import{S as Bas,i as Ias,s as Nas,e as a,k as l,w as v,t as o,M as $as,c as n,d as t,m as i,a as s,x as M,h as r,b as d,G as e,g as b,y as T,q as F,o as E,B as y,v as jas,L as V}from"../../chunks/vendor-hf-doc-builder.js";import{T as W$o}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 ae}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Z}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Pas(x){let h,C,u,f,p,m,g,ze,Pm,Vg,Vt,Zm,Vm,SZ,Wg,Qe,oo,Wm,zs,xZ,Hs,Ks,GZ,Jm,el,BZ,Um,Jg,Bn;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
`),Pm=a("code"),Vg=o("model_type"),Vt=o(" attribute is set to the same key you use when registering the config (here "),Zm=a("code"),Vm=o('"new-model"'),SZ=o(")."),Wg=l(),Qe=a("p"),oo=o("Likewise, if your "),Wm=a("code"),zs=o("NewModel"),xZ=o(" is a subclass of "),Hs=a("a"),Ks=o("PreTrainedModel"),GZ=o(`, make sure its
`),Jm=a("code"),el=o("config_class"),BZ=o(` attribute is set to the same class you use when registering the model (here
`),Um=a("code"),Jg=o("NewModelConfig"),Bn=o(")."),this.h()},l(ro){h=n(ro,"P",{});var Ie=s(h);C=r(Ie,"If your "),u=n(Ie,"CODE",{});var TQ=s(u);f=r(TQ,"NewModelConfig"),TQ.forEach(t),p=r(Ie," is a subclass of "),m=n(Ie,"CODE",{});var Xm=s(m);g=r(Xm,"~transformer.PretrainedConfig"),Xm.forEach(t),ze=r(Ie,`, make sure its
`),Pm=n(Ie,"CODE",{});var FQ=s(Pm);Vg=r(FQ,"model_type"),FQ.forEach(t),Vt=r(Ie," attribute is set to the same key you use when registering the config (here "),Zm=n(Ie,"CODE",{});var EQ=s(Zm);Vm=r(EQ,'"new-model"'),EQ.forEach(t),SZ=r(Ie,")."),Ie.forEach(t),Wg=i(ro),Qe=n(ro,"P",{});var jo=s(Qe);oo=r(jo,"Likewise, if your "),Wm=n(jo,"CODE",{});var In=s(Wm);zs=r(In,"NewModel"),In.forEach(t),xZ=r(jo," is a subclass of "),Hs=n(jo,"A",{href:!0});var yQ=s(Hs);Ks=r(yQ,"PreTrainedModel"),yQ.forEach(t),GZ=r(jo,`, make sure its
`),Jm=n(jo,"CODE",{});var Ug=s(Jm);el=r(Ug,"config_class"),Ug.forEach(t),BZ=r(jo,` attribute is set to the same class you use when registering the model (here
`),Um=n(jo,"CODE",{});var CQ=s(Um);Jg=r(CQ,"NewModelConfig"),CQ.forEach(t),Bn=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,Pm),e(Pm,Vg),e(h,Vt),e(h,Zm),e(Zm,Vm),e(h,SZ),b(ro,Wg,Ie),b(ro,Qe,Ie),e(Qe,oo),e(Qe,Wm),e(Wm,zs),e(Qe,xZ),e(Qe,Hs),e(Hs,Ks),e(Qe,GZ),e(Qe,Jm),e(Jm,el),e(Qe,BZ),e(Qe,Um),e(Um,Jg),e(Qe,Bn)},d(ro){ro&&t(h),ro&&t(Wg),ro&&t(Qe)}}}function Zas(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:V,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 Vas(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEElMjMlMjBEb3dubG9hZCUyMHZvY2FidWxhcnklMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtdW5jYXNlZCUyMiklMEElMEElMjMlMjBEb3dubG9hZCUyMHZvY2FidWxhcnklMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjAodXNlci11cGxvYWRlZCklMjBhbmQlMjBjYWNoZS4lMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJkYm1keiUyRmJlcnQtYmFzZS1nZXJtYW4tY2FzZWQlMjIpJTBBJTBBJTIzJTIwSWYlMjB2b2NhYnVsYXJ5JTIwZmlsZXMlMjBhcmUlMjBpbiUyMGElMjBkaXJlY3RvcnklMjAoZS5nLiUyMHRva2VuaXplciUyMHdhcyUyMHNhdmVkJTIwdXNpbmclMjAqc2F2ZV9wcmV0cmFpbmVkKCcuJTJGdGVzdCUyRnNhdmVkX21vZGVsJTJGJykqKSUwQSUyMyUyMHRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZ0ZXN0JTJGYmVydF9zYXZlZF9tb2RlbCUyRiUyMiklMEElMEElMjMlMjBEb3dubG9hZCUyMHZvY2FidWxhcnklMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBkZWZpbmUlMjBtb2RlbC1zcGVjaWZpYyUyMGFyZ3VtZW50cyUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMnJvYmVydGEtYmFzZSUyMiUyQyUyMGFkZF9wcmVmaXhfc3BhY2UlM0RUcnVlKQ==",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:V,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 Was(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 Jas(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:V,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 Uas(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 Xas(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:V,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 Das(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 qas(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:V,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 Oas(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:V,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 Qas(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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:V,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 Yas(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:V,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 zas(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, 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:V,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 Has(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:V,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 Kas(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:V,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 ens(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:V,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 ons(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:V,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 rns(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:V,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 tns(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:V,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 ans(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:V,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 nns(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:V,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 sns(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:V,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 lns(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:V,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 ins(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:V,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 dns(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:V,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 mns(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:V,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 cns(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:V,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 fns(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:V,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 hns(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:V,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 gns(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:V,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 uns(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",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:V,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 pns(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:V,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 _ns(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:V,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 bns(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:V,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 vns(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:V,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 Mns(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:V,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 Tns(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:V,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 Fns(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:V,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 Ens(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:V,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 yns(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:V,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 Cns(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:V,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 wns(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:V,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 Lns(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:V,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 Ans(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:V,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 Rns(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:V,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 kns(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:V,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 Sns(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:V,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 xns(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:V,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 Gns(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",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:V,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 Bns(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:V,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 Ins(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, 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:V,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 Nns(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:V,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 $ns(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, 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:V,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 jns(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:V,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 Pns(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:V,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 Zns(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:V,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 Vns(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:V,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 Wns(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:V,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 Jns(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, 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:V,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 Uns(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:V,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 Xns(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:V,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 Dns(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:V,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 qns(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:V,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 Ons(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:V,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 Qns(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:V,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 Yns(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:V,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 zns(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, 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:V,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 Hns(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:V,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 Kns(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, 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:V,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 ess(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:V,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 oss(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:V,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 rss(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:V,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 tss(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:V,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 ass(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:V,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 nss(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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:V,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 sss(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:V,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 lss(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:V,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 iss(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:V,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 dss(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:V,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 mss(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:V,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 css(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:V,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 fss(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:V,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 hss(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTWFza2VkSW1hZ2VNb2RlbGluZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmcuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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:V,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 gss(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:V,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 uss(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:V,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 pss(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:V,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 _ss(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:V,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 bss(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:V,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 vss(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:V,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 Mss(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:V,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 Tss(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, 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:V,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 Fss(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:V,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 Ess(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:V,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 yss(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:V,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 Css(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:V,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 wss(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:V,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 Lss(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:V,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 Ass(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:V,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 Rss(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:V,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 kss(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:V,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 Sss(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:V,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 xss(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:V,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 Gss(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:V,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 Bss(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:V,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 Iss(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:V,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 Nss(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:V,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 $ss(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:V,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 jss(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:V,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 Pss(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDVEMlMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ1RDLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNUQy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ1RDLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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:V,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 Zss(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:V,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 Vss(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, 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:V,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 Wss(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:V,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 Jss(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:V,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 Uss(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:V,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 Xss(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:V,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 Dss(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:V,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 qss(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:V,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 Oss(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:V,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 Qss(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:V,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 Yss(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:V,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 zss(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUYWJsZVF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZ0YXBhcy1iYXNlLWZpbmV0dW5lZC13dHElMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yVGFibGVRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGdGFwYXMtYmFzZS1maW5ldHVuZWQtd3RxJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRnRhcGFzX3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUYWJsZVF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZ0YXBhc19weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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:V,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 Hss(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:V,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 Kss(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:V,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 els(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:V,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 ols(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:V,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 rls(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:V,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 tls(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:V,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 als(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:V,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 nls(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:V,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 sls(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:V,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 lls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JWaXNpb24yU2VxLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXEuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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:V,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 ils(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:V,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 dls(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:V,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 mls(x){let 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m=new ae({}),Bn=new P({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMik=",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),IZ=new ae({}),NZ=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)`}}),Dg=new W$o({props:{warning:!0,$$slots:{default:[Pas]},$$scope:{ctx:x}}}),$Z=new ae({}),jZ=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#L882"}}),VZ=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;
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<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
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<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
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<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
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<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
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<li>a path to a <em>directory</em> containing a feature extractor file saved using the
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<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
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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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<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
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<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.,
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<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
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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.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#L130"}}),DM=new W$o({props:{$$slots:{default:[Das]},$$scope:{ctx:x}}}),qM=new Z({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[qas]},$$scope:{ctx:x}}}),fV=new G({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""}],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#L319"}}),hV=new ae({}),gV=new ae({}),uV=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#L1170"}}),_V=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><code>FalconConfig</code> configuration class: <code>FalconModel</code> (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/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/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/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#L407"}}),zM=new Z({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[Oas]},$$scope:{ctx:x}}}),bV=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#L438"}}),Cy=new Z({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[Qas]},$$scope:{ctx:x}}}),vV=new ae({}),MV=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#L527"}}),FV=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#L407"}}),Ly=new Z({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Yas]},$$scope:{ctx:x}}}),EV=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#L438"}}),$0=new Z({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[zas]},$$scope:{ctx:x}}}),yV=new ae({}),CV=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"}}),LV=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#L407"}}),P0=new Z({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[Has]},$$scope:{ctx:x}}}),AV=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#L438"}}),v5=new Z({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[Kas]},$$scope:{ctx:x}}}),RV=new ae({}),kV=new ae({}),SV=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#L1177"}}),GV=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/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#L407"}}),F5=new Z({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[ens]},$$scope:{ctx:x}}}),BV=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#L438"}}),S3=new Z({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[ons]},$$scope:{ctx:x}}}),IV=new ae({}),NV=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#L543"}}),jV=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#L407"}}),G3=new Z({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[rns]},$$scope:{ctx:x}}}),PV=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#L438"}}),n4=new Z({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[tns]},$$scope:{ctx:x}}}),ZV=new ae({}),VV=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"}}),JV=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#L407"}}),l4=new Z({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[ans]},$$scope:{ctx:x}}}),UV=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#L438"}}),E4=new Z({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[nns]},$$scope:{ctx:x}}}),XV=new ae({}),DV=new ae({}),qV=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#L1192"}}),QV=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><code>FalconConfig</code> configuration class: <code>FalconForCausalLM</code> (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/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#L407"}}),w4=new Z({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[sns]},$$scope:{ctx:x}}}),YV=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#L438"}}),S9=new Z({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[lns]},$$scope:{ctx:x}}}),zV=new ae({}),HV=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#L558"}}),eW=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#L407"}}),G9=new Z({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[ins]},$$scope:{ctx:x}}}),oW=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#L438"}}),z9=new Z({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[dns]},$$scope:{ctx:x}}}),rW=new ae({}),tW=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"}}),nW=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#L407"}}),K9=new Z({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[mns]},$$scope:{ctx:x}}}),sW=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#L438"}}),gC=new Z({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[cns]},$$scope:{ctx:x}}}),lW=new ae({}),iW=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#L1199"}}),mW=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#L407"}}),pC=new Z({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[fns]},$$scope:{ctx:x}}}),cW=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#L438"}}),dw=new Z({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[hns]},$$scope:{ctx:x}}}),fW=new ae({}),hW=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#L608"}}),uW=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#L407"}}),cw=new Z({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[gns]},$$scope:{ctx:x}}}),pW=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#L438"}}),Bw=new Z({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[uns]},$$scope:{ctx:x}}}),_W=new ae({}),bW=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"}}),MW=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#L407"}}),Nw=new Z({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[pns]},$$scope:{ctx:x}}}),TW=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#L438"}}),Ow=new Z({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[_ns]},$$scope:{ctx:x}}}),FW=new ae({}),yW=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#L1162"}}),CW=new ae({}),LW=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#L519"}}),AW=new ae({}),RW=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#L1206"}}),SW=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#L407"}}),Hw=new Z({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[bns]},$$scope:{ctx:x}}}),xW=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#L438"}}),E6=new Z({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[vns]},$$scope:{ctx:x}}}),GW=new ae({}),BW=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#L615"}}),NW=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#L407"}}),C6=new Z({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Mns]},$$scope:{ctx:x}}}),$W=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#L438"}}),N6=new Z({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Tns]},$$scope:{ctx:x}}}),jW=new ae({}),PW=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"}}),VW=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#L407"}}),j6=new Z({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Fns]},$$scope:{ctx:x}}}),WW=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#L438"}}),Q6=new Z({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Ens]},$$scope:{ctx:x}}}),JW=new ae({}),UW=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#L1215"}}),DW=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><code>FalconConfig</code> configuration class: <code>FalconForSequenceClassification</code> (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/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#L407"}}),z6=new Z({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[yns]},$$scope:{ctx:x}}}),qW=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#L438"}}),b7=new Z({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Cns]},$$scope:{ctx:x}}}),OW=new ae({}),QW=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#L624"}}),zW=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#L407"}}),M7=new Z({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[wns]},$$scope:{ctx:x}}}),HW=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#L438"}}),H7=new Z({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Lns]},$$scope:{ctx:x}}}),KW=new ae({}),eJ=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"}}),rJ=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#L407"}}),eL=new Z({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[Ans]},$$scope:{ctx:x}}}),tJ=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#L438"}}),fL=new Z({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Rns]},$$scope:{ctx:x}}}),aJ=new ae({}),nJ=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#L1271"}}),lJ=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#L407"}}),gL=new Z({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[kns]},$$scope:{ctx:x}}}),iJ=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#L438"}}),eA=new Z({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Sns]},$$scope:{ctx:x}}}),dJ=new ae({}),mJ=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#L671"}}),fJ=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/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#L407"}}),rA=new Z({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[xns]},$$scope:{ctx:x}}}),hJ=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#L438"}}),TA=new Z({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Gns]},$$scope:{ctx:x}}}),gJ=new ae({}),uJ=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"}}),_J=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#L407"}}),EA=new Z({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[Bns]},$$scope:{ctx:x}}}),bJ=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>
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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 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
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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.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;
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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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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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automatically loaded:</p>
<ul>
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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
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/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#L407"}}),IA=new Z({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Nns]},$$scope:{ctx:x}}}),EJ=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
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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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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
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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.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;
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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.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
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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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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>
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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/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#L407"}}),DA=new Z({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[jns]},$$scope:{ctx:x}}}),AJ=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;
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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
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
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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.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
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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.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;
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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.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#L438"}}),QA=new Z({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Pns]},$$scope:{ctx:x}}}),kJ=new ae({}),SJ=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"}}),GJ=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#L407"}}),zA=new Z({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Zns]},$$scope:{ctx:x}}}),BJ=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#L438"}}),KA=new Z({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Vns]},$$scope:{ctx:x}}}),IJ=new ae({}),NJ=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#L1264"}}),jJ=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><code>FalconConfig</code> configuration class: <code>FalconForTokenClassification</code> (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#L407"}}),oR=new Z({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[Wns]},$$scope:{ctx:x}}}),PJ=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#L438"}}),tk=new Z({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Jns]},$$scope:{ctx:x}}}),ZJ=new ae({}),VJ=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#L662"}}),JJ=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#L407"}}),nk=new Z({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[Uns]},$$scope:{ctx:x}}}),UJ=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#L438"}}),Rk=new Z({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Xns]},$$scope:{ctx:x}}}),XJ=new ae({}),DJ=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"}}),OJ=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#L407"}}),Sk=new Z({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[Dns]},$$scope:{ctx:x}}}),QJ=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#L438"}}),Vk=new Z({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[qns]},$$scope:{ctx:x}}}),YJ=new ae({}),zJ=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#L1224"}}),KJ=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><code>FalconConfig</code> configuration class: <code>FalconForQuestionAnswering</code> (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#L407"}}),Jk=new Z({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Ons]},$$scope:{ctx:x}}}),eU=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#L438"}}),KS=new Z({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Qns]},$$scope:{ctx:x}}}),oU=new ae({}),rU=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#L633"}}),aU=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#L407"}}),ox=new Z({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Yns]},$$scope:{ctx:x}}}),nU=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#L438"}}),yx=new Z({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[zns]},$$scope:{ctx:x}}}),sU=new ae({}),lU=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"}}),dU=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#L407"}}),wx=new Z({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Hns]},$$scope:{ctx:x}}}),mU=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#L438"}}),jx=new Z({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Kns]},$$scope:{ctx:x}}}),cU=new ae({}),hU=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#L1166"}}),gU=new ae({}),pU=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#L523"}}),_U=new ae({}),bU=new ae({}),vU=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#L1353"}}),TU=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#L407"}}),Jx=new Z({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[ess]},$$scope:{ctx:x}}}),FU=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#L438"}}),qx=new Z({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[oss]},$$scope:{ctx:x}}}),yU=new ae({}),CU=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#L1287"}}),LU=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#L407"}}),Qx=new Z({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[rss]},$$scope:{ctx:x}}}),AU=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#L438"}}),CG=new Z({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[tss]},$$scope:{ctx:x}}}),RU=new ae({}),kU=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#L574"}}),xU=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#L407"}}),LG=new Z({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[ass]},$$scope:{ctx:x}}}),GU=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
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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
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
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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.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;
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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.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
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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.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#L438"}}),$G=new Z({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[nss]},$$scope:{ctx:x}}}),BU=new ae({}),IU=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"}}),$U=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#L407"}}),PG=new Z({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[sss]},$$scope:{ctx:x}}}),jU=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#L438"}}),UG=new Z({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[lss]},$$scope:{ctx:x}}}),PU=new ae({}),ZU=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#L1360"}}),WU=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#L407"}}),DG=new Z({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[iss]},$$scope:{ctx:x}}}),JU=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#L438"}}),zG=new Z({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[dss]},$$scope:{ctx:x}}}),UU=new ae({}),XU=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#L1417"}}),qU=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#L407"}}),KG=new Z({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[mss]},$$scope:{ctx:x}}}),OU=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#L438"}}),sB=new Z({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[css]},$$scope:{ctx:x}}}),QU=new ae({}),YU=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#L565"}}),HU=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#L407"}}),iB=new Z({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[fss]},$$scope:{ctx:x}}}),KU=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>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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#L438"}}),cB=new Z({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[hss]},$$scope:{ctx:x}}}),oX=new ae({}),rX=new G({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1337"}}),aX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.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/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR 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.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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L407"}}),hB=new Z({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[gss]},$$scope:{ctx:x}}}),nX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.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
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<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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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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<li>A path to a <em>directory</em> containing model weights saved using
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<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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<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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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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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/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#L407"}}),AB=new Z({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[bss]},$$scope:{ctx:x}}}),uX=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;
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<li>A path to a <em>directory</em> containing model weights saved using
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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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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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.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;
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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
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
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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.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
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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
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/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#L407"}}),ZB=new Z({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[Mss]},$$scope:{ctx:x}}}),MX=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
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</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;
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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.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
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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.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#L438"}}),UB=new Z({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[Tss]},$$scope:{ctx:x}}}),TX=new ae({}),FX=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#L1328"}}),yX=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#L407"}}),DB=new Z({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[Fss]},$$scope:{ctx:x}}}),CX=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
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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#L438"}}),QB=new Z({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[Ess]},$$scope:{ctx:x}}}),wX=new ae({}),LX=new G({props:{name:"class transformers.AutoModelForUniversalSegmentation",anchor:"transformers.AutoModelForUniversalSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1319"}}),RX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForUniversalSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.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/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#L407"}}),zB=new Z({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[yss]},$$scope:{ctx:x}}}),kX=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.
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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
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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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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<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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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
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<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>
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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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<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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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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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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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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<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
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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
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/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>
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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>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
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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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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
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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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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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Can be either:</p>
<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
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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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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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
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
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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.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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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
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<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
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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
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#L438"}}),EI=new Z({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[Sss]},$$scope:{ctx:x}}}),qX=new ae({}),OX=new ae({}),QX=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#L1374"}}),zX=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#L407"}}),wI=new Z({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[xss]},$$scope:{ctx:x}}}),HX=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;
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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
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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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</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;
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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
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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
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
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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#L438"}}),PI=new Z({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[Gss]},$$scope:{ctx:x}}}),KX=new ae({}),eD=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#L534"}}),rD=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#L407"}}),VI=new Z({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[Bss]},$$scope:{ctx:x}}}),tD=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#L438"}}),JI=new Z({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[Iss]},$$scope:{ctx:x}}}),aD=new ae({}),nD=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#L1397"}}),lD=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#L407"}}),XI=new Z({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[Nss]},$$scope:{ctx:x}}}),iD=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#L438"}}),HI=new Z({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[$ss]},$$scope:{ctx:x}}}),dD=new ae({}),mD=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#L1381"}}),fD=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#L407"}}),eN=new Z({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[jss]},$$scope:{ctx:x}}}),hD=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
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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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<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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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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<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
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<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.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>
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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.
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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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</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
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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.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
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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.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;
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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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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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<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/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>
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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
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</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;
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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
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
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Path to a directory in which a downloaded pretrained model configuration should be cached if 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.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#L438"}}),CN=new Z({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Jss]},$$scope:{ctx:x}}}),CD=new ae({}),wD=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"}}),AD=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#L407"}}),LN=new Z({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Uss]},$$scope:{ctx:x}}}),RD=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#L438"}}),kN=new Z({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Xss]},$$scope:{ctx:x}}}),SD=new ae({}),xD=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#L1406"}}),BD=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#L407"}}),xN=new Z({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[Dss]},$$scope:{ctx:x}}}),ID=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
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.AutoModelForAudioXVector.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.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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioXVector.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.AutoModelForAudioXVector.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.AutoModelForAudioXVector.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.AutoModelForAudioXVector.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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioXVector.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.AutoModelForAudioXVector.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.AutoModelForAudioXVector.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.AutoModelForAudioXVector.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.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#L438"}}),PN=new Z({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[qss]},$$scope:{ctx:x}}}),ND=new ae({}),$D=new ae({}),jD=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#L1231"}}),ZD=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#L407"}}),WN=new Z({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Oss]},$$scope:{ctx:x}}}),VD=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>
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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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<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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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;
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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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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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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
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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
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#L407"}}),qN=new Z({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Yss]},$$scope:{ctx:x}}}),DD=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
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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
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
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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.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;
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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.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
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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.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.,
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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/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#L407"}}),zN=new Z({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[Hss]},$$scope:{ctx:x}}}),zD=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
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</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
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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.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
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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.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#L438"}}),r$=new Z({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[Kss]},$$scope:{ctx:x}}}),HD=new ae({}),KD=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#L640"}}),oq=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#L407"}}),a$=new Z({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[els]},$$scope:{ctx:x}}}),rq=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#L438"}}),s$=new Z({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[ols]},$$scope:{ctx:x}}}),tq=new ae({}),aq=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#L1242"}}),sq=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/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#L407"}}),i$=new Z({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[rls]},$$scope:{ctx:x}}}),lq=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
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.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#L438"}}),c$=new Z({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[tls]},$$scope:{ctx:x}}}),iq=new ae({}),dq=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#L1367"}}),cq=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#L407"}}),h$=new Z({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[als]},$$scope:{ctx:x}}}),fq=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
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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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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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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;
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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.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.,
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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/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>
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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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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
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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
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
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</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
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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 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.TFAutoModelForVision2Seq.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
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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
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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.FlaxAutoModelForVision2Seq.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
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.FlaxAutoModelForVision2Seq.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
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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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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
