP,"CODE",{});var Ajn=s(mHe);yDt=r(Ajn,"pretrained_model_name_or_path"),Ajn.forEach(t),CDt=r(kP,":"),kP.forEach(t),wDt=i(ln),ae=n(ln,"UL",{});var ne=s(ae);XL=n(ne,"LI",{});var x7o=s(XL);cHe=n(x7o,"STRONG",{});var Rjn=s(cHe);LDt=r(Rjn,"albert"),Rjn.forEach(t),ADt=r(x7o," \u2014 "),gMe=n(x7o,"A",{href:!0});var kjn=s(gMe);RDt=r(kjn,"AlbertForMultipleChoice"),kjn.forEach(t),kDt=r(x7o," (ALBERT model)"),x7o.forEach(t),SDt=i(ne),DL=n(ne,"LI",{});var G7o=s(DL);fHe=n(G7o,"STRONG",{});var Sjn=s(fHe);xDt=r(Sjn,"bert"),Sjn.forEach(t),GDt=r(G7o," \u2014 "),uMe=n(G7o,"A",{href:!0});var xjn=s(uMe);BDt=r(xjn,"BertForMultipleChoice"),xjn.forEach(t),IDt=r(G7o," (BERT model)"),G7o.forEach(t),NDt=i(ne),qL=n(ne,"LI",{});var B7o=s(qL);hHe=n(B7o,"STRONG",{});var Gjn=s(hHe);$Dt=r(Gjn,"big_bird"),Gjn.forEach(t),jDt=r(B7o," \u2014 "),pMe=n(B7o,"A",{href:!0});var Bjn=s(pMe);PDt=r(Bjn,"BigBirdForMultipleChoice"),Bjn.forEach(t),ZDt=r(B7o," (BigBird 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"),MMe=n(j7o,"A",{href:!0});var Wjn=s(MMe);nqt=r(Wjn,"Data2VecTextForMultipleChoice"),Wjn.forEach(t),sqt=r(j7o," (Data2VecText model)"),j7o.forEach(t),lqt=i(ne),HL=n(ne,"LI",{});var P7o=s(HL);bHe=n(P7o,"STRONG",{});var Jjn=s(bHe);iqt=r(Jjn,"deberta-v2"),Jjn.forEach(t),dqt=r(P7o," \u2014 "),TMe=n(P7o,"A",{href:!0});var Ujn=s(TMe);mqt=r(Ujn,"DebertaV2ForMultipleChoice"),Ujn.forEach(t),cqt=r(P7o," (DeBERTa-v2 model)"),P7o.forEach(t),fqt=i(ne),KL=n(ne,"LI",{});var Z7o=s(KL);vHe=n(Z7o,"STRONG",{});var Xjn=s(vHe);hqt=r(Xjn,"distilbert"),Xjn.forEach(t),gqt=r(Z7o," \u2014 "),FMe=n(Z7o,"A",{href:!0});var Djn=s(FMe);uqt=r(Djn,"DistilBertForMultipleChoice"),Djn.forEach(t),pqt=r(Z7o," (DistilBERT model)"),Z7o.forEach(t),_qt=i(ne),eA=n(ne,"LI",{});var V7o=s(eA);MHe=n(V7o,"STRONG",{});var qjn=s(MHe);bqt=r(qjn,"electra"),qjn.forEach(t),vqt=r(V7o," \u2014 "),EMe=n(V7o,"A",{href:!0});var Ojn=s(EMe);Mqt=r(Ojn,"ElectraForMultipleChoice"),Ojn.forEach(t),Tqt=r(V7o," (ELECTRA 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"),BMe=n(H7o,"A",{href:!0});var pPn=s(BMe);TOt=r(pPn,"MobileBertForMultipleChoice"),pPn.forEach(t),FOt=r(H7o," (MobileBERT model)"),H7o.forEach(t),EOt=i(ne),fA=n(ne,"LI",{});var K7o=s(fA);xHe=n(K7o,"STRONG",{});var _Pn=s(xHe);yOt=r(_Pn,"mpnet"),_Pn.forEach(t),COt=r(K7o," \u2014 "),IMe=n(K7o,"A",{href:!0});var bPn=s(IMe);wOt=r(bPn,"MPNetForMultipleChoice"),bPn.forEach(t),LOt=r(K7o," (MPNet model)"),K7o.forEach(t),AOt=i(ne),hA=n(ne,"LI",{});var eLo=s(hA);GHe=n(eLo,"STRONG",{});var vPn=s(GHe);ROt=r(vPn,"mra"),vPn.forEach(t),kOt=r(eLo," \u2014 "),NMe=n(eLo,"A",{href:!0});var MPn=s(NMe);SOt=r(MPn,"MraForMultipleChoice"),MPn.forEach(t),xOt=r(eLo," (MRA model)"),eLo.forEach(t),GOt=i(ne),gA=n(ne,"LI",{});var oLo=s(gA);BHe=n(oLo,"STRONG",{});var TPn=s(BHe);BOt=r(TPn,"nezha"),TPn.forEach(t),IOt=r(oLo," \u2014 "),$Me=n(oLo,"A",{href:!0});var FPn=s($Me);NOt=r(FPn,"NezhaForMultipleChoice"),FPn.forEach(t),$Ot=r(oLo," (Nezha model)"),oLo.forEach(t),jOt=i(ne),uA=n(ne,"LI",{});var rLo=s(uA);IHe=n(rLo,"STRONG",{});var EPn=s(IHe);POt=r(EPn,"nystromformer"),EPn.forEach(t),ZOt=r(rLo," \u2014 "),jMe=n(rLo,"A",{href:!0});var yPn=s(jMe);VOt=r(yPn,"NystromformerForMultipleChoice"),yPn.forEach(t),WOt=r(rLo," (Nystr\xF6mformer model)"),rLo.forEach(t),JOt=i(ne),pA=n(ne,"LI",{});var tLo=s(pA);NHe=n(tLo,"STRONG",{});var CPn=s(NHe);UOt=r(CPn,"qdqbert"),CPn.forEach(t),XOt=r(tLo," \u2014 "),PMe=n(tLo,"A",{href:!0});var wPn=s(PMe);DOt=r(wPn,"QDQBertForMultipleChoice"),wPn.forEach(t),qOt=r(tLo," (QDQBert model)"),tLo.forEach(t),OOt=i(ne),_A=n(ne,"LI",{});var aLo=s(_A);$He=n(aLo,"STRONG",{});var LPn=s($He);QOt=r(LPn,"rembert"),LPn.forEach(t),YOt=r(aLo," \u2014 "),ZMe=n(aLo,"A",{href:!0});var APn=s(ZMe);zOt=r(APn,"RemBertForMultipleChoice"),APn.forEach(t),HOt=r(aLo," (RemBERT model)"),aLo.forEach(t),KOt=i(ne),bA=n(ne,"LI",{});var nLo=s(bA);jHe=n(nLo,"STRONG",{});var RPn=s(jHe);eQt=r(RPn,"roberta"),RPn.forEach(t),oQt=r(nLo," \u2014 "),VMe=n(nLo,"A",{href:!0});var kPn=s(VMe);rQt=r(kPn,"RobertaForMultipleChoice"),kPn.forEach(t),tQt=r(nLo," (RoBERTa model)"),nLo.forEach(t),aQt=i(ne),vA=n(ne,"LI",{});var sLo=s(vA);PHe=n(sLo,"STRONG",{});var SPn=s(PHe);nQt=r(SPn,"roberta-prelayernorm"),SPn.forEach(t),sQt=r(sLo," \u2014 "),WMe=n(sLo,"A",{href:!0});var xPn=s(WMe);lQt=r(xPn,"RobertaPreLayerNormForMultipleChoice"),xPn.forEach(t),iQt=r(sLo," (RoBERTa-PreLayerNorm model)"),sLo.forEach(t),dQt=i(ne),MA=n(ne,"LI",{});var lLo=s(MA);ZHe=n(lLo,"STRONG",{});var GPn=s(ZHe);mQt=r(GPn,"roc_bert"),GPn.forEach(t),cQt=r(lLo," \u2014 "),JMe=n(lLo,"A",{href:!0});var BPn=s(JMe);fQt=r(BPn,"RoCBertForMultipleChoice"),BPn.forEach(t),hQt=r(lLo," (RoCBert model)"),lLo.forEach(t),gQt=i(ne),TA=n(ne,"LI",{});var iLo=s(TA);VHe=n(iLo,"STRONG",{});var IPn=s(VHe);uQt=r(IPn,"roformer"),IPn.forEach(t),pQt=r(iLo," \u2014 "),UMe=n(iLo,"A",{href:!0});var NPn=s(UMe);_Qt=r(NPn,"RoFormerForMultipleChoice"),NPn.forEach(t),bQt=r(iLo," (RoFormer 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with the `),HMe=n(awe,"A",{href:!0});var rZn=s(HMe);mYt=r(rZn,"from_pretrained()"),rZn.forEach(t),cYt=r(awe," class method or the "),KMe=n(awe,"A",{href:!0});var tZn=s(KMe);fYt=r(tZn,"from_config()"),tZn.forEach(t),hYt=r(awe,` class
method.`),awe.forEach(t),gYt=i(Sd),XJ=n(Sd,"P",{});var Gjo=s(XJ);uYt=r(Gjo,"This class cannot be instantiated directly using "),KHe=n(Gjo,"CODE",{});var aZn=s(KHe);pYt=r(aZn,"__init__()"),aZn.forEach(t),_Yt=r(Gjo," (throws an error)."),Gjo.forEach(t),bYt=i(Sd),ia=n(Sd,"DIV",{class:!0});var SP=s(ia);M(DJ.$$.fragment,SP),vYt=i(SP),eKe=n(SP,"P",{});var nZn=s(eKe);MYt=r(nZn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),nZn.forEach(t),TYt=i(SP),pf=n(SP,"P",{});var nwe=s(pf);FYt=r(nwe,`Note:
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model\u2019s configuration. Use `),eTe=n(nwe,"A",{href:!0});var lZn=s(eTe);CYt=r(lZn,"from_pretrained()"),lZn.forEach(t),wYt=r(nwe," to load the model weights."),nwe.forEach(t),LYt=i(SP),M(xA.$$.fragment,SP),SP.forEach(t),AYt=i(Sd),ut=n(Sd,"DIV",{class:!0});var xd=s(ut);M(qJ.$$.fragment,xd),RYt=i(xd),rKe=n(xd,"P",{});var iZn=s(rKe);kYt=r(iZn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),iZn.forEach(t),SYt=i(xd),ns=n(xd,"P",{});var xP=s(ns);xYt=r(xP,"The model class to instantiate is selected based on the "),tKe=n(xP,"CODE",{});var dZn=s(tKe);GYt=r(dZn,"model_type"),dZn.forEach(t),BYt=r(xP,` property of the config object (either
passed as an argument or loaded from `),aKe=n(xP,"CODE",{});var mZn=s(aKe);IYt=r(mZn,"pretrained_model_name_or_path"),mZn.forEach(t),NYt=r(xP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nKe=n(xP,"CODE",{});var cZn=s(nKe);$Yt=r(cZn,"pretrained_model_name_or_path"),cZn.forEach(t),jYt=r(xP,":"),xP.forEach(t),PYt=i(xd),Fe=n(xd,"UL",{});var Ee=s(Fe);GA=n(Ee,"LI",{});var _Lo=s(GA);sKe=n(_Lo,"STRONG",{});var fZn=s(sKe);ZYt=r(fZn,"albert"),fZn.forEach(t),VYt=r(_Lo," \u2014 "),oTe=n(_Lo,"A",{href:!0});var hZn=s(oTe);WYt=r(hZn,"TFAlbertForMultipleChoice"),hZn.forEach(t),JYt=r(_Lo," (ALBERT model)"),_Lo.forEach(t),UYt=i(Ee),BA=n(Ee,"LI",{});var bLo=s(BA);lKe=n(bLo,"STRONG",{});var gZn=s(lKe);XYt=r(gZn,"bert"),gZn.forEach(t),DYt=r(bLo," \u2014 "),rTe=n(bLo,"A",{href:!0});var uZn=s(rTe);qYt=r(uZn,"TFBertForMultipleChoice"),uZn.forEach(t),OYt=r(bLo," (BERT model)"),bLo.forEach(t),QYt=i(Ee),IA=n(Ee,"LI",{});var vLo=s(IA);iKe=n(vLo,"STRONG",{});var pZn=s(iKe);YYt=r(pZn,"camembert"),pZn.forEach(t),zYt=r(vLo," \u2014 "),tTe=n(vLo,"A",{href:!0});var _Zn=s(tTe);HYt=r(_Zn,"TFCamembertForMultipleChoice"),_Zn.forEach(t),KYt=r(vLo," (CamemBERT model)"),vLo.forEach(t),ezt=i(Ee),NA=n(Ee,"LI",{});var MLo=s(NA);dKe=n(MLo,"STRONG",{});var bZn=s(dKe);ozt=r(bZn,"convbert"),bZn.forEach(t),rzt=r(MLo," \u2014 "),aTe=n(MLo,"A",{href:!0});var vZn=s(aTe);tzt=r(vZn,"TFConvBertForMultipleChoice"),vZn.forEach(t),azt=r(MLo," (ConvBERT model)"),MLo.forEach(t),nzt=i(Ee),$A=n(Ee,"LI",{});var TLo=s($A);mKe=n(TLo,"STRONG",{});var MZn=s(mKe);szt=r(MZn,"deberta-v2"),MZn.forEach(t),lzt=r(TLo," \u2014 "),nTe=n(TLo,"A",{href:!0});var TZn=s(nTe);izt=r(TZn,"TFDebertaV2ForMultipleChoice"),TZn.forEach(t),dzt=r(TLo," (DeBERTa-v2 model)"),TLo.forEach(t),mzt=i(Ee),jA=n(Ee,"LI",{});var FLo=s(jA);cKe=n(FLo,"STRONG",{});var FZn=s(cKe);czt=r(FZn,"distilbert"),FZn.forEach(t),fzt=r(FLo," \u2014 "),sTe=n(FLo,"A",{href:!0});var EZn=s(sTe);hzt=r(EZn,"TFDistilBertForMultipleChoice"),EZn.forEach(t),gzt=r(FLo," (DistilBERT model)"),FLo.forEach(t),uzt=i(Ee),PA=n(Ee,"LI",{});var ELo=s(PA);fKe=n(ELo,"STRONG",{});var yZn=s(fKe);pzt=r(yZn,"electra"),yZn.forEach(t),_zt=r(ELo," \u2014 "),lTe=n(ELo,"A",{href:!0});var CZn=s(lTe);bzt=r(CZn,"TFElectraForMultipleChoice"),CZn.forEach(t),vzt=r(ELo," (ELECTRA model)"),ELo.forEach(t),Mzt=i(Ee),ZA=n(Ee,"LI",{});var yLo=s(ZA);hKe=n(yLo,"STRONG",{});var wZn=s(hKe);Tzt=r(wZn,"flaubert"),wZn.forEach(t),Fzt=r(yLo," \u2014 "),iTe=n(yLo,"A",{href:!0});var LZn=s(iTe);Ezt=r(LZn,"TFFlaubertForMultipleChoice"),LZn.forEach(t),yzt=r(yLo," (FlauBERT model)"),yLo.forEach(t),Czt=i(Ee),VA=n(Ee,"LI",{});var CLo=s(VA);gKe=n(CLo,"STRONG",{});var AZn=s(gKe);wzt=r(AZn,"funnel"),AZn.forEach(t),Lzt=r(CLo," \u2014 "),dTe=n(CLo,"A",{href:!0});var RZn=s(dTe);Azt=r(RZn,"TFFunnelForMultipleChoice"),RZn.forEach(t),Rzt=r(CLo," (Funnel Transformer model)"),CLo.forEach(t),kzt=i(Ee),WA=n(Ee,"LI",{});var wLo=s(WA);uKe=n(wLo,"STRONG",{});var kZn=s(uKe);Szt=r(kZn,"longformer"),kZn.forEach(t),xzt=r(wLo," \u2014 "),mTe=n(wLo,"A",{href:!0});var SZn=s(mTe);Gzt=r(SZn,"TFLongformerForMultipleChoice"),SZn.forEach(t),Bzt=r(wLo," (Longformer model)"),wLo.forEach(t),Izt=i(Ee),JA=n(Ee,"LI",{});var LLo=s(JA);pKe=n(LLo,"STRONG",{});var xZn=s(pKe);Nzt=r(xZn,"mobilebert"),xZn.forEach(t),$zt=r(LLo," \u2014 "),cTe=n(LLo,"A",{href:!0});var GZn=s(cTe);jzt=r(GZn,"TFMobileBertForMultipleChoice"),GZn.forEach(t),Pzt=r(LLo," (MobileBERT model)"),LLo.forEach(t),Zzt=i(Ee),UA=n(Ee,"LI",{});var ALo=s(UA);_Ke=n(ALo,"STRONG",{});var BZn=s(_Ke);Vzt=r(BZn,"mpnet"),BZn.forEach(t),Wzt=r(ALo," \u2014 "),fTe=n(ALo,"A",{href:!0});var IZn=s(fTe);Jzt=r(IZn,"TFMPNetForMultipleChoice"),IZn.forEach(t),Uzt=r(ALo," (MPNet model)"),ALo.forEach(t),Xzt=i(Ee),XA=n(Ee,"LI",{});var RLo=s(XA);bKe=n(RLo,"STRONG",{});var NZn=s(bKe);Dzt=r(NZn,"rembert"),NZn.forEach(t),qzt=r(RLo," \u2014 "),hTe=n(RLo,"A",{href:!0});var $Zn=s(hTe);Ozt=r($Zn,"TFRemBertForMultipleChoice"),$Zn.forEach(t),Qzt=r(RLo," (RemBERT model)"),RLo.forEach(t),Yzt=i(Ee),DA=n(Ee,"LI",{});var kLo=s(DA);vKe=n(kLo,"STRONG",{});var jZn=s(vKe);zzt=r(jZn,"roberta"),jZn.forEach(t),Hzt=r(kLo," \u2014 "),gTe=n(kLo,"A",{href:!0});var PZn=s(gTe);Kzt=r(PZn,"TFRobertaForMultipleChoice"),PZn.forEach(t),eHt=r(kLo," (RoBERTa model)"),kLo.forEach(t),oHt=i(Ee),qA=n(Ee,"LI",{});var SLo=s(qA);MKe=n(SLo,"STRONG",{});var ZZn=s(MKe);rHt=r(ZZn,"roberta-prelayernorm"),ZZn.forEach(t),tHt=r(SLo," \u2014 "),uTe=n(SLo,"A",{href:!0});var VZn=s(uTe);aHt=r(VZn,"TFRobertaPreLayerNormForMultipleChoice"),VZn.forEach(t),nHt=r(SLo," (RoBERTa-PreLayerNorm model)"),SLo.forEach(t),sHt=i(Ee),OA=n(Ee,"LI",{});var xLo=s(OA);TKe=n(xLo,"STRONG",{});var WZn=s(TKe);lHt=r(WZn,"roformer"),WZn.forEach(t),iHt=r(xLo," \u2014 "),pTe=n(xLo,"A",{href:!0});var JZn=s(pTe);dHt=r(JZn,"TFRoFormerForMultipleChoice"),JZn.forEach(t),mHt=r(xLo," (RoFormer model)"),xLo.forEach(t),cHt=i(Ee),QA=n(Ee,"LI",{});var GLo=s(QA);FKe=n(GLo,"STRONG",{});var UZn=s(FKe);fHt=r(UZn,"xlm"),UZn.forEach(t),hHt=r(GLo," \u2014 "),_Te=n(GLo,"A",{href:!0});var XZn=s(_Te);gHt=r(XZn,"TFXLMForMultipleChoice"),XZn.forEach(t),uHt=r(GLo," (XLM model)"),GLo.forEach(t),pHt=i(Ee),YA=n(Ee,"LI",{});var BLo=s(YA);EKe=n(BLo,"STRONG",{});var DZn=s(EKe);_Ht=r(DZn,"xlm-roberta"),DZn.forEach(t),bHt=r(BLo," \u2014 "),bTe=n(BLo,"A",{href:!0});var qZn=s(bTe);vHt=r(qZn,"TFXLMRobertaForMultipleChoice"),qZn.forEach(t),MHt=r(BLo," (XLM-RoBERTa model)"),BLo.forEach(t),THt=i(Ee),zA=n(Ee,"LI",{});var ILo=s(zA);yKe=n(ILo,"STRONG",{});var OZn=s(yKe);FHt=r(OZn,"xlnet"),OZn.forEach(t),EHt=r(ILo," \u2014 "),vTe=n(ILo,"A",{href:!0});var QZn=s(vTe);yHt=r(QZn,"TFXLNetForMultipleChoice"),QZn.forEach(t),CHt=r(ILo," (XLNet model)"),ILo.forEach(t),Ee.forEach(t),wHt=i(xd),M(HA.$$.fragment,xd),xd.forEach(t),Sd.forEach(t),IIo=i(c),_f=n(c,"H3",{class:!0});var Bjo=s(_f);KA=n(Bjo,"A",{id:!0,class:!0,href:!0});var YZn=s(KA);CKe=n(YZn,"SPAN",{});var zZn=s(CKe);M(OJ.$$.fragment,zZn),zZn.forEach(t),YZn.forEach(t),LHt=i(Bjo),wKe=n(Bjo,"SPAN",{});var HZn=s(wKe);AHt=r(HZn,"FlaxAutoModelForMultipleChoice"),HZn.forEach(t),Bjo.forEach(t),NIo=i(c),mr=n(c,"DIV",{class:!0});var Gd=s(mr);M(QJ.$$.fragment,Gd),RHt=i(Gd),bf=n(Gd,"P",{});var swe=s(bf);kHt=r(swe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),MTe=n(swe,"A",{href:!0});var KZn=s(MTe);SHt=r(KZn,"from_pretrained()"),KZn.forEach(t),xHt=r(swe," class method or the "),TTe=n(swe,"A",{href:!0});var eVn=s(TTe);GHt=r(eVn,"from_config()"),eVn.forEach(t),BHt=r(swe,` class
method.`),swe.forEach(t),IHt=i(Gd),YJ=n(Gd,"P",{});var Ijo=s(YJ);NHt=r(Ijo,"This class cannot be instantiated directly using "),LKe=n(Ijo,"CODE",{});var oVn=s(LKe);$Ht=r(oVn,"__init__()"),oVn.forEach(t),jHt=r(Ijo," (throws an error)."),Ijo.forEach(t),PHt=i(Gd),da=n(Gd,"DIV",{class:!0});var GP=s(da);M(zJ.$$.fragment,GP),ZHt=i(GP),AKe=n(GP,"P",{});var rVn=s(AKe);VHt=r(rVn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),rVn.forEach(t),WHt=i(GP),vf=n(GP,"P",{});var lwe=s(vf);JHt=r(lwe,`Note:
Loading a model from its configuration file does `),RKe=n(lwe,"STRONG",{});var tVn=s(RKe);UHt=r(tVn,"not"),tVn.forEach(t),XHt=r(lwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),FTe=n(lwe,"A",{href:!0});var aVn=s(FTe);DHt=r(aVn,"from_pretrained()"),aVn.forEach(t),qHt=r(lwe," to load the model weights."),lwe.forEach(t),OHt=i(GP),M(eR.$$.fragment,GP),GP.forEach(t),QHt=i(Gd),pt=n(Gd,"DIV",{class:!0});var Bd=s(pt);M(HJ.$$.fragment,Bd),YHt=i(Bd),kKe=n(Bd,"P",{});var nVn=s(kKe);zHt=r(nVn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),nVn.forEach(t),HHt=i(Bd),ss=n(Bd,"P",{});var BP=s(ss);KHt=r(BP,"The model class to instantiate is selected based on the "),SKe=n(BP,"CODE",{});var sVn=s(SKe);eKt=r(sVn,"model_type"),sVn.forEach(t),oKt=r(BP,` property of the config object (either
passed as an argument or loaded from `),xKe=n(BP,"CODE",{});var lVn=s(xKe);rKt=r(lVn,"pretrained_model_name_or_path"),lVn.forEach(t),tKt=r(BP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GKe=n(BP,"CODE",{});var iVn=s(GKe);aKt=r(iVn,"pretrained_model_name_or_path"),iVn.forEach(t),nKt=r(BP,":"),BP.forEach(t),sKt=i(Bd),Ue=n(Bd,"UL",{});var He=s(Ue);oR=n(He,"LI",{});var NLo=s(oR);BKe=n(NLo,"STRONG",{});var dVn=s(BKe);lKt=r(dVn,"albert"),dVn.forEach(t),iKt=r(NLo," \u2014 "),ETe=n(NLo,"A",{href:!0});var mVn=s(ETe);dKt=r(mVn,"FlaxAlbertForMultipleChoice"),mVn.forEach(t),mKt=r(NLo," (ALBERT model)"),NLo.forEach(t),cKt=i(He),rR=n(He,"LI",{});var $Lo=s(rR);IKe=n($Lo,"STRONG",{});var cVn=s(IKe);fKt=r(cVn,"bert"),cVn.forEach(t),hKt=r($Lo," \u2014 "),yTe=n($Lo,"A",{href:!0});var fVn=s(yTe);gKt=r(fVn,"FlaxBertForMultipleChoice"),fVn.forEach(t),uKt=r($Lo," (BERT model)"),$Lo.forEach(t),pKt=i(He),tR=n(He,"LI",{});var jLo=s(tR);NKe=n(jLo,"STRONG",{});var hVn=s(NKe);_Kt=r(hVn,"big_bird"),hVn.forEach(t),bKt=r(jLo," \u2014 "),CTe=n(jLo,"A",{href:!0});var gVn=s(CTe);vKt=r(gVn,"FlaxBigBirdForMultipleChoice"),gVn.forEach(t),MKt=r(jLo," (BigBird model)"),jLo.forEach(t),TKt=i(He),aR=n(He,"LI",{});var PLo=s(aR);$Ke=n(PLo,"STRONG",{});var uVn=s($Ke);FKt=r(uVn,"distilbert"),uVn.forEach(t),EKt=r(PLo," \u2014 "),wTe=n(PLo,"A",{href:!0});var pVn=s(wTe);yKt=r(pVn,"FlaxDistilBertForMultipleChoice"),pVn.forEach(t),CKt=r(PLo," (DistilBERT model)"),PLo.forEach(t),wKt=i(He),nR=n(He,"LI",{});var ZLo=s(nR);jKe=n(ZLo,"STRONG",{});var _Vn=s(jKe);LKt=r(_Vn,"electra"),_Vn.forEach(t),AKt=r(ZLo," \u2014 "),LTe=n(ZLo,"A",{href:!0});var bVn=s(LTe);RKt=r(bVn,"FlaxElectraForMultipleChoice"),bVn.forEach(t),kKt=r(ZLo," (ELECTRA model)"),ZLo.forEach(t),SKt=i(He),sR=n(He,"LI",{});var VLo=s(sR);PKe=n(VLo,"STRONG",{});var vVn=s(PKe);xKt=r(vVn,"roberta"),vVn.forEach(t),GKt=r(VLo," \u2014 "),ATe=n(VLo,"A",{href:!0});var MVn=s(ATe);BKt=r(MVn,"FlaxRobertaForMultipleChoice"),MVn.forEach(t),IKt=r(VLo," (RoBERTa model)"),VLo.forEach(t),NKt=i(He),lR=n(He,"LI",{});var WLo=s(lR);ZKe=n(WLo,"STRONG",{});var TVn=s(ZKe);$Kt=r(TVn,"roberta-prelayernorm"),TVn.forEach(t),jKt=r(WLo," \u2014 "),RTe=n(WLo,"A",{href:!0});var FVn=s(RTe);PKt=r(FVn,"FlaxRobertaPreLayerNormForMultipleChoice"),FVn.forEach(t),ZKt=r(WLo," (RoBERTa-PreLayerNorm model)"),WLo.forEach(t),VKt=i(He),iR=n(He,"LI",{});var JLo=s(iR);VKe=n(JLo,"STRONG",{});var EVn=s(VKe);WKt=r(EVn,"roformer"),EVn.forEach(t),JKt=r(JLo," \u2014 "),kTe=n(JLo,"A",{href:!0});var yVn=s(kTe);UKt=r(yVn,"FlaxRoFormerForMultipleChoice"),yVn.forEach(t),XKt=r(JLo," (RoFormer model)"),JLo.forEach(t),DKt=i(He),dR=n(He,"LI",{});var ULo=s(dR);WKe=n(ULo,"STRONG",{});var CVn=s(WKe);qKt=r(CVn,"xlm-roberta"),CVn.forEach(t),OKt=r(ULo," \u2014 "),STe=n(ULo,"A",{href:!0});var wVn=s(STe);QKt=r(wVn,"FlaxXLMRobertaForMultipleChoice"),wVn.forEach(t),YKt=r(ULo," (XLM-RoBERTa model)"),ULo.forEach(t),He.forEach(t),zKt=i(Bd),M(mR.$$.fragment,Bd),Bd.forEach(t),Gd.forEach(t),$Io=i(c),Mf=n(c,"H3",{class:!0});var Njo=s(Mf);cR=n(Njo,"A",{id:!0,class:!0,href:!0});var LVn=s(cR);JKe=n(LVn,"SPAN",{});var AVn=s(JKe);M(KJ.$$.fragment,AVn),AVn.forEach(t),LVn.forEach(t),HKt=i(Njo),UKe=n(Njo,"SPAN",{});var RVn=s(UKe);KKt=r(RVn,"AutoModelForNextSentencePrediction"),RVn.forEach(t),Njo.forEach(t),jIo=i(c),cr=n(c,"DIV",{class:!0});var Id=s(cr);M(eU.$$.fragment,Id),eea=i(Id),Tf=n(Id,"P",{});var iwe=s(Tf);oea=r(iwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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method.`),iwe.forEach(t),sea=i(Id),oU=n(Id,"P",{});var $jo=s(oU);lea=r($jo,"This class cannot be instantiated directly using "),XKe=n($jo,"CODE",{});var xVn=s(XKe);iea=r(xVn,"__init__()"),xVn.forEach(t),dea=r($jo," (throws an error)."),$jo.forEach(t),mea=i(Id),ma=n(Id,"DIV",{class:!0});var IP=s(ma);M(rU.$$.fragment,IP),cea=i(IP),DKe=n(IP,"P",{});var GVn=s(DKe);fea=r(GVn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),GVn.forEach(t),hea=i(IP),Ff=n(IP,"P",{});var dwe=s(Ff);gea=r(dwe,`Note:
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model\u2019s configuration. Use `),BTe=n(dwe,"A",{href:!0});var IVn=s(BTe);_ea=r(IVn,"from_pretrained()"),IVn.forEach(t),bea=r(dwe," to load the model weights."),dwe.forEach(t),vea=i(IP),M(fR.$$.fragment,IP),IP.forEach(t),Mea=i(Id),go=n(Id,"DIV",{class:!0});var dn=s(go);M(tU.$$.fragment,dn),Tea=i(dn),OKe=n(dn,"P",{});var NVn=s(OKe);Fea=r(NVn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),NVn.forEach(t),Eea=i(dn),ls=n(dn,"P",{});var NP=s(ls);yea=r(NP,"The model class to instantiate is selected based on the "),QKe=n(NP,"CODE",{});var $Vn=s(QKe);Cea=r($Vn,"model_type"),$Vn.forEach(t),wea=r(NP,` property of the config object (either
passed as an argument or loaded from `),YKe=n(NP,"CODE",{});var jVn=s(YKe);Lea=r(jVn,"pretrained_model_name_or_path"),jVn.forEach(t),Aea=r(NP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zKe=n(NP,"CODE",{});var PVn=s(zKe);Rea=r(PVn,"pretrained_model_name_or_path"),PVn.forEach(t),kea=r(NP,":"),NP.forEach(t),Sea=i(dn),eo=n(dn,"UL",{});var It=s(eo);hR=n(It,"LI",{});var XLo=s(hR);HKe=n(XLo,"STRONG",{});var ZVn=s(HKe);xea=r(ZVn,"bert"),ZVn.forEach(t),Gea=r(XLo," \u2014 "),ITe=n(XLo,"A",{href:!0});var VVn=s(ITe);Bea=r(VVn,"BertForNextSentencePrediction"),VVn.forEach(t),Iea=r(XLo," (BERT model)"),XLo.forEach(t),Nea=i(It),gR=n(It,"LI",{});var DLo=s(gR);KKe=n(DLo,"STRONG",{});var WVn=s(KKe);$ea=r(WVn,"ernie"),WVn.forEach(t),jea=r(DLo," \u2014 "),NTe=n(DLo,"A",{href:!0});var JVn=s(NTe);Pea=r(JVn,"ErnieForNextSentencePrediction"),JVn.forEach(t),Zea=r(DLo," (ERNIE model)"),DLo.forEach(t),Vea=i(It),uR=n(It,"LI",{});var qLo=s(uR);eeo=n(qLo,"STRONG",{});var UVn=s(eeo);Wea=r(UVn,"fnet"),UVn.forEach(t),Jea=r(qLo," \u2014 "),$Te=n(qLo,"A",{href:!0});var XVn=s($Te);Uea=r(XVn,"FNetForNextSentencePrediction"),XVn.forEach(t),Xea=r(qLo," (FNet model)"),qLo.forEach(t),Dea=i(It),pR=n(It,"LI",{});var OLo=s(pR);oeo=n(OLo,"STRONG",{});var DVn=s(oeo);qea=r(DVn,"megatron-bert"),DVn.forEach(t),Oea=r(OLo," \u2014 "),jTe=n(OLo,"A",{href:!0});var qVn=s(jTe);Qea=r(qVn,"MegatronBertForNextSentencePrediction"),qVn.forEach(t),Yea=r(OLo," (Megatron-BERT model)"),OLo.forEach(t),zea=i(It),_R=n(It,"LI",{});var QLo=s(_R);reo=n(QLo,"STRONG",{});var OVn=s(reo);Hea=r(OVn,"mobilebert"),OVn.forEach(t),Kea=r(QLo," \u2014 "),PTe=n(QLo,"A",{href:!0});var QVn=s(PTe);eoa=r(QVn,"MobileBertForNextSentencePrediction"),QVn.forEach(t),ooa=r(QLo," (MobileBERT model)"),QLo.forEach(t),roa=i(It),bR=n(It,"LI",{});var YLo=s(bR);teo=n(YLo,"STRONG",{});var YVn=s(teo);toa=r(YVn,"nezha"),YVn.forEach(t),aoa=r(YLo," \u2014 "),ZTe=n(YLo,"A",{href:!0});var zVn=s(ZTe);noa=r(zVn,"NezhaForNextSentencePrediction"),zVn.forEach(t),soa=r(YLo," (Nezha model)"),YLo.forEach(t),loa=i(It),vR=n(It,"LI",{});var zLo=s(vR);aeo=n(zLo,"STRONG",{});var HVn=s(aeo);ioa=r(HVn,"qdqbert"),HVn.forEach(t),doa=r(zLo," \u2014 "),VTe=n(zLo,"A",{href:!0});var KVn=s(VTe);moa=r(KVn,"QDQBertForNextSentencePrediction"),KVn.forEach(t),coa=r(zLo," (QDQBert model)"),zLo.forEach(t),It.forEach(t),foa=i(dn),MR=n(dn,"P",{});var HLo=s(MR);hoa=r(HLo,"The model is set in evaluation mode by default using "),neo=n(HLo,"CODE",{});var eWn=s(neo);goa=r(eWn,"model.eval()"),eWn.forEach(t),uoa=r(HLo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),seo=n(HLo,"CODE",{});var oWn=s(seo);poa=r(oWn,"model.train()"),oWn.forEach(t),HLo.forEach(t),_oa=i(dn),M(TR.$$.fragment,dn),dn.forEach(t),Id.forEach(t),PIo=i(c),Ef=n(c,"H3",{class:!0});var jjo=s(Ef);FR=n(jjo,"A",{id:!0,class:!0,href:!0});var rWn=s(FR);leo=n(rWn,"SPAN",{});var tWn=s(leo);M(aU.$$.fragment,tWn),tWn.forEach(t),rWn.forEach(t),boa=i(jjo),ieo=n(jjo,"SPAN",{});var aWn=s(ieo);voa=r(aWn,"TFAutoModelForNextSentencePrediction"),aWn.forEach(t),jjo.forEach(t),ZIo=i(c),fr=n(c,"DIV",{class:!0});var Nd=s(fr);M(nU.$$.fragment,Nd),Moa=i(Nd),yf=n(Nd,"P",{});var mwe=s(yf);Toa=r(mwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),WTe=n(mwe,"A",{href:!0});var nWn=s(WTe);Foa=r(nWn,"from_pretrained()"),nWn.forEach(t),Eoa=r(mwe," class method or the "),JTe=n(mwe,"A",{href:!0});var sWn=s(JTe);yoa=r(sWn,"from_config()"),sWn.forEach(t),Coa=r(mwe,` class
method.`),mwe.forEach(t),woa=i(Nd),sU=n(Nd,"P",{});var Pjo=s(sU);Loa=r(Pjo,"This class cannot be instantiated directly using "),deo=n(Pjo,"CODE",{});var lWn=s(deo);Aoa=r(lWn,"__init__()"),lWn.forEach(t),Roa=r(Pjo," (throws an error)."),Pjo.forEach(t),koa=i(Nd),ca=n(Nd,"DIV",{class:!0});var $P=s(ca);M(lU.$$.fragment,$P),Soa=i($P),meo=n($P,"P",{});var iWn=s(meo);xoa=r(iWn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),iWn.forEach(t),Goa=i($P),Cf=n($P,"P",{});var cwe=s(Cf);Boa=r(cwe,`Note:
Loading a model from its configuration file does `),ceo=n(cwe,"STRONG",{});var dWn=s(ceo);Ioa=r(dWn,"not"),dWn.forEach(t),Noa=r(cwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),UTe=n(cwe,"A",{href:!0});var mWn=s(UTe);$oa=r(mWn,"from_pretrained()"),mWn.forEach(t),joa=r(cwe," to load the model weights."),cwe.forEach(t),Poa=i($P),M(ER.$$.fragment,$P),$P.forEach(t),Zoa=i(Nd),_t=n(Nd,"DIV",{class:!0});var $d=s(_t);M(iU.$$.fragment,$d),Voa=i($d),feo=n($d,"P",{});var cWn=s(feo);Woa=r(cWn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),cWn.forEach(t),Joa=i($d),is=n($d,"P",{});var jP=s(is);Uoa=r(jP,"The model class to instantiate is selected based on the "),heo=n(jP,"CODE",{});var fWn=s(heo);Xoa=r(fWn,"model_type"),fWn.forEach(t),Doa=r(jP,` property of the config object (either
passed as an argument or loaded from `),geo=n(jP,"CODE",{});var hWn=s(geo);qoa=r(hWn,"pretrained_model_name_or_path"),hWn.forEach(t),Ooa=r(jP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ueo=n(jP,"CODE",{});var gWn=s(ueo);Qoa=r(gWn,"pretrained_model_name_or_path"),gWn.forEach(t),Yoa=r(jP,":"),jP.forEach(t),zoa=i($d),dU=n($d,"UL",{});var Zjo=s(dU);yR=n(Zjo,"LI",{});var KLo=s(yR);peo=n(KLo,"STRONG",{});var uWn=s(peo);Hoa=r(uWn,"bert"),uWn.forEach(t),Koa=r(KLo," \u2014 "),XTe=n(KLo,"A",{href:!0});var pWn=s(XTe);era=r(pWn,"TFBertForNextSentencePrediction"),pWn.forEach(t),ora=r(KLo," (BERT model)"),KLo.forEach(t),rra=i(Zjo),CR=n(Zjo,"LI",{});var eAo=s(CR);_eo=n(eAo,"STRONG",{});var _Wn=s(_eo);tra=r(_Wn,"mobilebert"),_Wn.forEach(t),ara=r(eAo," \u2014 "),DTe=n(eAo,"A",{href:!0});var bWn=s(DTe);nra=r(bWn,"TFMobileBertForNextSentencePrediction"),bWn.forEach(t),sra=r(eAo," (MobileBERT model)"),eAo.forEach(t),Zjo.forEach(t),lra=i($d),M(wR.$$.fragment,$d),$d.forEach(t),Nd.forEach(t),VIo=i(c),wf=n(c,"H3",{class:!0});var Vjo=s(wf);LR=n(Vjo,"A",{id:!0,class:!0,href:!0});var vWn=s(LR);beo=n(vWn,"SPAN",{});var MWn=s(beo);M(mU.$$.fragment,MWn),MWn.forEach(t),vWn.forEach(t),ira=i(Vjo),veo=n(Vjo,"SPAN",{});var TWn=s(veo);dra=r(TWn,"FlaxAutoModelForNextSentencePrediction"),TWn.forEach(t),Vjo.forEach(t),WIo=i(c),hr=n(c,"DIV",{class:!0});var jd=s(hr);M(cU.$$.fragment,jd),mra=i(jd),Lf=n(jd,"P",{});var fwe=s(Lf);cra=r(fwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),qTe=n(fwe,"A",{href:!0});var FWn=s(qTe);fra=r(FWn,"from_pretrained()"),FWn.forEach(t),hra=r(fwe," class method or the "),OTe=n(fwe,"A",{href:!0});var EWn=s(OTe);gra=r(EWn,"from_config()"),EWn.forEach(t),ura=r(fwe,` class
