import{S as pqa,i as _qa,s as vqa,e as a,k as l,w as F,t as o,M as bqa,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 M,q as E,o as C,B as w,v as Fqa,L as D}from"../../chunks/vendor-hf-doc-builder.js";import{T as Yuo}from"../../chunks/Tip-hf-doc-builder.js";import{D as R}from"../../chunks/Docstring-hf-doc-builder.js";import{C as I}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as te}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as N}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Tqa($){let g,b,u,m,p,c,h,Je,yd,rg,wt,xd,$d,nS,tg,We,Ze,kd,_s,sS,vs,bs,lS,Sd,Fs,iS,Rd,ag,ln;return{c(){g=a("p"),b=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
`),yd=a("code"),rg=o("model_type"),wt=o(" attribute is set to the same key you use when registering the config (here "),xd=a("code"),$d=o('"new-model"'),nS=o(")."),tg=l(),We=a("p"),Ze=o("Likewise, if your "),kd=a("code"),_s=o("NewModel"),sS=o(" is a subclass of "),vs=a("a"),bs=o("PreTrainedModel"),lS=o(`, make sure its
`),Sd=a("code"),Fs=o("config_class"),iS=o(` attribute is set to the same class you use when registering the model (here
`),Rd=a("code"),ag=o("NewModelConfig"),ln=o(")."),this.h()},l(Ke){g=n(Ke,"P",{});var ye=s(g);b=r(ye,"If your "),u=n(ye,"CODE",{});var kq=s(u);m=r(kq,"NewModelConfig"),kq.forEach(t),p=r(ye," is a subclass of "),c=n(ye,"CODE",{});var Pd=s(c);h=r(Pd,"~transformer.PretrainedConfig"),Pd.forEach(t),Je=r(ye,`, make sure its
`),yd=n(ye,"CODE",{});var Sq=s(yd);rg=r(Sq,"model_type"),Sq.forEach(t),wt=r(ye," attribute is set to the same key you use when registering the config (here "),xd=n(ye,"CODE",{});var Rq=s(xd);$d=r(Rq,'"new-model"'),Rq.forEach(t),nS=r(ye,")."),ye.forEach(t),tg=i(Ke),We=n(Ke,"P",{});var Po=s(We);Ze=r(Po,"Likewise, if your "),kd=n(Po,"CODE",{});var dn=s(kd);_s=r(dn,"NewModel"),dn.forEach(t),sS=r(Po," is a subclass of "),vs=n(Po,"A",{href:!0});var Pq=s(vs);bs=r(Pq,"PreTrainedModel"),Pq.forEach(t),lS=r(Po,`, make sure its
`),Sd=n(Po,"CODE",{});var ng=s(Sd);Fs=r(ng,"config_class"),ng.forEach(t),iS=r(Po,` attribute is set to the same class you use when registering the model (here
`),Rd=n(Po,"CODE",{});var Iq=s(Rd);ag=r(Iq,"NewModelConfig"),Iq.forEach(t),ln=r(Po,")."),Po.forEach(t),this.h()},h(){d(vs,"href","/docs/transformers/v4.25.1/en/main_classes/model#transformers.PreTrainedModel")},m(Ke,ye){v(Ke,g,ye),e(g,b),e(g,u),e(u,m),e(g,p),e(g,c),e(c,h),e(g,Je),e(g,yd),e(yd,rg),e(g,wt),e(g,xd),e(xd,$d),e(g,nS),v(Ke,tg,ye),v(Ke,We,ye),e(We,Ze),e(We,kd),e(kd,_s),e(We,sS),e(We,vs),e(vs,bs),e(We,lS),e(We,Sd),e(Sd,Fs),e(We,iS),e(We,Rd),e(Rd,ag),e(We,ln)},d(Ke){Ke&&t(g),Ke&&t(tg),Ke&&t(We)}}}function Mqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Eqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Cqa($){let g,b,u,m,p;return{c(){g=a("p"),b=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,b),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function wqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Aqa($){let g,b,u,m,p;return{c(){g=a("p"),b=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,b),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function Lqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function yqa($){let g,b,u,m,p;return{c(){g=a("p"),b=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,b),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function xqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function $qa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function kqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Sqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Rqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Pqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Iqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Bqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Nqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Dqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function qqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function jqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Gqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Oqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Vqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Xqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function zqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Qqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Wqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Uqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Hqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Jqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Yqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Zqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Kqa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function eja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function oja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function rja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function tja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function aja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function nja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function sja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function lja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function ija($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function dja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function cja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function fja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function mja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function gja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function hja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function uja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function pja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function _ja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function vja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function bja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Fja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Tja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Mja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Eja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Cja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function wja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Aja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Lja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function yja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function xja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function $ja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function kja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Sja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Rja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Pja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Ija($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Bja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Nja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Dja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function qja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function jja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Gja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Oja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Vja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Xja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function zja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Qja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Wja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Uja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Hja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Jja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Yja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Zja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function Kja($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function eGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function oGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function rGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function tGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function aGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function nGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function sGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function lGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function iGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function dGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function cGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function fGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function mGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function gGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function hGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function uGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function pGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function _Ga($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function vGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function bGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function FGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function TGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function MGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function EGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function CGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function wGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function AGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function LGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function yGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function xGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function $Ga($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function kGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function SGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function RGa($){let g,b,u,m,p;return m=new I({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"),b=o("Examples:"),u=l(),F(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);b=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,b),v(c,u,h),M(m,c,h),p=!0},p:D,i(c){p||(E(m.$$.fragment,c),p=!0)},o(c){C(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),w(m,c)}}}function PGa($){let 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c=new te({}),ln=new I({props:{code:'model = AutoModel.from_pretrained("bert-base-cased")',highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),dS=new te({}),cS=new I({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)`}}),lg=new Yuo({props:{warning:!0,$$slots:{default:[Tqa]},$$scope:{ctx:$}}}),fS=new te({}),mS=new R({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/configuration_auto.py#L698"}}),uS=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/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/v4.25.1/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
by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs(additional"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/configuration_auto.py#L721"}}),Wu=new N({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[Mqa]},$$scope:{ctx:$}}}),pS=new R({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""}],parametersDescription:[{anchor:"transformers.AutoConfig.register.model_type",description:"<strong>model_type</strong> (<code>str</code>) &#x2014; The model type like &#x201C;bert&#x201D; or &#x201C;gpt&#x201D;.",name:"model_type"},{anchor:"transformers.AutoConfig.register.config",description:'<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/configuration_auto.py#L844"}}),_S=new te({}),vS=new R({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/tokenization_auto.py#L459"}}),TS=new R({props:{name:"from_pretrained",anchor:"transformers.AutoTokenizer.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoTokenizer.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/v4.25.1/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;
Will be passed along to the Tokenizer <code>__init__()</code> method.`,name:"inputs"},{anchor:"transformers.AutoTokenizer.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoTokenizer.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoTokenizer.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.`,name:"subfolder"},{anchor:"transformers.AutoTokenizer.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to try to load the fast version of the tokenizer.`,name:"use_fast"},{anchor:"transformers.AutoTokenizer.from_pretrained.tokenizer_type",description:`<strong>tokenizer_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__()</code> for more details.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/tokenization_auto.py#L473"}}),Pp=new N({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[Eqa]},$$scope:{ctx:$}}}),MS=new R({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"}],parametersDescription:[{anchor:"transformers.AutoTokenizer.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizer</code>, <em>optional</em>) &#x2014;
The slow tokenizer to register.`,name:"slow_tokenizer_class"},{anchor:"transformers.AutoTokenizer.register.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/v4.25.1/src/transformers/models/auto/tokenization_auto.py#L674"}}),ES=new te({}),CS=new R({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/feature_extraction_auto.py#L211"}}),LS=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/v4.25.1/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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.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
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"use_auth_token"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/feature_extraction_auto.py#L225"}}),P_=new Yuo({props:{$$slots:{default:[Cqa]},$$scope:{ctx:$}}}),I_=new N({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[wqa]},$$scope:{ctx:$}}}),yS=new R({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoFeatureExtractor.register.feature_extractor_class",description:"<strong>feature_extractor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The feature extractor to register.",name:"feature_extractor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/feature_extraction_auto.py#L352"}}),xS=new te({}),$S=new R({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/image_processing_auto.py#L205"}}),RS=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/v4.25.1/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
they exist.`,name:"force_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoImageProcessor.from_pretrained.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
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"use_auth_token"},{anchor:"transformers.AutoImageProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoImageProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>image_processor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoImageProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoImageProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> image processor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/image_processing_auto.py#L219"}}),L1=new Yuo({props:{$$slots:{default:[Aqa]},$$scope:{ctx:$}}}),y1=new N({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[Lqa]},$$scope:{ctx:$}}}),PS=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/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoImageProcessor.register.image_processor_class",description:'<strong>image_processor_class</strong> (<a href="/docs/transformers/v4.25.1/en/internal/image_processing_utils#transformers.ImageProcessingMixin">ImageProcessingMixin</a>) &#x2014; The image processor to register.',name:"image_processor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/image_processing_auto.py#L364"}}),IS=new te({}),BS=new R({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/processing_auto.py#L99"}}),qS=new R({props:{name:"from_pretrained",anchor:"transformers.AutoProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a processor files saved using the <code>save_pretrained()</code> method,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.AutoProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoProcessor.from_pretrained.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
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"use_auth_token"},{anchor:"transformers.AutoProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/processing_auto.py#L113"}}),o2=new Yuo({props:{$$slots:{default:[yqa]},$$scope:{ctx:$}}}),r2=new N({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[xqa]},$$scope:{ctx:$}}}),jS=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/v4.25.1/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/v4.25.1/src/transformers/models/auto/processing_auto.py#L294"}}),GS=new te({}),OS=new R({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L928"}}),XS=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/v4.25.1/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/v4.25.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/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/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),n2=new N({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[$qa]},$$scope:{ctx:$}}}),zS=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),D4=new N({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[kqa]},$$scope:{ctx:$}}}),QS=new te({}),WS=new R({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L935"}}),HS=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),j4=new N({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[Sqa]},$$scope:{ctx:$}}}),JS=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),Db=new N({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Rqa]},$$scope:{ctx:$}}}),YS=new te({}),ZS=new R({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L950"}}),eR=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/v4.25.1/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/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),jb=new N({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[Pqa]},$$scope:{ctx:$}}}),oR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),k5=new N({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Iqa]},$$scope:{ctx:$}}}),rR=new te({}),tR=new R({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1093"}}),nR=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/v4.25.1/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/v4.25.1/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),R5=new N({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[Bqa]},$$scope:{ctx:$}}}),sR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),N5=new N({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[Nqa]},$$scope:{ctx:$}}}),iR=new te({}),dR=new R({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L957"}}),fR=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),q5=new N({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[Dqa]},$$scope:{ctx:$}}}),mR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),yF=new N({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[qqa]},$$scope:{ctx:$}}}),gR=new te({}),hR=new R({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L964"}}),pR=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/v4.25.1/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/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),$F=new N({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[jqa]},$$scope:{ctx:$}}}),_R=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),KF=new N({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Gqa]},$$scope:{ctx:$}}}),vR=new te({}),bR=new R({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L973"}}),TR=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),oT=new N({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[Oqa]},$$scope:{ctx:$}}}),MR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),sM=new N({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Vqa]},$$scope:{ctx:$}}}),ER=new te({}),CR=new R({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1029"}}),AR=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),iM=new N({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[Xqa]},$$scope:{ctx:$}}}),LR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),VM=new N({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[zqa]},$$scope:{ctx:$}}}),yR=new te({}),xR=new R({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1036"}}),kR=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/v4.25.1/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/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),zM=new N({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Qqa]},$$scope:{ctx:$}}}),SR=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/v4.25.1/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.AutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),eE=new N({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Wqa]},$$scope:{ctx:$}}}),RR=new te({}),PR=new R({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1022"}}),BR=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),rE=new N({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[Uqa]},$$scope:{ctx:$}}}),NR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),QE=new N({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Hqa]},$$scope:{ctx:$}}}),DR=new te({}),qR=new R({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L982"}}),GR=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),UE=new N({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Jqa]},$$scope:{ctx:$}}}),OR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),zC=new N({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Yqa]},$$scope:{ctx:$}}}),VR=new te({}),XR=new R({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L989"}}),QR=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/v4.25.1/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/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),WC=new N({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Zqa]},$$scope:{ctx:$}}}),WR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),JC=new N({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[Kqa]},$$scope:{ctx:$}}}),UR=new te({}),HR=new R({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1011"}}),YR=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/v4.25.1/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/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),ZC=new N({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[eja]},$$scope:{ctx:$}}}),ZR=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),t3=new N({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[oja]},$$scope:{ctx:$}}}),KR=new te({}),eP=new R({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1045"}}),rP=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/v4.25.1/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/v4.25.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),n3=new N({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[rja]},$$scope:{ctx:$}}}),tP=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/v4.25.1/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;