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model)"),vqr=l(),Qy=a("li"),j$e=a("strong"),Mqr=o("flaubert"),Tqr=o(" \u2014 "),kme=a("a"),Fqr=o("TFFlaubertModel"),Eqr=o(" (FlauBERT model)"),yqr=l(),Pi=a("li"),P$e=a("strong"),Cqr=o("funnel"),wqr=o(" \u2014 "),Sme=a("a"),Lqr=o("TFFunnelModel"),Aqr=o(" or "),xme=a("a"),Rqr=o("TFFunnelBaseModel"),kqr=o(" (Funnel Transformer model)"),Sqr=l(),Yy=a("li"),Z$e=a("strong"),xqr=o("gpt-sw3"),Gqr=o(" \u2014 "),Gme=a("a"),Bqr=o("TFGPT2Model"),Iqr=o(" (GPT-Sw3 model)"),Nqr=l(),zy=a("li"),V$e=a("strong"),$qr=o("gpt2"),jqr=o(" \u2014 "),Bme=a("a"),Pqr=o("TFGPT2Model"),Zqr=o(" (OpenAI GPT-2 model)"),Vqr=l(),Hy=a("li"),W$e=a("strong"),Wqr=o("gptj"),Jqr=o(" \u2014 "),Ime=a("a"),Uqr=o("TFGPTJModel"),Xqr=o(" (GPT-J model)"),Dqr=l(),Ky=a("li"),J$e=a("strong"),qqr=o("groupvit"),Oqr=o(" \u2014 "),Nme=a("a"),Qqr=o("TFGroupViTModel"),Yqr=o(" (GroupViT model)"),zqr=l(),e0=a("li"),U$e=a("strong"),Hqr=o("hubert"),Kqr=o(" \u2014 "),$me=a("a"),eOr=o("TFHubertModel"),oOr=o(" (Hubert model)"),rOr=l(),o0=a("li"),X$e=a("strong"),tOr=o("layoutlm"),aOr=o(" \u2014 "),jme=a("a"),nOr=o("TFLayoutLMModel"),sOr=o(" (LayoutLM model)"),lOr=l(),r0=a("li"),D$e=a("strong"),iOr=o("layoutlmv3"),dOr=o(" \u2014 "),Pme=a("a"),mOr=o("TFLayoutLMv3Model"),cOr=o(" (LayoutLMv3 model)"),fOr=l(),t0=a("li"),q$e=a("strong"),hOr=o("led"),gOr=o(" \u2014 "),Zme=a("a"),uOr=o("TFLEDModel"),pOr=o(" (LED model)"),_Or=l(),a0=a("li"),O$e=a("strong"),bOr=o("longformer"),vOr=o(" \u2014 "),Vme=a("a"),MOr=o("TFLongformerModel"),TOr=o(" (Longformer model)"),FOr=l(),n0=a("li"),Q$e=a("strong"),EOr=o("lxmert"),yOr=o(" \u2014 "),Wme=a("a"),COr=o("TFLxmertModel"),wOr=o(" (LXMERT model)"),LOr=l(),s0=a("li"),Y$e=a("strong"),AOr=o("marian"),ROr=o(" \u2014 "),Jme=a("a"),kOr=o("TFMarianModel"),SOr=o(" (Marian model)"),xOr=l(),l0=a("li"),z$e=a("strong"),GOr=o("mbart"),BOr=o(" \u2014 "),Ume=a("a"),IOr=o("TFMBartModel"),NOr=o(" (mBART model)"),$Or=l(),i0=a("li"),H$e=a("strong"),jOr=o("mobilebert"),POr=o(" \u2014 "),Xme=a("a"),ZOr=o("TFMobileBertModel"),VOr=o(" (MobileBERT model)"),WOr=l(),d0=a("li"),K$e=a("strong"),JOr=o("mobilevit"),UOr=o(" \u2014 "),Dme=a("a"),XOr=o("TFMobileViTModel"),DOr=o(" (MobileViT model)"),qOr=l(),m0=a("li"),eje=a("strong"),OOr=o("mpnet"),QOr=o(" \u2014 "),qme=a("a"),YOr=o("TFMPNetModel"),zOr=o(" (MPNet model)"),HOr=l(),c0=a("li"),oje=a("strong"),KOr=o("mt5"),eQr=o(" \u2014 "),Ome=a("a"),oQr=o("TFMT5Model"),rQr=o(" (MT5 model)"),tQr=l(),f0=a("li"),rje=a("strong"),aQr=o("openai-gpt"),nQr=o(" \u2014 "),Qme=a("a"),sQr=o("TFOpenAIGPTModel"),lQr=o(" (OpenAI GPT model)"),iQr=l(),h0=a("li"),tje=a("strong"),dQr=o("opt"),mQr=o(" \u2014 "),Yme=a("a"),cQr=o("TFOPTModel"),fQr=o(" (OPT model)"),hQr=l(),g0=a("li"),aje=a("strong"),gQr=o("pegasus"),uQr=o(" \u2014 "),zme=a("a"),pQr=o("TFPegasusModel"),_Qr=o(" (Pegasus model)"),bQr=l(),u0=a("li"),nje=a("strong"),vQr=o("regnet"),MQr=o(" \u2014 "),Hme=a("a"),TQr=o("TFRegNetModel"),FQr=o(" (RegNet model)"),EQr=l(),p0=a("li"),sje=a("strong"),yQr=o("rembert"),CQr=o(" \u2014 "),Kme=a("a"),wQr=o("TFRemBertModel"),LQr=o(" (RemBERT model)"),AQr=l(),_0=a("li"),lje=a("strong"),RQr=o("resnet"),kQr=o(" \u2014 "),ece=a("a"),SQr=o("TFResNetModel"),xQr=o(" (ResNet model)"),GQr=l(),b0=a("li"),ije=a("strong"),BQr=o("roberta"),IQr=o(" \u2014 "),oce=a("a"),NQr=o("TFRobertaModel"),$Qr=o(" (RoBERTa model)"),jQr=l(),v0=a("li"),dje=a("strong"),PQr=o("roberta-prelayernorm"),ZQr=o(" \u2014 "),rce=a("a"),VQr=o("TFRobertaPreLayerNormModel"),WQr=o(" (RoBERTa-PreLayerNorm model)"),JQr=l(),M0=a("li"),mje=a("strong"),UQr=o("roformer"),XQr=o(" \u2014 "),tce=a("a"),DQr=o("TFRoFormerModel"),qQr=o(" (RoFormer model)"),OQr=l(),T0=a("li"),cje=a("strong"),QQr=o("sam"),YQr=o(" \u2014 "),ace=a("a"),zQr=o("TFSamModel"),HQr=o(" (SAM model)"),KQr=l(),F0=a("li"),fje=a("strong"),eYr=o("segformer"),oYr=o(" \u2014 "),nce=a("a"),rYr=o("TFSegformerModel"),tYr=o(" (SegFormer model)"),aYr=l(),E0=a("li"),hje=a("strong"),nYr=o("speech_to_text"),sYr=o(" \u2014 "),sce=a("a"),lYr=o("TFSpeech2TextModel"),iYr=o(" (Speech2Text model)"),dYr=l(),y0=a("li"),gje=a("strong"),mYr=o("swin"),cYr=o(" \u2014 "),lce=a("a"),fYr=o("TFSwinModel"),hYr=o(" (Swin Transformer model)"),gYr=l(),C0=a("li"),uje=a("strong"),uYr=o("t5"),pYr=o(" \u2014 "),ice=a("a"),_Yr=o("TFT5Model"),bYr=o(" (T5 model)"),vYr=l(),w0=a("li"),pje=a("strong"),MYr=o("tapas"),TYr=o(" \u2014 "),dce=a("a"),FYr=o("TFTapasModel"),EYr=o(" (TAPAS model)"),yYr=l(),L0=a("li"),_je=a("strong"),CYr=o("transfo-xl"),wYr=o(" \u2014 "),mce=a("a"),LYr=o("TFTransfoXLModel"),AYr=o(" (Transformer-XL model)"),RYr=l(),A0=a("li"),bje=a("strong"),kYr=o("vision-text-dual-encoder"),SYr=o(" \u2014 "),cce=a("a"),xYr=o("TFVisionTextDualEncoderModel"),GYr=o(" (VisionTextDualEncoder model)"),BYr=l(),R0=a("li"),vje=a("strong"),IYr=o("vit"),NYr=o(" \u2014 "),fce=a("a"),$Yr=o("TFViTModel"),jYr=o(" (ViT model)"),PYr=l(),k0=a("li"),Mje=a("strong"),ZYr=o("vit_mae"),VYr=o(" \u2014 "),hce=a("a"),WYr=o("TFViTMAEModel"),JYr=o(" (ViTMAE model)"),UYr=l(),S0=a("li"),Tje=a("strong"),XYr=o("wav2vec2"),DYr=o(" \u2014 "),gce=a("a"),qYr=o("TFWav2Vec2Model"),OYr=o(" (Wav2Vec2 model)"),QYr=l(),x0=a("li"),Fje=a("strong"),YYr=o("whisper"),zYr=o(" \u2014 "),uce=a("a"),HYr=o("TFWhisperModel"),KYr=o(" (Whisper model)"),ezr=l(),G0=a("li"),Eje=a("strong"),ozr=o("xglm"),rzr=o(" \u2014 "),pce=a("a"),tzr=o("TFXGLMModel"),azr=o(" (XGLM model)"),nzr=l(),B0=a("li"),yje=a("strong"),szr=o("xlm"),lzr=o(" \u2014 "),_ce=a("a"),izr=o("TFXLMModel"),dzr=o(" (XLM model)"),mzr=l(),I0=a("li"),Cje=a("strong"),czr=o("xlm-roberta"),fzr=o(" \u2014 "),bce=a("a"),hzr=o("TFXLMRobertaModel"),gzr=o(" (XLM-RoBERTa model)"),uzr=l(),N0=a("li"),wje=a("strong"),pzr=o("xlnet"),_zr=o(" \u2014 "),vce=a("a"),bzr=o("TFXLNetModel"),vzr=o(" (XLNet model)"),Mzr=l(),v($0.$$.fragment),uxo=l(),lc=a("h3"),j0=a("a"),Lje=a("span"),v(yV.$$.fragment),Tzr=l(),Aje=a("span"),Fzr=o("FlaxAutoModel"),pxo=l(),Do=a("div"),v(CV.$$.fragment),Ezr=l(),ic=a("p"),yzr=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Mce=a("a"),Czr=o("from_pretrained()"),wzr=o(" class method or the "),Tce=a("a"),Lzr=o("from_config()"),Azr=o(` class
method.`),Rzr=l(),wV=a("p"),kzr=o("This class cannot be instantiated directly using "),Rje=a("code"),Szr=o("__init__()"),xzr=o(" (throws an error)."),Gzr=l(),Ut=a("div"),v(LV.$$.fragment),Bzr=l(),kje=a("p"),Izr=o("Instantiates one of the base model classes of the library from a configuration."),Nzr=l(),dc=a("p"),$zr=o(`Note:
Loading a model from its configuration file does `),Sje=a("strong"),jzr=o("not"),Pzr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Fce=a("a"),Zzr=o("from_pretrained()"),Vzr=o(" to load the model weights."),Wzr=l(),v(P0.$$.fragment),Jzr=l(),at=a("div"),v(AV.$$.fragment),Uzr=l(),xje=a("p"),Xzr=o("Instantiate one of the base model classes of the library from a pretrained model."),Dzr=l(),Vn=a("p"),qzr=o("The model class to instantiate is selected based on the "),Gje=a("code"),Ozr=o("model_type"),Qzr=o(` property of the config object (either
passed as an argument or loaded from `),Bje=a("code"),Yzr=o("pretrained_model_name_or_path"),zzr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ije=a("code"),Hzr=o("pretrained_model_name_or_path"),Kzr=o(":"),eHr=l(),se=a("ul"),Z0=a("li"),Nje=a("strong"),oHr=o("albert"),rHr=o(" \u2014 "),Ece=a("a"),tHr=o("FlaxAlbertModel"),aHr=o(" (ALBERT model)"),nHr=l(),V0=a("li"),$je=a("strong"),sHr=o("bart"),lHr=o(" \u2014 "),yce=a("a"),iHr=o("FlaxBartModel"),dHr=o(" (BART model)"),mHr=l(),W0=a("li"),jje=a("strong"),cHr=o("beit"),fHr=o(" \u2014 "),Cce=a("a"),hHr=o("FlaxBeitModel"),gHr=o(" (BEiT model)"),uHr=l(),J0=a("li"),Pje=a("strong"),pHr=o("bert"),_Hr=o(" \u2014 "),wce=a("a"),bHr=o("FlaxBertModel"),vHr=o(" (BERT model)"),MHr=l(),U0=a("li"),Zje=a("strong"),THr=o("big_bird"),FHr=o(" \u2014 "),Lce=a("a"),EHr=o("FlaxBigBirdModel"),yHr=o(" (BigBird model)"),CHr=l(),X0=a("li"),Vje=a("strong"),wHr=o("blenderbot"),LHr=o(" \u2014 "),Ace=a("a"),AHr=o("FlaxBlenderbotModel"),RHr=o(" (Blenderbot model)"),kHr=l(),D0=a("li"),Wje=a("strong"),SHr=o("blenderbot-small"),xHr=o(" \u2014 "),Rce=a("a"),GHr=o("FlaxBlenderbotSmallModel"),BHr=o(" (BlenderbotSmall model)"),IHr=l(),q0=a("li"),Jje=a("strong"),NHr=o("bloom"),$Hr=o(" \u2014 "),kce=a("a"),jHr=o("FlaxBloomModel"),PHr=o(" (BLOOM model)"),ZHr=l(),O0=a("li"),Uje=a("strong"),VHr=o("clip"),WHr=o(" \u2014 "),Sce=a("a"),JHr=o("FlaxCLIPModel"),UHr=o(" (CLIP model)"),XHr=l(),Q0=a("li"),Xje=a("strong"),DHr=o("distilbert"),qHr=o(" \u2014 "),xce=a("a"),OHr=o("FlaxDistilBertModel"),QHr=o(" (DistilBERT model)"),YHr=l(),Y0=a("li"),Dje=a("strong"),zHr=o("electra"),HHr=o(" \u2014 "),Gce=a("a"),KHr=o("FlaxElectraModel"),eKr=o(" (ELECTRA model)"),oKr=l(),z0=a("li"),qje=a("strong"),rKr=o("gpt-sw3"),tKr=o(" \u2014 "),Bce=a("a"),aKr=o("FlaxGPT2Model"),nKr=o(" (GPT-Sw3 model)"),sKr=l(),H0=a("li"),Oje=a("strong"),lKr=o("gpt2"),iKr=o(" \u2014 "),Ice=a("a"),dKr=o("FlaxGPT2Model"),mKr=o(" (OpenAI GPT-2 model)"),cKr=l(),K0=a("li"),Qje=a("strong"),fKr=o("gpt_neo"),hKr=o(" \u2014 "),Nce=a("a"),gKr=o("FlaxGPTNeoModel"),uKr=o(" (GPT Neo model)"),pKr=l(),e5=a("li"),Yje=a("strong"),_Kr=o("gptj"),bKr=o(" \u2014 "),$ce=a("a"),vKr=o("FlaxGPTJModel"),MKr=o(" (GPT-J model)"),TKr=l(),o5=a("li"),zje=a("strong"),FKr=o("longt5"),EKr=o(" \u2014 "),jce=a("a"),yKr=o("FlaxLongT5Model"),CKr=o(" (LongT5 model)"),wKr=l(),r5=a("li"),Hje=a("strong"),LKr=o("marian"),AKr=o(" \u2014 "),Pce=a("a"),RKr=o("FlaxMarianModel"),kKr=o(" (Marian model)"),SKr=l(),t5=a("li"),Kje=a("strong"),xKr=o("mbart"),GKr=o(" \u2014 "),Zce=a("a"),BKr=o("FlaxMBartModel"),IKr=o(" (mBART model)"),NKr=l(),a5=a("li"),ePe=a("strong"),$Kr=o("mt5"),jKr=o(" \u2014 "),Vce=a("a"),PKr=o("FlaxMT5Model"),ZKr=o(" (MT5 model)"),VKr=l(),n5=a("li"),oPe=a("strong"),WKr=o("opt"),JKr=o(" \u2014 "),Wce=a("a"),UKr=o("FlaxOPTModel"),XKr=o(" (OPT model)"),DKr=l(),s5=a("li"),rPe=a("strong"),qKr=o("pegasus"),OKr=o(" \u2014 "),Jce=a("a"),QKr=o("FlaxPegasusModel"),YKr=o(" (Pegasus model)"),zKr=l(),l5=a("li"),tPe=a("strong"),HKr=o("regnet"),KKr=o(" \u2014 "),Uce=a("a"),eet=o("FlaxRegNetModel"),oet=o(" (RegNet model)"),ret=l(),i5=a("li"),aPe=a("strong"),tet=o("resnet"),aet=o(" \u2014 "),Xce=a("a"),net=o("FlaxResNetModel"),set=o(" (ResNet model)"),iet=l(),d5=a("li"),nPe=a("strong"),det=o("roberta"),met=o(" \u2014 "),Dce=a("a"),cet=o("FlaxRobertaModel"),fet=o(" (RoBERTa model)"),het=l(),m5=a("li"),sPe=a("strong"),get=o("roberta-prelayernorm"),uet=o(" \u2014 "),qce=a("a"),pet=o("FlaxRobertaPreLayerNormModel"),_et=o(" (RoBERTa-PreLayerNorm model)"),bet=l(),c5=a("li"),lPe=a("strong"),vet=o("roformer"),Met=o(" \u2014 "),Oce=a("a"),Tet=o("FlaxRoFormerModel"),Fet=o(" (RoFormer model)"),Eet=l(),f5=a("li"),iPe=a("strong"),yet=o("t5"),Cet=o(" \u2014 "),Qce=a("a"),wet=o("FlaxT5Model"),Let=o(" (T5 model)"),Aet=l(),h5=a("li"),dPe=a("strong"),Ret=o("vision-text-dual-encoder"),ket=o(" \u2014 "),Yce=a("a"),Set=o("FlaxVisionTextDualEncoderModel"),xet=o(" (VisionTextDualEncoder model)"),Get=l(),g5=a("li"),mPe=a("strong"),Bet=o("vit"),Iet=o(" \u2014 "),zce=a("a"),Net=o("FlaxViTModel"),$et=o(" (ViT model)"),jet=l(),u5=a("li"),cPe=a("strong"),Pet=o("wav2vec2"),Zet=o(" \u2014 "),Hce=a("a"),Vet=o("FlaxWav2Vec2Model"),Wet=o(" (Wav2Vec2 model)"),Jet=l(),p5=a("li"),fPe=a("strong"),Uet=o("whisper"),Xet=o(" \u2014 "),Kce=a("a"),Det=o("FlaxWhisperModel"),qet=o(" (Whisper model)"),Oet=l(),_5=a("li"),hPe=a("strong"),Qet=o("xglm"),Yet=o(" \u2014 "),efe=a("a"),zet=o("FlaxXGLMModel"),Het=o(" (XGLM model)"),Ket=l(),b5=a("li"),gPe=a("strong"),eot=o("xlm-roberta"),oot=o(" \u2014 "),ofe=a("a"),rot=o("FlaxXLMRobertaModel"),tot=o(" (XLM-RoBERTa model)"),aot=l(),v(v5.$$.fragment),_xo=l(),mc=a("h2"),M5=a("a"),uPe=a("span"),v(RV.$$.fragment),not=l(),pPe=a("span"),sot=o("Generic pretraining classes"),bxo=l(),rfe=a("p"),lot=o("The following auto classes are available for instantiating a model with a pretraining 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with the `),tfe=a("a"),fot=o("from_pretrained()"),hot=o(" class method or the "),afe=a("a"),got=o("from_config()"),uot=o(` class
method.`),pot=l(),xV=a("p"),_ot=o("This class cannot be instantiated directly using "),vPe=a("code"),bot=o("__init__()"),vot=o(" (throws an error)."),Mot=l(),Xt=a("div"),v(GV.$$.fragment),Tot=l(),MPe=a("p"),Fot=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Eot=l(),hc=a("p"),yot=o(`Note:
Loading a model from its configuration file does `),TPe=a("strong"),Cot=o("not"),wot=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),nfe=a("a"),Lot=o("from_pretrained()"),Aot=o(" to load the model weights."),Rot=l(),v(F5.$$.fragment),kot=l(),lo=a("div"),v(BV.$$.fragment),Sot=l(),FPe=a("p"),xot=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Got=l(),Wn=a("p"),Bot=o("The model class to instantiate is selected based on the "),EPe=a("code"),Iot=o("model_type"),Not=o(` property of the config object (either
passed as an argument or loaded from `),yPe=a("code"),$ot=o("pretrained_model_name_or_path"),jot=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CPe=a("code"),Pot=o("pretrained_model_name_or_path"),Zot=o(":"),Vot=l(),X=a("ul"),E5=a("li"),wPe=a("strong"),Wot=o("albert"),Jot=o(" \u2014 "),sfe=a("a"),Uot=o("AlbertForPreTraining"),Xot=o(" (ALBERT model)"),Dot=l(),y5=a("li"),LPe=a("strong"),qot=o("bart"),Oot=o(" \u2014 "),lfe=a("a"),Qot=o("BartForConditionalGeneration"),Yot=o(" (BART model)"),zot=l(),C5=a("li"),APe=a("strong"),Hot=o("bert"),Kot=o(" \u2014 "),ife=a("a"),ert=o("BertForPreTraining"),ort=o(" (BERT model)"),rrt=l(),w5=a("li"),RPe=a("strong"),trt=o("big_bird"),art=o(" \u2014 "),dfe=a("a"),nrt=o("BigBirdForPreTraining"),srt=o(" (BigBird model)"),lrt=l(),L5=a("li"),kPe=a("strong"),irt=o("bloom"),drt=o(" \u2014 "),mfe=a("a"),mrt=o("BloomForCausalLM"),crt=o(" (BLOOM model)"),frt=l(),A5=a("li"),SPe=a("strong"),hrt=o("camembert"),grt=o(" \u2014 "),cfe=a("a"),urt=o("CamembertForMaskedLM"),prt=o(" (CamemBERT model)"),_rt=l(),R5=a("li"),xPe=a("strong"),brt=o("ctrl"),vrt=o(" \u2014 "),ffe=a("a"),Mrt=o("CTRLLMHeadModel"),Trt=o(" (CTRL model)"),Frt=l(),k5=a("li"),GPe=a("strong"),Ert=o("data2vec-text"),yrt=o(" \u2014 "),hfe=a("a"),Crt=o("Data2VecTextForMaskedLM"),wrt=o(" (Data2VecText model)"),Lrt=l(),S5=a("li"),BPe=a("strong"),Art=o("deberta"),Rrt=o(" \u2014 "),gfe=a("a"),krt=o("DebertaForMaskedLM"),Srt=o(" (DeBERTa model)"),xrt=l(),x5=a("li"),IPe=a("strong"),Grt=o("deberta-v2"),Brt=o(" \u2014 "),ufe=a("a"),Irt=o("DebertaV2ForMaskedLM"),Nrt=o(" (DeBERTa-v2 model)"),$rt=l(),G5=a("li"),NPe=a("strong"),jrt=o("distilbert"),Prt=o(" \u2014 "),pfe=a("a"),Zrt=o("DistilBertForMaskedLM"),Vrt=o(" (DistilBERT model)"),Wrt=l(),B5=a("li"),$Pe=a("strong"),Jrt=o("electra"),Urt=o(" \u2014 "),_fe=a("a"),Xrt=o("ElectraForPreTraining"),Drt=o(" (ELECTRA model)"),qrt=l(),I5=a("li"),jPe=a("strong"),Ort=o("ernie"),Qrt=o(" \u2014 "),bfe=a("a"),Yrt=o("ErnieForPreTraining"),zrt=o(" (ERNIE model)"),Hrt=l(),N5=a("li"),PPe=a("strong"),Krt=o("flaubert"),ett=o(" \u2014 "),vfe=a("a"),ott=o("FlaubertWithLMHeadModel"),rtt=o(" (FlauBERT model)"),ttt=l(),$5=a("li"),ZPe=a("strong"),att=o("flava"),ntt=o(" \u2014 "),Mfe=a("a"),stt=o("FlavaForPreTraining"),ltt=o(" (FLAVA model)"),itt=l(),j5=a("li"),VPe=a("strong"),dtt=o("fnet"),mtt=o(" \u2014 "),Tfe=a("a"),ctt=o("FNetForPreTraining"),ftt=o(" (FNet model)"),htt=l(),P5=a("li"),WPe=a("strong"),gtt=o("fsmt"),utt=o(" \u2014 "),Ffe=a("a"),ptt=o("FSMTForConditionalGeneration"),_tt=o(" (FairSeq Machine-Translation model)"),btt=l(),Z5=a("li"),JPe=a("strong"),vtt=o("funnel"),Mtt=o(" \u2014 "),Efe=a("a"),Ttt=o("FunnelForPreTraining"),Ftt=o(" (Funnel Transformer model)"),Ett=l(),V5=a("li"),UPe=a("strong"),ytt=o("gpt-sw3"),Ctt=o(" \u2014 "),yfe=a("a"),wtt=o("GPT2LMHeadModel"),Ltt=o(" (GPT-Sw3 model)"),Att=l(),W5=a("li"),XPe=a("strong"),Rtt=o("gpt2"),ktt=o(" \u2014 "),Cfe=a("a"),Stt=o("GPT2LMHeadModel"),xtt=o(" (OpenAI GPT-2 model)"),Gtt=l(),J5=a("li"),DPe=a("strong"),Btt=o("gpt_bigcode"),Itt=o(" \u2014 "),wfe=a("a"),Ntt=o("GPTBigCodeForCausalLM"),$tt=o(" (GPTBigCode model)"),jtt=l(),U5=a("li"),qPe=a("strong"),Ptt=o("gptsan-japanese"),Ztt=o(" \u2014 "),Lfe=a("a"),Vtt=o("GPTSanJapaneseForConditionalGeneration"),Wtt=o(" (GPTSAN-japanese model)"),Jtt=l(),X5=a("li"),OPe=a("strong"),Utt=o("ibert"),Xtt=o(" \u2014 "),Afe=a("a"),Dtt=o("IBertForMaskedLM"),qtt=o(" (I-BERT model)"),Ott=l(),D5=a("li"),QPe=a("strong"),Qtt=o("layoutlm"),Ytt=o(" \u2014 "),Rfe=a("a"),ztt=o("LayoutLMForMaskedLM"),Htt=o(" (LayoutLM model)"),Ktt=l(),q5=a("li"),YPe=a("strong"),eat=o("longformer"),oat=o(" \u2014 "),kfe=a("a"),rat=o("LongformerForMaskedLM"),tat=o(" (Longformer model)"),aat=l(),O5=a("li"),zPe=a("strong"),nat=o("luke"),sat=o(" \u2014 "),Sfe=a("a"),lat=o("LukeForMaskedLM"),iat=o(" (LUKE model)"),dat=l(),Q5=a("li"),HPe=a("strong"),mat=o("lxmert"),cat=o(" \u2014 "),xfe=a("a"),fat=o("LxmertForPreTraining"),hat=o(" (LXMERT model)"),gat=l(),Y5=a("li"),KPe=a("strong"),uat=o("mega"),pat=o(" \u2014 "),Gfe=a("a"),_at=o("MegaForMaskedLM"),bat=o(" (MEGA model)"),vat=l(),z5=a("li"),eZe=a("strong"),Mat=o("megatron-bert"),Tat=o(" \u2014 "),Bfe=a("a"),Fat=o("MegatronBertForPreTraining"),Eat=o(" (Megatron-BERT model)"),yat=l(),H5=a("li"),oZe=a("strong"),Cat=o("mobilebert"),wat=o(" \u2014 "),Ife=a("a"),Lat=o("MobileBertForPreTraining"),Aat=o(" (MobileBERT model)"),Rat=l(),K5=a("li"),rZe=a("strong"),kat=o("mpnet"),Sat=o(" \u2014 "),Nfe=a("a"),xat=o("MPNetForMaskedLM"),Gat=o(" (MPNet model)"),Bat=l(),e3=a("li"),tZe=a("strong"),Iat=o("mpt"),Nat=o(" \u2014 "),$fe=a("a"),$at=o("MptForCausalLM"),jat=o(" (MPT model)"),Pat=l(),o3=a("li"),aZe=a("strong"),Zat=o("mra"),Vat=o(" \u2014 "),jfe=a("a"),Wat=o("MraForMaskedLM"),Jat=o(" (MRA model)"),Uat=l(),r3=a("li"),nZe=a("strong"),Xat=o("mvp"),Dat=o(" \u2014 "),Pfe=a("a"),qat=o("MvpForConditionalGeneration"),Oat=o(" (MVP model)"),Qat=l(),t3=a("li"),sZe=a("strong"),Yat=o("nezha"),zat=o(" \u2014 "),Zfe=a("a"),Hat=o("NezhaForPreTraining"),Kat=o(" (Nezha model)"),ent=l(),a3=a("li"),lZe=a("strong"),ont=o("nllb-moe"),rnt=o(" \u2014 "),Vfe=a("a"),tnt=o("NllbMoeForConditionalGeneration"),ant=o(" (NLLB-MOE model)"),nnt=l(),n3=a("li"),iZe=a("strong"),snt=o("openai-gpt"),lnt=o(" \u2014 "),Wfe=a("a"),int=o("OpenAIGPTLMHeadModel"),dnt=o(" (OpenAI GPT model)"),mnt=l(),s3=a("li"),dZe=a("strong"),cnt=o("retribert"),fnt=o(" \u2014 "),Jfe=a("a"),hnt=o("RetriBertModel"),gnt=o(" (RetriBERT model)"),unt=l(),l3=a("li"),mZe=a("strong"),pnt=o("roberta"),_nt=o(" \u2014 "),Ufe=a("a"),bnt=o("RobertaForMaskedLM"),vnt=o(" (RoBERTa model)"),Mnt=l(),i3=a("li"),cZe=a("strong"),Tnt=o("roberta-prelayernorm"),Fnt=o(" \u2014 "),Xfe=a("a"),Ent=o("RobertaPreLayerNormForMaskedLM"),ynt=o(" (RoBERTa-PreLayerNorm model)"),Cnt=l(),d3=a("li"),fZe=a("strong"),wnt=o("roc_bert"),Lnt=o(" \u2014 "),Dfe=a("a"),Ant=o("RoCBertForPreTraining"),Rnt=o(" (RoCBert model)"),knt=l(),m3=a("li"),hZe=a("strong"),Snt=o("rwkv"),xnt=o(" \u2014 "),qfe=a("a"),Gnt=o("RwkvForCausalLM"),Bnt=o(" (RWKV model)"),Int=l(),c3=a("li"),gZe=a("strong"),Nnt=o("splinter"),$nt=o(" \u2014 "),Ofe=a("a"),jnt=o("SplinterForPreTraining"),Pnt=o(" (Splinter model)"),Znt=l(),f3=a("li"),uZe=a("strong"),Vnt=o("squeezebert"),Wnt=o(" \u2014 "),Qfe=a("a"),Jnt=o("SqueezeBertForMaskedLM"),Unt=o(" (SqueezeBERT model)"),Xnt=l(),h3=a("li"),pZe=a("strong"),Dnt=o("switch_transformers"),qnt=o(" \u2014 "),Yfe=a("a"),Ont=o("SwitchTransformersForConditionalGeneration"),Qnt=o(" (SwitchTransformers model)"),Ynt=l(),g3=a("li"),_Ze=a("strong"),znt=o("t5"),Hnt=o(" \u2014 "),zfe=a("a"),Knt=o("T5ForConditionalGeneration"),est=o(" (T5 model)"),ost=l(),u3=a("li"),bZe=a("strong"),rst=o("tapas"),tst=o(" \u2014 "),Hfe=a("a"),ast=o("TapasForMaskedLM"),nst=o(" (TAPAS model)"),sst=l(),p3=a("li"),vZe=a("strong"),lst=o("transfo-xl"),ist=o(" \u2014 "),Kfe=a("a"),dst=o("TransfoXLLMHeadModel"),mst=o(" (Transformer-XL model)"),cst=l(),_3=a("li"),MZe=a("strong"),fst=o("tvlt"),hst=o(" \u2014 "),ehe=a("a"),gst=o("TvltForPreTraining"),ust=o(" (TVLT model)"),pst=l(),b3=a("li"),TZe=a("strong"),_st=o("unispeech"),bst=o(" \u2014 "),ohe=a("a"),vst=o("UniSpeechForPreTraining"),Mst=o(" (UniSpeech model)"),Tst=l(),v3=a("li"),FZe=a("strong"),Fst=o("unispeech-sat"),Est=o(" \u2014 "),rhe=a("a"),yst=o("UniSpeechSatForPreTraining"),Cst=o(" (UniSpeechSat model)"),wst=l(),M3=a("li"),EZe=a("strong"),Lst=o("videomae"),Ast=o(" \u2014 "),the=a("a"),Rst=o("VideoMAEForPreTraining"),kst=o(" (VideoMAE model)"),Sst=l(),T3=a("li"),yZe=a("strong"),xst=o("visual_bert"),Gst=o(" \u2014 "),ahe=a("a"),Bst=o("VisualBertForPreTraining"),Ist=o(" (VisualBERT model)"),Nst=l(),F3=a("li"),CZe=a("strong"),$st=o("vit_mae"),jst=o(" \u2014 "),nhe=a("a"),Pst=o("ViTMAEForPreTraining"),Zst=o(" (ViTMAE model)"),Vst=l(),E3=a("li"),wZe=a("strong"),Wst=o("wav2vec2"),Jst=o(" \u2014 "),she=a("a"),Ust=o("Wav2Vec2ForPreTraining"),Xst=o(" (Wav2Vec2 model)"),Dst=l(),y3=a("li"),LZe=a("strong"),qst=o("wav2vec2-conformer"),Ost=o(" \u2014 "),lhe=a("a"),Qst=o("Wav2Vec2ConformerForPreTraining"),Yst=o(" (Wav2Vec2-Conformer model)"),zst=l(),C3=a("li"),AZe=a("strong"),Hst=o("xlm"),Kst=o(" \u2014 "),ihe=a("a"),elt=o("XLMWithLMHeadModel"),olt=o(" (XLM model)"),rlt=l(),w3=a("li"),RZe=a("strong"),tlt=o("xlm-roberta"),alt=o(" \u2014 "),dhe=a("a"),nlt=o("XLMRobertaForMaskedLM"),slt=o(" (XLM-RoBERTa model)"),llt=l(),L3=a("li"),kZe=a("strong"),ilt=o("xlm-roberta-xl"),dlt=o(" \u2014 "),mhe=a("a"),mlt=o("XLMRobertaXLForMaskedLM"),clt=o(" (XLM-RoBERTa-XL model)"),flt=l(),A3=a("li"),SZe=a("strong"),hlt=o("xlnet"),glt=o(" \u2014 "),che=a("a"),ult=o("XLNetLMHeadModel"),plt=o(" (XLNet model)"),_lt=l(),R3=a("li"),xZe=a("strong"),blt=o("xmod"),vlt=o(" \u2014 "),fhe=a("a"),Mlt=o("XmodForMaskedLM"),Tlt=o(" (X-MOD model)"),Flt=l(),k3=a("p"),Elt=o("The model is set in evaluation mode by default using "),GZe=a("code"),ylt=o("model.eval()"),Clt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),BZe=a("code"),wlt=o("model.train()"),Llt=l(),v(S3.$$.fragment),Txo=l(),gc=a("h3"),x3=a("a"),IZe=a("span"),v(IV.$$.fragment),Alt=l(),NZe=a("span"),Rlt=o("TFAutoModelForPreTraining"),Fxo=l(),Oo=a("div"),v(NV.$$.fragment),klt=l(),uc=a("p"),Slt=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 `),hhe=a("a"),xlt=o("from_pretrained()"),Glt=o(" class method or the "),ghe=a("a"),Blt=o("from_config()"),Ilt=o(` class
method.`),Nlt=l(),$V=a("p"),$lt=o("This class cannot be instantiated directly using "),$Ze=a("code"),jlt=o("__init__()"),Plt=o(" (throws an error)."),Zlt=l(),Dt=a("div"),v(jV.$$.fragment),Vlt=l(),jZe=a("p"),Wlt=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Jlt=l(),pc=a("p"),Ult=o(`Note:
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model\u2019s configuration. Use `),uhe=a("a"),qlt=o("from_pretrained()"),Olt=o(" to load the model weights."),Qlt=l(),v(G3.$$.fragment),Ylt=l(),nt=a("div"),v(PV.$$.fragment),zlt=l(),ZZe=a("p"),Hlt=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Klt=l(),Jn=a("p"),eit=o("The model class to instantiate is selected based on the "),VZe=a("code"),oit=o("model_type"),rit=o(` property of the config object (either
passed as an argument or loaded from `),WZe=a("code"),tit=o("pretrained_model_name_or_path"),ait=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JZe=a("code"),nit=o("pretrained_model_name_or_path"),sit=o(":"),lit=l(),fe=a("ul"),B3=a("li"),UZe=a("strong"),iit=o("albert"),dit=o(" \u2014 "),phe=a("a"),mit=o("TFAlbertForPreTraining"),cit=o(" (ALBERT model)"),fit=l(),I3=a("li"),XZe=a("strong"),hit=o("bart"),git=o(" \u2014 "),_he=a("a"),uit=o("TFBartForConditionalGeneration"),pit=o(" (BART model)"),_it=l(),N3=a("li"),DZe=a("strong"),bit=o("bert"),vit=o(" \u2014 "),bhe=a("a"),Mit=o("TFBertForPreTraining"),Tit=o(" (BERT model)"),Fit=l(),$3=a("li"),qZe=a("strong"),Eit=o("camembert"),yit=o(" \u2014 "),vhe=a("a"),Cit=o("TFCamembertForMaskedLM"),wit=o(" (CamemBERT model)"),Lit=l(),j3=a("li"),OZe=a("strong"),Ait=o("ctrl"),Rit=o(" \u2014 "),Mhe=a("a"),kit=o("TFCTRLLMHeadModel"),Sit=o(" (CTRL model)"),xit=l(),P3=a("li"),QZe=a("strong"),Git=o("distilbert"),Bit=o(" \u2014 "),The=a("a"),Iit=o("TFDistilBertForMaskedLM"),Nit=o(" (DistilBERT model)"),$it=l(),Z3=a("li"),YZe=a("strong"),jit=o("electra"),Pit=o(" \u2014 "),Fhe=a("a"),Zit=o("TFElectraForPreTraining"),Vit=o(" (ELECTRA model)"),Wit=l(),V3=a("li"),zZe=a("strong"),Jit=o("flaubert"),Uit=o(" \u2014 "),Ehe=a("a"),Xit=o("TFFlaubertWithLMHeadModel"),Dit=o(" (FlauBERT model)"),qit=l(),W3=a("li"),HZe=a("strong"),Oit=o("funnel"),Qit=o(" \u2014 "),yhe=a("a"),Yit=o("TFFunnelForPreTraining"),zit=o(" (Funnel Transformer model)"),Hit=l(),J3=a("li"),KZe=a("strong"),Kit=o("gpt-sw3"),edt=o(" \u2014 "),Che=a("a"),odt=o("TFGPT2LMHeadModel"),rdt=o(" (GPT-Sw3 model)"),tdt=l(),U3=a("li"),eVe=a("strong"),adt=o("gpt2"),ndt=o(" \u2014 "),whe=a("a"),sdt=o("TFGPT2LMHeadModel"),ldt=o(" (OpenAI GPT-2 model)"),idt=l(),X3=a("li"),oVe=a("strong"),ddt=o("layoutlm"),mdt=o(" \u2014 "),Lhe=a("a"),cdt=o("TFLayoutLMForMaskedLM"),fdt=o(" (LayoutLM model)"),hdt=l(),D3=a("li"),rVe=a("strong"),gdt=o("lxmert"),udt=o(" \u2014 "),Ahe=a("a"),pdt=o("TFLxmertForPreTraining"),_dt=o(" (LXMERT model)"),bdt=l(),q3=a("li"),tVe=a("strong"),vdt=o("mobilebert"),Mdt=o(" \u2014 "),Rhe=a("a"),Tdt=o("TFMobileBertForPreTraining"),Fdt=o(" (MobileBERT model)"),Edt=l(),O3=a("li"),aVe=a("strong"),ydt=o("mpnet"),Cdt=o(" \u2014 "),khe=a("a"),wdt=o("TFMPNetForMaskedLM"),Ldt=o(" (MPNet model)"),Adt=l(),Q3=a("li"),nVe=a("strong"),Rdt=o("openai-gpt"),kdt=o(" \u2014 "),She=a("a"),Sdt=o("TFOpenAIGPTLMHeadModel"),xdt=o(" (OpenAI GPT model)"),Gdt=l(),Y3=a("li"),sVe=a("strong"),Bdt=o("roberta"),Idt=o(" \u2014 "),xhe=a("a"),Ndt=o("TFRobertaForMaskedLM"),$dt=o(" (RoBERTa model)"),jdt=l(),z3=a("li"),lVe=a("strong"),Pdt=o("roberta-prelayernorm"),Zdt=o(" \u2014 "),Ghe=a("a"),Vdt=o("TFRobertaPreLayerNormForMaskedLM"),Wdt=o(" (RoBERTa-PreLayerNorm model)"),Jdt=l(),H3=a("li"),iVe=a("strong"),Udt=o("t5"),Xdt=o(" \u2014 "),Bhe=a("a"),Ddt=o("TFT5ForConditionalGeneration"),qdt=o(" (T5 model)"),Odt=l(),K3=a("li"),dVe=a("strong"),Qdt=o("tapas"),Ydt=o(" \u2014 "),Ihe=a("a"),zdt=o("TFTapasForMaskedLM"),Hdt=o(" (TAPAS model)"),Kdt=l(),e4=a("li"),mVe=a("strong"),emt=o("transfo-xl"),omt=o(" \u2014 "),Nhe=a("a"),rmt=o("TFTransfoXLLMHeadModel"),tmt=o(" (Transformer-XL model)"),amt=l(),o4=a("li"),cVe=a("strong"),nmt=o("vit_mae"),smt=o(" \u2014 "),$he=a("a"),lmt=o("TFViTMAEForPreTraining"),imt=o(" (ViTMAE model)"),dmt=l(),r4=a("li"),fVe=a("strong"),mmt=o("xlm"),cmt=o(" \u2014 "),jhe=a("a"),fmt=o("TFXLMWithLMHeadModel"),hmt=o(" (XLM model)"),gmt=l(),t4=a("li"),hVe=a("strong"),umt=o("xlm-roberta"),pmt=o(" \u2014 "),Phe=a("a"),_mt=o("TFXLMRobertaForMaskedLM"),bmt=o(" (XLM-RoBERTa model)"),vmt=l(),a4=a("li"),gVe=a("strong"),Mmt=o("xlnet"),Tmt=o(" \u2014 "),Zhe=a("a"),Fmt=o("TFXLNetLMHeadModel"),Emt=o(" (XLNet model)"),ymt=l(),v(n4.$$.fragment),Exo=l(),_c=a("h3"),s4=a("a"),uVe=a("span"),v(ZV.$$.fragment),Cmt=l(),pVe=a("span"),wmt=o("FlaxAutoModelForPreTraining"),yxo=l(),Qo=a("div"),v(VV.$$.fragment),Lmt=l(),bc=a("p"),Amt=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
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model\u2019s configuration. Use `),Jhe=a("a"),Umt=o("from_pretrained()"),Xmt=o(" to load the model weights."),Dmt=l(),v(l4.$$.fragment),qmt=l(),st=a("div"),v(UV.$$.fragment),Omt=l(),MVe=a("p"),Qmt=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Ymt=l(),Un=a("p"),zmt=o("The model class to instantiate is selected based on the "),TVe=a("code"),Hmt=o("model_type"),Kmt=o(` property of the config object (either
passed as an argument or loaded from `),FVe=a("code"),ect=o("pretrained_model_name_or_path"),oct=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EVe=a("code"),rct=o("pretrained_model_name_or_path"),tct=o(":"),act=l(),we=a("ul"),i4=a("li"),yVe=a("strong"),nct=o("albert"),sct=o(" \u2014 "),Uhe=a("a"),lct=o("FlaxAlbertForPreTraining"),ict=o(" (ALBERT model)"),dct=l(),d4=a("li"),CVe=a("strong"),mct=o("bart"),cct=o(" \u2014 "),Xhe=a("a"),fct=o("FlaxBartForConditionalGeneration"),hct=o(" (BART model)"),gct=l(),m4=a("li"),wVe=a("strong"),uct=o("bert"),pct=o(" \u2014 "),Dhe=a("a"),_ct=o("FlaxBertForPreTraining"),bct=o(" (BERT model)"),vct=l(),c4=a("li"),LVe=a("strong"),Mct=o("big_bird"),Tct=o(" \u2014 "),qhe=a("a"),Fct=o("FlaxBigBirdForPreTraining"),Ect=o(" (BigBird model)"),yct=l(),f4=a("li"),AVe=a("strong"),Cct=o("electra"),wct=o(" \u2014 "),Ohe=a("a"),Lct=o("FlaxElectraForPreTraining"),Act=o(" (ELECTRA model)"),Rct=l(),h4=a("li"),RVe=a("strong"),kct=o("longt5"),Sct=o(" \u2014 "),Qhe=a("a"),xct=o("FlaxLongT5ForConditionalGeneration"),Gct=o(" (LongT5 model)"),Bct=l(),g4=a("li"),kVe=a("strong"),Ict=o("mbart"),Nct=o(" \u2014 "),Yhe=a("a"),$ct=o("FlaxMBartForConditionalGeneration"),jct=o(" (mBART model)"),Pct=l(),u4=a("li"),SVe=a("strong"),Zct=o("mt5"),Vct=o(" \u2014 "),zhe=a("a"),Wct=o("FlaxMT5ForConditionalGeneration"),Jct=o(" (MT5 model)"),Uct=l(),p4=a("li"),xVe=a("strong"),Xct=o("roberta"),Dct=o(" \u2014 "),Hhe=a("a"),qct=o("FlaxRobertaForMaskedLM"),Oct=o(" (RoBERTa model)"),Qct=l(),_4=a("li"),GVe=a("strong"),Yct=o("roberta-prelayernorm"),zct=o(" \u2014 "),Khe=a("a"),Hct=o("FlaxRobertaPreLayerNormForMaskedLM"),Kct=o(" (RoBERTa-PreLayerNorm model)"),eft=l(),b4=a("li"),BVe=a("strong"),oft=o("roformer"),rft=o(" \u2014 "),ege=a("a"),tft=o("FlaxRoFormerForMaskedLM"),aft=o(" (RoFormer model)"),nft=l(),v4=a("li"),IVe=a("strong"),sft=o("t5"),lft=o(" \u2014 "),oge=a("a"),ift=o("FlaxT5ForConditionalGeneration"),dft=o(" (T5 model)"),mft=l(),M4=a("li"),NVe=a("strong"),cft=o("wav2vec2"),fft=o(" \u2014 "),rge=a("a"),hft=o("FlaxWav2Vec2ForPreTraining"),gft=o(" (Wav2Vec2 model)"),uft=l(),T4=a("li"),$Ve=a("strong"),pft=o("whisper"),_ft=o(" \u2014 "),tge=a("a"),bft=o("FlaxWhisperForConditionalGeneration"),vft=o(" (Whisper model)"),Mft=l(),F4=a("li"),jVe=a("strong"),Tft=o("xlm-roberta"),Fft=o(" \u2014 "),age=a("a"),Eft=o("FlaxXLMRobertaForMaskedLM"),yft=o(" (XLM-RoBERTa model)"),Cft=l(),v(E4.$$.fragment),Cxo=l(),Mc=a("h2"),y4=a("a"),PVe=a("span"),v(XV.$$.fragment),wft=l(),ZVe=a("span"),Lft=o("Natural Language Processing"),wxo=l(),nge=a("p"),Aft=o("The following auto classes are available for the following natural language processing tasks."),Lxo=l(),Tc=a("h3"),C4=a("a"),VVe=a("span"),v(DV.$$.fragment),Rft=l(),WVe=a("span"),kft=o("AutoModelForCausalLM"),Axo=l(),Yo=a("div"),v(qV.$$.fragment),Sft=l(),Fc=a("p"),xft=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
with the `),sge=a("a"),Gft=o("from_pretrained()"),Bft=o(" class method or the "),lge=a("a"),Ift=o("from_config()"),Nft=o(` class
method.`),$ft=l(),OV=a("p"),jft=o("This class cannot be instantiated directly using "),JVe=a("code"),Pft=o("__init__()"),Zft=o(" (throws an error)."),Vft=l(),Ot=a("div"),v(QV.$$.fragment),Wft=l(),UVe=a("p"),Jft=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Uft=l(),Ec=a("p"),Xft=o(`Note:
Loading a model from its configuration file does `),XVe=a("strong"),Dft=o("not"),qft=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),ige=a("a"),Oft=o("from_pretrained()"),Qft=o(" to load the model weights."),Yft=l(),v(w4.$$.fragment),zft=l(),io=a("div"),v(YV.$$.fragment),Hft=l(),DVe=a("p"),Kft=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),eht=l(),Xn=a("p"),oht=o("The model class to instantiate is selected based on the "),qVe=a("code"),rht=o("model_type"),tht=o(` property of the config object (either
passed as an argument or loaded from `),OVe=a("code"),aht=o("pretrained_model_name_or_path"),nht=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),QVe=a("code"),sht=o("pretrained_model_name_or_path"),lht=o(":"),iht=l(),Q=a("ul"),L4=a("li"),YVe=a("strong"),dht=o("bart"),mht=o(" \u2014 "),dge=a("a"),cht=o("BartForCausalLM"),fht=o(" (BART model)"),hht=l(),A4=a("li"),zVe=a("strong"),ght=o("bert"),uht=o(" \u2014 "),mge=a("a"),pht=o("BertLMHeadModel"),_ht=o(" (BERT model)"),bht=l(),R4=a("li"),HVe=a("strong"),vht=o("bert-generation"),Mht=o(" \u2014 "),cge=a("a"),Tht=o("BertGenerationDecoder"),Fht=o(" (Bert Generation model)"),Eht=l(),k4=a("li"),KVe=a("strong"),yht=o("big_bird"),Cht=o(" \u2014 "),fge=a("a"),wht=o("BigBirdForCausalLM"),Lht=o(" (BigBird model)"),Aht=l(),S4=a("li"),eWe=a("strong"),Rht=o("bigbird_pegasus"),kht=o(" \u2014 "),hge=a("a"),Sht=o("BigBirdPegasusForCausalLM"),xht=o(" (BigBird-Pegasus model)"),Ght=l(),x4=a("li"),oWe=a("strong"),Bht=o("biogpt"),Iht=o(" \u2014 "),gge=a("a"),Nht=o("BioGptForCausalLM"),$ht=o(" (BioGpt model)"),jht=l(),G4=a("li"),rWe=a("strong"),Pht=o("blenderbot"),Zht=o(" \u2014 "),uge=a("a"),Vht=o("BlenderbotForCausalLM"),Wht=o(" (Blenderbot model)"),Jht=l(),B4=a("li"),tWe=a("strong"),Uht=o("blenderbot-small"),Xht=o(" \u2014 "),pge=a("a"),Dht=o("BlenderbotSmallForCausalLM"),qht=o(" (BlenderbotSmall model)"),Oht=l(),I4=a("li"),aWe=a("strong"),Qht=o("bloom"),Yht=o(" \u2014 "),_ge=a("a"),zht=o("BloomForCausalLM"),Hht=o(" (BLOOM model)"),Kht=l(),N4=a("li"),nWe=a("strong"),egt=o("camembert"),ogt=o(" \u2014 "),bge=a("a"),rgt=o("CamembertForCausalLM"),tgt=o(" (CamemBERT model)"),agt=l(),$4=a("li"),sWe=a("strong"),ngt=o("codegen"),sgt=o(" \u2014 "),vge=a("a"),lgt=o("CodeGenForCausalLM"),igt=o(" (CodeGen model)"),dgt=l(),j4=a("li"),lWe=a("strong"),mgt=o("cpmant"),cgt=o(" \u2014 "),Mge=a("a"),fgt=o("CpmAntForCausalLM"),hgt=o(" (CPM-Ant model)"),ggt=l(),P4=a("li"),iWe=a("strong"),ugt=o("ctrl"),pgt=o(" \u2014 "),Tge=a("a"),_gt=o("CTRLLMHeadModel"),bgt=o(" (CTRL model)"),vgt=l(),Z4=a("li"),dWe=a("strong"),Mgt=o("data2vec-text"),Tgt=o(" \u2014 "),Fge=a("a"),Fgt=o("Data2VecTextForCausalLM"),Egt=o(" (Data2VecText model)"),ygt=l(),V4=a("li"),mWe=a("strong"),Cgt=o("electra"),wgt=o(" \u2014 "),Ege=a("a"),Lgt=o("ElectraForCausalLM"),Agt=o(" (ELECTRA model)"),Rgt=l(),W4=a("li"),cWe=a("strong"),kgt=o("ernie"),Sgt=o(" \u2014 "),yge=a("a"),xgt=o("ErnieForCausalLM"),Ggt=o(" (ERNIE model)"),Bgt=l(),J4=a("li"),fWe=a("strong"),Igt=o("falcon"),Ngt=o(" \u2014 "),hWe=a("code"),$gt=o("FalconForCausalLM"),jgt=o(" (Falcon model)"),Pgt=l(),U4=a("li"),gWe=a("strong"),Zgt=o("git"),Vgt=o(" \u2014 "),Cge=a("a"),Wgt=o("GitForCausalLM"),Jgt=o(" (GIT model)"),Ugt=l(),X4=a("li"),uWe=a("strong"),Xgt=o("gpt-sw3"),Dgt=o(" \u2014 "),wge=a("a"),qgt=o("GPT2LMHeadModel"),Ogt=o(" (GPT-Sw3 model)"),Qgt=l(),D4=a("li"),pWe=a("strong"),Ygt=o("gpt2"),zgt=o(" \u2014 "),Lge=a("a"),Hgt=o("GPT2LMHeadModel"),Kgt=o(" (OpenAI GPT-2 model)"),eut=l(),q4=a("li"),_We=a("strong"),out=o("gpt_bigcode"),rut=o(" \u2014 "),Age=a("a"),tut=o("GPTBigCodeForCausalLM"),aut=o(" (GPTBigCode model)"),nut=l(),O4=a("li"),bWe=a("strong"),sut=o("gpt_neo"),lut=o(" \u2014 "),Rge=a("a"),iut=o("GPTNeoForCausalLM"),dut=o(" (GPT Neo model)"),mut=l(),Q4=a("li"),vWe=a("strong"),cut=o("gpt_neox"),fut=o(" \u2014 "),kge=a("a"),hut=o("GPTNeoXForCausalLM"),gut=o(" (GPT NeoX model)"),uut=l(),Y4=a("li"),MWe=a("strong"),put=o("gpt_neox_japanese"),_ut=o(" \u2014 "),Sge=a("a"),but=o("GPTNeoXJapaneseForCausalLM"),vut=o(" (GPT NeoX Japanese model)"),Mut=l(),z4=a("li"),TWe=a("strong"),Tut=o("gptj"),Fut=o(" \u2014 "),xge=a("a"),Eut=o("GPTJForCausalLM"),yut=o(" (GPT-J model)"),Cut=l(),H4=a("li"),FWe=a("strong"),wut=o("llama"),Lut=o(" \u2014 "),Gge=a("a"),Aut=o("LlamaForCausalLM"),Rut=o(" (LLaMA model)"),kut=l(),K4=a("li"),EWe=a("strong"),Sut=o("marian"),xut=o(" \u2014 "),Bge=a("a"),Gut=o("MarianForCausalLM"),But=o(" (Marian model)"),Iut=l(),e9=a("li"),yWe=a("strong"),Nut=o("mbart"),$ut=o(" \u2014 "),Ige=a("a"),jut=o("MBartForCausalLM"),Put=o(" (mBART model)"),Zut=l(),o9=a("li"),CWe=a("strong"),Vut=o("mega"),Wut=o(" \u2014 "),Nge=a("a"),Jut=o("MegaForCausalLM"),Uut=o(" (MEGA model)"),Xut=l(),r9=a("li"),wWe=a("strong"),Dut=o("megatron-bert"),qut=o(" \u2014 "),$ge=a("a"),Out=o("MegatronBertForCausalLM"),Qut=o(" (Megatron-BERT model)"),Yut=l(),t9=a("li"),LWe=a("strong"),zut=o("mpt"),Hut=o(" \u2014 "),jge=a("a"),Kut=o("MptForCausalLM"),ept=o(" (MPT model)"),opt=l(),a9=a("li"),AWe=a("strong"),rpt=o("musicgen"),tpt=o(" \u2014 "),Pge=a("a"),apt=o("MusicgenForCausalLM"),npt=o(" (MusicGen model)"),spt=l(),n9=a("li"),RWe=a("strong"),lpt=o("mvp"),ipt=o(" \u2014 "),Zge=a("a"),dpt=o("MvpForCausalLM"),mpt=o(" (MVP model)"),cpt=l(),s9=a("li"),kWe=a("strong"),fpt=o("open-llama"),hpt=o(" \u2014 "),Vge=a("a"),gpt=o("OpenLlamaForCausalLM"),upt=o(" (OpenLlama model)"),ppt=l(),l9=a("li"),SWe=a("strong"),_pt=o("openai-gpt"),bpt=o(" \u2014 "),Wge=a("a"),vpt=o("OpenAIGPTLMHeadModel"),Mpt=o(" (OpenAI GPT model)"),Tpt=l(),i9=a("li"),xWe=a("strong"),Fpt=o("opt"),Ept=o(" \u2014 "),Jge=a("a"),ypt=o("OPTForCausalLM"),Cpt=o(" (OPT model)"),wpt=l(),d9=a("li"),GWe=a("strong"),Lpt=o("pegasus"),Apt=o(" \u2014 "),Uge=a("a"),Rpt=o("PegasusForCausalLM"),kpt=o(" (Pegasus model)"),Spt=l(),m9=a("li"),BWe=a("strong"),xpt=o("plbart"),Gpt=o(" \u2014 "),Xge=a("a"),Bpt=o("PLBartForCausalLM"),Ipt=o(" (PLBart model)"),Npt=l(),c9=a("li"),IWe=a("strong"),$pt=o("prophetnet"),jpt=o(" \u2014 "),Dge=a("a"),Ppt=o("ProphetNetForCausalLM"),Zpt=o(" (ProphetNet model)"),Vpt=l(),f9=a("li"),NWe=a("strong"),Wpt=o("qdqbert"),Jpt=o(" \u2014 "),qge=a("a"),Upt=o("QDQBertLMHeadModel"),Xpt=o(" (QDQBert model)"),Dpt=l(),h9=a("li"),$We=a("strong"),qpt=o("reformer"),Opt=o(" \u2014 "),Oge=a("a"),Qpt=o("ReformerModelWithLMHead"),Ypt=o(" (Reformer model)"),zpt=l(),g9=a("li"),jWe=a("strong"),Hpt=o("rembert"),Kpt=o(" \u2014 "),Qge=a("a"),e_t=o("RemBertForCausalLM"),o_t=o(" (RemBERT model)"),r_t=l(),u9=a("li"),PWe=a("strong"),t_t=o("roberta"),a_t=o(" \u2014 "),Yge=a("a"),n_t=o("RobertaForCausalLM"),s_t=o(" (RoBERTa model)"),l_t=l(),p9=a("li"),ZWe=a("strong"),i_t=o("roberta-prelayernorm"),d_t=o(" \u2014 "),zge=a("a"),m_t=o("RobertaPreLayerNormForCausalLM"),c_t=o(" (RoBERTa-PreLayerNorm model)"),f_t=l(),_9=a("li"),VWe=a("strong"),h_t=o("roc_bert"),g_t=o(" \u2014 "),Hge=a("a"),u_t=o("RoCBertForCausalLM"),p_t=o(" (RoCBert model)"),__t=l(),b9=a("li"),WWe=a("strong"),b_t=o("roformer"),v_t=o(" \u2014 "),Kge=a("a"),M_t=o("RoFormerForCausalLM"),T_t=o(" (RoFormer model)"),F_t=l(),v9=a("li"),JWe=a("strong"),E_t=o("rwkv"),y_t=o(" \u2014 "),eue=a("a"),C_t=o("RwkvForCausalLM"),w_t=o(" (RWKV model)"),L_t=l(),M9=a("li"),UWe=a("strong"),A_t=o("speech_to_text_2"),R_t=o(" \u2014 "),oue=a("a"),k_t=o("Speech2Text2ForCausalLM"),S_t=o(" (Speech2Text2 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model)"),h1t=l(),A9=a("li"),HWe=a("strong"),g1t=o("xlnet"),u1t=o(" \u2014 "),due=a("a"),p1t=o("XLNetLMHeadModel"),_1t=o(" (XLNet model)"),b1t=l(),R9=a("li"),KWe=a("strong"),v1t=o("xmod"),M1t=o(" \u2014 "),mue=a("a"),T1t=o("XmodForCausalLM"),F1t=o(" (X-MOD model)"),E1t=l(),k9=a("p"),y1t=o("The model is set in evaluation mode by default using "),eJe=a("code"),C1t=o("model.eval()"),w1t=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oJe=a("code"),L1t=o("model.train()"),A1t=l(),v(S9.$$.fragment),Rxo=l(),yc=a("h3"),x9=a("a"),rJe=a("span"),v(zV.$$.fragment),R1t=l(),tJe=a("span"),k1t=o("TFAutoModelForCausalLM"),kxo=l(),zo=a("div"),v(HV.$$.fragment),S1t=l(),Cc=a("p"),x1t=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
with the `),cue=a("a"),G1t=o("from_pretrained()"),B1t=o(" class method or the "),fue=a("a"),I1t=o("from_config()"),N1t=o(` class
method.`),$1t=l(),KV=a("p"),j1t=o("This class cannot be instantiated directly using "),aJe=a("code"),P1t=o("__init__()"),Z1t=o(" (throws an error)."),V1t=l(),Qt=a("div"),v(eW.$$.fragment),W1t=l(),nJe=a("p"),J1t=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),U1t=l(),wc=a("p"),X1t=o(`Note:
Loading a model from its configuration file does `),sJe=a("strong"),D1t=o("not"),q1t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),hue=a("a"),O1t=o("from_pretrained()"),Q1t=o(" to load the model weights."),Y1t=l(),v(G9.$$.fragment),z1t=l(),lt=a("div"),v(oW.$$.fragment),H1t=l(),lJe=a("p"),K1t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),ebt=l(),Dn=a("p"),obt=o("The model class to instantiate is selected based on the "),iJe=a("code"),rbt=o("model_type"),tbt=o(` property of the config object (either
passed as an argument or loaded from `),dJe=a("code"),abt=o("pretrained_model_name_or_path"),nbt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mJe=a("code"),sbt=o("pretrained_model_name_or_path"),lbt=o(":"),ibt=l(),Ee=a("ul"),B9=a("li"),cJe=a("strong"),dbt=o("bert"),mbt=o(" \u2014 "),gue=a("a"),cbt=o("TFBertLMHeadModel"),fbt=o(" (BERT model)"),hbt=l(),I9=a("li"),fJe=a("strong"),gbt=o("camembert"),ubt=o(" \u2014 "),uue=a("a"),pbt=o("TFCamembertForCausalLM"),_bt=o(" (CamemBERT model)"),bbt=l(),N9=a("li"),hJe=a("strong"),vbt=o("ctrl"),Mbt=o(" \u2014 "),pue=a("a"),Tbt=o("TFCTRLLMHeadModel"),Fbt=o(" (CTRL model)"),Ebt=l(),$9=a("li"),gJe=a("strong"),ybt=o("gpt-sw3"),Cbt=o(" \u2014 "),_ue=a("a"),wbt=o("TFGPT2LMHeadModel"),Lbt=o(" (GPT-Sw3 model)"),Abt=l(),j9=a("li"),uJe=a("strong"),Rbt=o("gpt2"),kbt=o(" \u2014 "),bue=a("a"),Sbt=o("TFGPT2LMHeadModel"),xbt=o(" (OpenAI GPT-2 model)"),Gbt=l(),P9=a("li"),pJe=a("strong"),Bbt=o("gptj"),Ibt=o(" \u2014 "),vue=a("a"),Nbt=o("TFGPTJForCausalLM"),$bt=o(" (GPT-J model)"),jbt=l(),Z9=a("li"),_Je=a("strong"),Pbt=o("openai-gpt"),Zbt=o(" \u2014 "),Mue=a("a"),Vbt=o("TFOpenAIGPTLMHeadModel"),Wbt=o(" (OpenAI GPT model)"),Jbt=l(),V9=a("li"),bJe=a("strong"),Ubt=o("opt"),Xbt=o(" \u2014 "),Tue=a("a"),Dbt=o("TFOPTForCausalLM"),qbt=o(" (OPT model)"),Obt=l(),W9=a("li"),vJe=a("strong"),Qbt=o("rembert"),Ybt=o(" \u2014 "),Fue=a("a"),zbt=o("TFRemBertForCausalLM"),Hbt=o(" (RemBERT model)"),Kbt=l(),J9=a("li"),MJe=a("strong"),e2t=o("roberta"),o2t=o(" \u2014 "),Eue=a("a"),r2t=o("TFRobertaForCausalLM"),t2t=o(" (RoBERTa model)"),a2t=l(),U9=a("li"),TJe=a("strong"),n2t=o("roberta-prelayernorm"),s2t=o(" \u2014 "),yue=a("a"),l2t=o("TFRobertaPreLayerNormForCausalLM"),i2t=o(" (RoBERTa-PreLayerNorm model)"),d2t=l(),X9=a("li"),FJe=a("strong"),m2t=o("roformer"),c2t=o(" \u2014 "),Cue=a("a"),f2t=o("TFRoFormerForCausalLM"),h2t=o(" (RoFormer model)"),g2t=l(),D9=a("li"),EJe=a("strong"),u2t=o("transfo-xl"),p2t=o(" \u2014 "),wue=a("a"),_2t=o("TFTransfoXLLMHeadModel"),b2t=o(" (Transformer-XL model)"),v2t=l(),q9=a("li"),yJe=a("strong"),M2t=o("xglm"),T2t=o(" \u2014 "),Lue=a("a"),F2t=o("TFXGLMForCausalLM"),E2t=o(" (XGLM model)"),y2t=l(),O9=a("li"),CJe=a("strong"),C2t=o("xlm"),w2t=o(" \u2014 "),Aue=a("a"),L2t=o("TFXLMWithLMHeadModel"),A2t=o(" (XLM model)"),R2t=l(),Q9=a("li"),wJe=a("strong"),k2t=o("xlm-roberta"),S2t=o(" \u2014 "),Rue=a("a"),x2t=o("TFXLMRobertaForCausalLM"),G2t=o(" (XLM-RoBERTa model)"),B2t=l(),Y9=a("li"),LJe=a("strong"),I2t=o("xlnet"),N2t=o(" \u2014 "),kue=a("a"),$2t=o("TFXLNetLMHeadModel"),j2t=o(" (XLNet model)"),P2t=l(),v(z9.$$.fragment),Sxo=l(),Lc=a("h3"),H9=a("a"),AJe=a("span"),v(rW.$$.fragment),Z2t=l(),RJe=a("span"),V2t=o("FlaxAutoModelForCausalLM"),xxo=l(),Ho=a("div"),v(tW.$$.fragment),W2t=l(),Ac=a("p"),J2t=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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method.`),O2t=l(),aW=a("p"),Q2t=o("This class cannot be instantiated directly using "),kJe=a("code"),Y2t=o("__init__()"),z2t=o(" (throws an error)."),H2t=l(),Yt=a("div"),v(nW.$$.fragment),K2t=l(),SJe=a("p"),evt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),ovt=l(),Rc=a("p"),rvt=o(`Note:
Loading a model from its configuration file does `),xJe=a("strong"),tvt=o("not"),avt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Gue=a("a"),nvt=o("from_pretrained()"),svt=o(" to load the model weights."),lvt=l(),v(K9.$$.fragment),ivt=l(),it=a("div"),v(sW.$$.fragment),dvt=l(),GJe=a("p"),mvt=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),cvt=l(),qn=a("p"),fvt=o("The model class to instantiate is selected based on the "),BJe=a("code"),hvt=o("model_type"),gvt=o(` property of the config object (either
passed as an argument or loaded from `),IJe=a("code"),uvt=o("pretrained_model_name_or_path"),pvt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),NJe=a("code"),_vt=o("pretrained_model_name_or_path"),bvt=o(":"),vvt=l(),Le=a("ul"),eC=a("li"),$Je=a("strong"),Mvt=o("bart"),Tvt=o(" \u2014 "),Bue=a("a"),Fvt=o("FlaxBartForCausalLM"),Evt=o(" (BART model)"),yvt=l(),oC=a("li"),jJe=a("strong"),Cvt=o("bert"),wvt=o(" \u2014 "),Iue=a("a"),Lvt=o("FlaxBertForCausalLM"),Avt=o(" (BERT model)"),Rvt=l(),rC=a("li"),PJe=a("strong"),kvt=o("big_bird"),Svt=o(" \u2014 "),Nue=a("a"),xvt=o("FlaxBigBirdForCausalLM"),Gvt=o(" (BigBird model)"),Bvt=l(),tC=a("li"),ZJe=a("strong"),Ivt=o("bloom"),Nvt=o(" \u2014 "),$ue=a("a"),$vt=o("FlaxBloomForCausalLM"),jvt=o(" (BLOOM model)"),Pvt=l(),aC=a("li"),VJe=a("strong"),Zvt=o("electra"),Vvt=o(" \u2014 "),jue=a("a"),Wvt=o("FlaxElectraForCausalLM"),Jvt=o(" (ELECTRA model)"),Uvt=l(),nC=a("li"),WJe=a("strong"),Xvt=o("gpt-sw3"),Dvt=o(" \u2014 "),Pue=a("a"),qvt=o("FlaxGPT2LMHeadModel"),Ovt=o(" (GPT-Sw3 model)"),Qvt=l(),sC=a("li"),JJe=a("strong"),Yvt=o("gpt2"),zvt=o(" \u2014 "),Zue=a("a"),Hvt=o("FlaxGPT2LMHeadModel"),Kvt=o(" (OpenAI GPT-2 model)"),eMt=l(),lC=a("li"),UJe=a("strong"),oMt=o("gpt_neo"),rMt=o(" \u2014 "),Vue=a("a"),tMt=o("FlaxGPTNeoForCausalLM"),aMt=o(" (GPT Neo model)"),nMt=l(),iC=a("li"),XJe=a("strong"),sMt=o("gptj"),lMt=o(" \u2014 "),Wue=a("a"),iMt=o("FlaxGPTJForCausalLM"),dMt=o(" (GPT-J model)"),mMt=l(),dC=a("li"),DJe=a("strong"),cMt=o("opt"),fMt=o(" \u2014 "),Jue=a("a"),hMt=o("FlaxOPTForCausalLM"),gMt=o(" (OPT model)"),uMt=l(),mC=a("li"),qJe=a("strong"),pMt=o("roberta"),_Mt=o(" \u2014 "),Uue=a("a"),bMt=o("FlaxRobertaForCausalLM"),vMt=o(" (RoBERTa model)"),MMt=l(),cC=a("li"),OJe=a("strong"),TMt=o("roberta-prelayernorm"),FMt=o(" \u2014 "),Xue=a("a"),EMt=o("FlaxRobertaPreLayerNormForCausalLM"),yMt=o(" (RoBERTa-PreLayerNorm model)"),CMt=l(),fC=a("li"),QJe=a("strong"),wMt=o("xglm"),LMt=o(" \u2014 "),Due=a("a"),AMt=o("FlaxXGLMForCausalLM"),RMt=o(" (XGLM model)"),kMt=l(),hC=a("li"),YJe=a("strong"),SMt=o("xlm-roberta"),xMt=o(" \u2014 "),que=a("a"),GMt=o("FlaxXLMRobertaForCausalLM"),BMt=o(" (XLM-RoBERTa model)"),IMt=l(),v(gC.$$.fragment),Gxo=l(),kc=a("h3"),uC=a("a"),zJe=a("span"),v(lW.$$.fragment),NMt=l(),HJe=a("span"),$Mt=o("AutoModelForMaskedLM"),Bxo=l(),Ko=a("div"),v(iW.$$.fragment),jMt=l(),Sc=a("p"),PMt=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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method.`),UMt=l(),dW=a("p"),XMt=o("This class cannot be instantiated directly using "),KJe=a("code"),DMt=o("__init__()"),qMt=o(" (throws an error)."),OMt=l(),zt=a("div"),v(mW.$$.fragment),QMt=l(),eUe=a("p"),YMt=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),zMt=l(),xc=a("p"),HMt=o(`Note:
Loading a model from its configuration file does `),oUe=a("strong"),KMt=o("not"),eTt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Yue=a("a"),oTt=o("from_pretrained()"),rTt=o(" to load the model weights."),tTt=l(),v(pC.$$.fragment),aTt=l(),mo=a("div"),v(cW.$$.fragment),nTt=l(),rUe=a("p"),sTt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),lTt=l(),On=a("p"),iTt=o("The model class to instantiate is selected based on the "),tUe=a("code"),dTt=o("model_type"),mTt=o(` property of the config object (either
passed as an argument or loaded from `),aUe=a("code"),cTt=o("pretrained_model_name_or_path"),fTt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nUe=a("code"),hTt=o("pretrained_model_name_or_path"),gTt=o(":"),uTt=l(),K=a("ul"),_C=a("li"),sUe=a("strong"),pTt=o("albert"),_Tt=o(" \u2014 "),zue=a("a"),bTt=o("AlbertForMaskedLM"),vTt=o(" (ALBERT model)"),MTt=l(),bC=a("li"),lUe=a("strong"),TTt=o("bart"),FTt=o(" \u2014 "),Hue=a("a"),ETt=o("BartForConditionalGeneration"),yTt=o(" (BART model)"),CTt=l(),vC=a("li"),iUe=a("strong"),wTt=o("bert"),LTt=o(" \u2014 "),Kue=a("a"),ATt=o("BertForMaskedLM"),RTt=o(" (BERT model)"),kTt=l(),MC=a("li"),dUe=a("strong"),STt=o("big_bird"),xTt=o(" \u2014 "),epe=a("a"),GTt=o("BigBirdForMaskedLM"),BTt=o(" (BigBird model)"),ITt=l(),TC=a("li"),mUe=a("strong"),NTt=o("camembert"),$Tt=o(" \u2014 "),ope=a("a"),jTt=o("CamembertForMaskedLM"),PTt=o(" (CamemBERT model)"),ZTt=l(),FC=a("li"),cUe=a("strong"),VTt=o("convbert"),WTt=o(" \u2014 "),rpe=a("a"),JTt=o("ConvBertForMaskedLM"),UTt=o(" (ConvBERT model)"),XTt=l(),EC=a("li"),fUe=a("strong"),DTt=o("data2vec-text"),qTt=o(" 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model\u2019s configuration. Use `),Jpe=a("a"),Q0t=o("from_pretrained()"),Y0t=o(" to load the model weights."),z0t=l(),v(cw.$$.fragment),H0t=l(),dt=a("div"),v(pW.$$.fragment),K0t=l(),nXe=a("p"),e5t=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),o5t=l(),Qn=a("p"),r5t=o("The model class to instantiate is selected based on the "),sXe=a("code"),t5t=o("model_type"),a5t=o(` property of the config object (either
passed as an argument or loaded from `),lXe=a("code"),n5t=o("pretrained_model_name_or_path"),s5t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iXe=a("code"),l5t=o("pretrained_model_name_or_path"),i5t=o(":"),d5t=l(),pe=a("ul"),fw=a("li"),dXe=a("strong"),m5t=o("albert"),c5t=o(" \u2014 "),Upe=a("a"),f5t=o("TFAlbertForMaskedLM"),h5t=o(" (ALBERT model)"),g5t=l(),hw=a("li"),mXe=a("strong"),u5t=o("bert"),p5t=o(" \u2014 "),Xpe=a("a"),_5t=o("TFBertForMaskedLM"),b5t=o(" (BERT model)"),v5t=l(),gw=a("li"),cXe=a("strong"),M5t=o("camembert"),T5t=o(" \u2014 "),Dpe=a("a"),F5t=o("TFCamembertForMaskedLM"),E5t=o(" (CamemBERT model)"),y5t=l(),uw=a("li"),fXe=a("strong"),C5t=o("convbert"),w5t=o(" \u2014 "),qpe=a("a"),L5t=o("TFConvBertForMaskedLM"),A5t=o(" (ConvBERT model)"),R5t=l(),pw=a("li"),hXe=a("strong"),k5t=o("deberta"),S5t=o(" \u2014 "),Ope=a("a"),x5t=o("TFDebertaForMaskedLM"),G5t=o(" (DeBERTa model)"),B5t=l(),_w=a("li"),gXe=a("strong"),I5t=o("deberta-v2"),N5t=o(" \u2014 "),Qpe=a("a"),$5t=o("TFDebertaV2ForMaskedLM"),j5t=o(" (DeBERTa-v2 model)"),P5t=l(),bw=a("li"),uXe=a("strong"),Z5t=o("distilbert"),V5t=o(" \u2014 "),Ype=a("a"),W5t=o("TFDistilBertForMaskedLM"),J5t=o(" (DistilBERT model)"),U5t=l(),vw=a("li"),pXe=a("strong"),X5t=o("electra"),D5t=o(" \u2014 "),zpe=a("a"),q5t=o("TFElectraForMaskedLM"),O5t=o(" (ELECTRA model)"),Q5t=l(),Mw=a("li"),_Xe=a("strong"),Y5t=o("esm"),z5t=o(" \u2014 "),Hpe=a("a"),H5t=o("TFEsmForMaskedLM"),K5t=o(" (ESM model)"),e3t=l(),Tw=a("li"),bXe=a("strong"),o3t=o("flaubert"),r3t=o(" \u2014 "),Kpe=a("a"),t3t=o("TFFlaubertWithLMHeadModel"),a3t=o(" (FlauBERT model)"),n3t=l(),Fw=a("li"),vXe=a("strong"),s3t=o("funnel"),l3t=o(" \u2014 "),e_e=a("a"),i3t=o("TFFunnelForMaskedLM"),d3t=o(" (Funnel Transformer model)"),m3t=l(),Ew=a("li"),MXe=a("strong"),c3t=o("layoutlm"),f3t=o(" \u2014 "),o_e=a("a"),h3t=o("TFLayoutLMForMaskedLM"),g3t=o(" (LayoutLM model)"),u3t=l(),yw=a("li"),TXe=a("strong"),p3t=o("longformer"),_3t=o(" \u2014 "),r_e=a("a"),b3t=o("TFLongformerForMaskedLM"),v3t=o(" (Longformer model)"),M3t=l(),Cw=a("li"),FXe=a("strong"),T3t=o("mobilebert"),F3t=o(" \u2014 "),t_e=a("a"),E3t=o("TFMobileBertForMaskedLM"),y3t=o(" (MobileBERT model)"),C3t=l(),ww=a("li"),EXe=a("strong"),w3t=o("mpnet"),L3t=o(" \u2014 "),a_e=a("a"),A3t=o("TFMPNetForMaskedLM"),R3t=o(" (MPNet model)"),k3t=l(),Lw=a("li"),yXe=a("strong"),S3t=o("rembert"),x3t=o(" \u2014 "),n_e=a("a"),G3t=o("TFRemBertForMaskedLM"),B3t=o(" (RemBERT model)"),I3t=l(),Aw=a("li"),CXe=a("strong"),N3t=o("roberta"),$3t=o(" \u2014 "),s_e=a("a"),j3t=o("TFRobertaForMaskedLM"),P3t=o(" (RoBERTa model)"),Z3t=l(),Rw=a("li"),wXe=a("strong"),V3t=o("roberta-prelayernorm"),W3t=o(" \u2014 "),l_e=a("a"),J3t=o("TFRobertaPreLayerNormForMaskedLM"),U3t=o(" (RoBERTa-PreLayerNorm model)"),X3t=l(),kw=a("li"),LXe=a("strong"),D3t=o("roformer"),q3t=o(" \u2014 "),i_e=a("a"),O3t=o("TFRoFormerForMaskedLM"),Q3t=o(" (RoFormer model)"),Y3t=l(),Sw=a("li"),AXe=a("strong"),z3t=o("tapas"),H3t=o(" \u2014 "),d_e=a("a"),K3t=o("TFTapasForMaskedLM"),e4t=o(" (TAPAS model)"),o4t=l(),xw=a("li"),RXe=a("strong"),r4t=o("xlm"),t4t=o(" \u2014 "),m_e=a("a"),a4t=o("TFXLMWithLMHeadModel"),n4t=o(" (XLM model)"),s4t=l(),Gw=a("li"),kXe=a("strong"),l4t=o("xlm-roberta"),i4t=o(" \u2014 "),c_e=a("a"),d4t=o("TFXLMRobertaForMaskedLM"),m4t=o(" (XLM-RoBERTa model)"),c4t=l(),v(Bw.$$.fragment),$xo=l(),Nc=a("h3"),Iw=a("a"),SXe=a("span"),v(_W.$$.fragment),f4t=l(),xXe=a("span"),h4t=o("FlaxAutoModelForMaskedLM"),jxo=l(),or=a("div"),v(bW.$$.fragment),g4t=l(),$c=a("p"),u4t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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passed as an argument or loaded from `),jXe=a("code"),V4t=o("pretrained_model_name_or_path"),W4t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PXe=a("code"),J4t=o("pretrained_model_name_or_path"),U4t=o(":"),X4t=l(),ke=a("ul"),$w=a("li"),ZXe=a("strong"),D4t=o("albert"),q4t=o(" \u2014 "),u_e=a("a"),O4t=o("FlaxAlbertForMaskedLM"),Q4t=o(" (ALBERT model)"),Y4t=l(),jw=a("li"),VXe=a("strong"),z4t=o("bart"),H4t=o(" \u2014 "),p_e=a("a"),K4t=o("FlaxBartForConditionalGeneration"),e9t=o(" (BART model)"),o9t=l(),Pw=a("li"),WXe=a("strong"),r9t=o("bert"),t9t=o(" \u2014 "),__e=a("a"),a9t=o("FlaxBertForMaskedLM"),n9t=o(" (BERT model)"),s9t=l(),Zw=a("li"),JXe=a("strong"),l9t=o("big_bird"),i9t=o(" \u2014 "),b_e=a("a"),d9t=o("FlaxBigBirdForMaskedLM"),m9t=o(" (BigBird model)"),c9t=l(),Vw=a("li"),UXe=a("strong"),f9t=o("distilbert"),h9t=o(" \u2014 "),v_e=a("a"),g9t=o("FlaxDistilBertForMaskedLM"),u9t=o(" (DistilBERT model)"),p9t=l(),Ww=a("li"),XXe=a("strong"),_9t=o("electra"),b9t=o(" \u2014 "),M_e=a("a"),v9t=o("FlaxElectraForMaskedLM"),M9t=o(" (ELECTRA model)"),T9t=l(),Jw=a("li"),DXe=a("strong"),F9t=o("mbart"),E9t=o(" \u2014 "),T_e=a("a"),y9t=o("FlaxMBartForConditionalGeneration"),C9t=o(" (mBART model)"),w9t=l(),Uw=a("li"),qXe=a("strong"),L9t=o("roberta"),A9t=o(" \u2014 "),F_e=a("a"),R9t=o("FlaxRobertaForMaskedLM"),k9t=o(" (RoBERTa model)"),S9t=l(),Xw=a("li"),OXe=a("strong"),x9t=o("roberta-prelayernorm"),G9t=o(" \u2014 "),E_e=a("a"),B9t=o("FlaxRobertaPreLayerNormForMaskedLM"),I9t=o(" (RoBERTa-PreLayerNorm model)"),N9t=l(),Dw=a("li"),QXe=a("strong"),$9t=o("roformer"),j9t=o(" \u2014 "),y_e=a("a"),P9t=o("FlaxRoFormerForMaskedLM"),Z9t=o(" (RoFormer model)"),V9t=l(),qw=a("li"),YXe=a("strong"),W9t=o("xlm-roberta"),J9t=o(" \u2014 "),C_e=a("a"),U9t=o("FlaxXLMRobertaForMaskedLM"),X9t=o(" (XLM-RoBERTa model)"),D9t=l(),v(Ow.$$.fragment),Pxo=l(),Pc=a("h3"),Qw=a("a"),zXe=a("span"),v(FW.$$.fragment),q9t=l(),HXe=a("span"),O9t=o("AutoModelForMaskGeneration"),Zxo=l(),EW=a("div"),v(yW.$$.fragment),Vxo=l(),Zc=a("h3"),Yw=a("a"),KXe=a("span"),v(CW.$$.fragment),Q9t=l(),eDe=a("span"),Y9t=o("TFAutoModelForMaskGeneration"),Wxo=l(),wW=a("div"),v(LW.$$.fragment),Jxo=l(),Vc=a("h3"),zw=a("a"),oDe=a("span"),v(AW.$$.fragment),z9t=l(),rDe=a("span"),H9t=o("AutoModelForSeq2SeqLM"),Uxo=l(),rr=a("div"),v(RW.$$.fragment),K9t=l(),Wc=a("p"),eCt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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method.`),nCt=l(),kW=a("p"),sCt=o("This class cannot be instantiated directly using "),tDe=a("code"),lCt=o("__init__()"),iCt=o(" (throws an error)."),dCt=l(),ea=a("div"),v(SW.$$.fragment),mCt=l(),aDe=a("p"),cCt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),fCt=l(),Jc=a("p"),hCt=o(`Note:
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passed as an argument or loaded from `),iDe=a("code"),wCt=o("pretrained_model_name_or_path"),LCt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dDe=a("code"),ACt=o("pretrained_model_name_or_path"),RCt=o(":"),kCt=l(),ge=a("ul"),Kw=a("li"),mDe=a("strong"),SCt=o("bart"),xCt=o(" \u2014 "),R_e=a("a"),GCt=o("BartForConditionalGeneration"),BCt=o(" (BART model)"),ICt=l(),e6=a("li"),cDe=a("strong"),NCt=o("bigbird_pegasus"),$Ct=o(" \u2014 "),k_e=a("a"),jCt=o("BigBirdPegasusForConditionalGeneration"),PCt=o(" (BigBird-Pegasus model)"),ZCt=l(),o6=a("li"),fDe=a("strong"),VCt=o("blenderbot"),WCt=o(" \u2014 "),S_e=a("a"),JCt=o("BlenderbotForConditionalGeneration"),UCt=o(" (Blenderbot model)"),XCt=l(),r6=a("li"),hDe=a("strong"),DCt=o("blenderbot-small"),qCt=o(" \u2014 "),x_e=a("a"),OCt=o("BlenderbotSmallForConditionalGeneration"),QCt=o(" (BlenderbotSmall model)"),YCt=l(),t6=a("li"),gDe=a("strong"),zCt=o("encoder-decoder"),HCt=o(" \u2014 "),G_e=a("a"),KCt=o("EncoderDecoderModel"),ewt=o(" (Encoder decoder model)"),owt=l(),a6=a("li"),uDe=a("strong"),rwt=o("fsmt"),twt=o(" \u2014 "),B_e=a("a"),awt=o("FSMTForConditionalGeneration"),nwt=o(" (FairSeq Machine-Translation model)"),swt=l(),n6=a("li"),pDe=a("strong"),lwt=o("gptsan-japanese"),iwt=o(" \u2014 "),I_e=a("a"),dwt=o("GPTSanJapaneseForConditionalGeneration"),mwt=o(" (GPTSAN-japanese model)"),cwt=l(),s6=a("li"),_De=a("strong"),fwt=o("led"),hwt=o(" \u2014 "),N_e=a("a"),gwt=o("LEDForConditionalGeneration"),uwt=o(" (LED model)"),pwt=l(),l6=a("li"),bDe=a("strong"),_wt=o("longt5"),bwt=o(" \u2014 "),$_e=a("a"),vwt=o("LongT5ForConditionalGeneration"),Mwt=o(" (LongT5 model)"),Twt=l(),i6=a("li"),vDe=a("strong"),Fwt=o("m2m_100"),Ewt=o(" \u2014 "),j_e=a("a"),ywt=o("M2M100ForConditionalGeneration"),Cwt=o(" (M2M100 model)"),wwt=l(),d6=a("li"),MDe=a("strong"),Lwt=o("marian"),Awt=o(" \u2014 "),P_e=a("a"),Rwt=o("MarianMTModel"),kwt=o(" (Marian model)"),Swt=l(),m6=a("li"),TDe=a("strong"),xwt=o("mbart"),Gwt=o(" \u2014 "),Z_e=a("a"),Bwt=o("MBartForConditionalGeneration"),Iwt=o(" (mBART model)"),Nwt=l(),c6=a("li"),FDe=a("strong"),$wt=o("mt5"),jwt=o(" \u2014 "),V_e=a("a"),Pwt=o("MT5ForConditionalGeneration"),Zwt=o(" (MT5 model)"),Vwt=l(),f6=a("li"),EDe=a("strong"),Wwt=o("mvp"),Jwt=o(" \u2014 "),W_e=a("a"),Uwt=o("MvpForConditionalGeneration"),Xwt=o(" (MVP model)"),Dwt=l(),h6=a("li"),yDe=a("strong"),qwt=o("nllb-moe"),Owt=o(" \u2014 "),J_e=a("a"),Qwt=o("NllbMoeForConditionalGeneration"),Ywt=o(" (NLLB-MOE model)"),zwt=l(),g6=a("li"),CDe=a("strong"),Hwt=o("pegasus"),Kwt=o(" \u2014 "),U_e=a("a"),e6t=o("PegasusForConditionalGeneration"),o6t=o(" (Pegasus model)"),r6t=l(),u6=a("li"),wDe=a("strong"),t6t=o("pegasus_x"),a6t=o(" \u2014 "),X_e=a("a"),n6t=o("PegasusXForConditionalGeneration"),s6t=o(" (PEGASUS-X model)"),l6t=l(),p6=a("li"),LDe=a("strong"),i6t=o("plbart"),d6t=o(" \u2014 "),D_e=a("a"),m6t=o("PLBartForConditionalGeneration"),c6t=o(" (PLBart model)"),f6t=l(),_6=a("li"),ADe=a("strong"),h6t=o("prophetnet"),g6t=o(" \u2014 "),q_e=a("a"),u6t=o("ProphetNetForConditionalGeneration"),p6t=o(" (ProphetNet model)"),_6t=l(),b6=a("li"),RDe=a("strong"),b6t=o("switch_transformers"),v6t=o(" \u2014 "),O_e=a("a"),M6t=o("SwitchTransformersForConditionalGeneration"),T6t=o(" (SwitchTransformers model)"),F6t=l(),v6=a("li"),kDe=a("strong"),E6t=o("t5"),y6t=o(" \u2014 "),Q_e=a("a"),C6t=o("T5ForConditionalGeneration"),w6t=o(" (T5 model)"),L6t=l(),M6=a("li"),SDe=a("strong"),A6t=o("umt5"),R6t=o(" \u2014 "),Y_e=a("a"),k6t=o("UMT5ForConditionalGeneration"),S6t=o(" (UMT5 model)"),x6t=l(),T6=a("li"),xDe=a("strong"),G6t=o("xlm-prophetnet"),B6t=o(" \u2014 "),z_e=a("a"),I6t=o("XLMProphetNetForConditionalGeneration"),N6t=o(" (XLM-ProphetNet model)"),$6t=l(),F6=a("p"),j6t=o("The model is set in evaluation mode by default using "),GDe=a("code"),P6t=o("model.eval()"),Z6t=o(` (so for instance, dropout modules are
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model\u2019s configuration. Use `),qEe=a("a"),qpa=o("from_pretrained()"),Opa=o(" to load the model weights."),Qpa=l(),v(wx.$$.fragment),Ypa=l(),Ft=a("div"),v(mU.$$.fragment),zpa=l(),jro=a("p"),Hpa=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),Kpa=l(),gs=a("p"),e_a=o("The model class to instantiate is selected based on the "),Pro=a("code"),o_a=o("model_type"),r_a=o(` property of the config object (either
passed as an argument or loaded from `),Zro=a("code"),t_a=o("pretrained_model_name_or_path"),a_a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vro=a("code"),n_a=o("pretrained_model_name_or_path"),s_a=o(":"),l_a=l(),xe=a("ul"),Lx=a("li"),Wro=a("strong"),i_a=o("albert"),d_a=o(" \u2014 "),OEe=a("a"),m_a=o("FlaxAlbertForQuestionAnswering"),c_a=o(" (ALBERT model)"),f_a=l(),Ax=a("li"),Jro=a("strong"),h_a=o("bart"),g_a=o(" \u2014 "),QEe=a("a"),u_a=o("FlaxBartForQuestionAnswering"),p_a=o(" (BART model)"),__a=l(),Rx=a("li"),Uro=a("strong"),b_a=o("bert"),v_a=o(" \u2014 "),YEe=a("a"),M_a=o("FlaxBertForQuestionAnswering"),T_a=o(" (BERT model)"),F_a=l(),kx=a("li"),Xro=a("strong"),E_a=o("big_bird"),y_a=o(" \u2014 "),zEe=a("a"),C_a=o("FlaxBigBirdForQuestionAnswering"),w_a=o(" (BigBird model)"),L_a=l(),Sx=a("li"),Dro=a("strong"),A_a=o("distilbert"),R_a=o(" \u2014 "),HEe=a("a"),k_a=o("FlaxDistilBertForQuestionAnswering"),S_a=o(" (DistilBERT model)"),x_a=l(),xx=a("li"),qro=a("strong"),G_a=o("electra"),B_a=o(" \u2014 "),KEe=a("a"),I_a=o("FlaxElectraForQuestionAnswering"),N_a=o(" (ELECTRA model)"),$_a=l(),Gx=a("li"),Oro=a("strong"),j_a=o("mbart"),P_a=o(" \u2014 "),eye=a("a"),Z_a=o("FlaxMBartForQuestionAnswering"),V_a=o(" (mBART model)"),W_a=l(),Bx=a("li"),Qro=a("strong"),J_a=o("roberta"),U_a=o(" \u2014 "),oye=a("a"),X_a=o("FlaxRobertaForQuestionAnswering"),D_a=o(" (RoBERTa model)"),q_a=l(),Ix=a("li"),Yro=a("strong"),O_a=o("roberta-prelayernorm"),Q_a=o(" \u2014 "),rye=a("a"),Y_a=o("FlaxRobertaPreLayerNormForQuestionAnswering"),z_a=o(" (RoBERTa-PreLayerNorm model)"),H_a=l(),Nx=a("li"),zro=a("strong"),K_a=o("roformer"),e1a=o(" \u2014 "),tye=a("a"),o1a=o("FlaxRoFormerForQuestionAnswering"),r1a=o(" (RoFormer model)"),t1a=l(),$x=a("li"),Hro=a("strong"),a1a=o("xlm-roberta"),n1a=o(" \u2014 "),aye=a("a"),s1a=o("FlaxXLMRobertaForQuestionAnswering"),l1a=o(" (XLM-RoBERTa model)"),i1a=l(),v(jx.$$.fragment),CGo=l(),Wf=a("h3"),Px=a("a"),Kro=a("span"),v(cU.$$.fragment),d1a=l(),eto=a("span"),m1a=o("AutoModelForTextEncoding"),wGo=l(),fU=a("div"),v(hU.$$.fragment),LGo=l(),Jf=a("h3"),Zx=a("a"),oto=a("span"),v(gU.$$.fragment),c1a=l(),rto=a("span"),f1a=o("TFAutoModelForTextEncoding"),AGo=l(),uU=a("div"),v(pU.$$.fragment),RGo=l(),Uf=a("h2"),Vx=a("a"),tto=a("span"),v(_U.$$.fragment),h1a=l(),ato=a("span"),g1a=o("Computer vision"),kGo=l(),nye=a("p"),u1a=o("The following auto classes are available for the following computer vision tasks."),SGo=l(),Xf=a("h3"),Wx=a("a"),nto=a("span"),v(bU.$$.fragment),p1a=l(),sto=a("span"),_1a=o("AutoModelForDepthEstimation"),xGo=l(),Mr=a("div"),v(vU.$$.fragment),b1a=l(),Df=a("p"),v1a=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 `),sye=a("a"),M1a=o("from_pretrained()"),T1a=o(" class method or the "),lye=a("a"),F1a=o("from_config()"),E1a=o(` class
method.`),y1a=l(),MU=a("p"),C1a=o("This class cannot be instantiated directly using "),lto=a("code"),w1a=o("__init__()"),L1a=o(" (throws an error)."),A1a=l(),ba=a("div"),v(TU.$$.fragment),R1a=l(),ito=a("p"),k1a=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),S1a=l(),qf=a("p"),x1a=o(`Note:
Loading a model from its configuration file does `),dto=a("strong"),G1a=o("not"),B1a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),iye=a("a"),I1a=o("from_pretrained()"),N1a=o(" to load the model weights."),$1a=l(),v(Jx.$$.fragment),j1a=l(),_o=a("div"),v(FU.$$.fragment),P1a=l(),mto=a("p"),Z1a=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),V1a=l(),us=a("p"),W1a=o("The model class to instantiate is selected based on the "),cto=a("code"),J1a=o("model_type"),U1a=o(` property of the config object (either
passed as an argument or loaded from `),fto=a("code"),X1a=o("pretrained_model_name_or_path"),D1a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hto=a("code"),q1a=o("pretrained_model_name_or_path"),O1a=o(":"),Q1a=l(),EU=a("ul"),Ux=a("li"),gto=a("strong"),Y1a=o("dpt"),z1a=o(" \u2014 "),dye=a("a"),H1a=o("DPTForDepthEstimation"),K1a=o(" (DPT model)"),eba=l(),Xx=a("li"),uto=a("strong"),oba=o("glpn"),rba=o(" \u2014 "),mye=a("a"),tba=o("GLPNForDepthEstimation"),aba=o(" (GLPN model)"),nba=l(),Dx=a("p"),sba=o("The model is set in evaluation mode by default using "),pto=a("code"),lba=o("model.eval()"),iba=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_to=a("code"),dba=o("model.train()"),mba=l(),v(qx.$$.fragment),GGo=l(),Of=a("h3"),Ox=a("a"),bto=a("span"),v(yU.$$.fragment),cba=l(),vto=a("span"),fba=o("AutoModelForImageClassification"),BGo=l(),Tr=a("div"),v(CU.$$.fragment),hba=l(),Qf=a("p"),gba=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 `),cye=a("a"),uba=o("from_pretrained()"),pba=o(" class method or the "),fye=a("a"),_ba=o("from_config()"),bba=o(` class