method.`),fwe.forEach(t),pra=i(jd),fU=n(jd,"P",{});var Wjo=s(fU);_ra=r(Wjo,"This class cannot be instantiated directly using "),Meo=n(Wjo,"CODE",{});var yWn=s(Meo);bra=r(yWn,"__init__()"),yWn.forEach(t),vra=r(Wjo," (throws an error)."),Wjo.forEach(t),Mra=i(jd),fa=n(jd,"DIV",{class:!0});var PP=s(fa);M(hU.$$.fragment,PP),Tra=i(PP),Teo=n(PP,"P",{});var CWn=s(Teo);Fra=r(CWn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),CWn.forEach(t),Era=i(PP),Af=n(PP,"P",{});var hwe=s(Af);yra=r(hwe,`Note:
Loading a model from its configuration file does `),Feo=n(hwe,"STRONG",{});var wWn=s(Feo);Cra=r(wWn,"not"),wWn.forEach(t),wra=r(hwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),QTe=n(hwe,"A",{href:!0});var LWn=s(QTe);Lra=r(LWn,"from_pretrained()"),LWn.forEach(t),Ara=r(hwe," to load the model weights."),hwe.forEach(t),Rra=i(PP),M(AR.$$.fragment,PP),PP.forEach(t),kra=i(jd),bt=n(jd,"DIV",{class:!0});var Pd=s(bt);M(gU.$$.fragment,Pd),Sra=i(Pd),Eeo=n(Pd,"P",{});var AWn=s(Eeo);xra=r(AWn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),AWn.forEach(t),Gra=i(Pd),ds=n(Pd,"P",{});var ZP=s(ds);Bra=r(ZP,"The model class to instantiate is selected based on the "),yeo=n(ZP,"CODE",{});var RWn=s(yeo);Ira=r(RWn,"model_type"),RWn.forEach(t),Nra=r(ZP,` property of the config object (either
passed as an argument or loaded from `),Ceo=n(ZP,"CODE",{});var kWn=s(Ceo);$ra=r(kWn,"pretrained_model_name_or_path"),kWn.forEach(t),jra=r(ZP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),weo=n(ZP,"CODE",{});var SWn=s(weo);Pra=r(SWn,"pretrained_model_name_or_path"),SWn.forEach(t),Zra=r(ZP,":"),ZP.forEach(t),Vra=i(Pd),Leo=n(Pd,"UL",{});var xWn=s(Leo);RR=n(xWn,"LI",{});var oAo=s(RR);Aeo=n(oAo,"STRONG",{});var GWn=s(Aeo);Wra=r(GWn,"bert"),GWn.forEach(t),Jra=r(oAo," \u2014 "),YTe=n(oAo,"A",{href:!0});var BWn=s(YTe);Ura=r(BWn,"FlaxBertForNextSentencePrediction"),BWn.forEach(t),Xra=r(oAo," (BERT model)"),oAo.forEach(t),xWn.forEach(t),Dra=i(Pd),M(kR.$$.fragment,Pd),Pd.forEach(t),jd.forEach(t),JIo=i(c),Rf=n(c,"H3",{class:!0});var Jjo=s(Rf);SR=n(Jjo,"A",{id:!0,class:!0,href:!0});var IWn=s(SR);Reo=n(IWn,"SPAN",{});var NWn=s(Reo);M(uU.$$.fragment,NWn),NWn.forEach(t),IWn.forEach(t),qra=i(Jjo),keo=n(Jjo,"SPAN",{});var $Wn=s(keo);Ora=r($Wn,"AutoModelForTokenClassification"),$Wn.forEach(t),Jjo.forEach(t),UIo=i(c),gr=n(c,"DIV",{class:!0});var Zd=s(gr);M(pU.$$.fragment,Zd),Qra=i(Zd),kf=n(Zd,"P",{});var gwe=s(kf);Yra=r(gwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),zTe=n(gwe,"A",{href:!0});var jWn=s(zTe);zra=r(jWn,"from_pretrained()"),jWn.forEach(t),Hra=r(gwe," class method or the "),HTe=n(gwe,"A",{href:!0});var PWn=s(HTe);Kra=r(PWn,"from_config()"),PWn.forEach(t),eta=r(gwe,` class
method.`),gwe.forEach(t),ota=i(Zd),_U=n(Zd,"P",{});var Ujo=s(_U);rta=r(Ujo,"This class cannot be instantiated directly using "),Seo=n(Ujo,"CODE",{});var ZWn=s(Seo);tta=r(ZWn,"__init__()"),ZWn.forEach(t),ata=r(Ujo," (throws an error)."),Ujo.forEach(t),nta=i(Zd),ha=n(Zd,"DIV",{class:!0});var VP=s(ha);M(bU.$$.fragment,VP),sta=i(VP),xeo=n(VP,"P",{});var VWn=s(xeo);lta=r(VWn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),VWn.forEach(t),ita=i(VP),Sf=n(VP,"P",{});var uwe=s(Sf);dta=r(uwe,`Note:
Loading a model from its configuration file does `),Geo=n(uwe,"STRONG",{});var WWn=s(Geo);mta=r(WWn,"not"),WWn.forEach(t),cta=r(uwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),KTe=n(uwe,"A",{href:!0});var JWn=s(KTe);fta=r(JWn,"from_pretrained()"),JWn.forEach(t),hta=r(uwe," to load the model weights."),uwe.forEach(t),gta=i(VP),M(xR.$$.fragment,VP),VP.forEach(t),uta=i(Zd),uo=n(Zd,"DIV",{class:!0});var mn=s(uo);M(vU.$$.fragment,mn),pta=i(mn),Beo=n(mn,"P",{});var UWn=s(Beo);_ta=r(UWn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),UWn.forEach(t),bta=i(mn),ms=n(mn,"P",{});var WP=s(ms);vta=r(WP,"The model class to instantiate is selected based on the "),Ieo=n(WP,"CODE",{});var XWn=s(Ieo);Mta=r(XWn,"model_type"),XWn.forEach(t),Tta=r(WP,` property of the config object (either
passed as an argument or loaded from `),Neo=n(WP,"CODE",{});var DWn=s(Neo);Fta=r(DWn,"pretrained_model_name_or_path"),DWn.forEach(t),Eta=r(WP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$eo=n(WP,"CODE",{});var qWn=s($eo);yta=r(qWn,"pretrained_model_name_or_path"),qWn.forEach(t),Cta=r(WP,":"),WP.forEach(t),wta=i(mn),z=n(mn,"UL",{});var H=s(z);GR=n(H,"LI",{});var rAo=s(GR);jeo=n(rAo,"STRONG",{});var OWn=s(jeo);Lta=r(OWn,"albert"),OWn.forEach(t),Ata=r(rAo," \u2014 "),eFe=n(rAo,"A",{href:!0});var QWn=s(eFe);Rta=r(QWn,"AlbertForTokenClassification"),QWn.forEach(t),kta=r(rAo," (ALBERT model)"),rAo.forEach(t),Sta=i(H),BR=n(H,"LI",{});var tAo=s(BR);Peo=n(tAo,"STRONG",{});var YWn=s(Peo);xta=r(YWn,"bert"),YWn.forEach(t),Gta=r(tAo," \u2014 "),oFe=n(tAo,"A",{href:!0});var zWn=s(oFe);Bta=r(zWn,"BertForTokenClassification"),zWn.forEach(t),Ita=r(tAo," (BERT model)"),tAo.forEach(t),Nta=i(H),IR=n(H,"LI",{});var aAo=s(IR);Zeo=n(aAo,"STRONG",{});var HWn=s(Zeo);$ta=r(HWn,"big_bird"),HWn.forEach(t),jta=r(aAo," \u2014 "),rFe=n(aAo,"A",{href:!0});var KWn=s(rFe);Pta=r(KWn,"BigBirdForTokenClassification"),KWn.forEach(t),Zta=r(aAo," (BigBird model)"),aAo.forEach(t),Vta=i(H),NR=n(H,"LI",{});var nAo=s(NR);Veo=n(nAo,"STRONG",{});var eJn=s(Veo);Wta=r(eJn,"biogpt"),eJn.forEach(t),Jta=r(nAo," \u2014 "),tFe=n(nAo,"A",{href:!0});var oJn=s(tFe);Uta=r(oJn,"BioGptForTokenClassification"),oJn.forEach(t),Xta=r(nAo," (BioGpt model)"),nAo.forEach(t),Dta=i(H),$R=n(H,"LI",{});var sAo=s($R);Weo=n(sAo,"STRONG",{});var rJn=s(Weo);qta=r(rJn,"bloom"),rJn.forEach(t),Ota=r(sAo," \u2014 "),aFe=n(sAo,"A",{href:!0});var tJn=s(aFe);Qta=r(tJn,"BloomForTokenClassification"),tJn.forEach(t),Yta=r(sAo," (BLOOM model)"),sAo.forEach(t),zta=i(H),jR=n(H,"LI",{});var lAo=s(jR);Jeo=n(lAo,"STRONG",{});var aJn=s(Jeo);Hta=r(aJn,"camembert"),aJn.forEach(t),Kta=r(lAo," \u2014 "),nFe=n(lAo,"A",{href:!0});var nJn=s(nFe);eaa=r(nJn,"CamembertForTokenClassification"),nJn.forEach(t),oaa=r(lAo," (CamemBERT model)"),lAo.forEach(t),raa=i(H),PR=n(H,"LI",{});var iAo=s(PR);Ueo=n(iAo,"STRONG",{});var sJn=s(Ueo);taa=r(sJn,"canine"),sJn.forEach(t),aaa=r(iAo," \u2014 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deactivated). To train the model, you should first set it back in training mode with `),Poo=n(rRo,"CODE",{});var QUn=s(Poo);kia=r(QUn,"model.train()"),QUn.forEach(t),rRo.forEach(t),Sia=i(mn),M(Bk.$$.fragment,mn),mn.forEach(t),Zd.forEach(t),XIo=i(c),xf=n(c,"H3",{class:!0});var Xjo=s(xf);Ik=n(Xjo,"A",{id:!0,class:!0,href:!0});var YUn=s(Ik);Zoo=n(YUn,"SPAN",{});var zUn=s(Zoo);M(MU.$$.fragment,zUn),zUn.forEach(t),YUn.forEach(t),xia=i(Xjo),Voo=n(Xjo,"SPAN",{});var HUn=s(Voo);Gia=r(HUn,"TFAutoModelForTokenClassification"),HUn.forEach(t),Xjo.forEach(t),DIo=i(c),ur=n(c,"DIV",{class:!0});var Vd=s(ur);M(TU.$$.fragment,Vd),Bia=i(Vd),Gf=n(Vd,"P",{});var pwe=s(Gf);Iia=r(pwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),eEe=n(pwe,"A",{href:!0});var KUn=s(eEe);Nia=r(KUn,"from_pretrained()"),KUn.forEach(t),$ia=r(pwe," class method or the "),oEe=n(pwe,"A",{href:!0});var eXn=s(oEe);jia=r(eXn,"from_config()"),eXn.forEach(t),Pia=r(pwe,` class
method.`),pwe.forEach(t),Zia=i(Vd),FU=n(Vd,"P",{});var Djo=s(FU);Via=r(Djo,"This class cannot be instantiated directly using "),Woo=n(Djo,"CODE",{});var oXn=s(Woo);Wia=r(oXn,"__init__()"),oXn.forEach(t),Jia=r(Djo," (throws an error)."),Djo.forEach(t),Uia=i(Vd),ga=n(Vd,"DIV",{class:!0});var JP=s(ga);M(EU.$$.fragment,JP),Xia=i(JP),Joo=n(JP,"P",{});var rXn=s(Joo);Dia=r(rXn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),rXn.forEach(t),qia=i(JP),Bf=n(JP,"P",{});var _we=s(Bf);Oia=r(_we,`Note:
Loading a model from its configuration file does `),Uoo=n(_we,"STRONG",{});var tXn=s(Uoo);Qia=r(tXn,"not"),tXn.forEach(t),Yia=r(_we,` load the model weights. It only affects the
model\u2019s configuration. Use `),rEe=n(_we,"A",{href:!0});var aXn=s(rEe);zia=r(aXn,"from_pretrained()"),aXn.forEach(t),Hia=r(_we," to load the model weights."),_we.forEach(t),Kia=i(JP),M(Nk.$$.fragment,JP),JP.forEach(t),eda=i(Vd),vt=n(Vd,"DIV",{class:!0});var Wd=s(vt);M(yU.$$.fragment,Wd),oda=i(Wd),Xoo=n(Wd,"P",{});var nXn=s(Xoo);rda=r(nXn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),nXn.forEach(t),tda=i(Wd),cs=n(Wd,"P",{});var UP=s(cs);ada=r(UP,"The model class to instantiate is selected based on the "),Doo=n(UP,"CODE",{});var sXn=s(Doo);nda=r(sXn,"model_type"),sXn.forEach(t),sda=r(UP,` property of the config object (either
passed as an argument or loaded from `),qoo=n(UP,"CODE",{});var lXn=s(qoo);lda=r(lXn,"pretrained_model_name_or_path"),lXn.forEach(t),ida=r(UP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ooo=n(UP,"CODE",{});var iXn=s(Ooo);dda=r(iXn,"pretrained_model_name_or_path"),iXn.forEach(t),mda=r(UP,":"),UP.forEach(t),cda=i(Wd),ue=n(Wd,"UL",{});var ve=s(ue);$k=n(ve,"LI",{});var tRo=s($k);Qoo=n(tRo,"STRONG",{});var dXn=s(Qoo);fda=r(dXn,"albert"),dXn.forEach(t),hda=r(tRo," \u2014 "),tEe=n(tRo,"A",{href:!0});var mXn=s(tEe);gda=r(mXn,"TFAlbertForTokenClassification"),mXn.forEach(t),uda=r(tRo," (ALBERT model)"),tRo.forEach(t),pda=i(ve),jk=n(ve,"LI",{});var aRo=s(jk);Yoo=n(aRo,"STRONG",{});var cXn=s(Yoo);_da=r(cXn,"bert"),cXn.forEach(t),bda=r(aRo," \u2014 "),aEe=n(aRo,"A",{href:!0});var fXn=s(aEe);vda=r(fXn,"TFBertForTokenClassification"),fXn.forEach(t),Mda=r(aRo," (BERT model)"),aRo.forEach(t),Tda=i(ve),Pk=n(ve,"LI",{});var nRo=s(Pk);zoo=n(nRo,"STRONG",{});var hXn=s(zoo);Fda=r(hXn,"camembert"),hXn.forEach(t),Eda=r(nRo," \u2014 "),nEe=n(nRo,"A",{href:!0});var gXn=s(nEe);yda=r(gXn,"TFCamembertForTokenClassification"),gXn.forEach(t),Cda=r(nRo," (CamemBERT model)"),nRo.forEach(t),wda=i(ve),Zk=n(ve,"LI",{});var sRo=s(Zk);Hoo=n(sRo,"STRONG",{});var uXn=s(Hoo);Lda=r(uXn,"convbert"),uXn.forEach(t),Ada=r(sRo," \u2014 "),sEe=n(sRo,"A",{href:!0});var pXn=s(sEe);Rda=r(pXn,"TFConvBertForTokenClassification"),pXn.forEach(t),kda=r(sRo," (ConvBERT model)"),sRo.forEach(t),Sda=i(ve),Vk=n(ve,"LI",{});var lRo=s(Vk);Koo=n(lRo,"STRONG",{});var _Xn=s(Koo);xda=r(_Xn,"deberta"),_Xn.forEach(t),Gda=r(lRo," \u2014 "),lEe=n(lRo,"A",{href:!0});var bXn=s(lEe);Bda=r(bXn,"TFDebertaForTokenClassification"),bXn.forEach(t),Ida=r(lRo," (DeBERTa model)"),lRo.forEach(t),Nda=i(ve),Wk=n(ve,"LI",{});var iRo=s(Wk);ero=n(iRo,"STRONG",{});var vXn=s(ero);$da=r(vXn,"deberta-v2"),vXn.forEach(t),jda=r(iRo," \u2014 "),iEe=n(iRo,"A",{href:!0});var MXn=s(iEe);Pda=r(MXn,"TFDebertaV2ForTokenClassification"),MXn.forEach(t),Zda=r(iRo," (DeBERTa-v2 model)"),iRo.forEach(t),Vda=i(ve),Jk=n(ve,"LI",{});var dRo=s(Jk);oro=n(dRo,"STRONG",{});var TXn=s(oro);Wda=r(TXn,"distilbert"),TXn.forEach(t),Jda=r(dRo," \u2014 "),dEe=n(dRo,"A",{href:!0});var FXn=s(dEe);Uda=r(FXn,"TFDistilBertForTokenClassification"),FXn.forEach(t),Xda=r(dRo," (DistilBERT model)"),dRo.forEach(t),Dda=i(ve),Uk=n(ve,"LI",{});var mRo=s(Uk);rro=n(mRo,"STRONG",{});var EXn=s(rro);qda=r(EXn,"electra"),EXn.forEach(t),Oda=r(mRo," \u2014 "),mEe=n(mRo,"A",{href:!0});var yXn=s(mEe);Qda=r(yXn,"TFElectraForTokenClassification"),yXn.forEach(t),Yda=r(mRo," (ELECTRA model)"),mRo.forEach(t),zda=i(ve),Xk=n(ve,"LI",{});var cRo=s(Xk);tro=n(cRo,"STRONG",{});var CXn=s(tro);Hda=r(CXn,"esm"),CXn.forEach(t),Kda=r(cRo," \u2014 "),cEe=n(cRo,"A",{href:!0});var wXn=s(cEe);ema=r(wXn,"TFEsmForTokenClassification"),wXn.forEach(t),oma=r(cRo," (ESM model)"),cRo.forEach(t),rma=i(ve),Dk=n(ve,"LI",{});var fRo=s(Dk);aro=n(fRo,"STRONG",{});var LXn=s(aro);tma=r(LXn,"flaubert"),LXn.forEach(t),ama=r(fRo," \u2014 "),fEe=n(fRo,"A",{href:!0});var AXn=s(fEe);nma=r(AXn,"TFFlaubertForTokenClassification"),AXn.forEach(t),sma=r(fRo," (FlauBERT model)"),fRo.forEach(t),lma=i(ve),qk=n(ve,"LI",{});var hRo=s(qk);nro=n(hRo,"STRONG",{});var RXn=s(nro);ima=r(RXn,"funnel"),RXn.forEach(t),dma=r(hRo," \u2014 "),hEe=n(hRo,"A",{href:!0});var kXn=s(hEe);mma=r(kXn,"TFFunnelForTokenClassification"),kXn.forEach(t),cma=r(hRo," (Funnel Transformer model)"),hRo.forEach(t),fma=i(ve),Ok=n(ve,"LI",{});var gRo=s(Ok);sro=n(gRo,"STRONG",{});var SXn=s(sro);hma=r(SXn,"layoutlm"),SXn.forEach(t),gma=r(gRo," \u2014 "),gEe=n(gRo,"A",{href:!0});var xXn=s(gEe);uma=r(xXn,"TFLayoutLMForTokenClassification"),xXn.forEach(t),pma=r(gRo," (LayoutLM model)"),gRo.forEach(t),_ma=i(ve),Qk=n(ve,"LI",{});var uRo=s(Qk);lro=n(uRo,"STRONG",{});var GXn=s(lro);bma=r(GXn,"layoutlmv3"),GXn.forEach(t),vma=r(uRo," \u2014 "),uEe=n(uRo,"A",{href:!0});var BXn=s(uEe);Mma=r(BXn,"TFLayoutLMv3ForTokenClassification"),BXn.forEach(t),Tma=r(uRo," (LayoutLMv3 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VXn=s(cro);jma=r(VXn,"rembert"),VXn.forEach(t),Pma=r(vRo," \u2014 "),vEe=n(vRo,"A",{href:!0});var WXn=s(vEe);Zma=r(WXn,"TFRemBertForTokenClassification"),WXn.forEach(t),Vma=r(vRo," (RemBERT model)"),vRo.forEach(t),Wma=i(ve),eS=n(ve,"LI",{});var MRo=s(eS);fro=n(MRo,"STRONG",{});var JXn=s(fro);Jma=r(JXn,"roberta"),JXn.forEach(t),Uma=r(MRo," \u2014 "),MEe=n(MRo,"A",{href:!0});var UXn=s(MEe);Xma=r(UXn,"TFRobertaForTokenClassification"),UXn.forEach(t),Dma=r(MRo," (RoBERTa model)"),MRo.forEach(t),qma=i(ve),oS=n(ve,"LI",{});var TRo=s(oS);hro=n(TRo,"STRONG",{});var XXn=s(hro);Oma=r(XXn,"roberta-prelayernorm"),XXn.forEach(t),Qma=r(TRo," \u2014 "),TEe=n(TRo,"A",{href:!0});var DXn=s(TEe);Yma=r(DXn,"TFRobertaPreLayerNormForTokenClassification"),DXn.forEach(t),zma=r(TRo," (RoBERTa-PreLayerNorm model)"),TRo.forEach(t),Hma=i(ve),rS=n(ve,"LI",{});var FRo=s(rS);gro=n(FRo,"STRONG",{});var qXn=s(gro);Kma=r(qXn,"roformer"),qXn.forEach(t),eca=r(FRo," \u2014 "),FEe=n(FRo,"A",{href:!0});var OXn=s(FEe);oca=r(OXn,"TFRoFormerForTokenClassification"),OXn.forEach(t),rca=r(FRo," (RoFormer model)"),FRo.forEach(t),tca=i(ve),tS=n(ve,"LI",{});var ERo=s(tS);uro=n(ERo,"STRONG",{});var QXn=s(uro);aca=r(QXn,"xlm"),QXn.forEach(t),nca=r(ERo," \u2014 "),EEe=n(ERo,"A",{href:!0});var YXn=s(EEe);sca=r(YXn,"TFXLMForTokenClassification"),YXn.forEach(t),lca=r(ERo," (XLM model)"),ERo.forEach(t),ica=i(ve),aS=n(ve,"LI",{});var yRo=s(aS);pro=n(yRo,"STRONG",{});var zXn=s(pro);dca=r(zXn,"xlm-roberta"),zXn.forEach(t),mca=r(yRo," \u2014 "),yEe=n(yRo,"A",{href:!0});var HXn=s(yEe);cca=r(HXn,"TFXLMRobertaForTokenClassification"),HXn.forEach(t),fca=r(yRo," (XLM-RoBERTa model)"),yRo.forEach(t),hca=i(ve),nS=n(ve,"LI",{});var CRo=s(nS);_ro=n(CRo,"STRONG",{});var KXn=s(_ro);gca=r(KXn,"xlnet"),KXn.forEach(t),uca=r(CRo," \u2014 "),CEe=n(CRo,"A",{href:!0});var eDn=s(CEe);pca=r(eDn,"TFXLNetForTokenClassification"),eDn.forEach(t),_ca=r(CRo," (XLNet model)"),CRo.forEach(t),ve.forEach(t),bca=i(Wd),M(sS.$$.fragment,Wd),Wd.forEach(t),Vd.forEach(t),qIo=i(c),If=n(c,"H3",{class:!0});var qjo=s(If);lS=n(qjo,"A",{id:!0,class:!0,href:!0});var oDn=s(lS);bro=n(oDn,"SPAN",{});var rDn=s(bro);M(CU.$$.fragment,rDn),rDn.forEach(t),oDn.forEach(t),vca=i(qjo),vro=n(qjo,"SPAN",{});var tDn=s(vro);Mca=r(tDn,"FlaxAutoModelForTokenClassification"),tDn.forEach(t),qjo.forEach(t),OIo=i(c),pr=n(c,"DIV",{class:!0});var Jd=s(pr);M(wU.$$.fragment,Jd),Tca=i(Jd),Nf=n(Jd,"P",{});var bwe=s(Nf);Fca=r(bwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),wEe=n(bwe,"A",{href:!0});var aDn=s(wEe);Eca=r(aDn,"from_pretrained()"),aDn.forEach(t),yca=r(bwe," class method or the "),LEe=n(bwe,"A",{href:!0});var nDn=s(LEe);Cca=r(nDn,"from_config()"),nDn.forEach(t),wca=r(bwe,` class
method.`),bwe.forEach(t),Lca=i(Jd),LU=n(Jd,"P",{});var Ojo=s(LU);Aca=r(Ojo,"This class cannot be instantiated directly using "),Mro=n(Ojo,"CODE",{});var sDn=s(Mro);Rca=r(sDn,"__init__()"),sDn.forEach(t),kca=r(Ojo," (throws an error)."),Ojo.forEach(t),Sca=i(Jd),ua=n(Jd,"DIV",{class:!0});var XP=s(ua);M(AU.$$.fragment,XP),xca=i(XP),Tro=n(XP,"P",{});var lDn=s(Tro);Gca=r(lDn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),lDn.forEach(t),Bca=i(XP),$f=n(XP,"P",{});var vwe=s($f);Ica=r(vwe,`Note:
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model\u2019s configuration. Use `),AEe=n(vwe,"A",{href:!0});var dDn=s(AEe);jca=r(dDn,"from_pretrained()"),dDn.forEach(t),Pca=r(vwe," to load the model weights."),vwe.forEach(t),Zca=i(XP),M(iS.$$.fragment,XP),XP.forEach(t),Vca=i(Jd),Mt=n(Jd,"DIV",{class:!0});var Ud=s(Mt);M(RU.$$.fragment,Ud),Wca=i(Ud),Ero=n(Ud,"P",{});var mDn=s(Ero);Jca=r(mDn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),mDn.forEach(t),Uca=i(Ud),fs=n(Ud,"P",{});var DP=s(fs);Xca=r(DP,"The model class to instantiate is selected based on the "),yro=n(DP,"CODE",{});var cDn=s(yro);Dca=r(cDn,"model_type"),cDn.forEach(t),qca=r(DP,` property of the config object (either
passed as an argument or loaded from `),Cro=n(DP,"CODE",{});var fDn=s(Cro);Oca=r(fDn,"pretrained_model_name_or_path"),fDn.forEach(t),Qca=r(DP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wro=n(DP,"CODE",{});var hDn=s(wro);Yca=r(hDn,"pretrained_model_name_or_path"),hDn.forEach(t),zca=r(DP,":"),DP.forEach(t),Hca=i(Ud),Xe=n(Ud,"UL",{});var Ke=s(Xe);dS=n(Ke,"LI",{});var wRo=s(dS);Lro=n(wRo,"STRONG",{});var gDn=s(Lro);Kca=r(gDn,"albert"),gDn.forEach(t),efa=r(wRo," \u2014 "),REe=n(wRo,"A",{href:!0});var uDn=s(REe);ofa=r(uDn,"FlaxAlbertForTokenClassification"),uDn.forEach(t),rfa=r(wRo," (ALBERT model)"),wRo.forEach(t),tfa=i(Ke),mS=n(Ke,"LI",{});var LRo=s(mS);Aro=n(LRo,"STRONG",{});var pDn=s(Aro);afa=r(pDn,"bert"),pDn.forEach(t),nfa=r(LRo," \u2014 "),kEe=n(LRo,"A",{href:!0});var _Dn=s(kEe);sfa=r(_Dn,"FlaxBertForTokenClassification"),_Dn.forEach(t),lfa=r(LRo," (BERT model)"),LRo.forEach(t),ifa=i(Ke),cS=n(Ke,"LI",{});var ARo=s(cS);Rro=n(ARo,"STRONG",{});var bDn=s(Rro);dfa=r(bDn,"big_bird"),bDn.forEach(t),mfa=r(ARo," \u2014 "),SEe=n(ARo,"A",{href:!0});var vDn=s(SEe);cfa=r(vDn,"FlaxBigBirdForTokenClassification"),vDn.forEach(t),ffa=r(ARo," (BigBird model)"),ARo.forEach(t),hfa=i(Ke),fS=n(Ke,"LI",{});var RRo=s(fS);kro=n(RRo,"STRONG",{});var MDn=s(kro);gfa=r(MDn,"distilbert"),MDn.forEach(t),ufa=r(RRo," \u2014 "),xEe=n(RRo,"A",{href:!0});var TDn=s(xEe);pfa=r(TDn,"FlaxDistilBertForTokenClassification"),TDn.forEach(t),_fa=r(RRo," (DistilBERT model)"),RRo.forEach(t),bfa=i(Ke),hS=n(Ke,"LI",{});var kRo=s(hS);Sro=n(kRo,"STRONG",{});var FDn=s(Sro);vfa=r(FDn,"electra"),FDn.forEach(t),Mfa=r(kRo," \u2014 "),GEe=n(kRo,"A",{href:!0});var EDn=s(GEe);Tfa=r(EDn,"FlaxElectraForTokenClassification"),EDn.forEach(t),Ffa=r(kRo," (ELECTRA model)"),kRo.forEach(t),Efa=i(Ke),gS=n(Ke,"LI",{});var SRo=s(gS);xro=n(SRo,"STRONG",{});var yDn=s(xro);yfa=r(yDn,"roberta"),yDn.forEach(t),Cfa=r(SRo," \u2014 "),BEe=n(SRo,"A",{href:!0});var CDn=s(BEe);wfa=r(CDn,"FlaxRobertaForTokenClassification"),CDn.forEach(t),Lfa=r(SRo," (RoBERTa model)"),SRo.forEach(t),Afa=i(Ke),uS=n(Ke,"LI",{});var xRo=s(uS);Gro=n(xRo,"STRONG",{});var wDn=s(Gro);Rfa=r(wDn,"roberta-prelayernorm"),wDn.forEach(t),kfa=r(xRo," \u2014 "),IEe=n(xRo,"A",{href:!0});var LDn=s(IEe);Sfa=r(LDn,"FlaxRobertaPreLayerNormForTokenClassification"),LDn.forEach(t),xfa=r(xRo," (RoBERTa-PreLayerNorm model)"),xRo.forEach(t),Gfa=i(Ke),pS=n(Ke,"LI",{});var GRo=s(pS);Bro=n(GRo,"STRONG",{});var ADn=s(Bro);Bfa=r(ADn,"roformer"),ADn.forEach(t),Ifa=r(GRo," \u2014 "),NEe=n(GRo,"A",{href:!0});var RDn=s(NEe);Nfa=r(RDn,"FlaxRoFormerForTokenClassification"),RDn.forEach(t),$fa=r(GRo," (RoFormer model)"),GRo.forEach(t),jfa=i(Ke),_S=n(Ke,"LI",{});var BRo=s(_S);Iro=n(BRo,"STRONG",{});var kDn=s(Iro);Pfa=r(kDn,"xlm-roberta"),kDn.forEach(t),Zfa=r(BRo," \u2014 "),$Ee=n(BRo,"A",{href:!0});var SDn=s($Ee);Vfa=r(SDn,"FlaxXLMRobertaForTokenClassification"),SDn.forEach(t),Wfa=r(BRo," (XLM-RoBERTa model)"),BRo.forEach(t),Ke.forEach(t),Jfa=i(Ud),M(bS.$$.fragment,Ud),Ud.forEach(t),Jd.forEach(t),QIo=i(c),jf=n(c,"H3",{class:!0});var Qjo=s(jf);vS=n(Qjo,"A",{id:!0,class:!0,href:!0});var xDn=s(vS);Nro=n(xDn,"SPAN",{});var GDn=s(Nro);M(kU.$$.fragment,GDn),GDn.forEach(t),xDn.forEach(t),Ufa=i(Qjo),$ro=n(Qjo,"SPAN",{});var BDn=s($ro);Xfa=r(BDn,"AutoModelForQuestionAnswering"),BDn.forEach(t),Qjo.forEach(t),YIo=i(c),_r=n(c,"DIV",{class:!0});var Xd=s(_r);M(SU.$$.fragment,Xd),Dfa=i(Xd),Pf=n(Xd,"P",{});var Mwe=s(Pf);qfa=r(Mwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),jEe=n(Mwe,"A",{href:!0});var IDn=s(jEe);Ofa=r(IDn,"from_pretrained()"),IDn.forEach(t),Qfa=r(Mwe," class method or the "),PEe=n(Mwe,"A",{href:!0});var NDn=s(PEe);Yfa=r(NDn,"from_config()"),NDn.forEach(t),zfa=r(Mwe,` class
method.`),Mwe.forEach(t),Hfa=i(Xd),xU=n(Xd,"P",{});var Yjo=s(xU);Kfa=r(Yjo,"This class cannot be instantiated directly using "),jro=n(Yjo,"CODE",{});var $Dn=s(jro);eha=r($Dn,"__init__()"),$Dn.forEach(t),oha=r(Yjo," (throws an error)."),Yjo.forEach(t),rha=i(Xd),pa=n(Xd,"DIV",{class:!0});var qP=s(pa);M(GU.$$.fragment,qP),tha=i(qP),Pro=n(qP,"P",{});var jDn=s(Pro);aha=r(jDn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),jDn.forEach(t),nha=i(qP),Zf=n(qP,"P",{});var Twe=s(Zf);sha=r(Twe,`Note:
Loading a model from its configuration file does `),Zro=n(Twe,"STRONG",{});var PDn=s(Zro);lha=r(PDn,"not"),PDn.forEach(t),iha=r(Twe,` load the model weights. It only affects the
model\u2019s configuration. Use `),ZEe=n(Twe,"A",{href:!0});var ZDn=s(ZEe);dha=r(ZDn,"from_pretrained()"),ZDn.forEach(t),mha=r(Twe," to load the model weights."),Twe.forEach(t),cha=i(qP),M(MS.$$.fragment,qP),qP.forEach(t),fha=i(Xd),po=n(Xd,"DIV",{class:!0});var cn=s(po);M(BU.$$.fragment,cn),hha=i(cn),Vro=n(cn,"P",{});var VDn=s(Vro);gha=r(VDn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),VDn.forEach(t),uha=i(cn),hs=n(cn,"P",{});var OP=s(hs);pha=r(OP,"The model class to instantiate is selected based on the "),Wro=n(OP,"CODE",{});var WDn=s(Wro);_ha=r(WDn,"model_type"),WDn.forEach(t),bha=r(OP,` property of the config object (either