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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The model class to instantiate is selected based on the configuration class:</p>
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<li><a href="/docs/transformers/v4.25.1/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
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<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/v4.25.1/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
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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/v4.25.1/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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<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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.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;
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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.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
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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.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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),k3=new N({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[nja]},$$scope:{ctx:$}}}),dP=new te({}),cP=new R({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1107"}}),mP=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/v4.25.1/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/v4.25.1/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),R3=new N({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[sja]},$$scope:{ctx:$}}}),gP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.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
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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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),B3=new N({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[lja]},$$scope:{ctx:$}}}),hP=new te({}),uP=new R({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1000"}}),_P=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/v4.25.1/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/v4.25.1/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),D3=new N({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[ija]},$$scope:{ctx:$}}}),vP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),G3=new N({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[dja]},$$scope:{ctx:$}}}),bP=new te({}),FP=new R({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1114"}}),MP=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/v4.25.1/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/v4.25.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),V3=new N({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[cja]},$$scope:{ctx:$}}}),EP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),o0=new N({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[fja]},$$scope:{ctx:$}}}),CP=new te({}),wP=new R({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1137"}}),LP=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/v4.25.1/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/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),t0=new N({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[mja]},$$scope:{ctx:$}}}),yP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),c0=new N({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[gja]},$$scope:{ctx:$}}}),xP=new te({}),$P=new R({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1121"}}),SP=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/v4.25.1/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/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),m0=new N({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[hja]},$$scope:{ctx:$}}}),RP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCTC.from_pretrained.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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),C0=new N({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[uja]},$$scope:{ctx:$}}}),PP=new te({}),IP=new R({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1128"}}),NP=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/v4.25.1/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/v4.25.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),A0=new N({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[pja]},$$scope:{ctx:$}}}),DP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),k0=new N({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[_ja]},$$scope:{ctx:$}}}),qP=new te({}),jP=new R({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1146"}}),OP=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/v4.25.1/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/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),R0=new N({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[vja]},$$scope:{ctx:$}}}),VP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),j0=new N({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[bja]},$$scope:{ctx:$}}}),XP=new te({}),zP=new R({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1157"}}),WP=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/v4.25.1/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/v4.25.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),O0=new N({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[Fja]},$$scope:{ctx:$}}}),UP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),U0=new N({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Tja]},$$scope:{ctx:$}}}),HP=new te({}),JP=new R({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1077"}}),ZP=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/v4.25.1/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/v4.25.1/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),J0=new N({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[Mja]},$$scope:{ctx:$}}}),KP=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),tw=new N({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[Eja]},$$scope:{ctx:$}}}),eI=new te({}),oI=new R({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1052"}}),tI=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/v4.25.1/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/v4.25.1/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),nw=new N({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[Cja]},$$scope:{ctx:$}}}),aI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),iw=new N({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[wja]},$$scope:{ctx:$}}}),nI=new te({}),sI=new R({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1059"}}),iI=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/v4.25.1/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/v4.25.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),cw=new N({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[Aja]},$$scope:{ctx:$}}}),dI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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
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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.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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
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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.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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),vw=new N({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[Lja]},$$scope:{ctx:$}}}),cI=new te({}),fI=new R({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1068"}}),gI=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/v4.25.1/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/v4.25.1/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),Fw=new N({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[yja]},$$scope:{ctx:$}}}),hI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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
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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.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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),Ew=new N({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[xja]},$$scope:{ctx:$}}}),uI=new te({}),pI=new R({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_auto.py#L1084"}}),vI=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/v4.25.1/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/v4.25.1/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),ww=new N({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[$ja]},$$scope:{ctx:$}}}),bI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),yw=new N({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[kja]},$$scope:{ctx:$}}}),FI=new te({}),TI=new R({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L444"}}),EI=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),$w=new N({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Sja]},$$scope:{ctx:$}}}),CI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),PA=new N({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[Rja]},$$scope:{ctx:$}}}),wI=new te({}),AI=new R({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L451"}}),yI=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),BA=new N({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[Pja]},$$scope:{ctx:$}}}),xI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),s6=new N({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Ija]},$$scope:{ctx:$}}}),$I=new te({}),kI=new R({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L466"}}),RI=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/v4.25.1/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/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),i6=new N({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[Bja]},$$scope:{ctx:$}}}),PI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),E6=new N({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Nja]},$$scope:{ctx:$}}}),II=new te({}),BI=new R({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L482"}}),DI=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/v4.25.1/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/v4.25.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/v4.25.1/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),w6=new N({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[Dja]},$$scope:{ctx:$}}}),qI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),I6=new N({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[qja]},$$scope:{ctx:$}}}),jI=new te({}),GI=new R({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L491"}}),VI=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/v4.25.1/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/v4.25.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),N6=new N({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[jja]},$$scope:{ctx:$}}}),XI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),G6=new N({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[Gja]},$$scope:{ctx:$}}}),zI=new te({}),QI=new R({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L507"}}),UI=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),V6=new N({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[Oja]},$$scope:{ctx:$}}}),HI=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),f8=new N({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Vja]},$$scope:{ctx:$}}}),JI=new te({}),YI=new R({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L514"}}),KI=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/v4.25.1/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/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),g8=new N({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Xja]},$$scope:{ctx:$}}}),eB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),C8=new N({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[zja]},$$scope:{ctx:$}}}),oB=new te({}),rB=new R({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L523"}}),aB=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),A8=new N({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[Qja]},$$scope:{ctx:$}}}),nB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),o7=new N({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Wja]},$$scope:{ctx:$}}}),sB=new te({}),lB=new R({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L570"}}),dB=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),t7=new N({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[Uja]},$$scope:{ctx:$}}}),cB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),T7=new N({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Hja]},$$scope:{ctx:$}}}),fB=new te({}),mB=new R({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L577"}}),hB=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/v4.25.1/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/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),E7=new N({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Jja]},$$scope:{ctx:$}}}),uB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),A7=new N({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Yja]},$$scope:{ctx:$}}}),_B=new te({}),vB=new R({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L550"}}),FB=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/v4.25.1/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/v4.25.1/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),y7=new N({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Zja]},$$scope:{ctx:$}}}),TB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.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.TFAutoModelForTableQuestionAnswering.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.TFAutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),$7=new N({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[Kja]},$$scope:{ctx:$}}}),MB=new te({}),EB=new R({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L539"}}),wB=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/v4.25.1/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/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),S7=new N({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[eGa]},$$scope:{ctx:$}}}),AB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),P7=new N({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[oGa]},$$scope:{ctx:$}}}),LB=new te({}),yB=new R({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L561"}}),$B=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),B7=new N({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[rGa]},$$scope:{ctx:$}}}),kB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),nL=new N({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[tGa]},$$scope:{ctx:$}}}),SB=new te({}),RB=new R({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L532"}}),IB=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),lL=new N({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[aGa]},$$scope:{ctx:$}}}),BB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),xL=new N({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[nGa]},$$scope:{ctx:$}}}),NB=new te({}),DB=new R({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L500"}}),jB=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/v4.25.1/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/v4.25.1/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),kL=new N({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[sGa]},$$scope:{ctx:$}}}),GB=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/v4.25.1/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.TFAutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/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/v4.25.1/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.TFAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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.TFAutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),RL=new N({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[lGa]},$$scope:{ctx:$}}}),OB=new te({}),VB=new R({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_tf_auto.py#L586"}}),zB=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/v4.25.1/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/v4.25.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),IL=new N({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[iGa]},$$scope:{ctx:$}}}),QB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),DL=new N({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[dGa]},$$scope:{ctx:$}}}),UB=new te({}),HB=new R({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L246"}}),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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),jL=new N({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[cGa]},$$scope:{ctx:$}}}),ZB=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),hy=new N({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[fGa]},$$scope:{ctx:$}}}),KB=new te({}),eN=new R({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L260"}}),rN=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/v4.25.1/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/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),py=new N({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[mGa]},$$scope:{ctx:$}}}),tN=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),Ly=new N({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[gGa]},$$scope:{ctx:$}}}),aN=new te({}),nN=new R({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L253"}}),lN=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),xy=new N({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[hGa]},$$scope:{ctx:$}}}),iN=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),Vy=new N({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[uGa]},$$scope:{ctx:$}}}),dN=new te({}),cN=new R({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L267"}}),mN=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),zy=new N({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[pGa]},$$scope:{ctx:$}}}),gN=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),r9=new N({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[_Ga]},$$scope:{ctx:$}}}),hN=new te({}),uN=new R({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L274"}}),_N=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/v4.25.1/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/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),a9=new N({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[vGa]},$$scope:{ctx:$}}}),vN=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),u9=new N({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[bGa]},$$scope:{ctx:$}}}),bN=new te({}),FN=new R({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L283"}}),MN=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),_9=new N({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[FGa]},$$scope:{ctx:$}}}),EN=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/v4.25.1/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/v4.25.1/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/v4.25.1/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
