import{S as r6n,i as t6n,s as a6n,e as a,k as l,w as b,t as o,M as n6n,c as n,d as t,m as i,a as s,x as T,h as r,b as d,G as e,g as v,y as F,q as M,o as E,B as C,v as s6n,L as D}from"../../chunks/vendor-hf-doc-builder.js";import{T as g8o}from"../../chunks/Tip-hf-doc-builder.js";import{D as R}from"../../chunks/Docstring-hf-doc-builder.js";import{C as P}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as te}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as G}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function l6n(S){let g,w,u,m,p,c,h,Je,hc,sh,It,uc,pc,rB,lh,He,oo,_c,qs,tB,js,Os,aB,vc,Vs,nB,bc,ih,wn;return{c(){g=a("p"),w=o("If your "),u=a("code"),m=o("NewModelConfig"),p=o(" is a subclass of "),c=a("code"),h=o("~transformer.PretrainedConfig"),Je=o(`, make sure its
`),hc=a("code"),sh=o("model_type"),It=o(" attribute is set to the same key you use when registering the config (here "),uc=a("code"),pc=o('"new-model"'),rB=o(")."),lh=l(),He=a("p"),oo=o("Likewise, if your "),_c=a("code"),qs=o("NewModel"),tB=o(" is a subclass of "),js=a("a"),Os=o("PreTrainedModel"),aB=o(`, make sure its
`),vc=a("code"),Vs=o("config_class"),nB=o(` attribute is set to the same class you use when registering the model (here
`),bc=a("code"),ih=o("NewModelConfig"),wn=o(")."),this.h()},l(ro){g=n(ro,"P",{});var Pe=s(g);w=r(Pe,"If your "),u=n(Pe,"CODE",{});var hz=s(u);m=r(hz,"NewModelConfig"),hz.forEach(t),p=r(Pe," is a subclass of "),c=n(Pe,"CODE",{});var Tc=s(c);h=r(Tc,"~transformer.PretrainedConfig"),Tc.forEach(t),Je=r(Pe,`, make sure its
`),hc=n(Pe,"CODE",{});var uz=s(hc);sh=r(uz,"model_type"),uz.forEach(t),It=r(Pe," attribute is set to the same key you use when registering the config (here "),uc=n(Pe,"CODE",{});var pz=s(uc);pc=r(pz,'"new-model"'),pz.forEach(t),rB=r(Pe,")."),Pe.forEach(t),lh=i(ro),He=n(ro,"P",{});var Bo=s(He);oo=r(Bo,"Likewise, if your "),_c=n(Bo,"CODE",{});var An=s(_c);qs=r(An,"NewModel"),An.forEach(t),tB=r(Bo," is a subclass of "),js=n(Bo,"A",{href:!0});var _z=s(js);Os=r(_z,"PreTrainedModel"),_z.forEach(t),aB=r(Bo,`, make sure its
`),vc=n(Bo,"CODE",{});var dh=s(vc);Vs=r(dh,"config_class"),dh.forEach(t),nB=r(Bo,` attribute is set to the same class you use when registering the model (here
`),bc=n(Bo,"CODE",{});var vz=s(bc);ih=r(vz,"NewModelConfig"),vz.forEach(t),wn=r(Bo,")."),Bo.forEach(t),this.h()},h(){d(js,"href","/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel")},m(ro,Pe){v(ro,g,Pe),e(g,w),e(g,u),e(u,m),e(g,p),e(g,c),e(c,h),e(g,Je),e(g,hc),e(hc,sh),e(g,It),e(g,uc),e(uc,pc),e(g,rB),v(ro,lh,Pe),v(ro,He,Pe),e(He,oo),e(He,_c),e(_c,qs),e(He,tB),e(He,js),e(js,Os),e(He,aB),e(He,vc),e(vc,Vs),e(He,nB),e(He,bc),e(bc,ih),e(He,wn)},d(ro){ro&&t(g),ro&&t(lh),ro&&t(He)}}}function i6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-uncased")

# Download configuration from huggingface.co (user-uploaded) and cache.
config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")

# If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
config = AutoConfig.from_pretrained("./test/bert_saved_model/")

# Load a specific configuration file.
config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")

# Change some config attributes when loading a pretrained config.
config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
config.output_attentions

config, unused_kwargs = AutoConfig.from_pretrained(
    "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
)
config.output_attentions

unused_kwargs`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function d6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoTokenizer

# Download vocabulary from huggingface.co and cache.
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Download vocabulary from huggingface.co (user-uploaded) and cache.
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")

# If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/")

# Download vocabulary from huggingface.co and define model-specific arguments
tokenizer = AutoTokenizer.from_pretrained("roberta-base", add_prefix_space=True)`,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>tokenizer = AutoTokenizer.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"># 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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function c6n(S){let g,w,u,m,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,w),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function f6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoFeatureExtractor

# Download feature extractor from huggingface.co and cache.
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")

# If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained('./test/saved_model/')*)
feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")`,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>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/&quot;</span>)`}}),{c(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function m6n(S){let g,w,u,m,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,w),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function g6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoImageProcessor

# Download image processor from huggingface.co and cache.
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")

# If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")`,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>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/&quot;</span>)`}}),{c(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function h6n(S){let g,w,u,m,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,w),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function u6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoProcessor

# Download processor from huggingface.co and cache.
processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")

# If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
processor = AutoProcessor.from_pretrained("./test/saved_model/")`,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>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/&quot;</span>)`}}),{c(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function p6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function _6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModel

# Download model and configuration from huggingface.co and cache.
model = AutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModel.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function v6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function b6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModel

# Download model and configuration from huggingface.co and cache.
model = TFAutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModel.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function T6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function F6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModel

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModel.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function M6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function E6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = AutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForPreTraining.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function C6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function w6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForPreTraining.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function A6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function L6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForPreTraining.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function y6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function x6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForCausalLM.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function S6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function $6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForCausalLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function k6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function R6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForCausalLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function P6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function I6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMaskedLM.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function N6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function B6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForMaskedLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function G6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function D6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForMaskedLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function q6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function j6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/t5_tf_model_config.json")
model = AutoModelForSeq2SeqLM.from_pretrained(
    "./tf_model/t5_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function O6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = TFAutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function V6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
model = TFAutoModelForSeq2SeqLM.from_pretrained(
    "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function X6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = FlaxAutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function z6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = FlaxAutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
    "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function W6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Q6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSequenceClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function U6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function H6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSequenceClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function J6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Z6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForSequenceClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Y6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function K6n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMultipleChoice.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function e8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function o8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForMultipleChoice.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function r8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function t8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForMultipleChoice.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function a8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function n8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = AutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForNextSentencePrediction.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function s8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function l8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForNextSentencePrediction.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function i8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function d8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function c8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function f8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForTokenClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function m8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function g8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForTokenClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function h8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function u8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForTokenClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function p8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function _8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForQuestionAnswering.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function v8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function b8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForQuestionAnswering.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function T8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function F8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForQuestionAnswering.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function M8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForDepthEstimation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForDepthEstimation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function E8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForDepthEstimation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForDepthEstimation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForDepthEstimation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForDepthEstimation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function C8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function w8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForImageClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function A8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function L8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function y8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function x8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function S8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForVideoClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForVideoClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function $8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForVideoClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVideoClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForVideoClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForVideoClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function k8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForMaskedImageModeling

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMaskedImageModeling.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function R8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForMaskedImageModeling

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMaskedImageModeling.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMaskedImageModeling.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMaskedImageModeling.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function P8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForObjectDetection

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForObjectDetection.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function I8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForObjectDetection

# Download model and configuration from huggingface.co and cache.
model = AutoModelForObjectDetection.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForObjectDetection.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForObjectDetection.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function N8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForImageSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForImageSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function B8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForImageSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForImageSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForImageSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForImageSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function G8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSemanticSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSemanticSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function D8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSemanticSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSemanticSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSemanticSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSemanticSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function q8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSemanticSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function j8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSemanticSegmentation.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function O8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForInstanceSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForInstanceSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function V8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForInstanceSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForInstanceSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForInstanceSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForInstanceSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function X8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForUniversalSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForUniversalSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function z8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForUniversalSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForUniversalSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForUniversalSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForUniversalSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function W8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForZeroShotImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Q8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotImageClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForZeroShotImageClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function U8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForZeroShotImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForZeroShotImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function H8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForZeroShotImageClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForZeroShotImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function J8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForZeroShotObjectDetection.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Z8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

# Download model and configuration from huggingface.co and cache.
model = AutoModelForZeroShotObjectDetection.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForZeroShotObjectDetection.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForZeroShotObjectDetection.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Y8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForAudioClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function K8n(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForAudioClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function eAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForAudioFrameClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioFrameClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function oAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForAudioFrameClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioFrameClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioFrameClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioFrameClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function rAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForCTC

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForCTC.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function tAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForCTC

# Download model and configuration from huggingface.co and cache.
model = AutoModelForCTC.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForCTC.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForCTC.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function aAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function nAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function sAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function lAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function iAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function dAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function cAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForAudioXVector

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioXVector.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function fAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForAudioXVector

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioXVector.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioXVector.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioXVector.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function mAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForTableQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = AutoModelForTableQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function gAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForTableQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

# Update configuration during loading
model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/tapas_tf_model_config.json")
model = AutoModelForTableQuestionAnswering.from_pretrained(
    "./tf_model/tapas_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function hAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = TFAutoModelForTableQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function uAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

# Update configuration during loading
model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/tapas_pt_model_config.json")
model = TFAutoModelForTableQuestionAnswering.from_pretrained(
    "./pt_model/tapas_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function pAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
model = AutoModelForDocumentQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function _An(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

# Update configuration during loading
model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/layoutlm_tf_model_config.json")
model = AutoModelForDocumentQuestionAnswering.from_pretrained(
    "./tf_model/layoutlm_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function vAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
model = TFAutoModelForDocumentQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function bAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

# Update configuration during loading
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/layoutlm_pt_model_config.json")
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
    "./pt_model/layoutlm_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function TAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = AutoModelForVisualQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function FAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

# Update configuration during loading
model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/vilt_tf_model_config.json")
model = AutoModelForVisualQuestionAnswering.from_pretrained(
    "./tf_model/vilt_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function MAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function EAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, AutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForVision2Seq.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function CAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function wAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, TFAutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForVision2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function AAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:D,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function LAn(S){let g,w,u,m,p;return m=new P({props:{code:`from transformers import AutoConfig, FlaxAutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