passed as an argument or loaded from `),Jro=n(OP,"CODE",{});var JDn=s(Jro);vha=r(JDn,"pretrained_model_name_or_path"),JDn.forEach(t),Mha=r(OP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Uro=n(OP,"CODE",{});var UDn=s(Uro);Tha=r(UDn,"pretrained_model_name_or_path"),UDn.forEach(t),Fha=r(OP,":"),OP.forEach(t),Eha=i(cn),X=n(cn,"UL",{});var O=s(X);TS=n(O,"LI",{});var IRo=s(TS);Xro=n(IRo,"STRONG",{});var XDn=s(Xro);yha=r(XDn,"albert"),XDn.forEach(t),Cha=r(IRo," \u2014 "),VEe=n(IRo,"A",{href:!0});var DDn=s(VEe);wha=r(DDn,"AlbertForQuestionAnswering"),DDn.forEach(t),Lha=r(IRo," (ALBERT model)"),IRo.forEach(t),Aha=i(O),FS=n(O,"LI",{});var NRo=s(FS);Dro=n(NRo,"STRONG",{});var qDn=s(Dro);Rha=r(qDn,"bart"),qDn.forEach(t),kha=r(NRo," \u2014 "),WEe=n(NRo,"A",{href:!0});var ODn=s(WEe);Sha=r(ODn,"BartForQuestionAnswering"),ODn.forEach(t),xha=r(NRo," (BART model)"),NRo.forEach(t),Gha=i(O),ES=n(O,"LI",{});var $Ro=s(ES);qro=n($Ro,"STRONG",{});var QDn=s(qro);Bha=r(QDn,"bert"),QDn.forEach(t),Iha=r($Ro," \u2014 "),JEe=n($Ro,"A",{href:!0});var YDn=s(JEe);Nha=r(YDn,"BertForQuestionAnswering"),YDn.forEach(t),$ha=r($Ro," (BERT model)"),$Ro.forEach(t),jha=i(O),yS=n(O,"LI",{});var jRo=s(yS);Oro=n(jRo,"STRONG",{});var zDn=s(Oro);Pha=r(zDn,"big_bird"),zDn.forEach(t),Zha=r(jRo," \u2014 "),UEe=n(jRo,"A",{href:!0});var HDn=s(UEe);Vha=r(HDn,"BigBirdForQuestionAnswering"),HDn.forEach(t),Wha=r(jRo," (BigBird model)"),jRo.forEach(t),Jha=i(O),CS=n(O,"LI",{});var PRo=s(CS);Qro=n(PRo,"STRONG",{});var KDn=s(Qro);Uha=r(KDn,"bigbird_pegasus"),KDn.forEach(t),Xha=r(PRo," \u2014 "),XEe=n(PRo,"A",{href:!0});var eqn=s(XEe);Dha=r(eqn,"BigBirdPegasusForQuestionAnswering"),eqn.forEach(t),qha=r(PRo," (BigBird-Pegasus model)"),PRo.forEach(t),Oha=i(O),wS=n(O,"LI",{});var ZRo=s(wS);Yro=n(ZRo,"STRONG",{});var oqn=s(Yro);Qha=r(oqn,"bloom"),oqn.forEach(t),Yha=r(ZRo," \u2014 "),DEe=n(ZRo,"A",{href:!0});var rqn=s(DEe);zha=r(rqn,"BloomForQuestionAnswering"),rqn.forEach(t),Hha=r(ZRo," (BLOOM model)"),ZRo.forEach(t),Kha=i(O),LS=n(O,"LI",{});var VRo=s(LS);zro=n(VRo,"STRONG",{});var tqn=s(zro);ega=r(tqn,"camembert"),tqn.forEach(t),oga=r(VRo," \u2014 "),qEe=n(VRo,"A",{href:!0});var aqn=s(qEe);rga=r(aqn,"CamembertForQuestionAnswering"),aqn.forEach(t),tga=r(VRo," (CamemBERT model)"),VRo.forEach(t),aga=i(O),AS=n(O,"LI",{});var WRo=s(AS);Hro=n(WRo,"STRONG",{});var nqn=s(Hro);nga=r(nqn,"canine"),nqn.forEach(t),sga=r(WRo," \u2014 "),OEe=n(WRo,"A",{href:!0});var sqn=s(OEe);lga=r(sqn,"CanineForQuestionAnswering"),sqn.forEach(t),iga=r(WRo," (CANINE model)"),WRo.forEach(t),dga=i(O),RS=n(O,"LI",{});var JRo=s(RS);Kro=n(JRo,"STRONG",{});var lqn=s(Kro);mga=r(lqn,"convbert"),lqn.forEach(t),cga=r(JRo," \u2014 "),QEe=n(JRo,"A",{href:!0});var iqn=s(QEe);fga=r(iqn,"ConvBertForQuestionAnswering"),iqn.forEach(t),hga=r(JRo," (ConvBERT model)"),JRo.forEach(t),gga=i(O),kS=n(O,"LI",{});var URo=s(kS);eto=n(URo,"STRONG",{});var dqn=s(eto);uga=r(dqn,"data2vec-text"),dqn.forEach(t),pga=r(URo," \u2014 "),YEe=n(URo,"A",{href:!0});var mqn=s(YEe);_ga=r(mqn,"Data2VecTextForQuestionAnswering"),mqn.forEach(t),bga=r(URo," (Data2VecText model)"),URo.forEach(t),vga=i(O),SS=n(O,"LI",{});var XRo=s(SS);oto=n(XRo,"STRONG",{});var cqn=s(oto);Mga=r(cqn,"deberta"),cqn.forEach(t),Tga=r(XRo," \u2014 "),zEe=n(XRo,"A",{href:!0});var fqn=s(zEe);Fga=r(fqn,"DebertaForQuestionAnswering"),fqn.forEach(t),Ega=r(XRo," (DeBERTa model)"),XRo.forEach(t),yga=i(O),xS=n(O,"LI",{});var DRo=s(xS);rto=n(DRo,"STRONG",{});var hqn=s(rto);Cga=r(hqn,"deberta-v2"),hqn.forEach(t),wga=r(DRo," \u2014 "),HEe=n(DRo,"A",{href:!0});var gqn=s(HEe);Lga=r(gqn,"DebertaV2ForQuestionAnswering"),gqn.forEach(t),Aga=r(DRo," (DeBERTa-v2 model)"),DRo.forEach(t),Rga=i(O),GS=n(O,"LI",{});var qRo=s(GS);tto=n(qRo,"STRONG",{});var uqn=s(tto);kga=r(uqn,"distilbert"),uqn.forEach(t),Sga=r(qRo," \u2014 "),KEe=n(qRo,"A",{href:!0});var pqn=s(KEe);xga=r(pqn,"DistilBertForQuestionAnswering"),pqn.forEach(t),Gga=r(qRo," (DistilBERT model)"),qRo.forEach(t),Bga=i(O),BS=n(O,"LI",{});var ORo=s(BS);ato=n(ORo,"STRONG",{});var _qn=s(ato);Iga=r(_qn,"electra"),_qn.forEach(t),Nga=r(ORo," \u2014 "),e0e=n(ORo,"A",{href:!0});var bqn=s(e0e);$ga=r(bqn,"ElectraForQuestionAnswering"),bqn.forEach(t),jga=r(ORo," (ELECTRA model)"),ORo.forEach(t),Pga=i(O),IS=n(O,"LI",{});var QRo=s(IS);nto=n(QRo,"STRONG",{});var vqn=s(nto);Zga=r(vqn,"ernie"),vqn.forEach(t),Vga=r(QRo," \u2014 "),o0e=n(QRo,"A",{href:!0});var Mqn=s(o0e);Wga=r(Mqn,"ErnieForQuestionAnswering"),Mqn.forEach(t),Jga=r(QRo," (ERNIE model)"),QRo.forEach(t),Uga=i(O),NS=n(O,"LI",{});var YRo=s(NS);sto=n(YRo,"STRONG",{});var Tqn=s(sto);Xga=r(Tqn,"ernie_m"),Tqn.forEach(t),Dga=r(YRo," \u2014 "),r0e=n(YRo,"A",{href:!0});var Fqn=s(r0e);qga=r(Fqn,"ErnieMForQuestionAnswering"),Fqn.forEach(t),Oga=r(YRo," (ErnieM model)"),YRo.forEach(t),Qga=i(O),$S=n(O,"LI",{});var zRo=s($S);lto=n(zRo,"STRONG",{});var Eqn=s(lto);Yga=r(Eqn,"falcon"),Eqn.forEach(t),zga=r(zRo," \u2014 "),t0e=n(zRo,"A",{href:!0});var yqn=s(t0e);Hga=r(yqn,"FalconForQuestionAnswering"),yqn.forEach(t),Kga=r(zRo," (Falcon model)"),zRo.forEach(t),eua=i(O),jS=n(O,"LI",{});var HRo=s(jS);ito=n(HRo,"STRONG",{});var Cqn=s(ito);oua=r(Cqn,"flaubert"),Cqn.forEach(t),rua=r(HRo," \u2014 "),a0e=n(HRo,"A",{href:!0});var wqn=s(a0e);tua=r(wqn,"FlaubertForQuestionAnsweringSimple"),wqn.forEach(t),aua=r(HRo," (FlauBERT model)"),HRo.forEach(t),nua=i(O),PS=n(O,"LI",{});var KRo=s(PS);dto=n(KRo,"STRONG",{});var Lqn=s(dto);sua=r(Lqn,"fnet"),Lqn.forEach(t),lua=r(KRo," \u2014 "),n0e=n(KRo,"A",{href:!0});var Aqn=s(n0e);iua=r(Aqn,"FNetForQuestionAnswering"),Aqn.forEach(t),dua=r(KRo," (FNet model)"),KRo.forEach(t),mua=i(O),ZS=n(O,"LI",{});var eko=s(ZS);mto=n(eko,"STRONG",{});var Rqn=s(mto);cua=r(Rqn,"funnel"),Rqn.forEach(t),fua=r(eko," \u2014 "),s0e=n(eko,"A",{href:!0});var kqn=s(s0e);hua=r(kqn,"FunnelForQuestionAnswering"),kqn.forEach(t),gua=r(eko," (Funnel Transformer model)"),eko.forEach(t),uua=i(O),VS=n(O,"LI",{});var oko=s(VS);cto=n(oko,"STRONG",{});var Sqn=s(cto);pua=r(Sqn,"gpt2"),Sqn.forEach(t),_ua=r(oko," \u2014 "),l0e=n(oko,"A",{href:!0});var xqn=s(l0e);bua=r(xqn,"GPT2ForQuestionAnswering"),xqn.forEach(t),vua=r(oko," (OpenAI GPT-2 model)"),oko.forEach(t),Mua=i(O),WS=n(O,"LI",{});var rko=s(WS);fto=n(rko,"STRONG",{});var Gqn=s(fto);Tua=r(Gqn,"gpt_neo"),Gqn.forEach(t),Fua=r(rko," \u2014 "),i0e=n(rko,"A",{href:!0});var Bqn=s(i0e);Eua=r(Bqn,"GPTNeoForQuestionAnswering"),Bqn.forEach(t),yua=r(rko," (GPT Neo model)"),rko.forEach(t),Cua=i(O),JS=n(O,"LI",{});var tko=s(JS);hto=n(tko,"STRONG",{});var Iqn=s(hto);wua=r(Iqn,"gpt_neox"),Iqn.forEach(t),Lua=r(tko," \u2014 "),d0e=n(tko,"A",{href:!0});var Nqn=s(d0e);Aua=r(Nqn,"GPTNeoXForQuestionAnswering"),Nqn.forEach(t),Rua=r(tko," (GPT NeoX model)"),tko.forEach(t),kua=i(O),US=n(O,"LI",{});var ako=s(US);gto=n(ako,"STRONG",{});var $qn=s(gto);Sua=r($qn,"gptj"),$qn.forEach(t),xua=r(ako," \u2014 "),m0e=n(ako,"A",{href:!0});var jqn=s(m0e);Gua=r(jqn,"GPTJForQuestionAnswering"),jqn.forEach(t),Bua=r(ako," (GPT-J model)"),ako.forEach(t),Iua=i(O),XS=n(O,"LI",{});var nko=s(XS);uto=n(nko,"STRONG",{});var Pqn=s(uto);Nua=r(Pqn,"ibert"),Pqn.forEach(t),$ua=r(nko," \u2014 "),c0e=n(nko,"A",{href:!0});var Zqn=s(c0e);jua=r(Zqn,"IBertForQuestionAnswering"),Zqn.forEach(t),Pua=r(nko," (I-BERT model)"),nko.forEach(t),Zua=i(O),DS=n(O,"LI",{});var sko=s(DS);pto=n(sko,"STRONG",{});var Vqn=s(pto);Vua=r(Vqn,"layoutlmv2"),Vqn.forEach(t),Wua=r(sko," \u2014 "),f0e=n(sko,"A",{href:!0});var Wqn=s(f0e);Jua=r(Wqn,"LayoutLMv2ForQuestionAnswering"),Wqn.forEach(t),Uua=r(sko," (LayoutLMv2 model)"),sko.forEach(t),Xua=i(O),qS=n(O,"LI",{});var lko=s(qS);_to=n(lko,"STRONG",{});var Jqn=s(_to);Dua=r(Jqn,"layoutlmv3"),Jqn.forEach(t),qua=r(lko," \u2014 "),h0e=n(lko,"A",{href:!0});var Uqn=s(h0e);Oua=r(Uqn,"LayoutLMv3ForQuestionAnswering"),Uqn.forEach(t),Qua=r(lko," (LayoutLMv3 model)"),lko.forEach(t),Yua=i(O),OS=n(O,"LI",{});var iko=s(OS);bto=n(iko,"STRONG",{});var Xqn=s(bto);zua=r(Xqn,"led"),Xqn.forEach(t),Hua=r(iko," \u2014 "),g0e=n(iko,"A",{href:!0});var Dqn=s(g0e);Kua=r(Dqn,"LEDForQuestionAnswering"),Dqn.forEach(t),epa=r(iko," (LED model)"),iko.forEach(t),opa=i(O),QS=n(O,"LI",{});var dko=s(QS);vto=n(dko,"STRONG",{});var qqn=s(vto);rpa=r(qqn,"lilt"),qqn.forEach(t),tpa=r(dko," \u2014 "),u0e=n(dko,"A",{href:!0});var Oqn=s(u0e);apa=r(Oqn,"LiltForQuestionAnswering"),Oqn.forEach(t),npa=r(dko," (LiLT model)"),dko.forEach(t),spa=i(O),YS=n(O,"LI",{});var mko=s(YS);Mto=n(mko,"STRONG",{});var Qqn=s(Mto);lpa=r(Qqn,"longformer"),Qqn.forEach(t),ipa=r(mko," \u2014 "),p0e=n(mko,"A",{href:!0});var Yqn=s(p0e);dpa=r(Yqn,"LongformerForQuestionAnswering"),Yqn.forEach(t),mpa=r(mko," (Longformer model)"),mko.forEach(t),cpa=i(O),zS=n(O,"LI",{});var cko=s(zS);Tto=n(cko,"STRONG",{});var zqn=s(Tto);fpa=r(zqn,"luke"),zqn.forEach(t),hpa=r(cko," \u2014 "),_0e=n(cko,"A",{href:!0});var Hqn=s(_0e);gpa=r(Hqn,"LukeForQuestionAnswering"),Hqn.forEach(t),upa=r(cko," (LUKE model)"),cko.forEach(t),ppa=i(O),HS=n(O,"LI",{});var fko=s(HS);Fto=n(fko,"STRONG",{});var 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method.`),Fwe.forEach(t),Eba=i(Dd),$U=n(Dd,"P",{});var Hjo=s($U);yba=r(Hjo,"This class cannot be instantiated directly using "),tao=n(Hjo,"CODE",{});var gQn=s(tao);Cba=r(gQn,"__init__()"),gQn.forEach(t),wba=r(Hjo," (throws an error)."),Hjo.forEach(t),Lba=i(Dd),_a=n(Dd,"DIV",{class:!0});var QP=s(_a);M(jU.$$.fragment,QP),Aba=i(QP),aao=n(QP,"P",{});var uQn=s(aao);Rba=r(uQn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),uQn.forEach(t),kba=i(QP),Jf=n(QP,"P",{});var Ewe=s(Jf);Sba=r(Ewe,`Note:
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model\u2019s configuration. Use `),z0e=n(Ewe,"A",{href:!0});var _Qn=s(z0e);Bba=r(_Qn,"from_pretrained()"),_Qn.forEach(t),Iba=r(Ewe," to load the model weights."),Ewe.forEach(t),Nba=i(QP),M(xx.$$.fragment,QP),QP.forEach(t),$ba=i(Dd),Tt=n(Dd,"DIV",{class:!0});var qd=s(Tt);M(PU.$$.fragment,qd),jba=i(qd),sao=n(qd,"P",{});var bQn=s(sao);Pba=r(bQn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),bQn.forEach(t),Zba=i(qd),gs=n(qd,"P",{});var YP=s(gs);Vba=r(YP,"The model class to instantiate is selected based on the "),lao=n(YP,"CODE",{});var vQn=s(lao);Wba=r(vQn,"model_type"),vQn.forEach(t),Jba=r(YP,` property of the config object (either
passed as an argument or loaded from `),iao=n(YP,"CODE",{});var MQn=s(iao);Uba=r(MQn,"pretrained_model_name_or_path"),MQn.forEach(t),Xba=r(YP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dao=n(YP,"CODE",{});var TQn=s(dao);Dba=r(TQn,"pretrained_model_name_or_path"),TQn.forEach(t),qba=r(YP,":"),YP.forEach(t),Oba=i(qd),_e=n(qd,"UL",{});var Te=s(_e);Gx=n(Te,"LI",{});var Uko=s(Gx);mao=n(Uko,"STRONG",{});var FQn=s(mao);Qba=r(FQn,"albert"),FQn.forEach(t),Yba=r(Uko," \u2014 "),H0e=n(Uko,"A",{href:!0});var EQn=s(H0e);zba=r(EQn,"TFAlbertForQuestionAnswering"),EQn.forEach(t),Hba=r(Uko," (ALBERT model)"),Uko.forEach(t),Kba=i(Te),Bx=n(Te,"LI",{});var Xko=s(Bx);cao=n(Xko,"STRONG",{});var yQn=s(cao);e2a=r(yQn,"bert"),yQn.forEach(t),o2a=r(Xko," \u2014 "),K0e=n(Xko,"A",{href:!0});var CQn=s(K0e);r2a=r(CQn,"TFBertForQuestionAnswering"),CQn.forEach(t),t2a=r(Xko," (BERT model)"),Xko.forEach(t),a2a=i(Te),Ix=n(Te,"LI",{});var Dko=s(Ix);fao=n(Dko,"STRONG",{});var wQn=s(fao);n2a=r(wQn,"camembert"),wQn.forEach(t),s2a=r(Dko," \u2014 "),eye=n(Dko,"A",{href:!0});var LQn=s(eye);l2a=r(LQn,"TFCamembertForQuestionAnswering"),LQn.forEach(t),i2a=r(Dko," (CamemBERT model)"),Dko.forEach(t),d2a=i(Te),Nx=n(Te,"LI",{});var qko=s(Nx);hao=n(qko,"STRONG",{});var AQn=s(hao);m2a=r(AQn,"convbert"),AQn.forEach(t),c2a=r(qko," \u2014 "),oye=n(qko,"A",{href:!0});var RQn=s(oye);f2a=r(RQn,"TFConvBertForQuestionAnswering"),RQn.forEach(t),h2a=r(qko," (ConvBERT model)"),qko.forEach(t),g2a=i(Te),$x=n(Te,"LI",{});var Oko=s($x);gao=n(Oko,"STRONG",{});var kQn=s(gao);u2a=r(kQn,"deberta"),kQn.forEach(t),p2a=r(Oko," \u2014 "),rye=n(Oko,"A",{href:!0});var SQn=s(rye);_2a=r(SQn,"TFDebertaForQuestionAnswering"),SQn.forEach(t),b2a=r(Oko," (DeBERTa model)"),Oko.forEach(t),v2a=i(Te),jx=n(Te,"LI",{});var Qko=s(jx);uao=n(Qko,"STRONG",{});var xQn=s(uao);M2a=r(xQn,"deberta-v2"),xQn.forEach(t),T2a=r(Qko," \u2014 "),tye=n(Qko,"A",{href:!0});var GQn=s(tye);F2a=r(GQn,"TFDebertaV2ForQuestionAnswering"),GQn.forEach(t),E2a=r(Qko," (DeBERTa-v2 model)"),Qko.forEach(t),y2a=i(Te),Px=n(Te,"LI",{});var Yko=s(Px);pao=n(Yko,"STRONG",{});var BQn=s(pao);C2a=r(BQn,"distilbert"),BQn.forEach(t),w2a=r(Yko," \u2014 "),aye=n(Yko,"A",{href:!0});var IQn=s(aye);L2a=r(IQn,"TFDistilBertForQuestionAnswering"),IQn.forEach(t),A2a=r(Yko," (DistilBERT model)"),Yko.forEach(t),R2a=i(Te),Zx=n(Te,"LI",{});var zko=s(Zx);_ao=n(zko,"STRONG",{});var NQn=s(_ao);k2a=r(NQn,"electra"),NQn.forEach(t),S2a=r(zko," \u2014 "),nye=n(zko,"A",{href:!0});var $Qn=s(nye);x2a=r($Qn,"TFElectraForQuestionAnswering"),$Qn.forEach(t),G2a=r(zko," (ELECTRA model)"),zko.forEach(t),B2a=i(Te),Vx=n(Te,"LI",{});var Hko=s(Vx);bao=n(Hko,"STRONG",{});var jQn=s(bao);I2a=r(jQn,"flaubert"),jQn.forEach(t),N2a=r(Hko," \u2014 "),sye=n(Hko,"A",{href:!0});var PQn=s(sye);$2a=r(PQn,"TFFlaubertForQuestionAnsweringSimple"),PQn.forEach(t),j2a=r(Hko," (FlauBERT model)"),Hko.forEach(t),P2a=i(Te),Wx=n(Te,"LI",{});var Kko=s(Wx);vao=n(Kko,"STRONG",{});var ZQn=s(vao);Z2a=r(ZQn,"funnel"),ZQn.forEach(t),V2a=r(Kko," \u2014 "),lye=n(Kko,"A",{href:!0});var VQn=s(lye);W2a=r(VQn,"TFFunnelForQuestionAnswering"),VQn.forEach(t),J2a=r(Kko," (Funnel Transformer model)"),Kko.forEach(t),U2a=i(Te),Jx=n(Te,"LI",{});var eSo=s(Jx);Mao=n(eSo,"STRONG",{});var WQn=s(Mao);X2a=r(WQn,"gptj"),WQn.forEach(t),D2a=r(eSo," \u2014 "),iye=n(eSo,"A",{href:!0});var JQn=s(iye);q2a=r(JQn,"TFGPTJForQuestionAnswering"),JQn.forEach(t),O2a=r(eSo," (GPT-J model)"),eSo.forEach(t),Q2a=i(Te),Ux=n(Te,"LI",{});var oSo=s(Ux);Tao=n(oSo,"STRONG",{});var UQn=s(Tao);Y2a=r(UQn,"layoutlmv3"),UQn.forEach(t),z2a=r(oSo," \u2014 "),dye=n(oSo,"A",{href:!0});var XQn=s(dye);H2a=r(XQn,"TFLayoutLMv3ForQuestionAnswering"),XQn.forEach(t),K2a=r(oSo," (LayoutLMv3 model)"),oSo.forEach(t),eva=i(Te),Xx=n(Te,"LI",{});var rSo=s(Xx);Fao=n(rSo,"STRONG",{});var DQn=s(Fao);ova=r(DQn,"longformer"),DQn.forEach(t),rva=r(rSo," \u2014 "),mye=n(rSo,"A",{href:!0});var qQn=s(mye);tva=r(qQn,"TFLongformerForQuestionAnswering"),qQn.forEach(t),ava=r(rSo," (Longformer model)"),rSo.forEach(t),nva=i(Te),Dx=n(Te,"LI",{});var tSo=s(Dx);Eao=n(tSo,"STRONG",{});var OQn=s(Eao);sva=r(OQn,"mobilebert"),OQn.forEach(t),lva=r(tSo," \u2014 "),cye=n(tSo,"A",{href:!0});var QQn=s(cye);iva=r(QQn,"TFMobileBertForQuestionAnswering"),QQn.forEach(t),dva=r(tSo," (MobileBERT model)"),tSo.forEach(t),mva=i(Te),qx=n(Te,"LI",{});var aSo=s(qx);yao=n(aSo,"STRONG",{});var YQn=s(yao);cva=r(YQn,"mpnet"),YQn.forEach(t),fva=r(aSo," \u2014 "),fye=n(aSo,"A",{href:!0});var zQn=s(fye);hva=r(zQn,"TFMPNetForQuestionAnswering"),zQn.forEach(t),gva=r(aSo," (MPNet model)"),aSo.forEach(t),uva=i(Te),Ox=n(Te,"LI",{});var nSo=s(Ox);Cao=n(nSo,"STRONG",{});var HQn=s(Cao);pva=r(HQn,"rembert"),HQn.forEach(t),_va=r(nSo," \u2014 "),hye=n(nSo,"A",{href:!0});var KQn=s(hye);bva=r(KQn,"TFRemBertForQuestionAnswering"),KQn.forEach(t),vva=r(nSo," (RemBERT model)"),nSo.forEach(t),Mva=i(Te),Qx=n(Te,"LI",{});var sSo=s(Qx);wao=n(sSo,"STRONG",{});var eYn=s(wao);Tva=r(eYn,"roberta"),eYn.forEach(t),Fva=r(sSo," \u2014 "),gye=n(sSo,"A",{href:!0});var oYn=s(gye);Eva=r(oYn,"TFRobertaForQuestionAnswering"),oYn.forEach(t),yva=r(sSo," (RoBERTa model)"),sSo.forEach(t),Cva=i(Te),Yx=n(Te,"LI",{});var lSo=s(Yx);Lao=n(lSo,"STRONG",{});var rYn=s(Lao);wva=r(rYn,"roberta-prelayernorm"),rYn.forEach(t),Lva=r(lSo," \u2014 "),uye=n(lSo,"A",{href:!0});var tYn=s(uye);Ava=r(tYn,"TFRobertaPreLayerNormForQuestionAnswering"),tYn.forEach(t),Rva=r(lSo," (RoBERTa-PreLayerNorm model)"),lSo.forEach(t),kva=i(Te),zx=n(Te,"LI",{});var iSo=s(zx);Aao=n(iSo,"STRONG",{});var aYn=s(Aao);Sva=r(aYn,"roformer"),aYn.forEach(t),xva=r(iSo," \u2014 "),pye=n(iSo,"A",{href:!0});var nYn=s(pye);Gva=r(nYn,"TFRoFormerForQuestionAnswering"),nYn.forEach(t),Bva=r(iSo," (RoFormer model)"),iSo.forEach(t),Iva=i(Te),Hx=n(Te,"LI",{});var dSo=s(Hx);Rao=n(dSo,"STRONG",{});var sYn=s(Rao);Nva=r(sYn,"xlm"),sYn.forEach(t),$va=r(dSo," \u2014 "),_ye=n(dSo,"A",{href:!0});var lYn=s(_ye);jva=r(lYn,"TFXLMForQuestionAnsweringSimple"),lYn.forEach(t),Pva=r(dSo," (XLM model)"),dSo.forEach(t),Zva=i(Te),Kx=n(Te,"LI",{});var mSo=s(Kx);kao=n(mSo,"STRONG",{});var iYn=s(kao);Vva=r(iYn,"xlm-roberta"),iYn.forEach(t),Wva=r(mSo," \u2014 "),bye=n(mSo,"A",{href:!0});var dYn=s(bye);Jva=r(dYn,"TFXLMRobertaForQuestionAnswering"),dYn.forEach(t),Uva=r(mSo," (XLM-RoBERTa model)"),mSo.forEach(t),Xva=i(Te),eG=n(Te,"LI",{});var cSo=s(eG);Sao=n(cSo,"STRONG",{});var mYn=s(Sao);Dva=r(mYn,"xlnet"),mYn.forEach(t),qva=r(cSo," \u2014 "),vye=n(cSo,"A",{href:!0});var cYn=s(vye);Ova=r(cYn,"TFXLNetForQuestionAnsweringSimple"),cYn.forEach(t),Qva=r(cSo," (XLNet model)"),cSo.forEach(t),Te.forEach(t),Yva=i(qd),M(oG.$$.fragment,qd),qd.forEach(t),Dd.forEach(t),KIo=i(c),Uf=n(c,"H3",{class:!0});var Kjo=s(Uf);rG=n(Kjo,"A",{id:!0,class:!0,href:!0});var fYn=s(rG);xao=n(fYn,"SPAN",{});var hYn=s(xao);M(ZU.$$.fragment,hYn),hYn.forEach(t),fYn.forEach(t),zva=i(Kjo),Gao=n(Kjo,"SPAN",{});var gYn=s(Gao);Hva=r(gYn,"FlaxAutoModelForQuestionAnswering"),gYn.forEach(t),Kjo.forEach(t),eNo=i(c),vr=n(c,"DIV",{class:!0});var Od=s(vr);M(VU.$$.fragment,Od),Kva=i(Od),Xf=n(Od,"P",{});var ywe=s(Xf);eMa=r(ywe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),Mye=n(ywe,"A",{href:!0});var uYn=s(Mye);oMa=r(uYn,"from_pretrained()"),uYn.forEach(t),rMa=r(ywe," class method or the "),Tye=n(ywe,"A",{href:!0});var pYn=s(Tye);tMa=r(pYn,"from_config()"),pYn.forEach(t),aMa=r(ywe,` class
method.`),ywe.forEach(t),nMa=i(Od),WU=n(Od,"P",{});var ePo=s(WU);sMa=r(ePo,"This class cannot be instantiated directly using "),Bao=n(ePo,"CODE",{});var _Yn=s(Bao);lMa=r(_Yn,"__init__()"),_Yn.forEach(t),iMa=r(ePo," (throws an error)."),ePo.forEach(t),dMa=i(Od),ba=n(Od,"DIV",{class:!0});var zP=s(ba);M(JU.$$.fragment,zP),mMa=i(zP),Iao=n(zP,"P",{});var bYn=s(Iao);cMa=r(bYn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),bYn.forEach(t),fMa=i(zP),Df=n(zP,"P",{});var Cwe=s(Df);hMa=r(Cwe,`Note:
Loading a model from its configuration file does `),Nao=n(Cwe,"STRONG",{});var vYn=s(Nao);gMa=r(vYn,"not"),vYn.forEach(t),uMa=r(Cwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Fye=n(Cwe,"A",{href:!0});var MYn=s(Fye);pMa=r(MYn,"from_pretrained()"),MYn.forEach(t),_Ma=r(Cwe," to load the model weights."),Cwe.forEach(t),bMa=i(zP),M(tG.$$.fragment,zP),zP.forEach(t),vMa=i(Od),Ft=n(Od,"DIV",{class:!0});var Qd=s(Ft);M(UU.$$.fragment,Qd),MMa=i(Qd),$ao=n(Qd,"P",{});var TYn=s($ao);TMa=r(TYn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),TYn.forEach(t),FMa=i(Qd),us=n(Qd,"P",{});var HP=s(us);EMa=r(HP,"The model class to instantiate is selected based on the "),jao=n(HP,"CODE",{});var FYn=s(jao);yMa=r(FYn,"model_type"),FYn.forEach(t),CMa=r(HP,` property of the config object (either
passed as an argument or loaded from `),Pao=n(HP,"CODE",{});var EYn=s(Pao);wMa=r(EYn,"pretrained_model_name_or_path"),EYn.forEach(t),LMa=r(HP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Zao=n(HP,"CODE",{});var yYn=s(Zao);AMa=r(yYn,"pretrained_model_name_or_path"),yYn.forEach(t),RMa=r(HP,":"),HP.forEach(t),kMa=i(Qd),xe=n(Qd,"UL",{});var Ve=s(xe);aG=n(Ve,"LI",{});var fSo=s(aG);Vao=n(fSo,"STRONG",{});var CYn=s(Vao);SMa=r(CYn,"albert"),CYn.forEach(t),xMa=r(fSo," \u2014 "),Eye=n(fSo,"A",{href:!0});var wYn=s(Eye);GMa=r(wYn,"FlaxAlbertForQuestionAnswering"),wYn.forEach(t),BMa=r(fSo," (ALBERT model)"),fSo.forEach(t),IMa=i(Ve),nG=n(Ve,"LI",{});var hSo=s(nG);Wao=n(hSo,"STRONG",{});var LYn=s(Wao);NMa=r(LYn,"bart"),LYn.forEach(t),$Ma=r(hSo," \u2014 "),yye=n(hSo,"A",{href:!0});var AYn=s(yye);jMa=r(AYn,"FlaxBartForQuestionAnswering"),AYn.forEach(t),PMa=r(hSo," (BART model)"),hSo.forEach(t),ZMa=i(Ve),sG=n(Ve,"LI",{});var gSo=s(sG);Jao=n(gSo,"STRONG",{});var RYn=s(Jao);VMa=r(RYn,"bert"),RYn.forEach(t),WMa=r(gSo," \u2014 "),Cye=n(gSo,"A",{href:!0});var kYn=s(Cye);JMa=r(kYn,"FlaxBertForQuestionAnswering"),kYn.forEach(t),UMa=r(gSo," (BERT model)"),gSo.forEach(t),XMa=i(Ve),lG=n(Ve,"LI",{});var uSo=s(lG);Uao=n(uSo,"STRONG",{});var SYn=s(Uao);DMa=r(SYn,"big_bird"),SYn.forEach(t),qMa=r(uSo," \u2014 "),wye=n(uSo,"A",{href:!0});var xYn=s(wye);OMa=r(xYn,"FlaxBigBirdForQuestionAnswering"),xYn.forEach(t),QMa=r(uSo," (BigBird model)"),uSo.forEach(t),YMa=i(Ve),iG=n(Ve,"LI",{});var pSo=s(iG);Xao=n(pSo,"STRONG",{});var GYn=s(Xao);zMa=r(GYn,"distilbert"),GYn.forEach(t),HMa=r(pSo," \u2014 "),Lye=n(pSo,"A",{href:!0});var BYn=s(Lye);KMa=r(BYn,"FlaxDistilBertForQuestionAnswering"),BYn.forEach(t),eTa=r(pSo," (DistilBERT model)"),pSo.forEach(t),oTa=i(Ve),dG=n(Ve,"LI",{});var _So=s(dG);Dao=n(_So,"STRONG",{});var IYn=s(Dao);rTa=r(IYn,"electra"),IYn.forEach(t),tTa=r(_So," \u2014 "),Aye=n(_So,"A",{href:!0});var NYn=s(Aye);aTa=r(NYn,"FlaxElectraForQuestionAnswering"),NYn.forEach(t),nTa=r(_So," (ELECTRA model)"),_So.forEach(t),sTa=i(Ve),mG=n(Ve,"LI",{});var bSo=s(mG);qao=n(bSo,"STRONG",{});var $Yn=s(qao);lTa=r($Yn,"mbart"),$Yn.forEach(t),iTa=r(bSo," \u2014 "),Rye=n(bSo,"A",{href:!0});var jYn=s(Rye);dTa=r(jYn,"FlaxMBartForQuestionAnswering"),jYn.forEach(t),mTa=r(bSo," (mBART model)"),bSo.forEach(t),cTa=i(Ve),cG=n(Ve,"LI",{});var vSo=s(cG);Oao=n(vSo,"STRONG",{});var PYn=s(Oao);fTa=r(PYn,"roberta"),PYn.forEach(t),hTa=r(vSo," \u2014 "),kye=n(vSo,"A",{href:!0});var ZYn=s(kye);gTa=r(ZYn,"FlaxRobertaForQuestionAnswering"),ZYn.forEach(t),uTa=r(vSo," (RoBERTa model)"),vSo.forEach(t),pTa=i(Ve),fG=n(Ve,"LI",{});var MSo=s(fG);Qao=n(MSo,"STRONG",{});var VYn=s(Qao);_Ta=r(VYn,"roberta-prelayernorm"),VYn.forEach(t),bTa=r(MSo," \u2014 "),Sye=n(MSo,"A",{href:!0});var WYn=s(Sye);vTa=r(WYn,"FlaxRobertaPreLayerNormForQuestionAnswering"),WYn.forEach(t),MTa=r(MSo," (RoBERTa-PreLayerNorm model)"),MSo.forEach(t),TTa=i(Ve),hG=n(Ve,"LI",{});var TSo=s(hG);Yao=n(TSo,"STRONG",{});var JYn=s(Yao);FTa=r(JYn,"roformer"),JYn.forEach(t),ETa=r(TSo," \u2014 "),xye=n(TSo,"A",{href:!0});var UYn=s(xye);yTa=r(UYn,"FlaxRoFormerForQuestionAnswering"),UYn.forEach(t),CTa=r(TSo," (RoFormer model)"),TSo.forEach(t),wTa=i(Ve),gG=n(Ve,"LI",{});var FSo=s(gG);zao=n(FSo,"STRONG",{});var XYn=s(zao);LTa=r(XYn,"xlm-roberta"),XYn.forEach(t),ATa=r(FSo," \u2014 "),Gye=n(FSo,"A",{href:!0});var DYn=s(Gye);RTa=r(DYn,"FlaxXLMRobertaForQuestionAnswering"),DYn.forEach(t),kTa=r(FSo," (XLM-RoBERTa model)"),FSo.forEach(t),Ve.forEach(t),STa=i(Qd),M(uG.$$.fragment,Qd),Qd.forEach(t),Od.forEach(t),oNo=i(c),qf=n(c,"H3",{class:!0});var oPo=s(qf);pG=n(oPo,"A",{id:!0,class:!0,href:!0});var qYn=s(pG);Hao=n(qYn,"SPAN",{});var OYn=s(Hao);M(XU.$$.fragment,OYn),OYn.forEach(t),qYn.forEach(t),xTa=i(oPo),Kao=n(oPo,"SPAN",{});var QYn=s(Kao);GTa=r(QYn,"AutoModelForTextEncoding"),QYn.forEach(t),oPo.forEach(t),rNo=i(c),DU=n(c,"DIV",{class:!0});var YYn=s(DU);M(qU.$$.fragment,YYn),YYn.forEach(t),tNo=i(c),Of=n(c,"H3",{class:!0});var rPo=s(Of);_G=n(rPo,"A",{id:!0,class:!0,href:!0});var zYn=s(_G);eno=n(zYn,"SPAN",{});var HYn=s(eno);M(OU.$$.fragment,HYn),HYn.forEach(t),zYn.forEach(t),BTa=i(rPo),ono=n(rPo,"SPAN",{});var KYn=s(ono);ITa=r(KYn,"TFAutoModelForTextEncoding"),KYn.forEach(t),rPo.forEach(t),aNo=i(c),QU=n(c,"DIV",{class:!0});var ezn=s(QU);M(YU.$$.fragment,ezn),ezn.forEach(t),nNo=i(c),Qf=n(c,"H2",{class:!0});var tPo=s(Qf);bG=n(tPo,"A",{id:!0,class:!0,href:!0});var ozn=s(bG);rno=n(ozn,"SPAN",{});var rzn=s(rno);M(zU.$$.fragment,rzn),rzn.forEach(t),ozn.forEach(t),NTa=i(tPo),tno=n(tPo,"SPAN",{});var tzn=s(tno);$Ta=r(tzn,"Computer vision"),tzn.forEach(t),tPo.forEach(t),sNo=i(c),Bye=n(c,"P",{});var azn=s(Bye);jTa=r(azn,"The following auto classes are available for the following computer vision tasks."),azn.forEach(t),lNo=i(c),Yf=n(c,"H3",{class:!0});var aPo=s(Yf);vG=n(aPo,"A",{id:!0,class:!0,href:!0});var nzn=s(vG);ano=n(nzn,"SPAN",{});var szn=s(ano);M(HU.$$.fragment,szn),szn.forEach(t),nzn.forEach(t),PTa=i(aPo),nno=n(aPo,"SPAN",{});var lzn=s(nno);ZTa=r(lzn,"AutoModelForDepthEstimation"),lzn.forEach(t),aPo.forEach(t),iNo=i(c),Mr=n(c,"DIV",{class:!0});var Yd=s(Mr);M(KU.$$.fragment,Yd),VTa=i(Yd),zf=n(Yd,"P",{});var wwe=s(zf);WTa=r(wwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),Iye=n(wwe,"A",{href:!0});var izn=s(Iye);JTa=r(izn,"from_pretrained()"),izn.forEach(t),UTa=r(wwe," class method or the "),Nye=n(wwe,"A",{href:!0});var dzn=s(Nye);XTa=r(dzn,"from_config()"),dzn.forEach(t),DTa=r(wwe,` class
method.`),wwe.forEach(t),qTa=i(Yd),eX=n(Yd,"P",{});var nPo=s(eX);OTa=r(nPo,"This class cannot be instantiated directly using "),sno=n(nPo,"CODE",{});var mzn=s(sno);QTa=r(mzn,"__init__()"),mzn.forEach(t),YTa=r(nPo," (throws an error)."),nPo.forEach(t),zTa=i(Yd),va=n(Yd,"DIV",{class:!0});var KP=s(va);M(oX.$$.fragment,KP),HTa=i(KP),lno=n(KP,"P",{});var czn=s(lno);KTa=r(czn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),czn.forEach(t),eFa=i(KP),Hf=n(KP,"P",{});var Lwe=s(Hf);oFa=r(Lwe,`Note:
Loading a model from its configuration file does `),ino=n(Lwe,"STRONG",{});var fzn=s(ino);rFa=r(fzn,"not"),fzn.forEach(t),tFa=r(Lwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$ye=n(Lwe,"A",{href:!0});var hzn=s($ye);aFa=r(hzn,"from_pretrained()"),hzn.forEach(t),nFa=r(Lwe," to load the model weights."),Lwe.forEach(t),sFa=i(KP),M(MG.$$.fragment,KP),KP.forEach(t),lFa=i(Yd),_o=n(Yd,"DIV",{class:!0});var fn=s(_o);M(rX.$$.fragment,fn),iFa=i(fn),dno=n(fn,"P",{});var gzn=s(dno);dFa=r(gzn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),gzn.forEach(t),mFa=i(fn),ps=n(fn,"P",{});var eZ=s(ps);cFa=r(eZ,"The model class to instantiate is selected based on the "),mno=n(eZ,"CODE",{});var uzn=s(mno);fFa=r(uzn,"model_type"),uzn.forEach(t),hFa=r(eZ,` property of the config object (either
passed as an argument or loaded from `),cno=n(eZ,"CODE",{});var pzn=s(cno);gFa=r(pzn,"pretrained_model_name_or_path"),pzn.forEach(t),uFa=r(eZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fno=n(eZ,"CODE",{});var _zn=s(fno);pFa=r(_zn,"pretrained_model_name_or_path"),_zn.forEach(t),_Fa=r(eZ,":"),eZ.forEach(t),bFa=i(fn),tX=n(fn,"UL",{});var sPo=s(tX);TG=n(sPo,"LI",{});var ESo=s(TG);hno=n(ESo,"STRONG",{});var bzn=s(hno);vFa=r(bzn,"dpt"),bzn.forEach(t),MFa=r(ESo," \u2014 "),jye=n(ESo,"A",{href:!0});var vzn=s(jye);TFa=r(vzn,"DPTForDepthEstimation"),vzn.forEach(t),FFa=r(ESo," (DPT model)"),ESo.forEach(t),EFa=i(sPo),FG=n(sPo,"LI",{});var ySo=s(FG);gno=n(ySo,"STRONG",{});var Mzn=s(gno);yFa=r(Mzn,"glpn"),Mzn.forEach(t),CFa=r(ySo," \u2014 "),Pye=n(ySo,"A",{href:!0});var Tzn=s(Pye);wFa=r(Tzn,"GLPNForDepthEstimation"),Tzn.forEach(t),LFa=r(ySo," (GLPN model)"),ySo.forEach(t),sPo.forEach(t),AFa=i(fn),EG=n(fn,"P",{});var CSo=s(EG);RFa=r(CSo,"The model is set in evaluation mode by default using "),uno=n(CSo,"CODE",{});var Fzn=s(uno);kFa=r(Fzn,"model.eval()"),Fzn.forEach(t),SFa=r(CSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),pno=n(CSo,"CODE",{});var Ezn=s(pno);xFa=r(Ezn,"model.train()"),Ezn.forEach(t),CSo.forEach(t),GFa=i(fn),M(yG.$$.fragment,fn),fn.forEach(t),Yd.forEach(t),dNo=i(c),Kf=n(c,"H3",{class:!0});var lPo=s(Kf);CG=n(lPo,"A",{id:!0,class:!0,href:!0});var yzn=s(CG);_no=n(yzn,"SPAN",{});var Czn=s(_no);M(aX.$$.fragment,Czn),Czn.forEach(t),yzn.forEach(t),BFa=i(lPo),bno=n(lPo,"SPAN",{});var wzn=s(bno);IFa=r(wzn,"AutoModelForImageClassification"),wzn.forEach(t),lPo.forEach(t),mNo=i(c),Tr=n(c,"DIV",{class:!0});var zd=s(Tr);M(nX.$$.fragment,zd),NFa=i(zd),eh=n(zd,"P",{});var Awe=s(eh);$Fa=r(Awe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),Zye=n(Awe,"A",{href:!0});var Lzn=s(Zye);jFa=r(Lzn,"from_pretrained()"),Lzn.forEach(t),PFa=r(Awe," class method or the "),Vye=n(Awe,"A",{href:!0});var Azn=s(Vye);ZFa=r(Azn,"from_config()"),Azn.forEach(t),VFa=r(Awe,` class