method.`),vba=l(),wU=a("p"),Mba=o("This class cannot be instantiated directly using "),Mto=a("code"),Tba=o("__init__()"),Fba=o(" (throws an error)."),Eba=l(),va=a("div"),v(LU.$$.fragment),yba=l(),Tto=a("p"),Cba=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),wba=l(),Yf=a("p"),Lba=o(`Note:
Loading a model from its configuration file does `),Fto=a("strong"),Aba=o("not"),Rba=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),hye=a("a"),kba=o("from_pretrained()"),Sba=o(" to load the model weights."),xba=l(),v(Qx.$$.fragment),Gba=l(),bo=a("div"),v(AU.$$.fragment),Bba=l(),Eto=a("p"),Iba=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Nba=l(),ps=a("p"),$ba=o("The model class to instantiate is selected based on the "),yto=a("code"),jba=o("model_type"),Pba=o(` property of the config object (either
passed as an argument or loaded from `),Cto=a("code"),Zba=o("pretrained_model_name_or_path"),Vba=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wto=a("code"),Wba=o("pretrained_model_name_or_path"),Jba=o(":"),Uba=l(),le=a("ul"),Yx=a("li"),Lto=a("strong"),Xba=o("beit"),Dba=o(" \u2014 "),gye=a("a"),qba=o("BeitForImageClassification"),Oba=o(" (BEiT model)"),Qba=l(),zx=a("li"),Ato=a("strong"),Yba=o("bit"),zba=o(" \u2014 "),uye=a("a"),Hba=o("BitForImageClassification"),Kba=o(" (BiT model)"),e2a=l(),Hx=a("li"),Rto=a("strong"),o2a=o("convnext"),r2a=o(" \u2014 "),pye=a("a"),t2a=o("ConvNextForImageClassification"),a2a=o(" (ConvNeXT model)"),n2a=l(),Kx=a("li"),kto=a("strong"),s2a=o("convnextv2"),l2a=o(" \u2014 "),_ye=a("a"),i2a=o("ConvNextV2ForImageClassification"),d2a=o(" (ConvNeXTV2 model)"),m2a=l(),eG=a("li"),Sto=a("strong"),c2a=o("cvt"),f2a=o(" \u2014 "),bye=a("a"),h2a=o("CvtForImageClassification"),g2a=o(" (CvT model)"),u2a=l(),oG=a("li"),xto=a("strong"),p2a=o("data2vec-vision"),_2a=o(" \u2014 "),vye=a("a"),b2a=o("Data2VecVisionForImageClassification"),v2a=o(" (Data2VecVision model)"),M2a=l(),Zi=a("li"),Gto=a("strong"),T2a=o("deit"),F2a=o(" \u2014 "),Mye=a("a"),E2a=o("DeiTForImageClassification"),y2a=o(" or "),Tye=a("a"),C2a=o("DeiTForImageClassificationWithTeacher"),w2a=o(" (DeiT model)"),L2a=l(),rG=a("li"),Bto=a("strong"),A2a=o("dinat"),R2a=o(" \u2014 "),Fye=a("a"),k2a=o("DinatForImageClassification"),S2a=o(" (DiNAT model)"),x2a=l(),tG=a("li"),Ito=a("strong"),G2a=o("dinov2"),B2a=o(" \u2014 "),Eye=a("a"),I2a=o("Dinov2ForImageClassification"),N2a=o(" (DINOv2 model)"),$2a=l(),Vi=a("li"),Nto=a("strong"),j2a=o("efficientformer"),P2a=o(" \u2014 "),yye=a("a"),Z2a=o("EfficientFormerForImageClassification"),V2a=o(" or "),Cye=a("a"),W2a=o("EfficientFormerForImageClassificationWithTeacher"),J2a=o(" (EfficientFormer model)"),U2a=l(),aG=a("li"),$to=a("strong"),X2a=o("efficientnet"),D2a=o(" \u2014 "),wye=a("a"),q2a=o("EfficientNetForImageClassification"),O2a=o(" (EfficientNet model)"),Q2a=l(),nG=a("li"),jto=a("strong"),Y2a=o("focalnet"),z2a=o(" \u2014 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"),Bye=a("a"),kva=o("MobileViTV2ForImageClassification"),Sva=o(" (MobileViTV2 model)"),xva=l(),cG=a("li"),Xto=a("strong"),Gva=o("nat"),Bva=o(" \u2014 "),Iye=a("a"),Iva=o("NatForImageClassification"),Nva=o(" (NAT model)"),$va=l(),Ma=a("li"),Dto=a("strong"),jva=o("perceiver"),Pva=o(" \u2014 "),Nye=a("a"),Zva=o("PerceiverForImageClassificationLearned"),Vva=o(" or "),$ye=a("a"),Wva=o("PerceiverForImageClassificationFourier"),Jva=o(" or "),jye=a("a"),Uva=o("PerceiverForImageClassificationConvProcessing"),Xva=o(" (Perceiver model)"),Dva=l(),fG=a("li"),qto=a("strong"),qva=o("poolformer"),Ova=o(" \u2014 "),Pye=a("a"),Qva=o("PoolFormerForImageClassification"),Yva=o(" (PoolFormer model)"),zva=l(),hG=a("li"),Oto=a("strong"),Hva=o("pvt"),Kva=o(" \u2014 "),Zye=a("a"),eMa=o("PvtForImageClassification"),oMa=o(" (PVT model)"),rMa=l(),gG=a("li"),Qto=a("strong"),tMa=o("regnet"),aMa=o(" \u2014 "),Vye=a("a"),nMa=o("RegNetForImageClassification"),sMa=o(" (RegNet model)"),lMa=l(),uG=a("li"),Yto=a("strong"),iMa=o("resnet"),dMa=o(" \u2014 "),Wye=a("a"),mMa=o("ResNetForImageClassification"),cMa=o(" (ResNet model)"),fMa=l(),pG=a("li"),zto=a("strong"),hMa=o("segformer"),gMa=o(" \u2014 "),Jye=a("a"),uMa=o("SegformerForImageClassification"),pMa=o(" (SegFormer model)"),_Ma=l(),_G=a("li"),Hto=a("strong"),bMa=o("swiftformer"),vMa=o(" \u2014 "),Uye=a("a"),MMa=o("SwiftFormerForImageClassification"),TMa=o(" (SwiftFormer model)"),FMa=l(),bG=a("li"),Kto=a("strong"),EMa=o("swin"),yMa=o(" \u2014 "),Xye=a("a"),CMa=o("SwinForImageClassification"),wMa=o(" (Swin Transformer model)"),LMa=l(),vG=a("li"),eao=a("strong"),AMa=o("swinv2"),RMa=o(" \u2014 "),Dye=a("a"),kMa=o("Swinv2ForImageClassification"),SMa=o(" (Swin Transformer V2 model)"),xMa=l(),MG=a("li"),oao=a("strong"),GMa=o("van"),BMa=o(" \u2014 "),qye=a("a"),IMa=o("VanForImageClassification"),NMa=o(" (VAN model)"),$Ma=l(),TG=a("li"),rao=a("strong"),jMa=o("vit"),PMa=o(" \u2014 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method.`),fTa=l(),SU=a("p"),hTa=o("This class cannot be instantiated directly using "),dao=a("code"),gTa=o("__init__()"),uTa=o(" (throws an error)."),pTa=l(),Ta=a("div"),v(xU.$$.fragment),_Ta=l(),mao=a("p"),bTa=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),vTa=l(),Kf=a("p"),MTa=o(`Note:
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model\u2019s configuration. Use `),Kye=a("a"),ETa=o("from_pretrained()"),yTa=o(" to load the model weights."),CTa=l(),v(LG.$$.fragment),wTa=l(),Et=a("div"),v(GU.$$.fragment),LTa=l(),fao=a("p"),ATa=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),RTa=l(),_s=a("p"),kTa=o("The model class to instantiate is selected based on the "),hao=a("code"),STa=o("model_type"),xTa=o(` property of the config object (either
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falling back to using pattern matching on `),uao=a("code"),ITa=o("pretrained_model_name_or_path"),NTa=o(":"),$Ta=l(),Ge=a("ul"),AG=a("li"),pao=a("strong"),jTa=o("convnext"),PTa=o(" \u2014 "),e0e=a("a"),ZTa=o("TFConvNextForImageClassification"),VTa=o(" (ConvNeXT model)"),WTa=l(),RG=a("li"),_ao=a("strong"),JTa=o("cvt"),UTa=o(" \u2014 "),o0e=a("a"),XTa=o("TFCvtForImageClassification"),DTa=o(" (CvT model)"),qTa=l(),kG=a("li"),bao=a("strong"),OTa=o("data2vec-vision"),QTa=o(" \u2014 "),r0e=a("a"),YTa=o("TFData2VecVisionForImageClassification"),zTa=o(" (Data2VecVision model)"),HTa=l(),Ji=a("li"),vao=a("strong"),KTa=o("deit"),eFa=o(" \u2014 "),t0e=a("a"),oFa=o("TFDeiTForImageClassification"),rFa=o(" or "),a0e=a("a"),tFa=o("TFDeiTForImageClassificationWithTeacher"),aFa=o(" (DeiT model)"),nFa=l(),Ui=a("li"),Mao=a("strong"),sFa=o("efficientformer"),lFa=o(" \u2014 "),n0e=a("a"),iFa=o("TFEfficientFormerForImageClassification"),dFa=o(" or "),s0e=a("a"),mFa=o("TFEfficientFormerForImageClassificationWithTeacher"),cFa=o(" (EfficientFormer model)"),fFa=l(),SG=a("li"),Tao=a("strong"),hFa=o("mobilevit"),gFa=o(" \u2014 "),l0e=a("a"),uFa=o("TFMobileViTForImageClassification"),pFa=o(" (MobileViT model)"),_Fa=l(),xG=a("li"),Fao=a("strong"),bFa=o("regnet"),vFa=o(" \u2014 "),i0e=a("a"),MFa=o("TFRegNetForImageClassification"),TFa=o(" (RegNet model)"),FFa=l(),GG=a("li"),Eao=a("strong"),EFa=o("resnet"),yFa=o(" \u2014 "),d0e=a("a"),CFa=o("TFResNetForImageClassification"),wFa=o(" (ResNet model)"),LFa=l(),BG=a("li"),yao=a("strong"),AFa=o("segformer"),RFa=o(" \u2014 "),m0e=a("a"),kFa=o("TFSegformerForImageClassification"),SFa=o(" (SegFormer model)"),xFa=l(),IG=a("li"),Cao=a("strong"),GFa=o("swin"),BFa=o(" \u2014 "),c0e=a("a"),IFa=o("TFSwinForImageClassification"),NFa=o(" (Swin Transformer model)"),$Fa=l(),NG=a("li"),wao=a("strong"),jFa=o("vit"),PFa=o(" \u2014 "),f0e=a("a"),ZFa=o("TFViTForImageClassification"),VFa=o(" (ViT model)"),WFa=l(),v($G.$$.fragment),$Go=l(),eh=a("h3"),jG=a("a"),Lao=a("span"),v(BU.$$.fragment),JFa=l(),Aao=a("span"),UFa=o("FlaxAutoModelForImageClassification"),jGo=l(),Er=a("div"),v(IU.$$.fragment),XFa=l(),oh=a("p"),DFa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
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passed as an argument or loaded from `),Bao=a("code"),bEa=o("pretrained_model_name_or_path"),vEa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Iao=a("code"),MEa=o("pretrained_model_name_or_path"),TEa=o(":"),FEa=l(),vs=a("ul"),ZG=a("li"),Nao=a("strong"),EEa=o("beit"),yEa=o(" \u2014 "),p0e=a("a"),CEa=o("FlaxBeitForImageClassification"),wEa=o(" (BEiT model)"),LEa=l(),VG=a("li"),$ao=a("strong"),AEa=o("regnet"),REa=o(" \u2014 "),_0e=a("a"),kEa=o("FlaxRegNetForImageClassification"),SEa=o(" (RegNet model)"),xEa=l(),WG=a("li"),jao=a("strong"),GEa=o("resnet"),BEa=o(" \u2014 "),b0e=a("a"),IEa=o("FlaxResNetForImageClassification"),NEa=o(" (ResNet model)"),$Ea=l(),JG=a("li"),Pao=a("strong"),jEa=o("vit"),PEa=o(" \u2014 "),v0e=a("a"),ZEa=o("FlaxViTForImageClassification"),VEa=o(" (ViT model)"),WEa=l(),v(UG.$$.fragment),PGo=l(),th=a("h3"),XG=a("a"),Zao=a("span"),v(PU.$$.fragment),JEa=l(),Vao=a("span"),UEa=o("AutoModelForVideoClassification"),ZGo=l(),yr=a("div"),v(ZU.$$.fragment),XEa=l(),ah=a("p"),DEa=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
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method.`),zEa=l(),VU=a("p"),HEa=o("This class cannot be instantiated directly using "),Wao=a("code"),KEa=o("__init__()"),eya=o(" (throws an error)."),oya=l(),Ea=a("div"),v(WU.$$.fragment),rya=l(),Jao=a("p"),tya=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),aya=l(),nh=a("p"),nya=o(`Note:
Loading a model from its configuration file does `),Uao=a("strong"),sya=o("not"),lya=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),F0e=a("a"),iya=o("from_pretrained()"),dya=o(" to load the model weights."),mya=l(),v(DG.$$.fragment),cya=l(),vo=a("div"),v(JU.$$.fragment),fya=l(),Xao=a("p"),hya=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),gya=l(),Ms=a("p"),uya=o("The model class to instantiate is selected based on the "),Dao=a("code"),pya=o("model_type"),_ya=o(` property of the config object (either
passed as an argument or loaded from `),qao=a("code"),bya=o("pretrained_model_name_or_path"),vya=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Oao=a("code"),Mya=o("pretrained_model_name_or_path"),Tya=o(":"),Fya=l(),sh=a("ul"),qG=a("li"),Qao=a("strong"),Eya=o("timesformer"),yya=o(" \u2014 "),E0e=a("a"),Cya=o("TimesformerForVideoClassification"),wya=o(" (TimeSformer model)"),Lya=l(),OG=a("li"),Yao=a("strong"),Aya=o("videomae"),Rya=o(" \u2014 "),y0e=a("a"),kya=o("VideoMAEForVideoClassification"),Sya=o(" (VideoMAE model)"),xya=l(),QG=a("li"),zao=a("strong"),Gya=o("vivit"),Bya=o(" \u2014 "),C0e=a("a"),Iya=o("VivitForVideoClassification"),Nya=o(" (ViViT model)"),$ya=l(),YG=a("p"),jya=o("The model is set in evaluation mode by default using "),Hao=a("code"),Pya=o("model.eval()"),Zya=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kao=a("code"),Vya=o("model.train()"),Wya=l(),v(zG.$$.fragment),VGo=l(),lh=a("h3"),HG=a("a"),eno=a("span"),v(UU.$$.fragment),Jya=l(),ono=a("span"),Uya=o("AutoModelForMaskedImageModeling"),WGo=l(),Cr=a("div"),v(XU.$$.fragment),Xya=l(),ih=a("p"),Dya=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 `),w0e=a("a"),qya=o("from_pretrained()"),Oya=o(" class method or the "),L0e=a("a"),Qya=o("from_config()"),Yya=o(` class
method.`),zya=l(),DU=a("p"),Hya=o("This class cannot be instantiated directly using "),rno=a("code"),Kya=o("__init__()"),e0a=o(" (throws an error)."),o0a=l(),ya=a("div"),v(qU.$$.fragment),r0a=l(),tno=a("p"),t0a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),a0a=l(),dh=a("p"),n0a=o(`Note:
Loading a model from its configuration file does `),ano=a("strong"),s0a=o("not"),l0a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),A0e=a("a"),i0a=o("from_pretrained()"),d0a=o(" to load the model weights."),m0a=l(),v(KG.$$.fragment),c0a=l(),Mo=a("div"),v(OU.$$.fragment),f0a=l(),nno=a("p"),h0a=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),g0a=l(),Ts=a("p"),u0a=o("The model class to instantiate is selected based on the "),sno=a("code"),p0a=o("model_type"),_0a=o(` property of the config object (either
passed as an argument or loaded from `),lno=a("code"),b0a=o("pretrained_model_name_or_path"),v0a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ino=a("code"),M0a=o("pretrained_model_name_or_path"),T0a=o(":"),F0a=l(),Nt=a("ul"),eB=a("li"),dno=a("strong"),E0a=o("deit"),y0a=o(" \u2014 "),R0e=a("a"),C0a=o("DeiTForMaskedImageModeling"),w0a=o(" (DeiT model)"),L0a=l(),oB=a("li"),mno=a("strong"),A0a=o("focalnet"),R0a=o(" \u2014 "),k0e=a("a"),k0a=o("FocalNetForMaskedImageModeling"),S0a=o(" (FocalNet model)"),x0a=l(),rB=a("li"),cno=a("strong"),G0a=o("swin"),B0a=o(" \u2014 "),S0e=a("a"),I0a=o("SwinForMaskedImageModeling"),N0a=o(" (Swin Transformer model)"),$0a=l(),tB=a("li"),fno=a("strong"),j0a=o("swinv2"),P0a=o(" \u2014 "),x0e=a("a"),Z0a=o("Swinv2ForMaskedImageModeling"),V0a=o(" (Swin Transformer V2 model)"),W0a=l(),aB=a("li"),hno=a("strong"),J0a=o("vit"),U0a=o(" \u2014 "),G0e=a("a"),X0a=o("ViTForMaskedImageModeling"),D0a=o(" (ViT model)"),q0a=l(),nB=a("p"),O0a=o("The model is set in evaluation mode by default using "),gno=a("code"),Q0a=o("model.eval()"),Y0a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),uno=a("code"),z0a=o("model.train()"),H0a=l(),v(sB.$$.fragment),JGo=l(),mh=a("h3"),lB=a("a"),pno=a("span"),v(QU.$$.fragment),K0a=l(),_no=a("span"),e5a=o("TFAutoModelForMaskedImageModeling"),UGo=l(),wr=a("div"),v(YU.$$.fragment),o5a=l(),ch=a("p"),r5a=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 `),B0e=a("a"),t5a=o("from_pretrained()"),a5a=o(" class method or the "),I0e=a("a"),n5a=o("from_config()"),s5a=o(` class
method.`),l5a=l(),zU=a("p"),i5a=o("This class cannot be instantiated directly using "),bno=a("code"),d5a=o("__init__()"),m5a=o(" (throws an error)."),c5a=l(),Ca=a("div"),v(HU.$$.fragment),f5a=l(),vno=a("p"),h5a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),g5a=l(),fh=a("p"),u5a=o(`Note:
Loading a model from its configuration file does `),Mno=a("strong"),p5a=o("not"),_5a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),N0e=a("a"),b5a=o("from_pretrained()"),v5a=o(" to load the model weights."),M5a=l(),v(iB.$$.fragment),T5a=l(),Ct=a("div"),v(KU.$$.fragment),F5a=l(),Tno=a("p"),E5a=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),y5a=l(),Fs=a("p"),C5a=o("The model class to instantiate is selected based on the "),Fno=a("code"),w5a=o("model_type"),L5a=o(` property of the config object (either
passed as an argument or loaded from `),Eno=a("code"),A5a=o("pretrained_model_name_or_path"),R5a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yno=a("code"),k5a=o("pretrained_model_name_or_path"),S5a=o(":"),x5a=l(),eX=a("ul"),dB=a("li"),Cno=a("strong"),G5a=o("deit"),B5a=o(" \u2014 "),$0e=a("a"),I5a=o("TFDeiTForMaskedImageModeling"),N5a=o(" (DeiT model)"),$5a=l(),mB=a("li"),wno=a("strong"),j5a=o("swin"),P5a=o(" \u2014 "),j0e=a("a"),Z5a=o("TFSwinForMaskedImageModeling"),V5a=o(" (Swin Transformer model)"),W5a=l(),v(cB.$$.fragment),XGo=l(),hh=a("h3"),fB=a("a"),Lno=a("span"),v(oX.$$.fragment),J5a=l(),Ano=a("span"),U5a=o("AutoModelForObjectDetection"),DGo=l(),Lr=a("div"),v(rX.$$.fragment),X5a=l(),gh=a("p"),D5a=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 `),P0e=a("a"),q5a=o("from_pretrained()"),O5a=o(" class method or the "),Z0e=a("a"),Q5a=o("from_config()"),Y5a=o(` class
method.`),z5a=l(),tX=a("p"),H5a=o("This class cannot be instantiated directly using "),Rno=a("code"),K5a=o("__init__()"),e3a=o(" (throws an error)."),o3a=l(),wa=a("div"),v(aX.$$.fragment),r3a=l(),kno=a("p"),t3a=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),a3a=l(),uh=a("p"),n3a=o(`Note:
Loading a model from its configuration file does `),Sno=a("strong"),s3a=o("not"),l3a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),V0e=a("a"),i3a=o("from_pretrained()"),d3a=o(" to load the model weights."),m3a=l(),v(hB.$$.fragment),c3a=l(),To=a("div"),v(nX.$$.fragment),f3a=l(),xno=a("p"),h3a=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),g3a=l(),Es=a("p"),u3a=o("The model class to instantiate is selected based on the "),Gno=a("code"),p3a=o("model_type"),_3a=o(` property of the config object (either
passed as an argument or loaded from `),Bno=a("code"),b3a=o("pretrained_model_name_or_path"),v3a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ino=a("code"),M3a=o("pretrained_model_name_or_path"),T3a=o(":"),F3a=l(),Ar=a("ul"),gB=a("li"),Nno=a("strong"),E3a=o("conditional_detr"),y3a=o(" \u2014 "),W0e=a("a"),C3a=o("ConditionalDetrForObjectDetection"),w3a=o(" (Conditional DETR model)"),L3a=l(),uB=a("li"),$no=a("strong"),A3a=o("deformable_detr"),R3a=o(" \u2014 "),J0e=a("a"),k3a=o("DeformableDetrForObjectDetection"),S3a=o(" (Deformable DETR model)"),x3a=l(),pB=a("li"),jno=a("strong"),G3a=o("deta"),B3a=o(" \u2014 "),U0e=a("a"),I3a=o("DetaForObjectDetection"),N3a=o(" (DETA model)"),$3a=l(),_B=a("li"),Pno=a("strong"),j3a=o("detr"),P3a=o(" \u2014 "),X0e=a("a"),Z3a=o("DetrForObjectDetection"),V3a=o(" (DETR model)"),W3a=l(),bB=a("li"),Zno=a("strong"),J3a=o("table-transformer"),U3a=o(" \u2014 "),D0e=a("a"),X3a=o("TableTransformerForObjectDetection"),D3a=o(" (Table Transformer model)"),q3a=l(),vB=a("li"),Vno=a("strong"),O3a=o("yolos"),Q3a=o(" \u2014 "),q0e=a("a"),Y3a=o("YolosForObjectDetection"),z3a=o(" (YOLOS model)"),H3a=l(),MB=a("p"),K3a=o("The model is set in evaluation mode by default using "),Wno=a("code"),e4a=o("model.eval()"),o4a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Jno=a("code"),r4a=o("model.train()"),t4a=l(),v(TB.$$.fragment),qGo=l(),ph=a("h3"),FB=a("a"),Uno=a("span"),v(sX.$$.fragment),a4a=l(),Xno=a("span"),n4a=o("AutoModelForImageSegmentation"),OGo=l(),Rr=a("div"),v(lX.$$.fragment),s4a=l(),_h=a("p"),l4a=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 `),O0e=a("a"),i4a=o("from_pretrained()"),d4a=o(" class method or the "),Q0e=a("a"),m4a=o("from_config()"),c4a=o(` class
method.`),f4a=l(),iX=a("p"),h4a=o("This class cannot be instantiated directly using "),Dno=a("code"),g4a=o("__init__()"),u4a=o(" (throws an error)."),p4a=l(),La=a("div"),v(dX.$$.fragment),_4a=l(),qno=a("p"),b4a=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),v4a=l(),bh=a("p"),M4a=o(`Note:
Loading a model from its configuration file does `),Ono=a("strong"),T4a=o("not"),F4a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Y0e=a("a"),E4a=o("from_pretrained()"),y4a=o(" to load the model weights."),C4a=l(),v(EB.$$.fragment),w4a=l(),Fo=a("div"),v(mX.$$.fragment),L4a=l(),Qno=a("p"),A4a=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),R4a=l(),ys=a("p"),k4a=o("The model class to instantiate is selected based on the "),Yno=a("code"),S4a=o("model_type"),x4a=o(` property of the config object (either
passed as an argument or loaded from `),zno=a("code"),G4a=o("pretrained_model_name_or_path"),B4a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Hno=a("code"),I4a=o("pretrained_model_name_or_path"),N4a=o(":"),$4a=l(),Kno=a("ul"),yB=a("li"),eso=a("strong"),j4a=o("detr"),P4a=o(" \u2014 "),z0e=a("a"),Z4a=o("DetrForSegmentation"),V4a=o(" (DETR model)"),W4a=l(),CB=a("p"),J4a=o("The model is set in evaluation mode by default using "),oso=a("code"),U4a=o("model.eval()"),X4a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),rso=a("code"),D4a=o("model.train()"),q4a=l(),v(wB.$$.fragment),QGo=l(),vh=a("h3"),LB=a("a"),tso=a("span"),v(cX.$$.fragment),O4a=l(),aso=a("span"),Q4a=o("AutoModelForSemanticSegmentation"),YGo=l(),kr=a("div"),v(fX.$$.fragment),Y4a=l(),Mh=a("p"),z4a=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 `),H0e=a("a"),H4a=o("from_pretrained()"),K4a=o(" class method or the "),K0e=a("a"),e9a=o("from_config()"),o9a=o(` class
method.`),r9a=l(),hX=a("p"),t9a=o("This class cannot be instantiated directly using "),nso=a("code"),a9a=o("__init__()"),n9a=o(" (throws an error)."),s9a=l(),Aa=a("div"),v(gX.$$.fragment),l9a=l(),sso=a("p"),i9a=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),d9a=l(),Th=a("p"),m9a=o(`Note:
Loading a model from its configuration file does `),lso=a("strong"),c9a=o("not"),f9a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),e5e=a("a"),h9a=o("from_pretrained()"),g9a=o(" to load the model weights."),u9a=l(),v(AB.$$.fragment),p9a=l(),Eo=a("div"),v(uX.$$.fragment),_9a=l(),iso=a("p"),b9a=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),v9a=l(),Cs=a("p"),M9a=o("The model class to instantiate is selected based on the "),dso=a("code"),T9a=o("model_type"),F9a=o(` property of the config object (either
passed as an argument or loaded from `),mso=a("code"),E9a=o("pretrained_model_name_or_path"),y9a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cso=a("code"),C9a=o("pretrained_model_name_or_path"),w9a=o(":"),L9a=l(),Ye=a("ul"),RB=a("li"),fso=a("strong"),A9a=o("beit"),R9a=o(" \u2014 "),o5e=a("a"),k9a=o("BeitForSemanticSegmentation"),S9a=o(" (BEiT model)"),x9a=l(),kB=a("li"),hso=a("strong"),G9a=o("data2vec-vision"),B9a=o(" \u2014 "),r5e=a("a"),I9a=o("Data2VecVisionForSemanticSegmentation"),N9a=o(" (Data2VecVision model)"),$9a=l(),SB=a("li"),gso=a("strong"),j9a=o("dpt"),P9a=o(" \u2014 "),t5e=a("a"),Z9a=o("DPTForSemanticSegmentation"),V9a=o(" (DPT model)"),W9a=l(),xB=a("li"),uso=a("strong"),J9a=o("mobilenet_v2"),U9a=o(" \u2014 "),a5e=a("a"),X9a=o("MobileNetV2ForSemanticSegmentation"),D9a=o(" (MobileNetV2 model)"),q9a=l(),GB=a("li"),pso=a("strong"),O9a=o("mobilevit"),Q9a=o(" \u2014 "),n5e=a("a"),Y9a=o("MobileViTForSemanticSegmentation"),z9a=o(" (MobileViT model)"),H9a=l(),BB=a("li"),_so=a("strong"),K9a=o("mobilevitv2"),eCa=o(" \u2014 "),s5e=a("a"),oCa=o("MobileViTV2ForSemanticSegmentation"),rCa=o(" (MobileViTV2 model)"),tCa=l(),IB=a("li"),bso=a("strong"),aCa=o("segformer"),nCa=o(" \u2014 "),l5e=a("a"),sCa=o("SegformerForSemanticSegmentation"),lCa=o(" (SegFormer model)"),iCa=l(),NB=a("li"),vso=a("strong"),dCa=o("upernet"),mCa=o(" \u2014 "),i5e=a("a"),cCa=o("UperNetForSemanticSegmentation"),fCa=o(" (UPerNet model)"),hCa=l(),$B=a("p"),gCa=o("The model is set in evaluation mode by default using "),Mso=a("code"),uCa=o("model.eval()"),pCa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Tso=a("code"),_Ca=o("model.train()"),bCa=l(),v(jB.$$.fragment),zGo=l(),Fh=a("h3"),PB=a("a"),Fso=a("span"),v(pX.$$.fragment),vCa=l(),Eso=a("span"),MCa=o("TFAutoModelForSemanticSegmentation"),HGo=l(),Sr=a("div"),v(_X.$$.fragment),TCa=l(),Eh=a("p"),FCa=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.`),LCa=l(),bX=a("p"),ACa=o("This class cannot be instantiated directly using "),yso=a("code"),RCa=o("__init__()"),kCa=o(" (throws an error)."),SCa=l(),Ra=a("div"),v(vX.$$.fragment),xCa=l(),Cso=a("p"),GCa=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),BCa=l(),yh=a("p"),ICa=o(`Note:
Loading a model from its configuration file does `),wso=a("strong"),NCa=o("not"),$Ca=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),c5e=a("a"),jCa=o("from_pretrained()"),PCa=o(" to load the model weights."),ZCa=l(),v(ZB.$$.fragment),VCa=l(),wt=a("div"),v(MX.$$.fragment),WCa=l(),Lso=a("p"),JCa=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),UCa=l(),ws=a("p"),XCa=o("The model class to instantiate is selected based on the "),Aso=a("code"),DCa=o("model_type"),qCa=o(` property of the config object (either
passed as an argument or loaded from `),Rso=a("code"),OCa=o("pretrained_model_name_or_path"),QCa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),kso=a("code"),YCa=o("pretrained_model_name_or_path"),zCa=o(":"),HCa=l(),Ch=a("ul"),VB=a("li"),Sso=a("strong"),KCa=o("data2vec-vision"),ewa=o(" \u2014 "),f5e=a("a"),owa=o("TFData2VecVisionForSemanticSegmentation"),rwa=o(" (Data2VecVision model)"),twa=l(),WB=a("li"),xso=a("strong"),awa=o("mobilevit"),nwa=o(" \u2014 "),h5e=a("a"),swa=o("TFMobileViTForSemanticSegmentation"),lwa=o(" (MobileViT model)"),iwa=l(),JB=a("li"),Gso=a("strong"),dwa=o("segformer"),mwa=o(" \u2014 "),g5e=a("a"),cwa=o("TFSegformerForSemanticSegmentation"),fwa=o(" (SegFormer model)"),hwa=l(),v(UB.$$.fragment),KGo=l(),wh=a("h3"),XB=a("a"),Bso=a("span"),v(TX.$$.fragment),gwa=l(),Iso=a("span"),uwa=o("AutoModelForInstanceSegmentation"),eBo=l(),xr=a("div"),v(FX.$$.fragment),pwa=l(),Lh=a("p"),_wa=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 `),u5e=a("a"),bwa=o("from_pretrained()"),vwa=o(" class method or the "),p5e=a("a"),Mwa=o("from_config()"),Twa=o(` class
method.`),Fwa=l(),EX=a("p"),Ewa=o("This class cannot be instantiated directly using "),Nso=a("code"),ywa=o("__init__()"),Cwa=o(" (throws an error)."),wwa=l(),ka=a("div"),v(yX.$$.fragment),Lwa=l(),$so=a("p"),Awa=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Rwa=l(),Ah=a("p"),kwa=o(`Note:
Loading a model from its configuration file does `),jso=a("strong"),Swa=o("not"),xwa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_5e=a("a"),Gwa=o("from_pretrained()"),Bwa=o(" to load the model weights."),Iwa=l(),v(DB.$$.fragment),Nwa=l(),yo=a("div"),v(CX.$$.fragment),$wa=l(),Pso=a("p"),jwa=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),Pwa=l(),Ls=a("p"),Zwa=o("The model class to instantiate is selected based on the "),Zso=a("code"),Vwa=o("model_type"),Wwa=o(` property of the config object (either
passed as an argument or loaded from `),Vso=a("code"),Jwa=o("pretrained_model_name_or_path"),Uwa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Wso=a("code"),Xwa=o("pretrained_model_name_or_path"),Dwa=o(":"),qwa=l(),Jso=a("ul"),qB=a("li"),Uso=a("strong"),Owa=o("maskformer"),Qwa=o(" \u2014 "),b5e=a("a"),Ywa=o("MaskFormerForInstanceSegmentation"),zwa=o(" (MaskFormer model)"),Hwa=l(),OB=a("p"),Kwa=o("The model is set in evaluation mode by default using "),Xso=a("code"),e6a=o("model.eval()"),o6a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Dso=a("code"),r6a=o("model.train()"),t6a=l(),v(QB.$$.fragment),oBo=l(),Rh=a("h3"),YB=a("a"),qso=a("span"),v(wX.$$.fragment),a6a=l(),Oso=a("span"),n6a=o("AutoModelForUniversalSegmentation"),rBo=l(),Gr=a("div"),v(LX.$$.fragment),s6a=l(),kh=a("p"),l6a=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
with the `),v5e=a("a"),i6a=o("from_pretrained()"),d6a=o(" class method or the "),M5e=a("a"),m6a=o("from_config()"),c6a=o(` class
method.`),f6a=l(),AX=a("p"),h6a=o("This class cannot be instantiated directly using "),Qso=a("code"),g6a=o("__init__()"),u6a=o(" (throws an error)."),p6a=l(),Sa=a("div"),v(RX.$$.fragment),_6a=l(),Yso=a("p"),b6a=o("Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),v6a=l(),Sh=a("p"),M6a=o(`Note:
Loading a model from its configuration file does `),zso=a("strong"),T6a=o("not"),F6a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),T5e=a("a"),E6a=o("from_pretrained()"),y6a=o(" to load the model weights."),C6a=l(),v(zB.$$.fragment),w6a=l(),Co=a("div"),v(kX.$$.fragment),L6a=l(),Hso=a("p"),A6a=o("Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),R6a=l(),As=a("p"),k6a=o("The model class to instantiate is selected based on the "),Kso=a("code"),S6a=o("model_type"),x6a=o(` property of the config object (either
passed as an argument or loaded from `),elo=a("code"),G6a=o("pretrained_model_name_or_path"),B6a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),olo=a("code"),I6a=o("pretrained_model_name_or_path"),N6a=o(":"),$6a=l(),Rs=a("ul"),HB=a("li"),rlo=a("strong"),j6a=o("detr"),P6a=o(" \u2014 "),F5e=a("a"),Z6a=o("DetrForSegmentation"),V6a=o(" (DETR model)"),W6a=l(),KB=a("li"),tlo=a("strong"),J6a=o("mask2former"),U6a=o(" \u2014 "),E5e=a("a"),X6a=o("Mask2FormerForUniversalSegmentation"),D6a=o(" (Mask2Former model)"),q6a=l(),eI=a("li"),alo=a("strong"),O6a=o("maskformer"),Q6a=o(" \u2014 "),y5e=a("a"),Y6a=o("MaskFormerForInstanceSegmentation"),z6a=o(" (MaskFormer model)"),H6a=l(),oI=a("li"),nlo=a("strong"),K6a=o("oneformer"),e8a=o(" \u2014 "),C5e=a("a"),o8a=o("OneFormerForUniversalSegmentation"),r8a=o(" (OneFormer model)"),t8a=l(),rI=a("p"),a8a=o("The model is set in evaluation mode by default using "),slo=a("code"),n8a=o("model.eval()"),s8a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),llo=a("code"),l8a=o("model.train()"),i8a=l(),v(tI.$$.fragment),tBo=l(),xh=a("h3"),aI=a("a"),ilo=a("span"),v(SX.$$.fragment),d8a=l(),dlo=a("span"),m8a=o("AutoModelForZeroShotImageClassification"),aBo=l(),Br=a("div"),v(xX.$$.fragment),c8a=l(),Gh=a("p"),f8a=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 `),w5e=a("a"),h8a=o("from_pretrained()"),g8a=o(" class method or the "),L5e=a("a"),u8a=o("from_config()"),p8a=o(` class
method.`),_8a=l(),GX=a("p"),b8a=o("This class cannot be instantiated directly using "),mlo=a("code"),v8a=o("__init__()"),M8a=o(" (throws an error)."),T8a=l(),xa=a("div"),v(BX.$$.fragment),F8a=l(),clo=a("p"),E8a=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),y8a=l(),Bh=a("p"),C8a=o(`Note:
Loading a model from its configuration file does `),flo=a("strong"),w8a=o("not"),L8a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),A5e=a("a"),A8a=o("from_pretrained()"),R8a=o(" to load the model weights."),k8a=l(),v(nI.$$.fragment),S8a=l(),wo=a("div"),v(IX.$$.fragment),x8a=l(),hlo=a("p"),G8a=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),B8a=l(),ks=a("p"),I8a=o("The model class to instantiate is selected based on the "),glo=a("code"),N8a=o("model_type"),$8a=o(` property of the config object (either
passed as an argument or loaded from `),ulo=a("code"),j8a=o("pretrained_model_name_or_path"),P8a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),plo=a("code"),Z8a=o("pretrained_model_name_or_path"),V8a=o(":"),W8a=l(),Ir=a("ul"),sI=a("li"),_lo=a("strong"),J8a=o("align"),U8a=o(" \u2014 "),R5e=a("a"),X8a=o("AlignModel"),D8a=o(" (ALIGN model)"),q8a=l(),lI=a("li"),blo=a("strong"),O8a=o("altclip"),Q8a=o(" \u2014 "),k5e=a("a"),Y8a=o("AltCLIPModel"),z8a=o(" (AltCLIP model)"),H8a=l(),iI=a("li"),vlo=a("strong"),K8a=o("blip"),e7a=o(" \u2014 "),S5e=a("a"),o7a=o("BlipModel"),r7a=o(" (BLIP model)"),t7a=l(),dI=a("li"),Mlo=a("strong"),a7a=o("chinese_clip"),n7a=o(" \u2014 "),x5e=a("a"),s7a=o("ChineseCLIPModel"),l7a=o(" (Chinese-CLIP model)"),i7a=l(),mI=a("li"),Tlo=a("strong"),d7a=o("clip"),m7a=o(" \u2014 "),G5e=a("a"),c7a=o("CLIPModel"),f7a=o(" (CLIP model)"),h7a=l(),cI=a("li"),Flo=a("strong"),g7a=o("clipseg"),u7a=o(" \u2014 "),B5e=a("a"),p7a=o("CLIPSegModel"),_7a=o(" (CLIPSeg model)"),b7a=l(),fI=a("p"),v7a=o("The model is set in evaluation mode by default using "),Elo=a("code"),M7a=o("model.eval()"),T7a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ylo=a("code"),F7a=o("model.train()"),E7a=l(),v(hI.$$.fragment),nBo=l(),Ih=a("h3"),gI=a("a"),Clo=a("span"),v(NX.$$.fragment),y7a=l(),wlo=a("span"),C7a=o("TFAutoModelForZeroShotImageClassification"),sBo=l(),Nr=a("div"),v($X.$$.fragment),w7a=l(),Nh=a("p"),L7a=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 `),I5e=a("a"),A7a=o("from_pretrained()"),R7a=o(" class method or the "),N5e=a("a"),k7a=o("from_config()"),S7a=o(` class
method.`),x7a=l(),jX=a("p"),G7a=o("This class cannot be instantiated directly using "),Llo=a("code"),B7a=o("__init__()"),I7a=o(" (throws an error)."),N7a=l(),Ga=a("div"),v(PX.$$.fragment),$7a=l(),Alo=a("p"),j7a=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),P7a=l(),$h=a("p"),Z7a=o(`Note:
Loading a model from its configuration file does `),Rlo=a("strong"),V7a=o("not"),W7a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),$5e=a("a"),J7a=o("from_pretrained()"),U7a=o(" to load the model weights."),X7a=l(),v(uI.$$.fragment),D7a=l(),Lt=a("div"),v(ZX.$$.fragment),q7a=l(),klo=a("p"),O7a=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Q7a=l(),Ss=a("p"),Y7a=o("The model class to instantiate is selected based on the "),Slo=a("code"),z7a=o("model_type"),H7a=o(` property of the config object (either
passed as an argument or loaded from `),xlo=a("code"),K7a=o("pretrained_model_name_or_path"),eLa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Glo=a("code"),oLa=o("pretrained_model_name_or_path"),rLa=o(":"),tLa=l(),VX=a("ul"),pI=a("li"),Blo=a("strong"),aLa=o("blip"),nLa=o(" \u2014 "),j5e=a("a"),sLa=o("TFBlipModel"),lLa=o(" (BLIP model)"),iLa=l(),_I=a("li"),Ilo=a("strong"),dLa=o("clip"),mLa=o(" \u2014 "),P5e=a("a"),cLa=o("TFCLIPModel"),fLa=o(" (CLIP model)"),hLa=l(),v(bI.$$.fragment),lBo=l(),jh=a("h3"),vI=a("a"),Nlo=a("span"),v(WX.$$.fragment),gLa=l(),$lo=a("span"),uLa=o("AutoModelForZeroShotObjectDetection"),iBo=l(),$r=a("div"),v(JX.$$.fragment),pLa=l(),Ph=a("p"),_La=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 `),Z5e=a("a"),bLa=o("from_pretrained()"),vLa=o(" class method or the "),V5e=a("a"),MLa=o("from_config()"),TLa=o(` class
method.`),FLa=l(),UX=a("p"),ELa=o("This class cannot be instantiated directly using "),jlo=a("code"),yLa=o("__init__()"),CLa=o(" (throws an error)."),wLa=l(),Ba=a("div"),v(XX.$$.fragment),LLa=l(),Plo=a("p"),ALa=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),RLa=l(),Zh=a("p"),kLa=o(`Note:
Loading a model from its configuration file does `),Zlo=a("strong"),SLa=o("not"),xLa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),W5e=a("a"),GLa=o("from_pretrained()"),BLa=o(" to load the model weights."),ILa=l(),v(MI.$$.fragment),NLa=l(),Lo=a("div"),v(DX.$$.fragment),$La=l(),Vlo=a("p"),jLa=o("Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),PLa=l(),xs=a("p"),ZLa=o("The model class to instantiate is selected based on the "),Wlo=a("code"),VLa=o("model_type"),WLa=o(` property of the config object (either
passed as an argument or loaded from `),Jlo=a("code"),JLa=o("pretrained_model_name_or_path"),ULa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ulo=a("code"),XLa=o("pretrained_model_name_or_path"),DLa=o(":"),qLa=l(),Xlo=a("ul"),TI=a("li"),Dlo=a("strong"),OLa=o("owlvit"),QLa=o(" \u2014 "),J5e=a("a"),YLa=o("OwlViTForObjectDetection"),zLa=o(" (OWL-ViT model)"),HLa=l(),FI=a("p"),KLa=o("The model is set in evaluation mode by default using "),qlo=a("code"),eAa=o("model.eval()"),oAa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Olo=a("code"),rAa=o("model.train()"),tAa=l(),v(EI.$$.fragment),dBo=l(),Vh=a("h2"),yI=a("a"),Qlo=a("span"),v(qX.$$.fragment),aAa=l(),Ylo=a("span"),nAa=o("Audio"),mBo=l(),U5e=a("p"),sAa=o("The following auto classes are available for the following audio tasks."),cBo=l(),Wh=a("h3"),CI=a("a"),zlo=a("span"),v(OX.$$.fragment),lAa=l(),Hlo=a("span"),iAa=o("AutoModelForAudioClassification"),fBo=l(),jr=a("div"),v(QX.$$.fragment),dAa=l(),Jh=a("p"),mAa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),X5e=a("a"),cAa=o("from_pretrained()"),fAa=o(" class method or the "),D5e=a("a"),hAa=o("from_config()"),gAa=o(` class
method.`),uAa=l(),YX=a("p"),pAa=o("This class cannot be instantiated directly using "),Klo=a("code"),_Aa=o("__init__()"),bAa=o(" (throws an error)."),vAa=l(),Ia=a("div"),v(zX.$$.fragment),MAa=l(),eio=a("p"),TAa=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),FAa=l(),Uh=a("p"),EAa=o(`Note:
Loading a model from its configuration file does `),oio=a("strong"),yAa=o("not"),CAa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),q5e=a("a"),wAa=o("from_pretrained()"),LAa=o(" to load the model weights."),AAa=l(),v(wI.$$.fragment),RAa=l(),Ao=a("div"),v(HX.$$.fragment),kAa=l(),rio=a("p"),SAa=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),xAa=l(),Gs=a("p"),GAa=o("The model class to instantiate is selected based on the "),tio=a("code"),BAa=o("model_type"),IAa=o(` property of the config object (either
passed as an argument or loaded from `),aio=a("code"),NAa=o("pretrained_model_name_or_path"),$Aa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nio=a("code"),jAa=o("pretrained_model_name_or_path"),PAa=o(":"),ZAa=l(),Be=a("ul"),LI=a("li"),sio=a("strong"),VAa=o("audio-spectrogram-transformer"),WAa=o(" \u2014 "),O5e=a("a"),JAa=o("ASTForAudioClassification"),UAa=o(" (Audio Spectrogram Transformer model)"),XAa=l(),AI=a("li"),lio=a("strong"),DAa=o("data2vec-audio"),qAa=o(" \u2014 "),Q5e=a("a"),OAa=o("Data2VecAudioForSequenceClassification"),QAa=o(" (Data2VecAudio model)"),YAa=l(),RI=a("li"),iio=a("strong"),zAa=o("hubert"),HAa=o(" \u2014 "),Y5e=a("a"),KAa=o("HubertForSequenceClassification"),eRa=o(" (Hubert model)"),oRa=l(),kI=a("li"),dio=a("strong"),rRa=o("sew"),tRa=o(" \u2014 "),z5e=a("a"),aRa=o("SEWForSequenceClassification"),nRa=o(" (SEW model)"),sRa=l(),SI=a("li"),mio=a("strong"),lRa=o("sew-d"),iRa=o(" \u2014 "),H5e=a("a"),dRa=o("SEWDForSequenceClassification"),mRa=o(" (SEW-D model)"),cRa=l(),xI=a("li"),cio=a("strong"),fRa=o("unispeech"),hRa=o(" \u2014 "),K5e=a("a"),gRa=o("UniSpeechForSequenceClassification"),uRa=o(" (UniSpeech model)"),pRa=l(),GI=a("li"),fio=a("strong"),_Ra=o("unispeech-sat"),bRa=o(" \u2014 "),e3e=a("a"),vRa=o("UniSpeechSatForSequenceClassification"),MRa=o(" (UniSpeechSat model)"),TRa=l(),BI=a("li"),hio=a("strong"),FRa=o("wav2vec2"),ERa=o(" \u2014 "),o3e=a("a"),yRa=o("Wav2Vec2ForSequenceClassification"),CRa=o(" (Wav2Vec2 model)"),wRa=l(),II=a("li"),gio=a("strong"),LRa=o("wav2vec2-conformer"),ARa=o(" \u2014 "),r3e=a("a"),RRa=o("Wav2Vec2ConformerForSequenceClassification"),kRa=o(" (Wav2Vec2-Conformer model)"),SRa=l(),NI=a("li"),uio=a("strong"),xRa=o("wavlm"),GRa=o(" \u2014 "),t3e=a("a"),BRa=o("WavLMForSequenceClassification"),IRa=o(" (WavLM model)"),NRa=l(),$I=a("li"),pio=a("strong"),$Ra=o("whisper"),jRa=o(" \u2014 "),a3e=a("a"),PRa=o("WhisperForAudioClassification"),ZRa=o(" (Whisper model)"),VRa=l(),jI=a("p"),WRa=o("The model is set in evaluation mode by default using "),_io=a("code"),JRa=o("model.eval()"),URa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),bio=a("code"),XRa=o("model.train()"),DRa=l(),v(PI.$$.fragment),hBo=l(),Xh=a("h3"),ZI=a("a"),vio=a("span"),v(KX.$$.fragment),qRa=l(),Mio=a("span"),ORa=o("AutoModelForAudioFrameClassification"),gBo=l(),Pr=a("div"),v(eD.$$.fragment),QRa=l(),Dh=a("p"),YRa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
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method.`),oka=l(),oD=a("p"),rka=o("This class cannot be instantiated directly using "),Tio=a("code"),tka=o("__init__()"),aka=o(" (throws an error)."),nka=l(),Na=a("div"),v(rD.$$.fragment),ska=l(),Fio=a("p"),lka=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),ika=l(),qh=a("p"),dka=o(`Note:
Loading a model from its configuration file does `),Eio=a("strong"),mka=o("not"),cka=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),l3e=a("a"),fka=o("from_pretrained()"),hka=o(" to load the model weights."),gka=l(),v(VI.$$.fragment),uka=l(),At=a("div"),v(tD.$$.fragment),pka=l(),yio=a("p"),_ka=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),bka=l(),Bs=a("p"),vka=o("The model class to instantiate is selected based on the "),Cio=a("code"),Mka=o("model_type"),Tka=o(` property of the config object (either
passed as an argument or loaded from `),wio=a("code"),Fka=o("pretrained_model_name_or_path"),Eka=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lio=a("code"),yka=o("pretrained_model_name_or_path"),Cka=o(":"),wka=l(),Aio=a("ul"),WI=a("li"),Rio=a("strong"),Lka=o("wav2vec2"),Aka=o(" \u2014 "),i3e=a("a"),Rka=o("TFWav2Vec2ForSequenceClassification"),kka=o(" (Wav2Vec2 model)"),Ska=l(),v(JI.$$.fragment),uBo=l(),Oh=a("h3"),UI=a("a"),kio=a("span"),v(aD.$$.fragment),xka=l(),Sio=a("span"),Gka=o("TFAutoModelForAudioFrameClassification"),pBo=l(),Zr=a("div"),v(nD.$$.fragment),Bka=l(),Qh=a("p"),Ika=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
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method.`),Zka=l(),sD=a("p"),Vka=o("This class cannot be instantiated directly using "),xio=a("code"),Wka=o("__init__()"),Jka=o(" (throws an error)."),Uka=l(),$a=a("div"),v(lD.$$.fragment),Xka=l(),Gio=a("p"),Dka=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),qka=l(),Yh=a("p"),Oka=o(`Note:
Loading a model from its configuration file does `),Bio=a("strong"),Qka=o("not"),Yka=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),c3e=a("a"),zka=o("from_pretrained()"),Hka=o(" to load the model weights."),Kka=l(),v(XI.$$.fragment),eSa=l(),Ro=a("div"),v(iD.$$.fragment),oSa=l(),Iio=a("p"),rSa=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),tSa=l(),Is=a("p"),aSa=o("The model class to instantiate is selected based on the "),Nio=a("code"),nSa=o("model_type"),sSa=o(` property of the config object (either
passed as an argument or loaded from `),$io=a("code"),lSa=o("pretrained_model_name_or_path"),iSa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jio=a("code"),dSa=o("pretrained_model_name_or_path"),mSa=o(":"),cSa=l(),$t=a("ul"),DI=a("li"),Pio=a("strong"),fSa=o("data2vec-audio"),hSa=o(" \u2014 "),f3e=a("a"),gSa=o("Data2VecAudioForAudioFrameClassification"),uSa=o(" (Data2VecAudio model)"),pSa=l(),qI=a("li"),Zio=a("strong"),_Sa=o("unispeech-sat"),bSa=o(" \u2014 "),h3e=a("a"),vSa=o("UniSpeechSatForAudioFrameClassification"),MSa=o(" (UniSpeechSat model)"),TSa=l(),OI=a("li"),Vio=a("strong"),FSa=o("wav2vec2"),ESa=o(" \u2014 "),g3e=a("a"),ySa=o("Wav2Vec2ForAudioFrameClassification"),CSa=o(" (Wav2Vec2 model)"),wSa=l(),QI=a("li"),Wio=a("strong"),LSa=o("wav2vec2-conformer"),ASa=o(" \u2014 "),u3e=a("a"),RSa=o("Wav2Vec2ConformerForAudioFrameClassification"),kSa=o(" (Wav2Vec2-Conformer model)"),SSa=l(),YI=a("li"),Jio=a("strong"),xSa=o("wavlm"),GSa=o(" \u2014 "),p3e=a("a"),BSa=o("WavLMForAudioFrameClassification"),ISa=o(" (WavLM model)"),NSa=l(),zI=a("p"),$Sa=o("The model is set in evaluation mode by default using "),Uio=a("code"),jSa=o("model.eval()"),PSa=o(` (so for instance, dropout modules are
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Znn=s(Cae);iEr=r(Znn,"WhisperFeatureExtractor"),Znn.forEach(t),dEr=r(W1o," (Whisper model)"),W1o.forEach(t),mEr=i(D),I2=n(D,"LI",{});var J1o=s(I2);eSe=n(J1o,"STRONG",{});var Vnn=s(eSe);cEr=r(Vnn,"xclip"),Vnn.forEach(t),fEr=r(J1o," \u2014 "),wae=n(J1o,"A",{href:!0});var Wnn=s(wae);hEr=r(Wnn,"CLIPFeatureExtractor"),Wnn.forEach(t),gEr=r(J1o," (X-CLIP model)"),J1o.forEach(t),uEr=i(D),N2=n(D,"LI",{});var U1o=s(N2);oSe=n(U1o,"STRONG",{});var Jnn=s(oSe);pEr=r(Jnn,"yolos"),Jnn.forEach(t),_Er=r(U1o," \u2014 "),Lae=n(U1o,"A",{href:!0});var Unn=s(Lae);bEr=r(Unn,"YolosFeatureExtractor"),Unn.forEach(t),vEr=r(U1o," (YOLOS model)"),U1o.forEach(t),D.forEach(t),MEr=i(za),M($2.$$.fragment,za),TEr=i(za),M(j2.$$.fragment,za),za.forEach(t),FEr=i(qi),P2=n(qi,"DIV",{class:!0});var eIo=s(P2);M(eV.$$.fragment,eIo),EEr=i(eIo),rSe=n(eIo,"P",{});var Xnn=s(rSe);yEr=r(Xnn,"Register a new feature extractor for this class."),Xnn.forEach(t),eIo.forEach(t),qi.forEach(t),nxo=i(c),zm=n(c,"H2",{class:!0});var oIo=s(zm);Z2=n(oIo,"A",{id:!0,class:!0,href:!0});var Dnn=s(Z2);tSe=n(Dnn,"SPAN",{});var qnn=s(tSe);M(oV.$$.fragment,qnn),qnn.forEach(t),Dnn.forEach(t),CEr=i(oIo),aSe=n(oIo,"SPAN",{});var Onn=s(aSe);wEr=r(Onn,"AutoImageProcessor"),Onn.forEach(t),oIo.forEach(t),sxo=i(c),Wo=n(c,"DIV",{class:!0});var Oi=s(Wo);M(rV.$$.fragment,Oi),LEr=i(Oi),tV=n(Oi,"P",{});var rIo=s(tV);AEr=r(rIo,`This is a generic image processor class that will be instantiated as one of the image processor classes of the
library when created with the `),Aae=n(rIo,"A",{href:!0});var Qnn=s(Aae);REr=r(Qnn,"AutoImageProcessor.from_pretrained()"),Qnn.forEach(t),kEr=r(rIo," class method."),rIo.forEach(t),SEr=i(Oi),aV=n(Oi,"P",{});var tIo=s(aV);xEr=r(tIo,"This class cannot be instantiated directly using "),nSe=n(tIo,"CODE",{});var Ynn=s(nSe);GEr=r(Ynn,"__init__()"),Ynn.forEach(t),BEr=r(tIo," (throws an error)."),tIo.forEach(t),IEr=i(Oi),ao=n(Oi,"DIV",{class:!0});var Ha=s(ao);M(nV.$$.fragment,Ha),NEr=i(Ha),sSe=n(Ha,"P",{});var znn=s(sSe);$Er=r(znn,"Instantiate one of the image processor classes of the library from a pretrained model vocabulary."),znn.forEach(t),jEr=i(Ha),jn=n(Ha,"P",{});var cj=s(jn);PEr=r(cj,"The image processor class to instantiate is selected based on the "),lSe=n(cj,"CODE",{});var Hnn=s(lSe);ZEr=r(Hnn,"model_type"),Hnn.forEach(t),VEr=r(cj,` property of the config object
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L2o=s(LM);Kxe=n(L2o,"STRONG",{});var Adn=s(Kxe);I6r=r(Adn,"owlvit"),Adn.forEach(t),N6r=r(L2o," \u2014 "),fse=n(L2o,"A",{href:!0});var Rdn=s(fse);$6r=r(Rdn,"OwlViTProcessor"),Rdn.forEach(t),j6r=r(L2o," (OWL-ViT model)"),L2o.forEach(t),P6r=i(re),AM=n(re,"LI",{});var A2o=s(AM);eGe=n(A2o,"STRONG",{});var kdn=s(eGe);Z6r=r(kdn,"pix2struct"),kdn.forEach(t),V6r=r(A2o," \u2014 "),hse=n(A2o,"A",{href:!0});var Sdn=s(hse);W6r=r(Sdn,"Pix2StructProcessor"),Sdn.forEach(t),J6r=r(A2o," (Pix2Struct model)"),A2o.forEach(t),U6r=i(re),RM=n(re,"LI",{});var R2o=s(RM);oGe=n(R2o,"STRONG",{});var xdn=s(oGe);X6r=r(xdn,"sam"),xdn.forEach(t),D6r=r(R2o," \u2014 "),gse=n(R2o,"A",{href:!0});var Gdn=s(gse);q6r=r(Gdn,"SamProcessor"),Gdn.forEach(t),O6r=r(R2o," (SAM model)"),R2o.forEach(t),Q6r=i(re),kM=n(re,"LI",{});var k2o=s(kM);rGe=n(k2o,"STRONG",{});var Bdn=s(rGe);Y6r=r(Bdn,"sew"),Bdn.forEach(t),z6r=r(k2o," \u2014 "),use=n(k2o,"A",{href:!0});var Idn=s(use);H6r=r(Idn,"Wav2Vec2Processor"),Idn.forEach(t),K6r=r(k2o," (SEW 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"),vse=n(B2o,"A",{href:!0});var Jdn=s(vse);b8r=r(Jdn,"SpeechT5Processor"),Jdn.forEach(t),v8r=r(B2o," (SpeechT5 model)"),B2o.forEach(t),M8r=i(re),IM=n(re,"LI",{});var I2o=s(IM);lGe=n(I2o,"STRONG",{});var Udn=s(lGe);T8r=r(Udn,"trocr"),Udn.forEach(t),F8r=r(I2o," \u2014 "),Mse=n(I2o,"A",{href:!0});var Xdn=s(Mse);E8r=r(Xdn,"TrOCRProcessor"),Xdn.forEach(t),y8r=r(I2o," (TrOCR model)"),I2o.forEach(t),C8r=i(re),NM=n(re,"LI",{});var N2o=s(NM);iGe=n(N2o,"STRONG",{});var Ddn=s(iGe);w8r=r(Ddn,"tvlt"),Ddn.forEach(t),L8r=r(N2o," \u2014 "),Tse=n(N2o,"A",{href:!0});var qdn=s(Tse);A8r=r(qdn,"TvltProcessor"),qdn.forEach(t),R8r=r(N2o," (TVLT model)"),N2o.forEach(t),k8r=i(re),$M=n(re,"LI",{});var $2o=s($M);dGe=n($2o,"STRONG",{});var Odn=s(dGe);S8r=r(Odn,"unispeech"),Odn.forEach(t),x8r=r($2o," \u2014 "),Fse=n($2o,"A",{href:!0});var Qdn=s(Fse);G8r=r(Qdn,"Wav2Vec2Processor"),Qdn.forEach(t),B8r=r($2o," (UniSpeech model)"),$2o.forEach(t),I8r=i(re),jM=n(re,"LI",{});var j2o=s(jM);mGe=n(j2o,"STRONG",{});var Ydn=s(mGe);N8r=r(Ydn,"unispeech-sat"),Ydn.forEach(t),$8r=r(j2o," \u2014 "),Ese=n(j2o,"A",{href:!0});var zdn=s(Ese);j8r=r(zdn,"Wav2Vec2Processor"),zdn.forEach(t),P8r=r(j2o," (UniSpeechSat model)"),j2o.forEach(t),Z8r=i(re),PM=n(re,"LI",{});var P2o=s(PM);cGe=n(P2o,"STRONG",{});var Hdn=s(cGe);V8r=r(Hdn,"vilt"),Hdn.forEach(t),W8r=r(P2o," \u2014 "),yse=n(P2o,"A",{href:!0});var Kdn=s(yse);J8r=r(Kdn,"ViltProcessor"),Kdn.forEach(t),U8r=r(P2o," (ViLT model)"),P2o.forEach(t),X8r=i(re),ZM=n(re,"LI",{});var Z2o=s(ZM);fGe=n(Z2o,"STRONG",{});var emn=s(fGe);D8r=r(emn,"vision-text-dual-encoder"),emn.forEach(t),q8r=r(Z2o," \u2014 "),Cse=n(Z2o,"A",{href:!0});var omn=s(Cse);O8r=r(omn,"VisionTextDualEncoderProcessor"),omn.forEach(t),Q8r=r(Z2o," (VisionTextDualEncoder model)"),Z2o.forEach(t),Y8r=i(re),VM=n(re,"LI",{});var V2o=s(VM);hGe=n(V2o,"STRONG",{});var rmn=s(hGe);z8r=r(rmn,"wav2vec2"),rmn.forEach(t),H8r=r(V2o," \u2014 "),wse=n(V2o,"A",{href:!0});var tmn=s(wse);K8r=r(tmn,"Wav2Vec2Processor"),tmn.forEach(t),e7r=r(V2o," (Wav2Vec2 model)"),V2o.forEach(t),o7r=i(re),WM=n(re,"LI",{});var W2o=s(WM);gGe=n(W2o,"STRONG",{});var amn=s(gGe);r7r=r(amn,"wav2vec2-conformer"),amn.forEach(t),t7r=r(W2o," \u2014 "),Lse=n(W2o,"A",{href:!0});var nmn=s(Lse);a7r=r(nmn,"Wav2Vec2Processor"),nmn.forEach(t),n7r=r(W2o," (Wav2Vec2-Conformer model)"),W2o.forEach(t),s7r=i(re),JM=n(re,"LI",{});var J2o=s(JM);uGe=n(J2o,"STRONG",{});var smn=s(uGe);l7r=r(smn,"wavlm"),smn.forEach(t),i7r=r(J2o," \u2014 "),Ase=n(J2o,"A",{href:!0});var lmn=s(Ase);d7r=r(lmn,"Wav2Vec2Processor"),lmn.forEach(t),m7r=r(J2o," (WavLM model)"),J2o.forEach(t),c7r=i(re),UM=n(re,"LI",{});var U2o=s(UM);pGe=n(U2o,"STRONG",{});var imn=s(pGe);f7r=r(imn,"whisper"),imn.forEach(t),h7r=r(U2o," \u2014 "),Rse=n(U2o,"A",{href:!0});var dmn=s(Rse);g7r=r(dmn,"WhisperProcessor"),dmn.forEach(t),u7r=r(U2o," (Whisper model)"),U2o.forEach(t),p7r=i(re),XM=n(re,"LI",{});var X2o=s(XM);_Ge=n(X2o,"STRONG",{});var mmn=s(_Ge);_7r=r(mmn,"xclip"),mmn.forEach(t),b7r=r(X2o," \u2014 "),kse=n(X2o,"A",{href:!0});var cmn=s(kse);v7r=r(cmn,"XCLIPProcessor"),cmn.forEach(t),M7r=r(X2o," (X-CLIP model)"),X2o.forEach(t),re.forEach(t),T7r=i(Ka),M(DM.$$.fragment,Ka),F7r=i(Ka),M(qM.$$.fragment,Ka),Ka.forEach(t),E7r=i(Qi),OM=n(Qi,"DIV",{class:!0});var iIo=s(OM);M(fV.$$.fragment,iIo),y7r=i(iIo),bGe=n(iIo,"P",{});var fmn=s(bGe);C7r=r(fmn,"Register a new processor for this class."),fmn.forEach(t),iIo.forEach(t),Qi.forEach(t),dxo=i(c),ec=n(c,"H2",{class:!0});var dIo=s(ec);QM=n(dIo,"A",{id:!0,class:!0,href:!0});var hmn=s(QM);vGe=n(hmn,"SPAN",{});var gmn=s(vGe);M(hV.$$.fragment,gmn),gmn.forEach(t),hmn.forEach(t),w7r=i(dIo),MGe=n(dIo,"SPAN",{});var umn=s(MGe);L7r=r(umn,"Generic model classes"),umn.forEach(t),dIo.forEach(t),mxo=i(c),Sse=n(c,"P",{});var pmn=s(Sse);A7r=r(pmn,"The following auto classes are available for instantiating a base model class without a specific head."),pmn.forEach(t),cxo=i(c),oc=n(c,"H3",{class:!0});var mIo=s(oc);YM=n(mIo,"A",{id:!0,class:!0,href:!0});var _mn=s(YM);TGe=n(_mn,"SPAN",{});var bmn=s(TGe);M(gV.$$.fragment,bmn),bmn.forEach(t),_mn.forEach(t),R7r=i(mIo),FGe=n(mIo,"SPAN",{});var vmn=s(FGe);k7r=r(vmn,"AutoModel"),vmn.forEach(t),mIo.forEach(t),fxo=i(c),Uo=n(c,"DIV",{class:!0});var Yi=s(Uo);M(uV.$$.fragment,Yi),S7r=i(Yi),rc=n(Yi,"P",{});var J4e=s(rc);x7r=r(J4e,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),xse=n(J4e,"A",{href:!0});var Mmn=s(xse);G7r=r(Mmn,"from_pretrained()"),Mmn.forEach(t),B7r=r(J4e," class method or the "),Gse=n(J4e,"A",{href:!0});var Tmn=s(Gse);I7r=r(Tmn,"from_config()"),Tmn.forEach(t),N7r=r(J4e,` class
method.`),J4e.forEach(t),$7r=i(Yi),pV=n(Yi,"P",{});var cIo=s(pV);j7r=r(cIo,"This class cannot be instantiated directly using "),EGe=n(cIo,"CODE",{});var Fmn=s(EGe);P7r=r(Fmn,"__init__()"),Fmn.forEach(t),Z7r=r(cIo," (throws an error)."),cIo.forEach(t),V7r=i(Yi),Wt=n(Yi,"DIV",{class:!0});var fj=s(Wt);M(_V.$$.fragment,fj),W7r=i(fj),yGe=n(fj,"P",{});var Emn=s(yGe);J7r=r(Emn,"Instantiates one of the base model classes of the library from a configuration."),Emn.forEach(t),U7r=i(fj),tc=n(fj,"P",{});var U4e=s(tc);X7r=r(U4e,`Note:
Loading a model from its configuration file does `),CGe=n(U4e,"STRONG",{});var ymn=s(CGe);D7r=r(ymn,"not"),ymn.forEach(t),q7r=r(U4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Bse=n(U4e,"A",{href:!0});var Cmn=s(Bse);O7r=r(Cmn,"from_pretrained()"),Cmn.forEach(t),Q7r=r(U4e," to load the model weights."),U4e.forEach(t),Y7r=i(fj),M(zM.$$.fragment,fj),fj.forEach(t),z7r=i(Yi),so=n(Yi,"DIV",{class:!0});var en=s(so);M(bV.$$.fragment,en),H7r=i(en),wGe=n(en,"P",{});var wmn=s(wGe);K7r=r(wmn,"Instantiate one of the base model classes of the library from a pretrained model."),wmn.forEach(t),eLr=i(en),Pn=n(en,"P",{});var hj=s(Pn);oLr=r(hj,"The model class to instantiate is selected based on the "),LGe=n(hj,"CODE",{});var Lmn=s(LGe);rLr=r(Lmn,"model_type"),Lmn.forEach(t),tLr=r(hj,` property of the config object (either
passed as an argument or loaded from `),AGe=n(hj,"CODE",{});var Amn=s(AGe);aLr=r(Amn,"pretrained_model_name_or_path"),Amn.forEach(t),nLr=r(hj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),RGe=n(hj,"CODE",{});var Rmn=s(RGe);sLr=r(Rmn,"pretrained_model_name_or_path"),Rmn.forEach(t),lLr=r(hj,":"),hj.forEach(t),iLr=i(en),A=n(en,"UL",{});var R=s(A);HM=n(R,"LI",{});var D2o=s(HM);kGe=n(D2o,"STRONG",{});var kmn=s(kGe);dLr=r(kmn,"albert"),kmn.forEach(t),mLr=r(D2o," \u2014 "),Ise=n(D2o,"A",{href:!0});var Smn=s(Ise);cLr=r(Smn,"AlbertModel"),Smn.forEach(t),fLr=r(D2o," (ALBERT model)"),D2o.forEach(t),hLr=i(R),KM=n(R,"LI",{});var q2o=s(KM);SGe=n(q2o,"STRONG",{});var xmn=s(SGe);gLr=r(xmn,"align"),xmn.forEach(t),uLr=r(q2o," \u2014 "),Nse=n(q2o,"A",{href:!0});var Gmn=s(Nse);pLr=r(Gmn,"AlignModel"),Gmn.forEach(t),_Lr=r(q2o," (ALIGN model)"),q2o.forEach(t),bLr=i(R),eT=n(R,"LI",{});var O2o=s(eT);xGe=n(O2o,"STRONG",{});var Bmn=s(xGe);vLr=r(Bmn,"altclip"),Bmn.forEach(t),MLr=r(O2o," \u2014 "),$se=n(O2o,"A",{href:!0});var Imn=s($se);TLr=r(Imn,"AltCLIPModel"),Imn.forEach(t),FLr=r(O2o," (AltCLIP model)"),O2o.forEach(t),ELr=i(R),oT=n(R,"LI",{});var Q2o=s(oT);GGe=n(Q2o,"STRONG",{});var Nmn=s(GGe);yLr=r(Nmn,"audio-spectrogram-transformer"),Nmn.forEach(t),CLr=r(Q2o," \u2014 "),jse=n(Q2o,"A",{href:!0});var $mn=s(jse);wLr=r($mn,"ASTModel"),$mn.forEach(t),LLr=r(Q2o," (Audio Spectrogram Transformer model)"),Q2o.forEach(t),ALr=i(R),rT=n(R,"LI",{});var Y2o=s(rT);BGe=n(Y2o,"STRONG",{});var jmn=s(BGe);RLr=r(jmn,"autoformer"),jmn.forEach(t),kLr=r(Y2o," \u2014 "),Pse=n(Y2o,"A",{href:!0});var Pmn=s(Pse);SLr=r(Pmn,"AutoformerModel"),Pmn.forEach(t),xLr=r(Y2o," (Autoformer model)"),Y2o.forEach(t),GLr=i(R),tT=n(R,"LI",{});var z2o=s(tT);IGe=n(z2o,"STRONG",{});var Zmn=s(IGe);BLr=r(Zmn,"bark"),Zmn.forEach(t),ILr=r(z2o," \u2014 "),Zse=n(z2o,"A",{href:!0});var Vmn=s(Zse);NLr=r(Vmn,"BarkModel"),Vmn.forEach(t),$Lr=r(z2o," (Bark model)"),z2o.forEach(t),jLr=i(R),aT=n(R,"LI",{});var H2o=s(aT);NGe=n(H2o,"STRONG",{});var Wmn=s(NGe);PLr=r(Wmn,"bart"),Wmn.forEach(t),ZLr=r(H2o," \u2014 "),Vse=n(H2o,"A",{href:!0});var Jmn=s(Vse);VLr=r(Jmn,"BartModel"),Jmn.forEach(t),WLr=r(H2o," (BART model)"),H2o.forEach(t),JLr=i(R),nT=n(R,"LI",{});var K2o=s(nT);$Ge=n(K2o,"STRONG",{});var Umn=s($Ge);ULr=r(Umn,"beit"),Umn.forEach(t),XLr=r(K2o," \u2014 "),Wse=n(K2o,"A",{href:!0});var Xmn=s(Wse);DLr=r(Xmn,"BeitModel"),Xmn.forEach(t),qLr=r(K2o," (BEiT model)"),K2o.forEach(t),OLr=i(R),sT=n(R,"LI",{});var evo=s(sT);jGe=n(evo,"STRONG",{});var Dmn=s(jGe);QLr=r(Dmn,"bert"),Dmn.forEach(t),YLr=r(evo," \u2014 "),Jse=n(evo,"A",{href:!0});var qmn=s(Jse);zLr=r(qmn,"BertModel"),qmn.forEach(t),HLr=r(evo," (BERT model)"),evo.forEach(t),KLr=i(R),lT=n(R,"LI",{});var ovo=s(lT);PGe=n(ovo,"STRONG",{});var Omn=s(PGe);eAr=r(Omn,"bert-generation"),Omn.forEach(t),oAr=r(ovo," \u2014 "),Use=n(ovo,"A",{href:!0});var Qmn=s(Use);rAr=r(Qmn,"BertGenerationEncoder"),Qmn.forEach(t),tAr=r(ovo," (Bert Generation model)"),ovo.forEach(t),aAr=i(R),iT=n(R,"LI",{});var rvo=s(iT);ZGe=n(rvo,"STRONG",{});var Ymn=s(ZGe);nAr=r(Ymn,"big_bird"),Ymn.forEach(t),sAr=r(rvo," \u2014 "),Xse=n(rvo,"A",{href:!0});var zmn=s(Xse);lAr=r(zmn,"BigBirdModel"),zmn.forEach(t),iAr=r(rvo," (BigBird model)"),rvo.forEach(t),dAr=i(R),dT=n(R,"LI",{});var tvo=s(dT);VGe=n(tvo,"STRONG",{});var Hmn=s(VGe);mAr=r(Hmn,"bigbird_pegasus"),Hmn.forEach(t),cAr=r(tvo," \u2014 "),Dse=n(tvo,"A",{href:!0});var Kmn=s(Dse);fAr=r(Kmn,"BigBirdPegasusModel"),Kmn.forEach(t),hAr=r(tvo," (BigBird-Pegasus model)"),tvo.forEach(t),gAr=i(R),mT=n(R,"LI",{});var avo=s(mT);WGe=n(avo,"STRONG",{});var ecn=s(WGe);uAr=r(ecn,"biogpt"),ecn.forEach(t),pAr=r(avo," \u2014 "),qse=n(avo,"A",{href:!0});var ocn=s(qse);_Ar=r(ocn,"BioGptModel"),ocn.forEach(t),bAr=r(avo," (BioGpt model)"),avo.forEach(t),vAr=i(R),cT=n(R,"LI",{});var nvo=s(cT);JGe=n(nvo,"STRONG",{});var rcn=s(JGe);MAr=r(rcn,"bit"),rcn.forEach(t),TAr=r(nvo," \u2014 "),Ose=n(nvo,"A",{href:!0});var tcn=s(Ose);FAr=r(tcn,"BitModel"),tcn.forEach(t),EAr=r(nvo," (BiT 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ccn=s(Hse);WAr=r(ccn,"Blip2Model"),ccn.forEach(t),JAr=r(dvo," (BLIP-2 model)"),dvo.forEach(t),UAr=i(R),pT=n(R,"LI",{});var mvo=s(pT);OGe=n(mvo,"STRONG",{});var fcn=s(OGe);XAr=r(fcn,"bloom"),fcn.forEach(t),DAr=r(mvo," \u2014 "),Kse=n(mvo,"A",{href:!0});var hcn=s(Kse);qAr=r(hcn,"BloomModel"),hcn.forEach(t),OAr=r(mvo," (BLOOM model)"),mvo.forEach(t),QAr=i(R),_T=n(R,"LI",{});var cvo=s(_T);QGe=n(cvo,"STRONG",{});var gcn=s(QGe);YAr=r(gcn,"bridgetower"),gcn.forEach(t),zAr=r(cvo," \u2014 "),ele=n(cvo,"A",{href:!0});var ucn=s(ele);HAr=r(ucn,"BridgeTowerModel"),ucn.forEach(t),KAr=r(cvo," (BridgeTower model)"),cvo.forEach(t),eRr=i(R),bT=n(R,"LI",{});var fvo=s(bT);YGe=n(fvo,"STRONG",{});var pcn=s(YGe);oRr=r(pcn,"camembert"),pcn.forEach(t),rRr=r(fvo," \u2014 "),ole=n(fvo,"A",{href:!0});var _cn=s(ole);tRr=r(_cn,"CamembertModel"),_cn.forEach(t),aRr=r(fvo," (CamemBERT model)"),fvo.forEach(t),nRr=i(R),vT=n(R,"LI",{});var hvo=s(vT);zGe=n(hvo,"STRONG",{});var 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l2n=s(yje);szr=r(l2n,"xlm"),l2n.forEach(t),lzr=r(CEo," \u2014 "),_ce=n(CEo,"A",{href:!0});var i2n=s(_ce);izr=r(i2n,"TFXLMModel"),i2n.forEach(t),dzr=r(CEo," (XLM model)"),CEo.forEach(t),mzr=i(W),I0=n(W,"LI",{});var wEo=s(I0);Cje=n(wEo,"STRONG",{});var d2n=s(Cje);czr=r(d2n,"xlm-roberta"),d2n.forEach(t),fzr=r(wEo," \u2014 "),bce=n(wEo,"A",{href:!0});var m2n=s(bce);hzr=r(m2n,"TFXLMRobertaModel"),m2n.forEach(t),gzr=r(wEo," (XLM-RoBERTa model)"),wEo.forEach(t),uzr=i(W),N0=n(W,"LI",{});var LEo=s(N0);wje=n(LEo,"STRONG",{});var c2n=s(wje);pzr=r(c2n,"xlnet"),c2n.forEach(t),_zr=r(LEo," \u2014 "),vce=n(LEo,"A",{href:!0});var f2n=s(vce);bzr=r(f2n,"TFXLNetModel"),f2n.forEach(t),vzr=r(LEo," (XLNet model)"),LEo.forEach(t),W.forEach(t),Mzr=i(Hi),M($0.$$.fragment,Hi),Hi.forEach(t),zi.forEach(t),uxo=i(c),lc=n(c,"H3",{class:!0});var gIo=s(lc);j0=n(gIo,"A",{id:!0,class:!0,href:!0});var h2n=s(j0);Lje=n(h2n,"SPAN",{});var g2n=s(Lje);M(yV.$$.fragment,g2n),g2n.forEach(t),h2n.forEach(t),Tzr=i(gIo),Aje=n(gIo,"SPAN",{});var u2n=s(Aje);Fzr=r(u2n,"FlaxAutoModel"),u2n.forEach(t),gIo.forEach(t),pxo=i(c),Do=n(c,"DIV",{class:!0});var Ki=s(Do);M(CV.$$.fragment,Ki),Ezr=i(Ki),ic=n(Ki,"P",{});var q4e=s(ic);yzr=r(q4e,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Mce=n(q4e,"A",{href:!0});var p2n=s(Mce);Czr=r(p2n,"from_pretrained()"),p2n.forEach(t),wzr=r(q4e," class method or the "),Tce=n(q4e,"A",{href:!0});var _2n=s(Tce);Lzr=r(_2n,"from_config()"),_2n.forEach(t),Azr=r(q4e,` class
method.`),q4e.forEach(t),Rzr=i(Ki),wV=n(Ki,"P",{});var uIo=s(wV);kzr=r(uIo,"This class cannot be instantiated directly using "),Rje=n(uIo,"CODE",{});var b2n=s(Rje);Szr=r(b2n,"__init__()"),b2n.forEach(t),xzr=r(uIo," (throws an error)."),uIo.forEach(t),Gzr=i(Ki),Ut=n(Ki,"DIV",{class:!0});var pj=s(Ut);M(LV.$$.fragment,pj),Bzr=i(pj),kje=n(pj,"P",{});var v2n=s(kje);Izr=r(v2n,"Instantiates one of the base model classes of the library from a configuration."),v2n.forEach(t),Nzr=i(pj),dc=n(pj,"P",{});var O4e=s(dc);$zr=r(O4e,`Note:
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model\u2019s configuration. Use `),Fce=n(O4e,"A",{href:!0});var T2n=s(Fce);Zzr=r(T2n,"from_pretrained()"),T2n.forEach(t),Vzr=r(O4e," to load the model weights."),O4e.forEach(t),Wzr=i(pj),M(P0.$$.fragment,pj),pj.forEach(t),Jzr=i(Ki),at=n(Ki,"DIV",{class:!0});var ed=s(at);M(AV.$$.fragment,ed),Uzr=i(ed),xje=n(ed,"P",{});var F2n=s(xje);Xzr=r(F2n,"Instantiate one of the base model classes of the library from a pretrained model."),F2n.forEach(t),Dzr=i(ed),Vn=n(ed,"P",{});var _j=s(Vn);qzr=r(_j,"The model class to instantiate is selected based on the "),Gje=n(_j,"CODE",{});var E2n=s(Gje);Ozr=r(E2n,"model_type"),E2n.forEach(t),Qzr=r(_j,` property of the config object (either
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falling back to using pattern matching on `),Ije=n(_j,"CODE",{});var C2n=s(Ije);Hzr=r(C2n,"pretrained_model_name_or_path"),C2n.forEach(t),Kzr=r(_j,":"),_j.forEach(t),eHr=i(ed),se=n(ed,"UL",{});var ie=s(se);Z0=n(ie,"LI",{});var AEo=s(Z0);Nje=n(AEo,"STRONG",{});var w2n=s(Nje);oHr=r(w2n,"albert"),w2n.forEach(t),rHr=r(AEo," \u2014 "),Ece=n(AEo,"A",{href:!0});var L2n=s(Ece);tHr=r(L2n,"FlaxAlbertModel"),L2n.forEach(t),aHr=r(AEo," (ALBERT model)"),AEo.forEach(t),nHr=i(ie),V0=n(ie,"LI",{});var REo=s(V0);$je=n(REo,"STRONG",{});var A2n=s($je);sHr=r(A2n,"bart"),A2n.forEach(t),lHr=r(REo," \u2014 "),yce=n(REo,"A",{href:!0});var R2n=s(yce);iHr=r(R2n,"FlaxBartModel"),R2n.forEach(t),dHr=r(REo," (BART model)"),REo.forEach(t),mHr=i(ie),W0=n(ie,"LI",{});var kEo=s(W0);jje=n(kEo,"STRONG",{});var k2n=s(jje);cHr=r(k2n,"beit"),k2n.forEach(t),fHr=r(kEo," \u2014 "),Cce=n(kEo,"A",{href:!0});var S2n=s(Cce);hHr=r(S2n,"FlaxBeitModel"),S2n.forEach(t),gHr=r(kEo," (BEiT 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P2n=s(Rce);GHr=r(P2n,"FlaxBlenderbotSmallModel"),P2n.forEach(t),BHr=r(BEo," (BlenderbotSmall model)"),BEo.forEach(t),IHr=i(ie),q0=n(ie,"LI",{});var IEo=s(q0);Jje=n(IEo,"STRONG",{});var Z2n=s(Jje);NHr=r(Z2n,"bloom"),Z2n.forEach(t),$Hr=r(IEo," \u2014 "),kce=n(IEo,"A",{href:!0});var V2n=s(kce);jHr=r(V2n,"FlaxBloomModel"),V2n.forEach(t),PHr=r(IEo," (BLOOM model)"),IEo.forEach(t),ZHr=i(ie),O0=n(ie,"LI",{});var NEo=s(O0);Uje=n(NEo,"STRONG",{});var W2n=s(Uje);VHr=r(W2n,"clip"),W2n.forEach(t),WHr=r(NEo," \u2014 "),Sce=n(NEo,"A",{href:!0});var J2n=s(Sce);JHr=r(J2n,"FlaxCLIPModel"),J2n.forEach(t),UHr=r(NEo," (CLIP model)"),NEo.forEach(t),XHr=i(ie),Q0=n(ie,"LI",{});var $Eo=s(Q0);Xje=n($Eo,"STRONG",{});var U2n=s(Xje);DHr=r(U2n,"distilbert"),U2n.forEach(t),qHr=r($Eo," \u2014 "),xce=n($Eo,"A",{href:!0});var X2n=s(xce);OHr=r(X2n,"FlaxDistilBertModel"),X2n.forEach(t),QHr=r($Eo," (DistilBERT model)"),$Eo.forEach(t),YHr=i(ie),Y0=n(ie,"LI",{});var jEo=s(Y0);Dje=n(jEo,"STRONG",{});var 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lvn=s(Zce);BKr=r(lvn,"FlaxMBartModel"),lvn.forEach(t),IKr=r(XEo," (mBART model)"),XEo.forEach(t),NKr=i(ie),a5=n(ie,"LI",{});var DEo=s(a5);ePe=n(DEo,"STRONG",{});var ivn=s(ePe);$Kr=r(ivn,"mt5"),ivn.forEach(t),jKr=r(DEo," \u2014 "),Vce=n(DEo,"A",{href:!0});var dvn=s(Vce);PKr=r(dvn,"FlaxMT5Model"),dvn.forEach(t),ZKr=r(DEo," (MT5 model)"),DEo.forEach(t),VKr=i(ie),n5=n(ie,"LI",{});var qEo=s(n5);oPe=n(qEo,"STRONG",{});var mvn=s(oPe);WKr=r(mvn,"opt"),mvn.forEach(t),JKr=r(qEo," \u2014 "),Wce=n(qEo,"A",{href:!0});var cvn=s(Wce);UKr=r(cvn,"FlaxOPTModel"),cvn.forEach(t),XKr=r(qEo," (OPT model)"),qEo.forEach(t),DKr=i(ie),s5=n(ie,"LI",{});var OEo=s(s5);rPe=n(OEo,"STRONG",{});var fvn=s(rPe);qKr=r(fvn,"pegasus"),fvn.forEach(t),OKr=r(OEo," \u2014 "),Jce=n(OEo,"A",{href:!0});var hvn=s(Jce);QKr=r(hvn,"FlaxPegasusModel"),hvn.forEach(t),YKr=r(OEo," (Pegasus model)"),OEo.forEach(t),zKr=i(ie),l5=n(ie,"LI",{});var QEo=s(l5);tPe=n(QEo,"STRONG",{});var gvn=s(tPe);HKr=r(gvn,"regnet"),gvn.forEach(t),KKr=r(QEo," \u2014 "),Uce=n(QEo,"A",{href:!0});var uvn=s(Uce);eet=r(uvn,"FlaxRegNetModel"),uvn.forEach(t),oet=r(QEo," (RegNet model)"),QEo.forEach(t),ret=i(ie),i5=n(ie,"LI",{});var YEo=s(i5);aPe=n(YEo,"STRONG",{});var pvn=s(aPe);tet=r(pvn,"resnet"),pvn.forEach(t),aet=r(YEo," \u2014 "),Xce=n(YEo,"A",{href:!0});var _vn=s(Xce);net=r(_vn,"FlaxResNetModel"),_vn.forEach(t),set=r(YEo," (ResNet model)"),YEo.forEach(t),iet=i(ie),d5=n(ie,"LI",{});var zEo=s(d5);nPe=n(zEo,"STRONG",{});var bvn=s(nPe);det=r(bvn,"roberta"),bvn.forEach(t),met=r(zEo," \u2014 "),Dce=n(zEo,"A",{href:!0});var vvn=s(Dce);cet=r(vvn,"FlaxRobertaModel"),vvn.forEach(t),fet=r(zEo," (RoBERTa model)"),zEo.forEach(t),het=i(ie),m5=n(ie,"LI",{});var HEo=s(m5);sPe=n(HEo,"STRONG",{});var Mvn=s(sPe);get=r(Mvn,"roberta-prelayernorm"),Mvn.forEach(t),uet=r(HEo," \u2014 "),qce=n(HEo,"A",{href:!0});var Tvn=s(qce);pet=r(Tvn,"FlaxRobertaPreLayerNormModel"),Tvn.forEach(t),_et=r(HEo," (RoBERTa-PreLayerNorm 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"),zce=n(ryo,"A",{href:!0});var Rvn=s(zce);Net=r(Rvn,"FlaxViTModel"),Rvn.forEach(t),$et=r(ryo," (ViT model)"),ryo.forEach(t),jet=i(ie),u5=n(ie,"LI",{});var tyo=s(u5);cPe=n(tyo,"STRONG",{});var kvn=s(cPe);Pet=r(kvn,"wav2vec2"),kvn.forEach(t),Zet=r(tyo," \u2014 "),Hce=n(tyo,"A",{href:!0});var Svn=s(Hce);Vet=r(Svn,"FlaxWav2Vec2Model"),Svn.forEach(t),Wet=r(tyo," (Wav2Vec2 model)"),tyo.forEach(t),Jet=i(ie),p5=n(ie,"LI",{});var ayo=s(p5);fPe=n(ayo,"STRONG",{});var xvn=s(fPe);Uet=r(xvn,"whisper"),xvn.forEach(t),Xet=r(ayo," \u2014 "),Kce=n(ayo,"A",{href:!0});var Gvn=s(Kce);Det=r(Gvn,"FlaxWhisperModel"),Gvn.forEach(t),qet=r(ayo," (Whisper model)"),ayo.forEach(t),Oet=i(ie),_5=n(ie,"LI",{});var nyo=s(_5);hPe=n(nyo,"STRONG",{});var Bvn=s(hPe);Qet=r(Bvn,"xglm"),Bvn.forEach(t),Yet=r(nyo," \u2014 "),efe=n(nyo,"A",{href:!0});var Ivn=s(efe);zet=r(Ivn,"FlaxXGLMModel"),Ivn.forEach(t),Het=r(nyo," (XGLM model)"),nyo.forEach(t),Ket=i(ie),b5=n(ie,"LI",{});var syo=s(b5);gPe=n(syo,"STRONG",{});var Nvn=s(gPe);eot=r(Nvn,"xlm-roberta"),Nvn.forEach(t),oot=r(syo," \u2014 "),ofe=n(syo,"A",{href:!0});var $vn=s(ofe);rot=r($vn,"FlaxXLMRobertaModel"),$vn.forEach(t),tot=r(syo," (XLM-RoBERTa model)"),syo.forEach(t),ie.forEach(t),aot=i(ed),M(v5.$$.fragment,ed),ed.forEach(t),Ki.forEach(t),_xo=i(c),mc=n(c,"H2",{class:!0});var pIo=s(mc);M5=n(pIo,"A",{id:!0,class:!0,href:!0});var jvn=s(M5);uPe=n(jvn,"SPAN",{});var Pvn=s(uPe);M(RV.$$.fragment,Pvn),Pvn.forEach(t),jvn.forEach(t),not=i(pIo),pPe=n(pIo,"SPAN",{});var Zvn=s(pPe);sot=r(Zvn,"Generic pretraining classes"),Zvn.forEach(t),pIo.forEach(t),bxo=i(c),rfe=n(c,"P",{});var Vvn=s(rfe);lot=r(Vvn,"The following auto classes are available for instantiating a model with a pretraining head."),Vvn.forEach(t),vxo=i(c),cc=n(c,"H3",{class:!0});var _Io=s(cc);T5=n(_Io,"A",{id:!0,class:!0,href:!0});var Wvn=s(T5);_Pe=n(Wvn,"SPAN",{});var Jvn=s(_Pe);M(kV.$$.fragment,Jvn),Jvn.forEach(t),Wvn.forEach(t),iot=i(_Io),bPe=n(_Io,"SPAN",{});var Uvn=s(bPe);dot=r(Uvn,"AutoModelForPreTraining"),Uvn.forEach(t),_Io.forEach(t),Mxo=i(c),qo=n(c,"DIV",{class:!0});var od=s(qo);M(SV.$$.fragment,od),mot=i(od),fc=n(od,"P",{});var Q4e=s(fc);cot=r(Q4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),tfe=n(Q4e,"A",{href:!0});var Xvn=s(tfe);fot=r(Xvn,"from_pretrained()"),Xvn.forEach(t),hot=r(Q4e," class method or the "),afe=n(Q4e,"A",{href:!0});var Dvn=s(afe);got=r(Dvn,"from_config()"),Dvn.forEach(t),uot=r(Q4e,` class