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

AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)`,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)`}}),fh=new g8o({props:{warning:!0,$$slots:{default:[l6n]},$$scope:{ctx:S}}}),iB=new te({}),dB=new R({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/configuration_auto.py#L785"}}),mB=new R({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/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> method,
e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a saved configuration JSON <em>file</em>, e.g.,
<code>./my_model_directory/configuration.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoConfig.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoConfig.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoConfig.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoConfig.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoConfig.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoConfig.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final configuration object.</p>
<p>If <code>True</code>, then this functions returns a <code>Tuple(config, unused_kwargs)</code> where <em>unused_kwargs</em> is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of <code>kwargs</code> which has not been used to update <code>config</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoConfig.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoConfig.from_pretrained.kwargs(additional",description:`<strong>kwargs(additional</strong> keyword arguments, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
single vocabulary file (like Bert or XLNet), e.g.: <code>./my_model_directory/vocab.txt</code>. (Not
applicable to all derived classes)</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoTokenizer.from_pretrained.inputs",description:`<strong>inputs</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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The configuration object used to dertermine the tokenizer class to instantiate.`,name:"config"},{anchor:"transformers.AutoTokenizer.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Whether or not to force the (re-)download the model weights and configuration files and override 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.AutoTokenizer.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
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Use a <a href="https://huggingface.co/docs/tokenizers/index" rel="nofollow">fast Rust-based tokenizer</a> if it is supported for
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Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
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The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizer</code>, <em>optional</em>) &#x2014;
The slow tokenizer to register.`,name:"slow_tokenizer_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizerFast</code>, <em>optional</em>) &#x2014;
The fast tokenizer to register.`,name:"slow_tokenizer_class"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/tokenization_auto.py#L712"}}),TB=new te({}),FB=new R({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/feature_extraction_auto.py#L220"}}),CB=new R({props:{name:"from_pretrained",anchor:"transformers.AutoFeatureExtractor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/v4.27.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
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Whether or not to delete incompletely received file. Attempts to resume the download if such a file
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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.AutoFeatureExtractor.from_pretrained.use_auth_token",description:`<strong>use_auth_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
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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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If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
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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 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/v4.27.1/src/transformers/models/auto/feature_extraction_auto.py#L234"}}),A2=new g8o({props:{$$slots:{default:[c6n]},$$scope:{ctx:S}}}),L2=new G({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[f6n]},$$scope:{ctx:S}}}),wB=new R({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoFeatureExtractor.register.feature_extractor_class",description:"<strong>feature_extractor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The feature extractor to register.",name:"feature_extractor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/feature_extraction_auto.py#L362"}}),AB=new te({}),LB=new R({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/image_processing_auto.py#L223"}}),SB=new R({props:{name:"from_pretrained",anchor:"transformers.AutoImageProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/v4.27.1/en/internal/image_processing_utils#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoImageProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoImageProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the image processor files and override the cached versions if
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Whether or not to delete incompletely received file. Attempts to resume the download if such a file
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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.AutoImageProcessor.from_pretrained.use_auth_token",description:`<strong>use_auth_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
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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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If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
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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 values in kwargs of any keys which are image processor attributes will be used to override the
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controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/image_processing_auto.py#L237"}}),Bv=new g8o({props:{$$slots:{default:[m6n]},$$scope:{ctx:S}}}),Gv=new G({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[g6n]},$$scope:{ctx:S}}}),$B=new R({props:{name:"register",anchor:"transformers.AutoImageProcessor.register",parameters:[{name:"config_class",val:""},{name:"image_processor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
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This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a processor files saved using the <code>save_pretrained()</code> method,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
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Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoProcessor.from_pretrained.use_auth_token",description:`<strong>use_auth_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
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.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
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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.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/v4.27.1/src/transformers/models/auto/processing_auto.py#L123"}}),M4=new g8o({props:{$$slots:{default:[h6n]},$$scope:{ctx:S}}}),E4=new G({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[u6n]},$$scope:{ctx:S}}}),BB=new R({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/v4.27.1/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/v4.27.1/src/transformers/models/auto/processing_auto.py#L305"}}),GB=new te({}),DB=new te({}),qB=new R({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1028"}}),OB=new R({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/v4.27.1/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/v4.27.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),L4=new G({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[p6n]},$$scope:{ctx:S}}}),VB=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),RT=new G({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[_6n]},$$scope:{ctx:S}}}),XB=new te({}),zB=new R({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L473"}}),QB=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),IT=new G({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[v6n]},$$scope:{ctx:S}}}),UB=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),OF=new G({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[b6n]},$$scope:{ctx:S}}}),HB=new te({}),JB=new R({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L260"}}),YB=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),XF=new G({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[T6n]},$$scope:{ctx:S}}}),KB=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),FM=new G({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[F6n]},$$scope:{ctx:S}}}),eG=new te({}),oG=new te({}),rG=new R({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1035"}}),aG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),CM=new G({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[M6n]},$$scope:{ctx:S}}}),nG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),LE=new G({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[E6n]},$$scope:{ctx:S}}}),sG=new te({}),lG=new R({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L480"}}),dG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),xE=new G({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[C6n]},$$scope:{ctx:S}}}),cG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),o3=new G({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[w6n]},$$scope:{ctx:S}}}),fG=new te({}),mG=new R({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L267"}}),hG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),t3=new G({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[A6n]},$$scope:{ctx:S}}}),uG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),b3=new G({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[L6n]},$$scope:{ctx:S}}}),pG=new te({}),_G=new te({}),vG=new R({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1050"}}),TG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),M3=new G({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[y6n]},$$scope:{ctx:S}}}),FG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),vC=new G({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[x6n]},$$scope:{ctx:S}}}),MG=new te({}),EG=new R({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L495"}}),wG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),TC=new G({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[S6n]},$$scope:{ctx:S}}}),AG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),GC=new G({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[$6n]},$$scope:{ctx:S}}}),LG=new te({}),yG=new R({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L274"}}),SG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),qC=new G({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[k6n]},$$scope:{ctx:S}}}),$G=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),e5=new G({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[R6n]},$$scope:{ctx:S}}}),kG=new te({}),RG=new R({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1057"}}),IG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),r5=new G({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[P6n]},$$scope:{ctx:S}}}),NG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),Q5=new G({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[I6n]},$$scope:{ctx:S}}}),BG=new te({}),GG=new R({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L545"}}),qG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),H5=new G({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[N6n]},$$scope:{ctx:S}}}),jG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),v0=new G({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[B6n]},$$scope:{ctx:S}}}),OG=new te({}),VG=new R({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L281"}}),zG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),T0=new G({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[G6n]},$$scope:{ctx:S}}}),WG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),k0=new G({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[D6n]},$$scope:{ctx:S}}}),QG=new te({}),UG=new R({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1064"}}),JG=new R({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/v4.27.1/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/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),P0=new G({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[q6n]},$$scope:{ctx:S}}}),ZG=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),tw=new G({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[j6n]},$$scope:{ctx:S}}}),YG=new te({}),KG=new R({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L552"}}),oD=new R({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/v4.27.1/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/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),nw=new G({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[O6n]},$$scope:{ctx:S}}}),rD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),pw=new G({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[V6n]},$$scope:{ctx:S}}}),tD=new te({}),aD=new R({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L288"}}),sD=new R({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/v4.27.1/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/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),vw=new G({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[X6n]},$$scope:{ctx:S}}}),lD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),xw=new G({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[z6n]},$$scope:{ctx:S}}}),iD=new te({}),dD=new R({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1073"}}),fD=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),$w=new G({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[W6n]},$$scope:{ctx:S}}}),mD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),q6=new G({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Q6n]},$$scope:{ctx:S}}}),gD=new te({}),hD=new R({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L561"}}),pD=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),O6=new G({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[U6n]},$$scope:{ctx:S}}}),_D=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),T8=new G({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[H6n]},$$scope:{ctx:S}}}),vD=new te({}),bD=new R({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L297"}}),FD=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),M8=new G({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[J6n]},$$scope:{ctx:S}}}),MD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),P8=new G({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Z6n]},$$scope:{ctx:S}}}),ED=new te({}),CD=new R({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1129"}}),AD=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),N8=new G({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[Y6n]},$$scope:{ctx:S}}}),LD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),TA=new G({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[K6n]},$$scope:{ctx:S}}}),yD=new te({}),xD=new R({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L608"}}),$D=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),MA=new G({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[e8n]},$$scope:{ctx:S}}}),kD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),jA=new G({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[o8n]},$$scope:{ctx:S}}}),RD=new te({}),PD=new R({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L322"}}),ND=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),VA=new G({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[r8n]},$$scope:{ctx:S}}}),BD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),KA=new G({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[t8n]},$$scope:{ctx:S}}}),GD=new te({}),DD=new R({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1136"}}),jD=new R({props:{name:"from_config",anchor:"transformers.AutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),o9=new G({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[a8n]},$$scope:{ctx:S}}}),OD=new R({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/v4.27.1/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
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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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
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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.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),c9=new G({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[n8n]},$$scope:{ctx:S}}}),VD=new te({}),XD=new R({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L615"}}),WD=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),m9=new G({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[s8n]},$$scope:{ctx:S}}}),QD=new R({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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),u9=new G({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[l8n]},$$scope:{ctx:S}}}),HD=new te({}),JD=new R({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L329"}}),YD=new R({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/v4.27.1/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/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),_9=new G({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[i8n]},$$scope:{ctx:S}}}),KD=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),b9=new G({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[d8n]},$$scope:{ctx:S}}}),eq=new te({}),oq=new R({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1122"}}),tq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),F9=new G({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[c8n]},$$scope:{ctx:S}}}),aq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),uL=new G({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[f8n]},$$scope:{ctx:S}}}),nq=new te({}),sq=new R({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L599"}}),iq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),_L=new G({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[m8n]},$$scope:{ctx:S}}}),dq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),jL=new G({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[g8n]},$$scope:{ctx:S}}}),cq=new te({}),fq=new R({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L313"}}),gq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),VL=new G({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[h8n]},$$scope:{ctx:S}}}),hq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),KL=new G({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[u8n]},$$scope:{ctx:S}}}),uq=new te({}),pq=new R({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1082"}}),vq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),oy=new G({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[p8n]},$$scope:{ctx:S}}}),bq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),ox=new G({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[_8n]},$$scope:{ctx:S}}}),Tq=new te({}),Fq=new R({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L570"}}),Eq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),tx=new G({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[v8n]},$$scope:{ctx:S}}}),Cq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),Ax=new G({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[b8n]},$$scope:{ctx:S}}}),wq=new te({}),Aq=new R({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L306"}}),yq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),yx=new G({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[T8n]},$$scope:{ctx:S}}}),xq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),qx=new G({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[F8n]},$$scope:{ctx:S}}}),Sq=new te({}),$q=new te({}),kq=new R({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1211"}}),Pq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),Vx=new G({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[M8n]},$$scope:{ctx:S}}}),Iq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),Qx=new G({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[E8n]},$$scope:{ctx:S}}}),Bq=new te({}),Gq=new R({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1145"}}),qq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),Hx=new G({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[C8n]},$$scope:{ctx:S}}}),jq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),TS=new G({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[w8n]},$$scope:{ctx:S}}}),Oq=new te({}),Vq=new R({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L511"}}),zq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),MS=new G({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[A8n]},$$scope:{ctx:S}}}),Wq=new R({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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),kS=new G({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[L8n]},$$scope:{ctx:S}}}),Qq=new te({}),Uq=new R({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L338"}}),Jq=new R({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/v4.27.1/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/v4.27.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),PS=new G({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[y8n]},$$scope:{ctx:S}}}),Zq=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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
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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.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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),BS=new G({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[x8n]},$$scope:{ctx:S}}}),Kq=new te({}),ej=new R({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1218"}}),rj=new R({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/v4.27.1/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/v4.27.1/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),DS=new G({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[S8n]},$$scope:{ctx:S}}}),tj=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),VS=new G({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[$8n]},$$scope:{ctx:S}}}),nj=new te({}),sj=new R({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1275"}}),ij=new R({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/v4.27.1/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/v4.27.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),zS=new G({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[k8n]},$$scope:{ctx:S}}}),dj=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),ZS=new G({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[R8n]},$$scope:{ctx:S}}}),cj=new te({}),fj=new R({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1195"}}),gj=new R({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/v4.27.1/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/v4.27.1/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),KS=new G({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[P8n]},$$scope:{ctx:S}}}),hj=new R({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
<a href="/docs/transformers/v4.27.1/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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/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.AutoModelForObjectDetection.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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForObjectDetection.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.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),l$=new G({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[I8n]},$$scope:{ctx:S}}}),uj=new te({}),pj=new R({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1161"}}),vj=new R({props:{name:"from_config",anchor:"transformers.AutoModelForImageSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),d$=new G({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[N8n]},$$scope:{ctx:S}}}),bj=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.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/v4.27.1/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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/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.AutoModelForImageSegmentation.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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),m$=new G({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[B8n]},$$scope:{ctx:S}}}),Tj=new te({}),Fj=new R({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1168"}}),Ej=new R({props:{name:"from_config",anchor:"transformers.AutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),h$=new G({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[G8n]},$$scope:{ctx:S}}}),Cj=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),E$=new G({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[D8n]},$$scope:{ctx:S}}}),wj=new te({}),Aj=new R({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L529"}}),yj=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),w$=new G({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[q8n]},$$scope:{ctx:S}}}),xj=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),x$=new G({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[j8n]},$$scope:{ctx:S}}}),Sj=new te({}),$j=new R({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1186"}}),Rj=new R({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/v4.27.1/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/v4.27.1/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),$$=new G({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[O8n]},$$scope:{ctx:S}}}),Pj=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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
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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),P$=new G({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[V8n]},$$scope:{ctx:S}}}),Ij=new te({}),Nj=new R({props:{name:"class transformers.AutoModelForUniversalSegmentation",anchor:"transformers.AutoModelForUniversalSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1177"}}),Gj=new R({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/v4.27.1/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/v4.27.1/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),N$=new G({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[X8n]},$$scope:{ctx:S}}}),Dj=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/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.AutoModelForUniversalSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForUniversalSegmentation.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.AutoModelForUniversalSegmentation.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.AutoModelForUniversalSegmentation.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.AutoModelForUniversalSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.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.AutoModelForUniversalSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),O$=new G({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[z8n]},$$scope:{ctx:S}}}),qj=new te({}),jj=new R({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1152"}}),Vj=new R({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),X$=new G({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[W8n]},$$scope:{ctx:S}}}),Xj=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/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.AutoModelForZeroShotImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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.AutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),Y$=new G({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[Q8n]},$$scope:{ctx:S}}}),zj=new te({}),Wj=new R({props:{name:"class transformers.TFAutoModelForZeroShotImageClassification",anchor:"transformers.TFAutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L520"}}),Uj=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),ek=new G({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[U8n]},$$scope:{ctx:S}}}),Hj=new R({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.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
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),rk=new G({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[H8n]},$$scope:{ctx:S}}}),Jj=new te({}),Zj=new R({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1202"}}),Kj=new R({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),ak=new G({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[J8n]},$$scope:{ctx:S}}}),eO=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.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.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.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.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),lk=new G({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[Z8n]},$$scope:{ctx:S}}}),oO=new te({}),rO=new te({}),tO=new R({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1232"}}),nO=new R({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/v4.27.1/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/v4.27.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),ck=new G({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[Y8n]},$$scope:{ctx:S}}}),sO=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),Ek=new G({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[K8n]},$$scope:{ctx:S}}}),lO=new te({}),iO=new R({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1255"}}),cO=new R({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/v4.27.1/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/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),wk=new G({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[eAn]},$$scope:{ctx:S}}}),fO=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),kk=new G({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[oAn]},$$scope:{ctx:S}}}),mO=new te({}),gO=new R({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1239"}}),uO=new R({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/v4.27.1/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/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),Pk=new G({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[rAn]},$$scope:{ctx:S}}}),pO=new R({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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCTC.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCTC.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCTC.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCTC.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCTC.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCTC.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCTC.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),Wk=new G({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[tAn]},$$scope:{ctx:S}}}),_O=new te({}),vO=new R({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1246"}}),TO=new R({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),Uk=new G({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[aAn]},$$scope:{ctx:S}}}),FO=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),eR=new G({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[nAn]},$$scope:{ctx:S}}}),MO=new te({}),EO=new R({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L624"}}),wO=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),rR=new G({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[sAn]},$$scope:{ctx:S}}}),AO=new R({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),nR=new G({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[lAn]},$$scope:{ctx:S}}}),yO=new te({}),xO=new R({props:{name:"class transformers.FlaxAutoModelForSpeechSeq2Seq",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_flax_auto.py#L354"}}),$O=new R({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),lR=new G({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[iAn]},$$scope:{ctx:S}}}),kO=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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.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
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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.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;
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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.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
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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
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),cR=new G({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[dAn]},$$scope:{ctx:S}}}),PO=new te({}),IO=new R({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1264"}}),BO=new R({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/v4.27.1/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/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),mR=new G({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[cAn]},$$scope:{ctx:S}}}),GO=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),bR=new G({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[fAn]},$$scope:{ctx:S}}}),DO=new te({}),qO=new te({}),jO=new R({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1089"}}),VO=new R({props:{name:"from_config",anchor:"transformers.AutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),MR=new G({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[mAn]},$$scope:{ctx:S}}}),XO=new R({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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTableQuestionAnswering.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.AutoModelForTableQuestionAnswering.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.AutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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.AutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),wR=new G({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[gAn]},$$scope:{ctx:S}}}),zO=new te({}),WO=new R({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L588"}}),UO=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),LR=new G({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[hAn]},$$scope:{ctx:S}}}),HO=new R({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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/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
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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.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),xR=new G({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[uAn]},$$scope:{ctx:S}}}),JO=new te({}),ZO=new R({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1111"}}),KO=new R({props:{name:"from_config",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),$R=new G({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[pAn]},$$scope:{ctx:S}}}),eV=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),NR=new G({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[_An]},$$scope:{ctx:S}}}),oV=new te({}),rV=new R({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L577"}}),aV=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),GR=new G({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[vAn]},$$scope:{ctx:S}}}),nV=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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
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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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),qR=new G({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[bAn]},$$scope:{ctx:S}}}),sV=new te({}),lV=new R({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1100"}}),dV=new R({props:{name:"from_config",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),OR=new G({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[TAn]},$$scope:{ctx:S}}}),cV=new R({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/v4.27.1/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/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/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.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),zR=new G({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[FAn]},$$scope:{ctx:S}}}),fV=new te({}),mV=new R({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_auto.py#L1225"}}),hV=new R({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),QR=new G({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[MAn]},$$scope:{ctx:S}}}),uV=new R({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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.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/v4.27.1/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/v4.27.1/src/transformers/models/auto/auto_factory.py#L418"}}),YR=new G({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[EAn]},$$scope:{ctx:S}}}),pV=new te({}),_V=new R({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/modeling_tf_auto.py#L538"}}),bV=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.27.1/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.27.1/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/auto/auto_factory.py#L390"}}),eP=new G({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[CAn]},$$scope:{ctx:S}}}),TV=new R({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.27.1/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
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(MobileNetV1 model)"),qfr=l(),fv=a("li"),K6e=a("strong"),jfr=o("mobilenet_v2"),Ofr=o(" \u2014 "),gee=a("a"),Vfr=o("MobileNetV2ImageProcessor"),Xfr=o(" (MobileNetV2 model)"),zfr=l(),mv=a("li"),e8e=a("strong"),Wfr=o("mobilevit"),Qfr=o(" \u2014 "),hee=a("a"),Ufr=o("MobileViTImageProcessor"),Hfr=o(" (MobileViT model)"),Jfr=l(),gv=a("li"),o8e=a("strong"),Zfr=o("nat"),Yfr=o(" \u2014 "),uee=a("a"),Kfr=o("ViTImageProcessor"),emr=o(" (NAT model)"),omr=l(),hv=a("li"),r8e=a("strong"),rmr=o("oneformer"),tmr=o(" \u2014 "),pee=a("a"),amr=o("OneFormerImageProcessor"),nmr=o(" (OneFormer model)"),smr=l(),uv=a("li"),t8e=a("strong"),lmr=o("owlvit"),imr=o(" \u2014 "),_ee=a("a"),dmr=o("OwlViTImageProcessor"),cmr=o(" (OWL-ViT model)"),fmr=l(),pv=a("li"),a8e=a("strong"),mmr=o("perceiver"),gmr=o(" \u2014 "),vee=a("a"),hmr=o("PerceiverImageProcessor"),umr=o(" (Perceiver model)"),pmr=l(),_v=a("li"),n8e=a("strong"),_mr=o("poolformer"),vmr=o(" \u2014 "),bee=a("a"),bmr=o("PoolFormerImageProcessor"),Tmr=o(" (PoolFormer model)"),Fmr=l(),vv=a("li"),s8e=a("strong"),Mmr=o("regnet"),Emr=o(" \u2014 "),Tee=a("a"),Cmr=o("ConvNextImageProcessor"),wmr=o(" (RegNet model)"),Amr=l(),bv=a("li"),l8e=a("strong"),Lmr=o("resnet"),ymr=o(" \u2014 "),Fee=a("a"),xmr=o("ConvNextImageProcessor"),Smr=o(" (ResNet model)"),$mr=l(),Tv=a("li"),i8e=a("strong"),kmr=o("segformer"),Rmr=o(" \u2014 "),Mee=a("a"),Pmr=o("SegformerImageProcessor"),Imr=o(" (SegFormer model)"),Nmr=l(),Fv=a("li"),d8e=a("strong"),Bmr=o("swin"),Gmr=o(" \u2014 "),Eee=a("a"),Dmr=o("ViTImageProcessor"),qmr=o(" (Swin Transformer model)"),jmr=l(),Mv=a("li"),c8e=a("strong"),Omr=o("swin2sr"),Vmr=o(" \u2014 "),Cee=a("a"),Xmr=o("Swin2SRImageProcessor"),zmr=o(" (Swin2SR model)"),Wmr=l(),Ev=a("li"),f8e=a("strong"),Qmr=o("swinv2"),Umr=o(" \u2014 "),wee=a("a"),Hmr=o("ViTImageProcessor"),Jmr=o(" (Swin Transformer V2 model)"),Zmr=l(),Cv=a("li"),m8e=a("strong"),Ymr=o("table-transformer"),Kmr=o(" \u2014 "),Aee=a("a"),egr=o("DetrImageProcessor"),ogr=o(" (Table 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model)"),Bgr=l(),kv=a("li"),T8e=a("strong"),Ggr=o("vit_hybrid"),Dgr=o(" \u2014 "),Pee=a("a"),qgr=o("ViTHybridImageProcessor"),jgr=o(" (ViT Hybrid model)"),Ogr=l(),Rv=a("li"),F8e=a("strong"),Vgr=o("vit_mae"),Xgr=o(" \u2014 "),Iee=a("a"),zgr=o("ViTImageProcessor"),Wgr=o(" (ViTMAE model)"),Qgr=l(),Pv=a("li"),M8e=a("strong"),Ugr=o("vit_msn"),Hgr=o(" \u2014 "),Nee=a("a"),Jgr=o("ViTImageProcessor"),Zgr=o(" (ViTMSN model)"),Ygr=l(),Iv=a("li"),E8e=a("strong"),Kgr=o("xclip"),ehr=o(" \u2014 "),Bee=a("a"),ohr=o("CLIPImageProcessor"),rhr=o(" (X-CLIP model)"),thr=l(),Nv=a("li"),C8e=a("strong"),ahr=o("yolos"),nhr=o(" \u2014 "),Gee=a("a"),shr=o("YolosImageProcessor"),lhr=o(" (YOLOS model)"),ihr=l(),b(Bv.$$.fragment),dhr=l(),b(Gv.$$.fragment),chr=l(),Dv=a("div"),b($B.$$.fragment),fhr=l(),w8e=a("p"),mhr=o("Register a new image processor for this 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with the `),Moe=a("a"),t1r=o("from_pretrained()"),a1r=o(" class method or the "),Eoe=a("a"),n1r=o("from_config()"),s1r=o(` class
method.`),l1r=l(),jB=a("p"),i1r=o("This class cannot be instantiated directly using "),vAe=a("code"),d1r=o("__init__()"),c1r=o(" (throws an error)."),f1r=l(),Nt=a("div"),b(OB.$$.fragment),m1r=l(),bAe=a("p"),g1r=o("Instantiates one of the base model classes of the library from a configuration."),h1r=l(),kc=a("p"),u1r=o(`Note:
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model\u2019s configuration. Use `),Coe=a("a"),v1r=o("from_pretrained()"),b1r=o(" to load the model weights."),T1r=l(),b(L4.$$.fragment),F1r=l(),so=a("div"),b(VB.$$.fragment),M1r=l(),FAe=a("p"),E1r=o("Instantiate one of the base model classes of the library from a pretrained model."),C1r=l(),Sn=a("p"),w1r=o("The model class to instantiate is selected based on the "),MAe=a("code"),A1r=o("model_type"),L1r=o(` property of the config object (either
passed as an argument or loaded from `),EAe=a("code"),y1r=o("pretrained_model_name_or_path"),x1r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CAe=a("code"),S1r=o("pretrained_model_name_or_path"),$1r=o(":"),k1r=l(),y=a("ul"),y4=a("li"),wAe=a("strong"),R1r=o("albert"),P1r=o(" \u2014 "),woe=a("a"),I1r=o("AlbertModel"),N1r=o(" (ALBERT model)"),B1r=l(),x4=a("li"),AAe=a("strong"),G1r=o("align"),D1r=o(" \u2014 "),Aoe=a("a"),q1r=o("AlignModel"),j1r=o(" (ALIGN model)"),O1r=l(),S4=a("li"),LAe=a("strong"),V1r=o("altclip"),X1r=o(" \u2014 "),Loe=a("a"),z1r=o("AltCLIPModel"),W1r=o(" (AltCLIP model)"),Q1r=l(),$4=a("li"),yAe=a("strong"),U1r=o("audio-spectrogram-transformer"),H1r=o(" \u2014 "),yoe=a("a"),J1r=o("ASTModel"),Z1r=o(" (Audio Spectrogram Transformer model)"),Y1r=l(),k4=a("li"),xAe=a("strong"),K1r=o("bart"),e2r=o(" \u2014 "),xoe=a("a"),o2r=o("BartModel"),r2r=o(" (BART model)"),t2r=l(),R4=a("li"),SAe=a("strong"),a2r=o("beit"),n2r=o(" \u2014 "),Soe=a("a"),s2r=o("BeitModel"),l2r=o(" (BEiT model)"),i2r=l(),P4=a("li"),$Ae=a("strong"),d2r=o("bert"),c2r=o(" \u2014 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method.`),YXr=l(),bG=a("p"),KXr=o("This class cannot be instantiated directly using "),rRe=a("code"),ezr=o("__init__()"),ozr=o(" (throws an error)."),rzr=l(),Ot=a("div"),b(TG.$$.fragment),tzr=l(),tRe=a("p"),azr=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),nzr=l(),Yc=a("p"),szr=o(`Note:
Loading a model from its configuration file does `),aRe=a("strong"),lzr=o("not"),izr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Tie=a("a"),dzr=o("from_pretrained()"),czr=o(" to load the model weights."),fzr=l(),b(M3.$$.fragment),mzr=l(),io=a("div"),b(FG.$$.fragment),gzr=l(),nRe=a("p"),hzr=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),uzr=l(),Nn=a("p"),pzr=o("The model class to instantiate is selected based on the "),sRe=a("code"),_zr=o("model_type"),vzr=o(` property of the config object (either
passed as an argument or loaded from `),lRe=a("code"),bzr=o("pretrained_model_name_or_path"),Tzr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iRe=a("code"),Fzr=o("pretrained_model_name_or_path"),Mzr=o(":"),Ezr=l(),H=a("ul"),E3=a("li"),dRe=a("strong"),Czr=o("bart"),wzr=o(" \u2014 "),Fie=a("a"),Azr=o("BartForCausalLM"),Lzr=o(" (BART model)"),yzr=l(),C3=a("li"),cRe=a("strong"),xzr=o("bert"),Szr=o(" \u2014 "),Mie=a("a"),$zr=o("BertLMHeadModel"),kzr=o(" (BERT model)"),Rzr=l(),w3=a("li"),fRe=a("strong"),Pzr=o("bert-generation"),Izr=o(" \u2014 "),Eie=a("a"),Nzr=o("BertGenerationDecoder"),Bzr=o(" (Bert Generation model)"),Gzr=l(),A3=a("li"),mRe=a("strong"),Dzr=o("big_bird"),qzr=o(" \u2014 "),Cie=a("a"),jzr=o("BigBirdForCausalLM"),Ozr=o(" (BigBird model)"),Vzr=l(),L3=a("li"),gRe=a("strong"),Xzr=o("bigbird_pegasus"),zzr=o(" \u2014 "),wie=a("a"),Wzr=o("BigBirdPegasusForCausalLM"),Qzr=o(" (BigBird-Pegasus model)"),Uzr=l(),y3=a("li"),hRe=a("strong"),Hzr=o("biogpt"),Jzr=o(" \u2014 "),Aie=a("a"),Zzr=o("BioGptForCausalLM"),Yzr=o(" (BioGpt 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with the `),ude=a("a"),tJr=o("from_pretrained()"),aJr=o(" class method or the "),pde=a("a"),nJr=o("from_config()"),sJr=o(` class
method.`),lJr=l(),CG=a("p"),iJr=o("This class cannot be instantiated directly using "),lPe=a("code"),dJr=o("__init__()"),cJr=o(" (throws an error)."),fJr=l(),Vt=a("div"),b(wG.$$.fragment),mJr=l(),iPe=a("p"),gJr=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),hJr=l(),of=a("p"),uJr=o(`Note:
Loading a model from its configuration file does `),dPe=a("strong"),pJr=o("not"),_Jr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_de=a("a"),vJr=o("from_pretrained()"),bJr=o(" to load the model weights."),TJr=l(),b(TC.$$.fragment),FJr=l(),tt=a("div"),b(AG.$$.fragment),MJr=l(),cPe=a("p"),EJr=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),CJr=l(),Bn=a("p"),wJr=o("The model class to instantiate is selected based on the "),fPe=a("code"),AJr=o("model_type"),LJr=o(` property of the config object (either
passed as an argument or loaded from `),mPe=a("code"),yJr=o("pretrained_model_name_or_path"),xJr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gPe=a("code"),SJr=o("pretrained_model_name_or_path"),$Jr=o(":"),kJr=l(),Ee=a("ul"),FC=a("li"),hPe=a("strong"),RJr=o("bert"),PJr=o(" \u2014 "),vde=a("a"),IJr=o("TFBertLMHeadModel"),NJr=o(" (BERT model)"),BJr=l(),MC=a("li"),uPe=a("strong"),GJr=o("camembert"),DJr=o(" \u2014 "),bde=a("a"),qJr=o("TFCamembertForCausalLM"),jJr=o(" (CamemBERT model)"),OJr=l(),EC=a("li"),pPe=a("strong"),VJr=o("ctrl"),XJr=o(" \u2014 "),Tde=a("a"),zJr=o("TFCTRLLMHeadModel"),WJr=o(" (CTRL model)"),QJr=l(),CC=a("li"),_Pe=a("strong"),UJr=o("gpt-sw3"),HJr=o(" \u2014 "),Fde=a("a"),JJr=o("TFGPT2LMHeadModel"),ZJr=o(" (GPT-Sw3 model)"),YJr=l(),wC=a("li"),vPe=a("strong"),KJr=o("gpt2"),eZr=o(" \u2014 "),Mde=a("a"),oZr=o("TFGPT2LMHeadModel"),rZr=o(" (OpenAI GPT-2 model)"),tZr=l(),AC=a("li"),bPe=a("strong"),aZr=o("gptj"),nZr=o(" \u2014 "),Ede=a("a"),sZr=o("TFGPTJForCausalLM"),lZr=o(" (GPT-J model)"),iZr=l(),LC=a("li"),TPe=a("strong"),dZr=o("openai-gpt"),cZr=o(" \u2014 "),Cde=a("a"),fZr=o("TFOpenAIGPTLMHeadModel"),mZr=o(" (OpenAI GPT model)"),gZr=l(),yC=a("li"),FPe=a("strong"),hZr=o("opt"),uZr=o(" \u2014 "),wde=a("a"),pZr=o("TFOPTForCausalLM"),_Zr=o(" (OPT model)"),vZr=l(),xC=a("li"),MPe=a("strong"),bZr=o("rembert"),TZr=o(" \u2014 "),Ade=a("a"),FZr=o("TFRemBertForCausalLM"),MZr=o(" (RemBERT model)"),EZr=l(),SC=a("li"),EPe=a("strong"),CZr=o("roberta"),wZr=o(" \u2014 "),Lde=a("a"),AZr=o("TFRobertaForCausalLM"),LZr=o(" (RoBERTa model)"),yZr=l(),$C=a("li"),CPe=a("strong"),xZr=o("roberta-prelayernorm"),SZr=o(" \u2014 "),yde=a("a"),$Zr=o("TFRobertaPreLayerNormForCausalLM"),kZr=o(" (RoBERTa-PreLayerNorm model)"),RZr=l(),kC=a("li"),wPe=a("strong"),PZr=o("roformer"),IZr=o(" \u2014 "),xde=a("a"),NZr=o("TFRoFormerForCausalLM"),BZr=o(" (RoFormer model)"),GZr=l(),RC=a("li"),APe=a("strong"),DZr=o("transfo-xl"),qZr=o(" \u2014 "),Sde=a("a"),jZr=o("TFTransfoXLLMHeadModel"),OZr=o(" (Transformer-XL model)"),VZr=l(),PC=a("li"),LPe=a("strong"),XZr=o("xglm"),zZr=o(" \u2014 "),$de=a("a"),WZr=o("TFXGLMForCausalLM"),QZr=o(" (XGLM model)"),UZr=l(),IC=a("li"),yPe=a("strong"),HZr=o("xlm"),JZr=o(" \u2014 "),kde=a("a"),ZZr=o("TFXLMWithLMHeadModel"),YZr=o(" (XLM model)"),KZr=l(),NC=a("li"),xPe=a("strong"),eYr=o("xlm-roberta"),oYr=o(" \u2014 "),Rde=a("a"),rYr=o("TFXLMRobertaForCausalLM"),tYr=o(" (XLM-RoBERTa model)"),aYr=l(),BC=a("li"),SPe=a("strong"),nYr=o("xlnet"),sYr=o(" \u2014 "),Pde=a("a"),lYr=o("TFXLNetLMHeadModel"),iYr=o(" (XLNet model)"),dYr=l(),b(GC.$$.fragment),ECo=l(),rf=a("h3"),DC=a("a"),$Pe=a("span"),b(LG.$$.fragment),cYr=l(),kPe=a("span"),fYr=o("FlaxAutoModelForCausalLM"),CCo=l(),Zo=a("div"),b(yG.$$.fragment),mYr=l(),tf=a("p"),gYr=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.`),vYr=l(),xG=a("p"),bYr=o("This class cannot be instantiated directly using "),RPe=a("code"),TYr=o("__init__()"),FYr=o(" (throws an error)."),MYr=l(),Xt=a("div"),b(SG.$$.fragment),EYr=l(),PPe=a("p"),CYr=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),wYr=l(),af=a("p"),AYr=o(`Note:
Loading a model from its configuration file does `),IPe=a("strong"),LYr=o("not"),yYr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Bde=a("a"),xYr=o("from_pretrained()"),SYr=o(" to load the model weights."),$Yr=l(),b(qC.$$.fragment),kYr=l(),at=a("div"),b($G.$$.fragment),RYr=l(),NPe=a("p"),PYr=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),IYr=l(),Gn=a("p"),NYr=o("The model class to instantiate is selected based on the "),BPe=a("code"),BYr=o("model_type"),GYr=o(` property of the config object (either
passed as an argument or loaded from `),GPe=a("code"),DYr=o("pretrained_model_name_or_path"),qYr=o(` if possible), or when it\u2019s missing, by
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model\u2019s configuration. Use `),ufe=a("a"),jlt=o("from_pretrained()"),Olt=o(" to load the model weights."),Vlt=l(),b(T0.$$.fragment),Xlt=l(),st=a("div"),b(WG.$$.fragment),zlt=l(),NNe=a("p"),Wlt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Qlt=l(),jn=a("p"),Ult=o("The model class to instantiate is selected based on the "),BNe=a("code"),Hlt=o("model_type"),Jlt=o(` property of the config object (either