method.`),Awe.forEach(t),WFa=i(zd),sX=n(zd,"P",{});var iPo=s(sX);JFa=r(iPo,"This class cannot be instantiated directly using "),vno=n(iPo,"CODE",{});var Rzn=s(vno);UFa=r(Rzn,"__init__()"),Rzn.forEach(t),XFa=r(iPo," (throws an error)."),iPo.forEach(t),DFa=i(zd),Ma=n(zd,"DIV",{class:!0});var oZ=s(Ma);M(lX.$$.fragment,oZ),qFa=i(oZ),Mno=n(oZ,"P",{});var kzn=s(Mno);OFa=r(kzn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),kzn.forEach(t),QFa=i(oZ),oh=n(oZ,"P",{});var Rwe=s(oh);YFa=r(Rwe,`Note:
Loading a model from its configuration file does `),Tno=n(Rwe,"STRONG",{});var Szn=s(Tno);zFa=r(Szn,"not"),Szn.forEach(t),HFa=r(Rwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Wye=n(Rwe,"A",{href:!0});var xzn=s(Wye);KFa=r(xzn,"from_pretrained()"),xzn.forEach(t),eEa=r(Rwe," to load the model weights."),Rwe.forEach(t),oEa=i(oZ),M(wG.$$.fragment,oZ),oZ.forEach(t),rEa=i(zd),bo=n(zd,"DIV",{class:!0});var hn=s(bo);M(iX.$$.fragment,hn),tEa=i(hn),Fno=n(hn,"P",{});var Gzn=s(Fno);aEa=r(Gzn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Gzn.forEach(t),nEa=i(hn),_s=n(hn,"P",{});var rZ=s(_s);sEa=r(rZ,"The model class to instantiate is selected based on the "),Eno=n(rZ,"CODE",{});var Bzn=s(Eno);lEa=r(Bzn,"model_type"),Bzn.forEach(t),iEa=r(rZ,` property of the config object (either
passed as an argument or loaded from `),yno=n(rZ,"CODE",{});var Izn=s(yno);dEa=r(Izn,"pretrained_model_name_or_path"),Izn.forEach(t),mEa=r(rZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Cno=n(rZ,"CODE",{});var Nzn=s(Cno);cEa=r(Nzn,"pretrained_model_name_or_path"),Nzn.forEach(t),fEa=r(rZ,":"),rZ.forEach(t),hEa=i(hn),le=n(hn,"UL",{});var me=s(le);LG=n(me,"LI",{});var wSo=s(LG);wno=n(wSo,"STRONG",{});var $zn=s(wno);gEa=r($zn,"beit"),$zn.forEach(t),uEa=r(wSo," \u2014 "),Jye=n(wSo,"A",{href:!0});var jzn=s(Jye);pEa=r(jzn,"BeitForImageClassification"),jzn.forEach(t),_Ea=r(wSo," (BEiT model)"),wSo.forEach(t),bEa=i(me),AG=n(me,"LI",{});var LSo=s(AG);Lno=n(LSo,"STRONG",{});var Pzn=s(Lno);vEa=r(Pzn,"bit"),Pzn.forEach(t),MEa=r(LSo," \u2014 "),Uye=n(LSo,"A",{href:!0});var Zzn=s(Uye);TEa=r(Zzn,"BitForImageClassification"),Zzn.forEach(t),FEa=r(LSo," (BiT model)"),LSo.forEach(t),EEa=i(me),RG=n(me,"LI",{});var ASo=s(RG);Ano=n(ASo,"STRONG",{});var Vzn=s(Ano);yEa=r(Vzn,"convnext"),Vzn.forEach(t),CEa=r(ASo," \u2014 "),Xye=n(ASo,"A",{href:!0});var Wzn=s(Xye);wEa=r(Wzn,"ConvNextForImageClassification"),Wzn.forEach(t),LEa=r(ASo," (ConvNeXT model)"),ASo.forEach(t),AEa=i(me),kG=n(me,"LI",{});var RSo=s(kG);Rno=n(RSo,"STRONG",{});var Jzn=s(Rno);REa=r(Jzn,"convnextv2"),Jzn.forEach(t),kEa=r(RSo," \u2014 "),Dye=n(RSo,"A",{href:!0});var Uzn=s(Dye);SEa=r(Uzn,"ConvNextV2ForImageClassification"),Uzn.forEach(t),xEa=r(RSo," (ConvNeXTV2 model)"),RSo.forEach(t),GEa=i(me),SG=n(me,"LI",{});var kSo=s(SG);kno=n(kSo,"STRONG",{});var Xzn=s(kno);BEa=r(Xzn,"cvt"),Xzn.forEach(t),IEa=r(kSo," \u2014 "),qye=n(kSo,"A",{href:!0});var Dzn=s(qye);NEa=r(Dzn,"CvtForImageClassification"),Dzn.forEach(t),$Ea=r(kSo," (CvT model)"),kSo.forEach(t),jEa=i(me),xG=n(me,"LI",{});var SSo=s(xG);Sno=n(SSo,"STRONG",{});var qzn=s(Sno);PEa=r(qzn,"data2vec-vision"),qzn.forEach(t),ZEa=r(SSo," \u2014 "),Oye=n(SSo,"A",{href:!0});var Ozn=s(Oye);VEa=r(Ozn,"Data2VecVisionForImageClassification"),Ozn.forEach(t),WEa=r(SSo," (Data2VecVision model)"),SSo.forEach(t),JEa=i(me),Ui=n(me,"LI",{});var lY=s(Ui);xno=n(lY,"STRONG",{});var Qzn=s(xno);UEa=r(Qzn,"deit"),Qzn.forEach(t),XEa=r(lY," \u2014 "),Qye=n(lY,"A",{href:!0});var Yzn=s(Qye);DEa=r(Yzn,"DeiTForImageClassification"),Yzn.forEach(t),qEa=r(lY," or "),Yye=n(lY,"A",{href:!0});var zzn=s(Yye);OEa=r(zzn,"DeiTForImageClassificationWithTeacher"),zzn.forEach(t),QEa=r(lY," (DeiT model)"),lY.forEach(t),YEa=i(me),GG=n(me,"LI",{});var xSo=s(GG);Gno=n(xSo,"STRONG",{});var Hzn=s(Gno);zEa=r(Hzn,"dinat"),Hzn.forEach(t),HEa=r(xSo," \u2014 "),zye=n(xSo,"A",{href:!0});var Kzn=s(zye);KEa=r(Kzn,"DinatForImageClassification"),Kzn.forEach(t),e0a=r(xSo," (DiNAT model)"),xSo.forEach(t),o0a=i(me),BG=n(me,"LI",{});var GSo=s(BG);Bno=n(GSo,"STRONG",{});var eHn=s(Bno);r0a=r(eHn,"dinov2"),eHn.forEach(t),t0a=r(GSo," \u2014 "),Hye=n(GSo,"A",{href:!0});var oHn=s(Hye);a0a=r(oHn,"Dinov2ForImageClassification"),oHn.forEach(t),n0a=r(GSo," (DINOv2 model)"),GSo.forEach(t),s0a=i(me),Xi=n(me,"LI",{});var iY=s(Xi);Ino=n(iY,"STRONG",{});var rHn=s(Ino);l0a=r(rHn,"efficientformer"),rHn.forEach(t),i0a=r(iY," \u2014 "),Kye=n(iY,"A",{href:!0});var tHn=s(Kye);d0a=r(tHn,"EfficientFormerForImageClassification"),tHn.forEach(t),m0a=r(iY," or "),e5e=n(iY,"A",{href:!0});var aHn=s(e5e);c0a=r(aHn,"EfficientFormerForImageClassificationWithTeacher"),aHn.forEach(t),f0a=r(iY," (EfficientFormer model)"),iY.forEach(t),h0a=i(me),IG=n(me,"LI",{});var BSo=s(IG);Nno=n(BSo,"STRONG",{});var nHn=s(Nno);g0a=r(nHn,"efficientnet"),nHn.forEach(t),u0a=r(BSo," \u2014 "),o5e=n(BSo,"A",{href:!0});var sHn=s(o5e);p0a=r(sHn,"EfficientNetForImageClassification"),sHn.forEach(t),_0a=r(BSo," (EfficientNet model)"),BSo.forEach(t),b0a=i(me),NG=n(me,"LI",{});var ISo=s(NG);$no=n(ISo,"STRONG",{});var lHn=s($no);v0a=r(lHn,"focalnet"),lHn.forEach(t),M0a=r(ISo," \u2014 "),r5e=n(ISo,"A",{href:!0});var iHn=s(r5e);T0a=r(iHn,"FocalNetForImageClassification"),iHn.forEach(t),F0a=r(ISo," (FocalNet model)"),ISo.forEach(t),E0a=i(me),$G=n(me,"LI",{});var NSo=s($G);jno=n(NSo,"STRONG",{});var dHn=s(jno);y0a=r(dHn,"imagegpt"),dHn.forEach(t),C0a=r(NSo," \u2014 "),t5e=n(NSo,"A",{href:!0});var mHn=s(t5e);w0a=r(mHn,"ImageGPTForImageClassification"),mHn.forEach(t),L0a=r(NSo," (ImageGPT model)"),NSo.forEach(t),A0a=i(me),Di=n(me,"LI",{});var dY=s(Di);Pno=n(dY,"STRONG",{});var cHn=s(Pno);R0a=r(cHn,"levit"),cHn.forEach(t),k0a=r(dY," \u2014 "),a5e=n(dY,"A",{href:!0});var fHn=s(a5e);S0a=r(fHn,"LevitForImageClassification"),fHn.forEach(t),x0a=r(dY," or "),n5e=n(dY,"A",{href:!0});var hHn=s(n5e);G0a=r(hHn,"LevitForImageClassificationWithTeacher"),hHn.forEach(t),B0a=r(dY," (LeViT model)"),dY.forEach(t),I0a=i(me),jG=n(me,"LI",{});var $So=s(jG);Zno=n($So,"STRONG",{});var gHn=s(Zno);N0a=r(gHn,"mobilenet_v1"),gHn.forEach(t),$0a=r($So," \u2014 "),s5e=n($So,"A",{href:!0});var uHn=s(s5e);j0a=r(uHn,"MobileNetV1ForImageClassification"),uHn.forEach(t),P0a=r($So," (MobileNetV1 model)"),$So.forEach(t),Z0a=i(me),PG=n(me,"LI",{});var jSo=s(PG);Vno=n(jSo,"STRONG",{});var pHn=s(Vno);V0a=r(pHn,"mobilenet_v2"),pHn.forEach(t),W0a=r(jSo," \u2014 "),l5e=n(jSo,"A",{href:!0});var _Hn=s(l5e);J0a=r(_Hn,"MobileNetV2ForImageClassification"),_Hn.forEach(t),U0a=r(jSo," (MobileNetV2 model)"),jSo.forEach(t),X0a=i(me),ZG=n(me,"LI",{});var PSo=s(ZG);Wno=n(PSo,"STRONG",{});var bHn=s(Wno);D0a=r(bHn,"mobilevit"),bHn.forEach(t),q0a=r(PSo," \u2014 "),i5e=n(PSo,"A",{href:!0});var vHn=s(i5e);O0a=r(vHn,"MobileViTForImageClassification"),vHn.forEach(t),Q0a=r(PSo," (MobileViT model)"),PSo.forEach(t),Y0a=i(me),VG=n(me,"LI",{});var ZSo=s(VG);Jno=n(ZSo,"STRONG",{});var MHn=s(Jno);z0a=r(MHn,"mobilevitv2"),MHn.forEach(t),H0a=r(ZSo," \u2014 "),d5e=n(ZSo,"A",{href:!0});var THn=s(d5e);K0a=r(THn,"MobileViTV2ForImageClassification"),THn.forEach(t),eya=r(ZSo," (MobileViTV2 model)"),ZSo.forEach(t),oya=i(me),WG=n(me,"LI",{});var VSo=s(WG);Uno=n(VSo,"STRONG",{});var FHn=s(Uno);rya=r(FHn,"nat"),FHn.forEach(t),tya=r(VSo," \u2014 "),m5e=n(VSo,"A",{href:!0});var EHn=s(m5e);aya=r(EHn,"NatForImageClassification"),EHn.forEach(t),nya=r(VSo," (NAT model)"),VSo.forEach(t),sya=i(me),Ta=n(me,"LI",{});var qg=s(Ta);Xno=n(qg,"STRONG",{});var yHn=s(Xno);lya=r(yHn,"perceiver"),yHn.forEach(t),iya=r(qg," \u2014 "),c5e=n(qg,"A",{href:!0});var CHn=s(c5e);dya=r(CHn,"PerceiverForImageClassificationLearned"),CHn.forEach(t),mya=r(qg," or "),f5e=n(qg,"A",{href:!0});var wHn=s(f5e);cya=r(wHn,"PerceiverForImageClassificationFourier"),wHn.forEach(t),fya=r(qg," or "),h5e=n(qg,"A",{href:!0});var LHn=s(h5e);hya=r(LHn,"PerceiverForImageClassificationConvProcessing"),LHn.forEach(t),gya=r(qg," (Perceiver model)"),qg.forEach(t),uya=i(me),JG=n(me,"LI",{});var WSo=s(JG);Dno=n(WSo,"STRONG",{});var AHn=s(Dno);pya=r(AHn,"poolformer"),AHn.forEach(t),_ya=r(WSo," \u2014 "),g5e=n(WSo,"A",{href:!0});var RHn=s(g5e);bya=r(RHn,"PoolFormerForImageClassification"),RHn.forEach(t),vya=r(WSo," (PoolFormer model)"),WSo.forEach(t),Mya=i(me),UG=n(me,"LI",{});var JSo=s(UG);qno=n(JSo,"STRONG",{});var kHn=s(qno);Tya=r(kHn,"pvt"),kHn.forEach(t),Fya=r(JSo," \u2014 "),u5e=n(JSo,"A",{href:!0});var SHn=s(u5e);Eya=r(SHn,"PvtForImageClassification"),SHn.forEach(t),yya=r(JSo," (PVT model)"),JSo.forEach(t),Cya=i(me),XG=n(me,"LI",{});var USo=s(XG);Ono=n(USo,"STRONG",{});var xHn=s(Ono);wya=r(xHn,"regnet"),xHn.forEach(t),Lya=r(USo," \u2014 "),p5e=n(USo,"A",{href:!0});var GHn=s(p5e);Aya=r(GHn,"RegNetForImageClassification"),GHn.forEach(t),Rya=r(USo," (RegNet model)"),USo.forEach(t),kya=i(me),DG=n(me,"LI",{});var XSo=s(DG);Qno=n(XSo,"STRONG",{});var BHn=s(Qno);Sya=r(BHn,"resnet"),BHn.forEach(t),xya=r(XSo," \u2014 "),_5e=n(XSo,"A",{href:!0});var IHn=s(_5e);Gya=r(IHn,"ResNetForImageClassification"),IHn.forEach(t),Bya=r(XSo," (ResNet model)"),XSo.forEach(t),Iya=i(me),qG=n(me,"LI",{});var DSo=s(qG);Yno=n(DSo,"STRONG",{});var NHn=s(Yno);Nya=r(NHn,"segformer"),NHn.forEach(t),$ya=r(DSo," \u2014 "),b5e=n(DSo,"A",{href:!0});var $Hn=s(b5e);jya=r($Hn,"SegformerForImageClassification"),$Hn.forEach(t),Pya=r(DSo," (SegFormer model)"),DSo.forEach(t),Zya=i(me),OG=n(me,"LI",{});var qSo=s(OG);zno=n(qSo,"STRONG",{});var jHn=s(zno);Vya=r(jHn,"swiftformer"),jHn.forEach(t),Wya=r(qSo," \u2014 "),v5e=n(qSo,"A",{href:!0});var PHn=s(v5e);Jya=r(PHn,"SwiftFormerForImageClassification"),PHn.forEach(t),Uya=r(qSo," (SwiftFormer model)"),qSo.forEach(t),Xya=i(me),QG=n(me,"LI",{});var OSo=s(QG);Hno=n(OSo,"STRONG",{});var ZHn=s(Hno);Dya=r(ZHn,"swin"),ZHn.forEach(t),qya=r(OSo," \u2014 "),M5e=n(OSo,"A",{href:!0});var VHn=s(M5e);Oya=r(VHn,"SwinForImageClassification"),VHn.forEach(t),Qya=r(OSo," (Swin Transformer model)"),OSo.forEach(t),Yya=i(me),YG=n(me,"LI",{});var QSo=s(YG);Kno=n(QSo,"STRONG",{});var WHn=s(Kno);zya=r(WHn,"swinv2"),WHn.forEach(t),Hya=r(QSo," \u2014 "),T5e=n(QSo,"A",{href:!0});var JHn=s(T5e);Kya=r(JHn,"Swinv2ForImageClassification"),JHn.forEach(t),e5a=r(QSo," (Swin Transformer V2 model)"),QSo.forEach(t),o5a=i(me),zG=n(me,"LI",{});var YSo=s(zG);eso=n(YSo,"STRONG",{});var UHn=s(eso);r5a=r(UHn,"van"),UHn.forEach(t),t5a=r(YSo," \u2014 "),F5e=n(YSo,"A",{href:!0});var XHn=s(F5e);a5a=r(XHn,"VanForImageClassification"),XHn.forEach(t),n5a=r(YSo," (VAN model)"),YSo.forEach(t),s5a=i(me),HG=n(me,"LI",{});var zSo=s(HG);oso=n(zSo,"STRONG",{});var DHn=s(oso);l5a=r(DHn,"vit"),DHn.forEach(t),i5a=r(zSo," \u2014 "),E5e=n(zSo,"A",{href:!0});var qHn=s(E5e);d5a=r(qHn,"ViTForImageClassification"),qHn.forEach(t),m5a=r(zSo," (ViT model)"),zSo.forEach(t),c5a=i(me),KG=n(me,"LI",{});var HSo=s(KG);rso=n(HSo,"STRONG",{});var OHn=s(rso);f5a=r(OHn,"vit_hybrid"),OHn.forEach(t),h5a=r(HSo," \u2014 "),y5e=n(HSo,"A",{href:!0});var QHn=s(y5e);g5a=r(QHn,"ViTHybridForImageClassification"),QHn.forEach(t),u5a=r(HSo," (ViT Hybrid model)"),HSo.forEach(t),p5a=i(me),eB=n(me,"LI",{});var KSo=s(eB);tso=n(KSo,"STRONG",{});var YHn=s(tso);_5a=r(YHn,"vit_msn"),YHn.forEach(t),b5a=r(KSo," \u2014 "),C5e=n(KSo,"A",{href:!0});var zHn=s(C5e);v5a=r(zHn,"ViTMSNForImageClassification"),zHn.forEach(t),M5a=r(KSo," (ViTMSN model)"),KSo.forEach(t),me.forEach(t),T5a=i(hn),oB=n(hn,"P",{});var exo=s(oB);F5a=r(exo,"The model is set in evaluation mode by default using "),aso=n(exo,"CODE",{});var HHn=s(aso);E5a=r(HHn,"model.eval()"),HHn.forEach(t),y5a=r(exo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nso=n(exo,"CODE",{});var KHn=s(nso);C5a=r(KHn,"model.train()"),KHn.forEach(t),exo.forEach(t),w5a=i(hn),M(rB.$$.fragment,hn),hn.forEach(t),zd.forEach(t),cNo=i(c),rh=n(c,"H3",{class:!0});var dPo=s(rh);tB=n(dPo,"A",{id:!0,class:!0,href:!0});var eKn=s(tB);sso=n(eKn,"SPAN",{});var oKn=s(sso);M(dX.$$.fragment,oKn),oKn.forEach(t),eKn.forEach(t),L5a=i(dPo),lso=n(dPo,"SPAN",{});var rKn=s(lso);A5a=r(rKn,"TFAutoModelForImageClassification"),rKn.forEach(t),dPo.forEach(t),fNo=i(c),Fr=n(c,"DIV",{class:!0});var Hd=s(Fr);M(mX.$$.fragment,Hd),R5a=i(Hd),th=n(Hd,"P",{});var kwe=s(th);k5a=r(kwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),w5e=n(kwe,"A",{href:!0});var tKn=s(w5e);S5a=r(tKn,"from_pretrained()"),tKn.forEach(t),x5a=r(kwe," class method or the "),L5e=n(kwe,"A",{href:!0});var aKn=s(L5e);G5a=r(aKn,"from_config()"),aKn.forEach(t),B5a=r(kwe,` class
method.`),kwe.forEach(t),I5a=i(Hd),cX=n(Hd,"P",{});var mPo=s(cX);N5a=r(mPo,"This class cannot be instantiated directly using "),iso=n(mPo,"CODE",{});var nKn=s(iso);$5a=r(nKn,"__init__()"),nKn.forEach(t),j5a=r(mPo," (throws an error)."),mPo.forEach(t),P5a=i(Hd),Fa=n(Hd,"DIV",{class:!0});var tZ=s(Fa);M(fX.$$.fragment,tZ),Z5a=i(tZ),dso=n(tZ,"P",{});var sKn=s(dso);V5a=r(sKn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),sKn.forEach(t),W5a=i(tZ),ah=n(tZ,"P",{});var Swe=s(ah);J5a=r(Swe,`Note:
Loading a model from its configuration file does `),mso=n(Swe,"STRONG",{});var lKn=s(mso);U5a=r(lKn,"not"),lKn.forEach(t),X5a=r(Swe,` load the model weights. It only affects the
model\u2019s configuration. Use `),A5e=n(Swe,"A",{href:!0});var iKn=s(A5e);D5a=r(iKn,"from_pretrained()"),iKn.forEach(t),q5a=r(Swe," to load the model weights."),Swe.forEach(t),O5a=i(tZ),M(aB.$$.fragment,tZ),tZ.forEach(t),Q5a=i(Hd),Et=n(Hd,"DIV",{class:!0});var Kd=s(Et);M(hX.$$.fragment,Kd),Y5a=i(Kd),cso=n(Kd,"P",{});var dKn=s(cso);z5a=r(dKn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),dKn.forEach(t),H5a=i(Kd),bs=n(Kd,"P",{});var aZ=s(bs);K5a=r(aZ,"The model class to instantiate is selected based on the "),fso=n(aZ,"CODE",{});var mKn=s(fso);e3a=r(mKn,"model_type"),mKn.forEach(t),o3a=r(aZ,` property of the config object (either
passed as an argument or loaded from `),hso=n(aZ,"CODE",{});var cKn=s(hso);r3a=r(cKn,"pretrained_model_name_or_path"),cKn.forEach(t),t3a=r(aZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gso=n(aZ,"CODE",{});var fKn=s(gso);a3a=r(fKn,"pretrained_model_name_or_path"),fKn.forEach(t),n3a=r(aZ,":"),aZ.forEach(t),s3a=i(Kd),Ge=n(Kd,"UL",{});var We=s(Ge);nB=n(We,"LI",{});var oxo=s(nB);uso=n(oxo,"STRONG",{});var hKn=s(uso);l3a=r(hKn,"convnext"),hKn.forEach(t),i3a=r(oxo," \u2014 "),R5e=n(oxo,"A",{href:!0});var gKn=s(R5e);d3a=r(gKn,"TFConvNextForImageClassification"),gKn.forEach(t),m3a=r(oxo," (ConvNeXT model)"),oxo.forEach(t),c3a=i(We),sB=n(We,"LI",{});var rxo=s(sB);pso=n(rxo,"STRONG",{});var uKn=s(pso);f3a=r(uKn,"cvt"),uKn.forEach(t),h3a=r(rxo," \u2014 "),k5e=n(rxo,"A",{href:!0});var pKn=s(k5e);g3a=r(pKn,"TFCvtForImageClassification"),pKn.forEach(t),u3a=r(rxo," (CvT model)"),rxo.forEach(t),p3a=i(We),lB=n(We,"LI",{});var txo=s(lB);_so=n(txo,"STRONG",{});var _Kn=s(_so);_3a=r(_Kn,"data2vec-vision"),_Kn.forEach(t),b3a=r(txo," \u2014 "),S5e=n(txo,"A",{href:!0});var bKn=s(S5e);v3a=r(bKn,"TFData2VecVisionForImageClassification"),bKn.forEach(t),M3a=r(txo," (Data2VecVision model)"),txo.forEach(t),T3a=i(We),qi=n(We,"LI",{});var mY=s(qi);bso=n(mY,"STRONG",{});var vKn=s(bso);F3a=r(vKn,"deit"),vKn.forEach(t),E3a=r(mY," \u2014 "),x5e=n(mY,"A",{href:!0});var MKn=s(x5e);y3a=r(MKn,"TFDeiTForImageClassification"),MKn.forEach(t),C3a=r(mY," or "),G5e=n(mY,"A",{href:!0});var TKn=s(G5e);w3a=r(TKn,"TFDeiTForImageClassificationWithTeacher"),TKn.forEach(t),L3a=r(mY," (DeiT model)"),mY.forEach(t),A3a=i(We),Oi=n(We,"LI",{});var cY=s(Oi);vso=n(cY,"STRONG",{});var FKn=s(vso);R3a=r(FKn,"efficientformer"),FKn.forEach(t),k3a=r(cY," \u2014 "),B5e=n(cY,"A",{href:!0});var EKn=s(B5e);S3a=r(EKn,"TFEfficientFormerForImageClassification"),EKn.forEach(t),x3a=r(cY," or "),I5e=n(cY,"A",{href:!0});var yKn=s(I5e);G3a=r(yKn,"TFEfficientFormerForImageClassificationWithTeacher"),yKn.forEach(t),B3a=r(cY," (EfficientFormer model)"),cY.forEach(t),I3a=i(We),iB=n(We,"LI",{});var axo=s(iB);Mso=n(axo,"STRONG",{});var CKn=s(Mso);N3a=r(CKn,"mobilevit"),CKn.forEach(t),$3a=r(axo," \u2014 "),N5e=n(axo,"A",{href:!0});var wKn=s(N5e);j3a=r(wKn,"TFMobileViTForImageClassification"),wKn.forEach(t),P3a=r(axo," (MobileViT model)"),axo.forEach(t),Z3a=i(We),dB=n(We,"LI",{});var nxo=s(dB);Tso=n(nxo,"STRONG",{});var LKn=s(Tso);V3a=r(LKn,"regnet"),LKn.forEach(t),W3a=r(nxo," \u2014 "),$5e=n(nxo,"A",{href:!0});var AKn=s($5e);J3a=r(AKn,"TFRegNetForImageClassification"),AKn.forEach(t),U3a=r(nxo," (RegNet model)"),nxo.forEach(t),X3a=i(We),mB=n(We,"LI",{});var sxo=s(mB);Fso=n(sxo,"STRONG",{});var RKn=s(Fso);D3a=r(RKn,"resnet"),RKn.forEach(t),q3a=r(sxo," \u2014 "),j5e=n(sxo,"A",{href:!0});var kKn=s(j5e);O3a=r(kKn,"TFResNetForImageClassification"),kKn.forEach(t),Q3a=r(sxo," (ResNet model)"),sxo.forEach(t),Y3a=i(We),cB=n(We,"LI",{});var lxo=s(cB);Eso=n(lxo,"STRONG",{});var SKn=s(Eso);z3a=r(SKn,"segformer"),SKn.forEach(t),H3a=r(lxo," \u2014 "),P5e=n(lxo,"A",{href:!0});var xKn=s(P5e);K3a=r(xKn,"TFSegformerForImageClassification"),xKn.forEach(t),e9a=r(lxo," (SegFormer model)"),lxo.forEach(t),o9a=i(We),fB=n(We,"LI",{});var ixo=s(fB);yso=n(ixo,"STRONG",{});var GKn=s(yso);r9a=r(GKn,"swin"),GKn.forEach(t),t9a=r(ixo," \u2014 "),Z5e=n(ixo,"A",{href:!0});var BKn=s(Z5e);a9a=r(BKn,"TFSwinForImageClassification"),BKn.forEach(t),n9a=r(ixo," (Swin Transformer model)"),ixo.forEach(t),s9a=i(We),hB=n(We,"LI",{});var dxo=s(hB);Cso=n(dxo,"STRONG",{});var IKn=s(Cso);l9a=r(IKn,"vit"),IKn.forEach(t),i9a=r(dxo," \u2014 "),V5e=n(dxo,"A",{href:!0});var NKn=s(V5e);d9a=r(NKn,"TFViTForImageClassification"),NKn.forEach(t),m9a=r(dxo," (ViT model)"),dxo.forEach(t),We.forEach(t),c9a=i(Kd),M(gB.$$.fragment,Kd),Kd.forEach(t),Hd.forEach(t),hNo=i(c),nh=n(c,"H3",{class:!0});var cPo=s(nh);uB=n(cPo,"A",{id:!0,class:!0,href:!0});var $Kn=s(uB);wso=n($Kn,"SPAN",{});var jKn=s(wso);M(gX.$$.fragment,jKn),jKn.forEach(t),$Kn.forEach(t),f9a=i(cPo),Lso=n(cPo,"SPAN",{});var PKn=s(Lso);h9a=r(PKn,"FlaxAutoModelForImageClassification"),PKn.forEach(t),cPo.forEach(t),gNo=i(c),Er=n(c,"DIV",{class:!0});var em=s(Er);M(uX.$$.fragment,em),g9a=i(em),sh=n(em,"P",{});var xwe=s(sh);u9a=r(xwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),W5e=n(xwe,"A",{href:!0});var ZKn=s(W5e);p9a=r(ZKn,"from_pretrained()"),ZKn.forEach(t),_9a=r(xwe," class method or the "),J5e=n(xwe,"A",{href:!0});var VKn=s(J5e);b9a=r(VKn,"from_config()"),VKn.forEach(t),v9a=r(xwe,` class
method.`),xwe.forEach(t),M9a=i(em),pX=n(em,"P",{});var fPo=s(pX);T9a=r(fPo,"This class cannot be instantiated directly using "),Aso=n(fPo,"CODE",{});var WKn=s(Aso);F9a=r(WKn,"__init__()"),WKn.forEach(t),E9a=r(fPo," (throws an error)."),fPo.forEach(t),y9a=i(em),Ea=n(em,"DIV",{class:!0});var nZ=s(Ea);M(_X.$$.fragment,nZ),C9a=i(nZ),Rso=n(nZ,"P",{});var JKn=s(Rso);w9a=r(JKn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),JKn.forEach(t),L9a=i(nZ),lh=n(nZ,"P",{});var Gwe=s(lh);A9a=r(Gwe,`Note:
Loading a model from its configuration file does `),kso=n(Gwe,"STRONG",{});var UKn=s(kso);R9a=r(UKn,"not"),UKn.forEach(t),k9a=r(Gwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),U5e=n(Gwe,"A",{href:!0});var XKn=s(U5e);S9a=r(XKn,"from_pretrained()"),XKn.forEach(t),x9a=r(Gwe," to load the model weights."),Gwe.forEach(t),G9a=i(nZ),M(pB.$$.fragment,nZ),nZ.forEach(t),B9a=i(em),yt=n(em,"DIV",{class:!0});var om=s(yt);M(bX.$$.fragment,om),I9a=i(om),Sso=n(om,"P",{});var DKn=s(Sso);N9a=r(DKn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),DKn.forEach(t),$9a=i(om),vs=n(om,"P",{});var sZ=s(vs);j9a=r(sZ,"The model class to instantiate is selected based on the "),xso=n(sZ,"CODE",{});var qKn=s(xso);P9a=r(qKn,"model_type"),qKn.forEach(t),Z9a=r(sZ,` property of the config object (either
passed as an argument or loaded from `),Gso=n(sZ,"CODE",{});var OKn=s(Gso);V9a=r(OKn,"pretrained_model_name_or_path"),OKn.forEach(t),W9a=r(sZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bso=n(sZ,"CODE",{});var QKn=s(Bso);J9a=r(QKn,"pretrained_model_name_or_path"),QKn.forEach(t),U9a=r(sZ,":"),sZ.forEach(t),X9a=i(om),Ms=n(om,"UL",{});var lZ=s(Ms);_B=n(lZ,"LI",{});var mxo=s(_B);Iso=n(mxo,"STRONG",{});var YKn=s(Iso);D9a=r(YKn,"beit"),YKn.forEach(t),q9a=r(mxo," \u2014 "),X5e=n(mxo,"A",{href:!0});var zKn=s(X5e);O9a=r(zKn,"FlaxBeitForImageClassification"),zKn.forEach(t),Q9a=r(mxo," (BEiT model)"),mxo.forEach(t),Y9a=i(lZ),bB=n(lZ,"LI",{});var cxo=s(bB);Nso=n(cxo,"STRONG",{});var HKn=s(Nso);z9a=r(HKn,"regnet"),HKn.forEach(t),H9a=r(cxo," \u2014 "),D5e=n(cxo,"A",{href:!0});var KKn=s(D5e);K9a=r(KKn,"FlaxRegNetForImageClassification"),KKn.forEach(t),e4a=r(cxo," (RegNet model)"),cxo.forEach(t),o4a=i(lZ),vB=n(lZ,"LI",{});var fxo=s(vB);$so=n(fxo,"STRONG",{});var ees=s($so);r4a=r(ees,"resnet"),ees.forEach(t),t4a=r(fxo," \u2014 "),q5e=n(fxo,"A",{href:!0});var oes=s(q5e);a4a=r(oes,"FlaxResNetForImageClassification"),oes.forEach(t),n4a=r(fxo," (ResNet model)"),fxo.forEach(t),s4a=i(lZ),MB=n(lZ,"LI",{});var hxo=s(MB);jso=n(hxo,"STRONG",{});var res=s(jso);l4a=r(res,"vit"),res.forEach(t),i4a=r(hxo," \u2014 "),O5e=n(hxo,"A",{href:!0});var tes=s(O5e);d4a=r(tes,"FlaxViTForImageClassification"),tes.forEach(t),m4a=r(hxo," (ViT model)"),hxo.forEach(t),lZ.forEach(t),c4a=i(om),M(TB.$$.fragment,om),om.forEach(t),em.forEach(t),uNo=i(c),ih=n(c,"H3",{class:!0});var hPo=s(ih);FB=n(hPo,"A",{id:!0,class:!0,href:!0});var aes=s(FB);Pso=n(aes,"SPAN",{});var nes=s(Pso);M(vX.$$.fragment,nes),nes.forEach(t),aes.forEach(t),f4a=i(hPo),Zso=n(hPo,"SPAN",{});var ses=s(Zso);h4a=r(ses,"AutoModelForVideoClassification"),ses.forEach(t),hPo.forEach(t),pNo=i(c),yr=n(c,"DIV",{class:!0});var rm=s(yr);M(MX.$$.fragment,rm),g4a=i(rm),dh=n(rm,"P",{});var Bwe=s(dh);u4a=r(Bwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),Q5e=n(Bwe,"A",{href:!0});var les=s(Q5e);p4a=r(les,"from_pretrained()"),les.forEach(t),_4a=r(Bwe," class method or the "),Y5e=n(Bwe,"A",{href:!0});var ies=s(Y5e);b4a=r(ies,"from_config()"),ies.forEach(t),v4a=r(Bwe,` class
method.`),Bwe.forEach(t),M4a=i(rm),TX=n(rm,"P",{});var gPo=s(TX);T4a=r(gPo,"This class cannot be instantiated directly using "),Vso=n(gPo,"CODE",{});var des=s(Vso);F4a=r(des,"__init__()"),des.forEach(t),E4a=r(gPo," (throws an error)."),gPo.forEach(t),y4a=i(rm),ya=n(rm,"DIV",{class:!0});var iZ=s(ya);M(FX.$$.fragment,iZ),C4a=i(iZ),Wso=n(iZ,"P",{});var mes=s(Wso);w4a=r(mes,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),mes.forEach(t),L4a=i(iZ),mh=n(iZ,"P",{});var Iwe=s(mh);A4a=r(Iwe,`Note:
Loading a model from its configuration file does `),Jso=n(Iwe,"STRONG",{});var ces=s(Jso);R4a=r(ces,"not"),ces.forEach(t),k4a=r(Iwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),z5e=n(Iwe,"A",{href:!0});var fes=s(z5e);S4a=r(fes,"from_pretrained()"),fes.forEach(t),x4a=r(Iwe," to load the model weights."),Iwe.forEach(t),G4a=i(iZ),M(EB.$$.fragment,iZ),iZ.forEach(t),B4a=i(rm),vo=n(rm,"DIV",{class:!0});var gn=s(vo);M(EX.$$.fragment,gn),I4a=i(gn),Uso=n(gn,"P",{});var hes=s(Uso);N4a=r(hes,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),hes.forEach(t),$4a=i(gn),Ts=n(gn,"P",{});var dZ=s(Ts);j4a=r(dZ,"The model class to instantiate is selected based on the "),Xso=n(dZ,"CODE",{});var ges=s(Xso);P4a=r(ges,"model_type"),ges.forEach(t),Z4a=r(dZ,` property of the config object (either
passed as an argument or loaded from `),Dso=n(dZ,"CODE",{});var ues=s(Dso);V4a=r(ues,"pretrained_model_name_or_path"),ues.forEach(t),W4a=r(dZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qso=n(dZ,"CODE",{});var pes=s(qso);J4a=r(pes,"pretrained_model_name_or_path"),pes.forEach(t),U4a=r(dZ,":"),dZ.forEach(t),X4a=i(gn),ch=n(gn,"UL",{});var Nwe=s(ch);yB=n(Nwe,"LI",{});var gxo=s(yB);Oso=n(gxo,"STRONG",{});var _es=s(Oso);D4a=r(_es,"timesformer"),_es.forEach(t),q4a=r(gxo," \u2014 "),H5e=n(gxo,"A",{href:!0});var bes=s(H5e);O4a=r(bes,"TimesformerForVideoClassification"),bes.forEach(t),Q4a=r(gxo," (TimeSformer model)"),gxo.forEach(t),Y4a=i(Nwe),CB=n(Nwe,"LI",{});var uxo=s(CB);Qso=n(uxo,"STRONG",{});var ves=s(Qso);z4a=r(ves,"videomae"),ves.forEach(t),H4a=r(uxo," \u2014 "),K5e=n(uxo,"A",{href:!0});var Mes=s(K5e);K4a=r(Mes,"VideoMAEForVideoClassification"),Mes.forEach(t),eCa=r(uxo," (VideoMAE model)"),uxo.forEach(t),oCa=i(Nwe),wB=n(Nwe,"LI",{});var pxo=s(wB);Yso=n(pxo,"STRONG",{});var Tes=s(Yso);rCa=r(Tes,"vivit"),Tes.forEach(t),tCa=r(pxo," \u2014 "),e3e=n(pxo,"A",{href:!0});var Fes=s(e3e);aCa=r(Fes,"VivitForVideoClassification"),Fes.forEach(t),nCa=r(pxo," (ViViT model)"),pxo.forEach(t),Nwe.forEach(t),sCa=i(gn),LB=n(gn,"P",{});var _xo=s(LB);lCa=r(_xo,"The model is set in evaluation mode by default using "),zso=n(_xo,"CODE",{});var Ees=s(zso);iCa=r(Ees,"model.eval()"),Ees.forEach(t),dCa=r(_xo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Hso=n(_xo,"CODE",{});var yes=s(Hso);mCa=r(yes,"model.train()"),yes.forEach(t),_xo.forEach(t),cCa=i(gn),M(AB.$$.fragment,gn),gn.forEach(t),rm.forEach(t),_No=i(c),fh=n(c,"H3",{class:!0});var uPo=s(fh);RB=n(uPo,"A",{id:!0,class:!0,href:!0});var Ces=s(RB);Kso=n(Ces,"SPAN",{});var wes=s(Kso);M(yX.$$.fragment,wes),wes.forEach(t),Ces.forEach(t),fCa=i(uPo),elo=n(uPo,"SPAN",{});var Les=s(elo);hCa=r(Les,"AutoModelForMaskedImageModeling"),Les.forEach(t),uPo.forEach(t),bNo=i(c),Cr=n(c,"DIV",{class:!0});var tm=s(Cr);M(CX.$$.fragment,tm),gCa=i(tm),hh=n(tm,"P",{});var $we=s(hh);uCa=r($we,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),o3e=n($we,"A",{href:!0});var Aes=s(o3e);pCa=r(Aes,"from_pretrained()"),Aes.forEach(t),_Ca=r($we," class method or the "),r3e=n($we,"A",{href:!0});var Res=s(r3e);bCa=r(Res,"from_config()"),Res.forEach(t),vCa=r($we,` class