method.`),Q4e.forEach(t),pot=i(od),xV=n(od,"P",{});var bIo=s(xV);_ot=r(bIo,"This class cannot be instantiated directly using "),vPe=n(bIo,"CODE",{});var qvn=s(vPe);bot=r(qvn,"__init__()"),qvn.forEach(t),vot=r(bIo," (throws an error)."),bIo.forEach(t),Mot=i(od),Xt=n(od,"DIV",{class:!0});var bj=s(Xt);M(GV.$$.fragment,bj),Tot=i(bj),MPe=n(bj,"P",{});var Ovn=s(MPe);Fot=r(Ovn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Ovn.forEach(t),Eot=i(bj),hc=n(bj,"P",{});var Y4e=s(hc);yot=r(Y4e,`Note:
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model\u2019s configuration. Use `),nfe=n(Y4e,"A",{href:!0});var Yvn=s(nfe);Lot=r(Yvn,"from_pretrained()"),Yvn.forEach(t),Aot=r(Y4e," to load the model weights."),Y4e.forEach(t),Rot=i(bj),M(F5.$$.fragment,bj),bj.forEach(t),kot=i(od),lo=n(od,"DIV",{class:!0});var on=s(lo);M(BV.$$.fragment,on),Sot=i(on),FPe=n(on,"P",{});var zvn=s(FPe);xot=r(zvn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),zvn.forEach(t),Got=i(on),Wn=n(on,"P",{});var vj=s(Wn);Bot=r(vj,"The model class to instantiate is selected based on the "),EPe=n(vj,"CODE",{});var Hvn=s(EPe);Iot=r(Hvn,"model_type"),Hvn.forEach(t),Not=r(vj,` property of the config object (either
passed as an argument or loaded from `),yPe=n(vj,"CODE",{});var Kvn=s(yPe);$ot=r(Kvn,"pretrained_model_name_or_path"),Kvn.forEach(t),jot=r(vj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CPe=n(vj,"CODE",{});var eMn=s(CPe);Pot=r(eMn,"pretrained_model_name_or_path"),eMn.forEach(t),Zot=r(vj,":"),vj.forEach(t),Vot=i(on),X=n(on,"UL",{});var O=s(X);E5=n(O,"LI",{});var lyo=s(E5);wPe=n(lyo,"STRONG",{});var oMn=s(wPe);Wot=r(oMn,"albert"),oMn.forEach(t),Jot=r(lyo," \u2014 "),sfe=n(lyo,"A",{href:!0});var rMn=s(sfe);Uot=r(rMn,"AlbertForPreTraining"),rMn.forEach(t),Xot=r(lyo," (ALBERT model)"),lyo.forEach(t),Dot=i(O),y5=n(O,"LI",{});var iyo=s(y5);LPe=n(iyo,"STRONG",{});var tMn=s(LPe);qot=r(tMn,"bart"),tMn.forEach(t),Oot=r(iyo," \u2014 "),lfe=n(iyo,"A",{href:!0});var aMn=s(lfe);Qot=r(aMn,"BartForConditionalGeneration"),aMn.forEach(t),Yot=r(iyo," (BART model)"),iyo.forEach(t),zot=i(O),C5=n(O,"LI",{});var dyo=s(C5);APe=n(dyo,"STRONG",{});var nMn=s(APe);Hot=r(nMn,"bert"),nMn.forEach(t),Kot=r(dyo," \u2014 "),ife=n(dyo,"A",{href:!0});var sMn=s(ife);ert=r(sMn,"BertForPreTraining"),sMn.forEach(t),ort=r(dyo," (BERT 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gMn=s(ffe);Mrt=r(gMn,"CTRLLMHeadModel"),gMn.forEach(t),Trt=r(hyo," (CTRL model)"),hyo.forEach(t),Frt=i(O),k5=n(O,"LI",{});var gyo=s(k5);GPe=n(gyo,"STRONG",{});var uMn=s(GPe);Ert=r(uMn,"data2vec-text"),uMn.forEach(t),yrt=r(gyo," \u2014 "),hfe=n(gyo,"A",{href:!0});var pMn=s(hfe);Crt=r(pMn,"Data2VecTextForMaskedLM"),pMn.forEach(t),wrt=r(gyo," (Data2VecText model)"),gyo.forEach(t),Lrt=i(O),S5=n(O,"LI",{});var uyo=s(S5);BPe=n(uyo,"STRONG",{});var _Mn=s(BPe);Art=r(_Mn,"deberta"),_Mn.forEach(t),Rrt=r(uyo," \u2014 "),gfe=n(uyo,"A",{href:!0});var bMn=s(gfe);krt=r(bMn,"DebertaForMaskedLM"),bMn.forEach(t),Srt=r(uyo," (DeBERTa model)"),uyo.forEach(t),xrt=i(O),x5=n(O,"LI",{});var pyo=s(x5);IPe=n(pyo,"STRONG",{});var vMn=s(IPe);Grt=r(vMn,"deberta-v2"),vMn.forEach(t),Brt=r(pyo," \u2014 "),ufe=n(pyo,"A",{href:!0});var MMn=s(ufe);Irt=r(MMn,"DebertaV2ForMaskedLM"),MMn.forEach(t),Nrt=r(pyo," (DeBERTa-v2 model)"),pyo.forEach(t),$rt=i(O),G5=n(O,"LI",{});var _yo=s(G5);NPe=n(_yo,"STRONG",{});var TMn=s(NPe);jrt=r(TMn,"distilbert"),TMn.forEach(t),Prt=r(_yo," \u2014 "),pfe=n(_yo,"A",{href:!0});var FMn=s(pfe);Zrt=r(FMn,"DistilBertForMaskedLM"),FMn.forEach(t),Vrt=r(_yo," (DistilBERT model)"),_yo.forEach(t),Wrt=i(O),B5=n(O,"LI",{});var byo=s(B5);$Pe=n(byo,"STRONG",{});var EMn=s($Pe);Jrt=r(EMn,"electra"),EMn.forEach(t),Urt=r(byo," \u2014 "),_fe=n(byo,"A",{href:!0});var yMn=s(_fe);Xrt=r(yMn,"ElectraForPreTraining"),yMn.forEach(t),Drt=r(byo," (ELECTRA model)"),byo.forEach(t),qrt=i(O),I5=n(O,"LI",{});var vyo=s(I5);jPe=n(vyo,"STRONG",{});var CMn=s(jPe);Ort=r(CMn,"ernie"),CMn.forEach(t),Qrt=r(vyo," \u2014 "),bfe=n(vyo,"A",{href:!0});var wMn=s(bfe);Yrt=r(wMn,"ErnieForPreTraining"),wMn.forEach(t),zrt=r(vyo," (ERNIE model)"),vyo.forEach(t),Hrt=i(O),N5=n(O,"LI",{});var Myo=s(N5);PPe=n(Myo,"STRONG",{});var LMn=s(PPe);Krt=r(LMn,"flaubert"),LMn.forEach(t),ett=r(Myo," \u2014 "),vfe=n(Myo,"A",{href:!0});var AMn=s(vfe);ott=r(AMn,"FlaubertWithLMHeadModel"),AMn.forEach(t),rtt=r(Myo," (FlauBERT 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Ayo=s(U5);qPe=n(Ayo,"STRONG",{});var JMn=s(qPe);Ptt=r(JMn,"gptsan-japanese"),JMn.forEach(t),Ztt=r(Ayo," \u2014 "),Lfe=n(Ayo,"A",{href:!0});var UMn=s(Lfe);Vtt=r(UMn,"GPTSanJapaneseForConditionalGeneration"),UMn.forEach(t),Wtt=r(Ayo," (GPTSAN-japanese model)"),Ayo.forEach(t),Jtt=i(O),X5=n(O,"LI",{});var Ryo=s(X5);OPe=n(Ryo,"STRONG",{});var XMn=s(OPe);Utt=r(XMn,"ibert"),XMn.forEach(t),Xtt=r(Ryo," \u2014 "),Afe=n(Ryo,"A",{href:!0});var DMn=s(Afe);Dtt=r(DMn,"IBertForMaskedLM"),DMn.forEach(t),qtt=r(Ryo," (I-BERT model)"),Ryo.forEach(t),Ott=i(O),D5=n(O,"LI",{});var kyo=s(D5);QPe=n(kyo,"STRONG",{});var qMn=s(QPe);Qtt=r(qMn,"layoutlm"),qMn.forEach(t),Ytt=r(kyo," \u2014 "),Rfe=n(kyo,"A",{href:!0});var OMn=s(Rfe);ztt=r(OMn,"LayoutLMForMaskedLM"),OMn.forEach(t),Htt=r(kyo," (LayoutLM model)"),kyo.forEach(t),Ktt=i(O),q5=n(O,"LI",{});var Syo=s(q5);YPe=n(Syo,"STRONG",{});var QMn=s(YPe);eat=r(QMn,"longformer"),QMn.forEach(t),oat=r(Syo," \u2014 "),kfe=n(Syo,"A",{href:!0});var YMn=s(kfe);rat=r(YMn,"LongformerForMaskedLM"),YMn.forEach(t),tat=r(Syo," (Longformer model)"),Syo.forEach(t),aat=i(O),O5=n(O,"LI",{});var xyo=s(O5);zPe=n(xyo,"STRONG",{});var zMn=s(zPe);nat=r(zMn,"luke"),zMn.forEach(t),sat=r(xyo," \u2014 "),Sfe=n(xyo,"A",{href:!0});var HMn=s(Sfe);lat=r(HMn,"LukeForMaskedLM"),HMn.forEach(t),iat=r(xyo," (LUKE model)"),xyo.forEach(t),dat=i(O),Q5=n(O,"LI",{});var Gyo=s(Q5);HPe=n(Gyo,"STRONG",{});var KMn=s(HPe);mat=r(KMn,"lxmert"),KMn.forEach(t),cat=r(Gyo," \u2014 "),xfe=n(Gyo,"A",{href:!0});var eTn=s(xfe);fat=r(eTn,"LxmertForPreTraining"),eTn.forEach(t),hat=r(Gyo," (LXMERT model)"),Gyo.forEach(t),gat=i(O),Y5=n(O,"LI",{});var Byo=s(Y5);KPe=n(Byo,"STRONG",{});var oTn=s(KPe);uat=r(oTn,"mega"),oTn.forEach(t),pat=r(Byo," \u2014 "),Gfe=n(Byo,"A",{href:!0});var rTn=s(Gfe);_at=r(rTn,"MegaForMaskedLM"),rTn.forEach(t),bat=r(Byo," (MEGA model)"),Byo.forEach(t),vat=i(O),z5=n(O,"LI",{});var Iyo=s(z5);eZe=n(Iyo,"STRONG",{});var tTn=s(eZe);Mat=r(tTn,"megatron-bert"),tTn.forEach(t),Tat=r(Iyo," \u2014 "),Bfe=n(Iyo,"A",{href:!0});var aTn=s(Bfe);Fat=r(aTn,"MegatronBertForPreTraining"),aTn.forEach(t),Eat=r(Iyo," (Megatron-BERT model)"),Iyo.forEach(t),yat=i(O),H5=n(O,"LI",{});var Nyo=s(H5);oZe=n(Nyo,"STRONG",{});var nTn=s(oZe);Cat=r(nTn,"mobilebert"),nTn.forEach(t),wat=r(Nyo," \u2014 "),Ife=n(Nyo,"A",{href:!0});var sTn=s(Ife);Lat=r(sTn,"MobileBertForPreTraining"),sTn.forEach(t),Aat=r(Nyo," (MobileBERT model)"),Nyo.forEach(t),Rat=i(O),K5=n(O,"LI",{});var $yo=s(K5);rZe=n($yo,"STRONG",{});var lTn=s(rZe);kat=r(lTn,"mpnet"),lTn.forEach(t),Sat=r($yo," \u2014 "),Nfe=n($yo,"A",{href:!0});var iTn=s(Nfe);xat=r(iTn,"MPNetForMaskedLM"),iTn.forEach(t),Gat=r($yo," (MPNet model)"),$yo.forEach(t),Bat=i(O),e3=n(O,"LI",{});var jyo=s(e3);tZe=n(jyo,"STRONG",{});var dTn=s(tZe);Iat=r(dTn,"mpt"),dTn.forEach(t),Nat=r(jyo," \u2014 "),$fe=n(jyo,"A",{href:!0});var mTn=s($fe);$at=r(mTn,"MptForCausalLM"),mTn.forEach(t),jat=r(jyo," (MPT 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bTn=s(Vfe);tnt=r(bTn,"NllbMoeForConditionalGeneration"),bTn.forEach(t),ant=r(Wyo," (NLLB-MOE model)"),Wyo.forEach(t),nnt=i(O),n3=n(O,"LI",{});var Jyo=s(n3);iZe=n(Jyo,"STRONG",{});var vTn=s(iZe);snt=r(vTn,"openai-gpt"),vTn.forEach(t),lnt=r(Jyo," \u2014 "),Wfe=n(Jyo,"A",{href:!0});var MTn=s(Wfe);int=r(MTn,"OpenAIGPTLMHeadModel"),MTn.forEach(t),dnt=r(Jyo," (OpenAI GPT model)"),Jyo.forEach(t),mnt=i(O),s3=n(O,"LI",{});var Uyo=s(s3);dZe=n(Uyo,"STRONG",{});var TTn=s(dZe);cnt=r(TTn,"retribert"),TTn.forEach(t),fnt=r(Uyo," \u2014 "),Jfe=n(Uyo,"A",{href:!0});var FTn=s(Jfe);hnt=r(FTn,"RetriBertModel"),FTn.forEach(t),gnt=r(Uyo," (RetriBERT model)"),Uyo.forEach(t),unt=i(O),l3=n(O,"LI",{});var Xyo=s(l3);mZe=n(Xyo,"STRONG",{});var ETn=s(mZe);pnt=r(ETn,"roberta"),ETn.forEach(t),_nt=r(Xyo," \u2014 "),Ufe=n(Xyo,"A",{href:!0});var yTn=s(Ufe);bnt=r(yTn,"RobertaForMaskedLM"),yTn.forEach(t),vnt=r(Xyo," (RoBERTa model)"),Xyo.forEach(t),Mnt=i(O),i3=n(O,"LI",{});var Dyo=s(i3);cZe=n(Dyo,"STRONG",{});var CTn=s(cZe);Tnt=r(CTn,"roberta-prelayernorm"),CTn.forEach(t),Fnt=r(Dyo," \u2014 "),Xfe=n(Dyo,"A",{href:!0});var wTn=s(Xfe);Ent=r(wTn,"RobertaPreLayerNormForMaskedLM"),wTn.forEach(t),ynt=r(Dyo," (RoBERTa-PreLayerNorm model)"),Dyo.forEach(t),Cnt=i(O),d3=n(O,"LI",{});var qyo=s(d3);fZe=n(qyo,"STRONG",{});var LTn=s(fZe);wnt=r(LTn,"roc_bert"),LTn.forEach(t),Lnt=r(qyo," \u2014 "),Dfe=n(qyo,"A",{href:!0});var ATn=s(Dfe);Ant=r(ATn,"RoCBertForPreTraining"),ATn.forEach(t),Rnt=r(qyo," (RoCBert model)"),qyo.forEach(t),knt=i(O),m3=n(O,"LI",{});var Oyo=s(m3);hZe=n(Oyo,"STRONG",{});var RTn=s(hZe);Snt=r(RTn,"rwkv"),RTn.forEach(t),xnt=r(Oyo," \u2014 "),qfe=n(Oyo,"A",{href:!0});var kTn=s(qfe);Gnt=r(kTn,"RwkvForCausalLM"),kTn.forEach(t),Bnt=r(Oyo," (RWKV model)"),Oyo.forEach(t),Int=i(O),c3=n(O,"LI",{});var Qyo=s(c3);gZe=n(Qyo,"STRONG",{});var STn=s(gZe);Nnt=r(STn,"splinter"),STn.forEach(t),$nt=r(Qyo," \u2014 "),Ofe=n(Qyo,"A",{href:!0});var xTn=s(Ofe);jnt=r(xTn,"SplinterForPreTraining"),xTn.forEach(t),Pnt=r(Qyo," (Splinter model)"),Qyo.forEach(t),Znt=i(O),f3=n(O,"LI",{});var Yyo=s(f3);uZe=n(Yyo,"STRONG",{});var GTn=s(uZe);Vnt=r(GTn,"squeezebert"),GTn.forEach(t),Wnt=r(Yyo," \u2014 "),Qfe=n(Yyo,"A",{href:!0});var BTn=s(Qfe);Jnt=r(BTn,"SqueezeBertForMaskedLM"),BTn.forEach(t),Unt=r(Yyo," (SqueezeBERT model)"),Yyo.forEach(t),Xnt=i(O),h3=n(O,"LI",{});var zyo=s(h3);pZe=n(zyo,"STRONG",{});var ITn=s(pZe);Dnt=r(ITn,"switch_transformers"),ITn.forEach(t),qnt=r(zyo," \u2014 "),Yfe=n(zyo,"A",{href:!0});var NTn=s(Yfe);Ont=r(NTn,"SwitchTransformersForConditionalGeneration"),NTn.forEach(t),Qnt=r(zyo," (SwitchTransformers model)"),zyo.forEach(t),Ynt=i(O),g3=n(O,"LI",{});var Hyo=s(g3);_Ze=n(Hyo,"STRONG",{});var $Tn=s(_Ze);znt=r($Tn,"t5"),$Tn.forEach(t),Hnt=r(Hyo," \u2014 "),zfe=n(Hyo,"A",{href:!0});var jTn=s(zfe);Knt=r(jTn,"T5ForConditionalGeneration"),jTn.forEach(t),est=r(Hyo," (T5 model)"),Hyo.forEach(t),ost=i(O),u3=n(O,"LI",{});var Kyo=s(u3);bZe=n(Kyo,"STRONG",{});var PTn=s(bZe);rst=r(PTn,"tapas"),PTn.forEach(t),tst=r(Kyo," \u2014 "),Hfe=n(Kyo,"A",{href:!0});var ZTn=s(Hfe);ast=r(ZTn,"TapasForMaskedLM"),ZTn.forEach(t),nst=r(Kyo," (TAPAS model)"),Kyo.forEach(t),sst=i(O),p3=n(O,"LI",{});var e0o=s(p3);vZe=n(e0o,"STRONG",{});var VTn=s(vZe);lst=r(VTn,"transfo-xl"),VTn.forEach(t),ist=r(e0o," \u2014 "),Kfe=n(e0o,"A",{href:!0});var WTn=s(Kfe);dst=r(WTn,"TransfoXLLMHeadModel"),WTn.forEach(t),mst=r(e0o," (Transformer-XL model)"),e0o.forEach(t),cst=i(O),_3=n(O,"LI",{});var o0o=s(_3);MZe=n(o0o,"STRONG",{});var JTn=s(MZe);fst=r(JTn,"tvlt"),JTn.forEach(t),hst=r(o0o," \u2014 "),ehe=n(o0o,"A",{href:!0});var UTn=s(ehe);gst=r(UTn,"TvltForPreTraining"),UTn.forEach(t),ust=r(o0o," (TVLT model)"),o0o.forEach(t),pst=i(O),b3=n(O,"LI",{});var r0o=s(b3);TZe=n(r0o,"STRONG",{});var XTn=s(TZe);_st=r(XTn,"unispeech"),XTn.forEach(t),bst=r(r0o," \u2014 "),ohe=n(r0o,"A",{href:!0});var DTn=s(ohe);vst=r(DTn,"UniSpeechForPreTraining"),DTn.forEach(t),Mst=r(r0o," (UniSpeech model)"),r0o.forEach(t),Tst=i(O),v3=n(O,"LI",{});var t0o=s(v3);FZe=n(t0o,"STRONG",{});var qTn=s(FZe);Fst=r(qTn,"unispeech-sat"),qTn.forEach(t),Est=r(t0o," \u2014 "),rhe=n(t0o,"A",{href:!0});var OTn=s(rhe);yst=r(OTn,"UniSpeechSatForPreTraining"),OTn.forEach(t),Cst=r(t0o," (UniSpeechSat model)"),t0o.forEach(t),wst=i(O),M3=n(O,"LI",{});var a0o=s(M3);EZe=n(a0o,"STRONG",{});var QTn=s(EZe);Lst=r(QTn,"videomae"),QTn.forEach(t),Ast=r(a0o," \u2014 "),the=n(a0o,"A",{href:!0});var YTn=s(the);Rst=r(YTn,"VideoMAEForPreTraining"),YTn.forEach(t),kst=r(a0o," (VideoMAE model)"),a0o.forEach(t),Sst=i(O),T3=n(O,"LI",{});var n0o=s(T3);yZe=n(n0o,"STRONG",{});var zTn=s(yZe);xst=r(zTn,"visual_bert"),zTn.forEach(t),Gst=r(n0o," \u2014 "),ahe=n(n0o,"A",{href:!0});var HTn=s(ahe);Bst=r(HTn,"VisualBertForPreTraining"),HTn.forEach(t),Ist=r(n0o," (VisualBERT model)"),n0o.forEach(t),Nst=i(O),F3=n(O,"LI",{});var s0o=s(F3);CZe=n(s0o,"STRONG",{});var KTn=s(CZe);$st=r(KTn,"vit_mae"),KTn.forEach(t),jst=r(s0o," \u2014 "),nhe=n(s0o,"A",{href:!0});var eFn=s(nhe);Pst=r(eFn,"ViTMAEForPreTraining"),eFn.forEach(t),Zst=r(s0o," (ViTMAE model)"),s0o.forEach(t),Vst=i(O),E3=n(O,"LI",{});var l0o=s(E3);wZe=n(l0o,"STRONG",{});var oFn=s(wZe);Wst=r(oFn,"wav2vec2"),oFn.forEach(t),Jst=r(l0o," \u2014 "),she=n(l0o,"A",{href:!0});var rFn=s(she);Ust=r(rFn,"Wav2Vec2ForPreTraining"),rFn.forEach(t),Xst=r(l0o," (Wav2Vec2 model)"),l0o.forEach(t),Dst=i(O),y3=n(O,"LI",{});var i0o=s(y3);LZe=n(i0o,"STRONG",{});var tFn=s(LZe);qst=r(tFn,"wav2vec2-conformer"),tFn.forEach(t),Ost=r(i0o," \u2014 "),lhe=n(i0o,"A",{href:!0});var aFn=s(lhe);Qst=r(aFn,"Wav2Vec2ConformerForPreTraining"),aFn.forEach(t),Yst=r(i0o," (Wav2Vec2-Conformer model)"),i0o.forEach(t),zst=i(O),C3=n(O,"LI",{});var d0o=s(C3);AZe=n(d0o,"STRONG",{});var nFn=s(AZe);Hst=r(nFn,"xlm"),nFn.forEach(t),Kst=r(d0o," \u2014 "),ihe=n(d0o,"A",{href:!0});var sFn=s(ihe);elt=r(sFn,"XLMWithLMHeadModel"),sFn.forEach(t),olt=r(d0o," (XLM model)"),d0o.forEach(t),rlt=i(O),w3=n(O,"LI",{});var m0o=s(w3);RZe=n(m0o,"STRONG",{});var lFn=s(RZe);tlt=r(lFn,"xlm-roberta"),lFn.forEach(t),alt=r(m0o," \u2014 "),dhe=n(m0o,"A",{href:!0});var iFn=s(dhe);nlt=r(iFn,"XLMRobertaForMaskedLM"),iFn.forEach(t),slt=r(m0o," (XLM-RoBERTa model)"),m0o.forEach(t),llt=i(O),L3=n(O,"LI",{});var c0o=s(L3);kZe=n(c0o,"STRONG",{});var dFn=s(kZe);ilt=r(dFn,"xlm-roberta-xl"),dFn.forEach(t),dlt=r(c0o," \u2014 "),mhe=n(c0o,"A",{href:!0});var mFn=s(mhe);mlt=r(mFn,"XLMRobertaXLForMaskedLM"),mFn.forEach(t),clt=r(c0o," (XLM-RoBERTa-XL model)"),c0o.forEach(t),flt=i(O),A3=n(O,"LI",{});var f0o=s(A3);SZe=n(f0o,"STRONG",{});var cFn=s(SZe);hlt=r(cFn,"xlnet"),cFn.forEach(t),glt=r(f0o," \u2014 "),che=n(f0o,"A",{href:!0});var fFn=s(che);ult=r(fFn,"XLNetLMHeadModel"),fFn.forEach(t),plt=r(f0o," (XLNet model)"),f0o.forEach(t),_lt=i(O),R3=n(O,"LI",{});var h0o=s(R3);xZe=n(h0o,"STRONG",{});var hFn=s(xZe);blt=r(hFn,"xmod"),hFn.forEach(t),vlt=r(h0o," \u2014 "),fhe=n(h0o,"A",{href:!0});var gFn=s(fhe);Mlt=r(gFn,"XmodForMaskedLM"),gFn.forEach(t),Tlt=r(h0o," (X-MOD model)"),h0o.forEach(t),O.forEach(t),Flt=i(on),k3=n(on,"P",{});var g0o=s(k3);Elt=r(g0o,"The model is set in evaluation mode by default using "),GZe=n(g0o,"CODE",{});var uFn=s(GZe);ylt=r(uFn,"model.eval()"),uFn.forEach(t),Clt=r(g0o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),BZe=n(g0o,"CODE",{});var pFn=s(BZe);wlt=r(pFn,"model.train()"),pFn.forEach(t),g0o.forEach(t),Llt=i(on),M(S3.$$.fragment,on),on.forEach(t),od.forEach(t),Txo=i(c),gc=n(c,"H3",{class:!0});var vIo=s(gc);x3=n(vIo,"A",{id:!0,class:!0,href:!0});var _Fn=s(x3);IZe=n(_Fn,"SPAN",{});var bFn=s(IZe);M(IV.$$.fragment,bFn),bFn.forEach(t),_Fn.forEach(t),Alt=i(vIo),NZe=n(vIo,"SPAN",{});var vFn=s(NZe);Rlt=r(vFn,"TFAutoModelForPreTraining"),vFn.forEach(t),vIo.forEach(t),Fxo=i(c),Oo=n(c,"DIV",{class:!0});var rd=s(Oo);M(NV.$$.fragment,rd),klt=i(rd),uc=n(rd,"P",{});var z4e=s(uc);Slt=r(z4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),hhe=n(z4e,"A",{href:!0});var MFn=s(hhe);xlt=r(MFn,"from_pretrained()"),MFn.forEach(t),Glt=r(z4e," class method or the "),ghe=n(z4e,"A",{href:!0});var TFn=s(ghe);Blt=r(TFn,"from_config()"),TFn.forEach(t),Ilt=r(z4e,` class
method.`),z4e.forEach(t),Nlt=i(rd),$V=n(rd,"P",{});var MIo=s($V);$lt=r(MIo,"This class cannot be instantiated directly using "),$Ze=n(MIo,"CODE",{});var FFn=s($Ze);jlt=r(FFn,"__init__()"),FFn.forEach(t),Plt=r(MIo," (throws an error)."),MIo.forEach(t),Zlt=i(rd),Dt=n(rd,"DIV",{class:!0});var Mj=s(Dt);M(jV.$$.fragment,Mj),Vlt=i(Mj),jZe=n(Mj,"P",{});var EFn=s(jZe);Wlt=r(EFn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),EFn.forEach(t),Jlt=i(Mj),pc=n(Mj,"P",{});var H4e=s(pc);Ult=r(H4e,`Note:
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model\u2019s configuration. Use `),uhe=n(H4e,"A",{href:!0});var CFn=s(uhe);qlt=r(CFn,"from_pretrained()"),CFn.forEach(t),Olt=r(H4e," to load the model weights."),H4e.forEach(t),Qlt=i(Mj),M(G3.$$.fragment,Mj),Mj.forEach(t),Ylt=i(rd),nt=n(rd,"DIV",{class:!0});var td=s(nt);M(PV.$$.fragment,td),zlt=i(td),ZZe=n(td,"P",{});var wFn=s(ZZe);Hlt=r(wFn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),wFn.forEach(t),Klt=i(td),Jn=n(td,"P",{});var Tj=s(Jn);eit=r(Tj,"The model class to instantiate is selected based on the "),VZe=n(Tj,"CODE",{});var LFn=s(VZe);oit=r(LFn,"model_type"),LFn.forEach(t),rit=r(Tj,` property of the config object (either
passed as an argument or loaded from `),WZe=n(Tj,"CODE",{});var AFn=s(WZe);tit=r(AFn,"pretrained_model_name_or_path"),AFn.forEach(t),ait=r(Tj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JZe=n(Tj,"CODE",{});var RFn=s(JZe);nit=r(RFn,"pretrained_model_name_or_path"),RFn.forEach(t),sit=r(Tj,":"),Tj.forEach(t),lit=i(td),fe=n(td,"UL",{});var he=s(fe);B3=n(he,"LI",{});var u0o=s(B3);UZe=n(u0o,"STRONG",{});var kFn=s(UZe);iit=r(kFn,"albert"),kFn.forEach(t),dit=r(u0o," \u2014 "),phe=n(u0o,"A",{href:!0});var SFn=s(phe);mit=r(SFn,"TFAlbertForPreTraining"),SFn.forEach(t),cit=r(u0o," (ALBERT model)"),u0o.forEach(t),fit=i(he),I3=n(he,"LI",{});var p0o=s(I3);XZe=n(p0o,"STRONG",{});var xFn=s(XZe);hit=r(xFn,"bart"),xFn.forEach(t),git=r(p0o," \u2014 "),_he=n(p0o,"A",{href:!0});var GFn=s(_he);uit=r(GFn,"TFBartForConditionalGeneration"),GFn.forEach(t),pit=r(p0o," (BART model)"),p0o.forEach(t),_it=i(he),N3=n(he,"LI",{});var _0o=s(N3);DZe=n(_0o,"STRONG",{});var BFn=s(DZe);bit=r(BFn,"bert"),BFn.forEach(t),vit=r(_0o," \u2014 "),bhe=n(_0o,"A",{href:!0});var IFn=s(bhe);Mit=r(IFn,"TFBertForPreTraining"),IFn.forEach(t),Tit=r(_0o," (BERT model)"),_0o.forEach(t),Fit=i(he),$3=n(he,"LI",{});var b0o=s($3);qZe=n(b0o,"STRONG",{});var NFn=s(qZe);Eit=r(NFn,"camembert"),NFn.forEach(t),yit=r(b0o," \u2014 "),vhe=n(b0o,"A",{href:!0});var $Fn=s(vhe);Cit=r($Fn,"TFCamembertForMaskedLM"),$Fn.forEach(t),wit=r(b0o," (CamemBERT model)"),b0o.forEach(t),Lit=i(he),j3=n(he,"LI",{});var v0o=s(j3);OZe=n(v0o,"STRONG",{});var jFn=s(OZe);Ait=r(jFn,"ctrl"),jFn.forEach(t),Rit=r(v0o," \u2014 "),Mhe=n(v0o,"A",{href:!0});var PFn=s(Mhe);kit=r(PFn,"TFCTRLLMHeadModel"),PFn.forEach(t),Sit=r(v0o," (CTRL model)"),v0o.forEach(t),xit=i(he),P3=n(he,"LI",{});var M0o=s(P3);QZe=n(M0o,"STRONG",{});var ZFn=s(QZe);Git=r(ZFn,"distilbert"),ZFn.forEach(t),Bit=r(M0o," \u2014 "),The=n(M0o,"A",{href:!0});var VFn=s(The);Iit=r(VFn,"TFDistilBertForMaskedLM"),VFn.forEach(t),Nit=r(M0o," (DistilBERT model)"),M0o.forEach(t),$it=i(he),Z3=n(he,"LI",{});var T0o=s(Z3);YZe=n(T0o,"STRONG",{});var WFn=s(YZe);jit=r(WFn,"electra"),WFn.forEach(t),Pit=r(T0o," \u2014 "),Fhe=n(T0o,"A",{href:!0});var JFn=s(Fhe);Zit=r(JFn,"TFElectraForPreTraining"),JFn.forEach(t),Vit=r(T0o," (ELECTRA model)"),T0o.forEach(t),Wit=i(he),V3=n(he,"LI",{});var F0o=s(V3);zZe=n(F0o,"STRONG",{});var UFn=s(zZe);Jit=r(UFn,"flaubert"),UFn.forEach(t),Uit=r(F0o," \u2014 "),Ehe=n(F0o,"A",{href:!0});var XFn=s(Ehe);Xit=r(XFn,"TFFlaubertWithLMHeadModel"),XFn.forEach(t),Dit=r(F0o," (FlauBERT model)"),F0o.forEach(t),qit=i(he),W3=n(he,"LI",{});var E0o=s(W3);HZe=n(E0o,"STRONG",{});var DFn=s(HZe);Oit=r(DFn,"funnel"),DFn.forEach(t),Qit=r(E0o," \u2014 "),yhe=n(E0o,"A",{href:!0});var qFn=s(yhe);Yit=r(qFn,"TFFunnelForPreTraining"),qFn.forEach(t),zit=r(E0o," (Funnel Transformer model)"),E0o.forEach(t),Hit=i(he),J3=n(he,"LI",{});var y0o=s(J3);KZe=n(y0o,"STRONG",{});var OFn=s(KZe);Kit=r(OFn,"gpt-sw3"),OFn.forEach(t),edt=r(y0o," \u2014 "),Che=n(y0o,"A",{href:!0});var QFn=s(Che);odt=r(QFn,"TFGPT2LMHeadModel"),QFn.forEach(t),rdt=r(y0o," (GPT-Sw3 model)"),y0o.forEach(t),tdt=i(he),U3=n(he,"LI",{});var C0o=s(U3);eVe=n(C0o,"STRONG",{});var YFn=s(eVe);adt=r(YFn,"gpt2"),YFn.forEach(t),ndt=r(C0o," \u2014 "),whe=n(C0o,"A",{href:!0});var zFn=s(whe);sdt=r(zFn,"TFGPT2LMHeadModel"),zFn.forEach(t),ldt=r(C0o," (OpenAI GPT-2 model)"),C0o.forEach(t),idt=i(he),X3=n(he,"LI",{});var w0o=s(X3);oVe=n(w0o,"STRONG",{});var HFn=s(oVe);ddt=r(HFn,"layoutlm"),HFn.forEach(t),mdt=r(w0o," \u2014 "),Lhe=n(w0o,"A",{href:!0});var KFn=s(Lhe);cdt=r(KFn,"TFLayoutLMForMaskedLM"),KFn.forEach(t),fdt=r(w0o," (LayoutLM model)"),w0o.forEach(t),hdt=i(he),D3=n(he,"LI",{});var L0o=s(D3);rVe=n(L0o,"STRONG",{});var eEn=s(rVe);gdt=r(eEn,"lxmert"),eEn.forEach(t),udt=r(L0o," \u2014 "),Ahe=n(L0o,"A",{href:!0});var oEn=s(Ahe);pdt=r(oEn,"TFLxmertForPreTraining"),oEn.forEach(t),_dt=r(L0o," (LXMERT model)"),L0o.forEach(t),bdt=i(he),q3=n(he,"LI",{});var A0o=s(q3);tVe=n(A0o,"STRONG",{});var rEn=s(tVe);vdt=r(rEn,"mobilebert"),rEn.forEach(t),Mdt=r(A0o," \u2014 "),Rhe=n(A0o,"A",{href:!0});var tEn=s(Rhe);Tdt=r(tEn,"TFMobileBertForPreTraining"),tEn.forEach(t),Fdt=r(A0o," (MobileBERT model)"),A0o.forEach(t),Edt=i(he),O3=n(he,"LI",{});var R0o=s(O3);aVe=n(R0o,"STRONG",{});var aEn=s(aVe);ydt=r(aEn,"mpnet"),aEn.forEach(t),Cdt=r(R0o," \u2014 "),khe=n(R0o,"A",{href:!0});var nEn=s(khe);wdt=r(nEn,"TFMPNetForMaskedLM"),nEn.forEach(t),Ldt=r(R0o," (MPNet model)"),R0o.forEach(t),Adt=i(he),Q3=n(he,"LI",{});var k0o=s(Q3);nVe=n(k0o,"STRONG",{});var sEn=s(nVe);Rdt=r(sEn,"openai-gpt"),sEn.forEach(t),kdt=r(k0o," \u2014 "),She=n(k0o,"A",{href:!0});var lEn=s(She);Sdt=r(lEn,"TFOpenAIGPTLMHeadModel"),lEn.forEach(t),xdt=r(k0o," (OpenAI GPT model)"),k0o.forEach(t),Gdt=i(he),Y3=n(he,"LI",{});var S0o=s(Y3);sVe=n(S0o,"STRONG",{});var iEn=s(sVe);Bdt=r(iEn,"roberta"),iEn.forEach(t),Idt=r(S0o," \u2014 "),xhe=n(S0o,"A",{href:!0});var dEn=s(xhe);Ndt=r(dEn,"TFRobertaForMaskedLM"),dEn.forEach(t),$dt=r(S0o," (RoBERTa model)"),S0o.forEach(t),jdt=i(he),z3=n(he,"LI",{});var x0o=s(z3);lVe=n(x0o,"STRONG",{});var mEn=s(lVe);Pdt=r(mEn,"roberta-prelayernorm"),mEn.forEach(t),Zdt=r(x0o," \u2014 "),Ghe=n(x0o,"A",{href:!0});var cEn=s(Ghe);Vdt=r(cEn,"TFRobertaPreLayerNormForMaskedLM"),cEn.forEach(t),Wdt=r(x0o," (RoBERTa-PreLayerNorm model)"),x0o.forEach(t),Jdt=i(he),H3=n(he,"LI",{});var G0o=s(H3);iVe=n(G0o,"STRONG",{});var fEn=s(iVe);Udt=r(fEn,"t5"),fEn.forEach(t),Xdt=r(G0o," \u2014 "),Bhe=n(G0o,"A",{href:!0});var hEn=s(Bhe);Ddt=r(hEn,"TFT5ForConditionalGeneration"),hEn.forEach(t),qdt=r(G0o," (T5 model)"),G0o.forEach(t),Odt=i(he),K3=n(he,"LI",{});var B0o=s(K3);dVe=n(B0o,"STRONG",{});var gEn=s(dVe);Qdt=r(gEn,"tapas"),gEn.forEach(t),Ydt=r(B0o," \u2014 "),Ihe=n(B0o,"A",{href:!0});var uEn=s(Ihe);zdt=r(uEn,"TFTapasForMaskedLM"),uEn.forEach(t),Hdt=r(B0o," (TAPAS model)"),B0o.forEach(t),Kdt=i(he),e4=n(he,"LI",{});var I0o=s(e4);mVe=n(I0o,"STRONG",{});var pEn=s(mVe);emt=r(pEn,"transfo-xl"),pEn.forEach(t),omt=r(I0o," \u2014 "),Nhe=n(I0o,"A",{href:!0});var _En=s(Nhe);rmt=r(_En,"TFTransfoXLLMHeadModel"),_En.forEach(t),tmt=r(I0o," (Transformer-XL model)"),I0o.forEach(t),amt=i(he),o4=n(he,"LI",{});var N0o=s(o4);cVe=n(N0o,"STRONG",{});var bEn=s(cVe);nmt=r(bEn,"vit_mae"),bEn.forEach(t),smt=r(N0o," \u2014 "),$he=n(N0o,"A",{href:!0});var vEn=s($he);lmt=r(vEn,"TFViTMAEForPreTraining"),vEn.forEach(t),imt=r(N0o," (ViTMAE model)"),N0o.forEach(t),dmt=i(he),r4=n(he,"LI",{});var $0o=s(r4);fVe=n($0o,"STRONG",{});var MEn=s(fVe);mmt=r(MEn,"xlm"),MEn.forEach(t),cmt=r($0o," \u2014 "),jhe=n($0o,"A",{href:!0});var TEn=s(jhe);fmt=r(TEn,"TFXLMWithLMHeadModel"),TEn.forEach(t),hmt=r($0o," (XLM model)"),$0o.forEach(t),gmt=i(he),t4=n(he,"LI",{});var j0o=s(t4);hVe=n(j0o,"STRONG",{});var FEn=s(hVe);umt=r(FEn,"xlm-roberta"),FEn.forEach(t),pmt=r(j0o," \u2014 "),Phe=n(j0o,"A",{href:!0});var EEn=s(Phe);_mt=r(EEn,"TFXLMRobertaForMaskedLM"),EEn.forEach(t),bmt=r(j0o," (XLM-RoBERTa model)"),j0o.forEach(t),vmt=i(he),a4=n(he,"LI",{});var P0o=s(a4);gVe=n(P0o,"STRONG",{});var yEn=s(gVe);Mmt=r(yEn,"xlnet"),yEn.forEach(t),Tmt=r(P0o," \u2014 "),Zhe=n(P0o,"A",{href:!0});var CEn=s(Zhe);Fmt=r(CEn,"TFXLNetLMHeadModel"),CEn.forEach(t),Emt=r(P0o," (XLNet model)"),P0o.forEach(t),he.forEach(t),ymt=i(td),M(n4.$$.fragment,td),td.forEach(t),rd.forEach(t),Exo=i(c),_c=n(c,"H3",{class:!0});var TIo=s(_c);s4=n(TIo,"A",{id:!0,class:!0,href:!0});var wEn=s(s4);uVe=n(wEn,"SPAN",{});var LEn=s(uVe);M(ZV.$$.fragment,LEn),LEn.forEach(t),wEn.forEach(t),Cmt=i(TIo),pVe=n(TIo,"SPAN",{});var AEn=s(pVe);wmt=r(AEn,"FlaxAutoModelForPreTraining"),AEn.forEach(t),TIo.forEach(t),yxo=i(c),Qo=n(c,"DIV",{class:!0});var ad=s(Qo);M(VV.$$.fragment,ad),Lmt=i(ad),bc=n(ad,"P",{});var K4e=s(bc);Amt=r(K4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Vhe=n(K4e,"A",{href:!0});var REn=s(Vhe);Rmt=r(REn,"from_pretrained()"),REn.forEach(t),kmt=r(K4e," class method or the "),Whe=n(K4e,"A",{href:!0});var kEn=s(Whe);Smt=r(kEn,"from_config()"),kEn.forEach(t),xmt=r(K4e,` class
method.`),K4e.forEach(t),Gmt=i(ad),WV=n(ad,"P",{});var FIo=s(WV);Bmt=r(FIo,"This class cannot be instantiated directly using "),_Ve=n(FIo,"CODE",{});var SEn=s(_Ve);Imt=r(SEn,"__init__()"),SEn.forEach(t),Nmt=r(FIo," (throws an error)."),FIo.forEach(t),$mt=i(ad),qt=n(ad,"DIV",{class:!0});var Fj=s(qt);M(JV.$$.fragment,Fj),jmt=i(Fj),bVe=n(Fj,"P",{});var xEn=s(bVe);Pmt=r(xEn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),xEn.forEach(t),Zmt=i(Fj),vc=n(Fj,"P",{});var e9e=s(vc);Vmt=r(e9e,`Note:
Loading a model from its configuration file does `),vVe=n(e9e,"STRONG",{});var GEn=s(vVe);Wmt=r(GEn,"not"),GEn.forEach(t),Jmt=r(e9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Jhe=n(e9e,"A",{href:!0});var BEn=s(Jhe);Umt=r(BEn,"from_pretrained()"),BEn.forEach(t),Xmt=r(e9e," to load the model weights."),e9e.forEach(t),Dmt=i(Fj),M(l4.$$.fragment,Fj),Fj.forEach(t),qmt=i(ad),st=n(ad,"DIV",{class:!0});var nd=s(st);M(UV.$$.fragment,nd),Omt=i(nd),MVe=n(nd,"P",{});var IEn=s(MVe);Qmt=r(IEn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),IEn.forEach(t),Ymt=i(nd),Un=n(nd,"P",{});var Ej=s(Un);zmt=r(Ej,"The model class to instantiate is selected based on the "),TVe=n(Ej,"CODE",{});var NEn=s(TVe);Hmt=r(NEn,"model_type"),NEn.forEach(t),Kmt=r(Ej,` property of the config object (either
passed as an argument or loaded from `),FVe=n(Ej,"CODE",{});var $En=s(FVe);ect=r($En,"pretrained_model_name_or_path"),$En.forEach(t),oct=r(Ej,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EVe=n(Ej,"CODE",{});var jEn=s(EVe);rct=r(jEn,"pretrained_model_name_or_path"),jEn.forEach(t),tct=r(Ej,":"),Ej.forEach(t),act=i(nd),we=n(nd,"UL",{});var Ae=s(we);i4=n(Ae,"LI",{});var Z0o=s(i4);yVe=n(Z0o,"STRONG",{});var PEn=s(yVe);nct=r(PEn,"albert"),PEn.forEach(t),sct=r(Z0o," \u2014 "),Uhe=n(Z0o,"A",{href:!0});var ZEn=s(Uhe);lct=r(ZEn,"FlaxAlbertForPreTraining"),ZEn.forEach(t),ict=r(Z0o," (ALBERT model)"),Z0o.forEach(t),dct=i(Ae),d4=n(Ae,"LI",{});var V0o=s(d4);CVe=n(V0o,"STRONG",{});var VEn=s(CVe);mct=r(VEn,"bart"),VEn.forEach(t),cct=r(V0o," \u2014 "),Xhe=n(V0o,"A",{href:!0});var WEn=s(Xhe);fct=r(WEn,"FlaxBartForConditionalGeneration"),WEn.forEach(t),hct=r(V0o," (BART model)"),V0o.forEach(t),gct=i(Ae),m4=n(Ae,"LI",{});var W0o=s(m4);wVe=n(W0o,"STRONG",{});var JEn=s(wVe);uct=r(JEn,"bert"),JEn.forEach(t),pct=r(W0o," \u2014 "),Dhe=n(W0o,"A",{href:!0});var UEn=s(Dhe);_ct=r(UEn,"FlaxBertForPreTraining"),UEn.forEach(t),bct=r(W0o," (BERT model)"),W0o.forEach(t),vct=i(Ae),c4=n(Ae,"LI",{});var J0o=s(c4);LVe=n(J0o,"STRONG",{});var XEn=s(LVe);Mct=r(XEn,"big_bird"),XEn.forEach(t),Tct=r(J0o," \u2014 "),qhe=n(J0o,"A",{href:!0});var DEn=s(qhe);Fct=r(DEn,"FlaxBigBirdForPreTraining"),DEn.forEach(t),Ect=r(J0o," (BigBird model)"),J0o.forEach(t),yct=i(Ae),f4=n(Ae,"LI",{});var U0o=s(f4);AVe=n(U0o,"STRONG",{});var qEn=s(AVe);Cct=r(qEn,"electra"),qEn.forEach(t),wct=r(U0o," \u2014 "),Ohe=n(U0o,"A",{href:!0});var OEn=s(Ohe);Lct=r(OEn,"FlaxElectraForPreTraining"),OEn.forEach(t),Act=r(U0o," (ELECTRA model)"),U0o.forEach(t),Rct=i(Ae),h4=n(Ae,"LI",{});var X0o=s(h4);RVe=n(X0o,"STRONG",{});var QEn=s(RVe);kct=r(QEn,"longt5"),QEn.forEach(t),Sct=r(X0o," \u2014 "),Qhe=n(X0o,"A",{href:!0});var YEn=s(Qhe);xct=r(YEn,"FlaxLongT5ForConditionalGeneration"),YEn.forEach(t),Gct=r(X0o," (LongT5 model)"),X0o.forEach(t),Bct=i(Ae),g4=n(Ae,"LI",{});var D0o=s(g4);kVe=n(D0o,"STRONG",{});var zEn=s(kVe);Ict=r(zEn,"mbart"),zEn.forEach(t),Nct=r(D0o," \u2014 "),Yhe=n(D0o,"A",{href:!0});var HEn=s(Yhe);$ct=r(HEn,"FlaxMBartForConditionalGeneration"),HEn.forEach(t),jct=r(D0o," (mBART model)"),D0o.forEach(t),Pct=i(Ae),u4=n(Ae,"LI",{});var q0o=s(u4);SVe=n(q0o,"STRONG",{});var KEn=s(SVe);Zct=r(KEn,"mt5"),KEn.forEach(t),Vct=r(q0o," \u2014 "),zhe=n(q0o,"A",{href:!0});var eyn=s(zhe);Wct=r(eyn,"FlaxMT5ForConditionalGeneration"),eyn.forEach(t),Jct=r(q0o," (MT5 model)"),q0o.forEach(t),Uct=i(Ae),p4=n(Ae,"LI",{});var O0o=s(p4);xVe=n(O0o,"STRONG",{});var oyn=s(xVe);Xct=r(oyn,"roberta"),oyn.forEach(t),Dct=r(O0o," \u2014 "),Hhe=n(O0o,"A",{href:!0});var ryn=s(Hhe);qct=r(ryn,"FlaxRobertaForMaskedLM"),ryn.forEach(t),Oct=r(O0o," (RoBERTa model)"),O0o.forEach(t),Qct=i(Ae),_4=n(Ae,"LI",{});var Q0o=s(_4);GVe=n(Q0o,"STRONG",{});var tyn=s(GVe);Yct=r(tyn,"roberta-prelayernorm"),tyn.forEach(t),zct=r(Q0o," \u2014 "),Khe=n(Q0o,"A",{href:!0});var ayn=s(Khe);Hct=r(ayn,"FlaxRobertaPreLayerNormForMaskedLM"),ayn.forEach(t),Kct=r(Q0o," (RoBERTa-PreLayerNorm model)"),Q0o.forEach(t),eft=i(Ae),b4=n(Ae,"LI",{});var Y0o=s(b4);BVe=n(Y0o,"STRONG",{});var nyn=s(BVe);oft=r(nyn,"roformer"),nyn.forEach(t),rft=r(Y0o," \u2014 "),ege=n(Y0o,"A",{href:!0});var syn=s(ege);tft=r(syn,"FlaxRoFormerForMaskedLM"),syn.forEach(t),aft=r(Y0o," (RoFormer model)"),Y0o.forEach(t),nft=i(Ae),v4=n(Ae,"LI",{});var z0o=s(v4);IVe=n(z0o,"STRONG",{});var lyn=s(IVe);sft=r(lyn,"t5"),lyn.forEach(t),lft=r(z0o," \u2014 "),oge=n(z0o,"A",{href:!0});var iyn=s(oge);ift=r(iyn,"FlaxT5ForConditionalGeneration"),iyn.forEach(t),dft=r(z0o," (T5 model)"),z0o.forEach(t),mft=i(Ae),M4=n(Ae,"LI",{});var H0o=s(M4);NVe=n(H0o,"STRONG",{});var dyn=s(NVe);cft=r(dyn,"wav2vec2"),dyn.forEach(t),fft=r(H0o," \u2014 "),rge=n(H0o,"A",{href:!0});var myn=s(rge);hft=r(myn,"FlaxWav2Vec2ForPreTraining"),myn.forEach(t),gft=r(H0o," (Wav2Vec2 model)"),H0o.forEach(t),uft=i(Ae),T4=n(Ae,"LI",{});var K0o=s(T4);$Ve=n(K0o,"STRONG",{});var cyn=s($Ve);pft=r(cyn,"whisper"),cyn.forEach(t),_ft=r(K0o," \u2014 "),tge=n(K0o,"A",{href:!0});var fyn=s(tge);bft=r(fyn,"FlaxWhisperForConditionalGeneration"),fyn.forEach(t),vft=r(K0o," (Whisper model)"),K0o.forEach(t),Mft=i(Ae),F4=n(Ae,"LI",{});var e5o=s(F4);jVe=n(e5o,"STRONG",{});var hyn=s(jVe);Tft=r(hyn,"xlm-roberta"),hyn.forEach(t),Fft=r(e5o," \u2014 "),age=n(e5o,"A",{href:!0});var gyn=s(age);Eft=r(gyn,"FlaxXLMRobertaForMaskedLM"),gyn.forEach(t),yft=r(e5o," (XLM-RoBERTa model)"),e5o.forEach(t),Ae.forEach(t),Cft=i(nd),M(E4.$$.fragment,nd),nd.forEach(t),ad.forEach(t),Cxo=i(c),Mc=n(c,"H2",{class:!0});var EIo=s(Mc);y4=n(EIo,"A",{id:!0,class:!0,href:!0});var uyn=s(y4);PVe=n(uyn,"SPAN",{});var pyn=s(PVe);M(XV.$$.fragment,pyn),pyn.forEach(t),uyn.forEach(t),wft=i(EIo),ZVe=n(EIo,"SPAN",{});var _yn=s(ZVe);Lft=r(_yn,"Natural Language Processing"),_yn.forEach(t),EIo.forEach(t),wxo=i(c),nge=n(c,"P",{});var byn=s(nge);Aft=r(byn,"The following auto classes are available for the following natural language processing tasks."),byn.forEach(t),Lxo=i(c),Tc=n(c,"H3",{class:!0});var yIo=s(Tc);C4=n(yIo,"A",{id:!0,class:!0,href:!0});var vyn=s(C4);VVe=n(vyn,"SPAN",{});var Myn=s(VVe);M(DV.$$.fragment,Myn),Myn.forEach(t),vyn.forEach(t),Rft=i(yIo),WVe=n(yIo,"SPAN",{});var Tyn=s(WVe);kft=r(Tyn,"AutoModelForCausalLM"),Tyn.forEach(t),yIo.forEach(t),Axo=i(c),Yo=n(c,"DIV",{class:!0});var sd=s(Yo);M(qV.$$.fragment,sd),Sft=i(sd),Fc=n(sd,"P",{});var o9e=s(Fc);xft=r(o9e,`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 `),sge=n(o9e,"A",{href:!0});var Fyn=s(sge);Gft=r(Fyn,"from_pretrained()"),Fyn.forEach(t),Bft=r(o9e," class method or the "),lge=n(o9e,"A",{href:!0});var Eyn=s(lge);Ift=r(Eyn,"from_config()"),Eyn.forEach(t),Nft=r(o9e,` class
method.`),o9e.forEach(t),$ft=i(sd),OV=n(sd,"P",{});var CIo=s(OV);jft=r(CIo,"This class cannot be instantiated directly using "),JVe=n(CIo,"CODE",{});var yyn=s(JVe);Pft=r(yyn,"__init__()"),yyn.forEach(t),Zft=r(CIo," (throws an error)."),CIo.forEach(t),Vft=i(sd),Ot=n(sd,"DIV",{class:!0});var yj=s(Ot);M(QV.$$.fragment,yj),Wft=i(yj),UVe=n(yj,"P",{});var Cyn=s(UVe);Jft=r(Cyn,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Cyn.forEach(t),Uft=i(yj),Ec=n(yj,"P",{});var r9e=s(Ec);Xft=r(r9e,`Note:
Loading a model from its configuration file does `),XVe=n(r9e,"STRONG",{});var wyn=s(XVe);Dft=r(wyn,"not"),wyn.forEach(t),qft=r(r9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),ige=n(r9e,"A",{href:!0});var Lyn=s(ige);Oft=r(Lyn,"from_pretrained()"),Lyn.forEach(t),Qft=r(r9e," to load the model weights."),r9e.forEach(t),Yft=i(yj),M(w4.$$.fragment,yj),yj.forEach(t),zft=i(sd),io=n(sd,"DIV",{class:!0});var rn=s(io);M(YV.$$.fragment,rn),Hft=i(rn),DVe=n(rn,"P",{});var Ayn=s(DVe);Kft=r(Ayn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Ayn.forEach(t),eht=i(rn),Xn=n(rn,"P",{});var Cj=s(Xn);oht=r(Cj,"The model class to instantiate is selected based on the "),qVe=n(Cj,"CODE",{});var Ryn=s(qVe);rht=r(Ryn,"model_type"),Ryn.forEach(t),tht=r(Cj,` property of the config object (either
passed as an argument or loaded from `),OVe=n(Cj,"CODE",{});var kyn=s(OVe);aht=r(kyn,"pretrained_model_name_or_path"),kyn.forEach(t),nht=r(Cj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),QVe=n(Cj,"CODE",{});var Syn=s(QVe);sht=r(Syn,"pretrained_model_name_or_path"),Syn.forEach(t),lht=r(Cj,":"),Cj.forEach(t),iht=i(rn),Q=n(rn,"UL",{});var Y=s(Q);L4=n(Y,"LI",{});var o5o=s(L4);YVe=n(o5o,"STRONG",{});var xyn=s(YVe);dht=r(xyn,"bart"),xyn.forEach(t),mht=r(o5o," \u2014 "),dge=n(o5o,"A",{href:!0});var Gyn=s(dge);cht=r(Gyn,"BartForCausalLM"),Gyn.forEach(t),fht=r(o5o," (BART model)"),o5o.forEach(t),hht=i(Y),A4=n(Y,"LI",{});var r5o=s(A4);zVe=n(r5o,"STRONG",{});var Byn=s(zVe);ght=r(Byn,"bert"),Byn.forEach(t),uht=r(r5o," \u2014 "),mge=n(r5o,"A",{href:!0});var Iyn=s(mge);pht=r(Iyn,"BertLMHeadModel"),Iyn.forEach(t),_ht=r(r5o," (BERT model)"),r5o.forEach(t),bht=i(Y),R4=n(Y,"LI",{});var t5o=s(R4);HVe=n(t5o,"STRONG",{});var Nyn=s(HVe);vht=r(Nyn,"bert-generation"),Nyn.forEach(t),Mht=r(t5o," \u2014 "),cge=n(t5o,"A",{href:!0});var $yn=s(cge);Tht=r($yn,"BertGenerationDecoder"),$yn.forEach(t),Fht=r(t5o," (Bert Generation 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c5o=s($4);sWe=n(c5o,"STRONG",{});var Hyn=s(sWe);ngt=r(Hyn,"codegen"),Hyn.forEach(t),sgt=r(c5o," \u2014 "),vge=n(c5o,"A",{href:!0});var Kyn=s(vge);lgt=r(Kyn,"CodeGenForCausalLM"),Kyn.forEach(t),igt=r(c5o," (CodeGen model)"),c5o.forEach(t),dgt=i(Y),j4=n(Y,"LI",{});var f5o=s(j4);lWe=n(f5o,"STRONG",{});var e0n=s(lWe);mgt=r(e0n,"cpmant"),e0n.forEach(t),cgt=r(f5o," \u2014 "),Mge=n(f5o,"A",{href:!0});var o0n=s(Mge);fgt=r(o0n,"CpmAntForCausalLM"),o0n.forEach(t),hgt=r(f5o," (CPM-Ant model)"),f5o.forEach(t),ggt=i(Y),P4=n(Y,"LI",{});var h5o=s(P4);iWe=n(h5o,"STRONG",{});var r0n=s(iWe);ugt=r(r0n,"ctrl"),r0n.forEach(t),pgt=r(h5o," \u2014 "),Tge=n(h5o,"A",{href:!0});var t0n=s(Tge);_gt=r(t0n,"CTRLLMHeadModel"),t0n.forEach(t),bgt=r(h5o," (CTRL model)"),h5o.forEach(t),vgt=i(Y),Z4=n(Y,"LI",{});var g5o=s(Z4);dWe=n(g5o,"STRONG",{});var a0n=s(dWe);Mgt=r(a0n,"data2vec-text"),a0n.forEach(t),Tgt=r(g5o," \u2014 "),Fge=n(g5o,"A",{href:!0});var n0n=s(Fge);Fgt=r(n0n,"Data2VecTextForCausalLM"),n0n.forEach(t),Egt=r(g5o," (Data2VecText model)"),g5o.forEach(t),ygt=i(Y),V4=n(Y,"LI",{});var u5o=s(V4);mWe=n(u5o,"STRONG",{});var s0n=s(mWe);Cgt=r(s0n,"electra"),s0n.forEach(t),wgt=r(u5o," \u2014 "),Ege=n(u5o,"A",{href:!0});var l0n=s(Ege);Lgt=r(l0n,"ElectraForCausalLM"),l0n.forEach(t),Agt=r(u5o," (ELECTRA model)"),u5o.forEach(t),Rgt=i(Y),W4=n(Y,"LI",{});var p5o=s(W4);cWe=n(p5o,"STRONG",{});var i0n=s(cWe);kgt=r(i0n,"ernie"),i0n.forEach(t),Sgt=r(p5o," \u2014 "),yge=n(p5o,"A",{href:!0});var d0n=s(yge);xgt=r(d0n,"ErnieForCausalLM"),d0n.forEach(t),Ggt=r(p5o," (ERNIE model)"),p5o.forEach(t),Bgt=i(Y),J4=n(Y,"LI",{});var _5o=s(J4);fWe=n(_5o,"STRONG",{});var m0n=s(fWe);Igt=r(m0n,"falcon"),m0n.forEach(t),Ngt=r(_5o," \u2014 "),hWe=n(_5o,"CODE",{});var c0n=s(hWe);$gt=r(c0n,"FalconForCausalLM"),c0n.forEach(t),jgt=r(_5o," (Falcon model)"),_5o.forEach(t),Pgt=i(Y),U4=n(Y,"LI",{});var b5o=s(U4);gWe=n(b5o,"STRONG",{});var f0n=s(gWe);Zgt=r(f0n,"git"),f0n.forEach(t),Vgt=r(b5o," \u2014 "),Cge=n(b5o,"A",{href:!0});var h0n=s(Cge);Wgt=r(h0n,"GitForCausalLM"),h0n.forEach(t),Jgt=r(b5o," (GIT model)"),b5o.forEach(t),Ugt=i(Y),X4=n(Y,"LI",{});var v5o=s(X4);uWe=n(v5o,"STRONG",{});var g0n=s(uWe);Xgt=r(g0n,"gpt-sw3"),g0n.forEach(t),Dgt=r(v5o," \u2014 "),wge=n(v5o,"A",{href:!0});var u0n=s(wge);qgt=r(u0n,"GPT2LMHeadModel"),u0n.forEach(t),Ogt=r(v5o," (GPT-Sw3 model)"),v5o.forEach(t),Qgt=i(Y),D4=n(Y,"LI",{});var M5o=s(D4);pWe=n(M5o,"STRONG",{});var p0n=s(pWe);Ygt=r(p0n,"gpt2"),p0n.forEach(t),zgt=r(M5o," \u2014 "),Lge=n(M5o,"A",{href:!0});var _0n=s(Lge);Hgt=r(_0n,"GPT2LMHeadModel"),_0n.forEach(t),Kgt=r(M5o," (OpenAI GPT-2 model)"),M5o.forEach(t),eut=i(Y),q4=n(Y,"LI",{});var T5o=s(q4);_We=n(T5o,"STRONG",{});var b0n=s(_We);out=r(b0n,"gpt_bigcode"),b0n.forEach(t),rut=r(T5o," \u2014 "),Age=n(T5o,"A",{href:!0});var v0n=s(Age);tut=r(v0n,"GPTBigCodeForCausalLM"),v0n.forEach(t),aut=r(T5o," (GPTBigCode 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with the `),cue=n(t9e,"A",{href:!0});var J5n=s(cue);G1t=r(J5n,"from_pretrained()"),J5n.forEach(t),B1t=r(t9e," class method or the "),fue=n(t9e,"A",{href:!0});var U5n=s(fue);I1t=r(U5n,"from_config()"),U5n.forEach(t),N1t=r(t9e,` class
method.`),t9e.forEach(t),$1t=i(ld),KV=n(ld,"P",{});var LIo=s(KV);j1t=r(LIo,"This class cannot be instantiated directly using "),aJe=n(LIo,"CODE",{});var X5n=s(aJe);P1t=r(X5n,"__init__()"),X5n.forEach(t),Z1t=r(LIo," (throws an error)."),LIo.forEach(t),V1t=i(ld),Qt=n(ld,"DIV",{class:!0});var wj=s(Qt);M(eW.$$.fragment,wj),W1t=i(wj),nJe=n(wj,"P",{});var D5n=s(nJe);J1t=r(D5n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),D5n.forEach(t),U1t=i(wj),wc=n(wj,"P",{});var a9e=s(wc);X1t=r(a9e,`Note:
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model\u2019s configuration. Use `),hue=n(a9e,"A",{href:!0});var O5n=s(hue);O1t=r(O5n,"from_pretrained()"),O5n.forEach(t),Q1t=r(a9e," to load the model weights."),a9e.forEach(t),Y1t=i(wj),M(G9.$$.fragment,wj),wj.forEach(t),z1t=i(ld),lt=n(ld,"DIV",{class:!0});var id=s(lt);M(oW.$$.fragment,id),H1t=i(id),lJe=n(id,"P",{});var Q5n=s(lJe);K1t=r(Q5n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Q5n.forEach(t),ebt=i(id),Dn=n(id,"P",{});var Lj=s(Dn);obt=r(Lj,"The model class to instantiate is selected based on the "),iJe=n(Lj,"CODE",{});var Y5n=s(iJe);rbt=r(Y5n,"model_type"),Y5n.forEach(t),tbt=r(Lj,` property of the config object (either
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falling back to using pattern matching on `),mJe=n(Lj,"CODE",{});var H5n=s(mJe);sbt=r(H5n,"pretrained_model_name_or_path"),H5n.forEach(t),lbt=r(Lj,":"),Lj.forEach(t),ibt=i(id),Ee=n(id,"UL",{});var Ce=s(Ee);B9=n(Ce,"LI",{});var n3o=s(B9);cJe=n(n3o,"STRONG",{});var K5n=s(cJe);dbt=r(K5n,"bert"),K5n.forEach(t),mbt=r(n3o," \u2014 "),gue=n(n3o,"A",{href:!0});var e3n=s(gue);cbt=r(e3n,"TFBertLMHeadModel"),e3n.forEach(t),fbt=r(n3o," (BERT model)"),n3o.forEach(t),hbt=i(Ce),I9=n(Ce,"LI",{});var s3o=s(I9);fJe=n(s3o,"STRONG",{});var o3n=s(fJe);gbt=r(o3n,"camembert"),o3n.forEach(t),ubt=r(s3o," \u2014 "),uue=n(s3o,"A",{href:!0});var r3n=s(uue);pbt=r(r3n,"TFCamembertForCausalLM"),r3n.forEach(t),_bt=r(s3o," (CamemBERT model)"),s3o.forEach(t),bbt=i(Ce),N9=n(Ce,"LI",{});var l3o=s(N9);hJe=n(l3o,"STRONG",{});var t3n=s(hJe);vbt=r(t3n,"ctrl"),t3n.forEach(t),Mbt=r(l3o," \u2014 "),pue=n(l3o,"A",{href:!0});var a3n=s(pue);Tbt=r(a3n,"TFCTRLLMHeadModel"),a3n.forEach(t),Fbt=r(l3o," (CTRL 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f3n=s(Mue);Vbt=r(f3n,"TFOpenAIGPTLMHeadModel"),f3n.forEach(t),Wbt=r(c3o," (OpenAI GPT model)"),c3o.forEach(t),Jbt=i(Ce),V9=n(Ce,"LI",{});var f3o=s(V9);bJe=n(f3o,"STRONG",{});var h3n=s(bJe);Ubt=r(h3n,"opt"),h3n.forEach(t),Xbt=r(f3o," \u2014 "),Tue=n(f3o,"A",{href:!0});var g3n=s(Tue);Dbt=r(g3n,"TFOPTForCausalLM"),g3n.forEach(t),qbt=r(f3o," (OPT model)"),f3o.forEach(t),Obt=i(Ce),W9=n(Ce,"LI",{});var h3o=s(W9);vJe=n(h3o,"STRONG",{});var u3n=s(vJe);Qbt=r(u3n,"rembert"),u3n.forEach(t),Ybt=r(h3o," \u2014 "),Fue=n(h3o,"A",{href:!0});var p3n=s(Fue);zbt=r(p3n,"TFRemBertForCausalLM"),p3n.forEach(t),Hbt=r(h3o," (RemBERT model)"),h3o.forEach(t),Kbt=i(Ce),J9=n(Ce,"LI",{});var g3o=s(J9);MJe=n(g3o,"STRONG",{});var _3n=s(MJe);e2t=r(_3n,"roberta"),_3n.forEach(t),o2t=r(g3o," \u2014 "),Eue=n(g3o,"A",{href:!0});var b3n=s(Eue);r2t=r(b3n,"TFRobertaForCausalLM"),b3n.forEach(t),t2t=r(g3o," (RoBERTa model)"),g3o.forEach(t),a2t=i(Ce),U9=n(Ce,"LI",{});var u3o=s(U9);TJe=n(u3o,"STRONG",{});var v3n=s(TJe);n2t=r(v3n,"roberta-prelayernorm"),v3n.forEach(t),s2t=r(u3o," \u2014 "),yue=n(u3o,"A",{href:!0});var M3n=s(yue);l2t=r(M3n,"TFRobertaPreLayerNormForCausalLM"),M3n.forEach(t),i2t=r(u3o," (RoBERTa-PreLayerNorm model)"),u3o.forEach(t),d2t=i(Ce),X9=n(Ce,"LI",{});var p3o=s(X9);FJe=n(p3o,"STRONG",{});var T3n=s(FJe);m2t=r(T3n,"roformer"),T3n.forEach(t),c2t=r(p3o," \u2014 "),Cue=n(p3o,"A",{href:!0});var F3n=s(Cue);f2t=r(F3n,"TFRoFormerForCausalLM"),F3n.forEach(t),h2t=r(p3o," (RoFormer model)"),p3o.forEach(t),g2t=i(Ce),D9=n(Ce,"LI",{});var _3o=s(D9);EJe=n(_3o,"STRONG",{});var E3n=s(EJe);u2t=r(E3n,"transfo-xl"),E3n.forEach(t),p2t=r(_3o," \u2014 "),wue=n(_3o,"A",{href:!0});var y3n=s(wue);_2t=r(y3n,"TFTransfoXLLMHeadModel"),y3n.forEach(t),b2t=r(_3o," (Transformer-XL model)"),_3o.forEach(t),v2t=i(Ce),q9=n(Ce,"LI",{});var b3o=s(q9);yJe=n(b3o,"STRONG",{});var C3n=s(yJe);M2t=r(C3n,"xglm"),C3n.forEach(t),T2t=r(b3o," \u2014 "),Lue=n(b3o,"A",{href:!0});var w3n=s(Lue);F2t=r(w3n,"TFXGLMForCausalLM"),w3n.forEach(t),E2t=r(b3o," (XGLM model)"),b3o.forEach(t),y2t=i(Ce),O9=n(Ce,"LI",{});var v3o=s(O9);CJe=n(v3o,"STRONG",{});var L3n=s(CJe);C2t=r(L3n,"xlm"),L3n.forEach(t),w2t=r(v3o," \u2014 "),Aue=n(v3o,"A",{href:!0});var A3n=s(Aue);L2t=r(A3n,"TFXLMWithLMHeadModel"),A3n.forEach(t),A2t=r(v3o," (XLM model)"),v3o.forEach(t),R2t=i(Ce),Q9=n(Ce,"LI",{});var M3o=s(Q9);wJe=n(M3o,"STRONG",{});var R3n=s(wJe);k2t=r(R3n,"xlm-roberta"),R3n.forEach(t),S2t=r(M3o," \u2014 "),Rue=n(M3o,"A",{href:!0});var k3n=s(Rue);x2t=r(k3n,"TFXLMRobertaForCausalLM"),k3n.forEach(t),G2t=r(M3o," (XLM-RoBERTa model)"),M3o.forEach(t),B2t=i(Ce),Y9=n(Ce,"LI",{});var T3o=s(Y9);LJe=n(T3o,"STRONG",{});var S3n=s(LJe);I2t=r(S3n,"xlnet"),S3n.forEach(t),N2t=r(T3o," \u2014 "),kue=n(T3o,"A",{href:!0});var x3n=s(kue);$2t=r(x3n,"TFXLNetLMHeadModel"),x3n.forEach(t),j2t=r(T3o," (XLNet model)"),T3o.forEach(t),Ce.forEach(t),P2t=i(id),M(z9.$$.fragment,id),id.forEach(t),ld.forEach(t),Sxo=i(c),Lc=n(c,"H3",{class:!0});var AIo=s(Lc);H9=n(AIo,"A",{id:!0,class:!0,href:!0});var G3n=s(H9);AJe=n(G3n,"SPAN",{});var B3n=s(AJe);M(rW.$$.fragment,B3n),B3n.forEach(t),G3n.forEach(t),Z2t=i(AIo),RJe=n(AIo,"SPAN",{});var I3n=s(RJe);V2t=r(I3n,"FlaxAutoModelForCausalLM"),I3n.forEach(t),AIo.forEach(t),xxo=i(c),Ho=n(c,"DIV",{class:!0});var dd=s(Ho);M(tW.$$.fragment,dd),W2t=i(dd),Ac=n(dd,"P",{});var n9e=s(Ac);J2t=r(n9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Sue=n(n9e,"A",{href:!0});var N3n=s(Sue);U2t=r(N3n,"from_pretrained()"),N3n.forEach(t),X2t=r(n9e," class method or the "),xue=n(n9e,"A",{href:!0});var $3n=s(xue);D2t=r($3n,"from_config()"),$3n.forEach(t),q2t=r(n9e,` class
method.`),n9e.forEach(t),O2t=i(dd),aW=n(dd,"P",{});var RIo=s(aW);Q2t=r(RIo,"This class cannot be instantiated directly using "),kJe=n(RIo,"CODE",{});var j3n=s(kJe);Y2t=r(j3n,"__init__()"),j3n.forEach(t),z2t=r(RIo," (throws an error)."),RIo.forEach(t),H2t=i(dd),Yt=n(dd,"DIV",{class:!0});var Aj=s(Yt);M(nW.$$.fragment,Aj),K2t=i(Aj),SJe=n(Aj,"P",{});var P3n=s(SJe);evt=r(P3n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),P3n.forEach(t),ovt=i(Aj),Rc=n(Aj,"P",{});var s9e=s(Rc);rvt=r(s9e,`Note:
Loading a model from its configuration file does `),xJe=n(s9e,"STRONG",{});var Z3n=s(xJe);tvt=r(Z3n,"not"),Z3n.forEach(t),avt=r(s9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Gue=n(s9e,"A",{href:!0});var V3n=s(Gue);nvt=r(V3n,"from_pretrained()"),V3n.forEach(t),svt=r(s9e," to load the model weights."),s9e.forEach(t),lvt=i(Aj),M(K9.$$.fragment,Aj),Aj.forEach(t),ivt=i(dd),it=n(dd,"DIV",{class:!0});var md=s(it);M(sW.$$.fragment,md),dvt=i(md),GJe=n(md,"P",{});var W3n=s(GJe);mvt=r(W3n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),W3n.forEach(t),cvt=i(md),qn=n(md,"P",{});var Rj=s(qn);fvt=r(Rj,"The model class to instantiate is selected based on the "),BJe=n(Rj,"CODE",{});var J3n=s(BJe);hvt=r(J3n,"model_type"),J3n.forEach(t),gvt=r(Rj,` property of the config object (either
passed as an argument or loaded from `),IJe=n(Rj,"CODE",{});var U3n=s(IJe);uvt=r(U3n,"pretrained_model_name_or_path"),U3n.forEach(t),pvt=r(Rj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),NJe=n(Rj,"CODE",{});var X3n=s(NJe);_vt=r(X3n,"pretrained_model_name_or_path"),X3n.forEach(t),bvt=r(Rj,":"),Rj.forEach(t),vvt=i(md),Le=n(md,"UL",{});var Re=s(Le);eC=n(Re,"LI",{});var F3o=s(eC);$Je=n(F3o,"STRONG",{});var D3n=s($Je);Mvt=r(D3n,"bart"),D3n.forEach(t),Tvt=r(F3o," \u2014 "),Bue=n(F3o,"A",{href:!0});var q3n=s(Bue);Fvt=r(q3n,"FlaxBartForCausalLM"),q3n.forEach(t),Evt=r(F3o," (BART model)"),F3o.forEach(t),yvt=i(Re),oC=n(Re,"LI",{});var E3o=s(oC);jJe=n(E3o,"STRONG",{});var O3n=s(jJe);Cvt=r(O3n,"bert"),O3n.forEach(t),wvt=r(E3o," \u2014 "),Iue=n(E3o,"A",{href:!0});var Q3n=s(Iue);Lvt=r(Q3n,"FlaxBertForCausalLM"),Q3n.forEach(t),Avt=r(E3o," (BERT model)"),E3o.forEach(t),Rvt=i(Re),rC=n(Re,"LI",{});var y3o=s(rC);PJe=n(y3o,"STRONG",{});var Y3n=s(PJe);kvt=r(Y3n,"big_bird"),Y3n.forEach(t),Svt=r(y3o," \u2014 "),Nue=n(y3o,"A",{href:!0});var z3n=s(Nue);xvt=r(z3n,"FlaxBigBirdForCausalLM"),z3n.forEach(t),Gvt=r(y3o," (BigBird model)"),y3o.forEach(t),Bvt=i(Re),tC=n(Re,"LI",{});var C3o=s(tC);ZJe=n(C3o,"STRONG",{});var H3n=s(ZJe);Ivt=r(H3n,"bloom"),H3n.forEach(t),Nvt=r(C3o," \u2014 "),$ue=n(C3o,"A",{href:!0});var K3n=s($ue);$vt=r(K3n,"FlaxBloomForCausalLM"),K3n.forEach(t),jvt=r(C3o," (BLOOM model)"),C3o.forEach(t),Pvt=i(Re),aC=n(Re,"LI",{});var w3o=s(aC);VJe=n(w3o,"STRONG",{});var e4n=s(VJe);Zvt=r(e4n,"electra"),e4n.forEach(t),Vvt=r(w3o," \u2014 "),jue=n(w3o,"A",{href:!0});var o4n=s(jue);Wvt=r(o4n,"FlaxElectraForCausalLM"),o4n.forEach(t),Jvt=r(w3o," (ELECTRA model)"),w3o.forEach(t),Uvt=i(Re),nC=n(Re,"LI",{});var L3o=s(nC);WJe=n(L3o,"STRONG",{});var r4n=s(WJe);Xvt=r(r4n,"gpt-sw3"),r4n.forEach(t),Dvt=r(L3o," \u2014 "),Pue=n(L3o,"A",{href:!0});var t4n=s(Pue);qvt=r(t4n,"FlaxGPT2LMHeadModel"),t4n.forEach(t),Ovt=r(L3o," (GPT-Sw3 model)"),L3o.forEach(t),Qvt=i(Re),sC=n(Re,"LI",{});var A3o=s(sC);JJe=n(A3o,"STRONG",{});var a4n=s(JJe);Yvt=r(a4n,"gpt2"),a4n.forEach(t),zvt=r(A3o," \u2014 "),Zue=n(A3o,"A",{href:!0});var n4n=s(Zue);Hvt=r(n4n,"FlaxGPT2LMHeadModel"),n4n.forEach(t),Kvt=r(A3o," (OpenAI GPT-2 model)"),A3o.forEach(t),eMt=i(Re),lC=n(Re,"LI",{});var R3o=s(lC);UJe=n(R3o,"STRONG",{});var s4n=s(UJe);oMt=r(s4n,"gpt_neo"),s4n.forEach(t),rMt=r(R3o," \u2014 "),Vue=n(R3o,"A",{href:!0});var l4n=s(Vue);tMt=r(l4n,"FlaxGPTNeoForCausalLM"),l4n.forEach(t),aMt=r(R3o," (GPT Neo model)"),R3o.forEach(t),nMt=i(Re),iC=n(Re,"LI",{});var k3o=s(iC);XJe=n(k3o,"STRONG",{});var i4n=s(XJe);sMt=r(i4n,"gptj"),i4n.forEach(t),lMt=r(k3o," \u2014 "),Wue=n(k3o,"A",{href:!0});var d4n=s(Wue);iMt=r(d4n,"FlaxGPTJForCausalLM"),d4n.forEach(t),dMt=r(k3o," (GPT-J model)"),k3o.forEach(t),mMt=i(Re),dC=n(Re,"LI",{});var S3o=s(dC);DJe=n(S3o,"STRONG",{});var m4n=s(DJe);cMt=r(m4n,"opt"),m4n.forEach(t),fMt=r(S3o," \u2014 "),Jue=n(S3o,"A",{href:!0});var c4n=s(Jue);hMt=r(c4n,"FlaxOPTForCausalLM"),c4n.forEach(t),gMt=r(S3o," (OPT model)"),S3o.forEach(t),uMt=i(Re),mC=n(Re,"LI",{});var x3o=s(mC);qJe=n(x3o,"STRONG",{});var f4n=s(qJe);pMt=r(f4n,"roberta"),f4n.forEach(t),_Mt=r(x3o," \u2014 "),Uue=n(x3o,"A",{href:!0});var h4n=s(Uue);bMt=r(h4n,"FlaxRobertaForCausalLM"),h4n.forEach(t),vMt=r(x3o," (RoBERTa model)"),x3o.forEach(t),MMt=i(Re),cC=n(Re,"LI",{});var G3o=s(cC);OJe=n(G3o,"STRONG",{});var g4n=s(OJe);TMt=r(g4n,"roberta-prelayernorm"),g4n.forEach(t),FMt=r(G3o," \u2014 "),Xue=n(G3o,"A",{href:!0});var u4n=s(Xue);EMt=r(u4n,"FlaxRobertaPreLayerNormForCausalLM"),u4n.forEach(t),yMt=r(G3o," (RoBERTa-PreLayerNorm model)"),G3o.forEach(t),CMt=i(Re),fC=n(Re,"LI",{});var B3o=s(fC);QJe=n(B3o,"STRONG",{});var p4n=s(QJe);wMt=r(p4n,"xglm"),p4n.forEach(t),LMt=r(B3o," \u2014 "),Due=n(B3o,"A",{href:!0});var _4n=s(Due);AMt=r(_4n,"FlaxXGLMForCausalLM"),_4n.forEach(t),RMt=r(B3o," (XGLM model)"),B3o.forEach(t),kMt=i(Re),hC=n(Re,"LI",{});var I3o=s(hC);YJe=n(I3o,"STRONG",{});var b4n=s(YJe);SMt=r(b4n,"xlm-roberta"),b4n.forEach(t),xMt=r(I3o," \u2014 "),que=n(I3o,"A",{href:!0});var v4n=s(que);GMt=r(v4n,"FlaxXLMRobertaForCausalLM"),v4n.forEach(t),BMt=r(I3o," (XLM-RoBERTa model)"),I3o.forEach(t),Re.forEach(t),IMt=i(md),M(gC.$$.fragment,md),md.forEach(t),dd.forEach(t),Gxo=i(c),kc=n(c,"H3",{class:!0});var kIo=s(kc);uC=n(kIo,"A",{id:!0,class:!0,href:!0});var M4n=s(uC);zJe=n(M4n,"SPAN",{});var T4n=s(zJe);M(lW.$$.fragment,T4n),T4n.forEach(t),M4n.forEach(t),NMt=i(kIo),HJe=n(kIo,"SPAN",{});var F4n=s(HJe);$Mt=r(F4n,"AutoModelForMaskedLM"),F4n.forEach(t),kIo.forEach(t),Bxo=i(c),Ko=n(c,"DIV",{class:!0});var cd=s(Ko);M(iW.$$.fragment,cd),jMt=i(cd),Sc=n(cd,"P",{});var l9e=s(Sc);PMt=r(l9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),Oue=n(l9e,"A",{href:!0});var E4n=s(Oue);ZMt=r(E4n,"from_pretrained()"),E4n.forEach(t),VMt=r(l9e," class method or the "),Que=n(l9e,"A",{href:!0});var y4n=s(Que);WMt=r(y4n,"from_config()"),y4n.forEach(t),JMt=r(l9e,` class
method.`),l9e.forEach(t),UMt=i(cd),dW=n(cd,"P",{});var SIo=s(dW);XMt=r(SIo,"This class cannot be instantiated directly using "),KJe=n(SIo,"CODE",{});var C4n=s(KJe);DMt=r(C4n,"__init__()"),C4n.forEach(t),qMt=r(SIo," (throws an error)."),SIo.forEach(t),OMt=i(cd),zt=n(cd,"DIV",{class:!0});var kj=s(zt);M(mW.$$.fragment,kj),QMt=i(kj),eUe=n(kj,"P",{});var w4n=s(eUe);YMt=r(w4n,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),w4n.forEach(t),zMt=i(kj),xc=n(kj,"P",{});var i9e=s(xc);HMt=r(i9e,`Note:
Loading a model from its configuration file does `),oUe=n(i9e,"STRONG",{});var L4n=s(oUe);KMt=r(L4n,"not"),L4n.forEach(t),eTt=r(i9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Yue=n(i9e,"A",{href:!0});var A4n=s(Yue);oTt=r(A4n,"from_pretrained()"),A4n.forEach(t),rTt=r(i9e," to load the model weights."),i9e.forEach(t),tTt=i(kj),M(pC.$$.fragment,kj),kj.forEach(t),aTt=i(cd),mo=n(cd,"DIV",{class:!0});var tn=s(mo);M(cW.$$.fragment,tn),nTt=i(tn),rUe=n(tn,"P",{});var R4n=s(rUe);sTt=r(R4n,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),R4n.forEach(t),lTt=i(tn),On=n(tn,"P",{});var Sj=s(On);iTt=r(Sj,"The model class to instantiate is selected based on the "),tUe=n(Sj,"CODE",{});var k4n=s(tUe);dTt=r(k4n,"model_type"),k4n.forEach(t),mTt=r(Sj,` property of the config object (either
passed as an argument or loaded from `),aUe=n(Sj,"CODE",{});var S4n=s(aUe);cTt=r(S4n,"pretrained_model_name_or_path"),S4n.forEach(t),fTt=r(Sj,` if possible), or when it\u2019s missing, by
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dCn=s(QUe);m0t=r(dCn,"xlm-roberta-xl"),dCn.forEach(t),c0t=r(y4o," \u2014 "),jpe=n(y4o,"A",{href:!0});var mCn=s(jpe);f0t=r(mCn,"XLMRobertaXLForMaskedLM"),mCn.forEach(t),h0t=r(y4o," (XLM-RoBERTa-XL model)"),y4o.forEach(t),g0t=i(ee),sw=n(ee,"LI",{});var C4o=s(sw);YUe=n(C4o,"STRONG",{});var cCn=s(YUe);u0t=r(cCn,"xmod"),cCn.forEach(t),p0t=r(C4o," \u2014 "),Ppe=n(C4o,"A",{href:!0});var fCn=s(Ppe);_0t=r(fCn,"XmodForMaskedLM"),fCn.forEach(t),b0t=r(C4o," (X-MOD model)"),C4o.forEach(t),v0t=i(ee),lw=n(ee,"LI",{});var w4o=s(lw);zUe=n(w4o,"STRONG",{});var hCn=s(zUe);M0t=r(hCn,"yoso"),hCn.forEach(t),T0t=r(w4o," \u2014 "),Zpe=n(w4o,"A",{href:!0});var gCn=s(Zpe);F0t=r(gCn,"YosoForMaskedLM"),gCn.forEach(t),E0t=r(w4o," (YOSO model)"),w4o.forEach(t),ee.forEach(t),y0t=i(tn),iw=n(tn,"P",{});var L4o=s(iw);C0t=r(L4o,"The model is set in evaluation mode by default using "),HUe=n(L4o,"CODE",{});var uCn=s(HUe);w0t=r(uCn,"model.eval()"),uCn.forEach(t),L0t=r(L4o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),KUe=n(L4o,"CODE",{});var pCn=s(KUe);A0t=r(pCn,"model.train()"),pCn.forEach(t),L4o.forEach(t),R0t=i(tn),M(dw.$$.fragment,tn),tn.forEach(t),cd.forEach(t),Ixo=i(c),Gc=n(c,"H3",{class:!0});var xIo=s(Gc);mw=n(xIo,"A",{id:!0,class:!0,href:!0});var _Cn=s(mw);eXe=n(_Cn,"SPAN",{});var bCn=s(eXe);M(fW.$$.fragment,bCn),bCn.forEach(t),_Cn.forEach(t),k0t=i(xIo),oXe=n(xIo,"SPAN",{});var vCn=s(oXe);S0t=r(vCn,"TFAutoModelForMaskedLM"),vCn.forEach(t),xIo.forEach(t),Nxo=i(c),er=n(c,"DIV",{class:!0});var fd=s(er);M(hW.$$.fragment,fd),x0t=i(fd),Bc=n(fd,"P",{});var d9e=s(Bc);G0t=r(d9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),Vpe=n(d9e,"A",{href:!0});var MCn=s(Vpe);B0t=r(MCn,"from_pretrained()"),MCn.forEach(t),I0t=r(d9e," class method or the "),Wpe=n(d9e,"A",{href:!0});var TCn=s(Wpe);N0t=r(TCn,"from_config()"),TCn.forEach(t),$0t=r(d9e,` class
method.`),d9e.forEach(t),j0t=i(fd),gW=n(fd,"P",{});var GIo=s(gW);P0t=r(GIo,"This class cannot be instantiated directly using "),rXe=n(GIo,"CODE",{});var FCn=s(rXe);Z0t=r(FCn,"__init__()"),FCn.forEach(t),V0t=r(GIo," (throws an error)."),GIo.forEach(t),W0t=i(fd),Ht=n(fd,"DIV",{class:!0});var xj=s(Ht);M(uW.$$.fragment,xj),J0t=i(xj),tXe=n(xj,"P",{});var ECn=s(tXe);U0t=r(ECn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),ECn.forEach(t),X0t=i(xj),Ic=n(xj,"P",{});var m9e=s(Ic);D0t=r(m9e,`Note:
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model\u2019s configuration. Use `),Jpe=n(m9e,"A",{href:!0});var CCn=s(Jpe);Q0t=r(CCn,"from_pretrained()"),CCn.forEach(t),Y0t=r(m9e," to load the model weights."),m9e.forEach(t),z0t=i(xj),M(cw.$$.fragment,xj),xj.forEach(t),H0t=i(fd),dt=n(fd,"DIV",{class:!0});var hd=s(dt);M(pW.$$.fragment,hd),K0t=i(hd),nXe=n(hd,"P",{});var wCn=s(nXe);e5t=r(wCn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),wCn.forEach(t),o5t=i(hd),Qn=n(hd,"P",{});var Gj=s(Qn);r5t=r(Gj,"The model class to instantiate is selected based on the "),sXe=n(Gj,"CODE",{});var LCn=s(sXe);t5t=r(LCn,"model_type"),LCn.forEach(t),a5t=r(Gj,` property of the config object (either
passed as an argument or loaded from `),lXe=n(Gj,"CODE",{});var ACn=s(lXe);n5t=r(ACn,"pretrained_model_name_or_path"),ACn.forEach(t),s5t=r(Gj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iXe=n(Gj,"CODE",{});var RCn=s(iXe);l5t=r(RCn,"pretrained_model_name_or_path"),RCn.forEach(t),i5t=r(Gj,":"),Gj.forEach(t),d5t=i(hd),pe=n(hd,"UL",{});var Me=s(pe);fw=n(Me,"LI",{});var A4o=s(fw);dXe=n(A4o,"STRONG",{});var kCn=s(dXe);m5t=r(kCn,"albert"),kCn.forEach(t),c5t=r(A4o," \u2014 "),Upe=n(A4o,"A",{href:!0});var SCn=s(Upe);f5t=r(SCn,"TFAlbertForMaskedLM"),SCn.forEach(t),h5t=r(A4o," (ALBERT model)"),A4o.forEach(t),g5t=i(Me),hw=n(Me,"LI",{});var R4o=s(hw);mXe=n(R4o,"STRONG",{});var xCn=s(mXe);u5t=r(xCn,"bert"),xCn.forEach(t),p5t=r(R4o," \u2014 "),Xpe=n(R4o,"A",{href:!0});var GCn=s(Xpe);_5t=r(GCn,"TFBertForMaskedLM"),GCn.forEach(t),b5t=r(R4o," (BERT model)"),R4o.forEach(t),v5t=i(Me),gw=n(Me,"LI",{});var k4o=s(gw);cXe=n(k4o,"STRONG",{});var BCn=s(cXe);M5t=r(BCn,"camembert"),BCn.forEach(t),T5t=r(k4o," \u2014 "),Dpe=n(k4o,"A",{href:!0});var ICn=s(Dpe);F5t=r(ICn,"TFCamembertForMaskedLM"),ICn.forEach(t),E5t=r(k4o," (CamemBERT 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twn=s(t_e);E3t=r(twn,"TFMobileBertForMaskedLM"),twn.forEach(t),y3t=r(V4o," (MobileBERT model)"),V4o.forEach(t),C3t=i(Me),ww=n(Me,"LI",{});var W4o=s(ww);EXe=n(W4o,"STRONG",{});var awn=s(EXe);w3t=r(awn,"mpnet"),awn.forEach(t),L3t=r(W4o," \u2014 "),a_e=n(W4o,"A",{href:!0});var nwn=s(a_e);A3t=r(nwn,"TFMPNetForMaskedLM"),nwn.forEach(t),R3t=r(W4o," (MPNet model)"),W4o.forEach(t),k3t=i(Me),Lw=n(Me,"LI",{});var J4o=s(Lw);yXe=n(J4o,"STRONG",{});var swn=s(yXe);S3t=r(swn,"rembert"),swn.forEach(t),x3t=r(J4o," \u2014 "),n_e=n(J4o,"A",{href:!0});var lwn=s(n_e);G3t=r(lwn,"TFRemBertForMaskedLM"),lwn.forEach(t),B3t=r(J4o," (RemBERT model)"),J4o.forEach(t),I3t=i(Me),Aw=n(Me,"LI",{});var U4o=s(Aw);CXe=n(U4o,"STRONG",{});var iwn=s(CXe);N3t=r(iwn,"roberta"),iwn.forEach(t),$3t=r(U4o," \u2014 "),s_e=n(U4o,"A",{href:!0});var dwn=s(s_e);j3t=r(dwn,"TFRobertaForMaskedLM"),dwn.forEach(t),P3t=r(U4o," (RoBERTa model)"),U4o.forEach(t),Z3t=i(Me),Rw=n(Me,"LI",{});var X4o=s(Rw);wXe=n(X4o,"STRONG",{});var 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_wn=s(m_e);a4t=r(_wn,"TFXLMWithLMHeadModel"),_wn.forEach(t),n4t=r(O4o," (XLM model)"),O4o.forEach(t),s4t=i(Me),Gw=n(Me,"LI",{});var Q4o=s(Gw);kXe=n(Q4o,"STRONG",{});var bwn=s(kXe);l4t=r(bwn,"xlm-roberta"),bwn.forEach(t),i4t=r(Q4o," \u2014 "),c_e=n(Q4o,"A",{href:!0});var vwn=s(c_e);d4t=r(vwn,"TFXLMRobertaForMaskedLM"),vwn.forEach(t),m4t=r(Q4o," (XLM-RoBERTa model)"),Q4o.forEach(t),Me.forEach(t),c4t=i(hd),M(Bw.$$.fragment,hd),hd.forEach(t),fd.forEach(t),$xo=i(c),Nc=n(c,"H3",{class:!0});var BIo=s(Nc);Iw=n(BIo,"A",{id:!0,class:!0,href:!0});var Mwn=s(Iw);SXe=n(Mwn,"SPAN",{});var Twn=s(SXe);M(_W.$$.fragment,Twn),Twn.forEach(t),Mwn.forEach(t),f4t=i(BIo),xXe=n(BIo,"SPAN",{});var Fwn=s(xXe);h4t=r(Fwn,"FlaxAutoModelForMaskedLM"),Fwn.forEach(t),BIo.forEach(t),jxo=i(c),or=n(c,"DIV",{class:!0});var gd=s(or);M(bW.$$.fragment,gd),g4t=i(gd),$c=n(gd,"P",{});var c9e=s($c);u4t=r(c9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language 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with the `),f_e=n(c9e,"A",{href:!0});var Ewn=s(f_e);p4t=r(Ewn,"from_pretrained()"),Ewn.forEach(t),_4t=r(c9e," class method or the "),h_e=n(c9e,"A",{href:!0});var ywn=s(h_e);b4t=r(ywn,"from_config()"),ywn.forEach(t),v4t=r(c9e,` class
method.`),c9e.forEach(t),M4t=i(gd),vW=n(gd,"P",{});var IIo=s(vW);T4t=r(IIo,"This class cannot be instantiated directly using "),GXe=n(IIo,"CODE",{});var Cwn=s(GXe);F4t=r(Cwn,"__init__()"),Cwn.forEach(t),E4t=r(IIo," (throws an error)."),IIo.forEach(t),y4t=i(gd),Kt=n(gd,"DIV",{class:!0});var Bj=s(Kt);M(MW.$$.fragment,Bj),C4t=i(Bj),BXe=n(Bj,"P",{});var wwn=s(BXe);w4t=r(wwn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),wwn.forEach(t),L4t=i(Bj),jc=n(Bj,"P",{});var f9e=s(jc);A4t=r(f9e,`Note:
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model\u2019s configuration. Use `),g_e=n(f9e,"A",{href:!0});var Awn=s(g_e);S4t=r(Awn,"from_pretrained()"),Awn.forEach(t),x4t=r(f9e," to load the model weights."),f9e.forEach(t),G4t=i(Bj),M(Nw.$$.fragment,Bj),Bj.forEach(t),B4t=i(gd),mt=n(gd,"DIV",{class:!0});var ud=s(mt);M(TW.$$.fragment,ud),I4t=i(ud),NXe=n(ud,"P",{});var Rwn=s(NXe);N4t=r(Rwn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Rwn.forEach(t),$4t=i(ud),Yn=n(ud,"P",{});var Ij=s(Yn);j4t=r(Ij,"The model class to instantiate is selected based on the "),$Xe=n(Ij,"CODE",{});var kwn=s($Xe);P4t=r(kwn,"model_type"),kwn.forEach(t),Z4t=r(Ij,` property of the config object (either