passed as an argument or loaded from `),GNe=a("code"),Zlt=o("pretrained_model_name_or_path"),Ylt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DNe=a("code"),Klt=o("pretrained_model_name_or_path"),eit=o(":"),oit=l(),Se=a("ul"),F0=a("li"),qNe=a("strong"),rit=o("albert"),tit=o(" \u2014 "),pfe=a("a"),ait=o("FlaxAlbertForMaskedLM"),nit=o(" (ALBERT model)"),sit=l(),M0=a("li"),jNe=a("strong"),lit=o("bart"),iit=o(" \u2014 "),_fe=a("a"),dit=o("FlaxBartForConditionalGeneration"),cit=o(" (BART model)"),fit=l(),E0=a("li"),ONe=a("strong"),mit=o("bert"),git=o(" \u2014 "),vfe=a("a"),hit=o("FlaxBertForMaskedLM"),uit=o(" (BERT model)"),pit=l(),C0=a("li"),VNe=a("strong"),_it=o("big_bird"),vit=o(" \u2014 "),bfe=a("a"),bit=o("FlaxBigBirdForMaskedLM"),Tit=o(" (BigBird model)"),Fit=l(),w0=a("li"),XNe=a("strong"),Mit=o("distilbert"),Eit=o(" \u2014 "),Tfe=a("a"),Cit=o("FlaxDistilBertForMaskedLM"),wit=o(" (DistilBERT model)"),Ait=l(),A0=a("li"),zNe=a("strong"),Lit=o("electra"),yit=o(" \u2014 "),Ffe=a("a"),xit=o("FlaxElectraForMaskedLM"),Sit=o(" (ELECTRA model)"),$it=l(),L0=a("li"),WNe=a("strong"),kit=o("mbart"),Rit=o(" \u2014 "),Mfe=a("a"),Pit=o("FlaxMBartForConditionalGeneration"),Iit=o(" (mBART model)"),Nit=l(),y0=a("li"),QNe=a("strong"),Bit=o("roberta"),Git=o(" \u2014 "),Efe=a("a"),Dit=o("FlaxRobertaForMaskedLM"),qit=o(" (RoBERTa model)"),jit=l(),x0=a("li"),UNe=a("strong"),Oit=o("roberta-prelayernorm"),Vit=o(" \u2014 "),Cfe=a("a"),Xit=o("FlaxRobertaPreLayerNormForMaskedLM"),zit=o(" (RoBERTa-PreLayerNorm model)"),Wit=l(),S0=a("li"),HNe=a("strong"),Qit=o("roformer"),Uit=o(" \u2014 "),wfe=a("a"),Hit=o("FlaxRoFormerForMaskedLM"),Jit=o(" (RoFormer model)"),Zit=l(),$0=a("li"),JNe=a("strong"),Yit=o("xlm-roberta"),Kit=o(" \u2014 "),Afe=a("a"),edt=o("FlaxXLMRobertaForMaskedLM"),odt=o(" (XLM-RoBERTa model)"),rdt=l(),b(k0.$$.fragment),$Co=l(),uf=a("h3"),R0=a("a"),ZNe=a("span"),b(QG.$$.fragment),tdt=l(),YNe=a("span"),adt=o("AutoModelForSeq2SeqLM"),kCo=l(),or=a("div"),b(UG.$$.fragment),ndt=l(),pf=a("p"),sdt=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
with the `),Lfe=a("a"),ldt=o("from_pretrained()"),idt=o(" class method or the "),yfe=a("a"),ddt=o("from_config()"),cdt=o(` class
method.`),fdt=l(),HG=a("p"),mdt=o("This class cannot be instantiated directly using "),KNe=a("code"),gdt=o("__init__()"),hdt=o(" (throws an error)."),udt=l(),Ut=a("div"),b(JG.$$.fragment),pdt=l(),eBe=a("p"),_dt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),vdt=l(),_f=a("p"),bdt=o(`Note:
Loading a model from its configuration file does `),oBe=a("strong"),Tdt=o("not"),Fdt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),xfe=a("a"),Mdt=o("from_pretrained()"),Edt=o(" to load the model weights."),Cdt=l(),b(P0.$$.fragment),wdt=l(),fo=a("div"),b(ZG.$$.fragment),Adt=l(),rBe=a("p"),Ldt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),ydt=l(),On=a("p"),xdt=o("The model class to instantiate is selected based on the "),tBe=a("code"),Sdt=o("model_type"),$dt=o(` property of the config object (either
passed as an argument or loaded from `),aBe=a("code"),kdt=o("pretrained_model_name_or_path"),Rdt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nBe=a("code"),Pdt=o("pretrained_model_name_or_path"),Idt=o(":"),Ndt=l(),ve=a("ul"),I0=a("li"),sBe=a("strong"),Bdt=o("bart"),Gdt=o(" \u2014 "),Sfe=a("a"),Ddt=o("BartForConditionalGeneration"),qdt=o(" (BART model)"),jdt=l(),N0=a("li"),lBe=a("strong"),Odt=o("bigbird_pegasus"),Vdt=o(" \u2014 "),$fe=a("a"),Xdt=o("BigBirdPegasusForConditionalGeneration"),zdt=o(" (BigBird-Pegasus model)"),Wdt=l(),B0=a("li"),iBe=a("strong"),Qdt=o("blenderbot"),Udt=o(" \u2014 "),kfe=a("a"),Hdt=o("BlenderbotForConditionalGeneration"),Jdt=o(" (Blenderbot model)"),Zdt=l(),G0=a("li"),dBe=a("strong"),Ydt=o("blenderbot-small"),Kdt=o(" \u2014 "),Rfe=a("a"),ect=o("BlenderbotSmallForConditionalGeneration"),oct=o(" (BlenderbotSmall model)"),rct=l(),D0=a("li"),cBe=a("strong"),tct=o("encoder-decoder"),act=o(" \u2014 "),Pfe=a("a"),nct=o("EncoderDecoderModel"),sct=o(" (Encoder decoder model)"),lct=l(),q0=a("li"),fBe=a("strong"),ict=o("fsmt"),dct=o(" \u2014 "),Ife=a("a"),cct=o("FSMTForConditionalGeneration"),fct=o(" (FairSeq Machine-Translation model)"),mct=l(),j0=a("li"),mBe=a("strong"),gct=o("gptsan-japanese"),hct=o(" \u2014 "),Nfe=a("a"),uct=o("GPTSanJapaneseForConditionalGeneration"),pct=o(" (GPTSAN-japanese model)"),_ct=l(),O0=a("li"),gBe=a("strong"),vct=o("led"),bct=o(" \u2014 "),Bfe=a("a"),Tct=o("LEDForConditionalGeneration"),Fct=o(" (LED model)"),Mct=l(),V0=a("li"),hBe=a("strong"),Ect=o("longt5"),Cct=o(" \u2014 "),Gfe=a("a"),wct=o("LongT5ForConditionalGeneration"),Act=o(" (LongT5 model)"),Lct=l(),X0=a("li"),uBe=a("strong"),yct=o("m2m_100"),xct=o(" \u2014 "),Dfe=a("a"),Sct=o("M2M100ForConditionalGeneration"),$ct=o(" (M2M100 model)"),kct=l(),z0=a("li"),pBe=a("strong"),Rct=o("marian"),Pct=o(" \u2014 "),qfe=a("a"),Ict=o("MarianMTModel"),Nct=o(" (Marian model)"),Bct=l(),W0=a("li"),_Be=a("strong"),Gct=o("mbart"),Dct=o(" \u2014 "),jfe=a("a"),qct=o("MBartForConditionalGeneration"),jct=o(" (mBART model)"),Oct=l(),Q0=a("li"),vBe=a("strong"),Vct=o("mt5"),Xct=o(" \u2014 "),Ofe=a("a"),zct=o("MT5ForConditionalGeneration"),Wct=o(" (MT5 model)"),Qct=l(),U0=a("li"),bBe=a("strong"),Uct=o("mvp"),Hct=o(" \u2014 "),Vfe=a("a"),Jct=o("MvpForConditionalGeneration"),Zct=o(" (MVP model)"),Yct=l(),H0=a("li"),TBe=a("strong"),Kct=o("pegasus"),eft=o(" \u2014 "),Xfe=a("a"),oft=o("PegasusForConditionalGeneration"),rft=o(" (Pegasus model)"),tft=l(),J0=a("li"),FBe=a("strong"),aft=o("pegasus_x"),nft=o(" \u2014 "),zfe=a("a"),sft=o("PegasusXForConditionalGeneration"),lft=o(" (PEGASUS-X model)"),ift=l(),Z0=a("li"),MBe=a("strong"),dft=o("plbart"),cft=o(" \u2014 "),Wfe=a("a"),fft=o("PLBartForConditionalGeneration"),mft=o(" (PLBart model)"),gft=l(),Y0=a("li"),EBe=a("strong"),hft=o("prophetnet"),uft=o(" \u2014 "),Qfe=a("a"),pft=o("ProphetNetForConditionalGeneration"),_ft=o(" (ProphetNet model)"),vft=l(),K0=a("li"),CBe=a("strong"),bft=o("switch_transformers"),Tft=o(" \u2014 "),Ufe=a("a"),Fft=o("SwitchTransformersForConditionalGeneration"),Mft=o(" (SwitchTransformers model)"),Eft=l(),ew=a("li"),wBe=a("strong"),Cft=o("t5"),wft=o(" \u2014 "),Hfe=a("a"),Aft=o("T5ForConditionalGeneration"),Lft=o(" (T5 model)"),yft=l(),ow=a("li"),ABe=a("strong"),xft=o("xlm-prophetnet"),Sft=o(" \u2014 "),Jfe=a("a"),$ft=o("XLMProphetNetForConditionalGeneration"),kft=o(" (XLM-ProphetNet model)"),Rft=l(),rw=a("p"),Pft=o("The model is set in evaluation mode by default using "),LBe=a("code"),Ift=o("model.eval()"),Nft=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),yBe=a("code"),Bft=o("model.train()"),Gft=l(),b(tw.$$.fragment),RCo=l(),vf=a("h3"),aw=a("a"),xBe=a("span"),b(YG.$$.fragment),Dft=l(),SBe=a("span"),qft=o("TFAutoModelForSeq2SeqLM"),PCo=l(),rr=a("div"),b(KG.$$.fragment),jft=l(),bf=a("p"),Oft=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
with the `),Zfe=a("a"),Vft=o("from_pretrained()"),Xft=o(" class method or the "),Yfe=a("a"),zft=o("from_config()"),Wft=o(` class
method.`),Qft=l(),eD=a("p"),Uft=o("This class cannot be instantiated directly using "),$Be=a("code"),Hft=o("__init__()"),Jft=o(" (throws an error)."),Zft=l(),Ht=a("div"),b(oD.$$.fragment),Yft=l(),kBe=a("p"),Kft=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),emt=l(),Tf=a("p"),omt=o(`Note:
Loading a model from its configuration file does `),RBe=a("strong"),rmt=o("not"),tmt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Kfe=a("a"),amt=o("from_pretrained()"),nmt=o(" to load the model weights."),smt=l(),b(nw.$$.fragment),lmt=l(),lt=a("div"),b(rD.$$.fragment),imt=l(),PBe=a("p"),dmt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),cmt=l(),Vn=a("p"),fmt=o("The model class to instantiate is selected based on the "),IBe=a("code"),mmt=o("model_type"),gmt=o(` property of the config object (either
passed as an argument or loaded from `),NBe=a("code"),hmt=o("pretrained_model_name_or_path"),umt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BBe=a("code"),pmt=o("pretrained_model_name_or_path"),_mt=o(":"),vmt=l(),Ie=a("ul"),sw=a("li"),GBe=a("strong"),bmt=o("bart"),Tmt=o(" \u2014 "),eme=a("a"),Fmt=o("TFBartForConditionalGeneration"),Mmt=o(" (BART model)"),Emt=l(),lw=a("li"),DBe=a("strong"),Cmt=o("blenderbot"),wmt=o(" \u2014 "),ome=a("a"),Amt=o("TFBlenderbotForConditionalGeneration"),Lmt=o(" (Blenderbot model)"),ymt=l(),iw=a("li"),qBe=a("strong"),xmt=o("blenderbot-small"),Smt=o(" \u2014 "),rme=a("a"),$mt=o("TFBlenderbotSmallForConditionalGeneration"),kmt=o(" (BlenderbotSmall model)"),Rmt=l(),dw=a("li"),jBe=a("strong"),Pmt=o("encoder-decoder"),Imt=o(" \u2014 "),tme=a("a"),Nmt=o("TFEncoderDecoderModel"),Bmt=o(" (Encoder decoder model)"),Gmt=l(),cw=a("li"),OBe=a("strong"),Dmt=o("led"),qmt=o(" \u2014 "),ame=a("a"),jmt=o("TFLEDForConditionalGeneration"),Omt=o(" (LED model)"),Vmt=l(),fw=a("li"),VBe=a("strong"),Xmt=o("marian"),zmt=o(" \u2014 "),nme=a("a"),Wmt=o("TFMarianMTModel"),Qmt=o(" (Marian model)"),Umt=l(),mw=a("li"),XBe=a("strong"),Hmt=o("mbart"),Jmt=o(" \u2014 "),sme=a("a"),Zmt=o("TFMBartForConditionalGeneration"),Ymt=o(" (mBART model)"),Kmt=l(),gw=a("li"),zBe=a("strong"),egt=o("mt5"),ogt=o(" \u2014 "),lme=a("a"),rgt=o("TFMT5ForConditionalGeneration"),tgt=o(" (MT5 model)"),agt=l(),hw=a("li"),WBe=a("strong"),ngt=o("pegasus"),sgt=o(" \u2014 "),ime=a("a"),lgt=o("TFPegasusForConditionalGeneration"),igt=o(" (Pegasus model)"),dgt=l(),uw=a("li"),QBe=a("strong"),cgt=o("t5"),fgt=o(" \u2014 "),dme=a("a"),mgt=o("TFT5ForConditionalGeneration"),ggt=o(" (T5 model)"),hgt=l(),b(pw.$$.fragment),ICo=l(),Ff=a("h3"),_w=a("a"),UBe=a("span"),b(tD.$$.fragment),ugt=l(),HBe=a("span"),pgt=o("FlaxAutoModelForSeq2SeqLM"),NCo=l(),tr=a("div"),b(aD.$$.fragment),_gt=l(),Mf=a("p"),vgt=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
with the `),cme=a("a"),bgt=o("from_pretrained()"),Tgt=o(" class method or the "),fme=a("a"),Fgt=o("from_config()"),Mgt=o(` class
method.`),Egt=l(),nD=a("p"),Cgt=o("This class cannot be instantiated directly using "),JBe=a("code"),wgt=o("__init__()"),Agt=o(" (throws an error)."),Lgt=l(),Jt=a("div"),b(sD.$$.fragment),ygt=l(),ZBe=a("p"),xgt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Sgt=l(),Ef=a("p"),$gt=o(`Note:
Loading a model from its configuration file does `),YBe=a("strong"),kgt=o("not"),Rgt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),mme=a("a"),Pgt=o("from_pretrained()"),Igt=o(" to load the model weights."),Ngt=l(),b(vw.$$.fragment),Bgt=l(),it=a("div"),b(lD.$$.fragment),Ggt=l(),KBe=a("p"),Dgt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),qgt=l(),Xn=a("p"),jgt=o("The model class to instantiate is selected based on the "),eGe=a("code"),Ogt=o("model_type"),Vgt=o(` property of the config object (either
passed as an argument or loaded from `),oGe=a("code"),Xgt=o("pretrained_model_name_or_path"),zgt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rGe=a("code"),Wgt=o("pretrained_model_name_or_path"),Qgt=o(":"),Ugt=l(),Ne=a("ul"),bw=a("li"),tGe=a("strong"),Hgt=o("bart"),Jgt=o(" \u2014 "),gme=a("a"),Zgt=o("FlaxBartForConditionalGeneration"),Ygt=o(" (BART model)"),Kgt=l(),Tw=a("li"),aGe=a("strong"),eht=o("blenderbot"),oht=o(" \u2014 "),hme=a("a"),rht=o("FlaxBlenderbotForConditionalGeneration"),tht=o(" (Blenderbot model)"),aht=l(),Fw=a("li"),nGe=a("strong"),nht=o("blenderbot-small"),sht=o(" \u2014 "),ume=a("a"),lht=o("FlaxBlenderbotSmallForConditionalGeneration"),iht=o(" (BlenderbotSmall model)"),dht=l(),Mw=a("li"),sGe=a("strong"),cht=o("encoder-decoder"),fht=o(" \u2014 "),pme=a("a"),mht=o("FlaxEncoderDecoderModel"),ght=o(" (Encoder decoder model)"),hht=l(),Ew=a("li"),lGe=a("strong"),uht=o("longt5"),pht=o(" \u2014 "),_me=a("a"),_ht=o("FlaxLongT5ForConditionalGeneration"),vht=o(" (LongT5 model)"),bht=l(),Cw=a("li"),iGe=a("strong"),Tht=o("marian"),Fht=o(" \u2014 "),vme=a("a"),Mht=o("FlaxMarianMTModel"),Eht=o(" (Marian model)"),Cht=l(),ww=a("li"),dGe=a("strong"),wht=o("mbart"),Aht=o(" \u2014 "),bme=a("a"),Lht=o("FlaxMBartForConditionalGeneration"),yht=o(" (mBART model)"),xht=l(),Aw=a("li"),cGe=a("strong"),Sht=o("mt5"),$ht=o(" \u2014 "),Tme=a("a"),kht=o("FlaxMT5ForConditionalGeneration"),Rht=o(" (MT5 model)"),Pht=l(),Lw=a("li"),fGe=a("strong"),Iht=o("pegasus"),Nht=o(" \u2014 "),Fme=a("a"),Bht=o("FlaxPegasusForConditionalGeneration"),Ght=o(" (Pegasus model)"),Dht=l(),yw=a("li"),mGe=a("strong"),qht=o("t5"),jht=o(" \u2014 "),Mme=a("a"),Oht=o("FlaxT5ForConditionalGeneration"),Vht=o(" (T5 model)"),Xht=l(),b(xw.$$.fragment),BCo=l(),Cf=a("h3"),Sw=a("a"),gGe=a("span"),b(iD.$$.fragment),zht=l(),hGe=a("span"),Wht=o("AutoModelForSequenceClassification"),GCo=l(),ar=a("div"),b(dD.$$.fragment),Qht=l(),wf=a("p"),Uht=o(`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 `),Eme=a("a"),Hht=o("from_pretrained()"),Jht=o(" class method or the "),Cme=a("a"),Zht=o("from_config()"),Yht=o(` class
method.`),Kht=l(),cD=a("p"),eut=o("This class cannot be instantiated directly using "),uGe=a("code"),out=o("__init__()"),rut=o(" (throws an error)."),tut=l(),Zt=a("div"),b(fD.$$.fragment),aut=l(),pGe=a("p"),nut=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),sut=l(),Af=a("p"),lut=o(`Note:
Loading a model from its configuration file does `),_Ge=a("strong"),iut=o("not"),dut=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),wme=a("a"),cut=o("from_pretrained()"),fut=o(" to load the model weights."),mut=l(),b($w.$$.fragment),gut=l(),mo=a("div"),b(mD.$$.fragment),hut=l(),vGe=a("p"),uut=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),put=l(),zn=a("p"),_ut=o("The model class to instantiate is selected based on the "),bGe=a("code"),vut=o("model_type"),but=o(` property of the config object (either
passed as an argument or loaded from `),TGe=a("code"),Tut=o("pretrained_model_name_or_path"),Fut=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FGe=a("code"),Mut=o("pretrained_model_name_or_path"),Eut=o(":"),Cut=l(),B=a("ul"),kw=a("li"),MGe=a("strong"),wut=o("albert"),Aut=o(" \u2014 "),Ame=a("a"),Lut=o("AlbertForSequenceClassification"),yut=o(" (ALBERT model)"),xut=l(),Rw=a("li"),EGe=a("strong"),Sut=o("bart"),$ut=o(" \u2014 "),Lme=a("a"),kut=o("BartForSequenceClassification"),Rut=o(" (BART model)"),Put=l(),Pw=a("li"),CGe=a("strong"),Iut=o("bert"),Nut=o(" \u2014 "),yme=a("a"),But=o("BertForSequenceClassification"),Gut=o(" (BERT model)"),Dut=l(),Iw=a("li"),wGe=a("strong"),qut=o("big_bird"),jut=o(" \u2014 "),xme=a("a"),Out=o("BigBirdForSequenceClassification"),Vut=o(" (BigBird model)"),Xut=l(),Nw=a("li"),AGe=a("strong"),zut=o("bigbird_pegasus"),Wut=o(" \u2014 "),Sme=a("a"),Qut=o("BigBirdPegasusForSequenceClassification"),Uut=o(" (BigBird-Pegasus model)"),Hut=l(),Bw=a("li"),LGe=a("strong"),Jut=o("bloom"),Zut=o(" \u2014 "),$me=a("a"),Yut=o("BloomForSequenceClassification"),Kut=o(" (BLOOM 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"),Gme=a("a"),Rpt=o("DebertaV2ForSequenceClassification"),Ppt=o(" (DeBERTa-v2 model)"),Ipt=l(),zw=a("li"),IGe=a("strong"),Npt=o("distilbert"),Bpt=o(" \u2014 "),Dme=a("a"),Gpt=o("DistilBertForSequenceClassification"),Dpt=o(" (DistilBERT model)"),qpt=l(),Ww=a("li"),NGe=a("strong"),jpt=o("electra"),Opt=o(" \u2014 "),qme=a("a"),Vpt=o("ElectraForSequenceClassification"),Xpt=o(" (ELECTRA model)"),zpt=l(),Qw=a("li"),BGe=a("strong"),Wpt=o("ernie"),Qpt=o(" \u2014 "),jme=a("a"),Upt=o("ErnieForSequenceClassification"),Hpt=o(" (ERNIE model)"),Jpt=l(),Uw=a("li"),GGe=a("strong"),Zpt=o("ernie_m"),Ypt=o(" \u2014 "),Ome=a("a"),Kpt=o("ErnieMForSequenceClassification"),e_t=o(" (ErnieM model)"),o_t=l(),Hw=a("li"),DGe=a("strong"),r_t=o("esm"),t_t=o(" \u2014 "),Vme=a("a"),a_t=o("EsmForSequenceClassification"),n_t=o(" (ESM model)"),s_t=l(),Jw=a("li"),qGe=a("strong"),l_t=o("flaubert"),i_t=o(" \u2014 "),Xme=a("a"),d_t=o("FlaubertForSequenceClassification"),c_t=o(" (FlauBERT 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falling back to using pattern matching on `),DDe=a("code"),O4t=o("pretrained_model_name_or_path"),V4t=o(":"),X4t=l(),se=a("ul"),V6=a("li"),qDe=a("strong"),z4t=o("albert"),W4t=o(" \u2014 "),Nge=a("a"),Q4t=o("TFAlbertForSequenceClassification"),U4t=o(" (ALBERT model)"),H4t=l(),X6=a("li"),jDe=a("strong"),J4t=o("bart"),Z4t=o(" \u2014 "),Bge=a("a"),Y4t=o("TFBartForSequenceClassification"),K4t=o(" (BART model)"),ebt=l(),z6=a("li"),ODe=a("strong"),obt=o("bert"),rbt=o(" \u2014 "),Gge=a("a"),tbt=o("TFBertForSequenceClassification"),abt=o(" (BERT model)"),nbt=l(),W6=a("li"),VDe=a("strong"),sbt=o("camembert"),lbt=o(" \u2014 "),Dge=a("a"),ibt=o("TFCamembertForSequenceClassification"),dbt=o(" (CamemBERT model)"),cbt=l(),Q6=a("li"),XDe=a("strong"),fbt=o("convbert"),mbt=o(" \u2014 "),qge=a("a"),gbt=o("TFConvBertForSequenceClassification"),hbt=o(" (ConvBERT model)"),ubt=l(),U6=a("li"),zDe=a("strong"),pbt=o("ctrl"),_bt=o(" \u2014 "),jge=a("a"),vbt=o("TFCTRLForSequenceClassification"),bbt=o(" (CTRL 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falling back to using pattern matching on `),vXe=a("code"),Pkt=o("pretrained_model_name_or_path"),Ikt=o(":"),Nkt=l(),he=a("ul"),vL=a("li"),bXe=a("strong"),Bkt=o("albert"),Gkt=o(" \u2014 "),i_e=a("a"),Dkt=o("TFAlbertForTokenClassification"),qkt=o(" (ALBERT model)"),jkt=l(),bL=a("li"),TXe=a("strong"),Okt=o("bert"),Vkt=o(" \u2014 "),d_e=a("a"),Xkt=o("TFBertForTokenClassification"),zkt=o(" (BERT model)"),Wkt=l(),TL=a("li"),FXe=a("strong"),Qkt=o("camembert"),Ukt=o(" \u2014 "),c_e=a("a"),Hkt=o("TFCamembertForTokenClassification"),Jkt=o(" (CamemBERT model)"),Zkt=l(),FL=a("li"),MXe=a("strong"),Ykt=o("convbert"),Kkt=o(" \u2014 "),f_e=a("a"),eRt=o("TFConvBertForTokenClassification"),oRt=o(" (ConvBERT model)"),rRt=l(),ML=a("li"),EXe=a("strong"),tRt=o("deberta"),aRt=o(" \u2014 "),m_e=a("a"),nRt=o("TFDebertaForTokenClassification"),sRt=o(" (DeBERTa model)"),lRt=l(),EL=a("li"),CXe=a("strong"),iRt=o("deberta-v2"),dRt=o(" \u2014 "),g_e=a("a"),cRt=o("TFDebertaV2ForTokenClassification"),fRt=o(" (DeBERTa-v2 model)"),mRt=l(),CL=a("li"),wXe=a("strong"),gRt=o("distilbert"),hRt=o(" \u2014 "),h_e=a("a"),uRt=o("TFDistilBertForTokenClassification"),pRt=o(" (DistilBERT model)"),_Rt=l(),wL=a("li"),AXe=a("strong"),vRt=o("electra"),bRt=o(" \u2014 "),u_e=a("a"),TRt=o("TFElectraForTokenClassification"),FRt=o(" (ELECTRA model)"),MRt=l(),AL=a("li"),LXe=a("strong"),ERt=o("esm"),CRt=o(" \u2014 "),p_e=a("a"),wRt=o("TFEsmForTokenClassification"),ARt=o(" (ESM model)"),LRt=l(),LL=a("li"),yXe=a("strong"),yRt=o("flaubert"),xRt=o(" \u2014 "),__e=a("a"),SRt=o("TFFlaubertForTokenClassification"),$Rt=o(" (FlauBERT model)"),kRt=l(),yL=a("li"),xXe=a("strong"),RRt=o("funnel"),PRt=o(" \u2014 "),v_e=a("a"),IRt=o("TFFunnelForTokenClassification"),NRt=o(" (Funnel Transformer model)"),BRt=l(),xL=a("li"),SXe=a("strong"),GRt=o("layoutlm"),DRt=o(" \u2014 "),b_e=a("a"),qRt=o("TFLayoutLMForTokenClassification"),jRt=o(" (LayoutLM model)"),ORt=l(),SL=a("li"),$Xe=a("strong"),VRt=o("layoutlmv3"),XRt=o(" \u2014 "),T_e=a("a"),zRt=o("TFLayoutLMv3ForTokenClassification"),WRt=o(" (LayoutLMv3 model)"),QRt=l(),$L=a("li"),kXe=a("strong"),URt=o("longformer"),HRt=o(" \u2014 "),F_e=a("a"),JRt=o("TFLongformerForTokenClassification"),ZRt=o(" (Longformer model)"),YRt=l(),kL=a("li"),RXe=a("strong"),KRt=o("mobilebert"),ePt=o(" \u2014 "),M_e=a("a"),oPt=o("TFMobileBertForTokenClassification"),rPt=o(" (MobileBERT model)"),tPt=l(),RL=a("li"),PXe=a("strong"),aPt=o("mpnet"),nPt=o(" \u2014 "),E_e=a("a"),sPt=o("TFMPNetForTokenClassification"),lPt=o(" (MPNet model)"),iPt=l(),PL=a("li"),IXe=a("strong"),dPt=o("rembert"),cPt=o(" \u2014 "),C_e=a("a"),fPt=o("TFRemBertForTokenClassification"),mPt=o(" (RemBERT model)"),gPt=l(),IL=a("li"),NXe=a("strong"),hPt=o("roberta"),uPt=o(" \u2014 "),w_e=a("a"),pPt=o("TFRobertaForTokenClassification"),_Pt=o(" (RoBERTa model)"),vPt=l(),NL=a("li"),BXe=a("strong"),bPt=o("roberta-prelayernorm"),TPt=o(" \u2014 "),A_e=a("a"),FPt=o("TFRobertaPreLayerNormForTokenClassification"),MPt=o(" (RoBERTa-PreLayerNorm model)"),EPt=l(),BL=a("li"),GXe=a("strong"),CPt=o("roformer"),wPt=o(" \u2014 "),L_e=a("a"),APt=o("TFRoFormerForTokenClassification"),LPt=o(" (RoFormer model)"),yPt=l(),GL=a("li"),DXe=a("strong"),xPt=o("xlm"),SPt=o(" \u2014 "),y_e=a("a"),$Pt=o("TFXLMForTokenClassification"),kPt=o(" (XLM model)"),RPt=l(),DL=a("li"),qXe=a("strong"),PPt=o("xlm-roberta"),IPt=o(" \u2014 "),x_e=a("a"),NPt=o("TFXLMRobertaForTokenClassification"),BPt=o(" (XLM-RoBERTa model)"),GPt=l(),qL=a("li"),jXe=a("strong"),DPt=o("xlnet"),qPt=o(" \u2014 "),S_e=a("a"),jPt=o("TFXLNetForTokenClassification"),OPt=o(" (XLNet model)"),VPt=l(),b(jL.$$.fragment),n5o=l(),tm=a("h3"),OL=a("a"),OXe=a("span"),b(cq.$$.fragment),XPt=l(),VXe=a("span"),zPt=o("FlaxAutoModelForTokenClassification"),s5o=l(),ur=a("div"),b(fq.$$.fragment),WPt=l(),am=a("p"),QPt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),ENt=l(),_q=a("p"),CNt=o("This class cannot be instantiated directly using "),ize=a("code"),wNt=o("__init__()"),ANt=o(" (throws an error)."),LNt=l(),da=a("div"),b(vq.$$.fragment),yNt=l(),dze=a("p"),xNt=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),SNt=l(),im=a("p"),$Nt=o(`Note:
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model\u2019s configuration. Use `),z_e=a("a"),PNt=o("from_pretrained()"),INt=o(" to load the model weights."),NNt=l(),b(oy.$$.fragment),BNt=l(),po=a("div"),b(bq.$$.fragment),GNt=l(),fze=a("p"),DNt=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),qNt=l(),ts=a("p"),jNt=o("The model class to instantiate is selected based on the "),mze=a("code"),ONt=o("model_type"),VNt=o(` property of the config object (either
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falling back to using pattern matching on `),hze=a("code"),WNt=o("pretrained_model_name_or_path"),QNt=o(":"),UNt=l(),Q=a("ul"),ry=a("li"),uze=a("strong"),HNt=o("albert"),JNt=o(" \u2014 "),W_e=a("a"),ZNt=o("AlbertForQuestionAnswering"),YNt=o(" (ALBERT model)"),KNt=l(),ty=a("li"),pze=a("strong"),eBt=o("bart"),oBt=o(" \u2014 "),Q_e=a("a"),rBt=o("BartForQuestionAnswering"),tBt=o(" (BART model)"),aBt=l(),ay=a("li"),_ze=a("strong"),nBt=o("bert"),sBt=o(" \u2014 "),U_e=a("a"),lBt=o("BertForQuestionAnswering"),iBt=o(" (BERT model)"),dBt=l(),ny=a("li"),vze=a("strong"),cBt=o("big_bird"),fBt=o(" \u2014 "),H_e=a("a"),mBt=o("BigBirdForQuestionAnswering"),gBt=o(" (BigBird model)"),hBt=l(),sy=a("li"),bze=a("strong"),uBt=o("bigbird_pegasus"),pBt=o(" \u2014 "),J_e=a("a"),_Bt=o("BigBirdPegasusForQuestionAnswering"),vBt=o(" (BigBird-Pegasus model)"),bBt=l(),ly=a("li"),Tze=a("strong"),TBt=o("bloom"),FBt=o(" \u2014 "),Z_e=a("a"),MBt=o("BloomForQuestionAnswering"),EBt=o(" (BLOOM 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method.`),Yjt=l(),Mq=a("p"),Kjt=o("This class cannot be instantiated directly using "),_We=a("code"),eOt=o("__init__()"),oOt=o(" (throws an error)."),rOt=l(),ca=a("div"),b(Eq.$$.fragment),tOt=l(),vWe=a("p"),aOt=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),nOt=l(),fm=a("p"),sOt=o(`Note:
Loading a model from its configuration file does `),bWe=a("strong"),lOt=o("not"),iOt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),W1e=a("a"),dOt=o("from_pretrained()"),cOt=o(" to load the model weights."),fOt=l(),b(tx.$$.fragment),mOt=l(),_t=a("div"),b(Cq.$$.fragment),gOt=l(),TWe=a("p"),hOt=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),uOt=l(),as=a("p"),pOt=o("The model class to instantiate is selected based on the "),FWe=a("code"),_Ot=o("model_type"),vOt=o(` property of the config object (either
passed as an argument or loaded from `),MWe=a("code"),bOt=o("pretrained_model_name_or_path"),TOt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EWe=a("code"),FOt=o("pretrained_model_name_or_path"),MOt=o(":"),EOt=l(),pe=a("ul"),ax=a("li"),CWe=a("strong"),COt=o("albert"),wOt=o(" \u2014 "),Q1e=a("a"),AOt=o("TFAlbertForQuestionAnswering"),LOt=o(" (ALBERT model)"),yOt=l(),nx=a("li"),wWe=a("strong"),xOt=o("bert"),SOt=o(" \u2014 "),U1e=a("a"),$Ot=o("TFBertForQuestionAnswering"),kOt=o(" (BERT model)"),ROt=l(),sx=a("li"),AWe=a("strong"),POt=o("camembert"),IOt=o(" \u2014 "),H1e=a("a"),NOt=o("TFCamembertForQuestionAnswering"),BOt=o(" (CamemBERT model)"),GOt=l(),lx=a("li"),LWe=a("strong"),DOt=o("convbert"),qOt=o(" \u2014 "),J1e=a("a"),jOt=o("TFConvBertForQuestionAnswering"),OOt=o(" (ConvBERT model)"),VOt=l(),ix=a("li"),yWe=a("strong"),XOt=o("deberta"),zOt=o(" \u2014 "),Z1e=a("a"),WOt=o("TFDebertaForQuestionAnswering"),QOt=o(" (DeBERTa model)"),UOt=l(),dx=a("li"),xWe=a("strong"),HOt=o("deberta-v2"),JOt=o(" \u2014 "),Y1e=a("a"),ZOt=o("TFDebertaV2ForQuestionAnswering"),YOt=o(" (DeBERTa-v2 model)"),KOt=l(),cx=a("li"),SWe=a("strong"),eVt=o("distilbert"),oVt=o(" \u2014 "),K1e=a("a"),rVt=o("TFDistilBertForQuestionAnswering"),tVt=o(" (DistilBERT model)"),aVt=l(),fx=a("li"),$We=a("strong"),nVt=o("electra"),sVt=o(" \u2014 "),e2e=a("a"),lVt=o("TFElectraForQuestionAnswering"),iVt=o(" (ELECTRA model)"),dVt=l(),mx=a("li"),kWe=a("strong"),cVt=o("flaubert"),fVt=o(" \u2014 "),o2e=a("a"),mVt=o("TFFlaubertForQuestionAnsweringSimple"),gVt=o(" (FlauBERT model)"),hVt=l(),gx=a("li"),RWe=a("strong"),uVt=o("funnel"),pVt=o(" \u2014 "),r2e=a("a"),_Vt=o("TFFunnelForQuestionAnswering"),vVt=o(" (Funnel Transformer model)"),bVt=l(),hx=a("li"),PWe=a("strong"),TVt=o("gptj"),FVt=o(" \u2014 "),t2e=a("a"),MVt=o("TFGPTJForQuestionAnswering"),EVt=o(" (GPT-J model)"),CVt=l(),ux=a("li"),IWe=a("strong"),wVt=o("layoutlmv3"),AVt=o(" \u2014 "),a2e=a("a"),LVt=o("TFLayoutLMv3ForQuestionAnswering"),yVt=o(" (LayoutLMv3 model)"),xVt=l(),px=a("li"),NWe=a("strong"),SVt=o("longformer"),$Vt=o(" \u2014 "),n2e=a("a"),kVt=o("TFLongformerForQuestionAnswering"),RVt=o(" (Longformer model)"),PVt=l(),_x=a("li"),BWe=a("strong"),IVt=o("mobilebert"),NVt=o(" \u2014 "),s2e=a("a"),BVt=o("TFMobileBertForQuestionAnswering"),GVt=o(" (MobileBERT model)"),DVt=l(),vx=a("li"),GWe=a("strong"),qVt=o("mpnet"),jVt=o(" \u2014 "),l2e=a("a"),OVt=o("TFMPNetForQuestionAnswering"),VVt=o(" (MPNet model)"),XVt=l(),bx=a("li"),DWe=a("strong"),zVt=o("rembert"),WVt=o(" \u2014 "),i2e=a("a"),QVt=o("TFRemBertForQuestionAnswering"),UVt=o(" (RemBERT model)"),HVt=l(),Tx=a("li"),qWe=a("strong"),JVt=o("roberta"),ZVt=o(" \u2014 "),d2e=a("a"),YVt=o("TFRobertaForQuestionAnswering"),KVt=o(" (RoBERTa model)"),eXt=l(),Fx=a("li"),jWe=a("strong"),oXt=o("roberta-prelayernorm"),rXt=o(" \u2014 "),c2e=a("a"),tXt=o("TFRobertaPreLayerNormForQuestionAnswering"),aXt=o(" (RoBERTa-PreLayerNorm model)"),nXt=l(),Mx=a("li"),OWe=a("strong"),sXt=o("roformer"),lXt=o(" \u2014 "),f2e=a("a"),iXt=o("TFRoFormerForQuestionAnswering"),dXt=o(" (RoFormer model)"),cXt=l(),Ex=a("li"),VWe=a("strong"),fXt=o("xlm"),mXt=o(" \u2014 "),m2e=a("a"),gXt=o("TFXLMForQuestionAnsweringSimple"),hXt=o(" (XLM model)"),uXt=l(),Cx=a("li"),XWe=a("strong"),pXt=o("xlm-roberta"),_Xt=o(" \u2014 "),g2e=a("a"),vXt=o("TFXLMRobertaForQuestionAnswering"),bXt=o(" (XLM-RoBERTa model)"),TXt=l(),wx=a("li"),zWe=a("strong"),FXt=o("xlnet"),MXt=o(" \u2014 "),h2e=a("a"),EXt=o("TFXLNetForQuestionAnsweringSimple"),CXt=o(" (XLNet model)"),wXt=l(),b(Ax.$$.fragment),f5o=l(),mm=a("h3"),Lx=a("a"),WWe=a("span"),b(wq.$$.fragment),AXt=l(),QWe=a("span"),LXt=o("FlaxAutoModelForQuestionAnswering"),m5o=l(),vr=a("div"),b(Aq.$$.fragment),yXt=l(),gm=a("p"),xXt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),u2e=a("a"),SXt=o("from_pretrained()"),$Xt=o(" class method or the "),p2e=a("a"),kXt=o("from_config()"),RXt=o(` class
method.`),PXt=l(),Lq=a("p"),IXt=o("This class cannot be instantiated directly using "),UWe=a("code"),NXt=o("__init__()"),BXt=o(" (throws an error)."),GXt=l(),fa=a("div"),b(yq.$$.fragment),DXt=l(),HWe=a("p"),qXt=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),jXt=l(),hm=a("p"),OXt=o(`Note:
Loading a model from its configuration file does `),JWe=a("strong"),VXt=o("not"),XXt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_2e=a("a"),zXt=o("from_pretrained()"),WXt=o(" to load the model weights."),QXt=l(),b(yx.$$.fragment),UXt=l(),vt=a("div"),b(xq.$$.fragment),HXt=l(),ZWe=a("p"),JXt=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),ZXt=l(),ns=a("p"),YXt=o("The model class to instantiate is selected based on the "),YWe=a("code"),KXt=o("model_type"),ezt=o(` property of the config object (either
passed as an argument or loaded from `),KWe=a("code"),ozt=o("pretrained_model_name_or_path"),rzt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eQe=a("code"),tzt=o("pretrained_model_name_or_path"),azt=o(":"),nzt=l(),ke=a("ul"),xx=a("li"),oQe=a("strong"),szt=o("albert"),lzt=o(" \u2014 "),v2e=a("a"),izt=o("FlaxAlbertForQuestionAnswering"),dzt=o(" (ALBERT model)"),czt=l(),Sx=a("li"),rQe=a("strong"),fzt=o("bart"),mzt=o(" \u2014 "),b2e=a("a"),gzt=o("FlaxBartForQuestionAnswering"),hzt=o(" (BART model)"),uzt=l(),$x=a("li"),tQe=a("strong"),pzt=o("bert"),_zt=o(" \u2014 "),T2e=a("a"),vzt=o("FlaxBertForQuestionAnswering"),bzt=o(" (BERT model)"),Tzt=l(),kx=a("li"),aQe=a("strong"),Fzt=o("big_bird"),Mzt=o(" \u2014 "),F2e=a("a"),Ezt=o("FlaxBigBirdForQuestionAnswering"),Czt=o(" (BigBird model)"),wzt=l(),Rx=a("li"),nQe=a("strong"),Azt=o("distilbert"),Lzt=o(" \u2014 "),M2e=a("a"),yzt=o("FlaxDistilBertForQuestionAnswering"),xzt=o(" (DistilBERT model)"),Szt=l(),Px=a("li"),sQe=a("strong"),$zt=o("electra"),kzt=o(" \u2014 "),E2e=a("a"),Rzt=o("FlaxElectraForQuestionAnswering"),Pzt=o(" (ELECTRA model)"),Izt=l(),Ix=a("li"),lQe=a("strong"),Nzt=o("mbart"),Bzt=o(" \u2014 "),C2e=a("a"),Gzt=o("FlaxMBartForQuestionAnswering"),Dzt=o(" (mBART model)"),qzt=l(),Nx=a("li"),iQe=a("strong"),jzt=o("roberta"),Ozt=o(" \u2014 "),w2e=a("a"),Vzt=o("FlaxRobertaForQuestionAnswering"),Xzt=o(" (RoBERTa model)"),zzt=l(),Bx=a("li"),dQe=a("strong"),Wzt=o("roberta-prelayernorm"),Qzt=o(" \u2014 "),A2e=a("a"),Uzt=o("FlaxRobertaPreLayerNormForQuestionAnswering"),Hzt=o(" (RoBERTa-PreLayerNorm model)"),Jzt=l(),Gx=a("li"),cQe=a("strong"),Zzt=o("roformer"),Yzt=o(" \u2014 "),L2e=a("a"),Kzt=o("FlaxRoFormerForQuestionAnswering"),eWt=o(" (RoFormer model)"),oWt=l(),Dx=a("li"),fQe=a("strong"),rWt=o("xlm-roberta"),tWt=o(" \u2014 "),y2e=a("a"),aWt=o("FlaxXLMRobertaForQuestionAnswering"),nWt=o(" (XLM-RoBERTa model)"),sWt=l(),b(qx.$$.fragment),g5o=l(),um=a("h2"),jx=a("a"),mQe=a("span"),b(Sq.$$.fragment),lWt=l(),gQe=a("span"),iWt=o("Computer vision"),h5o=l(),x2e=a("p"),dWt=o("The following auto classes are available for the following computer vision tasks."),u5o=l(),pm=a("h3"),Ox=a("a"),hQe=a("span"),b($q.$$.fragment),cWt=l(),uQe=a("span"),fWt=o("AutoModelForDepthEstimation"),p5o=l(),br=a("div"),b(kq.$$.fragment),mWt=l(),_m=a("p"),gWt=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
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method.`),vWt=l(),Rq=a("p"),bWt=o("This class cannot be instantiated directly using "),pQe=a("code"),TWt=o("__init__()"),FWt=o(" (throws an error)."),MWt=l(),ma=a("div"),b(Pq.$$.fragment),EWt=l(),_Qe=a("p"),CWt=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),wWt=l(),vm=a("p"),AWt=o(`Note:
Loading a model from its configuration file does `),vQe=a("strong"),LWt=o("not"),yWt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),k2e=a("a"),xWt=o("from_pretrained()"),SWt=o(" to load the model weights."),$Wt=l(),b(Vx.$$.fragment),kWt=l(),_o=a("div"),b(Iq.$$.fragment),RWt=l(),bQe=a("p"),PWt=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),IWt=l(),ss=a("p"),NWt=o("The model class to instantiate is selected based on the "),TQe=a("code"),BWt=o("model_type"),GWt=o(` property of the config object (either
passed as an argument or loaded from `),FQe=a("code"),DWt=o("pretrained_model_name_or_path"),qWt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MQe=a("code"),jWt=o("pretrained_model_name_or_path"),OWt=o(":"),VWt=l(),Nq=a("ul"),Xx=a("li"),EQe=a("strong"),XWt=o("dpt"),zWt=o(" \u2014 "),R2e=a("a"),WWt=o("DPTForDepthEstimation"),QWt=o(" (DPT model)"),UWt=l(),zx=a("li"),CQe=a("strong"),HWt=o("glpn"),JWt=o(" \u2014 "),P2e=a("a"),ZWt=o("GLPNForDepthEstimation"),YWt=o(" (GLPN model)"),KWt=l(),Wx=a("p"),eQt=o("The model is set in evaluation mode by default using "),wQe=a("code"),oQt=o("model.eval()"),rQt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),AQe=a("code"),tQt=o("model.train()"),aQt=l(),b(Qx.$$.fragment),_5o=l(),bm=a("h3"),Ux=a("a"),LQe=a("span"),b(Bq.$$.fragment),nQt=l(),yQe=a("span"),sQt=o("AutoModelForImageClassification"),v5o=l(),Tr=a("div"),b(Gq.$$.fragment),lQt=l(),Tm=a("p"),iQt=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 `),I2e=a("a"),dQt=o("from_pretrained()"),cQt=o(" class method or the "),N2e=a("a"),fQt=o("from_config()"),mQt=o(` class
method.`),gQt=l(),Dq=a("p"),hQt=o("This class cannot be instantiated directly using "),xQe=a("code"),uQt=o("__init__()"),pQt=o(" (throws an error)."),_Qt=l(),ga=a("div"),b(qq.$$.fragment),vQt=l(),SQe=a("p"),bQt=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),TQt=l(),Fm=a("p"),FQt=o(`Note:
Loading a model from its configuration file does `),$Qe=a("strong"),MQt=o("not"),EQt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),B2e=a("a"),CQt=o("from_pretrained()"),wQt=o(" to load the model weights."),AQt=l(),b(Hx.$$.fragment),LQt=l(),vo=a("div"),b(jq.$$.fragment),yQt=l(),kQe=a("p"),xQt=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),SQt=l(),ls=a("p"),$Qt=o("The model class to instantiate is selected based on the "),RQe=a("code"),kQt=o("model_type"),RQt=o(` property of the config object (either
passed as an argument or loaded from `),PQe=a("code"),PQt=o("pretrained_model_name_or_path"),IQt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),IQe=a("code"),NQt=o("pretrained_model_name_or_path"),BQt=o(":"),GQt=l(),ce=a("ul"),Jx=a("li"),NQe=a("strong"),DQt=o("beit"),qQt=o(" \u2014 "),G2e=a("a"),jQt=o("BeitForImageClassification"),OQt=o(" (BEiT model)"),VQt=l(),Zx=a("li"),BQe=a("strong"),XQt=o("bit"),zQt=o(" \u2014 "),D2e=a("a"),WQt=o("BitForImageClassification"),QQt=o(" (BiT model)"),UQt=l(),Yx=a("li"),GQe=a("strong"),HQt=o("convnext"),JQt=o(" \u2014 "),q2e=a("a"),ZQt=o("ConvNextForImageClassification"),YQt=o(" (ConvNeXT model)"),KQt=l(),Kx=a("li"),DQe=a("strong"),eUt=o("convnextv2"),oUt=o(" \u2014 "),j2e=a("a"),rUt=o("ConvNextV2ForImageClassification"),tUt=o(" (ConvNeXTV2 model)"),aUt=l(),eS=a("li"),qQe=a("strong"),nUt=o("cvt"),sUt=o(" \u2014 "),O2e=a("a"),lUt=o("CvtForImageClassification"),iUt=o(" (CvT model)"),dUt=l(),oS=a("li"),jQe=a("strong"),cUt=o("data2vec-vision"),fUt=o(" \u2014 "),V2e=a("a"),mUt=o("Data2VecVisionForImageClassification"),gUt=o(" (Data2VecVision model)"),hUt=l(),Fi=a("li"),OQe=a("strong"),uUt=o("deit"),pUt=o(" \u2014 "),X2e=a("a"),_Ut=o("DeiTForImageClassification"),vUt=o(" or "),z2e=a("a"),bUt=o("DeiTForImageClassificationWithTeacher"),TUt=o(" (DeiT model)"),FUt=l(),rS=a("li"),VQe=a("strong"),MUt=o("dinat"),EUt=o(" \u2014 "),W2e=a("a"),CUt=o("DinatForImageClassification"),wUt=o(" (DiNAT model)"),AUt=l(),Mi=a("li"),XQe=a("strong"),LUt=o("efficientformer"),yUt=o(" \u2014 "),Q2e=a("a"),xUt=o("EfficientFormerForImageClassification"),SUt=o(" or "),U2e=a("a"),$Ut=o("EfficientFormerForImageClassificationWithTeacher"),kUt=o(" (EfficientFormer model)"),RUt=l(),tS=a("li"),zQe=a("strong"),PUt=o("efficientnet"),IUt=o(" \u2014 "),H2e=a("a"),NUt=o("EfficientNetForImageClassification"),BUt=o(" (EfficientNet model)"),GUt=l(),aS=a("li"),WQe=a("strong"),DUt=o("imagegpt"),qUt=o(" \u2014 "),J2e=a("a"),jUt=o("ImageGPTForImageClassification"),OUt=o(" (ImageGPT model)"),VUt=l(),Ei=a("li"),QQe=a("strong"),XUt=o("levit"),zUt=o(" \u2014 "),Z2e=a("a"),WUt=o("LevitForImageClassification"),QUt=o(" or "),Y2e=a("a"),UUt=o("LevitForImageClassificationWithTeacher"),HUt=o(" (LeViT model)"),JUt=l(),nS=a("li"),UQe=a("strong"),ZUt=o("mobilenet_v1"),YUt=o(" \u2014 "),K2e=a("a"),KUt=o("MobileNetV1ForImageClassification"),eHt=o(" (MobileNetV1 model)"),oHt=l(),sS=a("li"),HQe=a("strong"),rHt=o("mobilenet_v2"),tHt=o(" \u2014 "),eve=a("a"),aHt=o("MobileNetV2ForImageClassification"),nHt=o(" (MobileNetV2 model)"),sHt=l(),lS=a("li"),JQe=a("strong"),lHt=o("mobilevit"),iHt=o(" \u2014 "),ove=a("a"),dHt=o("MobileViTForImageClassification"),cHt=o(" (MobileViT model)"),fHt=l(),iS=a("li"),ZQe=a("strong"),mHt=o("nat"),gHt=o(" \u2014 "),rve=a("a"),hHt=o("NatForImageClassification"),uHt=o(" (NAT model)"),pHt=l(),ha=a("li"),YQe=a("strong"),_Ht=o("perceiver"),vHt=o(" \u2014 "),tve=a("a"),bHt=o("PerceiverForImageClassificationLearned"),THt=o(" or "),ave=a("a"),FHt=o("PerceiverForImageClassificationFourier"),MHt=o(" or "),nve=a("a"),EHt=o("PerceiverForImageClassificationConvProcessing"),CHt=o(" (Perceiver model)"),wHt=l(),dS=a("li"),KQe=a("strong"),AHt=o("poolformer"),LHt=o(" \u2014 "),sve=a("a"),yHt=o("PoolFormerForImageClassification"),xHt=o(" (PoolFormer model)"),SHt=l(),cS=a("li"),eUe=a("strong"),$Ht=o("regnet"),kHt=o(" \u2014 "),lve=a("a"),RHt=o("RegNetForImageClassification"),PHt=o(" (RegNet model)"),IHt=l(),fS=a("li"),oUe=a("strong"),NHt=o("resnet"),BHt=o(" \u2014 "),ive=a("a"),GHt=o("ResNetForImageClassification"),DHt=o(" (ResNet model)"),qHt=l(),mS=a("li"),rUe=a("strong"),jHt=o("segformer"),OHt=o(" \u2014 "),dve=a("a"),VHt=o("SegformerForImageClassification"),XHt=o(" (SegFormer model)"),zHt=l(),gS=a("li"),tUe=a("strong"),WHt=o("swin"),QHt=o(" \u2014 "),cve=a("a"),UHt=o("SwinForImageClassification"),HHt=o(" (Swin Transformer model)"),JHt=l(),hS=a("li"),aUe=a("strong"),ZHt=o("swinv2"),YHt=o(" \u2014 "),fve=a("a"),KHt=o("Swinv2ForImageClassification"),eJt=o(" (Swin Transformer V2 model)"),oJt=l(),uS=a("li"),nUe=a("strong"),rJt=o("van"),tJt=o(" \u2014 "),mve=a("a"),aJt=o("VanForImageClassification"),nJt=o(" (VAN model)"),sJt=l(),pS=a("li"),sUe=a("strong"),lJt=o("vit"),iJt=o(" \u2014 "),gve=a("a"),dJt=o("ViTForImageClassification"),cJt=o(" (ViT model)"),fJt=l(),_S=a("li"),lUe=a("strong"),mJt=o("vit_hybrid"),gJt=o(" \u2014 "),hve=a("a"),hJt=o("ViTHybridForImageClassification"),uJt=o(" (ViT Hybrid model)"),pJt=l(),vS=a("li"),iUe=a("strong"),_Jt=o("vit_msn"),vJt=o(" \u2014 "),uve=a("a"),bJt=o("ViTMSNForImageClassification"),TJt=o(" (ViTMSN model)"),FJt=l(),bS=a("p"),MJt=o("The model is set in evaluation mode by default using "),dUe=a("code"),EJt=o("model.eval()"),CJt=o(` (so for instance, dropout modules are
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passed as an argument or loaded from `),BUe=a("code"),PYt=o("pretrained_model_name_or_path"),IYt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GUe=a("code"),NYt=o("pretrained_model_name_or_path"),BYt=o(":"),GYt=l(),Yq=a("ul"),IS=a("li"),DUe=a("strong"),DYt=o("beit"),qYt=o(" \u2014 "),Rve=a("a"),jYt=o("FlaxBeitForImageClassification"),OYt=o(" (BEiT model)"),VYt=l(),NS=a("li"),qUe=a("strong"),XYt=o("vit"),zYt=o(" \u2014 "),Pve=a("a"),WYt=o("FlaxViTForImageClassification"),QYt=o(" (ViT model)"),UYt=l(),b(BS.$$.fragment),E5o=l(),ym=a("h3"),GS=a("a"),jUe=a("span"),b(Kq.$$.fragment),HYt=l(),OUe=a("span"),JYt=o("AutoModelForVideoClassification"),C5o=l(),Er=a("div"),b(ej.$$.fragment),ZYt=l(),xm=a("p"),YYt=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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passed as an argument or loaded from `),UUe=a("code"),CKt=o("pretrained_model_name_or_path"),wKt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),HUe=a("code"),AKt=o("pretrained_model_name_or_path"),LKt=o(":"),yKt=l(),aj=a("ul"),qS=a("li"),JUe=a("strong"),xKt=o("timesformer"),SKt=o(" \u2014 "),Gve=a("a"),$Kt=o("TimesformerForVideoClassification"),kKt=o(" (TimeSformer model)"),RKt=l(),jS=a("li"),ZUe=a("strong"),PKt=o("videomae"),IKt=o(" \u2014 "),Dve=a("a"),NKt=o("VideoMAEForVideoClassification"),BKt=o(" (VideoMAE model)"),GKt=l(),OS=a("p"),DKt=o("The model is set in evaluation mode by default using "),YUe=a("code"),qKt=o("model.eval()"),jKt=o(` (so for instance, dropout modules are
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method.`),YKt=l(),lj=a("p"),KKt=o("This class cannot be instantiated directly using "),rHe=a("code"),eea=o("__init__()"),oea=o(" (throws an error)."),rea=l(),va=a("div"),b(ij.$$.fragment),tea=l(),tHe=a("p"),aea=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),nea=l(),Rm=a("p"),sea=o(`Note:
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model\u2019s configuration. Use `),Ove=a("a"),dea=o("from_pretrained()"),cea=o(" to load the model weights."),fea=l(),b(zS.$$.fragment),mea=l(),To=a("div"),b(dj.$$.fragment),gea=l(),nHe=a("p"),hea=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),uea=l(),fs=a("p"),pea=o("The model class to instantiate is selected based on the "),sHe=a("code"),_ea=o("model_type"),vea=o(` property of the config object (either
passed as an argument or loaded from `),lHe=a("code"),bea=o("pretrained_model_name_or_path"),Tea=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iHe=a("code"),Fea=o("pretrained_model_name_or_path"),Mea=o(":"),Eea=l(),ms=a("ul"),WS=a("li"),dHe=a("strong"),Cea=o("deit"),wea=o(" \u2014 "),Vve=a("a"),Aea=o("DeiTForMaskedImageModeling"),Lea=o(" (DeiT model)"),yea=l(),QS=a("li"),cHe=a("strong"),xea=o("swin"),Sea=o(" \u2014 "),Xve=a("a"),$ea=o("SwinForMaskedImageModeling"),kea=o(" (Swin Transformer model)"),Rea=l(),US=a("li"),fHe=a("strong"),Pea=o("swinv2"),Iea=o(" \u2014 "),zve=a("a"),Nea=o("Swinv2ForMaskedImageModeling"),Bea=o(" (Swin Transformer V2 model)"),Gea=l(),HS=a("li"),mHe=a("strong"),Dea=o("vit"),qea=o(" \u2014 "),Wve=a("a"),jea=o("ViTForMaskedImageModeling"),Oea=o(" (ViT model)"),Vea=l(),JS=a("p"),Xea=o("The model is set in evaluation mode by default using "),gHe=a("code"),zea=o("model.eval()"),Wea=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),hHe=a("code"),Qea=o("model.train()"),Uea=l(),b(ZS.$$.fragment),L5o=l(),Pm=a("h3"),YS=a("a"),uHe=a("span"),b(cj.$$.fragment),Hea=l(),pHe=a("span"),Jea=o("AutoModelForObjectDetection"),y5o=l(),wr=a("div"),b(fj.$$.fragment),Zea=l(),Im=a("p"),Yea=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 `),Qve=a("a"),Kea=o("from_pretrained()"),eoa=o(" class method or the "),Uve=a("a"),ooa=o("from_config()"),roa=o(` class
method.`),toa=l(),mj=a("p"),aoa=o("This class cannot be instantiated directly using "),_He=a("code"),noa=o("__init__()"),soa=o(" (throws an error)."),loa=l(),ba=a("div"),b(gj.$$.fragment),ioa=l(),vHe=a("p"),doa=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),coa=l(),Nm=a("p"),foa=o(`Note:
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passed as an argument or loaded from `),MHe=a("code"),Coa=o("pretrained_model_name_or_path"),woa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EHe=a("code"),Aoa=o("pretrained_model_name_or_path"),Loa=o(":"),yoa=l(),Ar=a("ul"),e$=a("li"),CHe=a("strong"),xoa=o("conditional_detr"),Soa=o(" \u2014 "),Jve=a("a"),$oa=o("ConditionalDetrForObjectDetection"),koa=o(" (Conditional DETR model)"),Roa=l(),o$=a("li"),wHe=a("strong"),Poa=o("deformable_detr"),Ioa=o(" \u2014 "),Zve=a("a"),Noa=o("DeformableDetrForObjectDetection"),Boa=o(" (Deformable DETR model)"),Goa=l(),r$=a("li"),AHe=a("strong"),Doa=o("deta"),qoa=o(" \u2014 "),Yve=a("a"),joa=o("DetaForObjectDetection"),Ooa=o(" (DETA model)"),Voa=l(),t$=a("li"),LHe=a("strong"),Xoa=o("detr"),zoa=o(" \u2014 "),Kve=a("a"),Woa=o("DetrForObjectDetection"),Qoa=o(" (DETR model)"),Uoa=l(),a$=a("li"),yHe=a("strong"),Hoa=o("table-transformer"),Joa=o(" \u2014 "),e4e=a("a"),Zoa=o("TableTransformerForObjectDetection"),Yoa=o(" (Table Transformer model)"),Koa=l(),n$=a("li"),xHe=a("strong"),era=o("yolos"),ora=o(" \u2014 "),o4e=a("a"),rra=o("YolosForObjectDetection"),tra=o(" (YOLOS model)"),ara=l(),s$=a("p"),nra=o("The model is set in evaluation mode by default using "),SHe=a("code"),sra=o("model.eval()"),lra=o(` (so for instance, dropout modules are
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method.`),vra=l(),_j=a("p"),bra=o("This class cannot be instantiated directly using "),PHe=a("code"),Tra=o("__init__()"),Fra=o(" (throws an error)."),Mra=l(),Ta=a("div"),b(vj.$$.fragment),Era=l(),IHe=a("p"),Cra=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),wra=l(),Dm=a("p"),Ara=o(`Note:
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passed as an argument or loaded from `),DHe=a("code"),Dra=o("pretrained_model_name_or_path"),qra=o(` if possible), or when it\u2019s missing, by
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with the `),s4e=a("a"),ata=o("from_pretrained()"),nta=o(" class method or the "),l4e=a("a"),sta=o("from_config()"),lta=o(` class
method.`),ita=l(),Mj=a("p"),dta=o("This class cannot be instantiated directly using "),QHe=a("code"),cta=o("__init__()"),fta=o(" (throws an error)."),mta=l(),Fa=a("div"),b(Ej.$$.fragment),gta=l(),UHe=a("p"),hta=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),uta=l(),Om=a("p"),pta=o(`Note:
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model\u2019s configuration. Use `),i4e=a("a"),bta=o("from_pretrained()"),Tta=o(" to load the model weights."),Fta=l(),b(h$.$$.fragment),Mta=l(),Eo=a("div"),b(Cj.$$.fragment),Eta=l(),JHe=a("p"),Cta=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),wta=l(),us=a("p"),Ata=o("The model class to instantiate is selected based on the "),ZHe=a("code"),Lta=o("model_type"),yta=o(` property of the config object (either
passed as an argument or loaded from `),YHe=a("code"),xta=o("pretrained_model_name_or_path"),Sta=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),KHe=a("code"),$ta=o("pretrained_model_name_or_path"),kta=o(":"),Rta=l(),eo=a("ul"),u$=a("li"),eJe=a("strong"),Pta=o("beit"),Ita=o(" \u2014 "),d4e=a("a"),Nta=o("BeitForSemanticSegmentation"),Bta=o(" (BEiT model)"),Gta=l(),p$=a("li"),oJe=a("strong"),Dta=o("data2vec-vision"),qta=o(" \u2014 "),c4e=a("a"),jta=o("Data2VecVisionForSemanticSegmentation"),Ota=o(" (Data2VecVision model)"),Vta=l(),_$=a("li"),rJe=a("strong"),Xta=o("dpt"),zta=o(" \u2014 "),f4e=a("a"),Wta=o("DPTForSemanticSegmentation"),Qta=o(" (DPT model)"),Uta=l(),v$=a("li"),tJe=a("strong"),Hta=o("mobilenet_v2"),Jta=o(" \u2014 "),m4e=a("a"),Zta=o("MobileNetV2ForSemanticSegmentation"),Yta=o(" (MobileNetV2 model)"),Kta=l(),b$=a("li"),aJe=a("strong"),eaa=o("mobilevit"),oaa=o(" \u2014 "),g4e=a("a"),raa=o("MobileViTForSemanticSegmentation"),taa=o(" (MobileViT model)"),aaa=l(),T$=a("li"),nJe=a("strong"),naa=o("segformer"),saa=o(" \u2014 "),h4e=a("a"),laa=o("SegformerForSemanticSegmentation"),iaa=o(" (SegFormer model)"),daa=l(),F$=a("li"),sJe=a("strong"),caa=o("upernet"),faa=o(" \u2014 "),u4e=a("a"),maa=o("UperNetForSemanticSegmentation"),gaa=o(" (UPerNet model)"),haa=l(),M$=a("p"),uaa=o("The model is set in evaluation mode by default using "),lJe=a("code"),paa=o("model.eval()"),_aa=o(` (so for instance, dropout modules are
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model\u2019s configuration. Use `),v4e=a("a"),Daa=o("from_pretrained()"),qaa=o(" to load the model weights."),jaa=l(),b(w$.$$.fragment),Oaa=l(),Ft=a("div"),b(xj.$$.fragment),Vaa=l(),hJe=a("p"),Xaa=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),zaa=l(),ps=a("p"),Waa=o("The model class to instantiate is selected based on the "),uJe=a("code"),Qaa=o("model_type"),Uaa=o(` property of the config object (either
passed as an argument or loaded from `),pJe=a("code"),Haa=o("pretrained_model_name_or_path"),Jaa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_Je=a("code"),Zaa=o("pretrained_model_name_or_path"),Yaa=o(":"),Kaa=l(),Wm=a("ul"),A$=a("li"),vJe=a("strong"),ena=o("data2vec-vision"),ona=o(" \u2014 "),b4e=a("a"),rna=o("TFData2VecVisionForSemanticSegmentation"),tna=o(" (Data2VecVision model)"),ana=l(),L$=a("li"),bJe=a("strong"),nna=o("mobilevit"),sna=o(" \u2014 "),T4e=a("a"),lna=o("TFMobileViTForSemanticSegmentation"),ina=o(" (MobileViT model)"),dna=l(),y$=a("li"),TJe=a("strong"),cna=o("segformer"),fna=o(" \u2014 "),F4e=a("a"),mna=o("TFSegformerForSemanticSegmentation"),gna=o(" (SegFormer model)"),hna=l(),b(x$.$$.fragment),I5o=l(),Qm=a("h3"),S$=a("a"),FJe=a("span"),b(Sj.$$.fragment),una=l(),MJe=a("span"),pna=o("AutoModelForInstanceSegmentation"),N5o=l(),Sr=a("div"),b($j.$$.fragment),_na=l(),Um=a("p"),vna=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 `),M4e=a("a"),bna=o("from_pretrained()"),Tna=o(" class method or the "),E4e=a("a"),Fna=o("from_config()"),Mna=o(` class
method.`),Ena=l(),kj=a("p"),Cna=o("This class cannot be instantiated directly using "),EJe=a("code"),wna=o("__init__()"),Ana=o(" (throws an error)."),Lna=l(),Ea=a("div"),b(Rj.$$.fragment),yna=l(),CJe=a("p"),xna=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Sna=l(),Hm=a("p"),$na=o(`Note:
Loading a model from its configuration file does `),wJe=a("strong"),kna=o("not"),Rna=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),C4e=a("a"),Pna=o("from_pretrained()"),Ina=o(" to load the model weights."),Nna=l(),b($$.$$.fragment),Bna=l(),Co=a("div"),b(Pj.$$.fragment),Gna=l(),AJe=a("p"),Dna=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),qna=l(),_s=a("p"),jna=o("The model class to instantiate is selected based on the "),LJe=a("code"),Ona=o("model_type"),Vna=o(` property of the config object (either
passed as an argument or loaded from `),yJe=a("code"),Xna=o("pretrained_model_name_or_path"),zna=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xJe=a("code"),Wna=o("pretrained_model_name_or_path"),Qna=o(":"),Una=l(),SJe=a("ul"),k$=a("li"),$Je=a("strong"),Hna=o("maskformer"),Jna=o(" \u2014 "),w4e=a("a"),Zna=o("MaskFormerForInstanceSegmentation"),Yna=o(" (MaskFormer model)"),Kna=l(),R$=a("p"),esa=o("The model is set in evaluation mode by default using "),kJe=a("code"),osa=o("model.eval()"),rsa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),RJe=a("code"),tsa=o("model.train()"),asa=l(),b(P$.$$.fragment),B5o=l(),Jm=a("h3"),I$=a("a"),PJe=a("span"),b(Ij.$$.fragment),nsa=l(),IJe=a("span"),ssa=o("AutoModelForUniversalSegmentation"),G5o=l(),$r=a("div"),b(Nj.$$.fragment),lsa=l(),Zm=a("p"),isa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the `),A4e=a("a"),dsa=o("from_pretrained()"),csa=o(" class method or the "),L4e=a("a"),fsa=o("from_config()"),msa=o(` class
method.`),gsa=l(),Bj=a("p"),hsa=o("This class cannot be instantiated directly using "),NJe=a("code"),usa=o("__init__()"),psa=o(" (throws an error)."),_sa=l(),Ca=a("div"),b(Gj.$$.fragment),vsa=l(),BJe=a("p"),bsa=o("Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),Tsa=l(),Ym=a("p"),Fsa=o(`Note:
Loading a model from its configuration file does `),GJe=a("strong"),Msa=o("not"),Esa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),y4e=a("a"),Csa=o("from_pretrained()"),wsa=o(" to load the model weights."),Asa=l(),b(N$.$$.fragment),Lsa=l(),wo=a("div"),b(Dj.$$.fragment),ysa=l(),DJe=a("p"),xsa=o("Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),Ssa=l(),vs=a("p"),$sa=o("The model class to instantiate is selected based on the "),qJe=a("code"),ksa=o("model_type"),Rsa=o(` property of the config object (either
passed as an argument or loaded from `),jJe=a("code"),Psa=o("pretrained_model_name_or_path"),Isa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OJe=a("code"),Nsa=o("pretrained_model_name_or_path"),Bsa=o(":"),Gsa=l(),bs=a("ul"),B$=a("li"),VJe=a("strong"),Dsa=o("detr"),qsa=o(" \u2014 "),x4e=a("a"),jsa=o("DetrForSegmentation"),Osa=o(" (DETR model)"),Vsa=l(),G$=a("li"),XJe=a("strong"),Xsa=o("mask2former"),zsa=o(" \u2014 "),S4e=a("a"),Wsa=o("Mask2FormerForUniversalSegmentation"),Qsa=o(" (Mask2Former model)"),Usa=l(),D$=a("li"),zJe=a("strong"),Hsa=o("maskformer"),Jsa=o(" \u2014 "),$4e=a("a"),Zsa=o("MaskFormerForInstanceSegmentation"),Ysa=o(" (MaskFormer model)"),Ksa=l(),q$=a("li"),WJe=a("strong"),ela=o("oneformer"),ola=o(" \u2014 "),k4e=a("a"),rla=o("OneFormerForUniversalSegmentation"),tla=o(" (OneFormer model)"),ala=l(),j$=a("p"),nla=o("The model is set in evaluation mode by default using "),QJe=a("code"),sla=o("model.eval()"),lla=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),UJe=a("code"),ila=o("model.train()"),dla=l(),b(O$.$$.fragment),D5o=l(),Km=a("h3"),V$=a("a"),HJe=a("span"),b(qj.$$.fragment),cla=l(),JJe=a("span"),fla=o("AutoModelForZeroShotImageClassification"),q5o=l(),kr=a("div"),b(jj.$$.fragment),mla=l(),eg=a("p"),gla=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),R4e=a("a"),hla=o("from_pretrained()"),ula=o(" class method or the "),P4e=a("a"),pla=o("from_config()"),_la=o(` class
method.`),vla=l(),Oj=a("p"),bla=o("This class cannot be instantiated directly using "),ZJe=a("code"),Tla=o("__init__()"),Fla=o(" (throws an error)."),Mla=l(),wa=a("div"),b(Vj.$$.fragment),Ela=l(),YJe=a("p"),Cla=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),wla=l(),og=a("p"),Ala=o(`Note:
Loading a model from its configuration file does `),KJe=a("strong"),Lla=o("not"),yla=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),I4e=a("a"),xla=o("from_pretrained()"),Sla=o(" to load the model weights."),$la=l(),b(X$.$$.fragment),kla=l(),Ao=a("div"),b(Xj.$$.fragment),Rla=l(),eZe=a("p"),Pla=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Ila=l(),Ts=a("p"),Nla=o("The model class to instantiate is selected based on the "),oZe=a("code"),Bla=o("model_type"),Gla=o(` property of the config object (either
passed as an argument or loaded from `),rZe=a("code"),Dla=o("pretrained_model_name_or_path"),qla=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tZe=a("code"),jla=o("pretrained_model_name_or_path"),Ola=o(":"),Vla=l(),Rr=a("ul"),z$=a("li"),aZe=a("strong"),Xla=o("align"),zla=o(" \u2014 "),N4e=a("a"),Wla=o("AlignModel"),Qla=o(" (ALIGN model)"),Ula=l(),W$=a("li"),nZe=a("strong"),Hla=o("altclip"),Jla=o(" \u2014 "),B4e=a("a"),Zla=o("AltCLIPModel"),Yla=o(" (AltCLIP model)"),Kla=l(),Q$=a("li"),sZe=a("strong"),eia=o("blip"),oia=o(" \u2014 "),G4e=a("a"),ria=o("BlipModel"),tia=o(" (BLIP model)"),aia=l(),U$=a("li"),lZe=a("strong"),nia=o("chinese_clip"),sia=o(" \u2014 "),D4e=a("a"),lia=o("ChineseCLIPModel"),iia=o(" (Chinese-CLIP model)"),dia=l(),H$=a("li"),iZe=a("strong"),cia=o("clip"),fia=o(" \u2014 "),q4e=a("a"),mia=o("CLIPModel"),gia=o(" (CLIP model)"),hia=l(),J$=a("li"),dZe=a("strong"),uia=o("clipseg"),pia=o(" \u2014 "),j4e=a("a"),_ia=o("CLIPSegModel"),via=o(" (CLIPSeg model)"),bia=l(),Z$=a("p"),Tia=o("The model is set in evaluation mode by default using "),cZe=a("code"),Fia=o("model.eval()"),Mia=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),fZe=a("code"),Eia=o("model.train()"),Cia=l(),b(Y$.$$.fragment),j5o=l(),rg=a("h3"),K$=a("a"),mZe=a("span"),b(zj.$$.fragment),wia=l(),gZe=a("span"),Aia=o("TFAutoModelForZeroShotImageClassification"),O5o=l(),Pr=a("div"),b(Wj.$$.fragment),Lia=l(),tg=a("p"),yia=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),O4e=a("a"),xia=o("from_pretrained()"),Sia=o(" class method or the "),V4e=a("a"),$ia=o("from_config()"),kia=o(` class
method.`),Ria=l(),Qj=a("p"),Pia=o("This class cannot be instantiated directly using "),hZe=a("code"),Iia=o("__init__()"),Nia=o(" (throws an error)."),Bia=l(),Aa=a("div"),b(Uj.$$.fragment),Gia=l(),uZe=a("p"),Dia=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),qia=l(),ag=a("p"),jia=o(`Note:
Loading a model from its configuration file does `),pZe=a("strong"),Oia=o("not"),Via=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),X4e=a("a"),Xia=o("from_pretrained()"),zia=o(" to load the model weights."),Wia=l(),b(ek.$$.fragment),Qia=l(),Mt=a("div"),b(Hj.$$.fragment),Uia=l(),_Ze=a("p"),Hia=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Jia=l(),Fs=a("p"),Zia=o("The model class to instantiate is selected based on the "),vZe=a("code"),Yia=o("model_type"),Kia=o(` property of the config object (either
passed as an argument or loaded from `),bZe=a("code"),eda=o("pretrained_model_name_or_path"),oda=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),TZe=a("code"),rda=o("pretrained_model_name_or_path"),tda=o(":"),ada=l(),FZe=a("ul"),ok=a("li"),MZe=a("strong"),nda=o("clip"),sda=o(" \u2014 "),z4e=a("a"),lda=o("TFCLIPModel"),ida=o(" (CLIP model)"),dda=l(),b(rk.$$.fragment),V5o=l(),ng=a("h3"),tk=a("a"),EZe=a("span"),b(Jj.$$.fragment),cda=l(),CZe=a("span"),fda=o("AutoModelForZeroShotObjectDetection"),X5o=l(),Ir=a("div"),b(Zj.$$.fragment),mda=l(),sg=a("p"),gda=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),W4e=a("a"),hda=o("from_pretrained()"),uda=o(" class method or the "),Q4e=a("a"),pda=o("from_config()"),_da=o(` class
method.`),vda=l(),Yj=a("p"),bda=o("This class cannot be instantiated directly using "),wZe=a("code"),Tda=o("__init__()"),Fda=o(" (throws an error)."),Mda=l(),La=a("div"),b(Kj.$$.fragment),Eda=l(),AZe=a("p"),Cda=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),wda=l(),lg=a("p"),Ada=o(`Note:
Loading a model from its configuration file does `),LZe=a("strong"),Lda=o("not"),yda=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),U4e=a("a"),xda=o("from_pretrained()"),Sda=o(" to load the model weights."),$da=l(),b(ak.$$.fragment),kda=l(),Lo=a("div"),b(eO.$$.fragment),Rda=l(),yZe=a("p"),Pda=o("Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),Ida=l(),Ms=a("p"),Nda=o("The model class to instantiate is selected based on the "),xZe=a("code"),Bda=o("model_type"),Gda=o(` property of the config object (either
passed as an argument or loaded from `),SZe=a("code"),Dda=o("pretrained_model_name_or_path"),qda=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$Ze=a("code"),jda=o("pretrained_model_name_or_path"),Oda=o(":"),Vda=l(),kZe=a("ul"),nk=a("li"),RZe=a("strong"),Xda=o("owlvit"),zda=o(" \u2014 "),H4e=a("a"),Wda=o("OwlViTForObjectDetection"),Qda=o(" (OWL-ViT model)"),Uda=l(),sk=a("p"),Hda=o("The model is set in evaluation mode by default using "),PZe=a("code"),Jda=o("model.eval()"),Zda=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),IZe=a("code"),Yda=o("model.train()"),Kda=l(),b(lk.$$.fragment),z5o=l(),ig=a("h2"),ik=a("a"),NZe=a("span"),b(oO.$$.fragment),eca=l(),BZe=a("span"),oca=o("Audio"),W5o=l(),J4e=a("p"),rca=o("The following auto classes are available for the following audio tasks."),Q5o=l(),dg=a("h3"),dk=a("a"),GZe=a("span"),b(rO.$$.fragment),tca=l(),DZe=a("span"),aca=o("AutoModelForAudioClassification"),U5o=l(),Nr=a("div"),b(tO.$$.fragment),nca=l(),cg=a("p"),sca=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 `),Z4e=a("a"),lca=o("from_pretrained()"),ica=o(" class method or the "),Y4e=a("a"),dca=o("from_config()"),cca=o(` class
method.`),fca=l(),aO=a("p"),mca=o("This class cannot be instantiated directly using "),qZe=a("code"),gca=o("__init__()"),hca=o(" (throws an error)."),uca=l(),ya=a("div"),b(nO.$$.fragment),pca=l(),jZe=a("p"),_ca=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),vca=l(),fg=a("p"),bca=o(`Note:
Loading a model from its configuration file does `),OZe=a("strong"),Tca=o("not"),Fca=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),K4e=a("a"),Mca=o("from_pretrained()"),Eca=o(" to load the model weights."),Cca=l(),b(ck.$$.fragment),wca=l(),yo=a("div"),b(sO.$$.fragment),Aca=l(),VZe=a("p"),Lca=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),yca=l(),Es=a("p"),xca=o("The model class to instantiate is selected based on the "),XZe=a("code"),Sca=o("model_type"),$ca=o(` property of the config object (either
passed as an argument or loaded from `),zZe=a("code"),kca=o("pretrained_model_name_or_path"),Rca=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WZe=a("code"),Pca=o("pretrained_model_name_or_path"),Ica=o(":"),Nca=l(),Re=a("ul"),fk=a("li"),QZe=a("strong"),Bca=o("audio-spectrogram-transformer"),Gca=o(" \u2014 "),ebe=a("a"),Dca=o("ASTForAudioClassification"),qca=o(" (Audio Spectrogram Transformer model)"),jca=l(),mk=a("li"),UZe=a("strong"),Oca=o("data2vec-audio"),Vca=o(" \u2014 "),obe=a("a"),Xca=o("Data2VecAudioForSequenceClassification"),zca=o(" (Data2VecAudio model)"),Wca=l(),gk=a("li"),HZe=a("strong"),Qca=o("hubert"),Uca=o(" \u2014 "),rbe=a("a"),Hca=o("HubertForSequenceClassification"),Jca=o(" (Hubert model)"),Zca=l(),hk=a("li"),JZe=a("strong"),Yca=o("sew"),Kca=o(" \u2014 "),tbe=a("a"),efa=o("SEWForSequenceClassification"),ofa=o(" (SEW model)"),rfa=l(),uk=a("li"),ZZe=a("strong"),tfa=o("sew-d"),afa=o(" \u2014 "),abe=a("a"),nfa=o("SEWDForSequenceClassification"),sfa=o(" (SEW-D model)"),lfa=l(),pk=a("li"),YZe=a("strong"),ifa=o("unispeech"),dfa=o(" \u2014 "),nbe=a("a"),cfa=o("UniSpeechForSequenceClassification"),ffa=o(" (UniSpeech model)"),mfa=l(),_k=a("li"),KZe=a("strong"),gfa=o("unispeech-sat"),hfa=o(" \u2014 "),sbe=a("a"),ufa=o("UniSpeechSatForSequenceClassification"),pfa=o(" (UniSpeechSat model)"),_fa=l(),vk=a("li"),eYe=a("strong"),vfa=o("wav2vec2"),bfa=o(" \u2014 "),lbe=a("a"),Tfa=o("Wav2Vec2ForSequenceClassification"),Ffa=o(" (Wav2Vec2 model)"),Mfa=l(),bk=a("li"),oYe=a("strong"),Efa=o("wav2vec2-conformer"),Cfa=o(" \u2014 "),ibe=a("a"),wfa=o("Wav2Vec2ConformerForSequenceClassification"),Afa=o(" (Wav2Vec2-Conformer model)"),Lfa=l(),Tk=a("li"),rYe=a("strong"),yfa=o("wavlm"),xfa=o(" \u2014 "),dbe=a("a"),Sfa=o("WavLMForSequenceClassification"),$fa=o(" (WavLM model)"),kfa=l(),Fk=a("li"),tYe=a("strong"),Rfa=o("whisper"),Pfa=o(" \u2014 "),cbe=a("a"),Ifa=o("WhisperForAudioClassification"),Nfa=o(" (Whisper model)"),Bfa=l(),Mk=a("p"),Gfa=o("The model is set in evaluation mode by default using "),aYe=a("code"),Dfa=o("model.eval()"),qfa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nYe=a("code"),jfa=o("model.train()"),Ofa=l(),b(Ek.$$.fragment),H5o=l(),mg=a("h3"),Ck=a("a"),sYe=a("span"),b(lO.$$.fragment),Vfa=l(),lYe=a("span"),Xfa=o("AutoModelForAudioFrameClassification"),J5o=l(),Br=a("div"),b(iO.$$.fragment),zfa=l(),gg=a("p"),Wfa=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
with the `),fbe=a("a"),Qfa=o("from_pretrained()"),Ufa=o(" class method or the "),mbe=a("a"),Hfa=o("from_config()"),Jfa=o(` class
method.`),Zfa=l(),dO=a("p"),Yfa=o("This class cannot be instantiated directly using "),iYe=a("code"),Kfa=o("__init__()"),ema=o(" (throws an error)."),oma=l(),xa=a("div"),b(cO.$$.fragment),rma=l(),dYe=a("p"),tma=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),ama=l(),hg=a("p"),nma=o(`Note:
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model\u2019s configuration. Use `),gbe=a("a"),ima=o("from_pretrained()"),dma=o(" to load the model weights."),cma=l(),b(wk.$$.fragment),fma=l(),xo=a("div"),b(fO.$$.fragment),mma=l(),fYe=a("p"),gma=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),hma=l(),Cs=a("p"),uma=o("The model class to instantiate is selected based on the "),mYe=a("code"),pma=o("model_type"),_ma=o(` property of the config object (either
passed as an argument or loaded from `),gYe=a("code"),vma=o("pretrained_model_name_or_path"),bma=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hYe=a("code"),Tma=o("pretrained_model_name_or_path"),Fma=o(":"),Mma=l(),$t=a("ul"),Ak=a("li"),uYe=a("strong"),Ema=o("data2vec-audio"),Cma=o(" \u2014 "),hbe=a("a"),wma=o("Data2VecAudioForAudioFrameClassification"),Ama=o(" (Data2VecAudio model)"),Lma=l(),Lk=a("li"),pYe=a("strong"),yma=o("unispeech-sat"),xma=o(" \u2014 "),ube=a("a"),Sma=o("UniSpeechSatForAudioFrameClassification"),$ma=o(" (UniSpeechSat model)"),kma=l(),yk=a("li"),_Ye=a("strong"),Rma=o("wav2vec2"),Pma=o(" \u2014 "),pbe=a("a"),Ima=o("Wav2Vec2ForAudioFrameClassification"),Nma=o(" (Wav2Vec2 model)"),Bma=l(),xk=a("li"),vYe=a("strong"),Gma=o("wav2vec2-conformer"),Dma=o(" \u2014 "),_be=a("a"),qma=o("Wav2Vec2ConformerForAudioFrameClassification"),jma=o(" (Wav2Vec2-Conformer model)"),Oma=l(),Sk=a("li"),bYe=a("strong"),Vma=o("wavlm"),Xma=o(" \u2014 "),vbe=a("a"),zma=o("WavLMForAudioFrameClassification"),Wma=o(" (WavLM model)"),Qma=l(),$k=a("p"),Uma=o("The model is set in evaluation mode by default using "),TYe=a("code"),Hma=o("model.eval()"),Jma=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),FYe=a("code"),Zma=o("model.train()"),Yma=l(),b(kk.$$.fragment),Z5o=l(),ug=a("h3"),Rk=a("a"),MYe=a("span"),b(mO.$$.fragment),Kma=l(),EYe=a("span"),ega=o("AutoModelForCTC"),Y5o=l(),Gr=a("div"),b(gO.$$.fragment),oga=l(),pg=a("p"),rga=o(`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 `),bbe=a("a"),tga=o("from_pretrained()"),aga=o(" class method or the "),Tbe=a("a"),nga=o("from_config()"),sga=o(` class
method.`),lga=l(),hO=a("p"),iga=o("This class cannot be instantiated directly using "),CYe=a("code"),dga=o("__init__()"),cga=o(" (throws an error)."),fga=l(),Sa=a("div"),b(uO.$$.fragment),mga=l(),wYe=a("p"),gga=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),hga=l(),_g=a("p"),uga=o(`Note:
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model)"),Cdo.forEach(t),S_r=i(ne),b4=n(ne,"LI",{});var wdo=s(b4);cAe=n(wdo,"STRONG",{});var Yka=s(cAe);$_r=r(Yka,"wavlm"),Yka.forEach(t),k_r=r(wdo," \u2014 "),voe=n(wdo,"A",{href:!0});var Kka=s(voe);R_r=r(Kka,"Wav2Vec2Processor"),Kka.forEach(t),P_r=r(wdo," (WavLM model)"),wdo.forEach(t),I_r=i(ne),T4=n(ne,"LI",{});var Ado=s(T4);fAe=n(Ado,"STRONG",{});var eRa=s(fAe);N_r=r(eRa,"whisper"),eRa.forEach(t),B_r=r(Ado," \u2014 "),boe=n(Ado,"A",{href:!0});var oRa=s(boe);G_r=r(oRa,"WhisperProcessor"),oRa.forEach(t),D_r=r(Ado," (Whisper model)"),Ado.forEach(t),q_r=i(ne),F4=n(ne,"LI",{});var Ldo=s(F4);mAe=n(Ldo,"STRONG",{});var rRa=s(mAe);j_r=r(rRa,"xclip"),rRa.forEach(t),O_r=r(Ldo," \u2014 "),Toe=n(Ldo,"A",{href:!0});var tRa=s(Toe);V_r=r(tRa,"XCLIPProcessor"),tRa.forEach(t),X_r=r(Ldo," (X-CLIP model)"),Ldo.forEach(t),ne.forEach(t),z_r=i(za),T(M4.$$.fragment,za),W_r=i(za),T(E4.$$.fragment,za),za.forEach(t),Q_r=i(xi),C4=n(xi,"DIV",{class:!0});var O0o=s(C4);T(BB.$$.fragment,O0o),U_r=i(O0o),gAe=n(O0o,"P",{});var aRa=s(gAe);H_r=r(aRa,"Register a new processor for this class."),aRa.forEach(t),O0o.forEach(t),xi.forEach(t),oCo=i(f),xc=n(f,"H2",{class:!0});var V0o=s(xc);w4=n(V0o,"A",{id:!0,class:!0,href:!0});var nRa=s(w4);hAe=n(nRa,"SPAN",{});var sRa=s(hAe);T(GB.$$.fragment,sRa),sRa.forEach(t),nRa.forEach(t),J_r=i(V0o),uAe=n(V0o,"SPAN",{});var lRa=s(uAe);Z_r=r(lRa,"Generic model classes"),lRa.forEach(t),V0o.forEach(t),rCo=i(f),Foe=n(f,"P",{});var iRa=s(Foe);Y_r=r(iRa,"The following auto classes are available for instantiating a base model class without a specific head."),iRa.forEach(t),tCo=i(f),Sc=n(f,"H3",{class:!0});var X0o=s(Sc);A4=n(X0o,"A",{id:!0,class:!0,href:!0});var dRa=s(A4);pAe=n(dRa,"SPAN",{});var cRa=s(pAe);T(DB.$$.fragment,cRa),cRa.forEach(t),dRa.forEach(t),K_r=i(X0o),_Ae=n(X0o,"SPAN",{});var fRa=s(_Ae);e1r=r(fRa,"AutoModel"),fRa.forEach(t),X0o.forEach(t),aCo=i(f),Vo=n(f,"DIV",{class:!0});var Si=s(Vo);T(qB.$$.fragment,Si),o1r=i(Si),$c=n(Si,"P",{});var j7e=s($c);r1r=r(j7e,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Moe=n(j7e,"A",{href:!0});var mRa=s(Moe);t1r=r(mRa,"from_pretrained()"),mRa.forEach(t),a1r=r(j7e," class method or the "),Eoe=n(j7e,"A",{href:!0});var gRa=s(Eoe);n1r=r(gRa,"from_config()"),gRa.forEach(t),s1r=r(j7e,` class
method.`),j7e.forEach(t),l1r=i(Si),jB=n(Si,"P",{});var z0o=s(jB);i1r=r(z0o,"This class cannot be instantiated directly using "),vAe=n(z0o,"CODE",{});var hRa=s(vAe);d1r=r(hRa,"__init__()"),hRa.forEach(t),c1r=r(z0o," (throws an error)."),z0o.forEach(t),f1r=i(Si),Nt=n(Si,"DIV",{class:!0});var jP=s(Nt);T(OB.$$.fragment,jP),m1r=i(jP),bAe=n(jP,"P",{});var uRa=s(bAe);g1r=r(uRa,"Instantiates one of the base model classes of the library from a configuration."),uRa.forEach(t),h1r=i(jP),kc=n(jP,"P",{});var O7e=s(kc);u1r=r(O7e,`Note:
Loading a model from its configuration file does `),TAe=n(O7e,"STRONG",{});var pRa=s(TAe);p1r=r(pRa,"not"),pRa.forEach(t),_1r=r(O7e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Coe=n(O7e,"A",{href:!0});var _Ra=s(Coe);v1r=r(_Ra,"from_pretrained()"),_Ra.forEach(t),b1r=r(O7e," to load the model weights."),O7e.forEach(t),T1r=i(jP),T(L4.$$.fragment,jP),jP.forEach(t),F1r=i(Si),so=n(Si,"DIV",{class:!0});var Wa=s(so);T(VB.$$.fragment,Wa),M1r=i(Wa),FAe=n(Wa,"P",{});var vRa=s(FAe);E1r=r(vRa,"Instantiate one of the base model classes of the library from a pretrained model."),vRa.forEach(t),C1r=i(Wa),Sn=n(Wa,"P",{});var OP=s(Sn);w1r=r(OP,"The model class to instantiate is selected based on the "),MAe=n(OP,"CODE",{});var bRa=s(MAe);A1r=r(bRa,"model_type"),bRa.forEach(t),L1r=r(OP,` property of the config object (either
passed as an argument or loaded from `),EAe=n(OP,"CODE",{});var TRa=s(EAe);y1r=r(TRa,"pretrained_model_name_or_path"),TRa.forEach(t),x1r=r(OP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CAe=n(OP,"CODE",{});var FRa=s(CAe);S1r=r(FRa,"pretrained_model_name_or_path"),FRa.forEach(t),$1r=r(OP,":"),OP.forEach(t),k1r=i(Wa),y=n(Wa,"UL",{});var x=s(y);y4=n(x,"LI",{});var ydo=s(y4);wAe=n(ydo,"STRONG",{});var MRa=s(wAe);R1r=r(MRa,"albert"),MRa.forEach(t),P1r=r(ydo," \u2014 "),woe=n(ydo,"A",{href:!0});var ERa=s(woe);I1r=r(ERa,"AlbertModel"),ERa.forEach(t),N1r=r(ydo," (ALBERT model)"),ydo.forEach(t),B1r=i(x),x4=n(x,"LI",{});var xdo=s(x4);AAe=n(xdo,"STRONG",{});var CRa=s(AAe);G1r=r(CRa,"align"),CRa.forEach(t),D1r=r(xdo," \u2014 "),Aoe=n(xdo,"A",{href:!0});var wRa=s(Aoe);q1r=r(wRa,"AlignModel"),wRa.forEach(t),j1r=r(xdo," (ALIGN model)"),xdo.forEach(t),O1r=i(x),S4=n(x,"LI",{});var Sdo=s(S4);LAe=n(Sdo,"STRONG",{});var ARa=s(LAe);V1r=r(ARa,"altclip"),ARa.forEach(t),X1r=r(Sdo," \u2014 "),Loe=n(Sdo,"A",{href:!0});var LRa=s(Loe);z1r=r(LRa,"AltCLIPModel"),LRa.forEach(t),W1r=r(Sdo," (AltCLIP model)"),Sdo.forEach(t),Q1r=i(x),$4=n(x,"LI",{});var $do=s($4);yAe=n($do,"STRONG",{});var yRa=s(yAe);U1r=r(yRa,"audio-spectrogram-transformer"),yRa.forEach(t),H1r=r($do," \u2014 "),yoe=n($do,"A",{href:!0});var xRa=s(yoe);J1r=r(xRa,"ASTModel"),xRa.forEach(t),Z1r=r($do," (Audio Spectrogram Transformer model)"),$do.forEach(t),Y1r=i(x),k4=n(x,"LI",{});var kdo=s(k4);xAe=n(kdo,"STRONG",{});var SRa=s(xAe);K1r=r(SRa,"bart"),SRa.forEach(t),e2r=r(kdo," \u2014 "),xoe=n(kdo,"A",{href:!0});var $Ra=s(xoe);o2r=r($Ra,"BartModel"),$Ra.forEach(t),r2r=r(kdo," (BART model)"),kdo.forEach(t),t2r=i(x),R4=n(x,"LI",{});var Rdo=s(R4);SAe=n(Rdo,"STRONG",{});var kRa=s(SAe);a2r=r(kRa,"beit"),kRa.forEach(t),n2r=r(Rdo," \u2014 "),Soe=n(Rdo,"A",{href:!0});var RRa=s(Soe);s2r=r(RRa,"BeitModel"),RRa.forEach(t),l2r=r(Rdo," (BEiT model)"),Rdo.forEach(t),i2r=i(x),P4=n(x,"LI",{});var Pdo=s(P4);$Ae=n(Pdo,"STRONG",{});var PRa=s($Ae);d2r=r(PRa,"bert"),PRa.forEach(t),c2r=r(Pdo," \u2014 "),$oe=n(Pdo,"A",{href:!0});var IRa=s($oe);f2r=r(IRa,"BertModel"),IRa.forEach(t),m2r=r(Pdo," (BERT model)"),Pdo.forEach(t),g2r=i(x),I4=n(x,"LI",{});var Ido=s(I4);kAe=n(Ido,"STRONG",{});var NRa=s(kAe);h2r=r(NRa,"bert-generation"),NRa.forEach(t),u2r=r(Ido," \u2014 "),koe=n(Ido,"A",{href:!0});var BRa=s(koe);p2r=r(BRa,"BertGenerationEncoder"),BRa.forEach(t),_2r=r(Ido," (Bert Generation model)"),Ido.forEach(t),v2r=i(x),N4=n(x,"LI",{});var Ndo=s(N4);RAe=n(Ndo,"STRONG",{});var GRa=s(RAe);b2r=r(GRa,"big_bird"),GRa.forEach(t),T2r=r(Ndo," \u2014 "),Roe=n(Ndo,"A",{href:!0});var DRa=s(Roe);F2r=r(DRa,"BigBirdModel"),DRa.forEach(t),M2r=r(Ndo," (BigBird model)"),Ndo.forEach(t),E2r=i(x),B4=n(x,"LI",{});var Bdo=s(B4);PAe=n(Bdo,"STRONG",{});var qRa=s(PAe);C2r=r(qRa,"bigbird_pegasus"),qRa.forEach(t),w2r=r(Bdo," \u2014 "),Poe=n(Bdo,"A",{href:!0});var jRa=s(Poe);A2r=r(jRa,"BigBirdPegasusModel"),jRa.forEach(t),L2r=r(Bdo," (BigBird-Pegasus model)"),Bdo.forEach(t),y2r=i(x),G4=n(x,"LI",{});var Gdo=s(G4);IAe=n(Gdo,"STRONG",{});var ORa=s(IAe);x2r=r(ORa,"biogpt"),ORa.forEach(t),S2r=r(Gdo," \u2014 "),Ioe=n(Gdo,"A",{href:!0});var VRa=s(Ioe);$2r=r(VRa,"BioGptModel"),VRa.forEach(t),k2r=r(Gdo," (BioGpt model)"),Gdo.forEach(t),R2r=i(x),D4=n(x,"LI",{});var Ddo=s(D4);NAe=n(Ddo,"STRONG",{});var XRa=s(NAe);P2r=r(XRa,"bit"),XRa.forEach(t),I2r=r(Ddo," \u2014 "),Noe=n(Ddo,"A",{href:!0});var zRa=s(Noe);N2r=r(zRa,"BitModel"),zRa.forEach(t),B2r=r(Ddo," (BiT model)"),Ddo.forEach(t),G2r=i(x),q4=n(x,"LI",{});var qdo=s(q4);BAe=n(qdo,"STRONG",{});var WRa=s(BAe);D2r=r(WRa,"blenderbot"),WRa.forEach(t),q2r=r(qdo," \u2014 "),Boe=n(qdo,"A",{href:!0});var QRa=s(Boe);j2r=r(QRa,"BlenderbotModel"),QRa.forEach(t),O2r=r(qdo," (Blenderbot model)"),qdo.forEach(t),V2r=i(x),j4=n(x,"LI",{});var jdo=s(j4);GAe=n(jdo,"STRONG",{});var URa=s(GAe);X2r=r(URa,"blenderbot-small"),URa.forEach(t),z2r=r(jdo," \u2014 "),Goe=n(jdo,"A",{href:!0});var HRa=s(Goe);W2r=r(HRa,"BlenderbotSmallModel"),HRa.forEach(t),Q2r=r(jdo," (BlenderbotSmall model)"),jdo.forEach(t),U2r=i(x),O4=n(x,"LI",{});var Odo=s(O4);DAe=n(Odo,"STRONG",{});var JRa=s(DAe);H2r=r(JRa,"blip"),JRa.forEach(t),J2r=r(Odo," \u2014 "),Doe=n(Odo,"A",{href:!0});var ZRa=s(Doe);Z2r=r(ZRa,"BlipModel"),ZRa.forEach(t),Y2r=r(Odo," (BLIP model)"),Odo.forEach(t),K2r=i(x),V4=n(x,"LI",{});var Vdo=s(V4);qAe=n(Vdo,"STRONG",{});var YRa=s(qAe);evr=r(YRa,"blip-2"),YRa.forEach(t),ovr=r(Vdo," \u2014 "),qoe=n(Vdo,"A",{href:!0});var KRa=s(qoe);rvr=r(KRa,"Blip2Model"),KRa.forEach(t),tvr=r(Vdo," (BLIP-2 model)"),Vdo.forEach(t),avr=i(x),X4=n(x,"LI",{});var Xdo=s(X4);jAe=n(Xdo,"STRONG",{});var ePa=s(jAe);nvr=r(ePa,"bloom"),ePa.forEach(t),svr=r(Xdo," \u2014 "),joe=n(Xdo,"A",{href:!0});var oPa=s(joe);lvr=r(oPa,"BloomModel"),oPa.forEach(t),ivr=r(Xdo," (BLOOM model)"),Xdo.forEach(t),dvr=i(x),z4=n(x,"LI",{});var zdo=s(z4);OAe=n(zdo,"STRONG",{});var rPa=s(OAe);cvr=r(rPa,"bridgetower"),rPa.forEach(t),fvr=r(zdo," \u2014 "),Ooe=n(zdo,"A",{href:!0});var tPa=s(Ooe);mvr=r(tPa,"BridgeTowerModel"),tPa.forEach(t),gvr=r(zdo," (BridgeTower model)"),zdo.forEach(t),hvr=i(x),W4=n(x,"LI",{});var Wdo=s(W4);VAe=n(Wdo,"STRONG",{});var aPa=s(VAe);uvr=r(aPa,"camembert"),aPa.forEach(t),pvr=r(Wdo," \u2014 "),Voe=n(Wdo,"A",{href:!0});var nPa=s(Voe);_vr=r(nPa,"CamembertModel"),nPa.forEach(t),vvr=r(Wdo," (CamemBERT model)"),Wdo.forEach(t),bvr=i(x),Q4=n(x,"LI",{});var Qdo=s(Q4);XAe=n(Qdo,"STRONG",{});var sPa=s(XAe);Tvr=r(sPa,"canine"),sPa.forEach(t),Fvr=r(Qdo," \u2014 "),Xoe=n(Qdo,"A",{href:!0});var lPa=s(Xoe);Mvr=r(lPa,"CanineModel"),lPa.forEach(t),Evr=r(Qdo," (CANINE model)"),Qdo.forEach(t),Cvr=i(x),U4=n(x,"LI",{});var Udo=s(U4);zAe=n(Udo,"STRONG",{});var iPa=s(zAe);wvr=r(iPa,"chinese_clip"),iPa.forEach(t),Avr=r(Udo," \u2014 "),zoe=n(Udo,"A",{href:!0});var dPa=s(zoe);Lvr=r(dPa,"ChineseCLIPModel"),dPa.forEach(t),yvr=r(Udo," (Chinese-CLIP model)"),Udo.forEach(t),xvr=i(x),H4=n(x,"LI",{});var Hdo=s(H4);WAe=n(Hdo,"STRONG",{});var cPa=s(WAe);Svr=r(cPa,"clap"),cPa.forEach(t),$vr=r(Hdo," \u2014 "),Woe=n(Hdo,"A",{href:!0});var fPa=s(Woe);kvr=r(fPa,"ClapModel"),fPa.forEach(t),Rvr=r(Hdo," (CLAP 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IOa=s(Jne);q$r=r(IOa,"FlaxBlenderbotSmallModel"),IOa.forEach(t),j$r=r(zgo," (BlenderbotSmall model)"),zgo.forEach(t),O$r=i(de),YF=n(de,"LI",{});var Wgo=s(YF);fSe=n(Wgo,"STRONG",{});var NOa=s(fSe);V$r=r(NOa,"clip"),NOa.forEach(t),X$r=r(Wgo," \u2014 "),Zne=n(Wgo,"A",{href:!0});var BOa=s(Zne);z$r=r(BOa,"FlaxCLIPModel"),BOa.forEach(t),W$r=r(Wgo," (CLIP model)"),Wgo.forEach(t),Q$r=i(de),KF=n(de,"LI",{});var Qgo=s(KF);mSe=n(Qgo,"STRONG",{});var GOa=s(mSe);U$r=r(GOa,"distilbert"),GOa.forEach(t),H$r=r(Qgo," \u2014 "),Yne=n(Qgo,"A",{href:!0});var DOa=s(Yne);J$r=r(DOa,"FlaxDistilBertModel"),DOa.forEach(t),Z$r=r(Qgo," (DistilBERT model)"),Qgo.forEach(t),Y$r=i(de),eM=n(de,"LI",{});var Ugo=s(eM);gSe=n(Ugo,"STRONG",{});var qOa=s(gSe);K$r=r(qOa,"electra"),qOa.forEach(t),ekr=r(Ugo," \u2014 "),Kne=n(Ugo,"A",{href:!0});var jOa=s(Kne);okr=r(jOa,"FlaxElectraModel"),jOa.forEach(t),rkr=r(Ugo," (ELECTRA model)"),Ugo.forEach(t),tkr=i(de),oM=n(de,"LI",{});var Hgo=s(oM);hSe=n(Hgo,"STRONG",{});var OOa=s(hSe);akr=r(OOa,"gpt-sw3"),OOa.forEach(t),nkr=r(Hgo," \u2014 "),ese=n(Hgo,"A",{href:!0});var VOa=s(ese);skr=r(VOa,"FlaxGPT2Model"),VOa.forEach(t),lkr=r(Hgo," (GPT-Sw3 model)"),Hgo.forEach(t),ikr=i(de),rM=n(de,"LI",{});var Jgo=s(rM);uSe=n(Jgo,"STRONG",{});var XOa=s(uSe);dkr=r(XOa,"gpt2"),XOa.forEach(t),ckr=r(Jgo," \u2014 "),ose=n(Jgo,"A",{href:!0});var zOa=s(ose);fkr=r(zOa,"FlaxGPT2Model"),zOa.forEach(t),mkr=r(Jgo," (OpenAI GPT-2 model)"),Jgo.forEach(t),gkr=i(de),tM=n(de,"LI",{});var Zgo=s(tM);pSe=n(Zgo,"STRONG",{});var WOa=s(pSe);hkr=r(WOa,"gpt_neo"),WOa.forEach(t),ukr=r(Zgo," \u2014 "),rse=n(Zgo,"A",{href:!0});var QOa=s(rse);pkr=r(QOa,"FlaxGPTNeoModel"),QOa.forEach(t),_kr=r(Zgo," (GPT Neo model)"),Zgo.forEach(t),vkr=i(de),aM=n(de,"LI",{});var Ygo=s(aM);_Se=n(Ygo,"STRONG",{});var UOa=s(_Se);bkr=r(UOa,"gptj"),UOa.forEach(t),Tkr=r(Ygo," \u2014 "),tse=n(Ygo,"A",{href:!0});var HOa=s(tse);Fkr=r(HOa,"FlaxGPTJModel"),HOa.forEach(t),Mkr=r(Ygo," (GPT-J model)"),Ygo.forEach(t),Ekr=i(de),nM=n(de,"LI",{});var Kgo=s(nM);vSe=n(Kgo,"STRONG",{});var JOa=s(vSe);Ckr=r(JOa,"longt5"),JOa.forEach(t),wkr=r(Kgo," \u2014 "),ase=n(Kgo,"A",{href:!0});var ZOa=s(ase);Akr=r(ZOa,"FlaxLongT5Model"),ZOa.forEach(t),Lkr=r(Kgo," (LongT5 model)"),Kgo.forEach(t),ykr=i(de),sM=n(de,"LI",{});var eho=s(sM);bSe=n(eho,"STRONG",{});var YOa=s(bSe);xkr=r(YOa,"marian"),YOa.forEach(t),Skr=r(eho," \u2014 "),nse=n(eho,"A",{href:!0});var KOa=s(nse);$kr=r(KOa,"FlaxMarianModel"),KOa.forEach(t),kkr=r(eho," (Marian model)"),eho.forEach(t),Rkr=i(de),lM=n(de,"LI",{});var oho=s(lM);TSe=n(oho,"STRONG",{});var eVa=s(TSe);Pkr=r(eVa,"mbart"),eVa.forEach(t),Ikr=r(oho," \u2014 "),sse=n(oho,"A",{href:!0});var oVa=s(sse);Nkr=r(oVa,"FlaxMBartModel"),oVa.forEach(t),Bkr=r(oho," (mBART model)"),oho.forEach(t),Gkr=i(de),iM=n(de,"LI",{});var rho=s(iM);FSe=n(rho,"STRONG",{});var rVa=s(FSe);Dkr=r(rVa,"mt5"),rVa.forEach(t),qkr=r(rho," \u2014 "),lse=n(rho,"A",{href:!0});var tVa=s(lse);jkr=r(tVa,"FlaxMT5Model"),tVa.forEach(t),Okr=r(rho," (MT5 model)"),rho.forEach(t),Vkr=i(de),dM=n(de,"LI",{});var tho=s(dM);MSe=n(tho,"STRONG",{});var aVa=s(MSe);Xkr=r(aVa,"opt"),aVa.forEach(t),zkr=r(tho," \u2014 "),ise=n(tho,"A",{href:!0});var nVa=s(ise);Wkr=r(nVa,"FlaxOPTModel"),nVa.forEach(t),Qkr=r(tho," (OPT model)"),tho.forEach(t),Ukr=i(de),cM=n(de,"LI",{});var aho=s(cM);ESe=n(aho,"STRONG",{});var sVa=s(ESe);Hkr=r(sVa,"pegasus"),sVa.forEach(t),Jkr=r(aho," \u2014 "),dse=n(aho,"A",{href:!0});var lVa=s(dse);Zkr=r(lVa,"FlaxPegasusModel"),lVa.forEach(t),Ykr=r(aho," (Pegasus model)"),aho.forEach(t),Kkr=i(de),fM=n(de,"LI",{});var nho=s(fM);CSe=n(nho,"STRONG",{});var iVa=s(CSe);eRr=r(iVa,"roberta"),iVa.forEach(t),oRr=r(nho," \u2014 "),cse=n(nho,"A",{href:!0});var dVa=s(cse);rRr=r(dVa,"FlaxRobertaModel"),dVa.forEach(t),tRr=r(nho," (RoBERTa model)"),nho.forEach(t),aRr=i(de),mM=n(de,"LI",{});var sho=s(mM);wSe=n(sho,"STRONG",{});var cVa=s(wSe);nRr=r(cVa,"roberta-prelayernorm"),cVa.forEach(t),sRr=r(sho," \u2014 "),fse=n(sho,"A",{href:!0});var fVa=s(fse);lRr=r(fVa,"FlaxRobertaPreLayerNormModel"),fVa.forEach(t),iRr=r(sho," (RoBERTa-PreLayerNorm model)"),sho.forEach(t),dRr=i(de),gM=n(de,"LI",{});var lho=s(gM);ASe=n(lho,"STRONG",{});var mVa=s(ASe);cRr=r(mVa,"roformer"),mVa.forEach(t),fRr=r(lho," \u2014 "),mse=n(lho,"A",{href:!0});var gVa=s(mse);mRr=r(gVa,"FlaxRoFormerModel"),gVa.forEach(t),gRr=r(lho," (RoFormer model)"),lho.forEach(t),hRr=i(de),hM=n(de,"LI",{});var iho=s(hM);LSe=n(iho,"STRONG",{});var hVa=s(LSe);uRr=r(hVa,"t5"),hVa.forEach(t),pRr=r(iho," \u2014 "),gse=n(iho,"A",{href:!0});var uVa=s(gse);_Rr=r(uVa,"FlaxT5Model"),uVa.forEach(t),vRr=r(iho," (T5 model)"),iho.forEach(t),bRr=i(de),uM=n(de,"LI",{});var dho=s(uM);ySe=n(dho,"STRONG",{});var pVa=s(ySe);TRr=r(pVa,"vision-text-dual-encoder"),pVa.forEach(t),FRr=r(dho," \u2014 "),hse=n(dho,"A",{href:!0});var _Va=s(hse);MRr=r(_Va,"FlaxVisionTextDualEncoderModel"),_Va.forEach(t),ERr=r(dho," (VisionTextDualEncoder model)"),dho.forEach(t),CRr=i(de),pM=n(de,"LI",{});var cho=s(pM);xSe=n(cho,"STRONG",{});var vVa=s(xSe);wRr=r(vVa,"vit"),vVa.forEach(t),ARr=r(cho," \u2014 "),use=n(cho,"A",{href:!0});var bVa=s(use);LRr=r(bVa,"FlaxViTModel"),bVa.forEach(t),yRr=r(cho," (ViT model)"),cho.forEach(t),xRr=i(de),_M=n(de,"LI",{});var fho=s(_M);SSe=n(fho,"STRONG",{});var TVa=s(SSe);SRr=r(TVa,"wav2vec2"),TVa.forEach(t),$Rr=r(fho," \u2014 "),pse=n(fho,"A",{href:!0});var FVa=s(pse);kRr=r(FVa,"FlaxWav2Vec2Model"),FVa.forEach(t),RRr=r(fho," (Wav2Vec2 model)"),fho.forEach(t),PRr=i(de),vM=n(de,"LI",{});var mho=s(vM);$Se=n(mho,"STRONG",{});var MVa=s($Se);IRr=r(MVa,"whisper"),MVa.forEach(t),NRr=r(mho," \u2014 "),_se=n(mho,"A",{href:!0});var EVa=s(_se);BRr=r(EVa,"FlaxWhisperModel"),EVa.forEach(t),GRr=r(mho," (Whisper model)"),mho.forEach(t),DRr=i(de),bM=n(de,"LI",{});var gho=s(bM);kSe=n(gho,"STRONG",{});var CVa=s(kSe);qRr=r(CVa,"xglm"),CVa.forEach(t),jRr=r(gho," \u2014 "),vse=n(gho,"A",{href:!0});var wVa=s(vse);ORr=r(wVa,"FlaxXGLMModel"),wVa.forEach(t),VRr=r(gho," (XGLM model)"),gho.forEach(t),XRr=i(de),TM=n(de,"LI",{});var hho=s(TM);RSe=n(hho,"STRONG",{});var AVa=s(RSe);zRr=r(AVa,"xlm-roberta"),AVa.forEach(t),WRr=r(hho," \u2014 "),bse=n(hho,"A",{href:!0});var LVa=s(bse);QRr=r(LVa,"FlaxXLMRobertaModel"),LVa.forEach(t),URr=r(hho," (XLM-RoBERTa model)"),hho.forEach(t),de.forEach(t),HRr=i(Pi),T(FM.$$.fragment,Pi),Pi.forEach(t),Ri.forEach(t),dCo=i(f),Dc=n(f,"H2",{class:!0});var J0o=s(Dc);MM=n(J0o,"A",{id:!0,class:!0,href:!0});var yVa=s(MM);PSe=n(yVa,"SPAN",{});var xVa=s(PSe);T(eG.$$.fragment,xVa),xVa.forEach(t),yVa.forEach(t),JRr=i(J0o),ISe=n(J0o,"SPAN",{});var SVa=s(ISe);ZRr=r(SVa,"Generic pretraining classes"),SVa.forEach(t),J0o.forEach(t),cCo=i(f),Tse=n(f,"P",{});var $Va=s(Tse);YRr=r($Va,"The following auto classes are available for instantiating a model with a pretraining head."),$Va.forEach(t),fCo=i(f),qc=n(f,"H3",{class:!0});var Z0o=s(qc);EM=n(Z0o,"A",{id:!0,class:!0,href:!0});var kVa=s(EM);NSe=n(kVa,"SPAN",{});var RVa=s(NSe);T(oG.$$.fragment,RVa),RVa.forEach(t),kVa.forEach(t),KRr=i(Z0o),BSe=n(Z0o,"SPAN",{});var PVa=s(BSe);ePr=r(PVa,"AutoModelForPreTraining"),PVa.forEach(t),Z0o.forEach(t),mCo=i(f),Wo=n(f,"DIV",{class:!0});var Ii=s(Wo);T(rG.$$.fragment,Ii),oPr=i(Ii),jc=n(Ii,"P",{});var Q7e=s(jc);rPr=r(Q7e,`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 `),Fse=n(Q7e,"A",{href:!0});var IVa=s(Fse);tPr=r(IVa,"from_pretrained()"),IVa.forEach(t),aPr=r(Q7e," class method or the "),Mse=n(Q7e,"A",{href:!0});var NVa=s(Mse);nPr=r(NVa,"from_config()"),NVa.forEach(t),sPr=r(Q7e,` class
method.`),Q7e.forEach(t),lPr=i(Ii),tG=n(Ii,"P",{});var Y0o=s(tG);iPr=r(Y0o,"This class cannot be instantiated directly using "),GSe=n(Y0o,"CODE",{});var BVa=s(GSe);dPr=r(BVa,"__init__()"),BVa.forEach(t),cPr=r(Y0o," (throws an error)."),Y0o.forEach(t),fPr=i(Ii),Dt=n(Ii,"DIV",{class:!0});var QP=s(Dt);T(aG.$$.fragment,QP),mPr=i(QP),DSe=n(QP,"P",{});var GVa=s(DSe);gPr=r(GVa,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),GVa.forEach(t),hPr=i(QP),Oc=n(QP,"P",{});var U7e=s(Oc);uPr=r(U7e,`Note:
Loading a model from its configuration file does `),qSe=n(U7e,"STRONG",{});var DVa=s(qSe);pPr=r(DVa,"not"),DVa.forEach(t),_Pr=r(U7e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ese=n(U7e,"A",{href:!0});var qVa=s(Ese);vPr=r(qVa,"from_pretrained()"),qVa.forEach(t),bPr=r(U7e," to load the model weights."),U7e.forEach(t),TPr=i(QP),T(CM.$$.fragment,QP),QP.forEach(t),FPr=i(Ii),lo=n(Ii,"DIV",{class:!0});var Qa=s(lo);T(nG.$$.fragment,Qa),MPr=i(Qa),jSe=n(Qa,"P",{});var jVa=s(jSe);EPr=r(jVa,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),jVa.forEach(t),CPr=i(Qa),Rn=n(Qa,"P",{});var UP=s(Rn);wPr=r(UP,"The model class to instantiate is selected based on the "),OSe=n(UP,"CODE",{});var OVa=s(OSe);APr=r(OVa,"model_type"),OVa.forEach(t),LPr=r(UP,` property of the config object (either
passed as an argument or loaded from `),VSe=n(UP,"CODE",{});var VVa=s(VSe);yPr=r(VVa,"pretrained_model_name_or_path"),VVa.forEach(t),xPr=r(UP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),XSe=n(UP,"CODE",{});var XVa=s(XSe);SPr=r(XVa,"pretrained_model_name_or_path"),XVa.forEach(t),$Pr=r(UP,":"),UP.forEach(t),kPr=i(Qa),z=n(Qa,"UL",{});var W=s(z);wM=n(W,"LI",{});var uho=s(wM);zSe=n(uho,"STRONG",{});var zVa=s(zSe);RPr=r(zVa,"albert"),zVa.forEach(t),PPr=r(uho," \u2014 "),Cse=n(uho,"A",{href:!0});var WVa=s(Cse);IPr=r(WVa,"AlbertForPreTraining"),WVa.forEach(t),NPr=r(uho," (ALBERT model)"),uho.forEach(t),BPr=i(W),AM=n(W,"LI",{});var pho=s(AM);WSe=n(pho,"STRONG",{});var QVa=s(WSe);GPr=r(QVa,"bart"),QVa.forEach(t),DPr=r(pho," \u2014 "),wse=n(pho,"A",{href:!0});var UVa=s(wse);qPr=r(UVa,"BartForConditionalGeneration"),UVa.forEach(t),jPr=r(pho," (BART model)"),pho.forEach(t),OPr=i(W),LM=n(W,"LI",{});var _ho=s(LM);QSe=n(_ho,"STRONG",{});var HVa=s(QSe);VPr=r(HVa,"bert"),HVa.forEach(t),XPr=r(_ho," \u2014 "),Ase=n(_ho,"A",{href:!0});var JVa=s(Ase);zPr=r(JVa,"BertForPreTraining"),JVa.forEach(t),WPr=r(_ho," (BERT 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falling back to using pattern matching on `),rke=n(JP,"CODE",{});var lWa=s(rke);Tqr=r(lWa,"pretrained_model_name_or_path"),lWa.forEach(t),Fqr=r(JP,":"),JP.forEach(t),Mqr=i(Bi),me=n(Bi,"UL",{});var ge=s(me);SE=n(ge,"LI",{});var _uo=s(SE);tke=n(_uo,"STRONG",{});var iWa=s(tke);Eqr=r(iWa,"albert"),iWa.forEach(t),Cqr=r(_uo," \u2014 "),yle=n(_uo,"A",{href:!0});var dWa=s(yle);wqr=r(dWa,"TFAlbertForPreTraining"),dWa.forEach(t),Aqr=r(_uo," (ALBERT model)"),_uo.forEach(t),Lqr=i(ge),$E=n(ge,"LI",{});var vuo=s($E);ake=n(vuo,"STRONG",{});var cWa=s(ake);yqr=r(cWa,"bart"),cWa.forEach(t),xqr=r(vuo," \u2014 "),xle=n(vuo,"A",{href:!0});var fWa=s(xle);Sqr=r(fWa,"TFBartForConditionalGeneration"),fWa.forEach(t),$qr=r(vuo," (BART model)"),vuo.forEach(t),kqr=i(ge),kE=n(ge,"LI",{});var buo=s(kE);nke=n(buo,"STRONG",{});var mWa=s(nke);Rqr=r(mWa,"bert"),mWa.forEach(t),Pqr=r(buo," \u2014 "),Sle=n(buo,"A",{href:!0});var gWa=s(Sle);Iqr=r(gWa,"TFBertForPreTraining"),gWa.forEach(t),Nqr=r(buo," (BERT 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method.`),Z7e.forEach(t),UOr=i(Gi),gG=n(Gi,"P",{});var rwo=s(gG);HOr=r(rwo,"This class cannot be instantiated directly using "),Ske=n(rwo,"CODE",{});var dQa=s(Ske);JOr=r(dQa,"__init__()"),dQa.forEach(t),ZOr=r(rwo," (throws an error)."),rwo.forEach(t),YOr=i(Gi),jt=n(Gi,"DIV",{class:!0});var ZP=s(jt);T(hG.$$.fragment,ZP),KOr=i(ZP),$ke=n(ZP,"P",{});var cQa=s($ke);eVr=r(cQa,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),cQa.forEach(t),oVr=i(ZP),Uc=n(ZP,"P",{});var Y7e=s(Uc);rVr=r(Y7e,`Note:
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model\u2019s configuration. Use `),oie=n(Y7e,"A",{href:!0});var mQa=s(oie);nVr=r(mQa,"from_pretrained()"),mQa.forEach(t),sVr=r(Y7e," to load the model weights."),Y7e.forEach(t),lVr=i(ZP),T(t3.$$.fragment,ZP),ZP.forEach(t),iVr=i(Gi),rt=n(Gi,"DIV",{class:!0});var Di=s(rt);T(uG.$$.fragment,Di),dVr=i(Di),Rke=n(Di,"P",{});var gQa=s(Rke);cVr=r(gQa,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),gQa.forEach(t),fVr=i(Di),In=n(Di,"P",{});var YP=s(In);mVr=r(YP,"The model class to instantiate is selected based on the "),Pke=n(YP,"CODE",{});var hQa=s(Pke);gVr=r(hQa,"model_type"),hQa.forEach(t),hVr=r(YP,` property of the config object (either
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tasks."),JQa.forEach(t),bCo=i(f),Jc=n(f,"H3",{class:!0});var awo=s(Jc);F3=n(awo,"A",{id:!0,class:!0,href:!0});var ZQa=s(F3);eRe=n(ZQa,"SPAN",{});var YQa=s(eRe);T(_G.$$.fragment,YQa),YQa.forEach(t),ZQa.forEach(t),XXr=i(awo),oRe=n(awo,"SPAN",{});var KQa=s(oRe);zXr=r(KQa,"AutoModelForCausalLM"),KQa.forEach(t),awo.forEach(t),TCo=i(f),Ho=n(f,"DIV",{class:!0});var qi=s(Ho);T(vG.$$.fragment,qi),WXr=i(qi),Zc=n(qi,"P",{});var K7e=s(Zc);QXr=r(K7e,`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 `),vie=n(K7e,"A",{href:!0});var eUa=s(vie);UXr=r(eUa,"from_pretrained()"),eUa.forEach(t),HXr=r(K7e," class method or the "),bie=n(K7e,"A",{href:!0});var oUa=s(bie);JXr=r(oUa,"from_config()"),oUa.forEach(t),ZXr=r(K7e,` class
method.`),K7e.forEach(t),YXr=i(qi),bG=n(qi,"P",{});var nwo=s(bG);KXr=r(nwo,"This class cannot be instantiated directly using "),rRe=n(nwo,"CODE",{});var rUa=s(rRe);ezr=r(rUa,"__init__()"),rUa.forEach(t),ozr=r(nwo," (throws an error)."),nwo.forEach(t),rzr=i(qi),Ot=n(qi,"DIV",{class:!0});var KP=s(Ot);T(TG.$$.fragment,KP),tzr=i(KP),tRe=n(KP,"P",{});var tUa=s(tRe);azr=r(tUa,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),tUa.forEach(t),nzr=i(KP),Yc=n(KP,"P",{});var eTe=s(Yc);szr=r(eTe,`Note:
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model\u2019s configuration. Use `),Tie=n(eTe,"A",{href:!0});var nUa=s(Tie);dzr=r(nUa,"from_pretrained()"),nUa.forEach(t),czr=r(eTe," to load the model weights."),eTe.forEach(t),fzr=i(KP),T(M3.$$.fragment,KP),KP.forEach(t),mzr=i(qi),io=n(qi,"DIV",{class:!0});var Ua=s(io);T(FG.$$.fragment,Ua),gzr=i(Ua),nRe=n(Ua,"P",{});var sUa=s(nRe);hzr=r(sUa,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),sUa.forEach(t),uzr=i(Ua),Nn=n(Ua,"P",{});var eI=s(Nn);pzr=r(eI,"The model class to instantiate is selected based on the "),sRe=n(eI,"CODE",{});var lUa=s(sRe);_zr=r(lUa,"model_type"),lUa.forEach(t),vzr=r(eI,` property of the config object (either
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falling back to using pattern matching on `),iRe=n(eI,"CODE",{});var dUa=s(iRe);Fzr=r(dUa,"pretrained_model_name_or_path"),dUa.forEach(t),Mzr=r(eI,":"),eI.forEach(t),Ezr=i(Ua),H=n(Ua,"UL",{});var J=s(H);E3=n(J,"LI",{});var tpo=s(E3);dRe=n(tpo,"STRONG",{});var cUa=s(dRe);Czr=r(cUa,"bart"),cUa.forEach(t),wzr=r(tpo," \u2014 "),Fie=n(tpo,"A",{href:!0});var fUa=s(Fie);Azr=r(fUa,"BartForCausalLM"),fUa.forEach(t),Lzr=r(tpo," (BART model)"),tpo.forEach(t),yzr=i(J),C3=n(J,"LI",{});var apo=s(C3);cRe=n(apo,"STRONG",{});var mUa=s(cRe);xzr=r(mUa,"bert"),mUa.forEach(t),Szr=r(apo," \u2014 "),Mie=n(apo,"A",{href:!0});var gUa=s(Mie);$zr=r(gUa,"BertLMHeadModel"),gUa.forEach(t),kzr=r(apo," (BERT model)"),apo.forEach(t),Rzr=i(J),w3=n(J,"LI",{});var npo=s(w3);fRe=n(npo,"STRONG",{});var hUa=s(fRe);Pzr=r(hUa,"bert-generation"),hUa.forEach(t),Izr=r(npo," \u2014 "),Eie=n(npo,"A",{href:!0});var uUa=s(Eie);Nzr=r(uUa,"BertGenerationDecoder"),uUa.forEach(t),Bzr=r(npo," (Bert Generation 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BUa=s(Pie);BWr=r(BUa,"ElectraForCausalLM"),BUa.forEach(t),GWr=r(ppo," (ELECTRA model)"),ppo.forEach(t),DWr=i(J),B3=n(J,"LI",{});var _po=s(B3);ERe=n(_po,"STRONG",{});var GUa=s(ERe);qWr=r(GUa,"ernie"),GUa.forEach(t),jWr=r(_po," \u2014 "),Iie=n(_po,"A",{href:!0});var DUa=s(Iie);OWr=r(DUa,"ErnieForCausalLM"),DUa.forEach(t),VWr=r(_po," (ERNIE model)"),_po.forEach(t),XWr=i(J),G3=n(J,"LI",{});var vpo=s(G3);CRe=n(vpo,"STRONG",{});var qUa=s(CRe);zWr=r(qUa,"git"),qUa.forEach(t),WWr=r(vpo," \u2014 "),Nie=n(vpo,"A",{href:!0});var jUa=s(Nie);QWr=r(jUa,"GitForCausalLM"),jUa.forEach(t),UWr=r(vpo," (GIT model)"),vpo.forEach(t),HWr=i(J),D3=n(J,"LI",{});var bpo=s(D3);wRe=n(bpo,"STRONG",{});var OUa=s(wRe);JWr=r(OUa,"gpt-sw3"),OUa.forEach(t),ZWr=r(bpo," \u2014 "),Bie=n(bpo,"A",{href:!0});var VUa=s(Bie);YWr=r(VUa,"GPT2LMHeadModel"),VUa.forEach(t),KWr=r(bpo," (GPT-Sw3 model)"),bpo.forEach(t),eQr=i(J),q3=n(J,"LI",{});var Tpo=s(q3);ARe=n(Tpo,"STRONG",{});var 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with the `),Ide=n(tTe,"A",{href:!0});var JJa=s(Ide);hYr=r(JJa,"from_pretrained()"),JJa.forEach(t),uYr=r(tTe," class method or the "),Nde=n(tTe,"A",{href:!0});var ZJa=s(Nde);pYr=r(ZJa,"from_config()"),ZJa.forEach(t),_Yr=r(tTe,` class
method.`),tTe.forEach(t),vYr=i(Vi),xG=n(Vi,"P",{});var dwo=s(xG);bYr=r(dwo,"This class cannot be instantiated directly using "),RPe=n(dwo,"CODE",{});var YJa=s(RPe);TYr=r(YJa,"__init__()"),YJa.forEach(t),FYr=r(dwo," (throws an error)."),dwo.forEach(t),MYr=i(Vi),Xt=n(Vi,"DIV",{class:!0});var tI=s(Xt);T(SG.$$.fragment,tI),EYr=i(tI),PPe=n(tI,"P",{});var KJa=s(PPe);CYr=r(KJa,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),KJa.forEach(t),wYr=i(tI),af=n(tI,"P",{});var aTe=s(af);AYr=r(aTe,`Note:
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model\u2019s configuration. Use `),Bde=n(aTe,"A",{href:!0});var oZa=s(Bde);xYr=r(oZa,"from_pretrained()"),oZa.forEach(t),SYr=r(aTe," to load the model weights."),aTe.forEach(t),$Yr=i(tI),T(qC.$$.fragment,tI),tI.forEach(t),kYr=i(Vi),at=n(Vi,"DIV",{class:!0});var Xi=s(at);T($G.$$.fragment,Xi),RYr=i(Xi),NPe=n(Xi,"P",{});var rZa=s(NPe);PYr=r(rZa,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),rZa.forEach(t),IYr=i(Xi),Gn=n(Xi,"P",{});var aI=s(Gn);NYr=r(aI,"The model class to instantiate is selected based on the "),BPe=n(aI,"CODE",{});var tZa=s(BPe);BYr=r(tZa,"model_type"),tZa.forEach(t),GYr=r(aI,` property of the config object (either
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falling back to using pattern matching on `),DPe=n(aI,"CODE",{});var nZa=s(DPe);jYr=r(nZa,"pretrained_model_name_or_path"),nZa.forEach(t),OYr=r(aI,":"),aI.forEach(t),VYr=i(Xi),ye=n(Xi,"UL",{});var xe=s(ye);jC=n(xe,"LI",{});var u_o=s(jC);qPe=n(u_o,"STRONG",{});var sZa=s(qPe);XYr=r(sZa,"bart"),sZa.forEach(t),zYr=r(u_o," \u2014 "),Gde=n(u_o,"A",{href:!0});var lZa=s(Gde);WYr=r(lZa,"FlaxBartForCausalLM"),lZa.forEach(t),QYr=r(u_o," (BART model)"),u_o.forEach(t),UYr=i(xe),OC=n(xe,"LI",{});var p_o=s(OC);jPe=n(p_o,"STRONG",{});var iZa=s(jPe);HYr=r(iZa,"bert"),iZa.forEach(t),JYr=r(p_o," \u2014 "),Dde=n(p_o,"A",{href:!0});var dZa=s(Dde);ZYr=r(dZa,"FlaxBertForCausalLM"),dZa.forEach(t),YYr=r(p_o," (BERT model)"),p_o.forEach(t),KYr=i(xe),VC=n(xe,"LI",{});var __o=s(VC);OPe=n(__o,"STRONG",{});var cZa=s(OPe);eKr=r(cZa,"big_bird"),cZa.forEach(t),oKr=r(__o," \u2014 "),qde=n(__o,"A",{href:!0});var fZa=s(qde);rKr=r(fZa,"FlaxBigBirdForCausalLM"),fZa.forEach(t),tKr=r(__o," (BigBird model)"),__o.forEach(t),aKr=i(xe),XC=n(xe,"LI",{});var v_o=s(XC);VPe=n(v_o,"STRONG",{});var mZa=s(VPe);nKr=r(mZa,"electra"),mZa.forEach(t),sKr=r(v_o," \u2014 "),jde=n(v_o,"A",{href:!0});var gZa=s(jde);lKr=r(gZa,"FlaxElectraForCausalLM"),gZa.forEach(t),iKr=r(v_o," (ELECTRA model)"),v_o.forEach(t),dKr=i(xe),zC=n(xe,"LI",{});var b_o=s(zC);XPe=n(b_o,"STRONG",{});var hZa=s(XPe);cKr=r(hZa,"gpt-sw3"),hZa.forEach(t),fKr=r(b_o," \u2014 "),Ode=n(b_o,"A",{href:!0});var uZa=s(Ode);mKr=r(uZa,"FlaxGPT2LMHeadModel"),uZa.forEach(t),gKr=r(b_o," (GPT-Sw3 model)"),b_o.forEach(t),hKr=i(xe),WC=n(xe,"LI",{});var T_o=s(WC);zPe=n(T_o,"STRONG",{});var pZa=s(zPe);uKr=r(pZa,"gpt2"),pZa.forEach(t),pKr=r(T_o," \u2014 "),Vde=n(T_o,"A",{href:!0});var _Za=s(Vde);_Kr=r(_Za,"FlaxGPT2LMHeadModel"),_Za.forEach(t),vKr=r(T_o," (OpenAI GPT-2 model)"),T_o.forEach(t),bKr=i(xe),QC=n(xe,"LI",{});var F_o=s(QC);WPe=n(F_o,"STRONG",{});var vZa=s(WPe);TKr=r(vZa,"gpt_neo"),vZa.forEach(t),FKr=r(F_o," \u2014 "),Xde=n(F_o,"A",{href:!0});var bZa=s(Xde);MKr=r(bZa,"FlaxGPTNeoForCausalLM"),bZa.forEach(t),EKr=r(F_o," (GPT Neo model)"),F_o.forEach(t),CKr=i(xe),UC=n(xe,"LI",{});var M_o=s(UC);QPe=n(M_o,"STRONG",{});var TZa=s(QPe);wKr=r(TZa,"gptj"),TZa.forEach(t),AKr=r(M_o," \u2014 "),zde=n(M_o,"A",{href:!0});var FZa=s(zde);LKr=r(FZa,"FlaxGPTJForCausalLM"),FZa.forEach(t),yKr=r(M_o," (GPT-J model)"),M_o.forEach(t),xKr=i(xe),HC=n(xe,"LI",{});var E_o=s(HC);UPe=n(E_o,"STRONG",{});var MZa=s(UPe);SKr=r(MZa,"opt"),MZa.forEach(t),$Kr=r(E_o," \u2014 "),Wde=n(E_o,"A",{href:!0});var EZa=s(Wde);kKr=r(EZa,"FlaxOPTForCausalLM"),EZa.forEach(t),RKr=r(E_o," (OPT model)"),E_o.forEach(t),PKr=i(xe),JC=n(xe,"LI",{});var C_o=s(JC);HPe=n(C_o,"STRONG",{});var CZa=s(HPe);IKr=r(CZa,"roberta"),CZa.forEach(t),NKr=r(C_o," \u2014 "),Qde=n(C_o,"A",{href:!0});var wZa=s(Qde);BKr=r(wZa,"FlaxRobertaForCausalLM"),wZa.forEach(t),GKr=r(C_o," (RoBERTa model)"),C_o.forEach(t),DKr=i(xe),ZC=n(xe,"LI",{});var w_o=s(ZC);JPe=n(w_o,"STRONG",{});var AZa=s(JPe);qKr=r(AZa,"roberta-prelayernorm"),AZa.forEach(t),jKr=r(w_o," \u2014 "),Ude=n(w_o,"A",{href:!0});var LZa=s(Ude);OKr=r(LZa,"FlaxRobertaPreLayerNormForCausalLM"),LZa.forEach(t),VKr=r(w_o," (RoBERTa-PreLayerNorm model)"),w_o.forEach(t),XKr=i(xe),YC=n(xe,"LI",{});var A_o=s(YC);ZPe=n(A_o,"STRONG",{});var yZa=s(ZPe);zKr=r(yZa,"xglm"),yZa.forEach(t),WKr=r(A_o," \u2014 "),Hde=n(A_o,"A",{href:!0});var xZa=s(Hde);QKr=r(xZa,"FlaxXGLMForCausalLM"),xZa.forEach(t),UKr=r(A_o," (XGLM model)"),A_o.forEach(t),HKr=i(xe),KC=n(xe,"LI",{});var L_o=s(KC);YPe=n(L_o,"STRONG",{});var SZa=s(YPe);JKr=r(SZa,"xlm-roberta"),SZa.forEach(t),ZKr=r(L_o," \u2014 "),Jde=n(L_o,"A",{href:!0});var $Za=s(Jde);YKr=r($Za,"FlaxXLMRobertaForCausalLM"),$Za.forEach(t),KKr=r(L_o," (XLM-RoBERTa model)"),L_o.forEach(t),xe.forEach(t),eet=i(Xi),T(e5.$$.fragment,Xi),Xi.forEach(t),Vi.forEach(t),wCo=i(f),nf=n(f,"H3",{class:!0});var cwo=s(nf);o5=n(cwo,"A",{id:!0,class:!0,href:!0});var kZa=s(o5);KPe=n(kZa,"SPAN",{});var RZa=s(KPe);T(kG.$$.fragment,RZa),RZa.forEach(t),kZa.forEach(t),oet=i(cwo),eIe=n(cwo,"SPAN",{});var PZa=s(eIe);ret=r(PZa,"AutoModelForMaskedLM"),PZa.forEach(t),cwo.forEach(t),ACo=i(f),Yo=n(f,"DIV",{class:!0});var zi=s(Yo);T(RG.$$.fragment,zi),tet=i(zi),sf=n(zi,"P",{});var nTe=s(sf);aet=r(nTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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model\u2019s configuration. Use `),Kde=n(sTe,"A",{href:!0});var qZa=s(Kde);Fet=r(qZa,"from_pretrained()"),qZa.forEach(t),Met=r(sTe," to load the model weights."),sTe.forEach(t),Eet=i(nI),T(r5.$$.fragment,nI),nI.forEach(t),Cet=i(zi),co=n(zi,"DIV",{class:!0});var Ha=s(co);T(NG.$$.fragment,Ha),wet=i(Ha),aIe=n(Ha,"P",{});var jZa=s(aIe);Aet=r(jZa,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),jZa.forEach(t),Let=i(Ha),Dn=n(Ha,"P",{});var sI=s(Dn);yet=r(sI,"The model class to instantiate is selected based on the "),nIe=n(sI,"CODE",{});var OZa=s(nIe);xet=r(OZa,"model_type"),OZa.forEach(t),Set=r(sI,` property of the config object (either
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model\u2019s configuration. Use `),Xce=n(iTe,"A",{href:!0});var RKa=s(Xce);nnt=r(RKa,"from_pretrained()"),RKa.forEach(t),snt=r(iTe," to load the model weights."),iTe.forEach(t),lnt=i(lI),T(H5.$$.fragment,lI),lI.forEach(t),int=i(Wi),nt=n(Wi,"DIV",{class:!0});var Qi=s(nt);T(jG.$$.fragment,Qi),dnt=i(Qi),nNe=n(Qi,"P",{});var PKa=s(nNe);cnt=r(PKa,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),PKa.forEach(t),fnt=i(Qi),qn=n(Qi,"P",{});var iI=s(qn);mnt=r(iI,"The model class to instantiate is selected based on the "),sNe=n(iI,"CODE",{});var IKa=s(sNe);gnt=r(IKa,"model_type"),IKa.forEach(t),hnt=r(iI,` property of the config object (either
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falling back to using pattern matching on `),iNe=n(iI,"CODE",{});var BKa=s(iNe);_nt=r(BKa,"pretrained_model_name_or_path"),BKa.forEach(t),vnt=r(iI,":"),iI.forEach(t),bnt=i(Qi),ue=n(Qi,"UL",{});var be=s(ue);J5=n(be,"LI",{});var v1o=s(J5);dNe=n(v1o,"STRONG",{});var GKa=s(dNe);Tnt=r(GKa,"albert"),GKa.forEach(t),Fnt=r(v1o," \u2014 "),zce=n(v1o,"A",{href:!0});var DKa=s(zce);Mnt=r(DKa,"TFAlbertForMaskedLM"),DKa.forEach(t),Ent=r(v1o," (ALBERT model)"),v1o.forEach(t),Cnt=i(be),Z5=n(be,"LI",{});var b1o=s(Z5);cNe=n(b1o,"STRONG",{});var qKa=s(cNe);wnt=r(qKa,"bert"),qKa.forEach(t),Ant=r(b1o," \u2014 "),Wce=n(b1o,"A",{href:!0});var jKa=s(Wce);Lnt=r(jKa,"TFBertForMaskedLM"),jKa.forEach(t),ynt=r(b1o," (BERT model)"),b1o.forEach(t),xnt=i(be),Y5=n(be,"LI",{});var T1o=s(Y5);fNe=n(T1o,"STRONG",{});var OKa=s(fNe);Snt=r(OKa,"camembert"),OKa.forEach(t),$nt=r(T1o," \u2014 "),Qce=n(T1o,"A",{href:!0});var VKa=s(Qce);knt=r(VKa,"TFCamembertForMaskedLM"),VKa.forEach(t),Rnt=r(T1o," (CamemBERT 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y1o=s(s0);bNe=n(y1o,"STRONG",{});var aen=s(bNe);pst=r(aen,"funnel"),aen.forEach(t),_st=r(y1o," \u2014 "),ofe=n(y1o,"A",{href:!0});var nen=s(ofe);vst=r(nen,"TFFunnelForMaskedLM"),nen.forEach(t),bst=r(y1o," (Funnel Transformer model)"),y1o.forEach(t),Tst=i(be),l0=n(be,"LI",{});var x1o=s(l0);TNe=n(x1o,"STRONG",{});var sen=s(TNe);Fst=r(sen,"layoutlm"),sen.forEach(t),Mst=r(x1o," \u2014 "),rfe=n(x1o,"A",{href:!0});var len=s(rfe);Est=r(len,"TFLayoutLMForMaskedLM"),len.forEach(t),Cst=r(x1o," (LayoutLM model)"),x1o.forEach(t),wst=i(be),i0=n(be,"LI",{});var S1o=s(i0);FNe=n(S1o,"STRONG",{});var ien=s(FNe);Ast=r(ien,"longformer"),ien.forEach(t),Lst=r(S1o," \u2014 "),tfe=n(S1o,"A",{href:!0});var den=s(tfe);yst=r(den,"TFLongformerForMaskedLM"),den.forEach(t),xst=r(S1o," (Longformer model)"),S1o.forEach(t),Sst=i(be),d0=n(be,"LI",{});var $1o=s(d0);MNe=n($1o,"STRONG",{});var cen=s(MNe);$st=r(cen,"mobilebert"),cen.forEach(t),kst=r($1o," \u2014 "),afe=n($1o,"A",{href:!0});var 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ven=s(ANe);Zst=r(ven,"roberta-prelayernorm"),ven.forEach(t),Yst=r(I1o," \u2014 "),ife=n(I1o,"A",{href:!0});var ben=s(ife);Kst=r(ben,"TFRobertaPreLayerNormForMaskedLM"),ben.forEach(t),elt=r(I1o," (RoBERTa-PreLayerNorm model)"),I1o.forEach(t),olt=i(be),h0=n(be,"LI",{});var N1o=s(h0);LNe=n(N1o,"STRONG",{});var Ten=s(LNe);rlt=r(Ten,"roformer"),Ten.forEach(t),tlt=r(N1o," \u2014 "),dfe=n(N1o,"A",{href:!0});var Fen=s(dfe);alt=r(Fen,"TFRoFormerForMaskedLM"),Fen.forEach(t),nlt=r(N1o," (RoFormer model)"),N1o.forEach(t),slt=i(be),u0=n(be,"LI",{});var B1o=s(u0);yNe=n(B1o,"STRONG",{});var Men=s(yNe);llt=r(Men,"tapas"),Men.forEach(t),ilt=r(B1o," \u2014 "),cfe=n(B1o,"A",{href:!0});var Een=s(cfe);dlt=r(Een,"TFTapasForMaskedLM"),Een.forEach(t),clt=r(B1o," (TAPAS model)"),B1o.forEach(t),flt=i(be),p0=n(be,"LI",{});var G1o=s(p0);xNe=n(G1o,"STRONG",{});var Cen=s(xNe);mlt=r(Cen,"xlm"),Cen.forEach(t),glt=r(G1o," \u2014 "),ffe=n(G1o,"A",{href:!0});var wen=s(ffe);hlt=r(wen,"TFXLMWithLMHeadModel"),wen.forEach(t),ult=r(G1o," (XLM model)"),G1o.forEach(t),plt=i(be),_0=n(be,"LI",{});var D1o=s(_0);SNe=n(D1o,"STRONG",{});var Aen=s(SNe);_lt=r(Aen,"xlm-roberta"),Aen.forEach(t),vlt=r(D1o," \u2014 "),mfe=n(D1o,"A",{href:!0});var Len=s(mfe);blt=r(Len,"TFXLMRobertaForMaskedLM"),Len.forEach(t),Tlt=r(D1o," (XLM-RoBERTa model)"),D1o.forEach(t),be.forEach(t),Flt=i(Qi),T(v0.$$.fragment,Qi),Qi.forEach(t),Wi.forEach(t),xCo=i(f),mf=n(f,"H3",{class:!0});var hwo=s(mf);b0=n(hwo,"A",{id:!0,class:!0,href:!0});var yen=s(b0);$Ne=n(yen,"SPAN",{});var xen=s($Ne);T(OG.$$.fragment,xen),xen.forEach(t),yen.forEach(t),Mlt=i(hwo),kNe=n(hwo,"SPAN",{});var Sen=s(kNe);Elt=r(Sen,"FlaxAutoModelForMaskedLM"),Sen.forEach(t),hwo.forEach(t),SCo=i(f),er=n(f,"DIV",{class:!0});var Ui=s(er);T(VG.$$.fragment,Ui),Clt=i(Ui),gf=n(Ui,"P",{});var dTe=s(gf);wlt=r(dTe,`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 `),gfe=n(dTe,"A",{href:!0});var $en=s(gfe);Alt=r($en,"from_pretrained()"),$en.forEach(t),Llt=r(dTe," class method or the "),hfe=n(dTe,"A",{href:!0});var ken=s(hfe);ylt=r(ken,"from_config()"),ken.forEach(t),xlt=r(dTe,` class
method.`),dTe.forEach(t),Slt=i(Ui),XG=n(Ui,"P",{});var uwo=s(XG);$lt=r(uwo,"This class cannot be instantiated directly using "),RNe=n(uwo,"CODE",{});var Ren=s(RNe);klt=r(Ren,"__init__()"),Ren.forEach(t),Rlt=r(uwo," (throws an error)."),uwo.forEach(t),Plt=i(Ui),Qt=n(Ui,"DIV",{class:!0});var dI=s(Qt);T(zG.$$.fragment,dI),Ilt=i(dI),PNe=n(dI,"P",{});var Pen=s(PNe);Nlt=r(Pen,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Pen.forEach(t),Blt=i(dI),hf=n(dI,"P",{});var cTe=s(hf);Glt=r(cTe,`Note:
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model\u2019s configuration. Use `),ufe=n(cTe,"A",{href:!0});var Nen=s(ufe);jlt=r(Nen,"from_pretrained()"),Nen.forEach(t),Olt=r(cTe," to load the model weights."),cTe.forEach(t),Vlt=i(dI),T(T0.$$.fragment,dI),dI.forEach(t),Xlt=i(Ui),st=n(Ui,"DIV",{class:!0});var Hi=s(st);T(WG.$$.fragment,Hi),zlt=i(Hi),NNe=n(Hi,"P",{});var Ben=s(NNe);Wlt=r(Ben,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Ben.forEach(t),Qlt=i(Hi),jn=n(Hi,"P",{});var cI=s(jn);Ult=r(cI,"The model class to instantiate is selected based on the "),BNe=n(cI,"CODE",{});var Gen=s(BNe);Hlt=r(Gen,"model_type"),Gen.forEach(t),Jlt=r(cI,` property of the config object (either