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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.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;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),y9=new N({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[TGa]},$$scope:{ctx:$}}}),CN=new te({}),wN=new R({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L292"}}),LN=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),$9=new N({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[MGa]},$$scope:{ctx:$}}}),yN=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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/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/v4.25.1/src/transformers/models/auto/auto_factory.py#L417"}}),G9=new N({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[EGa]},$$scope:{ctx:$}}}),xN=new te({}),$N=new R({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/modeling_flax_auto.py#L299"}}),SN=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/v4.25.1/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/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),V9=new N({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[CGa]},$$scope:{ctx:$}}}),RN=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/v4.25.1/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/v4.25.1/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/v4.25.1/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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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<li><a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),ex=new N({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[AGa]},$$scope:{ctx:$}}}),DN=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;
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<ul>
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<li>A path to a <em>directory</em> containing model weights saved using
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<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<li>The model was saved using <a href="/docs/transformers/v4.25.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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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/v4.25.1/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>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/models/auto/auto_factory.py#L389"}}),fx=new N({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[yGa]},$$scope:{ctx:$}}}),VN=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/v4.25.1/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
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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/v4.25.1/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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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.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;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.25.1/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>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.25.1/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
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(XLM-RoBERTa model)"),Mcr=l(),Ib=a("li"),jEe=a("strong"),Ecr=o("xlm-roberta-xl"),Ccr=o(" \u2014 "),kY=a("a"),wcr=o("XLMRobertaXLForMaskedLM"),Acr=o(" (XLM-RoBERTa-XL model)"),Lcr=l(),Bb=a("li"),GEe=a("strong"),ycr=o("xlnet"),xcr=o(" \u2014 "),SY=a("a"),$cr=o("XLNetLMHeadModel"),kcr=o(" (XLNet model)"),Scr=l(),Nb=a("p"),Rcr=o("The model is set in evaluation mode by default using "),OEe=a("code"),Pcr=o("model.eval()"),Icr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),VEe=a("code"),Bcr=o("model.train()"),Ncr=l(),F(Db.$$.fragment),wfo=l(),Hd=a("h2"),qb=a("a"),XEe=a("span"),F(YS.$$.fragment),Dcr=l(),zEe=a("span"),qcr=o("AutoModelForCausalLM"),Afo=l(),Oo=a("div"),F(ZS.$$.fragment),jcr=l(),Jd=a("p"),Gcr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
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method.`),Qcr=l(),KS=a("p"),Wcr=o("This class cannot be instantiated directly using "),QEe=a("code"),Ucr=o("__init__()"),Hcr=o(" (throws an error)."),Jcr=l(),yt=a("div"),F(eR.$$.fragment),Ycr=l(),WEe=a("p"),Zcr=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Kcr=l(),Yd=a("p"),efr=o(`Note:
Loading a model from its configuration file does `),UEe=a("strong"),ofr=o("not"),rfr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),IY=a("a"),tfr=o("from_pretrained()"),afr=o(" to load the model weights."),nfr=l(),F(jb.$$.fragment),sfr=l(),no=a("div"),F(oR.$$.fragment),lfr=l(),HEe=a("p"),ifr=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),dfr=l(),un=a("p"),cfr=o("The model class to instantiate is selected based on the "),JEe=a("code"),ffr=o("model_type"),mfr=o(` property of the config object (either
passed as an argument or loaded from `),YEe=a("code"),gfr=o("pretrained_model_name_or_path"),hfr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZEe=a("code"),ufr=o("pretrained_model_name_or_path"),pfr=o(":"),_fr=l(),U=a("ul"),Gb=a("li"),KEe=a("strong"),vfr=o("bart"),bfr=o(" \u2014 "),BY=a("a"),Ffr=o("BartForCausalLM"),Tfr=o(" (BART model)"),Mfr=l(),Ob=a("li"),eCe=a("strong"),Efr=o("bert"),Cfr=o(" \u2014 "),NY=a("a"),wfr=o("BertLMHeadModel"),Afr=o(" (BERT model)"),Lfr=l(),Vb=a("li"),oCe=a("strong"),yfr=o("bert-generation"),xfr=o(" \u2014 "),DY=a("a"),$fr=o("BertGenerationDecoder"),kfr=o(" (Bert Generation model)"),Sfr=l(),Xb=a("li"),rCe=a("strong"),Rfr=o("big_bird"),Pfr=o(" \u2014 "),qY=a("a"),Ifr=o("BigBirdForCausalLM"),Bfr=o(" (BigBird model)"),Nfr=l(),zb=a("li"),tCe=a("strong"),Dfr=o("bigbird_pegasus"),qfr=o(" \u2014 "),jY=a("a"),jfr=o("BigBirdPegasusForCausalLM"),Gfr=o(" (BigBird-Pegasus model)"),Ofr=l(),Qb=a("li"),aCe=a("strong"),Vfr=o("blenderbot"),Xfr=o(" \u2014 "),GY=a("a"),zfr=o("BlenderbotForCausalLM"),Qfr=o(" (Blenderbot 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passed as an argument or loaded from `),YCe=a("code"),Yur=o("pretrained_model_name_or_path"),Zur=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZCe=a("code"),Kur=o("pretrained_model_name_or_path"),epr=o(":"),opr=l(),lR=a("ul"),P5=a("li"),KCe=a("strong"),rpr=o("dpt"),tpr=o(" \u2014 "),yZ=a("a"),apr=o("DPTForDepthEstimation"),npr=o(" (DPT model)"),spr=l(),I5=a("li"),e3e=a("strong"),lpr=o("glpn"),ipr=o(" \u2014 "),xZ=a("a"),dpr=o("GLPNForDepthEstimation"),cpr=o(" (GLPN model)"),fpr=l(),B5=a("p"),mpr=o("The model is set in evaluation mode by default using "),o3e=a("code"),gpr=o("model.eval()"),hpr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),r3e=a("code"),upr=o("model.train()"),ppr=l(),F(N5.$$.fragment),xfo=l(),oc=a("h2"),D5=a("a"),t3e=a("span"),F(iR.$$.fragment),_pr=l(),a3e=a("span"),vpr=o("AutoModelForMaskedLM"),$fo=l(),Xo=a("div"),F(dR.$$.fragment),bpr=l(),rc=a("p"),Fpr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),$Z=a("a"),Tpr=o("from_pretrained()"),Mpr=o(" class method or the "),kZ=a("a"),Epr=o("from_config()"),Cpr=o(` class
method.`),wpr=l(),cR=a("p"),Apr=o("This class cannot be instantiated directly using "),n3e=a("code"),Lpr=o("__init__()"),ypr=o(" (throws an error)."),xpr=l(),$t=a("div"),F(fR.$$.fragment),$pr=l(),s3e=a("p"),kpr=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Spr=l(),tc=a("p"),Rpr=o(`Note:
Loading a model from its configuration file does `),l3e=a("strong"),Ppr=o("not"),Ipr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),SZ=a("a"),Bpr=o("from_pretrained()"),Npr=o(" to load the model weights."),Dpr=l(),F(q5.$$.fragment),qpr=l(),lo=a("div"),F(mR.$$.fragment),jpr=l(),i3e=a("p"),Gpr=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Opr=l(),_n=a("p"),Vpr=o("The model class to instantiate is selected based on the "),d3e=a("code"),Xpr=o("model_type"),zpr=o(` property of the config object (either
passed as an argument or loaded from `),c3e=a("code"),Qpr=o("pretrained_model_name_or_path"),Wpr=o(` if possible), or when it\u2019s missing, by
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with the `),bK=a("a"),Hvr=o("from_pretrained()"),Jvr=o(" class method or the "),FK=a("a"),Yvr=o("from_config()"),Zvr=o(` class
method.`),Kvr=l(),uR=a("p"),e4r=o("This class cannot be instantiated directly using "),a0e=a("code"),o4r=o("__init__()"),r4r=o(" (throws an error)."),t4r=l(),kt=a("div"),F(pR.$$.fragment),a4r=l(),n0e=a("p"),n4r=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),s4r=l(),sc=a("p"),l4r=o(`Note:
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model\u2019s configuration. Use `),TK=a("a"),c4r=o("from_pretrained()"),f4r=o(" to load the model weights."),m4r=l(),F($F.$$.fragment),g4r=l(),io=a("div"),F(_R.$$.fragment),h4r=l(),l0e=a("p"),u4r=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),p4r=l(),vn=a("p"),_4r=o("The model class to instantiate is selected based on the "),i0e=a("code"),v4r=o("model_type"),b4r=o(` property of the config object (either
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method.`),I5r=l(),FR=a("p"),B5r=o("This class cannot be instantiated directly using "),B0e=a("code"),N5r=o("__init__()"),D5r=o(" (throws an error)."),q5r=l(),St=a("div"),F(TR.$$.fragment),j5r=l(),N0e=a("p"),G5r=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),O5r=l(),dc=a("p"),V5r=o(`Note:
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passed as an argument or loaded from `),zAe=a("code"),X6r=o("pretrained_model_name_or_path"),z6r=o(` if possible), or when it\u2019s missing, by
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deactivated). To train the model, you should first set it back in training mode with `),o6e=a("code"),R8r=o("model.train()"),P8r=l(),F(eE.$$.fragment),qfo=l(),pc=a("h2"),oE=a("a"),r6e=a("span"),F(RR.$$.fragment),I8r=l(),t6e=a("span"),B8r=o("AutoModelForTokenClassification"),jfo=l(),Ho=a("div"),F(PR.$$.fragment),N8r=l(),_c=a("p"),D8r=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),Goe=a("a"),q8r=o("from_pretrained()"),j8r=o(" class method or the "),Ooe=a("a"),G8r=o("from_config()"),O8r=o(` class
method.`),V8r=l(),IR=a("p"),X8r=o("This class cannot be instantiated directly using "),a6e=a("code"),z8r=o("__init__()"),Q8r=o(" (throws an error)."),W8r=l(),It=a("div"),F(BR.$$.fragment),U8r=l(),n6e=a("p"),H8r=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),J8r=l(),vc=a("p"),Y8r=o(`Note:
Loading a model from its configuration file does `),s6e=a("strong"),Z8r=o("not"),K8r=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Voe=a("a"),e7r=o("from_pretrained()"),o7r=o(" to load the model weights."),r7r=l(),F(rE.$$.fragment),t7r=l(),go=a("div"),F(NR.$$.fragment),a7r=l(),l6e=a("p"),n7r=o("Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),s7r=l(),Mn=a("p"),l7r=o("The model class to instantiate is selected based on the "),i6e=a("code"),i7r=o("model_type"),d7r=o(` property of the config object (either
passed as an argument or loaded from `),d6e=a("code"),c7r=o("pretrained_model_name_or_path"),f7r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),c6e=a("code"),m7r=o("pretrained_model_name_or_path"),g7r=o(":"),h7r=l(),H=a("ul"),tE=a("li"),f6e=a("strong"),u7r=o("albert"),p7r=o(" \u2014 "),Xoe=a("a"),_7r=o("AlbertForTokenClassification"),v7r=o(" (ALBERT model)"),b7r=l(),aE=a("li"),m6e=a("strong"),F7r=o("bert"),T7r=o(" \u2014 "),zoe=a("a"),M7r=o("BertForTokenClassification"),E7r=o(" (BERT model)"),C7r=l(),nE=a("li"),g6e=a("strong"),w7r=o("big_bird"),A7r=o(" \u2014 "),Qoe=a("a"),L7r=o("BigBirdForTokenClassification"),y7r=o(" (BigBird model)"),x7r=l(),sE=a("li"),h6e=a("strong"),$7r=o("bloom"),k7r=o(" \u2014 "),Woe=a("a"),S7r=o("BloomForTokenClassification"),R7r=o(" (BLOOM model)"),P7r=l(),lE=a("li"),u6e=a("strong"),I7r=o("camembert"),B7r=o(" \u2014 "),Uoe=a("a"),N7r=o("CamembertForTokenClassification"),D7r=o(" (CamemBERT model)"),q7r=l(),iE=a("li"),p6e=a("strong"),j7r=o("canine"),G7r=o(" \u2014 "),Hoe=a("a"),O7r=o("CanineForTokenClassification"),V7r=o(" (CANINE 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"),Fte=a("a"),xRr=o("RobertaForQuestionAnswering"),$Rr=o(" (RoBERTa model)"),kRr=l(),IC=a("li"),J8e=a("strong"),SRr=o("roc_bert"),RRr=o(" \u2014 "),Tte=a("a"),PRr=o("RoCBertForQuestionAnswering"),IRr=o(" (RoCBert model)"),BRr=l(),BC=a("li"),Y8e=a("strong"),NRr=o("roformer"),DRr=o(" \u2014 "),Mte=a("a"),qRr=o("RoFormerForQuestionAnswering"),jRr=o(" (RoFormer model)"),GRr=l(),NC=a("li"),Z8e=a("strong"),ORr=o("splinter"),VRr=o(" \u2014 "),Ete=a("a"),XRr=o("SplinterForQuestionAnswering"),zRr=o(" (Splinter model)"),QRr=l(),DC=a("li"),K8e=a("strong"),WRr=o("squeezebert"),URr=o(" \u2014 "),Cte=a("a"),HRr=o("SqueezeBertForQuestionAnswering"),JRr=o(" (SqueezeBERT model)"),YRr=l(),qC=a("li"),e7e=a("strong"),ZRr=o("xlm"),KRr=o(" \u2014 "),wte=a("a"),ePr=o("XLMForQuestionAnsweringSimple"),oPr=o(" (XLM model)"),rPr=l(),jC=a("li"),o7e=a("strong"),tPr=o("xlm-roberta"),aPr=o(" \u2014 "),Ate=a("a"),nPr=o("XLMRobertaForQuestionAnswering"),sPr=o(" (XLM-RoBERTa model)"),lPr=l(),GC=a("li"),r7e=a("strong"),iPr=o("xlm-roberta-xl"),dPr=o(" \u2014 "),Lte=a("a"),cPr=o("XLMRobertaXLForQuestionAnswering"),fPr=o(" (XLM-RoBERTa-XL model)"),mPr=l(),OC=a("li"),t7e=a("strong"),gPr=o("xlnet"),hPr=o(" \u2014 "),yte=a("a"),uPr=o("XLNetForQuestionAnsweringSimple"),pPr=o(" (XLNet model)"),_Pr=l(),VC=a("li"),a7e=a("strong"),vPr=o("yoso"),bPr=o(" \u2014 "),xte=a("a"),FPr=o("YosoForQuestionAnswering"),TPr=o(" (YOSO model)"),MPr=l(),XC=a("p"),EPr=o("The model is set in evaluation mode by default using "),n7e=a("code"),CPr=o("model.eval()"),wPr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),s7e=a("code"),APr=o("model.train()"),LPr=l(),F(zC.$$.fragment),Vfo=l(),Mc=a("h2"),QC=a("a"),l7e=a("span"),F(VR.$$.fragment),yPr=l(),i7e=a("span"),xPr=o("AutoModelForTableQuestionAnswering"),Xfo=l(),Yo=a("div"),F(XR.$$.fragment),$Pr=l(),Ec=a("p"),kPr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),$te=a("a"),SPr=o("from_pretrained()"),RPr=o(" class method or the "),kte=a("a"),PPr=o("from_config()"),IPr=o(` class
method.`),BPr=l(),zR=a("p"),NPr=o("This class cannot be instantiated directly using "),d7e=a("code"),DPr=o("__init__()"),qPr=o(" (throws an error)."),jPr=l(),Nt=a("div"),F(QR.$$.fragment),GPr=l(),c7e=a("p"),OPr=o("Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),VPr=l(),Cc=a("p"),XPr=o(`Note:
Loading a model from its configuration file does `),f7e=a("strong"),zPr=o("not"),QPr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Ste=a("a"),WPr=o("from_pretrained()"),UPr=o(" to load the model weights."),HPr=l(),F(WC.$$.fragment),JPr=l(),uo=a("div"),F(WR.$$.fragment),YPr=l(),m7e=a("p"),ZPr=o("Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),KPr=l(),Cn=a("p"),eIr=o("The model class to instantiate is selected based on the "),g7e=a("code"),oIr=o("model_type"),rIr=o(` property of the config object (either
passed as an argument or loaded from `),h7e=a("code"),tIr=o("pretrained_model_name_or_path"),aIr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),u7e=a("code"),nIr=o("pretrained_model_name_or_path"),sIr=o(":"),lIr=l(),p7e=a("ul"),UC=a("li"),_7e=a("strong"),iIr=o("tapas"),dIr=o(" \u2014 "),Rte=a("a"),cIr=o("TapasForQuestionAnswering"),fIr=o(" (TAPAS model)"),mIr=l(),HC=a("p"),gIr=o("The model is set in evaluation mode by default using "),v7e=a("code"),hIr=o("model.eval()"),uIr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),b7e=a("code"),pIr=o("model.train()"),_Ir=l(),F(JC.$$.fragment),zfo=l(),wc=a("h2"),YC=a("a"),F7e=a("span"),F(UR.$$.fragment),vIr=l(),T7e=a("span"),bIr=o("AutoModelForDocumentQuestionAnswering"),Qfo=l(),Zo=a("div"),F(HR.$$.fragment),FIr=l(),Ac=a("p"),TIr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),Pte=a("a"),MIr=o("from_pretrained()"),EIr=o(" class method or the "),Ite=a("a"),CIr=o("from_config()"),wIr=o(` class
method.`),AIr=l(),JR=a("p"),LIr=o("This class cannot be instantiated directly using "),M7e=a("code"),yIr=o("__init__()"),xIr=o(" (throws an error)."),$Ir=l(),Dt=a("div"),F(YR.$$.fragment),kIr=l(),E7e=a("p"),SIr=o("Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),RIr=l(),Lc=a("p"),PIr=o(`Note:
Loading a model from its configuration file does `),C7e=a("strong"),IIr=o("not"),BIr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Bte=a("a"),NIr=o("from_pretrained()"),DIr=o(" to load the model weights."),qIr=l(),F(ZC.$$.fragment),jIr=l(),po=a("div"),F(ZR.$$.fragment),GIr=l(),w7e=a("p"),OIr=o("Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),VIr=l(),wn=a("p"),XIr=o("The model class to instantiate is selected based on the "),A7e=a("code"),zIr=o("model_type"),QIr=o(` property of the config object (either
passed as an argument or loaded from `),L7e=a("code"),WIr=o("pretrained_model_name_or_path"),UIr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),y7e=a("code"),HIr=o("pretrained_model_name_or_path"),JIr=o(":"),YIr=l(),yc=a("ul"),KC=a("li"),x7e=a("strong"),ZIr=o("layoutlm"),KIr=o(" \u2014 "),Nte=a("a"),eBr=o("LayoutLMForQuestionAnswering"),oBr=o(" (LayoutLM model)"),rBr=l(),e3=a("li"),$7e=a("strong"),tBr=o("layoutlmv2"),aBr=o(" \u2014 "),Dte=a("a"),nBr=o("LayoutLMv2ForQuestionAnswering"),sBr=o(" (LayoutLMv2 model)"),lBr=l(),o3=a("li"),k7e=a("strong"),iBr=o("layoutlmv3"),dBr=o(" \u2014 "),qte=a("a"),cBr=o("LayoutLMv3ForQuestionAnswering"),fBr=o(" (LayoutLMv3 model)"),mBr=l(),r3=a("p"),gBr=o("The model is set in evaluation mode by default using "),S7e=a("code"),hBr=o("model.eval()"),uBr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),R7e=a("code"),pBr=o("model.train()"),_Br=l(),F(t3.$$.fragment),Wfo=l(),xc=a("h2"),a3=a("a"),P7e=a("span"),F(KR.$$.fragment),vBr=l(),I7e=a("span"),bBr=o("AutoModelForImageClassification"),Ufo=l(),Ko=a("div"),F(eP.$$.fragment),FBr=l(),$c=a("p"),TBr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),jte=a("a"),MBr=o("from_pretrained()"),EBr=o(" class method or the "),Gte=a("a"),CBr=o("from_config()"),wBr=o(` class
method.`),ABr=l(),oP=a("p"),LBr=o("This class cannot be instantiated directly using "),B7e=a("code"),yBr=o("__init__()"),xBr=o(" (throws an error)."),$Br=l(),qt=a("div"),F(rP.$$.fragment),kBr=l(),N7e=a("p"),SBr=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),RBr=l(),kc=a("p"),PBr=o(`Note:
Loading a model from its configuration file does `),D7e=a("strong"),IBr=o("not"),BBr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Ote=a("a"),NBr=o("from_pretrained()"),DBr=o(" to load the model weights."),qBr=l(),F(n3.$$.fragment),jBr=l(),_o=a("div"),F(tP.$$.fragment),GBr=l(),q7e=a("p"),OBr=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),VBr=l(),An=a("p"),XBr=o("The model class to instantiate is selected based on the "),j7e=a("code"),zBr=o("model_type"),QBr=o(` property of the config object (either