passed as an argument or loaded from `),jXe=n(Ij,"CODE",{});var Swn=s(jXe);V4t=r(Swn,"pretrained_model_name_or_path"),Swn.forEach(t),W4t=r(Ij,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PXe=n(Ij,"CODE",{});var xwn=s(PXe);J4t=r(xwn,"pretrained_model_name_or_path"),xwn.forEach(t),U4t=r(Ij,":"),Ij.forEach(t),X4t=i(ud),ke=n(ud,"UL",{});var Pe=s(ke);$w=n(Pe,"LI",{});var Y4o=s($w);ZXe=n(Y4o,"STRONG",{});var Gwn=s(ZXe);D4t=r(Gwn,"albert"),Gwn.forEach(t),q4t=r(Y4o," \u2014 "),u_e=n(Y4o,"A",{href:!0});var Bwn=s(u_e);O4t=r(Bwn,"FlaxAlbertForMaskedLM"),Bwn.forEach(t),Q4t=r(Y4o," (ALBERT model)"),Y4o.forEach(t),Y4t=i(Pe),jw=n(Pe,"LI",{});var z4o=s(jw);VXe=n(z4o,"STRONG",{});var Iwn=s(VXe);z4t=r(Iwn,"bart"),Iwn.forEach(t),H4t=r(z4o," \u2014 "),p_e=n(z4o,"A",{href:!0});var Nwn=s(p_e);K4t=r(Nwn,"FlaxBartForConditionalGeneration"),Nwn.forEach(t),e9t=r(z4o," (BART model)"),z4o.forEach(t),o9t=i(Pe),Pw=n(Pe,"LI",{});var H4o=s(Pw);WXe=n(H4o,"STRONG",{});var $wn=s(WXe);r9t=r($wn,"bert"),$wn.forEach(t),t9t=r(H4o," \u2014 "),__e=n(H4o,"A",{href:!0});var jwn=s(__e);a9t=r(jwn,"FlaxBertForMaskedLM"),jwn.forEach(t),n9t=r(H4o," (BERT model)"),H4o.forEach(t),s9t=i(Pe),Zw=n(Pe,"LI",{});var K4o=s(Zw);JXe=n(K4o,"STRONG",{});var Pwn=s(JXe);l9t=r(Pwn,"big_bird"),Pwn.forEach(t),i9t=r(K4o," \u2014 "),b_e=n(K4o,"A",{href:!0});var Zwn=s(b_e);d9t=r(Zwn,"FlaxBigBirdForMaskedLM"),Zwn.forEach(t),m9t=r(K4o," (BigBird model)"),K4o.forEach(t),c9t=i(Pe),Vw=n(Pe,"LI",{});var e9o=s(Vw);UXe=n(e9o,"STRONG",{});var Vwn=s(UXe);f9t=r(Vwn,"distilbert"),Vwn.forEach(t),h9t=r(e9o," \u2014 "),v_e=n(e9o,"A",{href:!0});var Wwn=s(v_e);g9t=r(Wwn,"FlaxDistilBertForMaskedLM"),Wwn.forEach(t),u9t=r(e9o," (DistilBERT model)"),e9o.forEach(t),p9t=i(Pe),Ww=n(Pe,"LI",{});var o9o=s(Ww);XXe=n(o9o,"STRONG",{});var Jwn=s(XXe);_9t=r(Jwn,"electra"),Jwn.forEach(t),b9t=r(o9o," \u2014 "),M_e=n(o9o,"A",{href:!0});var Uwn=s(M_e);v9t=r(Uwn,"FlaxElectraForMaskedLM"),Uwn.forEach(t),M9t=r(o9o," (ELECTRA model)"),o9o.forEach(t),T9t=i(Pe),Jw=n(Pe,"LI",{});var r9o=s(Jw);DXe=n(r9o,"STRONG",{});var Xwn=s(DXe);F9t=r(Xwn,"mbart"),Xwn.forEach(t),E9t=r(r9o," \u2014 "),T_e=n(r9o,"A",{href:!0});var Dwn=s(T_e);y9t=r(Dwn,"FlaxMBartForConditionalGeneration"),Dwn.forEach(t),C9t=r(r9o," (mBART model)"),r9o.forEach(t),w9t=i(Pe),Uw=n(Pe,"LI",{});var t9o=s(Uw);qXe=n(t9o,"STRONG",{});var qwn=s(qXe);L9t=r(qwn,"roberta"),qwn.forEach(t),A9t=r(t9o," \u2014 "),F_e=n(t9o,"A",{href:!0});var Own=s(F_e);R9t=r(Own,"FlaxRobertaForMaskedLM"),Own.forEach(t),k9t=r(t9o," (RoBERTa model)"),t9o.forEach(t),S9t=i(Pe),Xw=n(Pe,"LI",{});var a9o=s(Xw);OXe=n(a9o,"STRONG",{});var Qwn=s(OXe);x9t=r(Qwn,"roberta-prelayernorm"),Qwn.forEach(t),G9t=r(a9o," \u2014 "),E_e=n(a9o,"A",{href:!0});var Ywn=s(E_e);B9t=r(Ywn,"FlaxRobertaPreLayerNormForMaskedLM"),Ywn.forEach(t),I9t=r(a9o," (RoBERTa-PreLayerNorm model)"),a9o.forEach(t),N9t=i(Pe),Dw=n(Pe,"LI",{});var n9o=s(Dw);QXe=n(n9o,"STRONG",{});var zwn=s(QXe);$9t=r(zwn,"roformer"),zwn.forEach(t),j9t=r(n9o," \u2014 "),y_e=n(n9o,"A",{href:!0});var Hwn=s(y_e);P9t=r(Hwn,"FlaxRoFormerForMaskedLM"),Hwn.forEach(t),Z9t=r(n9o," (RoFormer model)"),n9o.forEach(t),V9t=i(Pe),qw=n(Pe,"LI",{});var s9o=s(qw);YXe=n(s9o,"STRONG",{});var Kwn=s(YXe);W9t=r(Kwn,"xlm-roberta"),Kwn.forEach(t),J9t=r(s9o," \u2014 "),C_e=n(s9o,"A",{href:!0});var e6n=s(C_e);U9t=r(e6n,"FlaxXLMRobertaForMaskedLM"),e6n.forEach(t),X9t=r(s9o," (XLM-RoBERTa model)"),s9o.forEach(t),Pe.forEach(t),D9t=i(ud),M(Ow.$$.fragment,ud),ud.forEach(t),gd.forEach(t),Pxo=i(c),Pc=n(c,"H3",{class:!0});var NIo=s(Pc);Qw=n(NIo,"A",{id:!0,class:!0,href:!0});var o6n=s(Qw);zXe=n(o6n,"SPAN",{});var r6n=s(zXe);M(FW.$$.fragment,r6n),r6n.forEach(t),o6n.forEach(t),q9t=i(NIo),HXe=n(NIo,"SPAN",{});var t6n=s(HXe);O9t=r(t6n,"AutoModelForMaskGeneration"),t6n.forEach(t),NIo.forEach(t),Zxo=i(c),EW=n(c,"DIV",{class:!0});var a6n=s(EW);M(yW.$$.fragment,a6n),a6n.forEach(t),Vxo=i(c),Zc=n(c,"H3",{class:!0});var $Io=s(Zc);Yw=n($Io,"A",{id:!0,class:!0,href:!0});var n6n=s(Yw);KXe=n(n6n,"SPAN",{});var s6n=s(KXe);M(CW.$$.fragment,s6n),s6n.forEach(t),n6n.forEach(t),Q9t=i($Io),eDe=n($Io,"SPAN",{});var l6n=s(eDe);Y9t=r(l6n,"TFAutoModelForMaskGeneration"),l6n.forEach(t),$Io.forEach(t),Wxo=i(c),wW=n(c,"DIV",{class:!0});var i6n=s(wW);M(LW.$$.fragment,i6n),i6n.forEach(t),Jxo=i(c),Vc=n(c,"H3",{class:!0});var jIo=s(Vc);zw=n(jIo,"A",{id:!0,class:!0,href:!0});var d6n=s(zw);oDe=n(d6n,"SPAN",{});var m6n=s(oDe);M(AW.$$.fragment,m6n),m6n.forEach(t),d6n.forEach(t),z9t=i(jIo),rDe=n(jIo,"SPAN",{});var c6n=s(rDe);H9t=r(c6n,"AutoModelForSeq2SeqLM"),c6n.forEach(t),jIo.forEach(t),Uxo=i(c),rr=n(c,"DIV",{class:!0});var pd=s(rr);M(RW.$$.fragment,pd),K9t=i(pd),Wc=n(pd,"P",{});var h9e=s(Wc);eCt=r(h9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),w_e=n(h9e,"A",{href:!0});var f6n=s(w_e);oCt=r(f6n,"from_pretrained()"),f6n.forEach(t),rCt=r(h9e," class method or the "),L_e=n(h9e,"A",{href:!0});var h6n=s(L_e);tCt=r(h6n,"from_config()"),h6n.forEach(t),aCt=r(h9e,` class
method.`),h9e.forEach(t),nCt=i(pd),kW=n(pd,"P",{});var PIo=s(kW);sCt=r(PIo,"This class cannot be instantiated directly using "),tDe=n(PIo,"CODE",{});var g6n=s(tDe);lCt=r(g6n,"__init__()"),g6n.forEach(t),iCt=r(PIo," (throws an error)."),PIo.forEach(t),dCt=i(pd),ea=n(pd,"DIV",{class:!0});var Nj=s(ea);M(SW.$$.fragment,Nj),mCt=i(Nj),aDe=n(Nj,"P",{});var u6n=s(aDe);cCt=r(u6n,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),u6n.forEach(t),fCt=i(Nj),Jc=n(Nj,"P",{});var g9e=s(Jc);hCt=r(g9e,`Note:
Loading a model from its configuration file does `),nDe=n(g9e,"STRONG",{});var p6n=s(nDe);gCt=r(p6n,"not"),p6n.forEach(t),uCt=r(g9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),A_e=n(g9e,"A",{href:!0});var _6n=s(A_e);pCt=r(_6n,"from_pretrained()"),_6n.forEach(t),_Ct=r(g9e," to load the model weights."),g9e.forEach(t),bCt=i(Nj),M(Hw.$$.fragment,Nj),Nj.forEach(t),vCt=i(pd),co=n(pd,"DIV",{class:!0});var an=s(co);M(xW.$$.fragment,an),MCt=i(an),sDe=n(an,"P",{});var b6n=s(sDe);TCt=r(b6n,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),b6n.forEach(t),FCt=i(an),zn=n(an,"P",{});var $j=s(zn);ECt=r($j,"The model class to instantiate is selected based on the "),lDe=n($j,"CODE",{});var v6n=s(lDe);yCt=r(v6n,"model_type"),v6n.forEach(t),CCt=r($j,` property of the config object (either
passed as an argument or loaded from `),iDe=n($j,"CODE",{});var M6n=s(iDe);wCt=r(M6n,"pretrained_model_name_or_path"),M6n.forEach(t),LCt=r($j,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dDe=n($j,"CODE",{});var T6n=s(dDe);ACt=r(T6n,"pretrained_model_name_or_path"),T6n.forEach(t),RCt=r($j,":"),$j.forEach(t),kCt=i(an),ge=n(an,"UL",{});var be=s(ge);Kw=n(be,"LI",{});var l9o=s(Kw);mDe=n(l9o,"STRONG",{});var F6n=s(mDe);SCt=r(F6n,"bart"),F6n.forEach(t),xCt=r(l9o," \u2014 "),R_e=n(l9o,"A",{href:!0});var E6n=s(R_e);GCt=r(E6n,"BartForConditionalGeneration"),E6n.forEach(t),BCt=r(l9o," (BART model)"),l9o.forEach(t),ICt=i(be),e6=n(be,"LI",{});var i9o=s(e6);cDe=n(i9o,"STRONG",{});var y6n=s(cDe);NCt=r(y6n,"bigbird_pegasus"),y6n.forEach(t),$Ct=r(i9o," \u2014 "),k_e=n(i9o,"A",{href:!0});var C6n=s(k_e);jCt=r(C6n,"BigBirdPegasusForConditionalGeneration"),C6n.forEach(t),PCt=r(i9o," (BigBird-Pegasus model)"),i9o.forEach(t),ZCt=i(be),o6=n(be,"LI",{});var d9o=s(o6);fDe=n(d9o,"STRONG",{});var w6n=s(fDe);VCt=r(w6n,"blenderbot"),w6n.forEach(t),WCt=r(d9o," \u2014 "),S_e=n(d9o,"A",{href:!0});var L6n=s(S_e);JCt=r(L6n,"BlenderbotForConditionalGeneration"),L6n.forEach(t),UCt=r(d9o," (Blenderbot model)"),d9o.forEach(t),XCt=i(be),r6=n(be,"LI",{});var m9o=s(r6);hDe=n(m9o,"STRONG",{});var A6n=s(hDe);DCt=r(A6n,"blenderbot-small"),A6n.forEach(t),qCt=r(m9o," \u2014 "),x_e=n(m9o,"A",{href:!0});var R6n=s(x_e);OCt=r(R6n,"BlenderbotSmallForConditionalGeneration"),R6n.forEach(t),QCt=r(m9o," (BlenderbotSmall model)"),m9o.forEach(t),YCt=i(be),t6=n(be,"LI",{});var c9o=s(t6);gDe=n(c9o,"STRONG",{});var k6n=s(gDe);zCt=r(k6n,"encoder-decoder"),k6n.forEach(t),HCt=r(c9o," \u2014 "),G_e=n(c9o,"A",{href:!0});var S6n=s(G_e);KCt=r(S6n,"EncoderDecoderModel"),S6n.forEach(t),ewt=r(c9o," (Encoder decoder model)"),c9o.forEach(t),owt=i(be),a6=n(be,"LI",{});var f9o=s(a6);uDe=n(f9o,"STRONG",{});var x6n=s(uDe);rwt=r(x6n,"fsmt"),x6n.forEach(t),twt=r(f9o," \u2014 "),B_e=n(f9o,"A",{href:!0});var G6n=s(B_e);awt=r(G6n,"FSMTForConditionalGeneration"),G6n.forEach(t),nwt=r(f9o," (FairSeq Machine-Translation model)"),f9o.forEach(t),swt=i(be),n6=n(be,"LI",{});var h9o=s(n6);pDe=n(h9o,"STRONG",{});var B6n=s(pDe);lwt=r(B6n,"gptsan-japanese"),B6n.forEach(t),iwt=r(h9o," \u2014 "),I_e=n(h9o,"A",{href:!0});var I6n=s(I_e);dwt=r(I6n,"GPTSanJapaneseForConditionalGeneration"),I6n.forEach(t),mwt=r(h9o," (GPTSAN-japanese model)"),h9o.forEach(t),cwt=i(be),s6=n(be,"LI",{});var g9o=s(s6);_De=n(g9o,"STRONG",{});var N6n=s(_De);fwt=r(N6n,"led"),N6n.forEach(t),hwt=r(g9o," \u2014 "),N_e=n(g9o,"A",{href:!0});var $6n=s(N_e);gwt=r($6n,"LEDForConditionalGeneration"),$6n.forEach(t),uwt=r(g9o," (LED model)"),g9o.forEach(t),pwt=i(be),l6=n(be,"LI",{});var u9o=s(l6);bDe=n(u9o,"STRONG",{});var j6n=s(bDe);_wt=r(j6n,"longt5"),j6n.forEach(t),bwt=r(u9o," \u2014 "),$_e=n(u9o,"A",{href:!0});var P6n=s($_e);vwt=r(P6n,"LongT5ForConditionalGeneration"),P6n.forEach(t),Mwt=r(u9o," (LongT5 model)"),u9o.forEach(t),Twt=i(be),i6=n(be,"LI",{});var p9o=s(i6);vDe=n(p9o,"STRONG",{});var Z6n=s(vDe);Fwt=r(Z6n,"m2m_100"),Z6n.forEach(t),Ewt=r(p9o," \u2014 "),j_e=n(p9o,"A",{href:!0});var V6n=s(j_e);ywt=r(V6n,"M2M100ForConditionalGeneration"),V6n.forEach(t),Cwt=r(p9o," (M2M100 model)"),p9o.forEach(t),wwt=i(be),d6=n(be,"LI",{});var _9o=s(d6);MDe=n(_9o,"STRONG",{});var W6n=s(MDe);Lwt=r(W6n,"marian"),W6n.forEach(t),Awt=r(_9o," \u2014 "),P_e=n(_9o,"A",{href:!0});var J6n=s(P_e);Rwt=r(J6n,"MarianMTModel"),J6n.forEach(t),kwt=r(_9o," (Marian model)"),_9o.forEach(t),Swt=i(be),m6=n(be,"LI",{});var b9o=s(m6);TDe=n(b9o,"STRONG",{});var U6n=s(TDe);xwt=r(U6n,"mbart"),U6n.forEach(t),Gwt=r(b9o," \u2014 "),Z_e=n(b9o,"A",{href:!0});var X6n=s(Z_e);Bwt=r(X6n,"MBartForConditionalGeneration"),X6n.forEach(t),Iwt=r(b9o," (mBART model)"),b9o.forEach(t),Nwt=i(be),c6=n(be,"LI",{});var v9o=s(c6);FDe=n(v9o,"STRONG",{});var D6n=s(FDe);$wt=r(D6n,"mt5"),D6n.forEach(t),jwt=r(v9o," \u2014 "),V_e=n(v9o,"A",{href:!0});var q6n=s(V_e);Pwt=r(q6n,"MT5ForConditionalGeneration"),q6n.forEach(t),Zwt=r(v9o," (MT5 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"),X_e=n(E9o,"A",{href:!0});var o8n=s(X_e);n6t=r(o8n,"PegasusXForConditionalGeneration"),o8n.forEach(t),s6t=r(E9o," (PEGASUS-X model)"),E9o.forEach(t),l6t=i(be),p6=n(be,"LI",{});var y9o=s(p6);LDe=n(y9o,"STRONG",{});var r8n=s(LDe);i6t=r(r8n,"plbart"),r8n.forEach(t),d6t=r(y9o," \u2014 "),D_e=n(y9o,"A",{href:!0});var t8n=s(D_e);m6t=r(t8n,"PLBartForConditionalGeneration"),t8n.forEach(t),c6t=r(y9o," (PLBart model)"),y9o.forEach(t),f6t=i(be),_6=n(be,"LI",{});var C9o=s(_6);ADe=n(C9o,"STRONG",{});var a8n=s(ADe);h6t=r(a8n,"prophetnet"),a8n.forEach(t),g6t=r(C9o," \u2014 "),q_e=n(C9o,"A",{href:!0});var n8n=s(q_e);u6t=r(n8n,"ProphetNetForConditionalGeneration"),n8n.forEach(t),p6t=r(C9o," (ProphetNet model)"),C9o.forEach(t),_6t=i(be),b6=n(be,"LI",{});var w9o=s(b6);RDe=n(w9o,"STRONG",{});var s8n=s(RDe);b6t=r(s8n,"switch_transformers"),s8n.forEach(t),v6t=r(w9o," \u2014 "),O_e=n(w9o,"A",{href:!0});var l8n=s(O_e);M6t=r(l8n,"SwitchTransformersForConditionalGeneration"),l8n.forEach(t),T6t=r(w9o," (SwitchTransformers model)"),w9o.forEach(t),F6t=i(be),v6=n(be,"LI",{});var L9o=s(v6);kDe=n(L9o,"STRONG",{});var i8n=s(kDe);E6t=r(i8n,"t5"),i8n.forEach(t),y6t=r(L9o," \u2014 "),Q_e=n(L9o,"A",{href:!0});var d8n=s(Q_e);C6t=r(d8n,"T5ForConditionalGeneration"),d8n.forEach(t),w6t=r(L9o," (T5 model)"),L9o.forEach(t),L6t=i(be),M6=n(be,"LI",{});var A9o=s(M6);SDe=n(A9o,"STRONG",{});var m8n=s(SDe);A6t=r(m8n,"umt5"),m8n.forEach(t),R6t=r(A9o," \u2014 "),Y_e=n(A9o,"A",{href:!0});var c8n=s(Y_e);k6t=r(c8n,"UMT5ForConditionalGeneration"),c8n.forEach(t),S6t=r(A9o," (UMT5 model)"),A9o.forEach(t),x6t=i(be),T6=n(be,"LI",{});var R9o=s(T6);xDe=n(R9o,"STRONG",{});var f8n=s(xDe);G6t=r(f8n,"xlm-prophetnet"),f8n.forEach(t),B6t=r(R9o," \u2014 "),z_e=n(R9o,"A",{href:!0});var h8n=s(z_e);I6t=r(h8n,"XLMProphetNetForConditionalGeneration"),h8n.forEach(t),N6t=r(R9o," (XLM-ProphetNet model)"),R9o.forEach(t),be.forEach(t),$6t=i(an),F6=n(an,"P",{});var k9o=s(F6);j6t=r(k9o,"The model is set in evaluation mode by default using "),GDe=n(k9o,"CODE",{});var g8n=s(GDe);P6t=r(g8n,"model.eval()"),g8n.forEach(t),Z6t=r(k9o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),BDe=n(k9o,"CODE",{});var u8n=s(BDe);V6t=r(u8n,"model.train()"),u8n.forEach(t),k9o.forEach(t),W6t=i(an),M(E6.$$.fragment,an),an.forEach(t),pd.forEach(t),Xxo=i(c),Uc=n(c,"H3",{class:!0});var ZIo=s(Uc);y6=n(ZIo,"A",{id:!0,class:!0,href:!0});var p8n=s(y6);IDe=n(p8n,"SPAN",{});var _8n=s(IDe);M(GW.$$.fragment,_8n),_8n.forEach(t),p8n.forEach(t),J6t=i(ZIo),NDe=n(ZIo,"SPAN",{});var b8n=s(NDe);U6t=r(b8n,"TFAutoModelForSeq2SeqLM"),b8n.forEach(t),ZIo.forEach(t),Dxo=i(c),tr=n(c,"DIV",{class:!0});var _d=s(tr);M(BW.$$.fragment,_d),X6t=i(_d),Xc=n(_d,"P",{});var u9e=s(Xc);D6t=r(u9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),H_e=n(u9e,"A",{href:!0});var v8n=s(H_e);q6t=r(v8n,"from_pretrained()"),v8n.forEach(t),O6t=r(u9e," class method or the "),K_e=n(u9e,"A",{href:!0});var M8n=s(K_e);Q6t=r(M8n,"from_config()"),M8n.forEach(t),Y6t=r(u9e,` class
method.`),u9e.forEach(t),z6t=i(_d),IW=n(_d,"P",{});var VIo=s(IW);H6t=r(VIo,"This class cannot be instantiated directly using "),$De=n(VIo,"CODE",{});var T8n=s($De);K6t=r(T8n,"__init__()"),T8n.forEach(t),e8t=r(VIo," (throws an error)."),VIo.forEach(t),o8t=i(_d),oa=n(_d,"DIV",{class:!0});var jj=s(oa);M(NW.$$.fragment,jj),r8t=i(jj),jDe=n(jj,"P",{});var F8n=s(jDe);t8t=r(F8n,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),F8n.forEach(t),a8t=i(jj),Dc=n(jj,"P",{});var p9e=s(Dc);n8t=r(p9e,`Note:
Loading a model from its configuration file does `),PDe=n(p9e,"STRONG",{});var E8n=s(PDe);s8t=r(E8n,"not"),E8n.forEach(t),l8t=r(p9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),e1e=n(p9e,"A",{href:!0});var y8n=s(e1e);i8t=r(y8n,"from_pretrained()"),y8n.forEach(t),d8t=r(p9e," to load the model weights."),p9e.forEach(t),m8t=i(jj),M(C6.$$.fragment,jj),jj.forEach(t),c8t=i(_d),ct=n(_d,"DIV",{class:!0});var bd=s(ct);M($W.$$.fragment,bd),f8t=i(bd),ZDe=n(bd,"P",{});var C8n=s(ZDe);h8t=r(C8n,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),C8n.forEach(t),g8t=i(bd),Hn=n(bd,"P",{});var Pj=s(Hn);u8t=r(Pj,"The model class to instantiate is selected based on the "),VDe=n(Pj,"CODE",{});var w8n=s(VDe);p8t=r(w8n,"model_type"),w8n.forEach(t),_8t=r(Pj,` property of the config object (either
passed as an argument or loaded from `),WDe=n(Pj,"CODE",{});var L8n=s(WDe);b8t=r(L8n,"pretrained_model_name_or_path"),L8n.forEach(t),v8t=r(Pj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JDe=n(Pj,"CODE",{});var A8n=s(JDe);M8t=r(A8n,"pretrained_model_name_or_path"),A8n.forEach(t),T8t=r(Pj,":"),Pj.forEach(t),F8t=i(bd),Ne=n(bd,"UL",{});var De=s(Ne);w6=n(De,"LI",{});var S9o=s(w6);UDe=n(S9o,"STRONG",{});var R8n=s(UDe);E8t=r(R8n,"bart"),R8n.forEach(t),y8t=r(S9o," \u2014 "),o1e=n(S9o,"A",{href:!0});var k8n=s(o1e);C8t=r(k8n,"TFBartForConditionalGeneration"),k8n.forEach(t),w8t=r(S9o," (BART model)"),S9o.forEach(t),L8t=i(De),L6=n(De,"LI",{});var x9o=s(L6);XDe=n(x9o,"STRONG",{});var S8n=s(XDe);A8t=r(S8n,"blenderbot"),S8n.forEach(t),R8t=r(x9o," \u2014 "),r1e=n(x9o,"A",{href:!0});var x8n=s(r1e);k8t=r(x8n,"TFBlenderbotForConditionalGeneration"),x8n.forEach(t),S8t=r(x9o," (Blenderbot model)"),x9o.forEach(t),x8t=i(De),A6=n(De,"LI",{});var G9o=s(A6);DDe=n(G9o,"STRONG",{});var G8n=s(DDe);G8t=r(G8n,"blenderbot-small"),G8n.forEach(t),B8t=r(G9o," \u2014 "),t1e=n(G9o,"A",{href:!0});var B8n=s(t1e);I8t=r(B8n,"TFBlenderbotSmallForConditionalGeneration"),B8n.forEach(t),N8t=r(G9o," (BlenderbotSmall model)"),G9o.forEach(t),$8t=i(De),R6=n(De,"LI",{});var B9o=s(R6);qDe=n(B9o,"STRONG",{});var I8n=s(qDe);j8t=r(I8n,"encoder-decoder"),I8n.forEach(t),P8t=r(B9o," \u2014 "),a1e=n(B9o,"A",{href:!0});var N8n=s(a1e);Z8t=r(N8n,"TFEncoderDecoderModel"),N8n.forEach(t),V8t=r(B9o," (Encoder decoder model)"),B9o.forEach(t),W8t=i(De),k6=n(De,"LI",{});var I9o=s(k6);ODe=n(I9o,"STRONG",{});var $8n=s(ODe);J8t=r($8n,"led"),$8n.forEach(t),U8t=r(I9o," \u2014 "),n1e=n(I9o,"A",{href:!0});var j8n=s(n1e);X8t=r(j8n,"TFLEDForConditionalGeneration"),j8n.forEach(t),D8t=r(I9o," (LED model)"),I9o.forEach(t),q8t=i(De),S6=n(De,"LI",{});var N9o=s(S6);QDe=n(N9o,"STRONG",{});var P8n=s(QDe);O8t=r(P8n,"marian"),P8n.forEach(t),Q8t=r(N9o," \u2014 "),s1e=n(N9o,"A",{href:!0});var Z8n=s(s1e);Y8t=r(Z8n,"TFMarianMTModel"),Z8n.forEach(t),z8t=r(N9o," (Marian model)"),N9o.forEach(t),H8t=i(De),x6=n(De,"LI",{});var $9o=s(x6);YDe=n($9o,"STRONG",{});var V8n=s(YDe);K8t=r(V8n,"mbart"),V8n.forEach(t),e7t=r($9o," \u2014 "),l1e=n($9o,"A",{href:!0});var W8n=s(l1e);o7t=r(W8n,"TFMBartForConditionalGeneration"),W8n.forEach(t),r7t=r($9o," (mBART model)"),$9o.forEach(t),t7t=i(De),G6=n(De,"LI",{});var j9o=s(G6);zDe=n(j9o,"STRONG",{});var J8n=s(zDe);a7t=r(J8n,"mt5"),J8n.forEach(t),n7t=r(j9o," \u2014 "),i1e=n(j9o,"A",{href:!0});var U8n=s(i1e);s7t=r(U8n,"TFMT5ForConditionalGeneration"),U8n.forEach(t),l7t=r(j9o," (MT5 model)"),j9o.forEach(t),i7t=i(De),B6=n(De,"LI",{});var P9o=s(B6);HDe=n(P9o,"STRONG",{});var X8n=s(HDe);d7t=r(X8n,"pegasus"),X8n.forEach(t),m7t=r(P9o," \u2014 "),d1e=n(P9o,"A",{href:!0});var D8n=s(d1e);c7t=r(D8n,"TFPegasusForConditionalGeneration"),D8n.forEach(t),f7t=r(P9o," (Pegasus model)"),P9o.forEach(t),h7t=i(De),I6=n(De,"LI",{});var Z9o=s(I6);KDe=n(Z9o,"STRONG",{});var q8n=s(KDe);g7t=r(q8n,"t5"),q8n.forEach(t),u7t=r(Z9o," \u2014 "),m1e=n(Z9o,"A",{href:!0});var O8n=s(m1e);p7t=r(O8n,"TFT5ForConditionalGeneration"),O8n.forEach(t),_7t=r(Z9o," (T5 model)"),Z9o.forEach(t),De.forEach(t),b7t=i(bd),M(N6.$$.fragment,bd),bd.forEach(t),_d.forEach(t),qxo=i(c),qc=n(c,"H3",{class:!0});var WIo=s(qc);$6=n(WIo,"A",{id:!0,class:!0,href:!0});var Q8n=s($6);eqe=n(Q8n,"SPAN",{});var Y8n=s(eqe);M(jW.$$.fragment,Y8n),Y8n.forEach(t),Q8n.forEach(t),v7t=i(WIo),oqe=n(WIo,"SPAN",{});var z8n=s(oqe);M7t=r(z8n,"FlaxAutoModelForSeq2SeqLM"),z8n.forEach(t),WIo.forEach(t),Oxo=i(c),ar=n(c,"DIV",{class:!0});var vd=s(ar);M(PW.$$.fragment,vd),T7t=i(vd),Oc=n(vd,"P",{});var _9e=s(Oc);F7t=r(_9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),c1e=n(_9e,"A",{href:!0});var H8n=s(c1e);E7t=r(H8n,"from_pretrained()"),H8n.forEach(t),y7t=r(_9e," class method or the "),f1e=n(_9e,"A",{href:!0});var K8n=s(f1e);C7t=r(K8n,"from_config()"),K8n.forEach(t),w7t=r(_9e,` class
method.`),_9e.forEach(t),L7t=i(vd),ZW=n(vd,"P",{});var JIo=s(ZW);A7t=r(JIo,"This class cannot be instantiated directly using "),rqe=n(JIo,"CODE",{});var e7n=s(rqe);R7t=r(e7n,"__init__()"),e7n.forEach(t),k7t=r(JIo," (throws an error)."),JIo.forEach(t),S7t=i(vd),ra=n(vd,"DIV",{class:!0});var Zj=s(ra);M(VW.$$.fragment,Zj),x7t=i(Zj),tqe=n(Zj,"P",{});var o7n=s(tqe);G7t=r(o7n,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),o7n.forEach(t),B7t=i(Zj),Qc=n(Zj,"P",{});var b9e=s(Qc);I7t=r(b9e,`Note:
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model\u2019s configuration. Use `),h1e=n(b9e,"A",{href:!0});var t7n=s(h1e);j7t=r(t7n,"from_pretrained()"),t7n.forEach(t),P7t=r(b9e," to load the model weights."),b9e.forEach(t),Z7t=i(Zj),M(j6.$$.fragment,Zj),Zj.forEach(t),V7t=i(vd),ft=n(vd,"DIV",{class:!0});var Md=s(ft);M(WW.$$.fragment,Md),W7t=i(Md),nqe=n(Md,"P",{});var a7n=s(nqe);J7t=r(a7n,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),a7n.forEach(t),U7t=i(Md),Kn=n(Md,"P",{});var Vj=s(Kn);X7t=r(Vj,"The model class to instantiate is selected based on the "),sqe=n(Vj,"CODE",{});var n7n=s(sqe);D7t=r(n7n,"model_type"),n7n.forEach(t),q7t=r(Vj,` property of the config object (either
passed as an argument or loaded from `),lqe=n(Vj,"CODE",{});var s7n=s(lqe);O7t=r(s7n,"pretrained_model_name_or_path"),s7n.forEach(t),Q7t=r(Vj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iqe=n(Vj,"CODE",{});var l7n=s(iqe);Y7t=r(l7n,"pretrained_model_name_or_path"),l7n.forEach(t),z7t=r(Vj,":"),Vj.forEach(t),H7t=i(Md),$e=n(Md,"UL",{});var qe=s($e);P6=n(qe,"LI",{});var V9o=s(P6);dqe=n(V9o,"STRONG",{});var i7n=s(dqe);K7t=r(i7n,"bart"),i7n.forEach(t),eLt=r(V9o," \u2014 "),g1e=n(V9o,"A",{href:!0});var d7n=s(g1e);oLt=r(d7n,"FlaxBartForConditionalGeneration"),d7n.forEach(t),rLt=r(V9o," (BART model)"),V9o.forEach(t),tLt=i(qe),Z6=n(qe,"LI",{});var W9o=s(Z6);mqe=n(W9o,"STRONG",{});var m7n=s(mqe);aLt=r(m7n,"blenderbot"),m7n.forEach(t),nLt=r(W9o," \u2014 "),u1e=n(W9o,"A",{href:!0});var c7n=s(u1e);sLt=r(c7n,"FlaxBlenderbotForConditionalGeneration"),c7n.forEach(t),lLt=r(W9o," (Blenderbot model)"),W9o.forEach(t),iLt=i(qe),V6=n(qe,"LI",{});var J9o=s(V6);cqe=n(J9o,"STRONG",{});var f7n=s(cqe);dLt=r(f7n,"blenderbot-small"),f7n.forEach(t),mLt=r(J9o," \u2014 "),p1e=n(J9o,"A",{href:!0});var h7n=s(p1e);cLt=r(h7n,"FlaxBlenderbotSmallForConditionalGeneration"),h7n.forEach(t),fLt=r(J9o," (BlenderbotSmall model)"),J9o.forEach(t),hLt=i(qe),W6=n(qe,"LI",{});var U9o=s(W6);fqe=n(U9o,"STRONG",{});var g7n=s(fqe);gLt=r(g7n,"encoder-decoder"),g7n.forEach(t),uLt=r(U9o," \u2014 "),_1e=n(U9o,"A",{href:!0});var u7n=s(_1e);pLt=r(u7n,"FlaxEncoderDecoderModel"),u7n.forEach(t),_Lt=r(U9o," (Encoder decoder model)"),U9o.forEach(t),bLt=i(qe),J6=n(qe,"LI",{});var X9o=s(J6);hqe=n(X9o,"STRONG",{});var p7n=s(hqe);vLt=r(p7n,"longt5"),p7n.forEach(t),MLt=r(X9o," \u2014 "),b1e=n(X9o,"A",{href:!0});var _7n=s(b1e);TLt=r(_7n,"FlaxLongT5ForConditionalGeneration"),_7n.forEach(t),FLt=r(X9o," (LongT5 model)"),X9o.forEach(t),ELt=i(qe),U6=n(qe,"LI",{});var D9o=s(U6);gqe=n(D9o,"STRONG",{});var b7n=s(gqe);yLt=r(b7n,"marian"),b7n.forEach(t),CLt=r(D9o," \u2014 "),v1e=n(D9o,"A",{href:!0});var v7n=s(v1e);wLt=r(v7n,"FlaxMarianMTModel"),v7n.forEach(t),LLt=r(D9o," (Marian model)"),D9o.forEach(t),ALt=i(qe),X6=n(qe,"LI",{});var q9o=s(X6);uqe=n(q9o,"STRONG",{});var M7n=s(uqe);RLt=r(M7n,"mbart"),M7n.forEach(t),kLt=r(q9o," \u2014 "),M1e=n(q9o,"A",{href:!0});var T7n=s(M1e);SLt=r(T7n,"FlaxMBartForConditionalGeneration"),T7n.forEach(t),xLt=r(q9o," (mBART model)"),q9o.forEach(t),GLt=i(qe),D6=n(qe,"LI",{});var O9o=s(D6);pqe=n(O9o,"STRONG",{});var F7n=s(pqe);BLt=r(F7n,"mt5"),F7n.forEach(t),ILt=r(O9o," \u2014 "),T1e=n(O9o,"A",{href:!0});var E7n=s(T1e);NLt=r(E7n,"FlaxMT5ForConditionalGeneration"),E7n.forEach(t),$Lt=r(O9o," (MT5 model)"),O9o.forEach(t),jLt=i(qe),q6=n(qe,"LI",{});var Q9o=s(q6);_qe=n(Q9o,"STRONG",{});var y7n=s(_qe);PLt=r(y7n,"pegasus"),y7n.forEach(t),ZLt=r(Q9o," \u2014 "),F1e=n(Q9o,"A",{href:!0});var C7n=s(F1e);VLt=r(C7n,"FlaxPegasusForConditionalGeneration"),C7n.forEach(t),WLt=r(Q9o," (Pegasus model)"),Q9o.forEach(t),JLt=i(qe),O6=n(qe,"LI",{});var Y9o=s(O6);bqe=n(Y9o,"STRONG",{});var w7n=s(bqe);ULt=r(w7n,"t5"),w7n.forEach(t),XLt=r(Y9o," \u2014 "),E1e=n(Y9o,"A",{href:!0});var L7n=s(E1e);DLt=r(L7n,"FlaxT5ForConditionalGeneration"),L7n.forEach(t),qLt=r(Y9o," (T5 model)"),Y9o.forEach(t),qe.forEach(t),OLt=i(Md),M(Q6.$$.fragment,Md),Md.forEach(t),vd.forEach(t),Qxo=i(c),Yc=n(c,"H3",{class:!0});var UIo=s(Yc);Y6=n(UIo,"A",{id:!0,class:!0,href:!0});var A7n=s(Y6);vqe=n(A7n,"SPAN",{});var R7n=s(vqe);M(JW.$$.fragment,R7n),R7n.forEach(t),A7n.forEach(t),QLt=i(UIo),Mqe=n(UIo,"SPAN",{});var k7n=s(Mqe);YLt=r(k7n,"AutoModelForSequenceClassification"),k7n.forEach(t),UIo.forEach(t),Yxo=i(c),nr=n(c,"DIV",{class:!0});var Td=s(nr);M(UW.$$.fragment,Td),zLt=i(Td),zc=n(Td,"P",{});var v9e=s(zc);HLt=r(v9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),y1e=n(v9e,"A",{href:!0});var S7n=s(y1e);KLt=r(S7n,"from_pretrained()"),S7n.forEach(t),eAt=r(v9e," class method or the "),C1e=n(v9e,"A",{href:!0});var x7n=s(C1e);oAt=r(x7n,"from_config()"),x7n.forEach(t),rAt=r(v9e,` class
method.`),v9e.forEach(t),tAt=i(Td),XW=n(Td,"P",{});var XIo=s(XW);aAt=r(XIo,"This class cannot be instantiated directly using "),Tqe=n(XIo,"CODE",{});var G7n=s(Tqe);nAt=r(G7n,"__init__()"),G7n.forEach(t),sAt=r(XIo," (throws an error)."),XIo.forEach(t),lAt=i(Td),ta=n(Td,"DIV",{class:!0});var Wj=s(ta);M(DW.$$.fragment,Wj),iAt=i(Wj),Fqe=n(Wj,"P",{});var B7n=s(Fqe);dAt=r(B7n,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),B7n.forEach(t),mAt=i(Wj),Hc=n(Wj,"P",{});var M9e=s(Hc);cAt=r(M9e,`Note:
Loading a model from its configuration file does `),Eqe=n(M9e,"STRONG",{});var I7n=s(Eqe);fAt=r(I7n,"not"),I7n.forEach(t),hAt=r(M9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),w1e=n(M9e,"A",{href:!0});var N7n=s(w1e);gAt=r(N7n,"from_pretrained()"),N7n.forEach(t),uAt=r(M9e," to load the model weights."),M9e.forEach(t),pAt=i(Wj),M(z6.$$.fragment,Wj),Wj.forEach(t),_At=i(Td),fo=n(Td,"DIV",{class:!0});var nn=s(fo);M(qW.$$.fragment,nn),bAt=i(nn),yqe=n(nn,"P",{});var $7n=s(yqe);vAt=r($7n,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),$7n.forEach(t),MAt=i(nn),es=n(nn,"P",{});var Jj=s(es);TAt=r(Jj,"The model class to instantiate is selected based on the "),Cqe=n(Jj,"CODE",{});var j7n=s(Cqe);FAt=r(j7n,"model_type"),j7n.forEach(t),EAt=r(Jj,` property of the config object (either
passed as an argument or loaded from `),wqe=n(Jj,"CODE",{});var P7n=s(wqe);yAt=r(P7n,"pretrained_model_name_or_path"),P7n.forEach(t),CAt=r(Jj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lqe=n(Jj,"CODE",{});var Z7n=s(Lqe);wAt=r(Z7n,"pretrained_model_name_or_path"),Z7n.forEach(t),LAt=r(Jj,":"),Jj.forEach(t),AAt=i(nn),B=n(nn,"UL",{});var I=s(B);H6=n(I,"LI",{});var z9o=s(H6);Aqe=n(z9o,"STRONG",{});var V7n=s(Aqe);RAt=r(V7n,"albert"),V7n.forEach(t),kAt=r(z9o," \u2014 "),L1e=n(z9o,"A",{href:!0});var W7n=s(L1e);SAt=r(W7n,"AlbertForSequenceClassification"),W7n.forEach(t),xAt=r(z9o," (ALBERT model)"),z9o.forEach(t),GAt=i(I),K6=n(I,"LI",{});var H9o=s(K6);Rqe=n(H9o,"STRONG",{});var J7n=s(Rqe);BAt=r(J7n,"bart"),J7n.forEach(t),IAt=r(H9o," \u2014 "),A1e=n(H9o,"A",{href:!0});var U7n=s(A1e);NAt=r(U7n,"BartForSequenceClassification"),U7n.forEach(t),$At=r(H9o," (BART model)"),H9o.forEach(t),jAt=i(I),e8=n(I,"LI",{});var K9o=s(e8);kqe=n(K9o,"STRONG",{});var X7n=s(kqe);PAt=r(X7n,"bert"),X7n.forEach(t),ZAt=r(K9o," \u2014 "),R1e=n(K9o,"A",{href:!0});var D7n=s(R1e);VAt=r(D7n,"BertForSequenceClassification"),D7n.forEach(t),WAt=r(K9o," (BERT model)"),K9o.forEach(t),JAt=i(I),o8=n(I,"LI",{});var eCo=s(o8);Sqe=n(eCo,"STRONG",{});var q7n=s(Sqe);UAt=r(q7n,"big_bird"),q7n.forEach(t),XAt=r(eCo," \u2014 "),k1e=n(eCo,"A",{href:!0});var O7n=s(k1e);DAt=r(O7n,"BigBirdForSequenceClassification"),O7n.forEach(t),qAt=r(eCo," (BigBird model)"),eCo.forEach(t),OAt=i(I),r8=n(I,"LI",{});var oCo=s(r8);xqe=n(oCo,"STRONG",{});var Q7n=s(xqe);QAt=r(Q7n,"bigbird_pegasus"),Q7n.forEach(t),YAt=r(oCo," \u2014 "),S1e=n(oCo,"A",{href:!0});var Y7n=s(S1e);zAt=r(Y7n,"BigBirdPegasusForSequenceClassification"),Y7n.forEach(t),HAt=r(oCo," (BigBird-Pegasus model)"),oCo.forEach(t),KAt=i(I),t8=n(I,"LI",{});var rCo=s(t8);Gqe=n(rCo,"STRONG",{});var z7n=s(Gqe);eRt=r(z7n,"biogpt"),z7n.forEach(t),oRt=r(rCo," \u2014 "),x1e=n(rCo,"A",{href:!0});var H7n=s(x1e);rRt=r(H7n,"BioGptForSequenceClassification"),H7n.forEach(t),tRt=r(rCo," (BioGpt model)"),rCo.forEach(t),aRt=i(I),a8=n(I,"LI",{});var tCo=s(a8);Bqe=n(tCo,"STRONG",{});var K7n=s(Bqe);nRt=r(K7n,"bloom"),K7n.forEach(t),sRt=r(tCo," \u2014 "),G1e=n(tCo,"A",{href:!0});var eLn=s(G1e);lRt=r(eLn,"BloomForSequenceClassification"),eLn.forEach(t),iRt=r(tCo," (BLOOM model)"),tCo.forEach(t),dRt=i(I),n8=n(I,"LI",{});var aCo=s(n8);Iqe=n(aCo,"STRONG",{});var oLn=s(Iqe);mRt=r(oLn,"camembert"),oLn.forEach(t),cRt=r(aCo," \u2014 "),B1e=n(aCo,"A",{href:!0});var rLn=s(B1e);fRt=r(rLn,"CamembertForSequenceClassification"),rLn.forEach(t),hRt=r(aCo," (CamemBERT model)"),aCo.forEach(t),gRt=i(I),s8=n(I,"LI",{});var nCo=s(s8);Nqe=n(nCo,"STRONG",{});var tLn=s(Nqe);uRt=r(tLn,"canine"),tLn.forEach(t),pRt=r(nCo," \u2014 "),I1e=n(nCo,"A",{href:!0});var aLn=s(I1e);_Rt=r(aLn,"CanineForSequenceClassification"),aLn.forEach(t),bRt=r(nCo," (CANINE model)"),nCo.forEach(t),vRt=i(I),l8=n(I,"LI",{});var sCo=s(l8);$qe=n(sCo,"STRONG",{});var nLn=s($qe);MRt=r(nLn,"convbert"),nLn.forEach(t),TRt=r(sCo," \u2014 "),N1e=n(sCo,"A",{href:!0});var sLn=s(N1e);FRt=r(sLn,"ConvBertForSequenceClassification"),sLn.forEach(t),ERt=r(sCo," (ConvBERT model)"),sCo.forEach(t),yRt=i(I),i8=n(I,"LI",{});var lCo=s(i8);jqe=n(lCo,"STRONG",{});var lLn=s(jqe);CRt=r(lLn,"ctrl"),lLn.forEach(t),wRt=r(lCo," \u2014 "),$1e=n(lCo,"A",{href:!0});var iLn=s($1e);LRt=r(iLn,"CTRLForSequenceClassification"),iLn.forEach(t),ARt=r(lCo," (CTRL model)"),lCo.forEach(t),RRt=i(I),d8=n(I,"LI",{});var iCo=s(d8);Pqe=n(iCo,"STRONG",{});var dLn=s(Pqe);kRt=r(dLn,"data2vec-text"),dLn.forEach(t),SRt=r(iCo," \u2014 "),j1e=n(iCo,"A",{href:!0});var mLn=s(j1e);xRt=r(mLn,"Data2VecTextForSequenceClassification"),mLn.forEach(t),GRt=r(iCo," (Data2VecText model)"),iCo.forEach(t),BRt=i(I),m8=n(I,"LI",{});var dCo=s(m8);Zqe=n(dCo,"STRONG",{});var cLn=s(Zqe);IRt=r(cLn,"deberta"),cLn.forEach(t),NRt=r(dCo," \u2014 "),P1e=n(dCo,"A",{href:!0});var fLn=s(P1e);$Rt=r(fLn,"DebertaForSequenceClassification"),fLn.forEach(t),jRt=r(dCo," (DeBERTa model)"),dCo.forEach(t),PRt=i(I),c8=n(I,"LI",{});var mCo=s(c8);Vqe=n(mCo,"STRONG",{});var hLn=s(Vqe);ZRt=r(hLn,"deberta-v2"),hLn.forEach(t),VRt=r(mCo," \u2014 "),Z1e=n(mCo,"A",{href:!0});var gLn=s(Z1e);WRt=r(gLn,"DebertaV2ForSequenceClassification"),gLn.forEach(t),JRt=r(mCo," (DeBERTa-v2 model)"),mCo.forEach(t),URt=i(I),f8=n(I,"LI",{});var cCo=s(f8);Wqe=n(cCo,"STRONG",{});var uLn=s(Wqe);XRt=r(uLn,"distilbert"),uLn.forEach(t),DRt=r(cCo," \u2014 "),V1e=n(cCo,"A",{href:!0});var pLn=s(V1e);qRt=r(pLn,"DistilBertForSequenceClassification"),pLn.forEach(t),ORt=r(cCo," (DistilBERT model)"),cCo.forEach(t),QRt=i(I),h8=n(I,"LI",{});var fCo=s(h8);Jqe=n(fCo,"STRONG",{});var _Ln=s(Jqe);YRt=r(_Ln,"electra"),_Ln.forEach(t),zRt=r(fCo," \u2014 "),W1e=n(fCo,"A",{href:!0});var bLn=s(W1e);HRt=r(bLn,"ElectraForSequenceClassification"),bLn.forEach(t),KRt=r(fCo," (ELECTRA model)"),fCo.forEach(t),ekt=i(I),g8=n(I,"LI",{});var hCo=s(g8);Uqe=n(hCo,"STRONG",{});var vLn=s(Uqe);okt=r(vLn,"ernie"),vLn.forEach(t),rkt=r(hCo," \u2014 "),J1e=n(hCo,"A",{href:!0});var MLn=s(J1e);tkt=r(MLn,"ErnieForSequenceClassification"),MLn.forEach(t),akt=r(hCo," (ERNIE model)"),hCo.forEach(t),nkt=i(I),u8=n(I,"LI",{});var gCo=s(u8);Xqe=n(gCo,"STRONG",{});var TLn=s(Xqe);skt=r(TLn,"ernie_m"),TLn.forEach(t),lkt=r(gCo," \u2014 "),U1e=n(gCo,"A",{href:!0});var FLn=s(U1e);ikt=r(FLn,"ErnieMForSequenceClassification"),FLn.forEach(t),dkt=r(gCo," (ErnieM model)"),gCo.forEach(t),mkt=i(I),p8=n(I,"LI",{});var uCo=s(p8);Dqe=n(uCo,"STRONG",{});var ELn=s(Dqe);ckt=r(ELn,"esm"),ELn.forEach(t),fkt=r(uCo," \u2014 "),X1e=n(uCo,"A",{href:!0});var yLn=s(X1e);hkt=r(yLn,"EsmForSequenceClassification"),yLn.forEach(t),gkt=r(uCo," (ESM model)"),uCo.forEach(t),ukt=i(I),_8=n(I,"LI",{});var pCo=s(_8);qqe=n(pCo,"STRONG",{});var CLn=s(qqe);pkt=r(CLn,"falcon"),CLn.forEach(t),_kt=r(pCo," \u2014 "),Oqe=n(pCo,"CODE",{});var wLn=s(Oqe);bkt=r(wLn,"FalconForSequenceClassification"),wLn.forEach(t),vkt=r(pCo," (Falcon model)"),pCo.forEach(t),Mkt=i(I),b8=n(I,"LI",{});var _Co=s(b8);Qqe=n(_Co,"STRONG",{});var LLn=s(Qqe);Tkt=r(LLn,"flaubert"),LLn.forEach(t),Fkt=r(_Co," \u2014 "),D1e=n(_Co,"A",{href:!0});var ALn=s(D1e);Ekt=r(ALn,"FlaubertForSequenceClassification"),ALn.forEach(t),ykt=r(_Co," (FlauBERT model)"),_Co.forEach(t),Ckt=i(I),v8=n(I,"LI",{});var bCo=s(v8);Yqe=n(bCo,"STRONG",{});var RLn=s(Yqe);wkt=r(RLn,"fnet"),RLn.forEach(t),Lkt=r(bCo," \u2014 "),q1e=n(bCo,"A",{href:!0});var kLn=s(q1e);Akt=r(kLn,"FNetForSequenceClassification"),kLn.forEach(t),Rkt=r(bCo," (FNet model)"),bCo.forEach(t),kkt=i(I),M8=n(I,"LI",{});var vCo=s(M8);zqe=n(vCo,"STRONG",{});var SLn=s(zqe);Skt=r(SLn,"funnel"),SLn.forEach(t),xkt=r(vCo," \u2014 "),O1e=n(vCo,"A",{href:!0});var xLn=s(O1e);Gkt=r(xLn,"FunnelForSequenceClassification"),xLn.forEach(t),Bkt=r(vCo," (Funnel Transformer model)"),vCo.forEach(t),Ikt=i(I),T8=n(I,"LI",{});var MCo=s(T8);Hqe=n(MCo,"STRONG",{});var GLn=s(Hqe);Nkt=r(GLn,"gpt-sw3"),GLn.forEach(t),$kt=r(MCo," \u2014 "),Q1e=n(MCo,"A",{href:!0});var BLn=s(Q1e);jkt=r(BLn,"GPT2ForSequenceClassification"),BLn.forEach(t),Pkt=r(MCo," (GPT-Sw3 model)"),MCo.forEach(t),Zkt=i(I),F8=n(I,"LI",{});var TCo=s(F8);Kqe=n(TCo,"STRONG",{});var ILn=s(Kqe);Vkt=r(ILn,"gpt2"),ILn.forEach(t),Wkt=r(TCo," \u2014 "),Y1e=n(TCo,"A",{href:!0});var NLn=s(Y1e);Jkt=r(NLn,"GPT2ForSequenceClassification"),NLn.forEach(t),Ukt=r(TCo," (OpenAI GPT-2 model)"),TCo.forEach(t),Xkt=i(I),E8=n(I,"LI",{});var FCo=s(E8);eOe=n(FCo,"STRONG",{});var $Ln=s(eOe);Dkt=r($Ln,"gpt_bigcode"),$Ln.forEach(t),qkt=r(FCo," \u2014 "),z1e=n(FCo,"A",{href:!0});var jLn=s(z1e);Okt=r(jLn,"GPTBigCodeForSequenceClassification"),jLn.forEach(t),Qkt=r(FCo," (GPTBigCode model)"),FCo.forEach(t),Ykt=i(I),y8=n(I,"LI",{});var ECo=s(y8);oOe=n(ECo,"STRONG",{});var PLn=s(oOe);zkt=r(PLn,"gpt_neo"),PLn.forEach(t),Hkt=r(ECo," \u2014 "),H1e=n(ECo,"A",{href:!0});var ZLn=s(H1e);Kkt=r(ZLn,"GPTNeoForSequenceClassification"),ZLn.forEach(t),eSt=r(ECo," (GPT Neo model)"),ECo.forEach(t),oSt=i(I),C8=n(I,"LI",{});var yCo=s(C8);rOe=n(yCo,"STRONG",{});var VLn=s(rOe);rSt=r(VLn,"gpt_neox"),VLn.forEach(t),tSt=r(yCo," \u2014 "),K1e=n(yCo,"A",{href:!0});var WLn=s(K1e);aSt=r(WLn,"GPTNeoXForSequenceClassification"),WLn.forEach(t),nSt=r(yCo," (GPT NeoX model)"),yCo.forEach(t),sSt=i(I),w8=n(I,"LI",{});var CCo=s(w8);tOe=n(CCo,"STRONG",{});var JLn=s(tOe);lSt=r(JLn,"gptj"),JLn.forEach(t),iSt=r(CCo," \u2014 "),ebe=n(CCo,"A",{href:!0});var ULn=s(ebe);dSt=r(ULn,"GPTJForSequenceClassification"),ULn.forEach(t),mSt=r(CCo," (GPT-J model)"),CCo.forEach(t),cSt=i(I),L8=n(I,"LI",{});var wCo=s(L8);aOe=n(wCo,"STRONG",{});var XLn=s(aOe);fSt=r(XLn,"ibert"),XLn.forEach(t),hSt=r(wCo," \u2014 "),obe=n(wCo,"A",{href:!0});var DLn=s(obe);gSt=r(DLn,"IBertForSequenceClassification"),DLn.forEach(t),uSt=r(wCo," (I-BERT model)"),wCo.forEach(t),pSt=i(I),A8=n(I,"LI",{});var LCo=s(A8);nOe=n(LCo,"STRONG",{});var qLn=s(nOe);_St=r(qLn,"layoutlm"),qLn.forEach(t),bSt=r(LCo," \u2014 "),rbe=n(LCo,"A",{href:!0});var OLn=s(rbe);vSt=r(OLn,"LayoutLMForSequenceClassification"),OLn.forEach(t),MSt=r(LCo," (LayoutLM model)"),LCo.forEach(t),TSt=i(I),R8=n(I,"LI",{});var ACo=s(R8);sOe=n(ACo,"STRONG",{});var QLn=s(sOe);FSt=r(QLn,"layoutlmv2"),QLn.forEach(t),ESt=r(ACo," \u2014 "),tbe=n(ACo,"A",{href:!0});var YLn=s(tbe);ySt=r(YLn,"LayoutLMv2ForSequenceClassification"),YLn.forEach(t),CSt=r(ACo," (LayoutLMv2 model)"),ACo.forEach(t),wSt=i(I),k8=n(I,"LI",{});var RCo=s(k8);lOe=n(RCo,"STRONG",{});var zLn=s(lOe);LSt=r(zLn,"layoutlmv3"),zLn.forEach(t),ASt=r(RCo," \u2014 "),abe=n(RCo,"A",{href:!0});var HLn=s(abe);RSt=r(HLn,"LayoutLMv3ForSequenceClassification"),HLn.forEach(t),kSt=r(RCo," (LayoutLMv3 model)"),RCo.forEach(t),SSt=i(I),S8=n(I,"LI",{});var kCo=s(S8);iOe=n(kCo,"STRONG",{});var KLn=s(iOe);xSt=r(KLn,"led"),KLn.forEach(t),GSt=r(kCo," \u2014 "),nbe=n(kCo,"A",{href:!0});var eAn=s(nbe);BSt=r(eAn,"LEDForSequenceClassification"),eAn.forEach(t),ISt=r(kCo," (LED model)"),kCo.forEach(t),NSt=i(I),x8=n(I,"LI",{});var SCo=s(x8);dOe=n(SCo,"STRONG",{});var oAn=s(dOe);$St=r(oAn,"lilt"),oAn.forEach(t),jSt=r(SCo," \u2014 "),sbe=n(SCo,"A",{href:!0});var rAn=s(sbe);PSt=r(rAn,"LiltForSequenceClassification"),rAn.forEach(t),ZSt=r(SCo," (LiLT model)"),SCo.forEach(t),VSt=i(I),G8=n(I,"LI",{});var xCo=s(G8);mOe=n(xCo,"STRONG",{});var tAn=s(mOe);WSt=r(tAn,"llama"),tAn.forEach(t),JSt=r(xCo," \u2014 "),lbe=n(xCo,"A",{href:!0});var aAn=s(lbe);USt=r(aAn,"LlamaForSequenceClassification"),aAn.forEach(t),XSt=r(xCo," (LLaMA model)"),xCo.forEach(t),DSt=i(I),B8=n(I,"LI",{});var GCo=s(B8);cOe=n(GCo,"STRONG",{});var nAn=s(cOe);qSt=r(nAn,"longformer"),nAn.forEach(t),OSt=r(GCo," \u2014 "),ibe=n(GCo,"A",{href:!0});var sAn=s(ibe);QSt=r(sAn,"LongformerForSequenceClassification"),sAn.forEach(t),YSt=r(GCo," (Longformer model)"),GCo.forEach(t),zSt=i(I),I8=n(I,"LI",{});var BCo=s(I8);fOe=n(BCo,"STRONG",{});var lAn=s(fOe);HSt=r(lAn,"luke"),lAn.forEach(t),KSt=r(BCo," \u2014 "),dbe=n(BCo,"A",{href:!0});var iAn=s(dbe);ext=r(iAn,"LukeForSequenceClassification"),iAn.forEach(t),oxt=r(BCo," (LUKE model)"),BCo.forEach(t),rxt=i(I),N8=n(I,"LI",{});var ICo=s(N8);hOe=n(ICo,"STRONG",{});var dAn=s(hOe);txt=r(dAn,"markuplm"),dAn.forEach(t),axt=r(ICo," \u2014 "),mbe=n(ICo,"A",{href:!0});var mAn=s(mbe);nxt=r(mAn,"MarkupLMForSequenceClassification"),mAn.forEach(t),sxt=r(ICo," (MarkupLM model)"),ICo.forEach(t),lxt=i(I),$8=n(I,"LI",{});var NCo=s($8);gOe=n(NCo,"STRONG",{});var cAn=s(gOe);ixt=r(cAn,"mbart"),cAn.forEach(t),dxt=r(NCo," \u2014 "),cbe=n(NCo,"A",{href:!0});var fAn=s(cbe);mxt=r(fAn,"MBartForSequenceClassification"),fAn.forEach(t),cxt=r(NCo," (mBART model)"),NCo.forEach(t),fxt=i(I),j8=n(I,"LI",{});var $Co=s(j8);uOe=n($Co,"STRONG",{});var hAn=s(uOe);hxt=r(hAn,"mega"),hAn.forEach(t),gxt=r($Co," \u2014 "),fbe=n($Co,"A",{href:!0});var gAn=s(fbe);uxt=r(gAn,"MegaForSequenceClassification"),gAn.forEach(t),pxt=r($Co," (MEGA model)"),$Co.forEach(t),_xt=i(I),P8=n(I,"LI",{});var jCo=s(P8);pOe=n(jCo,"STRONG",{});var uAn=s(pOe);bxt=r(uAn,"megatron-bert"),uAn.forEach(t),vxt=r(jCo," \u2014 "),hbe=n(jCo,"A",{href:!0});var pAn=s(hbe);Mxt=r(pAn,"MegatronBertForSequenceClassification"),pAn.forEach(t),Txt=r(jCo," (Megatron-BERT model)"),jCo.forEach(t),Fxt=i(I),Z8=n(I,"LI",{});var PCo=s(Z8);_Oe=n(PCo,"STRONG",{});var _An=s(_Oe);Ext=r(_An,"mobilebert"),_An.forEach(t),yxt=r(PCo," \u2014 "),gbe=n(PCo,"A",{href:!0});var bAn=s(gbe);Cxt=r(bAn,"MobileBertForSequenceClassification"),bAn.forEach(t),wxt=r(PCo," (MobileBERT model)"),PCo.forEach(t),Lxt=i(I),V8=n(I,"LI",{});var ZCo=s(V8);bOe=n(ZCo,"STRONG",{});var vAn=s(bOe);Axt=r(vAn,"mpnet"),vAn.forEach(t),Rxt=r(ZCo," \u2014 "),ube=n(ZCo,"A",{href:!0});var MAn=s(ube);kxt=r(MAn,"MPNetForSequenceClassification"),MAn.forEach(t),Sxt=r(ZCo," (MPNet model)"),ZCo.forEach(t),xxt=i(I),W8=n(I,"LI",{});var VCo=s(W8);vOe=n(VCo,"STRONG",{});var TAn=s(vOe);Gxt=r(TAn,"mpt"),TAn.forEach(t),Bxt=r(VCo," \u2014 "),pbe=n(VCo,"A",{href:!0});var FAn=s(pbe);Ixt=r(FAn,"MptForSequenceClassification"),FAn.forEach(t),Nxt=r(VCo," (MPT model)"),VCo.forEach(t),$xt=i(I),J8=n(I,"LI",{});var WCo=s(J8);MOe=n(WCo,"STRONG",{});var EAn=s(MOe);jxt=r(EAn,"mra"),EAn.forEach(t),Pxt=r(WCo," \u2014 "),_be=n(WCo,"A",{href:!0});var yAn=s(_be);Zxt=r(yAn,"MraForSequenceClassification"),yAn.forEach(t),Vxt=r(WCo," (MRA model)"),WCo.forEach(t),Wxt=i(I),U8=n(I,"LI",{});var JCo=s(U8);TOe=n(JCo,"STRONG",{});var CAn=s(TOe);Jxt=r(CAn,"mt5"),CAn.forEach(t),Uxt=r(JCo," \u2014 "),bbe=n(JCo,"A",{href:!0});var wAn=s(bbe);Xxt=r(wAn,"MT5ForSequenceClassification"),wAn.forEach(t),Dxt=r(JCo," (MT5 model)"),JCo.forEach(t),qxt=i(I),X8=n(I,"LI",{});var UCo=s(X8);FOe=n(UCo,"STRONG",{});var LAn=s(FOe);Oxt=r(LAn,"mvp"),LAn.forEach(t),Qxt=r(UCo," \u2014 "),vbe=n(UCo,"A",{href:!0});var AAn=s(vbe);Yxt=r(AAn,"MvpForSequenceClassification"),AAn.forEach(t),zxt=r(UCo," (MVP model)"),UCo.forEach(t),Hxt=i(I),D8=n(I,"LI",{});var XCo=s(D8);EOe=n(XCo,"STRONG",{});var 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NAn=s(Ebe);pGt=r(NAn,"OpenAIGPTForSequenceClassification"),NAn.forEach(t),_Gt=r(OCo," (OpenAI GPT model)"),OCo.forEach(t),bGt=i(I),Y8=n(I,"LI",{});var QCo=s(Y8);LOe=n(QCo,"STRONG",{});var $An=s(LOe);vGt=r($An,"opt"),$An.forEach(t),MGt=r(QCo," \u2014 "),ybe=n(QCo,"A",{href:!0});var jAn=s(ybe);TGt=r(jAn,"OPTForSequenceClassification"),jAn.forEach(t),FGt=r(QCo," (OPT model)"),QCo.forEach(t),EGt=i(I),z8=n(I,"LI",{});var YCo=s(z8);AOe=n(YCo,"STRONG",{});var PAn=s(AOe);yGt=r(PAn,"perceiver"),PAn.forEach(t),CGt=r(YCo," \u2014 "),Cbe=n(YCo,"A",{href:!0});var ZAn=s(Cbe);wGt=r(ZAn,"PerceiverForSequenceClassification"),ZAn.forEach(t),LGt=r(YCo," (Perceiver model)"),YCo.forEach(t),AGt=i(I),H8=n(I,"LI",{});var zCo=s(H8);ROe=n(zCo,"STRONG",{});var VAn=s(ROe);RGt=r(VAn,"plbart"),VAn.forEach(t),kGt=r(zCo," \u2014 "),wbe=n(zCo,"A",{href:!0});var WAn=s(wbe);SGt=r(WAn,"PLBartForSequenceClassification"),WAn.forEach(t),xGt=r(zCo," (PLBart model)"),zCo.forEach(t),GGt=i(I),K8=n(I,"LI",{});var 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YAn=s(kbe);zGt=r(YAn,"RobertaForSequenceClassification"),YAn.forEach(t),HGt=r(owo," (RoBERTa model)"),owo.forEach(t),KGt=i(I),t7=n(I,"LI",{});var rwo=s(t7);BOe=n(rwo,"STRONG",{});var zAn=s(BOe);eBt=r(zAn,"roberta-prelayernorm"),zAn.forEach(t),oBt=r(rwo," \u2014 "),Sbe=n(rwo,"A",{href:!0});var HAn=s(Sbe);rBt=r(HAn,"RobertaPreLayerNormForSequenceClassification"),HAn.forEach(t),tBt=r(rwo," (RoBERTa-PreLayerNorm model)"),rwo.forEach(t),aBt=i(I),a7=n(I,"LI",{});var two=s(a7);IOe=n(two,"STRONG",{});var KAn=s(IOe);nBt=r(KAn,"roc_bert"),KAn.forEach(t),sBt=r(two," \u2014 "),xbe=n(two,"A",{href:!0});var eRn=s(xbe);lBt=r(eRn,"RoCBertForSequenceClassification"),eRn.forEach(t),iBt=r(two," (RoCBert model)"),two.forEach(t),dBt=i(I),n7=n(I,"LI",{});var awo=s(n7);NOe=n(awo,"STRONG",{});var oRn=s(NOe);mBt=r(oRn,"roformer"),oRn.forEach(t),cBt=r(awo," \u2014 "),Gbe=n(awo,"A",{href:!0});var rRn=s(Gbe);fBt=r(rRn,"RoFormerForSequenceClassification"),rRn.forEach(t),hBt=r(awo," (RoFormer 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method.`),T9e.forEach(t),RIt=i(Fd),YW=n(Fd,"P",{});var qIo=s(YW);kIt=r(qIo,"This class cannot be instantiated directly using "),HOe=n(qIo,"CODE",{});var xRn=s(HOe);SIt=r(xRn,"__init__()"),xRn.forEach(t),xIt=r(qIo," (throws an error)."),qIo.forEach(t),GIt=i(Fd),aa=n(Fd,"DIV",{class:!0});var Uj=s(aa);M(zW.$$.fragment,Uj),BIt=i(Uj),KOe=n(Uj,"P",{});var GRn=s(KOe);IIt=r(GRn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),GRn.forEach(t),NIt=i(Uj),of=n(Uj,"P",{});var F9e=s(of);$It=r(F9e,`Note:
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model\u2019s configuration. Use `),qbe=n(F9e,"A",{href:!0});var IRn=s(qbe);ZIt=r(IRn,"from_pretrained()"),IRn.forEach(t),VIt=r(F9e," to load the model weights."),F9e.forEach(t),WIt=i(Uj),M(M7.$$.fragment,Uj),Uj.forEach(t),JIt=i(Fd),ht=n(Fd,"DIV",{class:!0});var Ed=s(ht);M(HW.$$.fragment,Ed),UIt=i(Ed),oQe=n(Ed,"P",{});var NRn=s(oQe);XIt=r(NRn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),NRn.forEach(t),DIt=i(Ed),os=n(Ed,"P",{});var Xj=s(os);qIt=r(Xj,"The model class to instantiate is selected based on the "),rQe=n(Xj,"CODE",{});var $Rn=s(rQe);OIt=r($Rn,"model_type"),$Rn.forEach(t),QIt=r(Xj,` property of the config object (either
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falling back to using pattern matching on `),aQe=n(Xj,"CODE",{});var PRn=s(aQe);HIt=r(PRn,"pretrained_model_name_or_path"),PRn.forEach(t),KIt=r(Xj,":"),Xj.forEach(t),eNt=i(Ed),de=n(Ed,"UL",{});var ce=s(de);T7=n(ce,"LI",{});var _wo=s(T7);nQe=n(_wo,"STRONG",{});var ZRn=s(nQe);oNt=r(ZRn,"albert"),ZRn.forEach(t),rNt=r(_wo," \u2014 "),Obe=n(_wo,"A",{href:!0});var VRn=s(Obe);tNt=r(VRn,"TFAlbertForSequenceClassification"),VRn.forEach(t),aNt=r(_wo," (ALBERT model)"),_wo.forEach(t),nNt=i(ce),F7=n(ce,"LI",{});var bwo=s(F7);sQe=n(bwo,"STRONG",{});var WRn=s(sQe);sNt=r(WRn,"bart"),WRn.forEach(t),lNt=r(bwo," \u2014 "),Qbe=n(bwo,"A",{href:!0});var JRn=s(Qbe);iNt=r(JRn,"TFBartForSequenceClassification"),JRn.forEach(t),dNt=r(bwo," (BART model)"),bwo.forEach(t),mNt=i(ce),E7=n(ce,"LI",{});var vwo=s(E7);lQe=n(vwo,"STRONG",{});var URn=s(lQe);cNt=r(URn,"bert"),URn.forEach(t),fNt=r(vwo," \u2014 "),Ybe=n(vwo,"A",{href:!0});var XRn=s(Ybe);hNt=r(XRn,"TFBertForSequenceClassification"),XRn.forEach(t),gNt=r(vwo," 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nkn=s(t2e);ONt=r(nkn,"TFElectraForSequenceClassification"),nkn.forEach(t),QNt=r(wwo," (ELECTRA model)"),wwo.forEach(t),YNt=i(ce),S7=n(ce,"LI",{});var Lwo=s(S7);uQe=n(Lwo,"STRONG",{});var skn=s(uQe);zNt=r(skn,"esm"),skn.forEach(t),HNt=r(Lwo," \u2014 "),a2e=n(Lwo,"A",{href:!0});var lkn=s(a2e);KNt=r(lkn,"TFEsmForSequenceClassification"),lkn.forEach(t),e$t=r(Lwo," (ESM model)"),Lwo.forEach(t),o$t=i(ce),x7=n(ce,"LI",{});var Awo=s(x7);pQe=n(Awo,"STRONG",{});var ikn=s(pQe);r$t=r(ikn,"flaubert"),ikn.forEach(t),t$t=r(Awo," \u2014 "),n2e=n(Awo,"A",{href:!0});var dkn=s(n2e);a$t=r(dkn,"TFFlaubertForSequenceClassification"),dkn.forEach(t),n$t=r(Awo," (FlauBERT model)"),Awo.forEach(t),s$t=i(ce),G7=n(ce,"LI",{});var Rwo=s(G7);_Qe=n(Rwo,"STRONG",{});var mkn=s(_Qe);l$t=r(mkn,"funnel"),mkn.forEach(t),i$t=r(Rwo," \u2014 "),s2e=n(Rwo,"A",{href:!0});var ckn=s(s2e);d$t=r(ckn,"TFFunnelForSequenceClassification"),ckn.forEach(t),m$t=r(Rwo," (Funnel Transformer 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Ckn=s(h2e);U$t=r(Ckn,"TFMobileBertForSequenceClassification"),Ckn.forEach(t),X$t=r(Nwo," (MobileBERT model)"),Nwo.forEach(t),D$t=i(ce),V7=n(ce,"LI",{});var $wo=s(V7);CQe=n($wo,"STRONG",{});var wkn=s(CQe);q$t=r(wkn,"mpnet"),wkn.forEach(t),O$t=r($wo," \u2014 "),g2e=n($wo,"A",{href:!0});var Lkn=s(g2e);Q$t=r(Lkn,"TFMPNetForSequenceClassification"),Lkn.forEach(t),Y$t=r($wo," (MPNet model)"),$wo.forEach(t),z$t=i(ce),W7=n(ce,"LI",{});var jwo=s(W7);wQe=n(jwo,"STRONG",{});var Akn=s(wQe);H$t=r(Akn,"openai-gpt"),Akn.forEach(t),K$t=r(jwo," \u2014 "),u2e=n(jwo,"A",{href:!0});var Rkn=s(u2e);ejt=r(Rkn,"TFOpenAIGPTForSequenceClassification"),Rkn.forEach(t),ojt=r(jwo," (OpenAI GPT model)"),jwo.forEach(t),rjt=i(ce),J7=n(ce,"LI",{});var Pwo=s(J7);LQe=n(Pwo,"STRONG",{});var kkn=s(LQe);tjt=r(kkn,"rembert"),kkn.forEach(t),ajt=r(Pwo," \u2014 "),p2e=n(Pwo,"A",{href:!0});var Skn=s(p2e);njt=r(Skn,"TFRemBertForSequenceClassification"),Skn.forEach(t),sjt=r(Pwo," (RemBERT 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jkn=s(SQe);Ejt=r(jkn,"tapas"),jkn.forEach(t),yjt=r(Jwo," \u2014 "),M2e=n(Jwo,"A",{href:!0});var Pkn=s(M2e);Cjt=r(Pkn,"TFTapasForSequenceClassification"),Pkn.forEach(t),wjt=r(Jwo," (TAPAS model)"),Jwo.forEach(t),Ljt=i(ce),O7=n(ce,"LI",{});var Uwo=s(O7);xQe=n(Uwo,"STRONG",{});var Zkn=s(xQe);Ajt=r(Zkn,"transfo-xl"),Zkn.forEach(t),Rjt=r(Uwo," \u2014 "),T2e=n(Uwo,"A",{href:!0});var Vkn=s(T2e);kjt=r(Vkn,"TFTransfoXLForSequenceClassification"),Vkn.forEach(t),Sjt=r(Uwo," (Transformer-XL model)"),Uwo.forEach(t),xjt=i(ce),Q7=n(ce,"LI",{});var Xwo=s(Q7);GQe=n(Xwo,"STRONG",{});var Wkn=s(GQe);Gjt=r(Wkn,"xlm"),Wkn.forEach(t),Bjt=r(Xwo," \u2014 "),F2e=n(Xwo,"A",{href:!0});var Jkn=s(F2e);Ijt=r(Jkn,"TFXLMForSequenceClassification"),Jkn.forEach(t),Njt=r(Xwo," (XLM model)"),Xwo.forEach(t),$jt=i(ce),Y7=n(ce,"LI",{});var Dwo=s(Y7);BQe=n(Dwo,"STRONG",{});var Ukn=s(BQe);jjt=r(Ukn,"xlm-roberta"),Ukn.forEach(t),Pjt=r(Dwo," \u2014 "),E2e=n(Dwo,"A",{href:!0});var Xkn=s(E2e);Zjt=r(Xkn,"TFXLMRobertaForSequenceClassification"),Xkn.forEach(t),Vjt=r(Dwo," (XLM-RoBERTa model)"),Dwo.forEach(t),Wjt=i(ce),z7=n(ce,"LI",{});var qwo=s(z7);IQe=n(qwo,"STRONG",{});var Dkn=s(IQe);Jjt=r(Dkn,"xlnet"),Dkn.forEach(t),Ujt=r(qwo," \u2014 "),y2e=n(qwo,"A",{href:!0});var qkn=s(y2e);Xjt=r(qkn,"TFXLNetForSequenceClassification"),qkn.forEach(t),Djt=r(qwo," (XLNet model)"),qwo.forEach(t),ce.forEach(t),qjt=i(Ed),M(H7.$$.fragment,Ed),Ed.forEach(t),Fd.forEach(t),Kxo=i(c),rf=n(c,"H3",{class:!0});var OIo=s(rf);K7=n(OIo,"A",{id:!0,class:!0,href:!0});var Okn=s(K7);NQe=n(Okn,"SPAN",{});var Qkn=s(NQe);M(KW.$$.fragment,Qkn),Qkn.forEach(t),Okn.forEach(t),Ojt=i(OIo),$Qe=n(OIo,"SPAN",{});var Ykn=s($Qe);Qjt=r(Ykn,"FlaxAutoModelForSequenceClassification"),Ykn.forEach(t),OIo.forEach(t),eGo=i(c),lr=n(c,"DIV",{class:!0});var yd=s(lr);M(eJ.$$.fragment,yd),Yjt=i(yd),tf=n(yd,"P",{});var E9e=s(tf);zjt=r(E9e,`This is a generic model class that will be instantiated as one of the model classes of 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with the `),C2e=n(E9e,"A",{href:!0});var zkn=s(C2e);Hjt=r(zkn,"from_pretrained()"),zkn.forEach(t),Kjt=r(E9e," class method or the "),w2e=n(E9e,"A",{href:!0});var Hkn=s(w2e);ePt=r(Hkn,"from_config()"),Hkn.forEach(t),oPt=r(E9e,` class
method.`),E9e.forEach(t),rPt=i(yd),oJ=n(yd,"P",{});var QIo=s(oJ);tPt=r(QIo,"This class cannot be instantiated directly using "),jQe=n(QIo,"CODE",{});var Kkn=s(jQe);aPt=r(Kkn,"__init__()"),Kkn.forEach(t),nPt=r(QIo," (throws an error)."),QIo.forEach(t),sPt=i(yd),na=n(yd,"DIV",{class:!0});var Dj=s(na);M(rJ.$$.fragment,Dj),lPt=i(Dj),PQe=n(Dj,"P",{});var eSn=s(PQe);iPt=r(eSn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),eSn.forEach(t),dPt=i(Dj),af=n(Dj,"P",{});var y9e=s(af);mPt=r(y9e,`Note:
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model\u2019s configuration. Use `),L2e=n(y9e,"A",{href:!0});var rSn=s(L2e);hPt=r(rSn,"from_pretrained()"),rSn.forEach(t),gPt=r(y9e," to load the model weights."),y9e.forEach(t),uPt=i(Dj),M(eL.$$.fragment,Dj),Dj.forEach(t),pPt=i(yd),gt=n(yd,"DIV",{class:!0});var Cd=s(gt);M(tJ.$$.fragment,Cd),_Pt=i(Cd),VQe=n(Cd,"P",{});var tSn=s(VQe);bPt=r(tSn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),tSn.forEach(t),vPt=i(Cd),rs=n(Cd,"P",{});var qj=s(rs);MPt=r(qj,"The model class to instantiate is selected based on the "),WQe=n(qj,"CODE",{});var aSn=s(WQe);TPt=r(aSn,"model_type"),aSn.forEach(t),FPt=r(qj,` property of the config object (either
passed as an argument or loaded from `),JQe=n(qj,"CODE",{});var nSn=s(JQe);EPt=r(nSn,"pretrained_model_name_or_path"),nSn.forEach(t),yPt=r(qj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),UQe=n(qj,"CODE",{});var sSn=s(UQe);CPt=r(sSn,"pretrained_model_name_or_path"),sSn.forEach(t),wPt=r(qj,":"),qj.forEach(t),LPt=i(Cd),Se=n(Cd,"UL",{});var Ze=s(Se);oL=n(Ze,"LI",{});var Owo=s(oL);XQe=n(Owo,"STRONG",{});var lSn=s(XQe);APt=r(lSn,"albert"),lSn.forEach(t),RPt=r(Owo," \u2014 "),A2e=n(Owo,"A",{href:!0});var iSn=s(A2e);kPt=r(iSn,"FlaxAlbertForSequenceClassification"),iSn.forEach(t),SPt=r(Owo," (ALBERT model)"),Owo.forEach(t),xPt=i(Ze),rL=n(Ze,"LI",{});var Qwo=s(rL);DQe=n(Qwo,"STRONG",{});var dSn=s(DQe);GPt=r(dSn,"bart"),dSn.forEach(t),BPt=r(Qwo," \u2014 "),R2e=n(Qwo,"A",{href:!0});var mSn=s(R2e);IPt=r(mSn,"FlaxBartForSequenceClassification"),mSn.forEach(t),NPt=r(Qwo," (BART model)"),Qwo.forEach(t),$Pt=i(Ze),tL=n(Ze,"LI",{});var Ywo=s(tL);qQe=n(Ywo,"STRONG",{});var cSn=s(qQe);jPt=r(cSn,"bert"),cSn.forEach(t),PPt=r(Ywo," \u2014 "),k2e=n(Ywo,"A",{href:!0});var fSn=s(k2e);ZPt=r(fSn,"FlaxBertForSequenceClassification"),fSn.forEach(t),VPt=r(Ywo," (BERT model)"),Ywo.forEach(t),WPt=i(Ze),aL=n(Ze,"LI",{});var zwo=s(aL);OQe=n(zwo,"STRONG",{});var hSn=s(OQe);JPt=r(hSn,"big_bird"),hSn.forEach(t),UPt=r(zwo," \u2014 "),S2e=n(zwo,"A",{href:!0});var gSn=s(S2e);XPt=r(gSn,"FlaxBigBirdForSequenceClassification"),gSn.forEach(t),DPt=r(zwo," (BigBird model)"),zwo.forEach(t),qPt=i(Ze),nL=n(Ze,"LI",{});var Hwo=s(nL);QQe=n(Hwo,"STRONG",{});var uSn=s(QQe);OPt=r(uSn,"distilbert"),uSn.forEach(t),QPt=r(Hwo," \u2014 "),x2e=n(Hwo,"A",{href:!0});var pSn=s(x2e);YPt=r(pSn,"FlaxDistilBertForSequenceClassification"),pSn.forEach(t),zPt=r(Hwo," (DistilBERT model)"),Hwo.forEach(t),HPt=i(Ze),sL=n(Ze,"LI",{});var Kwo=s(sL);YQe=n(Kwo,"STRONG",{});var _Sn=s(YQe);KPt=r(_Sn,"electra"),_Sn.forEach(t),eZt=r(Kwo," \u2014 "),G2e=n(Kwo,"A",{href:!0});var bSn=s(G2e);oZt=r(bSn,"FlaxElectraForSequenceClassification"),bSn.forEach(t),rZt=r(Kwo," (ELECTRA model)"),Kwo.forEach(t),tZt=i(Ze),lL=n(Ze,"LI",{});var e6o=s(lL);zQe=n(e6o,"STRONG",{});var vSn=s(zQe);aZt=r(vSn,"mbart"),vSn.forEach(t),nZt=r(e6o," \u2014 "),B2e=n(e6o,"A",{href:!0});var MSn=s(B2e);sZt=r(MSn,"FlaxMBartForSequenceClassification"),MSn.forEach(t),lZt=r(e6o," (mBART model)"),e6o.forEach(t),iZt=i(Ze),iL=n(Ze,"LI",{});var o6o=s(iL);HQe=n(o6o,"STRONG",{});var TSn=s(HQe);dZt=r(TSn,"roberta"),TSn.forEach(t),mZt=r(o6o," \u2014 "),I2e=n(o6o,"A",{href:!0});var FSn=s(I2e);cZt=r(FSn,"FlaxRobertaForSequenceClassification"),FSn.forEach(t),fZt=r(o6o," (RoBERTa model)"),o6o.forEach(t),hZt=i(Ze),dL=n(Ze,"LI",{});var r6o=s(dL);KQe=n(r6o,"STRONG",{});var ESn=s(KQe);gZt=r(ESn,"roberta-prelayernorm"),ESn.forEach(t),uZt=r(r6o," \u2014 "),N2e=n(r6o,"A",{href:!0});var ySn=s(N2e);pZt=r(ySn,"FlaxRobertaPreLayerNormForSequenceClassification"),ySn.forEach(t),_Zt=r(r6o," (RoBERTa-PreLayerNorm model)"),r6o.forEach(t),bZt=i(Ze),mL=n(Ze,"LI",{});var t6o=s(mL);eYe=n(t6o,"STRONG",{});var CSn=s(eYe);vZt=r(CSn,"roformer"),CSn.forEach(t),MZt=r(t6o," \u2014 "),$2e=n(t6o,"A",{href:!0});var wSn=s($2e);TZt=r(wSn,"FlaxRoFormerForSequenceClassification"),wSn.forEach(t),FZt=r(t6o," (RoFormer model)"),t6o.forEach(t),EZt=i(Ze),cL=n(Ze,"LI",{});var a6o=s(cL);oYe=n(a6o,"STRONG",{});var LSn=s(oYe);yZt=r(LSn,"xlm-roberta"),LSn.forEach(t),CZt=r(a6o," \u2014 "),j2e=n(a6o,"A",{href:!0});var ASn=s(j2e);wZt=r(ASn,"FlaxXLMRobertaForSequenceClassification"),ASn.forEach(t),LZt=r(a6o," (XLM-RoBERTa model)"),a6o.forEach(t),Ze.forEach(t),AZt=i(Cd),M(fL.$$.fragment,Cd),Cd.forEach(t),yd.forEach(t),oGo=i(c),nf=n(c,"H3",{class:!0});var YIo=s(nf);hL=n(YIo,"A",{id:!0,class:!0,href:!0});var RSn=s(hL);rYe=n(RSn,"SPAN",{});var kSn=s(rYe);M(aJ.$$.fragment,kSn),kSn.forEach(t),RSn.forEach(t),RZt=i(YIo),tYe=n(YIo,"SPAN",{});var SSn=s(tYe);kZt=r(SSn,"AutoModelForMultipleChoice"),SSn.forEach(t),YIo.forEach(t),rGo=i(c),ir=n(c,"DIV",{class:!0});var wd=s(ir);M(nJ.$$.fragment,wd),SZt=i(wd),sf=n(wd,"P",{});var C9e=s(sf);xZt=r(C9e,`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 `),P2e=n(C9e,"A",{href:!0});var xSn=s(P2e);GZt=r(xSn,"from_pretrained()"),xSn.forEach(t),BZt=r(C9e," class method or the "),Z2e=n(C9e,"A",{href:!0});var GSn=s(Z2e);IZt=r(GSn,"from_config()"),GSn.forEach(t),NZt=r(C9e,` class
method.`),C9e.forEach(t),$Zt=i(wd),sJ=n(wd,"P",{});var zIo=s(sJ);jZt=r(zIo,"This class cannot be instantiated directly using "),aYe=n(zIo,"CODE",{});var BSn=s(aYe);PZt=r(BSn,"__init__()"),BSn.forEach(t),ZZt=r(zIo," (throws an error)."),zIo.forEach(t),VZt=i(wd),sa=n(wd,"DIV",{class:!0});var Oj=s(sa);M(lJ.$$.fragment,Oj),WZt=i(Oj),nYe=n(Oj,"P",{});var ISn=s(nYe);JZt=r(ISn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),ISn.forEach(t),UZt=i(Oj),lf=n(Oj,"P",{});var w9e=s(lf);XZt=r(w9e,`Note:
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model\u2019s configuration. Use `),V2e=n(w9e,"A",{href:!0});var $Sn=s(V2e);OZt=r($Sn,"from_pretrained()"),$Sn.forEach(t),QZt=r(w9e," to load the model weights."),w9e.forEach(t),YZt=i(Oj),M(gL.$$.fragment,Oj),Oj.forEach(t),zZt=i(wd),ho=n(wd,"DIV",{class:!0});var sn=s(ho);M(iJ.$$.fragment,sn),HZt=i(sn),lYe=n(sn,"P",{});var jSn=s(lYe);KZt=r(jSn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),jSn.forEach(t),eVt=i(sn),ts=n(sn,"P",{});var Qj=s(ts);oVt=r(Qj,"The model class to instantiate is selected based on the "),iYe=n(Qj,"CODE",{});var PSn=s(iYe);rVt=r(PSn,"model_type"),PSn.forEach(t),tVt=r(Qj,` property of the config object (either
passed as an argument or loaded from `),dYe=n(Qj,"CODE",{});var ZSn=s(dYe);aVt=r(ZSn,"pretrained_model_name_or_path"),ZSn.forEach(t),nVt=r(Qj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mYe=n(Qj,"CODE",{});var VSn=s(mYe);sVt=r(VSn,"pretrained_model_name_or_path"),VSn.forEach(t),lVt=r(Qj,":"),Qj.forEach(t),iVt=i(sn),te=n(sn,"UL",{});var ne=s(te);uL=n(ne,"LI",{});var n6o=s(uL);cYe=n(n6o,"STRONG",{});var WSn=s(cYe);dVt=r(WSn,"albert"),WSn.forEach(t),mVt=r(n6o," \u2014 "),W2e=n(n6o,"A",{href:!0});var JSn=s(W2e);cVt=r(JSn,"AlbertForMultipleChoice"),JSn.forEach(t),fVt=r(n6o," (ALBERT model)"),n6o.forEach(t),hVt=i(ne),pL=n(ne,"LI",{});var s6o=s(pL);fYe=n(s6o,"STRONG",{});var USn=s(fYe);gVt=r(USn,"bert"),USn.forEach(t),uVt=r(s6o," \u2014 "),J2e=n(s6o,"A",{href:!0});var XSn=s(J2e);pVt=r(XSn,"BertForMultipleChoice"),XSn.forEach(t),_Vt=r(s6o," (BERT model)"),s6o.forEach(t),bVt=i(ne),_L=n(ne,"LI",{});var l6o=s(_L);hYe=n(l6o,"STRONG",{});var DSn=s(hYe);vVt=r(DSn,"big_bird"),DSn.forEach(t),MVt=r(l6o," \u2014 "),U2e=n(l6o,"A",{href:!0});var qSn=s(U2e);TVt=r(qSn,"BigBirdForMultipleChoice"),qSn.forEach(t),FVt=r(l6o," (BigBird 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R6o=s(PL);IYe=n(R6o,"STRONG",{});var Nxn=s(IYe);TJt=r(Nxn,"nystromformer"),Nxn.forEach(t),FJt=r(R6o," \u2014 "),fve=n(R6o,"A",{href:!0});var $xn=s(fve);EJt=r($xn,"NystromformerForMultipleChoice"),$xn.forEach(t),yJt=r(R6o," (Nystr\xF6mformer model)"),R6o.forEach(t),CJt=i(ne),ZL=n(ne,"LI",{});var k6o=s(ZL);NYe=n(k6o,"STRONG",{});var jxn=s(NYe);wJt=r(jxn,"qdqbert"),jxn.forEach(t),LJt=r(k6o," \u2014 "),hve=n(k6o,"A",{href:!0});var Pxn=s(hve);AJt=r(Pxn,"QDQBertForMultipleChoice"),Pxn.forEach(t),RJt=r(k6o," (QDQBert model)"),k6o.forEach(t),kJt=i(ne),VL=n(ne,"LI",{});var S6o=s(VL);$Ye=n(S6o,"STRONG",{});var Zxn=s($Ye);SJt=r(Zxn,"rembert"),Zxn.forEach(t),xJt=r(S6o," \u2014 "),gve=n(S6o,"A",{href:!0});var Vxn=s(gve);GJt=r(Vxn,"RemBertForMultipleChoice"),Vxn.forEach(t),BJt=r(S6o," (RemBERT model)"),S6o.forEach(t),IJt=i(ne),WL=n(ne,"LI",{});var x6o=s(WL);jYe=n(x6o,"STRONG",{});var Wxn=s(jYe);NJt=r(Wxn,"roberta"),Wxn.forEach(t),$Jt=r(x6o," \u2014 "),uve=n(x6o,"A",{href:!0});var Jxn=s(uve);jJt=r(Jxn,"RobertaForMultipleChoice"),Jxn.forEach(t),PJt=r(x6o," (RoBERTa model)"),x6o.forEach(t),ZJt=i(ne),JL=n(ne,"LI",{});var G6o=s(JL);PYe=n(G6o,"STRONG",{});var Uxn=s(PYe);VJt=r(Uxn,"roberta-prelayernorm"),Uxn.forEach(t),WJt=r(G6o," \u2014 "),pve=n(G6o,"A",{href:!0});var Xxn=s(pve);JJt=r(Xxn,"RobertaPreLayerNormForMultipleChoice"),Xxn.forEach(t),UJt=r(G6o," (RoBERTa-PreLayerNorm model)"),G6o.forEach(t),XJt=i(ne),UL=n(ne,"LI",{});var B6o=s(UL);ZYe=n(B6o,"STRONG",{});var Dxn=s(ZYe);DJt=r(Dxn,"roc_bert"),Dxn.forEach(t),qJt=r(B6o," \u2014 "),_ve=n(B6o,"A",{href:!0});var qxn=s(_ve);OJt=r(qxn,"RoCBertForMultipleChoice"),qxn.forEach(t),QJt=r(B6o," (RoCBert model)"),B6o.forEach(t),YJt=i(ne),XL=n(ne,"LI",{});var I6o=s(XL);VYe=n(I6o,"STRONG",{});var Oxn=s(VYe);zJt=r(Oxn,"roformer"),Oxn.forEach(t),HJt=r(I6o," \u2014 "),bve=n(I6o,"A",{href:!0});var Qxn=s(bve);KJt=r(Qxn,"RoFormerForMultipleChoice"),Qxn.forEach(t),eUt=r(I6o," (RoFormer model)"),I6o.forEach(t),oUt=i(ne),DL=n(ne,"LI",{});var N6o=s(DL);WYe=n(N6o,"STRONG",{});var Yxn=s(WYe);rUt=r(Yxn,"squeezebert"),Yxn.forEach(t),tUt=r(N6o," \u2014 "),vve=n(N6o,"A",{href:!0});var zxn=s(vve);aUt=r(zxn,"SqueezeBertForMultipleChoice"),zxn.forEach(t),nUt=r(N6o," (SqueezeBERT model)"),N6o.forEach(t),sUt=i(ne),qL=n(ne,"LI",{});var $6o=s(qL);JYe=n($6o,"STRONG",{});var Hxn=s(JYe);lUt=r(Hxn,"xlm"),Hxn.forEach(t),iUt=r($6o," \u2014 "),Mve=n($6o,"A",{href:!0});var Kxn=s(Mve);dUt=r(Kxn,"XLMForMultipleChoice"),Kxn.forEach(t),mUt=r($6o," (XLM model)"),$6o.forEach(t),cUt=i(ne),OL=n(ne,"LI",{});var j6o=s(OL);UYe=n(j6o,"STRONG",{});var eGn=s(UYe);fUt=r(eGn,"xlm-roberta"),eGn.forEach(t),hUt=r(j6o," \u2014 "),Tve=n(j6o,"A",{href:!0});var oGn=s(Tve);gUt=r(oGn,"XLMRobertaForMultipleChoice"),oGn.forEach(t),uUt=r(j6o," (XLM-RoBERTa model)"),j6o.forEach(t),pUt=i(ne),QL=n(ne,"LI",{});var P6o=s(QL);XYe=n(P6o,"STRONG",{});var rGn=s(XYe);_Ut=r(rGn,"xlm-roberta-xl"),rGn.forEach(t),bUt=r(P6o," \u2014 "),Fve=n(P6o,"A",{href:!0});var tGn=s(Fve);vUt=r(tGn,"XLMRobertaXLForMultipleChoice"),tGn.forEach(t),MUt=r(P6o," (XLM-RoBERTa-XL model)"),P6o.forEach(t),TUt=i(ne),YL=n(ne,"LI",{});var Z6o=s(YL);DYe=n(Z6o,"STRONG",{});var aGn=s(DYe);FUt=r(aGn,"xlnet"),aGn.forEach(t),EUt=r(Z6o," \u2014 "),Eve=n(Z6o,"A",{href:!0});var nGn=s(Eve);yUt=r(nGn,"XLNetForMultipleChoice"),nGn.forEach(t),CUt=r(Z6o," (XLNet model)"),Z6o.forEach(t),wUt=i(ne),zL=n(ne,"LI",{});var V6o=s(zL);qYe=n(V6o,"STRONG",{});var sGn=s(qYe);LUt=r(sGn,"xmod"),sGn.forEach(t),AUt=r(V6o," \u2014 "),yve=n(V6o,"A",{href:!0});var lGn=s(yve);RUt=r(lGn,"XmodForMultipleChoice"),lGn.forEach(t),kUt=r(V6o," (X-MOD model)"),V6o.forEach(t),SUt=i(ne),HL=n(ne,"LI",{});var W6o=s(HL);OYe=n(W6o,"STRONG",{});var iGn=s(OYe);xUt=r(iGn,"yoso"),iGn.forEach(t),GUt=r(W6o," \u2014 "),Cve=n(W6o,"A",{href:!0});var dGn=s(Cve);BUt=r(dGn,"YosoForMultipleChoice"),dGn.forEach(t),IUt=r(W6o," (YOSO model)"),W6o.forEach(t),ne.forEach(t),NUt=i(sn),KL=n(sn,"P",{});var J6o=s(KL);$Ut=r(J6o,"The model is set in evaluation mode by default using "),QYe=n(J6o,"CODE",{});var mGn=s(QYe);jUt=r(mGn,"model.eval()"),mGn.forEach(t),PUt=r(J6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),YYe=n(J6o,"CODE",{});var cGn=s(YYe);ZUt=r(cGn,"model.train()"),cGn.forEach(t),J6o.forEach(t),VUt=i(sn),M(eA.$$.fragment,sn),sn.forEach(t),wd.forEach(t),tGo=i(c),df=n(c,"H3",{class:!0});var HIo=s(df);oA=n(HIo,"A",{id:!0,class:!0,href:!0});var fGn=s(oA);zYe=n(fGn,"SPAN",{});var hGn=s(zYe);M(dJ.$$.fragment,hGn),hGn.forEach(t),fGn.forEach(t),WUt=i(HIo),HYe=n(HIo,"SPAN",{});var gGn=s(HYe);JUt=r(gGn,"TFAutoModelForMultipleChoice"),gGn.forEach(t),HIo.forEach(t),aGo=i(c),dr=n(c,"DIV",{class:!0});var Ld=s(dr);M(mJ.$$.fragment,Ld),UUt=i(Ld),mf=n(Ld,"P",{});var L9e=s(mf);XUt=r(L9e,`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 `),wve=n(L9e,"A",{href:!0});var uGn=s(wve);DUt=r(uGn,"from_pretrained()"),uGn.forEach(t),qUt=r(L9e," class method or the "),Lve=n(L9e,"A",{href:!0});var pGn=s(Lve);OUt=r(pGn,"from_config()"),pGn.forEach(t),QUt=r(L9e,` class
method.`),L9e.forEach(t),YUt=i(Ld),cJ=n(Ld,"P",{});var KIo=s(cJ);zUt=r(KIo,"This class cannot be instantiated directly using "),KYe=n(KIo,"CODE",{});var _Gn=s(KYe);HUt=r(_Gn,"__init__()"),_Gn.forEach(t),KUt=r(KIo," (throws an error)."),KIo.forEach(t),eXt=i(Ld),la=n(Ld,"DIV",{class:!0});var Yj=s(la);M(fJ.$$.fragment,Yj),oXt=i(Yj),eze=n(Yj,"P",{});var bGn=s(eze);rXt=r(bGn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),bGn.forEach(t),tXt=i(Yj),cf=n(Yj,"P",{});var A9e=s(cf);aXt=r(A9e,`Note:
Loading a model from its configuration file does `),oze=n(A9e,"STRONG",{});var vGn=s(oze);nXt=r(vGn,"not"),vGn.forEach(t),sXt=r(A9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ave=n(A9e,"A",{href:!0});var MGn=s(Ave);lXt=r(MGn,"from_pretrained()"),MGn.forEach(t),iXt=r(A9e," to load the model weights."),A9e.forEach(t),dXt=i(Yj),M(rA.$$.fragment,Yj),Yj.forEach(t),mXt=i(Ld),ut=n(Ld,"DIV",{class:!0});var Ad=s(ut);M(hJ.$$.fragment,Ad),cXt=i(Ad),rze=n(Ad,"P",{});var TGn=s(rze);fXt=r(TGn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),TGn.forEach(t),hXt=i(Ad),as=n(Ad,"P",{});var zj=s(as);gXt=r(zj,"The model class to instantiate is selected based on the "),tze=n(zj,"CODE",{});var FGn=s(tze);uXt=r(FGn,"model_type"),FGn.forEach(t),pXt=r(zj,` property of the config object (either