method.`),$we.forEach(t),MCa=i(tm),wX=n(tm,"P",{});var pPo=s(wX);TCa=r(pPo,"This class cannot be instantiated directly using "),olo=n(pPo,"CODE",{});var kes=s(olo);FCa=r(kes,"__init__()"),kes.forEach(t),ECa=r(pPo," (throws an error)."),pPo.forEach(t),yCa=i(tm),Ca=n(tm,"DIV",{class:!0});var mZ=s(Ca);M(LX.$$.fragment,mZ),CCa=i(mZ),rlo=n(mZ,"P",{});var Ses=s(rlo);wCa=r(Ses,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),Ses.forEach(t),LCa=i(mZ),gh=n(mZ,"P",{});var jwe=s(gh);ACa=r(jwe,`Note:
Loading a model from its configuration file does `),tlo=n(jwe,"STRONG",{});var xes=s(tlo);RCa=r(xes,"not"),xes.forEach(t),kCa=r(jwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),t3e=n(jwe,"A",{href:!0});var Ges=s(t3e);SCa=r(Ges,"from_pretrained()"),Ges.forEach(t),xCa=r(jwe," to load the model weights."),jwe.forEach(t),GCa=i(mZ),M(kB.$$.fragment,mZ),mZ.forEach(t),BCa=i(tm),Mo=n(tm,"DIV",{class:!0});var un=s(Mo);M(AX.$$.fragment,un),ICa=i(un),alo=n(un,"P",{});var Bes=s(alo);NCa=r(Bes,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),Bes.forEach(t),$Ca=i(un),Fs=n(un,"P",{});var cZ=s(Fs);jCa=r(cZ,"The model class to instantiate is selected based on the "),nlo=n(cZ,"CODE",{});var Ies=s(nlo);PCa=r(Ies,"model_type"),Ies.forEach(t),ZCa=r(cZ,` property of the config object (either
passed as an argument or loaded from `),slo=n(cZ,"CODE",{});var Nes=s(slo);VCa=r(Nes,"pretrained_model_name_or_path"),Nes.forEach(t),WCa=r(cZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),llo=n(cZ,"CODE",{});var $es=s(llo);JCa=r($es,"pretrained_model_name_or_path"),$es.forEach(t),UCa=r(cZ,":"),cZ.forEach(t),XCa=i(un),Nt=n(un,"UL",{});var am=s(Nt);SB=n(am,"LI",{});var bxo=s(SB);ilo=n(bxo,"STRONG",{});var jes=s(ilo);DCa=r(jes,"deit"),jes.forEach(t),qCa=r(bxo," \u2014 "),a3e=n(bxo,"A",{href:!0});var Pes=s(a3e);OCa=r(Pes,"DeiTForMaskedImageModeling"),Pes.forEach(t),QCa=r(bxo," (DeiT model)"),bxo.forEach(t),YCa=i(am),xB=n(am,"LI",{});var vxo=s(xB);dlo=n(vxo,"STRONG",{});var Zes=s(dlo);zCa=r(Zes,"focalnet"),Zes.forEach(t),HCa=r(vxo," \u2014 "),n3e=n(vxo,"A",{href:!0});var Ves=s(n3e);KCa=r(Ves,"FocalNetForMaskedImageModeling"),Ves.forEach(t),ewa=r(vxo," (FocalNet model)"),vxo.forEach(t),owa=i(am),GB=n(am,"LI",{});var Mxo=s(GB);mlo=n(Mxo,"STRONG",{});var Wes=s(mlo);rwa=r(Wes,"swin"),Wes.forEach(t),twa=r(Mxo," \u2014 "),s3e=n(Mxo,"A",{href:!0});var Jes=s(s3e);awa=r(Jes,"SwinForMaskedImageModeling"),Jes.forEach(t),nwa=r(Mxo," (Swin Transformer model)"),Mxo.forEach(t),swa=i(am),BB=n(am,"LI",{});var Txo=s(BB);clo=n(Txo,"STRONG",{});var Ues=s(clo);lwa=r(Ues,"swinv2"),Ues.forEach(t),iwa=r(Txo," \u2014 "),l3e=n(Txo,"A",{href:!0});var Xes=s(l3e);dwa=r(Xes,"Swinv2ForMaskedImageModeling"),Xes.forEach(t),mwa=r(Txo," (Swin Transformer V2 model)"),Txo.forEach(t),cwa=i(am),IB=n(am,"LI",{});var Fxo=s(IB);flo=n(Fxo,"STRONG",{});var Des=s(flo);fwa=r(Des,"vit"),Des.forEach(t),hwa=r(Fxo," \u2014 "),i3e=n(Fxo,"A",{href:!0});var qes=s(i3e);gwa=r(qes,"ViTForMaskedImageModeling"),qes.forEach(t),uwa=r(Fxo," (ViT model)"),Fxo.forEach(t),am.forEach(t),pwa=i(un),NB=n(un,"P",{});var Exo=s(NB);_wa=r(Exo,"The model is set in evaluation mode by default using "),hlo=n(Exo,"CODE",{});var Oes=s(hlo);bwa=r(Oes,"model.eval()"),Oes.forEach(t),vwa=r(Exo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),glo=n(Exo,"CODE",{});var Qes=s(glo);Mwa=r(Qes,"model.train()"),Qes.forEach(t),Exo.forEach(t),Twa=i(un),M($B.$$.fragment,un),un.forEach(t),tm.forEach(t),vNo=i(c),uh=n(c,"H3",{class:!0});var _Po=s(uh);jB=n(_Po,"A",{id:!0,class:!0,href:!0});var Yes=s(jB);ulo=n(Yes,"SPAN",{});var zes=s(ulo);M(RX.$$.fragment,zes),zes.forEach(t),Yes.forEach(t),Fwa=i(_Po),plo=n(_Po,"SPAN",{});var Hes=s(plo);Ewa=r(Hes,"TFAutoModelForMaskedImageModeling"),Hes.forEach(t),_Po.forEach(t),MNo=i(c),wr=n(c,"DIV",{class:!0});var nm=s(wr);M(kX.$$.fragment,nm),ywa=i(nm),ph=n(nm,"P",{});var Pwe=s(ph);Cwa=r(Pwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),d3e=n(Pwe,"A",{href:!0});var Kes=s(d3e);wwa=r(Kes,"from_pretrained()"),Kes.forEach(t),Lwa=r(Pwe," class method or the "),m3e=n(Pwe,"A",{href:!0});var eos=s(m3e);Awa=r(eos,"from_config()"),eos.forEach(t),Rwa=r(Pwe,` class
method.`),Pwe.forEach(t),kwa=i(nm),SX=n(nm,"P",{});var bPo=s(SX);Swa=r(bPo,"This class cannot be instantiated directly using "),_lo=n(bPo,"CODE",{});var oos=s(_lo);xwa=r(oos,"__init__()"),oos.forEach(t),Gwa=r(bPo," (throws an error)."),bPo.forEach(t),Bwa=i(nm),wa=n(nm,"DIV",{class:!0});var fZ=s(wa);M(xX.$$.fragment,fZ),Iwa=i(fZ),blo=n(fZ,"P",{});var ros=s(blo);Nwa=r(ros,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),ros.forEach(t),$wa=i(fZ),_h=n(fZ,"P",{});var Zwe=s(_h);jwa=r(Zwe,`Note:
Loading a model from its configuration file does `),vlo=n(Zwe,"STRONG",{});var tos=s(vlo);Pwa=r(tos,"not"),tos.forEach(t),Zwa=r(Zwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),c3e=n(Zwe,"A",{href:!0});var aos=s(c3e);Vwa=r(aos,"from_pretrained()"),aos.forEach(t),Wwa=r(Zwe," to load the model weights."),Zwe.forEach(t),Jwa=i(fZ),M(PB.$$.fragment,fZ),fZ.forEach(t),Uwa=i(nm),Ct=n(nm,"DIV",{class:!0});var sm=s(Ct);M(GX.$$.fragment,sm),Xwa=i(sm),Mlo=n(sm,"P",{});var nos=s(Mlo);Dwa=r(nos,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),nos.forEach(t),qwa=i(sm),Es=n(sm,"P",{});var hZ=s(Es);Owa=r(hZ,"The model class to instantiate is selected based on the "),Tlo=n(hZ,"CODE",{});var sos=s(Tlo);Qwa=r(sos,"model_type"),sos.forEach(t),Ywa=r(hZ,` property of the config object (either
passed as an argument or loaded from `),Flo=n(hZ,"CODE",{});var los=s(Flo);zwa=r(los,"pretrained_model_name_or_path"),los.forEach(t),Hwa=r(hZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Elo=n(hZ,"CODE",{});var ios=s(Elo);Kwa=r(ios,"pretrained_model_name_or_path"),ios.forEach(t),e6a=r(hZ,":"),hZ.forEach(t),o6a=i(sm),BX=n(sm,"UL",{});var vPo=s(BX);ZB=n(vPo,"LI",{});var yxo=s(ZB);ylo=n(yxo,"STRONG",{});var dos=s(ylo);r6a=r(dos,"deit"),dos.forEach(t),t6a=r(yxo," \u2014 "),f3e=n(yxo,"A",{href:!0});var mos=s(f3e);a6a=r(mos,"TFDeiTForMaskedImageModeling"),mos.forEach(t),n6a=r(yxo," (DeiT model)"),yxo.forEach(t),s6a=i(vPo),VB=n(vPo,"LI",{});var Cxo=s(VB);Clo=n(Cxo,"STRONG",{});var cos=s(Clo);l6a=r(cos,"swin"),cos.forEach(t),i6a=r(Cxo," \u2014 "),h3e=n(Cxo,"A",{href:!0});var fos=s(h3e);d6a=r(fos,"TFSwinForMaskedImageModeling"),fos.forEach(t),m6a=r(Cxo," (Swin Transformer model)"),Cxo.forEach(t),vPo.forEach(t),c6a=i(sm),M(WB.$$.fragment,sm),sm.forEach(t),nm.forEach(t),TNo=i(c),bh=n(c,"H3",{class:!0});var MPo=s(bh);JB=n(MPo,"A",{id:!0,class:!0,href:!0});var hos=s(JB);wlo=n(hos,"SPAN",{});var gos=s(wlo);M(IX.$$.fragment,gos),gos.forEach(t),hos.forEach(t),f6a=i(MPo),Llo=n(MPo,"SPAN",{});var uos=s(Llo);h6a=r(uos,"AutoModelForObjectDetection"),uos.forEach(t),MPo.forEach(t),FNo=i(c),Lr=n(c,"DIV",{class:!0});var lm=s(Lr);M(NX.$$.fragment,lm),g6a=i(lm),vh=n(lm,"P",{});var Vwe=s(vh);u6a=r(Vwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),g3e=n(Vwe,"A",{href:!0});var pos=s(g3e);p6a=r(pos,"from_pretrained()"),pos.forEach(t),_6a=r(Vwe," class method or the "),u3e=n(Vwe,"A",{href:!0});var _os=s(u3e);b6a=r(_os,"from_config()"),_os.forEach(t),v6a=r(Vwe,` class
method.`),Vwe.forEach(t),M6a=i(lm),$X=n(lm,"P",{});var TPo=s($X);T6a=r(TPo,"This class cannot be instantiated directly using "),Alo=n(TPo,"CODE",{});var bos=s(Alo);F6a=r(bos,"__init__()"),bos.forEach(t),E6a=r(TPo," (throws an error)."),TPo.forEach(t),y6a=i(lm),La=n(lm,"DIV",{class:!0});var gZ=s(La);M(jX.$$.fragment,gZ),C6a=i(gZ),Rlo=n(gZ,"P",{});var vos=s(Rlo);w6a=r(vos,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),vos.forEach(t),L6a=i(gZ),Mh=n(gZ,"P",{});var Wwe=s(Mh);A6a=r(Wwe,`Note:
Loading a model from its configuration file does `),klo=n(Wwe,"STRONG",{});var Mos=s(klo);R6a=r(Mos,"not"),Mos.forEach(t),k6a=r(Wwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),p3e=n(Wwe,"A",{href:!0});var Tos=s(p3e);S6a=r(Tos,"from_pretrained()"),Tos.forEach(t),x6a=r(Wwe," to load the model weights."),Wwe.forEach(t),G6a=i(gZ),M(UB.$$.fragment,gZ),gZ.forEach(t),B6a=i(lm),To=n(lm,"DIV",{class:!0});var pn=s(To);M(PX.$$.fragment,pn),I6a=i(pn),Slo=n(pn,"P",{});var Fos=s(Slo);N6a=r(Fos,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),Fos.forEach(t),$6a=i(pn),ys=n(pn,"P",{});var uZ=s(ys);j6a=r(uZ,"The model class to instantiate is selected based on the "),xlo=n(uZ,"CODE",{});var Eos=s(xlo);P6a=r(Eos,"model_type"),Eos.forEach(t),Z6a=r(uZ,` property of the config object (either
passed as an argument or loaded from `),Glo=n(uZ,"CODE",{});var yos=s(Glo);V6a=r(yos,"pretrained_model_name_or_path"),yos.forEach(t),W6a=r(uZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Blo=n(uZ,"CODE",{});var Cos=s(Blo);J6a=r(Cos,"pretrained_model_name_or_path"),Cos.forEach(t),U6a=r(uZ,":"),uZ.forEach(t),X6a=i(pn),Ar=n(pn,"UL",{});var _n=s(Ar);XB=n(_n,"LI",{});var wxo=s(XB);Ilo=n(wxo,"STRONG",{});var wos=s(Ilo);D6a=r(wos,"conditional_detr"),wos.forEach(t),q6a=r(wxo," \u2014 "),_3e=n(wxo,"A",{href:!0});var Los=s(_3e);O6a=r(Los,"ConditionalDetrForObjectDetection"),Los.forEach(t),Q6a=r(wxo," (Conditional DETR model)"),wxo.forEach(t),Y6a=i(_n),DB=n(_n,"LI",{});var Lxo=s(DB);Nlo=n(Lxo,"STRONG",{});var Aos=s(Nlo);z6a=r(Aos,"deformable_detr"),Aos.forEach(t),H6a=r(Lxo," \u2014 "),b3e=n(Lxo,"A",{href:!0});var Ros=s(b3e);K6a=r(Ros,"DeformableDetrForObjectDetection"),Ros.forEach(t),e8a=r(Lxo," (Deformable DETR model)"),Lxo.forEach(t),o8a=i(_n),qB=n(_n,"LI",{});var Axo=s(qB);$lo=n(Axo,"STRONG",{});var kos=s($lo);r8a=r(kos,"deta"),kos.forEach(t),t8a=r(Axo," \u2014 "),v3e=n(Axo,"A",{href:!0});var Sos=s(v3e);a8a=r(Sos,"DetaForObjectDetection"),Sos.forEach(t),n8a=r(Axo," (DETA model)"),Axo.forEach(t),s8a=i(_n),OB=n(_n,"LI",{});var Rxo=s(OB);jlo=n(Rxo,"STRONG",{});var xos=s(jlo);l8a=r(xos,"detr"),xos.forEach(t),i8a=r(Rxo," \u2014 "),M3e=n(Rxo,"A",{href:!0});var Gos=s(M3e);d8a=r(Gos,"DetrForObjectDetection"),Gos.forEach(t),m8a=r(Rxo," (DETR model)"),Rxo.forEach(t),c8a=i(_n),QB=n(_n,"LI",{});var kxo=s(QB);Plo=n(kxo,"STRONG",{});var Bos=s(Plo);f8a=r(Bos,"table-transformer"),Bos.forEach(t),h8a=r(kxo," \u2014 "),T3e=n(kxo,"A",{href:!0});var Ios=s(T3e);g8a=r(Ios,"TableTransformerForObjectDetection"),Ios.forEach(t),u8a=r(kxo," (Table Transformer model)"),kxo.forEach(t),p8a=i(_n),YB=n(_n,"LI",{});var Sxo=s(YB);Zlo=n(Sxo,"STRONG",{});var Nos=s(Zlo);_8a=r(Nos,"yolos"),Nos.forEach(t),b8a=r(Sxo," \u2014 "),F3e=n(Sxo,"A",{href:!0});var $os=s(F3e);v8a=r($os,"YolosForObjectDetection"),$os.forEach(t),M8a=r(Sxo," (YOLOS model)"),Sxo.forEach(t),_n.forEach(t),T8a=i(pn),zB=n(pn,"P",{});var xxo=s(zB);F8a=r(xxo,"The model is set in evaluation mode by default using "),Vlo=n(xxo,"CODE",{});var jos=s(Vlo);E8a=r(jos,"model.eval()"),jos.forEach(t),y8a=r(xxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Wlo=n(xxo,"CODE",{});var Pos=s(Wlo);C8a=r(Pos,"model.train()"),Pos.forEach(t),xxo.forEach(t),w8a=i(pn),M(HB.$$.fragment,pn),pn.forEach(t),lm.forEach(t),ENo=i(c),Th=n(c,"H3",{class:!0});var FPo=s(Th);KB=n(FPo,"A",{id:!0,class:!0,href:!0});var Zos=s(KB);Jlo=n(Zos,"SPAN",{});var Vos=s(Jlo);M(ZX.$$.fragment,Vos),Vos.forEach(t),Zos.forEach(t),L8a=i(FPo),Ulo=n(FPo,"SPAN",{});var Wos=s(Ulo);A8a=r(Wos,"AutoModelForImageSegmentation"),Wos.forEach(t),FPo.forEach(t),yNo=i(c),Rr=n(c,"DIV",{class:!0});var im=s(Rr);M(VX.$$.fragment,im),R8a=i(im),Fh=n(im,"P",{});var Jwe=s(Fh);k8a=r(Jwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),E3e=n(Jwe,"A",{href:!0});var Jos=s(E3e);S8a=r(Jos,"from_pretrained()"),Jos.forEach(t),x8a=r(Jwe," class method or the "),y3e=n(Jwe,"A",{href:!0});var Uos=s(y3e);G8a=r(Uos,"from_config()"),Uos.forEach(t),B8a=r(Jwe,` class
method.`),Jwe.forEach(t),I8a=i(im),WX=n(im,"P",{});var EPo=s(WX);N8a=r(EPo,"This class cannot be instantiated directly using "),Xlo=n(EPo,"CODE",{});var Xos=s(Xlo);$8a=r(Xos,"__init__()"),Xos.forEach(t),j8a=r(EPo," (throws an error)."),EPo.forEach(t),P8a=i(im),Aa=n(im,"DIV",{class:!0});var pZ=s(Aa);M(JX.$$.fragment,pZ),Z8a=i(pZ),Dlo=n(pZ,"P",{});var Dos=s(Dlo);V8a=r(Dos,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Dos.forEach(t),W8a=i(pZ),Eh=n(pZ,"P",{});var Uwe=s(Eh);J8a=r(Uwe,`Note:
Loading a model from its configuration file does `),qlo=n(Uwe,"STRONG",{});var qos=s(qlo);U8a=r(qos,"not"),qos.forEach(t),X8a=r(Uwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),C3e=n(Uwe,"A",{href:!0});var Oos=s(C3e);D8a=r(Oos,"from_pretrained()"),Oos.forEach(t),q8a=r(Uwe," to load the model weights."),Uwe.forEach(t),O8a=i(pZ),M(eI.$$.fragment,pZ),pZ.forEach(t),Q8a=i(im),Fo=n(im,"DIV",{class:!0});var bn=s(Fo);M(UX.$$.fragment,bn),Y8a=i(bn),Olo=n(bn,"P",{});var Qos=s(Olo);z8a=r(Qos,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),Qos.forEach(t),H8a=i(bn),Cs=n(bn,"P",{});var _Z=s(Cs);K8a=r(_Z,"The model class to instantiate is selected based on the "),Qlo=n(_Z,"CODE",{});var Yos=s(Qlo);e7a=r(Yos,"model_type"),Yos.forEach(t),o7a=r(_Z,` property of the config object (either
passed as an argument or loaded from `),Ylo=n(_Z,"CODE",{});var zos=s(Ylo);r7a=r(zos,"pretrained_model_name_or_path"),zos.forEach(t),t7a=r(_Z,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zlo=n(_Z,"CODE",{});var Hos=s(zlo);a7a=r(Hos,"pretrained_model_name_or_path"),Hos.forEach(t),n7a=r(_Z,":"),_Z.forEach(t),s7a=i(bn),Hlo=n(bn,"UL",{});var Kos=s(Hlo);oI=n(Kos,"LI",{});var Gxo=s(oI);Klo=n(Gxo,"STRONG",{});var ers=s(Klo);l7a=r(ers,"detr"),ers.forEach(t),i7a=r(Gxo," \u2014 "),w3e=n(Gxo,"A",{href:!0});var ors=s(w3e);d7a=r(ors,"DetrForSegmentation"),ors.forEach(t),m7a=r(Gxo," (DETR model)"),Gxo.forEach(t),Kos.forEach(t),c7a=i(bn),rI=n(bn,"P",{});var Bxo=s(rI);f7a=r(Bxo,"The model is set in evaluation mode by default using "),eio=n(Bxo,"CODE",{});var rrs=s(eio);h7a=r(rrs,"model.eval()"),rrs.forEach(t),g7a=r(Bxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oio=n(Bxo,"CODE",{});var trs=s(oio);u7a=r(trs,"model.train()"),trs.forEach(t),Bxo.forEach(t),p7a=i(bn),M(tI.$$.fragment,bn),bn.forEach(t),im.forEach(t),CNo=i(c),yh=n(c,"H3",{class:!0});var yPo=s(yh);aI=n(yPo,"A",{id:!0,class:!0,href:!0});var ars=s(aI);rio=n(ars,"SPAN",{});var nrs=s(rio);M(XX.$$.fragment,nrs),nrs.forEach(t),ars.forEach(t),_7a=i(yPo),tio=n(yPo,"SPAN",{});var srs=s(tio);b7a=r(srs,"AutoModelForSemanticSegmentation"),srs.forEach(t),yPo.forEach(t),wNo=i(c),kr=n(c,"DIV",{class:!0});var dm=s(kr);M(DX.$$.fragment,dm),v7a=i(dm),Ch=n(dm,"P",{});var Xwe=s(Ch);M7a=r(Xwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),L3e=n(Xwe,"A",{href:!0});var lrs=s(L3e);T7a=r(lrs,"from_pretrained()"),lrs.forEach(t),F7a=r(Xwe," class method or the "),A3e=n(Xwe,"A",{href:!0});var irs=s(A3e);E7a=r(irs,"from_config()"),irs.forEach(t),y7a=r(Xwe,` class
method.`),Xwe.forEach(t),C7a=i(dm),qX=n(dm,"P",{});var CPo=s(qX);w7a=r(CPo,"This class cannot be instantiated directly using "),aio=n(CPo,"CODE",{});var drs=s(aio);L7a=r(drs,"__init__()"),drs.forEach(t),A7a=r(CPo," (throws an error)."),CPo.forEach(t),R7a=i(dm),Ra=n(dm,"DIV",{class:!0});var bZ=s(Ra);M(OX.$$.fragment,bZ),k7a=i(bZ),nio=n(bZ,"P",{});var mrs=s(nio);S7a=r(mrs,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),mrs.forEach(t),x7a=i(bZ),wh=n(bZ,"P",{});var Dwe=s(wh);G7a=r(Dwe,`Note:
Loading a model from its configuration file does `),sio=n(Dwe,"STRONG",{});var crs=s(sio);B7a=r(crs,"not"),crs.forEach(t),I7a=r(Dwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),R3e=n(Dwe,"A",{href:!0});var frs=s(R3e);N7a=r(frs,"from_pretrained()"),frs.forEach(t),$7a=r(Dwe," to load the model weights."),Dwe.forEach(t),j7a=i(bZ),M(nI.$$.fragment,bZ),bZ.forEach(t),P7a=i(dm),Eo=n(dm,"DIV",{class:!0});var vn=s(Eo);M(QX.$$.fragment,vn),Z7a=i(vn),lio=n(vn,"P",{});var hrs=s(lio);V7a=r(hrs,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),hrs.forEach(t),W7a=i(vn),ws=n(vn,"P",{});var vZ=s(ws);J7a=r(vZ,"The model class to instantiate is selected based on the "),iio=n(vZ,"CODE",{});var grs=s(iio);U7a=r(grs,"model_type"),grs.forEach(t),X7a=r(vZ,` property of the config object (either
passed as an argument or loaded from `),dio=n(vZ,"CODE",{});var urs=s(dio);D7a=r(urs,"pretrained_model_name_or_path"),urs.forEach(t),q7a=r(vZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mio=n(vZ,"CODE",{});var prs=s(mio);O7a=r(prs,"pretrained_model_name_or_path"),prs.forEach(t),Q7a=r(vZ,":"),vZ.forEach(t),Y7a=i(vn),Ye=n(vn,"UL",{});var $o=s(Ye);sI=n($o,"LI",{});var Ixo=s(sI);cio=n(Ixo,"STRONG",{});var _rs=s(cio);z7a=r(_rs,"beit"),_rs.forEach(t),H7a=r(Ixo," \u2014 "),k3e=n(Ixo,"A",{href:!0});var brs=s(k3e);K7a=r(brs,"BeitForSemanticSegmentation"),brs.forEach(t),eLa=r(Ixo," (BEiT model)"),Ixo.forEach(t),oLa=i($o),lI=n($o,"LI",{});var Nxo=s(lI);fio=n(Nxo,"STRONG",{});var vrs=s(fio);rLa=r(vrs,"data2vec-vision"),vrs.forEach(t),tLa=r(Nxo," \u2014 "),S3e=n(Nxo,"A",{href:!0});var Mrs=s(S3e);aLa=r(Mrs,"Data2VecVisionForSemanticSegmentation"),Mrs.forEach(t),nLa=r(Nxo," (Data2VecVision model)"),Nxo.forEach(t),sLa=i($o),iI=n($o,"LI",{});var $xo=s(iI);hio=n($xo,"STRONG",{});var Trs=s(hio);lLa=r(Trs,"dpt"),Trs.forEach(t),iLa=r($xo," \u2014 "),x3e=n($xo,"A",{href:!0});var Frs=s(x3e);dLa=r(Frs,"DPTForSemanticSegmentation"),Frs.forEach(t),mLa=r($xo," (DPT model)"),$xo.forEach(t),cLa=i($o),dI=n($o,"LI",{});var jxo=s(dI);gio=n(jxo,"STRONG",{});var Ers=s(gio);fLa=r(Ers,"mobilenet_v2"),Ers.forEach(t),hLa=r(jxo," \u2014 "),G3e=n(jxo,"A",{href:!0});var yrs=s(G3e);gLa=r(yrs,"MobileNetV2ForSemanticSegmentation"),yrs.forEach(t),uLa=r(jxo," (MobileNetV2 model)"),jxo.forEach(t),pLa=i($o),mI=n($o,"LI",{});var Pxo=s(mI);uio=n(Pxo,"STRONG",{});var Crs=s(uio);_La=r(Crs,"mobilevit"),Crs.forEach(t),bLa=r(Pxo," \u2014 "),B3e=n(Pxo,"A",{href:!0});var wrs=s(B3e);vLa=r(wrs,"MobileViTForSemanticSegmentation"),wrs.forEach(t),MLa=r(Pxo," (MobileViT model)"),Pxo.forEach(t),TLa=i($o),cI=n($o,"LI",{});var Zxo=s(cI);pio=n(Zxo,"STRONG",{});var Lrs=s(pio);FLa=r(Lrs,"mobilevitv2"),Lrs.forEach(t),ELa=r(Zxo," \u2014 "),I3e=n(Zxo,"A",{href:!0});var Ars=s(I3e);yLa=r(Ars,"MobileViTV2ForSemanticSegmentation"),Ars.forEach(t),CLa=r(Zxo," (MobileViTV2 model)"),Zxo.forEach(t),wLa=i($o),fI=n($o,"LI",{});var Vxo=s(fI);_io=n(Vxo,"STRONG",{});var Rrs=s(_io);LLa=r(Rrs,"segformer"),Rrs.forEach(t),ALa=r(Vxo," \u2014 "),N3e=n(Vxo,"A",{href:!0});var krs=s(N3e);RLa=r(krs,"SegformerForSemanticSegmentation"),krs.forEach(t),kLa=r(Vxo," (SegFormer model)"),Vxo.forEach(t),SLa=i($o),hI=n($o,"LI",{});var Wxo=s(hI);bio=n(Wxo,"STRONG",{});var Srs=s(bio);xLa=r(Srs,"upernet"),Srs.forEach(t),GLa=r(Wxo," \u2014 "),$3e=n(Wxo,"A",{href:!0});var xrs=s($3e);BLa=r(xrs,"UperNetForSemanticSegmentation"),xrs.forEach(t),ILa=r(Wxo," (UPerNet model)"),Wxo.forEach(t),$o.forEach(t),NLa=i(vn),gI=n(vn,"P",{});var Jxo=s(gI);$La=r(Jxo,"The model is set in evaluation mode by default using "),vio=n(Jxo,"CODE",{});var Grs=s(vio);jLa=r(Grs,"model.eval()"),Grs.forEach(t),PLa=r(Jxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Mio=n(Jxo,"CODE",{});var Brs=s(Mio);ZLa=r(Brs,"model.train()"),Brs.forEach(t),Jxo.forEach(t),VLa=i(vn),M(uI.$$.fragment,vn),vn.forEach(t),dm.forEach(t),LNo=i(c),Lh=n(c,"H3",{class:!0});var wPo=s(Lh);pI=n(wPo,"A",{id:!0,class:!0,href:!0});var Irs=s(pI);Tio=n(Irs,"SPAN",{});var Nrs=s(Tio);M(YX.$$.fragment,Nrs),Nrs.forEach(t),Irs.forEach(t),WLa=i(wPo),Fio=n(wPo,"SPAN",{});var $rs=s(Fio);JLa=r($rs,"TFAutoModelForSemanticSegmentation"),$rs.forEach(t),wPo.forEach(t),ANo=i(c),Sr=n(c,"DIV",{class:!0});var mm=s(Sr);M(zX.$$.fragment,mm),ULa=i(mm),Ah=n(mm,"P",{});var qwe=s(Ah);XLa=r(qwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),j3e=n(qwe,"A",{href:!0});var jrs=s(j3e);DLa=r(jrs,"from_pretrained()"),jrs.forEach(t),qLa=r(qwe," class method or the "),P3e=n(qwe,"A",{href:!0});var Prs=s(P3e);OLa=r(Prs,"from_config()"),Prs.forEach(t),QLa=r(qwe,` class
method.`),qwe.forEach(t),YLa=i(mm),HX=n(mm,"P",{});var LPo=s(HX);zLa=r(LPo,"This class cannot be instantiated directly using "),Eio=n(LPo,"CODE",{});var Zrs=s(Eio);HLa=r(Zrs,"__init__()"),Zrs.forEach(t),KLa=r(LPo," (throws an error)."),LPo.forEach(t),eAa=i(mm),ka=n(mm,"DIV",{class:!0});var MZ=s(ka);M(KX.$$.fragment,MZ),oAa=i(MZ),yio=n(MZ,"P",{});var Vrs=s(yio);rAa=r(Vrs,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Vrs.forEach(t),tAa=i(MZ),Rh=n(MZ,"P",{});var Owe=s(Rh);aAa=r(Owe,`Note:
Loading a model from its configuration file does `),Cio=n(Owe,"STRONG",{});var Wrs=s(Cio);nAa=r(Wrs,"not"),Wrs.forEach(t),sAa=r(Owe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Z3e=n(Owe,"A",{href:!0});var Jrs=s(Z3e);lAa=r(Jrs,"from_pretrained()"),Jrs.forEach(t),iAa=r(Owe," to load the model weights."),Owe.forEach(t),dAa=i(MZ),M(_I.$$.fragment,MZ),MZ.forEach(t),mAa=i(mm),wt=n(mm,"DIV",{class:!0});var cm=s(wt);M(eD.$$.fragment,cm),cAa=i(cm),wio=n(cm,"P",{});var Urs=s(wio);fAa=r(Urs,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Urs.forEach(t),hAa=i(cm),Ls=n(cm,"P",{});var TZ=s(Ls);gAa=r(TZ,"The model class to instantiate is selected based on the "),Lio=n(TZ,"CODE",{});var Xrs=s(Lio);uAa=r(Xrs,"model_type"),Xrs.forEach(t),pAa=r(TZ,` property of the config object (either
passed as an argument or loaded from `),Aio=n(TZ,"CODE",{});var Drs=s(Aio);_Aa=r(Drs,"pretrained_model_name_or_path"),Drs.forEach(t),bAa=r(TZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Rio=n(TZ,"CODE",{});var qrs=s(Rio);vAa=r(qrs,"pretrained_model_name_or_path"),qrs.forEach(t),MAa=r(TZ,":"),TZ.forEach(t),TAa=i(cm),kh=n(cm,"UL",{});var Qwe=s(kh);bI=n(Qwe,"LI",{});var Uxo=s(bI);kio=n(Uxo,"STRONG",{});var Ors=s(kio);FAa=r(Ors,"data2vec-vision"),Ors.forEach(t),EAa=r(Uxo," \u2014 "),V3e=n(Uxo,"A",{href:!0});var Qrs=s(V3e);yAa=r(Qrs,"TFData2VecVisionForSemanticSegmentation"),Qrs.forEach(t),CAa=r(Uxo," (Data2VecVision model)"),Uxo.forEach(t),wAa=i(Qwe),vI=n(Qwe,"LI",{});var Xxo=s(vI);Sio=n(Xxo,"STRONG",{});var Yrs=s(Sio);LAa=r(Yrs,"mobilevit"),Yrs.forEach(t),AAa=r(Xxo," \u2014 "),W3e=n(Xxo,"A",{href:!0});var zrs=s(W3e);RAa=r(zrs,"TFMobileViTForSemanticSegmentation"),zrs.forEach(t),kAa=r(Xxo," (MobileViT model)"),Xxo.forEach(t),SAa=i(Qwe),MI=n(Qwe,"LI",{});var Dxo=s(MI);xio=n(Dxo,"STRONG",{});var Hrs=s(xio);xAa=r(Hrs,"segformer"),Hrs.forEach(t),GAa=r(Dxo," \u2014 "),J3e=n(Dxo,"A",{href:!0});var Krs=s(J3e);BAa=r(Krs,"TFSegformerForSemanticSegmentation"),Krs.forEach(t),IAa=r(Dxo," (SegFormer model)"),Dxo.forEach(t),Qwe.forEach(t),NAa=i(cm),M(TI.$$.fragment,cm),cm.forEach(t),mm.forEach(t),RNo=i(c),Sh=n(c,"H3",{class:!0});var APo=s(Sh);FI=n(APo,"A",{id:!0,class:!0,href:!0});var ets=s(FI);Gio=n(ets,"SPAN",{});var ots=s(Gio);M(oD.$$.fragment,ots),ots.forEach(t),ets.forEach(t),$Aa=i(APo),Bio=n(APo,"SPAN",{});var rts=s(Bio);jAa=r(rts,"AutoModelForInstanceSegmentation"),rts.forEach(t),APo.forEach(t),kNo=i(c),xr=n(c,"DIV",{class:!0});var fm=s(xr);M(rD.$$.fragment,fm),PAa=i(fm),xh=n(fm,"P",{});var Ywe=s(xh);ZAa=r(Ywe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),U3e=n(Ywe,"A",{href:!0});var tts=s(U3e);VAa=r(tts,"from_pretrained()"),tts.forEach(t),WAa=r(Ywe," class method or the "),X3e=n(Ywe,"A",{href:!0});var ats=s(X3e);JAa=r(ats,"from_config()"),ats.forEach(t),UAa=r(Ywe,` class
method.`),Ywe.forEach(t),XAa=i(fm),tD=n(fm,"P",{});var RPo=s(tD);DAa=r(RPo,"This class cannot be instantiated directly using "),Iio=n(RPo,"CODE",{});var nts=s(Iio);qAa=r(nts,"__init__()"),nts.forEach(t),OAa=r(RPo," (throws an error)."),RPo.forEach(t),QAa=i(fm),Sa=n(fm,"DIV",{class:!0});var FZ=s(Sa);M(aD.$$.fragment,FZ),YAa=i(FZ),Nio=n(FZ,"P",{});var sts=s(Nio);zAa=r(sts,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),sts.forEach(t),HAa=i(FZ),Gh=n(FZ,"P",{});var zwe=s(Gh);KAa=r(zwe,`Note:
Loading a model from its configuration file does `),$io=n(zwe,"STRONG",{});var lts=s($io);eRa=r(lts,"not"),lts.forEach(t),oRa=r(zwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),D3e=n(zwe,"A",{href:!0});var its=s(D3e);rRa=r(its,"from_pretrained()"),its.forEach(t),tRa=r(zwe," to load the model weights."),zwe.forEach(t),aRa=i(FZ),M(EI.$$.fragment,FZ),FZ.forEach(t),nRa=i(fm),yo=n(fm,"DIV",{class:!0});var Mn=s(yo);M(nD.$$.fragment,Mn),sRa=i(Mn),jio=n(Mn,"P",{});var dts=s(jio);lRa=r(dts,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),dts.forEach(t),iRa=i(Mn),As=n(Mn,"P",{});var EZ=s(As);dRa=r(EZ,"The model class to instantiate is selected based on the "),Pio=n(EZ,"CODE",{});var mts=s(Pio);mRa=r(mts,"model_type"),mts.forEach(t),cRa=r(EZ,` property of the config object (either
passed as an argument or loaded from `),Zio=n(EZ,"CODE",{});var cts=s(Zio);fRa=r(cts,"pretrained_model_name_or_path"),cts.forEach(t),hRa=r(EZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vio=n(EZ,"CODE",{});var fts=s(Vio);gRa=r(fts,"pretrained_model_name_or_path"),fts.forEach(t),uRa=r(EZ,":"),EZ.forEach(t),pRa=i(Mn),Wio=n(Mn,"UL",{});var hts=s(Wio);yI=n(hts,"LI",{});var qxo=s(yI);Jio=n(qxo,"STRONG",{});var gts=s(Jio);_Ra=r(gts,"maskformer"),gts.forEach(t),bRa=r(qxo," \u2014 "),q3e=n(qxo,"A",{href:!0});var uts=s(q3e);vRa=r(uts,"MaskFormerForInstanceSegmentation"),uts.forEach(t),MRa=r(qxo," (MaskFormer model)"),qxo.forEach(t),hts.forEach(t),TRa=i(Mn),CI=n(Mn,"P",{});var Oxo=s(CI);FRa=r(Oxo,"The model is set in evaluation mode by default using "),Uio=n(Oxo,"CODE",{});var pts=s(Uio);ERa=r(pts,"model.eval()"),pts.forEach(t),yRa=r(Oxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xio=n(Oxo,"CODE",{});var _ts=s(Xio);CRa=r(_ts,"model.train()"),_ts.forEach(t),Oxo.forEach(t),wRa=i(Mn),M(wI.$$.fragment,Mn),Mn.forEach(t),fm.forEach(t),SNo=i(c),Bh=n(c,"H3",{class:!0});var kPo=s(Bh);LI=n(kPo,"A",{id:!0,class:!0,href:!0});var bts=s(LI);Dio=n(bts,"SPAN",{});var vts=s(Dio);M(sD.$$.fragment,vts),vts.forEach(t),bts.forEach(t),LRa=i(kPo),qio=n(kPo,"SPAN",{});var Mts=s(qio);ARa=r(Mts,"AutoModelForUniversalSegmentation"),Mts.forEach(t),kPo.forEach(t),xNo=i(c),Gr=n(c,"DIV",{class:!0});var hm=s(Gr);M(lD.$$.fragment,hm),RRa=i(hm),Ih=n(hm,"P",{});var Hwe=s(Ih);kRa=r(Hwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the `),O3e=n(Hwe,"A",{href:!0});var Tts=s(O3e);SRa=r(Tts,"from_pretrained()"),Tts.forEach(t),xRa=r(Hwe," class method or the "),Q3e=n(Hwe,"A",{href:!0});var Fts=s(Q3e);GRa=r(Fts,"from_config()"),Fts.forEach(t),BRa=r(Hwe,` class
method.`),Hwe.forEach(t),IRa=i(hm),iD=n(hm,"P",{});var SPo=s(iD);NRa=r(SPo,"This class cannot be instantiated directly using "),Oio=n(SPo,"CODE",{});var Ets=s(Oio);$Ra=r(Ets,"__init__()"),Ets.forEach(t),jRa=r(SPo," (throws an error)."),SPo.forEach(t),PRa=i(hm),xa=n(hm,"DIV",{class:!0});var yZ=s(xa);M(dD.$$.fragment,yZ),ZRa=i(yZ),Qio=n(yZ,"P",{});var yts=s(Qio);VRa=r(yts,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),yts.forEach(t),WRa=i(yZ),Nh=n(yZ,"P",{});var Kwe=s(Nh);JRa=r(Kwe,`Note:
Loading a model from its configuration file does `),Yio=n(Kwe,"STRONG",{});var Cts=s(Yio);URa=r(Cts,"not"),Cts.forEach(t),XRa=r(Kwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Y3e=n(Kwe,"A",{href:!0});var wts=s(Y3e);DRa=r(wts,"from_pretrained()"),wts.forEach(t),qRa=r(Kwe," to load the model weights."),Kwe.forEach(t),ORa=i(yZ),M(AI.$$.fragment,yZ),yZ.forEach(t),QRa=i(hm),Co=n(hm,"DIV",{class:!0});var Tn=s(Co);M(mD.$$.fragment,Tn),YRa=i(Tn),zio=n(Tn,"P",{});var Lts=s(zio);zRa=r(Lts,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),Lts.forEach(t),HRa=i(Tn),Rs=n(Tn,"P",{});var CZ=s(Rs);KRa=r(CZ,"The model class to instantiate is selected based on the "),Hio=n(CZ,"CODE",{});var Ats=s(Hio);eka=r(Ats,"model_type"),Ats.forEach(t),oka=r(CZ,` property of the config object (either
passed as an argument or loaded from `),Kio=n(CZ,"CODE",{});var Rts=s(Kio);rka=r(Rts,"pretrained_model_name_or_path"),Rts.forEach(t),tka=r(CZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),edo=n(CZ,"CODE",{});var kts=s(edo);aka=r(kts,"pretrained_model_name_or_path"),kts.forEach(t),nka=r(CZ,":"),CZ.forEach(t),ska=i(Tn),ks=n(Tn,"UL",{});var wZ=s(ks);RI=n(wZ,"LI",{});var Qxo=s(RI);odo=n(Qxo,"STRONG",{});var Sts=s(odo);lka=r(Sts,"detr"),Sts.forEach(t),ika=r(Qxo," \u2014 "),z3e=n(Qxo,"A",{href:!0});var xts=s(z3e);dka=r(xts,"DetrForSegmentation"),xts.forEach(t),mka=r(Qxo," (DETR model)"),Qxo.forEach(t),cka=i(wZ),kI=n(wZ,"LI",{});var Yxo=s(kI);rdo=n(Yxo,"STRONG",{});var Gts=s(rdo);fka=r(Gts,"mask2former"),Gts.forEach(t),hka=r(Yxo," \u2014 "),H3e=n(Yxo,"A",{href:!0});var Bts=s(H3e);gka=r(Bts,"Mask2FormerForUniversalSegmentation"),Bts.forEach(t),uka=r(Yxo," (Mask2Former model)"),Yxo.forEach(t),pka=i(wZ),SI=n(wZ,"LI",{});var zxo=s(SI);tdo=n(zxo,"STRONG",{});var Its=s(tdo);_ka=r(Its,"maskformer"),Its.forEach(t),bka=r(zxo," \u2014 "),K3e=n(zxo,"A",{href:!0});var Nts=s(K3e);vka=r(Nts,"MaskFormerForInstanceSegmentation"),Nts.forEach(t),Mka=r(zxo," (MaskFormer model)"),zxo.forEach(t),Tka=i(wZ),xI=n(wZ,"LI",{});var Hxo=s(xI);ado=n(Hxo,"STRONG",{});var $ts=s(ado);Fka=r($ts,"oneformer"),$ts.forEach(t),Eka=r(Hxo," \u2014 "),e9e=n(Hxo,"A",{href:!0});var jts=s(e9e);yka=r(jts,"OneFormerForUniversalSegmentation"),jts.forEach(t),Cka=r(Hxo," (OneFormer model)"),Hxo.forEach(t),wZ.forEach(t),wka=i(Tn),GI=n(Tn,"P",{});var Kxo=s(GI);Lka=r(Kxo,"The model is set in evaluation mode by default using "),ndo=n(Kxo,"CODE",{});var Pts=s(ndo);Aka=r(Pts,"model.eval()"),Pts.forEach(t),Rka=r(Kxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),sdo=n(Kxo,"CODE",{});var Zts=s(sdo);kka=r(Zts,"model.train()"),Zts.forEach(t),Kxo.forEach(t),Ska=i(Tn),M(BI.$$.fragment,Tn),Tn.forEach(t),hm.forEach(t),GNo=i(c),$h=n(c,"H3",{class:!0});var xPo=s($h);II=n(xPo,"A",{id:!0,class:!0,href:!0});var Vts=s(II);ldo=n(Vts,"SPAN",{});var Wts=s(ldo);M(cD.$$.fragment,Wts),Wts.forEach(t),Vts.forEach(t),xka=i(xPo),ido=n(xPo,"SPAN",{});var Jts=s(ido);Gka=r(Jts,"AutoModelForZeroShotImageClassification"),Jts.forEach(t),xPo.forEach(t),BNo=i(c),Br=n(c,"DIV",{class:!0});var gm=s(Br);M(fD.$$.fragment,gm),Bka=i(gm),jh=n(gm,"P",{});var e6e=s(jh);Ika=r(e6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),o9e=n(e6e,"A",{href:!0});var Uts=s(o9e);Nka=r(Uts,"from_pretrained()"),Uts.forEach(t),$ka=r(e6e," class method or the "),r9e=n(e6e,"A",{href:!0});var Xts=s(r9e);jka=r(Xts,"from_config()"),Xts.forEach(t),Pka=r(e6e,` class
method.`),e6e.forEach(t),Zka=i(gm),hD=n(gm,"P",{});var GPo=s(hD);Vka=r(GPo,"This class cannot be instantiated directly using "),ddo=n(GPo,"CODE",{});var Dts=s(ddo);Wka=r(Dts,"__init__()"),Dts.forEach(t),Jka=r(GPo," (throws an error)."),GPo.forEach(t),Uka=i(gm),Ga=n(gm,"DIV",{class:!0});var LZ=s(Ga);M(gD.$$.fragment,LZ),Xka=i(LZ),mdo=n(LZ,"P",{});var qts=s(mdo);Dka=r(qts,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),qts.forEach(t),qka=i(LZ),Ph=n(LZ,"P",{});var o6e=s(Ph);Oka=r(o6e,`Note:
Loading a model from its configuration file does `),cdo=n(o6e,"STRONG",{});var Ots=s(cdo);Qka=r(Ots,"not"),Ots.forEach(t),Yka=r(o6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),t9e=n(o6e,"A",{href:!0});var Qts=s(t9e);zka=r(Qts,"from_pretrained()"),Qts.forEach(t),Hka=r(o6e," to load the model weights."),o6e.forEach(t),Kka=i(LZ),M(NI.$$.fragment,LZ),LZ.forEach(t),eSa=i(gm),wo=n(gm,"DIV",{class:!0});var Fn=s(wo);M(uD.$$.fragment,Fn),oSa=i(Fn),fdo=n(Fn,"P",{});var Yts=s(fdo);rSa=r(Yts,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Yts.forEach(t),tSa=i(Fn),Ss=n(Fn,"P",{});var AZ=s(Ss);aSa=r(AZ,"The model class to instantiate is selected based on the "),hdo=n(AZ,"CODE",{});var zts=s(hdo);nSa=r(zts,"model_type"),zts.forEach(t),sSa=r(AZ,` property of the config object (either
passed as an argument or loaded from `),gdo=n(AZ,"CODE",{});var Hts=s(gdo);lSa=r(Hts,"pretrained_model_name_or_path"),Hts.forEach(t),iSa=r(AZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),udo=n(AZ,"CODE",{});var Kts=s(udo);dSa=r(Kts,"pretrained_model_name_or_path"),Kts.forEach(t),mSa=r(AZ,":"),AZ.forEach(t),cSa=i(Fn),Ir=n(Fn,"UL",{});var En=s(Ir);$I=n(En,"LI",{});var eGo=s($I);pdo=n(eGo,"STRONG",{});var eas=s(pdo);fSa=r(eas,"align"),eas.forEach(t),hSa=r(eGo," \u2014 "),a9e=n(eGo,"A",{href:!0});var oas=s(a9e);gSa=r(oas,"AlignModel"),oas.forEach(t),uSa=r(eGo," (ALIGN model)"),eGo.forEach(t),pSa=i(En),jI=n(En,"LI",{});var oGo=s(jI);_do=n(oGo,"STRONG",{});var ras=s(_do);_Sa=r(ras,"altclip"),ras.forEach(t),bSa=r(oGo," \u2014 "),n9e=n(oGo,"A",{href:!0});var tas=s(n9e);vSa=r(tas,"AltCLIPModel"),tas.forEach(t),MSa=r(oGo," (AltCLIP model)"),oGo.forEach(t),TSa=i(En),PI=n(En,"LI",{});var rGo=s(PI);bdo=n(rGo,"STRONG",{});var aas=s(bdo);FSa=r(aas,"blip"),aas.forEach(t),ESa=r(rGo," \u2014 "),s9e=n(rGo,"A",{href:!0});var nas=s(s9e);ySa=r(nas,"BlipModel"),nas.forEach(t),CSa=r(rGo," (BLIP model)"),rGo.forEach(t),wSa=i(En),ZI=n(En,"LI",{});var tGo=s(ZI);vdo=n(tGo,"STRONG",{});var sas=s(vdo);LSa=r(sas,"chinese_clip"),sas.forEach(t),ASa=r(tGo," \u2014 "),l9e=n(tGo,"A",{href:!0});var las=s(l9e);RSa=r(las,"ChineseCLIPModel"),las.forEach(t),kSa=r(tGo," (Chinese-CLIP model)"),tGo.forEach(t),SSa=i(En),VI=n(En,"LI",{});var aGo=s(VI);Mdo=n(aGo,"STRONG",{});var ias=s(Mdo);xSa=r(ias,"clip"),ias.forEach(t),GSa=r(aGo," \u2014 "),i9e=n(aGo,"A",{href:!0});var das=s(i9e);BSa=r(das,"CLIPModel"),das.forEach(t),ISa=r(aGo," (CLIP model)"),aGo.forEach(t),NSa=i(En),WI=n(En,"LI",{});var nGo=s(WI);Tdo=n(nGo,"STRONG",{});var mas=s(Tdo);$Sa=r(mas,"clipseg"),mas.forEach(t),jSa=r(nGo," \u2014 "),d9e=n(nGo,"A",{href:!0});var cas=s(d9e);PSa=r(cas,"CLIPSegModel"),cas.forEach(t),ZSa=r(nGo," (CLIPSeg model)"),nGo.forEach(t),En.forEach(t),VSa=i(Fn),JI=n(Fn,"P",{});var sGo=s(JI);WSa=r(sGo,"The model is set in evaluation mode by default using "),Fdo=n(sGo,"CODE",{});var fas=s(Fdo);JSa=r(fas,"model.eval()"),fas.forEach(t),USa=r(sGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Edo=n(sGo,"CODE",{});var has=s(Edo);XSa=r(has,"model.train()"),has.forEach(t),sGo.forEach(t),DSa=i(Fn),M(UI.$$.fragment,Fn),Fn.forEach(t),gm.forEach(t),INo=i(c),Zh=n(c,"H3",{class:!0});var BPo=s(Zh);XI=n(BPo,"A",{id:!0,class:!0,href:!0});var gas=s(XI);ydo=n(gas,"SPAN",{});var uas=s(ydo);M(pD.$$.fragment,uas),uas.forEach(t),gas.forEach(t),qSa=i(BPo),Cdo=n(BPo,"SPAN",{});var pas=s(Cdo);OSa=r(pas,"TFAutoModelForZeroShotImageClassification"),pas.forEach(t),BPo.forEach(t),NNo=i(c),Nr=n(c,"DIV",{class:!0});var um=s(Nr);M(_D.$$.fragment,um),QSa=i(um),Vh=n(um,"P",{});var r6e=s(Vh);YSa=r(r6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),m9e=n(r6e,"A",{href:!0});var _as=s(m9e);zSa=r(_as,"from_pretrained()"),_as.forEach(t),HSa=r(r6e," class method or the "),c9e=n(r6e,"A",{href:!0});var bas=s(c9e);KSa=r(bas,"from_config()"),bas.forEach(t),exa=r(r6e,` class
method.`),r6e.forEach(t),oxa=i(um),bD=n(um,"P",{});var IPo=s(bD);rxa=r(IPo,"This class cannot be instantiated directly using "),wdo=n(IPo,"CODE",{});var vas=s(wdo);txa=r(vas,"__init__()"),vas.forEach(t),axa=r(IPo," (throws an error)."),IPo.forEach(t),nxa=i(um),Ba=n(um,"DIV",{class:!0});var RZ=s(Ba);M(vD.$$.fragment,RZ),sxa=i(RZ),Ldo=n(RZ,"P",{});var Mas=s(Ldo);lxa=r(Mas,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),Mas.forEach(t),ixa=i(RZ),Wh=n(RZ,"P",{});var t6e=s(Wh);dxa=r(t6e,`Note:
Loading a model from its configuration file does `),Ado=n(t6e,"STRONG",{});var Tas=s(Ado);mxa=r(Tas,"not"),Tas.forEach(t),cxa=r(t6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),f9e=n(t6e,"A",{href:!0});var Fas=s(f9e);fxa=r(Fas,"from_pretrained()"),Fas.forEach(t),hxa=r(t6e," to load the model weights."),t6e.forEach(t),gxa=i(RZ),M(DI.$$.fragment,RZ),RZ.forEach(t),uxa=i(um),Lt=n(um,"DIV",{class:!0});var pm=s(Lt);M(MD.$$.fragment,pm),pxa=i(pm),Rdo=n(pm,"P",{});var Eas=s(Rdo);_xa=r(Eas,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Eas.forEach(t),bxa=i(pm),xs=n(pm,"P",{});var kZ=s(xs);vxa=r(kZ,"The model class to instantiate is selected based on the "),kdo=n(kZ,"CODE",{});var yas=s(kdo);Mxa=r(yas,"model_type"),yas.forEach(t),Txa=r(kZ,` property of the config object (either
passed as an argument or loaded from `),Sdo=n(kZ,"CODE",{});var Cas=s(Sdo);Fxa=r(Cas,"pretrained_model_name_or_path"),Cas.forEach(t),Exa=r(kZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xdo=n(kZ,"CODE",{});var was=s(xdo);yxa=r(was,"pretrained_model_name_or_path"),was.forEach(t),Cxa=r(kZ,":"),kZ.forEach(t),wxa=i(pm),TD=n(pm,"UL",{});var NPo=s(TD);qI=n(NPo,"LI",{});var lGo=s(qI);Gdo=n(lGo,"STRONG",{});var Las=s(Gdo);Lxa=r(Las,"blip"),Las.forEach(t),Axa=r(lGo," \u2014 "),h9e=n(lGo,"A",{href:!0});var Aas=s(h9e);Rxa=r(Aas,"TFBlipModel"),Aas.forEach(t),kxa=r(lGo," (BLIP model)"),lGo.forEach(t),Sxa=i(NPo),OI=n(NPo,"LI",{});var iGo=s(OI);Bdo=n(iGo,"STRONG",{});var Ras=s(Bdo);xxa=r(Ras,"clip"),Ras.forEach(t),Gxa=r(iGo," \u2014 "),g9e=n(iGo,"A",{href:!0});var kas=s(g9e);Bxa=r(kas,"TFCLIPModel"),kas.forEach(t),Ixa=r(iGo," (CLIP model)"),iGo.forEach(t),NPo.forEach(t),Nxa=i(pm),M(QI.$$.fragment,pm),pm.forEach(t),um.forEach(t),$No=i(c),Jh=n(c,"H3",{class:!0});var $Po=s(Jh);YI=n($Po,"A",{id:!0,class:!0,href:!0});var Sas=s(YI);Ido=n(Sas,"SPAN",{});var xas=s(Ido);M(FD.$$.fragment,xas),xas.forEach(t),Sas.forEach(t),$xa=i($Po),Ndo=n($Po,"SPAN",{});var Gas=s(Ndo);jxa=r(Gas,"AutoModelForZeroShotObjectDetection"),Gas.forEach(t),$Po.forEach(t),jNo=i(c),$r=n(c,"DIV",{class:!0});var _m=s($r);M(ED.$$.fragment,_m),Pxa=i(_m),Uh=n(_m,"P",{});var a6e=s(Uh);Zxa=r(a6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),u9e=n(a6e,"A",{href:!0});var Bas=s(u9e);Vxa=r(Bas,"from_pretrained()"),Bas.forEach(t),Wxa=r(a6e," class method or the "),p9e=n(a6e,"A",{href:!0});var Ias=s(p9e);Jxa=r(Ias,"from_config()"),Ias.forEach(t),Uxa=r(a6e,` class
method.`),a6e.forEach(t),Xxa=i(_m),yD=n(_m,"P",{});var jPo=s(yD);Dxa=r(jPo,"This class cannot be instantiated directly using "),$do=n(jPo,"CODE",{});var Nas=s($do);qxa=r(Nas,"__init__()"),Nas.forEach(t),Oxa=r(jPo," (throws an error)."),jPo.forEach(t),Qxa=i(_m),Ia=n(_m,"DIV",{class:!0});var SZ=s(Ia);M(CD.$$.fragment,SZ),Yxa=i(SZ),jdo=n(SZ,"P",{});var $as=s(jdo);zxa=r($as,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),$as.forEach(t),Hxa=i(SZ),Xh=n(SZ,"P",{});var n6e=s(Xh);Kxa=r(n6e,`Note:
Loading a model from its configuration file does `),Pdo=n(n6e,"STRONG",{});var jas=s(Pdo);eGa=r(jas,"not"),jas.forEach(t),oGa=r(n6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),_9e=n(n6e,"A",{href:!0});var Pas=s(_9e);rGa=r(Pas,"from_pretrained()"),Pas.forEach(t),tGa=r(n6e," to load the model weights."),n6e.forEach(t),aGa=i(SZ),M(zI.$$.fragment,SZ),SZ.forEach(t),nGa=i(_m),Lo=n(_m,"DIV",{class:!0});var yn=s(Lo);M(wD.$$.fragment,yn),sGa=i(yn),Zdo=n(yn,"P",{});var Zas=s(Zdo);lGa=r(Zas,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),Zas.forEach(t),iGa=i(yn),Gs=n(yn,"P",{});var xZ=s(Gs);dGa=r(xZ,"The model class to instantiate is selected based on the "),Vdo=n(xZ,"CODE",{});var Vas=s(Vdo);mGa=r(Vas,"model_type"),Vas.forEach(t),cGa=r(xZ,` property of the config object (either
passed as an argument or loaded from `),Wdo=n(xZ,"CODE",{});var Was=s(Wdo);fGa=r(Was,"pretrained_model_name_or_path"),Was.forEach(t),hGa=r(xZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jdo=n(xZ,"CODE",{});var Jas=s(Jdo);gGa=r(Jas,"pretrained_model_name_or_path"),Jas.forEach(t),uGa=r(xZ,":"),xZ.forEach(t),pGa=i(yn),Udo=n(yn,"UL",{});var Uas=s(Udo);HI=n(Uas,"LI",{});var dGo=s(HI);Xdo=n(dGo,"STRONG",{});var Xas=s(Xdo);_Ga=r(Xas,"owlvit"),Xas.forEach(t),bGa=r(dGo," \u2014 "),b9e=n(dGo,"A",{href:!0});var Das=s(b9e);vGa=r(Das,"OwlViTForObjectDetection"),Das.forEach(t),MGa=r(dGo," (OWL-ViT model)"),dGo.forEach(t),Uas.forEach(t),TGa=i(yn),KI=n(yn,"P",{});var mGo=s(KI);FGa=r(mGo,"The model is set in evaluation mode by default using "),Ddo=n(mGo,"CODE",{});var qas=s(Ddo);EGa=r(qas,"model.eval()"),qas.forEach(t),yGa=r(mGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),qdo=n(mGo,"CODE",{});var Oas=s(qdo);CGa=r(Oas,"model.train()"),Oas.forEach(t),mGo.forEach(t),wGa=i(yn),M(eN.$$.fragment,yn),yn.forEach(t),_m.forEach(t),PNo=i(c),Dh=n(c,"H2",{class:!0});var PPo=s(Dh);oN=n(PPo,"A",{id:!0,class:!0,href:!0});var Qas=s(oN);Odo=n(Qas,"SPAN",{});var Yas=s(Odo);M(LD.$$.fragment,Yas),Yas.forEach(t),Qas.forEach(t),LGa=i(PPo),Qdo=n(PPo,"SPAN",{});var zas=s(Qdo);AGa=r(zas,"Audio"),zas.forEach(t),PPo.forEach(t),ZNo=i(c),v9e=n(c,"P",{});var Has=s(v9e);RGa=r(Has,"The following auto classes are available for the following audio tasks."),Has.forEach(t),VNo=i(c),qh=n(c,"H3",{class:!0});var ZPo=s(qh);rN=n(ZPo,"A",{id:!0,class:!0,href:!0});var Kas=s(rN);Ydo=n(Kas,"SPAN",{});var ens=s(Ydo);M(AD.$$.fragment,ens),ens.forEach(t),Kas.forEach(t),kGa=i(ZPo),zdo=n(ZPo,"SPAN",{});var ons=s(zdo);SGa=r(ons,"AutoModelForAudioClassification"),ons.forEach(t),ZPo.forEach(t),WNo=i(c),jr=n(c,"DIV",{class:!0});var bm=s(jr);M(RD.$$.fragment,bm),xGa=i(bm),Oh=n(bm,"P",{});var s6e=s(Oh);GGa=r(s6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),M9e=n(s6e,"A",{href:!0});var rns=s(M9e);BGa=r(rns,"from_pretrained()"),rns.forEach(t),IGa=r(s6e," class method or the "),T9e=n(s6e,"A",{href:!0});var tns=s(T9e);NGa=r(tns,"from_config()"),tns.forEach(t),$Ga=r(s6e,` class
method.`),s6e.forEach(t),jGa=i(bm),kD=n(bm,"P",{});var VPo=s(kD);PGa=r(VPo,"This class cannot be instantiated directly using "),Hdo=n(VPo,"CODE",{});var ans=s(Hdo);ZGa=r(ans,"__init__()"),ans.forEach(t),VGa=r(VPo," (throws an error)."),VPo.forEach(t),WGa=i(bm),Na=n(bm,"DIV",{class:!0});var GZ=s(Na);M(SD.$$.fragment,GZ),JGa=i(GZ),Kdo=n(GZ,"P",{});var nns=s(Kdo);UGa=r(nns,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),nns.forEach(t),XGa=i(GZ),Qh=n(GZ,"P",{});var l6e=s(Qh);DGa=r(l6e,`Note:
Loading a model from its configuration file does `),emo=n(l6e,"STRONG",{});var sns=s(emo);qGa=r(sns,"not"),sns.forEach(t),OGa=r(l6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),F9e=n(l6e,"A",{href:!0});var lns=s(F9e);QGa=r(lns,"from_pretrained()"),lns.forEach(t),YGa=r(l6e," to load the model weights."),l6e.forEach(t),zGa=i(GZ),M(tN.$$.fragment,GZ),GZ.forEach(t),HGa=i(bm),Ao=n(bm,"DIV",{class:!0});var Cn=s(Ao);M(xD.$$.fragment,Cn),KGa=i(Cn),omo=n(Cn,"P",{});var ins=s(omo);eBa=r(ins,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),ins.forEach(t),oBa=i(Cn),Bs=n(Cn,"P",{});var BZ=s(Bs);rBa=r(BZ,"The model class to instantiate is selected based on the "),rmo=n(BZ,"CODE",{});var dns=s(rmo);tBa=r(dns,"model_type"),dns.forEach(t),aBa=r(BZ,` property of the config object (either
passed as an argument or loaded from `),tmo=n(BZ,"CODE",{});var mns=s(tmo);nBa=r(mns,"pretrained_model_name_or_path"),mns.forEach(t),sBa=r(BZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),amo=n(BZ,"CODE",{});var cns=s(amo);lBa=r(cns,"pretrained_model_name_or_path"),cns.forEach(t),iBa=r(BZ,":"),BZ.forEach(t),dBa=i(Cn),Be=n(Cn,"UL",{});var Je=s(Be);aN=n(Je,"LI",{});var cGo=s(aN);nmo=n(cGo,"STRONG",{});var fns=s(nmo);mBa=r(fns,"audio-spectrogram-transformer"),fns.forEach(t),cBa=r(cGo," \u2014 "),E9e=n(cGo,"A",{href:!0});var hns=s(E9e);fBa=r(hns,"ASTForAudioClassification"),hns.forEach(t),hBa=r(cGo," (Audio Spectrogram Transformer model)"),cGo.forEach(t),gBa=i(Je),nN=n(Je,"LI",{});var fGo=s(nN);smo=n(fGo,"STRONG",{});var gns=s(smo);uBa=r(gns,"data2vec-audio"),gns.forEach(t),pBa=r(fGo," \u2014 "),y9e=n(fGo,"A",{href:!0});var uns=s(y9e);_Ba=r(uns,"Data2VecAudioForSequenceClassification"),uns.forEach(t),bBa=r(fGo," (Data2VecAudio model)"),fGo.forEach(t),vBa=i(Je),sN=n(Je,"LI",{});var hGo=s(sN);lmo=n(hGo,"STRONG",{});var pns=s(lmo);MBa=r(pns,"hubert"),pns.forEach(t),TBa=r(hGo," \u2014 "),C9e=n(hGo,"A",{href:!0});var _ns=s(C9e);FBa=r(_ns,"HubertForSequenceClassification"),_ns.forEach(t),EBa=r(hGo," (Hubert model)"),hGo.forEach(t),yBa=i(Je),lN=n(Je,"LI",{});var gGo=s(lN);imo=n(gGo,"STRONG",{});var bns=s(imo);CBa=r(bns,"sew"),bns.forEach(t),wBa=r(gGo," \u2014 "),w9e=n(gGo,"A",{href:!0});var vns=s(w9e);LBa=r(vns,"SEWForSequenceClassification"),vns.forEach(t),ABa=r(gGo," (SEW model)"),gGo.forEach(t),RBa=i(Je),iN=n(Je,"LI",{});var uGo=s(iN);dmo=n(uGo,"STRONG",{});var Mns=s(dmo);kBa=r(Mns,"sew-d"),Mns.forEach(t),SBa=r(uGo," \u2014 "),L9e=n(uGo,"A",{href:!0});var Tns=s(L9e);xBa=r(Tns,"SEWDForSequenceClassification"),Tns.forEach(t),GBa=r(uGo," (SEW-D model)"),uGo.forEach(t),BBa=i(Je),dN=n(Je,"LI",{});var pGo=s(dN);mmo=n(pGo,"STRONG",{});var Fns=s(mmo);IBa=r(Fns,"unispeech"),Fns.forEach(t),NBa=r(pGo," \u2014 "),A9e=n(pGo,"A",{href:!0});var Ens=s(A9e);$Ba=r(Ens,"UniSpeechForSequenceClassification"),Ens.forEach(t),jBa=r(pGo," (UniSpeech model)"),pGo.forEach(t),PBa=i(Je),mN=n(Je,"LI",{});var _Go=s(mN);cmo=n(_Go,"STRONG",{});var yns=s(cmo);ZBa=r(yns,"unispeech-sat"),yns.forEach(t),VBa=r(_Go," \u2014 "),R9e=n(_Go,"A",{href:!0});var Cns=s(R9e);WBa=r(Cns,"UniSpeechSatForSequenceClassification"),Cns.forEach(t),JBa=r(_Go," (UniSpeechSat model)"),_Go.forEach(t),UBa=i(Je),cN=n(Je,"LI",{});var bGo=s(cN);fmo=n(bGo,"STRONG",{});var wns=s(fmo);XBa=r(wns,"wav2vec2"),wns.forEach(t),DBa=r(bGo," \u2014 "),k9e=n(bGo,"A",{href:!0});var Lns=s(k9e);qBa=r(Lns,"Wav2Vec2ForSequenceClassification"),Lns.forEach(t),OBa=r(bGo," (Wav2Vec2 model)"),bGo.forEach(t),QBa=i(Je),fN=n(Je,"LI",{});var vGo=s(fN);hmo=n(vGo,"STRONG",{});var Ans=s(hmo);YBa=r(Ans,"wav2vec2-conformer"),Ans.forEach(t),zBa=r(vGo," \u2014 "),S9e=n(vGo,"A",{href:!0});var Rns=s(S9e);HBa=r(Rns,"Wav2Vec2ConformerForSequenceClassification"),Rns.forEach(t),KBa=r(vGo," (Wav2Vec2-Conformer model)"),vGo.forEach(t),eIa=i(Je),hN=n(Je,"LI",{});var MGo=s(hN);gmo=n(MGo,"STRONG",{});var kns=s(gmo);oIa=r(kns,"wavlm"),kns.forEach(t),rIa=r(MGo," \u2014 "),x9e=n(MGo,"A",{href:!0});var Sns=s(x9e);tIa=r(Sns,"WavLMForSequenceClassification"),Sns.forEach(t),aIa=r(MGo," (WavLM model)"),MGo.forEach(t),nIa=i(Je),gN=n(Je,"LI",{});var TGo=s(gN);umo=n(TGo,"STRONG",{});var xns=s(umo);sIa=r(xns,"whisper"),xns.forEach(t),lIa=r(TGo," \u2014 "),G9e=n(TGo,"A",{href:!0});var Gns=s(G9e);iIa=r(Gns,"WhisperForAudioClassification"),Gns.forEach(t),dIa=r(TGo," (Whisper model)"),TGo.forEach(t),Je.forEach(t),mIa=i(Cn),uN=n(Cn,"P",{});var FGo=s(uN);cIa=r(FGo,"The model is set in evaluation mode by default using "),pmo=n(FGo,"CODE",{});var Bns=s(pmo);fIa=r(Bns,"model.eval()"),Bns.forEach(t),hIa=r(FGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_mo=n(FGo,"CODE",{});var Ins=s(_mo);gIa=r(Ins,"model.train()"),Ins.forEach(t),FGo.forEach(t),uIa=i(Cn),M(pN.$$.fragment,Cn),Cn.forEach(t),bm.forEach(t),JNo=i(c),Yh=n(c,"H3",{class:!0});var WPo=s(Yh);_N=n(WPo,"A",{id:!0,class:!0,href:!0});var Nns=s(_N);bmo=n(Nns,"SPAN",{});var $ns=s(bmo);M(GD.$$.fragment,$ns),$ns.forEach(t),Nns.forEach(t),pIa=i(WPo),vmo=n(WPo,"SPAN",{});var jns=s(vmo);_Ia=r(jns,"AutoModelForAudioFrameClassification"),jns.forEach(t),WPo.forEach(t),UNo=i(c),Pr=n(c,"DIV",{class:!0});var vm=s(Pr);M(BD.$$.fragment,vm),bIa=i(vm),zh=n(vm,"P",{});var i6e=s(zh);vIa=r(i6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),B9e=n(i6e,"A",{href:!0});var Pns=s(B9e);MIa=r(Pns,"from_pretrained()"),Pns.forEach(t),TIa=r(i6e," class method or the "),I9e=n(i6e,"A",{href:!0});var Zns=s(I9e);FIa=r(Zns,"from_config()"),Zns.forEach(t),EIa=r(i6e,` class
method.`),i6e.forEach(t),yIa=i(vm),ID=n(vm,"P",{});var JPo=s(ID);CIa=r(JPo,"This class cannot be instantiated directly using "),Mmo=n(JPo,"CODE",{});var Vns=s(Mmo);wIa=r(Vns,"__init__()"),Vns.forEach(t),LIa=r(JPo," (throws an error)."),JPo.forEach(t),AIa=i(vm),$a=n(vm,"DIV",{class:!0});var IZ=s($a);M(ND.$$.fragment,IZ),RIa=i(IZ),Tmo=n(IZ,"P",{});var Wns=s(Tmo);kIa=r(Wns,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Wns.forEach(t),SIa=i(IZ),Hh=n(IZ,"P",{});var d6e=s(Hh);xIa=r(d6e,`Note:
Loading a model from its configuration file does `),Fmo=n(d6e,"STRONG",{});var Jns=s(Fmo);GIa=r(Jns,"not"),Jns.forEach(t),BIa=r(d6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),N9e=n(d6e,"A",{href:!0});var Uns=s(N9e);IIa=r(Uns,"from_pretrained()"),Uns.forEach(t),NIa=r(d6e," to load the model weights."),d6e.forEach(t),$Ia=i(IZ),M(bN.$$.fragment,IZ),IZ.forEach(t),jIa=i(vm),At=n(vm,"DIV",{class:!0});var Mm=s(At);M($D.$$.fragment,Mm),PIa=i(Mm),Emo=n(Mm,"P",{});var Xns=s(Emo);ZIa=r(Xns,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Xns.forEach(t),VIa=i(Mm),Is=n(Mm,"P",{});var NZ=s(Is);WIa=r(NZ,"The model class to instantiate is selected based on the "),ymo=n(NZ,"CODE",{});var Dns=s(ymo);JIa=r(Dns,"model_type"),Dns.forEach(t),UIa=r(NZ,` property of the config object (either