passed as an argument or loaded from `),GNe=n(cI,"CODE",{});var Den=s(GNe);Zlt=r(Den,"pretrained_model_name_or_path"),Den.forEach(t),Ylt=r(cI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DNe=n(cI,"CODE",{});var qen=s(DNe);Klt=r(qen,"pretrained_model_name_or_path"),qen.forEach(t),eit=r(cI,":"),cI.forEach(t),oit=i(Hi),Se=n(Hi,"UL",{});var De=s(Se);F0=n(De,"LI",{});var q1o=s(F0);qNe=n(q1o,"STRONG",{});var jen=s(qNe);rit=r(jen,"albert"),jen.forEach(t),tit=r(q1o," \u2014 "),pfe=n(q1o,"A",{href:!0});var Oen=s(pfe);ait=r(Oen,"FlaxAlbertForMaskedLM"),Oen.forEach(t),nit=r(q1o," (ALBERT model)"),q1o.forEach(t),sit=i(De),M0=n(De,"LI",{});var j1o=s(M0);jNe=n(j1o,"STRONG",{});var Ven=s(jNe);lit=r(Ven,"bart"),Ven.forEach(t),iit=r(j1o," \u2014 "),_fe=n(j1o,"A",{href:!0});var Xen=s(_fe);dit=r(Xen,"FlaxBartForConditionalGeneration"),Xen.forEach(t),cit=r(j1o," (BART model)"),j1o.forEach(t),fit=i(De),E0=n(De,"LI",{});var O1o=s(E0);ONe=n(O1o,"STRONG",{});var zen=s(ONe);mit=r(zen,"bert"),zen.forEach(t),git=r(O1o," \u2014 "),vfe=n(O1o,"A",{href:!0});var Wen=s(vfe);hit=r(Wen,"FlaxBertForMaskedLM"),Wen.forEach(t),uit=r(O1o," (BERT model)"),O1o.forEach(t),pit=i(De),C0=n(De,"LI",{});var V1o=s(C0);VNe=n(V1o,"STRONG",{});var Qen=s(VNe);_it=r(Qen,"big_bird"),Qen.forEach(t),vit=r(V1o," \u2014 "),bfe=n(V1o,"A",{href:!0});var Uen=s(bfe);bit=r(Uen,"FlaxBigBirdForMaskedLM"),Uen.forEach(t),Tit=r(V1o," (BigBird model)"),V1o.forEach(t),Fit=i(De),w0=n(De,"LI",{});var X1o=s(w0);XNe=n(X1o,"STRONG",{});var Hen=s(XNe);Mit=r(Hen,"distilbert"),Hen.forEach(t),Eit=r(X1o," \u2014 "),Tfe=n(X1o,"A",{href:!0});var Jen=s(Tfe);Cit=r(Jen,"FlaxDistilBertForMaskedLM"),Jen.forEach(t),wit=r(X1o," (DistilBERT model)"),X1o.forEach(t),Ait=i(De),A0=n(De,"LI",{});var z1o=s(A0);zNe=n(z1o,"STRONG",{});var Zen=s(zNe);Lit=r(Zen,"electra"),Zen.forEach(t),yit=r(z1o," \u2014 "),Ffe=n(z1o,"A",{href:!0});var Yen=s(Ffe);xit=r(Yen,"FlaxElectraForMaskedLM"),Yen.forEach(t),Sit=r(z1o," (ELECTRA model)"),z1o.forEach(t),$it=i(De),L0=n(De,"LI",{});var W1o=s(L0);WNe=n(W1o,"STRONG",{});var Ken=s(WNe);kit=r(Ken,"mbart"),Ken.forEach(t),Rit=r(W1o," \u2014 "),Mfe=n(W1o,"A",{href:!0});var eon=s(Mfe);Pit=r(eon,"FlaxMBartForConditionalGeneration"),eon.forEach(t),Iit=r(W1o," (mBART model)"),W1o.forEach(t),Nit=i(De),y0=n(De,"LI",{});var Q1o=s(y0);QNe=n(Q1o,"STRONG",{});var oon=s(QNe);Bit=r(oon,"roberta"),oon.forEach(t),Git=r(Q1o," \u2014 "),Efe=n(Q1o,"A",{href:!0});var ron=s(Efe);Dit=r(ron,"FlaxRobertaForMaskedLM"),ron.forEach(t),qit=r(Q1o," (RoBERTa model)"),Q1o.forEach(t),jit=i(De),x0=n(De,"LI",{});var U1o=s(x0);UNe=n(U1o,"STRONG",{});var ton=s(UNe);Oit=r(ton,"roberta-prelayernorm"),ton.forEach(t),Vit=r(U1o," \u2014 "),Cfe=n(U1o,"A",{href:!0});var aon=s(Cfe);Xit=r(aon,"FlaxRobertaPreLayerNormForMaskedLM"),aon.forEach(t),zit=r(U1o," (RoBERTa-PreLayerNorm model)"),U1o.forEach(t),Wit=i(De),S0=n(De,"LI",{});var H1o=s(S0);HNe=n(H1o,"STRONG",{});var non=s(HNe);Qit=r(non,"roformer"),non.forEach(t),Uit=r(H1o," \u2014 "),wfe=n(H1o,"A",{href:!0});var son=s(wfe);Hit=r(son,"FlaxRoFormerForMaskedLM"),son.forEach(t),Jit=r(H1o," (RoFormer model)"),H1o.forEach(t),Zit=i(De),$0=n(De,"LI",{});var J1o=s($0);JNe=n(J1o,"STRONG",{});var lon=s(JNe);Yit=r(lon,"xlm-roberta"),lon.forEach(t),Kit=r(J1o," \u2014 "),Afe=n(J1o,"A",{href:!0});var ion=s(Afe);edt=r(ion,"FlaxXLMRobertaForMaskedLM"),ion.forEach(t),odt=r(J1o," (XLM-RoBERTa model)"),J1o.forEach(t),De.forEach(t),rdt=i(Hi),T(k0.$$.fragment,Hi),Hi.forEach(t),Ui.forEach(t),$Co=i(f),uf=n(f,"H3",{class:!0});var pwo=s(uf);R0=n(pwo,"A",{id:!0,class:!0,href:!0});var don=s(R0);ZNe=n(don,"SPAN",{});var con=s(ZNe);T(QG.$$.fragment,con),con.forEach(t),don.forEach(t),tdt=i(pwo),YNe=n(pwo,"SPAN",{});var fon=s(YNe);adt=r(fon,"AutoModelForSeq2SeqLM"),fon.forEach(t),pwo.forEach(t),kCo=i(f),or=n(f,"DIV",{class:!0});var Ji=s(or);T(UG.$$.fragment,Ji),ndt=i(Ji),pf=n(Ji,"P",{});var fTe=s(pf);sdt=r(fTe,`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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model\u2019s configuration. Use `),xfe=n(mTe,"A",{href:!0});var _on=s(xfe);Mdt=r(_on,"from_pretrained()"),_on.forEach(t),Edt=r(mTe," to load the model weights."),mTe.forEach(t),Cdt=i(fI),T(P0.$$.fragment,fI),fI.forEach(t),wdt=i(Ji),fo=n(Ji,"DIV",{class:!0});var Ja=s(fo);T(ZG.$$.fragment,Ja),Adt=i(Ja),rBe=n(Ja,"P",{});var von=s(rBe);Ldt=r(von,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),von.forEach(t),ydt=i(Ja),On=n(Ja,"P",{});var mI=s(On);xdt=r(mI,"The model class to instantiate is selected based on the "),tBe=n(mI,"CODE",{});var bon=s(tBe);Sdt=r(bon,"model_type"),bon.forEach(t),$dt=r(mI,` property of the config object (either
passed as an argument or loaded from `),aBe=n(mI,"CODE",{});var Ton=s(aBe);kdt=r(Ton,"pretrained_model_name_or_path"),Ton.forEach(t),Rdt=r(mI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nBe=n(mI,"CODE",{});var Fon=s(nBe);Pdt=r(Fon,"pretrained_model_name_or_path"),Fon.forEach(t),Idt=r(mI,":"),mI.forEach(t),Ndt=i(Ja),ve=n(Ja,"UL",{});var Fe=s(ve);I0=n(Fe,"LI",{});var Z1o=s(I0);sBe=n(Z1o,"STRONG",{});var Mon=s(sBe);Bdt=r(Mon,"bart"),Mon.forEach(t),Gdt=r(Z1o," \u2014 "),Sfe=n(Z1o,"A",{href:!0});var Eon=s(Sfe);Ddt=r(Eon,"BartForConditionalGeneration"),Eon.forEach(t),qdt=r(Z1o," (BART model)"),Z1o.forEach(t),jdt=i(Fe),N0=n(Fe,"LI",{});var Y1o=s(N0);lBe=n(Y1o,"STRONG",{});var Con=s(lBe);Odt=r(Con,"bigbird_pegasus"),Con.forEach(t),Vdt=r(Y1o," \u2014 "),$fe=n(Y1o,"A",{href:!0});var won=s($fe);Xdt=r(won,"BigBirdPegasusForConditionalGeneration"),won.forEach(t),zdt=r(Y1o," (BigBird-Pegasus model)"),Y1o.forEach(t),Wdt=i(Fe),B0=n(Fe,"LI",{});var K1o=s(B0);iBe=n(K1o,"STRONG",{});var Aon=s(iBe);Qdt=r(Aon,"blenderbot"),Aon.forEach(t),Udt=r(K1o," \u2014 "),kfe=n(K1o,"A",{href:!0});var Lon=s(kfe);Hdt=r(Lon,"BlenderbotForConditionalGeneration"),Lon.forEach(t),Jdt=r(K1o," (Blenderbot model)"),K1o.forEach(t),Zdt=i(Fe),G0=n(Fe,"LI",{});var e2o=s(G0);dBe=n(e2o,"STRONG",{});var yon=s(dBe);Ydt=r(yon,"blenderbot-small"),yon.forEach(t),Kdt=r(e2o," \u2014 "),Rfe=n(e2o,"A",{href:!0});var xon=s(Rfe);ect=r(xon,"BlenderbotSmallForConditionalGeneration"),xon.forEach(t),oct=r(e2o," (BlenderbotSmall model)"),e2o.forEach(t),rct=i(Fe),D0=n(Fe,"LI",{});var o2o=s(D0);cBe=n(o2o,"STRONG",{});var Son=s(cBe);tct=r(Son,"encoder-decoder"),Son.forEach(t),act=r(o2o," \u2014 "),Pfe=n(o2o,"A",{href:!0});var $on=s(Pfe);nct=r($on,"EncoderDecoderModel"),$on.forEach(t),sct=r(o2o," (Encoder decoder model)"),o2o.forEach(t),lct=i(Fe),q0=n(Fe,"LI",{});var r2o=s(q0);fBe=n(r2o,"STRONG",{});var kon=s(fBe);ict=r(kon,"fsmt"),kon.forEach(t),dct=r(r2o," \u2014 "),Ife=n(r2o,"A",{href:!0});var Ron=s(Ife);cct=r(Ron,"FSMTForConditionalGeneration"),Ron.forEach(t),fct=r(r2o," (FairSeq Machine-Translation model)"),r2o.forEach(t),mct=i(Fe),j0=n(Fe,"LI",{});var t2o=s(j0);mBe=n(t2o,"STRONG",{});var Pon=s(mBe);gct=r(Pon,"gptsan-japanese"),Pon.forEach(t),hct=r(t2o," \u2014 "),Nfe=n(t2o,"A",{href:!0});var Ion=s(Nfe);uct=r(Ion,"GPTSanJapaneseForConditionalGeneration"),Ion.forEach(t),pct=r(t2o," (GPTSAN-japanese model)"),t2o.forEach(t),_ct=i(Fe),O0=n(Fe,"LI",{});var a2o=s(O0);gBe=n(a2o,"STRONG",{});var Non=s(gBe);vct=r(Non,"led"),Non.forEach(t),bct=r(a2o," \u2014 "),Bfe=n(a2o,"A",{href:!0});var Bon=s(Bfe);Tct=r(Bon,"LEDForConditionalGeneration"),Bon.forEach(t),Fct=r(a2o," (LED model)"),a2o.forEach(t),Mct=i(Fe),V0=n(Fe,"LI",{});var n2o=s(V0);hBe=n(n2o,"STRONG",{});var Gon=s(hBe);Ect=r(Gon,"longt5"),Gon.forEach(t),Cct=r(n2o," \u2014 "),Gfe=n(n2o,"A",{href:!0});var Don=s(Gfe);wct=r(Don,"LongT5ForConditionalGeneration"),Don.forEach(t),Act=r(n2o," (LongT5 model)"),n2o.forEach(t),Lct=i(Fe),X0=n(Fe,"LI",{});var s2o=s(X0);uBe=n(s2o,"STRONG",{});var qon=s(uBe);yct=r(qon,"m2m_100"),qon.forEach(t),xct=r(s2o," \u2014 "),Dfe=n(s2o,"A",{href:!0});var jon=s(Dfe);Sct=r(jon,"M2M100ForConditionalGeneration"),jon.forEach(t),$ct=r(s2o," (M2M100 model)"),s2o.forEach(t),kct=i(Fe),z0=n(Fe,"LI",{});var l2o=s(z0);pBe=n(l2o,"STRONG",{});var Oon=s(pBe);Rct=r(Oon,"marian"),Oon.forEach(t),Pct=r(l2o," \u2014 "),qfe=n(l2o,"A",{href:!0});var Von=s(qfe);Ict=r(Von,"MarianMTModel"),Von.forEach(t),Nct=r(l2o," (Marian model)"),l2o.forEach(t),Bct=i(Fe),W0=n(Fe,"LI",{});var i2o=s(W0);_Be=n(i2o,"STRONG",{});var Xon=s(_Be);Gct=r(Xon,"mbart"),Xon.forEach(t),Dct=r(i2o," \u2014 "),jfe=n(i2o,"A",{href:!0});var zon=s(jfe);qct=r(zon,"MBartForConditionalGeneration"),zon.forEach(t),jct=r(i2o," (mBART model)"),i2o.forEach(t),Oct=i(Fe),Q0=n(Fe,"LI",{});var d2o=s(Q0);vBe=n(d2o,"STRONG",{});var Won=s(vBe);Vct=r(Won,"mt5"),Won.forEach(t),Xct=r(d2o," \u2014 "),Ofe=n(d2o,"A",{href:!0});var Qon=s(Ofe);zct=r(Qon,"MT5ForConditionalGeneration"),Qon.forEach(t),Wct=r(d2o," (MT5 model)"),d2o.forEach(t),Qct=i(Fe),U0=n(Fe,"LI",{});var c2o=s(U0);bBe=n(c2o,"STRONG",{});var Uon=s(bBe);Uct=r(Uon,"mvp"),Uon.forEach(t),Hct=r(c2o," \u2014 "),Vfe=n(c2o,"A",{href:!0});var Hon=s(Vfe);Jct=r(Hon,"MvpForConditionalGeneration"),Hon.forEach(t),Zct=r(c2o," (MVP model)"),c2o.forEach(t),Yct=i(Fe),H0=n(Fe,"LI",{});var f2o=s(H0);TBe=n(f2o,"STRONG",{});var Jon=s(TBe);Kct=r(Jon,"pegasus"),Jon.forEach(t),eft=r(f2o," \u2014 "),Xfe=n(f2o,"A",{href:!0});var Zon=s(Xfe);oft=r(Zon,"PegasusForConditionalGeneration"),Zon.forEach(t),rft=r(f2o," (Pegasus model)"),f2o.forEach(t),tft=i(Fe),J0=n(Fe,"LI",{});var m2o=s(J0);FBe=n(m2o,"STRONG",{});var Yon=s(FBe);aft=r(Yon,"pegasus_x"),Yon.forEach(t),nft=r(m2o," \u2014 "),zfe=n(m2o,"A",{href:!0});var Kon=s(zfe);sft=r(Kon,"PegasusXForConditionalGeneration"),Kon.forEach(t),lft=r(m2o," (PEGASUS-X model)"),m2o.forEach(t),ift=i(Fe),Z0=n(Fe,"LI",{});var g2o=s(Z0);MBe=n(g2o,"STRONG",{});var ern=s(MBe);dft=r(ern,"plbart"),ern.forEach(t),cft=r(g2o," \u2014 "),Wfe=n(g2o,"A",{href:!0});var orn=s(Wfe);fft=r(orn,"PLBartForConditionalGeneration"),orn.forEach(t),mft=r(g2o," (PLBart model)"),g2o.forEach(t),gft=i(Fe),Y0=n(Fe,"LI",{});var h2o=s(Y0);EBe=n(h2o,"STRONG",{});var rrn=s(EBe);hft=r(rrn,"prophetnet"),rrn.forEach(t),uft=r(h2o," \u2014 "),Qfe=n(h2o,"A",{href:!0});var trn=s(Qfe);pft=r(trn,"ProphetNetForConditionalGeneration"),trn.forEach(t),_ft=r(h2o," (ProphetNet model)"),h2o.forEach(t),vft=i(Fe),K0=n(Fe,"LI",{});var u2o=s(K0);CBe=n(u2o,"STRONG",{});var arn=s(CBe);bft=r(arn,"switch_transformers"),arn.forEach(t),Tft=r(u2o," \u2014 "),Ufe=n(u2o,"A",{href:!0});var nrn=s(Ufe);Fft=r(nrn,"SwitchTransformersForConditionalGeneration"),nrn.forEach(t),Mft=r(u2o," (SwitchTransformers model)"),u2o.forEach(t),Eft=i(Fe),ew=n(Fe,"LI",{});var p2o=s(ew);wBe=n(p2o,"STRONG",{});var srn=s(wBe);Cft=r(srn,"t5"),srn.forEach(t),wft=r(p2o," \u2014 "),Hfe=n(p2o,"A",{href:!0});var lrn=s(Hfe);Aft=r(lrn,"T5ForConditionalGeneration"),lrn.forEach(t),Lft=r(p2o," (T5 model)"),p2o.forEach(t),yft=i(Fe),ow=n(Fe,"LI",{});var _2o=s(ow);ABe=n(_2o,"STRONG",{});var irn=s(ABe);xft=r(irn,"xlm-prophetnet"),irn.forEach(t),Sft=r(_2o," \u2014 "),Jfe=n(_2o,"A",{href:!0});var drn=s(Jfe);$ft=r(drn,"XLMProphetNetForConditionalGeneration"),drn.forEach(t),kft=r(_2o," (XLM-ProphetNet model)"),_2o.forEach(t),Fe.forEach(t),Rft=i(Ja),rw=n(Ja,"P",{});var v2o=s(rw);Pft=r(v2o,"The model is set in evaluation mode by default using "),LBe=n(v2o,"CODE",{});var crn=s(LBe);Ift=r(crn,"model.eval()"),crn.forEach(t),Nft=r(v2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),yBe=n(v2o,"CODE",{});var frn=s(yBe);Bft=r(frn,"model.train()"),frn.forEach(t),v2o.forEach(t),Gft=i(Ja),T(tw.$$.fragment,Ja),Ja.forEach(t),Ji.forEach(t),RCo=i(f),vf=n(f,"H3",{class:!0});var vwo=s(vf);aw=n(vwo,"A",{id:!0,class:!0,href:!0});var mrn=s(aw);xBe=n(mrn,"SPAN",{});var grn=s(xBe);T(YG.$$.fragment,grn),grn.forEach(t),mrn.forEach(t),Dft=i(vwo),SBe=n(vwo,"SPAN",{});var hrn=s(SBe);qft=r(hrn,"TFAutoModelForSeq2SeqLM"),hrn.forEach(t),vwo.forEach(t),PCo=i(f),rr=n(f,"DIV",{class:!0});var Zi=s(rr);T(KG.$$.fragment,Zi),jft=i(Zi),bf=n(Zi,"P",{});var gTe=s(bf);Oft=r(gTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Zfe=n(gTe,"A",{href:!0});var urn=s(Zfe);Vft=r(urn,"from_pretrained()"),urn.forEach(t),Xft=r(gTe," class method or the "),Yfe=n(gTe,"A",{href:!0});var prn=s(Yfe);zft=r(prn,"from_config()"),prn.forEach(t),Wft=r(gTe,` class
method.`),gTe.forEach(t),Qft=i(Zi),eD=n(Zi,"P",{});var bwo=s(eD);Uft=r(bwo,"This class cannot be instantiated directly using "),$Be=n(bwo,"CODE",{});var _rn=s($Be);Hft=r(_rn,"__init__()"),_rn.forEach(t),Jft=r(bwo," (throws an error)."),bwo.forEach(t),Zft=i(Zi),Ht=n(Zi,"DIV",{class:!0});var gI=s(Ht);T(oD.$$.fragment,gI),Yft=i(gI),kBe=n(gI,"P",{});var vrn=s(kBe);Kft=r(vrn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),vrn.forEach(t),emt=i(gI),Tf=n(gI,"P",{});var hTe=s(Tf);omt=r(hTe,`Note:
Loading a model from its configuration file does `),RBe=n(hTe,"STRONG",{});var brn=s(RBe);rmt=r(brn,"not"),brn.forEach(t),tmt=r(hTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Kfe=n(hTe,"A",{href:!0});var Trn=s(Kfe);amt=r(Trn,"from_pretrained()"),Trn.forEach(t),nmt=r(hTe," to load the model weights."),hTe.forEach(t),smt=i(gI),T(nw.$$.fragment,gI),gI.forEach(t),lmt=i(Zi),lt=n(Zi,"DIV",{class:!0});var Yi=s(lt);T(rD.$$.fragment,Yi),imt=i(Yi),PBe=n(Yi,"P",{});var Frn=s(PBe);dmt=r(Frn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Frn.forEach(t),cmt=i(Yi),Vn=n(Yi,"P",{});var hI=s(Vn);fmt=r(hI,"The model class to instantiate is selected based on the "),IBe=n(hI,"CODE",{});var Mrn=s(IBe);mmt=r(Mrn,"model_type"),Mrn.forEach(t),gmt=r(hI,` property of the config object (either
passed as an argument or loaded from `),NBe=n(hI,"CODE",{});var Ern=s(NBe);hmt=r(Ern,"pretrained_model_name_or_path"),Ern.forEach(t),umt=r(hI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BBe=n(hI,"CODE",{});var Crn=s(BBe);pmt=r(Crn,"pretrained_model_name_or_path"),Crn.forEach(t),_mt=r(hI,":"),hI.forEach(t),vmt=i(Yi),Ie=n(Yi,"UL",{});var ze=s(Ie);sw=n(ze,"LI",{});var b2o=s(sw);GBe=n(b2o,"STRONG",{});var wrn=s(GBe);bmt=r(wrn,"bart"),wrn.forEach(t),Tmt=r(b2o," \u2014 "),eme=n(b2o,"A",{href:!0});var Arn=s(eme);Fmt=r(Arn,"TFBartForConditionalGeneration"),Arn.forEach(t),Mmt=r(b2o," (BART model)"),b2o.forEach(t),Emt=i(ze),lw=n(ze,"LI",{});var T2o=s(lw);DBe=n(T2o,"STRONG",{});var Lrn=s(DBe);Cmt=r(Lrn,"blenderbot"),Lrn.forEach(t),wmt=r(T2o," \u2014 "),ome=n(T2o,"A",{href:!0});var yrn=s(ome);Amt=r(yrn,"TFBlenderbotForConditionalGeneration"),yrn.forEach(t),Lmt=r(T2o," (Blenderbot model)"),T2o.forEach(t),ymt=i(ze),iw=n(ze,"LI",{});var F2o=s(iw);qBe=n(F2o,"STRONG",{});var xrn=s(qBe);xmt=r(xrn,"blenderbot-small"),xrn.forEach(t),Smt=r(F2o," \u2014 "),rme=n(F2o,"A",{href:!0});var Srn=s(rme);$mt=r(Srn,"TFBlenderbotSmallForConditionalGeneration"),Srn.forEach(t),kmt=r(F2o," (BlenderbotSmall model)"),F2o.forEach(t),Rmt=i(ze),dw=n(ze,"LI",{});var M2o=s(dw);jBe=n(M2o,"STRONG",{});var $rn=s(jBe);Pmt=r($rn,"encoder-decoder"),$rn.forEach(t),Imt=r(M2o," \u2014 "),tme=n(M2o,"A",{href:!0});var krn=s(tme);Nmt=r(krn,"TFEncoderDecoderModel"),krn.forEach(t),Bmt=r(M2o," (Encoder decoder model)"),M2o.forEach(t),Gmt=i(ze),cw=n(ze,"LI",{});var E2o=s(cw);OBe=n(E2o,"STRONG",{});var Rrn=s(OBe);Dmt=r(Rrn,"led"),Rrn.forEach(t),qmt=r(E2o," \u2014 "),ame=n(E2o,"A",{href:!0});var Prn=s(ame);jmt=r(Prn,"TFLEDForConditionalGeneration"),Prn.forEach(t),Omt=r(E2o," (LED model)"),E2o.forEach(t),Vmt=i(ze),fw=n(ze,"LI",{});var C2o=s(fw);VBe=n(C2o,"STRONG",{});var Irn=s(VBe);Xmt=r(Irn,"marian"),Irn.forEach(t),zmt=r(C2o," \u2014 "),nme=n(C2o,"A",{href:!0});var Nrn=s(nme);Wmt=r(Nrn,"TFMarianMTModel"),Nrn.forEach(t),Qmt=r(C2o," (Marian model)"),C2o.forEach(t),Umt=i(ze),mw=n(ze,"LI",{});var w2o=s(mw);XBe=n(w2o,"STRONG",{});var Brn=s(XBe);Hmt=r(Brn,"mbart"),Brn.forEach(t),Jmt=r(w2o," \u2014 "),sme=n(w2o,"A",{href:!0});var Grn=s(sme);Zmt=r(Grn,"TFMBartForConditionalGeneration"),Grn.forEach(t),Ymt=r(w2o," (mBART model)"),w2o.forEach(t),Kmt=i(ze),gw=n(ze,"LI",{});var A2o=s(gw);zBe=n(A2o,"STRONG",{});var Drn=s(zBe);egt=r(Drn,"mt5"),Drn.forEach(t),ogt=r(A2o," \u2014 "),lme=n(A2o,"A",{href:!0});var qrn=s(lme);rgt=r(qrn,"TFMT5ForConditionalGeneration"),qrn.forEach(t),tgt=r(A2o," (MT5 model)"),A2o.forEach(t),agt=i(ze),hw=n(ze,"LI",{});var L2o=s(hw);WBe=n(L2o,"STRONG",{});var jrn=s(WBe);ngt=r(jrn,"pegasus"),jrn.forEach(t),sgt=r(L2o," \u2014 "),ime=n(L2o,"A",{href:!0});var Orn=s(ime);lgt=r(Orn,"TFPegasusForConditionalGeneration"),Orn.forEach(t),igt=r(L2o," (Pegasus model)"),L2o.forEach(t),dgt=i(ze),uw=n(ze,"LI",{});var y2o=s(uw);QBe=n(y2o,"STRONG",{});var Vrn=s(QBe);cgt=r(Vrn,"t5"),Vrn.forEach(t),fgt=r(y2o," \u2014 "),dme=n(y2o,"A",{href:!0});var Xrn=s(dme);mgt=r(Xrn,"TFT5ForConditionalGeneration"),Xrn.forEach(t),ggt=r(y2o," (T5 model)"),y2o.forEach(t),ze.forEach(t),hgt=i(Yi),T(pw.$$.fragment,Yi),Yi.forEach(t),Zi.forEach(t),ICo=i(f),Ff=n(f,"H3",{class:!0});var Two=s(Ff);_w=n(Two,"A",{id:!0,class:!0,href:!0});var zrn=s(_w);UBe=n(zrn,"SPAN",{});var Wrn=s(UBe);T(tD.$$.fragment,Wrn),Wrn.forEach(t),zrn.forEach(t),ugt=i(Two),HBe=n(Two,"SPAN",{});var Qrn=s(HBe);pgt=r(Qrn,"FlaxAutoModelForSeq2SeqLM"),Qrn.forEach(t),Two.forEach(t),NCo=i(f),tr=n(f,"DIV",{class:!0});var Ki=s(tr);T(aD.$$.fragment,Ki),_gt=i(Ki),Mf=n(Ki,"P",{});var uTe=s(Mf);vgt=r(uTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),cme=n(uTe,"A",{href:!0});var Urn=s(cme);bgt=r(Urn,"from_pretrained()"),Urn.forEach(t),Tgt=r(uTe," class method or the "),fme=n(uTe,"A",{href:!0});var Hrn=s(fme);Fgt=r(Hrn,"from_config()"),Hrn.forEach(t),Mgt=r(uTe,` class
method.`),uTe.forEach(t),Egt=i(Ki),nD=n(Ki,"P",{});var Fwo=s(nD);Cgt=r(Fwo,"This class cannot be instantiated directly using "),JBe=n(Fwo,"CODE",{});var Jrn=s(JBe);wgt=r(Jrn,"__init__()"),Jrn.forEach(t),Agt=r(Fwo," (throws an error)."),Fwo.forEach(t),Lgt=i(Ki),Jt=n(Ki,"DIV",{class:!0});var uI=s(Jt);T(sD.$$.fragment,uI),ygt=i(uI),ZBe=n(uI,"P",{});var Zrn=s(ZBe);xgt=r(Zrn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Zrn.forEach(t),Sgt=i(uI),Ef=n(uI,"P",{});var pTe=s(Ef);$gt=r(pTe,`Note:
Loading a model from its configuration file does `),YBe=n(pTe,"STRONG",{});var Yrn=s(YBe);kgt=r(Yrn,"not"),Yrn.forEach(t),Rgt=r(pTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),mme=n(pTe,"A",{href:!0});var Krn=s(mme);Pgt=r(Krn,"from_pretrained()"),Krn.forEach(t),Igt=r(pTe," to load the model weights."),pTe.forEach(t),Ngt=i(uI),T(vw.$$.fragment,uI),uI.forEach(t),Bgt=i(Ki),it=n(Ki,"DIV",{class:!0});var ed=s(it);T(lD.$$.fragment,ed),Ggt=i(ed),KBe=n(ed,"P",{});var etn=s(KBe);Dgt=r(etn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),etn.forEach(t),qgt=i(ed),Xn=n(ed,"P",{});var pI=s(Xn);jgt=r(pI,"The model class to instantiate is selected based on the "),eGe=n(pI,"CODE",{});var otn=s(eGe);Ogt=r(otn,"model_type"),otn.forEach(t),Vgt=r(pI,` property of the config object (either
passed as an argument or loaded from `),oGe=n(pI,"CODE",{});var rtn=s(oGe);Xgt=r(rtn,"pretrained_model_name_or_path"),rtn.forEach(t),zgt=r(pI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rGe=n(pI,"CODE",{});var ttn=s(rGe);Wgt=r(ttn,"pretrained_model_name_or_path"),ttn.forEach(t),Qgt=r(pI,":"),pI.forEach(t),Ugt=i(ed),Ne=n(ed,"UL",{});var We=s(Ne);bw=n(We,"LI",{});var x2o=s(bw);tGe=n(x2o,"STRONG",{});var atn=s(tGe);Hgt=r(atn,"bart"),atn.forEach(t),Jgt=r(x2o," \u2014 "),gme=n(x2o,"A",{href:!0});var ntn=s(gme);Zgt=r(ntn,"FlaxBartForConditionalGeneration"),ntn.forEach(t),Ygt=r(x2o," (BART model)"),x2o.forEach(t),Kgt=i(We),Tw=n(We,"LI",{});var S2o=s(Tw);aGe=n(S2o,"STRONG",{});var stn=s(aGe);eht=r(stn,"blenderbot"),stn.forEach(t),oht=r(S2o," \u2014 "),hme=n(S2o,"A",{href:!0});var ltn=s(hme);rht=r(ltn,"FlaxBlenderbotForConditionalGeneration"),ltn.forEach(t),tht=r(S2o," (Blenderbot model)"),S2o.forEach(t),aht=i(We),Fw=n(We,"LI",{});var $2o=s(Fw);nGe=n($2o,"STRONG",{});var itn=s(nGe);nht=r(itn,"blenderbot-small"),itn.forEach(t),sht=r($2o," \u2014 "),ume=n($2o,"A",{href:!0});var dtn=s(ume);lht=r(dtn,"FlaxBlenderbotSmallForConditionalGeneration"),dtn.forEach(t),iht=r($2o," (BlenderbotSmall model)"),$2o.forEach(t),dht=i(We),Mw=n(We,"LI",{});var k2o=s(Mw);sGe=n(k2o,"STRONG",{});var ctn=s(sGe);cht=r(ctn,"encoder-decoder"),ctn.forEach(t),fht=r(k2o," \u2014 "),pme=n(k2o,"A",{href:!0});var ftn=s(pme);mht=r(ftn,"FlaxEncoderDecoderModel"),ftn.forEach(t),ght=r(k2o," (Encoder decoder model)"),k2o.forEach(t),hht=i(We),Ew=n(We,"LI",{});var R2o=s(Ew);lGe=n(R2o,"STRONG",{});var mtn=s(lGe);uht=r(mtn,"longt5"),mtn.forEach(t),pht=r(R2o," \u2014 "),_me=n(R2o,"A",{href:!0});var gtn=s(_me);_ht=r(gtn,"FlaxLongT5ForConditionalGeneration"),gtn.forEach(t),vht=r(R2o," (LongT5 model)"),R2o.forEach(t),bht=i(We),Cw=n(We,"LI",{});var P2o=s(Cw);iGe=n(P2o,"STRONG",{});var htn=s(iGe);Tht=r(htn,"marian"),htn.forEach(t),Fht=r(P2o," \u2014 "),vme=n(P2o,"A",{href:!0});var utn=s(vme);Mht=r(utn,"FlaxMarianMTModel"),utn.forEach(t),Eht=r(P2o," (Marian model)"),P2o.forEach(t),Cht=i(We),ww=n(We,"LI",{});var I2o=s(ww);dGe=n(I2o,"STRONG",{});var ptn=s(dGe);wht=r(ptn,"mbart"),ptn.forEach(t),Aht=r(I2o," \u2014 "),bme=n(I2o,"A",{href:!0});var _tn=s(bme);Lht=r(_tn,"FlaxMBartForConditionalGeneration"),_tn.forEach(t),yht=r(I2o," (mBART model)"),I2o.forEach(t),xht=i(We),Aw=n(We,"LI",{});var N2o=s(Aw);cGe=n(N2o,"STRONG",{});var vtn=s(cGe);Sht=r(vtn,"mt5"),vtn.forEach(t),$ht=r(N2o," \u2014 "),Tme=n(N2o,"A",{href:!0});var btn=s(Tme);kht=r(btn,"FlaxMT5ForConditionalGeneration"),btn.forEach(t),Rht=r(N2o," (MT5 model)"),N2o.forEach(t),Pht=i(We),Lw=n(We,"LI",{});var B2o=s(Lw);fGe=n(B2o,"STRONG",{});var Ttn=s(fGe);Iht=r(Ttn,"pegasus"),Ttn.forEach(t),Nht=r(B2o," \u2014 "),Fme=n(B2o,"A",{href:!0});var Ftn=s(Fme);Bht=r(Ftn,"FlaxPegasusForConditionalGeneration"),Ftn.forEach(t),Ght=r(B2o," (Pegasus model)"),B2o.forEach(t),Dht=i(We),yw=n(We,"LI",{});var G2o=s(yw);mGe=n(G2o,"STRONG",{});var Mtn=s(mGe);qht=r(Mtn,"t5"),Mtn.forEach(t),jht=r(G2o," \u2014 "),Mme=n(G2o,"A",{href:!0});var Etn=s(Mme);Oht=r(Etn,"FlaxT5ForConditionalGeneration"),Etn.forEach(t),Vht=r(G2o," (T5 model)"),G2o.forEach(t),We.forEach(t),Xht=i(ed),T(xw.$$.fragment,ed),ed.forEach(t),Ki.forEach(t),BCo=i(f),Cf=n(f,"H3",{class:!0});var Mwo=s(Cf);Sw=n(Mwo,"A",{id:!0,class:!0,href:!0});var Ctn=s(Sw);gGe=n(Ctn,"SPAN",{});var wtn=s(gGe);T(iD.$$.fragment,wtn),wtn.forEach(t),Ctn.forEach(t),zht=i(Mwo),hGe=n(Mwo,"SPAN",{});var Atn=s(hGe);Wht=r(Atn,"AutoModelForSequenceClassification"),Atn.forEach(t),Mwo.forEach(t),GCo=i(f),ar=n(f,"DIV",{class:!0});var od=s(ar);T(dD.$$.fragment,od),Qht=i(od),wf=n(od,"P",{});var _Te=s(wf);Uht=r(_Te,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Eme=n(_Te,"A",{href:!0});var Ltn=s(Eme);Hht=r(Ltn,"from_pretrained()"),Ltn.forEach(t),Jht=r(_Te," class method or the "),Cme=n(_Te,"A",{href:!0});var ytn=s(Cme);Zht=r(ytn,"from_config()"),ytn.forEach(t),Yht=r(_Te,` class
method.`),_Te.forEach(t),Kht=i(od),cD=n(od,"P",{});var Ewo=s(cD);eut=r(Ewo,"This class cannot be instantiated directly using "),uGe=n(Ewo,"CODE",{});var xtn=s(uGe);out=r(xtn,"__init__()"),xtn.forEach(t),rut=r(Ewo," (throws an error)."),Ewo.forEach(t),tut=i(od),Zt=n(od,"DIV",{class:!0});var _I=s(Zt);T(fD.$$.fragment,_I),aut=i(_I),pGe=n(_I,"P",{});var Stn=s(pGe);nut=r(Stn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Stn.forEach(t),sut=i(_I),Af=n(_I,"P",{});var vTe=s(Af);lut=r(vTe,`Note:
Loading a model from its configuration file does `),_Ge=n(vTe,"STRONG",{});var $tn=s(_Ge);iut=r($tn,"not"),$tn.forEach(t),dut=r(vTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),wme=n(vTe,"A",{href:!0});var ktn=s(wme);cut=r(ktn,"from_pretrained()"),ktn.forEach(t),fut=r(vTe," to load the model weights."),vTe.forEach(t),mut=i(_I),T($w.$$.fragment,_I),_I.forEach(t),gut=i(od),mo=n(od,"DIV",{class:!0});var Za=s(mo);T(mD.$$.fragment,Za),hut=i(Za),vGe=n(Za,"P",{});var Rtn=s(vGe);uut=r(Rtn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),Rtn.forEach(t),put=i(Za),zn=n(Za,"P",{});var vI=s(zn);_ut=r(vI,"The model class to instantiate is selected based on the "),bGe=n(vI,"CODE",{});var Ptn=s(bGe);vut=r(Ptn,"model_type"),Ptn.forEach(t),but=r(vI,` property of the config object (either
passed as an argument or loaded from `),TGe=n(vI,"CODE",{});var Itn=s(TGe);Tut=r(Itn,"pretrained_model_name_or_path"),Itn.forEach(t),Fut=r(vI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FGe=n(vI,"CODE",{});var Ntn=s(FGe);Mut=r(Ntn,"pretrained_model_name_or_path"),Ntn.forEach(t),Eut=r(vI,":"),vI.forEach(t),Cut=i(Za),B=n(Za,"UL",{});var V=s(B);kw=n(V,"LI",{});var D2o=s(kw);MGe=n(D2o,"STRONG",{});var Btn=s(MGe);wut=r(Btn,"albert"),Btn.forEach(t),Aut=r(D2o," \u2014 "),Ame=n(D2o,"A",{href:!0});var Gtn=s(Ame);Lut=r(Gtn,"AlbertForSequenceClassification"),Gtn.forEach(t),yut=r(D2o," (ALBERT model)"),D2o.forEach(t),xut=i(V),Rw=n(V,"LI",{});var q2o=s(Rw);EGe=n(q2o,"STRONG",{});var Dtn=s(EGe);Sut=r(Dtn,"bart"),Dtn.forEach(t),$ut=r(q2o," \u2014 "),Lme=n(q2o,"A",{href:!0});var qtn=s(Lme);kut=r(qtn,"BartForSequenceClassification"),qtn.forEach(t),Rut=r(q2o," (BART model)"),q2o.forEach(t),Put=i(V),Pw=n(V,"LI",{});var j2o=s(Pw);CGe=n(j2o,"STRONG",{});var jtn=s(CGe);Iut=r(jtn,"bert"),jtn.forEach(t),Nut=r(j2o," \u2014 "),yme=n(j2o,"A",{href:!0});var Otn=s(yme);But=r(Otn,"BertForSequenceClassification"),Otn.forEach(t),Gut=r(j2o," (BERT model)"),j2o.forEach(t),Dut=i(V),Iw=n(V,"LI",{});var O2o=s(Iw);wGe=n(O2o,"STRONG",{});var Vtn=s(wGe);qut=r(Vtn,"big_bird"),Vtn.forEach(t),jut=r(O2o," \u2014 "),xme=n(O2o,"A",{href:!0});var Xtn=s(xme);Out=r(Xtn,"BigBirdForSequenceClassification"),Xtn.forEach(t),Vut=r(O2o," (BigBird model)"),O2o.forEach(t),Xut=i(V),Nw=n(V,"LI",{});var V2o=s(Nw);AGe=n(V2o,"STRONG",{});var ztn=s(AGe);zut=r(ztn,"bigbird_pegasus"),ztn.forEach(t),Wut=r(V2o," \u2014 "),Sme=n(V2o,"A",{href:!0});var Wtn=s(Sme);Qut=r(Wtn,"BigBirdPegasusForSequenceClassification"),Wtn.forEach(t),Uut=r(V2o," (BigBird-Pegasus model)"),V2o.forEach(t),Hut=i(V),Bw=n(V,"LI",{});var X2o=s(Bw);LGe=n(X2o,"STRONG",{});var Qtn=s(LGe);Jut=r(Qtn,"bloom"),Qtn.forEach(t),Zut=r(X2o," \u2014 "),$me=n(X2o,"A",{href:!0});var Utn=s($me);Yut=r(Utn,"BloomForSequenceClassification"),Utn.forEach(t),Kut=r(X2o," (BLOOM model)"),X2o.forEach(t),ept=i(V),Gw=n(V,"LI",{});var z2o=s(Gw);yGe=n(z2o,"STRONG",{});var Htn=s(yGe);opt=r(Htn,"camembert"),Htn.forEach(t),rpt=r(z2o," \u2014 "),kme=n(z2o,"A",{href:!0});var Jtn=s(kme);tpt=r(Jtn,"CamembertForSequenceClassification"),Jtn.forEach(t),apt=r(z2o," (CamemBERT model)"),z2o.forEach(t),npt=i(V),Dw=n(V,"LI",{});var W2o=s(Dw);xGe=n(W2o,"STRONG",{});var Ztn=s(xGe);spt=r(Ztn,"canine"),Ztn.forEach(t),lpt=r(W2o," \u2014 "),Rme=n(W2o,"A",{href:!0});var Ytn=s(Rme);ipt=r(Ytn,"CanineForSequenceClassification"),Ytn.forEach(t),dpt=r(W2o," (CANINE model)"),W2o.forEach(t),cpt=i(V),qw=n(V,"LI",{});var Q2o=s(qw);SGe=n(Q2o,"STRONG",{});var Ktn=s(SGe);fpt=r(Ktn,"convbert"),Ktn.forEach(t),mpt=r(Q2o," \u2014 "),Pme=n(Q2o,"A",{href:!0});var ean=s(Pme);gpt=r(ean,"ConvBertForSequenceClassification"),ean.forEach(t),hpt=r(Q2o," (ConvBERT model)"),Q2o.forEach(t),upt=i(V),jw=n(V,"LI",{});var U2o=s(jw);$Ge=n(U2o,"STRONG",{});var oan=s($Ge);ppt=r(oan,"ctrl"),oan.forEach(t),_pt=r(U2o," \u2014 "),Ime=n(U2o,"A",{href:!0});var ran=s(Ime);vpt=r(ran,"CTRLForSequenceClassification"),ran.forEach(t),bpt=r(U2o," (CTRL model)"),U2o.forEach(t),Tpt=i(V),Ow=n(V,"LI",{});var H2o=s(Ow);kGe=n(H2o,"STRONG",{});var tan=s(kGe);Fpt=r(tan,"data2vec-text"),tan.forEach(t),Mpt=r(H2o," \u2014 "),Nme=n(H2o,"A",{href:!0});var aan=s(Nme);Ept=r(aan,"Data2VecTextForSequenceClassification"),aan.forEach(t),Cpt=r(H2o," (Data2VecText model)"),H2o.forEach(t),wpt=i(V),Vw=n(V,"LI",{});var J2o=s(Vw);RGe=n(J2o,"STRONG",{});var nan=s(RGe);Apt=r(nan,"deberta"),nan.forEach(t),Lpt=r(J2o," \u2014 "),Bme=n(J2o,"A",{href:!0});var san=s(Bme);ypt=r(san,"DebertaForSequenceClassification"),san.forEach(t),xpt=r(J2o," (DeBERTa model)"),J2o.forEach(t),Spt=i(V),Xw=n(V,"LI",{});var Z2o=s(Xw);PGe=n(Z2o,"STRONG",{});var lan=s(PGe);$pt=r(lan,"deberta-v2"),lan.forEach(t),kpt=r(Z2o," \u2014 "),Gme=n(Z2o,"A",{href:!0});var ian=s(Gme);Rpt=r(ian,"DebertaV2ForSequenceClassification"),ian.forEach(t),Ppt=r(Z2o," (DeBERTa-v2 model)"),Z2o.forEach(t),Ipt=i(V),zw=n(V,"LI",{});var Y2o=s(zw);IGe=n(Y2o,"STRONG",{});var dan=s(IGe);Npt=r(dan,"distilbert"),dan.forEach(t),Bpt=r(Y2o," \u2014 "),Dme=n(Y2o,"A",{href:!0});var can=s(Dme);Gpt=r(can,"DistilBertForSequenceClassification"),can.forEach(t),Dpt=r(Y2o," (DistilBERT model)"),Y2o.forEach(t),qpt=i(V),Ww=n(V,"LI",{});var K2o=s(Ww);NGe=n(K2o,"STRONG",{});var fan=s(NGe);jpt=r(fan,"electra"),fan.forEach(t),Opt=r(K2o," \u2014 "),qme=n(K2o,"A",{href:!0});var man=s(qme);Vpt=r(man,"ElectraForSequenceClassification"),man.forEach(t),Xpt=r(K2o," (ELECTRA model)"),K2o.forEach(t),zpt=i(V),Qw=n(V,"LI",{});var evo=s(Qw);BGe=n(evo,"STRONG",{});var gan=s(BGe);Wpt=r(gan,"ernie"),gan.forEach(t),Qpt=r(evo," \u2014 "),jme=n(evo,"A",{href:!0});var han=s(jme);Upt=r(han,"ErnieForSequenceClassification"),han.forEach(t),Hpt=r(evo," (ERNIE model)"),evo.forEach(t),Jpt=i(V),Uw=n(V,"LI",{});var ovo=s(Uw);GGe=n(ovo,"STRONG",{});var uan=s(GGe);Zpt=r(uan,"ernie_m"),uan.forEach(t),Ypt=r(ovo," \u2014 "),Ome=n(ovo,"A",{href:!0});var pan=s(Ome);Kpt=r(pan,"ErnieMForSequenceClassification"),pan.forEach(t),e_t=r(ovo," (ErnieM model)"),ovo.forEach(t),o_t=i(V),Hw=n(V,"LI",{});var rvo=s(Hw);DGe=n(rvo,"STRONG",{});var _an=s(DGe);r_t=r(_an,"esm"),_an.forEach(t),t_t=r(rvo," \u2014 "),Vme=n(rvo,"A",{href:!0});var van=s(Vme);a_t=r(van,"EsmForSequenceClassification"),van.forEach(t),n_t=r(rvo," (ESM model)"),rvo.forEach(t),s_t=i(V),Jw=n(V,"LI",{});var tvo=s(Jw);qGe=n(tvo,"STRONG",{});var ban=s(qGe);l_t=r(ban,"flaubert"),ban.forEach(t),i_t=r(tvo," \u2014 "),Xme=n(tvo,"A",{href:!0});var Tan=s(Xme);d_t=r(Tan,"FlaubertForSequenceClassification"),Tan.forEach(t),c_t=r(tvo," (FlauBERT model)"),tvo.forEach(t),f_t=i(V),Zw=n(V,"LI",{});var avo=s(Zw);jGe=n(avo,"STRONG",{});var Fan=s(jGe);m_t=r(Fan,"fnet"),Fan.forEach(t),g_t=r(avo," \u2014 "),zme=n(avo,"A",{href:!0});var Man=s(zme);h_t=r(Man,"FNetForSequenceClassification"),Man.forEach(t),u_t=r(avo," (FNet model)"),avo.forEach(t),p_t=i(V),Yw=n(V,"LI",{});var nvo=s(Yw);OGe=n(nvo,"STRONG",{});var Ean=s(OGe);__t=r(Ean,"funnel"),Ean.forEach(t),v_t=r(nvo," \u2014 "),Wme=n(nvo,"A",{href:!0});var Can=s(Wme);b_t=r(Can,"FunnelForSequenceClassification"),Can.forEach(t),T_t=r(nvo," (Funnel Transformer model)"),nvo.forEach(t),F_t=i(V),Kw=n(V,"LI",{});var svo=s(Kw);VGe=n(svo,"STRONG",{});var wan=s(VGe);M_t=r(wan,"gpt-sw3"),wan.forEach(t),E_t=r(svo," \u2014 "),Qme=n(svo,"A",{href:!0});var Aan=s(Qme);C_t=r(Aan,"GPT2ForSequenceClassification"),Aan.forEach(t),w_t=r(svo," (GPT-Sw3 model)"),svo.forEach(t),A_t=i(V),e6=n(V,"LI",{});var lvo=s(e6);XGe=n(lvo,"STRONG",{});var Lan=s(XGe);L_t=r(Lan,"gpt2"),Lan.forEach(t),y_t=r(lvo," \u2014 "),Ume=n(lvo,"A",{href:!0});var yan=s(Ume);x_t=r(yan,"GPT2ForSequenceClassification"),yan.forEach(t),S_t=r(lvo," (OpenAI GPT-2 model)"),lvo.forEach(t),$_t=i(V),o6=n(V,"LI",{});var ivo=s(o6);zGe=n(ivo,"STRONG",{});var xan=s(zGe);k_t=r(xan,"gpt_neo"),xan.forEach(t),R_t=r(ivo," 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model\u2019s configuration. Use `),uhe=n(MTe,"A",{href:!0});var yln=s(uhe);tFt=r(yln,"from_pretrained()"),yln.forEach(t),aFt=r(MTe," to load the model weights."),MTe.forEach(t),nFt=i(FI),T(M8.$$.fragment,FI),FI.forEach(t),sFt=i(ad),ct=n(ad,"DIV",{class:!0});var nd=s(ct);T(MD.$$.fragment,nd),lFt=i(nd),Mqe=n(nd,"P",{});var xln=s(Mqe);iFt=r(xln,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),xln.forEach(t),dFt=i(nd),Qn=n(nd,"P",{});var MI=s(Qn);cFt=r(MI,"The model class to instantiate is selected based on the "),Eqe=n(MI,"CODE",{});var Sln=s(Eqe);fFt=r(Sln,"model_type"),Sln.forEach(t),mFt=r(MI,` property of the config object (either
passed as an argument or loaded from `),Cqe=n(MI,"CODE",{});var $ln=s(Cqe);gFt=r($ln,"pretrained_model_name_or_path"),$ln.forEach(t),hFt=r(MI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wqe=n(MI,"CODE",{});var kln=s(wqe);uFt=r(kln,"pretrained_model_name_or_path"),kln.forEach(t),pFt=r(MI,":"),MI.forEach(t),_Ft=i(nd),$e=n(nd,"UL",{});var qe=s($e);E8=n(qe,"LI",{});var C4o=s(E8);Aqe=n(C4o,"STRONG",{});var Rln=s(Aqe);vFt=r(Rln,"albert"),Rln.forEach(t),bFt=r(C4o," \u2014 "),phe=n(C4o,"A",{href:!0});var Pln=s(phe);TFt=r(Pln,"FlaxAlbertForSequenceClassification"),Pln.forEach(t),FFt=r(C4o," (ALBERT model)"),C4o.forEach(t),MFt=i(qe),C8=n(qe,"LI",{});var w4o=s(C8);Lqe=n(w4o,"STRONG",{});var Iln=s(Lqe);EFt=r(Iln,"bart"),Iln.forEach(t),CFt=r(w4o," \u2014 "),_he=n(w4o,"A",{href:!0});var Nln=s(_he);wFt=r(Nln,"FlaxBartForSequenceClassification"),Nln.forEach(t),AFt=r(w4o," (BART model)"),w4o.forEach(t),LFt=i(qe),w8=n(qe,"LI",{});var A4o=s(w8);yqe=n(A4o,"STRONG",{});var Bln=s(yqe);yFt=r(Bln,"bert"),Bln.forEach(t),xFt=r(A4o," \u2014 "),vhe=n(A4o,"A",{href:!0});var Gln=s(vhe);SFt=r(Gln,"FlaxBertForSequenceClassification"),Gln.forEach(t),$Ft=r(A4o," (BERT model)"),A4o.forEach(t),kFt=i(qe),A8=n(qe,"LI",{});var L4o=s(A8);xqe=n(L4o,"STRONG",{});var Dln=s(xqe);RFt=r(Dln,"big_bird"),Dln.forEach(t),PFt=r(L4o," \u2014 "),bhe=n(L4o,"A",{href:!0});var qln=s(bhe);IFt=r(qln,"FlaxBigBirdForSequenceClassification"),qln.forEach(t),NFt=r(L4o," (BigBird model)"),L4o.forEach(t),BFt=i(qe),L8=n(qe,"LI",{});var y4o=s(L8);Sqe=n(y4o,"STRONG",{});var jln=s(Sqe);GFt=r(jln,"distilbert"),jln.forEach(t),DFt=r(y4o," \u2014 "),The=n(y4o,"A",{href:!0});var Oln=s(The);qFt=r(Oln,"FlaxDistilBertForSequenceClassification"),Oln.forEach(t),jFt=r(y4o," (DistilBERT model)"),y4o.forEach(t),OFt=i(qe),y8=n(qe,"LI",{});var x4o=s(y8);$qe=n(x4o,"STRONG",{});var Vln=s($qe);VFt=r(Vln,"electra"),Vln.forEach(t),XFt=r(x4o," \u2014 "),Fhe=n(x4o,"A",{href:!0});var Xln=s(Fhe);zFt=r(Xln,"FlaxElectraForSequenceClassification"),Xln.forEach(t),WFt=r(x4o," (ELECTRA model)"),x4o.forEach(t),QFt=i(qe),x8=n(qe,"LI",{});var S4o=s(x8);kqe=n(S4o,"STRONG",{});var zln=s(kqe);UFt=r(zln,"mbart"),zln.forEach(t),HFt=r(S4o," \u2014 "),Mhe=n(S4o,"A",{href:!0});var Wln=s(Mhe);JFt=r(Wln,"FlaxMBartForSequenceClassification"),Wln.forEach(t),ZFt=r(S4o," (mBART model)"),S4o.forEach(t),YFt=i(qe),S8=n(qe,"LI",{});var $4o=s(S8);Rqe=n($4o,"STRONG",{});var Qln=s(Rqe);KFt=r(Qln,"roberta"),Qln.forEach(t),eMt=r($4o," \u2014 "),Ehe=n($4o,"A",{href:!0});var Uln=s(Ehe);oMt=r(Uln,"FlaxRobertaForSequenceClassification"),Uln.forEach(t),rMt=r($4o," (RoBERTa model)"),$4o.forEach(t),tMt=i(qe),$8=n(qe,"LI",{});var k4o=s($8);Pqe=n(k4o,"STRONG",{});var Hln=s(Pqe);aMt=r(Hln,"roberta-prelayernorm"),Hln.forEach(t),nMt=r(k4o," \u2014 "),Che=n(k4o,"A",{href:!0});var Jln=s(Che);sMt=r(Jln,"FlaxRobertaPreLayerNormForSequenceClassification"),Jln.forEach(t),lMt=r(k4o," (RoBERTa-PreLayerNorm model)"),k4o.forEach(t),iMt=i(qe),k8=n(qe,"LI",{});var R4o=s(k8);Iqe=n(R4o,"STRONG",{});var Zln=s(Iqe);dMt=r(Zln,"roformer"),Zln.forEach(t),cMt=r(R4o," \u2014 "),whe=n(R4o,"A",{href:!0});var Yln=s(whe);fMt=r(Yln,"FlaxRoFormerForSequenceClassification"),Yln.forEach(t),mMt=r(R4o," (RoFormer model)"),R4o.forEach(t),gMt=i(qe),R8=n(qe,"LI",{});var P4o=s(R8);Nqe=n(P4o,"STRONG",{});var Kln=s(Nqe);hMt=r(Kln,"xlm-roberta"),Kln.forEach(t),uMt=r(P4o," \u2014 "),Ahe=n(P4o,"A",{href:!0});var ein=s(Ahe);pMt=r(ein,"FlaxXLMRobertaForSequenceClassification"),ein.forEach(t),_Mt=r(P4o," (XLM-RoBERTa model)"),P4o.forEach(t),qe.forEach(t),vMt=i(nd),T(P8.$$.fragment,nd),nd.forEach(t),ad.forEach(t),VCo=i(f),Rf=n(f,"H3",{class:!0});var ywo=s(Rf);I8=n(ywo,"A",{id:!0,class:!0,href:!0});var oin=s(I8);Bqe=n(oin,"SPAN",{});var rin=s(Bqe);T(ED.$$.fragment,rin),rin.forEach(t),oin.forEach(t),bMt=i(ywo),Gqe=n(ywo,"SPAN",{});var tin=s(Gqe);TMt=r(tin,"AutoModelForMultipleChoice"),tin.forEach(t),ywo.forEach(t),XCo=i(f),lr=n(f,"DIV",{class:!0});var sd=s(lr);T(CD.$$.fragment,sd),FMt=i(sd),Pf=n(sd,"P",{});var ETe=s(Pf);MMt=r(ETe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),Lhe=n(ETe,"A",{href:!0});var ain=s(Lhe);EMt=r(ain,"from_pretrained()"),ain.forEach(t),CMt=r(ETe," class method or the "),yhe=n(ETe,"A",{href:!0});var nin=s(yhe);wMt=r(nin,"from_config()"),nin.forEach(t),AMt=r(ETe,` class
method.`),ETe.forEach(t),LMt=i(sd),wD=n(sd,"P",{});var xwo=s(wD);yMt=r(xwo,"This class cannot be instantiated directly using "),Dqe=n(xwo,"CODE",{});var sin=s(Dqe);xMt=r(sin,"__init__()"),sin.forEach(t),SMt=r(xwo," (throws an error)."),xwo.forEach(t),$Mt=i(sd),ea=n(sd,"DIV",{class:!0});var EI=s(ea);T(AD.$$.fragment,EI),kMt=i(EI),qqe=n(EI,"P",{});var lin=s(qqe);RMt=r(lin,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),lin.forEach(t),PMt=i(EI),If=n(EI,"P",{});var CTe=s(If);IMt=r(CTe,`Note:
Loading a model from its configuration file does `),jqe=n(CTe,"STRONG",{});var iin=s(jqe);NMt=r(iin,"not"),iin.forEach(t),BMt=r(CTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),xhe=n(CTe,"A",{href:!0});var din=s(xhe);GMt=r(din,"from_pretrained()"),din.forEach(t),DMt=r(CTe," to load the model weights."),CTe.forEach(t),qMt=i(EI),T(N8.$$.fragment,EI),EI.forEach(t),jMt=i(sd),go=n(sd,"DIV",{class:!0});var Ya=s(go);T(LD.$$.fragment,Ya),OMt=i(Ya),Oqe=n(Ya,"P",{});var cin=s(Oqe);VMt=r(cin,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),cin.forEach(t),XMt=i(Ya),Un=n(Ya,"P",{});var CI=s(Un);zMt=r(CI,"The model class to instantiate is selected based on the "),Vqe=n(CI,"CODE",{});var fin=s(Vqe);WMt=r(fin,"model_type"),fin.forEach(t),QMt=r(CI,` property of the config object (either
passed as an argument or loaded from `),Xqe=n(CI,"CODE",{});var min=s(Xqe);UMt=r(min,"pretrained_model_name_or_path"),min.forEach(t),HMt=r(CI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zqe=n(CI,"CODE",{});var gin=s(zqe);JMt=r(gin,"pretrained_model_name_or_path"),gin.forEach(t),ZMt=r(CI,":"),CI.forEach(t),YMt=i(Ya),oe=n(Ya,"UL",{});var ae=s(oe);B8=n(ae,"LI",{});var I4o=s(B8);Wqe=n(I4o,"STRONG",{});var hin=s(Wqe);KMt=r(hin,"albert"),hin.forEach(t),eEt=r(I4o," \u2014 "),She=n(I4o,"A",{href:!0});var uin=s(She);oEt=r(uin,"AlbertForMultipleChoice"),uin.forEach(t),rEt=r(I4o," (ALBERT model)"),I4o.forEach(t),tEt=i(ae),G8=n(ae,"LI",{});var N4o=s(G8);Qqe=n(N4o,"STRONG",{});var pin=s(Qqe);aEt=r(pin,"bert"),pin.forEach(t),nEt=r(N4o," \u2014 "),$he=n(N4o,"A",{href:!0});var _in=s($he);sEt=r(_in,"BertForMultipleChoice"),_in.forEach(t),lEt=r(N4o," (BERT model)"),N4o.forEach(t),iEt=i(ae),D8=n(ae,"LI",{});var B4o=s(D8);Uqe=n(B4o,"STRONG",{});var vin=s(Uqe);dEt=r(vin,"big_bird"),vin.forEach(t),cEt=r(B4o," \u2014 "),khe=n(B4o,"A",{href:!0});var bin=s(khe);fEt=r(bin,"BigBirdForMultipleChoice"),bin.forEach(t),mEt=r(B4o," (BigBird model)"),B4o.forEach(t),gEt=i(ae),q8=n(ae,"LI",{});var G4o=s(q8);Hqe=n(G4o,"STRONG",{});var Tin=s(Hqe);hEt=r(Tin,"camembert"),Tin.forEach(t),uEt=r(G4o," \u2014 "),Rhe=n(G4o,"A",{href:!0});var Fin=s(Rhe);pEt=r(Fin,"CamembertForMultipleChoice"),Fin.forEach(t),_Et=r(G4o," (CamemBERT model)"),G4o.forEach(t),vEt=i(ae),j8=n(ae,"LI",{});var D4o=s(j8);Jqe=n(D4o,"STRONG",{});var Min=s(Jqe);bEt=r(Min,"canine"),Min.forEach(t),TEt=r(D4o," \u2014 "),Phe=n(D4o,"A",{href:!0});var Ein=s(Phe);FEt=r(Ein,"CanineForMultipleChoice"),Ein.forEach(t),MEt=r(D4o," (CANINE model)"),D4o.forEach(t),EEt=i(ae),O8=n(ae,"LI",{});var q4o=s(O8);Zqe=n(q4o,"STRONG",{});var Cin=s(Zqe);CEt=r(Cin,"convbert"),Cin.forEach(t),wEt=r(q4o," \u2014 "),Ihe=n(q4o,"A",{href:!0});var win=s(Ihe);AEt=r(win,"ConvBertForMultipleChoice"),win.forEach(t),LEt=r(q4o," (ConvBERT model)"),q4o.forEach(t),yEt=i(ae),V8=n(ae,"LI",{});var j4o=s(V8);Yqe=n(j4o,"STRONG",{});var Ain=s(Yqe);xEt=r(Ain,"data2vec-text"),Ain.forEach(t),SEt=r(j4o," \u2014 "),Nhe=n(j4o,"A",{href:!0});var Lin=s(Nhe);$Et=r(Lin,"Data2VecTextForMultipleChoice"),Lin.forEach(t),kEt=r(j4o," (Data2VecText model)"),j4o.forEach(t),REt=i(ae),X8=n(ae,"LI",{});var O4o=s(X8);Kqe=n(O4o,"STRONG",{});var yin=s(Kqe);PEt=r(yin,"deberta-v2"),yin.forEach(t),IEt=r(O4o," \u2014 "),Bhe=n(O4o,"A",{href:!0});var xin=s(Bhe);NEt=r(xin,"DebertaV2ForMultipleChoice"),xin.forEach(t),BEt=r(O4o," (DeBERTa-v2 model)"),O4o.forEach(t),GEt=i(ae),z8=n(ae,"LI",{});var V4o=s(z8);eje=n(V4o,"STRONG",{});var Sin=s(eje);DEt=r(Sin,"distilbert"),Sin.forEach(t),qEt=r(V4o," \u2014 "),Ghe=n(V4o,"A",{href:!0});var $in=s(Ghe);jEt=r($in,"DistilBertForMultipleChoice"),$in.forEach(t),OEt=r(V4o," (DistilBERT model)"),V4o.forEach(t),VEt=i(ae),W8=n(ae,"LI",{});var X4o=s(W8);oje=n(X4o,"STRONG",{});var kin=s(oje);XEt=r(kin,"electra"),kin.forEach(t),zEt=r(X4o," \u2014 "),Dhe=n(X4o,"A",{href:!0});var Rin=s(Dhe);WEt=r(Rin,"ElectraForMultipleChoice"),Rin.forEach(t),QEt=r(X4o," (ELECTRA model)"),X4o.forEach(t),UEt=i(ae),Q8=n(ae,"LI",{});var z4o=s(Q8);rje=n(z4o,"STRONG",{});var Pin=s(rje);HEt=r(Pin,"ernie"),Pin.forEach(t),JEt=r(z4o," \u2014 "),qhe=n(z4o,"A",{href:!0});var Iin=s(qhe);ZEt=r(Iin,"ErnieForMultipleChoice"),Iin.forEach(t),YEt=r(z4o," (ERNIE model)"),z4o.forEach(t),KEt=i(ae),U8=n(ae,"LI",{});var W4o=s(U8);tje=n(W4o,"STRONG",{});var Nin=s(tje);e3t=r(Nin,"ernie_m"),Nin.forEach(t),o3t=r(W4o," \u2014 "),jhe=n(W4o,"A",{href:!0});var Bin=s(jhe);r3t=r(Bin,"ErnieMForMultipleChoice"),Bin.forEach(t),t3t=r(W4o," (ErnieM model)"),W4o.forEach(t),a3t=i(ae),H8=n(ae,"LI",{});var Q4o=s(H8);aje=n(Q4o,"STRONG",{});var Gin=s(aje);n3t=r(Gin,"flaubert"),Gin.forEach(t),s3t=r(Q4o," \u2014 "),Ohe=n(Q4o,"A",{href:!0});var Din=s(Ohe);l3t=r(Din,"FlaubertForMultipleChoice"),Din.forEach(t),i3t=r(Q4o," (FlauBERT model)"),Q4o.forEach(t),d3t=i(ae),J8=n(ae,"LI",{});var U4o=s(J8);nje=n(U4o,"STRONG",{});var qin=s(nje);c3t=r(qin,"fnet"),qin.forEach(t),f3t=r(U4o," \u2014 "),Vhe=n(U4o,"A",{href:!0});var jin=s(Vhe);m3t=r(jin,"FNetForMultipleChoice"),jin.forEach(t),g3t=r(U4o," (FNet model)"),U4o.forEach(t),h3t=i(ae),Z8=n(ae,"LI",{});var H4o=s(Z8);sje=n(H4o,"STRONG",{});var Oin=s(sje);u3t=r(Oin,"funnel"),Oin.forEach(t),p3t=r(H4o," \u2014 "),Xhe=n(H4o,"A",{href:!0});var Vin=s(Xhe);_3t=r(Vin,"FunnelForMultipleChoice"),Vin.forEach(t),v3t=r(H4o," (Funnel Transformer model)"),H4o.forEach(t),b3t=i(ae),Y8=n(ae,"LI",{});var J4o=s(Y8);lje=n(J4o,"STRONG",{});var Xin=s(lje);T3t=r(Xin,"ibert"),Xin.forEach(t),F3t=r(J4o," \u2014 "),zhe=n(J4o,"A",{href:!0});var zin=s(zhe);M3t=r(zin,"IBertForMultipleChoice"),zin.forEach(t),E3t=r(J4o," (I-BERT model)"),J4o.forEach(t),C3t=i(ae),K8=n(ae,"LI",{});var Z4o=s(K8);ije=n(Z4o,"STRONG",{});var Win=s(ije);w3t=r(Win,"longformer"),Win.forEach(t),A3t=r(Z4o," \u2014 "),Whe=n(Z4o,"A",{href:!0});var Qin=s(Whe);L3t=r(Qin,"LongformerForMultipleChoice"),Qin.forEach(t),y3t=r(Z4o," (Longformer model)"),Z4o.forEach(t),x3t=i(ae),eA=n(ae,"LI",{});var Y4o=s(eA);dje=n(Y4o,"STRONG",{});var Uin=s(dje);S3t=r(Uin,"luke"),Uin.forEach(t),$3t=r(Y4o," \u2014 "),Qhe=n(Y4o,"A",{href:!0});var Hin=s(Qhe);k3t=r(Hin,"LukeForMultipleChoice"),Hin.forEach(t),R3t=r(Y4o," (LUKE model)"),Y4o.forEach(t),P3t=i(ae),oA=n(ae,"LI",{});var K4o=s(oA);cje=n(K4o,"STRONG",{});var Jin=s(cje);I3t=r(Jin,"megatron-bert"),Jin.forEach(t),N3t=r(K4o," \u2014 "),Uhe=n(K4o,"A",{href:!0});var Zin=s(Uhe);B3t=r(Zin,"MegatronBertForMultipleChoice"),Zin.forEach(t),G3t=r(K4o," (Megatron-BERT model)"),K4o.forEach(t),D3t=i(ae),rA=n(ae,"LI",{});var ebo=s(rA);fje=n(ebo,"STRONG",{});var Yin=s(fje);q3t=r(Yin,"mobilebert"),Yin.forEach(t),j3t=r(ebo," \u2014 "),Hhe=n(ebo,"A",{href:!0});var Kin=s(Hhe);O3t=r(Kin,"MobileBertForMultipleChoice"),Kin.forEach(t),V3t=r(ebo," (MobileBERT model)"),ebo.forEach(t),X3t=i(ae),tA=n(ae,"LI",{});var obo=s(tA);mje=n(obo,"STRONG",{});var edn=s(mje);z3t=r(edn,"mpnet"),edn.forEach(t),W3t=r(obo," \u2014 "),Jhe=n(obo,"A",{href:!0});var odn=s(Jhe);Q3t=r(odn,"MPNetForMultipleChoice"),odn.forEach(t),U3t=r(obo," (MPNet model)"),obo.forEach(t),H3t=i(ae),aA=n(ae,"LI",{});var rbo=s(aA);gje=n(rbo,"STRONG",{});var rdn=s(gje);J3t=r(rdn,"nezha"),rdn.forEach(t),Z3t=r(rbo," \u2014 "),Zhe=n(rbo,"A",{href:!0});var tdn=s(Zhe);Y3t=r(tdn,"NezhaForMultipleChoice"),tdn.forEach(t),K3t=r(rbo," (Nezha model)"),rbo.forEach(t),eCt=i(ae),nA=n(ae,"LI",{});var tbo=s(nA);hje=n(tbo,"STRONG",{});var adn=s(hje);oCt=r(adn,"nystromformer"),adn.forEach(t),rCt=r(tbo," \u2014 "),Yhe=n(tbo,"A",{href:!0});var ndn=s(Yhe);tCt=r(ndn,"NystromformerForMultipleChoice"),ndn.forEach(t),aCt=r(tbo," (Nystr\xF6mformer model)"),tbo.forEach(t),nCt=i(ae),sA=n(ae,"LI",{});var abo=s(sA);uje=n(abo,"STRONG",{});var sdn=s(uje);sCt=r(sdn,"qdqbert"),sdn.forEach(t),lCt=r(abo," \u2014 "),Khe=n(abo,"A",{href:!0});var ldn=s(Khe);iCt=r(ldn,"QDQBertForMultipleChoice"),ldn.forEach(t),dCt=r(abo," (QDQBert 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hdn=s(bje);ACt=r(hdn,"roc_bert"),hdn.forEach(t),LCt=r(ibo," \u2014 "),tue=n(ibo,"A",{href:!0});var udn=s(tue);yCt=r(udn,"RoCBertForMultipleChoice"),udn.forEach(t),xCt=r(ibo," (RoCBert model)"),ibo.forEach(t),SCt=i(ae),fA=n(ae,"LI",{});var dbo=s(fA);Tje=n(dbo,"STRONG",{});var pdn=s(Tje);$Ct=r(pdn,"roformer"),pdn.forEach(t),kCt=r(dbo," \u2014 "),aue=n(dbo,"A",{href:!0});var _dn=s(aue);RCt=r(_dn,"RoFormerForMultipleChoice"),_dn.forEach(t),PCt=r(dbo," (RoFormer model)"),dbo.forEach(t),ICt=i(ae),mA=n(ae,"LI",{});var cbo=s(mA);Fje=n(cbo,"STRONG",{});var vdn=s(Fje);NCt=r(vdn,"squeezebert"),vdn.forEach(t),BCt=r(cbo," \u2014 "),nue=n(cbo,"A",{href:!0});var bdn=s(nue);GCt=r(bdn,"SqueezeBertForMultipleChoice"),bdn.forEach(t),DCt=r(cbo," (SqueezeBERT model)"),cbo.forEach(t),qCt=i(ae),gA=n(ae,"LI",{});var fbo=s(gA);Mje=n(fbo,"STRONG",{});var Tdn=s(Mje);jCt=r(Tdn,"xlm"),Tdn.forEach(t),OCt=r(fbo," \u2014 "),sue=n(fbo,"A",{href:!0});var Fdn=s(sue);VCt=r(Fdn,"XLMForMultipleChoice"),Fdn.forEach(t),XCt=r(fbo," (XLM model)"),fbo.forEach(t),zCt=i(ae),hA=n(ae,"LI",{});var mbo=s(hA);Eje=n(mbo,"STRONG",{});var Mdn=s(Eje);WCt=r(Mdn,"xlm-roberta"),Mdn.forEach(t),QCt=r(mbo," \u2014 "),lue=n(mbo,"A",{href:!0});var Edn=s(lue);UCt=r(Edn,"XLMRobertaForMultipleChoice"),Edn.forEach(t),HCt=r(mbo," (XLM-RoBERTa model)"),mbo.forEach(t),JCt=i(ae),uA=n(ae,"LI",{});var gbo=s(uA);Cje=n(gbo,"STRONG",{});var Cdn=s(Cje);ZCt=r(Cdn,"xlm-roberta-xl"),Cdn.forEach(t),YCt=r(gbo," \u2014 "),iue=n(gbo,"A",{href:!0});var wdn=s(iue);KCt=r(wdn,"XLMRobertaXLForMultipleChoice"),wdn.forEach(t),e5t=r(gbo," (XLM-RoBERTa-XL model)"),gbo.forEach(t),o5t=i(ae),pA=n(ae,"LI",{});var hbo=s(pA);wje=n(hbo,"STRONG",{});var Adn=s(wje);r5t=r(Adn,"xlnet"),Adn.forEach(t),t5t=r(hbo," \u2014 "),due=n(hbo,"A",{href:!0});var Ldn=s(due);a5t=r(Ldn,"XLNetForMultipleChoice"),Ldn.forEach(t),n5t=r(hbo," (XLNet model)"),hbo.forEach(t),s5t=i(ae),_A=n(ae,"LI",{});var ubo=s(_A);Aje=n(ubo,"STRONG",{});var ydn=s(Aje);l5t=r(ydn,"xmod"),ydn.forEach(t),i5t=r(ubo," \u2014 "),cue=n(ubo,"A",{href:!0});var xdn=s(cue);d5t=r(xdn,"XmodForMultipleChoice"),xdn.forEach(t),c5t=r(ubo," (X-MOD model)"),ubo.forEach(t),f5t=i(ae),vA=n(ae,"LI",{});var pbo=s(vA);Lje=n(pbo,"STRONG",{});var Sdn=s(Lje);m5t=r(Sdn,"yoso"),Sdn.forEach(t),g5t=r(pbo," \u2014 "),fue=n(pbo,"A",{href:!0});var $dn=s(fue);h5t=r($dn,"YosoForMultipleChoice"),$dn.forEach(t),u5t=r(pbo," (YOSO model)"),pbo.forEach(t),ae.forEach(t),p5t=i(Ya),bA=n(Ya,"P",{});var _bo=s(bA);_5t=r(_bo,"The model is set in evaluation mode by default using "),yje=n(_bo,"CODE",{});var kdn=s(yje);v5t=r(kdn,"model.eval()"),kdn.forEach(t),b5t=r(_bo,` (so for instance, dropout modules are
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with the `),mue=n(wTe,"A",{href:!0});var Bdn=s(mue);A5t=r(Bdn,"from_pretrained()"),Bdn.forEach(t),L5t=r(wTe," class method or the "),gue=n(wTe,"A",{href:!0});var Gdn=s(gue);y5t=r(Gdn,"from_config()"),Gdn.forEach(t),x5t=r(wTe,` class
method.`),wTe.forEach(t),S5t=i(ld),SD=n(ld,"P",{});var $wo=s(SD);$5t=r($wo,"This class cannot be instantiated directly using "),kje=n($wo,"CODE",{});var Ddn=s(kje);k5t=r(Ddn,"__init__()"),Ddn.forEach(t),R5t=r($wo," (throws an error)."),$wo.forEach(t),P5t=i(ld),oa=n(ld,"DIV",{class:!0});var wI=s(oa);T($D.$$.fragment,wI),I5t=i(wI),Rje=n(wI,"P",{});var qdn=s(Rje);N5t=r(qdn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),qdn.forEach(t),B5t=i(wI),Gf=n(wI,"P",{});var ATe=s(Gf);G5t=r(ATe,`Note:
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model\u2019s configuration. Use `),hue=n(ATe,"A",{href:!0});var Odn=s(hue);j5t=r(Odn,"from_pretrained()"),Odn.forEach(t),O5t=r(ATe," to load the model weights."),ATe.forEach(t),V5t=i(wI),T(MA.$$.fragment,wI),wI.forEach(t),X5t=i(ld),ft=n(ld,"DIV",{class:!0});var id=s(ft);T(kD.$$.fragment,id),z5t=i(id),Ije=n(id,"P",{});var Vdn=s(Ije);W5t=r(Vdn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),Vdn.forEach(t),Q5t=i(id),Hn=n(id,"P",{});var AI=s(Hn);U5t=r(AI,"The model class to instantiate is selected based on the "),Nje=n(AI,"CODE",{});var Xdn=s(Nje);H5t=r(Xdn,"model_type"),Xdn.forEach(t),J5t=r(AI,` property of the config object (either
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falling back to using pattern matching on `),Gje=n(AI,"CODE",{});var Wdn=s(Gje);K5t=r(Wdn,"pretrained_model_name_or_path"),Wdn.forEach(t),e0t=r(AI,":"),AI.forEach(t),o0t=i(id),Me=n(id,"UL",{});var Ce=s(Me);EA=n(Ce,"LI",{});var vbo=s(EA);Dje=n(vbo,"STRONG",{});var Qdn=s(Dje);r0t=r(Qdn,"albert"),Qdn.forEach(t),t0t=r(vbo," \u2014 "),uue=n(vbo,"A",{href:!0});var Udn=s(uue);a0t=r(Udn,"TFAlbertForMultipleChoice"),Udn.forEach(t),n0t=r(vbo," (ALBERT model)"),vbo.forEach(t),s0t=i(Ce),CA=n(Ce,"LI",{});var bbo=s(CA);qje=n(bbo,"STRONG",{});var Hdn=s(qje);l0t=r(Hdn,"bert"),Hdn.forEach(t),i0t=r(bbo," \u2014 "),pue=n(bbo,"A",{href:!0});var Jdn=s(pue);d0t=r(Jdn,"TFBertForMultipleChoice"),Jdn.forEach(t),c0t=r(bbo," (BERT model)"),bbo.forEach(t),f0t=i(Ce),wA=n(Ce,"LI",{});var Tbo=s(wA);jje=n(Tbo,"STRONG",{});var Zdn=s(jje);m0t=r(Zdn,"camembert"),Zdn.forEach(t),g0t=r(Tbo," \u2014 "),_ue=n(Tbo,"A",{href:!0});var Ydn=s(_ue);h0t=r(Ydn,"TFCamembertForMultipleChoice"),Ydn.forEach(t),u0t=r(Tbo," (CamemBERT 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method.`),LTe.forEach(t),Wwt=i(dd),ID=n(dd,"P",{});var Rwo=s(ID);Qwt=r(Rwo,"This class cannot be instantiated directly using "),nOe=n(Rwo,"CODE",{});var Pcn=s(nOe);Uwt=r(Pcn,"__init__()"),Pcn.forEach(t),Hwt=r(Rwo," (throws an error)."),Rwo.forEach(t),Jwt=i(dd),ra=n(dd,"DIV",{class:!0});var LI=s(ra);T(ND.$$.fragment,LI),Zwt=i(LI),sOe=n(LI,"P",{});var Icn=s(sOe);Ywt=r(Icn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Icn.forEach(t),Kwt=i(LI),jf=n(LI,"P",{});var yTe=s(jf);e6t=r(yTe,`Note:
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model\u2019s configuration. Use `),Iue=n(yTe,"A",{href:!0});var Bcn=s(Iue);t6t=r(Bcn,"from_pretrained()"),Bcn.forEach(t),a6t=r(yTe," to load the model weights."),yTe.forEach(t),n6t=i(LI),T(VA.$$.fragment,LI),LI.forEach(t),s6t=i(dd),mt=n(dd,"DIV",{class:!0});var cd=s(mt);T(BD.$$.fragment,cd),l6t=i(cd),iOe=n(cd,"P",{});var Gcn=s(iOe);i6t=r(Gcn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),Gcn.forEach(t),d6t=i(cd),Jn=n(cd,"P",{});var yI=s(Jn);c6t=r(yI,"The model class to instantiate is selected based on the "),dOe=n(yI,"CODE",{});var Dcn=s(dOe);f6t=r(Dcn,"model_type"),Dcn.forEach(t),m6t=r(yI,` property of the config object (either