passed as an argument or loaded from `),aze=n(zj,"CODE",{});var EGn=s(aze);_Xt=r(EGn,"pretrained_model_name_or_path"),EGn.forEach(t),bXt=r(zj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nze=n(zj,"CODE",{});var yGn=s(nze);vXt=r(yGn,"pretrained_model_name_or_path"),yGn.forEach(t),MXt=r(zj,":"),zj.forEach(t),TXt=i(Ad),Fe=n(Ad,"UL",{});var ye=s(Fe);tA=n(ye,"LI",{});var U6o=s(tA);sze=n(U6o,"STRONG",{});var CGn=s(sze);FXt=r(CGn,"albert"),CGn.forEach(t),EXt=r(U6o," \u2014 "),Rve=n(U6o,"A",{href:!0});var wGn=s(Rve);yXt=r(wGn,"TFAlbertForMultipleChoice"),wGn.forEach(t),CXt=r(U6o," (ALBERT model)"),U6o.forEach(t),wXt=i(ye),aA=n(ye,"LI",{});var X6o=s(aA);lze=n(X6o,"STRONG",{});var LGn=s(lze);LXt=r(LGn,"bert"),LGn.forEach(t),AXt=r(X6o," \u2014 "),kve=n(X6o,"A",{href:!0});var AGn=s(kve);RXt=r(AGn,"TFBertForMultipleChoice"),AGn.forEach(t),kXt=r(X6o," (BERT model)"),X6o.forEach(t),SXt=i(ye),nA=n(ye,"LI",{});var D6o=s(nA);ize=n(D6o,"STRONG",{});var RGn=s(ize);xXt=r(RGn,"camembert"),RGn.forEach(t),GXt=r(D6o," \u2014 "),Sve=n(D6o,"A",{href:!0});var kGn=s(Sve);BXt=r(kGn,"TFCamembertForMultipleChoice"),kGn.forEach(t),IXt=r(D6o," (CamemBERT model)"),D6o.forEach(t),NXt=i(ye),sA=n(ye,"LI",{});var q6o=s(sA);dze=n(q6o,"STRONG",{});var SGn=s(dze);$Xt=r(SGn,"convbert"),SGn.forEach(t),jXt=r(q6o," \u2014 "),xve=n(q6o,"A",{href:!0});var xGn=s(xve);PXt=r(xGn,"TFConvBertForMultipleChoice"),xGn.forEach(t),ZXt=r(q6o," (ConvBERT model)"),q6o.forEach(t),VXt=i(ye),lA=n(ye,"LI",{});var O6o=s(lA);mze=n(O6o,"STRONG",{});var GGn=s(mze);WXt=r(GGn,"distilbert"),GGn.forEach(t),JXt=r(O6o," \u2014 "),Gve=n(O6o,"A",{href:!0});var BGn=s(Gve);UXt=r(BGn,"TFDistilBertForMultipleChoice"),BGn.forEach(t),XXt=r(O6o," (DistilBERT model)"),O6o.forEach(t),DXt=i(ye),iA=n(ye,"LI",{});var Q6o=s(iA);cze=n(Q6o,"STRONG",{});var IGn=s(cze);qXt=r(IGn,"electra"),IGn.forEach(t),OXt=r(Q6o," \u2014 "),Bve=n(Q6o,"A",{href:!0});var NGn=s(Bve);QXt=r(NGn,"TFElectraForMultipleChoice"),NGn.forEach(t),YXt=r(Q6o," (ELECTRA model)"),Q6o.forEach(t),zXt=i(ye),dA=n(ye,"LI",{});var Y6o=s(dA);fze=n(Y6o,"STRONG",{});var $Gn=s(fze);HXt=r($Gn,"flaubert"),$Gn.forEach(t),KXt=r(Y6o," 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with the `),qve=n(R9e,"A",{href:!0});var mBn=s(qve);iqt=r(mBn,"from_pretrained()"),mBn.forEach(t),dqt=r(R9e," class method or the "),Ove=n(R9e,"A",{href:!0});var cBn=s(Ove);mqt=r(cBn,"from_config()"),cBn.forEach(t),cqt=r(R9e,` class
method.`),R9e.forEach(t),fqt=i(Rd),pJ=n(Rd,"P",{});var oNo=s(pJ);hqt=r(oNo,"This class cannot be instantiated directly using "),wze=n(oNo,"CODE",{});var fBn=s(wze);gqt=r(fBn,"__init__()"),fBn.forEach(t),uqt=r(oNo," (throws an error)."),oNo.forEach(t),pqt=i(Rd),ia=n(Rd,"DIV",{class:!0});var Hj=s(ia);M(_J.$$.fragment,Hj),_qt=i(Hj),Lze=n(Hj,"P",{});var hBn=s(Lze);bqt=r(hBn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),hBn.forEach(t),vqt=i(Hj),gf=n(Hj,"P",{});var k9e=s(gf);Mqt=r(k9e,`Note:
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model\u2019s configuration. Use `),Qve=n(k9e,"A",{href:!0});var uBn=s(Qve);Eqt=r(uBn,"from_pretrained()"),uBn.forEach(t),yqt=r(k9e," to load the model weights."),k9e.forEach(t),Cqt=i(Hj),M(EA.$$.fragment,Hj),Hj.forEach(t),wqt=i(Rd),pt=n(Rd,"DIV",{class:!0});var kd=s(pt);M(bJ.$$.fragment,kd),Lqt=i(kd),Rze=n(kd,"P",{});var pBn=s(Rze);Aqt=r(pBn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),pBn.forEach(t),Rqt=i(kd),ns=n(kd,"P",{});var Kj=s(ns);kqt=r(Kj,"The model class to instantiate is selected based on the "),kze=n(Kj,"CODE",{});var _Bn=s(kze);Sqt=r(_Bn,"model_type"),_Bn.forEach(t),xqt=r(Kj,` property of the config object (either
passed as an argument or loaded from `),Sze=n(Kj,"CODE",{});var bBn=s(Sze);Gqt=r(bBn,"pretrained_model_name_or_path"),bBn.forEach(t),Bqt=r(Kj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xze=n(Kj,"CODE",{});var vBn=s(xze);Iqt=r(vBn,"pretrained_model_name_or_path"),vBn.forEach(t),Nqt=r(Kj,":"),Kj.forEach(t),$qt=i(kd),Ue=n(kd,"UL",{});var He=s(Ue);yA=n(He,"LI",{});var i8o=s(yA);Gze=n(i8o,"STRONG",{});var MBn=s(Gze);jqt=r(MBn,"albert"),MBn.forEach(t),Pqt=r(i8o," \u2014 "),Yve=n(i8o,"A",{href:!0});var TBn=s(Yve);Zqt=r(TBn,"FlaxAlbertForMultipleChoice"),TBn.forEach(t),Vqt=r(i8o," (ALBERT model)"),i8o.forEach(t),Wqt=i(He),CA=n(He,"LI",{});var d8o=s(CA);Bze=n(d8o,"STRONG",{});var FBn=s(Bze);Jqt=r(FBn,"bert"),FBn.forEach(t),Uqt=r(d8o," \u2014 "),zve=n(d8o,"A",{href:!0});var EBn=s(zve);Xqt=r(EBn,"FlaxBertForMultipleChoice"),EBn.forEach(t),Dqt=r(d8o," (BERT model)"),d8o.forEach(t),qqt=i(He),wA=n(He,"LI",{});var m8o=s(wA);Ize=n(m8o,"STRONG",{});var yBn=s(Ize);Oqt=r(yBn,"big_bird"),yBn.forEach(t),Qqt=r(m8o," \u2014 "),Hve=n(m8o,"A",{href:!0});var CBn=s(Hve);Yqt=r(CBn,"FlaxBigBirdForMultipleChoice"),CBn.forEach(t),zqt=r(m8o," (BigBird model)"),m8o.forEach(t),Hqt=i(He),LA=n(He,"LI",{});var c8o=s(LA);Nze=n(c8o,"STRONG",{});var wBn=s(Nze);Kqt=r(wBn,"distilbert"),wBn.forEach(t),eOt=r(c8o," \u2014 "),Kve=n(c8o,"A",{href:!0});var LBn=s(Kve);oOt=r(LBn,"FlaxDistilBertForMultipleChoice"),LBn.forEach(t),rOt=r(c8o," (DistilBERT model)"),c8o.forEach(t),tOt=i(He),AA=n(He,"LI",{});var f8o=s(AA);$ze=n(f8o,"STRONG",{});var ABn=s($ze);aOt=r(ABn,"electra"),ABn.forEach(t),nOt=r(f8o," \u2014 "),eMe=n(f8o,"A",{href:!0});var RBn=s(eMe);sOt=r(RBn,"FlaxElectraForMultipleChoice"),RBn.forEach(t),lOt=r(f8o," (ELECTRA model)"),f8o.forEach(t),iOt=i(He),RA=n(He,"LI",{});var h8o=s(RA);jze=n(h8o,"STRONG",{});var kBn=s(jze);dOt=r(kBn,"roberta"),kBn.forEach(t),mOt=r(h8o," \u2014 "),oMe=n(h8o,"A",{href:!0});var SBn=s(oMe);cOt=r(SBn,"FlaxRobertaForMultipleChoice"),SBn.forEach(t),fOt=r(h8o," (RoBERTa model)"),h8o.forEach(t),hOt=i(He),kA=n(He,"LI",{});var g8o=s(kA);Pze=n(g8o,"STRONG",{});var xBn=s(Pze);gOt=r(xBn,"roberta-prelayernorm"),xBn.forEach(t),uOt=r(g8o," \u2014 "),rMe=n(g8o,"A",{href:!0});var GBn=s(rMe);pOt=r(GBn,"FlaxRobertaPreLayerNormForMultipleChoice"),GBn.forEach(t),_Ot=r(g8o," (RoBERTa-PreLayerNorm model)"),g8o.forEach(t),bOt=i(He),SA=n(He,"LI",{});var u8o=s(SA);Zze=n(u8o,"STRONG",{});var BBn=s(Zze);vOt=r(BBn,"roformer"),BBn.forEach(t),MOt=r(u8o," \u2014 "),tMe=n(u8o,"A",{href:!0});var IBn=s(tMe);TOt=r(IBn,"FlaxRoFormerForMultipleChoice"),IBn.forEach(t),FOt=r(u8o," (RoFormer model)"),u8o.forEach(t),EOt=i(He),xA=n(He,"LI",{});var p8o=s(xA);Vze=n(p8o,"STRONG",{});var NBn=s(Vze);yOt=r(NBn,"xlm-roberta"),NBn.forEach(t),COt=r(p8o," \u2014 "),aMe=n(p8o,"A",{href:!0});var $Bn=s(aMe);wOt=r($Bn,"FlaxXLMRobertaForMultipleChoice"),$Bn.forEach(t),LOt=r(p8o," (XLM-RoBERTa model)"),p8o.forEach(t),He.forEach(t),AOt=i(kd),M(GA.$$.fragment,kd),kd.forEach(t),Rd.forEach(t),lGo=i(c),uf=n(c,"H3",{class:!0});var rNo=s(uf);BA=n(rNo,"A",{id:!0,class:!0,href:!0});var jBn=s(BA);Wze=n(jBn,"SPAN",{});var PBn=s(Wze);M(vJ.$$.fragment,PBn),PBn.forEach(t),jBn.forEach(t),ROt=i(rNo),Jze=n(rNo,"SPAN",{});var ZBn=s(Jze);kOt=r(ZBn,"AutoModelForNextSentencePrediction"),ZBn.forEach(t),rNo.forEach(t),iGo=i(c),cr=n(c,"DIV",{class:!0});var Sd=s(cr);M(MJ.$$.fragment,Sd),SOt=i(Sd),pf=n(Sd,"P",{});var S9e=s(pf);xOt=r(S9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),nMe=n(S9e,"A",{href:!0});var VBn=s(nMe);GOt=r(VBn,"from_pretrained()"),VBn.forEach(t),BOt=r(S9e," class method or the "),sMe=n(S9e,"A",{href:!0});var WBn=s(sMe);IOt=r(WBn,"from_config()"),WBn.forEach(t),NOt=r(S9e,` class
method.`),S9e.forEach(t),$Ot=i(Sd),TJ=n(Sd,"P",{});var tNo=s(TJ);jOt=r(tNo,"This class cannot be instantiated directly using "),Uze=n(tNo,"CODE",{});var JBn=s(Uze);POt=r(JBn,"__init__()"),JBn.forEach(t),ZOt=r(tNo," (throws an error)."),tNo.forEach(t),VOt=i(Sd),da=n(Sd,"DIV",{class:!0});var eP=s(da);M(FJ.$$.fragment,eP),WOt=i(eP),Xze=n(eP,"P",{});var UBn=s(Xze);JOt=r(UBn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),UBn.forEach(t),UOt=i(eP),_f=n(eP,"P",{});var x9e=s(_f);XOt=r(x9e,`Note:
Loading a model from its configuration file does `),Dze=n(x9e,"STRONG",{});var XBn=s(Dze);DOt=r(XBn,"not"),XBn.forEach(t),qOt=r(x9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),lMe=n(x9e,"A",{href:!0});var DBn=s(lMe);OOt=r(DBn,"from_pretrained()"),DBn.forEach(t),QOt=r(x9e," to load the model weights."),x9e.forEach(t),YOt=i(eP),M(IA.$$.fragment,eP),eP.forEach(t),zOt=i(Sd),go=n(Sd,"DIV",{class:!0});var ln=s(go);M(EJ.$$.fragment,ln),HOt=i(ln),qze=n(ln,"P",{});var qBn=s(qze);KOt=r(qBn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),qBn.forEach(t),eQt=i(ln),ss=n(ln,"P",{});var oP=s(ss);oQt=r(oP,"The model class to instantiate is selected based on the "),Oze=n(oP,"CODE",{});var OBn=s(Oze);rQt=r(OBn,"model_type"),OBn.forEach(t),tQt=r(oP,` property of the config object (either
passed as an argument or loaded from `),Qze=n(oP,"CODE",{});var QBn=s(Qze);aQt=r(QBn,"pretrained_model_name_or_path"),QBn.forEach(t),nQt=r(oP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yze=n(oP,"CODE",{});var YBn=s(Yze);sQt=r(YBn,"pretrained_model_name_or_path"),YBn.forEach(t),lQt=r(oP,":"),oP.forEach(t),iQt=i(ln),eo=n(ln,"UL",{});var It=s(eo);NA=n(It,"LI",{});var _8o=s(NA);zze=n(_8o,"STRONG",{});var zBn=s(zze);dQt=r(zBn,"bert"),zBn.forEach(t),mQt=r(_8o," \u2014 "),iMe=n(_8o,"A",{href:!0});var HBn=s(iMe);cQt=r(HBn,"BertForNextSentencePrediction"),HBn.forEach(t),fQt=r(_8o," (BERT model)"),_8o.forEach(t),hQt=i(It),$A=n(It,"LI",{});var b8o=s($A);Hze=n(b8o,"STRONG",{});var KBn=s(Hze);gQt=r(KBn,"ernie"),KBn.forEach(t),uQt=r(b8o," \u2014 "),dMe=n(b8o,"A",{href:!0});var eIn=s(dMe);pQt=r(eIn,"ErnieForNextSentencePrediction"),eIn.forEach(t),_Qt=r(b8o," (ERNIE model)"),b8o.forEach(t),bQt=i(It),jA=n(It,"LI",{});var v8o=s(jA);Kze=n(v8o,"STRONG",{});var oIn=s(Kze);vQt=r(oIn,"fnet"),oIn.forEach(t),MQt=r(v8o," \u2014 "),mMe=n(v8o,"A",{href:!0});var rIn=s(mMe);TQt=r(rIn,"FNetForNextSentencePrediction"),rIn.forEach(t),FQt=r(v8o," (FNet model)"),v8o.forEach(t),EQt=i(It),PA=n(It,"LI",{});var M8o=s(PA);eHe=n(M8o,"STRONG",{});var tIn=s(eHe);yQt=r(tIn,"megatron-bert"),tIn.forEach(t),CQt=r(M8o," \u2014 "),cMe=n(M8o,"A",{href:!0});var aIn=s(cMe);wQt=r(aIn,"MegatronBertForNextSentencePrediction"),aIn.forEach(t),LQt=r(M8o," (Megatron-BERT model)"),M8o.forEach(t),AQt=i(It),ZA=n(It,"LI",{});var T8o=s(ZA);oHe=n(T8o,"STRONG",{});var nIn=s(oHe);RQt=r(nIn,"mobilebert"),nIn.forEach(t),kQt=r(T8o," \u2014 "),fMe=n(T8o,"A",{href:!0});var sIn=s(fMe);SQt=r(sIn,"MobileBertForNextSentencePrediction"),sIn.forEach(t),xQt=r(T8o," (MobileBERT model)"),T8o.forEach(t),GQt=i(It),VA=n(It,"LI",{});var F8o=s(VA);rHe=n(F8o,"STRONG",{});var lIn=s(rHe);BQt=r(lIn,"nezha"),lIn.forEach(t),IQt=r(F8o," \u2014 "),hMe=n(F8o,"A",{href:!0});var iIn=s(hMe);NQt=r(iIn,"NezhaForNextSentencePrediction"),iIn.forEach(t),$Qt=r(F8o," (Nezha model)"),F8o.forEach(t),jQt=i(It),WA=n(It,"LI",{});var E8o=s(WA);tHe=n(E8o,"STRONG",{});var dIn=s(tHe);PQt=r(dIn,"qdqbert"),dIn.forEach(t),ZQt=r(E8o," \u2014 "),gMe=n(E8o,"A",{href:!0});var mIn=s(gMe);VQt=r(mIn,"QDQBertForNextSentencePrediction"),mIn.forEach(t),WQt=r(E8o," (QDQBert model)"),E8o.forEach(t),It.forEach(t),JQt=i(ln),JA=n(ln,"P",{});var y8o=s(JA);UQt=r(y8o,"The model is set in evaluation mode by default using "),aHe=n(y8o,"CODE",{});var cIn=s(aHe);XQt=r(cIn,"model.eval()"),cIn.forEach(t),DQt=r(y8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nHe=n(y8o,"CODE",{});var fIn=s(nHe);qQt=r(fIn,"model.train()"),fIn.forEach(t),y8o.forEach(t),OQt=i(ln),M(UA.$$.fragment,ln),ln.forEach(t),Sd.forEach(t),dGo=i(c),bf=n(c,"H3",{class:!0});var aNo=s(bf);XA=n(aNo,"A",{id:!0,class:!0,href:!0});var hIn=s(XA);sHe=n(hIn,"SPAN",{});var gIn=s(sHe);M(yJ.$$.fragment,gIn),gIn.forEach(t),hIn.forEach(t),QQt=i(aNo),lHe=n(aNo,"SPAN",{});var uIn=s(lHe);YQt=r(uIn,"TFAutoModelForNextSentencePrediction"),uIn.forEach(t),aNo.forEach(t),mGo=i(c),fr=n(c,"DIV",{class:!0});var xd=s(fr);M(CJ.$$.fragment,xd),zQt=i(xd),vf=n(xd,"P",{});var G9e=s(vf);HQt=r(G9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),uMe=n(G9e,"A",{href:!0});var pIn=s(uMe);KQt=r(pIn,"from_pretrained()"),pIn.forEach(t),eYt=r(G9e," class method or the "),pMe=n(G9e,"A",{href:!0});var _In=s(pMe);oYt=r(_In,"from_config()"),_In.forEach(t),rYt=r(G9e,` class
method.`),G9e.forEach(t),tYt=i(xd),wJ=n(xd,"P",{});var nNo=s(wJ);aYt=r(nNo,"This class cannot be instantiated directly using "),iHe=n(nNo,"CODE",{});var bIn=s(iHe);nYt=r(bIn,"__init__()"),bIn.forEach(t),sYt=r(nNo," (throws an error)."),nNo.forEach(t),lYt=i(xd),ma=n(xd,"DIV",{class:!0});var rP=s(ma);M(LJ.$$.fragment,rP),iYt=i(rP),dHe=n(rP,"P",{});var vIn=s(dHe);dYt=r(vIn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),vIn.forEach(t),mYt=i(rP),Mf=n(rP,"P",{});var B9e=s(Mf);cYt=r(B9e,`Note:
Loading a model from its configuration file does `),mHe=n(B9e,"STRONG",{});var MIn=s(mHe);fYt=r(MIn,"not"),MIn.forEach(t),hYt=r(B9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),_Me=n(B9e,"A",{href:!0});var TIn=s(_Me);gYt=r(TIn,"from_pretrained()"),TIn.forEach(t),uYt=r(B9e," to load the model weights."),B9e.forEach(t),pYt=i(rP),M(DA.$$.fragment,rP),rP.forEach(t),_Yt=i(xd),_t=n(xd,"DIV",{class:!0});var Gd=s(_t);M(AJ.$$.fragment,Gd),bYt=i(Gd),cHe=n(Gd,"P",{});var FIn=s(cHe);vYt=r(FIn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),FIn.forEach(t),MYt=i(Gd),ls=n(Gd,"P",{});var tP=s(ls);TYt=r(tP,"The model class to instantiate is selected based on the "),fHe=n(tP,"CODE",{});var EIn=s(fHe);FYt=r(EIn,"model_type"),EIn.forEach(t),EYt=r(tP,` property of the config object (either
passed as an argument or loaded from `),hHe=n(tP,"CODE",{});var yIn=s(hHe);yYt=r(yIn,"pretrained_model_name_or_path"),yIn.forEach(t),CYt=r(tP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gHe=n(tP,"CODE",{});var CIn=s(gHe);wYt=r(CIn,"pretrained_model_name_or_path"),CIn.forEach(t),LYt=r(tP,":"),tP.forEach(t),AYt=i(Gd),RJ=n(Gd,"UL",{});var sNo=s(RJ);qA=n(sNo,"LI",{});var C8o=s(qA);uHe=n(C8o,"STRONG",{});var wIn=s(uHe);RYt=r(wIn,"bert"),wIn.forEach(t),kYt=r(C8o," \u2014 "),bMe=n(C8o,"A",{href:!0});var LIn=s(bMe);SYt=r(LIn,"TFBertForNextSentencePrediction"),LIn.forEach(t),xYt=r(C8o," (BERT model)"),C8o.forEach(t),GYt=i(sNo),OA=n(sNo,"LI",{});var w8o=s(OA);pHe=n(w8o,"STRONG",{});var AIn=s(pHe);BYt=r(AIn,"mobilebert"),AIn.forEach(t),IYt=r(w8o," \u2014 "),vMe=n(w8o,"A",{href:!0});var RIn=s(vMe);NYt=r(RIn,"TFMobileBertForNextSentencePrediction"),RIn.forEach(t),$Yt=r(w8o," (MobileBERT model)"),w8o.forEach(t),sNo.forEach(t),jYt=i(Gd),M(QA.$$.fragment,Gd),Gd.forEach(t),xd.forEach(t),cGo=i(c),Tf=n(c,"H3",{class:!0});var lNo=s(Tf);YA=n(lNo,"A",{id:!0,class:!0,href:!0});var kIn=s(YA);_He=n(kIn,"SPAN",{});var SIn=s(_He);M(kJ.$$.fragment,SIn),SIn.forEach(t),kIn.forEach(t),PYt=i(lNo),bHe=n(lNo,"SPAN",{});var xIn=s(bHe);ZYt=r(xIn,"FlaxAutoModelForNextSentencePrediction"),xIn.forEach(t),lNo.forEach(t),fGo=i(c),hr=n(c,"DIV",{class:!0});var Bd=s(hr);M(SJ.$$.fragment,Bd),VYt=i(Bd),Ff=n(Bd,"P",{});var I9e=s(Ff);WYt=r(I9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),MMe=n(I9e,"A",{href:!0});var GIn=s(MMe);JYt=r(GIn,"from_pretrained()"),GIn.forEach(t),UYt=r(I9e," class method or the "),TMe=n(I9e,"A",{href:!0});var BIn=s(TMe);XYt=r(BIn,"from_config()"),BIn.forEach(t),DYt=r(I9e,` class
method.`),I9e.forEach(t),qYt=i(Bd),xJ=n(Bd,"P",{});var iNo=s(xJ);OYt=r(iNo,"This class cannot be instantiated directly using "),vHe=n(iNo,"CODE",{});var IIn=s(vHe);QYt=r(IIn,"__init__()"),IIn.forEach(t),YYt=r(iNo," (throws an error)."),iNo.forEach(t),zYt=i(Bd),ca=n(Bd,"DIV",{class:!0});var aP=s(ca);M(GJ.$$.fragment,aP),HYt=i(aP),MHe=n(aP,"P",{});var NIn=s(MHe);KYt=r(NIn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),NIn.forEach(t),ezt=i(aP),Ef=n(aP,"P",{});var N9e=s(Ef);ozt=r(N9e,`Note:
Loading a model from its configuration file does `),THe=n(N9e,"STRONG",{});var $In=s(THe);rzt=r($In,"not"),$In.forEach(t),tzt=r(N9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),FMe=n(N9e,"A",{href:!0});var jIn=s(FMe);azt=r(jIn,"from_pretrained()"),jIn.forEach(t),nzt=r(N9e," to load the model weights."),N9e.forEach(t),szt=i(aP),M(zA.$$.fragment,aP),aP.forEach(t),lzt=i(Bd),bt=n(Bd,"DIV",{class:!0});var Id=s(bt);M(BJ.$$.fragment,Id),izt=i(Id),FHe=n(Id,"P",{});var PIn=s(FHe);dzt=r(PIn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),PIn.forEach(t),mzt=i(Id),is=n(Id,"P",{});var nP=s(is);czt=r(nP,"The model class to instantiate is selected based on the "),EHe=n(nP,"CODE",{});var ZIn=s(EHe);fzt=r(ZIn,"model_type"),ZIn.forEach(t),hzt=r(nP,` property of the config object (either
passed as an argument or loaded from `),yHe=n(nP,"CODE",{});var VIn=s(yHe);gzt=r(VIn,"pretrained_model_name_or_path"),VIn.forEach(t),uzt=r(nP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CHe=n(nP,"CODE",{});var WIn=s(CHe);pzt=r(WIn,"pretrained_model_name_or_path"),WIn.forEach(t),_zt=r(nP,":"),nP.forEach(t),bzt=i(Id),wHe=n(Id,"UL",{});var JIn=s(wHe);HA=n(JIn,"LI",{});var L8o=s(HA);LHe=n(L8o,"STRONG",{});var UIn=s(LHe);vzt=r(UIn,"bert"),UIn.forEach(t),Mzt=r(L8o," \u2014 "),EMe=n(L8o,"A",{href:!0});var XIn=s(EMe);Tzt=r(XIn,"FlaxBertForNextSentencePrediction"),XIn.forEach(t),Fzt=r(L8o," (BERT model)"),L8o.forEach(t),JIn.forEach(t),Ezt=i(Id),M(KA.$$.fragment,Id),Id.forEach(t),Bd.forEach(t),hGo=i(c),yf=n(c,"H3",{class:!0});var dNo=s(yf);eR=n(dNo,"A",{id:!0,class:!0,href:!0});var DIn=s(eR);AHe=n(DIn,"SPAN",{});var qIn=s(AHe);M(IJ.$$.fragment,qIn),qIn.forEach(t),DIn.forEach(t),yzt=i(dNo),RHe=n(dNo,"SPAN",{});var OIn=s(RHe);Czt=r(OIn,"AutoModelForTokenClassification"),OIn.forEach(t),dNo.forEach(t),gGo=i(c),gr=n(c,"DIV",{class:!0});var Nd=s(gr);M(NJ.$$.fragment,Nd),wzt=i(Nd),Cf=n(Nd,"P",{});var $9e=s(Cf);Lzt=r($9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),yMe=n($9e,"A",{href:!0});var QIn=s(yMe);Azt=r(QIn,"from_pretrained()"),QIn.forEach(t),Rzt=r($9e," class method or the "),CMe=n($9e,"A",{href:!0});var YIn=s(CMe);kzt=r(YIn,"from_config()"),YIn.forEach(t),Szt=r($9e,` class
method.`),$9e.forEach(t),xzt=i(Nd),$J=n(Nd,"P",{});var mNo=s($J);Gzt=r(mNo,"This class cannot be instantiated directly using "),kHe=n(mNo,"CODE",{});var zIn=s(kHe);Bzt=r(zIn,"__init__()"),zIn.forEach(t),Izt=r(mNo," (throws an error)."),mNo.forEach(t),Nzt=i(Nd),fa=n(Nd,"DIV",{class:!0});var sP=s(fa);M(jJ.$$.fragment,sP),$zt=i(sP),SHe=n(sP,"P",{});var HIn=s(SHe);jzt=r(HIn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),HIn.forEach(t),Pzt=i(sP),wf=n(sP,"P",{});var j9e=s(wf);Zzt=r(j9e,`Note:
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model\u2019s configuration. Use `),wMe=n(j9e,"A",{href:!0});var eNn=s(wMe);Jzt=r(eNn,"from_pretrained()"),eNn.forEach(t),Uzt=r(j9e," to load the model weights."),j9e.forEach(t),Xzt=i(sP),M(oR.$$.fragment,sP),sP.forEach(t),Dzt=i(Nd),uo=n(Nd,"DIV",{class:!0});var dn=s(uo);M(PJ.$$.fragment,dn),qzt=i(dn),GHe=n(dn,"P",{});var oNn=s(GHe);Ozt=r(oNn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),oNn.forEach(t),Qzt=i(dn),ds=n(dn,"P",{});var lP=s(ds);Yzt=r(lP,"The model class to instantiate is selected based on the "),BHe=n(lP,"CODE",{});var rNn=s(BHe);zzt=r(rNn,"model_type"),rNn.forEach(t),Hzt=r(lP,` property of the config object (either
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X8o=s(vR);oKe=n(X8o,"STRONG",{});var PNn=s(oKe);IKt=r(PNn,"flaubert"),PNn.forEach(t),NKt=r(X8o," \u2014 "),JMe=n(X8o,"A",{href:!0});var ZNn=s(JMe);$Kt=r(ZNn,"FlaubertForTokenClassification"),ZNn.forEach(t),jKt=r(X8o," (FlauBERT model)"),X8o.forEach(t),PKt=i(H),MR=n(H,"LI",{});var D8o=s(MR);rKe=n(D8o,"STRONG",{});var VNn=s(rKe);ZKt=r(VNn,"fnet"),VNn.forEach(t),VKt=r(D8o," \u2014 "),UMe=n(D8o,"A",{href:!0});var WNn=s(UMe);WKt=r(WNn,"FNetForTokenClassification"),WNn.forEach(t),JKt=r(D8o," (FNet model)"),D8o.forEach(t),UKt=i(H),TR=n(H,"LI",{});var q8o=s(TR);tKe=n(q8o,"STRONG",{});var JNn=s(tKe);XKt=r(JNn,"funnel"),JNn.forEach(t),DKt=r(q8o," \u2014 "),XMe=n(q8o,"A",{href:!0});var UNn=s(XMe);qKt=r(UNn,"FunnelForTokenClassification"),UNn.forEach(t),OKt=r(q8o," (Funnel Transformer model)"),q8o.forEach(t),QKt=i(H),FR=n(H,"LI",{});var O8o=s(FR);aKe=n(O8o,"STRONG",{});var XNn=s(aKe);YKt=r(XNn,"gpt-sw3"),XNn.forEach(t),zKt=r(O8o," \u2014 "),DMe=n(O8o,"A",{href:!0});var DNn=s(DMe);HKt=r(DNn,"GPT2ForTokenClassification"),DNn.forEach(t),KKt=r(O8o," (GPT-Sw3 model)"),O8o.forEach(t),eea=i(H),ER=n(H,"LI",{});var Q8o=s(ER);nKe=n(Q8o,"STRONG",{});var qNn=s(nKe);oea=r(qNn,"gpt2"),qNn.forEach(t),rea=r(Q8o," \u2014 "),qMe=n(Q8o,"A",{href:!0});var ONn=s(qMe);tea=r(ONn,"GPT2ForTokenClassification"),ONn.forEach(t),aea=r(Q8o," (OpenAI GPT-2 model)"),Q8o.forEach(t),nea=i(H),yR=n(H,"LI",{});var Y8o=s(yR);sKe=n(Y8o,"STRONG",{});var QNn=s(sKe);sea=r(QNn,"gpt_bigcode"),QNn.forEach(t),lea=r(Y8o," \u2014 "),OMe=n(Y8o,"A",{href:!0});var YNn=s(OMe);iea=r(YNn,"GPTBigCodeForTokenClassification"),YNn.forEach(t),dea=r(Y8o," (GPTBigCode model)"),Y8o.forEach(t),mea=i(H),CR=n(H,"LI",{});var z8o=s(CR);lKe=n(z8o,"STRONG",{});var zNn=s(lKe);cea=r(zNn,"gpt_neo"),zNn.forEach(t),fea=r(z8o," \u2014 "),QMe=n(z8o,"A",{href:!0});var HNn=s(QMe);hea=r(HNn,"GPTNeoForTokenClassification"),HNn.forEach(t),gea=r(z8o," (GPT Neo model)"),z8o.forEach(t),uea=i(H),wR=n(H,"LI",{});var H8o=s(wR);iKe=n(H8o,"STRONG",{});var KNn=s(iKe);pea=r(KNn,"gpt_neox"),KNn.forEach(t),_ea=r(H8o," \u2014 "),YMe=n(H8o,"A",{href:!0});var e$n=s(YMe);bea=r(e$n,"GPTNeoXForTokenClassification"),e$n.forEach(t),vea=r(H8o," (GPT NeoX model)"),H8o.forEach(t),Mea=i(H),LR=n(H,"LI",{});var K8o=s(LR);dKe=n(K8o,"STRONG",{});var o$n=s(dKe);Tea=r(o$n,"ibert"),o$n.forEach(t),Fea=r(K8o," \u2014 "),zMe=n(K8o,"A",{href:!0});var r$n=s(zMe);Eea=r(r$n,"IBertForTokenClassification"),r$n.forEach(t),yea=r(K8o," (I-BERT model)"),K8o.forEach(t),Cea=i(H),AR=n(H,"LI",{});var e7o=s(AR);mKe=n(e7o,"STRONG",{});var t$n=s(mKe);wea=r(t$n,"layoutlm"),t$n.forEach(t),Lea=r(e7o," \u2014 "),HMe=n(e7o,"A",{href:!0});var a$n=s(HMe);Aea=r(a$n,"LayoutLMForTokenClassification"),a$n.forEach(t),Rea=r(e7o," (LayoutLM model)"),e7o.forEach(t),kea=i(H),RR=n(H,"LI",{});var o7o=s(RR);cKe=n(o7o,"STRONG",{});var n$n=s(cKe);Sea=r(n$n,"layoutlmv2"),n$n.forEach(t),xea=r(o7o," \u2014 "),KMe=n(o7o,"A",{href:!0});var s$n=s(KMe);Gea=r(s$n,"LayoutLMv2ForTokenClassification"),s$n.forEach(t),Bea=r(o7o," (LayoutLMv2 model)"),o7o.forEach(t),Iea=i(H),kR=n(H,"LI",{});var r7o=s(kR);fKe=n(r7o,"STRONG",{});var l$n=s(fKe);Nea=r(l$n,"layoutlmv3"),l$n.forEach(t),$ea=r(r7o," \u2014 "),eTe=n(r7o,"A",{href:!0});var i$n=s(eTe);jea=r(i$n,"LayoutLMv3ForTokenClassification"),i$n.forEach(t),Pea=r(r7o," (LayoutLMv3 model)"),r7o.forEach(t),Zea=i(H),SR=n(H,"LI",{});var t7o=s(SR);hKe=n(t7o,"STRONG",{});var d$n=s(hKe);Vea=r(d$n,"lilt"),d$n.forEach(t),Wea=r(t7o," \u2014 "),oTe=n(t7o,"A",{href:!0});var m$n=s(oTe);Jea=r(m$n,"LiltForTokenClassification"),m$n.forEach(t),Uea=r(t7o," (LiLT model)"),t7o.forEach(t),Xea=i(H),xR=n(H,"LI",{});var a7o=s(xR);gKe=n(a7o,"STRONG",{});var c$n=s(gKe);Dea=r(c$n,"longformer"),c$n.forEach(t),qea=r(a7o," \u2014 "),rTe=n(a7o,"A",{href:!0});var f$n=s(rTe);Oea=r(f$n,"LongformerForTokenClassification"),f$n.forEach(t),Qea=r(a7o," (Longformer model)"),a7o.forEach(t),Yea=i(H),GR=n(H,"LI",{});var n7o=s(GR);uKe=n(n7o,"STRONG",{});var h$n=s(uKe);zea=r(h$n,"luke"),h$n.forEach(t),Hea=r(n7o," \u2014 "),tTe=n(n7o,"A",{href:!0});var g$n=s(tTe);Kea=r(g$n,"LukeForTokenClassification"),g$n.forEach(t),eoa=r(n7o," (LUKE model)"),n7o.forEach(t),ooa=i(H),BR=n(H,"LI",{});var s7o=s(BR);pKe=n(s7o,"STRONG",{});var u$n=s(pKe);roa=r(u$n,"markuplm"),u$n.forEach(t),toa=r(s7o," \u2014 "),aTe=n(s7o,"A",{href:!0});var p$n=s(aTe);aoa=r(p$n,"MarkupLMForTokenClassification"),p$n.forEach(t),noa=r(s7o," (MarkupLM model)"),s7o.forEach(t),soa=i(H),IR=n(H,"LI",{});var l7o=s(IR);_Ke=n(l7o,"STRONG",{});var _$n=s(_Ke);loa=r(_$n,"mega"),_$n.forEach(t),ioa=r(l7o," \u2014 "),nTe=n(l7o,"A",{href:!0});var b$n=s(nTe);doa=r(b$n,"MegaForTokenClassification"),b$n.forEach(t),moa=r(l7o," (MEGA model)"),l7o.forEach(t),coa=i(H),NR=n(H,"LI",{});var i7o=s(NR);bKe=n(i7o,"STRONG",{});var v$n=s(bKe);foa=r(v$n,"megatron-bert"),v$n.forEach(t),hoa=r(i7o," \u2014 "),sTe=n(i7o,"A",{href:!0});var M$n=s(sTe);goa=r(M$n,"MegatronBertForTokenClassification"),M$n.forEach(t),uoa=r(i7o," (Megatron-BERT model)"),i7o.forEach(t),poa=i(H),$R=n(H,"LI",{});var d7o=s($R);vKe=n(d7o,"STRONG",{});var T$n=s(vKe);_oa=r(T$n,"mobilebert"),T$n.forEach(t),boa=r(d7o," \u2014 "),lTe=n(d7o,"A",{href:!0});var F$n=s(lTe);voa=r(F$n,"MobileBertForTokenClassification"),F$n.forEach(t),Moa=r(d7o," (MobileBERT model)"),d7o.forEach(t),Toa=i(H),jR=n(H,"LI",{});var m7o=s(jR);MKe=n(m7o,"STRONG",{});var E$n=s(MKe);Foa=r(E$n,"mpnet"),E$n.forEach(t),Eoa=r(m7o," \u2014 "),iTe=n(m7o,"A",{href:!0});var y$n=s(iTe);yoa=r(y$n,"MPNetForTokenClassification"),y$n.forEach(t),Coa=r(m7o," (MPNet model)"),m7o.forEach(t),woa=i(H),PR=n(H,"LI",{});var c7o=s(PR);TKe=n(c7o,"STRONG",{});var C$n=s(TKe);Loa=r(C$n,"mpt"),C$n.forEach(t),Aoa=r(c7o," \u2014 "),dTe=n(c7o,"A",{href:!0});var w$n=s(dTe);Roa=r(w$n,"MptForTokenClassification"),w$n.forEach(t),koa=r(c7o," (MPT model)"),c7o.forEach(t),Soa=i(H),ZR=n(H,"LI",{});var 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deactivated). To train the model, you should first set it back in training mode with `),PKe=n(A7o,"CODE",{});var sjn=s(PKe);lta=r(sjn,"model.train()"),sjn.forEach(t),A7o.forEach(t),ita=i(dn),M(tk.$$.fragment,dn),dn.forEach(t),Nd.forEach(t),uGo=i(c),Lf=n(c,"H3",{class:!0});var cNo=s(Lf);ak=n(cNo,"A",{id:!0,class:!0,href:!0});var ljn=s(ak);ZKe=n(ljn,"SPAN",{});var ijn=s(ZKe);M(ZJ.$$.fragment,ijn),ijn.forEach(t),ljn.forEach(t),dta=i(cNo),VKe=n(cNo,"SPAN",{});var djn=s(VKe);mta=r(djn,"TFAutoModelForTokenClassification"),djn.forEach(t),cNo.forEach(t),pGo=i(c),ur=n(c,"DIV",{class:!0});var $d=s(ur);M(VJ.$$.fragment,$d),cta=i($d),Af=n($d,"P",{});var P9e=s(Af);fta=r(P9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),wTe=n(P9e,"A",{href:!0});var mjn=s(wTe);hta=r(mjn,"from_pretrained()"),mjn.forEach(t),gta=r(P9e," class method or the "),LTe=n(P9e,"A",{href:!0});var cjn=s(LTe);uta=r(cjn,"from_config()"),cjn.forEach(t),pta=r(P9e,` class
method.`),P9e.forEach(t),_ta=i($d),WJ=n($d,"P",{});var fNo=s(WJ);bta=r(fNo,"This class cannot be instantiated directly using "),WKe=n(fNo,"CODE",{});var fjn=s(WKe);vta=r(fjn,"__init__()"),fjn.forEach(t),Mta=r(fNo," (throws an error)."),fNo.forEach(t),Tta=i($d),ha=n($d,"DIV",{class:!0});var iP=s(ha);M(JJ.$$.fragment,iP),Fta=i(iP),JKe=n(iP,"P",{});var hjn=s(JKe);Eta=r(hjn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),hjn.forEach(t),yta=i(iP),Rf=n(iP,"P",{});var Z9e=s(Rf);Cta=r(Z9e,`Note:
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model\u2019s configuration. Use `),ATe=n(Z9e,"A",{href:!0});var ujn=s(ATe);Ata=r(ujn,"from_pretrained()"),ujn.forEach(t),Rta=r(Z9e," to load the model weights."),Z9e.forEach(t),kta=i(iP),M(nk.$$.fragment,iP),iP.forEach(t),Sta=i($d),vt=n($d,"DIV",{class:!0});var jd=s(vt);M(UJ.$$.fragment,jd),xta=i(jd),XKe=n(jd,"P",{});var pjn=s(XKe);Gta=r(pjn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),pjn.forEach(t),Bta=i(jd),ms=n(jd,"P",{});var dP=s(ms);Ita=r(dP,"The model class to instantiate is selected based on the "),DKe=n(dP,"CODE",{});var _jn=s(DKe);Nta=r(_jn,"model_type"),_jn.forEach(t),$ta=r(dP,` property of the config object (either
passed as an argument or loaded from `),qKe=n(dP,"CODE",{});var bjn=s(qKe);jta=r(bjn,"pretrained_model_name_or_path"),bjn.forEach(t),Pta=r(dP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OKe=n(dP,"CODE",{});var vjn=s(OKe);Zta=r(vjn,"pretrained_model_name_or_path"),vjn.forEach(t),Vta=r(dP,":"),dP.forEach(t),Wta=i(jd),ue=n(jd,"UL",{});var ve=s(ue);sk=n(ve,"LI",{});var R7o=s(sk);QKe=n(R7o,"STRONG",{});var Mjn=s(QKe);Jta=r(Mjn,"albert"),Mjn.forEach(t),Uta=r(R7o," \u2014 "),RTe=n(R7o,"A",{href:!0});var Tjn=s(RTe);Xta=r(Tjn,"TFAlbertForTokenClassification"),Tjn.forEach(t),Dta=r(R7o," (ALBERT model)"),R7o.forEach(t),qta=i(ve),lk=n(ve,"LI",{});var k7o=s(lk);YKe=n(k7o,"STRONG",{});var Fjn=s(YKe);Ota=r(Fjn,"bert"),Fjn.forEach(t),Qta=r(k7o," \u2014 "),kTe=n(k7o,"A",{href:!0});var Ejn=s(kTe);Yta=r(Ejn,"TFBertForTokenClassification"),Ejn.forEach(t),zta=r(k7o," (BERT model)"),k7o.forEach(t),Hta=i(ve),ik=n(ve,"LI",{});var S7o=s(ik);zKe=n(S7o,"STRONG",{});var yjn=s(zKe);Kta=r(yjn,"camembert"),yjn.forEach(t),eaa=r(S7o," \u2014 "),STe=n(S7o,"A",{href:!0});var Cjn=s(STe);oaa=r(Cjn,"TFCamembertForTokenClassification"),Cjn.forEach(t),raa=r(S7o," 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xjn=s(oeo);vaa=r(xjn,"distilbert"),xjn.forEach(t),Maa=r(I7o," \u2014 "),ITe=n(I7o,"A",{href:!0});var Gjn=s(ITe);Taa=r(Gjn,"TFDistilBertForTokenClassification"),Gjn.forEach(t),Faa=r(I7o," (DistilBERT model)"),I7o.forEach(t),Eaa=i(ve),hk=n(ve,"LI",{});var N7o=s(hk);reo=n(N7o,"STRONG",{});var Bjn=s(reo);yaa=r(Bjn,"electra"),Bjn.forEach(t),Caa=r(N7o," \u2014 "),NTe=n(N7o,"A",{href:!0});var Ijn=s(NTe);waa=r(Ijn,"TFElectraForTokenClassification"),Ijn.forEach(t),Laa=r(N7o," (ELECTRA model)"),N7o.forEach(t),Aaa=i(ve),gk=n(ve,"LI",{});var $7o=s(gk);teo=n($7o,"STRONG",{});var Njn=s(teo);Raa=r(Njn,"esm"),Njn.forEach(t),kaa=r($7o," \u2014 "),$Te=n($7o,"A",{href:!0});var $jn=s($Te);Saa=r($jn,"TFEsmForTokenClassification"),$jn.forEach(t),xaa=r($7o," (ESM model)"),$7o.forEach(t),Gaa=i(ve),uk=n(ve,"LI",{});var j7o=s(uk);aeo=n(j7o,"STRONG",{});var jjn=s(aeo);Baa=r(jjn,"flaubert"),jjn.forEach(t),Iaa=r(j7o," \u2014 "),jTe=n(j7o,"A",{href:!0});var Pjn=s(jTe);Naa=r(Pjn,"TFFlaubertForTokenClassification"),Pjn.forEach(t),$aa=r(j7o," (FlauBERT model)"),j7o.forEach(t),jaa=i(ve),pk=n(ve,"LI",{});var P7o=s(pk);neo=n(P7o,"STRONG",{});var Zjn=s(neo);Paa=r(Zjn,"funnel"),Zjn.forEach(t),Zaa=r(P7o," \u2014 "),PTe=n(P7o,"A",{href:!0});var Vjn=s(PTe);Vaa=r(Vjn,"TFFunnelForTokenClassification"),Vjn.forEach(t),Waa=r(P7o," (Funnel Transformer model)"),P7o.forEach(t),Jaa=i(ve),_k=n(ve,"LI",{});var Z7o=s(_k);seo=n(Z7o,"STRONG",{});var Wjn=s(seo);Uaa=r(Wjn,"layoutlm"),Wjn.forEach(t),Xaa=r(Z7o," \u2014 "),ZTe=n(Z7o,"A",{href:!0});var Jjn=s(ZTe);Daa=r(Jjn,"TFLayoutLMForTokenClassification"),Jjn.forEach(t),qaa=r(Z7o," (LayoutLM model)"),Z7o.forEach(t),Oaa=i(ve),bk=n(ve,"LI",{});var V7o=s(bk);leo=n(V7o,"STRONG",{});var Ujn=s(leo);Qaa=r(Ujn,"layoutlmv3"),Ujn.forEach(t),Yaa=r(V7o," \u2014 "),VTe=n(V7o,"A",{href:!0});var Xjn=s(VTe);zaa=r(Xjn,"TFLayoutLMv3ForTokenClassification"),Xjn.forEach(t),Haa=r(V7o," (LayoutLMv3 model)"),V7o.forEach(t),Kaa=i(ve),vk=n(ve,"LI",{});var W7o=s(vk);ieo=n(W7o,"STRONG",{});var Djn=s(ieo);ena=r(Djn,"longformer"),Djn.forEach(t),ona=r(W7o," \u2014 "),WTe=n(W7o,"A",{href:!0});var qjn=s(WTe);rna=r(qjn,"TFLongformerForTokenClassification"),qjn.forEach(t),tna=r(W7o," (Longformer model)"),W7o.forEach(t),ana=i(ve),Mk=n(ve,"LI",{});var J7o=s(Mk);deo=n(J7o,"STRONG",{});var Ojn=s(deo);nna=r(Ojn,"mobilebert"),Ojn.forEach(t),sna=r(J7o," \u2014 "),JTe=n(J7o,"A",{href:!0});var Qjn=s(JTe);lna=r(Qjn,"TFMobileBertForTokenClassification"),Qjn.forEach(t),ina=r(J7o," (MobileBERT model)"),J7o.forEach(t),dna=i(ve),Tk=n(ve,"LI",{});var U7o=s(Tk);meo=n(U7o,"STRONG",{});var Yjn=s(meo);mna=r(Yjn,"mpnet"),Yjn.forEach(t),cna=r(U7o," \u2014 "),UTe=n(U7o,"A",{href:!0});var zjn=s(UTe);fna=r(zjn,"TFMPNetForTokenClassification"),zjn.forEach(t),hna=r(U7o," (MPNet model)"),U7o.forEach(t),gna=i(ve),Fk=n(ve,"LI",{});var X7o=s(Fk);ceo=n(X7o,"STRONG",{});var Hjn=s(ceo);una=r(Hjn,"rembert"),Hjn.forEach(t),pna=r(X7o," \u2014 "),XTe=n(X7o,"A",{href:!0});var Kjn=s(XTe);_na=r(Kjn,"TFRemBertForTokenClassification"),Kjn.forEach(t),bna=r(X7o," (RemBERT model)"),X7o.forEach(t),vna=i(ve),Ek=n(ve,"LI",{});var D7o=s(Ek);feo=n(D7o,"STRONG",{});var ePn=s(feo);Mna=r(ePn,"roberta"),ePn.forEach(t),Tna=r(D7o," \u2014 "),DTe=n(D7o,"A",{href:!0});var oPn=s(DTe);Fna=r(oPn,"TFRobertaForTokenClassification"),oPn.forEach(t),Ena=r(D7o," (RoBERTa model)"),D7o.forEach(t),yna=i(ve),yk=n(ve,"LI",{});var q7o=s(yk);heo=n(q7o,"STRONG",{});var rPn=s(heo);Cna=r(rPn,"roberta-prelayernorm"),rPn.forEach(t),wna=r(q7o," \u2014 "),qTe=n(q7o,"A",{href:!0});var tPn=s(qTe);Lna=r(tPn,"TFRobertaPreLayerNormForTokenClassification"),tPn.forEach(t),Ana=r(q7o," (RoBERTa-PreLayerNorm model)"),q7o.forEach(t),Rna=i(ve),Ck=n(ve,"LI",{});var O7o=s(Ck);geo=n(O7o,"STRONG",{});var aPn=s(geo);kna=r(aPn,"roformer"),aPn.forEach(t),Sna=r(O7o," \u2014 "),OTe=n(O7o,"A",{href:!0});var nPn=s(OTe);xna=r(nPn,"TFRoFormerForTokenClassification"),nPn.forEach(t),Gna=r(O7o," (RoFormer model)"),O7o.forEach(t),Bna=i(ve),wk=n(ve,"LI",{});var Q7o=s(wk);ueo=n(Q7o,"STRONG",{});var sPn=s(ueo);Ina=r(sPn,"xlm"),sPn.forEach(t),Nna=r(Q7o," \u2014 "),QTe=n(Q7o,"A",{href:!0});var lPn=s(QTe);$na=r(lPn,"TFXLMForTokenClassification"),lPn.forEach(t),jna=r(Q7o," (XLM model)"),Q7o.forEach(t),Pna=i(ve),Lk=n(ve,"LI",{});var Y7o=s(Lk);peo=n(Y7o,"STRONG",{});var iPn=s(peo);Zna=r(iPn,"xlm-roberta"),iPn.forEach(t),Vna=r(Y7o," \u2014 "),YTe=n(Y7o,"A",{href:!0});var dPn=s(YTe);Wna=r(dPn,"TFXLMRobertaForTokenClassification"),dPn.forEach(t),Jna=r(Y7o," (XLM-RoBERTa model)"),Y7o.forEach(t),Una=i(ve),Ak=n(ve,"LI",{});var z7o=s(Ak);_eo=n(z7o,"STRONG",{});var mPn=s(_eo);Xna=r(mPn,"xlnet"),mPn.forEach(t),Dna=r(z7o," \u2014 "),zTe=n(z7o,"A",{href:!0});var cPn=s(zTe);qna=r(cPn,"TFXLNetForTokenClassification"),cPn.forEach(t),Ona=r(z7o," (XLNet 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with the `),HTe=n(V9e,"A",{href:!0});var uPn=s(HTe);esa=r(uPn,"from_pretrained()"),uPn.forEach(t),osa=r(V9e," class method or the "),KTe=n(V9e,"A",{href:!0});var pPn=s(KTe);rsa=r(pPn,"from_config()"),pPn.forEach(t),tsa=r(V9e,` class
method.`),V9e.forEach(t),asa=i(Pd),qJ=n(Pd,"P",{});var gNo=s(qJ);nsa=r(gNo,"This class cannot be instantiated directly using "),Meo=n(gNo,"CODE",{});var _Pn=s(Meo);ssa=r(_Pn,"__init__()"),_Pn.forEach(t),lsa=r(gNo," (throws an error)."),gNo.forEach(t),isa=i(Pd),ga=n(Pd,"DIV",{class:!0});var mP=s(ga);M(OJ.$$.fragment,mP),dsa=i(mP),Teo=n(mP,"P",{});var bPn=s(Teo);msa=r(bPn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),bPn.forEach(t),csa=i(mP),xf=n(mP,"P",{});var W9e=s(xf);fsa=r(W9e,`Note:
Loading a model from its configuration file does `),Feo=n(W9e,"STRONG",{});var vPn=s(Feo);hsa=r(vPn,"not"),vPn.forEach(t),gsa=r(W9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),eFe=n(W9e,"A",{href:!0});var MPn=s(eFe);usa=r(MPn,"from_pretrained()"),MPn.forEach(t),psa=r(W9e," to load the model weights."),W9e.forEach(t),_sa=i(mP),M(Sk.$$.fragment,mP),mP.forEach(t),bsa=i(Pd),Mt=n(Pd,"DIV",{class:!0});var Zd=s(Mt);M(QJ.$$.fragment,Zd),vsa=i(Zd),Eeo=n(Zd,"P",{});var TPn=s(Eeo);Msa=r(TPn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),TPn.forEach(t),Tsa=i(Zd),cs=n(Zd,"P",{});var cP=s(cs);Fsa=r(cP,"The model class to instantiate is selected based on the "),yeo=n(cP,"CODE",{});var FPn=s(yeo);Esa=r(FPn,"model_type"),FPn.forEach(t),ysa=r(cP,` property of the config object (either
passed as an argument or loaded from `),Ceo=n(cP,"CODE",{});var EPn=s(Ceo);Csa=r(EPn,"pretrained_model_name_or_path"),EPn.forEach(t),wsa=r(cP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),weo=n(cP,"CODE",{});var yPn=s(weo);Lsa=r(yPn,"pretrained_model_name_or_path"),yPn.forEach(t),Asa=r(cP,":"),cP.forEach(t),Rsa=i(Zd),Xe=n(Zd,"UL",{});var Ke=s(Xe);xk=n(Ke,"LI",{});var H7o=s(xk);Leo=n(H7o,"STRONG",{});var CPn=s(Leo);ksa=r(CPn,"albert"),CPn.forEach(t),Ssa=r(H7o," \u2014 "),oFe=n(H7o,"A",{href:!0});var wPn=s(oFe);xsa=r(wPn,"FlaxAlbertForTokenClassification"),wPn.forEach(t),Gsa=r(H7o," (ALBERT model)"),H7o.forEach(t),Bsa=i(Ke),Gk=n(Ke,"LI",{});var K7o=s(Gk);Aeo=n(K7o,"STRONG",{});var LPn=s(Aeo);Isa=r(LPn,"bert"),LPn.forEach(t),Nsa=r(K7o," \u2014 "),rFe=n(K7o,"A",{href:!0});var APn=s(rFe);$sa=r(APn,"FlaxBertForTokenClassification"),APn.forEach(t),jsa=r(K7o," (BERT model)"),K7o.forEach(t),Psa=i(Ke),Bk=n(Ke,"LI",{});var eLo=s(Bk);Reo=n(eLo,"STRONG",{});var RPn=s(Reo);Zsa=r(RPn,"big_bird"),RPn.forEach(t),Vsa=r(eLo," \u2014 "),tFe=n(eLo,"A",{href:!0});var kPn=s(tFe);Wsa=r(kPn,"FlaxBigBirdForTokenClassification"),kPn.forEach(t),Jsa=r(eLo," (BigBird model)"),eLo.forEach(t),Usa=i(Ke),Ik=n(Ke,"LI",{});var oLo=s(Ik);keo=n(oLo,"STRONG",{});var SPn=s(keo);Xsa=r(SPn,"distilbert"),SPn.forEach(t),Dsa=r(oLo," \u2014 "),aFe=n(oLo,"A",{href:!0});var xPn=s(aFe);qsa=r(xPn,"FlaxDistilBertForTokenClassification"),xPn.forEach(t),Osa=r(oLo," (DistilBERT model)"),oLo.forEach(t),Qsa=i(Ke),Nk=n(Ke,"LI",{});var rLo=s(Nk);Seo=n(rLo,"STRONG",{});var GPn=s(Seo);Ysa=r(GPn,"electra"),GPn.forEach(t),zsa=r(rLo," \u2014 "),nFe=n(rLo,"A",{href:!0});var BPn=s(nFe);Hsa=r(BPn,"FlaxElectraForTokenClassification"),BPn.forEach(t),Ksa=r(rLo," (ELECTRA model)"),rLo.forEach(t),ela=i(Ke),$k=n(Ke,"LI",{});var tLo=s($k);xeo=n(tLo,"STRONG",{});var IPn=s(xeo);ola=r(IPn,"roberta"),IPn.forEach(t),rla=r(tLo," \u2014 "),sFe=n(tLo,"A",{href:!0});var NPn=s(sFe);tla=r(NPn,"FlaxRobertaForTokenClassification"),NPn.forEach(t),ala=r(tLo," (RoBERTa model)"),tLo.forEach(t),nla=i(Ke),jk=n(Ke,"LI",{});var aLo=s(jk);Geo=n(aLo,"STRONG",{});var $Pn=s(Geo);sla=r($Pn,"roberta-prelayernorm"),$Pn.forEach(t),lla=r(aLo," \u2014 "),lFe=n(aLo,"A",{href:!0});var jPn=s(lFe);ila=r(jPn,"FlaxRobertaPreLayerNormForTokenClassification"),jPn.forEach(t),dla=r(aLo," (RoBERTa-PreLayerNorm model)"),aLo.forEach(t),mla=i(Ke),Pk=n(Ke,"LI",{});var nLo=s(Pk);Beo=n(nLo,"STRONG",{});var PPn=s(Beo);cla=r(PPn,"roformer"),PPn.forEach(t),fla=r(nLo," \u2014 "),iFe=n(nLo,"A",{href:!0});var ZPn=s(iFe);hla=r(ZPn,"FlaxRoFormerForTokenClassification"),ZPn.forEach(t),gla=r(nLo," (RoFormer model)"),nLo.forEach(t),ula=i(Ke),Zk=n(Ke,"LI",{});var sLo=s(Zk);Ieo=n(sLo,"STRONG",{});var VPn=s(Ieo);pla=r(VPn,"xlm-roberta"),VPn.forEach(t),_la=r(sLo," \u2014 "),dFe=n(sLo,"A",{href:!0});var WPn=s(dFe);bla=r(WPn,"FlaxXLMRobertaForTokenClassification"),WPn.forEach(t),vla=r(sLo," (XLM-RoBERTa model)"),sLo.forEach(t),Ke.forEach(t),Mla=i(Zd),M(Vk.$$.fragment,Zd),Zd.forEach(t),Pd.forEach(t),vGo=i(c),Gf=n(c,"H3",{class:!0});var uNo=s(Gf);Wk=n(uNo,"A",{id:!0,class:!0,href:!0});var JPn=s(Wk);Neo=n(JPn,"SPAN",{});var UPn=s(Neo);M(YJ.$$.fragment,UPn),UPn.forEach(t),JPn.forEach(t),Tla=i(uNo),$eo=n(uNo,"SPAN",{});var XPn=s($eo);Fla=r(XPn,"AutoModelForQuestionAnswering"),XPn.forEach(t),uNo.forEach(t),MGo=i(c),_r=n(c,"DIV",{class:!0});var Vd=s(_r);M(zJ.$$.fragment,Vd),Ela=i(Vd),Bf=n(Vd,"P",{});var J9e=s(Bf);yla=r(J9e,`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
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method.`),J9e.forEach(t),Rla=i(Vd),HJ=n(Vd,"P",{});var pNo=s(HJ);kla=r(pNo,"This class cannot be instantiated directly using "),jeo=n(pNo,"CODE",{});var OPn=s(jeo);Sla=r(OPn,"__init__()"),OPn.forEach(t),xla=r(pNo," (throws an error)."),pNo.forEach(t),Gla=i(Vd),ua=n(Vd,"DIV",{class:!0});var fP=s(ua);M(KJ.$$.fragment,fP),Bla=i(fP),Peo=n(fP,"P",{});var QPn=s(Peo);Ila=r(QPn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),QPn.forEach(t),Nla=i(fP),If=n(fP,"P",{});var U9e=s(If);$la=r(U9e,`Note:
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model\u2019s configuration. Use `),fFe=n(U9e,"A",{href:!0});var zPn=s(fFe);Zla=r(zPn,"from_pretrained()"),zPn.forEach(t),Vla=r(U9e," to load the model weights."),U9e.forEach(t),Wla=i(fP),M(Jk.$$.fragment,fP),fP.forEach(t),Jla=i(Vd),po=n(Vd,"DIV",{class:!0});var mn=s(po);M(eU.$$.fragment,mn),Ula=i(mn),Veo=n(mn,"P",{});var HPn=s(Veo);Xla=r(HPn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),HPn.forEach(t),Dla=i(mn),fs=n(mn,"P",{});var hP=s(fs);qla=r(hP,"The model class to instantiate is selected based on the "),Weo=n(hP,"CODE",{});var KPn=s(Weo);Ola=r(KPn,"model_type"),KPn.forEach(t),Qla=r(hP,` property of the config object (either
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falling back to using pattern matching on `),Ueo=n(hP,"CODE",{});var oZn=s(Ueo);Hla=r(oZn,"pretrained_model_name_or_path"),oZn.forEach(t),Kla=r(hP,":"),hP.forEach(t),eia=i(mn),U=n(mn,"UL",{});var q=s(U);Uk=n(q,"LI",{});var lLo=s(Uk);Xeo=n(lLo,"STRONG",{});var rZn=s(Xeo);oia=r(rZn,"albert"),rZn.forEach(t),ria=r(lLo," \u2014 "),hFe=n(lLo,"A",{href:!0});var tZn=s(hFe);tia=r(tZn,"AlbertForQuestionAnswering"),tZn.forEach(t),aia=r(lLo," (ALBERT model)"),lLo.forEach(t),nia=i(q),Xk=n(q,"LI",{});var iLo=s(Xk);Deo=n(iLo,"STRONG",{});var aZn=s(Deo);sia=r(aZn,"bart"),aZn.forEach(t),lia=r(iLo," \u2014 "),gFe=n(iLo,"A",{href:!0});var nZn=s(gFe);iia=r(nZn,"BartForQuestionAnswering"),nZn.forEach(t),dia=r(iLo," (BART model)"),iLo.forEach(t),mia=i(q),Dk=n(q,"LI",{});var dLo=s(Dk);qeo=n(dLo,"STRONG",{});var sZn=s(qeo);cia=r(sZn,"bert"),sZn.forEach(t),fia=r(dLo," \u2014 "),uFe=n(dLo,"A",{href:!0});var lZn=s(uFe);hia=r(lZn,"BertForQuestionAnswering"),lZn.forEach(t),gia=r(dLo," (BERT 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dAo=s(DS);Ooo=n(dAo,"STRONG",{});var sWn=s(Ooo);cha=r(sWn,"xlm"),sWn.forEach(t),fha=r(dAo," \u2014 "),gEe=n(dAo,"A",{href:!0});var lWn=s(gEe);hha=r(lWn,"XLMForQuestionAnsweringSimple"),lWn.forEach(t),gha=r(dAo," (XLM model)"),dAo.forEach(t),uha=i(q),qS=n(q,"LI",{});var mAo=s(qS);Qoo=n(mAo,"STRONG",{});var iWn=s(Qoo);pha=r(iWn,"xlm-roberta"),iWn.forEach(t),_ha=r(mAo," \u2014 "),uEe=n(mAo,"A",{href:!0});var dWn=s(uEe);bha=r(dWn,"XLMRobertaForQuestionAnswering"),dWn.forEach(t),vha=r(mAo," (XLM-RoBERTa model)"),mAo.forEach(t),Mha=i(q),OS=n(q,"LI",{});var cAo=s(OS);Yoo=n(cAo,"STRONG",{});var mWn=s(Yoo);Tha=r(mWn,"xlm-roberta-xl"),mWn.forEach(t),Fha=r(cAo," \u2014 "),pEe=n(cAo,"A",{href:!0});var cWn=s(pEe);Eha=r(cWn,"XLMRobertaXLForQuestionAnswering"),cWn.forEach(t),yha=r(cAo," (XLM-RoBERTa-XL model)"),cAo.forEach(t),Cha=i(q),QS=n(q,"LI",{});var fAo=s(QS);zoo=n(fAo,"STRONG",{});var fWn=s(zoo);wha=r(fWn,"xlnet"),fWn.forEach(t),Lha=r(fAo," \u2014 "),_Ee=n(fAo,"A",{href:!0});var hWn=s(_Ee);Aha=r(hWn,"XLNetForQuestionAnsweringSimple"),hWn.forEach(t),Rha=r(fAo," (XLNet model)"),fAo.forEach(t),kha=i(q),YS=n(q,"LI",{});var hAo=s(YS);Hoo=n(hAo,"STRONG",{});var gWn=s(Hoo);Sha=r(gWn,"xmod"),gWn.forEach(t),xha=r(hAo," \u2014 "),bEe=n(hAo,"A",{href:!0});var uWn=s(bEe);Gha=r(uWn,"XmodForQuestionAnswering"),uWn.forEach(t),Bha=r(hAo," (X-MOD model)"),hAo.forEach(t),Iha=i(q),zS=n(q,"LI",{});var gAo=s(zS);Koo=n(gAo,"STRONG",{});var pWn=s(Koo);Nha=r(pWn,"yoso"),pWn.forEach(t),$ha=r(gAo," \u2014 "),vEe=n(gAo,"A",{href:!0});var _Wn=s(vEe);jha=r(_Wn,"YosoForQuestionAnswering"),_Wn.forEach(t),Pha=r(gAo," (YOSO model)"),gAo.forEach(t),q.forEach(t),Zha=i(mn),HS=n(mn,"P",{});var uAo=s(HS);Vha=r(uAo,"The model is set in evaluation mode by default using "),ero=n(uAo,"CODE",{});var bWn=s(ero);Wha=r(bWn,"model.eval()"),bWn.forEach(t),Jha=r(uAo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oro=n(uAo,"CODE",{});var vWn=s(oro);Uha=r(vWn,"model.train()"),vWn.forEach(t),uAo.forEach(t),Xha=i(mn),M(KS.$$.fragment,mn),mn.forEach(t),Vd.forEach(t),TGo=i(c),Nf=n(c,"H3",{class:!0});var _No=s(Nf);ex=n(_No,"A",{id:!0,class:!0,href:!0});var MWn=s(ex);rro=n(MWn,"SPAN",{});var TWn=s(rro);M(oU.$$.fragment,TWn),TWn.forEach(t),MWn.forEach(t),Dha=i(_No),tro=n(_No,"SPAN",{});var FWn=s(tro);qha=r(FWn,"TFAutoModelForQuestionAnswering"),FWn.forEach(t),_No.forEach(t),FGo=i(c),br=n(c,"DIV",{class:!0});var Wd=s(br);M(rU.$$.fragment,Wd),Oha=i(Wd),$f=n(Wd,"P",{});var X9e=s($f);Qha=r(X9e,`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 `),MEe=n(X9e,"A",{href:!0});var EWn=s(MEe);Yha=r(EWn,"from_pretrained()"),EWn.forEach(t),zha=r(X9e," class method or the "),TEe=n(X9e,"A",{href:!0});var yWn=s(TEe);Hha=r(yWn,"from_config()"),yWn.forEach(t),Kha=r(X9e,` class
method.`),X9e.forEach(t),ega=i(Wd),tU=n(Wd,"P",{});var bNo=s(tU);oga=r(bNo,"This class cannot be instantiated directly using "),aro=n(bNo,"CODE",{});var CWn=s(aro);rga=r(CWn,"__init__()"),CWn.forEach(t),tga=r(bNo," (throws an error)."),bNo.forEach(t),aga=i(Wd),pa=n(Wd,"DIV",{class:!0});var gP=s(pa);M(aU.$$.fragment,gP),nga=i(gP),nro=n(gP,"P",{});var wWn=s(nro);sga=r(wWn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),wWn.forEach(t),lga=i(gP),jf=n(gP,"P",{});var D9e=s(jf);iga=r(D9e,`Note:
Loading a model from its configuration file does `),sro=n(D9e,"STRONG",{});var LWn=s(sro);dga=r(LWn,"not"),LWn.forEach(t),mga=r(D9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),FEe=n(D9e,"A",{href:!0});var AWn=s(FEe);cga=r(AWn,"from_pretrained()"),AWn.forEach(t),fga=r(D9e," to load the model weights."),D9e.forEach(t),hga=i(gP),M(ox.$$.fragment,gP),gP.forEach(t),gga=i(Wd),Tt=n(Wd,"DIV",{class:!0});var Jd=s(Tt);M(nU.$$.fragment,Jd),uga=i(Jd),lro=n(Jd,"P",{});var RWn=s(lro);pga=r(RWn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),RWn.forEach(t),_ga=i(Jd),hs=n(Jd,"P",{});var uP=s(hs);bga=r(uP,"The model class to instantiate is selected based on the "),iro=n(uP,"CODE",{});var kWn=s(iro);vga=r(kWn,"model_type"),kWn.forEach(t),Mga=r(uP,` property of the config object (either
passed as an argument or loaded from `),dro=n(uP,"CODE",{});var SWn=s(dro);Tga=r(SWn,"pretrained_model_name_or_path"),SWn.forEach(t),Fga=r(uP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mro=n(uP,"CODE",{});var xWn=s(mro);Ega=r(xWn,"pretrained_model_name_or_path"),xWn.forEach(t),yga=r(uP,":"),uP.forEach(t),Cga=i(Jd),_e=n(Jd,"UL",{});var Te=s(_e);rx=n(Te,"LI",{});var pAo=s(rx);cro=n(pAo,"STRONG",{});var GWn=s(cro);wga=r(GWn,"albert"),GWn.forEach(t),Lga=r(pAo," \u2014 "),EEe=n(pAo,"A",{href:!0});var BWn=s(EEe);Aga=r(BWn,"TFAlbertForQuestionAnswering"),BWn.forEach(t),Rga=r(pAo," (ALBERT model)"),pAo.forEach(t),kga=i(Te),tx=n(Te,"LI",{});var _Ao=s(tx);fro=n(_Ao,"STRONG",{});var IWn=s(fro);Sga=r(IWn,"bert"),IWn.forEach(t),xga=r(_Ao," \u2014 "),yEe=n(_Ao,"A",{href:!0});var NWn=s(yEe);Gga=r(NWn,"TFBertForQuestionAnswering"),NWn.forEach(t),Bga=r(_Ao," (BERT model)"),_Ao.forEach(t),Iga=i(Te),ax=n(Te,"LI",{});var bAo=s(ax);hro=n(bAo,"STRONG",{});var $Wn=s(hro);Nga=r($Wn,"camembert"),$Wn.forEach(t),$ga=r(bAo," \u2014 "),CEe=n(bAo,"A",{href:!0});var jWn=s(CEe);jga=r(jWn,"TFCamembertForQuestionAnswering"),jWn.forEach(t),Pga=r(bAo," (CamemBERT model)"),bAo.forEach(t),Zga=i(Te),nx=n(Te,"LI",{});var vAo=s(nx);gro=n(vAo,"STRONG",{});var PWn=s(gro);Vga=r(PWn,"convbert"),PWn.forEach(t),Wga=r(vAo," \u2014 "),wEe=n(vAo,"A",{href:!0});var ZWn=s(wEe);Jga=r(ZWn,"TFConvBertForQuestionAnswering"),ZWn.forEach(t),Uga=r(vAo," (ConvBERT model)"),vAo.forEach(t),Xga=i(Te),sx=n(Te,"LI",{});var MAo=s(sx);uro=n(MAo,"STRONG",{});var VWn=s(uro);Dga=r(VWn,"deberta"),VWn.forEach(t),qga=r(MAo," \u2014 "),LEe=n(MAo,"A",{href:!0});var WWn=s(LEe);Oga=r(WWn,"TFDebertaForQuestionAnswering"),WWn.forEach(t),Qga=r(MAo," (DeBERTa model)"),MAo.forEach(t),Yga=i(Te),lx=n(Te,"LI",{});var TAo=s(lx);pro=n(TAo,"STRONG",{});var JWn=s(pro);zga=r(JWn,"deberta-v2"),JWn.forEach(t),Hga=r(TAo," \u2014 "),AEe=n(TAo,"A",{href:!0});var UWn=s(AEe);Kga=r(UWn,"TFDebertaV2ForQuestionAnswering"),UWn.forEach(t),eua=r(TAo," (DeBERTa-v2 model)"),TAo.forEach(t),oua=i(Te),ix=n(Te,"LI",{});var FAo=s(ix);_ro=n(FAo,"STRONG",{});var XWn=s(_ro);rua=r(XWn,"distilbert"),XWn.forEach(t),tua=r(FAo," \u2014 "),REe=n(FAo,"A",{href:!0});var DWn=s(REe);aua=r(DWn,"TFDistilBertForQuestionAnswering"),DWn.forEach(t),nua=r(FAo," (DistilBERT model)"),FAo.forEach(t),sua=i(Te),dx=n(Te,"LI",{});var EAo=s(dx);bro=n(EAo,"STRONG",{});var qWn=s(bro);lua=r(qWn,"electra"),qWn.forEach(t),iua=r(EAo," \u2014 "),kEe=n(EAo,"A",{href:!0});var OWn=s(kEe);dua=r(OWn,"TFElectraForQuestionAnswering"),OWn.forEach(t),mua=r(EAo," (ELECTRA model)"),EAo.forEach(t),cua=i(Te),mx=n(Te,"LI",{});var yAo=s(mx);vro=n(yAo,"STRONG",{});var QWn=s(vro);fua=r(QWn,"flaubert"),QWn.forEach(t),hua=r(yAo," \u2014 "),SEe=n(yAo,"A",{href:!0});var YWn=s(SEe);gua=r(YWn,"TFFlaubertForQuestionAnsweringSimple"),YWn.forEach(t),uua=r(yAo," (FlauBERT model)"),yAo.forEach(t),pua=i(Te),cx=n(Te,"LI",{});var CAo=s(cx);Mro=n(CAo,"STRONG",{});var zWn=s(Mro);_ua=r(zWn,"funnel"),zWn.forEach(t),bua=r(CAo," \u2014 "),xEe=n(CAo,"A",{href:!0});var HWn=s(xEe);vua=r(HWn,"TFFunnelForQuestionAnswering"),HWn.forEach(t),Mua=r(CAo," (Funnel Transformer model)"),CAo.forEach(t),Tua=i(Te),fx=n(Te,"LI",{});var wAo=s(fx);Tro=n(wAo,"STRONG",{});var KWn=s(Tro);Fua=r(KWn,"gptj"),KWn.forEach(t),Eua=r(wAo," \u2014 "),GEe=n(wAo,"A",{href:!0});var eJn=s(GEe);yua=r(eJn,"TFGPTJForQuestionAnswering"),eJn.forEach(t),Cua=r(wAo," (GPT-J model)"),wAo.forEach(t),wua=i(Te),hx=n(Te,"LI",{});var LAo=s(hx);Fro=n(LAo,"STRONG",{});var oJn=s(Fro);Lua=r(oJn,"layoutlmv3"),oJn.forEach(t),Aua=r(LAo," \u2014 "),BEe=n(LAo,"A",{href:!0});var rJn=s(BEe);Rua=r(rJn,"TFLayoutLMv3ForQuestionAnswering"),rJn.forEach(t),kua=r(LAo," (LayoutLMv3 model)"),LAo.forEach(t),Sua=i(Te),gx=n(Te,"LI",{});var AAo=s(gx);Ero=n(AAo,"STRONG",{});var tJn=s(Ero);xua=r(tJn,"longformer"),tJn.forEach(t),Gua=r(AAo," \u2014 "),IEe=n(AAo,"A",{href:!0});var aJn=s(IEe);Bua=r(aJn,"TFLongformerForQuestionAnswering"),aJn.forEach(t),Iua=r(AAo," (Longformer model)"),AAo.forEach(t),Nua=i(Te),ux=n(Te,"LI",{});var RAo=s(ux);yro=n(RAo,"STRONG",{});var nJn=s(yro);$ua=r(nJn,"mobilebert"),nJn.forEach(t),jua=r(RAo," \u2014 "),NEe=n(RAo,"A",{href:!0});var sJn=s(NEe);Pua=r(sJn,"TFMobileBertForQuestionAnswering"),sJn.forEach(t),Zua=r(RAo," (MobileBERT model)"),RAo.forEach(t),Vua=i(Te),px=n(Te,"LI",{});var kAo=s(px);Cro=n(kAo,"STRONG",{});var lJn=s(Cro);Wua=r(lJn,"mpnet"),lJn.forEach(t),Jua=r(kAo," \u2014 "),$Ee=n(kAo,"A",{href:!0});var iJn=s($Ee);Uua=r(iJn,"TFMPNetForQuestionAnswering"),iJn.forEach(t),Xua=r(kAo," (MPNet model)"),kAo.forEach(t),Dua=i(Te),_x=n(Te,"LI",{});var SAo=s(_x);wro=n(SAo,"STRONG",{});var dJn=s(wro);qua=r(dJn,"rembert"),dJn.forEach(t),Oua=r(SAo," \u2014 "),jEe=n(SAo,"A",{href:!0});var mJn=s(jEe);Qua=r(mJn,"TFRemBertForQuestionAnswering"),mJn.forEach(t),Yua=r(SAo," (RemBERT model)"),SAo.forEach(t),zua=i(Te),bx=n(Te,"LI",{});var xAo=s(bx);Lro=n(xAo,"STRONG",{});var cJn=s(Lro);Hua=r(cJn,"roberta"),cJn.forEach(t),Kua=r(xAo," \u2014 "),PEe=n(xAo,"A",{href:!0});var fJn=s(PEe);epa=r(fJn,"TFRobertaForQuestionAnswering"),fJn.forEach(t),opa=r(xAo," (RoBERTa model)"),xAo.forEach(t),rpa=i(Te),vx=n(Te,"LI",{});var GAo=s(vx);Aro=n(GAo,"STRONG",{});var hJn=s(Aro);tpa=r(hJn,"roberta-prelayernorm"),hJn.forEach(t),apa=r(GAo," \u2014 "),ZEe=n(GAo,"A",{href:!0});var gJn=s(ZEe);npa=r(gJn,"TFRobertaPreLayerNormForQuestionAnswering"),gJn.forEach(t),spa=r(GAo," (RoBERTa-PreLayerNorm model)"),GAo.forEach(t),lpa=i(Te),Mx=n(Te,"LI",{});var BAo=s(Mx);Rro=n(BAo,"STRONG",{});var uJn=s(Rro);ipa=r(uJn,"roformer"),uJn.forEach(t),dpa=r(BAo," \u2014 "),VEe=n(BAo,"A",{href:!0});var pJn=s(VEe);mpa=r(pJn,"TFRoFormerForQuestionAnswering"),pJn.forEach(t),cpa=r(BAo," (RoFormer model)"),BAo.forEach(t),fpa=i(Te),Tx=n(Te,"LI",{});var IAo=s(Tx);kro=n(IAo,"STRONG",{});var _Jn=s(kro);hpa=r(_Jn,"xlm"),_Jn.forEach(t),gpa=r(IAo," \u2014 "),WEe=n(IAo,"A",{href:!0});var bJn=s(WEe);upa=r(bJn,"TFXLMForQuestionAnsweringSimple"),bJn.forEach(t),ppa=r(IAo," (XLM model)"),IAo.forEach(t),_pa=i(Te),Fx=n(Te,"LI",{});var NAo=s(Fx);Sro=n(NAo,"STRONG",{});var vJn=s(Sro);bpa=r(vJn,"xlm-roberta"),vJn.forEach(t),vpa=r(NAo," \u2014 "),JEe=n(NAo,"A",{href:!0});var MJn=s(JEe);Mpa=r(MJn,"TFXLMRobertaForQuestionAnswering"),MJn.forEach(t),Tpa=r(NAo," (XLM-RoBERTa model)"),NAo.forEach(t),Fpa=i(Te),Ex=n(Te,"LI",{});var $Ao=s(Ex);xro=n($Ao,"STRONG",{});var TJn=s(xro);Epa=r(TJn,"xlnet"),TJn.forEach(t),ypa=r($Ao," \u2014 "),UEe=n($Ao,"A",{href:!0});var FJn=s(UEe);Cpa=r(FJn,"TFXLNetForQuestionAnsweringSimple"),FJn.forEach(t),wpa=r($Ao," (XLNet model)"),$Ao.forEach(t),Te.forEach(t),Lpa=i(Jd),M(yx.$$.fragment,Jd),Jd.forEach(t),Wd.forEach(t),EGo=i(c),Pf=n(c,"H3",{class:!0});var vNo=s(Pf);Cx=n(vNo,"A",{id:!0,class:!0,href:!0});var EJn=s(Cx);Gro=n(EJn,"SPAN",{});var yJn=s(Gro);M(sU.$$.fragment,yJn),yJn.forEach(t),EJn.forEach(t),Apa=i(vNo),Bro=n(vNo,"SPAN",{});var CJn=s(Bro);Rpa=r(CJn,"FlaxAutoModelForQuestionAnswering"),CJn.forEach(t),vNo.forEach(t),yGo=i(c),vr=n(c,"DIV",{class:!0});var Ud=s(vr);M(lU.$$.fragment,Ud),kpa=i(Ud),Zf=n(Ud,"P",{});var q9e=s(Zf);Spa=r(q9e,`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 `),XEe=n(q9e,"A",{href:!0});var wJn=s(XEe);xpa=r(wJn,"from_pretrained()"),wJn.forEach(t),Gpa=r(q9e," class method or the "),DEe=n(q9e,"A",{href:!0});var LJn=s(DEe);Bpa=r(LJn,"from_config()"),LJn.forEach(t),Ipa=r(q9e,` class
method.`),q9e.forEach(t),Npa=i(Ud),iU=n(Ud,"P",{});var MNo=s(iU);$pa=r(MNo,"This class cannot be instantiated directly using "),Iro=n(MNo,"CODE",{});var AJn=s(Iro);jpa=r(AJn,"__init__()"),AJn.forEach(t),Ppa=r(MNo," (throws an error)."),MNo.forEach(t),Zpa=i(Ud),_a=n(Ud,"DIV",{class:!0});var pP=s(_a);M(dU.$$.fragment,pP),Vpa=i(pP),Nro=n(pP,"P",{});var RJn=s(Nro);Wpa=r(RJn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),RJn.forEach(t),Jpa=i(pP),Vf=n(pP,"P",{});var O9e=s(Vf);Upa=r(O9e,`Note:
Loading a model from its configuration file does `),$ro=n(O9e,"STRONG",{});var kJn=s($ro);Xpa=r(kJn,"not"),kJn.forEach(t),Dpa=r(O9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),qEe=n(O9e,"A",{href:!0});var SJn=s(qEe);qpa=r(SJn,"from_pretrained()"),SJn.forEach(t),Opa=r(O9e," to load the model weights."),O9e.forEach(t),Qpa=i(pP),M(wx.$$.fragment,pP),pP.forEach(t),Ypa=i(Ud),Ft=n(Ud,"DIV",{class:!0});var Xd=s(Ft);M(mU.$$.fragment,Xd),zpa=i(Xd),jro=n(Xd,"P",{});var xJn=s(jro);Hpa=r(xJn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),xJn.forEach(t),Kpa=i(Xd),gs=n(Xd,"P",{});var _P=s(gs);e_a=r(_P,"The model class to instantiate is selected based on the "),Pro=n(_P,"CODE",{});var GJn=s(Pro);o_a=r(GJn,"model_type"),GJn.forEach(t),r_a=r(_P,` property of the config object (either
passed as an argument or loaded from `),Zro=n(_P,"CODE",{});var BJn=s(Zro);t_a=r(BJn,"pretrained_model_name_or_path"),BJn.forEach(t),a_a=r(_P,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vro=n(_P,"CODE",{});var IJn=s(Vro);n_a=r(IJn,"pretrained_model_name_or_path"),IJn.forEach(t),s_a=r(_P,":"),_P.forEach(t),l_a=i(Xd),xe=n(Xd,"UL",{});var Ve=s(xe);Lx=n(Ve,"LI",{});var jAo=s(Lx);Wro=n(jAo,"STRONG",{});var NJn=s(Wro);i_a=r(NJn,"albert"),NJn.forEach(t),d_a=r(jAo," \u2014 "),OEe=n(jAo,"A",{href:!0});var $Jn=s(OEe);m_a=r($Jn,"FlaxAlbertForQuestionAnswering"),$Jn.forEach(t),c_a=r(jAo," (ALBERT model)"),jAo.forEach(t),f_a=i(Ve),Ax=n(Ve,"LI",{});var PAo=s(Ax);Jro=n(PAo,"STRONG",{});var jJn=s(Jro);h_a=r(jJn,"bart"),jJn.forEach(t),g_a=r(PAo," \u2014 "),QEe=n(PAo,"A",{href:!0});var PJn=s(QEe);u_a=r(PJn,"FlaxBartForQuestionAnswering"),PJn.forEach(t),p_a=r(PAo," (BART model)"),PAo.forEach(t),__a=i(Ve),Rx=n(Ve,"LI",{});var ZAo=s(Rx);Uro=n(ZAo,"STRONG",{});var ZJn=s(Uro);b_a=r(ZJn,"bert"),ZJn.forEach(t),v_a=r(ZAo," \u2014 "),YEe=n(ZAo,"A",{href:!0});var VJn=s(YEe);M_a=r(VJn,"FlaxBertForQuestionAnswering"),VJn.forEach(t),T_a=r(ZAo," (BERT model)"),ZAo.forEach(t),F_a=i(Ve),kx=n(Ve,"LI",{});var VAo=s(kx);Xro=n(VAo,"STRONG",{});var WJn=s(Xro);E_a=r(WJn,"big_bird"),WJn.forEach(t),y_a=r(VAo," \u2014 "),zEe=n(VAo,"A",{href:!0});var JJn=s(zEe);C_a=r(JJn,"FlaxBigBirdForQuestionAnswering"),JJn.forEach(t),w_a=r(VAo," (BigBird model)"),VAo.forEach(t),L_a=i(Ve),Sx=n(Ve,"LI",{});var WAo=s(Sx);Dro=n(WAo,"STRONG",{});var UJn=s(Dro);A_a=r(UJn,"distilbert"),UJn.forEach(t),R_a=r(WAo," \u2014 "),HEe=n(WAo,"A",{href:!0});var XJn=s(HEe);k_a=r(XJn,"FlaxDistilBertForQuestionAnswering"),XJn.forEach(t),S_a=r(WAo," (DistilBERT model)"),WAo.forEach(t),x_a=i(Ve),xx=n(Ve,"LI",{});var JAo=s(xx);qro=n(JAo,"STRONG",{});var DJn=s(qro);G_a=r(DJn,"electra"),DJn.forEach(t),B_a=r(JAo," \u2014 "),KEe=n(JAo,"A",{href:!0});var qJn=s(KEe);I_a=r(qJn,"FlaxElectraForQuestionAnswering"),qJn.forEach(t),N_a=r(JAo," (ELECTRA model)"),JAo.forEach(t),$_a=i(Ve),Gx=n(Ve,"LI",{});var UAo=s(Gx);Oro=n(UAo,"STRONG",{});var OJn=s(Oro);j_a=r(OJn,"mbart"),OJn.forEach(t),P_a=r(UAo," \u2014 "),eye=n(UAo,"A",{href:!0});var QJn=s(eye);Z_a=r(QJn,"FlaxMBartForQuestionAnswering"),QJn.forEach(t),V_a=r(UAo," (mBART model)"),UAo.forEach(t),W_a=i(Ve),Bx=n(Ve,"LI",{});var XAo=s(Bx);Qro=n(XAo,"STRONG",{});var YJn=s(Qro);J_a=r(YJn,"roberta"),YJn.forEach(t),U_a=r(XAo," \u2014 "),oye=n(XAo,"A",{href:!0});var zJn=s(oye);X_a=r(zJn,"FlaxRobertaForQuestionAnswering"),zJn.forEach(t),D_a=r(XAo," (RoBERTa model)"),XAo.forEach(t),q_a=i(Ve),Ix=n(Ve,"LI",{});var DAo=s(Ix);Yro=n(DAo,"STRONG",{});var HJn=s(Yro);O_a=r(HJn,"roberta-prelayernorm"),HJn.forEach(t),Q_a=r(DAo," \u2014 "),rye=n(DAo,"A",{href:!0});var KJn=s(rye);Y_a=r(KJn,"FlaxRobertaPreLayerNormForQuestionAnswering"),KJn.forEach(t),z_a=r(DAo," (RoBERTa-PreLayerNorm model)"),DAo.forEach(t),H_a=i(Ve),Nx=n(Ve,"LI",{});var qAo=s(Nx);zro=n(qAo,"STRONG",{});var eUn=s(zro);K_a=r(eUn,"roformer"),eUn.forEach(t),e1a=r(qAo," \u2014 "),tye=n(qAo,"A",{href:!0});var oUn=s(tye);o1a=r(oUn,"FlaxRoFormerForQuestionAnswering"),oUn.forEach(t),r1a=r(qAo," (RoFormer model)"),qAo.forEach(t),t1a=i(Ve),$x=n(Ve,"LI",{});var OAo=s($x);Hro=n(OAo,"STRONG",{});var rUn=s(Hro);a1a=r(rUn,"xlm-roberta"),rUn.forEach(t),n1a=r(OAo," \u2014 "),aye=n(OAo,"A",{href:!0});var tUn=s(aye);s1a=r(tUn,"FlaxXLMRobertaForQuestionAnswering"),tUn.forEach(t),l1a=r(OAo," (XLM-RoBERTa model)"),OAo.forEach(t),Ve.forEach(t),i1a=i(Xd),M(jx.$$.fragment,Xd),Xd.forEach(t),Ud.forEach(t),CGo=i(c),Wf=n(c,"H3",{class:!0});var TNo=s(Wf);Px=n(TNo,"A",{id:!0,class:!0,href:!0});var aUn=s(Px);Kro=n(aUn,"SPAN",{});var nUn=s(Kro);M(cU.$$.fragment,nUn),nUn.forEach(t),aUn.forEach(t),d1a=i(TNo),eto=n(TNo,"SPAN",{});var sUn=s(eto);m1a=r(sUn,"AutoModelForTextEncoding"),sUn.forEach(t),TNo.forEach(t),wGo=i(c),fU=n(c,"DIV",{class:!0});var lUn=s(fU);M(hU.$$.fragment,lUn),lUn.forEach(t),LGo=i(c),Jf=n(c,"H3",{class:!0});var FNo=s(Jf);Zx=n(FNo,"A",{id:!0,class:!0,href:!0});var iUn=s(Zx);oto=n(iUn,"SPAN",{});var dUn=s(oto);M(gU.$$.fragment,dUn),dUn.forEach(t),iUn.forEach(t),c1a=i(FNo),rto=n(FNo,"SPAN",{});var mUn=s(rto);f1a=r(mUn,"TFAutoModelForTextEncoding"),mUn.forEach(t),FNo.forEach(t),AGo=i(c),uU=n(c,"DIV",{class:!0});var cUn=s(uU);M(pU.$$.fragment,cUn),cUn.forEach(t),RGo=i(c),Uf=n(c,"H2",{class:!0});var ENo=s(Uf);Vx=n(ENo,"A",{id:!0,class:!0,href:!0});var fUn=s(Vx);tto=n(fUn,"SPAN",{});var hUn=s(tto);M(_U.$$.fragment,hUn),hUn.forEach(t),fUn.forEach(t),h1a=i(ENo),ato=n(ENo,"SPAN",{});var gUn=s(ato);g1a=r(gUn,"Computer vision"),gUn.forEach(t),ENo.forEach(t),kGo=i(c),nye=n(c,"P",{});var uUn=s(nye);u1a=r(uUn,"The following auto classes are available for the following computer vision tasks."),uUn.forEach(t),SGo=i(c),Xf=n(c,"H3",{class:!0});var yNo=s(Xf);Wx=n(yNo,"A",{id:!0,class:!0,href:!0});var pUn=s(Wx);nto=n(pUn,"SPAN",{});var _Un=s(nto);M(bU.$$.fragment,_Un),_Un.forEach(t),pUn.forEach(t),p1a=i(yNo),sto=n(yNo,"SPAN",{});var bUn=s(sto);_1a=r(bUn,"AutoModelForDepthEstimation"),bUn.forEach(t),yNo.forEach(t),xGo=i(c),Mr=n(c,"DIV",{class:!0});var Dd=s(Mr);M(vU.$$.fragment,Dd),b1a=i(Dd),Df=n(Dd,"P",{});var Q9e=s(Df);v1a=r(Q9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),sye=n(Q9e,"A",{href:!0});var vUn=s(sye);M1a=r(vUn,"from_pretrained()"),vUn.forEach(t),T1a=r(Q9e," class method or the "),lye=n(Q9e,"A",{href:!0});var MUn=s(lye);F1a=r(MUn,"from_config()"),MUn.forEach(t),E1a=r(Q9e,` class
method.`),Q9e.forEach(t),y1a=i(Dd),MU=n(Dd,"P",{});var CNo=s(MU);C1a=r(CNo,"This class cannot be instantiated directly using "),lto=n(CNo,"CODE",{});var TUn=s(lto);w1a=r(TUn,"__init__()"),TUn.forEach(t),L1a=r(CNo," (throws an error)."),CNo.forEach(t),A1a=i(Dd),ba=n(Dd,"DIV",{class:!0});var bP=s(ba);M(TU.$$.fragment,bP),R1a=i(bP),ito=n(bP,"P",{});var FUn=s(ito);k1a=r(FUn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),FUn.forEach(t),S1a=i(bP),qf=n(bP,"P",{});var Y9e=s(qf);x1a=r(Y9e,`Note:
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model\u2019s configuration. Use `),iye=n(Y9e,"A",{href:!0});var yUn=s(iye);I1a=r(yUn,"from_pretrained()"),yUn.forEach(t),N1a=r(Y9e," to load the model weights."),Y9e.forEach(t),$1a=i(bP),M(Jx.$$.fragment,bP),bP.forEach(t),j1a=i(Dd),_o=n(Dd,"DIV",{class:!0});var cn=s(_o);M(FU.$$.fragment,cn),P1a=i(cn),mto=n(cn,"P",{});var CUn=s(mto);Z1a=r(CUn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),CUn.forEach(t),V1a=i(cn),us=n(cn,"P",{});var vP=s(us);W1a=r(vP,"The model class to instantiate is selected based on the "),cto=n(vP,"CODE",{});var wUn=s(cto);J1a=r(wUn,"model_type"),wUn.forEach(t),U1a=r(vP,` property of the config object (either
passed as an argument or loaded from `),fto=n(vP,"CODE",{});var LUn=s(fto);X1a=r(LUn,"pretrained_model_name_or_path"),LUn.forEach(t),D1a=r(vP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hto=n(vP,"CODE",{});var AUn=s(hto);q1a=r(AUn,"pretrained_model_name_or_path"),AUn.forEach(t),O1a=r(vP,":"),vP.forEach(t),Q1a=i(cn),EU=n(cn,"UL",{});var wNo=s(EU);Ux=n(wNo,"LI",{});var QAo=s(Ux);gto=n(QAo,"STRONG",{});var RUn=s(gto);Y1a=r(RUn,"dpt"),RUn.forEach(t),z1a=r(QAo," \u2014 "),dye=n(QAo,"A",{href:!0});var kUn=s(dye);H1a=r(kUn,"DPTForDepthEstimation"),kUn.forEach(t),K1a=r(QAo," (DPT model)"),QAo.forEach(t),eba=i(wNo),Xx=n(wNo,"LI",{});var YAo=s(Xx);uto=n(YAo,"STRONG",{});var SUn=s(uto);oba=r(SUn,"glpn"),SUn.forEach(t),rba=r(YAo," \u2014 "),mye=n(YAo,"A",{href:!0});var xUn=s(mye);tba=r(xUn,"GLPNForDepthEstimation"),xUn.forEach(t),aba=r(YAo," (GLPN model)"),YAo.forEach(t),wNo.forEach(t),nba=i(cn),Dx=n(cn,"P",{});var zAo=s(Dx);sba=r(zAo,"The model is set in evaluation mode by default using "),pto=n(zAo,"CODE",{});var GUn=s(pto);lba=r(GUn,"model.eval()"),GUn.forEach(t),iba=r(zAo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_to=n(zAo,"CODE",{});var BUn=s(_to);dba=r(BUn,"model.train()"),BUn.forEach(t),zAo.forEach(t),mba=i(cn),M(qx.$$.fragment,cn),cn.forEach(t),Dd.forEach(t),GGo=i(c),Of=n(c,"H3",{class:!0});var LNo=s(Of);Ox=n(LNo,"A",{id:!0,class:!0,href:!0});var IUn=s(Ox);bto=n(IUn,"SPAN",{});var NUn=s(bto);M(yU.$$.fragment,NUn),NUn.forEach(t),IUn.forEach(t),cba=i(LNo),vto=n(LNo,"SPAN",{});var $Un=s(vto);fba=r($Un,"AutoModelForImageClassification"),$Un.forEach(t),LNo.forEach(t),BGo=i(c),Tr=n(c,"DIV",{class:!0});var qd=s(Tr);M(CU.$$.fragment,qd),hba=i(qd),Qf=n(qd,"P",{});var z9e=s(Qf);gba=r(z9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),cye=n(z9e,"A",{href:!0});var jUn=s(cye);uba=r(jUn,"from_pretrained()"),jUn.forEach(t),pba=r(z9e," class method or the "),fye=n(z9e,"A",{href:!0});var PUn=s(fye);_ba=r(PUn,"from_config()"),PUn.forEach(t),bba=r(z9e,` class
method.`),z9e.forEach(t),vba=i(qd),wU=n(qd,"P",{});var ANo=s(wU);Mba=r(ANo,"This class cannot be instantiated directly using "),Mto=n(ANo,"CODE",{});var ZUn=s(Mto);Tba=r(ZUn,"__init__()"),ZUn.forEach(t),Fba=r(ANo," (throws an error)."),ANo.forEach(t),Eba=i(qd),va=n(qd,"DIV",{class:!0});var MP=s(va);M(LU.$$.fragment,MP),yba=i(MP),Tto=n(MP,"P",{});var VUn=s(Tto);Cba=r(VUn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),VUn.forEach(t),wba=i(MP),Yf=n(MP,"P",{});var H9e=s(Yf);Lba=r(H9e,`Note:
Loading a model from its configuration file does `),Fto=n(H9e,"STRONG",{});var WUn=s(Fto);Aba=r(WUn,"not"),WUn.forEach(t),Rba=r(H9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),hye=n(H9e,"A",{href:!0});var JUn=s(hye);kba=r(JUn,"from_pretrained()"),JUn.forEach(t),Sba=r(H9e," to load the model weights."),H9e.forEach(t),xba=i(MP),M(Qx.$$.fragment,MP),MP.forEach(t),Gba=i(qd),bo=n(qd,"DIV",{class:!0});var fn=s(bo);M(AU.$$.fragment,fn),Bba=i(fn),Eto=n(fn,"P",{});var UUn=s(Eto);Iba=r(UUn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),UUn.forEach(t),Nba=i(fn),ps=n(fn,"P",{});var TP=s(ps);$ba=r(TP,"The model class to instantiate is selected based on the "),yto=n(TP,"CODE",{});var XUn=s(yto);jba=r(XUn,"model_type"),XUn.forEach(t),Pba=r(TP,` property of the config object (either