passed as an argument or loaded from `),Cmo=n(NZ,"CODE",{});var qns=s(Cmo);XIa=r(qns,"pretrained_model_name_or_path"),qns.forEach(t),DIa=r(NZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wmo=n(NZ,"CODE",{});var Ons=s(wmo);qIa=r(Ons,"pretrained_model_name_or_path"),Ons.forEach(t),OIa=r(NZ,":"),NZ.forEach(t),QIa=i(Mm),Lmo=n(Mm,"UL",{});var Qns=s(Lmo);vN=n(Qns,"LI",{});var EGo=s(vN);Amo=n(EGo,"STRONG",{});var Yns=s(Amo);YIa=r(Yns,"wav2vec2"),Yns.forEach(t),zIa=r(EGo," \u2014 "),$9e=n(EGo,"A",{href:!0});var zns=s($9e);HIa=r(zns,"TFWav2Vec2ForSequenceClassification"),zns.forEach(t),KIa=r(EGo," (Wav2Vec2 model)"),EGo.forEach(t),Qns.forEach(t),eNa=i(Mm),M(MN.$$.fragment,Mm),Mm.forEach(t),vm.forEach(t),XNo=i(c),Kh=n(c,"H3",{class:!0});var UPo=s(Kh);TN=n(UPo,"A",{id:!0,class:!0,href:!0});var Hns=s(TN);Rmo=n(Hns,"SPAN",{});var Kns=s(Rmo);M(jD.$$.fragment,Kns),Kns.forEach(t),Hns.forEach(t),oNa=i(UPo),kmo=n(UPo,"SPAN",{});var ess=s(kmo);rNa=r(ess,"TFAutoModelForAudioFrameClassification"),ess.forEach(t),UPo.forEach(t),DNo=i(c),Zr=n(c,"DIV",{class:!0});var Tm=s(Zr);M(PD.$$.fragment,Tm),tNa=i(Tm),eg=n(Tm,"P",{});var m6e=s(eg);aNa=r(m6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),j9e=n(m6e,"A",{href:!0});var oss=s(j9e);nNa=r(oss,"from_pretrained()"),oss.forEach(t),sNa=r(m6e," class method or the "),P9e=n(m6e,"A",{href:!0});var rss=s(P9e);lNa=r(rss,"from_config()"),rss.forEach(t),iNa=r(m6e,` class
method.`),m6e.forEach(t),dNa=i(Tm),ZD=n(Tm,"P",{});var XPo=s(ZD);mNa=r(XPo,"This class cannot be instantiated directly using "),Smo=n(XPo,"CODE",{});var tss=s(Smo);cNa=r(tss,"__init__()"),tss.forEach(t),fNa=r(XPo," (throws an error)."),XPo.forEach(t),hNa=i(Tm),ja=n(Tm,"DIV",{class:!0});var $Z=s(ja);M(VD.$$.fragment,$Z),gNa=i($Z),xmo=n($Z,"P",{});var ass=s(xmo);uNa=r(ass,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),ass.forEach(t),pNa=i($Z),og=n($Z,"P",{});var c6e=s(og);_Na=r(c6e,`Note:
Loading a model from its configuration file does `),Gmo=n(c6e,"STRONG",{});var nss=s(Gmo);bNa=r(nss,"not"),nss.forEach(t),vNa=r(c6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Z9e=n(c6e,"A",{href:!0});var sss=s(Z9e);MNa=r(sss,"from_pretrained()"),sss.forEach(t),TNa=r(c6e," to load the model weights."),c6e.forEach(t),FNa=i($Z),M(FN.$$.fragment,$Z),$Z.forEach(t),ENa=i(Tm),Ro=n(Tm,"DIV",{class:!0});var wn=s(Ro);M(WD.$$.fragment,wn),yNa=i(wn),Bmo=n(wn,"P",{});var lss=s(Bmo);CNa=r(lss,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),lss.forEach(t),wNa=i(wn),Ns=n(wn,"P",{});var jZ=s(Ns);LNa=r(jZ,"The model class to instantiate is selected based on the "),Imo=n(jZ,"CODE",{});var iss=s(Imo);ANa=r(iss,"model_type"),iss.forEach(t),RNa=r(jZ,` property of the config object (either
passed as an argument or loaded from `),Nmo=n(jZ,"CODE",{});var dss=s(Nmo);kNa=r(dss,"pretrained_model_name_or_path"),dss.forEach(t),SNa=r(jZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$mo=n(jZ,"CODE",{});var mss=s($mo);xNa=r(mss,"pretrained_model_name_or_path"),mss.forEach(t),GNa=r(jZ,":"),jZ.forEach(t),BNa=i(wn),$t=n(wn,"UL",{});var Fm=s($t);EN=n(Fm,"LI",{});var yGo=s(EN);jmo=n(yGo,"STRONG",{});var css=s(jmo);INa=r(css,"data2vec-audio"),css.forEach(t),NNa=r(yGo," \u2014 "),V9e=n(yGo,"A",{href:!0});var fss=s(V9e);$Na=r(fss,"Data2VecAudioForAudioFrameClassification"),fss.forEach(t),jNa=r(yGo," (Data2VecAudio model)"),yGo.forEach(t),PNa=i(Fm),yN=n(Fm,"LI",{});var CGo=s(yN);Pmo=n(CGo,"STRONG",{});var hss=s(Pmo);ZNa=r(hss,"unispeech-sat"),hss.forEach(t),VNa=r(CGo," \u2014 "),W9e=n(CGo,"A",{href:!0});var gss=s(W9e);WNa=r(gss,"UniSpeechSatForAudioFrameClassification"),gss.forEach(t),JNa=r(CGo," (UniSpeechSat model)"),CGo.forEach(t),UNa=i(Fm),CN=n(Fm,"LI",{});var wGo=s(CN);Zmo=n(wGo,"STRONG",{});var uss=s(Zmo);XNa=r(uss,"wav2vec2"),uss.forEach(t),DNa=r(wGo," \u2014 "),J9e=n(wGo,"A",{href:!0});var pss=s(J9e);qNa=r(pss,"Wav2Vec2ForAudioFrameClassification"),pss.forEach(t),ONa=r(wGo," (Wav2Vec2 model)"),wGo.forEach(t),QNa=i(Fm),wN=n(Fm,"LI",{});var LGo=s(wN);Vmo=n(LGo,"STRONG",{});var _ss=s(Vmo);YNa=r(_ss,"wav2vec2-conformer"),_ss.forEach(t),zNa=r(LGo," \u2014 "),U9e=n(LGo,"A",{href:!0});var bss=s(U9e);HNa=r(bss,"Wav2Vec2ConformerForAudioFrameClassification"),bss.forEach(t),KNa=r(LGo," (Wav2Vec2-Conformer model)"),LGo.forEach(t),e$a=i(Fm),LN=n(Fm,"LI",{});var AGo=s(LN);Wmo=n(AGo,"STRONG",{});var vss=s(Wmo);o$a=r(vss,"wavlm"),vss.forEach(t),r$a=r(AGo," \u2014 "),X9e=n(AGo,"A",{href:!0});var Mss=s(X9e);t$a=r(Mss,"WavLMForAudioFrameClassification"),Mss.forEach(t),a$a=r(AGo," (WavLM model)"),AGo.forEach(t),Fm.forEach(t),n$a=i(wn),AN=n(wn,"P",{});var RGo=s(AN);s$a=r(RGo,"The model is set in evaluation mode by default using "),Jmo=n(RGo,"CODE",{});var Tss=s(Jmo);l$a=r(Tss,"model.eval()"),Tss.forEach(t),i$a=r(RGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Umo=n(RGo,"CODE",{});var Fss=s(Umo);d$a=r(Fss,"model.train()"),Fss.forEach(t),RGo.forEach(t),m$a=i(wn),M(RN.$$.fragment,wn),wn.forEach(t),Tm.forEach(t),qNo=i(c),rg=n(c,"H3",{class:!0});var DPo=s(rg);kN=n(DPo,"A",{id:!0,class:!0,href:!0});var Ess=s(kN);Xmo=n(Ess,"SPAN",{});var yss=s(Xmo);M(JD.$$.fragment,yss),yss.forEach(t),Ess.forEach(t),c$a=i(DPo),Dmo=n(DPo,"SPAN",{});var Css=s(Dmo);f$a=r(Css,"AutoModelForCTC"),Css.forEach(t),DPo.forEach(t),ONo=i(c),Vr=n(c,"DIV",{class:!0});var Em=s(Vr);M(UD.$$.fragment,Em),h$a=i(Em),tg=n(Em,"P",{});var f6e=s(tg);g$a=r(f6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),D9e=n(f6e,"A",{href:!0});var wss=s(D9e);u$a=r(wss,"from_pretrained()"),wss.forEach(t),p$a=r(f6e," class method or the "),q9e=n(f6e,"A",{href:!0});var Lss=s(q9e);_$a=r(Lss,"from_config()"),Lss.forEach(t),b$a=r(f6e,` class
method.`),f6e.forEach(t),v$a=i(Em),XD=n(Em,"P",{});var qPo=s(XD);M$a=r(qPo,"This class cannot be instantiated directly using "),qmo=n(qPo,"CODE",{});var Ass=s(qmo);T$a=r(Ass,"__init__()"),Ass.forEach(t),F$a=r(qPo," (throws an error)."),qPo.forEach(t),E$a=i(Em),Pa=n(Em,"DIV",{class:!0});var PZ=s(Pa);M(DD.$$.fragment,PZ),y$a=i(PZ),Omo=n(PZ,"P",{});var Rss=s(Omo);C$a=r(Rss,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),Rss.forEach(t),w$a=i(PZ),ag=n(PZ,"P",{});var h6e=s(ag);L$a=r(h6e,`Note:
Loading a model from its configuration file does `),Qmo=n(h6e,"STRONG",{});var kss=s(Qmo);A$a=r(kss,"not"),kss.forEach(t),R$a=r(h6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),O9e=n(h6e,"A",{href:!0});var Sss=s(O9e);k$a=r(Sss,"from_pretrained()"),Sss.forEach(t),S$a=r(h6e," to load the model weights."),h6e.forEach(t),x$a=i(PZ),M(SN.$$.fragment,PZ),PZ.forEach(t),G$a=i(Em),ko=n(Em,"DIV",{class:!0});var Ln=s(ko);M(qD.$$.fragment,Ln),B$a=i(Ln),Ymo=n(Ln,"P",{});var xss=s(Ymo);I$a=r(xss,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),xss.forEach(t),N$a=i(Ln),$s=n(Ln,"P",{});var ZZ=s($s);$$a=r(ZZ,"The model class to instantiate is selected based on the "),zmo=n(ZZ,"CODE",{});var Gss=s(zmo);j$a=r(Gss,"model_type"),Gss.forEach(t),P$a=r(ZZ,` property of the config object (either
passed as an argument or loaded from `),Hmo=n(ZZ,"CODE",{});var Bss=s(Hmo);Z$a=r(Bss,"pretrained_model_name_or_path"),Bss.forEach(t),V$a=r(ZZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kmo=n(ZZ,"CODE",{});var Iss=s(Kmo);W$a=r(Iss,"pretrained_model_name_or_path"),Iss.forEach(t),J$a=r(ZZ,":"),ZZ.forEach(t),U$a=i(Ln),je=n(Ln,"UL",{});var Oe=s(je);xN=n(Oe,"LI",{});var kGo=s(xN);eco=n(kGo,"STRONG",{});var Nss=s(eco);X$a=r(Nss,"data2vec-audio"),Nss.forEach(t),D$a=r(kGo," \u2014 "),Q9e=n(kGo,"A",{href:!0});var $ss=s(Q9e);q$a=r($ss,"Data2VecAudioForCTC"),$ss.forEach(t),O$a=r(kGo," (Data2VecAudio model)"),kGo.forEach(t),Q$a=i(Oe),GN=n(Oe,"LI",{});var SGo=s(GN);oco=n(SGo,"STRONG",{});var jss=s(oco);Y$a=r(jss,"hubert"),jss.forEach(t),z$a=r(SGo," \u2014 "),Y9e=n(SGo,"A",{href:!0});var Pss=s(Y9e);H$a=r(Pss,"HubertForCTC"),Pss.forEach(t),K$a=r(SGo," (Hubert model)"),SGo.forEach(t),eja=i(Oe),BN=n(Oe,"LI",{});var xGo=s(BN);rco=n(xGo,"STRONG",{});var Zss=s(rco);oja=r(Zss,"mctct"),Zss.forEach(t),rja=r(xGo," \u2014 "),z9e=n(xGo,"A",{href:!0});var Vss=s(z9e);tja=r(Vss,"MCTCTForCTC"),Vss.forEach(t),aja=r(xGo," (M-CTC-T model)"),xGo.forEach(t),nja=i(Oe),IN=n(Oe,"LI",{});var GGo=s(IN);tco=n(GGo,"STRONG",{});var Wss=s(tco);sja=r(Wss,"sew"),Wss.forEach(t),lja=r(GGo," \u2014 "),H9e=n(GGo,"A",{href:!0});var Jss=s(H9e);ija=r(Jss,"SEWForCTC"),Jss.forEach(t),dja=r(GGo," (SEW model)"),GGo.forEach(t),mja=i(Oe),NN=n(Oe,"LI",{});var BGo=s(NN);aco=n(BGo,"STRONG",{});var Uss=s(aco);cja=r(Uss,"sew-d"),Uss.forEach(t),fja=r(BGo," \u2014 "),K9e=n(BGo,"A",{href:!0});var Xss=s(K9e);hja=r(Xss,"SEWDForCTC"),Xss.forEach(t),gja=r(BGo," (SEW-D model)"),BGo.forEach(t),uja=i(Oe),$N=n(Oe,"LI",{});var IGo=s($N);nco=n(IGo,"STRONG",{});var Dss=s(nco);pja=r(Dss,"unispeech"),Dss.forEach(t),_ja=r(IGo," \u2014 "),e4e=n(IGo,"A",{href:!0});var qss=s(e4e);bja=r(qss,"UniSpeechForCTC"),qss.forEach(t),vja=r(IGo," (UniSpeech model)"),IGo.forEach(t),Mja=i(Oe),jN=n(Oe,"LI",{});var NGo=s(jN);sco=n(NGo,"STRONG",{});var Oss=s(sco);Tja=r(Oss,"unispeech-sat"),Oss.forEach(t),Fja=r(NGo," \u2014 "),o4e=n(NGo,"A",{href:!0});var Qss=s(o4e);Eja=r(Qss,"UniSpeechSatForCTC"),Qss.forEach(t),yja=r(NGo," (UniSpeechSat model)"),NGo.forEach(t),Cja=i(Oe),PN=n(Oe,"LI",{});var $Go=s(PN);lco=n($Go,"STRONG",{});var Yss=s(lco);wja=r(Yss,"wav2vec2"),Yss.forEach(t),Lja=r($Go," \u2014 "),r4e=n($Go,"A",{href:!0});var zss=s(r4e);Aja=r(zss,"Wav2Vec2ForCTC"),zss.forEach(t),Rja=r($Go," (Wav2Vec2 model)"),$Go.forEach(t),kja=i(Oe),ZN=n(Oe,"LI",{});var jGo=s(ZN);ico=n(jGo,"STRONG",{});var Hss=s(ico);Sja=r(Hss,"wav2vec2-conformer"),Hss.forEach(t),xja=r(jGo," \u2014 "),t4e=n(jGo,"A",{href:!0});var Kss=s(t4e);Gja=r(Kss,"Wav2Vec2ConformerForCTC"),Kss.forEach(t),Bja=r(jGo," (Wav2Vec2-Conformer model)"),jGo.forEach(t),Ija=i(Oe),VN=n(Oe,"LI",{});var PGo=s(VN);dco=n(PGo,"STRONG",{});var els=s(dco);Nja=r(els,"wavlm"),els.forEach(t),$ja=r(PGo," \u2014 "),a4e=n(PGo,"A",{href:!0});var ols=s(a4e);jja=r(ols,"WavLMForCTC"),ols.forEach(t),Pja=r(PGo," (WavLM model)"),PGo.forEach(t),Oe.forEach(t),Zja=i(Ln),WN=n(Ln,"P",{});var ZGo=s(WN);Vja=r(ZGo,"The model is set in evaluation mode by default using "),mco=n(ZGo,"CODE",{});var rls=s(mco);Wja=r(rls,"model.eval()"),rls.forEach(t),Jja=r(ZGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cco=n(ZGo,"CODE",{});var tls=s(cco);Uja=r(tls,"model.train()"),tls.forEach(t),ZGo.forEach(t),Xja=i(Ln),M(JN.$$.fragment,Ln),Ln.forEach(t),Em.forEach(t),QNo=i(c),ng=n(c,"H3",{class:!0});var OPo=s(ng);UN=n(OPo,"A",{id:!0,class:!0,href:!0});var als=s(UN);fco=n(als,"SPAN",{});var nls=s(fco);M(OD.$$.fragment,nls),nls.forEach(t),als.forEach(t),Dja=i(OPo),hco=n(OPo,"SPAN",{});var sls=s(hco);qja=r(sls,"AutoModelForSpeechSeq2Seq"),sls.forEach(t),OPo.forEach(t),YNo=i(c),Wr=n(c,"DIV",{class:!0});var ym=s(Wr);M(QD.$$.fragment,ym),Oja=i(ym),sg=n(ym,"P",{});var g6e=s(sg);Qja=r(g6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),n4e=n(g6e,"A",{href:!0});var lls=s(n4e);Yja=r(lls,"from_pretrained()"),lls.forEach(t),zja=r(g6e," class method or the "),s4e=n(g6e,"A",{href:!0});var ils=s(s4e);Hja=r(ils,"from_config()"),ils.forEach(t),Kja=r(g6e,` class
method.`),g6e.forEach(t),ePa=i(ym),YD=n(ym,"P",{});var QPo=s(YD);oPa=r(QPo,"This class cannot be instantiated directly using "),gco=n(QPo,"CODE",{});var dls=s(gco);rPa=r(dls,"__init__()"),dls.forEach(t),tPa=r(QPo," (throws an error)."),QPo.forEach(t),aPa=i(ym),Za=n(ym,"DIV",{class:!0});var VZ=s(Za);M(zD.$$.fragment,VZ),nPa=i(VZ),uco=n(VZ,"P",{});var mls=s(uco);sPa=r(mls,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),mls.forEach(t),lPa=i(VZ),lg=n(VZ,"P",{});var u6e=s(lg);iPa=r(u6e,`Note:
Loading a model from its configuration file does `),pco=n(u6e,"STRONG",{});var cls=s(pco);dPa=r(cls,"not"),cls.forEach(t),mPa=r(u6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),l4e=n(u6e,"A",{href:!0});var fls=s(l4e);cPa=r(fls,"from_pretrained()"),fls.forEach(t),fPa=r(u6e," to load the model weights."),u6e.forEach(t),hPa=i(VZ),M(XN.$$.fragment,VZ),VZ.forEach(t),gPa=i(ym),So=n(ym,"DIV",{class:!0});var An=s(So);M(HD.$$.fragment,An),uPa=i(An),_co=n(An,"P",{});var hls=s(_co);pPa=r(hls,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),hls.forEach(t),_Pa=i(An),js=n(An,"P",{});var WZ=s(js);bPa=r(WZ,"The model class to instantiate is selected based on the "),bco=n(WZ,"CODE",{});var gls=s(bco);vPa=r(gls,"model_type"),gls.forEach(t),MPa=r(WZ,` property of the config object (either
passed as an argument or loaded from `),vco=n(WZ,"CODE",{});var uls=s(vco);TPa=r(uls,"pretrained_model_name_or_path"),uls.forEach(t),FPa=r(WZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Mco=n(WZ,"CODE",{});var pls=s(Mco);EPa=r(pls,"pretrained_model_name_or_path"),pls.forEach(t),yPa=r(WZ,":"),WZ.forEach(t),CPa=i(An),jt=n(An,"UL",{});var Cm=s(jt);DN=n(Cm,"LI",{});var VGo=s(DN);Tco=n(VGo,"STRONG",{});var _ls=s(Tco);wPa=r(_ls,"pop2piano"),_ls.forEach(t),LPa=r(VGo," \u2014 "),i4e=n(VGo,"A",{href:!0});var bls=s(i4e);APa=r(bls,"Pop2PianoForConditionalGeneration"),bls.forEach(t),RPa=r(VGo," (Pop2Piano model)"),VGo.forEach(t),kPa=i(Cm),qN=n(Cm,"LI",{});var WGo=s(qN);Fco=n(WGo,"STRONG",{});var vls=s(Fco);SPa=r(vls,"speech-encoder-decoder"),vls.forEach(t),xPa=r(WGo," \u2014 "),d4e=n(WGo,"A",{href:!0});var Mls=s(d4e);GPa=r(Mls,"SpeechEncoderDecoderModel"),Mls.forEach(t),BPa=r(WGo," (Speech Encoder decoder model)"),WGo.forEach(t),IPa=i(Cm),ON=n(Cm,"LI",{});var JGo=s(ON);Eco=n(JGo,"STRONG",{});var Tls=s(Eco);NPa=r(Tls,"speech_to_text"),Tls.forEach(t),$Pa=r(JGo," \u2014 "),m4e=n(JGo,"A",{href:!0});var Fls=s(m4e);jPa=r(Fls,"Speech2TextForConditionalGeneration"),Fls.forEach(t),PPa=r(JGo," (Speech2Text model)"),JGo.forEach(t),ZPa=i(Cm),QN=n(Cm,"LI",{});var UGo=s(QN);yco=n(UGo,"STRONG",{});var Els=s(yco);VPa=r(Els,"speecht5"),Els.forEach(t),WPa=r(UGo," \u2014 "),c4e=n(UGo,"A",{href:!0});var yls=s(c4e);JPa=r(yls,"SpeechT5ForSpeechToText"),yls.forEach(t),UPa=r(UGo," (SpeechT5 model)"),UGo.forEach(t),XPa=i(Cm),YN=n(Cm,"LI",{});var XGo=s(YN);Cco=n(XGo,"STRONG",{});var Cls=s(Cco);DPa=r(Cls,"whisper"),Cls.forEach(t),qPa=r(XGo," \u2014 "),f4e=n(XGo,"A",{href:!0});var wls=s(f4e);OPa=r(wls,"WhisperForConditionalGeneration"),wls.forEach(t),QPa=r(XGo," (Whisper model)"),XGo.forEach(t),Cm.forEach(t),YPa=i(An),zN=n(An,"P",{});var DGo=s(zN);zPa=r(DGo,"The model is set in evaluation mode by default using "),wco=n(DGo,"CODE",{});var Lls=s(wco);HPa=r(Lls,"model.eval()"),Lls.forEach(t),KPa=r(DGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Lco=n(DGo,"CODE",{});var Als=s(Lco);eZa=r(Als,"model.train()"),Als.forEach(t),DGo.forEach(t),oZa=i(An),M(HN.$$.fragment,An),An.forEach(t),ym.forEach(t),zNo=i(c),ig=n(c,"H3",{class:!0});var YPo=s(ig);KN=n(YPo,"A",{id:!0,class:!0,href:!0});var Rls=s(KN);Aco=n(Rls,"SPAN",{});var kls=s(Aco);M(KD.$$.fragment,kls),kls.forEach(t),Rls.forEach(t),rZa=i(YPo),Rco=n(YPo,"SPAN",{});var Sls=s(Rco);tZa=r(Sls,"TFAutoModelForSpeechSeq2Seq"),Sls.forEach(t),YPo.forEach(t),HNo=i(c),Jr=n(c,"DIV",{class:!0});var wm=s(Jr);M(eq.$$.fragment,wm),aZa=i(wm),dg=n(wm,"P",{});var p6e=s(dg);nZa=r(p6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),h4e=n(p6e,"A",{href:!0});var xls=s(h4e);sZa=r(xls,"from_pretrained()"),xls.forEach(t),lZa=r(p6e," class method or the "),g4e=n(p6e,"A",{href:!0});var Gls=s(g4e);iZa=r(Gls,"from_config()"),Gls.forEach(t),dZa=r(p6e,` class
method.`),p6e.forEach(t),mZa=i(wm),oq=n(wm,"P",{});var zPo=s(oq);cZa=r(zPo,"This class cannot be instantiated directly using "),kco=n(zPo,"CODE",{});var Bls=s(kco);fZa=r(Bls,"__init__()"),Bls.forEach(t),hZa=r(zPo," (throws an error)."),zPo.forEach(t),gZa=i(wm),Va=n(wm,"DIV",{class:!0});var JZ=s(Va);M(rq.$$.fragment,JZ),uZa=i(JZ),Sco=n(JZ,"P",{});var Ils=s(Sco);pZa=r(Ils,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Ils.forEach(t),_Za=i(JZ),mg=n(JZ,"P",{});var _6e=s(mg);bZa=r(_6e,`Note:
Loading a model from its configuration file does `),xco=n(_6e,"STRONG",{});var Nls=s(xco);vZa=r(Nls,"not"),Nls.forEach(t),MZa=r(_6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),u4e=n(_6e,"A",{href:!0});var $ls=s(u4e);TZa=r($ls,"from_pretrained()"),$ls.forEach(t),FZa=r(_6e," to load the model weights."),_6e.forEach(t),EZa=i(JZ),M(e$.$$.fragment,JZ),JZ.forEach(t),yZa=i(wm),Rt=n(wm,"DIV",{class:!0});var Lm=s(Rt);M(tq.$$.fragment,Lm),CZa=i(Lm),Gco=n(Lm,"P",{});var jls=s(Gco);wZa=r(jls,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),jls.forEach(t),LZa=i(Lm),Ps=n(Lm,"P",{});var UZ=s(Ps);AZa=r(UZ,"The model class to instantiate is selected based on the "),Bco=n(UZ,"CODE",{});var Pls=s(Bco);RZa=r(Pls,"model_type"),Pls.forEach(t),kZa=r(UZ,` property of the config object (either
passed as an argument or loaded from `),Ico=n(UZ,"CODE",{});var Zls=s(Ico);SZa=r(Zls,"pretrained_model_name_or_path"),Zls.forEach(t),xZa=r(UZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Nco=n(UZ,"CODE",{});var Vls=s(Nco);GZa=r(Vls,"pretrained_model_name_or_path"),Vls.forEach(t),BZa=r(UZ,":"),UZ.forEach(t),IZa=i(Lm),aq=n(Lm,"UL",{});var HPo=s(aq);o$=n(HPo,"LI",{});var qGo=s(o$);$co=n(qGo,"STRONG",{});var Wls=s($co);NZa=r(Wls,"speech_to_text"),Wls.forEach(t),$Za=r(qGo," \u2014 "),p4e=n(qGo,"A",{href:!0});var Jls=s(p4e);jZa=r(Jls,"TFSpeech2TextForConditionalGeneration"),Jls.forEach(t),PZa=r(qGo," (Speech2Text model)"),qGo.forEach(t),ZZa=i(HPo),r$=n(HPo,"LI",{});var OGo=s(r$);jco=n(OGo,"STRONG",{});var Uls=s(jco);VZa=r(Uls,"whisper"),Uls.forEach(t),WZa=r(OGo," \u2014 "),_4e=n(OGo,"A",{href:!0});var Xls=s(_4e);JZa=r(Xls,"TFWhisperForConditionalGeneration"),Xls.forEach(t),UZa=r(OGo," (Whisper model)"),OGo.forEach(t),HPo.forEach(t),XZa=i(Lm),M(t$.$$.fragment,Lm),Lm.forEach(t),wm.forEach(t),KNo=i(c),cg=n(c,"H3",{class:!0});var KPo=s(cg);a$=n(KPo,"A",{id:!0,class:!0,href:!0});var Dls=s(a$);Pco=n(Dls,"SPAN",{});var qls=s(Pco);M(nq.$$.fragment,qls),qls.forEach(t),Dls.forEach(t),DZa=i(KPo),Zco=n(KPo,"SPAN",{});var Ols=s(Zco);qZa=r(Ols,"FlaxAutoModelForSpeechSeq2Seq"),Ols.forEach(t),KPo.forEach(t),e$o=i(c),Ur=n(c,"DIV",{class:!0});var Am=s(Ur);M(sq.$$.fragment,Am),OZa=i(Am),fg=n(Am,"P",{});var b6e=s(fg);QZa=r(b6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),b4e=n(b6e,"A",{href:!0});var Qls=s(b4e);YZa=r(Qls,"from_pretrained()"),Qls.forEach(t),zZa=r(b6e," class method or the "),v4e=n(b6e,"A",{href:!0});var Yls=s(v4e);HZa=r(Yls,"from_config()"),Yls.forEach(t),KZa=r(b6e,` class
method.`),b6e.forEach(t),eVa=i(Am),lq=n(Am,"P",{});var eZo=s(lq);oVa=r(eZo,"This class cannot be instantiated directly using "),Vco=n(eZo,"CODE",{});var zls=s(Vco);rVa=r(zls,"__init__()"),zls.forEach(t),tVa=r(eZo," (throws an error)."),eZo.forEach(t),aVa=i(Am),Wa=n(Am,"DIV",{class:!0});var XZ=s(Wa);M(iq.$$.fragment,XZ),nVa=i(XZ),Wco=n(XZ,"P",{});var Hls=s(Wco);sVa=r(Hls,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Hls.forEach(t),lVa=i(XZ),hg=n(XZ,"P",{});var v6e=s(hg);iVa=r(v6e,`Note:
Loading a model from its configuration file does `),Jco=n(v6e,"STRONG",{});var Kls=s(Jco);dVa=r(Kls,"not"),Kls.forEach(t),mVa=r(v6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),M4e=n(v6e,"A",{href:!0});var eis=s(M4e);cVa=r(eis,"from_pretrained()"),eis.forEach(t),fVa=r(v6e," to load the model weights."),v6e.forEach(t),hVa=i(XZ),M(n$.$$.fragment,XZ),XZ.forEach(t),gVa=i(Am),kt=n(Am,"DIV",{class:!0});var Rm=s(kt);M(dq.$$.fragment,Rm),uVa=i(Rm),Uco=n(Rm,"P",{});var ois=s(Uco);pVa=r(ois,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),ois.forEach(t),_Va=i(Rm),Zs=n(Rm,"P",{});var DZ=s(Zs);bVa=r(DZ,"The model class to instantiate is selected based on the "),Xco=n(DZ,"CODE",{});var ris=s(Xco);vVa=r(ris,"model_type"),ris.forEach(t),MVa=r(DZ,` property of the config object (either
passed as an argument or loaded from `),Dco=n(DZ,"CODE",{});var tis=s(Dco);TVa=r(tis,"pretrained_model_name_or_path"),tis.forEach(t),FVa=r(DZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qco=n(DZ,"CODE",{});var ais=s(qco);EVa=r(ais,"pretrained_model_name_or_path"),ais.forEach(t),yVa=r(DZ,":"),DZ.forEach(t),CVa=i(Rm),mq=n(Rm,"UL",{});var oZo=s(mq);s$=n(oZo,"LI",{});var QGo=s(s$);Oco=n(QGo,"STRONG",{});var nis=s(Oco);wVa=r(nis,"speech-encoder-decoder"),nis.forEach(t),LVa=r(QGo," \u2014 "),T4e=n(QGo,"A",{href:!0});var sis=s(T4e);AVa=r(sis,"FlaxSpeechEncoderDecoderModel"),sis.forEach(t),RVa=r(QGo," (Speech Encoder decoder model)"),QGo.forEach(t),kVa=i(oZo),l$=n(oZo,"LI",{});var YGo=s(l$);Qco=n(YGo,"STRONG",{});var lis=s(Qco);SVa=r(lis,"whisper"),lis.forEach(t),xVa=r(YGo," \u2014 "),F4e=n(YGo,"A",{href:!0});var iis=s(F4e);GVa=r(iis,"FlaxWhisperForConditionalGeneration"),iis.forEach(t),BVa=r(YGo," (Whisper model)"),YGo.forEach(t),oZo.forEach(t),IVa=i(Rm),M(i$.$$.fragment,Rm),Rm.forEach(t),Am.forEach(t),o$o=i(c),gg=n(c,"H3",{class:!0});var rZo=s(gg);d$=n(rZo,"A",{id:!0,class:!0,href:!0});var dis=s(d$);Yco=n(dis,"SPAN",{});var mis=s(Yco);M(cq.$$.fragment,mis),mis.forEach(t),dis.forEach(t),NVa=i(rZo),zco=n(rZo,"SPAN",{});var cis=s(zco);$Va=r(cis,"AutoModelForAudioXVector"),cis.forEach(t),rZo.forEach(t),r$o=i(c),Xr=n(c,"DIV",{class:!0});var km=s(Xr);M(fq.$$.fragment,km),jVa=i(km),ug=n(km,"P",{});var M6e=s(ug);PVa=r(M6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),E4e=n(M6e,"A",{href:!0});var fis=s(E4e);ZVa=r(fis,"from_pretrained()"),fis.forEach(t),VVa=r(M6e," class method or the "),y4e=n(M6e,"A",{href:!0});var his=s(y4e);WVa=r(his,"from_config()"),his.forEach(t),JVa=r(M6e,` class
method.`),M6e.forEach(t),UVa=i(km),hq=n(km,"P",{});var tZo=s(hq);XVa=r(tZo,"This class cannot be instantiated directly using "),Hco=n(tZo,"CODE",{});var gis=s(Hco);DVa=r(gis,"__init__()"),gis.forEach(t),qVa=r(tZo," (throws an error)."),tZo.forEach(t),OVa=i(km),Ja=n(km,"DIV",{class:!0});var qZ=s(Ja);M(gq.$$.fragment,qZ),QVa=i(qZ),Kco=n(qZ,"P",{});var uis=s(Kco);YVa=r(uis,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),uis.forEach(t),zVa=i(qZ),pg=n(qZ,"P",{});var T6e=s(pg);HVa=r(T6e,`Note:
Loading a model from its configuration file does `),efo=n(T6e,"STRONG",{});var pis=s(efo);KVa=r(pis,"not"),pis.forEach(t),eWa=r(T6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),C4e=n(T6e,"A",{href:!0});var _is=s(C4e);oWa=r(_is,"from_pretrained()"),_is.forEach(t),rWa=r(T6e," to load the model weights."),T6e.forEach(t),tWa=i(qZ),M(m$.$$.fragment,qZ),qZ.forEach(t),aWa=i(km),xo=n(km,"DIV",{class:!0});var Rn=s(xo);M(uq.$$.fragment,Rn),nWa=i(Rn),ofo=n(Rn,"P",{});var bis=s(ofo);sWa=r(bis,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),bis.forEach(t),lWa=i(Rn),Vs=n(Rn,"P",{});var OZ=s(Vs);iWa=r(OZ,"The model class to instantiate is selected based on the "),rfo=n(OZ,"CODE",{});var vis=s(rfo);dWa=r(vis,"model_type"),vis.forEach(t),mWa=r(OZ,` property of the config object (either
passed as an argument or loaded from `),tfo=n(OZ,"CODE",{});var Mis=s(tfo);cWa=r(Mis,"pretrained_model_name_or_path"),Mis.forEach(t),fWa=r(OZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),afo=n(OZ,"CODE",{});var Tis=s(afo);hWa=r(Tis,"pretrained_model_name_or_path"),Tis.forEach(t),gWa=r(OZ,":"),OZ.forEach(t),uWa=i(Rn),Pt=n(Rn,"UL",{});var Sm=s(Pt);c$=n(Sm,"LI",{});var zGo=s(c$);nfo=n(zGo,"STRONG",{});var Fis=s(nfo);pWa=r(Fis,"data2vec-audio"),Fis.forEach(t),_Wa=r(zGo," \u2014 "),w4e=n(zGo,"A",{href:!0});var Eis=s(w4e);bWa=r(Eis,"Data2VecAudioForXVector"),Eis.forEach(t),vWa=r(zGo," (Data2VecAudio model)"),zGo.forEach(t),MWa=i(Sm),f$=n(Sm,"LI",{});var HGo=s(f$);sfo=n(HGo,"STRONG",{});var yis=s(sfo);TWa=r(yis,"unispeech-sat"),yis.forEach(t),FWa=r(HGo," \u2014 "),L4e=n(HGo,"A",{href:!0});var Cis=s(L4e);EWa=r(Cis,"UniSpeechSatForXVector"),Cis.forEach(t),yWa=r(HGo," (UniSpeechSat model)"),HGo.forEach(t),CWa=i(Sm),h$=n(Sm,"LI",{});var KGo=s(h$);lfo=n(KGo,"STRONG",{});var wis=s(lfo);wWa=r(wis,"wav2vec2"),wis.forEach(t),LWa=r(KGo," \u2014 "),A4e=n(KGo,"A",{href:!0});var Lis=s(A4e);AWa=r(Lis,"Wav2Vec2ForXVector"),Lis.forEach(t),RWa=r(KGo," (Wav2Vec2 model)"),KGo.forEach(t),kWa=i(Sm),g$=n(Sm,"LI",{});var eBo=s(g$);ifo=n(eBo,"STRONG",{});var Ais=s(ifo);SWa=r(Ais,"wav2vec2-conformer"),Ais.forEach(t),xWa=r(eBo," \u2014 "),R4e=n(eBo,"A",{href:!0});var Ris=s(R4e);GWa=r(Ris,"Wav2Vec2ConformerForXVector"),Ris.forEach(t),BWa=r(eBo," (Wav2Vec2-Conformer model)"),eBo.forEach(t),IWa=i(Sm),u$=n(Sm,"LI",{});var oBo=s(u$);dfo=n(oBo,"STRONG",{});var kis=s(dfo);NWa=r(kis,"wavlm"),kis.forEach(t),$Wa=r(oBo," \u2014 "),k4e=n(oBo,"A",{href:!0});var Sis=s(k4e);jWa=r(Sis,"WavLMForXVector"),Sis.forEach(t),PWa=r(oBo," (WavLM model)"),oBo.forEach(t),Sm.forEach(t),ZWa=i(Rn),p$=n(Rn,"P",{});var rBo=s(p$);VWa=r(rBo,"The model is set in evaluation mode by default using "),mfo=n(rBo,"CODE",{});var xis=s(mfo);WWa=r(xis,"model.eval()"),xis.forEach(t),JWa=r(rBo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cfo=n(rBo,"CODE",{});var Gis=s(cfo);UWa=r(Gis,"model.train()"),Gis.forEach(t),rBo.forEach(t),XWa=i(Rn),M(_$.$$.fragment,Rn),Rn.forEach(t),km.forEach(t),t$o=i(c),_g=n(c,"H3",{class:!0});var aZo=s(_g);b$=n(aZo,"A",{id:!0,class:!0,href:!0});var Bis=s(b$);ffo=n(Bis,"SPAN",{});var Iis=s(ffo);M(pq.$$.fragment,Iis),Iis.forEach(t),Bis.forEach(t),DWa=i(aZo),hfo=n(aZo,"SPAN",{});var Nis=s(hfo);qWa=r(Nis,"AutoModelForTextToSpectrogram"),Nis.forEach(t),aZo.forEach(t),a$o=i(c),_q=n(c,"DIV",{class:!0});var $is=s(_q);M(bq.$$.fragment,$is),$is.forEach(t),n$o=i(c),bg=n(c,"H3",{class:!0});var nZo=s(bg);v$=n(nZo,"A",{id:!0,class:!0,href:!0});var jis=s(v$);gfo=n(jis,"SPAN",{});var Pis=s(gfo);M(vq.$$.fragment,Pis),Pis.forEach(t),jis.forEach(t),OWa=i(nZo),ufo=n(nZo,"SPAN",{});var Zis=s(ufo);QWa=r(Zis,"AutoModelForTextToWaveform"),Zis.forEach(t),nZo.forEach(t),s$o=i(c),Mq=n(c,"DIV",{class:!0});var Vis=s(Mq);M(Tq.$$.fragment,Vis),Vis.forEach(t),l$o=i(c),vg=n(c,"H2",{class:!0});var sZo=s(vg);M$=n(sZo,"A",{id:!0,class:!0,href:!0});var Wis=s(M$);pfo=n(Wis,"SPAN",{});var Jis=s(pfo);M(Fq.$$.fragment,Jis),Jis.forEach(t),Wis.forEach(t),YWa=i(sZo),_fo=n(sZo,"SPAN",{});var Uis=s(_fo);zWa=r(Uis,"Multimodal"),Uis.forEach(t),sZo.forEach(t),i$o=i(c),S4e=n(c,"P",{});var Xis=s(S4e);HWa=r(Xis,"The following auto classes are available for the following multimodal tasks."),Xis.forEach(t),d$o=i(c),Mg=n(c,"H3",{class:!0});var lZo=s(Mg);T$=n(lZo,"A",{id:!0,class:!0,href:!0});var Dis=s(T$);bfo=n(Dis,"SPAN",{});var qis=s(bfo);M(Eq.$$.fragment,qis),qis.forEach(t),Dis.forEach(t),KWa=i(lZo),vfo=n(lZo,"SPAN",{});var Ois=s(vfo);eJa=r(Ois,"AutoModelForTableQuestionAnswering"),Ois.forEach(t),lZo.forEach(t),m$o=i(c),Dr=n(c,"DIV",{class:!0});var xm=s(Dr);M(yq.$$.fragment,xm),oJa=i(xm),Tg=n(xm,"P",{});var F6e=s(Tg);rJa=r(F6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),x4e=n(F6e,"A",{href:!0});var Qis=s(x4e);tJa=r(Qis,"from_pretrained()"),Qis.forEach(t),aJa=r(F6e," class method or the "),G4e=n(F6e,"A",{href:!0});var Yis=s(G4e);nJa=r(Yis,"from_config()"),Yis.forEach(t),sJa=r(F6e,` class