passed as an argument or loaded from `),cOe=n(yI,"CODE",{});var qcn=s(cOe);g6t=r(qcn,"pretrained_model_name_or_path"),qcn.forEach(t),h6t=r(yI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fOe=n(yI,"CODE",{});var jcn=s(fOe);u6t=r(jcn,"pretrained_model_name_or_path"),jcn.forEach(t),p6t=r(yI,":"),yI.forEach(t),_6t=i(cd),Ve=n(cd,"UL",{});var Ze=s(Ve);XA=n(Ze,"LI",{});var Nbo=s(XA);mOe=n(Nbo,"STRONG",{});var Ocn=s(mOe);v6t=r(Ocn,"albert"),Ocn.forEach(t),b6t=r(Nbo," \u2014 "),Nue=n(Nbo,"A",{href:!0});var Vcn=s(Nue);T6t=r(Vcn,"FlaxAlbertForMultipleChoice"),Vcn.forEach(t),F6t=r(Nbo," (ALBERT model)"),Nbo.forEach(t),M6t=i(Ze),zA=n(Ze,"LI",{});var Bbo=s(zA);gOe=n(Bbo,"STRONG",{});var Xcn=s(gOe);E6t=r(Xcn,"bert"),Xcn.forEach(t),C6t=r(Bbo," \u2014 "),Bue=n(Bbo,"A",{href:!0});var zcn=s(Bue);w6t=r(zcn,"FlaxBertForMultipleChoice"),zcn.forEach(t),A6t=r(Bbo," (BERT model)"),Bbo.forEach(t),L6t=i(Ze),WA=n(Ze,"LI",{});var Gbo=s(WA);hOe=n(Gbo,"STRONG",{});var Wcn=s(hOe);y6t=r(Wcn,"big_bird"),Wcn.forEach(t),x6t=r(Gbo," \u2014 "),Gue=n(Gbo,"A",{href:!0});var Qcn=s(Gue);S6t=r(Qcn,"FlaxBigBirdForMultipleChoice"),Qcn.forEach(t),$6t=r(Gbo," (BigBird model)"),Gbo.forEach(t),k6t=i(Ze),QA=n(Ze,"LI",{});var Dbo=s(QA);uOe=n(Dbo,"STRONG",{});var Ucn=s(uOe);R6t=r(Ucn,"distilbert"),Ucn.forEach(t),P6t=r(Dbo," \u2014 "),Due=n(Dbo,"A",{href:!0});var Hcn=s(Due);I6t=r(Hcn,"FlaxDistilBertForMultipleChoice"),Hcn.forEach(t),N6t=r(Dbo," (DistilBERT model)"),Dbo.forEach(t),B6t=i(Ze),UA=n(Ze,"LI",{});var qbo=s(UA);pOe=n(qbo,"STRONG",{});var Jcn=s(pOe);G6t=r(Jcn,"electra"),Jcn.forEach(t),D6t=r(qbo," \u2014 "),que=n(qbo,"A",{href:!0});var Zcn=s(que);q6t=r(Zcn,"FlaxElectraForMultipleChoice"),Zcn.forEach(t),j6t=r(qbo," (ELECTRA model)"),qbo.forEach(t),O6t=i(Ze),HA=n(Ze,"LI",{});var jbo=s(HA);_Oe=n(jbo,"STRONG",{});var Ycn=s(_Oe);V6t=r(Ycn,"roberta"),Ycn.forEach(t),X6t=r(jbo," \u2014 "),jue=n(jbo,"A",{href:!0});var Kcn=s(jue);z6t=r(Kcn,"FlaxRobertaForMultipleChoice"),Kcn.forEach(t),W6t=r(jbo," (RoBERTa model)"),jbo.forEach(t),Q6t=i(Ze),JA=n(Ze,"LI",{});var Obo=s(JA);vOe=n(Obo,"STRONG",{});var efn=s(vOe);U6t=r(efn,"roberta-prelayernorm"),efn.forEach(t),H6t=r(Obo," \u2014 "),Oue=n(Obo,"A",{href:!0});var ofn=s(Oue);J6t=r(ofn,"FlaxRobertaPreLayerNormForMultipleChoice"),ofn.forEach(t),Z6t=r(Obo," (RoBERTa-PreLayerNorm model)"),Obo.forEach(t),Y6t=i(Ze),ZA=n(Ze,"LI",{});var Vbo=s(ZA);bOe=n(Vbo,"STRONG",{});var rfn=s(bOe);K6t=r(rfn,"roformer"),rfn.forEach(t),e8t=r(Vbo," \u2014 "),Vue=n(Vbo,"A",{href:!0});var tfn=s(Vue);o8t=r(tfn,"FlaxRoFormerForMultipleChoice"),tfn.forEach(t),r8t=r(Vbo," (RoFormer model)"),Vbo.forEach(t),t8t=i(Ze),YA=n(Ze,"LI",{});var Xbo=s(YA);TOe=n(Xbo,"STRONG",{});var afn=s(TOe);a8t=r(afn,"xlm-roberta"),afn.forEach(t),n8t=r(Xbo," \u2014 "),Xue=n(Xbo,"A",{href:!0});var nfn=s(Xue);s8t=r(nfn,"FlaxXLMRobertaForMultipleChoice"),nfn.forEach(t),l8t=r(Xbo," (XLM-RoBERTa model)"),Xbo.forEach(t),Ze.forEach(t),i8t=i(cd),T(KA.$$.fragment,cd),cd.forEach(t),dd.forEach(t),HCo=i(f),Of=n(f,"H3",{class:!0});var Pwo=s(Of);e9=n(Pwo,"A",{id:!0,class:!0,href:!0});var sfn=s(e9);FOe=n(sfn,"SPAN",{});var lfn=s(FOe);T(GD.$$.fragment,lfn),lfn.forEach(t),sfn.forEach(t),d8t=i(Pwo),MOe=n(Pwo,"SPAN",{});var ifn=s(MOe);c8t=r(ifn,"AutoModelForNextSentencePrediction"),ifn.forEach(t),Pwo.forEach(t),JCo=i(f),cr=n(f,"DIV",{class:!0});var fd=s(cr);T(DD.$$.fragment,fd),f8t=i(fd),Vf=n(fd,"P",{});var xTe=s(Vf);m8t=r(xTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),zue=n(xTe,"A",{href:!0});var dfn=s(zue);g8t=r(dfn,"from_pretrained()"),dfn.forEach(t),h8t=r(xTe," class method or the "),Wue=n(xTe,"A",{href:!0});var cfn=s(Wue);u8t=r(cfn,"from_config()"),cfn.forEach(t),p8t=r(xTe,` class
method.`),xTe.forEach(t),_8t=i(fd),qD=n(fd,"P",{});var Iwo=s(qD);v8t=r(Iwo,"This class cannot be instantiated directly using "),EOe=n(Iwo,"CODE",{});var ffn=s(EOe);b8t=r(ffn,"__init__()"),ffn.forEach(t),T8t=r(Iwo," (throws an error)."),Iwo.forEach(t),F8t=i(fd),ta=n(fd,"DIV",{class:!0});var xI=s(ta);T(jD.$$.fragment,xI),M8t=i(xI),COe=n(xI,"P",{});var mfn=s(COe);E8t=r(mfn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),mfn.forEach(t),C8t=i(xI),Xf=n(xI,"P",{});var STe=s(Xf);w8t=r(STe,`Note:
Loading a model from its configuration file does `),wOe=n(STe,"STRONG",{});var gfn=s(wOe);A8t=r(gfn,"not"),gfn.forEach(t),L8t=r(STe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Que=n(STe,"A",{href:!0});var hfn=s(Que);y8t=r(hfn,"from_pretrained()"),hfn.forEach(t),x8t=r(STe," to load the model weights."),STe.forEach(t),S8t=i(xI),T(o9.$$.fragment,xI),xI.forEach(t),$8t=i(fd),ho=n(fd,"DIV",{class:!0});var Ka=s(ho);T(OD.$$.fragment,Ka),k8t=i(Ka),AOe=n(Ka,"P",{});var ufn=s(AOe);R8t=r(ufn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),ufn.forEach(t),P8t=i(Ka),Zn=n(Ka,"P",{});var SI=s(Zn);I8t=r(SI,"The model class to instantiate is selected based on the "),LOe=n(SI,"CODE",{});var pfn=s(LOe);N8t=r(pfn,"model_type"),pfn.forEach(t),B8t=r(SI,` property of the config object (either
passed as an argument or loaded from `),yOe=n(SI,"CODE",{});var _fn=s(yOe);G8t=r(_fn,"pretrained_model_name_or_path"),_fn.forEach(t),D8t=r(SI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xOe=n(SI,"CODE",{});var vfn=s(xOe);q8t=r(vfn,"pretrained_model_name_or_path"),vfn.forEach(t),j8t=r(SI,":"),SI.forEach(t),O8t=i(Ka),Ke=n(Ka,"UL",{});var xt=s(Ke);r9=n(xt,"LI",{});var zbo=s(r9);SOe=n(zbo,"STRONG",{});var bfn=s(SOe);V8t=r(bfn,"bert"),bfn.forEach(t),X8t=r(zbo," \u2014 "),Uue=n(zbo,"A",{href:!0});var Tfn=s(Uue);z8t=r(Tfn,"BertForNextSentencePrediction"),Tfn.forEach(t),W8t=r(zbo," (BERT model)"),zbo.forEach(t),Q8t=i(xt),t9=n(xt,"LI",{});var Wbo=s(t9);$Oe=n(Wbo,"STRONG",{});var Ffn=s($Oe);U8t=r(Ffn,"ernie"),Ffn.forEach(t),H8t=r(Wbo," \u2014 "),Hue=n(Wbo,"A",{href:!0});var Mfn=s(Hue);J8t=r(Mfn,"ErnieForNextSentencePrediction"),Mfn.forEach(t),Z8t=r(Wbo," (ERNIE model)"),Wbo.forEach(t),Y8t=i(xt),a9=n(xt,"LI",{});var Qbo=s(a9);kOe=n(Qbo,"STRONG",{});var Efn=s(kOe);K8t=r(Efn,"fnet"),Efn.forEach(t),eAt=r(Qbo," \u2014 "),Jue=n(Qbo,"A",{href:!0});var Cfn=s(Jue);oAt=r(Cfn,"FNetForNextSentencePrediction"),Cfn.forEach(t),rAt=r(Qbo," (FNet model)"),Qbo.forEach(t),tAt=i(xt),n9=n(xt,"LI",{});var Ubo=s(n9);ROe=n(Ubo,"STRONG",{});var wfn=s(ROe);aAt=r(wfn,"megatron-bert"),wfn.forEach(t),nAt=r(Ubo," \u2014 "),Zue=n(Ubo,"A",{href:!0});var Afn=s(Zue);sAt=r(Afn,"MegatronBertForNextSentencePrediction"),Afn.forEach(t),lAt=r(Ubo," (Megatron-BERT model)"),Ubo.forEach(t),iAt=i(xt),s9=n(xt,"LI",{});var Hbo=s(s9);POe=n(Hbo,"STRONG",{});var Lfn=s(POe);dAt=r(Lfn,"mobilebert"),Lfn.forEach(t),cAt=r(Hbo," \u2014 "),Yue=n(Hbo,"A",{href:!0});var yfn=s(Yue);fAt=r(yfn,"MobileBertForNextSentencePrediction"),yfn.forEach(t),mAt=r(Hbo," (MobileBERT model)"),Hbo.forEach(t),gAt=i(xt),l9=n(xt,"LI",{});var Jbo=s(l9);IOe=n(Jbo,"STRONG",{});var xfn=s(IOe);hAt=r(xfn,"nezha"),xfn.forEach(t),uAt=r(Jbo," \u2014 "),Kue=n(Jbo,"A",{href:!0});var Sfn=s(Kue);pAt=r(Sfn,"NezhaForNextSentencePrediction"),Sfn.forEach(t),_At=r(Jbo," (Nezha model)"),Jbo.forEach(t),vAt=i(xt),i9=n(xt,"LI",{});var Zbo=s(i9);NOe=n(Zbo,"STRONG",{});var $fn=s(NOe);bAt=r($fn,"qdqbert"),$fn.forEach(t),TAt=r(Zbo," \u2014 "),epe=n(Zbo,"A",{href:!0});var kfn=s(epe);FAt=r(kfn,"QDQBertForNextSentencePrediction"),kfn.forEach(t),MAt=r(Zbo," (QDQBert model)"),Zbo.forEach(t),xt.forEach(t),EAt=i(Ka),d9=n(Ka,"P",{});var Ybo=s(d9);CAt=r(Ybo,"The model is set in evaluation mode by default using "),BOe=n(Ybo,"CODE",{});var Rfn=s(BOe);wAt=r(Rfn,"model.eval()"),Rfn.forEach(t),AAt=r(Ybo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),GOe=n(Ybo,"CODE",{});var Pfn=s(GOe);LAt=r(Pfn,"model.train()"),Pfn.forEach(t),Ybo.forEach(t),yAt=i(Ka),T(c9.$$.fragment,Ka),Ka.forEach(t),fd.forEach(t),ZCo=i(f),zf=n(f,"H3",{class:!0});var Nwo=s(zf);f9=n(Nwo,"A",{id:!0,class:!0,href:!0});var Ifn=s(f9);DOe=n(Ifn,"SPAN",{});var Nfn=s(DOe);T(VD.$$.fragment,Nfn),Nfn.forEach(t),Ifn.forEach(t),xAt=i(Nwo),qOe=n(Nwo,"SPAN",{});var Bfn=s(qOe);SAt=r(Bfn,"TFAutoModelForNextSentencePrediction"),Bfn.forEach(t),Nwo.forEach(t),YCo=i(f),fr=n(f,"DIV",{class:!0});var md=s(fr);T(XD.$$.fragment,md),$At=i(md),Wf=n(md,"P",{});var $Te=s(Wf);kAt=r($Te,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),ope=n($Te,"A",{href:!0});var Gfn=s(ope);RAt=r(Gfn,"from_pretrained()"),Gfn.forEach(t),PAt=r($Te," class method or the "),rpe=n($Te,"A",{href:!0});var Dfn=s(rpe);IAt=r(Dfn,"from_config()"),Dfn.forEach(t),NAt=r($Te,` class
method.`),$Te.forEach(t),BAt=i(md),zD=n(md,"P",{});var Bwo=s(zD);GAt=r(Bwo,"This class cannot be instantiated directly using "),jOe=n(Bwo,"CODE",{});var qfn=s(jOe);DAt=r(qfn,"__init__()"),qfn.forEach(t),qAt=r(Bwo," (throws an error)."),Bwo.forEach(t),jAt=i(md),aa=n(md,"DIV",{class:!0});var $I=s(aa);T(WD.$$.fragment,$I),OAt=i($I),OOe=n($I,"P",{});var jfn=s(OOe);VAt=r(jfn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),jfn.forEach(t),XAt=i($I),Qf=n($I,"P",{});var kTe=s(Qf);zAt=r(kTe,`Note:
Loading a model from its configuration file does `),VOe=n(kTe,"STRONG",{});var Ofn=s(VOe);WAt=r(Ofn,"not"),Ofn.forEach(t),QAt=r(kTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),tpe=n(kTe,"A",{href:!0});var Vfn=s(tpe);UAt=r(Vfn,"from_pretrained()"),Vfn.forEach(t),HAt=r(kTe," to load the model weights."),kTe.forEach(t),JAt=i($I),T(m9.$$.fragment,$I),$I.forEach(t),ZAt=i(md),gt=n(md,"DIV",{class:!0});var gd=s(gt);T(QD.$$.fragment,gd),YAt=i(gd),XOe=n(gd,"P",{});var Xfn=s(XOe);KAt=r(Xfn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),Xfn.forEach(t),e9t=i(gd),Yn=n(gd,"P",{});var kI=s(Yn);o9t=r(kI,"The model class to instantiate is selected based on the "),zOe=n(kI,"CODE",{});var zfn=s(zOe);r9t=r(zfn,"model_type"),zfn.forEach(t),t9t=r(kI,` property of the config object (either
passed as an argument or loaded from `),WOe=n(kI,"CODE",{});var Wfn=s(WOe);a9t=r(Wfn,"pretrained_model_name_or_path"),Wfn.forEach(t),n9t=r(kI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),QOe=n(kI,"CODE",{});var Qfn=s(QOe);s9t=r(Qfn,"pretrained_model_name_or_path"),Qfn.forEach(t),l9t=r(kI,":"),kI.forEach(t),i9t=i(gd),UD=n(gd,"UL",{});var Gwo=s(UD);g9=n(Gwo,"LI",{});var Kbo=s(g9);UOe=n(Kbo,"STRONG",{});var Ufn=s(UOe);d9t=r(Ufn,"bert"),Ufn.forEach(t),c9t=r(Kbo," \u2014 "),ape=n(Kbo,"A",{href:!0});var Hfn=s(ape);f9t=r(Hfn,"TFBertForNextSentencePrediction"),Hfn.forEach(t),m9t=r(Kbo," (BERT model)"),Kbo.forEach(t),g9t=i(Gwo),h9=n(Gwo,"LI",{});var e7o=s(h9);HOe=n(e7o,"STRONG",{});var Jfn=s(HOe);h9t=r(Jfn,"mobilebert"),Jfn.forEach(t),u9t=r(e7o," \u2014 "),npe=n(e7o,"A",{href:!0});var Zfn=s(npe);p9t=r(Zfn,"TFMobileBertForNextSentencePrediction"),Zfn.forEach(t),_9t=r(e7o," (MobileBERT model)"),e7o.forEach(t),Gwo.forEach(t),v9t=i(gd),T(u9.$$.fragment,gd),gd.forEach(t),md.forEach(t),KCo=i(f),Uf=n(f,"H3",{class:!0});var Dwo=s(Uf);p9=n(Dwo,"A",{id:!0,class:!0,href:!0});var Yfn=s(p9);JOe=n(Yfn,"SPAN",{});var Kfn=s(JOe);T(HD.$$.fragment,Kfn),Kfn.forEach(t),Yfn.forEach(t),b9t=i(Dwo),ZOe=n(Dwo,"SPAN",{});var emn=s(ZOe);T9t=r(emn,"FlaxAutoModelForNextSentencePrediction"),emn.forEach(t),Dwo.forEach(t),e5o=i(f),mr=n(f,"DIV",{class:!0});var hd=s(mr);T(JD.$$.fragment,hd),F9t=i(hd),Hf=n(hd,"P",{});var RTe=s(Hf);M9t=r(RTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),spe=n(RTe,"A",{href:!0});var omn=s(spe);E9t=r(omn,"from_pretrained()"),omn.forEach(t),C9t=r(RTe," class method or the "),lpe=n(RTe,"A",{href:!0});var rmn=s(lpe);w9t=r(rmn,"from_config()"),rmn.forEach(t),A9t=r(RTe,` class
method.`),RTe.forEach(t),L9t=i(hd),ZD=n(hd,"P",{});var qwo=s(ZD);y9t=r(qwo,"This class cannot be instantiated directly using "),YOe=n(qwo,"CODE",{});var tmn=s(YOe);x9t=r(tmn,"__init__()"),tmn.forEach(t),S9t=r(qwo," (throws an error)."),qwo.forEach(t),$9t=i(hd),na=n(hd,"DIV",{class:!0});var RI=s(na);T(YD.$$.fragment,RI),k9t=i(RI),KOe=n(RI,"P",{});var amn=s(KOe);R9t=r(amn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),amn.forEach(t),P9t=i(RI),Jf=n(RI,"P",{});var PTe=s(Jf);I9t=r(PTe,`Note:
Loading a model from its configuration file does `),eVe=n(PTe,"STRONG",{});var nmn=s(eVe);N9t=r(nmn,"not"),nmn.forEach(t),B9t=r(PTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),ipe=n(PTe,"A",{href:!0});var smn=s(ipe);G9t=r(smn,"from_pretrained()"),smn.forEach(t),D9t=r(PTe," to load the model weights."),PTe.forEach(t),q9t=i(RI),T(_9.$$.fragment,RI),RI.forEach(t),j9t=i(hd),ht=n(hd,"DIV",{class:!0});var ud=s(ht);T(KD.$$.fragment,ud),O9t=i(ud),oVe=n(ud,"P",{});var lmn=s(oVe);V9t=r(lmn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),lmn.forEach(t),X9t=i(ud),Kn=n(ud,"P",{});var PI=s(Kn);z9t=r(PI,"The model class to instantiate is selected based on the "),rVe=n(PI,"CODE",{});var imn=s(rVe);W9t=r(imn,"model_type"),imn.forEach(t),Q9t=r(PI,` property of the config object (either
passed as an argument or loaded from `),tVe=n(PI,"CODE",{});var dmn=s(tVe);U9t=r(dmn,"pretrained_model_name_or_path"),dmn.forEach(t),H9t=r(PI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aVe=n(PI,"CODE",{});var cmn=s(aVe);J9t=r(cmn,"pretrained_model_name_or_path"),cmn.forEach(t),Z9t=r(PI,":"),PI.forEach(t),Y9t=i(ud),nVe=n(ud,"UL",{});var fmn=s(nVe);v9=n(fmn,"LI",{});var o7o=s(v9);sVe=n(o7o,"STRONG",{});var mmn=s(sVe);K9t=r(mmn,"bert"),mmn.forEach(t),eLt=r(o7o," \u2014 "),dpe=n(o7o,"A",{href:!0});var gmn=s(dpe);oLt=r(gmn,"FlaxBertForNextSentencePrediction"),gmn.forEach(t),rLt=r(o7o," (BERT model)"),o7o.forEach(t),fmn.forEach(t),tLt=i(ud),T(b9.$$.fragment,ud),ud.forEach(t),hd.forEach(t),o5o=i(f),Zf=n(f,"H3",{class:!0});var jwo=s(Zf);T9=n(jwo,"A",{id:!0,class:!0,href:!0});var hmn=s(T9);lVe=n(hmn,"SPAN",{});var umn=s(lVe);T(eq.$$.fragment,umn),umn.forEach(t),hmn.forEach(t),aLt=i(jwo),iVe=n(jwo,"SPAN",{});var pmn=s(iVe);nLt=r(pmn,"AutoModelForTokenClassification"),pmn.forEach(t),jwo.forEach(t),r5o=i(f),gr=n(f,"DIV",{class:!0});var pd=s(gr);T(oq.$$.fragment,pd),sLt=i(pd),Yf=n(pd,"P",{});var ITe=s(Yf);lLt=r(ITe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),cpe=n(ITe,"A",{href:!0});var _mn=s(cpe);iLt=r(_mn,"from_pretrained()"),_mn.forEach(t),dLt=r(ITe," class method or the "),fpe=n(ITe,"A",{href:!0});var vmn=s(fpe);cLt=r(vmn,"from_config()"),vmn.forEach(t),fLt=r(ITe,` class
method.`),ITe.forEach(t),mLt=i(pd),rq=n(pd,"P",{});var Owo=s(rq);gLt=r(Owo,"This class cannot be instantiated directly using "),dVe=n(Owo,"CODE",{});var bmn=s(dVe);hLt=r(bmn,"__init__()"),bmn.forEach(t),uLt=r(Owo," (throws an error)."),Owo.forEach(t),pLt=i(pd),sa=n(pd,"DIV",{class:!0});var II=s(sa);T(tq.$$.fragment,II),_Lt=i(II),cVe=n(II,"P",{});var Tmn=s(cVe);vLt=r(Tmn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),Tmn.forEach(t),bLt=i(II),Kf=n(II,"P",{});var NTe=s(Kf);TLt=r(NTe,`Note:
Loading a model from its configuration file does `),fVe=n(NTe,"STRONG",{});var Fmn=s(fVe);FLt=r(Fmn,"not"),Fmn.forEach(t),MLt=r(NTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),mpe=n(NTe,"A",{href:!0});var Mmn=s(mpe);ELt=r(Mmn,"from_pretrained()"),Mmn.forEach(t),CLt=r(NTe," to load the model weights."),NTe.forEach(t),wLt=i(II),T(F9.$$.fragment,II),II.forEach(t),ALt=i(pd),uo=n(pd,"DIV",{class:!0});var en=s(uo);T(aq.$$.fragment,en),LLt=i(en),mVe=n(en,"P",{});var Emn=s(mVe);yLt=r(Emn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),Emn.forEach(t),xLt=i(en),es=n(en,"P",{});var NI=s(es);SLt=r(NI,"The model class to instantiate is selected based on the "),gVe=n(NI,"CODE",{});var Cmn=s(gVe);$Lt=r(Cmn,"model_type"),Cmn.forEach(t),kLt=r(NI,` property of the config object (either
passed as an argument or loaded from `),hVe=n(NI,"CODE",{});var wmn=s(hVe);RLt=r(wmn,"pretrained_model_name_or_path"),wmn.forEach(t),PLt=r(NI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),uVe=n(NI,"CODE",{});var Amn=s(uVe);ILt=r(Amn,"pretrained_model_name_or_path"),Amn.forEach(t),NLt=r(NI,":"),NI.forEach(t),BLt=i(en),Z=n(en,"UL",{});var Y=s(Z);M9=n(Y,"LI",{});var r7o=s(M9);pVe=n(r7o,"STRONG",{});var Lmn=s(pVe);GLt=r(Lmn,"albert"),Lmn.forEach(t),DLt=r(r7o," \u2014 "),gpe=n(r7o,"A",{href:!0});var ymn=s(gpe);qLt=r(ymn,"AlbertForTokenClassification"),ymn.forEach(t),jLt=r(r7o," (ALBERT model)"),r7o.forEach(t),OLt=i(Y),E9=n(Y,"LI",{});var t7o=s(E9);_Ve=n(t7o,"STRONG",{});var xmn=s(_Ve);VLt=r(xmn,"bert"),xmn.forEach(t),XLt=r(t7o," \u2014 "),hpe=n(t7o,"A",{href:!0});var Smn=s(hpe);zLt=r(Smn,"BertForTokenClassification"),Smn.forEach(t),WLt=r(t7o," (BERT model)"),t7o.forEach(t),QLt=i(Y),C9=n(Y,"LI",{});var a7o=s(C9);vVe=n(a7o,"STRONG",{});var $mn=s(vVe);ULt=r($mn,"big_bird"),$mn.forEach(t),HLt=r(a7o," \u2014 "),upe=n(a7o,"A",{href:!0});var kmn=s(upe);JLt=r(kmn,"BigBirdForTokenClassification"),kmn.forEach(t),ZLt=r(a7o," (BigBird model)"),a7o.forEach(t),YLt=i(Y),w9=n(Y,"LI",{});var n7o=s(w9);bVe=n(n7o,"STRONG",{});var Rmn=s(bVe);KLt=r(Rmn,"bloom"),Rmn.forEach(t),eyt=r(n7o," \u2014 "),ppe=n(n7o,"A",{href:!0});var Pmn=s(ppe);oyt=r(Pmn,"BloomForTokenClassification"),Pmn.forEach(t),ryt=r(n7o," (BLOOM model)"),n7o.forEach(t),tyt=i(Y),A9=n(Y,"LI",{});var s7o=s(A9);TVe=n(s7o,"STRONG",{});var Imn=s(TVe);ayt=r(Imn,"camembert"),Imn.forEach(t),nyt=r(s7o," \u2014 "),_pe=n(s7o,"A",{href:!0});var Nmn=s(_pe);syt=r(Nmn,"CamembertForTokenClassification"),Nmn.forEach(t),lyt=r(s7o," (CamemBERT model)"),s7o.forEach(t),iyt=i(Y),L9=n(Y,"LI",{});var l7o=s(L9);FVe=n(l7o,"STRONG",{});var Bmn=s(FVe);dyt=r(Bmn,"canine"),Bmn.forEach(t),cyt=r(l7o," \u2014 "),vpe=n(l7o,"A",{href:!0});var Gmn=s(vpe);fyt=r(Gmn,"CanineForTokenClassification"),Gmn.forEach(t),myt=r(l7o," (CANINE model)"),l7o.forEach(t),gyt=i(Y),y9=n(Y,"LI",{});var i7o=s(y9);MVe=n(i7o,"STRONG",{});var Dmn=s(MVe);hyt=r(Dmn,"convbert"),Dmn.forEach(t),uyt=r(i7o," \u2014 "),bpe=n(i7o,"A",{href:!0});var qmn=s(bpe);pyt=r(qmn,"ConvBertForTokenClassification"),qmn.forEach(t),_yt=r(i7o," (ConvBERT model)"),i7o.forEach(t),vyt=i(Y),x9=n(Y,"LI",{});var d7o=s(x9);EVe=n(d7o,"STRONG",{});var jmn=s(EVe);byt=r(jmn,"data2vec-text"),jmn.forEach(t),Tyt=r(d7o," \u2014 "),Tpe=n(d7o,"A",{href:!0});var Omn=s(Tpe);Fyt=r(Omn,"Data2VecTextForTokenClassification"),Omn.forEach(t),Myt=r(d7o," (Data2VecText model)"),d7o.forEach(t),Eyt=i(Y),S9=n(Y,"LI",{});var c7o=s(S9);CVe=n(c7o,"STRONG",{});var Vmn=s(CVe);Cyt=r(Vmn,"deberta"),Vmn.forEach(t),wyt=r(c7o," \u2014 "),Fpe=n(c7o,"A",{href:!0});var Xmn=s(Fpe);Ayt=r(Xmn,"DebertaForTokenClassification"),Xmn.forEach(t),Lyt=r(c7o," (DeBERTa model)"),c7o.forEach(t),yyt=i(Y),$9=n(Y,"LI",{});var f7o=s($9);wVe=n(f7o,"STRONG",{});var zmn=s(wVe);xyt=r(zmn,"deberta-v2"),zmn.forEach(t),Syt=r(f7o," \u2014 "),Mpe=n(f7o,"A",{href:!0});var Wmn=s(Mpe);$yt=r(Wmn,"DebertaV2ForTokenClassification"),Wmn.forEach(t),kyt=r(f7o," (DeBERTa-v2 model)"),f7o.forEach(t),Ryt=i(Y),k9=n(Y,"LI",{});var m7o=s(k9);AVe=n(m7o,"STRONG",{});var Qmn=s(AVe);Pyt=r(Qmn,"distilbert"),Qmn.forEach(t),Iyt=r(m7o," \u2014 "),Epe=n(m7o,"A",{href:!0});var Umn=s(Epe);Nyt=r(Umn,"DistilBertForTokenClassification"),Umn.forEach(t),Byt=r(m7o," (DistilBERT model)"),m7o.forEach(t),Gyt=i(Y),R9=n(Y,"LI",{});var g7o=s(R9);LVe=n(g7o,"STRONG",{});var Hmn=s(LVe);Dyt=r(Hmn,"electra"),Hmn.forEach(t),qyt=r(g7o," \u2014 "),Cpe=n(g7o,"A",{href:!0});var Jmn=s(Cpe);jyt=r(Jmn,"ElectraForTokenClassification"),Jmn.forEach(t),Oyt=r(g7o," (ELECTRA model)"),g7o.forEach(t),Vyt=i(Y),P9=n(Y,"LI",{});var h7o=s(P9);yVe=n(h7o,"STRONG",{});var Zmn=s(yVe);Xyt=r(Zmn,"ernie"),Zmn.forEach(t),zyt=r(h7o," \u2014 "),wpe=n(h7o,"A",{href:!0});var Ymn=s(wpe);Wyt=r(Ymn,"ErnieForTokenClassification"),Ymn.forEach(t),Qyt=r(h7o," (ERNIE model)"),h7o.forEach(t),Uyt=i(Y),I9=n(Y,"LI",{});var u7o=s(I9);xVe=n(u7o,"STRONG",{});var Kmn=s(xVe);Hyt=r(Kmn,"ernie_m"),Kmn.forEach(t),Jyt=r(u7o," \u2014 "),Ape=n(u7o,"A",{href:!0});var egn=s(Ape);Zyt=r(egn,"ErnieMForTokenClassification"),egn.forEach(t),Yyt=r(u7o," (ErnieM model)"),u7o.forEach(t),Kyt=i(Y),N9=n(Y,"LI",{});var p7o=s(N9);SVe=n(p7o,"STRONG",{});var ogn=s(SVe);ext=r(ogn,"esm"),ogn.forEach(t),oxt=r(p7o," \u2014 "),Lpe=n(p7o,"A",{href:!0});var rgn=s(Lpe);rxt=r(rgn,"EsmForTokenClassification"),rgn.forEach(t),txt=r(p7o," (ESM model)"),p7o.forEach(t),axt=i(Y),B9=n(Y,"LI",{});var _7o=s(B9);$Ve=n(_7o,"STRONG",{});var tgn=s($Ve);nxt=r(tgn,"flaubert"),tgn.forEach(t),sxt=r(_7o," \u2014 "),ype=n(_7o,"A",{href:!0});var agn=s(ype);lxt=r(agn,"FlaubertForTokenClassification"),agn.forEach(t),ixt=r(_7o," (FlauBERT model)"),_7o.forEach(t),dxt=i(Y),G9=n(Y,"LI",{});var v7o=s(G9);kVe=n(v7o,"STRONG",{});var ngn=s(kVe);cxt=r(ngn,"fnet"),ngn.forEach(t),fxt=r(v7o," \u2014 "),xpe=n(v7o,"A",{href:!0});var sgn=s(xpe);mxt=r(sgn,"FNetForTokenClassification"),sgn.forEach(t),gxt=r(v7o," (FNet model)"),v7o.forEach(t),hxt=i(Y),D9=n(Y,"LI",{});var b7o=s(D9);RVe=n(b7o,"STRONG",{});var lgn=s(RVe);uxt=r(lgn,"funnel"),lgn.forEach(t),pxt=r(b7o," \u2014 "),Spe=n(b7o,"A",{href:!0});var ign=s(Spe);_xt=r(ign,"FunnelForTokenClassification"),ign.forEach(t),vxt=r(b7o," (Funnel Transformer model)"),b7o.forEach(t),bxt=i(Y),q9=n(Y,"LI",{});var T7o=s(q9);PVe=n(T7o,"STRONG",{});var dgn=s(PVe);Txt=r(dgn,"gpt-sw3"),dgn.forEach(t),Fxt=r(T7o," \u2014 "),$pe=n(T7o,"A",{href:!0});var cgn=s($pe);Mxt=r(cgn,"GPT2ForTokenClassification"),cgn.forEach(t),Ext=r(T7o," (GPT-Sw3 model)"),T7o.forEach(t),Cxt=i(Y),j9=n(Y,"LI",{});var F7o=s(j9);IVe=n(F7o,"STRONG",{});var fgn=s(IVe);wxt=r(fgn,"gpt2"),fgn.forEach(t),Axt=r(F7o," \u2014 "),kpe=n(F7o,"A",{href:!0});var mgn=s(kpe);Lxt=r(mgn,"GPT2ForTokenClassification"),mgn.forEach(t),yxt=r(F7o," (OpenAI GPT-2 model)"),F7o.forEach(t),xxt=i(Y),O9=n(Y,"LI",{});var M7o=s(O9);NVe=n(M7o,"STRONG",{});var ggn=s(NVe);Sxt=r(ggn,"ibert"),ggn.forEach(t),$xt=r(M7o," \u2014 "),Rpe=n(M7o,"A",{href:!0});var hgn=s(Rpe);kxt=r(hgn,"IBertForTokenClassification"),hgn.forEach(t),Rxt=r(M7o," (I-BERT model)"),M7o.forEach(t),Pxt=i(Y),V9=n(Y,"LI",{});var E7o=s(V9);BVe=n(E7o,"STRONG",{});var ugn=s(BVe);Ixt=r(ugn,"layoutlm"),ugn.forEach(t),Nxt=r(E7o," \u2014 "),Ppe=n(E7o,"A",{href:!0});var pgn=s(Ppe);Bxt=r(pgn,"LayoutLMForTokenClassification"),pgn.forEach(t),Gxt=r(E7o," (LayoutLM model)"),E7o.forEach(t),Dxt=i(Y),X9=n(Y,"LI",{});var C7o=s(X9);GVe=n(C7o,"STRONG",{});var _gn=s(GVe);qxt=r(_gn,"layoutlmv2"),_gn.forEach(t),jxt=r(C7o," \u2014 "),Ipe=n(C7o,"A",{href:!0});var vgn=s(Ipe);Oxt=r(vgn,"LayoutLMv2ForTokenClassification"),vgn.forEach(t),Vxt=r(C7o," (LayoutLMv2 model)"),C7o.forEach(t),Xxt=i(Y),z9=n(Y,"LI",{});var w7o=s(z9);DVe=n(w7o,"STRONG",{});var bgn=s(DVe);zxt=r(bgn,"layoutlmv3"),bgn.forEach(t),Wxt=r(w7o," \u2014 "),Npe=n(w7o,"A",{href:!0});var Tgn=s(Npe);Qxt=r(Tgn,"LayoutLMv3ForTokenClassification"),Tgn.forEach(t),Uxt=r(w7o," (LayoutLMv3 model)"),w7o.forEach(t),Hxt=i(Y),W9=n(Y,"LI",{});var A7o=s(W9);qVe=n(A7o,"STRONG",{});var Fgn=s(qVe);Jxt=r(Fgn,"lilt"),Fgn.forEach(t),Zxt=r(A7o," \u2014 "),Bpe=n(A7o,"A",{href:!0});var Mgn=s(Bpe);Yxt=r(Mgn,"LiltForTokenClassification"),Mgn.forEach(t),Kxt=r(A7o," (LiLT model)"),A7o.forEach(t),eSt=i(Y),Q9=n(Y,"LI",{});var L7o=s(Q9);jVe=n(L7o,"STRONG",{});var Egn=s(jVe);oSt=r(Egn,"longformer"),Egn.forEach(t),rSt=r(L7o," \u2014 "),Gpe=n(L7o,"A",{href:!0});var Cgn=s(Gpe);tSt=r(Cgn,"LongformerForTokenClassification"),Cgn.forEach(t),aSt=r(L7o," (Longformer model)"),L7o.forEach(t),nSt=i(Y),U9=n(Y,"LI",{});var y7o=s(U9);OVe=n(y7o,"STRONG",{});var wgn=s(OVe);sSt=r(wgn,"luke"),wgn.forEach(t),lSt=r(y7o," \u2014 "),Dpe=n(y7o,"A",{href:!0});var Agn=s(Dpe);iSt=r(Agn,"LukeForTokenClassification"),Agn.forEach(t),dSt=r(y7o," (LUKE model)"),y7o.forEach(t),cSt=i(Y),H9=n(Y,"LI",{});var x7o=s(H9);VVe=n(x7o,"STRONG",{});var Lgn=s(VVe);fSt=r(Lgn,"markuplm"),Lgn.forEach(t),mSt=r(x7o," \u2014 "),qpe=n(x7o,"A",{href:!0});var ygn=s(qpe);gSt=r(ygn,"MarkupLMForTokenClassification"),ygn.forEach(t),hSt=r(x7o," (MarkupLM model)"),x7o.forEach(t),uSt=i(Y),J9=n(Y,"LI",{});var S7o=s(J9);XVe=n(S7o,"STRONG",{});var xgn=s(XVe);pSt=r(xgn,"megatron-bert"),xgn.forEach(t),_St=r(S7o," \u2014 "),jpe=n(S7o,"A",{href:!0});var Sgn=s(jpe);vSt=r(Sgn,"MegatronBertForTokenClassification"),Sgn.forEach(t),bSt=r(S7o," (Megatron-BERT model)"),S7o.forEach(t),TSt=i(Y),Z9=n(Y,"LI",{});var $7o=s(Z9);zVe=n($7o,"STRONG",{});var $gn=s(zVe);FSt=r($gn,"mobilebert"),$gn.forEach(t),MSt=r($7o," \u2014 "),Ope=n($7o,"A",{href:!0});var kgn=s(Ope);ESt=r(kgn,"MobileBertForTokenClassification"),kgn.forEach(t),CSt=r($7o," (MobileBERT model)"),$7o.forEach(t),wSt=i(Y),Y9=n(Y,"LI",{});var k7o=s(Y9);WVe=n(k7o,"STRONG",{});var Rgn=s(WVe);ASt=r(Rgn,"mpnet"),Rgn.forEach(t),LSt=r(k7o," \u2014 "),Vpe=n(k7o,"A",{href:!0});var Pgn=s(Vpe);ySt=r(Pgn,"MPNetForTokenClassification"),Pgn.forEach(t),xSt=r(k7o," (MPNet model)"),k7o.forEach(t),SSt=i(Y),K9=n(Y,"LI",{});var R7o=s(K9);QVe=n(R7o,"STRONG",{});var Ign=s(QVe);$St=r(Ign,"nezha"),Ign.forEach(t),kSt=r(R7o," \u2014 "),Xpe=n(R7o,"A",{href:!0});var Ngn=s(Xpe);RSt=r(Ngn,"NezhaForTokenClassification"),Ngn.forEach(t),PSt=r(R7o," (Nezha model)"),R7o.forEach(t),ISt=i(Y),eL=n(Y,"LI",{});var P7o=s(eL);UVe=n(P7o,"STRONG",{});var Bgn=s(UVe);NSt=r(Bgn,"nystromformer"),Bgn.forEach(t),BSt=r(P7o," \u2014 "),zpe=n(P7o,"A",{href:!0});var Ggn=s(zpe);GSt=r(Ggn,"NystromformerForTokenClassification"),Ggn.forEach(t),DSt=r(P7o," (Nystr\xF6mformer model)"),P7o.forEach(t),qSt=i(Y),oL=n(Y,"LI",{});var I7o=s(oL);HVe=n(I7o,"STRONG",{});var Dgn=s(HVe);jSt=r(Dgn,"qdqbert"),Dgn.forEach(t),OSt=r(I7o," \u2014 "),Wpe=n(I7o,"A",{href:!0});var qgn=s(Wpe);VSt=r(qgn,"QDQBertForTokenClassification"),qgn.forEach(t),XSt=r(I7o," (QDQBert model)"),I7o.forEach(t),zSt=i(Y),rL=n(Y,"LI",{});var N7o=s(rL);JVe=n(N7o,"STRONG",{});var jgn=s(JVe);WSt=r(jgn,"rembert"),jgn.forEach(t),QSt=r(N7o," \u2014 "),Qpe=n(N7o,"A",{href:!0});var Ogn=s(Qpe);USt=r(Ogn,"RemBertForTokenClassification"),Ogn.forEach(t),HSt=r(N7o," (RemBERT model)"),N7o.forEach(t),JSt=i(Y),tL=n(Y,"LI",{});var B7o=s(tL);ZVe=n(B7o,"STRONG",{});var Vgn=s(ZVe);ZSt=r(Vgn,"roberta"),Vgn.forEach(t),YSt=r(B7o," \u2014 "),Upe=n(B7o,"A",{href:!0});var Xgn=s(Upe);KSt=r(Xgn,"RobertaForTokenClassification"),Xgn.forEach(t),e$t=r(B7o," (RoBERTa model)"),B7o.forEach(t),o$t=i(Y),aL=n(Y,"LI",{});var G7o=s(aL);YVe=n(G7o,"STRONG",{});var zgn=s(YVe);r$t=r(zgn,"roberta-prelayernorm"),zgn.forEach(t),t$t=r(G7o," \u2014 "),Hpe=n(G7o,"A",{href:!0});var Wgn=s(Hpe);a$t=r(Wgn,"RobertaPreLayerNormForTokenClassification"),Wgn.forEach(t),n$t=r(G7o," (RoBERTa-PreLayerNorm model)"),G7o.forEach(t),s$t=i(Y),nL=n(Y,"LI",{});var D7o=s(nL);KVe=n(D7o,"STRONG",{});var Qgn=s(KVe);l$t=r(Qgn,"roc_bert"),Qgn.forEach(t),i$t=r(D7o," \u2014 "),Jpe=n(D7o,"A",{href:!0});var Ugn=s(Jpe);d$t=r(Ugn,"RoCBertForTokenClassification"),Ugn.forEach(t),c$t=r(D7o," (RoCBert 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falling back to using pattern matching on `),vXe=n(GI,"CODE",{});var whn=s(vXe);Pkt=r(whn,"pretrained_model_name_or_path"),whn.forEach(t),Ikt=r(GI,":"),GI.forEach(t),Nkt=i(vd),he=n(vd,"UL",{});var _e=s(he);vL=n(_e,"LI",{});var H7o=s(vL);bXe=n(H7o,"STRONG",{});var Ahn=s(bXe);Bkt=r(Ahn,"albert"),Ahn.forEach(t),Gkt=r(H7o," \u2014 "),i_e=n(H7o,"A",{href:!0});var Lhn=s(i_e);Dkt=r(Lhn,"TFAlbertForTokenClassification"),Lhn.forEach(t),qkt=r(H7o," (ALBERT model)"),H7o.forEach(t),jkt=i(_e),bL=n(_e,"LI",{});var J7o=s(bL);TXe=n(J7o,"STRONG",{});var yhn=s(TXe);Okt=r(yhn,"bert"),yhn.forEach(t),Vkt=r(J7o," \u2014 "),d_e=n(J7o,"A",{href:!0});var xhn=s(d_e);Xkt=r(xhn,"TFBertForTokenClassification"),xhn.forEach(t),zkt=r(J7o," (BERT model)"),J7o.forEach(t),Wkt=i(_e),TL=n(_e,"LI",{});var Z7o=s(TL);FXe=n(Z7o,"STRONG",{});var Shn=s(FXe);Qkt=r(Shn,"camembert"),Shn.forEach(t),Ukt=r(Z7o," \u2014 "),c_e=n(Z7o,"A",{href:!0});var $hn=s(c_e);Hkt=r($hn,"TFCamembertForTokenClassification"),$hn.forEach(t),Jkt=r(Z7o," 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method.`),DTe.forEach(t),YPt=i(bd),mq=n(bd,"P",{});var Wwo=s(mq);KPt=r(Wwo,"This class cannot be instantiated directly using "),XXe=n(Wwo,"CODE",{});var Eun=s(XXe);eIt=r(Eun,"__init__()"),Eun.forEach(t),oIt=r(Wwo," (throws an error)."),Wwo.forEach(t),rIt=i(bd),ia=n(bd,"DIV",{class:!0});var DI=s(ia);T(gq.$$.fragment,DI),tIt=i(DI),zXe=n(DI,"P",{});var Cun=s(zXe);aIt=r(Cun,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),Cun.forEach(t),nIt=i(DI),nm=n(DI,"P",{});var qTe=s(nm);sIt=r(qTe,`Note:
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model\u2019s configuration. Use `),R_e=n(qTe,"A",{href:!0});var Aun=s(R_e);dIt=r(Aun,"from_pretrained()"),Aun.forEach(t),cIt=r(qTe," to load the model weights."),qTe.forEach(t),fIt=i(DI),T(VL.$$.fragment,DI),DI.forEach(t),mIt=i(bd),pt=n(bd,"DIV",{class:!0});var Td=s(pt);T(hq.$$.fragment,Td),gIt=i(Td),QXe=n(Td,"P",{});var Lun=s(QXe);hIt=r(Lun,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),Lun.forEach(t),uIt=i(Td),rs=n(Td,"P",{});var qI=s(rs);pIt=r(qI,"The model class to instantiate is selected based on the "),UXe=n(qI,"CODE",{});var yun=s(UXe);_It=r(yun,"model_type"),yun.forEach(t),vIt=r(qI,` property of the config object (either
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falling back to using pattern matching on `),JXe=n(qI,"CODE",{});var Sun=s(JXe);FIt=r(Sun,"pretrained_model_name_or_path"),Sun.forEach(t),MIt=r(qI,":"),qI.forEach(t),EIt=i(Td),Xe=n(Td,"UL",{});var Ye=s(Xe);XL=n(Ye,"LI",{});var vTo=s(XL);ZXe=n(vTo,"STRONG",{});var $un=s(ZXe);CIt=r($un,"albert"),$un.forEach(t),wIt=r(vTo," \u2014 "),P_e=n(vTo,"A",{href:!0});var kun=s(P_e);AIt=r(kun,"FlaxAlbertForTokenClassification"),kun.forEach(t),LIt=r(vTo," (ALBERT model)"),vTo.forEach(t),yIt=i(Ye),zL=n(Ye,"LI",{});var bTo=s(zL);YXe=n(bTo,"STRONG",{});var Run=s(YXe);xIt=r(Run,"bert"),Run.forEach(t),SIt=r(bTo," \u2014 "),I_e=n(bTo,"A",{href:!0});var Pun=s(I_e);$It=r(Pun,"FlaxBertForTokenClassification"),Pun.forEach(t),kIt=r(bTo," (BERT model)"),bTo.forEach(t),RIt=i(Ye),WL=n(Ye,"LI",{});var TTo=s(WL);KXe=n(TTo,"STRONG",{});var Iun=s(KXe);PIt=r(Iun,"big_bird"),Iun.forEach(t),IIt=r(TTo," \u2014 "),N_e=n(TTo,"A",{href:!0});var Nun=s(N_e);NIt=r(Nun,"FlaxBigBirdForTokenClassification"),Nun.forEach(t),BIt=r(TTo," (BigBird model)"),TTo.forEach(t),GIt=i(Ye),QL=n(Ye,"LI",{});var FTo=s(QL);eze=n(FTo,"STRONG",{});var Bun=s(eze);DIt=r(Bun,"distilbert"),Bun.forEach(t),qIt=r(FTo," \u2014 "),B_e=n(FTo,"A",{href:!0});var Gun=s(B_e);jIt=r(Gun,"FlaxDistilBertForTokenClassification"),Gun.forEach(t),OIt=r(FTo," (DistilBERT model)"),FTo.forEach(t),VIt=i(Ye),UL=n(Ye,"LI",{});var MTo=s(UL);oze=n(MTo,"STRONG",{});var Dun=s(oze);XIt=r(Dun,"electra"),Dun.forEach(t),zIt=r(MTo," \u2014 "),G_e=n(MTo,"A",{href:!0});var qun=s(G_e);WIt=r(qun,"FlaxElectraForTokenClassification"),qun.forEach(t),QIt=r(MTo," (ELECTRA model)"),MTo.forEach(t),UIt=i(Ye),HL=n(Ye,"LI",{});var ETo=s(HL);rze=n(ETo,"STRONG",{});var jun=s(rze);HIt=r(jun,"roberta"),jun.forEach(t),JIt=r(ETo," \u2014 "),D_e=n(ETo,"A",{href:!0});var Oun=s(D_e);ZIt=r(Oun,"FlaxRobertaForTokenClassification"),Oun.forEach(t),YIt=r(ETo," (RoBERTa model)"),ETo.forEach(t),KIt=i(Ye),JL=n(Ye,"LI",{});var CTo=s(JL);tze=n(CTo,"STRONG",{});var Vun=s(tze);eNt=r(Vun,"roberta-prelayernorm"),Vun.forEach(t),oNt=r(CTo," \u2014 "),q_e=n(CTo,"A",{href:!0});var Xun=s(q_e);rNt=r(Xun,"FlaxRobertaPreLayerNormForTokenClassification"),Xun.forEach(t),tNt=r(CTo," (RoBERTa-PreLayerNorm model)"),CTo.forEach(t),aNt=i(Ye),ZL=n(Ye,"LI",{});var wTo=s(ZL);aze=n(wTo,"STRONG",{});var zun=s(aze);nNt=r(zun,"roformer"),zun.forEach(t),sNt=r(wTo," \u2014 "),j_e=n(wTo,"A",{href:!0});var Wun=s(j_e);lNt=r(Wun,"FlaxRoFormerForTokenClassification"),Wun.forEach(t),iNt=r(wTo," (RoFormer model)"),wTo.forEach(t),dNt=i(Ye),YL=n(Ye,"LI",{});var ATo=s(YL);nze=n(ATo,"STRONG",{});var Qun=s(nze);cNt=r(Qun,"xlm-roberta"),Qun.forEach(t),fNt=r(ATo," \u2014 "),O_e=n(ATo,"A",{href:!0});var Uun=s(O_e);mNt=r(Uun,"FlaxXLMRobertaForTokenClassification"),Uun.forEach(t),gNt=r(ATo," (XLM-RoBERTa model)"),ATo.forEach(t),Ye.forEach(t),hNt=i(Td),T(KL.$$.fragment,Td),Td.forEach(t),bd.forEach(t),l5o=i(f),sm=n(f,"H3",{class:!0});var Qwo=s(sm);ey=n(Qwo,"A",{id:!0,class:!0,href:!0});var Hun=s(ey);sze=n(Hun,"SPAN",{});var Jun=s(sze);T(uq.$$.fragment,Jun),Jun.forEach(t),Hun.forEach(t),uNt=i(Qwo),lze=n(Qwo,"SPAN",{});var Zun=s(lze);pNt=r(Zun,"AutoModelForQuestionAnswering"),Zun.forEach(t),Qwo.forEach(t),i5o=i(f),pr=n(f,"DIV",{class:!0});var Fd=s(pr);T(pq.$$.fragment,Fd),_Nt=i(Fd),lm=n(Fd,"P",{});var jTe=s(lm);vNt=r(jTe,`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 `),V_e=n(jTe,"A",{href:!0});var Yun=s(V_e);bNt=r(Yun,"from_pretrained()"),Yun.forEach(t),TNt=r(jTe," class method or the "),X_e=n(jTe,"A",{href:!0});var Kun=s(X_e);FNt=r(Kun,"from_config()"),Kun.forEach(t),MNt=r(jTe,` class
method.`),jTe.forEach(t),ENt=i(Fd),_q=n(Fd,"P",{});var Uwo=s(_q);CNt=r(Uwo,"This class cannot be instantiated directly using "),ize=n(Uwo,"CODE",{});var epn=s(ize);wNt=r(epn,"__init__()"),epn.forEach(t),ANt=r(Uwo," (throws an error)."),Uwo.forEach(t),LNt=i(Fd),da=n(Fd,"DIV",{class:!0});var jI=s(da);T(vq.$$.fragment,jI),yNt=i(jI),dze=n(jI,"P",{});var opn=s(dze);xNt=r(opn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),opn.forEach(t),SNt=i(jI),im=n(jI,"P",{});var OTe=s(im);$Nt=r(OTe,`Note:
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model\u2019s configuration. Use `),z_e=n(OTe,"A",{href:!0});var tpn=s(z_e);PNt=r(tpn,"from_pretrained()"),tpn.forEach(t),INt=r(OTe," to load the model weights."),OTe.forEach(t),NNt=i(jI),T(oy.$$.fragment,jI),jI.forEach(t),BNt=i(Fd),po=n(Fd,"DIV",{class:!0});var on=s(po);T(bq.$$.fragment,on),GNt=i(on),fze=n(on,"P",{});var apn=s(fze);DNt=r(apn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),apn.forEach(t),qNt=i(on),ts=n(on,"P",{});var OI=s(ts);jNt=r(OI,"The model class to instantiate is selected based on the "),mze=n(OI,"CODE",{});var npn=s(mze);ONt=r(npn,"model_type"),npn.forEach(t),VNt=r(OI,` property of the config object (either
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falling back to using pattern matching on `),hze=n(OI,"CODE",{});var lpn=s(hze);WNt=r(lpn,"pretrained_model_name_or_path"),lpn.forEach(t),QNt=r(OI,":"),OI.forEach(t),UNt=i(on),Q=n(on,"UL",{});var U=s(Q);ry=n(U,"LI",{});var LTo=s(ry);uze=n(LTo,"STRONG",{});var ipn=s(uze);HNt=r(ipn,"albert"),ipn.forEach(t),JNt=r(LTo," \u2014 "),W_e=n(LTo,"A",{href:!0});var dpn=s(W_e);ZNt=r(dpn,"AlbertForQuestionAnswering"),dpn.forEach(t),YNt=r(LTo," (ALBERT model)"),LTo.forEach(t),KNt=i(U),ty=n(U,"LI",{});var yTo=s(ty);pze=n(yTo,"STRONG",{});var cpn=s(pze);eBt=r(cpn,"bart"),cpn.forEach(t),oBt=r(yTo," \u2014 "),Q_e=n(yTo,"A",{href:!0});var fpn=s(Q_e);rBt=r(fpn,"BartForQuestionAnswering"),fpn.forEach(t),tBt=r(yTo," (BART model)"),yTo.forEach(t),aBt=i(U),ay=n(U,"LI",{});var xTo=s(ay);_ze=n(xTo,"STRONG",{});var mpn=s(_ze);nBt=r(mpn,"bert"),mpn.forEach(t),sBt=r(xTo," \u2014 "),U_e=n(xTo,"A",{href:!0});var gpn=s(U_e);lBt=r(gpn,"BertForQuestionAnswering"),gpn.forEach(t),iBt=r(xTo," (BERT 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"),d2e=n(OFo,"A",{href:!0});var Y1n=s(d2e);YVt=r(Y1n,"TFRobertaForQuestionAnswering"),Y1n.forEach(t),KVt=r(OFo," (RoBERTa model)"),OFo.forEach(t),eXt=i(Te),Fx=n(Te,"LI",{});var VFo=s(Fx);jWe=n(VFo,"STRONG",{});var K1n=s(jWe);oXt=r(K1n,"roberta-prelayernorm"),K1n.forEach(t),rXt=r(VFo," \u2014 "),c2e=n(VFo,"A",{href:!0});var e2n=s(c2e);tXt=r(e2n,"TFRobertaPreLayerNormForQuestionAnswering"),e2n.forEach(t),aXt=r(VFo," (RoBERTa-PreLayerNorm model)"),VFo.forEach(t),nXt=i(Te),Mx=n(Te,"LI",{});var XFo=s(Mx);OWe=n(XFo,"STRONG",{});var o2n=s(OWe);sXt=r(o2n,"roformer"),o2n.forEach(t),lXt=r(XFo," \u2014 "),f2e=n(XFo,"A",{href:!0});var r2n=s(f2e);iXt=r(r2n,"TFRoFormerForQuestionAnswering"),r2n.forEach(t),dXt=r(XFo," (RoFormer model)"),XFo.forEach(t),cXt=i(Te),Ex=n(Te,"LI",{});var zFo=s(Ex);VWe=n(zFo,"STRONG",{});var t2n=s(VWe);fXt=r(t2n,"xlm"),t2n.forEach(t),mXt=r(zFo," \u2014 "),m2e=n(zFo,"A",{href:!0});var a2n=s(m2e);gXt=r(a2n,"TFXLMForQuestionAnsweringSimple"),a2n.forEach(t),hXt=r(zFo," (XLM model)"),zFo.forEach(t),uXt=i(Te),Cx=n(Te,"LI",{});var WFo=s(Cx);XWe=n(WFo,"STRONG",{});var n2n=s(XWe);pXt=r(n2n,"xlm-roberta"),n2n.forEach(t),_Xt=r(WFo," \u2014 "),g2e=n(WFo,"A",{href:!0});var s2n=s(g2e);vXt=r(s2n,"TFXLMRobertaForQuestionAnswering"),s2n.forEach(t),bXt=r(WFo," (XLM-RoBERTa model)"),WFo.forEach(t),TXt=i(Te),wx=n(Te,"LI",{});var QFo=s(wx);zWe=n(QFo,"STRONG",{});var l2n=s(zWe);FXt=r(l2n,"xlnet"),l2n.forEach(t),MXt=r(QFo," \u2014 "),h2e=n(QFo,"A",{href:!0});var i2n=s(h2e);EXt=r(i2n,"TFXLNetForQuestionAnsweringSimple"),i2n.forEach(t),CXt=r(QFo," (XLNet model)"),QFo.forEach(t),Te.forEach(t),wXt=i(Ed),T(Ax.$$.fragment,Ed),Ed.forEach(t),Md.forEach(t),f5o=i(f),mm=n(f,"H3",{class:!0});var Zwo=s(mm);Lx=n(Zwo,"A",{id:!0,class:!0,href:!0});var d2n=s(Lx);WWe=n(d2n,"SPAN",{});var c2n=s(WWe);T(wq.$$.fragment,c2n),c2n.forEach(t),d2n.forEach(t),AXt=i(Zwo),QWe=n(Zwo,"SPAN",{});var f2n=s(QWe);LXt=r(f2n,"FlaxAutoModelForQuestionAnswering"),f2n.forEach(t),Zwo.forEach(t),m5o=i(f),vr=n(f,"DIV",{class:!0});var Cd=s(vr);T(Aq.$$.fragment,Cd),yXt=i(Cd),gm=n(Cd,"P",{});var zTe=s(gm);xXt=r(zTe,`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 `),u2e=n(zTe,"A",{href:!0});var m2n=s(u2e);SXt=r(m2n,"from_pretrained()"),m2n.forEach(t),$Xt=r(zTe," class method or the "),p2e=n(zTe,"A",{href:!0});var g2n=s(p2e);kXt=r(g2n,"from_config()"),g2n.forEach(t),RXt=r(zTe,` class
method.`),zTe.forEach(t),PXt=i(Cd),Lq=n(Cd,"P",{});var Ywo=s(Lq);IXt=r(Ywo,"This class cannot be instantiated directly using "),UWe=n(Ywo,"CODE",{});var h2n=s(UWe);NXt=r(h2n,"__init__()"),h2n.forEach(t),BXt=r(Ywo," (throws an error)."),Ywo.forEach(t),GXt=i(Cd),fa=n(Cd,"DIV",{class:!0});var zI=s(fa);T(yq.$$.fragment,zI),DXt=i(zI),HWe=n(zI,"P",{});var u2n=s(HWe);qXt=r(u2n,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),u2n.forEach(t),jXt=i(zI),hm=n(zI,"P",{});var WTe=s(hm);OXt=r(WTe,`Note:
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model\u2019s configuration. Use `),_2e=n(WTe,"A",{href:!0});var _2n=s(_2e);zXt=r(_2n,"from_pretrained()"),_2n.forEach(t),WXt=r(WTe," to load the model weights."),WTe.forEach(t),QXt=i(zI),T(yx.$$.fragment,zI),zI.forEach(t),UXt=i(Cd),vt=n(Cd,"DIV",{class:!0});var wd=s(vt);T(xq.$$.fragment,wd),HXt=i(wd),ZWe=n(wd,"P",{});var v2n=s(ZWe);JXt=r(v2n,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),v2n.forEach(t),ZXt=i(wd),ns=n(wd,"P",{});var WI=s(ns);YXt=r(WI,"The model class to instantiate is selected based on the "),YWe=n(WI,"CODE",{});var b2n=s(YWe);KXt=r(b2n,"model_type"),b2n.forEach(t),ezt=r(WI,` property of the config object (either
passed as an argument or loaded from `),KWe=n(WI,"CODE",{});var T2n=s(KWe);ozt=r(T2n,"pretrained_model_name_or_path"),T2n.forEach(t),rzt=r(WI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eQe=n(WI,"CODE",{});var F2n=s(eQe);tzt=r(F2n,"pretrained_model_name_or_path"),F2n.forEach(t),azt=r(WI,":"),WI.forEach(t),nzt=i(wd),ke=n(wd,"UL",{});var je=s(ke);xx=n(je,"LI",{});var UFo=s(xx);oQe=n(UFo,"STRONG",{});var M2n=s(oQe);szt=r(M2n,"albert"),M2n.forEach(t),lzt=r(UFo," \u2014 "),v2e=n(UFo,"A",{href:!0});var E2n=s(v2e);izt=r(E2n,"FlaxAlbertForQuestionAnswering"),E2n.forEach(t),dzt=r(UFo," (ALBERT model)"),UFo.forEach(t),czt=i(je),Sx=n(je,"LI",{});var HFo=s(Sx);rQe=n(HFo,"STRONG",{});var C2n=s(rQe);fzt=r(C2n,"bart"),C2n.forEach(t),mzt=r(HFo," \u2014 "),b2e=n(HFo,"A",{href:!0});var w2n=s(b2e);gzt=r(w2n,"FlaxBartForQuestionAnswering"),w2n.forEach(t),hzt=r(HFo," (BART model)"),HFo.forEach(t),uzt=i(je),$x=n(je,"LI",{});var JFo=s($x);tQe=n(JFo,"STRONG",{});var A2n=s(tQe);pzt=r(A2n,"bert"),A2n.forEach(t),_zt=r(JFo," \u2014 "),T2e=n(JFo,"A",{href:!0});var L2n=s(T2e);vzt=r(L2n,"FlaxBertForQuestionAnswering"),L2n.forEach(t),bzt=r(JFo," (BERT model)"),JFo.forEach(t),Tzt=i(je),kx=n(je,"LI",{});var ZFo=s(kx);aQe=n(ZFo,"STRONG",{});var y2n=s(aQe);Fzt=r(y2n,"big_bird"),y2n.forEach(t),Mzt=r(ZFo," \u2014 "),F2e=n(ZFo,"A",{href:!0});var x2n=s(F2e);Ezt=r(x2n,"FlaxBigBirdForQuestionAnswering"),x2n.forEach(t),Czt=r(ZFo," (BigBird model)"),ZFo.forEach(t),wzt=i(je),Rx=n(je,"LI",{});var YFo=s(Rx);nQe=n(YFo,"STRONG",{});var S2n=s(nQe);Azt=r(S2n,"distilbert"),S2n.forEach(t),Lzt=r(YFo," \u2014 "),M2e=n(YFo,"A",{href:!0});var $2n=s(M2e);yzt=r($2n,"FlaxDistilBertForQuestionAnswering"),$2n.forEach(t),xzt=r(YFo," (DistilBERT model)"),YFo.forEach(t),Szt=i(je),Px=n(je,"LI",{});var KFo=s(Px);sQe=n(KFo,"STRONG",{});var k2n=s(sQe);$zt=r(k2n,"electra"),k2n.forEach(t),kzt=r(KFo," \u2014 "),E2e=n(KFo,"A",{href:!0});var R2n=s(E2e);Rzt=r(R2n,"FlaxElectraForQuestionAnswering"),R2n.forEach(t),Pzt=r(KFo," (ELECTRA model)"),KFo.forEach(t),Izt=i(je),Ix=n(je,"LI",{});var eMo=s(Ix);lQe=n(eMo,"STRONG",{});var P2n=s(lQe);Nzt=r(P2n,"mbart"),P2n.forEach(t),Bzt=r(eMo," \u2014 "),C2e=n(eMo,"A",{href:!0});var I2n=s(C2e);Gzt=r(I2n,"FlaxMBartForQuestionAnswering"),I2n.forEach(t),Dzt=r(eMo," (mBART model)"),eMo.forEach(t),qzt=i(je),Nx=n(je,"LI",{});var oMo=s(Nx);iQe=n(oMo,"STRONG",{});var N2n=s(iQe);jzt=r(N2n,"roberta"),N2n.forEach(t),Ozt=r(oMo," \u2014 "),w2e=n(oMo,"A",{href:!0});var B2n=s(w2e);Vzt=r(B2n,"FlaxRobertaForQuestionAnswering"),B2n.forEach(t),Xzt=r(oMo," (RoBERTa model)"),oMo.forEach(t),zzt=i(je),Bx=n(je,"LI",{});var rMo=s(Bx);dQe=n(rMo,"STRONG",{});var G2n=s(dQe);Wzt=r(G2n,"roberta-prelayernorm"),G2n.forEach(t),Qzt=r(rMo," \u2014 "),A2e=n(rMo,"A",{href:!0});var D2n=s(A2e);Uzt=r(D2n,"FlaxRobertaPreLayerNormForQuestionAnswering"),D2n.forEach(t),Hzt=r(rMo," (RoBERTa-PreLayerNorm model)"),rMo.forEach(t),Jzt=i(je),Gx=n(je,"LI",{});var tMo=s(Gx);cQe=n(tMo,"STRONG",{});var q2n=s(cQe);Zzt=r(q2n,"roformer"),q2n.forEach(t),Yzt=r(tMo," \u2014 "),L2e=n(tMo,"A",{href:!0});var j2n=s(L2e);Kzt=r(j2n,"FlaxRoFormerForQuestionAnswering"),j2n.forEach(t),eWt=r(tMo," (RoFormer model)"),tMo.forEach(t),oWt=i(je),Dx=n(je,"LI",{});var aMo=s(Dx);fQe=n(aMo,"STRONG",{});var O2n=s(fQe);rWt=r(O2n,"xlm-roberta"),O2n.forEach(t),tWt=r(aMo," \u2014 "),y2e=n(aMo,"A",{href:!0});var V2n=s(y2e);aWt=r(V2n,"FlaxXLMRobertaForQuestionAnswering"),V2n.forEach(t),nWt=r(aMo," (XLM-RoBERTa model)"),aMo.forEach(t),je.forEach(t),sWt=i(wd),T(qx.$$.fragment,wd),wd.forEach(t),Cd.forEach(t),g5o=i(f),um=n(f,"H2",{class:!0});var Kwo=s(um);jx=n(Kwo,"A",{id:!0,class:!0,href:!0});var X2n=s(jx);mQe=n(X2n,"SPAN",{});var z2n=s(mQe);T(Sq.$$.fragment,z2n),z2n.forEach(t),X2n.forEach(t),lWt=i(Kwo),gQe=n(Kwo,"SPAN",{});var W2n=s(gQe);iWt=r(W2n,"Computer vision"),W2n.forEach(t),Kwo.forEach(t),h5o=i(f),x2e=n(f,"P",{});var Q2n=s(x2e);dWt=r(Q2n,"The following auto classes are available for the following computer vision tasks."),Q2n.forEach(t),u5o=i(f),pm=n(f,"H3",{class:!0});var e6o=s(pm);Ox=n(e6o,"A",{id:!0,class:!0,href:!0});var U2n=s(Ox);hQe=n(U2n,"SPAN",{});var H2n=s(hQe);T($q.$$.fragment,H2n),H2n.forEach(t),U2n.forEach(t),cWt=i(e6o),uQe=n(e6o,"SPAN",{});var J2n=s(uQe);fWt=r(J2n,"AutoModelForDepthEstimation"),J2n.forEach(t),e6o.forEach(t),p5o=i(f),br=n(f,"DIV",{class:!0});var Ad=s(br);T(kq.$$.fragment,Ad),mWt=i(Ad),_m=n(Ad,"P",{});var QTe=s(_m);gWt=r(QTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),S2e=n(QTe,"A",{href:!0});var Z2n=s(S2e);hWt=r(Z2n,"from_pretrained()"),Z2n.forEach(t),uWt=r(QTe," class method or the "),$2e=n(QTe,"A",{href:!0});var Y2n=s($2e);pWt=r(Y2n,"from_config()"),Y2n.forEach(t),_Wt=r(QTe,` class
method.`),QTe.forEach(t),vWt=i(Ad),Rq=n(Ad,"P",{});var o6o=s(Rq);bWt=r(o6o,"This class cannot be instantiated directly using "),pQe=n(o6o,"CODE",{});var K2n=s(pQe);TWt=r(K2n,"__init__()"),K2n.forEach(t),FWt=r(o6o," (throws an error)."),o6o.forEach(t),MWt=i(Ad),ma=n(Ad,"DIV",{class:!0});var QI=s(ma);T(Pq.$$.fragment,QI),EWt=i(QI),_Qe=n(QI,"P",{});var evn=s(_Qe);CWt=r(evn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),evn.forEach(t),wWt=i(QI),vm=n(QI,"P",{});var UTe=s(vm);AWt=r(UTe,`Note:
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model\u2019s configuration. Use `),k2e=n(UTe,"A",{href:!0});var rvn=s(k2e);xWt=r(rvn,"from_pretrained()"),rvn.forEach(t),SWt=r(UTe," to load the model weights."),UTe.forEach(t),$Wt=i(QI),T(Vx.$$.fragment,QI),QI.forEach(t),kWt=i(Ad),_o=n(Ad,"DIV",{class:!0});var rn=s(_o);T(Iq.$$.fragment,rn),RWt=i(rn),bQe=n(rn,"P",{});var tvn=s(bQe);PWt=r(tvn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),tvn.forEach(t),IWt=i(rn),ss=n(rn,"P",{});var UI=s(ss);NWt=r(UI,"The model class to instantiate is selected based on the "),TQe=n(UI,"CODE",{});var avn=s(TQe);BWt=r(avn,"model_type"),avn.forEach(t),GWt=r(UI,` property of the config object (either
passed as an argument or loaded from `),FQe=n(UI,"CODE",{});var nvn=s(FQe);DWt=r(nvn,"pretrained_model_name_or_path"),nvn.forEach(t),qWt=r(UI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MQe=n(UI,"CODE",{});var svn=s(MQe);jWt=r(svn,"pretrained_model_name_or_path"),svn.forEach(t),OWt=r(UI,":"),UI.forEach(t),VWt=i(rn),Nq=n(rn,"UL",{});var r6o=s(Nq);Xx=n(r6o,"LI",{});var nMo=s(Xx);EQe=n(nMo,"STRONG",{});var lvn=s(EQe);XWt=r(lvn,"dpt"),lvn.forEach(t),zWt=r(nMo," \u2014 "),R2e=n(nMo,"A",{href:!0});var ivn=s(R2e);WWt=r(ivn,"DPTForDepthEstimation"),ivn.forEach(t),QWt=r(nMo," (DPT model)"),nMo.forEach(t),UWt=i(r6o),zx=n(r6o,"LI",{});var sMo=s(zx);CQe=n(sMo,"STRONG",{});var dvn=s(CQe);HWt=r(dvn,"glpn"),dvn.forEach(t),JWt=r(sMo," \u2014 "),P2e=n(sMo,"A",{href:!0});var cvn=s(P2e);ZWt=r(cvn,"GLPNForDepthEstimation"),cvn.forEach(t),YWt=r(sMo," (GLPN model)"),sMo.forEach(t),r6o.forEach(t),KWt=i(rn),Wx=n(rn,"P",{});var lMo=s(Wx);eQt=r(lMo,"The model is set in evaluation mode by default using "),wQe=n(lMo,"CODE",{});var fvn=s(wQe);oQt=r(fvn,"model.eval()"),fvn.forEach(t),rQt=r(lMo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),AQe=n(lMo,"CODE",{});var mvn=s(AQe);tQt=r(mvn,"model.train()"),mvn.forEach(t),lMo.forEach(t),aQt=i(rn),T(Qx.$$.fragment,rn),rn.forEach(t),Ad.forEach(t),_5o=i(f),bm=n(f,"H3",{class:!0});var t6o=s(bm);Ux=n(t6o,"A",{id:!0,class:!0,href:!0});var gvn=s(Ux);LQe=n(gvn,"SPAN",{});var hvn=s(LQe);T(Bq.$$.fragment,hvn),hvn.forEach(t),gvn.forEach(t),nQt=i(t6o),yQe=n(t6o,"SPAN",{});var uvn=s(yQe);sQt=r(uvn,"AutoModelForImageClassification"),uvn.forEach(t),t6o.forEach(t),v5o=i(f),Tr=n(f,"DIV",{class:!0});var Ld=s(Tr);T(Gq.$$.fragment,Ld),lQt=i(Ld),Tm=n(Ld,"P",{});var HTe=s(Tm);iQt=r(HTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),I2e=n(HTe,"A",{href:!0});var pvn=s(I2e);dQt=r(pvn,"from_pretrained()"),pvn.forEach(t),cQt=r(HTe," class method or the "),N2e=n(HTe,"A",{href:!0});var _vn=s(N2e);fQt=r(_vn,"from_config()"),_vn.forEach(t),mQt=r(HTe,` class
method.`),HTe.forEach(t),gQt=i(Ld),Dq=n(Ld,"P",{});var a6o=s(Dq);hQt=r(a6o,"This class cannot be instantiated directly using "),xQe=n(a6o,"CODE",{});var vvn=s(xQe);uQt=r(vvn,"__init__()"),vvn.forEach(t),pQt=r(a6o," (throws an error)."),a6o.forEach(t),_Qt=i(Ld),ga=n(Ld,"DIV",{class:!0});var HI=s(ga);T(qq.$$.fragment,HI),vQt=i(HI),SQe=n(HI,"P",{});var bvn=s(SQe);bQt=r(bvn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),bvn.forEach(t),TQt=i(HI),Fm=n(HI,"P",{});var JTe=s(Fm);FQt=r(JTe,`Note:
Loading a model from its configuration file does `),$Qe=n(JTe,"STRONG",{});var Tvn=s($Qe);MQt=r(Tvn,"not"),Tvn.forEach(t),EQt=r(JTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),B2e=n(JTe,"A",{href:!0});var Fvn=s(B2e);CQt=r(Fvn,"from_pretrained()"),Fvn.forEach(t),wQt=r(JTe," to load the model weights."),JTe.forEach(t),AQt=i(HI),T(Hx.$$.fragment,HI),HI.forEach(t),LQt=i(Ld),vo=n(Ld,"DIV",{class:!0});var tn=s(vo);T(jq.$$.fragment,tn),yQt=i(tn),kQe=n(tn,"P",{});var Mvn=s(kQe);xQt=r(Mvn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Mvn.forEach(t),SQt=i(tn),ls=n(tn,"P",{});var JI=s(ls);$Qt=r(JI,"The model class to instantiate is selected based on the "),RQe=n(JI,"CODE",{});var Evn=s(RQe);kQt=r(Evn,"model_type"),Evn.forEach(t),RQt=r(JI,` property of the config object (either
passed as an argument or loaded from `),PQe=n(JI,"CODE",{});var Cvn=s(PQe);PQt=r(Cvn,"pretrained_model_name_or_path"),Cvn.forEach(t),IQt=r(JI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),IQe=n(JI,"CODE",{});var wvn=s(IQe);NQt=r(wvn,"pretrained_model_name_or_path"),wvn.forEach(t),BQt=r(JI,":"),JI.forEach(t),GQt=i(tn),ce=n(tn,"UL",{});var fe=s(ce);Jx=n(fe,"LI",{});var iMo=s(Jx);NQe=n(iMo,"STRONG",{});var Avn=s(NQe);DQt=r(Avn,"beit"),Avn.forEach(t),qQt=r(iMo," \u2014 "),G2e=n(iMo,"A",{href:!0});var Lvn=s(G2e);jQt=r(Lvn,"BeitForImageClassification"),Lvn.forEach(t),OQt=r(iMo," (BEiT model)"),iMo.forEach(t),VQt=i(fe),Zx=n(fe,"LI",{});var dMo=s(Zx);BQe=n(dMo,"STRONG",{});var yvn=s(BQe);XQt=r(yvn,"bit"),yvn.forEach(t),zQt=r(dMo," \u2014 "),D2e=n(dMo,"A",{href:!0});var xvn=s(D2e);WQt=r(xvn,"BitForImageClassification"),xvn.forEach(t),QQt=r(dMo," (BiT model)"),dMo.forEach(t),UQt=i(fe),Yx=n(fe,"LI",{});var cMo=s(Yx);GQe=n(cMo,"STRONG",{});var Svn=s(GQe);HQt=r(Svn,"convnext"),Svn.forEach(t),JQt=r(cMo," \u2014 "),q2e=n(cMo,"A",{href:!0});var $vn=s(q2e);ZQt=r($vn,"ConvNextForImageClassification"),$vn.forEach(t),YQt=r(cMo," (ConvNeXT model)"),cMo.forEach(t),KQt=i(fe),Kx=n(fe,"LI",{});var fMo=s(Kx);DQe=n(fMo,"STRONG",{});var kvn=s(DQe);eUt=r(kvn,"convnextv2"),kvn.forEach(t),oUt=r(fMo," \u2014 "),j2e=n(fMo,"A",{href:!0});var Rvn=s(j2e);rUt=r(Rvn,"ConvNextV2ForImageClassification"),Rvn.forEach(t),tUt=r(fMo," (ConvNeXTV2 model)"),fMo.forEach(t),aUt=i(fe),eS=n(fe,"LI",{});var mMo=s(eS);qQe=n(mMo,"STRONG",{});var Pvn=s(qQe);nUt=r(Pvn,"cvt"),Pvn.forEach(t),sUt=r(mMo," \u2014 "),O2e=n(mMo,"A",{href:!0});var Ivn=s(O2e);lUt=r(Ivn,"CvtForImageClassification"),Ivn.forEach(t),iUt=r(mMo," (CvT model)"),mMo.forEach(t),dUt=i(fe),oS=n(fe,"LI",{});var gMo=s(oS);jQe=n(gMo,"STRONG",{});var Nvn=s(jQe);cUt=r(Nvn,"data2vec-vision"),Nvn.forEach(t),fUt=r(gMo," \u2014 "),V2e=n(gMo,"A",{href:!0});var Bvn=s(V2e);mUt=r(Bvn,"Data2VecVisionForImageClassification"),Bvn.forEach(t),gUt=r(gMo," (Data2VecVision model)"),gMo.forEach(t),hUt=i(fe),Fi=n(fe,"LI",{});var cz=s(Fi);OQe=n(cz,"STRONG",{});var Gvn=s(OQe);uUt=r(Gvn,"deit"),Gvn.forEach(t),pUt=r(cz," \u2014 "),X2e=n(cz,"A",{href:!0});var Dvn=s(X2e);_Ut=r(Dvn,"DeiTForImageClassification"),Dvn.forEach(t),vUt=r(cz," or "),z2e=n(cz,"A",{href:!0});var qvn=s(z2e);bUt=r(qvn,"DeiTForImageClassificationWithTeacher"),qvn.forEach(t),TUt=r(cz," (DeiT model)"),cz.forEach(t),FUt=i(fe),rS=n(fe,"LI",{});var hMo=s(rS);VQe=n(hMo,"STRONG",{});var jvn=s(VQe);MUt=r(jvn,"dinat"),jvn.forEach(t),EUt=r(hMo," \u2014 "),W2e=n(hMo,"A",{href:!0});var Ovn=s(W2e);CUt=r(Ovn,"DinatForImageClassification"),Ovn.forEach(t),wUt=r(hMo," (DiNAT model)"),hMo.forEach(t),AUt=i(fe),Mi=n(fe,"LI",{});var fz=s(Mi);XQe=n(fz,"STRONG",{});var Vvn=s(XQe);LUt=r(Vvn,"efficientformer"),Vvn.forEach(t),yUt=r(fz," \u2014 "),Q2e=n(fz,"A",{href:!0});var Xvn=s(Q2e);xUt=r(Xvn,"EfficientFormerForImageClassification"),Xvn.forEach(t),SUt=r(fz," or "),U2e=n(fz,"A",{href:!0});var zvn=s(U2e);$Ut=r(zvn,"EfficientFormerForImageClassificationWithTeacher"),zvn.forEach(t),kUt=r(fz," (EfficientFormer model)"),fz.forEach(t),RUt=i(fe),tS=n(fe,"LI",{});var uMo=s(tS);zQe=n(uMo,"STRONG",{});var Wvn=s(zQe);PUt=r(Wvn,"efficientnet"),Wvn.forEach(t),IUt=r(uMo," \u2014 "),H2e=n(uMo,"A",{href:!0});var Qvn=s(H2e);NUt=r(Qvn,"EfficientNetForImageClassification"),Qvn.forEach(t),BUt=r(uMo," (EfficientNet model)"),uMo.forEach(t),GUt=i(fe),aS=n(fe,"LI",{});var pMo=s(aS);WQe=n(pMo,"STRONG",{});var Uvn=s(WQe);DUt=r(Uvn,"imagegpt"),Uvn.forEach(t),qUt=r(pMo," \u2014 "),J2e=n(pMo,"A",{href:!0});var Hvn=s(J2e);jUt=r(Hvn,"ImageGPTForImageClassification"),Hvn.forEach(t),OUt=r(pMo," (ImageGPT model)"),pMo.forEach(t),VUt=i(fe),Ei=n(fe,"LI",{});var mz=s(Ei);QQe=n(mz,"STRONG",{});var Jvn=s(QQe);XUt=r(Jvn,"levit"),Jvn.forEach(t),zUt=r(mz," \u2014 "),Z2e=n(mz,"A",{href:!0});var Zvn=s(Z2e);WUt=r(Zvn,"LevitForImageClassification"),Zvn.forEach(t),QUt=r(mz," or "),Y2e=n(mz,"A",{href:!0});var Yvn=s(Y2e);UUt=r(Yvn,"LevitForImageClassificationWithTeacher"),Yvn.forEach(t),HUt=r(mz," (LeViT model)"),mz.forEach(t),JUt=i(fe),nS=n(fe,"LI",{});var _Mo=s(nS);UQe=n(_Mo,"STRONG",{});var Kvn=s(UQe);ZUt=r(Kvn,"mobilenet_v1"),Kvn.forEach(t),YUt=r(_Mo," \u2014 "),K2e=n(_Mo,"A",{href:!0});var e4n=s(K2e);KUt=r(e4n,"MobileNetV1ForImageClassification"),e4n.forEach(t),eHt=r(_Mo," (MobileNetV1 model)"),_Mo.forEach(t),oHt=i(fe),sS=n(fe,"LI",{});var vMo=s(sS);HQe=n(vMo,"STRONG",{});var o4n=s(HQe);rHt=r(o4n,"mobilenet_v2"),o4n.forEach(t),tHt=r(vMo," \u2014 "),eve=n(vMo,"A",{href:!0});var r4n=s(eve);aHt=r(r4n,"MobileNetV2ForImageClassification"),r4n.forEach(t),nHt=r(vMo," (MobileNetV2 model)"),vMo.forEach(t),sHt=i(fe),lS=n(fe,"LI",{});var bMo=s(lS);JQe=n(bMo,"STRONG",{});var t4n=s(JQe);lHt=r(t4n,"mobilevit"),t4n.forEach(t),iHt=r(bMo," \u2014 "),ove=n(bMo,"A",{href:!0});var a4n=s(ove);dHt=r(a4n,"MobileViTForImageClassification"),a4n.forEach(t),cHt=r(bMo," (MobileViT model)"),bMo.forEach(t),fHt=i(fe),iS=n(fe,"LI",{});var TMo=s(iS);ZQe=n(TMo,"STRONG",{});var n4n=s(ZQe);mHt=r(n4n,"nat"),n4n.forEach(t),gHt=r(TMo," \u2014 "),rve=n(TMo,"A",{href:!0});var s4n=s(rve);hHt=r(s4n,"NatForImageClassification"),s4n.forEach(t),uHt=r(TMo," (NAT model)"),TMo.forEach(t),pHt=i(fe),ha=n(fe,"LI",{});var nh=s(ha);YQe=n(nh,"STRONG",{});var l4n=s(YQe);_Ht=r(l4n,"perceiver"),l4n.forEach(t),vHt=r(nh," \u2014 "),tve=n(nh,"A",{href:!0});var i4n=s(tve);bHt=r(i4n,"PerceiverForImageClassificationLearned"),i4n.forEach(t),THt=r(nh," or "),ave=n(nh,"A",{href:!0});var d4n=s(ave);FHt=r(d4n,"PerceiverForImageClassificationFourier"),d4n.forEach(t),MHt=r(nh," or "),nve=n(nh,"A",{href:!0});var c4n=s(nve);EHt=r(c4n,"PerceiverForImageClassificationConvProcessing"),c4n.forEach(t),CHt=r(nh," (Perceiver model)"),nh.forEach(t),wHt=i(fe),dS=n(fe,"LI",{});var FMo=s(dS);KQe=n(FMo,"STRONG",{});var f4n=s(KQe);AHt=r(f4n,"poolformer"),f4n.forEach(t),LHt=r(FMo," \u2014 "),sve=n(FMo,"A",{href:!0});var m4n=s(sve);yHt=r(m4n,"PoolFormerForImageClassification"),m4n.forEach(t),xHt=r(FMo," (PoolFormer model)"),FMo.forEach(t),SHt=i(fe),cS=n(fe,"LI",{});var MMo=s(cS);eUe=n(MMo,"STRONG",{});var g4n=s(eUe);$Ht=r(g4n,"regnet"),g4n.forEach(t),kHt=r(MMo," \u2014 "),lve=n(MMo,"A",{href:!0});var h4n=s(lve);RHt=r(h4n,"RegNetForImageClassification"),h4n.forEach(t),PHt=r(MMo," (RegNet model)"),MMo.forEach(t),IHt=i(fe),fS=n(fe,"LI",{});var EMo=s(fS);oUe=n(EMo,"STRONG",{});var u4n=s(oUe);NHt=r(u4n,"resnet"),u4n.forEach(t),BHt=r(EMo," \u2014 "),ive=n(EMo,"A",{href:!0});var p4n=s(ive);GHt=r(p4n,"ResNetForImageClassification"),p4n.forEach(t),DHt=r(EMo," (ResNet model)"),EMo.forEach(t),qHt=i(fe),mS=n(fe,"LI",{});var CMo=s(mS);rUe=n(CMo,"STRONG",{});var _4n=s(rUe);jHt=r(_4n,"segformer"),_4n.forEach(t),OHt=r(CMo," \u2014 "),dve=n(CMo,"A",{href:!0});var v4n=s(dve);VHt=r(v4n,"SegformerForImageClassification"),v4n.forEach(t),XHt=r(CMo," (SegFormer model)"),CMo.forEach(t),zHt=i(fe),gS=n(fe,"LI",{});var wMo=s(gS);tUe=n(wMo,"STRONG",{});var b4n=s(tUe);WHt=r(b4n,"swin"),b4n.forEach(t),QHt=r(wMo," \u2014 "),cve=n(wMo,"A",{href:!0});var T4n=s(cve);UHt=r(T4n,"SwinForImageClassification"),T4n.forEach(t),HHt=r(wMo," (Swin Transformer model)"),wMo.forEach(t),JHt=i(fe),hS=n(fe,"LI",{});var AMo=s(hS);aUe=n(AMo,"STRONG",{});var F4n=s(aUe);ZHt=r(F4n,"swinv2"),F4n.forEach(t),YHt=r(AMo," \u2014 "),fve=n(AMo,"A",{href:!0});var M4n=s(fve);KHt=r(M4n,"Swinv2ForImageClassification"),M4n.forEach(t),eJt=r(AMo," (Swin Transformer V2 model)"),AMo.forEach(t),oJt=i(fe),uS=n(fe,"LI",{});var LMo=s(uS);nUe=n(LMo,"STRONG",{});var E4n=s(nUe);rJt=r(E4n,"van"),E4n.forEach(t),tJt=r(LMo," \u2014 "),mve=n(LMo,"A",{href:!0});var C4n=s(mve);aJt=r(C4n,"VanForImageClassification"),C4n.forEach(t),nJt=r(LMo," (VAN model)"),LMo.forEach(t),sJt=i(fe),pS=n(fe,"LI",{});var yMo=s(pS);sUe=n(yMo,"STRONG",{});var w4n=s(sUe);lJt=r(w4n,"vit"),w4n.forEach(t),iJt=r(yMo," \u2014 "),gve=n(yMo,"A",{href:!0});var A4n=s(gve);dJt=r(A4n,"ViTForImageClassification"),A4n.forEach(t),cJt=r(yMo," (ViT model)"),yMo.forEach(t),fJt=i(fe),_S=n(fe,"LI",{});var xMo=s(_S);lUe=n(xMo,"STRONG",{});var L4n=s(lUe);mJt=r(L4n,"vit_hybrid"),L4n.forEach(t),gJt=r(xMo," \u2014 "),hve=n(xMo,"A",{href:!0});var y4n=s(hve);hJt=r(y4n,"ViTHybridForImageClassification"),y4n.forEach(t),uJt=r(xMo," (ViT Hybrid model)"),xMo.forEach(t),pJt=i(fe),vS=n(fe,"LI",{});var SMo=s(vS);iUe=n(SMo,"STRONG",{});var x4n=s(iUe);_Jt=r(x4n,"vit_msn"),x4n.forEach(t),vJt=r(SMo," \u2014 "),uve=n(SMo,"A",{href:!0});var S4n=s(uve);bJt=r(S4n,"ViTMSNForImageClassification"),S4n.forEach(t),TJt=r(SMo," (ViTMSN model)"),SMo.forEach(t),fe.forEach(t),FJt=i(tn),bS=n(tn,"P",{});var $Mo=s(bS);MJt=r($Mo,"The model is set in evaluation mode by default using "),dUe=n($Mo,"CODE",{});var $4n=s(dUe);EJt=r($4n,"model.eval()"),$4n.forEach(t),CJt=r($Mo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cUe=n($Mo,"CODE",{});var k4n=s(cUe);wJt=r(k4n,"model.train()"),k4n.forEach(t),$Mo.forEach(t),AJt=i(tn),T(TS.$$.fragment,tn),tn.forEach(t),Ld.forEach(t),b5o=i(f),Mm=n(f,"H3",{class:!0});var n6o=s(Mm);FS=n(n6o,"A",{id:!0,class:!0,href:!0});var R4n=s(FS);fUe=n(R4n,"SPAN",{});var P4n=s(fUe);T(Oq.$$.fragment,P4n),P4n.forEach(t),R4n.forEach(t),LJt=i(n6o),mUe=n(n6o,"SPAN",{});var I4n=s(mUe);yJt=r(I4n,"TFAutoModelForImageClassification"),I4n.forEach(t),n6o.forEach(t),T5o=i(f),Fr=n(f,"DIV",{class:!0});var yd=s(Fr);T(Vq.$$.fragment,yd),xJt=i(yd),Em=n(yd,"P",{});var ZTe=s(Em);SJt=r(ZTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),pve=n(ZTe,"A",{href:!0});var N4n=s(pve);$Jt=r(N4n,"from_pretrained()"),N4n.forEach(t),kJt=r(ZTe," class method or the "),_ve=n(ZTe,"A",{href:!0});var B4n=s(_ve);RJt=r(B4n,"from_config()"),B4n.forEach(t),PJt=r(ZTe,` class