passed as an argument or loaded from `),Cto=n(TP,"CODE",{});var DUn=s(Cto);Zba=r(DUn,"pretrained_model_name_or_path"),DUn.forEach(t),Vba=r(TP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wto=n(TP,"CODE",{});var qUn=s(wto);Wba=r(qUn,"pretrained_model_name_or_path"),qUn.forEach(t),Jba=r(TP,":"),TP.forEach(t),Uba=i(fn),le=n(fn,"UL",{});var me=s(le);Yx=n(me,"LI",{});var HAo=s(Yx);Lto=n(HAo,"STRONG",{});var OUn=s(Lto);Xba=r(OUn,"beit"),OUn.forEach(t),Dba=r(HAo," \u2014 "),gye=n(HAo,"A",{href:!0});var QUn=s(gye);qba=r(QUn,"BeitForImageClassification"),QUn.forEach(t),Oba=r(HAo," (BEiT model)"),HAo.forEach(t),Qba=i(me),zx=n(me,"LI",{});var KAo=s(zx);Ato=n(KAo,"STRONG",{});var YUn=s(Ato);Yba=r(YUn,"bit"),YUn.forEach(t),zba=r(KAo," \u2014 "),uye=n(KAo,"A",{href:!0});var zUn=s(uye);Hba=r(zUn,"BitForImageClassification"),zUn.forEach(t),Kba=r(KAo," (BiT model)"),KAo.forEach(t),e2a=i(me),Hx=n(me,"LI",{});var eRo=s(Hx);Rto=n(eRo,"STRONG",{});var HUn=s(Rto);o2a=r(HUn,"convnext"),HUn.forEach(t),r2a=r(eRo," \u2014 "),pye=n(eRo,"A",{href:!0});var KUn=s(pye);t2a=r(KUn,"ConvNextForImageClassification"),KUn.forEach(t),a2a=r(eRo," (ConvNeXT model)"),eRo.forEach(t),n2a=i(me),Kx=n(me,"LI",{});var oRo=s(Kx);kto=n(oRo,"STRONG",{});var eXn=s(kto);s2a=r(eXn,"convnextv2"),eXn.forEach(t),l2a=r(oRo," \u2014 "),_ye=n(oRo,"A",{href:!0});var oXn=s(_ye);i2a=r(oXn,"ConvNextV2ForImageClassification"),oXn.forEach(t),d2a=r(oRo," (ConvNeXTV2 model)"),oRo.forEach(t),m2a=i(me),eG=n(me,"LI",{});var rRo=s(eG);Sto=n(rRo,"STRONG",{});var rXn=s(Sto);c2a=r(rXn,"cvt"),rXn.forEach(t),f2a=r(rRo," \u2014 "),bye=n(rRo,"A",{href:!0});var tXn=s(bye);h2a=r(tXn,"CvtForImageClassification"),tXn.forEach(t),g2a=r(rRo," (CvT model)"),rRo.forEach(t),u2a=i(me),oG=n(me,"LI",{});var tRo=s(oG);xto=n(tRo,"STRONG",{});var aXn=s(xto);p2a=r(aXn,"data2vec-vision"),aXn.forEach(t),_2a=r(tRo," \u2014 "),vye=n(tRo,"A",{href:!0});var nXn=s(vye);b2a=r(nXn,"Data2VecVisionForImageClassification"),nXn.forEach(t),v2a=r(tRo," (Data2VecVision model)"),tRo.forEach(t),M2a=i(me),Zi=n(me,"LI",{});var pQ=s(Zi);Gto=n(pQ,"STRONG",{});var sXn=s(Gto);T2a=r(sXn,"deit"),sXn.forEach(t),F2a=r(pQ," \u2014 "),Mye=n(pQ,"A",{href:!0});var lXn=s(Mye);E2a=r(lXn,"DeiTForImageClassification"),lXn.forEach(t),y2a=r(pQ," or "),Tye=n(pQ,"A",{href:!0});var iXn=s(Tye);C2a=r(iXn,"DeiTForImageClassificationWithTeacher"),iXn.forEach(t),w2a=r(pQ," (DeiT model)"),pQ.forEach(t),L2a=i(me),rG=n(me,"LI",{});var aRo=s(rG);Bto=n(aRo,"STRONG",{});var dXn=s(Bto);A2a=r(dXn,"dinat"),dXn.forEach(t),R2a=r(aRo," \u2014 "),Fye=n(aRo,"A",{href:!0});var mXn=s(Fye);k2a=r(mXn,"DinatForImageClassification"),mXn.forEach(t),S2a=r(aRo," (DiNAT model)"),aRo.forEach(t),x2a=i(me),tG=n(me,"LI",{});var nRo=s(tG);Ito=n(nRo,"STRONG",{});var cXn=s(Ito);G2a=r(cXn,"dinov2"),cXn.forEach(t),B2a=r(nRo," \u2014 "),Eye=n(nRo,"A",{href:!0});var fXn=s(Eye);I2a=r(fXn,"Dinov2ForImageClassification"),fXn.forEach(t),N2a=r(nRo," (DINOv2 model)"),nRo.forEach(t),$2a=i(me),Vi=n(me,"LI",{});var _Q=s(Vi);Nto=n(_Q,"STRONG",{});var hXn=s(Nto);j2a=r(hXn,"efficientformer"),hXn.forEach(t),P2a=r(_Q," \u2014 "),yye=n(_Q,"A",{href:!0});var gXn=s(yye);Z2a=r(gXn,"EfficientFormerForImageClassification"),gXn.forEach(t),V2a=r(_Q," or "),Cye=n(_Q,"A",{href:!0});var uXn=s(Cye);W2a=r(uXn,"EfficientFormerForImageClassificationWithTeacher"),uXn.forEach(t),J2a=r(_Q," (EfficientFormer model)"),_Q.forEach(t),U2a=i(me),aG=n(me,"LI",{});var sRo=s(aG);$to=n(sRo,"STRONG",{});var pXn=s($to);X2a=r(pXn,"efficientnet"),pXn.forEach(t),D2a=r(sRo," \u2014 "),wye=n(sRo,"A",{href:!0});var _Xn=s(wye);q2a=r(_Xn,"EfficientNetForImageClassification"),_Xn.forEach(t),O2a=r(sRo," (EfficientNet model)"),sRo.forEach(t),Q2a=i(me),nG=n(me,"LI",{});var lRo=s(nG);jto=n(lRo,"STRONG",{});var bXn=s(jto);Y2a=r(bXn,"focalnet"),bXn.forEach(t),z2a=r(lRo," \u2014 "),Lye=n(lRo,"A",{href:!0});var vXn=s(Lye);H2a=r(vXn,"FocalNetForImageClassification"),vXn.forEach(t),K2a=r(lRo," (FocalNet model)"),lRo.forEach(t),eva=i(me),sG=n(me,"LI",{});var iRo=s(sG);Pto=n(iRo,"STRONG",{});var MXn=s(Pto);ova=r(MXn,"imagegpt"),MXn.forEach(t),rva=r(iRo," \u2014 "),Aye=n(iRo,"A",{href:!0});var TXn=s(Aye);tva=r(TXn,"ImageGPTForImageClassification"),TXn.forEach(t),ava=r(iRo," (ImageGPT model)"),iRo.forEach(t),nva=i(me),Wi=n(me,"LI",{});var bQ=s(Wi);Zto=n(bQ,"STRONG",{});var FXn=s(Zto);sva=r(FXn,"levit"),FXn.forEach(t),lva=r(bQ," \u2014 "),Rye=n(bQ,"A",{href:!0});var EXn=s(Rye);iva=r(EXn,"LevitForImageClassification"),EXn.forEach(t),dva=r(bQ," or "),kye=n(bQ,"A",{href:!0});var yXn=s(kye);mva=r(yXn,"LevitForImageClassificationWithTeacher"),yXn.forEach(t),cva=r(bQ," (LeViT model)"),bQ.forEach(t),fva=i(me),lG=n(me,"LI",{});var dRo=s(lG);Vto=n(dRo,"STRONG",{});var CXn=s(Vto);hva=r(CXn,"mobilenet_v1"),CXn.forEach(t),gva=r(dRo," \u2014 "),Sye=n(dRo,"A",{href:!0});var wXn=s(Sye);uva=r(wXn,"MobileNetV1ForImageClassification"),wXn.forEach(t),pva=r(dRo," (MobileNetV1 model)"),dRo.forEach(t),_va=i(me),iG=n(me,"LI",{});var mRo=s(iG);Wto=n(mRo,"STRONG",{});var LXn=s(Wto);bva=r(LXn,"mobilenet_v2"),LXn.forEach(t),vva=r(mRo," \u2014 "),xye=n(mRo,"A",{href:!0});var AXn=s(xye);Mva=r(AXn,"MobileNetV2ForImageClassification"),AXn.forEach(t),Tva=r(mRo," (MobileNetV2 model)"),mRo.forEach(t),Fva=i(me),dG=n(me,"LI",{});var cRo=s(dG);Jto=n(cRo,"STRONG",{});var RXn=s(Jto);Eva=r(RXn,"mobilevit"),RXn.forEach(t),yva=r(cRo," \u2014 "),Gye=n(cRo,"A",{href:!0});var kXn=s(Gye);Cva=r(kXn,"MobileViTForImageClassification"),kXn.forEach(t),wva=r(cRo," (MobileViT model)"),cRo.forEach(t),Lva=i(me),mG=n(me,"LI",{});var fRo=s(mG);Uto=n(fRo,"STRONG",{});var SXn=s(Uto);Ava=r(SXn,"mobilevitv2"),SXn.forEach(t),Rva=r(fRo," \u2014 "),Bye=n(fRo,"A",{href:!0});var xXn=s(Bye);kva=r(xXn,"MobileViTV2ForImageClassification"),xXn.forEach(t),Sva=r(fRo," (MobileViTV2 model)"),fRo.forEach(t),xva=i(me),cG=n(me,"LI",{});var hRo=s(cG);Xto=n(hRo,"STRONG",{});var GXn=s(Xto);Gva=r(GXn,"nat"),GXn.forEach(t),Bva=r(hRo," \u2014 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uRo=s(hG);Oto=n(uRo,"STRONG",{});var VXn=s(Oto);Hva=r(VXn,"pvt"),VXn.forEach(t),Kva=r(uRo," \u2014 "),Zye=n(uRo,"A",{href:!0});var WXn=s(Zye);eMa=r(WXn,"PvtForImageClassification"),WXn.forEach(t),oMa=r(uRo," (PVT model)"),uRo.forEach(t),rMa=i(me),gG=n(me,"LI",{});var pRo=s(gG);Qto=n(pRo,"STRONG",{});var JXn=s(Qto);tMa=r(JXn,"regnet"),JXn.forEach(t),aMa=r(pRo," \u2014 "),Vye=n(pRo,"A",{href:!0});var UXn=s(Vye);nMa=r(UXn,"RegNetForImageClassification"),UXn.forEach(t),sMa=r(pRo," (RegNet model)"),pRo.forEach(t),lMa=i(me),uG=n(me,"LI",{});var _Ro=s(uG);Yto=n(_Ro,"STRONG",{});var XXn=s(Yto);iMa=r(XXn,"resnet"),XXn.forEach(t),dMa=r(_Ro," \u2014 "),Wye=n(_Ro,"A",{href:!0});var DXn=s(Wye);mMa=r(DXn,"ResNetForImageClassification"),DXn.forEach(t),cMa=r(_Ro," (ResNet model)"),_Ro.forEach(t),fMa=i(me),pG=n(me,"LI",{});var bRo=s(pG);zto=n(bRo,"STRONG",{});var qXn=s(zto);hMa=r(qXn,"segformer"),qXn.forEach(t),gMa=r(bRo," \u2014 "),Jye=n(bRo,"A",{href:!0});var OXn=s(Jye);uMa=r(OXn,"SegformerForImageClassification"),OXn.forEach(t),pMa=r(bRo," (SegFormer model)"),bRo.forEach(t),_Ma=i(me),_G=n(me,"LI",{});var vRo=s(_G);Hto=n(vRo,"STRONG",{});var QXn=s(Hto);bMa=r(QXn,"swiftformer"),QXn.forEach(t),vMa=r(vRo," \u2014 "),Uye=n(vRo,"A",{href:!0});var YXn=s(Uye);MMa=r(YXn,"SwiftFormerForImageClassification"),YXn.forEach(t),TMa=r(vRo," (SwiftFormer model)"),vRo.forEach(t),FMa=i(me),bG=n(me,"LI",{});var MRo=s(bG);Kto=n(MRo,"STRONG",{});var zXn=s(Kto);EMa=r(zXn,"swin"),zXn.forEach(t),yMa=r(MRo," \u2014 "),Xye=n(MRo,"A",{href:!0});var HXn=s(Xye);CMa=r(HXn,"SwinForImageClassification"),HXn.forEach(t),wMa=r(MRo," (Swin Transformer model)"),MRo.forEach(t),LMa=i(me),vG=n(me,"LI",{});var TRo=s(vG);eao=n(TRo,"STRONG",{});var KXn=s(eao);AMa=r(KXn,"swinv2"),KXn.forEach(t),RMa=r(TRo," \u2014 "),Dye=n(TRo,"A",{href:!0});var eDn=s(Dye);kMa=r(eDn,"Swinv2ForImageClassification"),eDn.forEach(t),SMa=r(TRo," (Swin Transformer V2 model)"),TRo.forEach(t),xMa=i(me),MG=n(me,"LI",{});var FRo=s(MG);oao=n(FRo,"STRONG",{});var oDn=s(oao);GMa=r(oDn,"van"),oDn.forEach(t),BMa=r(FRo," \u2014 "),qye=n(FRo,"A",{href:!0});var rDn=s(qye);IMa=r(rDn,"VanForImageClassification"),rDn.forEach(t),NMa=r(FRo," (VAN model)"),FRo.forEach(t),$Ma=i(me),TG=n(me,"LI",{});var ERo=s(TG);rao=n(ERo,"STRONG",{});var tDn=s(rao);jMa=r(tDn,"vit"),tDn.forEach(t),PMa=r(ERo," \u2014 "),Oye=n(ERo,"A",{href:!0});var aDn=s(Oye);ZMa=r(aDn,"ViTForImageClassification"),aDn.forEach(t),VMa=r(ERo," (ViT model)"),ERo.forEach(t),WMa=i(me),FG=n(me,"LI",{});var yRo=s(FG);tao=n(yRo,"STRONG",{});var nDn=s(tao);JMa=r(nDn,"vit_hybrid"),nDn.forEach(t),UMa=r(yRo," \u2014 "),Qye=n(yRo,"A",{href:!0});var sDn=s(Qye);XMa=r(sDn,"ViTHybridForImageClassification"),sDn.forEach(t),DMa=r(yRo," (ViT Hybrid model)"),yRo.forEach(t),qMa=i(me),EG=n(me,"LI",{});var CRo=s(EG);aao=n(CRo,"STRONG",{});var lDn=s(aao);OMa=r(lDn,"vit_msn"),lDn.forEach(t),QMa=r(CRo," \u2014 "),Yye=n(CRo,"A",{href:!0});var iDn=s(Yye);YMa=r(iDn,"ViTMSNForImageClassification"),iDn.forEach(t),zMa=r(CRo," (ViTMSN model)"),CRo.forEach(t),me.forEach(t),HMa=i(fn),yG=n(fn,"P",{});var wRo=s(yG);KMa=r(wRo,"The model is set in evaluation mode by default using "),nao=n(wRo,"CODE",{});var dDn=s(nao);eTa=r(dDn,"model.eval()"),dDn.forEach(t),oTa=r(wRo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),sao=n(wRo,"CODE",{});var mDn=s(sao);rTa=r(mDn,"model.train()"),mDn.forEach(t),wRo.forEach(t),tTa=i(fn),M(CG.$$.fragment,fn),fn.forEach(t),qd.forEach(t),IGo=i(c),zf=n(c,"H3",{class:!0});var RNo=s(zf);wG=n(RNo,"A",{id:!0,class:!0,href:!0});var cDn=s(wG);lao=n(cDn,"SPAN",{});var fDn=s(lao);M(RU.$$.fragment,fDn),fDn.forEach(t),cDn.forEach(t),aTa=i(RNo),iao=n(RNo,"SPAN",{});var hDn=s(iao);nTa=r(hDn,"TFAutoModelForImageClassification"),hDn.forEach(t),RNo.forEach(t),NGo=i(c),Fr=n(c,"DIV",{class:!0});var Od=s(Fr);M(kU.$$.fragment,Od),sTa=i(Od),Hf=n(Od,"P",{});var K9e=s(Hf);lTa=r(K9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),zye=n(K9e,"A",{href:!0});var gDn=s(zye);iTa=r(gDn,"from_pretrained()"),gDn.forEach(t),dTa=r(K9e," class method or the "),Hye=n(K9e,"A",{href:!0});var uDn=s(Hye);mTa=r(uDn,"from_config()"),uDn.forEach(t),cTa=r(K9e,` class
method.`),K9e.forEach(t),fTa=i(Od),SU=n(Od,"P",{});var kNo=s(SU);hTa=r(kNo,"This class cannot be instantiated directly using "),dao=n(kNo,"CODE",{});var pDn=s(dao);gTa=r(pDn,"__init__()"),pDn.forEach(t),uTa=r(kNo," (throws an error)."),kNo.forEach(t),pTa=i(Od),Ta=n(Od,"DIV",{class:!0});var FP=s(Ta);M(xU.$$.fragment,FP),_Ta=i(FP),mao=n(FP,"P",{});var _Dn=s(mao);bTa=r(_Dn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),_Dn.forEach(t),vTa=i(FP),Kf=n(FP,"P",{});var eCe=s(Kf);MTa=r(eCe,`Note:
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model\u2019s configuration. Use `),Kye=n(eCe,"A",{href:!0});var vDn=s(Kye);ETa=r(vDn,"from_pretrained()"),vDn.forEach(t),yTa=r(eCe," to load the model weights."),eCe.forEach(t),CTa=i(FP),M(LG.$$.fragment,FP),FP.forEach(t),wTa=i(Od),Et=n(Od,"DIV",{class:!0});var Qd=s(Et);M(GU.$$.fragment,Qd),LTa=i(Qd),fao=n(Qd,"P",{});var MDn=s(fao);ATa=r(MDn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),MDn.forEach(t),RTa=i(Qd),_s=n(Qd,"P",{});var EP=s(_s);kTa=r(EP,"The model class to instantiate is selected based on the "),hao=n(EP,"CODE",{});var TDn=s(hao);STa=r(TDn,"model_type"),TDn.forEach(t),xTa=r(EP,` property of the config object (either
passed as an argument or loaded from `),gao=n(EP,"CODE",{});var FDn=s(gao);GTa=r(FDn,"pretrained_model_name_or_path"),FDn.forEach(t),BTa=r(EP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),uao=n(EP,"CODE",{});var EDn=s(uao);ITa=r(EDn,"pretrained_model_name_or_path"),EDn.forEach(t),NTa=r(EP,":"),EP.forEach(t),$Ta=i(Qd),Ge=n(Qd,"UL",{});var We=s(Ge);AG=n(We,"LI",{});var LRo=s(AG);pao=n(LRo,"STRONG",{});var yDn=s(pao);jTa=r(yDn,"convnext"),yDn.forEach(t),PTa=r(LRo," \u2014 "),e0e=n(LRo,"A",{href:!0});var CDn=s(e0e);ZTa=r(CDn,"TFConvNextForImageClassification"),CDn.forEach(t),VTa=r(LRo," (ConvNeXT model)"),LRo.forEach(t),WTa=i(We),RG=n(We,"LI",{});var ARo=s(RG);_ao=n(ARo,"STRONG",{});var wDn=s(_ao);JTa=r(wDn,"cvt"),wDn.forEach(t),UTa=r(ARo," \u2014 "),o0e=n(ARo,"A",{href:!0});var LDn=s(o0e);XTa=r(LDn,"TFCvtForImageClassification"),LDn.forEach(t),DTa=r(ARo," (CvT model)"),ARo.forEach(t),qTa=i(We),kG=n(We,"LI",{});var RRo=s(kG);bao=n(RRo,"STRONG",{});var ADn=s(bao);OTa=r(ADn,"data2vec-vision"),ADn.forEach(t),QTa=r(RRo," \u2014 "),r0e=n(RRo,"A",{href:!0});var RDn=s(r0e);YTa=r(RDn,"TFData2VecVisionForImageClassification"),RDn.forEach(t),zTa=r(RRo," (Data2VecVision model)"),RRo.forEach(t),HTa=i(We),Ji=n(We,"LI",{});var vQ=s(Ji);vao=n(vQ,"STRONG",{});var kDn=s(vao);KTa=r(kDn,"deit"),kDn.forEach(t),eFa=r(vQ," \u2014 "),t0e=n(vQ,"A",{href:!0});var SDn=s(t0e);oFa=r(SDn,"TFDeiTForImageClassification"),SDn.forEach(t),rFa=r(vQ," or "),a0e=n(vQ,"A",{href:!0});var xDn=s(a0e);tFa=r(xDn,"TFDeiTForImageClassificationWithTeacher"),xDn.forEach(t),aFa=r(vQ," (DeiT model)"),vQ.forEach(t),nFa=i(We),Ui=n(We,"LI",{});var MQ=s(Ui);Mao=n(MQ,"STRONG",{});var GDn=s(Mao);sFa=r(GDn,"efficientformer"),GDn.forEach(t),lFa=r(MQ," \u2014 "),n0e=n(MQ,"A",{href:!0});var BDn=s(n0e);iFa=r(BDn,"TFEfficientFormerForImageClassification"),BDn.forEach(t),dFa=r(MQ," or "),s0e=n(MQ,"A",{href:!0});var IDn=s(s0e);mFa=r(IDn,"TFEfficientFormerForImageClassificationWithTeacher"),IDn.forEach(t),cFa=r(MQ," (EfficientFormer model)"),MQ.forEach(t),fFa=i(We),SG=n(We,"LI",{});var kRo=s(SG);Tao=n(kRo,"STRONG",{});var NDn=s(Tao);hFa=r(NDn,"mobilevit"),NDn.forEach(t),gFa=r(kRo," \u2014 "),l0e=n(kRo,"A",{href:!0});var $Dn=s(l0e);uFa=r($Dn,"TFMobileViTForImageClassification"),$Dn.forEach(t),pFa=r(kRo," (MobileViT model)"),kRo.forEach(t),_Fa=i(We),xG=n(We,"LI",{});var SRo=s(xG);Fao=n(SRo,"STRONG",{});var jDn=s(Fao);bFa=r(jDn,"regnet"),jDn.forEach(t),vFa=r(SRo," \u2014 "),i0e=n(SRo,"A",{href:!0});var PDn=s(i0e);MFa=r(PDn,"TFRegNetForImageClassification"),PDn.forEach(t),TFa=r(SRo," (RegNet model)"),SRo.forEach(t),FFa=i(We),GG=n(We,"LI",{});var xRo=s(GG);Eao=n(xRo,"STRONG",{});var ZDn=s(Eao);EFa=r(ZDn,"resnet"),ZDn.forEach(t),yFa=r(xRo," \u2014 "),d0e=n(xRo,"A",{href:!0});var VDn=s(d0e);CFa=r(VDn,"TFResNetForImageClassification"),VDn.forEach(t),wFa=r(xRo," (ResNet model)"),xRo.forEach(t),LFa=i(We),BG=n(We,"LI",{});var GRo=s(BG);yao=n(GRo,"STRONG",{});var WDn=s(yao);AFa=r(WDn,"segformer"),WDn.forEach(t),RFa=r(GRo," \u2014 "),m0e=n(GRo,"A",{href:!0});var JDn=s(m0e);kFa=r(JDn,"TFSegformerForImageClassification"),JDn.forEach(t),SFa=r(GRo," (SegFormer model)"),GRo.forEach(t),xFa=i(We),IG=n(We,"LI",{});var BRo=s(IG);Cao=n(BRo,"STRONG",{});var UDn=s(Cao);GFa=r(UDn,"swin"),UDn.forEach(t),BFa=r(BRo," \u2014 "),c0e=n(BRo,"A",{href:!0});var XDn=s(c0e);IFa=r(XDn,"TFSwinForImageClassification"),XDn.forEach(t),NFa=r(BRo," (Swin Transformer model)"),BRo.forEach(t),$Fa=i(We),NG=n(We,"LI",{});var IRo=s(NG);wao=n(IRo,"STRONG",{});var DDn=s(wao);jFa=r(DDn,"vit"),DDn.forEach(t),PFa=r(IRo," \u2014 "),f0e=n(IRo,"A",{href:!0});var qDn=s(f0e);ZFa=r(qDn,"TFViTForImageClassification"),qDn.forEach(t),VFa=r(IRo," (ViT model)"),IRo.forEach(t),We.forEach(t),WFa=i(Qd),M($G.$$.fragment,Qd),Qd.forEach(t),Od.forEach(t),$Go=i(c),eh=n(c,"H3",{class:!0});var SNo=s(eh);jG=n(SNo,"A",{id:!0,class:!0,href:!0});var ODn=s(jG);Lao=n(ODn,"SPAN",{});var QDn=s(Lao);M(BU.$$.fragment,QDn),QDn.forEach(t),ODn.forEach(t),JFa=i(SNo),Aao=n(SNo,"SPAN",{});var YDn=s(Aao);UFa=r(YDn,"FlaxAutoModelForImageClassification"),YDn.forEach(t),SNo.forEach(t),jGo=i(c),Er=n(c,"DIV",{class:!0});var Yd=s(Er);M(IU.$$.fragment,Yd),XFa=i(Yd),oh=n(Yd,"P",{});var oCe=s(oh);DFa=r(oCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),h0e=n(oCe,"A",{href:!0});var zDn=s(h0e);qFa=r(zDn,"from_pretrained()"),zDn.forEach(t),OFa=r(oCe," class method or the "),g0e=n(oCe,"A",{href:!0});var HDn=s(g0e);QFa=r(HDn,"from_config()"),HDn.forEach(t),YFa=r(oCe,` class
method.`),oCe.forEach(t),zFa=i(Yd),NU=n(Yd,"P",{});var xNo=s(NU);HFa=r(xNo,"This class cannot be instantiated directly using "),Rao=n(xNo,"CODE",{});var KDn=s(Rao);KFa=r(KDn,"__init__()"),KDn.forEach(t),eEa=r(xNo," (throws an error)."),xNo.forEach(t),oEa=i(Yd),Fa=n(Yd,"DIV",{class:!0});var yP=s(Fa);M($U.$$.fragment,yP),rEa=i(yP),kao=n(yP,"P",{});var eqn=s(kao);tEa=r(eqn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),eqn.forEach(t),aEa=i(yP),rh=n(yP,"P",{});var rCe=s(rh);nEa=r(rCe,`Note:
Loading a model from its configuration file does `),Sao=n(rCe,"STRONG",{});var oqn=s(Sao);sEa=r(oqn,"not"),oqn.forEach(t),lEa=r(rCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),u0e=n(rCe,"A",{href:!0});var rqn=s(u0e);iEa=r(rqn,"from_pretrained()"),rqn.forEach(t),dEa=r(rCe," to load the model weights."),rCe.forEach(t),mEa=i(yP),M(PG.$$.fragment,yP),yP.forEach(t),cEa=i(Yd),yt=n(Yd,"DIV",{class:!0});var zd=s(yt);M(jU.$$.fragment,zd),fEa=i(zd),xao=n(zd,"P",{});var tqn=s(xao);hEa=r(tqn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),tqn.forEach(t),gEa=i(zd),bs=n(zd,"P",{});var CP=s(bs);uEa=r(CP,"The model class to instantiate is selected based on the "),Gao=n(CP,"CODE",{});var aqn=s(Gao);pEa=r(aqn,"model_type"),aqn.forEach(t),_Ea=r(CP,` property of the config object (either
passed as an argument or loaded from `),Bao=n(CP,"CODE",{});var nqn=s(Bao);bEa=r(nqn,"pretrained_model_name_or_path"),nqn.forEach(t),vEa=r(CP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Iao=n(CP,"CODE",{});var sqn=s(Iao);MEa=r(sqn,"pretrained_model_name_or_path"),sqn.forEach(t),TEa=r(CP,":"),CP.forEach(t),FEa=i(zd),vs=n(zd,"UL",{});var wP=s(vs);ZG=n(wP,"LI",{});var NRo=s(ZG);Nao=n(NRo,"STRONG",{});var lqn=s(Nao);EEa=r(lqn,"beit"),lqn.forEach(t),yEa=r(NRo," \u2014 "),p0e=n(NRo,"A",{href:!0});var iqn=s(p0e);CEa=r(iqn,"FlaxBeitForImageClassification"),iqn.forEach(t),wEa=r(NRo," (BEiT model)"),NRo.forEach(t),LEa=i(wP),VG=n(wP,"LI",{});var $Ro=s(VG);$ao=n($Ro,"STRONG",{});var dqn=s($ao);AEa=r(dqn,"regnet"),dqn.forEach(t),REa=r($Ro," \u2014 "),_0e=n($Ro,"A",{href:!0});var mqn=s(_0e);kEa=r(mqn,"FlaxRegNetForImageClassification"),mqn.forEach(t),SEa=r($Ro," (RegNet model)"),$Ro.forEach(t),xEa=i(wP),WG=n(wP,"LI",{});var jRo=s(WG);jao=n(jRo,"STRONG",{});var cqn=s(jao);GEa=r(cqn,"resnet"),cqn.forEach(t),BEa=r(jRo," \u2014 "),b0e=n(jRo,"A",{href:!0});var fqn=s(b0e);IEa=r(fqn,"FlaxResNetForImageClassification"),fqn.forEach(t),NEa=r(jRo," (ResNet model)"),jRo.forEach(t),$Ea=i(wP),JG=n(wP,"LI",{});var PRo=s(JG);Pao=n(PRo,"STRONG",{});var hqn=s(Pao);jEa=r(hqn,"vit"),hqn.forEach(t),PEa=r(PRo," \u2014 "),v0e=n(PRo,"A",{href:!0});var gqn=s(v0e);ZEa=r(gqn,"FlaxViTForImageClassification"),gqn.forEach(t),VEa=r(PRo," (ViT model)"),PRo.forEach(t),wP.forEach(t),WEa=i(zd),M(UG.$$.fragment,zd),zd.forEach(t),Yd.forEach(t),PGo=i(c),th=n(c,"H3",{class:!0});var GNo=s(th);XG=n(GNo,"A",{id:!0,class:!0,href:!0});var uqn=s(XG);Zao=n(uqn,"SPAN",{});var pqn=s(Zao);M(PU.$$.fragment,pqn),pqn.forEach(t),uqn.forEach(t),JEa=i(GNo),Vao=n(GNo,"SPAN",{});var _qn=s(Vao);UEa=r(_qn,"AutoModelForVideoClassification"),_qn.forEach(t),GNo.forEach(t),ZGo=i(c),yr=n(c,"DIV",{class:!0});var Hd=s(yr);M(ZU.$$.fragment,Hd),XEa=i(Hd),ah=n(Hd,"P",{});var tCe=s(ah);DEa=r(tCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),M0e=n(tCe,"A",{href:!0});var bqn=s(M0e);qEa=r(bqn,"from_pretrained()"),bqn.forEach(t),OEa=r(tCe," class method or the "),T0e=n(tCe,"A",{href:!0});var vqn=s(T0e);QEa=r(vqn,"from_config()"),vqn.forEach(t),YEa=r(tCe,` class
method.`),tCe.forEach(t),zEa=i(Hd),VU=n(Hd,"P",{});var BNo=s(VU);HEa=r(BNo,"This class cannot be instantiated directly using "),Wao=n(BNo,"CODE",{});var Mqn=s(Wao);KEa=r(Mqn,"__init__()"),Mqn.forEach(t),eya=r(BNo," (throws an error)."),BNo.forEach(t),oya=i(Hd),Ea=n(Hd,"DIV",{class:!0});var LP=s(Ea);M(WU.$$.fragment,LP),rya=i(LP),Jao=n(LP,"P",{});var Tqn=s(Jao);tya=r(Tqn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),Tqn.forEach(t),aya=i(LP),nh=n(LP,"P",{});var aCe=s(nh);nya=r(aCe,`Note:
Loading a model from its configuration file does `),Uao=n(aCe,"STRONG",{});var Fqn=s(Uao);sya=r(Fqn,"not"),Fqn.forEach(t),lya=r(aCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),F0e=n(aCe,"A",{href:!0});var Eqn=s(F0e);iya=r(Eqn,"from_pretrained()"),Eqn.forEach(t),dya=r(aCe," to load the model weights."),aCe.forEach(t),mya=i(LP),M(DG.$$.fragment,LP),LP.forEach(t),cya=i(Hd),vo=n(Hd,"DIV",{class:!0});var hn=s(vo);M(JU.$$.fragment,hn),fya=i(hn),Xao=n(hn,"P",{});var yqn=s(Xao);hya=r(yqn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),yqn.forEach(t),gya=i(hn),Ms=n(hn,"P",{});var AP=s(Ms);uya=r(AP,"The model class to instantiate is selected based on the "),Dao=n(AP,"CODE",{});var Cqn=s(Dao);pya=r(Cqn,"model_type"),Cqn.forEach(t),_ya=r(AP,` property of the config object (either
passed as an argument or loaded from `),qao=n(AP,"CODE",{});var wqn=s(qao);bya=r(wqn,"pretrained_model_name_or_path"),wqn.forEach(t),vya=r(AP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Oao=n(AP,"CODE",{});var Lqn=s(Oao);Mya=r(Lqn,"pretrained_model_name_or_path"),Lqn.forEach(t),Tya=r(AP,":"),AP.forEach(t),Fya=i(hn),sh=n(hn,"UL",{});var nCe=s(sh);qG=n(nCe,"LI",{});var ZRo=s(qG);Qao=n(ZRo,"STRONG",{});var Aqn=s(Qao);Eya=r(Aqn,"timesformer"),Aqn.forEach(t),yya=r(ZRo," \u2014 "),E0e=n(ZRo,"A",{href:!0});var Rqn=s(E0e);Cya=r(Rqn,"TimesformerForVideoClassification"),Rqn.forEach(t),wya=r(ZRo," (TimeSformer model)"),ZRo.forEach(t),Lya=i(nCe),OG=n(nCe,"LI",{});var VRo=s(OG);Yao=n(VRo,"STRONG",{});var kqn=s(Yao);Aya=r(kqn,"videomae"),kqn.forEach(t),Rya=r(VRo," \u2014 "),y0e=n(VRo,"A",{href:!0});var Sqn=s(y0e);kya=r(Sqn,"VideoMAEForVideoClassification"),Sqn.forEach(t),Sya=r(VRo," (VideoMAE model)"),VRo.forEach(t),xya=i(nCe),QG=n(nCe,"LI",{});var WRo=s(QG);zao=n(WRo,"STRONG",{});var xqn=s(zao);Gya=r(xqn,"vivit"),xqn.forEach(t),Bya=r(WRo," \u2014 "),C0e=n(WRo,"A",{href:!0});var Gqn=s(C0e);Iya=r(Gqn,"VivitForVideoClassification"),Gqn.forEach(t),Nya=r(WRo," (ViViT model)"),WRo.forEach(t),nCe.forEach(t),$ya=i(hn),YG=n(hn,"P",{});var JRo=s(YG);jya=r(JRo,"The model is set in evaluation mode by default using "),Hao=n(JRo,"CODE",{});var Bqn=s(Hao);Pya=r(Bqn,"model.eval()"),Bqn.forEach(t),Zya=r(JRo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kao=n(JRo,"CODE",{});var Iqn=s(Kao);Vya=r(Iqn,"model.train()"),Iqn.forEach(t),JRo.forEach(t),Wya=i(hn),M(zG.$$.fragment,hn),hn.forEach(t),Hd.forEach(t),VGo=i(c),lh=n(c,"H3",{class:!0});var INo=s(lh);HG=n(INo,"A",{id:!0,class:!0,href:!0});var Nqn=s(HG);eno=n(Nqn,"SPAN",{});var $qn=s(eno);M(UU.$$.fragment,$qn),$qn.forEach(t),Nqn.forEach(t),Jya=i(INo),ono=n(INo,"SPAN",{});var jqn=s(ono);Uya=r(jqn,"AutoModelForMaskedImageModeling"),jqn.forEach(t),INo.forEach(t),WGo=i(c),Cr=n(c,"DIV",{class:!0});var Kd=s(Cr);M(XU.$$.fragment,Kd),Xya=i(Kd),ih=n(Kd,"P",{});var sCe=s(ih);Dya=r(sCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),w0e=n(sCe,"A",{href:!0});var Pqn=s(w0e);qya=r(Pqn,"from_pretrained()"),Pqn.forEach(t),Oya=r(sCe," class method or the "),L0e=n(sCe,"A",{href:!0});var Zqn=s(L0e);Qya=r(Zqn,"from_config()"),Zqn.forEach(t),Yya=r(sCe,` class
method.`),sCe.forEach(t),zya=i(Kd),DU=n(Kd,"P",{});var NNo=s(DU);Hya=r(NNo,"This class cannot be instantiated directly using "),rno=n(NNo,"CODE",{});var Vqn=s(rno);Kya=r(Vqn,"__init__()"),Vqn.forEach(t),e0a=r(NNo," (throws an error)."),NNo.forEach(t),o0a=i(Kd),ya=n(Kd,"DIV",{class:!0});var RP=s(ya);M(qU.$$.fragment,RP),r0a=i(RP),tno=n(RP,"P",{});var Wqn=s(tno);t0a=r(Wqn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),Wqn.forEach(t),a0a=i(RP),dh=n(RP,"P",{});var lCe=s(dh);n0a=r(lCe,`Note:
Loading a model from its configuration file does `),ano=n(lCe,"STRONG",{});var Jqn=s(ano);s0a=r(Jqn,"not"),Jqn.forEach(t),l0a=r(lCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),A0e=n(lCe,"A",{href:!0});var Uqn=s(A0e);i0a=r(Uqn,"from_pretrained()"),Uqn.forEach(t),d0a=r(lCe," to load the model weights."),lCe.forEach(t),m0a=i(RP),M(KG.$$.fragment,RP),RP.forEach(t),c0a=i(Kd),Mo=n(Kd,"DIV",{class:!0});var gn=s(Mo);M(OU.$$.fragment,gn),f0a=i(gn),nno=n(gn,"P",{});var Xqn=s(nno);h0a=r(Xqn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),Xqn.forEach(t),g0a=i(gn),Ts=n(gn,"P",{});var kP=s(Ts);u0a=r(kP,"The model class to instantiate is selected based on the "),sno=n(kP,"CODE",{});var Dqn=s(sno);p0a=r(Dqn,"model_type"),Dqn.forEach(t),_0a=r(kP,` property of the config object (either
passed as an argument or loaded from `),lno=n(kP,"CODE",{});var qqn=s(lno);b0a=r(qqn,"pretrained_model_name_or_path"),qqn.forEach(t),v0a=r(kP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ino=n(kP,"CODE",{});var Oqn=s(ino);M0a=r(Oqn,"pretrained_model_name_or_path"),Oqn.forEach(t),T0a=r(kP,":"),kP.forEach(t),F0a=i(gn),Nt=n(gn,"UL",{});var em=s(Nt);eB=n(em,"LI",{});var URo=s(eB);dno=n(URo,"STRONG",{});var Qqn=s(dno);E0a=r(Qqn,"deit"),Qqn.forEach(t),y0a=r(URo," \u2014 "),R0e=n(URo,"A",{href:!0});var Yqn=s(R0e);C0a=r(Yqn,"DeiTForMaskedImageModeling"),Yqn.forEach(t),w0a=r(URo," (DeiT model)"),URo.forEach(t),L0a=i(em),oB=n(em,"LI",{});var XRo=s(oB);mno=n(XRo,"STRONG",{});var zqn=s(mno);A0a=r(zqn,"focalnet"),zqn.forEach(t),R0a=r(XRo," \u2014 "),k0e=n(XRo,"A",{href:!0});var Hqn=s(k0e);k0a=r(Hqn,"FocalNetForMaskedImageModeling"),Hqn.forEach(t),S0a=r(XRo," (FocalNet model)"),XRo.forEach(t),x0a=i(em),rB=n(em,"LI",{});var DRo=s(rB);cno=n(DRo,"STRONG",{});var Kqn=s(cno);G0a=r(Kqn,"swin"),Kqn.forEach(t),B0a=r(DRo," \u2014 "),S0e=n(DRo,"A",{href:!0});var eOn=s(S0e);I0a=r(eOn,"SwinForMaskedImageModeling"),eOn.forEach(t),N0a=r(DRo," (Swin Transformer model)"),DRo.forEach(t),$0a=i(em),tB=n(em,"LI",{});var qRo=s(tB);fno=n(qRo,"STRONG",{});var oOn=s(fno);j0a=r(oOn,"swinv2"),oOn.forEach(t),P0a=r(qRo," \u2014 "),x0e=n(qRo,"A",{href:!0});var rOn=s(x0e);Z0a=r(rOn,"Swinv2ForMaskedImageModeling"),rOn.forEach(t),V0a=r(qRo," (Swin Transformer V2 model)"),qRo.forEach(t),W0a=i(em),aB=n(em,"LI",{});var ORo=s(aB);hno=n(ORo,"STRONG",{});var tOn=s(hno);J0a=r(tOn,"vit"),tOn.forEach(t),U0a=r(ORo," \u2014 "),G0e=n(ORo,"A",{href:!0});var aOn=s(G0e);X0a=r(aOn,"ViTForMaskedImageModeling"),aOn.forEach(t),D0a=r(ORo," (ViT model)"),ORo.forEach(t),em.forEach(t),q0a=i(gn),nB=n(gn,"P",{});var QRo=s(nB);O0a=r(QRo,"The model is set in evaluation mode by default using "),gno=n(QRo,"CODE",{});var nOn=s(gno);Q0a=r(nOn,"model.eval()"),nOn.forEach(t),Y0a=r(QRo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),uno=n(QRo,"CODE",{});var sOn=s(uno);z0a=r(sOn,"model.train()"),sOn.forEach(t),QRo.forEach(t),H0a=i(gn),M(sB.$$.fragment,gn),gn.forEach(t),Kd.forEach(t),JGo=i(c),mh=n(c,"H3",{class:!0});var $No=s(mh);lB=n($No,"A",{id:!0,class:!0,href:!0});var lOn=s(lB);pno=n(lOn,"SPAN",{});var iOn=s(pno);M(QU.$$.fragment,iOn),iOn.forEach(t),lOn.forEach(t),K0a=i($No),_no=n($No,"SPAN",{});var dOn=s(_no);e5a=r(dOn,"TFAutoModelForMaskedImageModeling"),dOn.forEach(t),$No.forEach(t),UGo=i(c),wr=n(c,"DIV",{class:!0});var om=s(wr);M(YU.$$.fragment,om),o5a=i(om),ch=n(om,"P",{});var iCe=s(ch);r5a=r(iCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),B0e=n(iCe,"A",{href:!0});var mOn=s(B0e);t5a=r(mOn,"from_pretrained()"),mOn.forEach(t),a5a=r(iCe," class method or the "),I0e=n(iCe,"A",{href:!0});var cOn=s(I0e);n5a=r(cOn,"from_config()"),cOn.forEach(t),s5a=r(iCe,` class
method.`),iCe.forEach(t),l5a=i(om),zU=n(om,"P",{});var jNo=s(zU);i5a=r(jNo,"This class cannot be instantiated directly using "),bno=n(jNo,"CODE",{});var fOn=s(bno);d5a=r(fOn,"__init__()"),fOn.forEach(t),m5a=r(jNo," (throws an error)."),jNo.forEach(t),c5a=i(om),Ca=n(om,"DIV",{class:!0});var SP=s(Ca);M(HU.$$.fragment,SP),f5a=i(SP),vno=n(SP,"P",{});var hOn=s(vno);h5a=r(hOn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),hOn.forEach(t),g5a=i(SP),fh=n(SP,"P",{});var dCe=s(fh);u5a=r(dCe,`Note:
Loading a model from its configuration file does `),Mno=n(dCe,"STRONG",{});var gOn=s(Mno);p5a=r(gOn,"not"),gOn.forEach(t),_5a=r(dCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),N0e=n(dCe,"A",{href:!0});var uOn=s(N0e);b5a=r(uOn,"from_pretrained()"),uOn.forEach(t),v5a=r(dCe," to load the model weights."),dCe.forEach(t),M5a=i(SP),M(iB.$$.fragment,SP),SP.forEach(t),T5a=i(om),Ct=n(om,"DIV",{class:!0});var rm=s(Ct);M(KU.$$.fragment,rm),F5a=i(rm),Tno=n(rm,"P",{});var pOn=s(Tno);E5a=r(pOn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),pOn.forEach(t),y5a=i(rm),Fs=n(rm,"P",{});var xP=s(Fs);C5a=r(xP,"The model class to instantiate is selected based on the "),Fno=n(xP,"CODE",{});var _On=s(Fno);w5a=r(_On,"model_type"),_On.forEach(t),L5a=r(xP,` property of the config object (either
passed as an argument or loaded from `),Eno=n(xP,"CODE",{});var bOn=s(Eno);A5a=r(bOn,"pretrained_model_name_or_path"),bOn.forEach(t),R5a=r(xP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yno=n(xP,"CODE",{});var vOn=s(yno);k5a=r(vOn,"pretrained_model_name_or_path"),vOn.forEach(t),S5a=r(xP,":"),xP.forEach(t),x5a=i(rm),eX=n(rm,"UL",{});var PNo=s(eX);dB=n(PNo,"LI",{});var YRo=s(dB);Cno=n(YRo,"STRONG",{});var MOn=s(Cno);G5a=r(MOn,"deit"),MOn.forEach(t),B5a=r(YRo," \u2014 "),$0e=n(YRo,"A",{href:!0});var TOn=s($0e);I5a=r(TOn,"TFDeiTForMaskedImageModeling"),TOn.forEach(t),N5a=r(YRo," (DeiT model)"),YRo.forEach(t),$5a=i(PNo),mB=n(PNo,"LI",{});var zRo=s(mB);wno=n(zRo,"STRONG",{});var FOn=s(wno);j5a=r(FOn,"swin"),FOn.forEach(t),P5a=r(zRo," \u2014 "),j0e=n(zRo,"A",{href:!0});var EOn=s(j0e);Z5a=r(EOn,"TFSwinForMaskedImageModeling"),EOn.forEach(t),V5a=r(zRo," (Swin Transformer model)"),zRo.forEach(t),PNo.forEach(t),W5a=i(rm),M(cB.$$.fragment,rm),rm.forEach(t),om.forEach(t),XGo=i(c),hh=n(c,"H3",{class:!0});var ZNo=s(hh);fB=n(ZNo,"A",{id:!0,class:!0,href:!0});var yOn=s(fB);Lno=n(yOn,"SPAN",{});var COn=s(Lno);M(oX.$$.fragment,COn),COn.forEach(t),yOn.forEach(t),J5a=i(ZNo),Ano=n(ZNo,"SPAN",{});var wOn=s(Ano);U5a=r(wOn,"AutoModelForObjectDetection"),wOn.forEach(t),ZNo.forEach(t),DGo=i(c),Lr=n(c,"DIV",{class:!0});var tm=s(Lr);M(rX.$$.fragment,tm),X5a=i(tm),gh=n(tm,"P",{});var mCe=s(gh);D5a=r(mCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),P0e=n(mCe,"A",{href:!0});var LOn=s(P0e);q5a=r(LOn,"from_pretrained()"),LOn.forEach(t),O5a=r(mCe," class method or the "),Z0e=n(mCe,"A",{href:!0});var AOn=s(Z0e);Q5a=r(AOn,"from_config()"),AOn.forEach(t),Y5a=r(mCe,` class
method.`),mCe.forEach(t),z5a=i(tm),tX=n(tm,"P",{});var VNo=s(tX);H5a=r(VNo,"This class cannot be instantiated directly using "),Rno=n(VNo,"CODE",{});var ROn=s(Rno);K5a=r(ROn,"__init__()"),ROn.forEach(t),e3a=r(VNo," (throws an error)."),VNo.forEach(t),o3a=i(tm),wa=n(tm,"DIV",{class:!0});var GP=s(wa);M(aX.$$.fragment,GP),r3a=i(GP),kno=n(GP,"P",{});var kOn=s(kno);t3a=r(kOn,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),kOn.forEach(t),a3a=i(GP),uh=n(GP,"P",{});var cCe=s(uh);n3a=r(cCe,`Note:
Loading a model from its configuration file does `),Sno=n(cCe,"STRONG",{});var SOn=s(Sno);s3a=r(SOn,"not"),SOn.forEach(t),l3a=r(cCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),V0e=n(cCe,"A",{href:!0});var xOn=s(V0e);i3a=r(xOn,"from_pretrained()"),xOn.forEach(t),d3a=r(cCe," to load the model weights."),cCe.forEach(t),m3a=i(GP),M(hB.$$.fragment,GP),GP.forEach(t),c3a=i(tm),To=n(tm,"DIV",{class:!0});var un=s(To);M(nX.$$.fragment,un),f3a=i(un),xno=n(un,"P",{});var GOn=s(xno);h3a=r(GOn,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),GOn.forEach(t),g3a=i(un),Es=n(un,"P",{});var BP=s(Es);u3a=r(BP,"The model class to instantiate is selected based on the "),Gno=n(BP,"CODE",{});var BOn=s(Gno);p3a=r(BOn,"model_type"),BOn.forEach(t),_3a=r(BP,` property of the config object (either
passed as an argument or loaded from `),Bno=n(BP,"CODE",{});var IOn=s(Bno);b3a=r(IOn,"pretrained_model_name_or_path"),IOn.forEach(t),v3a=r(BP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ino=n(BP,"CODE",{});var NOn=s(Ino);M3a=r(NOn,"pretrained_model_name_or_path"),NOn.forEach(t),T3a=r(BP,":"),BP.forEach(t),F3a=i(un),Ar=n(un,"UL",{});var pn=s(Ar);gB=n(pn,"LI",{});var HRo=s(gB);Nno=n(HRo,"STRONG",{});var $On=s(Nno);E3a=r($On,"conditional_detr"),$On.forEach(t),y3a=r(HRo," \u2014 "),W0e=n(HRo,"A",{href:!0});var jOn=s(W0e);C3a=r(jOn,"ConditionalDetrForObjectDetection"),jOn.forEach(t),w3a=r(HRo," (Conditional DETR model)"),HRo.forEach(t),L3a=i(pn),uB=n(pn,"LI",{});var KRo=s(uB);$no=n(KRo,"STRONG",{});var POn=s($no);A3a=r(POn,"deformable_detr"),POn.forEach(t),R3a=r(KRo," \u2014 "),J0e=n(KRo,"A",{href:!0});var ZOn=s(J0e);k3a=r(ZOn,"DeformableDetrForObjectDetection"),ZOn.forEach(t),S3a=r(KRo," (Deformable DETR model)"),KRo.forEach(t),x3a=i(pn),pB=n(pn,"LI",{});var eko=s(pB);jno=n(eko,"STRONG",{});var VOn=s(jno);G3a=r(VOn,"deta"),VOn.forEach(t),B3a=r(eko," \u2014 "),U0e=n(eko,"A",{href:!0});var WOn=s(U0e);I3a=r(WOn,"DetaForObjectDetection"),WOn.forEach(t),N3a=r(eko," (DETA model)"),eko.forEach(t),$3a=i(pn),_B=n(pn,"LI",{});var oko=s(_B);Pno=n(oko,"STRONG",{});var JOn=s(Pno);j3a=r(JOn,"detr"),JOn.forEach(t),P3a=r(oko," \u2014 "),X0e=n(oko,"A",{href:!0});var UOn=s(X0e);Z3a=r(UOn,"DetrForObjectDetection"),UOn.forEach(t),V3a=r(oko," (DETR model)"),oko.forEach(t),W3a=i(pn),bB=n(pn,"LI",{});var rko=s(bB);Zno=n(rko,"STRONG",{});var XOn=s(Zno);J3a=r(XOn,"table-transformer"),XOn.forEach(t),U3a=r(rko," \u2014 "),D0e=n(rko,"A",{href:!0});var DOn=s(D0e);X3a=r(DOn,"TableTransformerForObjectDetection"),DOn.forEach(t),D3a=r(rko," (Table Transformer model)"),rko.forEach(t),q3a=i(pn),vB=n(pn,"LI",{});var tko=s(vB);Vno=n(tko,"STRONG",{});var qOn=s(Vno);O3a=r(qOn,"yolos"),qOn.forEach(t),Q3a=r(tko," \u2014 "),q0e=n(tko,"A",{href:!0});var OOn=s(q0e);Y3a=r(OOn,"YolosForObjectDetection"),OOn.forEach(t),z3a=r(tko," (YOLOS model)"),tko.forEach(t),pn.forEach(t),H3a=i(un),MB=n(un,"P",{});var ako=s(MB);K3a=r(ako,"The model is set in evaluation mode by default using "),Wno=n(ako,"CODE",{});var QOn=s(Wno);e4a=r(QOn,"model.eval()"),QOn.forEach(t),o4a=r(ako,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Jno=n(ako,"CODE",{});var YOn=s(Jno);r4a=r(YOn,"model.train()"),YOn.forEach(t),ako.forEach(t),t4a=i(un),M(TB.$$.fragment,un),un.forEach(t),tm.forEach(t),qGo=i(c),ph=n(c,"H3",{class:!0});var WNo=s(ph);FB=n(WNo,"A",{id:!0,class:!0,href:!0});var zOn=s(FB);Uno=n(zOn,"SPAN",{});var HOn=s(Uno);M(sX.$$.fragment,HOn),HOn.forEach(t),zOn.forEach(t),a4a=i(WNo),Xno=n(WNo,"SPAN",{});var KOn=s(Xno);n4a=r(KOn,"AutoModelForImageSegmentation"),KOn.forEach(t),WNo.forEach(t),OGo=i(c),Rr=n(c,"DIV",{class:!0});var am=s(Rr);M(lX.$$.fragment,am),s4a=i(am),_h=n(am,"P",{});var fCe=s(_h);l4a=r(fCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),O0e=n(fCe,"A",{href:!0});var eQn=s(O0e);i4a=r(eQn,"from_pretrained()"),eQn.forEach(t),d4a=r(fCe," class method or the "),Q0e=n(fCe,"A",{href:!0});var oQn=s(Q0e);m4a=r(oQn,"from_config()"),oQn.forEach(t),c4a=r(fCe,` class
method.`),fCe.forEach(t),f4a=i(am),iX=n(am,"P",{});var JNo=s(iX);h4a=r(JNo,"This class cannot be instantiated directly using "),Dno=n(JNo,"CODE",{});var rQn=s(Dno);g4a=r(rQn,"__init__()"),rQn.forEach(t),u4a=r(JNo," (throws an error)."),JNo.forEach(t),p4a=i(am),La=n(am,"DIV",{class:!0});var IP=s(La);M(dX.$$.fragment,IP),_4a=i(IP),qno=n(IP,"P",{});var tQn=s(qno);b4a=r(tQn,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),tQn.forEach(t),v4a=i(IP),bh=n(IP,"P",{});var hCe=s(bh);M4a=r(hCe,`Note:
Loading a model from its configuration file does `),Ono=n(hCe,"STRONG",{});var aQn=s(Ono);T4a=r(aQn,"not"),aQn.forEach(t),F4a=r(hCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Y0e=n(hCe,"A",{href:!0});var nQn=s(Y0e);E4a=r(nQn,"from_pretrained()"),nQn.forEach(t),y4a=r(hCe," to load the model weights."),hCe.forEach(t),C4a=i(IP),M(EB.$$.fragment,IP),IP.forEach(t),w4a=i(am),Fo=n(am,"DIV",{class:!0});var _n=s(Fo);M(mX.$$.fragment,_n),L4a=i(_n),Qno=n(_n,"P",{});var sQn=s(Qno);A4a=r(sQn,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),sQn.forEach(t),R4a=i(_n),ys=n(_n,"P",{});var NP=s(ys);k4a=r(NP,"The model class to instantiate is selected based on the "),Yno=n(NP,"CODE",{});var lQn=s(Yno);S4a=r(lQn,"model_type"),lQn.forEach(t),x4a=r(NP,` property of the config object (either
passed as an argument or loaded from `),zno=n(NP,"CODE",{});var iQn=s(zno);G4a=r(iQn,"pretrained_model_name_or_path"),iQn.forEach(t),B4a=r(NP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Hno=n(NP,"CODE",{});var dQn=s(Hno);I4a=r(dQn,"pretrained_model_name_or_path"),dQn.forEach(t),N4a=r(NP,":"),NP.forEach(t),$4a=i(_n),Kno=n(_n,"UL",{});var mQn=s(Kno);yB=n(mQn,"LI",{});var nko=s(yB);eso=n(nko,"STRONG",{});var cQn=s(eso);j4a=r(cQn,"detr"),cQn.forEach(t),P4a=r(nko," \u2014 "),z0e=n(nko,"A",{href:!0});var fQn=s(z0e);Z4a=r(fQn,"DetrForSegmentation"),fQn.forEach(t),V4a=r(nko," (DETR model)"),nko.forEach(t),mQn.forEach(t),W4a=i(_n),CB=n(_n,"P",{});var sko=s(CB);J4a=r(sko,"The model is set in evaluation mode by default using "),oso=n(sko,"CODE",{});var hQn=s(oso);U4a=r(hQn,"model.eval()"),hQn.forEach(t),X4a=r(sko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),rso=n(sko,"CODE",{});var gQn=s(rso);D4a=r(gQn,"model.train()"),gQn.forEach(t),sko.forEach(t),q4a=i(_n),M(wB.$$.fragment,_n),_n.forEach(t),am.forEach(t),QGo=i(c),vh=n(c,"H3",{class:!0});var UNo=s(vh);LB=n(UNo,"A",{id:!0,class:!0,href:!0});var uQn=s(LB);tso=n(uQn,"SPAN",{});var pQn=s(tso);M(cX.$$.fragment,pQn),pQn.forEach(t),uQn.forEach(t),O4a=i(UNo),aso=n(UNo,"SPAN",{});var _Qn=s(aso);Q4a=r(_Qn,"AutoModelForSemanticSegmentation"),_Qn.forEach(t),UNo.forEach(t),YGo=i(c),kr=n(c,"DIV",{class:!0});var nm=s(kr);M(fX.$$.fragment,nm),Y4a=i(nm),Mh=n(nm,"P",{});var gCe=s(Mh);z4a=r(gCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),H0e=n(gCe,"A",{href:!0});var bQn=s(H0e);H4a=r(bQn,"from_pretrained()"),bQn.forEach(t),K4a=r(gCe," class method or the "),K0e=n(gCe,"A",{href:!0});var vQn=s(K0e);e9a=r(vQn,"from_config()"),vQn.forEach(t),o9a=r(gCe,` class
method.`),gCe.forEach(t),r9a=i(nm),hX=n(nm,"P",{});var XNo=s(hX);t9a=r(XNo,"This class cannot be instantiated directly using "),nso=n(XNo,"CODE",{});var MQn=s(nso);a9a=r(MQn,"__init__()"),MQn.forEach(t),n9a=r(XNo," (throws an error)."),XNo.forEach(t),s9a=i(nm),Aa=n(nm,"DIV",{class:!0});var $P=s(Aa);M(gX.$$.fragment,$P),l9a=i($P),sso=n($P,"P",{});var TQn=s(sso);i9a=r(TQn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),TQn.forEach(t),d9a=i($P),Th=n($P,"P",{});var uCe=s(Th);m9a=r(uCe,`Note:
Loading a model from its configuration file does `),lso=n(uCe,"STRONG",{});var FQn=s(lso);c9a=r(FQn,"not"),FQn.forEach(t),f9a=r(uCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),e5e=n(uCe,"A",{href:!0});var EQn=s(e5e);h9a=r(EQn,"from_pretrained()"),EQn.forEach(t),g9a=r(uCe," to load the model weights."),uCe.forEach(t),u9a=i($P),M(AB.$$.fragment,$P),$P.forEach(t),p9a=i(nm),Eo=n(nm,"DIV",{class:!0});var bn=s(Eo);M(uX.$$.fragment,bn),_9a=i(bn),iso=n(bn,"P",{});var yQn=s(iso);b9a=r(yQn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),yQn.forEach(t),v9a=i(bn),Cs=n(bn,"P",{});var jP=s(Cs);M9a=r(jP,"The model class to instantiate is selected based on the "),dso=n(jP,"CODE",{});var CQn=s(dso);T9a=r(CQn,"model_type"),CQn.forEach(t),F9a=r(jP,` property of the config object (either
passed as an argument or loaded from `),mso=n(jP,"CODE",{});var wQn=s(mso);E9a=r(wQn,"pretrained_model_name_or_path"),wQn.forEach(t),y9a=r(jP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cso=n(jP,"CODE",{});var LQn=s(cso);C9a=r(LQn,"pretrained_model_name_or_path"),LQn.forEach(t),w9a=r(jP,":"),jP.forEach(t),L9a=i(bn),Ye=n(bn,"UL",{});var $o=s(Ye);RB=n($o,"LI",{});var lko=s(RB);fso=n(lko,"STRONG",{});var AQn=s(fso);A9a=r(AQn,"beit"),AQn.forEach(t),R9a=r(lko," \u2014 "),o5e=n(lko,"A",{href:!0});var RQn=s(o5e);k9a=r(RQn,"BeitForSemanticSegmentation"),RQn.forEach(t),S9a=r(lko," (BEiT model)"),lko.forEach(t),x9a=i($o),kB=n($o,"LI",{});var iko=s(kB);hso=n(iko,"STRONG",{});var kQn=s(hso);G9a=r(kQn,"data2vec-vision"),kQn.forEach(t),B9a=r(iko," \u2014 "),r5e=n(iko,"A",{href:!0});var SQn=s(r5e);I9a=r(SQn,"Data2VecVisionForSemanticSegmentation"),SQn.forEach(t),N9a=r(iko," (Data2VecVision model)"),iko.forEach(t),$9a=i($o),SB=n($o,"LI",{});var dko=s(SB);gso=n(dko,"STRONG",{});var xQn=s(gso);j9a=r(xQn,"dpt"),xQn.forEach(t),P9a=r(dko," \u2014 "),t5e=n(dko,"A",{href:!0});var GQn=s(t5e);Z9a=r(GQn,"DPTForSemanticSegmentation"),GQn.forEach(t),V9a=r(dko," (DPT model)"),dko.forEach(t),W9a=i($o),xB=n($o,"LI",{});var mko=s(xB);uso=n(mko,"STRONG",{});var BQn=s(uso);J9a=r(BQn,"mobilenet_v2"),BQn.forEach(t),U9a=r(mko," \u2014 "),a5e=n(mko,"A",{href:!0});var IQn=s(a5e);X9a=r(IQn,"MobileNetV2ForSemanticSegmentation"),IQn.forEach(t),D9a=r(mko," (MobileNetV2 model)"),mko.forEach(t),q9a=i($o),GB=n($o,"LI",{});var cko=s(GB);pso=n(cko,"STRONG",{});var NQn=s(pso);O9a=r(NQn,"mobilevit"),NQn.forEach(t),Q9a=r(cko," \u2014 "),n5e=n(cko,"A",{href:!0});var $Qn=s(n5e);Y9a=r($Qn,"MobileViTForSemanticSegmentation"),$Qn.forEach(t),z9a=r(cko," (MobileViT model)"),cko.forEach(t),H9a=i($o),BB=n($o,"LI",{});var fko=s(BB);_so=n(fko,"STRONG",{});var jQn=s(_so);K9a=r(jQn,"mobilevitv2"),jQn.forEach(t),eCa=r(fko," \u2014 "),s5e=n(fko,"A",{href:!0});var PQn=s(s5e);oCa=r(PQn,"MobileViTV2ForSemanticSegmentation"),PQn.forEach(t),rCa=r(fko," (MobileViTV2 model)"),fko.forEach(t),tCa=i($o),IB=n($o,"LI",{});var hko=s(IB);bso=n(hko,"STRONG",{});var ZQn=s(bso);aCa=r(ZQn,"segformer"),ZQn.forEach(t),nCa=r(hko," \u2014 "),l5e=n(hko,"A",{href:!0});var VQn=s(l5e);sCa=r(VQn,"SegformerForSemanticSegmentation"),VQn.forEach(t),lCa=r(hko," (SegFormer model)"),hko.forEach(t),iCa=i($o),NB=n($o,"LI",{});var gko=s(NB);vso=n(gko,"STRONG",{});var WQn=s(vso);dCa=r(WQn,"upernet"),WQn.forEach(t),mCa=r(gko," \u2014 "),i5e=n(gko,"A",{href:!0});var JQn=s(i5e);cCa=r(JQn,"UperNetForSemanticSegmentation"),JQn.forEach(t),fCa=r(gko," (UPerNet model)"),gko.forEach(t),$o.forEach(t),hCa=i(bn),$B=n(bn,"P",{});var uko=s($B);gCa=r(uko,"The model is set in evaluation mode by default using "),Mso=n(uko,"CODE",{});var UQn=s(Mso);uCa=r(UQn,"model.eval()"),UQn.forEach(t),pCa=r(uko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Tso=n(uko,"CODE",{});var XQn=s(Tso);_Ca=r(XQn,"model.train()"),XQn.forEach(t),uko.forEach(t),bCa=i(bn),M(jB.$$.fragment,bn),bn.forEach(t),nm.forEach(t),zGo=i(c),Fh=n(c,"H3",{class:!0});var DNo=s(Fh);PB=n(DNo,"A",{id:!0,class:!0,href:!0});var DQn=s(PB);Fso=n(DQn,"SPAN",{});var qQn=s(Fso);M(pX.$$.fragment,qQn),qQn.forEach(t),DQn.forEach(t),vCa=i(DNo),Eso=n(DNo,"SPAN",{});var OQn=s(Eso);MCa=r(OQn,"TFAutoModelForSemanticSegmentation"),OQn.forEach(t),DNo.forEach(t),HGo=i(c),Sr=n(c,"DIV",{class:!0});var sm=s(Sr);M(_X.$$.fragment,sm),TCa=i(sm),Eh=n(sm,"P",{});var pCe=s(Eh);FCa=r(pCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),d5e=n(pCe,"A",{href:!0});var QQn=s(d5e);ECa=r(QQn,"from_pretrained()"),QQn.forEach(t),yCa=r(pCe," class method or the "),m5e=n(pCe,"A",{href:!0});var YQn=s(m5e);CCa=r(YQn,"from_config()"),YQn.forEach(t),wCa=r(pCe,` class
method.`),pCe.forEach(t),LCa=i(sm),bX=n(sm,"P",{});var qNo=s(bX);ACa=r(qNo,"This class cannot be instantiated directly using "),yso=n(qNo,"CODE",{});var zQn=s(yso);RCa=r(zQn,"__init__()"),zQn.forEach(t),kCa=r(qNo," (throws an error)."),qNo.forEach(t),SCa=i(sm),Ra=n(sm,"DIV",{class:!0});var PP=s(Ra);M(vX.$$.fragment,PP),xCa=i(PP),Cso=n(PP,"P",{});var HQn=s(Cso);GCa=r(HQn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),HQn.forEach(t),BCa=i(PP),yh=n(PP,"P",{});var _Ce=s(yh);ICa=r(_Ce,`Note:
Loading a model from its configuration file does `),wso=n(_Ce,"STRONG",{});var KQn=s(wso);NCa=r(KQn,"not"),KQn.forEach(t),$Ca=r(_Ce,` load the model weights. It only affects the
model\u2019s configuration. Use `),c5e=n(_Ce,"A",{href:!0});var eYn=s(c5e);jCa=r(eYn,"from_pretrained()"),eYn.forEach(t),PCa=r(_Ce," to load the model weights."),_Ce.forEach(t),ZCa=i(PP),M(ZB.$$.fragment,PP),PP.forEach(t),VCa=i(sm),wt=n(sm,"DIV",{class:!0});var lm=s(wt);M(MX.$$.fragment,lm),WCa=i(lm),Lso=n(lm,"P",{});var oYn=s(Lso);JCa=r(oYn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),oYn.forEach(t),UCa=i(lm),ws=n(lm,"P",{});var ZP=s(ws);XCa=r(ZP,"The model class to instantiate is selected based on the "),Aso=n(ZP,"CODE",{});var rYn=s(Aso);DCa=r(rYn,"model_type"),rYn.forEach(t),qCa=r(ZP,` property of the config object (either
passed as an argument or loaded from `),Rso=n(ZP,"CODE",{});var tYn=s(Rso);OCa=r(tYn,"pretrained_model_name_or_path"),tYn.forEach(t),QCa=r(ZP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),kso=n(ZP,"CODE",{});var aYn=s(kso);YCa=r(aYn,"pretrained_model_name_or_path"),aYn.forEach(t),zCa=r(ZP,":"),ZP.forEach(t),HCa=i(lm),Ch=n(lm,"UL",{});var bCe=s(Ch);VB=n(bCe,"LI",{});var pko=s(VB);Sso=n(pko,"STRONG",{});var nYn=s(Sso);KCa=r(nYn,"data2vec-vision"),nYn.forEach(t),ewa=r(pko," \u2014 "),f5e=n(pko,"A",{href:!0});var sYn=s(f5e);owa=r(sYn,"TFData2VecVisionForSemanticSegmentation"),sYn.forEach(t),rwa=r(pko," (Data2VecVision model)"),pko.forEach(t),twa=i(bCe),WB=n(bCe,"LI",{});var _ko=s(WB);xso=n(_ko,"STRONG",{});var lYn=s(xso);awa=r(lYn,"mobilevit"),lYn.forEach(t),nwa=r(_ko," \u2014 "),h5e=n(_ko,"A",{href:!0});var iYn=s(h5e);swa=r(iYn,"TFMobileViTForSemanticSegmentation"),iYn.forEach(t),lwa=r(_ko," (MobileViT model)"),_ko.forEach(t),iwa=i(bCe),JB=n(bCe,"LI",{});var bko=s(JB);Gso=n(bko,"STRONG",{});var dYn=s(Gso);dwa=r(dYn,"segformer"),dYn.forEach(t),mwa=r(bko," \u2014 "),g5e=n(bko,"A",{href:!0});var mYn=s(g5e);cwa=r(mYn,"TFSegformerForSemanticSegmentation"),mYn.forEach(t),fwa=r(bko," (SegFormer model)"),bko.forEach(t),bCe.forEach(t),hwa=i(lm),M(UB.$$.fragment,lm),lm.forEach(t),sm.forEach(t),KGo=i(c),wh=n(c,"H3",{class:!0});var ONo=s(wh);XB=n(ONo,"A",{id:!0,class:!0,href:!0});var cYn=s(XB);Bso=n(cYn,"SPAN",{});var fYn=s(Bso);M(TX.$$.fragment,fYn),fYn.forEach(t),cYn.forEach(t),gwa=i(ONo),Iso=n(ONo,"SPAN",{});var hYn=s(Iso);uwa=r(hYn,"AutoModelForInstanceSegmentation"),hYn.forEach(t),ONo.forEach(t),eBo=i(c),xr=n(c,"DIV",{class:!0});var im=s(xr);M(FX.$$.fragment,im),pwa=i(im),Lh=n(im,"P",{});var vCe=s(Lh);_wa=r(vCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),u5e=n(vCe,"A",{href:!0});var gYn=s(u5e);bwa=r(gYn,"from_pretrained()"),gYn.forEach(t),vwa=r(vCe," class method or the "),p5e=n(vCe,"A",{href:!0});var uYn=s(p5e);Mwa=r(uYn,"from_config()"),uYn.forEach(t),Twa=r(vCe,` class
method.`),vCe.forEach(t),Fwa=i(im),EX=n(im,"P",{});var QNo=s(EX);Ewa=r(QNo,"This class cannot be instantiated directly using "),Nso=n(QNo,"CODE",{});var pYn=s(Nso);ywa=r(pYn,"__init__()"),pYn.forEach(t),Cwa=r(QNo," (throws an error)."),QNo.forEach(t),wwa=i(im),ka=n(im,"DIV",{class:!0});var VP=s(ka);M(yX.$$.fragment,VP),Lwa=i(VP),$so=n(VP,"P",{});var _Yn=s($so);Awa=r(_Yn,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),_Yn.forEach(t),Rwa=i(VP),Ah=n(VP,"P",{});var MCe=s(Ah);kwa=r(MCe,`Note:
Loading a model from its configuration file does `),jso=n(MCe,"STRONG",{});var bYn=s(jso);Swa=r(bYn,"not"),bYn.forEach(t),xwa=r(MCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),_5e=n(MCe,"A",{href:!0});var vYn=s(_5e);Gwa=r(vYn,"from_pretrained()"),vYn.forEach(t),Bwa=r(MCe," to load the model weights."),MCe.forEach(t),Iwa=i(VP),M(DB.$$.fragment,VP),VP.forEach(t),Nwa=i(im),yo=n(im,"DIV",{class:!0});var vn=s(yo);M(CX.$$.fragment,vn),$wa=i(vn),Pso=n(vn,"P",{});var MYn=s(Pso);jwa=r(MYn,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),MYn.forEach(t),Pwa=i(vn),Ls=n(vn,"P",{});var WP=s(Ls);Zwa=r(WP,"The model class to instantiate is selected based on the "),Zso=n(WP,"CODE",{});var TYn=s(Zso);Vwa=r(TYn,"model_type"),TYn.forEach(t),Wwa=r(WP,` property of the config object (either
passed as an argument or loaded from `),Vso=n(WP,"CODE",{});var FYn=s(Vso);Jwa=r(FYn,"pretrained_model_name_or_path"),FYn.forEach(t),Uwa=r(WP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Wso=n(WP,"CODE",{});var EYn=s(Wso);Xwa=r(EYn,"pretrained_model_name_or_path"),EYn.forEach(t),Dwa=r(WP,":"),WP.forEach(t),qwa=i(vn),Jso=n(vn,"UL",{});var yYn=s(Jso);qB=n(yYn,"LI",{});var vko=s(qB);Uso=n(vko,"STRONG",{});var CYn=s(Uso);Owa=r(CYn,"maskformer"),CYn.forEach(t),Qwa=r(vko," \u2014 "),b5e=n(vko,"A",{href:!0});var wYn=s(b5e);Ywa=r(wYn,"MaskFormerForInstanceSegmentation"),wYn.forEach(t),zwa=r(vko," (MaskFormer model)"),vko.forEach(t),yYn.forEach(t),Hwa=i(vn),OB=n(vn,"P",{});var Mko=s(OB);Kwa=r(Mko,"The model is set in evaluation mode by default using "),Xso=n(Mko,"CODE",{});var LYn=s(Xso);e6a=r(LYn,"model.eval()"),LYn.forEach(t),o6a=r(Mko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Dso=n(Mko,"CODE",{});var AYn=s(Dso);r6a=r(AYn,"model.train()"),AYn.forEach(t),Mko.forEach(t),t6a=i(vn),M(QB.$$.fragment,vn),vn.forEach(t),im.forEach(t),oBo=i(c),Rh=n(c,"H3",{class:!0});var YNo=s(Rh);YB=n(YNo,"A",{id:!0,class:!0,href:!0});var RYn=s(YB);qso=n(RYn,"SPAN",{});var kYn=s(qso);M(wX.$$.fragment,kYn),kYn.forEach(t),RYn.forEach(t),a6a=i(YNo),Oso=n(YNo,"SPAN",{});var SYn=s(Oso);n6a=r(SYn,"AutoModelForUniversalSegmentation"),SYn.forEach(t),YNo.forEach(t),rBo=i(c),Gr=n(c,"DIV",{class:!0});var dm=s(Gr);M(LX.$$.fragment,dm),s6a=i(dm),kh=n(dm,"P",{});var TCe=s(kh);l6a=r(TCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the `),v5e=n(TCe,"A",{href:!0});var xYn=s(v5e);i6a=r(xYn,"from_pretrained()"),xYn.forEach(t),d6a=r(TCe," class method or the "),M5e=n(TCe,"A",{href:!0});var GYn=s(M5e);m6a=r(GYn,"from_config()"),GYn.forEach(t),c6a=r(TCe,` class
method.`),TCe.forEach(t),f6a=i(dm),AX=n(dm,"P",{});var zNo=s(AX);h6a=r(zNo,"This class cannot be instantiated directly using "),Qso=n(zNo,"CODE",{});var BYn=s(Qso);g6a=r(BYn,"__init__()"),BYn.forEach(t),u6a=r(zNo," (throws an error)."),zNo.forEach(t),p6a=i(dm),Sa=n(dm,"DIV",{class:!0});var JP=s(Sa);M(RX.$$.fragment,JP),_6a=i(JP),Yso=n(JP,"P",{});var IYn=s(Yso);b6a=r(IYn,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),IYn.forEach(t),v6a=i(JP),Sh=n(JP,"P",{});var FCe=s(Sh);M6a=r(FCe,`Note:
Loading a model from its configuration file does `),zso=n(FCe,"STRONG",{});var NYn=s(zso);T6a=r(NYn,"not"),NYn.forEach(t),F6a=r(FCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),T5e=n(FCe,"A",{href:!0});var $Yn=s(T5e);E6a=r($Yn,"from_pretrained()"),$Yn.forEach(t),y6a=r(FCe," to load the model weights."),FCe.forEach(t),C6a=i(JP),M(zB.$$.fragment,JP),JP.forEach(t),w6a=i(dm),Co=n(dm,"DIV",{class:!0});var Mn=s(Co);M(kX.$$.fragment,Mn),L6a=i(Mn),Hso=n(Mn,"P",{});var jYn=s(Hso);A6a=r(jYn,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),jYn.forEach(t),R6a=i(Mn),As=n(Mn,"P",{});var UP=s(As);k6a=r(UP,"The model class to instantiate is selected based on the "),Kso=n(UP,"CODE",{});var PYn=s(Kso);S6a=r(PYn,"model_type"),PYn.forEach(t),x6a=r(UP,` property of the config object (either
passed as an argument or loaded from `),elo=n(UP,"CODE",{});var ZYn=s(elo);G6a=r(ZYn,"pretrained_model_name_or_path"),ZYn.forEach(t),B6a=r(UP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),olo=n(UP,"CODE",{});var VYn=s(olo);I6a=r(VYn,"pretrained_model_name_or_path"),VYn.forEach(t),N6a=r(UP,":"),UP.forEach(t),$6a=i(Mn),Rs=n(Mn,"UL",{});var XP=s(Rs);HB=n(XP,"LI",{});var Tko=s(HB);rlo=n(Tko,"STRONG",{});var WYn=s(rlo);j6a=r(WYn,"detr"),WYn.forEach(t),P6a=r(Tko," \u2014 "),F5e=n(Tko,"A",{href:!0});var JYn=s(F5e);Z6a=r(JYn,"DetrForSegmentation"),JYn.forEach(t),V6a=r(Tko," (DETR model)"),Tko.forEach(t),W6a=i(XP),KB=n(XP,"LI",{});var Fko=s(KB);tlo=n(Fko,"STRONG",{});var UYn=s(tlo);J6a=r(UYn,"mask2former"),UYn.forEach(t),U6a=r(Fko," \u2014 "),E5e=n(Fko,"A",{href:!0});var XYn=s(E5e);X6a=r(XYn,"Mask2FormerForUniversalSegmentation"),XYn.forEach(t),D6a=r(Fko," (Mask2Former model)"),Fko.forEach(t),q6a=i(XP),eI=n(XP,"LI",{});var Eko=s(eI);alo=n(Eko,"STRONG",{});var DYn=s(alo);O6a=r(DYn,"maskformer"),DYn.forEach(t),Q6a=r(Eko," \u2014 "),y5e=n(Eko,"A",{href:!0});var qYn=s(y5e);Y6a=r(qYn,"MaskFormerForInstanceSegmentation"),qYn.forEach(t),z6a=r(Eko," (MaskFormer model)"),Eko.forEach(t),H6a=i(XP),oI=n(XP,"LI",{});var yko=s(oI);nlo=n(yko,"STRONG",{});var OYn=s(nlo);K6a=r(OYn,"oneformer"),OYn.forEach(t),e8a=r(yko," \u2014 "),C5e=n(yko,"A",{href:!0});var QYn=s(C5e);o8a=r(QYn,"OneFormerForUniversalSegmentation"),QYn.forEach(t),r8a=r(yko," (OneFormer model)"),yko.forEach(t),XP.forEach(t),t8a=i(Mn),rI=n(Mn,"P",{});var Cko=s(rI);a8a=r(Cko,"The model is set in evaluation mode by default using "),slo=n(Cko,"CODE",{});var YYn=s(slo);n8a=r(YYn,"model.eval()"),YYn.forEach(t),s8a=r(Cko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),llo=n(Cko,"CODE",{});var zYn=s(llo);l8a=r(zYn,"model.train()"),zYn.forEach(t),Cko.forEach(t),i8a=i(Mn),M(tI.$$.fragment,Mn),Mn.forEach(t),dm.forEach(t),tBo=i(c),xh=n(c,"H3",{class:!0});var HNo=s(xh);aI=n(HNo,"A",{id:!0,class:!0,href:!0});var HYn=s(aI);ilo=n(HYn,"SPAN",{});var KYn=s(ilo);M(SX.$$.fragment,KYn),KYn.forEach(t),HYn.forEach(t),d8a=i(HNo),dlo=n(HNo,"SPAN",{});var ezn=s(dlo);m8a=r(ezn,"AutoModelForZeroShotImageClassification"),ezn.forEach(t),HNo.forEach(t),aBo=i(c),Br=n(c,"DIV",{class:!0});var mm=s(Br);M(xX.$$.fragment,mm),c8a=i(mm),Gh=n(mm,"P",{});var ECe=s(Gh);f8a=r(ECe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),w5e=n(ECe,"A",{href:!0});var ozn=s(w5e);h8a=r(ozn,"from_pretrained()"),ozn.forEach(t),g8a=r(ECe," class method or the "),L5e=n(ECe,"A",{href:!0});var rzn=s(L5e);u8a=r(rzn,"from_config()"),rzn.forEach(t),p8a=r(ECe,` class
method.`),ECe.forEach(t),_8a=i(mm),GX=n(mm,"P",{});var KNo=s(GX);b8a=r(KNo,"This class cannot be instantiated directly using "),mlo=n(KNo,"CODE",{});var tzn=s(mlo);v8a=r(tzn,"__init__()"),tzn.forEach(t),M8a=r(KNo," (throws an error)."),KNo.forEach(t),T8a=i(mm),xa=n(mm,"DIV",{class:!0});var DP=s(xa);M(BX.$$.fragment,DP),F8a=i(DP),clo=n(DP,"P",{});var azn=s(clo);E8a=r(azn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),azn.forEach(t),y8a=i(DP),Bh=n(DP,"P",{});var yCe=s(Bh);C8a=r(yCe,`Note:
Loading a model from its configuration file does `),flo=n(yCe,"STRONG",{});var nzn=s(flo);w8a=r(nzn,"not"),nzn.forEach(t),L8a=r(yCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),A5e=n(yCe,"A",{href:!0});var szn=s(A5e);A8a=r(szn,"from_pretrained()"),szn.forEach(t),R8a=r(yCe," to load the model weights."),yCe.forEach(t),k8a=i(DP),M(nI.$$.fragment,DP),DP.forEach(t),S8a=i(mm),wo=n(mm,"DIV",{class:!0});var Tn=s(wo);M(IX.$$.fragment,Tn),x8a=i(Tn),hlo=n(Tn,"P",{});var lzn=s(hlo);G8a=r(lzn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),lzn.forEach(t),B8a=i(Tn),ks=n(Tn,"P",{});var qP=s(ks);I8a=r(qP,"The model class to instantiate is selected based on the "),glo=n(qP,"CODE",{});var izn=s(glo);N8a=r(izn,"model_type"),izn.forEach(t),$8a=r(qP,` property of the config object (either
passed as an argument or loaded from `),ulo=n(qP,"CODE",{});var dzn=s(ulo);j8a=r(dzn,"pretrained_model_name_or_path"),dzn.forEach(t),P8a=r(qP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),plo=n(qP,"CODE",{});var mzn=s(plo);Z8a=r(mzn,"pretrained_model_name_or_path"),mzn.forEach(t),V8a=r(qP,":"),qP.forEach(t),W8a=i(Tn),Ir=n(Tn,"UL",{});var Fn=s(Ir);sI=n(Fn,"LI",{});var wko=s(sI);_lo=n(wko,"STRONG",{});var czn=s(_lo);J8a=r(czn,"align"),czn.forEach(t),U8a=r(wko," \u2014 "),R5e=n(wko,"A",{href:!0});var fzn=s(R5e);X8a=r(fzn,"AlignModel"),fzn.forEach(t),D8a=r(wko," (ALIGN model)"),wko.forEach(t),q8a=i(Fn),lI=n(Fn,"LI",{});var Lko=s(lI);blo=n(Lko,"STRONG",{});var hzn=s(blo);O8a=r(hzn,"altclip"),hzn.forEach(t),Q8a=r(Lko," \u2014 "),k5e=n(Lko,"A",{href:!0});var gzn=s(k5e);Y8a=r(gzn,"AltCLIPModel"),gzn.forEach(t),z8a=r(Lko," (AltCLIP model)"),Lko.forEach(t),H8a=i(Fn),iI=n(Fn,"LI",{});var Ako=s(iI);vlo=n(Ako,"STRONG",{});var uzn=s(vlo);K8a=r(uzn,"blip"),uzn.forEach(t),e7a=r(Ako," \u2014 "),S5e=n(Ako,"A",{href:!0});var pzn=s(S5e);o7a=r(pzn,"BlipModel"),pzn.forEach(t),r7a=r(Ako," (BLIP model)"),Ako.forEach(t),t7a=i(Fn),dI=n(Fn,"LI",{});var Rko=s(dI);Mlo=n(Rko,"STRONG",{});var _zn=s(Mlo);a7a=r(_zn,"chinese_clip"),_zn.forEach(t),n7a=r(Rko," \u2014 "),x5e=n(Rko,"A",{href:!0});var bzn=s(x5e);s7a=r(bzn,"ChineseCLIPModel"),bzn.forEach(t),l7a=r(Rko," (Chinese-CLIP model)"),Rko.forEach(t),i7a=i(Fn),mI=n(Fn,"LI",{});var kko=s(mI);Tlo=n(kko,"STRONG",{});var vzn=s(Tlo);d7a=r(vzn,"clip"),vzn.forEach(t),m7a=r(kko," \u2014 "),G5e=n(kko,"A",{href:!0});var Mzn=s(G5e);c7a=r(Mzn,"CLIPModel"),Mzn.forEach(t),f7a=r(kko," (CLIP model)"),kko.forEach(t),h7a=i(Fn),cI=n(Fn,"LI",{});var Sko=s(cI);Flo=n(Sko,"STRONG",{});var Tzn=s(Flo);g7a=r(Tzn,"clipseg"),Tzn.forEach(t),u7a=r(Sko," \u2014 "),B5e=n(Sko,"A",{href:!0});var Fzn=s(B5e);p7a=r(Fzn,"CLIPSegModel"),Fzn.forEach(t),_7a=r(Sko," (CLIPSeg model)"),Sko.forEach(t),Fn.forEach(t),b7a=i(Tn),fI=n(Tn,"P",{});var xko=s(fI);v7a=r(xko,"The model is set in evaluation mode by default using "),Elo=n(xko,"CODE",{});var Ezn=s(Elo);M7a=r(Ezn,"model.eval()"),Ezn.forEach(t),T7a=r(xko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ylo=n(xko,"CODE",{});var yzn=s(ylo);F7a=r(yzn,"model.train()"),yzn.forEach(t),xko.forEach(t),E7a=i(Tn),M(hI.$$.fragment,Tn),Tn.forEach(t),mm.forEach(t),nBo=i(c),Ih=n(c,"H3",{class:!0});var e$o=s(Ih);gI=n(e$o,"A",{id:!0,class:!0,href:!0});var Czn=s(gI);Clo=n(Czn,"SPAN",{});var wzn=s(Clo);M(NX.$$.fragment,wzn),wzn.forEach(t),Czn.forEach(t),y7a=i(e$o),wlo=n(e$o,"SPAN",{});var Lzn=s(wlo);C7a=r(Lzn,"TFAutoModelForZeroShotImageClassification"),Lzn.forEach(t),e$o.forEach(t),sBo=i(c),Nr=n(c,"DIV",{class:!0});var cm=s(Nr);M($X.$$.fragment,cm),w7a=i(cm),Nh=n(cm,"P",{});var CCe=s(Nh);L7a=r(CCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),I5e=n(CCe,"A",{href:!0});var Azn=s(I5e);A7a=r(Azn,"from_pretrained()"),Azn.forEach(t),R7a=r(CCe," class method or the "),N5e=n(CCe,"A",{href:!0});var Rzn=s(N5e);k7a=r(Rzn,"from_config()"),Rzn.forEach(t),S7a=r(CCe,` class
method.`),CCe.forEach(t),x7a=i(cm),jX=n(cm,"P",{});var o$o=s(jX);G7a=r(o$o,"This class cannot be instantiated directly using "),Llo=n(o$o,"CODE",{});var kzn=s(Llo);B7a=r(kzn,"__init__()"),kzn.forEach(t),I7a=r(o$o," (throws an error)."),o$o.forEach(t),N7a=i(cm),Ga=n(cm,"DIV",{class:!0});var OP=s(Ga);M(PX.$$.fragment,OP),$7a=i(OP),Alo=n(OP,"P",{});var Szn=s(Alo);j7a=r(Szn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),Szn.forEach(t),P7a=i(OP),$h=n(OP,"P",{});var wCe=s($h);Z7a=r(wCe,`Note:
Loading a model from its configuration file does `),Rlo=n(wCe,"STRONG",{});var xzn=s(Rlo);V7a=r(xzn,"not"),xzn.forEach(t),W7a=r(wCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$5e=n(wCe,"A",{href:!0});var Gzn=s($5e);J7a=r(Gzn,"from_pretrained()"),Gzn.forEach(t),U7a=r(wCe," to load the model weights."),wCe.forEach(t),X7a=i(OP),M(uI.$$.fragment,OP),OP.forEach(t),D7a=i(cm),Lt=n(cm,"DIV",{class:!0});var fm=s(Lt);M(ZX.$$.fragment,fm),q7a=i(fm),klo=n(fm,"P",{});var Bzn=s(klo);O7a=r(Bzn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Bzn.forEach(t),Q7a=i(fm),Ss=n(fm,"P",{});var QP=s(Ss);Y7a=r(QP,"The model class to instantiate is selected based on the "),Slo=n(QP,"CODE",{});var Izn=s(Slo);z7a=r(Izn,"model_type"),Izn.forEach(t),H7a=r(QP,` property of the config object (either
passed as an argument or loaded from `),xlo=n(QP,"CODE",{});var Nzn=s(xlo);K7a=r(Nzn,"pretrained_model_name_or_path"),Nzn.forEach(t),eLa=r(QP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Glo=n(QP,"CODE",{});var $zn=s(Glo);oLa=r($zn,"pretrained_model_name_or_path"),$zn.forEach(t),rLa=r(QP,":"),QP.forEach(t),tLa=i(fm),VX=n(fm,"UL",{});var r$o=s(VX);pI=n(r$o,"LI",{});var Gko=s(pI);Blo=n(Gko,"STRONG",{});var jzn=s(Blo);aLa=r(jzn,"blip"),jzn.forEach(t),nLa=r(Gko," \u2014 "),j5e=n(Gko,"A",{href:!0});var Pzn=s(j5e);sLa=r(Pzn,"TFBlipModel"),Pzn.forEach(t),lLa=r(Gko," (BLIP model)"),Gko.forEach(t),iLa=i(r$o),_I=n(r$o,"LI",{});var Bko=s(_I);Ilo=n(Bko,"STRONG",{});var Zzn=s(Ilo);dLa=r(Zzn,"clip"),Zzn.forEach(t),mLa=r(Bko," \u2014 "),P5e=n(Bko,"A",{href:!0});var Vzn=s(P5e);cLa=r(Vzn,"TFCLIPModel"),Vzn.forEach(t),fLa=r(Bko," (CLIP model)"),Bko.forEach(t),r$o.forEach(t),hLa=i(fm),M(bI.$$.fragment,fm),fm.forEach(t),cm.forEach(t),lBo=i(c),jh=n(c,"H3",{class:!0});var t$o=s(jh);vI=n(t$o,"A",{id:!0,class:!0,href:!0});var Wzn=s(vI);Nlo=n(Wzn,"SPAN",{});var Jzn=s(Nlo);M(WX.$$.fragment,Jzn),Jzn.forEach(t),Wzn.forEach(t),gLa=i(t$o),$lo=n(t$o,"SPAN",{});var Uzn=s($lo);uLa=r(Uzn,"AutoModelForZeroShotObjectDetection"),Uzn.forEach(t),t$o.forEach(t),iBo=i(c),$r=n(c,"DIV",{class:!0});var hm=s($r);M(JX.$$.fragment,hm),pLa=i(hm),Ph=n(hm,"P",{});var LCe=s(Ph);_La=r(LCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),Z5e=n(LCe,"A",{href:!0});var Xzn=s(Z5e);bLa=r(Xzn,"from_pretrained()"),Xzn.forEach(t),vLa=r(LCe," class method or the "),V5e=n(LCe,"A",{href:!0});var Dzn=s(V5e);MLa=r(Dzn,"from_config()"),Dzn.forEach(t),TLa=r(LCe,` class
method.`),LCe.forEach(t),FLa=i(hm),UX=n(hm,"P",{});var a$o=s(UX);ELa=r(a$o,"This class cannot be instantiated directly using "),jlo=n(a$o,"CODE",{});var qzn=s(jlo);yLa=r(qzn,"__init__()"),qzn.forEach(t),CLa=r(a$o," (throws an error)."),a$o.forEach(t),wLa=i(hm),Ba=n(hm,"DIV",{class:!0});var YP=s(Ba);M(XX.$$.fragment,YP),LLa=i(YP),Plo=n(YP,"P",{});var Ozn=s(Plo);ALa=r(Ozn,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),Ozn.forEach(t),RLa=i(YP),Zh=n(YP,"P",{});var ACe=s(Zh);kLa=r(ACe,`Note:
Loading a model from its configuration file does `),Zlo=n(ACe,"STRONG",{});var Qzn=s(Zlo);SLa=r(Qzn,"not"),Qzn.forEach(t),xLa=r(ACe,` load the model weights. It only affects the
model\u2019s configuration. Use `),W5e=n(ACe,"A",{href:!0});var Yzn=s(W5e);GLa=r(Yzn,"from_pretrained()"),Yzn.forEach(t),BLa=r(ACe," to load the model weights."),ACe.forEach(t),ILa=i(YP),M(MI.$$.fragment,YP),YP.forEach(t),NLa=i(hm),Lo=n(hm,"DIV",{class:!0});var En=s(Lo);M(DX.$$.fragment,En),$La=i(En),Vlo=n(En,"P",{});var zzn=s(Vlo);jLa=r(zzn,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),zzn.forEach(t),PLa=i(En),xs=n(En,"P",{});var zP=s(xs);ZLa=r(zP,"The model class to instantiate is selected based on the "),Wlo=n(zP,"CODE",{});var Hzn=s(Wlo);VLa=r(Hzn,"model_type"),Hzn.forEach(t),WLa=r(zP,` property of the config object (either