passed as an argument or loaded from `),G7e=a("code"),WBr=o("pretrained_model_name_or_path"),UBr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),O7e=a("code"),HBr=o("pretrained_model_name_or_path"),JBr=o(":"),YBr=l(),fe=a("ul"),s3=a("li"),V7e=a("strong"),ZBr=o("beit"),KBr=o(" \u2014 "),Vte=a("a"),eNr=o("BeitForImageClassification"),oNr=o(" (BEiT model)"),rNr=l(),l3=a("li"),X7e=a("strong"),tNr=o("convnext"),aNr=o(" \u2014 "),Xte=a("a"),nNr=o("ConvNextForImageClassification"),sNr=o(" (ConvNeXT model)"),lNr=l(),i3=a("li"),z7e=a("strong"),iNr=o("cvt"),dNr=o(" \u2014 "),zte=a("a"),cNr=o("CvtForImageClassification"),fNr=o(" (CvT model)"),mNr=l(),d3=a("li"),Q7e=a("strong"),gNr=o("data2vec-vision"),hNr=o(" \u2014 "),Qte=a("a"),uNr=o("Data2VecVisionForImageClassification"),pNr=o(" (Data2VecVision model)"),_Nr=l(),jl=a("li"),W7e=a("strong"),vNr=o("deit"),bNr=o(" \u2014 "),Wte=a("a"),FNr=o("DeiTForImageClassification"),TNr=o(" or "),Ute=a("a"),MNr=o("DeiTForImageClassificationWithTeacher"),ENr=o(" (DeiT model)"),CNr=l(),c3=a("li"),U7e=a("strong"),wNr=o("dinat"),ANr=o(" \u2014 "),Hte=a("a"),LNr=o("DinatForImageClassification"),yNr=o(" (DiNAT model)"),xNr=l(),f3=a("li"),H7e=a("strong"),$Nr=o("imagegpt"),kNr=o(" \u2014 "),Jte=a("a"),SNr=o("ImageGPTForImageClassification"),RNr=o(" (ImageGPT model)"),PNr=l(),Gl=a("li"),J7e=a("strong"),INr=o("levit"),BNr=o(" \u2014 "),Yte=a("a"),NNr=o("LevitForImageClassification"),DNr=o(" or "),Zte=a("a"),qNr=o("LevitForImageClassificationWithTeacher"),jNr=o(" (LeViT model)"),GNr=l(),m3=a("li"),Y7e=a("strong"),ONr=o("mobilenet_v1"),VNr=o(" \u2014 "),Kte=a("a"),XNr=o("MobileNetV1ForImageClassification"),zNr=o(" (MobileNetV1 model)"),QNr=l(),g3=a("li"),Z7e=a("strong"),WNr=o("mobilenet_v2"),UNr=o(" \u2014 "),eae=a("a"),HNr=o("MobileNetV2ForImageClassification"),JNr=o(" (MobileNetV2 model)"),YNr=l(),h3=a("li"),K7e=a("strong"),ZNr=o("mobilevit"),KNr=o(" \u2014 "),oae=a("a"),eDr=o("MobileViTForImageClassification"),oDr=o(" (MobileViT model)"),rDr=l(),u3=a("li"),eLe=a("strong"),tDr=o("nat"),aDr=o(" \u2014 "),rae=a("a"),nDr=o("NatForImageClassification"),sDr=o(" (NAT model)"),lDr=l(),jt=a("li"),oLe=a("strong"),iDr=o("perceiver"),dDr=o(" \u2014 "),tae=a("a"),cDr=o("PerceiverForImageClassificationLearned"),fDr=o(" or "),aae=a("a"),mDr=o("PerceiverForImageClassificationFourier"),gDr=o(" or "),nae=a("a"),hDr=o("PerceiverForImageClassificationConvProcessing"),uDr=o(" (Perceiver model)"),pDr=l(),p3=a("li"),rLe=a("strong"),_Dr=o("poolformer"),vDr=o(" \u2014 "),sae=a("a"),bDr=o("PoolFormerForImageClassification"),FDr=o(" (PoolFormer model)"),TDr=l(),_3=a("li"),tLe=a("strong"),MDr=o("regnet"),EDr=o(" \u2014 "),lae=a("a"),CDr=o("RegNetForImageClassification"),wDr=o(" (RegNet model)"),ADr=l(),v3=a("li"),aLe=a("strong"),LDr=o("resnet"),yDr=o(" \u2014 "),iae=a("a"),xDr=o("ResNetForImageClassification"),$Dr=o(" (ResNet model)"),kDr=l(),b3=a("li"),nLe=a("strong"),SDr=o("segformer"),RDr=o(" \u2014 "),dae=a("a"),PDr=o("SegformerForImageClassification"),IDr=o(" (SegFormer model)"),BDr=l(),F3=a("li"),sLe=a("strong"),NDr=o("swin"),DDr=o(" \u2014 "),cae=a("a"),qDr=o("SwinForImageClassification"),jDr=o(" (Swin Transformer model)"),GDr=l(),T3=a("li"),lLe=a("strong"),ODr=o("swinv2"),VDr=o(" \u2014 "),fae=a("a"),XDr=o("Swinv2ForImageClassification"),zDr=o(" (Swin Transformer V2 model)"),QDr=l(),M3=a("li"),iLe=a("strong"),WDr=o("van"),UDr=o(" \u2014 "),mae=a("a"),HDr=o("VanForImageClassification"),JDr=o(" (VAN model)"),YDr=l(),E3=a("li"),dLe=a("strong"),ZDr=o("vit"),KDr=o(" \u2014 "),gae=a("a"),eqr=o("ViTForImageClassification"),oqr=o(" (ViT model)"),rqr=l(),C3=a("li"),cLe=a("strong"),tqr=o("vit_msn"),aqr=o(" \u2014 "),hae=a("a"),nqr=o("ViTMSNForImageClassification"),sqr=o(" (ViTMSN model)"),lqr=l(),w3=a("p"),iqr=o("The model is set in evaluation mode by default using "),fLe=a("code"),dqr=o("model.eval()"),cqr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),mLe=a("code"),fqr=o("model.train()"),mqr=l(),F(A3.$$.fragment),Hfo=l(),Sc=a("h2"),L3=a("a"),gLe=a("span"),F(aP.$$.fragment),gqr=l(),hLe=a("span"),hqr=o("AutoModelForVideoClassification"),Jfo=l(),er=a("div"),F(nP.$$.fragment),uqr=l(),Rc=a("p"),pqr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),uae=a("a"),_qr=o("from_pretrained()"),vqr=o(" class method or the "),pae=a("a"),bqr=o("from_config()"),Fqr=o(` class
method.`),Tqr=l(),sP=a("p"),Mqr=o("This class cannot be instantiated directly using "),uLe=a("code"),Eqr=o("__init__()"),Cqr=o(" (throws an error)."),wqr=l(),Gt=a("div"),F(lP.$$.fragment),Aqr=l(),pLe=a("p"),Lqr=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),yqr=l(),Pc=a("p"),xqr=o(`Note:
Loading a model from its configuration file does `),_Le=a("strong"),$qr=o("not"),kqr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_ae=a("a"),Sqr=o("from_pretrained()"),Rqr=o(" to load the model weights."),Pqr=l(),F(y3.$$.fragment),Iqr=l(),vo=a("div"),F(iP.$$.fragment),Bqr=l(),vLe=a("p"),Nqr=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),Dqr=l(),Ln=a("p"),qqr=o("The model class to instantiate is selected based on the "),bLe=a("code"),jqr=o("model_type"),Gqr=o(` property of the config object (either
passed as an argument or loaded from `),FLe=a("code"),Oqr=o("pretrained_model_name_or_path"),Vqr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),TLe=a("code"),Xqr=o("pretrained_model_name_or_path"),zqr=o(":"),Qqr=l(),MLe=a("ul"),x3=a("li"),ELe=a("strong"),Wqr=o("videomae"),Uqr=o(" \u2014 "),vae=a("a"),Hqr=o("VideoMAEForVideoClassification"),Jqr=o(" (VideoMAE model)"),Yqr=l(),$3=a("p"),Zqr=o("The model is set in evaluation mode by default using "),CLe=a("code"),Kqr=o("model.eval()"),ejr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wLe=a("code"),ojr=o("model.train()"),rjr=l(),F(k3.$$.fragment),Yfo=l(),Ic=a("h2"),S3=a("a"),ALe=a("span"),F(dP.$$.fragment),tjr=l(),LLe=a("span"),ajr=o("AutoModelForVision2Seq"),Zfo=l(),or=a("div"),F(cP.$$.fragment),njr=l(),Bc=a("p"),sjr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),bae=a("a"),ljr=o("from_pretrained()"),ijr=o(" class method or the "),Fae=a("a"),djr=o("from_config()"),cjr=o(` class
method.`),fjr=l(),fP=a("p"),mjr=o("This class cannot be instantiated directly using "),yLe=a("code"),gjr=o("__init__()"),hjr=o(" (throws an error)."),ujr=l(),Ot=a("div"),F(mP.$$.fragment),pjr=l(),xLe=a("p"),_jr=o("Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),vjr=l(),Nc=a("p"),bjr=o(`Note:
Loading a model from its configuration file does `),$Le=a("strong"),Fjr=o("not"),Tjr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Tae=a("a"),Mjr=o("from_pretrained()"),Ejr=o(" to load the model weights."),Cjr=l(),F(R3.$$.fragment),wjr=l(),bo=a("div"),F(gP.$$.fragment),Ajr=l(),kLe=a("p"),Ljr=o("Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),yjr=l(),yn=a("p"),xjr=o("The model class to instantiate is selected based on the "),SLe=a("code"),$jr=o("model_type"),kjr=o(` property of the config object (either
passed as an argument or loaded from `),RLe=a("code"),Sjr=o("pretrained_model_name_or_path"),Rjr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PLe=a("code"),Pjr=o("pretrained_model_name_or_path"),Ijr=o(":"),Bjr=l(),ILe=a("ul"),P3=a("li"),BLe=a("strong"),Njr=o("vision-encoder-decoder"),Djr=o(" \u2014 "),Mae=a("a"),qjr=o("VisionEncoderDecoderModel"),jjr=o(" (Vision Encoder decoder model)"),Gjr=l(),I3=a("p"),Ojr=o("The model is set in evaluation mode by default using "),NLe=a("code"),Vjr=o("model.eval()"),Xjr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),DLe=a("code"),zjr=o("model.train()"),Qjr=l(),F(B3.$$.fragment),Kfo=l(),Dc=a("h2"),N3=a("a"),qLe=a("span"),F(hP.$$.fragment),Wjr=l(),jLe=a("span"),Ujr=o("AutoModelForVisualQuestionAnswering"),emo=l(),rr=a("div"),F(uP.$$.fragment),Hjr=l(),qc=a("p"),Jjr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),Eae=a("a"),Yjr=o("from_pretrained()"),Zjr=o(" class method or the "),Cae=a("a"),Kjr=o("from_config()"),eGr=o(` class
method.`),oGr=l(),pP=a("p"),rGr=o("This class cannot be instantiated directly using "),GLe=a("code"),tGr=o("__init__()"),aGr=o(" (throws an error)."),nGr=l(),Vt=a("div"),F(_P.$$.fragment),sGr=l(),OLe=a("p"),lGr=o("Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),iGr=l(),jc=a("p"),dGr=o(`Note:
Loading a model from its configuration file does `),VLe=a("strong"),cGr=o("not"),fGr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),wae=a("a"),mGr=o("from_pretrained()"),gGr=o(" to load the model weights."),hGr=l(),F(D3.$$.fragment),uGr=l(),Fo=a("div"),F(vP.$$.fragment),pGr=l(),XLe=a("p"),_Gr=o("Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),vGr=l(),xn=a("p"),bGr=o("The model class to instantiate is selected based on the "),zLe=a("code"),FGr=o("model_type"),TGr=o(` property of the config object (either
passed as an argument or loaded from `),QLe=a("code"),MGr=o("pretrained_model_name_or_path"),EGr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WLe=a("code"),CGr=o("pretrained_model_name_or_path"),wGr=o(":"),AGr=l(),ULe=a("ul"),q3=a("li"),HLe=a("strong"),LGr=o("vilt"),yGr=o(" \u2014 "),Aae=a("a"),xGr=o("ViltForQuestionAnswering"),$Gr=o(" (ViLT model)"),kGr=l(),j3=a("p"),SGr=o("The model is set in evaluation mode by default using "),JLe=a("code"),RGr=o("model.eval()"),PGr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),YLe=a("code"),IGr=o("model.train()"),BGr=l(),F(G3.$$.fragment),omo=l(),Gc=a("h2"),O3=a("a"),ZLe=a("span"),F(bP.$$.fragment),NGr=l(),KLe=a("span"),DGr=o("AutoModelForAudioClassification"),rmo=l(),tr=a("div"),F(FP.$$.fragment),qGr=l(),Oc=a("p"),jGr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),Lae=a("a"),GGr=o("from_pretrained()"),OGr=o(" class method or the "),yae=a("a"),VGr=o("from_config()"),XGr=o(` class
method.`),zGr=l(),TP=a("p"),QGr=o("This class cannot be instantiated directly using "),eye=a("code"),WGr=o("__init__()"),UGr=o(" (throws an error)."),HGr=l(),Xt=a("div"),F(MP.$$.fragment),JGr=l(),oye=a("p"),YGr=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),ZGr=l(),Vc=a("p"),KGr=o(`Note:
Loading a model from its configuration file does `),rye=a("strong"),eOr=o("not"),oOr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),xae=a("a"),rOr=o("from_pretrained()"),tOr=o(" to load the model weights."),aOr=l(),F(V3.$$.fragment),nOr=l(),To=a("div"),F(EP.$$.fragment),sOr=l(),tye=a("p"),lOr=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),iOr=l(),$n=a("p"),dOr=o("The model class to instantiate is selected based on the "),aye=a("code"),cOr=o("model_type"),fOr=o(` property of the config object (either
passed as an argument or loaded from `),nye=a("code"),mOr=o("pretrained_model_name_or_path"),gOr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sye=a("code"),hOr=o("pretrained_model_name_or_path"),uOr=o(":"),pOr=l(),xe=a("ul"),X3=a("li"),lye=a("strong"),_Or=o("audio-spectrogram-transformer"),vOr=o(" \u2014 "),$ae=a("a"),bOr=o("ASTForAudioClassification"),FOr=o(" (Audio Spectrogram Transformer model)"),TOr=l(),z3=a("li"),iye=a("strong"),MOr=o("data2vec-audio"),EOr=o(" \u2014 "),kae=a("a"),COr=o("Data2VecAudioForSequenceClassification"),wOr=o(" (Data2VecAudio model)"),AOr=l(),Q3=a("li"),dye=a("strong"),LOr=o("hubert"),yOr=o(" \u2014 "),Sae=a("a"),xOr=o("HubertForSequenceClassification"),$Or=o(" (Hubert model)"),kOr=l(),W3=a("li"),cye=a("strong"),SOr=o("sew"),ROr=o(" \u2014 "),Rae=a("a"),POr=o("SEWForSequenceClassification"),IOr=o(" (SEW model)"),BOr=l(),U3=a("li"),fye=a("strong"),NOr=o("sew-d"),DOr=o(" \u2014 "),Pae=a("a"),qOr=o("SEWDForSequenceClassification"),jOr=o(" (SEW-D model)"),GOr=l(),H3=a("li"),mye=a("strong"),OOr=o("unispeech"),VOr=o(" \u2014 "),Iae=a("a"),XOr=o("UniSpeechForSequenceClassification"),zOr=o(" (UniSpeech model)"),QOr=l(),J3=a("li"),gye=a("strong"),WOr=o("unispeech-sat"),UOr=o(" \u2014 "),Bae=a("a"),HOr=o("UniSpeechSatForSequenceClassification"),JOr=o(" (UniSpeechSat model)"),YOr=l(),Y3=a("li"),hye=a("strong"),ZOr=o("wav2vec2"),KOr=o(" \u2014 "),Nae=a("a"),eVr=o("Wav2Vec2ForSequenceClassification"),oVr=o(" (Wav2Vec2 model)"),rVr=l(),Z3=a("li"),uye=a("strong"),tVr=o("wav2vec2-conformer"),aVr=o(" \u2014 "),Dae=a("a"),nVr=o("Wav2Vec2ConformerForSequenceClassification"),sVr=o(" (Wav2Vec2-Conformer model)"),lVr=l(),K3=a("li"),pye=a("strong"),iVr=o("wavlm"),dVr=o(" \u2014 "),qae=a("a"),cVr=o("WavLMForSequenceClassification"),fVr=o(" (WavLM model)"),mVr=l(),e0=a("p"),gVr=o("The model is set in evaluation mode by default using "),_ye=a("code"),hVr=o("model.eval()"),uVr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),vye=a("code"),pVr=o("model.train()"),_Vr=l(),F(o0.$$.fragment),tmo=l(),Xc=a("h2"),r0=a("a"),bye=a("span"),F(CP.$$.fragment),vVr=l(),Fye=a("span"),bVr=o("AutoModelForAudioFrameClassification"),amo=l(),ar=a("div"),F(wP.$$.fragment),FVr=l(),zc=a("p"),TVr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),jae=a("a"),MVr=o("from_pretrained()"),EVr=o(" class method or the "),Gae=a("a"),CVr=o("from_config()"),wVr=o(` class
method.`),AVr=l(),AP=a("p"),LVr=o("This class cannot be instantiated directly using "),Tye=a("code"),yVr=o("__init__()"),xVr=o(" (throws an error)."),$Vr=l(),zt=a("div"),F(LP.$$.fragment),kVr=l(),Mye=a("p"),SVr=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),RVr=l(),Qc=a("p"),PVr=o(`Note:
Loading a model from its configuration file does `),Eye=a("strong"),IVr=o("not"),BVr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Oae=a("a"),NVr=o("from_pretrained()"),DVr=o(" to load the model weights."),qVr=l(),F(t0.$$.fragment),jVr=l(),Mo=a("div"),F(yP.$$.fragment),GVr=l(),Cye=a("p"),OVr=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),VVr=l(),kn=a("p"),XVr=o("The model class to instantiate is selected based on the "),wye=a("code"),zVr=o("model_type"),QVr=o(` property of the config object (either
passed as an argument or loaded from `),Aye=a("code"),WVr=o("pretrained_model_name_or_path"),UVr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lye=a("code"),HVr=o("pretrained_model_name_or_path"),JVr=o(":"),YVr=l(),Ft=a("ul"),a0=a("li"),yye=a("strong"),ZVr=o("data2vec-audio"),KVr=o(" \u2014 "),Vae=a("a"),eXr=o("Data2VecAudioForAudioFrameClassification"),oXr=o(" (Data2VecAudio model)"),rXr=l(),n0=a("li"),xye=a("strong"),tXr=o("unispeech-sat"),aXr=o(" \u2014 "),Xae=a("a"),nXr=o("UniSpeechSatForAudioFrameClassification"),sXr=o(" (UniSpeechSat model)"),lXr=l(),s0=a("li"),$ye=a("strong"),iXr=o("wav2vec2"),dXr=o(" \u2014 "),zae=a("a"),cXr=o("Wav2Vec2ForAudioFrameClassification"),fXr=o(" (Wav2Vec2 model)"),mXr=l(),l0=a("li"),kye=a("strong"),gXr=o("wav2vec2-conformer"),hXr=o(" \u2014 "),Qae=a("a"),uXr=o("Wav2Vec2ConformerForAudioFrameClassification"),pXr=o(" (Wav2Vec2-Conformer model)"),_Xr=l(),i0=a("li"),Sye=a("strong"),vXr=o("wavlm"),bXr=o(" \u2014 "),Wae=a("a"),FXr=o("WavLMForAudioFrameClassification"),TXr=o(" (WavLM model)"),MXr=l(),d0=a("p"),EXr=o("The model is set in evaluation mode by default using "),Rye=a("code"),CXr=o("model.eval()"),wXr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pye=a("code"),AXr=o("model.train()"),LXr=l(),F(c0.$$.fragment),nmo=l(),Wc=a("h2"),f0=a("a"),Iye=a("span"),F(xP.$$.fragment),yXr=l(),Bye=a("span"),xXr=o("AutoModelForCTC"),smo=l(),nr=a("div"),F($P.$$.fragment),$Xr=l(),Uc=a("p"),kXr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),Uae=a("a"),SXr=o("from_pretrained()"),RXr=o(" class method or the "),Hae=a("a"),PXr=o("from_config()"),IXr=o(` class
method.`),BXr=l(),kP=a("p"),NXr=o("This class cannot be instantiated directly using "),Nye=a("code"),DXr=o("__init__()"),qXr=o(" (throws an error)."),jXr=l(),Qt=a("div"),F(SP.$$.fragment),GXr=l(),Dye=a("p"),OXr=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),VXr=l(),Hc=a("p"),XXr=o(`Note:
Loading a model from its configuration file does `),qye=a("strong"),zXr=o("not"),QXr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Jae=a("a"),WXr=o("from_pretrained()"),UXr=o(" to load the model weights."),HXr=l(),F(m0.$$.fragment),JXr=l(),Eo=a("div"),F(RP.$$.fragment),YXr=l(),jye=a("p"),ZXr=o("Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),KXr=l(),Sn=a("p"),ezr=o("The model class to instantiate is selected based on the "),Gye=a("code"),ozr=o("model_type"),rzr=o(` property of the config object (either
passed as an argument or loaded from `),Oye=a("code"),tzr=o("pretrained_model_name_or_path"),azr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vye=a("code"),nzr=o("pretrained_model_name_or_path"),szr=o(":"),lzr=l(),$e=a("ul"),g0=a("li"),Xye=a("strong"),izr=o("data2vec-audio"),dzr=o(" \u2014 "),Yae=a("a"),czr=o("Data2VecAudioForCTC"),fzr=o(" (Data2VecAudio model)"),mzr=l(),h0=a("li"),zye=a("strong"),gzr=o("hubert"),hzr=o(" \u2014 "),Zae=a("a"),uzr=o("HubertForCTC"),pzr=o(" (Hubert model)"),_zr=l(),u0=a("li"),Qye=a("strong"),vzr=o("mctct"),bzr=o(" \u2014 "),Kae=a("a"),Fzr=o("MCTCTForCTC"),Tzr=o(" (M-CTC-T model)"),Mzr=l(),p0=a("li"),Wye=a("strong"),Ezr=o("sew"),Czr=o(" \u2014 "),ene=a("a"),wzr=o("SEWForCTC"),Azr=o(" (SEW model)"),Lzr=l(),_0=a("li"),Uye=a("strong"),yzr=o("sew-d"),xzr=o(" \u2014 "),one=a("a"),$zr=o("SEWDForCTC"),kzr=o(" (SEW-D model)"),Szr=l(),v0=a("li"),Hye=a("strong"),Rzr=o("unispeech"),Pzr=o(" \u2014 "),rne=a("a"),Izr=o("UniSpeechForCTC"),Bzr=o(" (UniSpeech model)"),Nzr=l(),b0=a("li"),Jye=a("strong"),Dzr=o("unispeech-sat"),qzr=o(" \u2014 "),tne=a("a"),jzr=o("UniSpeechSatForCTC"),Gzr=o(" (UniSpeechSat model)"),Ozr=l(),F0=a("li"),Yye=a("strong"),Vzr=o("wav2vec2"),Xzr=o(" \u2014 "),ane=a("a"),zzr=o("Wav2Vec2ForCTC"),Qzr=o(" (Wav2Vec2 model)"),Wzr=l(),T0=a("li"),Zye=a("strong"),Uzr=o("wav2vec2-conformer"),Hzr=o(" \u2014 "),nne=a("a"),Jzr=o("Wav2Vec2ConformerForCTC"),Yzr=o(" (Wav2Vec2-Conformer model)"),Zzr=l(),M0=a("li"),Kye=a("strong"),Kzr=o("wavlm"),eQr=o(" \u2014 "),sne=a("a"),oQr=o("WavLMForCTC"),rQr=o(" (WavLM model)"),tQr=l(),E0=a("p"),aQr=o("The model is set in evaluation mode by default using "),e9e=a("code"),nQr=o("model.eval()"),sQr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),o9e=a("code"),lQr=o("model.train()"),iQr=l(),F(C0.$$.fragment),lmo=l(),Jc=a("h2"),w0=a("a"),r9e=a("span"),F(PP.$$.fragment),dQr=l(),t9e=a("span"),cQr=o("AutoModelForSpeechSeq2Seq"),imo=l(),sr=a("div"),F(IP.$$.fragment),fQr=l(),Yc=a("p"),mQr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),lne=a("a"),gQr=o("from_pretrained()"),hQr=o(" class method or the "),ine=a("a"),uQr=o("from_config()"),pQr=o(` class
method.`),_Qr=l(),BP=a("p"),vQr=o("This class cannot be instantiated directly using "),a9e=a("code"),bQr=o("__init__()"),FQr=o(" (throws an error)."),TQr=l(),Wt=a("div"),F(NP.$$.fragment),MQr=l(),n9e=a("p"),EQr=o("Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),CQr=l(),Zc=a("p"),wQr=o(`Note:
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model\u2019s configuration. Use `),dne=a("a"),yQr=o("from_pretrained()"),xQr=o(" to load the model weights."),$Qr=l(),F(A0.$$.fragment),kQr=l(),Co=a("div"),F(DP.$$.fragment),SQr=l(),l9e=a("p"),RQr=o("Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),PQr=l(),Rn=a("p"),IQr=o("The model class to instantiate is selected based on the "),i9e=a("code"),BQr=o("model_type"),NQr=o(` property of the config object (either
passed as an argument or loaded from `),d9e=a("code"),DQr=o("pretrained_model_name_or_path"),qQr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),c9e=a("code"),jQr=o("pretrained_model_name_or_path"),GQr=o(":"),OQr=l(),Kc=a("ul"),L0=a("li"),f9e=a("strong"),VQr=o("speech-encoder-decoder"),XQr=o(" \u2014 "),cne=a("a"),zQr=o("SpeechEncoderDecoderModel"),QQr=o(" (Speech Encoder decoder model)"),WQr=l(),y0=a("li"),m9e=a("strong"),UQr=o("speech_to_text"),HQr=o(" \u2014 "),fne=a("a"),JQr=o("Speech2TextForConditionalGeneration"),YQr=o(" (Speech2Text model)"),ZQr=l(),x0=a("li"),g9e=a("strong"),KQr=o("whisper"),eWr=o(" \u2014 "),mne=a("a"),oWr=o("WhisperForConditionalGeneration"),rWr=o(" (Whisper model)"),tWr=l(),$0=a("p"),aWr=o("The model is set in evaluation mode by default using "),h9e=a("code"),nWr=o("model.eval()"),sWr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),u9e=a("code"),lWr=o("model.train()"),iWr=l(),F(k0.$$.fragment),dmo=l(),ef=a("h2"),S0=a("a"),p9e=a("span"),F(qP.$$.fragment),dWr=l(),_9e=a("span"),cWr=o("AutoModelForAudioXVector"),cmo=l(),lr=a("div"),F(jP.$$.fragment),fWr=l(),of=a("p"),mWr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),gne=a("a"),gWr=o("from_pretrained()"),hWr=o(" class method or the "),hne=a("a"),uWr=o("from_config()"),pWr=o(` class
method.`),_Wr=l(),GP=a("p"),vWr=o("This class cannot be instantiated directly using "),v9e=a("code"),bWr=o("__init__()"),FWr=o(" (throws an error)."),TWr=l(),Ut=a("div"),F(OP.$$.fragment),MWr=l(),b9e=a("p"),EWr=o("Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),CWr=l(),rf=a("p"),wWr=o(`Note:
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model\u2019s configuration. Use `),une=a("a"),yWr=o("from_pretrained()"),xWr=o(" to load the model weights."),$Wr=l(),F(R0.$$.fragment),kWr=l(),wo=a("div"),F(VP.$$.fragment),SWr=l(),T9e=a("p"),RWr=o("Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),PWr=l(),Pn=a("p"),IWr=o("The model class to instantiate is selected based on the "),M9e=a("code"),BWr=o("model_type"),NWr=o(` property of the config object (either