method.`),ZTe.forEach(t),IJt=i(yd),Xq=n(yd,"P",{});var s6o=s(Xq);NJt=r(s6o,"This class cannot be instantiated directly using "),gUe=n(s6o,"CODE",{});var G4n=s(gUe);BJt=r(G4n,"__init__()"),G4n.forEach(t),GJt=r(s6o," (throws an error)."),s6o.forEach(t),DJt=i(yd),ua=n(yd,"DIV",{class:!0});var ZI=s(ua);T(zq.$$.fragment,ZI),qJt=i(ZI),hUe=n(ZI,"P",{});var D4n=s(hUe);jJt=r(D4n,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),D4n.forEach(t),OJt=i(ZI),Cm=n(ZI,"P",{});var YTe=s(Cm);VJt=r(YTe,`Note:
Loading a model from its configuration file does `),uUe=n(YTe,"STRONG",{});var q4n=s(uUe);XJt=r(q4n,"not"),q4n.forEach(t),zJt=r(YTe,` load the model weights. It only affects the
model\u2019s configuration. Use `),vve=n(YTe,"A",{href:!0});var j4n=s(vve);WJt=r(j4n,"from_pretrained()"),j4n.forEach(t),QJt=r(YTe," to load the model weights."),YTe.forEach(t),UJt=i(ZI),T(MS.$$.fragment,ZI),ZI.forEach(t),HJt=i(yd),bt=n(yd,"DIV",{class:!0});var xd=s(bt);T(Wq.$$.fragment,xd),JJt=i(xd),pUe=n(xd,"P",{});var O4n=s(pUe);ZJt=r(O4n,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),O4n.forEach(t),YJt=i(xd),is=n(xd,"P",{});var YI=s(is);KJt=r(YI,"The model class to instantiate is selected based on the "),_Ue=n(YI,"CODE",{});var V4n=s(_Ue);eZt=r(V4n,"model_type"),V4n.forEach(t),oZt=r(YI,` property of the config object (either
passed as an argument or loaded from `),vUe=n(YI,"CODE",{});var X4n=s(vUe);rZt=r(X4n,"pretrained_model_name_or_path"),X4n.forEach(t),tZt=r(YI,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bUe=n(YI,"CODE",{});var z4n=s(bUe);aZt=r(z4n,"pretrained_model_name_or_path"),z4n.forEach(t),nZt=r(YI,":"),YI.forEach(t),sZt=i(xd),Be=n(xd,"UL",{});var Qe=s(Be);ES=n(Qe,"LI",{});var kMo=s(ES);TUe=n(kMo,"STRONG",{});var W4n=s(TUe);lZt=r(W4n,"convnext"),W4n.forEach(t),iZt=r(kMo," \u2014 "),bve=n(kMo,"A",{href:!0});var Q4n=s(bve);dZt=r(Q4n,"TFConvNextForImageClassification"),Q4n.forEach(t),cZt=r(kMo," (ConvNeXT model)"),kMo.forEach(t),fZt=i(Qe),CS=n(Qe,"LI",{});var RMo=s(CS);FUe=n(RMo,"STRONG",{});var U4n=s(FUe);mZt=r(U4n,"cvt"),U4n.forEach(t),gZt=r(RMo," \u2014 "),Tve=n(RMo,"A",{href:!0});var H4n=s(Tve);hZt=r(H4n,"TFCvtForImageClassification"),H4n.forEach(t),uZt=r(RMo," (CvT model)"),RMo.forEach(t),pZt=i(Qe),wS=n(Qe,"LI",{});var PMo=s(wS);MUe=n(PMo,"STRONG",{});var J4n=s(MUe);_Zt=r(J4n,"data2vec-vision"),J4n.forEach(t),vZt=r(PMo," \u2014 "),Fve=n(PMo,"A",{href:!0});var Z4n=s(Fve);bZt=r(Z4n,"TFData2VecVisionForImageClassification"),Z4n.forEach(t),TZt=r(PMo," (Data2VecVision model)"),PMo.forEach(t),FZt=i(Qe),Ci=n(Qe,"LI",{});var gz=s(Ci);EUe=n(gz,"STRONG",{});var Y4n=s(EUe);MZt=r(Y4n,"deit"),Y4n.forEach(t),EZt=r(gz," \u2014 "),Mve=n(gz,"A",{href:!0});var K4n=s(Mve);CZt=r(K4n,"TFDeiTForImageClassification"),K4n.forEach(t),wZt=r(gz," or "),Eve=n(gz,"A",{href:!0});var ebn=s(Eve);AZt=r(ebn,"TFDeiTForImageClassificationWithTeacher"),ebn.forEach(t),LZt=r(gz," (DeiT model)"),gz.forEach(t),yZt=i(Qe),AS=n(Qe,"LI",{});var IMo=s(AS);CUe=n(IMo,"STRONG",{});var obn=s(CUe);xZt=r(obn,"mobilevit"),obn.forEach(t),SZt=r(IMo," \u2014 "),Cve=n(IMo,"A",{href:!0});var rbn=s(Cve);$Zt=r(rbn,"TFMobileViTForImageClassification"),rbn.forEach(t),kZt=r(IMo," (MobileViT model)"),IMo.forEach(t),RZt=i(Qe),LS=n(Qe,"LI",{});var NMo=s(LS);wUe=n(NMo,"STRONG",{});var tbn=s(wUe);PZt=r(tbn,"regnet"),tbn.forEach(t),IZt=r(NMo," \u2014 "),wve=n(NMo,"A",{href:!0});var abn=s(wve);NZt=r(abn,"TFRegNetForImageClassification"),abn.forEach(t),BZt=r(NMo," (RegNet model)"),NMo.forEach(t),GZt=i(Qe),yS=n(Qe,"LI",{});var BMo=s(yS);AUe=n(BMo,"STRONG",{});var nbn=s(AUe);DZt=r(nbn,"resnet"),nbn.forEach(t),qZt=r(BMo," \u2014 "),Ave=n(BMo,"A",{href:!0});var sbn=s(Ave);jZt=r(sbn,"TFResNetForImageClassification"),sbn.forEach(t),OZt=r(BMo," (ResNet model)"),BMo.forEach(t),VZt=i(Qe),xS=n(Qe,"LI",{});var GMo=s(xS);LUe=n(GMo,"STRONG",{});var lbn=s(LUe);XZt=r(lbn,"segformer"),lbn.forEach(t),zZt=r(GMo," \u2014 "),Lve=n(GMo,"A",{href:!0});var ibn=s(Lve);WZt=r(ibn,"TFSegformerForImageClassification"),ibn.forEach(t),QZt=r(GMo," (SegFormer model)"),GMo.forEach(t),UZt=i(Qe),SS=n(Qe,"LI",{});var DMo=s(SS);yUe=n(DMo,"STRONG",{});var dbn=s(yUe);HZt=r(dbn,"swin"),dbn.forEach(t),JZt=r(DMo," \u2014 "),yve=n(DMo,"A",{href:!0});var cbn=s(yve);ZZt=r(cbn,"TFSwinForImageClassification"),cbn.forEach(t),YZt=r(DMo," (Swin Transformer model)"),DMo.forEach(t),KZt=i(Qe),$S=n(Qe,"LI",{});var qMo=s($S);xUe=n(qMo,"STRONG",{});var fbn=s(xUe);eYt=r(fbn,"vit"),fbn.forEach(t),oYt=r(qMo," \u2014 "),xve=n(qMo,"A",{href:!0});var mbn=s(xve);rYt=r(mbn,"TFViTForImageClassification"),mbn.forEach(t),tYt=r(qMo," (ViT model)"),qMo.forEach(t),Qe.forEach(t),aYt=i(xd),T(kS.$$.fragment,xd),xd.forEach(t),yd.forEach(t),F5o=i(f),wm=n(f,"H3",{class:!0});var l6o=s(wm);RS=n(l6o,"A",{id:!0,class:!0,href:!0});var gbn=s(RS);SUe=n(gbn,"SPAN",{});var hbn=s(SUe);T(Qq.$$.fragment,hbn),hbn.forEach(t),gbn.forEach(t),nYt=i(l6o),$Ue=n(l6o,"SPAN",{});var ubn=s($Ue);sYt=r(ubn,"FlaxAutoModelForImageClassification"),ubn.forEach(t),l6o.forEach(t),M5o=i(f),Mr=n(f,"DIV",{class:!0});var Sd=s(Mr);T(Uq.$$.fragment,Sd),lYt=i(Sd),Am=n(Sd,"P",{});var KTe=s(Am);iYt=r(KTe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),Sve=n(KTe,"A",{href:!0});var pbn=s(Sve);dYt=r(pbn,"from_pretrained()"),pbn.forEach(t),cYt=r(KTe," class method or the "),$ve=n(KTe,"A",{href:!0});var _bn=s($ve);fYt=r(_bn,"from_config()"),_bn.forEach(t),mYt=r(KTe,` class
method.`),KTe.forEach(t),gYt=i(Sd),Hq=n(Sd,"P",{});var i6o=s(Hq);hYt=r(i6o,"This class cannot be instantiated directly using "),kUe=n(i6o,"CODE",{});var vbn=s(kUe);uYt=r(vbn,"__init__()"),vbn.forEach(t),pYt=r(i6o," (throws an error)."),i6o.forEach(t),_Yt=i(Sd),pa=n(Sd,"DIV",{class:!0});var KI=s(pa);T(Jq.$$.fragment,KI),vYt=i(KI),RUe=n(KI,"P",{});var bbn=s(RUe);bYt=r(bbn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),bbn.forEach(t),TYt=i(KI),Lm=n(KI,"P",{});var eFe=s(Lm);FYt=r(eFe,`Note:
Loading a model from its configuration file does `),PUe=n(eFe,"STRONG",{});var Tbn=s(PUe);MYt=r(Tbn,"not"),Tbn.forEach(t),EYt=r(eFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),kve=n(eFe,"A",{href:!0});var Fbn=s(kve);CYt=r(Fbn,"from_pretrained()"),Fbn.forEach(t),wYt=r(eFe," to load the model weights."),eFe.forEach(t),AYt=i(KI),T(PS.$$.fragment,KI),KI.forEach(t),LYt=i(Sd),Tt=n(Sd,"DIV",{class:!0});var $d=s(Tt);T(Zq.$$.fragment,$d),yYt=i($d),IUe=n($d,"P",{});var Mbn=s(IUe);xYt=r(Mbn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Mbn.forEach(t),SYt=i($d),ds=n($d,"P",{});var eN=s(ds);$Yt=r(eN,"The model class to instantiate is selected based on the "),NUe=n(eN,"CODE",{});var Ebn=s(NUe);kYt=r(Ebn,"model_type"),Ebn.forEach(t),RYt=r(eN,` property of the config object (either
passed as an argument or loaded from `),BUe=n(eN,"CODE",{});var Cbn=s(BUe);PYt=r(Cbn,"pretrained_model_name_or_path"),Cbn.forEach(t),IYt=r(eN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GUe=n(eN,"CODE",{});var wbn=s(GUe);NYt=r(wbn,"pretrained_model_name_or_path"),wbn.forEach(t),BYt=r(eN,":"),eN.forEach(t),GYt=i($d),Yq=n($d,"UL",{});var d6o=s(Yq);IS=n(d6o,"LI",{});var jMo=s(IS);DUe=n(jMo,"STRONG",{});var Abn=s(DUe);DYt=r(Abn,"beit"),Abn.forEach(t),qYt=r(jMo," \u2014 "),Rve=n(jMo,"A",{href:!0});var Lbn=s(Rve);jYt=r(Lbn,"FlaxBeitForImageClassification"),Lbn.forEach(t),OYt=r(jMo," (BEiT model)"),jMo.forEach(t),VYt=i(d6o),NS=n(d6o,"LI",{});var OMo=s(NS);qUe=n(OMo,"STRONG",{});var ybn=s(qUe);XYt=r(ybn,"vit"),ybn.forEach(t),zYt=r(OMo," \u2014 "),Pve=n(OMo,"A",{href:!0});var xbn=s(Pve);WYt=r(xbn,"FlaxViTForImageClassification"),xbn.forEach(t),QYt=r(OMo," (ViT model)"),OMo.forEach(t),d6o.forEach(t),UYt=i($d),T(BS.$$.fragment,$d),$d.forEach(t),Sd.forEach(t),E5o=i(f),ym=n(f,"H3",{class:!0});var c6o=s(ym);GS=n(c6o,"A",{id:!0,class:!0,href:!0});var Sbn=s(GS);jUe=n(Sbn,"SPAN",{});var $bn=s(jUe);T(Kq.$$.fragment,$bn),$bn.forEach(t),Sbn.forEach(t),HYt=i(c6o),OUe=n(c6o,"SPAN",{});var kbn=s(OUe);JYt=r(kbn,"AutoModelForVideoClassification"),kbn.forEach(t),c6o.forEach(t),C5o=i(f),Er=n(f,"DIV",{class:!0});var kd=s(Er);T(ej.$$.fragment,kd),ZYt=i(kd),xm=n(kd,"P",{});var oFe=s(xm);YYt=r(oFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),Ive=n(oFe,"A",{href:!0});var Rbn=s(Ive);KYt=r(Rbn,"from_pretrained()"),Rbn.forEach(t),eKt=r(oFe," class method or the "),Nve=n(oFe,"A",{href:!0});var Pbn=s(Nve);oKt=r(Pbn,"from_config()"),Pbn.forEach(t),rKt=r(oFe,` class
method.`),oFe.forEach(t),tKt=i(kd),oj=n(kd,"P",{});var f6o=s(oj);aKt=r(f6o,"This class cannot be instantiated directly using "),VUe=n(f6o,"CODE",{});var Ibn=s(VUe);nKt=r(Ibn,"__init__()"),Ibn.forEach(t),sKt=r(f6o," (throws an error)."),f6o.forEach(t),lKt=i(kd),_a=n(kd,"DIV",{class:!0});var oN=s(_a);T(rj.$$.fragment,oN),iKt=i(oN),XUe=n(oN,"P",{});var Nbn=s(XUe);dKt=r(Nbn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),Nbn.forEach(t),cKt=i(oN),Sm=n(oN,"P",{});var rFe=s(Sm);fKt=r(rFe,`Note:
Loading a model from its configuration file does `),zUe=n(rFe,"STRONG",{});var Bbn=s(zUe);mKt=r(Bbn,"not"),Bbn.forEach(t),gKt=r(rFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Bve=n(rFe,"A",{href:!0});var Gbn=s(Bve);hKt=r(Gbn,"from_pretrained()"),Gbn.forEach(t),uKt=r(rFe," to load the model weights."),rFe.forEach(t),pKt=i(oN),T(DS.$$.fragment,oN),oN.forEach(t),_Kt=i(kd),bo=n(kd,"DIV",{class:!0});var an=s(bo);T(tj.$$.fragment,an),vKt=i(an),WUe=n(an,"P",{});var Dbn=s(WUe);bKt=r(Dbn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),Dbn.forEach(t),TKt=i(an),cs=n(an,"P",{});var rN=s(cs);FKt=r(rN,"The model class to instantiate is selected based on the "),QUe=n(rN,"CODE",{});var qbn=s(QUe);MKt=r(qbn,"model_type"),qbn.forEach(t),EKt=r(rN,` property of the config object (either
passed as an argument or loaded from `),UUe=n(rN,"CODE",{});var jbn=s(UUe);CKt=r(jbn,"pretrained_model_name_or_path"),jbn.forEach(t),wKt=r(rN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),HUe=n(rN,"CODE",{});var Obn=s(HUe);AKt=r(Obn,"pretrained_model_name_or_path"),Obn.forEach(t),LKt=r(rN,":"),rN.forEach(t),yKt=i(an),aj=n(an,"UL",{});var m6o=s(aj);qS=n(m6o,"LI",{});var VMo=s(qS);JUe=n(VMo,"STRONG",{});var Vbn=s(JUe);xKt=r(Vbn,"timesformer"),Vbn.forEach(t),SKt=r(VMo," \u2014 "),Gve=n(VMo,"A",{href:!0});var Xbn=s(Gve);$Kt=r(Xbn,"TimesformerForVideoClassification"),Xbn.forEach(t),kKt=r(VMo," (TimeSformer model)"),VMo.forEach(t),RKt=i(m6o),jS=n(m6o,"LI",{});var XMo=s(jS);ZUe=n(XMo,"STRONG",{});var zbn=s(ZUe);PKt=r(zbn,"videomae"),zbn.forEach(t),IKt=r(XMo," \u2014 "),Dve=n(XMo,"A",{href:!0});var Wbn=s(Dve);NKt=r(Wbn,"VideoMAEForVideoClassification"),Wbn.forEach(t),BKt=r(XMo," (VideoMAE model)"),XMo.forEach(t),m6o.forEach(t),GKt=i(an),OS=n(an,"P",{});var zMo=s(OS);DKt=r(zMo,"The model is set in evaluation mode by default using "),YUe=n(zMo,"CODE",{});var Qbn=s(YUe);qKt=r(Qbn,"model.eval()"),Qbn.forEach(t),jKt=r(zMo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),KUe=n(zMo,"CODE",{});var Ubn=s(KUe);OKt=r(Ubn,"model.train()"),Ubn.forEach(t),zMo.forEach(t),VKt=i(an),T(VS.$$.fragment,an),an.forEach(t),kd.forEach(t),w5o=i(f),$m=n(f,"H3",{class:!0});var g6o=s($m);XS=n(g6o,"A",{id:!0,class:!0,href:!0});var Hbn=s(XS);eHe=n(Hbn,"SPAN",{});var Jbn=s(eHe);T(nj.$$.fragment,Jbn),Jbn.forEach(t),Hbn.forEach(t),XKt=i(g6o),oHe=n(g6o,"SPAN",{});var Zbn=s(oHe);zKt=r(Zbn,"AutoModelForMaskedImageModeling"),Zbn.forEach(t),g6o.forEach(t),A5o=i(f),Cr=n(f,"DIV",{class:!0});var Rd=s(Cr);T(sj.$$.fragment,Rd),WKt=i(Rd),km=n(Rd,"P",{});var tFe=s(km);QKt=r(tFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),qve=n(tFe,"A",{href:!0});var Ybn=s(qve);UKt=r(Ybn,"from_pretrained()"),Ybn.forEach(t),HKt=r(tFe," class method or the "),jve=n(tFe,"A",{href:!0});var Kbn=s(jve);JKt=r(Kbn,"from_config()"),Kbn.forEach(t),ZKt=r(tFe,` class
method.`),tFe.forEach(t),YKt=i(Rd),lj=n(Rd,"P",{});var h6o=s(lj);KKt=r(h6o,"This class cannot be instantiated directly using "),rHe=n(h6o,"CODE",{});var e7n=s(rHe);eea=r(e7n,"__init__()"),e7n.forEach(t),oea=r(h6o," (throws an error)."),h6o.forEach(t),rea=i(Rd),va=n(Rd,"DIV",{class:!0});var tN=s(va);T(ij.$$.fragment,tN),tea=i(tN),tHe=n(tN,"P",{});var o7n=s(tHe);aea=r(o7n,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),o7n.forEach(t),nea=i(tN),Rm=n(tN,"P",{});var aFe=s(Rm);sea=r(aFe,`Note:
Loading a model from its configuration file does `),aHe=n(aFe,"STRONG",{});var r7n=s(aHe);lea=r(r7n,"not"),r7n.forEach(t),iea=r(aFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ove=n(aFe,"A",{href:!0});var t7n=s(Ove);dea=r(t7n,"from_pretrained()"),t7n.forEach(t),cea=r(aFe," to load the model weights."),aFe.forEach(t),fea=i(tN),T(zS.$$.fragment,tN),tN.forEach(t),mea=i(Rd),To=n(Rd,"DIV",{class:!0});var nn=s(To);T(dj.$$.fragment,nn),gea=i(nn),nHe=n(nn,"P",{});var a7n=s(nHe);hea=r(a7n,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),a7n.forEach(t),uea=i(nn),fs=n(nn,"P",{});var aN=s(fs);pea=r(aN,"The model class to instantiate is selected based on the "),sHe=n(aN,"CODE",{});var n7n=s(sHe);_ea=r(n7n,"model_type"),n7n.forEach(t),vea=r(aN,` property of the config object (either
passed as an argument or loaded from `),lHe=n(aN,"CODE",{});var s7n=s(lHe);bea=r(s7n,"pretrained_model_name_or_path"),s7n.forEach(t),Tea=r(aN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iHe=n(aN,"CODE",{});var l7n=s(iHe);Fea=r(l7n,"pretrained_model_name_or_path"),l7n.forEach(t),Mea=r(aN,":"),aN.forEach(t),Eea=i(nn),ms=n(nn,"UL",{});var nN=s(ms);WS=n(nN,"LI",{});var WMo=s(WS);dHe=n(WMo,"STRONG",{});var i7n=s(dHe);Cea=r(i7n,"deit"),i7n.forEach(t),wea=r(WMo," \u2014 "),Vve=n(WMo,"A",{href:!0});var d7n=s(Vve);Aea=r(d7n,"DeiTForMaskedImageModeling"),d7n.forEach(t),Lea=r(WMo," (DeiT model)"),WMo.forEach(t),yea=i(nN),QS=n(nN,"LI",{});var QMo=s(QS);cHe=n(QMo,"STRONG",{});var c7n=s(cHe);xea=r(c7n,"swin"),c7n.forEach(t),Sea=r(QMo," \u2014 "),Xve=n(QMo,"A",{href:!0});var f7n=s(Xve);$ea=r(f7n,"SwinForMaskedImageModeling"),f7n.forEach(t),kea=r(QMo," (Swin Transformer model)"),QMo.forEach(t),Rea=i(nN),US=n(nN,"LI",{});var UMo=s(US);fHe=n(UMo,"STRONG",{});var m7n=s(fHe);Pea=r(m7n,"swinv2"),m7n.forEach(t),Iea=r(UMo," \u2014 "),zve=n(UMo,"A",{href:!0});var g7n=s(zve);Nea=r(g7n,"Swinv2ForMaskedImageModeling"),g7n.forEach(t),Bea=r(UMo," (Swin Transformer V2 model)"),UMo.forEach(t),Gea=i(nN),HS=n(nN,"LI",{});var HMo=s(HS);mHe=n(HMo,"STRONG",{});var h7n=s(mHe);Dea=r(h7n,"vit"),h7n.forEach(t),qea=r(HMo," \u2014 "),Wve=n(HMo,"A",{href:!0});var u7n=s(Wve);jea=r(u7n,"ViTForMaskedImageModeling"),u7n.forEach(t),Oea=r(HMo," (ViT model)"),HMo.forEach(t),nN.forEach(t),Vea=i(nn),JS=n(nn,"P",{});var JMo=s(JS);Xea=r(JMo,"The model is set in evaluation mode by default using "),gHe=n(JMo,"CODE",{});var p7n=s(gHe);zea=r(p7n,"model.eval()"),p7n.forEach(t),Wea=r(JMo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),hHe=n(JMo,"CODE",{});var _7n=s(hHe);Qea=r(_7n,"model.train()"),_7n.forEach(t),JMo.forEach(t),Uea=i(nn),T(ZS.$$.fragment,nn),nn.forEach(t),Rd.forEach(t),L5o=i(f),Pm=n(f,"H3",{class:!0});var u6o=s(Pm);YS=n(u6o,"A",{id:!0,class:!0,href:!0});var v7n=s(YS);uHe=n(v7n,"SPAN",{});var b7n=s(uHe);T(cj.$$.fragment,b7n),b7n.forEach(t),v7n.forEach(t),Hea=i(u6o),pHe=n(u6o,"SPAN",{});var T7n=s(pHe);Jea=r(T7n,"AutoModelForObjectDetection"),T7n.forEach(t),u6o.forEach(t),y5o=i(f),wr=n(f,"DIV",{class:!0});var Pd=s(wr);T(fj.$$.fragment,Pd),Zea=i(Pd),Im=n(Pd,"P",{});var nFe=s(Im);Yea=r(nFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),Qve=n(nFe,"A",{href:!0});var F7n=s(Qve);Kea=r(F7n,"from_pretrained()"),F7n.forEach(t),eoa=r(nFe," class method or the "),Uve=n(nFe,"A",{href:!0});var M7n=s(Uve);ooa=r(M7n,"from_config()"),M7n.forEach(t),roa=r(nFe,` class
method.`),nFe.forEach(t),toa=i(Pd),mj=n(Pd,"P",{});var p6o=s(mj);aoa=r(p6o,"This class cannot be instantiated directly using "),_He=n(p6o,"CODE",{});var E7n=s(_He);noa=r(E7n,"__init__()"),E7n.forEach(t),soa=r(p6o," (throws an error)."),p6o.forEach(t),loa=i(Pd),ba=n(Pd,"DIV",{class:!0});var sN=s(ba);T(gj.$$.fragment,sN),ioa=i(sN),vHe=n(sN,"P",{});var C7n=s(vHe);doa=r(C7n,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),C7n.forEach(t),coa=i(sN),Nm=n(sN,"P",{});var sFe=s(Nm);foa=r(sFe,`Note:
Loading a model from its configuration file does `),bHe=n(sFe,"STRONG",{});var w7n=s(bHe);moa=r(w7n,"not"),w7n.forEach(t),goa=r(sFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Hve=n(sFe,"A",{href:!0});var A7n=s(Hve);hoa=r(A7n,"from_pretrained()"),A7n.forEach(t),uoa=r(sFe," to load the model weights."),sFe.forEach(t),poa=i(sN),T(KS.$$.fragment,sN),sN.forEach(t),_oa=i(Pd),Fo=n(Pd,"DIV",{class:!0});var sn=s(Fo);T(hj.$$.fragment,sn),voa=i(sn),THe=n(sn,"P",{});var L7n=s(THe);boa=r(L7n,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),L7n.forEach(t),Toa=i(sn),gs=n(sn,"P",{});var lN=s(gs);Foa=r(lN,"The model class to instantiate is selected based on the "),FHe=n(lN,"CODE",{});var y7n=s(FHe);Moa=r(y7n,"model_type"),y7n.forEach(t),Eoa=r(lN,` property of the config object (either
passed as an argument or loaded from `),MHe=n(lN,"CODE",{});var x7n=s(MHe);Coa=r(x7n,"pretrained_model_name_or_path"),x7n.forEach(t),woa=r(lN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EHe=n(lN,"CODE",{});var S7n=s(EHe);Aoa=r(S7n,"pretrained_model_name_or_path"),S7n.forEach(t),Loa=r(lN,":"),lN.forEach(t),yoa=i(sn),Ar=n(sn,"UL",{});var ln=s(Ar);e$=n(ln,"LI",{});var ZMo=s(e$);CHe=n(ZMo,"STRONG",{});var $7n=s(CHe);xoa=r($7n,"conditional_detr"),$7n.forEach(t),Soa=r(ZMo," \u2014 "),Jve=n(ZMo,"A",{href:!0});var k7n=s(Jve);$oa=r(k7n,"ConditionalDetrForObjectDetection"),k7n.forEach(t),koa=r(ZMo," (Conditional DETR model)"),ZMo.forEach(t),Roa=i(ln),o$=n(ln,"LI",{});var YMo=s(o$);wHe=n(YMo,"STRONG",{});var R7n=s(wHe);Poa=r(R7n,"deformable_detr"),R7n.forEach(t),Ioa=r(YMo," \u2014 "),Zve=n(YMo,"A",{href:!0});var P7n=s(Zve);Noa=r(P7n,"DeformableDetrForObjectDetection"),P7n.forEach(t),Boa=r(YMo," (Deformable DETR model)"),YMo.forEach(t),Goa=i(ln),r$=n(ln,"LI",{});var KMo=s(r$);AHe=n(KMo,"STRONG",{});var I7n=s(AHe);Doa=r(I7n,"deta"),I7n.forEach(t),qoa=r(KMo," \u2014 "),Yve=n(KMo,"A",{href:!0});var N7n=s(Yve);joa=r(N7n,"DetaForObjectDetection"),N7n.forEach(t),Ooa=r(KMo," (DETA model)"),KMo.forEach(t),Voa=i(ln),t$=n(ln,"LI",{});var eEo=s(t$);LHe=n(eEo,"STRONG",{});var B7n=s(LHe);Xoa=r(B7n,"detr"),B7n.forEach(t),zoa=r(eEo," \u2014 "),Kve=n(eEo,"A",{href:!0});var G7n=s(Kve);Woa=r(G7n,"DetrForObjectDetection"),G7n.forEach(t),Qoa=r(eEo," (DETR model)"),eEo.forEach(t),Uoa=i(ln),a$=n(ln,"LI",{});var oEo=s(a$);yHe=n(oEo,"STRONG",{});var D7n=s(yHe);Hoa=r(D7n,"table-transformer"),D7n.forEach(t),Joa=r(oEo," \u2014 "),e4e=n(oEo,"A",{href:!0});var q7n=s(e4e);Zoa=r(q7n,"TableTransformerForObjectDetection"),q7n.forEach(t),Yoa=r(oEo," (Table Transformer model)"),oEo.forEach(t),Koa=i(ln),n$=n(ln,"LI",{});var rEo=s(n$);xHe=n(rEo,"STRONG",{});var j7n=s(xHe);era=r(j7n,"yolos"),j7n.forEach(t),ora=r(rEo," \u2014 "),o4e=n(rEo,"A",{href:!0});var O7n=s(o4e);rra=r(O7n,"YolosForObjectDetection"),O7n.forEach(t),tra=r(rEo," (YOLOS model)"),rEo.forEach(t),ln.forEach(t),ara=i(sn),s$=n(sn,"P",{});var tEo=s(s$);nra=r(tEo,"The model is set in evaluation mode by default using "),SHe=n(tEo,"CODE",{});var V7n=s(SHe);sra=r(V7n,"model.eval()"),V7n.forEach(t),lra=r(tEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),$He=n(tEo,"CODE",{});var X7n=s($He);ira=r(X7n,"model.train()"),X7n.forEach(t),tEo.forEach(t),dra=i(sn),T(l$.$$.fragment,sn),sn.forEach(t),Pd.forEach(t),x5o=i(f),Bm=n(f,"H3",{class:!0});var _6o=s(Bm);i$=n(_6o,"A",{id:!0,class:!0,href:!0});var z7n=s(i$);kHe=n(z7n,"SPAN",{});var W7n=s(kHe);T(uj.$$.fragment,W7n),W7n.forEach(t),z7n.forEach(t),cra=i(_6o),RHe=n(_6o,"SPAN",{});var Q7n=s(RHe);fra=r(Q7n,"AutoModelForImageSegmentation"),Q7n.forEach(t),_6o.forEach(t),S5o=i(f),Lr=n(f,"DIV",{class:!0});var Id=s(Lr);T(pj.$$.fragment,Id),mra=i(Id),Gm=n(Id,"P",{});var lFe=s(Gm);gra=r(lFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),r4e=n(lFe,"A",{href:!0});var U7n=s(r4e);hra=r(U7n,"from_pretrained()"),U7n.forEach(t),ura=r(lFe," class method or the "),t4e=n(lFe,"A",{href:!0});var H7n=s(t4e);pra=r(H7n,"from_config()"),H7n.forEach(t),_ra=r(lFe,` class
method.`),lFe.forEach(t),vra=i(Id),_j=n(Id,"P",{});var v6o=s(_j);bra=r(v6o,"This class cannot be instantiated directly using "),PHe=n(v6o,"CODE",{});var J7n=s(PHe);Tra=r(J7n,"__init__()"),J7n.forEach(t),Fra=r(v6o," (throws an error)."),v6o.forEach(t),Mra=i(Id),Ta=n(Id,"DIV",{class:!0});var iN=s(Ta);T(vj.$$.fragment,iN),Era=i(iN),IHe=n(iN,"P",{});var Z7n=s(IHe);Cra=r(Z7n,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Z7n.forEach(t),wra=i(iN),Dm=n(iN,"P",{});var iFe=s(Dm);Ara=r(iFe,`Note:
Loading a model from its configuration file does `),NHe=n(iFe,"STRONG",{});var Y7n=s(NHe);Lra=r(Y7n,"not"),Y7n.forEach(t),yra=r(iFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),a4e=n(iFe,"A",{href:!0});var K7n=s(a4e);xra=r(K7n,"from_pretrained()"),K7n.forEach(t),Sra=r(iFe," to load the model weights."),iFe.forEach(t),$ra=i(iN),T(d$.$$.fragment,iN),iN.forEach(t),kra=i(Id),Mo=n(Id,"DIV",{class:!0});var dn=s(Mo);T(bj.$$.fragment,dn),Rra=i(dn),BHe=n(dn,"P",{});var eTn=s(BHe);Pra=r(eTn,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),eTn.forEach(t),Ira=i(dn),hs=n(dn,"P",{});var dN=s(hs);Nra=r(dN,"The model class to instantiate is selected based on the "),GHe=n(dN,"CODE",{});var oTn=s(GHe);Bra=r(oTn,"model_type"),oTn.forEach(t),Gra=r(dN,` property of the config object (either
passed as an argument or loaded from `),DHe=n(dN,"CODE",{});var rTn=s(DHe);Dra=r(rTn,"pretrained_model_name_or_path"),rTn.forEach(t),qra=r(dN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qHe=n(dN,"CODE",{});var tTn=s(qHe);jra=r(tTn,"pretrained_model_name_or_path"),tTn.forEach(t),Ora=r(dN,":"),dN.forEach(t),Vra=i(dn),jHe=n(dn,"UL",{});var aTn=s(jHe);c$=n(aTn,"LI",{});var aEo=s(c$);OHe=n(aEo,"STRONG",{});var nTn=s(OHe);Xra=r(nTn,"detr"),nTn.forEach(t),zra=r(aEo," \u2014 "),n4e=n(aEo,"A",{href:!0});var sTn=s(n4e);Wra=r(sTn,"DetrForSegmentation"),sTn.forEach(t),Qra=r(aEo," (DETR model)"),aEo.forEach(t),aTn.forEach(t),Ura=i(dn),f$=n(dn,"P",{});var nEo=s(f$);Hra=r(nEo,"The model is set in evaluation mode by default using "),VHe=n(nEo,"CODE",{});var lTn=s(VHe);Jra=r(lTn,"model.eval()"),lTn.forEach(t),Zra=r(nEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),XHe=n(nEo,"CODE",{});var iTn=s(XHe);Yra=r(iTn,"model.train()"),iTn.forEach(t),nEo.forEach(t),Kra=i(dn),T(m$.$$.fragment,dn),dn.forEach(t),Id.forEach(t),$5o=i(f),qm=n(f,"H3",{class:!0});var b6o=s(qm);g$=n(b6o,"A",{id:!0,class:!0,href:!0});var dTn=s(g$);zHe=n(dTn,"SPAN",{});var cTn=s(zHe);T(Tj.$$.fragment,cTn),cTn.forEach(t),dTn.forEach(t),eta=i(b6o),WHe=n(b6o,"SPAN",{});var fTn=s(WHe);ota=r(fTn,"AutoModelForSemanticSegmentation"),fTn.forEach(t),b6o.forEach(t),k5o=i(f),yr=n(f,"DIV",{class:!0});var Nd=s(yr);T(Fj.$$.fragment,Nd),rta=i(Nd),jm=n(Nd,"P",{});var dFe=s(jm);tta=r(dFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),s4e=n(dFe,"A",{href:!0});var mTn=s(s4e);ata=r(mTn,"from_pretrained()"),mTn.forEach(t),nta=r(dFe," class method or the "),l4e=n(dFe,"A",{href:!0});var gTn=s(l4e);sta=r(gTn,"from_config()"),gTn.forEach(t),lta=r(dFe,` class
method.`),dFe.forEach(t),ita=i(Nd),Mj=n(Nd,"P",{});var T6o=s(Mj);dta=r(T6o,"This class cannot be instantiated directly using "),QHe=n(T6o,"CODE",{});var hTn=s(QHe);cta=r(hTn,"__init__()"),hTn.forEach(t),fta=r(T6o," (throws an error)."),T6o.forEach(t),mta=i(Nd),Fa=n(Nd,"DIV",{class:!0});var cN=s(Fa);T(Ej.$$.fragment,cN),gta=i(cN),UHe=n(cN,"P",{});var uTn=s(UHe);hta=r(uTn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),uTn.forEach(t),uta=i(cN),Om=n(cN,"P",{});var cFe=s(Om);pta=r(cFe,`Note:
Loading a model from its configuration file does `),HHe=n(cFe,"STRONG",{});var pTn=s(HHe);_ta=r(pTn,"not"),pTn.forEach(t),vta=r(cFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),i4e=n(cFe,"A",{href:!0});var _Tn=s(i4e);bta=r(_Tn,"from_pretrained()"),_Tn.forEach(t),Tta=r(cFe," to load the model weights."),cFe.forEach(t),Fta=i(cN),T(h$.$$.fragment,cN),cN.forEach(t),Mta=i(Nd),Eo=n(Nd,"DIV",{class:!0});var cn=s(Eo);T(Cj.$$.fragment,cn),Eta=i(cn),JHe=n(cn,"P",{});var vTn=s(JHe);Cta=r(vTn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),vTn.forEach(t),wta=i(cn),us=n(cn,"P",{});var fN=s(us);Ata=r(fN,"The model class to instantiate is selected based on the "),ZHe=n(fN,"CODE",{});var bTn=s(ZHe);Lta=r(bTn,"model_type"),bTn.forEach(t),yta=r(fN,` property of the config object (either
passed as an argument or loaded from `),YHe=n(fN,"CODE",{});var TTn=s(YHe);xta=r(TTn,"pretrained_model_name_or_path"),TTn.forEach(t),Sta=r(fN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),KHe=n(fN,"CODE",{});var FTn=s(KHe);$ta=r(FTn,"pretrained_model_name_or_path"),FTn.forEach(t),kta=r(fN,":"),fN.forEach(t),Rta=i(cn),eo=n(cn,"UL",{});var St=s(eo);u$=n(St,"LI",{});var sEo=s(u$);eJe=n(sEo,"STRONG",{});var MTn=s(eJe);Pta=r(MTn,"beit"),MTn.forEach(t),Ita=r(sEo," \u2014 "),d4e=n(sEo,"A",{href:!0});var ETn=s(d4e);Nta=r(ETn,"BeitForSemanticSegmentation"),ETn.forEach(t),Bta=r(sEo," (BEiT model)"),sEo.forEach(t),Gta=i(St),p$=n(St,"LI",{});var lEo=s(p$);oJe=n(lEo,"STRONG",{});var CTn=s(oJe);Dta=r(CTn,"data2vec-vision"),CTn.forEach(t),qta=r(lEo," \u2014 "),c4e=n(lEo,"A",{href:!0});var wTn=s(c4e);jta=r(wTn,"Data2VecVisionForSemanticSegmentation"),wTn.forEach(t),Ota=r(lEo," (Data2VecVision model)"),lEo.forEach(t),Vta=i(St),_$=n(St,"LI",{});var iEo=s(_$);rJe=n(iEo,"STRONG",{});var ATn=s(rJe);Xta=r(ATn,"dpt"),ATn.forEach(t),zta=r(iEo," \u2014 "),f4e=n(iEo,"A",{href:!0});var LTn=s(f4e);Wta=r(LTn,"DPTForSemanticSegmentation"),LTn.forEach(t),Qta=r(iEo," (DPT model)"),iEo.forEach(t),Uta=i(St),v$=n(St,"LI",{});var dEo=s(v$);tJe=n(dEo,"STRONG",{});var yTn=s(tJe);Hta=r(yTn,"mobilenet_v2"),yTn.forEach(t),Jta=r(dEo," \u2014 "),m4e=n(dEo,"A",{href:!0});var xTn=s(m4e);Zta=r(xTn,"MobileNetV2ForSemanticSegmentation"),xTn.forEach(t),Yta=r(dEo," (MobileNetV2 model)"),dEo.forEach(t),Kta=i(St),b$=n(St,"LI",{});var cEo=s(b$);aJe=n(cEo,"STRONG",{});var STn=s(aJe);eaa=r(STn,"mobilevit"),STn.forEach(t),oaa=r(cEo," \u2014 "),g4e=n(cEo,"A",{href:!0});var $Tn=s(g4e);raa=r($Tn,"MobileViTForSemanticSegmentation"),$Tn.forEach(t),taa=r(cEo," (MobileViT model)"),cEo.forEach(t),aaa=i(St),T$=n(St,"LI",{});var fEo=s(T$);nJe=n(fEo,"STRONG",{});var kTn=s(nJe);naa=r(kTn,"segformer"),kTn.forEach(t),saa=r(fEo," \u2014 "),h4e=n(fEo,"A",{href:!0});var RTn=s(h4e);laa=r(RTn,"SegformerForSemanticSegmentation"),RTn.forEach(t),iaa=r(fEo," (SegFormer model)"),fEo.forEach(t),daa=i(St),F$=n(St,"LI",{});var mEo=s(F$);sJe=n(mEo,"STRONG",{});var PTn=s(sJe);caa=r(PTn,"upernet"),PTn.forEach(t),faa=r(mEo," \u2014 "),u4e=n(mEo,"A",{href:!0});var ITn=s(u4e);maa=r(ITn,"UperNetForSemanticSegmentation"),ITn.forEach(t),gaa=r(mEo," (UPerNet model)"),mEo.forEach(t),St.forEach(t),haa=i(cn),M$=n(cn,"P",{});var gEo=s(M$);uaa=r(gEo,"The model is set in evaluation mode by default using "),lJe=n(gEo,"CODE",{});var NTn=s(lJe);paa=r(NTn,"model.eval()"),NTn.forEach(t),_aa=r(gEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),iJe=n(gEo,"CODE",{});var BTn=s(iJe);vaa=r(BTn,"model.train()"),BTn.forEach(t),gEo.forEach(t),baa=i(cn),T(E$.$$.fragment,cn),cn.forEach(t),Nd.forEach(t),R5o=i(f),Vm=n(f,"H3",{class:!0});var F6o=s(Vm);C$=n(F6o,"A",{id:!0,class:!0,href:!0});var GTn=s(C$);dJe=n(GTn,"SPAN",{});var DTn=s(dJe);T(wj.$$.fragment,DTn),DTn.forEach(t),GTn.forEach(t),Taa=i(F6o),cJe=n(F6o,"SPAN",{});var qTn=s(cJe);Faa=r(qTn,"TFAutoModelForSemanticSegmentation"),qTn.forEach(t),F6o.forEach(t),P5o=i(f),xr=n(f,"DIV",{class:!0});var Bd=s(xr);T(Aj.$$.fragment,Bd),Maa=i(Bd),Xm=n(Bd,"P",{});var fFe=s(Xm);Eaa=r(fFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),p4e=n(fFe,"A",{href:!0});var jTn=s(p4e);Caa=r(jTn,"from_pretrained()"),jTn.forEach(t),waa=r(fFe," class method or the "),_4e=n(fFe,"A",{href:!0});var OTn=s(_4e);Aaa=r(OTn,"from_config()"),OTn.forEach(t),Laa=r(fFe,` class
method.`),fFe.forEach(t),yaa=i(Bd),Lj=n(Bd,"P",{});var M6o=s(Lj);xaa=r(M6o,"This class cannot be instantiated directly using "),fJe=n(M6o,"CODE",{});var VTn=s(fJe);Saa=r(VTn,"__init__()"),VTn.forEach(t),$aa=r(M6o," (throws an error)."),M6o.forEach(t),kaa=i(Bd),Ma=n(Bd,"DIV",{class:!0});var mN=s(Ma);T(yj.$$.fragment,mN),Raa=i(mN),mJe=n(mN,"P",{});var XTn=s(mJe);Paa=r(XTn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),XTn.forEach(t),Iaa=i(mN),zm=n(mN,"P",{});var mFe=s(zm);Naa=r(mFe,`Note:
Loading a model from its configuration file does `),gJe=n(mFe,"STRONG",{});var zTn=s(gJe);Baa=r(zTn,"not"),zTn.forEach(t),Gaa=r(mFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),v4e=n(mFe,"A",{href:!0});var WTn=s(v4e);Daa=r(WTn,"from_pretrained()"),WTn.forEach(t),qaa=r(mFe," to load the model weights."),mFe.forEach(t),jaa=i(mN),T(w$.$$.fragment,mN),mN.forEach(t),Oaa=i(Bd),Ft=n(Bd,"DIV",{class:!0});var Gd=s(Ft);T(xj.$$.fragment,Gd),Vaa=i(Gd),hJe=n(Gd,"P",{});var QTn=s(hJe);Xaa=r(QTn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),QTn.forEach(t),zaa=i(Gd),ps=n(Gd,"P",{});var gN=s(ps);Waa=r(gN,"The model class to instantiate is selected based on the "),uJe=n(gN,"CODE",{});var UTn=s(uJe);Qaa=r(UTn,"model_type"),UTn.forEach(t),Uaa=r(gN,` property of the config object (either
passed as an argument or loaded from `),pJe=n(gN,"CODE",{});var HTn=s(pJe);Haa=r(HTn,"pretrained_model_name_or_path"),HTn.forEach(t),Jaa=r(gN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_Je=n(gN,"CODE",{});var JTn=s(_Je);Zaa=r(JTn,"pretrained_model_name_or_path"),JTn.forEach(t),Yaa=r(gN,":"),gN.forEach(t),Kaa=i(Gd),Wm=n(Gd,"UL",{});var gFe=s(Wm);A$=n(gFe,"LI",{});var hEo=s(A$);vJe=n(hEo,"STRONG",{});var ZTn=s(vJe);ena=r(ZTn,"data2vec-vision"),ZTn.forEach(t),ona=r(hEo," \u2014 "),b4e=n(hEo,"A",{href:!0});var YTn=s(b4e);rna=r(YTn,"TFData2VecVisionForSemanticSegmentation"),YTn.forEach(t),tna=r(hEo," (Data2VecVision model)"),hEo.forEach(t),ana=i(gFe),L$=n(gFe,"LI",{});var uEo=s(L$);bJe=n(uEo,"STRONG",{});var KTn=s(bJe);nna=r(KTn,"mobilevit"),KTn.forEach(t),sna=r(uEo," \u2014 "),T4e=n(uEo,"A",{href:!0});var eFn=s(T4e);lna=r(eFn,"TFMobileViTForSemanticSegmentation"),eFn.forEach(t),ina=r(uEo," (MobileViT model)"),uEo.forEach(t),dna=i(gFe),y$=n(gFe,"LI",{});var pEo=s(y$);TJe=n(pEo,"STRONG",{});var oFn=s(TJe);cna=r(oFn,"segformer"),oFn.forEach(t),fna=r(pEo," \u2014 "),F4e=n(pEo,"A",{href:!0});var rFn=s(F4e);mna=r(rFn,"TFSegformerForSemanticSegmentation"),rFn.forEach(t),gna=r(pEo," (SegFormer model)"),pEo.forEach(t),gFe.forEach(t),hna=i(Gd),T(x$.$$.fragment,Gd),Gd.forEach(t),Bd.forEach(t),I5o=i(f),Qm=n(f,"H3",{class:!0});var E6o=s(Qm);S$=n(E6o,"A",{id:!0,class:!0,href:!0});var tFn=s(S$);FJe=n(tFn,"SPAN",{});var aFn=s(FJe);T(Sj.$$.fragment,aFn),aFn.forEach(t),tFn.forEach(t),una=i(E6o),MJe=n(E6o,"SPAN",{});var nFn=s(MJe);pna=r(nFn,"AutoModelForInstanceSegmentation"),nFn.forEach(t),E6o.forEach(t),N5o=i(f),Sr=n(f,"DIV",{class:!0});var Dd=s(Sr);T($j.$$.fragment,Dd),_na=i(Dd),Um=n(Dd,"P",{});var hFe=s(Um);vna=r(hFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),M4e=n(hFe,"A",{href:!0});var sFn=s(M4e);bna=r(sFn,"from_pretrained()"),sFn.forEach(t),Tna=r(hFe," class method or the "),E4e=n(hFe,"A",{href:!0});var lFn=s(E4e);Fna=r(lFn,"from_config()"),lFn.forEach(t),Mna=r(hFe,` class
method.`),hFe.forEach(t),Ena=i(Dd),kj=n(Dd,"P",{});var C6o=s(kj);Cna=r(C6o,"This class cannot be instantiated directly using "),EJe=n(C6o,"CODE",{});var iFn=s(EJe);wna=r(iFn,"__init__()"),iFn.forEach(t),Ana=r(C6o," (throws an error)."),C6o.forEach(t),Lna=i(Dd),Ea=n(Dd,"DIV",{class:!0});var hN=s(Ea);T(Rj.$$.fragment,hN),yna=i(hN),CJe=n(hN,"P",{});var dFn=s(CJe);xna=r(dFn,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),dFn.forEach(t),Sna=i(hN),Hm=n(hN,"P",{});var uFe=s(Hm);$na=r(uFe,`Note:
Loading a model from its configuration file does `),wJe=n(uFe,"STRONG",{});var cFn=s(wJe);kna=r(cFn,"not"),cFn.forEach(t),Rna=r(uFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),C4e=n(uFe,"A",{href:!0});var fFn=s(C4e);Pna=r(fFn,"from_pretrained()"),fFn.forEach(t),Ina=r(uFe," to load the model weights."),uFe.forEach(t),Nna=i(hN),T($$.$$.fragment,hN),hN.forEach(t),Bna=i(Dd),Co=n(Dd,"DIV",{class:!0});var fn=s(Co);T(Pj.$$.fragment,fn),Gna=i(fn),AJe=n(fn,"P",{});var mFn=s(AJe);Dna=r(mFn,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),mFn.forEach(t),qna=i(fn),_s=n(fn,"P",{});var uN=s(_s);jna=r(uN,"The model class to instantiate is selected based on the "),LJe=n(uN,"CODE",{});var gFn=s(LJe);Ona=r(gFn,"model_type"),gFn.forEach(t),Vna=r(uN,` property of the config object (either
passed as an argument or loaded from `),yJe=n(uN,"CODE",{});var hFn=s(yJe);Xna=r(hFn,"pretrained_model_name_or_path"),hFn.forEach(t),zna=r(uN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xJe=n(uN,"CODE",{});var uFn=s(xJe);Wna=r(uFn,"pretrained_model_name_or_path"),uFn.forEach(t),Qna=r(uN,":"),uN.forEach(t),Una=i(fn),SJe=n(fn,"UL",{});var pFn=s(SJe);k$=n(pFn,"LI",{});var _Eo=s(k$);$Je=n(_Eo,"STRONG",{});var _Fn=s($Je);Hna=r(_Fn,"maskformer"),_Fn.forEach(t),Jna=r(_Eo," \u2014 "),w4e=n(_Eo,"A",{href:!0});var vFn=s(w4e);Zna=r(vFn,"MaskFormerForInstanceSegmentation"),vFn.forEach(t),Yna=r(_Eo," (MaskFormer model)"),_Eo.forEach(t),pFn.forEach(t),Kna=i(fn),R$=n(fn,"P",{});var vEo=s(R$);esa=r(vEo,"The model is set in evaluation mode by default using "),kJe=n(vEo,"CODE",{});var bFn=s(kJe);osa=r(bFn,"model.eval()"),bFn.forEach(t),rsa=r(vEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),RJe=n(vEo,"CODE",{});var TFn=s(RJe);tsa=r(TFn,"model.train()"),TFn.forEach(t),vEo.forEach(t),asa=i(fn),T(P$.$$.fragment,fn),fn.forEach(t),Dd.forEach(t),B5o=i(f),Jm=n(f,"H3",{class:!0});var w6o=s(Jm);I$=n(w6o,"A",{id:!0,class:!0,href:!0});var FFn=s(I$);PJe=n(FFn,"SPAN",{});var MFn=s(PJe);T(Ij.$$.fragment,MFn),MFn.forEach(t),FFn.forEach(t),nsa=i(w6o),IJe=n(w6o,"SPAN",{});var EFn=s(IJe);ssa=r(EFn,"AutoModelForUniversalSegmentation"),EFn.forEach(t),w6o.forEach(t),G5o=i(f),$r=n(f,"DIV",{class:!0});var qd=s($r);T(Nj.$$.fragment,qd),lsa=i(qd),Zm=n(qd,"P",{});var pFe=s(Zm);isa=r(pFe,`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 `),A4e=n(pFe,"A",{href:!0});var CFn=s(A4e);dsa=r(CFn,"from_pretrained()"),CFn.forEach(t),csa=r(pFe," class method or the "),L4e=n(pFe,"A",{href:!0});var wFn=s(L4e);fsa=r(wFn,"from_config()"),wFn.forEach(t),msa=r(pFe,` class
method.`),pFe.forEach(t),gsa=i(qd),Bj=n(qd,"P",{});var A6o=s(Bj);hsa=r(A6o,"This class cannot be instantiated directly using "),NJe=n(A6o,"CODE",{});var AFn=s(NJe);usa=r(AFn,"__init__()"),AFn.forEach(t),psa=r(A6o," (throws an error)."),A6o.forEach(t),_sa=i(qd),Ca=n(qd,"DIV",{class:!0});var pN=s(Ca);T(Gj.$$.fragment,pN),vsa=i(pN),BJe=n(pN,"P",{});var LFn=s(BJe);bsa=r(LFn,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),LFn.forEach(t),Tsa=i(pN),Ym=n(pN,"P",{});var _Fe=s(Ym);Fsa=r(_Fe,`Note:
Loading a model from its configuration file does `),GJe=n(_Fe,"STRONG",{});var yFn=s(GJe);Msa=r(yFn,"not"),yFn.forEach(t),Esa=r(_Fe,` load the model weights. It only affects the
model\u2019s configuration. Use `),y4e=n(_Fe,"A",{href:!0});var xFn=s(y4e);Csa=r(xFn,"from_pretrained()"),xFn.forEach(t),wsa=r(_Fe," to load the model weights."),_Fe.forEach(t),Asa=i(pN),T(N$.$$.fragment,pN),pN.forEach(t),Lsa=i(qd),wo=n(qd,"DIV",{class:!0});var mn=s(wo);T(Dj.$$.fragment,mn),ysa=i(mn),DJe=n(mn,"P",{});var SFn=s(DJe);xsa=r(SFn,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),SFn.forEach(t),Ssa=i(mn),vs=n(mn,"P",{});var _N=s(vs);$sa=r(_N,"The model class to instantiate is selected based on the "),qJe=n(_N,"CODE",{});var $Fn=s(qJe);ksa=r($Fn,"model_type"),$Fn.forEach(t),Rsa=r(_N,` property of the config object (either
passed as an argument or loaded from `),jJe=n(_N,"CODE",{});var kFn=s(jJe);Psa=r(kFn,"pretrained_model_name_or_path"),kFn.forEach(t),Isa=r(_N,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OJe=n(_N,"CODE",{});var RFn=s(OJe);Nsa=r(RFn,"pretrained_model_name_or_path"),RFn.forEach(t),Bsa=r(_N,":"),_N.forEach(t),Gsa=i(mn),bs=n(mn,"UL",{});var vN=s(bs);B$=n(vN,"LI",{});var bEo=s(B$);VJe=n(bEo,"STRONG",{});var PFn=s(VJe);Dsa=r(PFn,"detr"),PFn.forEach(t),qsa=r(bEo," \u2014 "),x4e=n(bEo,"A",{href:!0});var IFn=s(x4e);jsa=r(IFn,"DetrForSegmentation"),IFn.forEach(t),Osa=r(bEo," (DETR model)"),bEo.forEach(t),Vsa=i(vN),G$=n(vN,"LI",{});var TEo=s(G$);XJe=n(TEo,"STRONG",{});var NFn=s(XJe);Xsa=r(NFn,"mask2former"),NFn.forEach(t),zsa=r(TEo," \u2014 "),S4e=n(TEo,"A",{href:!0});var BFn=s(S4e);Wsa=r(BFn,"Mask2FormerForUniversalSegmentation"),BFn.forEach(t),Qsa=r(TEo," (Mask2Former model)"),TEo.forEach(t),Usa=i(vN),D$=n(vN,"LI",{});var FEo=s(D$);zJe=n(FEo,"STRONG",{});var GFn=s(zJe);Hsa=r(GFn,"maskformer"),GFn.forEach(t),Jsa=r(FEo," \u2014 "),$4e=n(FEo,"A",{href:!0});var DFn=s($4e);Zsa=r(DFn,"MaskFormerForInstanceSegmentation"),DFn.forEach(t),Ysa=r(FEo," (MaskFormer model)"),FEo.forEach(t),Ksa=i(vN),q$=n(vN,"LI",{});var MEo=s(q$);WJe=n(MEo,"STRONG",{});var qFn=s(WJe);ela=r(qFn,"oneformer"),qFn.forEach(t),ola=r(MEo," \u2014 "),k4e=n(MEo,"A",{href:!0});var jFn=s(k4e);rla=r(jFn,"OneFormerForUniversalSegmentation"),jFn.forEach(t),tla=r(MEo," (OneFormer model)"),MEo.forEach(t),vN.forEach(t),ala=i(mn),j$=n(mn,"P",{});var EEo=s(j$);nla=r(EEo,"The model is set in evaluation mode by default using "),QJe=n(EEo,"CODE",{});var OFn=s(QJe);sla=r(OFn,"model.eval()"),OFn.forEach(t),lla=r(EEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),UJe=n(EEo,"CODE",{});var VFn=s(UJe);ila=r(VFn,"model.train()"),VFn.forEach(t),EEo.forEach(t),dla=i(mn),T(O$.$$.fragment,mn),mn.forEach(t),qd.forEach(t),D5o=i(f),Km=n(f,"H3",{class:!0});var L6o=s(Km);V$=n(L6o,"A",{id:!0,class:!0,href:!0});var XFn=s(V$);HJe=n(XFn,"SPAN",{});var zFn=s(HJe);T(qj.$$.fragment,zFn),zFn.forEach(t),XFn.forEach(t),cla=i(L6o),JJe=n(L6o,"SPAN",{});var WFn=s(JJe);fla=r(WFn,"AutoModelForZeroShotImageClassification"),WFn.forEach(t),L6o.forEach(t),q5o=i(f),kr=n(f,"DIV",{class:!0});var jd=s(kr);T(jj.$$.fragment,jd),mla=i(jd),eg=n(jd,"P",{});var vFe=s(eg);gla=r(vFe,`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 `),R4e=n(vFe,"A",{href:!0});var QFn=s(R4e);hla=r(QFn,"from_pretrained()"),QFn.forEach(t),ula=r(vFe," class method or the "),P4e=n(vFe,"A",{href:!0});var UFn=s(P4e);pla=r(UFn,"from_config()"),UFn.forEach(t),_la=r(vFe,` class
method.`),vFe.forEach(t),vla=i(jd),Oj=n(jd,"P",{});var y6o=s(Oj);bla=r(y6o,"This class cannot be instantiated directly using "),ZJe=n(y6o,"CODE",{});var HFn=s(ZJe);Tla=r(HFn,"__init__()"),HFn.forEach(t),Fla=r(y6o," (throws an error)."),y6o.forEach(t),Mla=i(jd),wa=n(jd,"DIV",{class:!0});var bN=s(wa);T(Vj.$$.fragment,bN),Ela=i(bN),YJe=n(bN,"P",{});var JFn=s(YJe);Cla=r(JFn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),JFn.forEach(t),wla=i(bN),og=n(bN,"P",{});var bFe=s(og);Ala=r(bFe,`Note:
Loading a model from its configuration file does `),KJe=n(bFe,"STRONG",{});var ZFn=s(KJe);Lla=r(ZFn,"not"),ZFn.forEach(t),yla=r(bFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),I4e=n(bFe,"A",{href:!0});var YFn=s(I4e);xla=r(YFn,"from_pretrained()"),YFn.forEach(t),Sla=r(bFe," to load the model weights."),bFe.forEach(t),$la=i(bN),T(X$.$$.fragment,bN),bN.forEach(t),kla=i(jd),Ao=n(jd,"DIV",{class:!0});var gn=s(Ao);T(Xj.$$.fragment,gn),Rla=i(gn),eZe=n(gn,"P",{});var KFn=s(eZe);Pla=r(KFn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),KFn.forEach(t),Ila=i(gn),Ts=n(gn,"P",{});var TN=s(Ts);Nla=r(TN,"The model class to instantiate is selected based on the "),oZe=n(TN,"CODE",{});var eMn=s(oZe);Bla=r(eMn,"model_type"),eMn.forEach(t),Gla=r(TN,` property of the config object (either
passed as an argument or loaded from `),rZe=n(TN,"CODE",{});var oMn=s(rZe);Dla=r(oMn,"pretrained_model_name_or_path"),oMn.forEach(t),qla=r(TN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tZe=n(TN,"CODE",{});var rMn=s(tZe);jla=r(rMn,"pretrained_model_name_or_path"),rMn.forEach(t),Ola=r(TN,":"),TN.forEach(t),Vla=i(gn),Rr=n(gn,"UL",{});var hn=s(Rr);z$=n(hn,"LI",{});var CEo=s(z$);aZe=n(CEo,"STRONG",{});var tMn=s(aZe);Xla=r(tMn,"align"),tMn.forEach(t),zla=r(CEo," \u2014 "),N4e=n(CEo,"A",{href:!0});var aMn=s(N4e);Wla=r(aMn,"AlignModel"),aMn.forEach(t),Qla=r(CEo," (ALIGN model)"),CEo.forEach(t),Ula=i(hn),W$=n(hn,"LI",{});var wEo=s(W$);nZe=n(wEo,"STRONG",{});var nMn=s(nZe);Hla=r(nMn,"altclip"),nMn.forEach(t),Jla=r(wEo," \u2014 "),B4e=n(wEo,"A",{href:!0});var sMn=s(B4e);Zla=r(sMn,"AltCLIPModel"),sMn.forEach(t),Yla=r(wEo," (AltCLIP model)"),wEo.forEach(t),Kla=i(hn),Q$=n(hn,"LI",{});var AEo=s(Q$);sZe=n(AEo,"STRONG",{});var lMn=s(sZe);eia=r(lMn,"blip"),lMn.forEach(t),oia=r(AEo," \u2014 "),G4e=n(AEo,"A",{href:!0});var iMn=s(G4e);ria=r(iMn,"BlipModel"),iMn.forEach(t),tia=r(AEo," (BLIP model)"),AEo.forEach(t),aia=i(hn),U$=n(hn,"LI",{});var LEo=s(U$);lZe=n(LEo,"STRONG",{});var dMn=s(lZe);nia=r(dMn,"chinese_clip"),dMn.forEach(t),sia=r(LEo," \u2014 "),D4e=n(LEo,"A",{href:!0});var cMn=s(D4e);lia=r(cMn,"ChineseCLIPModel"),cMn.forEach(t),iia=r(LEo," (Chinese-CLIP model)"),LEo.forEach(t),dia=i(hn),H$=n(hn,"LI",{});var yEo=s(H$);iZe=n(yEo,"STRONG",{});var fMn=s(iZe);cia=r(fMn,"clip"),fMn.forEach(t),fia=r(yEo," \u2014 "),q4e=n(yEo,"A",{href:!0});var mMn=s(q4e);mia=r(mMn,"CLIPModel"),mMn.forEach(t),gia=r(yEo," (CLIP model)"),yEo.forEach(t),hia=i(hn),J$=n(hn,"LI",{});var xEo=s(J$);dZe=n(xEo,"STRONG",{});var gMn=s(dZe);uia=r(gMn,"clipseg"),gMn.forEach(t),pia=r(xEo," \u2014 "),j4e=n(xEo,"A",{href:!0});var hMn=s(j4e);_ia=r(hMn,"CLIPSegModel"),hMn.forEach(t),via=r(xEo," (CLIPSeg model)"),xEo.forEach(t),hn.forEach(t),bia=i(gn),Z$=n(gn,"P",{});var SEo=s(Z$);Tia=r(SEo,"The model is set in evaluation mode by default using "),cZe=n(SEo,"CODE",{});var uMn=s(cZe);Fia=r(uMn,"model.eval()"),uMn.forEach(t),Mia=r(SEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),fZe=n(SEo,"CODE",{});var pMn=s(fZe);Eia=r(pMn,"model.train()"),pMn.forEach(t),SEo.forEach(t),Cia=i(gn),T(Y$.$$.fragment,gn),gn.forEach(t),jd.forEach(t),j5o=i(f),rg=n(f,"H3",{class:!0});var x6o=s(rg);K$=n(x6o,"A",{id:!0,class:!0,href:!0});var _Mn=s(K$);mZe=n(_Mn,"SPAN",{});var vMn=s(mZe);T(zj.$$.fragment,vMn),vMn.forEach(t),_Mn.forEach(t),wia=i(x6o),gZe=n(x6o,"SPAN",{});var bMn=s(gZe);Aia=r(bMn,"TFAutoModelForZeroShotImageClassification"),bMn.forEach(t),x6o.forEach(t),O5o=i(f),Pr=n(f,"DIV",{class:!0});var Od=s(Pr);T(Wj.$$.fragment,Od),Lia=i(Od),tg=n(Od,"P",{});var TFe=s(tg);yia=r(TFe,`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 `),O4e=n(TFe,"A",{href:!0});var TMn=s(O4e);xia=r(TMn,"from_pretrained()"),TMn.forEach(t),Sia=r(TFe," class method or the "),V4e=n(TFe,"A",{href:!0});var FMn=s(V4e);$ia=r(FMn,"from_config()"),FMn.forEach(t),kia=r(TFe,` class
method.`),TFe.forEach(t),Ria=i(Od),Qj=n(Od,"P",{});var S6o=s(Qj);Pia=r(S6o,"This class cannot be instantiated directly using "),hZe=n(S6o,"CODE",{});var MMn=s(hZe);Iia=r(MMn,"__init__()"),MMn.forEach(t),Nia=r(S6o," (throws an error)."),S6o.forEach(t),Bia=i(Od),Aa=n(Od,"DIV",{class:!0});var FN=s(Aa);T(Uj.$$.fragment,FN),Gia=i(FN),uZe=n(FN,"P",{});var EMn=s(uZe);Dia=r(EMn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),EMn.forEach(t),qia=i(FN),ag=n(FN,"P",{});var FFe=s(ag);jia=r(FFe,`Note:
Loading a model from its configuration file does `),pZe=n(FFe,"STRONG",{});var CMn=s(pZe);Oia=r(CMn,"not"),CMn.forEach(t),Via=r(FFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),X4e=n(FFe,"A",{href:!0});var wMn=s(X4e);Xia=r(wMn,"from_pretrained()"),wMn.forEach(t),zia=r(FFe," to load the model weights."),FFe.forEach(t),Wia=i(FN),T(ek.$$.fragment,FN),FN.forEach(t),Qia=i(Od),Mt=n(Od,"DIV",{class:!0});var Vd=s(Mt);T(Hj.$$.fragment,Vd),Uia=i(Vd),_Ze=n(Vd,"P",{});var AMn=s(_Ze);Hia=r(AMn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),AMn.forEach(t),Jia=i(Vd),Fs=n(Vd,"P",{});var MN=s(Fs);Zia=r(MN,"The model class to instantiate is selected based on the "),vZe=n(MN,"CODE",{});var LMn=s(vZe);Yia=r(LMn,"model_type"),LMn.forEach(t),Kia=r(MN,` property of the config object (either
passed as an argument or loaded from `),bZe=n(MN,"CODE",{});var yMn=s(bZe);eda=r(yMn,"pretrained_model_name_or_path"),yMn.forEach(t),oda=r(MN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),TZe=n(MN,"CODE",{});var xMn=s(TZe);rda=r(xMn,"pretrained_model_name_or_path"),xMn.forEach(t),tda=r(MN,":"),MN.forEach(t),ada=i(Vd),FZe=n(Vd,"UL",{});var SMn=s(FZe);ok=n(SMn,"LI",{});var $Eo=s(ok);MZe=n($Eo,"STRONG",{});var $Mn=s(MZe);nda=r($Mn,"clip"),$Mn.forEach(t),sda=r($Eo," \u2014 "),z4e=n($Eo,"A",{href:!0});var kMn=s(z4e);lda=r(kMn,"TFCLIPModel"),kMn.forEach(t),ida=r($Eo," (CLIP model)"),$Eo.forEach(t),SMn.forEach(t),dda=i(Vd),T(rk.$$.fragment,Vd),Vd.forEach(t),Od.forEach(t),V5o=i(f),ng=n(f,"H3",{class:!0});var $6o=s(ng);tk=n($6o,"A",{id:!0,class:!0,href:!0});var RMn=s(tk);EZe=n(RMn,"SPAN",{});var PMn=s(EZe);T(Jj.$$.fragment,PMn),PMn.forEach(t),RMn.forEach(t),cda=i($6o),CZe=n($6o,"SPAN",{});var IMn=s(CZe);fda=r(IMn,"AutoModelForZeroShotObjectDetection"),IMn.forEach(t),$6o.forEach(t),X5o=i(f),Ir=n(f,"DIV",{class:!0});var Xd=s(Ir);T(Zj.$$.fragment,Xd),mda=i(Xd),sg=n(Xd,"P",{});var MFe=s(sg);gda=r(MFe,`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 `),W4e=n(MFe,"A",{href:!0});var NMn=s(W4e);hda=r(NMn,"from_pretrained()"),NMn.forEach(t),uda=r(MFe," class method or the "),Q4e=n(MFe,"A",{href:!0});var BMn=s(Q4e);pda=r(BMn,"from_config()"),BMn.forEach(t),_da=r(MFe,` class
method.`),MFe.forEach(t),vda=i(Xd),Yj=n(Xd,"P",{});var k6o=s(Yj);bda=r(k6o,"This class cannot be instantiated directly using "),wZe=n(k6o,"CODE",{});var GMn=s(wZe);Tda=r(GMn,"__init__()"),GMn.forEach(t),Fda=r(k6o," (throws an error)."),k6o.forEach(t),Mda=i(Xd),La=n(Xd,"DIV",{class:!0});var EN=s(La);T(Kj.$$.fragment,EN),Eda=i(EN),AZe=n(EN,"P",{});var DMn=s(AZe);Cda=r(DMn,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),DMn.forEach(t),wda=i(EN),lg=n(EN,"P",{});var EFe=s(lg);Ada=r(EFe,`Note:
Loading a model from its configuration file does `),LZe=n(EFe,"STRONG",{});var qMn=s(LZe);Lda=r(qMn,"not"),qMn.forEach(t),yda=r(EFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),U4e=n(EFe,"A",{href:!0});var jMn=s(U4e);xda=r(jMn,"from_pretrained()"),jMn.forEach(t),Sda=r(EFe," to load the model weights."),EFe.forEach(t),$da=i(EN),T(ak.$$.fragment,EN),EN.forEach(t),kda=i(Xd),Lo=n(Xd,"DIV",{class:!0});var un=s(Lo);T(eO.$$.fragment,un),Rda=i(un),yZe=n(un,"P",{});var OMn=s(yZe);Pda=r(OMn,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),OMn.forEach(t),Ida=i(un),Ms=n(un,"P",{});var CN=s(Ms);Nda=r(CN,"The model class to instantiate is selected based on the "),xZe=n(CN,"CODE",{});var VMn=s(xZe);Bda=r(VMn,"model_type"),VMn.forEach(t),Gda=r(CN,` property of the config object (either