method.`),F6e.forEach(t),lJa=i(xm),Cq=n(xm,"P",{});var iZo=s(Cq);iJa=r(iZo,"This class cannot be instantiated directly using "),Mfo=n(iZo,"CODE",{});var zis=s(Mfo);dJa=r(zis,"__init__()"),zis.forEach(t),mJa=r(iZo," (throws an error)."),iZo.forEach(t),cJa=i(xm),Ua=n(xm,"DIV",{class:!0});var QZ=s(Ua);M(wq.$$.fragment,QZ),fJa=i(QZ),Tfo=n(QZ,"P",{});var His=s(Tfo);hJa=r(His,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),His.forEach(t),gJa=i(QZ),Fg=n(QZ,"P",{});var E6e=s(Fg);uJa=r(E6e,`Note:
Loading a model from its configuration file does `),Ffo=n(E6e,"STRONG",{});var Kis=s(Ffo);pJa=r(Kis,"not"),Kis.forEach(t),_Ja=r(E6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),B4e=n(E6e,"A",{href:!0});var eds=s(B4e);bJa=r(eds,"from_pretrained()"),eds.forEach(t),vJa=r(E6e," to load the model weights."),E6e.forEach(t),MJa=i(QZ),M(F$.$$.fragment,QZ),QZ.forEach(t),TJa=i(xm),Go=n(xm,"DIV",{class:!0});var kn=s(Go);M(Lq.$$.fragment,kn),FJa=i(kn),Efo=n(kn,"P",{});var ods=s(Efo);EJa=r(ods,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),ods.forEach(t),yJa=i(kn),Ws=n(kn,"P",{});var YZ=s(Ws);CJa=r(YZ,"The model class to instantiate is selected based on the "),yfo=n(YZ,"CODE",{});var rds=s(yfo);wJa=r(rds,"model_type"),rds.forEach(t),LJa=r(YZ,` property of the config object (either
passed as an argument or loaded from `),Cfo=n(YZ,"CODE",{});var tds=s(Cfo);AJa=r(tds,"pretrained_model_name_or_path"),tds.forEach(t),RJa=r(YZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wfo=n(YZ,"CODE",{});var ads=s(wfo);kJa=r(ads,"pretrained_model_name_or_path"),ads.forEach(t),SJa=r(YZ,":"),YZ.forEach(t),xJa=i(kn),Lfo=n(kn,"UL",{});var nds=s(Lfo);E$=n(nds,"LI",{});var tBo=s(E$);Afo=n(tBo,"STRONG",{});var sds=s(Afo);GJa=r(sds,"tapas"),sds.forEach(t),BJa=r(tBo," \u2014 "),I4e=n(tBo,"A",{href:!0});var lds=s(I4e);IJa=r(lds,"TapasForQuestionAnswering"),lds.forEach(t),NJa=r(tBo," (TAPAS model)"),tBo.forEach(t),nds.forEach(t),$Ja=i(kn),y$=n(kn,"P",{});var aBo=s(y$);jJa=r(aBo,"The model is set in evaluation mode by default using "),Rfo=n(aBo,"CODE",{});var ids=s(Rfo);PJa=r(ids,"model.eval()"),ids.forEach(t),ZJa=r(aBo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),kfo=n(aBo,"CODE",{});var dds=s(kfo);VJa=r(dds,"model.train()"),dds.forEach(t),aBo.forEach(t),WJa=i(kn),M(C$.$$.fragment,kn),kn.forEach(t),xm.forEach(t),c$o=i(c),Eg=n(c,"H3",{class:!0});var dZo=s(Eg);w$=n(dZo,"A",{id:!0,class:!0,href:!0});var mds=s(w$);Sfo=n(mds,"SPAN",{});var cds=s(Sfo);M(Aq.$$.fragment,cds),cds.forEach(t),mds.forEach(t),JJa=i(dZo),xfo=n(dZo,"SPAN",{});var fds=s(xfo);UJa=r(fds,"TFAutoModelForTableQuestionAnswering"),fds.forEach(t),dZo.forEach(t),f$o=i(c),qr=n(c,"DIV",{class:!0});var Gm=s(qr);M(Rq.$$.fragment,Gm),XJa=i(Gm),yg=n(Gm,"P",{});var y6e=s(yg);DJa=r(y6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),N4e=n(y6e,"A",{href:!0});var hds=s(N4e);qJa=r(hds,"from_pretrained()"),hds.forEach(t),OJa=r(y6e," class method or the "),$4e=n(y6e,"A",{href:!0});var gds=s($4e);QJa=r(gds,"from_config()"),gds.forEach(t),YJa=r(y6e,` class
method.`),y6e.forEach(t),zJa=i(Gm),kq=n(Gm,"P",{});var mZo=s(kq);HJa=r(mZo,"This class cannot be instantiated directly using "),Gfo=n(mZo,"CODE",{});var uds=s(Gfo);KJa=r(uds,"__init__()"),uds.forEach(t),eUa=r(mZo," (throws an error)."),mZo.forEach(t),oUa=i(Gm),Xa=n(Gm,"DIV",{class:!0});var zZ=s(Xa);M(Sq.$$.fragment,zZ),rUa=i(zZ),Bfo=n(zZ,"P",{});var pds=s(Bfo);tUa=r(pds,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),pds.forEach(t),aUa=i(zZ),Cg=n(zZ,"P",{});var C6e=s(Cg);nUa=r(C6e,`Note:
Loading a model from its configuration file does `),Ifo=n(C6e,"STRONG",{});var _ds=s(Ifo);sUa=r(_ds,"not"),_ds.forEach(t),lUa=r(C6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),j4e=n(C6e,"A",{href:!0});var bds=s(j4e);iUa=r(bds,"from_pretrained()"),bds.forEach(t),dUa=r(C6e," to load the model weights."),C6e.forEach(t),mUa=i(zZ),M(L$.$$.fragment,zZ),zZ.forEach(t),cUa=i(Gm),St=n(Gm,"DIV",{class:!0});var Bm=s(St);M(xq.$$.fragment,Bm),fUa=i(Bm),Nfo=n(Bm,"P",{});var vds=s(Nfo);hUa=r(vds,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),vds.forEach(t),gUa=i(Bm),Js=n(Bm,"P",{});var HZ=s(Js);uUa=r(HZ,"The model class to instantiate is selected based on the "),$fo=n(HZ,"CODE",{});var Mds=s($fo);pUa=r(Mds,"model_type"),Mds.forEach(t),_Ua=r(HZ,` property of the config object (either
passed as an argument or loaded from `),jfo=n(HZ,"CODE",{});var Tds=s(jfo);bUa=r(Tds,"pretrained_model_name_or_path"),Tds.forEach(t),vUa=r(HZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pfo=n(HZ,"CODE",{});var Fds=s(Pfo);MUa=r(Fds,"pretrained_model_name_or_path"),Fds.forEach(t),TUa=r(HZ,":"),HZ.forEach(t),FUa=i(Bm),Zfo=n(Bm,"UL",{});var Eds=s(Zfo);A$=n(Eds,"LI",{});var nBo=s(A$);Vfo=n(nBo,"STRONG",{});var yds=s(Vfo);EUa=r(yds,"tapas"),yds.forEach(t),yUa=r(nBo," \u2014 "),P4e=n(nBo,"A",{href:!0});var Cds=s(P4e);CUa=r(Cds,"TFTapasForQuestionAnswering"),Cds.forEach(t),wUa=r(nBo," (TAPAS model)"),nBo.forEach(t),Eds.forEach(t),LUa=i(Bm),M(R$.$$.fragment,Bm),Bm.forEach(t),Gm.forEach(t),h$o=i(c),wg=n(c,"H3",{class:!0});var cZo=s(wg);k$=n(cZo,"A",{id:!0,class:!0,href:!0});var wds=s(k$);Wfo=n(wds,"SPAN",{});var Lds=s(Wfo);M(Gq.$$.fragment,Lds),Lds.forEach(t),wds.forEach(t),AUa=i(cZo),Jfo=n(cZo,"SPAN",{});var Ads=s(Jfo);RUa=r(Ads,"AutoModelForDocumentQuestionAnswering"),Ads.forEach(t),cZo.forEach(t),g$o=i(c),Or=n(c,"DIV",{class:!0});var Im=s(Or);M(Bq.$$.fragment,Im),kUa=i(Im),Lg=n(Im,"P",{});var w6e=s(Lg);SUa=r(w6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),Z4e=n(w6e,"A",{href:!0});var Rds=s(Z4e);xUa=r(Rds,"from_pretrained()"),Rds.forEach(t),GUa=r(w6e," class method or the "),V4e=n(w6e,"A",{href:!0});var kds=s(V4e);BUa=r(kds,"from_config()"),kds.forEach(t),IUa=r(w6e,` class
method.`),w6e.forEach(t),NUa=i(Im),Iq=n(Im,"P",{});var fZo=s(Iq);$Ua=r(fZo,"This class cannot be instantiated directly using "),Ufo=n(fZo,"CODE",{});var Sds=s(Ufo);jUa=r(Sds,"__init__()"),Sds.forEach(t),PUa=r(fZo," (throws an error)."),fZo.forEach(t),ZUa=i(Im),Da=n(Im,"DIV",{class:!0});var KZ=s(Da);M(Nq.$$.fragment,KZ),VUa=i(KZ),Xfo=n(KZ,"P",{});var xds=s(Xfo);WUa=r(xds,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),xds.forEach(t),JUa=i(KZ),Ag=n(KZ,"P",{});var L6e=s(Ag);UUa=r(L6e,`Note:
Loading a model from its configuration file does `),Dfo=n(L6e,"STRONG",{});var Gds=s(Dfo);XUa=r(Gds,"not"),Gds.forEach(t),DUa=r(L6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),W4e=n(L6e,"A",{href:!0});var Bds=s(W4e);qUa=r(Bds,"from_pretrained()"),Bds.forEach(t),OUa=r(L6e," to load the model weights."),L6e.forEach(t),QUa=i(KZ),M(S$.$$.fragment,KZ),KZ.forEach(t),YUa=i(Im),Bo=n(Im,"DIV",{class:!0});var Sn=s(Bo);M($q.$$.fragment,Sn),zUa=i(Sn),qfo=n(Sn,"P",{});var Ids=s(qfo);HUa=r(Ids,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),Ids.forEach(t),KUa=i(Sn),Us=n(Sn,"P",{});var eV=s(Us);eXa=r(eV,"The model class to instantiate is selected based on the "),Ofo=n(eV,"CODE",{});var Nds=s(Ofo);oXa=r(Nds,"model_type"),Nds.forEach(t),rXa=r(eV,` property of the config object (either
passed as an argument or loaded from `),Qfo=n(eV,"CODE",{});var $ds=s(Qfo);tXa=r($ds,"pretrained_model_name_or_path"),$ds.forEach(t),aXa=r(eV,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yfo=n(eV,"CODE",{});var jds=s(Yfo);nXa=r(jds,"pretrained_model_name_or_path"),jds.forEach(t),sXa=r(eV,":"),eV.forEach(t),lXa=i(Sn),Rg=n(Sn,"UL",{});var A6e=s(Rg);x$=n(A6e,"LI",{});var sBo=s(x$);zfo=n(sBo,"STRONG",{});var Pds=s(zfo);iXa=r(Pds,"layoutlm"),Pds.forEach(t),dXa=r(sBo," \u2014 "),J4e=n(sBo,"A",{href:!0});var Zds=s(J4e);mXa=r(Zds,"LayoutLMForQuestionAnswering"),Zds.forEach(t),cXa=r(sBo," (LayoutLM model)"),sBo.forEach(t),fXa=i(A6e),G$=n(A6e,"LI",{});var lBo=s(G$);Hfo=n(lBo,"STRONG",{});var Vds=s(Hfo);hXa=r(Vds,"layoutlmv2"),Vds.forEach(t),gXa=r(lBo," \u2014 "),U4e=n(lBo,"A",{href:!0});var Wds=s(U4e);uXa=r(Wds,"LayoutLMv2ForQuestionAnswering"),Wds.forEach(t),pXa=r(lBo," (LayoutLMv2 model)"),lBo.forEach(t),_Xa=i(A6e),B$=n(A6e,"LI",{});var iBo=s(B$);Kfo=n(iBo,"STRONG",{});var Jds=s(Kfo);bXa=r(Jds,"layoutlmv3"),Jds.forEach(t),vXa=r(iBo," \u2014 "),X4e=n(iBo,"A",{href:!0});var Uds=s(X4e);MXa=r(Uds,"LayoutLMv3ForQuestionAnswering"),Uds.forEach(t),TXa=r(iBo," (LayoutLMv3 model)"),iBo.forEach(t),A6e.forEach(t),FXa=i(Sn),I$=n(Sn,"P",{});var dBo=s(I$);EXa=r(dBo,"The model is set in evaluation mode by default using "),eho=n(dBo,"CODE",{});var Xds=s(eho);yXa=r(Xds,"model.eval()"),Xds.forEach(t),CXa=r(dBo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oho=n(dBo,"CODE",{});var Dds=s(oho);wXa=r(Dds,"model.train()"),Dds.forEach(t),dBo.forEach(t),LXa=i(Sn),M(N$.$$.fragment,Sn),Sn.forEach(t),Im.forEach(t),u$o=i(c),kg=n(c,"H3",{class:!0});var hZo=s(kg);$$=n(hZo,"A",{id:!0,class:!0,href:!0});var qds=s($$);rho=n(qds,"SPAN",{});var Ods=s(rho);M(jq.$$.fragment,Ods),Ods.forEach(t),qds.forEach(t),AXa=i(hZo),tho=n(hZo,"SPAN",{});var Qds=s(tho);RXa=r(Qds,"TFAutoModelForDocumentQuestionAnswering"),Qds.forEach(t),hZo.forEach(t),p$o=i(c),Qr=n(c,"DIV",{class:!0});var Nm=s(Qr);M(Pq.$$.fragment,Nm),kXa=i(Nm),Sg=n(Nm,"P",{});var R6e=s(Sg);SXa=r(R6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),D4e=n(R6e,"A",{href:!0});var Yds=s(D4e);xXa=r(Yds,"from_pretrained()"),Yds.forEach(t),GXa=r(R6e," class method or the "),q4e=n(R6e,"A",{href:!0});var zds=s(q4e);BXa=r(zds,"from_config()"),zds.forEach(t),IXa=r(R6e,` class
method.`),R6e.forEach(t),NXa=i(Nm),Zq=n(Nm,"P",{});var gZo=s(Zq);$Xa=r(gZo,"This class cannot be instantiated directly using "),aho=n(gZo,"CODE",{});var Hds=s(aho);jXa=r(Hds,"__init__()"),Hds.forEach(t),PXa=r(gZo," (throws an error)."),gZo.forEach(t),ZXa=i(Nm),qa=n(Nm,"DIV",{class:!0});var oV=s(qa);M(Vq.$$.fragment,oV),VXa=i(oV),nho=n(oV,"P",{});var Kds=s(nho);WXa=r(Kds,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),Kds.forEach(t),JXa=i(oV),xg=n(oV,"P",{});var k6e=s(xg);UXa=r(k6e,`Note:
Loading a model from its configuration file does `),sho=n(k6e,"STRONG",{});var ems=s(sho);XXa=r(ems,"not"),ems.forEach(t),DXa=r(k6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),O4e=n(k6e,"A",{href:!0});var oms=s(O4e);qXa=r(oms,"from_pretrained()"),oms.forEach(t),OXa=r(k6e," to load the model weights."),k6e.forEach(t),QXa=i(oV),M(j$.$$.fragment,oV),oV.forEach(t),YXa=i(Nm),xt=n(Nm,"DIV",{class:!0});var $m=s(xt);M(Wq.$$.fragment,$m),zXa=i($m),lho=n($m,"P",{});var rms=s(lho);HXa=r(rms,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),rms.forEach(t),KXa=i($m),Xs=n($m,"P",{});var rV=s(Xs);eDa=r(rV,"The model class to instantiate is selected based on the "),iho=n(rV,"CODE",{});var tms=s(iho);oDa=r(tms,"model_type"),tms.forEach(t),rDa=r(rV,` property of the config object (either
passed as an argument or loaded from `),dho=n(rV,"CODE",{});var ams=s(dho);tDa=r(ams,"pretrained_model_name_or_path"),ams.forEach(t),aDa=r(rV,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mho=n(rV,"CODE",{});var nms=s(mho);nDa=r(nms,"pretrained_model_name_or_path"),nms.forEach(t),sDa=r(rV,":"),rV.forEach(t),lDa=i($m),cho=n($m,"UL",{});var sms=s(cho);P$=n(sms,"LI",{});var mBo=s(P$);fho=n(mBo,"STRONG",{});var lms=s(fho);iDa=r(lms,"layoutlm"),lms.forEach(t),dDa=r(mBo," \u2014 "),Q4e=n(mBo,"A",{href:!0});var ims=s(Q4e);mDa=r(ims,"TFLayoutLMForQuestionAnswering"),ims.forEach(t),cDa=r(mBo," (LayoutLM model)"),mBo.forEach(t),sms.forEach(t),fDa=i($m),M(Z$.$$.fragment,$m),$m.forEach(t),Nm.forEach(t),_$o=i(c),Gg=n(c,"H3",{class:!0});var uZo=s(Gg);V$=n(uZo,"A",{id:!0,class:!0,href:!0});var dms=s(V$);hho=n(dms,"SPAN",{});var mms=s(hho);M(Jq.$$.fragment,mms),mms.forEach(t),dms.forEach(t),hDa=i(uZo),gho=n(uZo,"SPAN",{});var cms=s(gho);gDa=r(cms,"AutoModelForVisualQuestionAnswering"),cms.forEach(t),uZo.forEach(t),b$o=i(c),Yr=n(c,"DIV",{class:!0});var jm=s(Yr);M(Uq.$$.fragment,jm),uDa=i(jm),Bg=n(jm,"P",{});var S6e=s(Bg);pDa=r(S6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),Y4e=n(S6e,"A",{href:!0});var fms=s(Y4e);_Da=r(fms,"from_pretrained()"),fms.forEach(t),bDa=r(S6e," class method or the "),z4e=n(S6e,"A",{href:!0});var hms=s(z4e);vDa=r(hms,"from_config()"),hms.forEach(t),MDa=r(S6e,` class
method.`),S6e.forEach(t),TDa=i(jm),Xq=n(jm,"P",{});var pZo=s(Xq);FDa=r(pZo,"This class cannot be instantiated directly using "),uho=n(pZo,"CODE",{});var gms=s(uho);EDa=r(gms,"__init__()"),gms.forEach(t),yDa=r(pZo," (throws an error)."),pZo.forEach(t),CDa=i(jm),Oa=n(jm,"DIV",{class:!0});var tV=s(Oa);M(Dq.$$.fragment,tV),wDa=i(tV),pho=n(tV,"P",{});var ums=s(pho);LDa=r(ums,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),ums.forEach(t),ADa=i(tV),Ig=n(tV,"P",{});var x6e=s(Ig);RDa=r(x6e,`Note:
Loading a model from its configuration file does `),_ho=n(x6e,"STRONG",{});var pms=s(_ho);kDa=r(pms,"not"),pms.forEach(t),SDa=r(x6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),H4e=n(x6e,"A",{href:!0});var _ms=s(H4e);xDa=r(_ms,"from_pretrained()"),_ms.forEach(t),GDa=r(x6e," to load the model weights."),x6e.forEach(t),BDa=i(tV),M(W$.$$.fragment,tV),tV.forEach(t),IDa=i(jm),Io=n(jm,"DIV",{class:!0});var xn=s(Io);M(qq.$$.fragment,xn),NDa=i(xn),bho=n(xn,"P",{});var bms=s(bho);$Da=r(bms,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),bms.forEach(t),jDa=i(xn),Ds=n(xn,"P",{});var aV=s(Ds);PDa=r(aV,"The model class to instantiate is selected based on the "),vho=n(aV,"CODE",{});var vms=s(vho);ZDa=r(vms,"model_type"),vms.forEach(t),VDa=r(aV,` property of the config object (either
passed as an argument or loaded from `),Mho=n(aV,"CODE",{});var Mms=s(Mho);WDa=r(Mms,"pretrained_model_name_or_path"),Mms.forEach(t),JDa=r(aV,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tho=n(aV,"CODE",{});var Tms=s(Tho);UDa=r(Tms,"pretrained_model_name_or_path"),Tms.forEach(t),XDa=r(aV,":"),aV.forEach(t),DDa=i(xn),Oq=n(xn,"UL",{});var _Zo=s(Oq);J$=n(_Zo,"LI",{});var cBo=s(J$);Fho=n(cBo,"STRONG",{});var Fms=s(Fho);qDa=r(Fms,"blip-2"),Fms.forEach(t),ODa=r(cBo," \u2014 "),K4e=n(cBo,"A",{href:!0});var Ems=s(K4e);QDa=r(Ems,"Blip2ForConditionalGeneration"),Ems.forEach(t),YDa=r(cBo," (BLIP-2 model)"),cBo.forEach(t),zDa=i(_Zo),U$=n(_Zo,"LI",{});var fBo=s(U$);Eho=n(fBo,"STRONG",{});var yms=s(Eho);HDa=r(yms,"vilt"),yms.forEach(t),KDa=r(fBo," \u2014 "),eCe=n(fBo,"A",{href:!0});var Cms=s(eCe);eqa=r(Cms,"ViltForQuestionAnswering"),Cms.forEach(t),oqa=r(fBo," (ViLT model)"),fBo.forEach(t),_Zo.forEach(t),rqa=i(xn),X$=n(xn,"P",{});var hBo=s(X$);tqa=r(hBo,"The model is set in evaluation mode by default using "),yho=n(hBo,"CODE",{});var wms=s(yho);aqa=r(wms,"model.eval()"),wms.forEach(t),nqa=r(hBo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Cho=n(hBo,"CODE",{});var Lms=s(Cho);sqa=r(Lms,"model.train()"),Lms.forEach(t),hBo.forEach(t),lqa=i(xn),M(D$.$$.fragment,xn),xn.forEach(t),jm.forEach(t),v$o=i(c),Ng=n(c,"H3",{class:!0});var bZo=s(Ng);q$=n(bZo,"A",{id:!0,class:!0,href:!0});var Ams=s(q$);who=n(Ams,"SPAN",{});var Rms=s(who);M(Qq.$$.fragment,Rms),Rms.forEach(t),Ams.forEach(t),iqa=i(bZo),Lho=n(bZo,"SPAN",{});var kms=s(Lho);dqa=r(kms,"AutoModelForVision2Seq"),kms.forEach(t),bZo.forEach(t),M$o=i(c),zr=n(c,"DIV",{class:!0});var Pm=s(zr);M(Yq.$$.fragment,Pm),mqa=i(Pm),$g=n(Pm,"P",{});var G6e=s($g);cqa=r(G6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),oCe=n(G6e,"A",{href:!0});var Sms=s(oCe);fqa=r(Sms,"from_pretrained()"),Sms.forEach(t),hqa=r(G6e," class method or the "),rCe=n(G6e,"A",{href:!0});var xms=s(rCe);gqa=r(xms,"from_config()"),xms.forEach(t),uqa=r(G6e,` class
method.`),G6e.forEach(t),pqa=i(Pm),zq=n(Pm,"P",{});var vZo=s(zq);_qa=r(vZo,"This class cannot be instantiated directly using "),Aho=n(vZo,"CODE",{});var Gms=s(Aho);bqa=r(Gms,"__init__()"),Gms.forEach(t),vqa=r(vZo," (throws an error)."),vZo.forEach(t),Mqa=i(Pm),Qa=n(Pm,"DIV",{class:!0});var nV=s(Qa);M(Hq.$$.fragment,nV),Tqa=i(nV),Rho=n(nV,"P",{});var Bms=s(Rho);Fqa=r(Bms,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Bms.forEach(t),Eqa=i(nV),jg=n(nV,"P",{});var B6e=s(jg);yqa=r(B6e,`Note:
Loading a model from its configuration file does `),kho=n(B6e,"STRONG",{});var Ims=s(kho);Cqa=r(Ims,"not"),Ims.forEach(t),wqa=r(B6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),tCe=n(B6e,"A",{href:!0});var Nms=s(tCe);Lqa=r(Nms,"from_pretrained()"),Nms.forEach(t),Aqa=r(B6e," to load the model weights."),B6e.forEach(t),Rqa=i(nV),M(O$.$$.fragment,nV),nV.forEach(t),kqa=i(Pm),No=n(Pm,"DIV",{class:!0});var Gn=s(No);M(Kq.$$.fragment,Gn),Sqa=i(Gn),Sho=n(Gn,"P",{});var $ms=s(Sho);xqa=r($ms,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),$ms.forEach(t),Gqa=i(Gn),qs=n(Gn,"P",{});var sV=s(qs);Bqa=r(sV,"The model class to instantiate is selected based on the "),xho=n(sV,"CODE",{});var jms=s(xho);Iqa=r(jms,"model_type"),jms.forEach(t),Nqa=r(sV,` property of the config object (either
passed as an argument or loaded from `),Gho=n(sV,"CODE",{});var Pms=s(Gho);$qa=r(Pms,"pretrained_model_name_or_path"),Pms.forEach(t),jqa=r(sV,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bho=n(sV,"CODE",{});var Zms=s(Bho);Pqa=r(Zms,"pretrained_model_name_or_path"),Zms.forEach(t),Zqa=r(sV,":"),sV.forEach(t),Vqa=i(Gn),Hr=n(Gn,"UL",{});var Bn=s(Hr);Q$=n(Bn,"LI",{});var gBo=s(Q$);Iho=n(gBo,"STRONG",{});var Vms=s(Iho);Wqa=r(Vms,"blip"),Vms.forEach(t),Jqa=r(gBo," \u2014 "),aCe=n(gBo,"A",{href:!0});var Wms=s(aCe);Uqa=r(Wms,"BlipForConditionalGeneration"),Wms.forEach(t),Xqa=r(gBo," (BLIP model)"),gBo.forEach(t),Dqa=i(Bn),Y$=n(Bn,"LI",{});var uBo=s(Y$);Nho=n(uBo,"STRONG",{});var Jms=s(Nho);qqa=r(Jms,"blip-2"),Jms.forEach(t),Oqa=r(uBo," \u2014 "),nCe=n(uBo,"A",{href:!0});var Ums=s(nCe);Qqa=r(Ums,"Blip2ForConditionalGeneration"),Ums.forEach(t),Yqa=r(uBo," (BLIP-2 model)"),uBo.forEach(t),zqa=i(Bn),z$=n(Bn,"LI",{});var pBo=s(z$);$ho=n(pBo,"STRONG",{});var Xms=s($ho);Hqa=r(Xms,"git"),Xms.forEach(t),Kqa=r(pBo," \u2014 "),sCe=n(pBo,"A",{href:!0});var Dms=s(sCe);eOa=r(Dms,"GitForCausalLM"),Dms.forEach(t),oOa=r(pBo," (GIT model)"),pBo.forEach(t),rOa=i(Bn),H$=n(Bn,"LI",{});var _Bo=s(H$);jho=n(_Bo,"STRONG",{});var qms=s(jho);tOa=r(qms,"instructblip"),qms.forEach(t),aOa=r(_Bo," \u2014 "),lCe=n(_Bo,"A",{href:!0});var Oms=s(lCe);nOa=r(Oms,"InstructBlipForConditionalGeneration"),Oms.forEach(t),sOa=r(_Bo," (InstructBLIP model)"),_Bo.forEach(t),lOa=i(Bn),K$=n(Bn,"LI",{});var bBo=s(K$);Pho=n(bBo,"STRONG",{});var Qms=s(Pho);iOa=r(Qms,"pix2struct"),Qms.forEach(t),dOa=r(bBo," \u2014 "),iCe=n(bBo,"A",{href:!0});var Yms=s(iCe);mOa=r(Yms,"Pix2StructForConditionalGeneration"),Yms.forEach(t),cOa=r(bBo," (Pix2Struct model)"),bBo.forEach(t),fOa=i(Bn),ej=n(Bn,"LI",{});var vBo=s(ej);Zho=n(vBo,"STRONG",{});var zms=s(Zho);hOa=r(zms,"vision-encoder-decoder"),zms.forEach(t),gOa=r(vBo," \u2014 "),dCe=n(vBo,"A",{href:!0});var Hms=s(dCe);uOa=r(Hms,"VisionEncoderDecoderModel"),Hms.forEach(t),pOa=r(vBo," (Vision Encoder decoder model)"),vBo.forEach(t),Bn.forEach(t),_Oa=i(Gn),oj=n(Gn,"P",{});var MBo=s(oj);bOa=r(MBo,"The model is set in evaluation mode by default using "),Vho=n(MBo,"CODE",{});var Kms=s(Vho);vOa=r(Kms,"model.eval()"),Kms.forEach(t),MOa=r(MBo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Who=n(MBo,"CODE",{});var ecs=s(Who);TOa=r(ecs,"model.train()"),ecs.forEach(t),MBo.forEach(t),FOa=i(Gn),M(rj.$$.fragment,Gn),Gn.forEach(t),Pm.forEach(t),T$o=i(c),Pg=n(c,"H3",{class:!0});var MZo=s(Pg);tj=n(MZo,"A",{id:!0,class:!0,href:!0});var ocs=s(tj);Jho=n(ocs,"SPAN",{});var rcs=s(Jho);M(eO.$$.fragment,rcs),rcs.forEach(t),ocs.forEach(t),EOa=i(MZo),Uho=n(MZo,"SPAN",{});var tcs=s(Uho);yOa=r(tcs,"TFAutoModelForVision2Seq"),tcs.forEach(t),MZo.forEach(t),F$o=i(c),Kr=n(c,"DIV",{class:!0});var Zm=s(Kr);M(oO.$$.fragment,Zm),COa=i(Zm),Zg=n(Zm,"P",{});var I6e=s(Zg);wOa=r(I6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),mCe=n(I6e,"A",{href:!0});var acs=s(mCe);LOa=r(acs,"from_pretrained()"),acs.forEach(t),AOa=r(I6e," class method or the "),cCe=n(I6e,"A",{href:!0});var ncs=s(cCe);ROa=r(ncs,"from_config()"),ncs.forEach(t),kOa=r(I6e,` class
method.`),I6e.forEach(t),SOa=i(Zm),rO=n(Zm,"P",{});var TZo=s(rO);xOa=r(TZo,"This class cannot be instantiated directly using "),Xho=n(TZo,"CODE",{});var scs=s(Xho);GOa=r(scs,"__init__()"),scs.forEach(t),BOa=r(TZo," (throws an error)."),TZo.forEach(t),IOa=i(Zm),Ya=n(Zm,"DIV",{class:!0});var lV=s(Ya);M(tO.$$.fragment,lV),NOa=i(lV),Dho=n(lV,"P",{});var lcs=s(Dho);$Oa=r(lcs,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),lcs.forEach(t),jOa=i(lV),Vg=n(lV,"P",{});var N6e=s(Vg);POa=r(N6e,`Note:
Loading a model from its configuration file does `),qho=n(N6e,"STRONG",{});var ics=s(qho);ZOa=r(ics,"not"),ics.forEach(t),VOa=r(N6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),fCe=n(N6e,"A",{href:!0});var dcs=s(fCe);WOa=r(dcs,"from_pretrained()"),dcs.forEach(t),JOa=r(N6e," to load the model weights."),N6e.forEach(t),UOa=i(lV),M(aj.$$.fragment,lV),lV.forEach(t),XOa=i(Zm),Gt=n(Zm,"DIV",{class:!0});var Vm=s(Gt);M(aO.$$.fragment,Vm),DOa=i(Vm),Oho=n(Vm,"P",{});var mcs=s(Oho);qOa=r(mcs,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),mcs.forEach(t),OOa=i(Vm),Os=n(Vm,"P",{});var iV=s(Os);QOa=r(iV,"The model class to instantiate is selected based on the "),Qho=n(iV,"CODE",{});var ccs=s(Qho);YOa=r(ccs,"model_type"),ccs.forEach(t),zOa=r(iV,` property of the config object (either
passed as an argument or loaded from `),Yho=n(iV,"CODE",{});var fcs=s(Yho);HOa=r(fcs,"pretrained_model_name_or_path"),fcs.forEach(t),KOa=r(iV,` if possible), or when it\u2019s missing, by
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NextImageProcessor"),d(Nse,"href","/docs/transformers/main/en/model_doc/sam#transformers.SamImageProcessor"),d($se,"href","/docs/transformers/main/en/model_doc/segformer#transformers.SegformerImageProcessor"),d(jse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Pse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Zse,"href","/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRImageProcessor"),d(Vse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Wse,"href","/docs/transformers/main/en/model_doc/detr#transformers.DetrImageProcessor"),d(Jse,"href","/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEImageProcessor"),d(Use,"href","/docs/transformers/main/en/model_doc/tvlt#transformers.TvltImageProcessor"),d(Xse,"href","/docs/transformers/main/en/model_doc/segformer#transformers.SegformerImageProcessor"),d(Dse,"href","/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextImageProcessor"),d(qse,"href","/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEImageProcessor"),d(Ose,"href","/docs/transformers/main/en/model_doc/vilt#transformers.ViltImageProcessor"),d(Qse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Yse,"href","/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridImageProcessor"),d(zse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Hse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Kse,"href","/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor"),d(ele,"href","/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor"),d(ao,"class","docstring 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