passed as an argument or loaded from `),E9e=a("code"),DWr=o("pretrained_model_name_or_path"),qWr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),C9e=a("code"),jWr=o("pretrained_model_name_or_path"),GWr=o(":"),OWr=l(),Tt=a("ul"),P0=a("li"),w9e=a("strong"),VWr=o("data2vec-audio"),XWr=o(" \u2014 "),pne=a("a"),zWr=o("Data2VecAudioForXVector"),QWr=o(" (Data2VecAudio model)"),WWr=l(),I0=a("li"),A9e=a("strong"),UWr=o("unispeech-sat"),HWr=o(" \u2014 "),_ne=a("a"),JWr=o("UniSpeechSatForXVector"),YWr=o(" (UniSpeechSat model)"),ZWr=l(),B0=a("li"),L9e=a("strong"),KWr=o("wav2vec2"),eUr=o(" \u2014 "),vne=a("a"),oUr=o("Wav2Vec2ForXVector"),rUr=o(" (Wav2Vec2 model)"),tUr=l(),N0=a("li"),y9e=a("strong"),aUr=o("wav2vec2-conformer"),nUr=o(" \u2014 "),bne=a("a"),sUr=o("Wav2Vec2ConformerForXVector"),lUr=o(" (Wav2Vec2-Conformer model)"),iUr=l(),D0=a("li"),x9e=a("strong"),dUr=o("wavlm"),cUr=o(" \u2014 "),Fne=a("a"),fUr=o("WavLMForXVector"),mUr=o(" (WavLM model)"),gUr=l(),q0=a("p"),hUr=o("The model is set in evaluation mode by default using "),$9e=a("code"),uUr=o("model.eval()"),pUr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),k9e=a("code"),_Ur=o("model.train()"),vUr=l(),F(j0.$$.fragment),fmo=l(),tf=a("h2"),G0=a("a"),S9e=a("span"),F(XP.$$.fragment),bUr=l(),R9e=a("span"),FUr=o("AutoModelForMaskedImageModeling"),mmo=l(),ir=a("div"),F(zP.$$.fragment),TUr=l(),af=a("p"),MUr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
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method.`),LUr=l(),QP=a("p"),yUr=o("This class cannot be instantiated directly using "),P9e=a("code"),xUr=o("__init__()"),$Ur=o(" (throws an error)."),kUr=l(),Ht=a("div"),F(WP.$$.fragment),SUr=l(),I9e=a("p"),RUr=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),PUr=l(),nf=a("p"),IUr=o(`Note:
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passed as an argument or loaded from `),q9e=a("code"),UUr=o("pretrained_model_name_or_path"),HUr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),j9e=a("code"),JUr=o("pretrained_model_name_or_path"),YUr=o(":"),ZUr=l(),Bn=a("ul"),V0=a("li"),G9e=a("strong"),KUr=o("deit"),eHr=o(" \u2014 "),Cne=a("a"),oHr=o("DeiTForMaskedImageModeling"),rHr=o(" (DeiT model)"),tHr=l(),X0=a("li"),O9e=a("strong"),aHr=o("swin"),nHr=o(" \u2014 "),wne=a("a"),sHr=o("SwinForMaskedImageModeling"),lHr=o(" (Swin Transformer model)"),iHr=l(),z0=a("li"),V9e=a("strong"),dHr=o("swinv2"),cHr=o(" \u2014 "),Ane=a("a"),fHr=o("Swinv2ForMaskedImageModeling"),mHr=o(" (Swin Transformer V2 model)"),gHr=l(),Q0=a("li"),X9e=a("strong"),hHr=o("vit"),uHr=o(" \u2014 "),Lne=a("a"),pHr=o("ViTForMaskedImageModeling"),_Hr=o(" (ViT model)"),vHr=l(),W0=a("p"),bHr=o("The model is set in evaluation mode by default using "),z9e=a("code"),FHr=o("model.eval()"),THr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Q9e=a("code"),MHr=o("model.train()"),EHr=l(),F(U0.$$.fragment),gmo=l(),sf=a("h2"),H0=a("a"),W9e=a("span"),F(HP.$$.fragment),CHr=l(),U9e=a("span"),wHr=o("AutoModelForObjectDetection"),hmo=l(),dr=a("div"),F(JP.$$.fragment),AHr=l(),lf=a("p"),LHr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),yne=a("a"),yHr=o("from_pretrained()"),xHr=o(" class method or the "),xne=a("a"),$Hr=o("from_config()"),kHr=o(` class
method.`),SHr=l(),YP=a("p"),RHr=o("This class cannot be instantiated directly using "),H9e=a("code"),PHr=o("__init__()"),IHr=o(" (throws an error)."),BHr=l(),Jt=a("div"),F(ZP.$$.fragment),NHr=l(),J9e=a("p"),DHr=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),qHr=l(),df=a("p"),jHr=o(`Note:
Loading a model from its configuration file does `),Y9e=a("strong"),GHr=o("not"),OHr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),$ne=a("a"),VHr=o("from_pretrained()"),XHr=o(" to load the model weights."),zHr=l(),F(J0.$$.fragment),QHr=l(),Lo=a("div"),F(KP.$$.fragment),WHr=l(),Z9e=a("p"),UHr=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),HHr=l(),Nn=a("p"),JHr=o("The model class to instantiate is selected based on the "),K9e=a("code"),YHr=o("model_type"),ZHr=o(` property of the config object (either
passed as an argument or loaded from `),exe=a("code"),KHr=o("pretrained_model_name_or_path"),eJr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),oxe=a("code"),oJr=o("pretrained_model_name_or_path"),rJr=o(":"),tJr=l(),Mt=a("ul"),Y0=a("li"),rxe=a("strong"),aJr=o("conditional_detr"),nJr=o(" \u2014 "),kne=a("a"),sJr=o("ConditionalDetrForObjectDetection"),lJr=o(" (Conditional DETR model)"),iJr=l(),Z0=a("li"),txe=a("strong"),dJr=o("deformable_detr"),cJr=o(" \u2014 "),Sne=a("a"),fJr=o("DeformableDetrForObjectDetection"),mJr=o(" (Deformable DETR model)"),gJr=l(),K0=a("li"),axe=a("strong"),hJr=o("detr"),uJr=o(" \u2014 "),Rne=a("a"),pJr=o("DetrForObjectDetection"),_Jr=o(" (DETR model)"),vJr=l(),ew=a("li"),nxe=a("strong"),bJr=o("table-transformer"),FJr=o(" \u2014 "),Pne=a("a"),TJr=o("TableTransformerForObjectDetection"),MJr=o(" (Table Transformer model)"),EJr=l(),ow=a("li"),sxe=a("strong"),CJr=o("yolos"),wJr=o(" \u2014 "),Ine=a("a"),AJr=o("YolosForObjectDetection"),LJr=o(" (YOLOS model)"),yJr=l(),rw=a("p"),xJr=o("The model is set in evaluation mode by default using "),lxe=a("code"),$Jr=o("model.eval()"),kJr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ixe=a("code"),SJr=o("model.train()"),RJr=l(),F(tw.$$.fragment),umo=l(),cf=a("h2"),aw=a("a"),dxe=a("span"),F(eI.$$.fragment),PJr=l(),cxe=a("span"),IJr=o("AutoModelForImageSegmentation"),pmo=l(),cr=a("div"),F(oI.$$.fragment),BJr=l(),ff=a("p"),NJr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),Bne=a("a"),DJr=o("from_pretrained()"),qJr=o(" class method or the "),Nne=a("a"),jJr=o("from_config()"),GJr=o(` class
method.`),OJr=l(),rI=a("p"),VJr=o("This class cannot be instantiated directly using "),fxe=a("code"),XJr=o("__init__()"),zJr=o(" (throws an error)."),QJr=l(),Yt=a("div"),F(tI.$$.fragment),WJr=l(),mxe=a("p"),UJr=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),HJr=l(),mf=a("p"),JJr=o(`Note:
Loading a model from its configuration file does `),gxe=a("strong"),YJr=o("not"),ZJr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Dne=a("a"),KJr=o("from_pretrained()"),eYr=o(" to load the model weights."),oYr=l(),F(nw.$$.fragment),rYr=l(),yo=a("div"),F(aI.$$.fragment),tYr=l(),hxe=a("p"),aYr=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),nYr=l(),Dn=a("p"),sYr=o("The model class to instantiate is selected based on the "),uxe=a("code"),lYr=o("model_type"),iYr=o(` property of the config object (either
passed as an argument or loaded from `),pxe=a("code"),dYr=o("pretrained_model_name_or_path"),cYr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_xe=a("code"),fYr=o("pretrained_model_name_or_path"),mYr=o(":"),gYr=l(),vxe=a("ul"),sw=a("li"),bxe=a("strong"),hYr=o("detr"),uYr=o(" \u2014 "),qne=a("a"),pYr=o("DetrForSegmentation"),_Yr=o(" (DETR model)"),vYr=l(),lw=a("p"),bYr=o("The model is set in evaluation mode by default using "),Fxe=a("code"),FYr=o("model.eval()"),TYr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Txe=a("code"),MYr=o("model.train()"),EYr=l(),F(iw.$$.fragment),_mo=l(),gf=a("h2"),dw=a("a"),Mxe=a("span"),F(nI.$$.fragment),CYr=l(),Exe=a("span"),wYr=o("AutoModelForSemanticSegmentation"),vmo=l(),fr=a("div"),F(sI.$$.fragment),AYr=l(),hf=a("p"),LYr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),jne=a("a"),yYr=o("from_pretrained()"),xYr=o(" class method or the "),Gne=a("a"),$Yr=o("from_config()"),kYr=o(` class
method.`),SYr=l(),lI=a("p"),RYr=o("This class cannot be instantiated directly using "),Cxe=a("code"),PYr=o("__init__()"),IYr=o(" (throws an error)."),BYr=l(),Zt=a("div"),F(iI.$$.fragment),NYr=l(),wxe=a("p"),DYr=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),qYr=l(),uf=a("p"),jYr=o(`Note:
Loading a model from its configuration file does `),Axe=a("strong"),GYr=o("not"),OYr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),One=a("a"),VYr=o("from_pretrained()"),XYr=o(" to load the model weights."),zYr=l(),F(cw.$$.fragment),QYr=l(),xo=a("div"),F(dI.$$.fragment),WYr=l(),Lxe=a("p"),UYr=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),HYr=l(),qn=a("p"),JYr=o("The model class to instantiate is selected based on the "),yxe=a("code"),YYr=o("model_type"),ZYr=o(` property of the config object (either
passed as an argument or loaded from `),xxe=a("code"),KYr=o("pretrained_model_name_or_path"),eZr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$xe=a("code"),oZr=o("pretrained_model_name_or_path"),rZr=o(":"),tZr=l(),mr=a("ul"),fw=a("li"),kxe=a("strong"),aZr=o("beit"),nZr=o(" \u2014 "),Vne=a("a"),sZr=o("BeitForSemanticSegmentation"),lZr=o(" (BEiT model)"),iZr=l(),mw=a("li"),Sxe=a("strong"),dZr=o("data2vec-vision"),cZr=o(" \u2014 "),Xne=a("a"),fZr=o("Data2VecVisionForSemanticSegmentation"),mZr=o(" (Data2VecVision model)"),gZr=l(),gw=a("li"),Rxe=a("strong"),hZr=o("dpt"),uZr=o(" \u2014 "),zne=a("a"),pZr=o("DPTForSemanticSegmentation"),_Zr=o(" (DPT model)"),vZr=l(),hw=a("li"),Pxe=a("strong"),bZr=o("mobilenet_v2"),FZr=o(" \u2014 "),Qne=a("a"),TZr=o("MobileNetV2ForSemanticSegmentation"),MZr=o(" (MobileNetV2 model)"),EZr=l(),uw=a("li"),Ixe=a("strong"),CZr=o("mobilevit"),wZr=o(" \u2014 "),Wne=a("a"),AZr=o("MobileViTForSemanticSegmentation"),LZr=o(" (MobileViT model)"),yZr=l(),pw=a("li"),Bxe=a("strong"),xZr=o("segformer"),$Zr=o(" \u2014 "),Une=a("a"),kZr=o("SegformerForSemanticSegmentation"),SZr=o(" (SegFormer model)"),RZr=l(),_w=a("p"),PZr=o("The model is set in evaluation mode by default using "),Nxe=a("code"),IZr=o("model.eval()"),BZr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Dxe=a("code"),NZr=o("model.train()"),DZr=l(),F(vw.$$.fragment),bmo=l(),pf=a("h2"),bw=a("a"),qxe=a("span"),F(cI.$$.fragment),qZr=l(),jxe=a("span"),jZr=o("AutoModelForInstanceSegmentation"),Fmo=l(),gr=a("div"),F(fI.$$.fragment),GZr=l(),_f=a("p"),OZr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),Hne=a("a"),VZr=o("from_pretrained()"),XZr=o(" class method or the "),Jne=a("a"),zZr=o("from_config()"),QZr=o(` class
method.`),WZr=l(),mI=a("p"),UZr=o("This class cannot be instantiated directly using "),Gxe=a("code"),HZr=o("__init__()"),JZr=o(" (throws an error)."),YZr=l(),Kt=a("div"),F(gI.$$.fragment),ZZr=l(),Oxe=a("p"),KZr=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),eKr=l(),vf=a("p"),oKr=o(`Note:
Loading a model from its configuration file does `),Vxe=a("strong"),rKr=o("not"),tKr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Yne=a("a"),aKr=o("from_pretrained()"),nKr=o(" to load the model weights."),sKr=l(),F(Fw.$$.fragment),lKr=l(),$o=a("div"),F(hI.$$.fragment),iKr=l(),Xxe=a("p"),dKr=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),cKr=l(),jn=a("p"),fKr=o("The model class to instantiate is selected based on the "),zxe=a("code"),mKr=o("model_type"),gKr=o(` property of the config object (either
passed as an argument or loaded from `),Qxe=a("code"),hKr=o("pretrained_model_name_or_path"),uKr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Wxe=a("code"),pKr=o("pretrained_model_name_or_path"),_Kr=o(":"),vKr=l(),Uxe=a("ul"),Tw=a("li"),Hxe=a("strong"),bKr=o("maskformer"),FKr=o(" \u2014 "),Zne=a("a"),TKr=o("MaskFormerForInstanceSegmentation"),MKr=o(" (MaskFormer model)"),EKr=l(),Mw=a("p"),CKr=o("The model is set in evaluation mode by default using "),Jxe=a("code"),wKr=o("model.eval()"),AKr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Yxe=a("code"),LKr=o("model.train()"),yKr=l(),F(Ew.$$.fragment),Tmo=l(),bf=a("h2"),Cw=a("a"),Zxe=a("span"),F(uI.$$.fragment),xKr=l(),Kxe=a("span"),$Kr=o("AutoModelForZeroShotObjectDetection"),Mmo=l(),hr=a("div"),F(pI.$$.fragment),kKr=l(),Ff=a("p"),SKr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),Kne=a("a"),RKr=o("from_pretrained()"),PKr=o(" class method or the "),ese=a("a"),IKr=o("from_config()"),BKr=o(` class
method.`),NKr=l(),_I=a("p"),DKr=o("This class cannot be instantiated directly using "),e$e=a("code"),qKr=o("__init__()"),jKr=o(" (throws an error)."),GKr=l(),ea=a("div"),F(vI.$$.fragment),OKr=l(),o$e=a("p"),VKr=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),XKr=l(),Tf=a("p"),zKr=o(`Note:
Loading a model from its configuration file does `),r$e=a("strong"),QKr=o("not"),WKr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),ose=a("a"),UKr=o("from_pretrained()"),HKr=o(" to load the model weights."),JKr=l(),F(ww.$$.fragment),YKr=l(),ko=a("div"),F(bI.$$.fragment),ZKr=l(),t$e=a("p"),KKr=o("Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),eet=l(),Gn=a("p"),oet=o("The model class to instantiate is selected based on the "),a$e=a("code"),ret=o("model_type"),tet=o(` property of the config object (either
passed as an argument or loaded from `),n$e=a("code"),aet=o("pretrained_model_name_or_path"),net=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),s$e=a("code"),set=o("pretrained_model_name_or_path"),iet=o(":"),det=l(),l$e=a("ul"),Aw=a("li"),i$e=a("strong"),cet=o("owlvit"),fet=o(" \u2014 "),rse=a("a"),met=o("OwlViTForObjectDetection"),get=o(" (OWL-ViT model)"),het=l(),Lw=a("p"),uet=o("The model is set in evaluation mode by default using "),d$e=a("code"),pet=o("model.eval()"),_et=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),c$e=a("code"),vet=o("model.train()"),bet=l(),F(yw.$$.fragment),Emo=l(),Mf=a("h2"),xw=a("a"),f$e=a("span"),F(FI.$$.fragment),Fet=l(),m$e=a("span"),Tet=o("TFAutoModel"),Cmo=l(),ur=a("div"),F(TI.$$.fragment),Met=l(),Ef=a("p"),Eet=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),tse=a("a"),Cet=o("from_pretrained()"),wet=o(" class method or the "),ase=a("a"),Aet=o("from_config()"),Let=o(` class
method.`),yet=l(),MI=a("p"),xet=o("This class cannot be instantiated directly using "),g$e=a("code"),$et=o("__init__()"),ket=o(" (throws an error)."),Set=l(),oa=a("div"),F(EI.$$.fragment),Ret=l(),h$e=a("p"),Pet=o("Instantiates one of the base model classes of the library from a configuration."),Iet=l(),Cf=a("p"),Bet=o(`Note:
Loading a model from its configuration file does `),u$e=a("strong"),Net=o("not"),Det=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),nse=a("a"),qet=o("from_pretrained()"),jet=o(" to load the model weights."),Get=l(),F($w.$$.fragment),Oet=l(),zr=a("div"),F(CI.$$.fragment),Vet=l(),p$e=a("p"),Xet=o("Instantiate one of the base model classes of the library from a pretrained model."),zet=l(),On=a("p"),Qet=o("The model class to instantiate is selected based on the "),_$e=a("code"),Wet=o("model_type"),Uet=o(` property of the config object (either
passed as an argument or loaded from `),v$e=a("code"),Het=o("pretrained_model_name_or_path"),Jet=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),b$e=a("code"),Yet=o("pretrained_model_name_or_path"),Zet=o(":"),Ket=l(),P=a("ul"),kw=a("li"),F$e=a("strong"),eot=o("albert"),oot=o(" \u2014 "),sse=a("a"),rot=o("TFAlbertModel"),tot=o(" (ALBERT model)"),aot=l(),Sw=a("li"),T$e=a("strong"),not=o("bart"),sot=o(" \u2014 "),lse=a("a"),lot=o("TFBartModel"),iot=o(" (BART model)"),dot=l(),Rw=a("li"),M$e=a("strong"),cot=o("bert"),fot=o(" \u2014 "),ise=a("a"),mot=o("TFBertModel"),got=o(" (BERT model)"),hot=l(),Pw=a("li"),E$e=a("strong"),uot=o("blenderbot"),pot=o(" \u2014 "),dse=a("a"),_ot=o("TFBlenderbotModel"),vot=o(" (Blenderbot model)"),bot=l(),Iw=a("li"),C$e=a("strong"),Fot=o("blenderbot-small"),Tot=o(" \u2014 "),cse=a("a"),Mot=o("TFBlenderbotSmallModel"),Eot=o(" (BlenderbotSmall model)"),Cot=l(),Bw=a("li"),w$e=a("strong"),wot=o("camembert"),Aot=o(" \u2014 "),fse=a("a"),Lot=o("TFCamembertModel"),yot=o(" (CamemBERT model)"),xot=l(),Nw=a("li"),A$e=a("strong"),$ot=o("clip"),kot=o(" \u2014 "),mse=a("a"),Sot=o("TFCLIPModel"),Rot=o(" (CLIP model)"),Pot=l(),Dw=a("li"),L$e=a("strong"),Iot=o("convbert"),Bot=o(" \u2014 "),gse=a("a"),Not=o("TFConvBertModel"),Dot=o(" (ConvBERT model)"),qot=l(),qw=a("li"),y$e=a("strong"),jot=o("convnext"),Got=o(" \u2014 "),hse=a("a"),Oot=o("TFConvNextModel"),Vot=o(" (ConvNeXT model)"),Xot=l(),jw=a("li"),x$e=a("strong"),zot=o("ctrl"),Qot=o(" \u2014 "),use=a("a"),Wot=o("TFCTRLModel"),Uot=o(" (CTRL model)"),Hot=l(),Gw=a("li"),$$e=a("strong"),Jot=o("cvt"),Yot=o(" \u2014 "),pse=a("a"),Zot=o("TFCvtModel"),Kot=o(" (CvT model)"),ert=l(),Ow=a("li"),k$e=a("strong"),ort=o("data2vec-vision"),rrt=o(" \u2014 "),_se=a("a"),trt=o("TFData2VecVisionModel"),art=o(" (Data2VecVision model)"),nrt=l(),Vw=a("li"),S$e=a("strong"),srt=o("deberta"),lrt=o(" \u2014 "),vse=a("a"),irt=o("TFDebertaModel"),drt=o(" (DeBERTa model)"),crt=l(),Xw=a("li"),R$e=a("strong"),frt=o("deberta-v2"),mrt=o(" \u2014 "),bse=a("a"),grt=o("TFDebertaV2Model"),hrt=o(" (DeBERTa-v2 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model\u2019s configuration. Use `),rie=a("a"),Cft=o("from_pretrained()"),wft=o(" to load the model weights."),Aft=l(),F(w6.$$.fragment),Lft=l(),Ur=a("div"),F(qI.$$.fragment),yft=l(),$Se=a("p"),xft=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),$ft=l(),zn=a("p"),kft=o("The model class to instantiate is selected based on the "),kSe=a("code"),Sft=o("model_type"),Rft=o(` property of the config object (either
passed as an argument or loaded from `),SSe=a("code"),Pft=o("pretrained_model_name_or_path"),Ift=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),RSe=a("code"),Bft=o("pretrained_model_name_or_path"),Nft=o(":"),Dft=l(),ke=a("ul"),A6=a("li"),PSe=a("strong"),qft=o("convnext"),jft=o(" \u2014 "),tie=a("a"),Gft=o("TFConvNextForImageClassification"),Oft=o(" (ConvNeXT model)"),Vft=l(),L6=a("li"),ISe=a("strong"),Xft=o("cvt"),zft=o(" \u2014 "),aie=a("a"),Qft=o("TFCvtForImageClassification"),Wft=o(" (CvT model)"),Uft=l(),y6=a("li"),BSe=a("strong"),Hft=o("data2vec-vision"),Jft=o(" \u2014 "),nie=a("a"),Yft=o("TFData2VecVisionForImageClassification"),Zft=o(" (Data2VecVision model)"),Kft=l(),Vl=a("li"),NSe=a("strong"),emt=o("deit"),omt=o(" \u2014 "),sie=a("a"),rmt=o("TFDeiTForImageClassification"),tmt=o(" or "),lie=a("a"),amt=o("TFDeiTForImageClassificationWithTeacher"),nmt=o(" (DeiT model)"),smt=l(),x6=a("li"),DSe=a("strong"),lmt=o("mobilevit"),imt=o(" \u2014 "),iie=a("a"),dmt=o("TFMobileViTForImageClassification"),cmt=o(" (MobileViT model)"),fmt=l(),$6=a("li"),qSe=a("strong"),mmt=o("regnet"),gmt=o(" \u2014 "),die=a("a"),hmt=o("TFRegNetForImageClassification"),umt=o(" (RegNet model)"),pmt=l(),k6=a("li"),jSe=a("strong"),_mt=o("resnet"),vmt=o(" \u2014 "),cie=a("a"),bmt=o("TFResNetForImageClassification"),Fmt=o(" (ResNet model)"),Tmt=l(),S6=a("li"),GSe=a("strong"),Mmt=o("segformer"),Emt=o(" \u2014 "),fie=a("a"),Cmt=o("TFSegformerForImageClassification"),wmt=o(" (SegFormer model)"),Amt=l(),R6=a("li"),OSe=a("strong"),Lmt=o("swin"),ymt=o(" \u2014 "),mie=a("a"),xmt=o("TFSwinForImageClassification"),$mt=o(" (Swin Transformer model)"),kmt=l(),P6=a("li"),VSe=a("strong"),Smt=o("vit"),Rmt=o(" \u2014 "),gie=a("a"),Pmt=o("TFViTForImageClassification"),Imt=o(" (ViT model)"),Bmt=l(),F(I6.$$.fragment),kmo=l(),Pf=a("h2"),B6=a("a"),XSe=a("span"),F(jI.$$.fragment),Nmt=l(),zSe=a("span"),Dmt=o("TFAutoModelForSemanticSegmentation"),Smo=l(),br=a("div"),F(GI.$$.fragment),qmt=l(),If=a("p"),jmt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),hie=a("a"),Gmt=o("from_pretrained()"),Omt=o(" class method or the "),uie=a("a"),Vmt=o("from_config()"),Xmt=o(` class
method.`),zmt=l(),OI=a("p"),Qmt=o("This class cannot be instantiated directly using "),QSe=a("code"),Wmt=o("__init__()"),Umt=o(" (throws an error)."),Hmt=l(),na=a("div"),F(VI.$$.fragment),Jmt=l(),WSe=a("p"),Ymt=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Zmt=l(),Bf=a("p"),Kmt=o(`Note:
Loading a model from its configuration file does `),USe=a("strong"),egt=o("not"),ogt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),pie=a("a"),rgt=o("from_pretrained()"),tgt=o(" to load the model weights."),agt=l(),F(N6.$$.fragment),ngt=l(),Hr=a("div"),F(XI.$$.fragment),sgt=l(),HSe=a("p"),lgt=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),igt=l(),Qn=a("p"),dgt=o("The model class to instantiate is selected based on the "),JSe=a("code"),cgt=o("model_type"),fgt=o(` property of the config object (either
passed as an argument or loaded from `),YSe=a("code"),mgt=o("pretrained_model_name_or_path"),ggt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZSe=a("code"),hgt=o("pretrained_model_name_or_path"),ugt=o(":"),pgt=l(),Nf=a("ul"),D6=a("li"),KSe=a("strong"),_gt=o("data2vec-vision"),vgt=o(" \u2014 "),_ie=a("a"),bgt=o("TFData2VecVisionForSemanticSegmentation"),Fgt=o(" (Data2VecVision model)"),Tgt=l(),q6=a("li"),eRe=a("strong"),Mgt=o("mobilevit"),Egt=o(" \u2014 "),vie=a("a"),Cgt=o("TFMobileViTForSemanticSegmentation"),wgt=o(" (MobileViT model)"),Agt=l(),j6=a("li"),oRe=a("strong"),Lgt=o("segformer"),ygt=o(" \u2014 "),bie=a("a"),xgt=o("TFSegformerForSemanticSegmentation"),$gt=o(" (SegFormer model)"),kgt=l(),F(G6.$$.fragment),Rmo=l(),Df=a("h2"),O6=a("a"),rRe=a("span"),F(zI.$$.fragment),Sgt=l(),tRe=a("span"),Rgt=o("TFAutoModelForMaskedLM"),Pmo=l(),Fr=a("div"),F(QI.$$.fragment),Pgt=l(),qf=a("p"),Igt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),Fie=a("a"),Bgt=o("from_pretrained()"),Ngt=o(" class method or the "),Tie=a("a"),Dgt=o("from_config()"),qgt=o(` class
method.`),jgt=l(),WI=a("p"),Ggt=o("This class cannot be instantiated directly using "),aRe=a("code"),Ogt=o("__init__()"),Vgt=o(" (throws an error)."),Xgt=l(),sa=a("div"),F(UI.$$.fragment),zgt=l(),nRe=a("p"),Qgt=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Wgt=l(),jf=a("p"),Ugt=o(`Note:
Loading a model from its configuration file does `),sRe=a("strong"),Hgt=o("not"),Jgt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Mie=a("a"),Ygt=o("from_pretrained()"),Zgt=o(" to load the model weights."),Kgt=l(),F(V6.$$.fragment),eht=l(),Jr=a("div"),F(HI.$$.fragment),oht=l(),lRe=a("p"),rht=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),tht=l(),Wn=a("p"),aht=o("The model class to instantiate is selected based on the "),iRe=a("code"),nht=o("model_type"),sht=o(` property of the config object (either
passed as an argument or loaded from `),dRe=a("code"),lht=o("pretrained_model_name_or_path"),iht=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cRe=a("code"),dht=o("pretrained_model_name_or_path"),cht=o(":"),fht=l(),ue=a("ul"),X6=a("li"),fRe=a("strong"),mht=o("albert"),ght=o(" \u2014 "),Eie=a("a"),hht=o("TFAlbertForMaskedLM"),uht=o(" (ALBERT model)"),pht=l(),z6=a("li"),mRe=a("strong"),_ht=o("bert"),vht=o(" \u2014 "),Cie=a("a"),bht=o("TFBertForMaskedLM"),Fht=o(" (BERT model)"),Tht=l(),Q6=a("li"),gRe=a("strong"),Mht=o("camembert"),Eht=o(" \u2014 "),wie=a("a"),Cht=o("TFCamembertForMaskedLM"),wht=o(" (CamemBERT model)"),Aht=l(),W6=a("li"),hRe=a("strong"),Lht=o("convbert"),yht=o(" \u2014 "),Aie=a("a"),xht=o("TFConvBertForMaskedLM"),$ht=o(" (ConvBERT model)"),kht=l(),U6=a("li"),uRe=a("strong"),Sht=o("deberta"),Rht=o(" \u2014 "),Lie=a("a"),Pht=o("TFDebertaForMaskedLM"),Iht=o(" (DeBERTa model)"),Bht=l(),H6=a("li"),pRe=a("strong"),Nht=o("deberta-v2"),Dht=o(" \u2014 "),yie=a("a"),qht=o("TFDebertaV2ForMaskedLM"),jht=o(" (DeBERTa-v2 model)"),Ght=l(),J6=a("li"),_Re=a("strong"),Oht=o("distilbert"),Vht=o(" \u2014 "),xie=a("a"),Xht=o("TFDistilBertForMaskedLM"),zht=o(" (DistilBERT model)"),Qht=l(),Y6=a("li"),vRe=a("strong"),Wht=o("electra"),Uht=o(" \u2014 "),$ie=a("a"),Hht=o("TFElectraForMaskedLM"),Jht=o(" (ELECTRA model)"),Yht=l(),Z6=a("li"),bRe=a("strong"),Zht=o("esm"),Kht=o(" \u2014 "),kie=a("a"),eut=o("TFEsmForMaskedLM"),out=o(" (ESM model)"),rut=l(),K6=a("li"),FRe=a("strong"),tut=o("flaubert"),aut=o(" \u2014 "),Sie=a("a"),nut=o("TFFlaubertWithLMHeadModel"),sut=o(" (FlauBERT model)"),lut=l(),e8=a("li"),TRe=a("strong"),iut=o("funnel"),dut=o(" \u2014 "),Rie=a("a"),cut=o("TFFunnelForMaskedLM"),fut=o(" (Funnel Transformer model)"),mut=l(),o8=a("li"),MRe=a("strong"),gut=o("layoutlm"),hut=o(" \u2014 "),Pie=a("a"),uut=o("TFLayoutLMForMaskedLM"),put=o(" (LayoutLM model)"),_ut=l(),r8=a("li"),ERe=a("strong"),vut=o("longformer"),but=o(" \u2014 "),Iie=a("a"),Fut=o("TFLongformerForMaskedLM"),Tut=o(" (Longformer model)"),Mut=l(),t8=a("li"),CRe=a("strong"),Eut=o("mobilebert"),Cut=o(" \u2014 "),Bie=a("a"),wut=o("TFMobileBertForMaskedLM"),Aut=o(" (MobileBERT model)"),Lut=l(),a8=a("li"),wRe=a("strong"),yut=o("mpnet"),xut=o(" \u2014 "),Nie=a("a"),$ut=o("TFMPNetForMaskedLM"),kut=o(" (MPNet model)"),Sut=l(),n8=a("li"),ARe=a("strong"),Rut=o("rembert"),Put=o(" \u2014 "),Die=a("a"),Iut=o("TFRemBertForMaskedLM"),But=o(" (RemBERT model)"),Nut=l(),s8=a("li"),LRe=a("strong"),Dut=o("roberta"),qut=o(" \u2014 "),qie=a("a"),jut=o("TFRobertaForMaskedLM"),Gut=o(" (RoBERTa model)"),Out=l(),l8=a("li"),yRe=a("strong"),Vut=o("roformer"),Xut=o(" \u2014 "),jie=a("a"),zut=o("TFRoFormerForMaskedLM"),Qut=o(" (RoFormer model)"),Wut=l(),i8=a("li"),xRe=a("strong"),Uut=o("tapas"),Hut=o(" \u2014 "),Gie=a("a"),Jut=o("TFTapasForMaskedLM"),Yut=o(" (TAPAS model)"),Zut=l(),d8=a("li"),$Re=a("strong"),Kut=o("xlm"),ept=o(" \u2014 "),Oie=a("a"),opt=o("TFXLMWithLMHeadModel"),rpt=o(" (XLM model)"),tpt=l(),c8=a("li"),kRe=a("strong"),apt=o("xlm-roberta"),npt=o(" \u2014 "),Vie=a("a"),spt=o("TFXLMRobertaForMaskedLM"),lpt=o(" (XLM-RoBERTa model)"),ipt=l(),F(f8.$$.fragment),Imo=l(),Gf=a("h2"),m8=a("a"),SRe=a("span"),F(JI.$$.fragment),dpt=l(),RRe=a("span"),cpt=o("TFAutoModelForSeq2SeqLM"),Bmo=l(),Tr=a("div"),F(YI.$$.fragment),fpt=l(),Of=a("p"),mpt=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 `),Xie=a("a"),gpt=o("from_pretrained()"),hpt=o(" class method or the "),zie=a("a"),upt=o("from_config()"),ppt=o(` class
method.`),_pt=l(),ZI=a("p"),vpt=o("This class cannot be instantiated directly using "),PRe=a("code"),bpt=o("__init__()"),Fpt=o(" (throws an error)."),Tpt=l(),la=a("div"),F(KI.$$.fragment),Mpt=l(),IRe=a("p"),Ept=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Cpt=l(),Vf=a("p"),wpt=o(`Note:
Loading a model from its configuration file does `),BRe=a("strong"),Apt=o("not"),Lpt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Qie=a("a"),ypt=o("from_pretrained()"),xpt=o(" to load the model weights."),$pt=l(),F(g8.$$.fragment),kpt=l(),Yr=a("div"),F(eB.$$.fragment),Spt=l(),NRe=a("p"),Rpt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Ppt=l(),Un=a("p"),Ipt=o("The model class to instantiate is selected based on the "),DRe=a("code"),Bpt=o("model_type"),Npt=o(` property of the config object (either
passed as an argument or loaded from `),qRe=a("code"),Dpt=o("pretrained_model_name_or_path"),qpt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jRe=a("code"),jpt=o("pretrained_model_name_or_path"),Gpt=o(":"),Opt=l(),Se=a("ul"),h8=a("li"),GRe=a("strong"),Vpt=o("bart"),Xpt=o(" \u2014 "),Wie=a("a"),zpt=o("TFBartForConditionalGeneration"),Qpt=o(" (BART model)"),Wpt=l(),u8=a("li"),ORe=a("strong"),Upt=o("blenderbot"),Hpt=o(" \u2014 "),Uie=a("a"),Jpt=o("TFBlenderbotForConditionalGeneration"),Ypt=o(" (Blenderbot model)"),Zpt=l(),p8=a("li"),VRe=a("strong"),Kpt=o("blenderbot-small"),e_t=o(" \u2014 "),Hie=a("a"),o_t=o("TFBlenderbotSmallForConditionalGeneration"),r_t=o(" (BlenderbotSmall model)"),t_t=l(),_8=a("li"),XRe=a("strong"),a_t=o("encoder-decoder"),n_t=o(" \u2014 "),Jie=a("a"),s_t=o("TFEncoderDecoderModel"),l_t=o(" (Encoder decoder model)"),i_t=l(),v8=a("li"),zRe=a("strong"),d_t=o("led"),c_t=o(" \u2014 "),Yie=a("a"),f_t=o("TFLEDForConditionalGeneration"),m_t=o(" (LED model)"),g_t=l(),b8=a("li"),QRe=a("strong"),h_t=o("marian"),u_t=o(" \u2014 "),Zie=a("a"),p_t=o("TFMarianMTModel"),__t=o(" (Marian model)"),v_t=l(),F8=a("li"),WRe=a("strong"),b_t=o("mbart"),F_t=o(" \u2014 "),Kie=a("a"),T_t=o("TFMBartForConditionalGeneration"),M_t=o(" (mBART model)"),E_t=l(),T8=a("li"),URe=a("strong"),C_t=o("mt5"),w_t=o(" \u2014 "),ede=a("a"),A_t=o("TFMT5ForConditionalGeneration"),L_t=o(" (MT5 model)"),y_t=l(),M8=a("li"),HRe=a("strong"),x_t=o("pegasus"),$_t=o(" \u2014 "),ode=a("a"),k_t=o("TFPegasusForConditionalGeneration"),S_t=o(" (Pegasus model)"),R_t=l(),E8=a("li"),JRe=a("strong"),P_t=o("t5"),I_t=o(" \u2014 "),rde=a("a"),B_t=o("TFT5ForConditionalGeneration"),N_t=o(" (T5 model)"),D_t=l(),F(C8.$$.fragment),Nmo=l(),Xf=a("h2"),w8=a("a"),YRe=a("span"),F(oB.$$.fragment),q_t=l(),ZRe=a("span"),j_t=o("TFAutoModelForSequenceClassification"),Dmo=l(),Mr=a("div"),F(rB.$$.fragment),G_t=l(),zf=a("p"),O_t=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
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method.`),W_t=l(),tB=a("p"),U_t=o("This class cannot be instantiated directly using "),KRe=a("code"),H_t=o("__init__()"),J_t=o(" (throws an error)."),Y_t=l(),ia=a("div"),F(aB.$$.fragment),Z_t=l(),ePe=a("p"),K_t=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),e1t=l(),Qf=a("p"),o1t=o(`Note:
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falling back to using pattern matching on `),MNe=a("code"),GAt=o("pretrained_model_name_or_path"),OAt=o(":"),VAt=l(),ne=a("ul"),GL=a("li"),ENe=a("strong"),XAt=o("albert"),zAt=o(" \u2014 "),Ffe=a("a"),QAt=o("FlaxAlbertModel"),WAt=o(" (ALBERT model)"),UAt=l(),OL=a("li"),CNe=a("strong"),HAt=o("bart"),JAt=o(" \u2014 "),Tfe=a("a"),YAt=o("FlaxBartModel"),ZAt=o(" (BART model)"),KAt=l(),VL=a("li"),wNe=a("strong"),e6t=o("beit"),o6t=o(" \u2014 "),Mfe=a("a"),r6t=o("FlaxBeitModel"),t6t=o(" (BEiT model)"),a6t=l(),XL=a("li"),ANe=a("strong"),n6t=o("bert"),s6t=o(" \u2014 "),Efe=a("a"),l6t=o("FlaxBertModel"),i6t=o(" (BERT model)"),d6t=l(),zL=a("li"),LNe=a("strong"),c6t=o("big_bird"),f6t=o(" \u2014 "),Cfe=a("a"),m6t=o("FlaxBigBirdModel"),g6t=o(" (BigBird model)"),h6t=l(),QL=a("li"),yNe=a("strong"),u6t=o("blenderbot"),p6t=o(" \u2014 "),wfe=a("a"),_6t=o("FlaxBlenderbotModel"),v6t=o(" (Blenderbot model)"),b6t=l(),WL=a("li"),xNe=a("strong"),F6t=o("blenderbot-small"),T6t=o(" \u2014 "),Afe=a("a"),M6t=o("FlaxBlenderbotSmallModel"),E6t=o(" (BlenderbotSmall model)"),C6t=l(),UL=a("li"),$Ne=a("strong"),w6t=o("clip"),A6t=o(" \u2014 "),Lfe=a("a"),L6t=o("FlaxCLIPModel"),y6t=o(" (CLIP model)"),x6t=l(),HL=a("li"),kNe=a("strong"),$6t=o("distilbert"),k6t=o(" \u2014 "),yfe=a("a"),S6t=o("FlaxDistilBertModel"),R6t=o(" (DistilBERT model)"),P6t=l(),JL=a("li"),SNe=a("strong"),I6t=o("electra"),B6t=o(" \u2014 "),xfe=a("a"),N6t=o("FlaxElectraModel"),D6t=o(" (ELECTRA model)"),q6t=l(),YL=a("li"),RNe=a("strong"),j6t=o("gpt2"),G6t=o(" \u2014 "),$fe=a("a"),O6t=o("FlaxGPT2Model"),V6t=o(" (OpenAI GPT-2 model)"),X6t=l(),ZL=a("li"),PNe=a("strong"),z6t=o("gpt_neo"),Q6t=o(" \u2014 "),kfe=a("a"),W6t=o("FlaxGPTNeoModel"),U6t=o(" (GPT Neo model)"),H6t=l(),KL=a("li"),INe=a("strong"),J6t=o("gptj"),Y6t=o(" \u2014 "),Sfe=a("a"),Z6t=o("FlaxGPTJModel"),K6t=o(" (GPT-J model)"),e8t=l(),ey=a("li"),BNe=a("strong"),o8t=o("longt5"),r8t=o(" \u2014 "),Rfe=a("a"),t8t=o("FlaxLongT5Model"),a8t=o(" (LongT5 model)"),n8t=l(),oy=a("li"),NNe=a("strong"),s8t=o("marian"),l8t=o(" \u2014 "),Pfe=a("a"),i8t=o("FlaxMarianModel"),d8t=o(" (Marian model)"),c8t=l(),ry=a("li"),DNe=a("strong"),f8t=o("mbart"),m8t=o(" \u2014 "),Ife=a("a"),g8t=o("FlaxMBartModel"),h8t=o(" (mBART model)"),u8t=l(),ty=a("li"),qNe=a("strong"),p8t=o("mt5"),_8t=o(" \u2014 "),Bfe=a("a"),v8t=o("FlaxMT5Model"),b8t=o(" (MT5 model)"),F8t=l(),ay=a("li"),jNe=a("strong"),T8t=o("opt"),M8t=o(" \u2014 "),Nfe=a("a"),E8t=o("FlaxOPTModel"),C8t=o(" (OPT model)"),w8t=l(),ny=a("li"),GNe=a("strong"),A8t=o("pegasus"),L8t=o(" \u2014 "),Dfe=a("a"),y8t=o("FlaxPegasusModel"),x8t=o(" (Pegasus model)"),$8t=l(),sy=a("li"),ONe=a("strong"),k8t=o("roberta"),S8t=o(" \u2014 "),qfe=a("a"),R8t=o("FlaxRobertaModel"),P8t=o(" (RoBERTa model)"),I8t=l(),ly=a("li"),VNe=a("strong"),B8t=o("roformer"),N8t=o(" \u2014 "),jfe=a("a"),D8t=o("FlaxRoFormerModel"),q8t=o(" (RoFormer model)"),j8t=l(),iy=a("li"),XNe=a("strong"),G8t=o("t5"),O8t=o(" \u2014 "),Gfe=a("a"),V8t=o("FlaxT5Model"),X8t=o(" (T5 model)"),z8t=l(),dy=a("li"),zNe=a("strong"),Q8t=o("vision-text-dual-encoder"),W8t=o(" \u2014 "),Ofe=a("a"),U8t=o("FlaxVisionTextDualEncoderModel"),H8t=o(" (VisionTextDualEncoder model)"),J8t=l(),cy=a("li"),QNe=a("strong"),Y8t=o("vit"),Z8t=o(" \u2014 "),Vfe=a("a"),K8t=o("FlaxViTModel"),e7t=o(" (ViT model)"),o7t=l(),fy=a("li"),WNe=a("strong"),r7t=o("wav2vec2"),t7t=o(" \u2014 "),Xfe=a("a"),a7t=o("FlaxWav2Vec2Model"),n7t=o(" (Wav2Vec2 model)"),s7t=l(),my=a("li"),UNe=a("strong"),l7t=o("xglm"),i7t=o(" \u2014 "),zfe=a("a"),d7t=o("FlaxXGLMModel"),c7t=o(" (XGLM model)"),f7t=l(),gy=a("li"),HNe=a("strong"),m7t=o("xlm-roberta"),g7t=o(" \u2014 "),Qfe=a("a"),h7t=o("FlaxXLMRobertaModel"),u7t=o(" (XLM-RoBERTa model)"),p7t=l(),F(hy.$$.fragment),tgo=l(),Fm=a("h2"),uy=a("a"),JNe=a("span"),F(KB.$$.fragment),_7t=l(),YNe=a("span"),v7t=o("FlaxAutoModelForCausalLM"),ago=l(),Sr=a("div"),F(eN.$$.fragment),b7t=l(),Tm=a("p"),F7t=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.`),w7t=l(),oN=a("p"),A7t=o("This class cannot be instantiated directly using "),ZNe=a("code"),L7t=o("__init__()"),y7t=o(" (throws an error)."),x7t=l(),va=a("div"),F(rN.$$.fragment),$7t=l(),KNe=a("p"),k7t=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),S7t=l(),Mm=a("p"),R7t=o(`Note:
Loading a model from its configuration file does `),eDe=a("strong"),P7t=o("not"),I7t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Hfe=a("a"),B7t=o("from_pretrained()"),N7t=o(" to load the model weights."),D7t=l(),F(py.$$.fragment),q7t=l(),it=a("div"),F(tN.$$.fragment),j7t=l(),oDe=a("p"),G7t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),O7t=l(),ns=a("p"),V7t=o("The model class to instantiate is selected based on the "),rDe=a("code"),X7t=o("model_type"),z7t=o(` property of the config object (either
passed as an argument or loaded from `),tDe=a("code"),Q7t=o("pretrained_model_name_or_path"),W7t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aDe=a("code"),U7t=o("pretrained_model_name_or_path"),H7t=o(":"),J7t=l(),Re=a("ul"),_y=a("li"),nDe=a("strong"),Y7t=o("bart"),Z7t=o(" \u2014 "),Jfe=a("a"),K7t=o("FlaxBartForCausalLM"),eLt=o(" (BART model)"),oLt=l(),vy=a("li"),sDe=a("strong"),rLt=o("bert"),tLt=o(" \u2014 "),Yfe=a("a"),aLt=o("FlaxBertForCausalLM"),nLt=o(" (BERT model)"),sLt=l(),by=a("li"),lDe=a("strong"),lLt=o("big_bird"),iLt=o(" \u2014 "),Zfe=a("a"),dLt=o("FlaxBigBirdForCausalLM"),cLt=o(" (BigBird model)"),fLt=l(),Fy=a("li"),iDe=a("strong"),mLt=o("electra"),gLt=o(" \u2014 "),Kfe=a("a"),hLt=o("FlaxElectraForCausalLM"),uLt=o(" (ELECTRA model)"),pLt=l(),Ty=a("li"),dDe=a("strong"),_Lt=o("gpt2"),vLt=o(" \u2014 "),eme=a("a"),bLt=o("FlaxGPT2LMHeadModel"),FLt=o(" (OpenAI GPT-2 model)"),TLt=l(),My=a("li"),cDe=a("strong"),MLt=o("gpt_neo"),ELt=o(" \u2014 "),ome=a("a"),CLt=o("FlaxGPTNeoForCausalLM"),wLt=o(" (GPT Neo model)"),ALt=l(),Ey=a("li"),fDe=a("strong"),LLt=o("gptj"),yLt=o(" \u2014 "),rme=a("a"),xLt=o("FlaxGPTJForCausalLM"),$Lt=o(" (GPT-J model)"),kLt=l(),Cy=a("li"),mDe=a("strong"),SLt=o("opt"),RLt=o(" \u2014 "),tme=a("a"),PLt=o("FlaxOPTForCausalLM"),ILt=o(" (OPT model)"),BLt=l(),wy=a("li"),gDe=a("strong"),NLt=o("roberta"),DLt=o(" \u2014 "),ame=a("a"),qLt=o("FlaxRobertaForCausalLM"),jLt=o(" (RoBERTa model)"),GLt=l(),Ay=a("li"),hDe=a("strong"),OLt=o("xglm"),VLt=o(" \u2014 "),nme=a("a"),XLt=o("FlaxXGLMForCausalLM"),zLt=o(" (XGLM model)"),QLt=l(),F(Ly.$$.fragment),ngo=l(),Em=a("h2"),yy=a("a"),uDe=a("span"),F(aN.$$.fragment),WLt=l(),pDe=a("span"),ULt=o("FlaxAutoModelForPreTraining"),sgo=l(),Rr=a("div"),F(nN.$$.fragment),HLt=l(),Cm=a("p"),JLt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),sme=a("a"),YLt=o("from_pretrained()"),ZLt=o(" class method or the "),lme=a("a"),KLt=o("from_config()"),eyt=o(` class
method.`),oyt=l(),sN=a("p"),ryt=o("This class cannot be instantiated directly using "),_De=a("code"),tyt=o("__init__()"),ayt=o(" (throws an error)."),nyt=l(),ba=a("div"),F(lN.$$.fragment),syt=l(),vDe=a("p"),lyt=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),iyt=l(),wm=a("p"),dyt=o(`Note:
Loading a model from its configuration file does `),bDe=a("strong"),cyt=o("not"),fyt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),ime=a("a"),myt=o("from_pretrained()"),gyt=o(" to load the model weights."),hyt=l(),F(xy.$$.fragment),uyt=l(),dt=a("div"),F(iN.$$.fragment),pyt=l(),FDe=a("p"),_yt=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),vyt=l(),ss=a("p"),byt=o("The model class to instantiate is selected based on the "),TDe=a("code"),Fyt=o("model_type"),Tyt=o(` property of the config object (either
passed as an argument or loaded from `),MDe=a("code"),Myt=o("pretrained_model_name_or_path"),Eyt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EDe=a("code"),Cyt=o("pretrained_model_name_or_path"),wyt=o(":"),Ayt=l(),we=a("ul"),$y=a("li"),CDe=a("strong"),Lyt=o("albert"),yyt=o(" \u2014 "),dme=a("a"),xyt=o("FlaxAlbertForPreTraining"),$yt=o(" (ALBERT model)"),kyt=l(),ky=a("li"),wDe=a("strong"),Syt=o("bart"),Ryt=o(" \u2014 "),cme=a("a"),Pyt=o("FlaxBartForConditionalGeneration"),Iyt=o(" (BART model)"),Byt=l(),Sy=a("li"),ADe=a("strong"),Nyt=o("bert"),Dyt=o(" \u2014 "),fme=a("a"),qyt=o("FlaxBertForPreTraining"),jyt=o(" (BERT model)"),Gyt=l(),Ry=a("li"),LDe=a("strong"),Oyt=o("big_bird"),Vyt=o(" \u2014 "),mme=a("a"),Xyt=o("FlaxBigBirdForPreTraining"),zyt=o(" (BigBird model)"),Qyt=l(),Py=a("li"),yDe=a("strong"),Wyt=o("electra"),Uyt=o(" \u2014 "),gme=a("a"),Hyt=o("FlaxElectraForPreTraining"),Jyt=o(" (ELECTRA model)"),Yyt=l(),Iy=a("li"),xDe=a("strong"),Zyt=o("longt5"),Kyt=o(" \u2014 "),hme=a("a"),e9t=o("FlaxLongT5ForConditionalGeneration"),o9t=o(" (LongT5 model)"),r9t=l(),By=a("li"),$De=a("strong"),t9t=o("mbart"),a9t=o(" \u2014 "),ume=a("a"),n9t=o("FlaxMBartForConditionalGeneration"),s9t=o(" (mBART model)"),l9t=l(),Ny=a("li"),kDe=a("strong"),i9t=o("mt5"),d9t=o(" \u2014 "),pme=a("a"),c9t=o("FlaxMT5ForConditionalGeneration"),f9t=o(" (MT5 model)"),m9t=l(),Dy=a("li"),SDe=a("strong"),g9t=o("roberta"),h9t=o(" \u2014 "),_me=a("a"),u9t=o("FlaxRobertaForMaskedLM"),p9t=o(" (RoBERTa model)"),_9t=l(),qy=a("li"),RDe=a("strong"),v9t=o("roformer"),b9t=o(" \u2014 "),vme=a("a"),F9t=o("FlaxRoFormerForMaskedLM"),T9t=o(" (RoFormer model)"),M9t=l(),jy=a("li"),PDe=a("strong"),E9t=o("t5"),C9t=o(" \u2014 "),bme=a("a"),w9t=o("FlaxT5ForConditionalGeneration"),A9t=o(" (T5 model)"),L9t=l(),Gy=a("li"),IDe=a("strong"),y9t=o("wav2vec2"),x9t=o(" \u2014 "),Fme=a("a"),$9t=o("FlaxWav2Vec2ForPreTraining"),k9t=o(" (Wav2Vec2 model)"),S9t=l(),Oy=a("li"),BDe=a("strong"),R9t=o("xlm-roberta"),P9t=o(" \u2014 "),Tme=a("a"),I9t=o("FlaxXLMRobertaForMaskedLM"),B9t=o(" (XLM-RoBERTa model)"),N9t=l(),F(Vy.$$.fragment),lgo=l(),Am=a("h2"),Xy=a("a"),NDe=a("span"),F(dN.$$.fragment),D9t=l(),DDe=a("span"),q9t=o("FlaxAutoModelForMaskedLM"),igo=l(),Pr=a("div"),F(cN.$$.fragment),j9t=l(),Lm=a("p"),G9t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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method.`),Q9t=l(),fN=a("p"),W9t=o("This class cannot be instantiated directly using "),qDe=a("code"),U9t=o("__init__()"),H9t=o(" (throws an error)."),J9t=l(),Fa=a("div"),F(mN.$$.fragment),Y9t=l(),jDe=a("p"),Z9t=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),K9t=l(),ym=a("p"),ext=o(`Note:
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model\u2019s configuration. Use `),Cme=a("a"),txt=o("from_pretrained()"),axt=o(" to load the model weights."),nxt=l(),F(zy.$$.fragment),sxt=l(),ct=a("div"),F(gN.$$.fragment),lxt=l(),ODe=a("p"),ixt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),dxt=l(),ls=a("p"),cxt=o("The model class to instantiate is selected based on the "),VDe=a("code"),fxt=o("model_type"),mxt=o(` property of the config object (either
passed as an argument or loaded from `),XDe=a("code"),gxt=o("pretrained_model_name_or_path"),hxt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zDe=a("code"),uxt=o("pretrained_model_name_or_path"),pxt=o(":"),_xt=l(),Pe=a("ul"),Qy=a("li"),QDe=a("strong"),vxt=o("albert"),bxt=o(" \u2014 "),wme=a("a"),Fxt=o("FlaxAlbertForMaskedLM"),Txt=o(" (ALBERT model)"),Mxt=l(),Wy=a("li"),WDe=a("strong"),Ext=o("bart"),Cxt=o(" \u2014 "),Ame=a("a"),wxt=o("FlaxBartForConditionalGeneration"),Axt=o(" (BART model)"),Lxt=l(),Uy=a("li"),UDe=a("strong"),yxt=o("bert"),xxt=o(" \u2014 "),Lme=a("a"),$xt=o("FlaxBertForMaskedLM"),kxt=o(" (BERT model)"),Sxt=l(),Hy=a("li"),HDe=a("strong"),Rxt=o("big_bird"),Pxt=o(" \u2014 "),yme=a("a"),Ixt=o("FlaxBigBirdForMaskedLM"),Bxt=o(" (BigBird model)"),Nxt=l(),Jy=a("li"),JDe=a("strong"),Dxt=o("distilbert"),qxt=o(" \u2014 "),xme=a("a"),jxt=o("FlaxDistilBertForMaskedLM"),Gxt=o(" (DistilBERT model)"),Oxt=l(),Yy=a("li"),YDe=a("strong"),Vxt=o("electra"),Xxt=o(" \u2014 "),$me=a("a"),zxt=o("FlaxElectraForMaskedLM"),Qxt=o(" (ELECTRA model)"),Wxt=l(),Zy=a("li"),ZDe=a("strong"),Uxt=o("mbart"),Hxt=o(" \u2014 "),kme=a("a"),Jxt=o("FlaxMBartForConditionalGeneration"),Yxt=o(" (mBART model)"),Zxt=l(),Ky=a("li"),KDe=a("strong"),Kxt=o("roberta"),e$t=o(" \u2014 "),Sme=a("a"),o$t=o("FlaxRobertaForMaskedLM"),r$t=o(" (RoBERTa model)"),t$t=l(),e9=a("li"),eqe=a("strong"),a$t=o("roformer"),n$t=o(" \u2014 "),Rme=a("a"),s$t=o("FlaxRoFormerForMaskedLM"),l$t=o(" (RoFormer model)"),i$t=l(),o9=a("li"),oqe=a("strong"),d$t=o("xlm-roberta"),c$t=o(" \u2014 "),Pme=a("a"),f$t=o("FlaxXLMRobertaForMaskedLM"),m$t=o(" (XLM-RoBERTa model)"),g$t=l(),F(r9.$$.fragment),dgo=l(),xm=a("h2"),t9=a("a"),rqe=a("span"),F(hN.$$.fragment),h$t=l(),tqe=a("span"),u$t=o("FlaxAutoModelForSeq2SeqLM"),cgo=l(),Ir=a("div"),F(uN.$$.fragment),p$t=l(),$m=a("p"),_$t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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model\u2019s configuration. Use `),Nme=a("a"),R$t=o("from_pretrained()"),P$t=o(" to load the model weights."),I$t=l(),F(a9.$$.fragment),B$t=l(),ft=a("div"),F(vN.$$.fragment),N$t=l(),lqe=a("p"),D$t=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),q$t=l(),is=a("p"),j$t=o("The model class to instantiate is selected based on the "),iqe=a("code"),G$t=o("model_type"),O$t=o(` property of the config object (either
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passed as an argument or loaded from `),hGe=a("code"),jqt=o("pretrained_model_name_or_path"),Gqt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),uGe=a("code"),Oqt=o("pretrained_model_name_or_path"),Vqt=o(":"),Xqt=l(),pGe=a("ul"),Tx=a("li"),_Ge=a("strong"),zqt=o("vision-encoder-decoder"),Qqt=o(" \u2014 "),ehe=a("a"),Wqt=o("FlaxVisionEncoderDecoderModel"),Uqt=o(" (Vision Encoder decoder model)"),Hqt=l(),F(Mx.$$.fragment),this.h()},l(f){const _=bqa('[data-svelte="svelte-1phssyn"]',document.head);g=n(_,"META",{name:!0,content:!0}),_.forEach(t),b=i(f),u=n(f,"H1",{class:!0});var oD=s(u);m=n(oD,"A",{id:!0,class:!0,href:!0});var vGe=s(m);p=n(vGe,"SPAN",{});var bGe=s(p);T(c.$$.fragment,bGe),bGe.forEach(t),vGe.forEach(t),h=i(oD),Je=n(oD,"SPAN",{});var FGe=s(Je);yd=r(FGe,"Auto Classes"),FGe.forEach(t),oD.forEach(t),rg=i(f),wt=n(f,"P",{});var rD=s(wt);xd=r(rD,`In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