passed as an argument or loaded from `),Jlo=n(zP,"CODE",{});var Kzn=s(Jlo);JLa=r(Kzn,"pretrained_model_name_or_path"),Kzn.forEach(t),ULa=r(zP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ulo=n(zP,"CODE",{});var eHn=s(Ulo);XLa=r(eHn,"pretrained_model_name_or_path"),eHn.forEach(t),DLa=r(zP,":"),zP.forEach(t),qLa=i(En),Xlo=n(En,"UL",{});var oHn=s(Xlo);TI=n(oHn,"LI",{});var Iko=s(TI);Dlo=n(Iko,"STRONG",{});var rHn=s(Dlo);OLa=r(rHn,"owlvit"),rHn.forEach(t),QLa=r(Iko," \u2014 "),J5e=n(Iko,"A",{href:!0});var tHn=s(J5e);YLa=r(tHn,"OwlViTForObjectDetection"),tHn.forEach(t),zLa=r(Iko," (OWL-ViT model)"),Iko.forEach(t),oHn.forEach(t),HLa=i(En),FI=n(En,"P",{});var Nko=s(FI);KLa=r(Nko,"The model is set in evaluation mode by default using "),qlo=n(Nko,"CODE",{});var aHn=s(qlo);eAa=r(aHn,"model.eval()"),aHn.forEach(t),oAa=r(Nko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Olo=n(Nko,"CODE",{});var nHn=s(Olo);rAa=r(nHn,"model.train()"),nHn.forEach(t),Nko.forEach(t),tAa=i(En),M(EI.$$.fragment,En),En.forEach(t),hm.forEach(t),dBo=i(c),Vh=n(c,"H2",{class:!0});var n$o=s(Vh);yI=n(n$o,"A",{id:!0,class:!0,href:!0});var sHn=s(yI);Qlo=n(sHn,"SPAN",{});var lHn=s(Qlo);M(qX.$$.fragment,lHn),lHn.forEach(t),sHn.forEach(t),aAa=i(n$o),Ylo=n(n$o,"SPAN",{});var iHn=s(Ylo);nAa=r(iHn,"Audio"),iHn.forEach(t),n$o.forEach(t),mBo=i(c),U5e=n(c,"P",{});var dHn=s(U5e);sAa=r(dHn,"The following auto classes are available for the following audio tasks."),dHn.forEach(t),cBo=i(c),Wh=n(c,"H3",{class:!0});var s$o=s(Wh);CI=n(s$o,"A",{id:!0,class:!0,href:!0});var mHn=s(CI);zlo=n(mHn,"SPAN",{});var cHn=s(zlo);M(OX.$$.fragment,cHn),cHn.forEach(t),mHn.forEach(t),lAa=i(s$o),Hlo=n(s$o,"SPAN",{});var fHn=s(Hlo);iAa=r(fHn,"AutoModelForAudioClassification"),fHn.forEach(t),s$o.forEach(t),fBo=i(c),jr=n(c,"DIV",{class:!0});var gm=s(jr);M(QX.$$.fragment,gm),dAa=i(gm),Jh=n(gm,"P",{});var RCe=s(Jh);mAa=r(RCe,`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 `),X5e=n(RCe,"A",{href:!0});var hHn=s(X5e);cAa=r(hHn,"from_pretrained()"),hHn.forEach(t),fAa=r(RCe," class method or the "),D5e=n(RCe,"A",{href:!0});var gHn=s(D5e);hAa=r(gHn,"from_config()"),gHn.forEach(t),gAa=r(RCe,` class
method.`),RCe.forEach(t),uAa=i(gm),YX=n(gm,"P",{});var l$o=s(YX);pAa=r(l$o,"This class cannot be instantiated directly using "),Klo=n(l$o,"CODE",{});var uHn=s(Klo);_Aa=r(uHn,"__init__()"),uHn.forEach(t),bAa=r(l$o," (throws an error)."),l$o.forEach(t),vAa=i(gm),Ia=n(gm,"DIV",{class:!0});var HP=s(Ia);M(zX.$$.fragment,HP),MAa=i(HP),eio=n(HP,"P",{});var pHn=s(eio);TAa=r(pHn,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),pHn.forEach(t),FAa=i(HP),Uh=n(HP,"P",{});var kCe=s(Uh);EAa=r(kCe,`Note:
Loading a model from its configuration file does `),oio=n(kCe,"STRONG",{});var _Hn=s(oio);yAa=r(_Hn,"not"),_Hn.forEach(t),CAa=r(kCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),q5e=n(kCe,"A",{href:!0});var bHn=s(q5e);wAa=r(bHn,"from_pretrained()"),bHn.forEach(t),LAa=r(kCe," to load the model weights."),kCe.forEach(t),AAa=i(HP),M(wI.$$.fragment,HP),HP.forEach(t),RAa=i(gm),Ao=n(gm,"DIV",{class:!0});var yn=s(Ao);M(HX.$$.fragment,yn),kAa=i(yn),rio=n(yn,"P",{});var vHn=s(rio);SAa=r(vHn,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),vHn.forEach(t),xAa=i(yn),Gs=n(yn,"P",{});var KP=s(Gs);GAa=r(KP,"The model class to instantiate is selected based on the "),tio=n(KP,"CODE",{});var MHn=s(tio);BAa=r(MHn,"model_type"),MHn.forEach(t),IAa=r(KP,` property of the config object (either
passed as an argument or loaded from `),aio=n(KP,"CODE",{});var THn=s(aio);NAa=r(THn,"pretrained_model_name_or_path"),THn.forEach(t),$Aa=r(KP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nio=n(KP,"CODE",{});var FHn=s(nio);jAa=r(FHn,"pretrained_model_name_or_path"),FHn.forEach(t),PAa=r(KP,":"),KP.forEach(t),ZAa=i(yn),Be=n(yn,"UL",{});var Je=s(Be);LI=n(Je,"LI",{});var $ko=s(LI);sio=n($ko,"STRONG",{});var EHn=s(sio);VAa=r(EHn,"audio-spectrogram-transformer"),EHn.forEach(t),WAa=r($ko," \u2014 "),O5e=n($ko,"A",{href:!0});var yHn=s(O5e);JAa=r(yHn,"ASTForAudioClassification"),yHn.forEach(t),UAa=r($ko," (Audio Spectrogram Transformer model)"),$ko.forEach(t),XAa=i(Je),AI=n(Je,"LI",{});var jko=s(AI);lio=n(jko,"STRONG",{});var CHn=s(lio);DAa=r(CHn,"data2vec-audio"),CHn.forEach(t),qAa=r(jko," \u2014 "),Q5e=n(jko,"A",{href:!0});var wHn=s(Q5e);OAa=r(wHn,"Data2VecAudioForSequenceClassification"),wHn.forEach(t),QAa=r(jko," (Data2VecAudio model)"),jko.forEach(t),YAa=i(Je),RI=n(Je,"LI",{});var Pko=s(RI);iio=n(Pko,"STRONG",{});var LHn=s(iio);zAa=r(LHn,"hubert"),LHn.forEach(t),HAa=r(Pko," \u2014 "),Y5e=n(Pko,"A",{href:!0});var AHn=s(Y5e);KAa=r(AHn,"HubertForSequenceClassification"),AHn.forEach(t),eRa=r(Pko," (Hubert model)"),Pko.forEach(t),oRa=i(Je),kI=n(Je,"LI",{});var Zko=s(kI);dio=n(Zko,"STRONG",{});var RHn=s(dio);rRa=r(RHn,"sew"),RHn.forEach(t),tRa=r(Zko," \u2014 "),z5e=n(Zko,"A",{href:!0});var kHn=s(z5e);aRa=r(kHn,"SEWForSequenceClassification"),kHn.forEach(t),nRa=r(Zko," (SEW model)"),Zko.forEach(t),sRa=i(Je),SI=n(Je,"LI",{});var Vko=s(SI);mio=n(Vko,"STRONG",{});var SHn=s(mio);lRa=r(SHn,"sew-d"),SHn.forEach(t),iRa=r(Vko," \u2014 "),H5e=n(Vko,"A",{href:!0});var xHn=s(H5e);dRa=r(xHn,"SEWDForSequenceClassification"),xHn.forEach(t),mRa=r(Vko," (SEW-D model)"),Vko.forEach(t),cRa=i(Je),xI=n(Je,"LI",{});var Wko=s(xI);cio=n(Wko,"STRONG",{});var GHn=s(cio);fRa=r(GHn,"unispeech"),GHn.forEach(t),hRa=r(Wko," \u2014 "),K5e=n(Wko,"A",{href:!0});var BHn=s(K5e);gRa=r(BHn,"UniSpeechForSequenceClassification"),BHn.forEach(t),uRa=r(Wko," (UniSpeech model)"),Wko.forEach(t),pRa=i(Je),GI=n(Je,"LI",{});var Jko=s(GI);fio=n(Jko,"STRONG",{});var IHn=s(fio);_Ra=r(IHn,"unispeech-sat"),IHn.forEach(t),bRa=r(Jko," \u2014 "),e3e=n(Jko,"A",{href:!0});var NHn=s(e3e);vRa=r(NHn,"UniSpeechSatForSequenceClassification"),NHn.forEach(t),MRa=r(Jko," (UniSpeechSat model)"),Jko.forEach(t),TRa=i(Je),BI=n(Je,"LI",{});var Uko=s(BI);hio=n(Uko,"STRONG",{});var $Hn=s(hio);FRa=r($Hn,"wav2vec2"),$Hn.forEach(t),ERa=r(Uko," \u2014 "),o3e=n(Uko,"A",{href:!0});var jHn=s(o3e);yRa=r(jHn,"Wav2Vec2ForSequenceClassification"),jHn.forEach(t),CRa=r(Uko," (Wav2Vec2 model)"),Uko.forEach(t),wRa=i(Je),II=n(Je,"LI",{});var Xko=s(II);gio=n(Xko,"STRONG",{});var PHn=s(gio);LRa=r(PHn,"wav2vec2-conformer"),PHn.forEach(t),ARa=r(Xko," \u2014 "),r3e=n(Xko,"A",{href:!0});var ZHn=s(r3e);RRa=r(ZHn,"Wav2Vec2ConformerForSequenceClassification"),ZHn.forEach(t),kRa=r(Xko," (Wav2Vec2-Conformer model)"),Xko.forEach(t),SRa=i(Je),NI=n(Je,"LI",{});var Dko=s(NI);uio=n(Dko,"STRONG",{});var VHn=s(uio);xRa=r(VHn,"wavlm"),VHn.forEach(t),GRa=r(Dko," \u2014 "),t3e=n(Dko,"A",{href:!0});var WHn=s(t3e);BRa=r(WHn,"WavLMForSequenceClassification"),WHn.forEach(t),IRa=r(Dko," (WavLM model)"),Dko.forEach(t),NRa=i(Je),$I=n(Je,"LI",{});var qko=s($I);pio=n(qko,"STRONG",{});var JHn=s(pio);$Ra=r(JHn,"whisper"),JHn.forEach(t),jRa=r(qko," \u2014 "),a3e=n(qko,"A",{href:!0});var UHn=s(a3e);PRa=r(UHn,"WhisperForAudioClassification"),UHn.forEach(t),ZRa=r(qko," (Whisper model)"),qko.forEach(t),Je.forEach(t),VRa=i(yn),jI=n(yn,"P",{});var Oko=s(jI);WRa=r(Oko,"The model is set in evaluation mode by default using "),_io=n(Oko,"CODE",{});var XHn=s(_io);JRa=r(XHn,"model.eval()"),XHn.forEach(t),URa=r(Oko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),bio=n(Oko,"CODE",{});var DHn=s(bio);XRa=r(DHn,"model.train()"),DHn.forEach(t),Oko.forEach(t),DRa=i(yn),M(PI.$$.fragment,yn),yn.forEach(t),gm.forEach(t),hBo=i(c),Xh=n(c,"H3",{class:!0});var i$o=s(Xh);ZI=n(i$o,"A",{id:!0,class:!0,href:!0});var qHn=s(ZI);vio=n(qHn,"SPAN",{});var OHn=s(vio);M(KX.$$.fragment,OHn),OHn.forEach(t),qHn.forEach(t),qRa=i(i$o),Mio=n(i$o,"SPAN",{});var QHn=s(Mio);ORa=r(QHn,"AutoModelForAudioFrameClassification"),QHn.forEach(t),i$o.forEach(t),gBo=i(c),Pr=n(c,"DIV",{class:!0});var um=s(Pr);M(eD.$$.fragment,um),QRa=i(um),Dh=n(um,"P",{});var SCe=s(Dh);YRa=r(SCe,`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 `),n3e=n(SCe,"A",{href:!0});var YHn=s(n3e);zRa=r(YHn,"from_pretrained()"),YHn.forEach(t),HRa=r(SCe," class method or the "),s3e=n(SCe,"A",{href:!0});var zHn=s(s3e);KRa=r(zHn,"from_config()"),zHn.forEach(t),eka=r(SCe,` class
method.`),SCe.forEach(t),oka=i(um),oD=n(um,"P",{});var d$o=s(oD);rka=r(d$o,"This class cannot be instantiated directly using "),Tio=n(d$o,"CODE",{});var HHn=s(Tio);tka=r(HHn,"__init__()"),HHn.forEach(t),aka=r(d$o," (throws an error)."),d$o.forEach(t),nka=i(um),Na=n(um,"DIV",{class:!0});var eZ=s(Na);M(rD.$$.fragment,eZ),ska=i(eZ),Fio=n(eZ,"P",{});var KHn=s(Fio);lka=r(KHn,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),KHn.forEach(t),ika=i(eZ),qh=n(eZ,"P",{});var xCe=s(qh);dka=r(xCe,`Note:
Loading a model from its configuration file does `),Eio=n(xCe,"STRONG",{});var eKn=s(Eio);mka=r(eKn,"not"),eKn.forEach(t),cka=r(xCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),l3e=n(xCe,"A",{href:!0});var oKn=s(l3e);fka=r(oKn,"from_pretrained()"),oKn.forEach(t),hka=r(xCe," to load the model weights."),xCe.forEach(t),gka=i(eZ),M(VI.$$.fragment,eZ),eZ.forEach(t),uka=i(um),At=n(um,"DIV",{class:!0});var pm=s(At);M(tD.$$.fragment,pm),pka=i(pm),yio=n(pm,"P",{});var rKn=s(yio);_ka=r(rKn,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),rKn.forEach(t),bka=i(pm),Bs=n(pm,"P",{});var oZ=s(Bs);vka=r(oZ,"The model class to instantiate is selected based on the "),Cio=n(oZ,"CODE",{});var tKn=s(Cio);Mka=r(tKn,"model_type"),tKn.forEach(t),Tka=r(oZ,` property of the config object (either
passed as an argument or loaded from `),wio=n(oZ,"CODE",{});var aKn=s(wio);Fka=r(aKn,"pretrained_model_name_or_path"),aKn.forEach(t),Eka=r(oZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lio=n(oZ,"CODE",{});var nKn=s(Lio);yka=r(nKn,"pretrained_model_name_or_path"),nKn.forEach(t),Cka=r(oZ,":"),oZ.forEach(t),wka=i(pm),Aio=n(pm,"UL",{});var sKn=s(Aio);WI=n(sKn,"LI",{});var Qko=s(WI);Rio=n(Qko,"STRONG",{});var lKn=s(Rio);Lka=r(lKn,"wav2vec2"),lKn.forEach(t),Aka=r(Qko," \u2014 "),i3e=n(Qko,"A",{href:!0});var iKn=s(i3e);Rka=r(iKn,"TFWav2Vec2ForSequenceClassification"),iKn.forEach(t),kka=r(Qko," (Wav2Vec2 model)"),Qko.forEach(t),sKn.forEach(t),Ska=i(pm),M(JI.$$.fragment,pm),pm.forEach(t),um.forEach(t),uBo=i(c),Oh=n(c,"H3",{class:!0});var m$o=s(Oh);UI=n(m$o,"A",{id:!0,class:!0,href:!0});var dKn=s(UI);kio=n(dKn,"SPAN",{});var mKn=s(kio);M(aD.$$.fragment,mKn),mKn.forEach(t),dKn.forEach(t),xka=i(m$o),Sio=n(m$o,"SPAN",{});var cKn=s(Sio);Gka=r(cKn,"TFAutoModelForAudioFrameClassification"),cKn.forEach(t),m$o.forEach(t),pBo=i(c),Zr=n(c,"DIV",{class:!0});var _m=s(Zr);M(nD.$$.fragment,_m),Bka=i(_m),Qh=n(_m,"P",{});var GCe=s(Qh);Ika=r(GCe,`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 `),d3e=n(GCe,"A",{href:!0});var fKn=s(d3e);Nka=r(fKn,"from_pretrained()"),fKn.forEach(t),$ka=r(GCe," class method or the "),m3e=n(GCe,"A",{href:!0});var hKn=s(m3e);jka=r(hKn,"from_config()"),hKn.forEach(t),Pka=r(GCe,` class
method.`),GCe.forEach(t),Zka=i(_m),sD=n(_m,"P",{});var c$o=s(sD);Vka=r(c$o,"This class cannot be instantiated directly using "),xio=n(c$o,"CODE",{});var gKn=s(xio);Wka=r(gKn,"__init__()"),gKn.forEach(t),Jka=r(c$o," (throws an error)."),c$o.forEach(t),Uka=i(_m),$a=n(_m,"DIV",{class:!0});var rZ=s($a);M(lD.$$.fragment,rZ),Xka=i(rZ),Gio=n(rZ,"P",{});var uKn=s(Gio);Dka=r(uKn,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),uKn.forEach(t),qka=i(rZ),Yh=n(rZ,"P",{});var BCe=s(Yh);Oka=r(BCe,`Note:
Loading a model from its configuration file does `),Bio=n(BCe,"STRONG",{});var pKn=s(Bio);Qka=r(pKn,"not"),pKn.forEach(t),Yka=r(BCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),c3e=n(BCe,"A",{href:!0});var _Kn=s(c3e);zka=r(_Kn,"from_pretrained()"),_Kn.forEach(t),Hka=r(BCe," to load the model weights."),BCe.forEach(t),Kka=i(rZ),M(XI.$$.fragment,rZ),rZ.forEach(t),eSa=i(_m),Ro=n(_m,"DIV",{class:!0});var Cn=s(Ro);M(iD.$$.fragment,Cn),oSa=i(Cn),Iio=n(Cn,"P",{});var bKn=s(Iio);rSa=r(bKn,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),bKn.forEach(t),tSa=i(Cn),Is=n(Cn,"P",{});var tZ=s(Is);aSa=r(tZ,"The model class to instantiate is selected based on the "),Nio=n(tZ,"CODE",{});var vKn=s(Nio);nSa=r(vKn,"model_type"),vKn.forEach(t),sSa=r(tZ,` property of the config object (either
passed as an argument or loaded from `),$io=n(tZ,"CODE",{});var MKn=s($io);lSa=r(MKn,"pretrained_model_name_or_path"),MKn.forEach(t),iSa=r(tZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jio=n(tZ,"CODE",{});var TKn=s(jio);dSa=r(TKn,"pretrained_model_name_or_path"),TKn.forEach(t),mSa=r(tZ,":"),tZ.forEach(t),cSa=i(Cn),$t=n(Cn,"UL",{});var bm=s($t);DI=n(bm,"LI",{});var Yko=s(DI);Pio=n(Yko,"STRONG",{});var FKn=s(Pio);fSa=r(FKn,"data2vec-audio"),FKn.forEach(t),hSa=r(Yko," \u2014 "),f3e=n(Yko,"A",{href:!0});var EKn=s(f3e);gSa=r(EKn,"Data2VecAudioForAudioFrameClassification"),EKn.forEach(t),uSa=r(Yko," (Data2VecAudio model)"),Yko.forEach(t),pSa=i(bm),qI=n(bm,"LI",{});var zko=s(qI);Zio=n(zko,"STRONG",{});var yKn=s(Zio);_Sa=r(yKn,"unispeech-sat"),yKn.forEach(t),bSa=r(zko," \u2014 "),h3e=n(zko,"A",{href:!0});var CKn=s(h3e);vSa=r(CKn,"UniSpeechSatForAudioFrameClassification"),CKn.forEach(t),MSa=r(zko," (UniSpeechSat model)"),zko.forEach(t),TSa=i(bm),OI=n(bm,"LI",{});var Hko=s(OI);Vio=n(Hko,"STRONG",{});var wKn=s(Vio);FSa=r(wKn,"wav2vec2"),wKn.forEach(t),ESa=r(Hko," \u2014 "),g3e=n(Hko,"A",{href:!0});var LKn=s(g3e);ySa=r(LKn,"Wav2Vec2ForAudioFrameClassification"),LKn.forEach(t),CSa=r(Hko," (Wav2Vec2 model)"),Hko.forEach(t),wSa=i(bm),QI=n(bm,"LI",{});var Kko=s(QI);Wio=n(Kko,"STRONG",{});var AKn=s(Wio);LSa=r(AKn,"wav2vec2-conformer"),AKn.forEach(t),ASa=r(Kko," \u2014 "),u3e=n(Kko,"A",{href:!0});var RKn=s(u3e);RSa=r(RKn,"Wav2Vec2ConformerForAudioFrameClassification"),RKn.forEach(t),kSa=r(Kko," (Wav2Vec2-Conformer model)"),Kko.forEach(t),SSa=i(bm),YI=n(bm,"LI",{});var eSo=s(YI);Jio=n(eSo,"STRONG",{});var kKn=s(Jio);xSa=r(kKn,"wavlm"),kKn.forEach(t),GSa=r(eSo," \u2014 "),p3e=n(eSo,"A",{href:!0});var SKn=s(p3e);BSa=r(SKn,"WavLMForAudioFrameClassification"),SKn.forEach(t),ISa=r(eSo," (WavLM model)"),eSo.forEach(t),bm.forEach(t),NSa=i(Cn),zI=n(Cn,"P",{});var oSo=s(zI);$Sa=r(oSo,"The model is set in evaluation mode by default using "),Uio=n(oSo,"CODE",{});var xKn=s(Uio);jSa=r(xKn,"model.eval()"),xKn.forEach(t),PSa=r(oSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xio=n(oSo,"CODE",{});var GKn=s(Xio);ZSa=r(GKn,"model.train()"),GKn.forEach(t),oSo.forEach(t),VSa=i(Cn),M(HI.$$.fragment,Cn),Cn.forEach(t),_m.forEach(t),_Bo=i(c),zh=n(c,"H3",{class:!0});var f$o=s(zh);KI=n(f$o,"A",{id:!0,class:!0,href:!0});var BKn=s(KI);Dio=n(BKn,"SPAN",{});var IKn=s(Dio);M(dD.$$.fragment,IKn),IKn.forEach(t),BKn.forEach(t),WSa=i(f$o),qio=n(f$o,"SPAN",{});var NKn=s(qio);JSa=r(NKn,"AutoModelForCTC"),NKn.forEach(t),f$o.forEach(t),bBo=i(c),Vr=n(c,"DIV",{class:!0});var vm=s(Vr);M(mD.$$.fragment,vm),USa=i(vm),Hh=n(vm,"P",{});var ICe=s(Hh);XSa=r(ICe,`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 `),_3e=n(ICe,"A",{href:!0});var $Kn=s(_3e);DSa=r($Kn,"from_pretrained()"),$Kn.forEach(t),qSa=r(ICe," class method or the "),b3e=n(ICe,"A",{href:!0});var jKn=s(b3e);OSa=r(jKn,"from_config()"),jKn.forEach(t),QSa=r(ICe,` class
method.`),ICe.forEach(t),YSa=i(vm),cD=n(vm,"P",{});var h$o=s(cD);zSa=r(h$o,"This class cannot be instantiated directly using "),Oio=n(h$o,"CODE",{});var PKn=s(Oio);HSa=r(PKn,"__init__()"),PKn.forEach(t),KSa=r(h$o," (throws an error)."),h$o.forEach(t),exa=i(vm),ja=n(vm,"DIV",{class:!0});var aZ=s(ja);M(fD.$$.fragment,aZ),oxa=i(aZ),Qio=n(aZ,"P",{});var ZKn=s(Qio);rxa=r(ZKn,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),ZKn.forEach(t),txa=i(aZ),Kh=n(aZ,"P",{});var NCe=s(Kh);axa=r(NCe,`Note:
Loading a model from its configuration file does `),Yio=n(NCe,"STRONG",{});var VKn=s(Yio);nxa=r(VKn,"not"),VKn.forEach(t),sxa=r(NCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),v3e=n(NCe,"A",{href:!0});var WKn=s(v3e);lxa=r(WKn,"from_pretrained()"),WKn.forEach(t),ixa=r(NCe," to load the model weights."),NCe.forEach(t),dxa=i(aZ),M(eN.$$.fragment,aZ),aZ.forEach(t),mxa=i(vm),ko=n(vm,"DIV",{class:!0});var wn=s(ko);M(hD.$$.fragment,wn),cxa=i(wn),zio=n(wn,"P",{});var JKn=s(zio);fxa=r(JKn,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),JKn.forEach(t),hxa=i(wn),Ns=n(wn,"P",{});var nZ=s(Ns);gxa=r(nZ,"The model class to instantiate is selected based on the "),Hio=n(nZ,"CODE",{});var UKn=s(Hio);uxa=r(UKn,"model_type"),UKn.forEach(t),pxa=r(nZ,` property of the config object (either
passed as an argument or loaded from `),Kio=n(nZ,"CODE",{});var XKn=s(Kio);_xa=r(XKn,"pretrained_model_name_or_path"),XKn.forEach(t),bxa=r(nZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),edo=n(nZ,"CODE",{});var DKn=s(edo);vxa=r(DKn,"pretrained_model_name_or_path"),DKn.forEach(t),Mxa=r(nZ,":"),nZ.forEach(t),Txa=i(wn),je=n(wn,"UL",{});var Oe=s(je);oN=n(Oe,"LI",{});var rSo=s(oN);odo=n(rSo,"STRONG",{});var qKn=s(odo);Fxa=r(qKn,"data2vec-audio"),qKn.forEach(t),Exa=r(rSo," \u2014 "),M3e=n(rSo,"A",{href:!0});var OKn=s(M3e);yxa=r(OKn,"Data2VecAudioForCTC"),OKn.forEach(t),Cxa=r(rSo," (Data2VecAudio model)"),rSo.forEach(t),wxa=i(Oe),rN=n(Oe,"LI",{});var tSo=s(rN);rdo=n(tSo,"STRONG",{});var QKn=s(rdo);Lxa=r(QKn,"hubert"),QKn.forEach(t),Axa=r(tSo," \u2014 "),T3e=n(tSo,"A",{href:!0});var YKn=s(T3e);Rxa=r(YKn,"HubertForCTC"),YKn.forEach(t),kxa=r(tSo," (Hubert model)"),tSo.forEach(t),Sxa=i(Oe),tN=n(Oe,"LI",{});var aSo=s(tN);tdo=n(aSo,"STRONG",{});var zKn=s(tdo);xxa=r(zKn,"mctct"),zKn.forEach(t),Gxa=r(aSo," \u2014 "),F3e=n(aSo,"A",{href:!0});var HKn=s(F3e);Bxa=r(HKn,"MCTCTForCTC"),HKn.forEach(t),Ixa=r(aSo," (M-CTC-T model)"),aSo.forEach(t),Nxa=i(Oe),aN=n(Oe,"LI",{});var nSo=s(aN);ado=n(nSo,"STRONG",{});var KKn=s(ado);$xa=r(KKn,"sew"),KKn.forEach(t),jxa=r(nSo," \u2014 "),E3e=n(nSo,"A",{href:!0});var ees=s(E3e);Pxa=r(ees,"SEWForCTC"),ees.forEach(t),Zxa=r(nSo," (SEW model)"),nSo.forEach(t),Vxa=i(Oe),nN=n(Oe,"LI",{});var sSo=s(nN);ndo=n(sSo,"STRONG",{});var oes=s(ndo);Wxa=r(oes,"sew-d"),oes.forEach(t),Jxa=r(sSo," \u2014 "),y3e=n(sSo,"A",{href:!0});var res=s(y3e);Uxa=r(res,"SEWDForCTC"),res.forEach(t),Xxa=r(sSo," (SEW-D model)"),sSo.forEach(t),Dxa=i(Oe),sN=n(Oe,"LI",{});var lSo=s(sN);sdo=n(lSo,"STRONG",{});var tes=s(sdo);qxa=r(tes,"unispeech"),tes.forEach(t),Oxa=r(lSo," \u2014 "),C3e=n(lSo,"A",{href:!0});var aes=s(C3e);Qxa=r(aes,"UniSpeechForCTC"),aes.forEach(t),Yxa=r(lSo," (UniSpeech model)"),lSo.forEach(t),zxa=i(Oe),lN=n(Oe,"LI",{});var iSo=s(lN);ldo=n(iSo,"STRONG",{});var nes=s(ldo);Hxa=r(nes,"unispeech-sat"),nes.forEach(t),Kxa=r(iSo," \u2014 "),w3e=n(iSo,"A",{href:!0});var ses=s(w3e);eGa=r(ses,"UniSpeechSatForCTC"),ses.forEach(t),oGa=r(iSo," (UniSpeechSat model)"),iSo.forEach(t),rGa=i(Oe),iN=n(Oe,"LI",{});var dSo=s(iN);ido=n(dSo,"STRONG",{});var les=s(ido);tGa=r(les,"wav2vec2"),les.forEach(t),aGa=r(dSo," \u2014 "),L3e=n(dSo,"A",{href:!0});var ies=s(L3e);nGa=r(ies,"Wav2Vec2ForCTC"),ies.forEach(t),sGa=r(dSo," (Wav2Vec2 model)"),dSo.forEach(t),lGa=i(Oe),dN=n(Oe,"LI",{});var mSo=s(dN);ddo=n(mSo,"STRONG",{});var des=s(ddo);iGa=r(des,"wav2vec2-conformer"),des.forEach(t),dGa=r(mSo," \u2014 "),A3e=n(mSo,"A",{href:!0});var mes=s(A3e);mGa=r(mes,"Wav2Vec2ConformerForCTC"),mes.forEach(t),cGa=r(mSo," (Wav2Vec2-Conformer model)"),mSo.forEach(t),fGa=i(Oe),mN=n(Oe,"LI",{});var cSo=s(mN);mdo=n(cSo,"STRONG",{});var ces=s(mdo);hGa=r(ces,"wavlm"),ces.forEach(t),gGa=r(cSo," \u2014 "),R3e=n(cSo,"A",{href:!0});var fes=s(R3e);uGa=r(fes,"WavLMForCTC"),fes.forEach(t),pGa=r(cSo," (WavLM model)"),cSo.forEach(t),Oe.forEach(t),_Ga=i(wn),cN=n(wn,"P",{});var fSo=s(cN);bGa=r(fSo,"The model is set in evaluation mode by default using "),cdo=n(fSo,"CODE",{});var hes=s(cdo);vGa=r(hes,"model.eval()"),hes.forEach(t),MGa=r(fSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),fdo=n(fSo,"CODE",{});var ges=s(fdo);TGa=r(ges,"model.train()"),ges.forEach(t),fSo.forEach(t),FGa=i(wn),M(fN.$$.fragment,wn),wn.forEach(t),vm.forEach(t),vBo=i(c),eg=n(c,"H3",{class:!0});var g$o=s(eg);hN=n(g$o,"A",{id:!0,class:!0,href:!0});var ues=s(hN);hdo=n(ues,"SPAN",{});var pes=s(hdo);M(gD.$$.fragment,pes),pes.forEach(t),ues.forEach(t),EGa=i(g$o),gdo=n(g$o,"SPAN",{});var _es=s(gdo);yGa=r(_es,"AutoModelForSpeechSeq2Seq"),_es.forEach(t),g$o.forEach(t),MBo=i(c),Wr=n(c,"DIV",{class:!0});var Mm=s(Wr);M(uD.$$.fragment,Mm),CGa=i(Mm),og=n(Mm,"P",{});var $Ce=s(og);wGa=r($Ce,`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 `),k3e=n($Ce,"A",{href:!0});var bes=s(k3e);LGa=r(bes,"from_pretrained()"),bes.forEach(t),AGa=r($Ce," class method or the "),S3e=n($Ce,"A",{href:!0});var ves=s(S3e);RGa=r(ves,"from_config()"),ves.forEach(t),kGa=r($Ce,` class
method.`),$Ce.forEach(t),SGa=i(Mm),pD=n(Mm,"P",{});var u$o=s(pD);xGa=r(u$o,"This class cannot be instantiated directly using "),udo=n(u$o,"CODE",{});var Mes=s(udo);GGa=r(Mes,"__init__()"),Mes.forEach(t),BGa=r(u$o," (throws an error)."),u$o.forEach(t),IGa=i(Mm),Pa=n(Mm,"DIV",{class:!0});var sZ=s(Pa);M(_D.$$.fragment,sZ),NGa=i(sZ),pdo=n(sZ,"P",{});var Tes=s(pdo);$Ga=r(Tes,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Tes.forEach(t),jGa=i(sZ),rg=n(sZ,"P",{});var jCe=s(rg);PGa=r(jCe,`Note:
Loading a model from its configuration file does `),_do=n(jCe,"STRONG",{});var Fes=s(_do);ZGa=r(Fes,"not"),Fes.forEach(t),VGa=r(jCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),x3e=n(jCe,"A",{href:!0});var Ees=s(x3e);WGa=r(Ees,"from_pretrained()"),Ees.forEach(t),JGa=r(jCe," to load the model weights."),jCe.forEach(t),UGa=i(sZ),M(gN.$$.fragment,sZ),sZ.forEach(t),XGa=i(Mm),So=n(Mm,"DIV",{class:!0});var Ln=s(So);M(bD.$$.fragment,Ln),DGa=i(Ln),bdo=n(Ln,"P",{});var yes=s(bdo);qGa=r(yes,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),yes.forEach(t),OGa=i(Ln),$s=n(Ln,"P",{});var lZ=s($s);QGa=r(lZ,"The model class to instantiate is selected based on the "),vdo=n(lZ,"CODE",{});var Ces=s(vdo);YGa=r(Ces,"model_type"),Ces.forEach(t),zGa=r(lZ,` property of the config object (either
passed as an argument or loaded from `),Mdo=n(lZ,"CODE",{});var wes=s(Mdo);HGa=r(wes,"pretrained_model_name_or_path"),wes.forEach(t),KGa=r(lZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tdo=n(lZ,"CODE",{});var Les=s(Tdo);eBa=r(Les,"pretrained_model_name_or_path"),Les.forEach(t),oBa=r(lZ,":"),lZ.forEach(t),rBa=i(Ln),js=n(Ln,"UL",{});var iZ=s(js);uN=n(iZ,"LI",{});var hSo=s(uN);Fdo=n(hSo,"STRONG",{});var Aes=s(Fdo);tBa=r(Aes,"speech-encoder-decoder"),Aes.forEach(t),aBa=r(hSo," \u2014 "),G3e=n(hSo,"A",{href:!0});var Res=s(G3e);nBa=r(Res,"SpeechEncoderDecoderModel"),Res.forEach(t),sBa=r(hSo," (Speech Encoder decoder model)"),hSo.forEach(t),lBa=i(iZ),pN=n(iZ,"LI",{});var gSo=s(pN);Edo=n(gSo,"STRONG",{});var kes=s(Edo);iBa=r(kes,"speech_to_text"),kes.forEach(t),dBa=r(gSo," \u2014 "),B3e=n(gSo,"A",{href:!0});var Ses=s(B3e);mBa=r(Ses,"Speech2TextForConditionalGeneration"),Ses.forEach(t),cBa=r(gSo," (Speech2Text model)"),gSo.forEach(t),fBa=i(iZ),_N=n(iZ,"LI",{});var uSo=s(_N);ydo=n(uSo,"STRONG",{});var xes=s(ydo);hBa=r(xes,"speecht5"),xes.forEach(t),gBa=r(uSo," \u2014 "),I3e=n(uSo,"A",{href:!0});var Ges=s(I3e);uBa=r(Ges,"SpeechT5ForSpeechToText"),Ges.forEach(t),pBa=r(uSo," (SpeechT5 model)"),uSo.forEach(t),_Ba=i(iZ),bN=n(iZ,"LI",{});var pSo=s(bN);Cdo=n(pSo,"STRONG",{});var Bes=s(Cdo);bBa=r(Bes,"whisper"),Bes.forEach(t),vBa=r(pSo," \u2014 "),N3e=n(pSo,"A",{href:!0});var Ies=s(N3e);MBa=r(Ies,"WhisperForConditionalGeneration"),Ies.forEach(t),TBa=r(pSo," (Whisper model)"),pSo.forEach(t),iZ.forEach(t),FBa=i(Ln),vN=n(Ln,"P",{});var _So=s(vN);EBa=r(_So,"The model is set in evaluation mode by default using "),wdo=n(_So,"CODE",{});var Nes=s(wdo);yBa=r(Nes,"model.eval()"),Nes.forEach(t),CBa=r(_So,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ldo=n(_So,"CODE",{});var $es=s(Ldo);wBa=r($es,"model.train()"),$es.forEach(t),_So.forEach(t),LBa=i(Ln),M(MN.$$.fragment,Ln),Ln.forEach(t),Mm.forEach(t),TBo=i(c),tg=n(c,"H3",{class:!0});var p$o=s(tg);TN=n(p$o,"A",{id:!0,class:!0,href:!0});var jes=s(TN);Ado=n(jes,"SPAN",{});var Pes=s(Ado);M(vD.$$.fragment,Pes),Pes.forEach(t),jes.forEach(t),ABa=i(p$o),Rdo=n(p$o,"SPAN",{});var Zes=s(Rdo);RBa=r(Zes,"TFAutoModelForSpeechSeq2Seq"),Zes.forEach(t),p$o.forEach(t),FBo=i(c),Jr=n(c,"DIV",{class:!0});var Tm=s(Jr);M(MD.$$.fragment,Tm),kBa=i(Tm),ag=n(Tm,"P",{});var PCe=s(ag);SBa=r(PCe,`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 `),$3e=n(PCe,"A",{href:!0});var Ves=s($3e);xBa=r(Ves,"from_pretrained()"),Ves.forEach(t),GBa=r(PCe," class method or the "),j3e=n(PCe,"A",{href:!0});var Wes=s(j3e);BBa=r(Wes,"from_config()"),Wes.forEach(t),IBa=r(PCe,` class
method.`),PCe.forEach(t),NBa=i(Tm),TD=n(Tm,"P",{});var _$o=s(TD);$Ba=r(_$o,"This class cannot be instantiated directly using "),kdo=n(_$o,"CODE",{});var Jes=s(kdo);jBa=r(Jes,"__init__()"),Jes.forEach(t),PBa=r(_$o," (throws an error)."),_$o.forEach(t),ZBa=i(Tm),Za=n(Tm,"DIV",{class:!0});var dZ=s(Za);M(FD.$$.fragment,dZ),VBa=i(dZ),Sdo=n(dZ,"P",{});var Ues=s(Sdo);WBa=r(Ues,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Ues.forEach(t),JBa=i(dZ),ng=n(dZ,"P",{});var ZCe=s(ng);UBa=r(ZCe,`Note:
Loading a model from its configuration file does `),xdo=n(ZCe,"STRONG",{});var Xes=s(xdo);XBa=r(Xes,"not"),Xes.forEach(t),DBa=r(ZCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),P3e=n(ZCe,"A",{href:!0});var Des=s(P3e);qBa=r(Des,"from_pretrained()"),Des.forEach(t),OBa=r(ZCe," to load the model weights."),ZCe.forEach(t),QBa=i(dZ),M(FN.$$.fragment,dZ),dZ.forEach(t),YBa=i(Tm),Rt=n(Tm,"DIV",{class:!0});var Fm=s(Rt);M(ED.$$.fragment,Fm),zBa=i(Fm),Gdo=n(Fm,"P",{});var qes=s(Gdo);HBa=r(qes,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),qes.forEach(t),KBa=i(Fm),Ps=n(Fm,"P",{});var mZ=s(Ps);eIa=r(mZ,"The model class to instantiate is selected based on the "),Bdo=n(mZ,"CODE",{});var Oes=s(Bdo);oIa=r(Oes,"model_type"),Oes.forEach(t),rIa=r(mZ,` property of the config object (either
passed as an argument or loaded from `),Ido=n(mZ,"CODE",{});var Qes=s(Ido);tIa=r(Qes,"pretrained_model_name_or_path"),Qes.forEach(t),aIa=r(mZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ndo=n(mZ,"CODE",{});var Yes=s(Ndo);nIa=r(Yes,"pretrained_model_name_or_path"),Yes.forEach(t),sIa=r(mZ,":"),mZ.forEach(t),lIa=i(Fm),yD=n(Fm,"UL",{});var b$o=s(yD);EN=n(b$o,"LI",{});var bSo=s(EN);$do=n(bSo,"STRONG",{});var zes=s($do);iIa=r(zes,"speech_to_text"),zes.forEach(t),dIa=r(bSo," \u2014 "),Z3e=n(bSo,"A",{href:!0});var Hes=s(Z3e);mIa=r(Hes,"TFSpeech2TextForConditionalGeneration"),Hes.forEach(t),cIa=r(bSo," (Speech2Text model)"),bSo.forEach(t),fIa=i(b$o),yN=n(b$o,"LI",{});var vSo=s(yN);jdo=n(vSo,"STRONG",{});var Kes=s(jdo);hIa=r(Kes,"whisper"),Kes.forEach(t),gIa=r(vSo," \u2014 "),V3e=n(vSo,"A",{href:!0});var eos=s(V3e);uIa=r(eos,"TFWhisperForConditionalGeneration"),eos.forEach(t),pIa=r(vSo," (Whisper model)"),vSo.forEach(t),b$o.forEach(t),_Ia=i(Fm),M(CN.$$.fragment,Fm),Fm.forEach(t),Tm.forEach(t),EBo=i(c),sg=n(c,"H3",{class:!0});var v$o=s(sg);wN=n(v$o,"A",{id:!0,class:!0,href:!0});var oos=s(wN);Pdo=n(oos,"SPAN",{});var ros=s(Pdo);M(CD.$$.fragment,ros),ros.forEach(t),oos.forEach(t),bIa=i(v$o),Zdo=n(v$o,"SPAN",{});var tos=s(Zdo);vIa=r(tos,"FlaxAutoModelForSpeechSeq2Seq"),tos.forEach(t),v$o.forEach(t),yBo=i(c),Ur=n(c,"DIV",{class:!0});var Em=s(Ur);M(wD.$$.fragment,Em),MIa=i(Em),lg=n(Em,"P",{});var VCe=s(lg);TIa=r(VCe,`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 `),W3e=n(VCe,"A",{href:!0});var aos=s(W3e);FIa=r(aos,"from_pretrained()"),aos.forEach(t),EIa=r(VCe," class method or the "),J3e=n(VCe,"A",{href:!0});var nos=s(J3e);yIa=r(nos,"from_config()"),nos.forEach(t),CIa=r(VCe,` class
method.`),VCe.forEach(t),wIa=i(Em),LD=n(Em,"P",{});var M$o=s(LD);LIa=r(M$o,"This class cannot be instantiated directly using "),Vdo=n(M$o,"CODE",{});var sos=s(Vdo);AIa=r(sos,"__init__()"),sos.forEach(t),RIa=r(M$o," (throws an error)."),M$o.forEach(t),kIa=i(Em),Va=n(Em,"DIV",{class:!0});var cZ=s(Va);M(AD.$$.fragment,cZ),SIa=i(cZ),Wdo=n(cZ,"P",{});var los=s(Wdo);xIa=r(los,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),los.forEach(t),GIa=i(cZ),ig=n(cZ,"P",{});var WCe=s(ig);BIa=r(WCe,`Note:
Loading a model from its configuration file does `),Jdo=n(WCe,"STRONG",{});var ios=s(Jdo);IIa=r(ios,"not"),ios.forEach(t),NIa=r(WCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),U3e=n(WCe,"A",{href:!0});var dos=s(U3e);$Ia=r(dos,"from_pretrained()"),dos.forEach(t),jIa=r(WCe," to load the model weights."),WCe.forEach(t),PIa=i(cZ),M(LN.$$.fragment,cZ),cZ.forEach(t),ZIa=i(Em),kt=n(Em,"DIV",{class:!0});var ym=s(kt);M(RD.$$.fragment,ym),VIa=i(ym),Udo=n(ym,"P",{});var mos=s(Udo);WIa=r(mos,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),mos.forEach(t),JIa=i(ym),Zs=n(ym,"P",{});var fZ=s(Zs);UIa=r(fZ,"The model class to instantiate is selected based on the "),Xdo=n(fZ,"CODE",{});var cos=s(Xdo);XIa=r(cos,"model_type"),cos.forEach(t),DIa=r(fZ,` property of the config object (either
passed as an argument or loaded from `),Ddo=n(fZ,"CODE",{});var fos=s(Ddo);qIa=r(fos,"pretrained_model_name_or_path"),fos.forEach(t),OIa=r(fZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qdo=n(fZ,"CODE",{});var hos=s(qdo);QIa=r(hos,"pretrained_model_name_or_path"),hos.forEach(t),YIa=r(fZ,":"),fZ.forEach(t),zIa=i(ym),kD=n(ym,"UL",{});var T$o=s(kD);AN=n(T$o,"LI",{});var MSo=s(AN);Odo=n(MSo,"STRONG",{});var gos=s(Odo);HIa=r(gos,"speech-encoder-decoder"),gos.forEach(t),KIa=r(MSo," \u2014 "),X3e=n(MSo,"A",{href:!0});var uos=s(X3e);eNa=r(uos,"FlaxSpeechEncoderDecoderModel"),uos.forEach(t),oNa=r(MSo," (Speech Encoder decoder model)"),MSo.forEach(t),rNa=i(T$o),RN=n(T$o,"LI",{});var TSo=s(RN);Qdo=n(TSo,"STRONG",{});var pos=s(Qdo);tNa=r(pos,"whisper"),pos.forEach(t),aNa=r(TSo," \u2014 "),D3e=n(TSo,"A",{href:!0});var _os=s(D3e);nNa=r(_os,"FlaxWhisperForConditionalGeneration"),_os.forEach(t),sNa=r(TSo," (Whisper model)"),TSo.forEach(t),T$o.forEach(t),lNa=i(ym),M(kN.$$.fragment,ym),ym.forEach(t),Em.forEach(t),CBo=i(c),dg=n(c,"H3",{class:!0});var F$o=s(dg);SN=n(F$o,"A",{id:!0,class:!0,href:!0});var bos=s(SN);Ydo=n(bos,"SPAN",{});var vos=s(Ydo);M(SD.$$.fragment,vos),vos.forEach(t),bos.forEach(t),iNa=i(F$o),zdo=n(F$o,"SPAN",{});var Mos=s(zdo);dNa=r(Mos,"AutoModelForAudioXVector"),Mos.forEach(t),F$o.forEach(t),wBo=i(c),Xr=n(c,"DIV",{class:!0});var Cm=s(Xr);M(xD.$$.fragment,Cm),mNa=i(Cm),mg=n(Cm,"P",{});var JCe=s(mg);cNa=r(JCe,`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 `),q3e=n(JCe,"A",{href:!0});var Tos=s(q3e);fNa=r(Tos,"from_pretrained()"),Tos.forEach(t),hNa=r(JCe," class method or the "),O3e=n(JCe,"A",{href:!0});var Fos=s(O3e);gNa=r(Fos,"from_config()"),Fos.forEach(t),uNa=r(JCe,` class
method.`),JCe.forEach(t),pNa=i(Cm),GD=n(Cm,"P",{});var E$o=s(GD);_Na=r(E$o,"This class cannot be instantiated directly using "),Hdo=n(E$o,"CODE",{});var Eos=s(Hdo);bNa=r(Eos,"__init__()"),Eos.forEach(t),vNa=r(E$o," (throws an error)."),E$o.forEach(t),MNa=i(Cm),Wa=n(Cm,"DIV",{class:!0});var hZ=s(Wa);M(BD.$$.fragment,hZ),TNa=i(hZ),Kdo=n(hZ,"P",{});var yos=s(Kdo);FNa=r(yos,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),yos.forEach(t),ENa=i(hZ),cg=n(hZ,"P",{});var UCe=s(cg);yNa=r(UCe,`Note:
Loading a model from its configuration file does `),emo=n(UCe,"STRONG",{});var Cos=s(emo);CNa=r(Cos,"not"),Cos.forEach(t),wNa=r(UCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Q3e=n(UCe,"A",{href:!0});var wos=s(Q3e);LNa=r(wos,"from_pretrained()"),wos.forEach(t),ANa=r(UCe," to load the model weights."),UCe.forEach(t),RNa=i(hZ),M(xN.$$.fragment,hZ),hZ.forEach(t),kNa=i(Cm),xo=n(Cm,"DIV",{class:!0});var An=s(xo);M(ID.$$.fragment,An),SNa=i(An),omo=n(An,"P",{});var Los=s(omo);xNa=r(Los,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),Los.forEach(t),GNa=i(An),Vs=n(An,"P",{});var gZ=s(Vs);BNa=r(gZ,"The model class to instantiate is selected based on the "),rmo=n(gZ,"CODE",{});var Aos=s(rmo);INa=r(Aos,"model_type"),Aos.forEach(t),NNa=r(gZ,` property of the config object (either
passed as an argument or loaded from `),tmo=n(gZ,"CODE",{});var Ros=s(tmo);$Na=r(Ros,"pretrained_model_name_or_path"),Ros.forEach(t),jNa=r(gZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),amo=n(gZ,"CODE",{});var kos=s(amo);PNa=r(kos,"pretrained_model_name_or_path"),kos.forEach(t),ZNa=r(gZ,":"),gZ.forEach(t),VNa=i(An),jt=n(An,"UL",{});var wm=s(jt);GN=n(wm,"LI",{});var FSo=s(GN);nmo=n(FSo,"STRONG",{});var Sos=s(nmo);WNa=r(Sos,"data2vec-audio"),Sos.forEach(t),JNa=r(FSo," \u2014 "),Y3e=n(FSo,"A",{href:!0});var xos=s(Y3e);UNa=r(xos,"Data2VecAudioForXVector"),xos.forEach(t),XNa=r(FSo," (Data2VecAudio model)"),FSo.forEach(t),DNa=i(wm),BN=n(wm,"LI",{});var ESo=s(BN);smo=n(ESo,"STRONG",{});var Gos=s(smo);qNa=r(Gos,"unispeech-sat"),Gos.forEach(t),ONa=r(ESo," \u2014 "),z3e=n(ESo,"A",{href:!0});var Bos=s(z3e);QNa=r(Bos,"UniSpeechSatForXVector"),Bos.forEach(t),YNa=r(ESo," (UniSpeechSat model)"),ESo.forEach(t),zNa=i(wm),IN=n(wm,"LI",{});var ySo=s(IN);lmo=n(ySo,"STRONG",{});var Ios=s(lmo);HNa=r(Ios,"wav2vec2"),Ios.forEach(t),KNa=r(ySo," \u2014 "),H3e=n(ySo,"A",{href:!0});var Nos=s(H3e);e$a=r(Nos,"Wav2Vec2ForXVector"),Nos.forEach(t),o$a=r(ySo," (Wav2Vec2 model)"),ySo.forEach(t),r$a=i(wm),NN=n(wm,"LI",{});var CSo=s(NN);imo=n(CSo,"STRONG",{});var $os=s(imo);t$a=r($os,"wav2vec2-conformer"),$os.forEach(t),a$a=r(CSo," \u2014 "),K3e=n(CSo,"A",{href:!0});var jos=s(K3e);n$a=r(jos,"Wav2Vec2ConformerForXVector"),jos.forEach(t),s$a=r(CSo," (Wav2Vec2-Conformer model)"),CSo.forEach(t),l$a=i(wm),$N=n(wm,"LI",{});var wSo=s($N);dmo=n(wSo,"STRONG",{});var Pos=s(dmo);i$a=r(Pos,"wavlm"),Pos.forEach(t),d$a=r(wSo," \u2014 "),e4e=n(wSo,"A",{href:!0});var Zos=s(e4e);m$a=r(Zos,"WavLMForXVector"),Zos.forEach(t),c$a=r(wSo," (WavLM model)"),wSo.forEach(t),wm.forEach(t),f$a=i(An),jN=n(An,"P",{});var LSo=s(jN);h$a=r(LSo,"The model is set in evaluation mode by default using "),mmo=n(LSo,"CODE",{});var Vos=s(mmo);g$a=r(Vos,"model.eval()"),Vos.forEach(t),u$a=r(LSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cmo=n(LSo,"CODE",{});var Wos=s(cmo);p$a=r(Wos,"model.train()"),Wos.forEach(t),LSo.forEach(t),_$a=i(An),M(PN.$$.fragment,An),An.forEach(t),Cm.forEach(t),LBo=i(c),fg=n(c,"H2",{class:!0});var y$o=s(fg);ZN=n(y$o,"A",{id:!0,class:!0,href:!0});var Jos=s(ZN);fmo=n(Jos,"SPAN",{});var Uos=s(fmo);M(ND.$$.fragment,Uos),Uos.forEach(t),Jos.forEach(t),b$a=i(y$o),hmo=n(y$o,"SPAN",{});var Xos=s(hmo);v$a=r(Xos,"Multimodal"),Xos.forEach(t),y$o.forEach(t),ABo=i(c),o4e=n(c,"P",{});var Dos=s(o4e);M$a=r(Dos,"The following auto classes are available for the following multimodal tasks."),Dos.forEach(t),RBo=i(c),hg=n(c,"H3",{class:!0});var C$o=s(hg);VN=n(C$o,"A",{id:!0,class:!0,href:!0});var qos=s(VN);gmo=n(qos,"SPAN",{});var Oos=s(gmo);M($D.$$.fragment,Oos),Oos.forEach(t),qos.forEach(t),T$a=i(C$o),umo=n(C$o,"SPAN",{});var Qos=s(umo);F$a=r(Qos,"AutoModelForTableQuestionAnswering"),Qos.forEach(t),C$o.forEach(t),kBo=i(c),Dr=n(c,"DIV",{class:!0});var Lm=s(Dr);M(jD.$$.fragment,Lm),E$a=i(Lm),gg=n(Lm,"P",{});var XCe=s(gg);y$a=r(XCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),r4e=n(XCe,"A",{href:!0});var Yos=s(r4e);C$a=r(Yos,"from_pretrained()"),Yos.forEach(t),w$a=r(XCe," class method or the "),t4e=n(XCe,"A",{href:!0});var zos=s(t4e);L$a=r(zos,"from_config()"),zos.forEach(t),A$a=r(XCe,` class
method.`),XCe.forEach(t),R$a=i(Lm),PD=n(Lm,"P",{});var w$o=s(PD);k$a=r(w$o,"This class cannot be instantiated directly using "),pmo=n(w$o,"CODE",{});var Hos=s(pmo);S$a=r(Hos,"__init__()"),Hos.forEach(t),x$a=r(w$o," (throws an error)."),w$o.forEach(t),G$a=i(Lm),Ja=n(Lm,"DIV",{class:!0});var uZ=s(Ja);M(ZD.$$.fragment,uZ),B$a=i(uZ),_mo=n(uZ,"P",{});var Kos=s(_mo);I$a=r(Kos,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),Kos.forEach(t),N$a=i(uZ),ug=n(uZ,"P",{});var DCe=s(ug);$$a=r(DCe,`Note:
Loading a model from its configuration file does `),bmo=n(DCe,"STRONG",{});var ers=s(bmo);j$a=r(ers,"not"),ers.forEach(t),P$a=r(DCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),a4e=n(DCe,"A",{href:!0});var ors=s(a4e);Z$a=r(ors,"from_pretrained()"),ors.forEach(t),V$a=r(DCe," to load the model weights."),DCe.forEach(t),W$a=i(uZ),M(WN.$$.fragment,uZ),uZ.forEach(t),J$a=i(Lm),Go=n(Lm,"DIV",{class:!0});var Rn=s(Go);M(VD.$$.fragment,Rn),U$a=i(Rn),vmo=n(Rn,"P",{});var rrs=s(vmo);X$a=r(rrs,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),rrs.forEach(t),D$a=i(Rn),Ws=n(Rn,"P",{});var pZ=s(Ws);q$a=r(pZ,"The model class to instantiate is selected based on the "),Mmo=n(pZ,"CODE",{});var trs=s(Mmo);O$a=r(trs,"model_type"),trs.forEach(t),Q$a=r(pZ,` property of the config object (either
passed as an argument or loaded from `),Tmo=n(pZ,"CODE",{});var ars=s(Tmo);Y$a=r(ars,"pretrained_model_name_or_path"),ars.forEach(t),z$a=r(pZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Fmo=n(pZ,"CODE",{});var nrs=s(Fmo);H$a=r(nrs,"pretrained_model_name_or_path"),nrs.forEach(t),K$a=r(pZ,":"),pZ.forEach(t),eja=i(Rn),Emo=n(Rn,"UL",{});var srs=s(Emo);JN=n(srs,"LI",{});var ASo=s(JN);ymo=n(ASo,"STRONG",{});var lrs=s(ymo);oja=r(lrs,"tapas"),lrs.forEach(t),rja=r(ASo," \u2014 "),n4e=n(ASo,"A",{href:!0});var irs=s(n4e);tja=r(irs,"TapasForQuestionAnswering"),irs.forEach(t),aja=r(ASo," (TAPAS model)"),ASo.forEach(t),srs.forEach(t),nja=i(Rn),UN=n(Rn,"P",{});var RSo=s(UN);sja=r(RSo,"The model is set in evaluation mode by default using "),Cmo=n(RSo,"CODE",{});var drs=s(Cmo);lja=r(drs,"model.eval()"),drs.forEach(t),ija=r(RSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wmo=n(RSo,"CODE",{});var mrs=s(wmo);dja=r(mrs,"model.train()"),mrs.forEach(t),RSo.forEach(t),mja=i(Rn),M(XN.$$.fragment,Rn),Rn.forEach(t),Lm.forEach(t),SBo=i(c),pg=n(c,"H3",{class:!0});var L$o=s(pg);DN=n(L$o,"A",{id:!0,class:!0,href:!0});var crs=s(DN);Lmo=n(crs,"SPAN",{});var frs=s(Lmo);M(WD.$$.fragment,frs),frs.forEach(t),crs.forEach(t),cja=i(L$o),Amo=n(L$o,"SPAN",{});var hrs=s(Amo);fja=r(hrs,"TFAutoModelForTableQuestionAnswering"),hrs.forEach(t),L$o.forEach(t),xBo=i(c),qr=n(c,"DIV",{class:!0});var Am=s(qr);M(JD.$$.fragment,Am),hja=i(Am),_g=n(Am,"P",{});var qCe=s(_g);gja=r(qCe,`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 `),s4e=n(qCe,"A",{href:!0});var grs=s(s4e);uja=r(grs,"from_pretrained()"),grs.forEach(t),pja=r(qCe," class method or the "),l4e=n(qCe,"A",{href:!0});var urs=s(l4e);_ja=r(urs,"from_config()"),urs.forEach(t),bja=r(qCe,` class
method.`),qCe.forEach(t),vja=i(Am),UD=n(Am,"P",{});var A$o=s(UD);Mja=r(A$o,"This class cannot be instantiated directly using "),Rmo=n(A$o,"CODE",{});var prs=s(Rmo);Tja=r(prs,"__init__()"),prs.forEach(t),Fja=r(A$o," (throws an error)."),A$o.forEach(t),Eja=i(Am),Ua=n(Am,"DIV",{class:!0});var _Z=s(Ua);M(XD.$$.fragment,_Z),yja=i(_Z),kmo=n(_Z,"P",{});var _rs=s(kmo);Cja=r(_rs,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),_rs.forEach(t),wja=i(_Z),bg=n(_Z,"P",{});var OCe=s(bg);Lja=r(OCe,`Note:
Loading a model from its configuration file does `),Smo=n(OCe,"STRONG",{});var brs=s(Smo);Aja=r(brs,"not"),brs.forEach(t),Rja=r(OCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),i4e=n(OCe,"A",{href:!0});var vrs=s(i4e);kja=r(vrs,"from_pretrained()"),vrs.forEach(t),Sja=r(OCe," to load the model weights."),OCe.forEach(t),xja=i(_Z),M(qN.$$.fragment,_Z),_Z.forEach(t),Gja=i(Am),St=n(Am,"DIV",{class:!0});var Rm=s(St);M(DD.$$.fragment,Rm),Bja=i(Rm),xmo=n(Rm,"P",{});var Mrs=s(xmo);Ija=r(Mrs,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Mrs.forEach(t),Nja=i(Rm),Js=n(Rm,"P",{});var bZ=s(Js);$ja=r(bZ,"The model class to instantiate is selected based on the "),Gmo=n(bZ,"CODE",{});var Trs=s(Gmo);jja=r(Trs,"model_type"),Trs.forEach(t),Pja=r(bZ,` property of the config object (either
passed as an argument or loaded from `),Bmo=n(bZ,"CODE",{});var Frs=s(Bmo);Zja=r(Frs,"pretrained_model_name_or_path"),Frs.forEach(t),Vja=r(bZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Imo=n(bZ,"CODE",{});var Ers=s(Imo);Wja=r(Ers,"pretrained_model_name_or_path"),Ers.forEach(t),Jja=r(bZ,":"),bZ.forEach(t),Uja=i(Rm),Nmo=n(Rm,"UL",{});var yrs=s(Nmo);ON=n(yrs,"LI",{});var kSo=s(ON);$mo=n(kSo,"STRONG",{});var Crs=s($mo);Xja=r(Crs,"tapas"),Crs.forEach(t),Dja=r(kSo," \u2014 "),d4e=n(kSo,"A",{href:!0});var wrs=s(d4e);qja=r(wrs,"TFTapasForQuestionAnswering"),wrs.forEach(t),Oja=r(kSo," (TAPAS model)"),kSo.forEach(t),yrs.forEach(t),Qja=i(Rm),M(QN.$$.fragment,Rm),Rm.forEach(t),Am.forEach(t),GBo=i(c),vg=n(c,"H3",{class:!0});var R$o=s(vg);YN=n(R$o,"A",{id:!0,class:!0,href:!0});var Lrs=s(YN);jmo=n(Lrs,"SPAN",{});var Ars=s(jmo);M(qD.$$.fragment,Ars),Ars.forEach(t),Lrs.forEach(t),Yja=i(R$o),Pmo=n(R$o,"SPAN",{});var Rrs=s(Pmo);zja=r(Rrs,"AutoModelForDocumentQuestionAnswering"),Rrs.forEach(t),R$o.forEach(t),BBo=i(c),Or=n(c,"DIV",{class:!0});var km=s(Or);M(OD.$$.fragment,km),Hja=i(km),Mg=n(km,"P",{});var QCe=s(Mg);Kja=r(QCe,`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 `),m4e=n(QCe,"A",{href:!0});var krs=s(m4e);ePa=r(krs,"from_pretrained()"),krs.forEach(t),oPa=r(QCe," class method or the "),c4e=n(QCe,"A",{href:!0});var Srs=s(c4e);rPa=r(Srs,"from_config()"),Srs.forEach(t),tPa=r(QCe,` class
method.`),QCe.forEach(t),aPa=i(km),QD=n(km,"P",{});var k$o=s(QD);nPa=r(k$o,"This class cannot be instantiated directly using "),Zmo=n(k$o,"CODE",{});var xrs=s(Zmo);sPa=r(xrs,"__init__()"),xrs.forEach(t),lPa=r(k$o," (throws an error)."),k$o.forEach(t),iPa=i(km),Xa=n(km,"DIV",{class:!0});var vZ=s(Xa);M(YD.$$.fragment,vZ),dPa=i(vZ),Vmo=n(vZ,"P",{});var Grs=s(Vmo);mPa=r(Grs,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),Grs.forEach(t),cPa=i(vZ),Tg=n(vZ,"P",{});var YCe=s(Tg);fPa=r(YCe,`Note:
Loading a model from its configuration file does `),Wmo=n(YCe,"STRONG",{});var Brs=s(Wmo);hPa=r(Brs,"not"),Brs.forEach(t),gPa=r(YCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),f4e=n(YCe,"A",{href:!0});var Irs=s(f4e);uPa=r(Irs,"from_pretrained()"),Irs.forEach(t),pPa=r(YCe," to load the model weights."),YCe.forEach(t),_Pa=i(vZ),M(zN.$$.fragment,vZ),vZ.forEach(t),bPa=i(km),Bo=n(km,"DIV",{class:!0});var kn=s(Bo);M(zD.$$.fragment,kn),vPa=i(kn),Jmo=n(kn,"P",{});var Nrs=s(Jmo);MPa=r(Nrs,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),Nrs.forEach(t),TPa=i(kn),Us=n(kn,"P",{});var MZ=s(Us);FPa=r(MZ,"The model class to instantiate is selected based on the "),Umo=n(MZ,"CODE",{});var $rs=s(Umo);EPa=r($rs,"model_type"),$rs.forEach(t),yPa=r(MZ,` property of the config object (either
passed as an argument or loaded from `),Xmo=n(MZ,"CODE",{});var jrs=s(Xmo);CPa=r(jrs,"pretrained_model_name_or_path"),jrs.forEach(t),wPa=r(MZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dmo=n(MZ,"CODE",{});var Prs=s(Dmo);LPa=r(Prs,"pretrained_model_name_or_path"),Prs.forEach(t),APa=r(MZ,":"),MZ.forEach(t),RPa=i(kn),Fg=n(kn,"UL",{});var zCe=s(Fg);HN=n(zCe,"LI",{});var SSo=s(HN);qmo=n(SSo,"STRONG",{});var Zrs=s(qmo);kPa=r(Zrs,"layoutlm"),Zrs.forEach(t),SPa=r(SSo," \u2014 "),h4e=n(SSo,"A",{href:!0});var Vrs=s(h4e);xPa=r(Vrs,"LayoutLMForQuestionAnswering"),Vrs.forEach(t),GPa=r(SSo," (LayoutLM model)"),SSo.forEach(t),BPa=i(zCe),KN=n(zCe,"LI",{});var xSo=s(KN);Omo=n(xSo,"STRONG",{});var Wrs=s(Omo);IPa=r(Wrs,"layoutlmv2"),Wrs.forEach(t),NPa=r(xSo," \u2014 "),g4e=n(xSo,"A",{href:!0});var Jrs=s(g4e);$Pa=r(Jrs,"LayoutLMv2ForQuestionAnswering"),Jrs.forEach(t),jPa=r(xSo," (LayoutLMv2 model)"),xSo.forEach(t),PPa=i(zCe),e$=n(zCe,"LI",{});var GSo=s(e$);Qmo=n(GSo,"STRONG",{});var Urs=s(Qmo);ZPa=r(Urs,"layoutlmv3"),Urs.forEach(t),VPa=r(GSo," \u2014 "),u4e=n(GSo,"A",{href:!0});var Xrs=s(u4e);WPa=r(Xrs,"LayoutLMv3ForQuestionAnswering"),Xrs.forEach(t),JPa=r(GSo," (LayoutLMv3 model)"),GSo.forEach(t),zCe.forEach(t),UPa=i(kn),o$=n(kn,"P",{});var BSo=s(o$);XPa=r(BSo,"The model is set in evaluation mode by default using "),Ymo=n(BSo,"CODE",{});var Drs=s(Ymo);DPa=r(Drs,"model.eval()"),Drs.forEach(t),qPa=r(BSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),zmo=n(BSo,"CODE",{});var qrs=s(zmo);OPa=r(qrs,"model.train()"),qrs.forEach(t),BSo.forEach(t),QPa=i(kn),M(r$.$$.fragment,kn),kn.forEach(t),km.forEach(t),IBo=i(c),Eg=n(c,"H3",{class:!0});var S$o=s(Eg);t$=n(S$o,"A",{id:!0,class:!0,href:!0});var Ors=s(t$);Hmo=n(Ors,"SPAN",{});var Qrs=s(Hmo);M(HD.$$.fragment,Qrs),Qrs.forEach(t),Ors.forEach(t),YPa=i(S$o),Kmo=n(S$o,"SPAN",{});var Yrs=s(Kmo);zPa=r(Yrs,"TFAutoModelForDocumentQuestionAnswering"),Yrs.forEach(t),S$o.forEach(t),NBo=i(c),Qr=n(c,"DIV",{class:!0});var Sm=s(Qr);M(KD.$$.fragment,Sm),HPa=i(Sm),yg=n(Sm,"P",{});var HCe=s(yg);KPa=r(HCe,`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 `),p4e=n(HCe,"A",{href:!0});var zrs=s(p4e);eZa=r(zrs,"from_pretrained()"),zrs.forEach(t),oZa=r(HCe," class method or the "),_4e=n(HCe,"A",{href:!0});var Hrs=s(_4e);rZa=r(Hrs,"from_config()"),Hrs.forEach(t),tZa=r(HCe,` class
method.`),HCe.forEach(t),aZa=i(Sm),eq=n(Sm,"P",{});var x$o=s(eq);nZa=r(x$o,"This class cannot be instantiated directly using "),eco=n(x$o,"CODE",{});var Krs=s(eco);sZa=r(Krs,"__init__()"),Krs.forEach(t),lZa=r(x$o," (throws an error)."),x$o.forEach(t),iZa=i(Sm),Da=n(Sm,"DIV",{class:!0});var TZ=s(Da);M(oq.$$.fragment,TZ),dZa=i(TZ),oco=n(TZ,"P",{});var ets=s(oco);mZa=r(ets,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),ets.forEach(t),cZa=i(TZ),Cg=n(TZ,"P",{});var KCe=s(Cg);fZa=r(KCe,`Note:
Loading a model from its configuration file does `),rco=n(KCe,"STRONG",{});var ots=s(rco);hZa=r(ots,"not"),ots.forEach(t),gZa=r(KCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),b4e=n(KCe,"A",{href:!0});var rts=s(b4e);uZa=r(rts,"from_pretrained()"),rts.forEach(t),pZa=r(KCe," to load the model weights."),KCe.forEach(t),_Za=i(TZ),M(a$.$$.fragment,TZ),TZ.forEach(t),bZa=i(Sm),xt=n(Sm,"DIV",{class:!0});var xm=s(xt);M(rq.$$.fragment,xm),vZa=i(xm),tco=n(xm,"P",{});var tts=s(tco);MZa=r(tts,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),tts.forEach(t),TZa=i(xm),Xs=n(xm,"P",{});var FZ=s(Xs);FZa=r(FZ,"The model class to instantiate is selected based on the "),aco=n(FZ,"CODE",{});var ats=s(aco);EZa=r(ats,"model_type"),ats.forEach(t),yZa=r(FZ,` property of the config object (either
passed as an argument or loaded from `),nco=n(FZ,"CODE",{});var nts=s(nco);CZa=r(nts,"pretrained_model_name_or_path"),nts.forEach(t),wZa=r(FZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sco=n(FZ,"CODE",{});var sts=s(sco);LZa=r(sts,"pretrained_model_name_or_path"),sts.forEach(t),AZa=r(FZ,":"),FZ.forEach(t),RZa=i(xm),lco=n(xm,"UL",{});var lts=s(lco);n$=n(lts,"LI",{});var ISo=s(n$);ico=n(ISo,"STRONG",{});var its=s(ico);kZa=r(its,"layoutlm"),its.forEach(t),SZa=r(ISo," \u2014 "),v4e=n(ISo,"A",{href:!0});var dts=s(v4e);xZa=r(dts,"TFLayoutLMForQuestionAnswering"),dts.forEach(t),GZa=r(ISo," (LayoutLM model)"),ISo.forEach(t),lts.forEach(t),BZa=i(xm),M(s$.$$.fragment,xm),xm.forEach(t),Sm.forEach(t),$Bo=i(c),wg=n(c,"H3",{class:!0});var G$o=s(wg);l$=n(G$o,"A",{id:!0,class:!0,href:!0});var mts=s(l$);dco=n(mts,"SPAN",{});var cts=s(dco);M(tq.$$.fragment,cts),cts.forEach(t),mts.forEach(t),IZa=i(G$o),mco=n(G$o,"SPAN",{});var fts=s(mco);NZa=r(fts,"AutoModelForVisualQuestionAnswering"),fts.forEach(t),G$o.forEach(t),jBo=i(c),Yr=n(c,"DIV",{class:!0});var Gm=s(Yr);M(aq.$$.fragment,Gm),$Za=i(Gm),Lg=n(Gm,"P",{});var ewe=s(Lg);jZa=r(ewe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),M4e=n(ewe,"A",{href:!0});var hts=s(M4e);PZa=r(hts,"from_pretrained()"),hts.forEach(t),ZZa=r(ewe," class method or the "),T4e=n(ewe,"A",{href:!0});var gts=s(T4e);VZa=r(gts,"from_config()"),gts.forEach(t),WZa=r(ewe,` class
method.`),ewe.forEach(t),JZa=i(Gm),nq=n(Gm,"P",{});var B$o=s(nq);UZa=r(B$o,"This class cannot be instantiated directly using "),cco=n(B$o,"CODE",{});var uts=s(cco);XZa=r(uts,"__init__()"),uts.forEach(t),DZa=r(B$o," (throws an error)."),B$o.forEach(t),qZa=i(Gm),qa=n(Gm,"DIV",{class:!0});var EZ=s(qa);M(sq.$$.fragment,EZ),OZa=i(EZ),fco=n(EZ,"P",{});var pts=s(fco);QZa=r(pts,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),pts.forEach(t),YZa=i(EZ),Ag=n(EZ,"P",{});var owe=s(Ag);zZa=r(owe,`Note:
Loading a model from its configuration file does `),hco=n(owe,"STRONG",{});var _ts=s(hco);HZa=r(_ts,"not"),_ts.forEach(t),KZa=r(owe,` load the model weights. It only affects the
model\u2019s configuration. Use `),F4e=n(owe,"A",{href:!0});var bts=s(F4e);eVa=r(bts,"from_pretrained()"),bts.forEach(t),oVa=r(owe," to load the model weights."),owe.forEach(t),rVa=i(EZ),M(i$.$$.fragment,EZ),EZ.forEach(t),tVa=i(Gm),Io=n(Gm,"DIV",{class:!0});var Sn=s(Io);M(lq.$$.fragment,Sn),aVa=i(Sn),gco=n(Sn,"P",{});var vts=s(gco);nVa=r(vts,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),vts.forEach(t),sVa=i(Sn),Ds=n(Sn,"P",{});var yZ=s(Ds);lVa=r(yZ,"The model class to instantiate is selected based on the "),uco=n(yZ,"CODE",{});var Mts=s(uco);iVa=r(Mts,"model_type"),Mts.forEach(t),dVa=r(yZ,` property of the config object (either
passed as an argument or loaded from `),pco=n(yZ,"CODE",{});var Tts=s(pco);mVa=r(Tts,"pretrained_model_name_or_path"),Tts.forEach(t),cVa=r(yZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_co=n(yZ,"CODE",{});var Fts=s(_co);fVa=r(Fts,"pretrained_model_name_or_path"),Fts.forEach(t),hVa=r(yZ,":"),yZ.forEach(t),gVa=i(Sn),bco=n(Sn,"UL",{});var Ets=s(bco);d$=n(Ets,"LI",{});var NSo=s(d$);vco=n(NSo,"STRONG",{});var yts=s(vco);uVa=r(yts,"vilt"),yts.forEach(t),pVa=r(NSo," \u2014 "),E4e=n(NSo,"A",{href:!0});var Cts=s(E4e);_Va=r(Cts,"ViltForQuestionAnswering"),Cts.forEach(t),bVa=r(NSo," (ViLT model)"),NSo.forEach(t),Ets.forEach(t),vVa=i(Sn),m$=n(Sn,"P",{});var $So=s(m$);MVa=r($So,"The model is set in evaluation mode by default using "),Mco=n($So,"CODE",{});var wts=s(Mco);TVa=r(wts,"model.eval()"),wts.forEach(t),FVa=r($So,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Tco=n($So,"CODE",{});var Lts=s(Tco);EVa=r(Lts,"model.train()"),Lts.forEach(t),$So.forEach(t),yVa=i(Sn),M(c$.$$.fragment,Sn),Sn.forEach(t),Gm.forEach(t),PBo=i(c),Rg=n(c,"H3",{class:!0});var I$o=s(Rg);f$=n(I$o,"A",{id:!0,class:!0,href:!0});var Ats=s(f$);Fco=n(Ats,"SPAN",{});var Rts=s(Fco);M(iq.$$.fragment,Rts),Rts.forEach(t),Ats.forEach(t),CVa=i(I$o),Eco=n(I$o,"SPAN",{});var kts=s(Eco);wVa=r(kts,"AutoModelForVision2Seq"),kts.forEach(t),I$o.forEach(t),ZBo=i(c),zr=n(c,"DIV",{class:!0});var Bm=s(zr);M(dq.$$.fragment,Bm),LVa=i(Bm),kg=n(Bm,"P",{});var rwe=s(kg);AVa=r(rwe,`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 `),y4e=n(rwe,"A",{href:!0});var Sts=s(y4e);RVa=r(Sts,"from_pretrained()"),Sts.forEach(t),kVa=r(rwe," class method or the "),C4e=n(rwe,"A",{href:!0});var xts=s(C4e);SVa=r(xts,"from_config()"),xts.forEach(t),xVa=r(rwe,` class
method.`),rwe.forEach(t),GVa=i(Bm),mq=n(Bm,"P",{});var N$o=s(mq);BVa=r(N$o,"This class cannot be instantiated directly using "),yco=n(N$o,"CODE",{});var Gts=s(yco);IVa=r(Gts,"__init__()"),Gts.forEach(t),NVa=r(N$o," (throws an error)."),N$o.forEach(t),$Va=i(Bm),Oa=n(Bm,"DIV",{class:!0});var CZ=s(Oa);M(cq.$$.fragment,CZ),jVa=i(CZ),Cco=n(CZ,"P",{});var Bts=s(Cco);PVa=r(Bts,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Bts.forEach(t),ZVa=i(CZ),Sg=n(CZ,"P",{});var twe=s(Sg);VVa=r(twe,`Note:
Loading a model from its configuration file does `),wco=n(twe,"STRONG",{});var Its=s(wco);WVa=r(Its,"not"),Its.forEach(t),JVa=r(twe,` load the model weights. It only affects the
model\u2019s configuration. Use `),w4e=n(twe,"A",{href:!0});var Nts=s(w4e);UVa=r(Nts,"from_pretrained()"),Nts.forEach(t),XVa=r(twe," to load the model weights."),twe.forEach(t),DVa=i(CZ),M(h$.$$.fragment,CZ),CZ.forEach(t),qVa=i(Bm),No=n(Bm,"DIV",{class:!0});var xn=s(No);M(fq.$$.fragment,xn),OVa=i(xn),Lco=n(xn,"P",{});var $ts=s(Lco);QVa=r($ts,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),$ts.forEach(t),YVa=i(xn),qs=n(xn,"P",{});var wZ=s(qs);zVa=r(wZ,"The model class to instantiate is selected based on the "),Aco=n(wZ,"CODE",{});var jts=s(Aco);HVa=r(jts,"model_type"),jts.forEach(t),KVa=r(wZ,` property of the config object (either
passed as an argument or loaded from `),Rco=n(wZ,"CODE",{});var Pts=s(Rco);eWa=r(Pts,"pretrained_model_name_or_path"),Pts.forEach(t),oWa=r(wZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),kco=n(wZ,"CODE",{});var Zts=s(kco);rWa=r(Zts,"pretrained_model_name_or_path"),Zts.forEach(t),tWa=r(wZ,":"),wZ.forEach(t),aWa=i(xn),Hr=n(xn,"UL",{});var Gn=s(Hr);g$=n(Gn,"LI",{});var jSo=s(g$);Sco=n(jSo,"STRONG",{});var Vts=s(Sco);nWa=r(Vts,"blip"),Vts.forEach(t),sWa=r(jSo," \u2014 "),L4e=n(jSo,"A",{href:!0});var Wts=s(L4e);lWa=r(Wts,"BlipForConditionalGeneration"),Wts.forEach(t),iWa=r(jSo," (BLIP model)"),jSo.forEach(t),dWa=i(Gn),u$=n(Gn,"LI",{});var PSo=s(u$);xco=n(PSo,"STRONG",{});var Jts=s(xco);mWa=r(Jts,"blip-2"),Jts.forEach(t),cWa=r(PSo," \u2014 "),A4e=n(PSo,"A",{href:!0});var Uts=s(A4e);fWa=r(Uts,"Blip2ForConditionalGeneration"),Uts.forEach(t),hWa=r(PSo," (BLIP-2 model)"),PSo.forEach(t),gWa=i(Gn),p$=n(Gn,"LI",{});var ZSo=s(p$);Gco=n(ZSo,"STRONG",{});var Xts=s(Gco);uWa=r(Xts,"git"),Xts.forEach(t),pWa=r(ZSo," \u2014 "),R4e=n(ZSo,"A",{href:!0});var Dts=s(R4e);_Wa=r(Dts,"GitForCausalLM"),Dts.forEach(t),bWa=r(ZSo," (GIT model)"),ZSo.forEach(t),vWa=i(Gn),_$=n(Gn,"LI",{});var VSo=s(_$);Bco=n(VSo,"STRONG",{});var qts=s(Bco);MWa=r(qts,"instructblip"),qts.forEach(t),TWa=r(VSo," \u2014 "),k4e=n(VSo,"A",{href:!0});var Ots=s(k4e);FWa=r(Ots,"InstructBlipForConditionalGeneration"),Ots.forEach(t),EWa=r(VSo," (InstructBLIP model)"),VSo.forEach(t),yWa=i(Gn),b$=n(Gn,"LI",{});var WSo=s(b$);Ico=n(WSo,"STRONG",{});var Qts=s(Ico);CWa=r(Qts,"pix2struct"),Qts.forEach(t),wWa=r(WSo," \u2014 "),S4e=n(WSo,"A",{href:!0});var Yts=s(S4e);LWa=r(Yts,"Pix2StructForConditionalGeneration"),Yts.forEach(t),AWa=r(WSo," (Pix2Struct model)"),WSo.forEach(t),RWa=i(Gn),v$=n(Gn,"LI",{});var JSo=s(v$);Nco=n(JSo,"STRONG",{});var zts=s(Nco);kWa=r(zts,"vision-encoder-decoder"),zts.forEach(t),SWa=r(JSo," \u2014 "),x4e=n(JSo,"A",{href:!0});var Hts=s(x4e);xWa=r(Hts,"VisionEncoderDecoderModel"),Hts.forEach(t),GWa=r(JSo," (Vision Encoder decoder model)"),JSo.forEach(t),Gn.forEach(t),BWa=i(xn),M$=n(xn,"P",{});var USo=s(M$);IWa=r(USo,"The model is set in evaluation mode by default using "),$co=n(USo,"CODE",{});var Kts=s($co);NWa=r(Kts,"model.eval()"),Kts.forEach(t),$Wa=r(USo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),jco=n(USo,"CODE",{});var eas=s(jco);jWa=r(eas,"model.train()"),eas.forEach(t),USo.forEach(t),PWa=i(xn),M(T$.$$.fragment,xn),xn.forEach(t),Bm.forEach(t),VBo=i(c),xg=n(c,"H3",{class:!0});var $$o=s(xg);F$=n($$o,"A",{id:!0,class:!0,href:!0});var oas=s(F$);Pco=n(oas,"SPAN",{});var ras=s(Pco);M(hq.$$.fragment,ras),ras.forEach(t),oas.forEach(t),ZWa=i($$o),Zco=n($$o,"SPAN",{});var tas=s(Zco);VWa=r(tas,"TFAutoModelForVision2Seq"),tas.forEach(t),$$o.forEach(t),WBo=i(c),Kr=n(c,"DIV",{class:!0});var Im=s(Kr);M(gq.$$.fragment,Im),WWa=i(Im),Gg=n(Im,"P",{});var awe=s(Gg);JWa=r(awe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),G4e=n(awe,"A",{href:!0});var aas=s(G4e);UWa=r(aas,"from_pretrained()"),aas.forEach(t),XWa=r(awe," class method or the "),B4e=n(awe,"A",{href:!0});var nas=s(B4e);DWa=r(nas,"from_config()"),nas.forEach(t),qWa=r(awe,` class
method.`),awe.forEach(t),OWa=i(Im),uq=n(Im,"P",{});var j$o=s(uq);QWa=r(j$o,"This class cannot be instantiated directly using "),Vco=n(j$o,"CODE",{});var sas=s(Vco);YWa=r(sas,"__init__()"),sas.forEach(t),zWa=r(j$o," (throws an error)."),j$o.forEach(t),HWa=i(Im),Qa=n(Im,"DIV",{class:!0});var LZ=s(Qa);M(pq.$$.fragment,LZ),KWa=i(LZ),Wco=n(LZ,"P",{});var las=s(Wco);eJa=r(las,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),las.forEach(t),oJa=i(LZ),Bg=n(LZ,"P",{});var nwe=s(Bg);rJa=r(nwe,`Note:
Loading a model from its configuration file does `),Jco=n(nwe,"STRONG",{});var ias=s(Jco);tJa=r(ias,"not"),ias.forEach(t),aJa=r(nwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),I4e=n(nwe,"A",{href:!0});var das=s(I4e);nJa=r(das,"from_pretrained()"),das.forEach(t),sJa=r(nwe," to load the model weights."),nwe.forEach(t),lJa=i(LZ),M(E$.$$.fragment,LZ),LZ.forEach(t),iJa=i(Im),Gt=n(Im,"DIV",{class:!0});var Nm=s(Gt);M(_q.$$.fragment,Nm),dJa=i(Nm),Uco=n(Nm,"P",{});var mas=s(Uco);mJa=r(mas,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),mas.forEach(t),cJa=i(Nm),Os=n(Nm,"P",{});var AZ=s(Os);fJa=r(AZ,"The model class to instantiate is selected based on the "),Xco=n(AZ,"CODE",{});var cas=s(Xco);hJa=r(cas,"model_type"),cas.forEach(t),gJa=r(AZ,` property of the config object (either
passed as an argument or loaded from `),Dco=n(AZ,"CODE",{});var fas=s(Dco);uJa=r(fas,"pretrained_model_name_or_path"),fas.forEach(t),pJa=r(AZ,` if possible), or when it\u2019s missing, by
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classes"},{local:"natural-language-processing",sections:[{local:"transformers.AutoModelForCausalLM",title:"AutoModelForCausalLM"},{local:"transformers.TFAutoModelForCausalLM",title:"TFAutoModelForCausalLM"},{local:"transformers.FlaxAutoModelForCausalLM",title:"FlaxAutoModelForCausalLM"},{local:"transformers.AutoModelForMaskedLM",title:"AutoModelForMaskedLM"},{local:"transformers.TFAutoModelForMaskedLM",title:"TFAutoModelForMaskedLM"},{local:"transformers.FlaxAutoModelForMaskedLM",title:"FlaxAutoModelForMaskedLM"},{local:"transformers.AutoModelForMaskGeneration",title:"AutoModelForMaskGeneration"},{local:"transformers.TFAutoModelForMaskGeneration",title:"TFAutoModelForMaskGeneration"},{local:"transformers.AutoModelForSeq2SeqLM",title:"AutoModelForSeq2SeqLM"},{local:"transformers.TFAutoModelForSeq2SeqLM",title:"TFAutoModelForSeq2SeqLM"},{local:"transformers.FlaxAutoModelForSeq2SeqLM",title:"FlaxAutoModelForSeq2SeqLM"},{local:"transformers.AutoModelForSequenceClassification",title:"AutoModelForSequenceClassification"},{local:"transformers.TFAutoModelForSequenceClassification",title:"TFAutoModelForSequenceClassification"},{local:"transformers.FlaxAutoModelForSequenceClassification",title:"FlaxAutoModelForSequenceClassification"},{local:"transformers.AutoModelForMultipleChoice",title:"AutoModelForMultipleChoice"},{local:"transformers.TFAutoModelForMultipleChoice",title:"TFAutoModelForMultipleChoice"},{local:"transformers.FlaxAutoModelForMultipleChoice",title:"FlaxAutoModelForMultipleChoice"},{local:"transformers.AutoModelForNextSentencePrediction",title:"AutoModelForNextSentencePrediction"},{local:"transformers.TFAutoModelForNextSentencePrediction",title:"TFAutoModelForNextSentencePrediction"},{local:"transformers.FlaxAutoModelForNextSentencePrediction",title:"FlaxAutoModelForNextSentencePrediction"},{local:"transformers.AutoModelForTokenClassification",title:"AutoModelForTokenClassification"},{local:"transformers.TFAutoModelForTokenClassification",title:"TFAutoModelForTokenClassification"},{local:"transformers.FlaxAutoModelForTokenClassification",title:"FlaxAutoModelForTokenClassification"},{local:"transformers.AutoModelForQuestionAnswering",title:"AutoModelForQuestionAnswering"},{local:"transformers.TFAutoModelForQuestionAnswering",title:"TFAutoModelForQuestionAnswering"},{local:"transformers.FlaxAutoModelForQuestionAnswering",title:"FlaxAutoModelForQuestionAnswering"},{local:"transformers.AutoModelForTextEncoding",title:"AutoModelForTextEncoding"},{local:"transformers.TFAutoModelForTextEncoding",title:"TFAutoModelForTextEncoding"}],title:"Natural Language Processing"},{local:"computer-vision",sections:[{local:"transformers.AutoModelForDepthEstimation",title:"AutoModelForDepthEstimation"},{local:"transformers.AutoModelForImageClassification",title:"AutoModelForImageClassification"},{local:"transformers.TFAutoModelForImageClassification",title:"TFAutoModelForImageClassification"},{local:"transformers.FlaxAutoModelForImageClassification",title:"FlaxAutoModelForImageClassification"},{local:"transformers.AutoModelForVideoClassification",title:"AutoModelForVideoClassification"},{local:"transformers.AutoModelForMaskedImageModeling",title:"AutoModelForMaskedImageModeling"},{local:"transformers.TFAutoModelForMaskedImageModeling",title:"TFAutoModelForMaskedImageModeling"},{local:"transformers.AutoModelForObjectDetection",title:"AutoModelForObjectDetection"},{local:"transformers.AutoModelForImageSegmentation",title:"AutoModelForImageSegmentation"},{local:"transformers.AutoModelForSemanticSegmentation",title:"AutoModelForSemanticSegmentation"},{local:"transformers.TFAutoModelForSemanticSegmentation",title:"TFAutoModelForSemanticSegmentation"},{local:"transformers.AutoModelForInstanceSegmentation",title:"AutoModelForInstanceSegmentation"},{local:"transformers.AutoModelForUniversalSegmentation",title:"AutoModelForUniversalSegmentation"},{local:"transformers.AutoModelForZeroShotImageClassification",title:"AutoModelForZeroShotImageClassification"},{local:"transformers.TFAutoModelForZeroShotImageClassification",title:"TFAutoModelForZeroShotImageClassification"},{local:"transformers.AutoModelForZeroShotObjectDetection",title:"AutoModelForZeroShotObjectDetection"}],title:"Computer vision"},{local:"audio",sections:[{local:"transformers.AutoModelForAudioClassification",title:"AutoModelForAudioClassification"},{local:"transformers.TFAutoModelForAudioClassification",title:"AutoModelForAudioFrameClassification"},{local:"transformers.AutoModelForAudioFrameClassification",title:"TFAutoModelForAudioFrameClassification"},{local:"transformers.AutoModelForCTC",title:"AutoModelForCTC"},{local:"transformers.AutoModelForSpeechSeq2Seq",title:"AutoModelForSpeechSeq2Seq"},{local:"transformers.TFAutoModelForSpeechSeq2Seq",title:"TFAutoModelForSpeechSeq2Seq"},{local:"transformers.FlaxAutoModelForSpeechSeq2Seq",title:"FlaxAutoModelForSpeechSeq2Seq"},{local:"transformers.AutoModelForAudioXVector",title:"AutoModelForAudioXVector"}],title:"Audio"},{local:"multimodal",sections:[{local:"transformers.AutoModelForTableQuestionAnswering",title:"AutoModelForTableQuestionAnswering"},{local:"transformers.TFAutoModelForTableQuestionAnswering",title:"TFAutoModelForTableQuestionAnswering"},{local:"transformers.AutoModelForDocumentQuestionAnswering",title:"AutoModelForDocumentQuestionAnswering"},{local:"transformers.TFAutoModelForDocumentQuestionAnswering",title:"TFAutoModelForDocumentQuestionAnswering"},{local:"transformers.AutoModelForVisualQuestionAnswering",title:"AutoModelForVisualQuestionAnswering"},{local:"transformers.AutoModelForVision2Seq",title:"AutoModelForVision2Seq"},{local:"transformers.TFAutoModelForVision2Seq",title:"TFAutoModelForVision2Seq"},{local:"transformers.FlaxAutoModelForVision2Seq",title:"FlaxAutoModelForVision2Seq"}],title:"Multimodal"}],title:"Auto Classes"};function fls(x){return jas(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class vls extends Bas{constructor(h){super();Ias(this,h,fls,mls,Nas,{})}}export{vls as default,cls as metadata};