passed as an argument or loaded from `),SZe=n(CN,"CODE",{});var XMn=s(SZe);Dda=r(XMn,"pretrained_model_name_or_path"),XMn.forEach(t),qda=r(CN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$Ze=n(CN,"CODE",{});var zMn=s($Ze);jda=r(zMn,"pretrained_model_name_or_path"),zMn.forEach(t),Oda=r(CN,":"),CN.forEach(t),Vda=i(un),kZe=n(un,"UL",{});var WMn=s(kZe);nk=n(WMn,"LI",{});var kEo=s(nk);RZe=n(kEo,"STRONG",{});var QMn=s(RZe);Xda=r(QMn,"owlvit"),QMn.forEach(t),zda=r(kEo," \u2014 "),H4e=n(kEo,"A",{href:!0});var UMn=s(H4e);Wda=r(UMn,"OwlViTForObjectDetection"),UMn.forEach(t),Qda=r(kEo," (OWL-ViT model)"),kEo.forEach(t),WMn.forEach(t),Uda=i(un),sk=n(un,"P",{});var REo=s(sk);Hda=r(REo,"The model is set in evaluation mode by default using "),PZe=n(REo,"CODE",{});var HMn=s(PZe);Jda=r(HMn,"model.eval()"),HMn.forEach(t),Zda=r(REo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),IZe=n(REo,"CODE",{});var JMn=s(IZe);Yda=r(JMn,"model.train()"),JMn.forEach(t),REo.forEach(t),Kda=i(un),T(lk.$$.fragment,un),un.forEach(t),Xd.forEach(t),z5o=i(f),ig=n(f,"H2",{class:!0});var R6o=s(ig);ik=n(R6o,"A",{id:!0,class:!0,href:!0});var ZMn=s(ik);NZe=n(ZMn,"SPAN",{});var YMn=s(NZe);T(oO.$$.fragment,YMn),YMn.forEach(t),ZMn.forEach(t),eca=i(R6o),BZe=n(R6o,"SPAN",{});var KMn=s(BZe);oca=r(KMn,"Audio"),KMn.forEach(t),R6o.forEach(t),W5o=i(f),J4e=n(f,"P",{});var eEn=s(J4e);rca=r(eEn,"The following auto classes are available for the following audio tasks."),eEn.forEach(t),Q5o=i(f),dg=n(f,"H3",{class:!0});var P6o=s(dg);dk=n(P6o,"A",{id:!0,class:!0,href:!0});var oEn=s(dk);GZe=n(oEn,"SPAN",{});var rEn=s(GZe);T(rO.$$.fragment,rEn),rEn.forEach(t),oEn.forEach(t),tca=i(P6o),DZe=n(P6o,"SPAN",{});var tEn=s(DZe);aca=r(tEn,"AutoModelForAudioClassification"),tEn.forEach(t),P6o.forEach(t),U5o=i(f),Nr=n(f,"DIV",{class:!0});var zd=s(Nr);T(tO.$$.fragment,zd),nca=i(zd),cg=n(zd,"P",{});var CFe=s(cg);sca=r(CFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),Z4e=n(CFe,"A",{href:!0});var aEn=s(Z4e);lca=r(aEn,"from_pretrained()"),aEn.forEach(t),ica=r(CFe," class method or the "),Y4e=n(CFe,"A",{href:!0});var nEn=s(Y4e);dca=r(nEn,"from_config()"),nEn.forEach(t),cca=r(CFe,` class
method.`),CFe.forEach(t),fca=i(zd),aO=n(zd,"P",{});var I6o=s(aO);mca=r(I6o,"This class cannot be instantiated directly using "),qZe=n(I6o,"CODE",{});var sEn=s(qZe);gca=r(sEn,"__init__()"),sEn.forEach(t),hca=r(I6o," (throws an error)."),I6o.forEach(t),uca=i(zd),ya=n(zd,"DIV",{class:!0});var wN=s(ya);T(nO.$$.fragment,wN),pca=i(wN),jZe=n(wN,"P",{});var lEn=s(jZe);_ca=r(lEn,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),lEn.forEach(t),vca=i(wN),fg=n(wN,"P",{});var wFe=s(fg);bca=r(wFe,`Note:
Loading a model from its configuration file does `),OZe=n(wFe,"STRONG",{});var iEn=s(OZe);Tca=r(iEn,"not"),iEn.forEach(t),Fca=r(wFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),K4e=n(wFe,"A",{href:!0});var dEn=s(K4e);Mca=r(dEn,"from_pretrained()"),dEn.forEach(t),Eca=r(wFe," to load the model weights."),wFe.forEach(t),Cca=i(wN),T(ck.$$.fragment,wN),wN.forEach(t),wca=i(zd),yo=n(zd,"DIV",{class:!0});var pn=s(yo);T(sO.$$.fragment,pn),Aca=i(pn),VZe=n(pn,"P",{});var cEn=s(VZe);Lca=r(cEn,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),cEn.forEach(t),yca=i(pn),Es=n(pn,"P",{});var AN=s(Es);xca=r(AN,"The model class to instantiate is selected based on the "),XZe=n(AN,"CODE",{});var fEn=s(XZe);Sca=r(fEn,"model_type"),fEn.forEach(t),$ca=r(AN,` property of the config object (either
passed as an argument or loaded from `),zZe=n(AN,"CODE",{});var mEn=s(zZe);kca=r(mEn,"pretrained_model_name_or_path"),mEn.forEach(t),Rca=r(AN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WZe=n(AN,"CODE",{});var gEn=s(WZe);Pca=r(gEn,"pretrained_model_name_or_path"),gEn.forEach(t),Ica=r(AN,":"),AN.forEach(t),Nca=i(pn),Re=n(pn,"UL",{});var Oe=s(Re);fk=n(Oe,"LI",{});var PEo=s(fk);QZe=n(PEo,"STRONG",{});var hEn=s(QZe);Bca=r(hEn,"audio-spectrogram-transformer"),hEn.forEach(t),Gca=r(PEo," \u2014 "),ebe=n(PEo,"A",{href:!0});var uEn=s(ebe);Dca=r(uEn,"ASTForAudioClassification"),uEn.forEach(t),qca=r(PEo," (Audio Spectrogram Transformer model)"),PEo.forEach(t),jca=i(Oe),mk=n(Oe,"LI",{});var IEo=s(mk);UZe=n(IEo,"STRONG",{});var pEn=s(UZe);Oca=r(pEn,"data2vec-audio"),pEn.forEach(t),Vca=r(IEo," \u2014 "),obe=n(IEo,"A",{href:!0});var _En=s(obe);Xca=r(_En,"Data2VecAudioForSequenceClassification"),_En.forEach(t),zca=r(IEo," (Data2VecAudio model)"),IEo.forEach(t),Wca=i(Oe),gk=n(Oe,"LI",{});var NEo=s(gk);HZe=n(NEo,"STRONG",{});var vEn=s(HZe);Qca=r(vEn,"hubert"),vEn.forEach(t),Uca=r(NEo," \u2014 "),rbe=n(NEo,"A",{href:!0});var bEn=s(rbe);Hca=r(bEn,"HubertForSequenceClassification"),bEn.forEach(t),Jca=r(NEo," (Hubert model)"),NEo.forEach(t),Zca=i(Oe),hk=n(Oe,"LI",{});var BEo=s(hk);JZe=n(BEo,"STRONG",{});var TEn=s(JZe);Yca=r(TEn,"sew"),TEn.forEach(t),Kca=r(BEo," \u2014 "),tbe=n(BEo,"A",{href:!0});var FEn=s(tbe);efa=r(FEn,"SEWForSequenceClassification"),FEn.forEach(t),ofa=r(BEo," (SEW model)"),BEo.forEach(t),rfa=i(Oe),uk=n(Oe,"LI",{});var GEo=s(uk);ZZe=n(GEo,"STRONG",{});var MEn=s(ZZe);tfa=r(MEn,"sew-d"),MEn.forEach(t),afa=r(GEo," \u2014 "),abe=n(GEo,"A",{href:!0});var EEn=s(abe);nfa=r(EEn,"SEWDForSequenceClassification"),EEn.forEach(t),sfa=r(GEo," (SEW-D model)"),GEo.forEach(t),lfa=i(Oe),pk=n(Oe,"LI",{});var DEo=s(pk);YZe=n(DEo,"STRONG",{});var CEn=s(YZe);ifa=r(CEn,"unispeech"),CEn.forEach(t),dfa=r(DEo," \u2014 "),nbe=n(DEo,"A",{href:!0});var wEn=s(nbe);cfa=r(wEn,"UniSpeechForSequenceClassification"),wEn.forEach(t),ffa=r(DEo," (UniSpeech model)"),DEo.forEach(t),mfa=i(Oe),_k=n(Oe,"LI",{});var qEo=s(_k);KZe=n(qEo,"STRONG",{});var AEn=s(KZe);gfa=r(AEn,"unispeech-sat"),AEn.forEach(t),hfa=r(qEo," \u2014 "),sbe=n(qEo,"A",{href:!0});var LEn=s(sbe);ufa=r(LEn,"UniSpeechSatForSequenceClassification"),LEn.forEach(t),pfa=r(qEo," (UniSpeechSat model)"),qEo.forEach(t),_fa=i(Oe),vk=n(Oe,"LI",{});var jEo=s(vk);eYe=n(jEo,"STRONG",{});var yEn=s(eYe);vfa=r(yEn,"wav2vec2"),yEn.forEach(t),bfa=r(jEo," \u2014 "),lbe=n(jEo,"A",{href:!0});var xEn=s(lbe);Tfa=r(xEn,"Wav2Vec2ForSequenceClassification"),xEn.forEach(t),Ffa=r(jEo," (Wav2Vec2 model)"),jEo.forEach(t),Mfa=i(Oe),bk=n(Oe,"LI",{});var OEo=s(bk);oYe=n(OEo,"STRONG",{});var SEn=s(oYe);Efa=r(SEn,"wav2vec2-conformer"),SEn.forEach(t),Cfa=r(OEo," \u2014 "),ibe=n(OEo,"A",{href:!0});var $En=s(ibe);wfa=r($En,"Wav2Vec2ConformerForSequenceClassification"),$En.forEach(t),Afa=r(OEo," (Wav2Vec2-Conformer model)"),OEo.forEach(t),Lfa=i(Oe),Tk=n(Oe,"LI",{});var VEo=s(Tk);rYe=n(VEo,"STRONG",{});var kEn=s(rYe);yfa=r(kEn,"wavlm"),kEn.forEach(t),xfa=r(VEo," \u2014 "),dbe=n(VEo,"A",{href:!0});var REn=s(dbe);Sfa=r(REn,"WavLMForSequenceClassification"),REn.forEach(t),$fa=r(VEo," (WavLM model)"),VEo.forEach(t),kfa=i(Oe),Fk=n(Oe,"LI",{});var XEo=s(Fk);tYe=n(XEo,"STRONG",{});var PEn=s(tYe);Rfa=r(PEn,"whisper"),PEn.forEach(t),Pfa=r(XEo," \u2014 "),cbe=n(XEo,"A",{href:!0});var IEn=s(cbe);Ifa=r(IEn,"WhisperForAudioClassification"),IEn.forEach(t),Nfa=r(XEo," (Whisper model)"),XEo.forEach(t),Oe.forEach(t),Bfa=i(pn),Mk=n(pn,"P",{});var zEo=s(Mk);Gfa=r(zEo,"The model is set in evaluation mode by default using "),aYe=n(zEo,"CODE",{});var NEn=s(aYe);Dfa=r(NEn,"model.eval()"),NEn.forEach(t),qfa=r(zEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nYe=n(zEo,"CODE",{});var BEn=s(nYe);jfa=r(BEn,"model.train()"),BEn.forEach(t),zEo.forEach(t),Ofa=i(pn),T(Ek.$$.fragment,pn),pn.forEach(t),zd.forEach(t),H5o=i(f),mg=n(f,"H3",{class:!0});var N6o=s(mg);Ck=n(N6o,"A",{id:!0,class:!0,href:!0});var GEn=s(Ck);sYe=n(GEn,"SPAN",{});var DEn=s(sYe);T(lO.$$.fragment,DEn),DEn.forEach(t),GEn.forEach(t),Vfa=i(N6o),lYe=n(N6o,"SPAN",{});var qEn=s(lYe);Xfa=r(qEn,"AutoModelForAudioFrameClassification"),qEn.forEach(t),N6o.forEach(t),J5o=i(f),Br=n(f,"DIV",{class:!0});var Wd=s(Br);T(iO.$$.fragment,Wd),zfa=i(Wd),gg=n(Wd,"P",{});var AFe=s(gg);Wfa=r(AFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),fbe=n(AFe,"A",{href:!0});var jEn=s(fbe);Qfa=r(jEn,"from_pretrained()"),jEn.forEach(t),Ufa=r(AFe," class method or the "),mbe=n(AFe,"A",{href:!0});var OEn=s(mbe);Hfa=r(OEn,"from_config()"),OEn.forEach(t),Jfa=r(AFe,` class
method.`),AFe.forEach(t),Zfa=i(Wd),dO=n(Wd,"P",{});var B6o=s(dO);Yfa=r(B6o,"This class cannot be instantiated directly using "),iYe=n(B6o,"CODE",{});var VEn=s(iYe);Kfa=r(VEn,"__init__()"),VEn.forEach(t),ema=r(B6o," (throws an error)."),B6o.forEach(t),oma=i(Wd),xa=n(Wd,"DIV",{class:!0});var LN=s(xa);T(cO.$$.fragment,LN),rma=i(LN),dYe=n(LN,"P",{});var XEn=s(dYe);tma=r(XEn,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),XEn.forEach(t),ama=i(LN),hg=n(LN,"P",{});var LFe=s(hg);nma=r(LFe,`Note:
Loading a model from its configuration file does `),cYe=n(LFe,"STRONG",{});var zEn=s(cYe);sma=r(zEn,"not"),zEn.forEach(t),lma=r(LFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),gbe=n(LFe,"A",{href:!0});var WEn=s(gbe);ima=r(WEn,"from_pretrained()"),WEn.forEach(t),dma=r(LFe," to load the model weights."),LFe.forEach(t),cma=i(LN),T(wk.$$.fragment,LN),LN.forEach(t),fma=i(Wd),xo=n(Wd,"DIV",{class:!0});var _n=s(xo);T(fO.$$.fragment,_n),mma=i(_n),fYe=n(_n,"P",{});var QEn=s(fYe);gma=r(QEn,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),QEn.forEach(t),hma=i(_n),Cs=n(_n,"P",{});var yN=s(Cs);uma=r(yN,"The model class to instantiate is selected based on the "),mYe=n(yN,"CODE",{});var UEn=s(mYe);pma=r(UEn,"model_type"),UEn.forEach(t),_ma=r(yN,` property of the config object (either
passed as an argument or loaded from `),gYe=n(yN,"CODE",{});var HEn=s(gYe);vma=r(HEn,"pretrained_model_name_or_path"),HEn.forEach(t),bma=r(yN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hYe=n(yN,"CODE",{});var JEn=s(hYe);Tma=r(JEn,"pretrained_model_name_or_path"),JEn.forEach(t),Fma=r(yN,":"),yN.forEach(t),Mma=i(_n),$t=n(_n,"UL",{});var Qd=s($t);Ak=n(Qd,"LI",{});var WEo=s(Ak);uYe=n(WEo,"STRONG",{});var ZEn=s(uYe);Ema=r(ZEn,"data2vec-audio"),ZEn.forEach(t),Cma=r(WEo," \u2014 "),hbe=n(WEo,"A",{href:!0});var YEn=s(hbe);wma=r(YEn,"Data2VecAudioForAudioFrameClassification"),YEn.forEach(t),Ama=r(WEo," (Data2VecAudio model)"),WEo.forEach(t),Lma=i(Qd),Lk=n(Qd,"LI",{});var QEo=s(Lk);pYe=n(QEo,"STRONG",{});var KEn=s(pYe);yma=r(KEn,"unispeech-sat"),KEn.forEach(t),xma=r(QEo," \u2014 "),ube=n(QEo,"A",{href:!0});var e3n=s(ube);Sma=r(e3n,"UniSpeechSatForAudioFrameClassification"),e3n.forEach(t),$ma=r(QEo," (UniSpeechSat model)"),QEo.forEach(t),kma=i(Qd),yk=n(Qd,"LI",{});var UEo=s(yk);_Ye=n(UEo,"STRONG",{});var o3n=s(_Ye);Rma=r(o3n,"wav2vec2"),o3n.forEach(t),Pma=r(UEo," \u2014 "),pbe=n(UEo,"A",{href:!0});var r3n=s(pbe);Ima=r(r3n,"Wav2Vec2ForAudioFrameClassification"),r3n.forEach(t),Nma=r(UEo," (Wav2Vec2 model)"),UEo.forEach(t),Bma=i(Qd),xk=n(Qd,"LI",{});var HEo=s(xk);vYe=n(HEo,"STRONG",{});var t3n=s(vYe);Gma=r(t3n,"wav2vec2-conformer"),t3n.forEach(t),Dma=r(HEo," \u2014 "),_be=n(HEo,"A",{href:!0});var a3n=s(_be);qma=r(a3n,"Wav2Vec2ConformerForAudioFrameClassification"),a3n.forEach(t),jma=r(HEo," (Wav2Vec2-Conformer model)"),HEo.forEach(t),Oma=i(Qd),Sk=n(Qd,"LI",{});var JEo=s(Sk);bYe=n(JEo,"STRONG",{});var n3n=s(bYe);Vma=r(n3n,"wavlm"),n3n.forEach(t),Xma=r(JEo," \u2014 "),vbe=n(JEo,"A",{href:!0});var s3n=s(vbe);zma=r(s3n,"WavLMForAudioFrameClassification"),s3n.forEach(t),Wma=r(JEo," (WavLM model)"),JEo.forEach(t),Qd.forEach(t),Qma=i(_n),$k=n(_n,"P",{});var ZEo=s($k);Uma=r(ZEo,"The model is set in evaluation mode by default using "),TYe=n(ZEo,"CODE",{});var l3n=s(TYe);Hma=r(l3n,"model.eval()"),l3n.forEach(t),Jma=r(ZEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),FYe=n(ZEo,"CODE",{});var i3n=s(FYe);Zma=r(i3n,"model.train()"),i3n.forEach(t),ZEo.forEach(t),Yma=i(_n),T(kk.$$.fragment,_n),_n.forEach(t),Wd.forEach(t),Z5o=i(f),ug=n(f,"H3",{class:!0});var G6o=s(ug);Rk=n(G6o,"A",{id:!0,class:!0,href:!0});var d3n=s(Rk);MYe=n(d3n,"SPAN",{});var c3n=s(MYe);T(mO.$$.fragment,c3n),c3n.forEach(t),d3n.forEach(t),Kma=i(G6o),EYe=n(G6o,"SPAN",{});var f3n=s(EYe);ega=r(f3n,"AutoModelForCTC"),f3n.forEach(t),G6o.forEach(t),Y5o=i(f),Gr=n(f,"DIV",{class:!0});var Ud=s(Gr);T(gO.$$.fragment,Ud),oga=i(Ud),pg=n(Ud,"P",{});var yFe=s(pg);rga=r(yFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),bbe=n(yFe,"A",{href:!0});var m3n=s(bbe);tga=r(m3n,"from_pretrained()"),m3n.forEach(t),aga=r(yFe," class method or the "),Tbe=n(yFe,"A",{href:!0});var g3n=s(Tbe);nga=r(g3n,"from_config()"),g3n.forEach(t),sga=r(yFe,` class
method.`),yFe.forEach(t),lga=i(Ud),hO=n(Ud,"P",{});var D6o=s(hO);iga=r(D6o,"This class cannot be instantiated directly using "),CYe=n(D6o,"CODE",{});var h3n=s(CYe);dga=r(h3n,"__init__()"),h3n.forEach(t),cga=r(D6o," (throws an error)."),D6o.forEach(t),fga=i(Ud),Sa=n(Ud,"DIV",{class:!0});var xN=s(Sa);T(uO.$$.fragment,xN),mga=i(xN),wYe=n(xN,"P",{});var u3n=s(wYe);gga=r(u3n,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),u3n.forEach(t),hga=i(xN),_g=n(xN,"P",{});var xFe=s(_g);uga=r(xFe,`Note:
Loading a model from its configuration file does `),AYe=n(xFe,"STRONG",{});var p3n=s(AYe);pga=r(p3n,"not"),p3n.forEach(t),_ga=r(xFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Fbe=n(xFe,"A",{href:!0});var _3n=s(Fbe);vga=r(_3n,"from_pretrained()"),_3n.forEach(t),bga=r(xFe," to load the model weights."),xFe.forEach(t),Tga=i(xN),T(Pk.$$.fragment,xN),xN.forEach(t),Fga=i(Ud),So=n(Ud,"DIV",{class:!0});var vn=s(So);T(pO.$$.fragment,vn),Mga=i(vn),LYe=n(vn,"P",{});var v3n=s(LYe);Ega=r(v3n,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),v3n.forEach(t),Cga=i(vn),ws=n(vn,"P",{});var SN=s(ws);wga=r(SN,"The model class to instantiate is selected based on the "),yYe=n(SN,"CODE",{});var b3n=s(yYe);Aga=r(b3n,"model_type"),b3n.forEach(t),Lga=r(SN,` property of the config object (either
passed as an argument or loaded from `),xYe=n(SN,"CODE",{});var T3n=s(xYe);yga=r(T3n,"pretrained_model_name_or_path"),T3n.forEach(t),xga=r(SN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),SYe=n(SN,"CODE",{});var F3n=s(SYe);Sga=r(F3n,"pretrained_model_name_or_path"),F3n.forEach(t),$ga=r(SN,":"),SN.forEach(t),kga=i(vn),Ge=n(vn,"UL",{});var Ue=s(Ge);Ik=n(Ue,"LI",{});var YEo=s(Ik);$Ye=n(YEo,"STRONG",{});var M3n=s($Ye);Rga=r(M3n,"data2vec-audio"),M3n.forEach(t),Pga=r(YEo," \u2014 "),Mbe=n(YEo,"A",{href:!0});var E3n=s(Mbe);Iga=r(E3n,"Data2VecAudioForCTC"),E3n.forEach(t),Nga=r(YEo," (Data2VecAudio model)"),YEo.forEach(t),Bga=i(Ue),Nk=n(Ue,"LI",{});var KEo=s(Nk);kYe=n(KEo,"STRONG",{});var C3n=s(kYe);Gga=r(C3n,"hubert"),C3n.forEach(t),Dga=r(KEo," \u2014 "),Ebe=n(KEo,"A",{href:!0});var w3n=s(Ebe);qga=r(w3n,"HubertForCTC"),w3n.forEach(t),jga=r(KEo," (Hubert model)"),KEo.forEach(t),Oga=i(Ue),Bk=n(Ue,"LI",{});var e3o=s(Bk);RYe=n(e3o,"STRONG",{});var A3n=s(RYe);Vga=r(A3n,"mctct"),A3n.forEach(t),Xga=r(e3o," \u2014 "),Cbe=n(e3o,"A",{href:!0});var L3n=s(Cbe);zga=r(L3n,"MCTCTForCTC"),L3n.forEach(t),Wga=r(e3o," (M-CTC-T model)"),e3o.forEach(t),Qga=i(Ue),Gk=n(Ue,"LI",{});var o3o=s(Gk);PYe=n(o3o,"STRONG",{});var y3n=s(PYe);Uga=r(y3n,"sew"),y3n.forEach(t),Hga=r(o3o," \u2014 "),wbe=n(o3o,"A",{href:!0});var x3n=s(wbe);Jga=r(x3n,"SEWForCTC"),x3n.forEach(t),Zga=r(o3o," (SEW model)"),o3o.forEach(t),Yga=i(Ue),Dk=n(Ue,"LI",{});var r3o=s(Dk);IYe=n(r3o,"STRONG",{});var S3n=s(IYe);Kga=r(S3n,"sew-d"),S3n.forEach(t),eha=r(r3o," \u2014 "),Abe=n(r3o,"A",{href:!0});var $3n=s(Abe);oha=r($3n,"SEWDForCTC"),$3n.forEach(t),rha=r(r3o," (SEW-D model)"),r3o.forEach(t),tha=i(Ue),qk=n(Ue,"LI",{});var t3o=s(qk);NYe=n(t3o,"STRONG",{});var k3n=s(NYe);aha=r(k3n,"unispeech"),k3n.forEach(t),nha=r(t3o," \u2014 "),Lbe=n(t3o,"A",{href:!0});var R3n=s(Lbe);sha=r(R3n,"UniSpeechForCTC"),R3n.forEach(t),lha=r(t3o," (UniSpeech model)"),t3o.forEach(t),iha=i(Ue),jk=n(Ue,"LI",{});var a3o=s(jk);BYe=n(a3o,"STRONG",{});var P3n=s(BYe);dha=r(P3n,"unispeech-sat"),P3n.forEach(t),cha=r(a3o," \u2014 "),ybe=n(a3o,"A",{href:!0});var I3n=s(ybe);fha=r(I3n,"UniSpeechSatForCTC"),I3n.forEach(t),mha=r(a3o," (UniSpeechSat model)"),a3o.forEach(t),gha=i(Ue),Ok=n(Ue,"LI",{});var n3o=s(Ok);GYe=n(n3o,"STRONG",{});var N3n=s(GYe);hha=r(N3n,"wav2vec2"),N3n.forEach(t),uha=r(n3o," \u2014 "),xbe=n(n3o,"A",{href:!0});var B3n=s(xbe);pha=r(B3n,"Wav2Vec2ForCTC"),B3n.forEach(t),_ha=r(n3o," (Wav2Vec2 model)"),n3o.forEach(t),vha=i(Ue),Vk=n(Ue,"LI",{});var s3o=s(Vk);DYe=n(s3o,"STRONG",{});var G3n=s(DYe);bha=r(G3n,"wav2vec2-conformer"),G3n.forEach(t),Tha=r(s3o," \u2014 "),Sbe=n(s3o,"A",{href:!0});var D3n=s(Sbe);Fha=r(D3n,"Wav2Vec2ConformerForCTC"),D3n.forEach(t),Mha=r(s3o," (Wav2Vec2-Conformer model)"),s3o.forEach(t),Eha=i(Ue),Xk=n(Ue,"LI",{});var l3o=s(Xk);qYe=n(l3o,"STRONG",{});var q3n=s(qYe);Cha=r(q3n,"wavlm"),q3n.forEach(t),wha=r(l3o," \u2014 "),$be=n(l3o,"A",{href:!0});var j3n=s($be);Aha=r(j3n,"WavLMForCTC"),j3n.forEach(t),Lha=r(l3o," (WavLM model)"),l3o.forEach(t),Ue.forEach(t),yha=i(vn),zk=n(vn,"P",{});var i3o=s(zk);xha=r(i3o,"The model is set in evaluation mode by default using "),jYe=n(i3o,"CODE",{});var O3n=s(jYe);Sha=r(O3n,"model.eval()"),O3n.forEach(t),$ha=r(i3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),OYe=n(i3o,"CODE",{});var V3n=s(OYe);kha=r(V3n,"model.train()"),V3n.forEach(t),i3o.forEach(t),Rha=i(vn),T(Wk.$$.fragment,vn),vn.forEach(t),Ud.forEach(t),K5o=i(f),vg=n(f,"H3",{class:!0});var q6o=s(vg);Qk=n(q6o,"A",{id:!0,class:!0,href:!0});var X3n=s(Qk);VYe=n(X3n,"SPAN",{});var z3n=s(VYe);T(_O.$$.fragment,z3n),z3n.forEach(t),X3n.forEach(t),Pha=i(q6o),XYe=n(q6o,"SPAN",{});var W3n=s(XYe);Iha=r(W3n,"AutoModelForSpeechSeq2Seq"),W3n.forEach(t),q6o.forEach(t),e0o=i(f),Dr=n(f,"DIV",{class:!0});var Hd=s(Dr);T(vO.$$.fragment,Hd),Nha=i(Hd),bg=n(Hd,"P",{});var SFe=s(bg);Bha=r(SFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),kbe=n(SFe,"A",{href:!0});var Q3n=s(kbe);Gha=r(Q3n,"from_pretrained()"),Q3n.forEach(t),Dha=r(SFe," class method or the "),Rbe=n(SFe,"A",{href:!0});var U3n=s(Rbe);qha=r(U3n,"from_config()"),U3n.forEach(t),jha=r(SFe,` class
method.`),SFe.forEach(t),Oha=i(Hd),bO=n(Hd,"P",{});var j6o=s(bO);Vha=r(j6o,"This class cannot be instantiated directly using "),zYe=n(j6o,"CODE",{});var H3n=s(zYe);Xha=r(H3n,"__init__()"),H3n.forEach(t),zha=r(j6o," (throws an error)."),j6o.forEach(t),Wha=i(Hd),$a=n(Hd,"DIV",{class:!0});var $N=s($a);T(TO.$$.fragment,$N),Qha=i($N),WYe=n($N,"P",{});var J3n=s(WYe);Uha=r(J3n,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),J3n.forEach(t),Hha=i($N),Tg=n($N,"P",{});var $Fe=s(Tg);Jha=r($Fe,`Note:
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model\u2019s configuration. Use `),Pbe=n($Fe,"A",{href:!0});var Y3n=s(Pbe);Kha=r(Y3n,"from_pretrained()"),Y3n.forEach(t),eua=r($Fe," to load the model weights."),$Fe.forEach(t),oua=i($N),T(Uk.$$.fragment,$N),$N.forEach(t),rua=i(Hd),$o=n(Hd,"DIV",{class:!0});var bn=s($o);T(FO.$$.fragment,bn),tua=i(bn),UYe=n(bn,"P",{});var K3n=s(UYe);aua=r(K3n,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),K3n.forEach(t),nua=i(bn),As=n(bn,"P",{});var kN=s(As);sua=r(kN,"The model class to instantiate is selected based on the "),HYe=n(kN,"CODE",{});var eCn=s(HYe);lua=r(eCn,"model_type"),eCn.forEach(t),iua=r(kN,` property of the config object (either
passed as an argument or loaded from `),JYe=n(kN,"CODE",{});var oCn=s(JYe);dua=r(oCn,"pretrained_model_name_or_path"),oCn.forEach(t),cua=r(kN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZYe=n(kN,"CODE",{});var rCn=s(ZYe);fua=r(rCn,"pretrained_model_name_or_path"),rCn.forEach(t),mua=r(kN,":"),kN.forEach(t),gua=i(bn),Ls=n(bn,"UL",{});var RN=s(Ls);Hk=n(RN,"LI",{});var d3o=s(Hk);YYe=n(d3o,"STRONG",{});var tCn=s(YYe);hua=r(tCn,"speech-encoder-decoder"),tCn.forEach(t),uua=r(d3o," \u2014 "),Ibe=n(d3o,"A",{href:!0});var aCn=s(Ibe);pua=r(aCn,"SpeechEncoderDecoderModel"),aCn.forEach(t),_ua=r(d3o," (Speech Encoder decoder model)"),d3o.forEach(t),vua=i(RN),Jk=n(RN,"LI",{});var c3o=s(Jk);KYe=n(c3o,"STRONG",{});var nCn=s(KYe);bua=r(nCn,"speech_to_text"),nCn.forEach(t),Tua=r(c3o," \u2014 "),Nbe=n(c3o,"A",{href:!0});var sCn=s(Nbe);Fua=r(sCn,"Speech2TextForConditionalGeneration"),sCn.forEach(t),Mua=r(c3o," (Speech2Text model)"),c3o.forEach(t),Eua=i(RN),Zk=n(RN,"LI",{});var f3o=s(Zk);eKe=n(f3o,"STRONG",{});var lCn=s(eKe);Cua=r(lCn,"speecht5"),lCn.forEach(t),wua=r(f3o," \u2014 "),Bbe=n(f3o,"A",{href:!0});var iCn=s(Bbe);Aua=r(iCn,"SpeechT5ForSpeechToText"),iCn.forEach(t),Lua=r(f3o," (SpeechT5 model)"),f3o.forEach(t),yua=i(RN),Yk=n(RN,"LI",{});var m3o=s(Yk);oKe=n(m3o,"STRONG",{});var dCn=s(oKe);xua=r(dCn,"whisper"),dCn.forEach(t),Sua=r(m3o," \u2014 "),Gbe=n(m3o,"A",{href:!0});var cCn=s(Gbe);$ua=r(cCn,"WhisperForConditionalGeneration"),cCn.forEach(t),kua=r(m3o," (Whisper model)"),m3o.forEach(t),RN.forEach(t),Rua=i(bn),Kk=n(bn,"P",{});var g3o=s(Kk);Pua=r(g3o,"The model is set in evaluation mode by default using "),rKe=n(g3o,"CODE",{});var fCn=s(rKe);Iua=r(fCn,"model.eval()"),fCn.forEach(t),Nua=r(g3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),tKe=n(g3o,"CODE",{});var mCn=s(tKe);Bua=r(mCn,"model.train()"),mCn.forEach(t),g3o.forEach(t),Gua=i(bn),T(eR.$$.fragment,bn),bn.forEach(t),Hd.forEach(t),o0o=i(f),Fg=n(f,"H3",{class:!0});var O6o=s(Fg);oR=n(O6o,"A",{id:!0,class:!0,href:!0});var gCn=s(oR);aKe=n(gCn,"SPAN",{});var hCn=s(aKe);T(MO.$$.fragment,hCn),hCn.forEach(t),gCn.forEach(t),Dua=i(O6o),nKe=n(O6o,"SPAN",{});var uCn=s(nKe);qua=r(uCn,"TFAutoModelForSpeechSeq2Seq"),uCn.forEach(t),O6o.forEach(t),r0o=i(f),qr=n(f,"DIV",{class:!0});var Jd=s(qr);T(EO.$$.fragment,Jd),jua=i(Jd),Mg=n(Jd,"P",{});var kFe=s(Mg);Oua=r(kFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),Dbe=n(kFe,"A",{href:!0});var pCn=s(Dbe);Vua=r(pCn,"from_pretrained()"),pCn.forEach(t),Xua=r(kFe," class method or the "),qbe=n(kFe,"A",{href:!0});var _Cn=s(qbe);zua=r(_Cn,"from_config()"),_Cn.forEach(t),Wua=r(kFe,` class
method.`),kFe.forEach(t),Qua=i(Jd),CO=n(Jd,"P",{});var V6o=s(CO);Uua=r(V6o,"This class cannot be instantiated directly using "),sKe=n(V6o,"CODE",{});var vCn=s(sKe);Hua=r(vCn,"__init__()"),vCn.forEach(t),Jua=r(V6o," (throws an error)."),V6o.forEach(t),Zua=i(Jd),ka=n(Jd,"DIV",{class:!0});var PN=s(ka);T(wO.$$.fragment,PN),Yua=i(PN),lKe=n(PN,"P",{});var bCn=s(lKe);Kua=r(bCn,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),bCn.forEach(t),epa=i(PN),Eg=n(PN,"P",{});var RFe=s(Eg);opa=r(RFe,`Note:
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model\u2019s configuration. Use `),jbe=n(RFe,"A",{href:!0});var FCn=s(jbe);apa=r(FCn,"from_pretrained()"),FCn.forEach(t),npa=r(RFe," to load the model weights."),RFe.forEach(t),spa=i(PN),T(rR.$$.fragment,PN),PN.forEach(t),lpa=i(Jd),Et=n(Jd,"DIV",{class:!0});var Zd=s(Et);T(AO.$$.fragment,Zd),ipa=i(Zd),dKe=n(Zd,"P",{});var MCn=s(dKe);dpa=r(MCn,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),MCn.forEach(t),cpa=i(Zd),ys=n(Zd,"P",{});var IN=s(ys);fpa=r(IN,"The model class to instantiate is selected based on the "),cKe=n(IN,"CODE",{});var ECn=s(cKe);mpa=r(ECn,"model_type"),ECn.forEach(t),gpa=r(IN,` property of the config object (either
passed as an argument or loaded from `),fKe=n(IN,"CODE",{});var CCn=s(fKe);hpa=r(CCn,"pretrained_model_name_or_path"),CCn.forEach(t),upa=r(IN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mKe=n(IN,"CODE",{});var wCn=s(mKe);ppa=r(wCn,"pretrained_model_name_or_path"),wCn.forEach(t),_pa=r(IN,":"),IN.forEach(t),vpa=i(Zd),LO=n(Zd,"UL",{});var X6o=s(LO);tR=n(X6o,"LI",{});var h3o=s(tR);gKe=n(h3o,"STRONG",{});var ACn=s(gKe);bpa=r(ACn,"speech_to_text"),ACn.forEach(t),Tpa=r(h3o," \u2014 "),Obe=n(h3o,"A",{href:!0});var LCn=s(Obe);Fpa=r(LCn,"TFSpeech2TextForConditionalGeneration"),LCn.forEach(t),Mpa=r(h3o," (Speech2Text model)"),h3o.forEach(t),Epa=i(X6o),aR=n(X6o,"LI",{});var u3o=s(aR);hKe=n(u3o,"STRONG",{});var yCn=s(hKe);Cpa=r(yCn,"whisper"),yCn.forEach(t),wpa=r(u3o," \u2014 "),Vbe=n(u3o,"A",{href:!0});var xCn=s(Vbe);Apa=r(xCn,"TFWhisperForConditionalGeneration"),xCn.forEach(t),Lpa=r(u3o," (Whisper model)"),u3o.forEach(t),X6o.forEach(t),ypa=i(Zd),T(nR.$$.fragment,Zd),Zd.forEach(t),Jd.forEach(t),t0o=i(f),Cg=n(f,"H3",{class:!0});var z6o=s(Cg);sR=n(z6o,"A",{id:!0,class:!0,href:!0});var SCn=s(sR);uKe=n(SCn,"SPAN",{});var $Cn=s(uKe);T(yO.$$.fragment,$Cn),$Cn.forEach(t),SCn.forEach(t),xpa=i(z6o),pKe=n(z6o,"SPAN",{});var kCn=s(pKe);Spa=r(kCn,"FlaxAutoModelForSpeechSeq2Seq"),kCn.forEach(t),z6o.forEach(t),a0o=i(f),jr=n(f,"DIV",{class:!0});var Yd=s(jr);T(xO.$$.fragment,Yd),$pa=i(Yd),wg=n(Yd,"P",{});var PFe=s(wg);kpa=r(PFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),Xbe=n(PFe,"A",{href:!0});var RCn=s(Xbe);Rpa=r(RCn,"from_pretrained()"),RCn.forEach(t),Ppa=r(PFe," class method or the "),zbe=n(PFe,"A",{href:!0});var PCn=s(zbe);Ipa=r(PCn,"from_config()"),PCn.forEach(t),Npa=r(PFe,` class
method.`),PFe.forEach(t),Bpa=i(Yd),SO=n(Yd,"P",{});var W6o=s(SO);Gpa=r(W6o,"This class cannot be instantiated directly using "),_Ke=n(W6o,"CODE",{});var ICn=s(_Ke);Dpa=r(ICn,"__init__()"),ICn.forEach(t),qpa=r(W6o," (throws an error)."),W6o.forEach(t),jpa=i(Yd),Ra=n(Yd,"DIV",{class:!0});var NN=s(Ra);T($O.$$.fragment,NN),Opa=i(NN),vKe=n(NN,"P",{});var NCn=s(vKe);Vpa=r(NCn,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),NCn.forEach(t),Xpa=i(NN),Ag=n(NN,"P",{});var IFe=s(Ag);zpa=r(IFe,`Note:
Loading a model from its configuration file does `),bKe=n(IFe,"STRONG",{});var BCn=s(bKe);Wpa=r(BCn,"not"),BCn.forEach(t),Qpa=r(IFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Wbe=n(IFe,"A",{href:!0});var GCn=s(Wbe);Upa=r(GCn,"from_pretrained()"),GCn.forEach(t),Hpa=r(IFe," to load the model weights."),IFe.forEach(t),Jpa=i(NN),T(lR.$$.fragment,NN),NN.forEach(t),Zpa=i(Yd),Ct=n(Yd,"DIV",{class:!0});var Kd=s(Ct);T(kO.$$.fragment,Kd),Ypa=i(Kd),TKe=n(Kd,"P",{});var DCn=s(TKe);Kpa=r(DCn,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),DCn.forEach(t),e_a=i(Kd),xs=n(Kd,"P",{});var BN=s(xs);o_a=r(BN,"The model class to instantiate is selected based on the "),FKe=n(BN,"CODE",{});var qCn=s(FKe);r_a=r(qCn,"model_type"),qCn.forEach(t),t_a=r(BN,` property of the config object (either
passed as an argument or loaded from `),MKe=n(BN,"CODE",{});var jCn=s(MKe);a_a=r(jCn,"pretrained_model_name_or_path"),jCn.forEach(t),n_a=r(BN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EKe=n(BN,"CODE",{});var OCn=s(EKe);s_a=r(OCn,"pretrained_model_name_or_path"),OCn.forEach(t),l_a=r(BN,":"),BN.forEach(t),i_a=i(Kd),RO=n(Kd,"UL",{});var Q6o=s(RO);iR=n(Q6o,"LI",{});var p3o=s(iR);CKe=n(p3o,"STRONG",{});var VCn=s(CKe);d_a=r(VCn,"speech-encoder-decoder"),VCn.forEach(t),c_a=r(p3o," \u2014 "),Qbe=n(p3o,"A",{href:!0});var XCn=s(Qbe);f_a=r(XCn,"FlaxSpeechEncoderDecoderModel"),XCn.forEach(t),m_a=r(p3o," (Speech Encoder decoder model)"),p3o.forEach(t),g_a=i(Q6o),dR=n(Q6o,"LI",{});var _3o=s(dR);wKe=n(_3o,"STRONG",{});var zCn=s(wKe);h_a=r(zCn,"whisper"),zCn.forEach(t),u_a=r(_3o," \u2014 "),Ube=n(_3o,"A",{href:!0});var WCn=s(Ube);p_a=r(WCn,"FlaxWhisperForConditionalGeneration"),WCn.forEach(t),__a=r(_3o," (Whisper model)"),_3o.forEach(t),Q6o.forEach(t),v_a=i(Kd),T(cR.$$.fragment,Kd),Kd.forEach(t),Yd.forEach(t),n0o=i(f),Lg=n(f,"H3",{class:!0});var U6o=s(Lg);fR=n(U6o,"A",{id:!0,class:!0,href:!0});var QCn=s(fR);AKe=n(QCn,"SPAN",{});var UCn=s(AKe);T(PO.$$.fragment,UCn),UCn.forEach(t),QCn.forEach(t),b_a=i(U6o),LKe=n(U6o,"SPAN",{});var HCn=s(LKe);T_a=r(HCn,"AutoModelForAudioXVector"),HCn.forEach(t),U6o.forEach(t),s0o=i(f),Or=n(f,"DIV",{class:!0});var ec=s(Or);T(IO.$$.fragment,ec),F_a=i(ec),yg=n(ec,"P",{});var NFe=s(yg);M_a=r(NFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),Hbe=n(NFe,"A",{href:!0});var JCn=s(Hbe);E_a=r(JCn,"from_pretrained()"),JCn.forEach(t),C_a=r(NFe," class method or the "),Jbe=n(NFe,"A",{href:!0});var ZCn=s(Jbe);w_a=r(ZCn,"from_config()"),ZCn.forEach(t),A_a=r(NFe,` class
method.`),NFe.forEach(t),L_a=i(ec),NO=n(ec,"P",{});var H6o=s(NO);y_a=r(H6o,"This class cannot be instantiated directly using "),yKe=n(H6o,"CODE",{});var YCn=s(yKe);x_a=r(YCn,"__init__()"),YCn.forEach(t),S_a=r(H6o," (throws an error)."),H6o.forEach(t),$_a=i(ec),Pa=n(ec,"DIV",{class:!0});var GN=s(Pa);T(BO.$$.fragment,GN),k_a=i(GN),xKe=n(GN,"P",{});var KCn=s(xKe);R_a=r(KCn,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),KCn.forEach(t),P_a=i(GN),xg=n(GN,"P",{});var BFe=s(xg);I_a=r(BFe,`Note:
Loading a model from its configuration file does `),SKe=n(BFe,"STRONG",{});var e5n=s(SKe);N_a=r(e5n,"not"),e5n.forEach(t),B_a=r(BFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Zbe=n(BFe,"A",{href:!0});var o5n=s(Zbe);G_a=r(o5n,"from_pretrained()"),o5n.forEach(t),D_a=r(BFe," to load the model weights."),BFe.forEach(t),q_a=i(GN),T(mR.$$.fragment,GN),GN.forEach(t),j_a=i(ec),ko=n(ec,"DIV",{class:!0});var Tn=s(ko);T(GO.$$.fragment,Tn),O_a=i(Tn),$Ke=n(Tn,"P",{});var r5n=s($Ke);V_a=r(r5n,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),r5n.forEach(t),X_a=i(Tn),Ss=n(Tn,"P",{});var DN=s(Ss);z_a=r(DN,"The model class to instantiate is selected based on the "),kKe=n(DN,"CODE",{});var t5n=s(kKe);W_a=r(t5n,"model_type"),t5n.forEach(t),Q_a=r(DN,` property of the config object (either
passed as an argument or loaded from `),RKe=n(DN,"CODE",{});var a5n=s(RKe);U_a=r(a5n,"pretrained_model_name_or_path"),a5n.forEach(t),H_a=r(DN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PKe=n(DN,"CODE",{});var n5n=s(PKe);J_a=r(n5n,"pretrained_model_name_or_path"),n5n.forEach(t),Z_a=r(DN,":"),DN.forEach(t),Y_a=i(Tn),kt=n(Tn,"UL",{});var oc=s(kt);gR=n(oc,"LI",{});var v3o=s(gR);IKe=n(v3o,"STRONG",{});var s5n=s(IKe);K_a=r(s5n,"data2vec-audio"),s5n.forEach(t),e1a=r(v3o," \u2014 "),Ybe=n(v3o,"A",{href:!0});var l5n=s(Ybe);o1a=r(l5n,"Data2VecAudioForXVector"),l5n.forEach(t),r1a=r(v3o," (Data2VecAudio model)"),v3o.forEach(t),t1a=i(oc),hR=n(oc,"LI",{});var b3o=s(hR);NKe=n(b3o,"STRONG",{});var i5n=s(NKe);a1a=r(i5n,"unispeech-sat"),i5n.forEach(t),n1a=r(b3o," \u2014 "),Kbe=n(b3o,"A",{href:!0});var d5n=s(Kbe);s1a=r(d5n,"UniSpeechSatForXVector"),d5n.forEach(t),l1a=r(b3o," (UniSpeechSat model)"),b3o.forEach(t),i1a=i(oc),uR=n(oc,"LI",{});var T3o=s(uR);BKe=n(T3o,"STRONG",{});var c5n=s(BKe);d1a=r(c5n,"wav2vec2"),c5n.forEach(t),c1a=r(T3o," \u2014 "),e7e=n(T3o,"A",{href:!0});var f5n=s(e7e);f1a=r(f5n,"Wav2Vec2ForXVector"),f5n.forEach(t),m1a=r(T3o," (Wav2Vec2 model)"),T3o.forEach(t),g1a=i(oc),pR=n(oc,"LI",{});var F3o=s(pR);GKe=n(F3o,"STRONG",{});var m5n=s(GKe);h1a=r(m5n,"wav2vec2-conformer"),m5n.forEach(t),u1a=r(F3o," \u2014 "),o7e=n(F3o,"A",{href:!0});var g5n=s(o7e);p1a=r(g5n,"Wav2Vec2ConformerForXVector"),g5n.forEach(t),_1a=r(F3o," (Wav2Vec2-Conformer model)"),F3o.forEach(t),v1a=i(oc),_R=n(oc,"LI",{});var M3o=s(_R);DKe=n(M3o,"STRONG",{});var h5n=s(DKe);b1a=r(h5n,"wavlm"),h5n.forEach(t),T1a=r(M3o," \u2014 "),r7e=n(M3o,"A",{href:!0});var u5n=s(r7e);F1a=r(u5n,"WavLMForXVector"),u5n.forEach(t),M1a=r(M3o," (WavLM model)"),M3o.forEach(t),oc.forEach(t),E1a=i(Tn),vR=n(Tn,"P",{});var E3o=s(vR);C1a=r(E3o,"The model is set in evaluation mode by default using "),qKe=n(E3o,"CODE",{});var p5n=s(qKe);w1a=r(p5n,"model.eval()"),p5n.forEach(t),A1a=r(E3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),jKe=n(E3o,"CODE",{});var _5n=s(jKe);L1a=r(_5n,"model.train()"),_5n.forEach(t),E3o.forEach(t),y1a=i(Tn),T(bR.$$.fragment,Tn),Tn.forEach(t),ec.forEach(t),l0o=i(f),Sg=n(f,"H2",{class:!0});var J6o=s(Sg);TR=n(J6o,"A",{id:!0,class:!0,href:!0});var v5n=s(TR);OKe=n(v5n,"SPAN",{});var b5n=s(OKe);T(DO.$$.fragment,b5n),b5n.forEach(t),v5n.forEach(t),x1a=i(J6o),VKe=n(J6o,"SPAN",{});var T5n=s(VKe);S1a=r(T5n,"Multimodal"),T5n.forEach(t),J6o.forEach(t),i0o=i(f),t7e=n(f,"P",{});var F5n=s(t7e);$1a=r(F5n,"The following auto classes are available for the following multimodal tasks."),F5n.forEach(t),d0o=i(f),$g=n(f,"H3",{class:!0});var Z6o=s($g);FR=n(Z6o,"A",{id:!0,class:!0,href:!0});var M5n=s(FR);XKe=n(M5n,"SPAN",{});var E5n=s(XKe);T(qO.$$.fragment,E5n),E5n.forEach(t),M5n.forEach(t),k1a=i(Z6o),zKe=n(Z6o,"SPAN",{});var C5n=s(zKe);R1a=r(C5n,"AutoModelForTableQuestionAnswering"),C5n.forEach(t),Z6o.forEach(t),c0o=i(f),Vr=n(f,"DIV",{class:!0});var rc=s(Vr);T(jO.$$.fragment,rc),P1a=i(rc),kg=n(rc,"P",{});var GFe=s(kg);I1a=r(GFe,`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 `),a7e=n(GFe,"A",{href:!0});var w5n=s(a7e);N1a=r(w5n,"from_pretrained()"),w5n.forEach(t),B1a=r(GFe," class method or the "),n7e=n(GFe,"A",{href:!0});var A5n=s(n7e);G1a=r(A5n,"from_config()"),A5n.forEach(t),D1a=r(GFe,` class
method.`),GFe.forEach(t),q1a=i(rc),OO=n(rc,"P",{});var Y6o=s(OO);j1a=r(Y6o,"This class cannot be instantiated directly using "),WKe=n(Y6o,"CODE",{});var L5n=s(WKe);O1a=r(L5n,"__init__()"),L5n.forEach(t),V1a=r(Y6o," (throws an error)."),Y6o.forEach(t),X1a=i(rc),Ia=n(rc,"DIV",{class:!0});var qN=s(Ia);T(VO.$$.fragment,qN),z1a=i(qN),QKe=n(qN,"P",{});var y5n=s(QKe);W1a=r(y5n,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),y5n.forEach(t),Q1a=i(qN),Rg=n(qN,"P",{});var DFe=s(Rg);U1a=r(DFe,`Note:
Loading a model from its configuration file does `),UKe=n(DFe,"STRONG",{});var x5n=s(UKe);H1a=r(x5n,"not"),x5n.forEach(t),J1a=r(DFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),s7e=n(DFe,"A",{href:!0});var S5n=s(s7e);Z1a=r(S5n,"from_pretrained()"),S5n.forEach(t),Y1a=r(DFe," to load the model weights."),DFe.forEach(t),K1a=i(qN),T(MR.$$.fragment,qN),qN.forEach(t),e2a=i(rc),Ro=n(rc,"DIV",{class:!0});var Fn=s(Ro);T(XO.$$.fragment,Fn),o2a=i(Fn),HKe=n(Fn,"P",{});var $5n=s(HKe);r2a=r($5n,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),$5n.forEach(t),t2a=i(Fn),$s=n(Fn,"P",{});var jN=s($s);a2a=r(jN,"The model class to instantiate is selected based on the "),JKe=n(jN,"CODE",{});var k5n=s(JKe);n2a=r(k5n,"model_type"),k5n.forEach(t),s2a=r(jN,` property of the config object (either
passed as an argument or loaded from `),ZKe=n(jN,"CODE",{});var R5n=s(ZKe);l2a=r(R5n,"pretrained_model_name_or_path"),R5n.forEach(t),i2a=r(jN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),YKe=n(jN,"CODE",{});var P5n=s(YKe);d2a=r(P5n,"pretrained_model_name_or_path"),P5n.forEach(t),c2a=r(jN,":"),jN.forEach(t),f2a=i(Fn),KKe=n(Fn,"UL",{});var I5n=s(KKe);ER=n(I5n,"LI",{});var C3o=s(ER);eeo=n(C3o,"STRONG",{});var N5n=s(eeo);m2a=r(N5n,"tapas"),N5n.forEach(t),g2a=r(C3o," \u2014 "),l7e=n(C3o,"A",{href:!0});var B5n=s(l7e);h2a=r(B5n,"TapasForQuestionAnswering"),B5n.forEach(t),u2a=r(C3o," (TAPAS model)"),C3o.forEach(t),I5n.forEach(t),p2a=i(Fn),CR=n(Fn,"P",{});var w3o=s(CR);_2a=r(w3o,"The model is set in evaluation mode by default using "),oeo=n(w3o,"CODE",{});var G5n=s(oeo);v2a=r(G5n,"model.eval()"),G5n.forEach(t),b2a=r(w3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),reo=n(w3o,"CODE",{});var D5n=s(reo);T2a=r(D5n,"model.train()"),D5n.forEach(t),w3o.forEach(t),F2a=i(Fn),T(wR.$$.fragment,Fn),Fn.forEach(t),rc.forEach(t),f0o=i(f),Pg=n(f,"H3",{class:!0});var K6o=s(Pg);AR=n(K6o,"A",{id:!0,class:!0,href:!0});var q5n=s(AR);teo=n(q5n,"SPAN",{});var j5n=s(teo);T(zO.$$.fragment,j5n),j5n.forEach(t),q5n.forEach(t),M2a=i(K6o),aeo=n(K6o,"SPAN",{});var O5n=s(aeo);E2a=r(O5n,"TFAutoModelForTableQuestionAnswering"),O5n.forEach(t),K6o.forEach(t),m0o=i(f),Xr=n(f,"DIV",{class:!0});var tc=s(Xr);T(WO.$$.fragment,tc),C2a=i(tc),Ig=n(tc,"P",{});var qFe=s(Ig);w2a=r(qFe,`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 `),i7e=n(qFe,"A",{href:!0});var V5n=s(i7e);A2a=r(V5n,"from_pretrained()"),V5n.forEach(t),L2a=r(qFe," class method or the "),d7e=n(qFe,"A",{href:!0});var X5n=s(d7e);y2a=r(X5n,"from_config()"),X5n.forEach(t),x2a=r(qFe,` class
method.`),qFe.forEach(t),S2a=i(tc),QO=n(tc,"P",{});var e8o=s(QO);$2a=r(e8o,"This class cannot be instantiated directly using "),neo=n(e8o,"CODE",{});var z5n=s(neo);k2a=r(z5n,"__init__()"),z5n.forEach(t),R2a=r(e8o," (throws an error)."),e8o.forEach(t),P2a=i(tc),Na=n(tc,"DIV",{class:!0});var ON=s(Na);T(UO.$$.fragment,ON),I2a=i(ON),seo=n(ON,"P",{});var W5n=s(seo);N2a=r(W5n,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),W5n.forEach(t),B2a=i(ON),Ng=n(ON,"P",{});var jFe=s(Ng);G2a=r(jFe,`Note:
Loading a model from its configuration file does `),leo=n(jFe,"STRONG",{});var Q5n=s(leo);D2a=r(Q5n,"not"),Q5n.forEach(t),q2a=r(jFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),c7e=n(jFe,"A",{href:!0});var U5n=s(c7e);j2a=r(U5n,"from_pretrained()"),U5n.forEach(t),O2a=r(jFe," to load the model weights."),jFe.forEach(t),V2a=i(ON),T(LR.$$.fragment,ON),ON.forEach(t),X2a=i(tc),wt=n(tc,"DIV",{class:!0});var ac=s(wt);T(HO.$$.fragment,ac),z2a=i(ac),ieo=n(ac,"P",{});var H5n=s(ieo);W2a=r(H5n,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),H5n.forEach(t),Q2a=i(ac),ks=n(ac,"P",{});var VN=s(ks);U2a=r(VN,"The model class to instantiate is selected based on the "),deo=n(VN,"CODE",{});var J5n=s(deo);H2a=r(J5n,"model_type"),J5n.forEach(t),J2a=r(VN,` property of the config object (either
passed as an argument or loaded from `),ceo=n(VN,"CODE",{});var Z5n=s(ceo);Z2a=r(Z5n,"pretrained_model_name_or_path"),Z5n.forEach(t),Y2a=r(VN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),feo=n(VN,"CODE",{});var Y5n=s(feo);K2a=r(Y5n,"pretrained_model_name_or_path"),Y5n.forEach(t),eva=r(VN,":"),VN.forEach(t),ova=i(ac),meo=n(ac,"UL",{});var K5n=s(meo);yR=n(K5n,"LI",{});var A3o=s(yR);geo=n(A3o,"STRONG",{});var e0n=s(geo);rva=r(e0n,"tapas"),e0n.forEach(t),tva=r(A3o," \u2014 "),f7e=n(A3o,"A",{href:!0});var o0n=s(f7e);ava=r(o0n,"TFTapasForQuestionAnswering"),o0n.forEach(t),nva=r(A3o," (TAPAS model)"),A3o.forEach(t),K5n.forEach(t),sva=i(ac),T(xR.$$.fragment,ac),ac.forEach(t),tc.forEach(t),g0o=i(f),Bg=n(f,"H3",{class:!0});var o8o=s(Bg);SR=n(o8o,"A",{id:!0,class:!0,href:!0});var r0n=s(SR);heo=n(r0n,"SPAN",{});var t0n=s(heo);T(JO.$$.fragment,t0n),t0n.forEach(t),r0n.forEach(t),lva=i(o8o),ueo=n(o8o,"SPAN",{});var a0n=s(ueo);iva=r(a0n,"AutoModelForDocumentQuestionAnswering"),a0n.forEach(t),o8o.forEach(t),h0o=i(f),zr=n(f,"DIV",{class:!0});var nc=s(zr);T(ZO.$$.fragment,nc),dva=i(nc),Gg=n(nc,"P",{});var OFe=s(Gg);cva=r(OFe,`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 `),m7e=n(OFe,"A",{href:!0});var n0n=s(m7e);fva=r(n0n,"from_pretrained()"),n0n.forEach(t),mva=r(OFe," class method or the "),g7e=n(OFe,"A",{href:!0});var s0n=s(g7e);gva=r(s0n,"from_config()"),s0n.forEach(t),hva=r(OFe,` class
method.`),OFe.forEach(t),uva=i(nc),YO=n(nc,"P",{});var r8o=s(YO);pva=r(r8o,"This class cannot be instantiated directly using "),peo=n(r8o,"CODE",{});var l0n=s(peo);_va=r(l0n,"__init__()"),l0n.forEach(t),vva=r(r8o," (throws an error)."),r8o.forEach(t),bva=i(nc),Ba=n(nc,"DIV",{class:!0});var XN=s(Ba);T(KO.$$.fragment,XN),Tva=i(XN),_eo=n(XN,"P",{});var i0n=s(_eo);Fva=r(i0n,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),i0n.forEach(t),Mva=i(XN),Dg=n(XN,"P",{});var VFe=s(Dg);Eva=r(VFe,`Note:
Loading a model from its configuration file does `),veo=n(VFe,"STRONG",{});var d0n=s(veo);Cva=r(d0n,"not"),d0n.forEach(t),wva=r(VFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),h7e=n(VFe,"A",{href:!0});var c0n=s(h7e);Ava=r(c0n,"from_pretrained()"),c0n.forEach(t),Lva=r(VFe," to load the model weights."),VFe.forEach(t),yva=i(XN),T($R.$$.fragment,XN),XN.forEach(t),xva=i(nc),Po=n(nc,"DIV",{class:!0});var Mn=s(Po);T(eV.$$.fragment,Mn),Sva=i(Mn),beo=n(Mn,"P",{});var f0n=s(beo);$va=r(f0n,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),f0n.forEach(t),kva=i(Mn),Rs=n(Mn,"P",{});var zN=s(Rs);Rva=r(zN,"The model class to instantiate is selected based on the "),Teo=n(zN,"CODE",{});var m0n=s(Teo);Pva=r(m0n,"model_type"),m0n.forEach(t),Iva=r(zN,` property of the config object (either
passed as an argument or loaded from `),Feo=n(zN,"CODE",{});var g0n=s(Feo);Nva=r(g0n,"pretrained_model_name_or_path"),g0n.forEach(t),Bva=r(zN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Meo=n(zN,"CODE",{});var h0n=s(Meo);Gva=r(h0n,"pretrained_model_name_or_path"),h0n.forEach(t),Dva=r(zN,":"),zN.forEach(t),qva=i(Mn),qg=n(Mn,"UL",{});var XFe=s(qg);kR=n(XFe,"LI",{});var L3o=s(kR);Eeo=n(L3o,"STRONG",{});var u0n=s(Eeo);jva=r(u0n,"layoutlm"),u0n.forEach(t),Ova=r(L3o," \u2014 "),u7e=n(L3o,"A",{href:!0});var p0n=s(u7e);Vva=r(p0n,"LayoutLMForQuestionAnswering"),p0n.forEach(t),Xva=r(L3o," (LayoutLM model)"),L3o.forEach(t),zva=i(XFe),RR=n(XFe,"LI",{});var y3o=s(RR);Ceo=n(y3o,"STRONG",{});var _0n=s(Ceo);Wva=r(_0n,"layoutlmv2"),_0n.forEach(t),Qva=r(y3o," \u2014 "),p7e=n(y3o,"A",{href:!0});var v0n=s(p7e);Uva=r(v0n,"LayoutLMv2ForQuestionAnswering"),v0n.forEach(t),Hva=r(y3o," (LayoutLMv2 model)"),y3o.forEach(t),Jva=i(XFe),PR=n(XFe,"LI",{});var x3o=s(PR);weo=n(x3o,"STRONG",{});var b0n=s(weo);Zva=r(b0n,"layoutlmv3"),b0n.forEach(t),Yva=r(x3o," \u2014 "),_7e=n(x3o,"A",{href:!0});var T0n=s(_7e);Kva=r(T0n,"LayoutLMv3ForQuestionAnswering"),T0n.forEach(t),e4a=r(x3o," (LayoutLMv3 model)"),x3o.forEach(t),XFe.forEach(t),o4a=i(Mn),IR=n(Mn,"P",{});var S3o=s(IR);r4a=r(S3o,"The model is set in evaluation mode by default using "),Aeo=n(S3o,"CODE",{});var F0n=s(Aeo);t4a=r(F0n,"model.eval()"),F0n.forEach(t),a4a=r(S3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Leo=n(S3o,"CODE",{});var M0n=s(Leo);n4a=r(M0n,"model.train()"),M0n.forEach(t),S3o.forEach(t),s4a=i(Mn),T(NR.$$.fragment,Mn),Mn.forEach(t),nc.forEach(t),u0o=i(f),jg=n(f,"H3",{class:!0});var t8o=s(jg);BR=n(t8o,"A",{id:!0,class:!0,href:!0});var E0n=s(BR);yeo=n(E0n,"SPAN",{});var C0n=s(yeo);T(oV.$$.fragment,C0n),C0n.forEach(t),E0n.forEach(t),l4a=i(t8o),xeo=n(t8o,"SPAN",{});var w0n=s(xeo);i4a=r(w0n,"TFAutoModelForDocumentQuestionAnswering"),w0n.forEach(t),t8o.forEach(t),p0o=i(f),Wr=n(f,"DIV",{class:!0});var sc=s(Wr);T(rV.$$.fragment,sc),d4a=i(sc),Og=n(sc,"P",{});var zFe=s(Og);c4a=r(zFe,`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 `),v7e=n(zFe,"A",{href:!0});var A0n=s(v7e);f4a=r(A0n,"from_pretrained()"),A0n.forEach(t),m4a=r(zFe," class method or the "),b7e=n(zFe,"A",{href:!0});var L0n=s(b7e);g4a=r(L0n,"from_config()"),L0n.forEach(t),h4a=r(zFe,` class
method.`),zFe.forEach(t),u4a=i(sc),tV=n(sc,"P",{});var a8o=s(tV);p4a=r(a8o,"This class cannot be instantiated directly using "),Seo=n(a8o,"CODE",{});var y0n=s(Seo);_4a=r(y0n,"__init__()"),y0n.forEach(t),v4a=r(a8o," (throws an error)."),a8o.forEach(t),b4a=i(sc),Ga=n(sc,"DIV",{class:!0});var WN=s(Ga);T(aV.$$.fragment,WN),T4a=i(WN),$eo=n(WN,"P",{});var x0n=s($eo);F4a=r(x0n,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),x0n.forEach(t),M4a=i(WN),Vg=n(WN,"P",{});var WFe=s(Vg);E4a=r(WFe,`Note:
Loading a model from its configuration file does `),keo=n(WFe,"STRONG",{});var S0n=s(keo);C4a=r(S0n,"not"),S0n.forEach(t),w4a=r(WFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),T7e=n(WFe,"A",{href:!0});var $0n=s(T7e);A4a=r($0n,"from_pretrained()"),$0n.forEach(t),L4a=r(WFe," to load the model weights."),WFe.forEach(t),y4a=i(WN),T(GR.$$.fragment,WN),WN.forEach(t),x4a=i(sc),At=n(sc,"DIV",{class:!0});var lc=s(At);T(nV.$$.fragment,lc),S4a=i(lc),Reo=n(lc,"P",{});var k0n=s(Reo);$4a=r(k0n,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),k0n.forEach(t),k4a=i(lc),Ps=n(lc,"P",{});var QN=s(Ps);R4a=r(QN,"The model class to instantiate is selected based on the "),Peo=n(QN,"CODE",{});var R0n=s(Peo);P4a=r(R0n,"model_type"),R0n.forEach(t),I4a=r(QN,` property of the config object (either
passed as an argument or loaded from `),Ieo=n(QN,"CODE",{});var P0n=s(Ieo);N4a=r(P0n,"pretrained_model_name_or_path"),P0n.forEach(t),B4a=r(QN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Neo=n(QN,"CODE",{});var I0n=s(Neo);G4a=r(I0n,"pretrained_model_name_or_path"),I0n.forEach(t),D4a=r(QN,":"),QN.forEach(t),q4a=i(lc),Beo=n(lc,"UL",{});var N0n=s(Beo);DR=n(N0n,"LI",{});var $3o=s(DR);Geo=n($3o,"STRONG",{});var B0n=s(Geo);j4a=r(B0n,"layoutlm"),B0n.forEach(t),O4a=r($3o," \u2014 "),F7e=n($3o,"A",{href:!0});var G0n=s(F7e);V4a=r(G0n,"TFLayoutLMForQuestionAnswering"),G0n.forEach(t),X4a=r($3o," (LayoutLM model)"),$3o.forEach(t),N0n.forEach(t),z4a=i(lc),T(qR.$$.fragment,lc),lc.forEach(t),sc.forEach(t),_0o=i(f),Xg=n(f,"H3",{class:!0});var n8o=s(Xg);jR=n(n8o,"A",{id:!0,class:!0,href:!0});var D0n=s(jR);Deo=n(D0n,"SPAN",{});var q0n=s(Deo);T(sV.$$.fragment,q0n),q0n.forEach(t),D0n.forEach(t),W4a=i(n8o),qeo=n(n8o,"SPAN",{});var j0n=s(qeo);Q4a=r(j0n,"AutoModelForVisualQuestionAnswering"),j0n.forEach(t),n8o.forEach(t),v0o=i(f),Qr=n(f,"DIV",{class:!0});var ic=s(Qr);T(lV.$$.fragment,ic),U4a=i(ic),zg=n(ic,"P",{});var QFe=s(zg);H4a=r(QFe,`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 `),M7e=n(QFe,"A",{href:!0});var O0n=s(M7e);J4a=r(O0n,"from_pretrained()"),O0n.forEach(t),Z4a=r(QFe," class method or the "),E7e=n(QFe,"A",{href:!0});var V0n=s(E7e);Y4a=r(V0n,"from_config()"),V0n.forEach(t),K4a=r(QFe,` class
method.`),QFe.forEach(t),eba=i(ic),iV=n(ic,"P",{});var s8o=s(iV);oba=r(s8o,"This class cannot be instantiated directly using "),jeo=n(s8o,"CODE",{});var X0n=s(jeo);rba=r(X0n,"__init__()"),X0n.forEach(t),tba=r(s8o," (throws an error)."),s8o.forEach(t),aba=i(ic),Da=n(ic,"DIV",{class:!0});var UN=s(Da);T(dV.$$.fragment,UN),nba=i(UN),Oeo=n(UN,"P",{});var z0n=s(Oeo);sba=r(z0n,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),z0n.forEach(t),lba=i(UN),Wg=n(UN,"P",{});var UFe=s(Wg);iba=r(UFe,`Note:
Loading a model from its configuration file does `),Veo=n(UFe,"STRONG",{});var W0n=s(Veo);dba=r(W0n,"not"),W0n.forEach(t),cba=r(UFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),C7e=n(UFe,"A",{href:!0});var Q0n=s(C7e);fba=r(Q0n,"from_pretrained()"),Q0n.forEach(t),mba=r(UFe," to load the model weights."),UFe.forEach(t),gba=i(UN),T(OR.$$.fragment,UN),UN.forEach(t),hba=i(ic),Io=n(ic,"DIV",{class:!0});var En=s(Io);T(cV.$$.fragment,En),uba=i(En),Xeo=n(En,"P",{});var U0n=s(Xeo);pba=r(U0n,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),U0n.forEach(t),_ba=i(En),Is=n(En,"P",{});var HN=s(Is);vba=r(HN,"The model class to instantiate is selected based on the "),zeo=n(HN,"CODE",{});var H0n=s(zeo);bba=r(H0n,"model_type"),H0n.forEach(t),Tba=r(HN,` property of the config object (either
passed as an argument or loaded from `),Weo=n(HN,"CODE",{});var J0n=s(Weo);Fba=r(J0n,"pretrained_model_name_or_path"),J0n.forEach(t),Mba=r(HN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Qeo=n(HN,"CODE",{});var Z0n=s(Qeo);Eba=r(Z0n,"pretrained_model_name_or_path"),Z0n.forEach(t),Cba=r(HN,":"),HN.forEach(t),wba=i(En),Ueo=n(En,"UL",{});var Y0n=s(Ueo);VR=n(Y0n,"LI",{});var k3o=s(VR);Heo=n(k3o,"STRONG",{});var K0n=s(Heo);Aba=r(K0n,"vilt"),K0n.forEach(t),Lba=r(k3o," \u2014 "),w7e=n(k3o,"A",{href:!0});var ewn=s(w7e);yba=r(ewn,"ViltForQuestionAnswering"),ewn.forEach(t),xba=r(k3o," (ViLT model)"),k3o.forEach(t),Y0n.forEach(t),Sba=i(En),XR=n(En,"P",{});var R3o=s(XR);$ba=r(R3o,"The model is set in evaluation mode by default using "),Jeo=n(R3o,"CODE",{});var own=s(Jeo);kba=r(own,"model.eval()"),own.forEach(t),Rba=r(R3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Zeo=n(R3o,"CODE",{});var rwn=s(Zeo);Pba=r(rwn,"model.train()"),rwn.forEach(t),R3o.forEach(t),Iba=i(En),T(zR.$$.fragment,En),En.forEach(t),ic.forEach(t),b0o=i(f),Qg=n(f,"H3",{class:!0});var l8o=s(Qg);WR=n(l8o,"A",{id:!0,class:!0,href:!0});var twn=s(WR);Yeo=n(twn,"SPAN",{});var awn=s(Yeo);T(fV.$$.fragment,awn),awn.forEach(t),twn.forEach(t),Nba=i(l8o),Keo=n(l8o,"SPAN",{});var nwn=s(Keo);Bba=r(nwn,"AutoModelForVision2Seq"),nwn.forEach(t),l8o.forEach(t),T0o=i(f),Ur=n(f,"DIV",{class:!0});var dc=s(Ur);T(mV.$$.fragment,dc),Gba=i(dc),Ug=n(dc,"P",{});var HFe=s(Ug);Dba=r(HFe,`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 `),A7e=n(HFe,"A",{href:!0});var swn=s(A7e);qba=r(swn,"from_pretrained()"),swn.forEach(t),jba=r(HFe," class method or the "),L7e=n(HFe,"A",{href:!0});var lwn=s(L7e);Oba=r(lwn,"from_config()"),lwn.forEach(t),Vba=r(HFe,` class
method.`),HFe.forEach(t),Xba=i(dc),gV=n(dc,"P",{});var i8o=s(gV);zba=r(i8o,"This class cannot be instantiated directly using "),eoo=n(i8o,"CODE",{});var iwn=s(eoo);Wba=r(iwn,"__init__()"),iwn.forEach(t),Qba=r(i8o," (throws an error)."),i8o.forEach(t),Uba=i(dc),qa=n(dc,"DIV",{class:!0});var JN=s(qa);T(hV.$$.fragment,JN),Hba=i(JN),ooo=n(JN,"P",{});var dwn=s(ooo);Jba=r(dwn,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),dwn.forEach(t),Zba=i(JN),Hg=n(JN,"P",{});var JFe=s(Hg);Yba=r(JFe,`Note:
Loading a model from its configuration file does `),roo=n(JFe,"STRONG",{});var cwn=s(roo);Kba=r(cwn,"not"),cwn.forEach(t),e7a=r(JFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),y7e=n(JFe,"A",{href:!0});var fwn=s(y7e);o7a=r(fwn,"from_pretrained()"),fwn.forEach(t),r7a=r(JFe," to load the model weights."),JFe.forEach(t),t7a=i(JN),T(QR.$$.fragment,JN),JN.forEach(t),a7a=i(dc),No=n(dc,"DIV",{class:!0});var Cn=s(No);T(uV.$$.fragment,Cn),n7a=i(Cn),too=n(Cn,"P",{});var mwn=s(too);s7a=r(mwn,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),mwn.forEach(t),l7a=i(Cn),Ns=n(Cn,"P",{});var ZN=s(Ns);i7a=r(ZN,"The model class to instantiate is selected based on the "),aoo=n(ZN,"CODE",{});var gwn=s(aoo);d7a=r(gwn,"model_type"),gwn.forEach(t),c7a=r(ZN,` property of the config object (either
passed as an argument or loaded from `),noo=n(ZN,"CODE",{});var hwn=s(noo);f7a=r(hwn,"pretrained_model_name_or_path"),hwn.forEach(t),m7a=r(ZN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),soo=n(ZN,"CODE",{});var uwn=s(soo);g7a=r(uwn,"pretrained_model_name_or_path"),uwn.forEach(t),h7a=r(ZN,":"),ZN.forEach(t),u7a=i(Cn),Jg=n(Cn,"UL",{});var ZFe=s(Jg);UR=n(ZFe,"LI",{});var P3o=s(UR);loo=n(P3o,"STRONG",{});var pwn=s(loo);p7a=r(pwn,"blip"),pwn.forEach(t),_7a=r(P3o," \u2014 "),x7e=n(P3o,"A",{href:!0});var _wn=s(x7e);v7a=r(_wn,"BlipForConditionalGeneration"),_wn.forEach(t),b7a=r(P3o," (BLIP model)"),P3o.forEach(t),T7a=i(ZFe),HR=n(ZFe,"LI",{});var I3o=s(HR);ioo=n(I3o,"STRONG",{});var vwn=s(ioo);F7a=r(vwn,"blip-2"),vwn.forEach(t),M7a=r(I3o," \u2014 "),S7e=n(I3o,"A",{href:!0});var bwn=s(S7e);E7a=r(bwn,"Blip2ForConditionalGeneration"),bwn.forEach(t),C7a=r(I3o," (BLIP-2 model)"),I3o.forEach(t),w7a=i(ZFe),JR=n(ZFe,"LI",{});var N3o=s(JR);doo=n(N3o,"STRONG",{});var Twn=s(doo);A7a=r(Twn,"vision-encoder-decoder"),Twn.forEach(t),L7a=r(N3o," \u2014 "),$7e=n(N3o,"A",{href:!0});var Fwn=s($7e);y7a=r(Fwn,"VisionEncoderDecoderModel"),Fwn.forEach(t),x7a=r(N3o," (Vision Encoder decoder model)"),N3o.forEach(t),ZFe.forEach(t),S7a=i(Cn),ZR=n(Cn,"P",{});var B3o=s(ZR);$7a=r(B3o,"The model is set in evaluation mode by default using "),coo=n(B3o,"CODE",{});var Mwn=s(coo);k7a=r(Mwn,"model.eval()"),Mwn.forEach(t),R7a=r(B3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),foo=n(B3o,"CODE",{});var Ewn=s(foo);P7a=r(Ewn,"model.train()"),Ewn.forEach(t),B3o.forEach(t),I7a=i(Cn),T(YR.$$.fragment,Cn),Cn.forEach(t),dc.forEach(t),F0o=i(f),Zg=n(f,"H3",{class:!0});var d8o=s(Zg);KR=n(d8o,"A",{id:!0,class:!0,href:!0});var Cwn=s(KR);moo=n(Cwn,"SPAN",{});var wwn=s(moo);T(pV.$$.fragment,wwn),wwn.forEach(t),Cwn.forEach(t),N7a=i(d8o),goo=n(d8o,"SPAN",{});var Awn=s(goo);B7a=r(Awn,"TFAutoModelForVision2Seq"),Awn.forEach(t),d8o.forEach(t),M0o=i(f),Hr=n(f,"DIV",{class:!0});var cc=s(Hr);T(_V.$$.fragment,cc),G7a=i(cc),Yg=n(cc,"P",{});var YFe=s(Yg);D7a=r(YFe,`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
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falling back to using pattern matching on `),Too=n(KN,"CODE",{});var Nwn=s(Too);gTa=r(Nwn,"pretrained_model_name_or_path"),Nwn.forEach(t),hTa=r(KN,":"),KN.forEach(t),uTa=i(fc),Foo=n(fc,"UL",{});var Bwn=s(Foo);oP=n(Bwn,"LI",{});var G3o=s(oP);Moo=n(G3o,"STRONG",{});var Gwn=s(Moo);pTa=r(Gwn,"vision-encoder-decoder"),Gwn.forEach(t),_Ta=r(G3o," \u2014 "),I7e=n(G3o,"A",{href:!0});var Dwn=s(I7e);vTa=r(Dwn,"TFVisionEncoderDecoderModel"),Dwn.forEach(t),bTa=r(G3o," (Vision Encoder decoder model)"),G3o.forEach(t),Bwn.forEach(t),TTa=i(fc),T(rP.$$.fragment,fc),fc.forEach(t),cc.forEach(t),E0o=i(f),eh=n(f,"H3",{class:!0});var f8o=s(eh);tP=n(f8o,"A",{id:!0,class:!0,href:!0});var qwn=s(tP);Eoo=n(qwn,"SPAN",{});var jwn=s(Eoo);T(FV.$$.fragment,jwn),jwn.forEach(t),qwn.forEach(t),FTa=i(f8o),Coo=n(f8o,"SPAN",{});var Own=s(Coo);MTa=r(Own,"FlaxAutoModelForVision2Seq"),Own.forEach(t),f8o.forEach(t),C0o=i(f),Jr=n(f,"DIV",{class:!0});var mc=s(Jr);T(MV.$$.fragment,mc),ETa=i(mc),oh=n(mc,"P",{});var eMe=s(oh);CTa=r(eMe,`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 `),N7e=n(eMe,"A",{href:!0});var Vwn=s(N7e);wTa=r(Vwn,"from_pretrained()"),Vwn.forEach(t),ATa=r(eMe," class method or the "),B7e=n(eMe,"A",{href:!0});var Xwn=s(B7e);LTa=r(Xwn,"from_config()"),Xwn.forEach(t),yTa=r(eMe,` class
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