are supplying to the `),$d=n(rD,"CODE",{});var TGe=s($d);nS=r(TGe,"from_pretrained()"),TGe.forEach(t),tg=r(rD,` method. AutoClasses are here to do this job for you so that you
automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.`),rD.forEach(t),We=i(f),Ze=n(f,"P",{});var ps=s(Ze);kd=r(ps,"Instantiating one of "),_s=n(ps,"A",{href:!0});var MGe=s(_s);sS=r(MGe,"AutoConfig"),MGe.forEach(t),vs=r(ps,", "),bs=n(ps,"A",{href:!0});var EGe=s(bs);lS=r(EGe,"AutoModel"),EGe.forEach(t),Sd=r(ps,`, and
`),Fs=n(ps,"A",{href:!0});var CGe=s(Fs);iS=r(CGe,"AutoTokenizer"),CGe.forEach(t),Rd=r(ps," will directly create a class of the relevant architecture. For instance"),ps.forEach(t),ag=i(f),T(ln.$$.fragment,f),Ke=i(f),ye=n(f,"P",{});var tD=s(ye);kq=r(tD,"will create a model that is an instance of "),Pd=n(tD,"A",{href:!0});var wGe=s(Pd);Sq=r(wGe,"BertModel"),wGe.forEach(t),Rq=r(tD,"."),tD.forEach(t),Po=i(f),dn=n(f,"P",{});var aD=s(dn);Pq=r(aD,"There is one class of "),ng=n(aD,"CODE",{});var AGe=s(ng);Iq=r(AGe,"AutoModel"),AGe.forEach(t),Zuo=r(aD," for each task, and for each backend (PyTorch, TensorFlow, or Flax)."),aD.forEach(t),sfo=i(f),Id=n(f,"H2",{class:!0});var nD=s(Id);sg=n(nD,"A",{id:!0,class:!0,href:!0});var LGe=s(sg);dpe=n(LGe,"SPAN",{});var yGe=s(dpe);T(dS.$$.fragment,yGe),yGe.forEach(t),LGe.forEach(t),Kuo=i(nD),cpe=n(nD,"SPAN",{});var xGe=s(cpe);epo=r(xGe,"Extending the Auto Classes"),xGe.forEach(t),nD.forEach(t),lfo=i(f),Ts=n(f,"P",{});var Km=s(Ts);opo=r(Km,`Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model `),fpe=n(Km,"CODE",{});var $Ge=s(fpe);rpo=r($Ge,"NewModel"),$Ge.forEach(t),tpo=r(Km,", make sure you have a "),mpe=n(Km,"CODE",{});var kGe=s(mpe);apo=r(kGe,"NewModelConfig"),kGe.forEach(t),npo=r(Km,` then you can add those to the auto
classes like this:`),Km.forEach(t),ifo=i(f),T(cS.$$.fragment,f),dfo=i(f),Bq=n(f,"P",{});var SGe=s(Bq);spo=r(SGe,"You will then be able to use the auto classes like you would usually do!"),SGe.forEach(t),cfo=i(f),T(lg.$$.fragment,f),ffo=i(f),Bd=n(f,"H2",{class:!0});var sD=s(Bd);ig=n(sD,"A",{id:!0,class:!0,href:!0});var RGe=s(ig);gpe=n(RGe,"SPAN",{});var PGe=s(gpe);T(fS.$$.fragment,PGe),PGe.forEach(t),RGe.forEach(t),lpo=i(sD),hpe=n(sD,"SPAN",{});var IGe=s(hpe);ipo=r(IGe,"AutoConfig"),IGe.forEach(t),sD.forEach(t),mfo=i(f),Io=n(f,"DIV",{class:!0});var Et=s(Io);T(mS.$$.fragment,Et),dpo=i(Et),gS=n(Et,"P",{});var lD=s(gS);cpo=r(lD,`This is a generic configuration class that will be instantiated as one of the configuration classes of the library
when created with the `),Nq=n(lD,"A",{href:!0});var BGe=s(Nq);fpo=r(BGe,"from_pretrained()"),BGe.forEach(t),mpo=r(lD," class method."),lD.forEach(t),gpo=i(Et),hS=n(Et,"P",{});var iD=s(hS);hpo=r(iD,"This class cannot be instantiated directly using "),upe=n(iD,"CODE",{});var NGe=s(upe);upo=r(NGe,"__init__()"),NGe.forEach(t),ppo=r(iD," (throws an error)."),iD.forEach(t),_po=i(Et),Vr=n(Et,"DIV",{class:!0});var Ct=s(Vr);T(uS.$$.fragment,Ct),vpo=i(Ct),ppe=n(Ct,"P",{});var DGe=s(ppe);bpo=r(DGe,"Instantiate one of the configuration classes of the library from a pretrained model configuration."),DGe.forEach(t),Fpo=i(Ct),Nd=n(Ct,"P",{});var eg=s(Nd);Tpo=r(eg,"The configuration class to instantiate is selected based on the "),_pe=n(eg,"CODE",{});var qGe=s(_pe);Mpo=r(qGe,"model_type"),qGe.forEach(t),Epo=r(eg,` property of the config object that
is loaded, or when it\u2019s missing, by falling back to using pattern matching on `),vpe=n(eg,"CODE",{});var jGe=s(vpe);Cpo=r(jGe,"pretrained_model_name_or_path"),jGe.forEach(t),wpo=r(eg,":"),eg.forEach(t),Apo=i(Ct),A=n(Ct,"UL",{});var L=s(A);dg=n(L,"LI",{});var Ex=s(dg);bpe=n(Ex,"STRONG",{});var GGe=s(bpe);Lpo=r(GGe,"albert"),GGe.forEach(t),ypo=r(Ex," \u2014 "),Dq=n(Ex,"A",{href:!0});var OGe=s(Dq);xpo=r(OGe,"AlbertConfig"),OGe.forEach(t),$po=r(Ex," (ALBERT model)"),Ex.forEach(t),kpo=i(L),cg=n(L,"LI",{});var Cx=s(cg);Fpe=n(Cx,"STRONG",{});var VGe=s(Fpe);Spo=r(VGe,"audio-spectrogram-transformer"),VGe.forEach(t),Rpo=r(Cx," \u2014 "),qq=n(Cx,"A",{href:!0});var XGe=s(qq);Ppo=r(XGe,"ASTConfig"),XGe.forEach(t),Ipo=r(Cx," (Audio Spectrogram Transformer model)"),Cx.forEach(t),Bpo=i(L),fg=n(L,"LI",{});var wx=s(fg);Tpe=n(wx,"STRONG",{});var zGe=s(Tpe);Npo=r(zGe,"bart"),zGe.forEach(t),Dpo=r(wx," \u2014 "),jq=n(wx,"A",{href:!0});var QGe=s(jq);qpo=r(QGe,"BartConfig"),QGe.forEach(t),jpo=r(wx," 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eOe=s(Xq);n_o=r(eOe,"BigBirdConfig"),eOe.forEach(t),s_o=r(xx," (BigBird model)"),xx.forEach(t),l_o=i(L),pg=n(L,"LI",{});var $x=s(pg);Ape=n($x,"STRONG",{});var oOe=s(Ape);i_o=r(oOe,"bigbird_pegasus"),oOe.forEach(t),d_o=r($x," \u2014 "),zq=n($x,"A",{href:!0});var rOe=s(zq);c_o=r(rOe,"BigBirdPegasusConfig"),rOe.forEach(t),f_o=r($x," (BigBird-Pegasus model)"),$x.forEach(t),m_o=i(L),_g=n(L,"LI",{});var kx=s(_g);Lpe=n(kx,"STRONG",{});var tOe=s(Lpe);g_o=r(tOe,"blenderbot"),tOe.forEach(t),h_o=r(kx," \u2014 "),Qq=n(kx,"A",{href:!0});var aOe=s(Qq);u_o=r(aOe,"BlenderbotConfig"),aOe.forEach(t),p_o=r(kx," (Blenderbot model)"),kx.forEach(t),__o=i(L),vg=n(L,"LI",{});var Sx=s(vg);ype=n(Sx,"STRONG",{});var nOe=s(ype);v_o=r(nOe,"blenderbot-small"),nOe.forEach(t),b_o=r(Sx," \u2014 "),Wq=n(Sx,"A",{href:!0});var sOe=s(Wq);F_o=r(sOe,"BlenderbotSmallConfig"),sOe.forEach(t),T_o=r(Sx," (BlenderbotSmall model)"),Sx.forEach(t),M_o=i(L),bg=n(L,"LI",{});var Rx=s(bg);xpe=n(Rx,"STRONG",{});var 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xKt=s(f5e);AOo=r(xKt,"vit_mae"),xKt.forEach(t),LOo=r(bWe," \u2014 "),CW=n(bWe,"A",{href:!0});var $Kt=s(CW);yOo=r($Kt,"ViTImageProcessor"),$Kt.forEach(t),xOo=r(bWe," (ViTMAE model)"),bWe.forEach(t),$Oo=i(J),C1=n(J,"LI",{});var FWe=s(C1);m5e=n(FWe,"STRONG",{});var kKt=s(m5e);kOo=r(kKt,"vit_msn"),kKt.forEach(t),SOo=r(FWe," \u2014 "),wW=n(FWe,"A",{href:!0});var SKt=s(wW);ROo=r(SKt,"ViTImageProcessor"),SKt.forEach(t),POo=r(FWe," (ViTMSN model)"),FWe.forEach(t),IOo=i(J),w1=n(J,"LI",{});var TWe=s(w1);g5e=n(TWe,"STRONG",{});var RKt=s(g5e);BOo=r(RKt,"xclip"),RKt.forEach(t),NOo=r(TWe," \u2014 "),AW=n(TWe,"A",{href:!0});var PKt=s(AW);DOo=r(PKt,"CLIPImageProcessor"),PKt.forEach(t),qOo=r(TWe," (X-CLIP model)"),TWe.forEach(t),jOo=i(J),A1=n(J,"LI",{});var MWe=s(A1);h5e=n(MWe,"STRONG",{});var IKt=s(h5e);GOo=r(IKt,"yolos"),IKt.forEach(t),OOo=r(MWe," \u2014 "),LW=n(MWe,"A",{href:!0});var BKt=s(LW);VOo=r(BKt,"YolosImageProcessor"),BKt.forEach(t),XOo=r(MWe," (YOLOS 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created with the `),yW=n(jgo,"A",{href:!0});var GKt=s(yW);eVo=r(GKt,"AutoProcessor.from_pretrained()"),GKt.forEach(t),oVo=r(jgo," class method."),jgo.forEach(t),rVo=i(Ul),DS=n(Ul,"P",{});var Ggo=s(DS);tVo=r(Ggo,"This class cannot be instantiated directly using "),v5e=n(Ggo,"CODE",{});var OKt=s(v5e);aVo=r(OKt,"__init__()"),OKt.forEach(t),nVo=r(Ggo," (throws an error)."),Ggo.forEach(t),sVo=i(Ul),ro=n(Ul,"DIV",{class:!0});var ka=s(ro);T(qS.$$.fragment,ka),lVo=i(ka),b5e=n(ka,"P",{});var VKt=s(b5e);iVo=r(VKt,"Instantiate one of the processor classes of the library from a pretrained model vocabulary."),VKt.forEach(t),dVo=i(ka),Od=n(ka,"P",{});var ohe=s(Od);cVo=r(ohe,"The processor class to instantiate is selected based on the "),F5e=n(ohe,"CODE",{});var XKt=s(F5e);fVo=r(XKt,"model_type"),XKt.forEach(t),mVo=r(ohe,` property of the config object (either
passed as an argument or loaded from `),T5e=n(ohe,"CODE",{});var zKt=s(T5e);gVo=r(zKt,"pretrained_model_name_or_path"),zKt.forEach(t),hVo=r(ohe," if possible):"),ohe.forEach(t),uVo=i(ka),ie=n(ka,"UL",{});var ce=s(ie);k1=n(ce,"LI",{});var EWe=s(k1);M5e=n(EWe,"STRONG",{});var QKt=s(M5e);pVo=r(QKt,"chinese_clip"),QKt.forEach(t),_Vo=r(EWe," \u2014 "),xW=n(EWe,"A",{href:!0});var WKt=s(xW);vVo=r(WKt,"ChineseCLIPProcessor"),WKt.forEach(t),bVo=r(EWe," (Chinese-CLIP model)"),EWe.forEach(t),FVo=i(ce),S1=n(ce,"LI",{});var CWe=s(S1);E5e=n(CWe,"STRONG",{});var UKt=s(E5e);TVo=r(UKt,"clip"),UKt.forEach(t),MVo=r(CWe," \u2014 "),$W=n(CWe,"A",{href:!0});var HKt=s($W);EVo=r(HKt,"CLIPProcessor"),HKt.forEach(t),CVo=r(CWe," (CLIP model)"),CWe.forEach(t),wVo=i(ce),R1=n(ce,"LI",{});var wWe=s(R1);C5e=n(wWe,"STRONG",{});var JKt=s(C5e);AVo=r(JKt,"clipseg"),JKt.forEach(t),LVo=r(wWe," \u2014 "),kW=n(wWe,"A",{href:!0});var YKt=s(kW);yVo=r(YKt,"CLIPSegProcessor"),YKt.forEach(t),xVo=r(wWe," (CLIPSeg 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nea=s(IW);UVo=r(nea,"LayoutLMv3Processor"),nea.forEach(t),HVo=r(xWe," (LayoutLMv3 model)"),xWe.forEach(t),JVo=i(ce),D1=n(ce,"LI",{});var $We=s(D1);x5e=n($We,"STRONG",{});var sea=s(x5e);YVo=r(sea,"layoutxlm"),sea.forEach(t),ZVo=r($We," \u2014 "),BW=n($We,"A",{href:!0});var lea=s(BW);KVo=r(lea,"LayoutXLMProcessor"),lea.forEach(t),eXo=r($We," (LayoutXLM model)"),$We.forEach(t),oXo=i(ce),q1=n(ce,"LI",{});var kWe=s(q1);$5e=n(kWe,"STRONG",{});var iea=s($5e);rXo=r(iea,"markuplm"),iea.forEach(t),tXo=r(kWe," \u2014 "),NW=n(kWe,"A",{href:!0});var dea=s(NW);aXo=r(dea,"MarkupLMProcessor"),dea.forEach(t),nXo=r(kWe," (MarkupLM model)"),kWe.forEach(t),sXo=i(ce),j1=n(ce,"LI",{});var SWe=s(j1);k5e=n(SWe,"STRONG",{});var cea=s(k5e);lXo=r(cea,"owlvit"),cea.forEach(t),iXo=r(SWe," \u2014 "),DW=n(SWe,"A",{href:!0});var fea=s(DW);dXo=r(fea,"OwlViTProcessor"),fea.forEach(t),cXo=r(SWe," (OWL-ViT model)"),SWe.forEach(t),fXo=i(ce),G1=n(ce,"LI",{});var RWe=s(G1);S5e=n(RWe,"STRONG",{});var 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Lea=s(QW);HXo=r(Lea,"ViltProcessor"),Lea.forEach(t),JXo=r(jWe," (ViLT model)"),jWe.forEach(t),YXo=i(ce),H1=n(ce,"LI",{});var GWe=s(H1);j5e=n(GWe,"STRONG",{});var yea=s(j5e);ZXo=r(yea,"vision-text-dual-encoder"),yea.forEach(t),KXo=r(GWe," \u2014 "),WW=n(GWe,"A",{href:!0});var xea=s(WW);ezo=r(xea,"VisionTextDualEncoderProcessor"),xea.forEach(t),ozo=r(GWe," (VisionTextDualEncoder model)"),GWe.forEach(t),rzo=i(ce),J1=n(ce,"LI",{});var OWe=s(J1);G5e=n(OWe,"STRONG",{});var $ea=s(G5e);tzo=r($ea,"wav2vec2"),$ea.forEach(t),azo=r(OWe," \u2014 "),UW=n(OWe,"A",{href:!0});var kea=s(UW);nzo=r(kea,"Wav2Vec2Processor"),kea.forEach(t),szo=r(OWe," (Wav2Vec2 model)"),OWe.forEach(t),lzo=i(ce),Y1=n(ce,"LI",{});var VWe=s(Y1);O5e=n(VWe,"STRONG",{});var Sea=s(O5e);izo=r(Sea,"wav2vec2-conformer"),Sea.forEach(t),dzo=r(VWe," \u2014 "),HW=n(VWe,"A",{href:!0});var Rea=s(HW);czo=r(Rea,"Wav2Vec2Processor"),Rea.forEach(t),fzo=r(VWe," (Wav2Vec2-Conformer model)"),VWe.forEach(t),mzo=i(ce),Z1=n(ce,"LI",{});var XWe=s(Z1);V5e=n(XWe,"STRONG",{});var Pea=s(V5e);gzo=r(Pea,"wavlm"),Pea.forEach(t),hzo=r(XWe," \u2014 "),JW=n(XWe,"A",{href:!0});var Iea=s(JW);uzo=r(Iea,"Wav2Vec2Processor"),Iea.forEach(t),pzo=r(XWe," (WavLM model)"),XWe.forEach(t),_zo=i(ce),K1=n(ce,"LI",{});var zWe=s(K1);X5e=n(zWe,"STRONG",{});var Bea=s(X5e);vzo=r(Bea,"whisper"),Bea.forEach(t),bzo=r(zWe," \u2014 "),YW=n(zWe,"A",{href:!0});var Nea=s(YW);Fzo=r(Nea,"WhisperProcessor"),Nea.forEach(t),Tzo=r(zWe," (Whisper model)"),zWe.forEach(t),Mzo=i(ce),e2=n(ce,"LI",{});var QWe=s(e2);z5e=n(QWe,"STRONG",{});var Dea=s(z5e);Ezo=r(Dea,"xclip"),Dea.forEach(t),Czo=r(QWe," \u2014 "),ZW=n(QWe,"A",{href:!0});var qea=s(ZW);wzo=r(qea,"XCLIPProcessor"),qea.forEach(t),Azo=r(QWe," (X-CLIP model)"),QWe.forEach(t),ce.forEach(t),Lzo=i(ka),T(o2.$$.fragment,ka),yzo=i(ka),T(r2.$$.fragment,ka),ka.forEach(t),xzo=i(Ul),t2=n(Ul,"DIV",{class:!0});var Ogo=s(t2);T(jS.$$.fragment,Ogo),$zo=i(Ogo),Q5e=n(Ogo,"P",{});var jea=s(Q5e);kzo=r(jea,"Register a new processor 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with the `),KW=n(rhe,"A",{href:!0});var Xea=s(KW);Bzo=r(Xea,"from_pretrained()"),Xea.forEach(t),Nzo=r(rhe," class method or the "),eU=n(rhe,"A",{href:!0});var zea=s(eU);Dzo=r(zea,"from_config()"),zea.forEach(t),qzo=r(rhe,` class
method.`),rhe.forEach(t),jzo=i(Hl),VS=n(Hl,"P",{});var Xgo=s(VS);Gzo=r(Xgo,"This class cannot be instantiated directly using "),H5e=n(Xgo,"CODE",{});var Qea=s(H5e);Ozo=r(Qea,"__init__()"),Qea.forEach(t),Vzo=r(Xgo," (throws an error)."),Xgo.forEach(t),Xzo=i(Hl),At=n(Hl,"DIV",{class:!0});var e$=s(At);T(XS.$$.fragment,e$),zzo=i(e$),J5e=n(e$,"P",{});var Wea=s(J5e);Qzo=r(Wea,"Instantiates one of the base model classes of the library from a configuration."),Wea.forEach(t),Wzo=i(e$),zd=n(e$,"P",{});var the=s(zd);Uzo=r(the,`Note:
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csa=s(RJ);fnr=r(csa,"YosoModel"),csa.forEach(t),mnr=r(EJe," (YOSO model)"),EJe.forEach(t),x.forEach(t),gnr=i(Sa),N4=n(Sa,"P",{});var CJe=s(N4);hnr=r(CJe,"The model is set in evaluation mode by default using "),IMe=n(CJe,"CODE",{});var fsa=s(IMe);unr=r(fsa,"model.eval()"),fsa.forEach(t),pnr=r(CJe,` (so for instance, dropout modules are
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cYe=s(Ab);kEe=n(cYe,"STRONG",{});var Jla=s(kEe);Sdr=r(Jla,"unispeech"),Jla.forEach(t),Rdr=r(cYe," \u2014 "),MY=n(cYe,"A",{href:!0});var Yla=s(MY);Pdr=r(Yla,"UniSpeechForPreTraining"),Yla.forEach(t),Idr=r(cYe," (UniSpeech model)"),cYe.forEach(t),Bdr=i(z),Lb=n(z,"LI",{});var fYe=s(Lb);SEe=n(fYe,"STRONG",{});var Zla=s(SEe);Ndr=r(Zla,"unispeech-sat"),Zla.forEach(t),Ddr=r(fYe," \u2014 "),EY=n(fYe,"A",{href:!0});var Kla=s(EY);qdr=r(Kla,"UniSpeechSatForPreTraining"),Kla.forEach(t),jdr=r(fYe," (UniSpeechSat model)"),fYe.forEach(t),Gdr=i(z),yb=n(z,"LI",{});var mYe=s(yb);REe=n(mYe,"STRONG",{});var eia=s(REe);Odr=r(eia,"videomae"),eia.forEach(t),Vdr=r(mYe," \u2014 "),CY=n(mYe,"A",{href:!0});var oia=s(CY);Xdr=r(oia,"VideoMAEForPreTraining"),oia.forEach(t),zdr=r(mYe," (VideoMAE model)"),mYe.forEach(t),Qdr=i(z),xb=n(z,"LI",{});var gYe=s(xb);PEe=n(gYe,"STRONG",{});var ria=s(PEe);Wdr=r(ria,"visual_bert"),ria.forEach(t),Udr=r(gYe," \u2014 "),wY=n(gYe,"A",{href:!0});var tia=s(wY);Hdr=r(tia,"VisualBertForPreTraining"),tia.forEach(t),Jdr=r(gYe," (VisualBERT model)"),gYe.forEach(t),Ydr=i(z),$b=n(z,"LI",{});var hYe=s($b);IEe=n(hYe,"STRONG",{});var aia=s(IEe);Zdr=r(aia,"vit_mae"),aia.forEach(t),Kdr=r(hYe," \u2014 "),AY=n(hYe,"A",{href:!0});var nia=s(AY);ecr=r(nia,"ViTMAEForPreTraining"),nia.forEach(t),ocr=r(hYe," (ViTMAE model)"),hYe.forEach(t),rcr=i(z),kb=n(z,"LI",{});var uYe=s(kb);BEe=n(uYe,"STRONG",{});var sia=s(BEe);tcr=r(sia,"wav2vec2"),sia.forEach(t),acr=r(uYe," \u2014 "),LY=n(uYe,"A",{href:!0});var lia=s(LY);ncr=r(lia,"Wav2Vec2ForPreTraining"),lia.forEach(t),scr=r(uYe," (Wav2Vec2 model)"),uYe.forEach(t),lcr=i(z),Sb=n(z,"LI",{});var pYe=s(Sb);NEe=n(pYe,"STRONG",{});var iia=s(NEe);icr=r(iia,"wav2vec2-conformer"),iia.forEach(t),dcr=r(pYe," \u2014 "),yY=n(pYe,"A",{href:!0});var dia=s(yY);ccr=r(dia,"Wav2Vec2ConformerForPreTraining"),dia.forEach(t),fcr=r(pYe," (Wav2Vec2-Conformer model)"),pYe.forEach(t),mcr=i(z),Rb=n(z,"LI",{});var 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_ia=s(SY);$cr=r(_ia,"XLNetLMHeadModel"),_ia.forEach(t),kcr=r(FYe," (XLNet model)"),FYe.forEach(t),z.forEach(t),Scr=i(Ra),Nb=n(Ra,"P",{});var TYe=s(Nb);Rcr=r(TYe,"The model is set in evaluation mode by default using "),OEe=n(TYe,"CODE",{});var via=s(OEe);Pcr=r(via,"model.eval()"),via.forEach(t),Icr=r(TYe,` (so for instance, dropout modules are
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with the `),RY=n(she,"A",{href:!0});var Eia=s(RY);Ocr=r(Eia,"from_pretrained()"),Eia.forEach(t),Vcr=r(she," class method or the "),PY=n(she,"A",{href:!0});var Cia=s(PY);Xcr=r(Cia,"from_config()"),Cia.forEach(t),zcr=r(she,` class
method.`),she.forEach(t),Qcr=i(Yl),KS=n(Yl,"P",{});var Ugo=s(KS);Wcr=r(Ugo,"This class cannot be instantiated directly using "),QEe=n(Ugo,"CODE",{});var wia=s(QEe);Ucr=r(wia,"__init__()"),wia.forEach(t),Hcr=r(Ugo," (throws an error)."),Ugo.forEach(t),Jcr=i(Yl),yt=n(Yl,"DIV",{class:!0});var a$=s(yt);T(eR.$$.fragment,a$),Ycr=i(a$),WEe=n(a$,"P",{});var Aia=s(WEe);Zcr=r(Aia,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Aia.forEach(t),Kcr=i(a$),Yd=n(a$,"P",{});var lhe=s(Yd);efr=r(lhe,`Note:
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model\u2019s configuration. Use `),IY=n(lhe,"A",{href:!0});var yia=s(IY);tfr=r(yia,"from_pretrained()"),yia.forEach(t),afr=r(lhe," to load the model weights."),lhe.forEach(t),nfr=i(a$),T(jb.$$.fragment,a$),a$.forEach(t),sfr=i(Yl),no=n(Yl,"DIV",{class:!0});var Pa=s(no);T(oR.$$.fragment,Pa),lfr=i(Pa),HEe=n(Pa,"P",{});var xia=s(HEe);ifr=r(xia,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),xia.forEach(t),dfr=i(Pa),un=n(Pa,"P",{});var n$=s(un);cfr=r(n$,"The model class to instantiate is selected based on the "),JEe=n(n$,"CODE",{});var $ia=s(JEe);ffr=r($ia,"model_type"),$ia.forEach(t),mfr=r(n$,` property of the config object (either
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falling back to using pattern matching on `),ZEe=n(n$,"CODE",{});var Sia=s(ZEe);ufr=r(Sia,"pretrained_model_name_or_path"),Sia.forEach(t),pfr=r(n$,":"),n$.forEach(t),_fr=i(Pa),U=n(Pa,"UL",{});var Y=s(U);Gb=n(Y,"LI",{});var MYe=s(Gb);KEe=n(MYe,"STRONG",{});var Ria=s(KEe);vfr=r(Ria,"bart"),Ria.forEach(t),bfr=r(MYe," \u2014 "),BY=n(MYe,"A",{href:!0});var Pia=s(BY);Ffr=r(Pia,"BartForCausalLM"),Pia.forEach(t),Tfr=r(MYe," (BART model)"),MYe.forEach(t),Mfr=i(Y),Ob=n(Y,"LI",{});var EYe=s(Ob);eCe=n(EYe,"STRONG",{});var Iia=s(eCe);Efr=r(Iia,"bert"),Iia.forEach(t),Cfr=r(EYe," \u2014 "),NY=n(EYe,"A",{href:!0});var Bia=s(NY);wfr=r(Bia,"BertLMHeadModel"),Bia.forEach(t),Afr=r(EYe," (BERT model)"),EYe.forEach(t),Lfr=i(Y),Vb=n(Y,"LI",{});var CYe=s(Vb);oCe=n(CYe,"STRONG",{});var Nia=s(oCe);yfr=r(Nia,"bert-generation"),Nia.forEach(t),xfr=r(CYe," \u2014 "),DY=n(CYe,"A",{href:!0});var Dia=s(DY);$fr=r(Dia,"BertGenerationDecoder"),Dia.forEach(t),kfr=r(CYe," (Bert Generation 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hda=s(KY);Ymr=r(hda,"GPTNeoXJapaneseForCausalLM"),hda.forEach(t),Zmr=r(qYe," (GPT NeoX Japanese model)"),qYe.forEach(t),Kmr=i(Y),n5=n(Y,"LI",{});var jYe=s(n5);_Ce=n(jYe,"STRONG",{});var uda=s(_Ce);egr=r(uda,"gptj"),uda.forEach(t),ogr=r(jYe," \u2014 "),eZ=n(jYe,"A",{href:!0});var pda=s(eZ);rgr=r(pda,"GPTJForCausalLM"),pda.forEach(t),tgr=r(jYe," (GPT-J model)"),jYe.forEach(t),agr=i(Y),s5=n(Y,"LI",{});var GYe=s(s5);vCe=n(GYe,"STRONG",{});var _da=s(vCe);ngr=r(_da,"marian"),_da.forEach(t),sgr=r(GYe," \u2014 "),oZ=n(GYe,"A",{href:!0});var vda=s(oZ);lgr=r(vda,"MarianForCausalLM"),vda.forEach(t),igr=r(GYe," (Marian model)"),GYe.forEach(t),dgr=i(Y),l5=n(Y,"LI",{});var OYe=s(l5);bCe=n(OYe,"STRONG",{});var bda=s(bCe);cgr=r(bda,"mbart"),bda.forEach(t),fgr=r(OYe," \u2014 "),rZ=n(OYe,"A",{href:!0});var Fda=s(rZ);mgr=r(Fda,"MBartForCausalLM"),Fda.forEach(t),ggr=r(OYe," (mBART model)"),OYe.forEach(t),hgr=i(Y),i5=n(Y,"LI",{});var VYe=s(i5);FCe=n(VYe,"STRONG",{});var Tda=s(FCe);ugr=r(Tda,"megatron-bert"),Tda.forEach(t),pgr=r(VYe," \u2014 "),tZ=n(VYe,"A",{href:!0});var Mda=s(tZ);_gr=r(Mda,"MegatronBertForCausalLM"),Mda.forEach(t),vgr=r(VYe," (Megatron-BERT model)"),VYe.forEach(t),bgr=i(Y),d5=n(Y,"LI",{});var XYe=s(d5);TCe=n(XYe,"STRONG",{});var Eda=s(TCe);Fgr=r(Eda,"mvp"),Eda.forEach(t),Tgr=r(XYe," \u2014 "),aZ=n(XYe,"A",{href:!0});var Cda=s(aZ);Mgr=r(Cda,"MvpForCausalLM"),Cda.forEach(t),Egr=r(XYe," (MVP model)"),XYe.forEach(t),Cgr=i(Y),c5=n(Y,"LI",{});var zYe=s(c5);MCe=n(zYe,"STRONG",{});var wda=s(MCe);wgr=r(wda,"openai-gpt"),wda.forEach(t),Agr=r(zYe," \u2014 "),nZ=n(zYe,"A",{href:!0});var Ada=s(nZ);Lgr=r(Ada,"OpenAIGPTLMHeadModel"),Ada.forEach(t),ygr=r(zYe," (OpenAI GPT model)"),zYe.forEach(t),xgr=i(Y),f5=n(Y,"LI",{});var QYe=s(f5);ECe=n(QYe,"STRONG",{});var Lda=s(ECe);$gr=r(Lda,"opt"),Lda.forEach(t),kgr=r(QYe," \u2014 "),sZ=n(QYe,"A",{href:!0});var yda=s(sZ);Sgr=r(yda,"OPTForCausalLM"),yda.forEach(t),Rgr=r(QYe," (OPT model)"),QYe.forEach(t),Pgr=i(Y),m5=n(Y,"LI",{});var WYe=s(m5);CCe=n(WYe,"STRONG",{});var xda=s(CCe);Igr=r(xda,"pegasus"),xda.forEach(t),Bgr=r(WYe," \u2014 "),lZ=n(WYe,"A",{href:!0});var $da=s(lZ);Ngr=r($da,"PegasusForCausalLM"),$da.forEach(t),Dgr=r(WYe," (Pegasus model)"),WYe.forEach(t),qgr=i(Y),g5=n(Y,"LI",{});var UYe=s(g5);wCe=n(UYe,"STRONG",{});var kda=s(wCe);jgr=r(kda,"plbart"),kda.forEach(t),Ggr=r(UYe," \u2014 "),iZ=n(UYe,"A",{href:!0});var Sda=s(iZ);Ogr=r(Sda,"PLBartForCausalLM"),Sda.forEach(t),Vgr=r(UYe," (PLBart model)"),UYe.forEach(t),Xgr=i(Y),h5=n(Y,"LI",{});var HYe=s(h5);ACe=n(HYe,"STRONG",{});var Rda=s(ACe);zgr=r(Rda,"prophetnet"),Rda.forEach(t),Qgr=r(HYe," \u2014 "),dZ=n(HYe,"A",{href:!0});var Pda=s(dZ);Wgr=r(Pda,"ProphetNetForCausalLM"),Pda.forEach(t),Ugr=r(HYe," (ProphetNet model)"),HYe.forEach(t),Hgr=i(Y),u5=n(Y,"LI",{});var JYe=s(u5);LCe=n(JYe,"STRONG",{});var Ida=s(LCe);Jgr=r(Ida,"qdqbert"),Ida.forEach(t),Ygr=r(JYe," \u2014 "),cZ=n(JYe,"A",{href:!0});var Bda=s(cZ);Zgr=r(Bda,"QDQBertLMHeadModel"),Bda.forEach(t),Kgr=r(JYe," (QDQBert model)"),JYe.forEach(t),ehr=i(Y),p5=n(Y,"LI",{});var YYe=s(p5);yCe=n(YYe,"STRONG",{});var Nda=s(yCe);ohr=r(Nda,"reformer"),Nda.forEach(t),rhr=r(YYe," \u2014 "),fZ=n(YYe,"A",{href:!0});var Dda=s(fZ);thr=r(Dda,"ReformerModelWithLMHead"),Dda.forEach(t),ahr=r(YYe," (Reformer model)"),YYe.forEach(t),nhr=i(Y),_5=n(Y,"LI",{});var ZYe=s(_5);xCe=n(ZYe,"STRONG",{});var qda=s(xCe);shr=r(qda,"rembert"),qda.forEach(t),lhr=r(ZYe," \u2014 "),mZ=n(ZYe,"A",{href:!0});var jda=s(mZ);ihr=r(jda,"RemBertForCausalLM"),jda.forEach(t),dhr=r(ZYe," (RemBERT model)"),ZYe.forEach(t),chr=i(Y),v5=n(Y,"LI",{});var KYe=s(v5);$Ce=n(KYe,"STRONG",{});var Gda=s($Ce);fhr=r(Gda,"roberta"),Gda.forEach(t),mhr=r(KYe," \u2014 "),gZ=n(KYe,"A",{href:!0});var Oda=s(gZ);ghr=r(Oda,"RobertaForCausalLM"),Oda.forEach(t),hhr=r(KYe," (RoBERTa model)"),KYe.forEach(t),uhr=i(Y),b5=n(Y,"LI",{});var eZe=s(b5);kCe=n(eZe,"STRONG",{});var Vda=s(kCe);phr=r(Vda,"roc_bert"),Vda.forEach(t),_hr=r(eZe," \u2014 "),hZ=n(eZe,"A",{href:!0});var Xda=s(hZ);vhr=r(Xda,"RoCBertForCausalLM"),Xda.forEach(t),bhr=r(eZe," (RoCBert model)"),eZe.forEach(t),Fhr=i(Y),F5=n(Y,"LI",{});var oZe=s(F5);SCe=n(oZe,"STRONG",{});var zda=s(SCe);Thr=r(zda,"roformer"),zda.forEach(t),Mhr=r(oZe," \u2014 "),uZ=n(oZe,"A",{href:!0});var Qda=s(uZ);Ehr=r(Qda,"RoFormerForCausalLM"),Qda.forEach(t),Chr=r(oZe," (RoFormer model)"),oZe.forEach(t),whr=i(Y),T5=n(Y,"LI",{});var rZe=s(T5);RCe=n(rZe,"STRONG",{});var Wda=s(RCe);Ahr=r(Wda,"speech_to_text_2"),Wda.forEach(t),Lhr=r(rZe," \u2014 "),pZ=n(rZe,"A",{href:!0});var Uda=s(pZ);yhr=r(Uda,"Speech2Text2ForCausalLM"),Uda.forEach(t),xhr=r(rZe," (Speech2Text2 model)"),rZe.forEach(t),$hr=i(Y),M5=n(Y,"LI",{});var tZe=s(M5);PCe=n(tZe,"STRONG",{});var Hda=s(PCe);khr=r(Hda,"transfo-xl"),Hda.forEach(t),Shr=r(tZe," \u2014 "),_Z=n(tZe,"A",{href:!0});var Jda=s(_Z);Rhr=r(Jda,"TransfoXLLMHeadModel"),Jda.forEach(t),Phr=r(tZe," (Transformer-XL model)"),tZe.forEach(t),Ihr=i(Y),E5=n(Y,"LI",{});var aZe=s(E5);ICe=n(aZe,"STRONG",{});var Yda=s(ICe);Bhr=r(Yda,"trocr"),Yda.forEach(t),Nhr=r(aZe," \u2014 "),vZ=n(aZe,"A",{href:!0});var Zda=s(vZ);Dhr=r(Zda,"TrOCRForCausalLM"),Zda.forEach(t),qhr=r(aZe," (TrOCR model)"),aZe.forEach(t),jhr=i(Y),C5=n(Y,"LI",{});var nZe=s(C5);BCe=n(nZe,"STRONG",{});var Kda=s(BCe);Ghr=r(Kda,"xglm"),Kda.forEach(t),Ohr=r(nZe," \u2014 "),bZ=n(nZe,"A",{href:!0});var eca=s(bZ);Vhr=r(eca,"XGLMForCausalLM"),eca.forEach(t),Xhr=r(nZe," (XGLM model)"),nZe.forEach(t),zhr=i(Y),w5=n(Y,"LI",{});var sZe=s(w5);NCe=n(sZe,"STRONG",{});var oca=s(NCe);Qhr=r(oca,"xlm"),oca.forEach(t),Whr=r(sZe," \u2014 "),FZ=n(sZe,"A",{href:!0});var rca=s(FZ);Uhr=r(rca,"XLMWithLMHeadModel"),rca.forEach(t),Hhr=r(sZe," (XLM model)"),sZe.forEach(t),Jhr=i(Y),A5=n(Y,"LI",{});var lZe=s(A5);DCe=n(lZe,"STRONG",{});var tca=s(DCe);Yhr=r(tca,"xlm-prophetnet"),tca.forEach(t),Zhr=r(lZe," \u2014 "),TZ=n(lZe,"A",{href:!0});var aca=s(TZ);Khr=r(aca,"XLMProphetNetForCausalLM"),aca.forEach(t),eur=r(lZe," (XLM-ProphetNet model)"),lZe.forEach(t),our=i(Y),L5=n(Y,"LI",{});var iZe=s(L5);qCe=n(iZe,"STRONG",{});var nca=s(qCe);rur=r(nca,"xlm-roberta"),nca.forEach(t),tur=r(iZe," \u2014 "),MZ=n(iZe,"A",{href:!0});var sca=s(MZ);aur=r(sca,"XLMRobertaForCausalLM"),sca.forEach(t),nur=r(iZe," (XLM-RoBERTa model)"),iZe.forEach(t),sur=i(Y),y5=n(Y,"LI",{});var dZe=s(y5);jCe=n(dZe,"STRONG",{});var lca=s(jCe);lur=r(lca,"xlm-roberta-xl"),lca.forEach(t),iur=r(dZe," \u2014 "),EZ=n(dZe,"A",{href:!0});var ica=s(EZ);dur=r(ica,"XLMRobertaXLForCausalLM"),ica.forEach(t),cur=r(dZe," (XLM-RoBERTa-XL model)"),dZe.forEach(t),fur=i(Y),x5=n(Y,"LI",{});var cZe=s(x5);GCe=n(cZe,"STRONG",{});var dca=s(GCe);mur=r(dca,"xlnet"),dca.forEach(t),gur=r(cZe," \u2014 "),CZ=n(cZe,"A",{href:!0});var cca=s(CZ);hur=r(cca,"XLNetLMHeadModel"),cca.forEach(t),uur=r(cZe," (XLNet model)"),cZe.forEach(t),Y.forEach(t),pur=i(Pa),$5=n(Pa,"P",{});var fZe=s($5);_ur=r(fZe,"The model is set in evaluation mode by default using "),OCe=n(fZe,"CODE",{});var fca=s(OCe);vur=r(fca,"model.eval()"),fca.forEach(t),bur=r(fZe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),VCe=n(fZe,"CODE",{});var mca=s(VCe);Fur=r(mca,"model.train()"),mca.forEach(t),fZe.forEach(t),Tur=i(Pa),T(k5.$$.fragment,Pa),Pa.forEach(t),Yl.forEach(t),Lfo=i(f),Zd=n(f,"H2",{class:!0});var Hgo=s(Zd);S5=n(Hgo,"A",{id:!0,class:!0,href:!0});var gca=s(S5);XCe=n(gca,"SPAN",{});var hca=s(XCe);T(rR.$$.fragment,hca),hca.forEach(t),gca.forEach(t),Mur=i(Hgo),zCe=n(Hgo,"SPAN",{});var uca=s(zCe);Eur=r(uca,"AutoModelForDepthEstimation"),uca.forEach(t),Hgo.forEach(t),yfo=i(f),Vo=n(f,"DIV",{class:!0});var Zl=s(Vo);T(tR.$$.fragment,Zl),Cur=i(Zl),Kd=n(Zl,"P",{});var ihe=s(Kd);wur=r(ihe,`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 `),wZ=n(ihe,"A",{href:!0});var pca=s(wZ);Aur=r(pca,"from_pretrained()"),pca.forEach(t),Lur=r(ihe," class method or the "),AZ=n(ihe,"A",{href:!0});var _ca=s(AZ);yur=r(_ca,"from_config()"),_ca.forEach(t),xur=r(ihe,` class
method.`),ihe.forEach(t),$ur=i(Zl),aR=n(Zl,"P",{});var Jgo=s(aR);kur=r(Jgo,"This class cannot be instantiated directly using "),QCe=n(Jgo,"CODE",{});var vca=s(QCe);Sur=r(vca,"__init__()"),vca.forEach(t),Rur=r(Jgo," (throws an error)."),Jgo.forEach(t),Pur=i(Zl),xt=n(Zl,"DIV",{class:!0});var s$=s(xt);T(nR.$$.fragment,s$),Iur=i(s$),WCe=n(s$,"P",{});var bca=s(WCe);Bur=r(bca,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),bca.forEach(t),Nur=i(s$),ec=n(s$,"P",{});var dhe=s(ec);Dur=r(dhe,`Note:
Loading a model from its configuration file does `),UCe=n(dhe,"STRONG",{});var Fca=s(UCe);qur=r(Fca,"not"),Fca.forEach(t),jur=r(dhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),LZ=n(dhe,"A",{href:!0});var Tca=s(LZ);Gur=r(Tca,"from_pretrained()"),Tca.forEach(t),Our=r(dhe," to load the model weights."),dhe.forEach(t),Vur=i(s$),T(R5.$$.fragment,s$),s$.forEach(t),Xur=i(Zl),so=n(Zl,"DIV",{class:!0});var Ia=s(so);T(sR.$$.fragment,Ia),zur=i(Ia),HCe=n(Ia,"P",{});var Mca=s(HCe);Qur=r(Mca,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),Mca.forEach(t),Wur=i(Ia),pn=n(Ia,"P",{});var l$=s(pn);Uur=r(l$,"The model class to instantiate is selected based on the "),JCe=n(l$,"CODE",{});var Eca=s(JCe);Hur=r(Eca,"model_type"),Eca.forEach(t),Jur=r(l$,` property of the config object (either
passed as an argument or loaded from `),YCe=n(l$,"CODE",{});var Cca=s(YCe);Yur=r(Cca,"pretrained_model_name_or_path"),Cca.forEach(t),Zur=r(l$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZCe=n(l$,"CODE",{});var wca=s(ZCe);Kur=r(wca,"pretrained_model_name_or_path"),wca.forEach(t),epr=r(l$,":"),l$.forEach(t),opr=i(Ia),lR=n(Ia,"UL",{});var Ygo=s(lR);P5=n(Ygo,"LI",{});var mZe=s(P5);KCe=n(mZe,"STRONG",{});var Aca=s(KCe);rpr=r(Aca,"dpt"),Aca.forEach(t),tpr=r(mZe," \u2014 "),yZ=n(mZe,"A",{href:!0});var Lca=s(yZ);apr=r(Lca,"DPTForDepthEstimation"),Lca.forEach(t),npr=r(mZe," (DPT model)"),mZe.forEach(t),spr=i(Ygo),I5=n(Ygo,"LI",{});var gZe=s(I5);e3e=n(gZe,"STRONG",{});var yca=s(e3e);lpr=r(yca,"glpn"),yca.forEach(t),ipr=r(gZe," \u2014 "),xZ=n(gZe,"A",{href:!0});var xca=s(xZ);dpr=r(xca,"GLPNForDepthEstimation"),xca.forEach(t),cpr=r(gZe," (GLPN model)"),gZe.forEach(t),Ygo.forEach(t),fpr=i(Ia),B5=n(Ia,"P",{});var hZe=s(B5);mpr=r(hZe,"The model is set in evaluation mode by default using "),o3e=n(hZe,"CODE",{});var $ca=s(o3e);gpr=r($ca,"model.eval()"),$ca.forEach(t),hpr=r(hZe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),r3e=n(hZe,"CODE",{});var kca=s(r3e);upr=r(kca,"model.train()"),kca.forEach(t),hZe.forEach(t),ppr=i(Ia),T(N5.$$.fragment,Ia),Ia.forEach(t),Zl.forEach(t),xfo=i(f),oc=n(f,"H2",{class:!0});var Zgo=s(oc);D5=n(Zgo,"A",{id:!0,class:!0,href:!0});var Sca=s(D5);t3e=n(Sca,"SPAN",{});var Rca=s(t3e);T(iR.$$.fragment,Rca),Rca.forEach(t),Sca.forEach(t),_pr=i(Zgo),a3e=n(Zgo,"SPAN",{});var Pca=s(a3e);vpr=r(Pca,"AutoModelForMaskedLM"),Pca.forEach(t),Zgo.forEach(t),$fo=i(f),Xo=n(f,"DIV",{class:!0});var Kl=s(Xo);T(dR.$$.fragment,Kl),bpr=i(Kl),rc=n(Kl,"P",{});var che=s(rc);Fpr=r(che,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),$Z=n(che,"A",{href:!0});var Ica=s($Z);Tpr=r(Ica,"from_pretrained()"),Ica.forEach(t),Mpr=r(che," class method or the "),kZ=n(che,"A",{href:!0});var Bca=s(kZ);Epr=r(Bca,"from_config()"),Bca.forEach(t),Cpr=r(che,` class
method.`),che.forEach(t),wpr=i(Kl),cR=n(Kl,"P",{});var Kgo=s(cR);Apr=r(Kgo,"This class cannot be instantiated directly using "),n3e=n(Kgo,"CODE",{});var Nca=s(n3e);Lpr=r(Nca,"__init__()"),Nca.forEach(t),ypr=r(Kgo," (throws an error)."),Kgo.forEach(t),xpr=i(Kl),$t=n(Kl,"DIV",{class:!0});var i$=s($t);T(fR.$$.fragment,i$),$pr=i(i$),s3e=n(i$,"P",{});var Dca=s(s3e);kpr=r(Dca,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Dca.forEach(t),Spr=i(i$),tc=n(i$,"P",{});var fhe=s(tc);Rpr=r(fhe,`Note:
Loading a model from its configuration file does `),l3e=n(fhe,"STRONG",{});var qca=s(l3e);Ppr=r(qca,"not"),qca.forEach(t),Ipr=r(fhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),SZ=n(fhe,"A",{href:!0});var jca=s(SZ);Bpr=r(jca,"from_pretrained()"),jca.forEach(t),Npr=r(fhe," to load the model weights."),fhe.forEach(t),Dpr=i(i$),T(q5.$$.fragment,i$),i$.forEach(t),qpr=i(Kl),lo=n(Kl,"DIV",{class:!0});var Ba=s(lo);T(mR.$$.fragment,Ba),jpr=i(Ba),i3e=n(Ba,"P",{});var Gca=s(i3e);Gpr=r(Gca,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Gca.forEach(t),Opr=i(Ba),_n=n(Ba,"P",{});var d$=s(_n);Vpr=r(d$,"The model class to instantiate is selected based on the "),d3e=n(d$,"CODE",{});var Oca=s(d3e);Xpr=r(Oca,"model_type"),Oca.forEach(t),zpr=r(d$,` property of the config object (either
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falling back to using pattern matching on `),f3e=n(d$,"CODE",{});var Xca=s(f3e);Upr=r(Xca,"pretrained_model_name_or_path"),Xca.forEach(t),Hpr=r(d$,":"),d$.forEach(t),Jpr=i(Ba),Z=n(Ba,"UL",{});var ee=s(Z);j5=n(ee,"LI",{});var uZe=s(j5);m3e=n(uZe,"STRONG",{});var zca=s(m3e);Ypr=r(zca,"albert"),zca.forEach(t),Zpr=r(uZe," \u2014 "),RZ=n(uZe,"A",{href:!0});var Qca=s(RZ);Kpr=r(Qca,"AlbertForMaskedLM"),Qca.forEach(t),e_r=r(uZe," (ALBERT model)"),uZe.forEach(t),o_r=i(ee),G5=n(ee,"LI",{});var pZe=s(G5);g3e=n(pZe,"STRONG",{});var Wca=s(g3e);r_r=r(Wca,"bart"),Wca.forEach(t),t_r=r(pZe," \u2014 "),PZ=n(pZe,"A",{href:!0});var Uca=s(PZ);a_r=r(Uca,"BartForConditionalGeneration"),Uca.forEach(t),n_r=r(pZe," (BART model)"),pZe.forEach(t),s_r=i(ee),O5=n(ee,"LI",{});var _Ze=s(O5);h3e=n(_Ze,"STRONG",{});var Hca=s(h3e);l_r=r(Hca,"bert"),Hca.forEach(t),i_r=r(_Ze," \u2014 "),IZ=n(_Ze,"A",{href:!0});var Jca=s(IZ);d_r=r(Jca,"BertForMaskedLM"),Jca.forEach(t),c_r=r(_Ze," (BERT 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falling back to using pattern matching on `),c0e=n(f$,"CODE",{});var Rma=s(c0e);M4r=r(Rma,"pretrained_model_name_or_path"),Rma.forEach(t),E4r=r(f$,":"),f$.forEach(t),C4r=i(Na),he=n(Na,"UL",{});var be=s(he);kF=n(be,"LI",{});var tKe=s(kF);f0e=n(tKe,"STRONG",{});var Pma=s(f0e);w4r=r(Pma,"bart"),Pma.forEach(t),A4r=r(tKe," \u2014 "),MK=n(tKe,"A",{href:!0});var Ima=s(MK);L4r=r(Ima,"BartForConditionalGeneration"),Ima.forEach(t),y4r=r(tKe," (BART model)"),tKe.forEach(t),x4r=i(be),SF=n(be,"LI",{});var aKe=s(SF);m0e=n(aKe,"STRONG",{});var Bma=s(m0e);$4r=r(Bma,"bigbird_pegasus"),Bma.forEach(t),k4r=r(aKe," \u2014 "),EK=n(aKe,"A",{href:!0});var Nma=s(EK);S4r=r(Nma,"BigBirdPegasusForConditionalGeneration"),Nma.forEach(t),R4r=r(aKe," (BigBird-Pegasus model)"),aKe.forEach(t),P4r=i(be),RF=n(be,"LI",{});var nKe=s(RF);g0e=n(nKe,"STRONG",{});var Dma=s(g0e);I4r=r(Dma,"blenderbot"),Dma.forEach(t),B4r=r(nKe," \u2014 "),CK=n(nKe,"A",{href:!0});var 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model\u2019s configuration. Use `),Yee=n(_he,"A",{href:!0});var Kua=s(Yee);Q3r=r(Kua,"from_pretrained()"),Kua.forEach(t),W3r=r(_he," to load the model weights."),_he.forEach(t),U3r=i(h$),T(iM.$$.fragment,h$),h$.forEach(t),H3r=i(ri),fo=n(ri,"DIV",{class:!0});var qa=s(fo);T(LR.$$.fragment,qa),J3r=i(qa),Kwe=n(qa,"P",{});var epa=s(Kwe);Y3r=r(epa,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),epa.forEach(t),Z3r=i(qa),Fn=n(qa,"P",{});var u$=s(Fn);K3r=r(u$,"The model class to instantiate is selected based on the "),eAe=n(u$,"CODE",{});var opa=s(eAe);e0r=r(opa,"model_type"),opa.forEach(t),o0r=r(u$,` property of the config object (either
passed as an argument or loaded from `),oAe=n(u$,"CODE",{});var rpa=s(oAe);r0r=r(rpa,"pretrained_model_name_or_path"),rpa.forEach(t),t0r=r(u$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rAe=n(u$,"CODE",{});var tpa=s(rAe);a0r=r(tpa,"pretrained_model_name_or_path"),tpa.forEach(t),n0r=r(u$,":"),u$.forEach(t),s0r=i(qa),oe=n(qa,"UL",{});var re=s(oe);dM=n(re,"LI",{});var xeo=s(dM);tAe=n(xeo,"STRONG",{});var apa=s(tAe);l0r=r(apa,"albert"),apa.forEach(t),i0r=r(xeo," \u2014 "),Zee=n(xeo,"A",{href:!0});var npa=s(Zee);d0r=r(npa,"AlbertForMultipleChoice"),npa.forEach(t),c0r=r(xeo," (ALBERT model)"),xeo.forEach(t),f0r=i(re),cM=n(re,"LI",{});var $eo=s(cM);aAe=n($eo,"STRONG",{});var spa=s(aAe);m0r=r(spa,"bert"),spa.forEach(t),g0r=r($eo," \u2014 "),Kee=n($eo,"A",{href:!0});var lpa=s(Kee);h0r=r(lpa,"BertForMultipleChoice"),lpa.forEach(t),u0r=r($eo," (BERT model)"),$eo.forEach(t),p0r=i(re),fM=n(re,"LI",{});var keo=s(fM);nAe=n(keo,"STRONG",{});var ipa=s(nAe);_0r=r(ipa,"big_bird"),ipa.forEach(t),v0r=r(keo," \u2014 "),eoe=n(keo,"A",{href:!0});var dpa=s(eoe);b0r=r(dpa,"BigBirdForMultipleChoice"),dpa.forEach(t),F0r=r(keo," (BigBird model)"),keo.forEach(t),T0r=i(re),mM=n(re,"LI",{});var Seo=s(mM);sAe=n(Seo,"STRONG",{});var cpa=s(sAe);M0r=r(cpa,"camembert"),cpa.forEach(t),E0r=r(Seo," \u2014 "),ooe=n(Seo,"A",{href:!0});var fpa=s(ooe);C0r=r(fpa,"CamembertForMultipleChoice"),fpa.forEach(t),w0r=r(Seo," (CamemBERT model)"),Seo.forEach(t),A0r=i(re),gM=n(re,"LI",{});var Reo=s(gM);lAe=n(Reo,"STRONG",{});var mpa=s(lAe);L0r=r(mpa,"canine"),mpa.forEach(t),y0r=r(Reo," \u2014 "),roe=n(Reo,"A",{href:!0});var gpa=s(roe);x0r=r(gpa,"CanineForMultipleChoice"),gpa.forEach(t),$0r=r(Reo," (CANINE model)"),Reo.forEach(t),k0r=i(re),hM=n(re,"LI",{});var Peo=s(hM);iAe=n(Peo,"STRONG",{});var hpa=s(iAe);S0r=r(hpa,"convbert"),hpa.forEach(t),R0r=r(Peo," \u2014 "),toe=n(Peo,"A",{href:!0});var upa=s(toe);P0r=r(upa,"ConvBertForMultipleChoice"),upa.forEach(t),I0r=r(Peo," (ConvBERT model)"),Peo.forEach(t),B0r=i(re),uM=n(re,"LI",{});var Ieo=s(uM);dAe=n(Ieo,"STRONG",{});var ppa=s(dAe);N0r=r(ppa,"data2vec-text"),ppa.forEach(t),D0r=r(Ieo," \u2014 "),aoe=n(Ieo,"A",{href:!0});var _pa=s(aoe);q0r=r(_pa,"Data2VecTextForMultipleChoice"),_pa.forEach(t),j0r=r(Ieo," (Data2VecText model)"),Ieo.forEach(t),G0r=i(re),pM=n(re,"LI",{});var Beo=s(pM);cAe=n(Beo,"STRONG",{});var vpa=s(cAe);O0r=r(vpa,"deberta-v2"),vpa.forEach(t),V0r=r(Beo," \u2014 "),noe=n(Beo,"A",{href:!0});var bpa=s(noe);X0r=r(bpa,"DebertaV2ForMultipleChoice"),bpa.forEach(t),z0r=r(Beo," (DeBERTa-v2 model)"),Beo.forEach(t),Q0r=i(re),_M=n(re,"LI",{});var Neo=s(_M);fAe=n(Neo,"STRONG",{});var Fpa=s(fAe);W0r=r(Fpa,"distilbert"),Fpa.forEach(t),U0r=r(Neo," \u2014 "),soe=n(Neo,"A",{href:!0});var Tpa=s(soe);H0r=r(Tpa,"DistilBertForMultipleChoice"),Tpa.forEach(t),J0r=r(Neo," (DistilBERT model)"),Neo.forEach(t),Y0r=i(re),vM=n(re,"LI",{});var Deo=s(vM);mAe=n(Deo,"STRONG",{});var Mpa=s(mAe);Z0r=r(Mpa,"electra"),Mpa.forEach(t),K0r=r(Deo," \u2014 "),loe=n(Deo,"A",{href:!0});var Epa=s(loe);ewr=r(Epa,"ElectraForMultipleChoice"),Epa.forEach(t),owr=r(Deo," (ELECTRA model)"),Deo.forEach(t),rwr=i(re),bM=n(re,"LI",{});var qeo=s(bM);gAe=n(qeo,"STRONG",{});var Cpa=s(gAe);twr=r(Cpa,"ernie"),Cpa.forEach(t),awr=r(qeo," \u2014 "),ioe=n(qeo,"A",{href:!0});var wpa=s(ioe);nwr=r(wpa,"ErnieForMultipleChoice"),wpa.forEach(t),swr=r(qeo," (ERNIE model)"),qeo.forEach(t),lwr=i(re),FM=n(re,"LI",{});var jeo=s(FM);hAe=n(jeo,"STRONG",{});var Apa=s(hAe);iwr=r(Apa,"flaubert"),Apa.forEach(t),dwr=r(jeo," \u2014 "),doe=n(jeo,"A",{href:!0});var Lpa=s(doe);cwr=r(Lpa,"FlaubertForMultipleChoice"),Lpa.forEach(t),fwr=r(jeo," (FlauBERT model)"),jeo.forEach(t),mwr=i(re),TM=n(re,"LI",{});var Geo=s(TM);uAe=n(Geo,"STRONG",{});var ypa=s(uAe);gwr=r(ypa,"fnet"),ypa.forEach(t),hwr=r(Geo," \u2014 "),coe=n(Geo,"A",{href:!0});var xpa=s(coe);uwr=r(xpa,"FNetForMultipleChoice"),xpa.forEach(t),pwr=r(Geo," (FNet model)"),Geo.forEach(t),_wr=i(re),MM=n(re,"LI",{});var Oeo=s(MM);pAe=n(Oeo,"STRONG",{});var $pa=s(pAe);vwr=r($pa,"funnel"),$pa.forEach(t),bwr=r(Oeo," \u2014 "),foe=n(Oeo,"A",{href:!0});var kpa=s(foe);Fwr=r(kpa,"FunnelForMultipleChoice"),kpa.forEach(t),Twr=r(Oeo," (Funnel Transformer model)"),Oeo.forEach(t),Mwr=i(re),EM=n(re,"LI",{});var Veo=s(EM);_Ae=n(Veo,"STRONG",{});var Spa=s(_Ae);Ewr=r(Spa,"ibert"),Spa.forEach(t),Cwr=r(Veo," \u2014 "),moe=n(Veo,"A",{href:!0});var Rpa=s(moe);wwr=r(Rpa,"IBertForMultipleChoice"),Rpa.forEach(t),Awr=r(Veo," (I-BERT model)"),Veo.forEach(t),Lwr=i(re),CM=n(re,"LI",{});var Xeo=s(CM);vAe=n(Xeo,"STRONG",{});var Ppa=s(vAe);ywr=r(Ppa,"longformer"),Ppa.forEach(t),xwr=r(Xeo," \u2014 "),goe=n(Xeo,"A",{href:!0});var Ipa=s(goe);$wr=r(Ipa,"LongformerForMultipleChoice"),Ipa.forEach(t),kwr=r(Xeo," (Longformer model)"),Xeo.forEach(t),Swr=i(re),wM=n(re,"LI",{});var zeo=s(wM);bAe=n(zeo,"STRONG",{});var Bpa=s(bAe);Rwr=r(Bpa,"luke"),Bpa.forEach(t),Pwr=r(zeo," \u2014 "),hoe=n(zeo,"A",{href:!0});var Npa=s(hoe);Iwr=r(Npa,"LukeForMultipleChoice"),Npa.forEach(t),Bwr=r(zeo," (LUKE 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"),voe=n(Heo,"A",{href:!0});var zpa=s(voe);oAr=r(zpa,"NezhaForMultipleChoice"),zpa.forEach(t),rAr=r(Heo," (Nezha model)"),Heo.forEach(t),tAr=i(re),$M=n(re,"LI",{});var Jeo=s($M);CAe=n(Jeo,"STRONG",{});var Qpa=s(CAe);aAr=r(Qpa,"nystromformer"),Qpa.forEach(t),nAr=r(Jeo," \u2014 "),boe=n(Jeo,"A",{href:!0});var Wpa=s(boe);sAr=r(Wpa,"NystromformerForMultipleChoice"),Wpa.forEach(t),lAr=r(Jeo," (Nystr\xF6mformer model)"),Jeo.forEach(t),iAr=i(re),kM=n(re,"LI",{});var Yeo=s(kM);wAe=n(Yeo,"STRONG",{});var Upa=s(wAe);dAr=r(Upa,"qdqbert"),Upa.forEach(t),cAr=r(Yeo," \u2014 "),Foe=n(Yeo,"A",{href:!0});var Hpa=s(Foe);fAr=r(Hpa,"QDQBertForMultipleChoice"),Hpa.forEach(t),mAr=r(Yeo," (QDQBert model)"),Yeo.forEach(t),gAr=i(re),SM=n(re,"LI",{});var Zeo=s(SM);AAe=n(Zeo,"STRONG",{});var Jpa=s(AAe);hAr=r(Jpa,"rembert"),Jpa.forEach(t),uAr=r(Zeo," \u2014 "),Toe=n(Zeo,"A",{href:!0});var Ypa=s(Toe);pAr=r(Ypa,"RemBertForMultipleChoice"),Ypa.forEach(t),_Ar=r(Zeo," (RemBERT 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"),woe=n(roo,"A",{href:!0});var n_a=s(woe);BAr=r(n_a,"SqueezeBertForMultipleChoice"),n_a.forEach(t),NAr=r(roo," (SqueezeBERT model)"),roo.forEach(t),DAr=i(re),NM=n(re,"LI",{});var too=s(NM);kAe=n(too,"STRONG",{});var s_a=s(kAe);qAr=r(s_a,"xlm"),s_a.forEach(t),jAr=r(too," \u2014 "),Aoe=n(too,"A",{href:!0});var l_a=s(Aoe);GAr=r(l_a,"XLMForMultipleChoice"),l_a.forEach(t),OAr=r(too," (XLM model)"),too.forEach(t),VAr=i(re),DM=n(re,"LI",{});var aoo=s(DM);SAe=n(aoo,"STRONG",{});var i_a=s(SAe);XAr=r(i_a,"xlm-roberta"),i_a.forEach(t),zAr=r(aoo," \u2014 "),Loe=n(aoo,"A",{href:!0});var d_a=s(Loe);QAr=r(d_a,"XLMRobertaForMultipleChoice"),d_a.forEach(t),WAr=r(aoo," (XLM-RoBERTa model)"),aoo.forEach(t),UAr=i(re),qM=n(re,"LI",{});var noo=s(qM);RAe=n(noo,"STRONG",{});var c_a=s(RAe);HAr=r(c_a,"xlm-roberta-xl"),c_a.forEach(t),JAr=r(noo," \u2014 "),yoe=n(noo,"A",{href:!0});var f_a=s(yoe);YAr=r(f_a,"XLMRobertaXLForMultipleChoice"),f_a.forEach(t),ZAr=r(noo," (XLM-RoBERTa-XL 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deactivated). To train the model, you should first set it back in training mode with `),NAe=n(ioo,"CODE",{});var __a=s(NAe);g6r=r(__a,"model.train()"),__a.forEach(t),ioo.forEach(t),h6r=i(qa),T(VM.$$.fragment,qa),qa.forEach(t),ri.forEach(t),Nfo=i(f),gc=n(f,"H2",{class:!0});var sho=s(gc);XM=n(sho,"A",{id:!0,class:!0,href:!0});var v_a=s(XM);DAe=n(v_a,"SPAN",{});var b_a=s(DAe);T(yR.$$.fragment,b_a),b_a.forEach(t),v_a.forEach(t),u6r=i(sho),qAe=n(sho,"SPAN",{});var F_a=s(qAe);p6r=r(F_a,"AutoModelForNextSentencePrediction"),F_a.forEach(t),sho.forEach(t),Dfo=i(f),Uo=n(f,"DIV",{class:!0});var ti=s(Uo);T(xR.$$.fragment,ti),_6r=i(ti),hc=n(ti,"P",{});var vhe=s(hc);v6r=r(vhe,`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 `),koe=n(vhe,"A",{href:!0});var T_a=s(koe);b6r=r(T_a,"from_pretrained()"),T_a.forEach(t),F6r=r(vhe," class method or the "),Soe=n(vhe,"A",{href:!0});var M_a=s(Soe);T6r=r(M_a,"from_config()"),M_a.forEach(t),M6r=r(vhe,` class
method.`),vhe.forEach(t),E6r=i(ti),$R=n(ti,"P",{});var lho=s($R);C6r=r(lho,"This class cannot be instantiated directly using "),jAe=n(lho,"CODE",{});var E_a=s(jAe);w6r=r(E_a,"__init__()"),E_a.forEach(t),A6r=r(lho," (throws an error)."),lho.forEach(t),L6r=i(ti),Pt=n(ti,"DIV",{class:!0});var p$=s(Pt);T(kR.$$.fragment,p$),y6r=i(p$),GAe=n(p$,"P",{});var C_a=s(GAe);x6r=r(C_a,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),C_a.forEach(t),$6r=i(p$),uc=n(p$,"P",{});var bhe=s(uc);k6r=r(bhe,`Note:
Loading a model from its configuration file does `),OAe=n(bhe,"STRONG",{});var w_a=s(OAe);S6r=r(w_a,"not"),w_a.forEach(t),R6r=r(bhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Roe=n(bhe,"A",{href:!0});var A_a=s(Roe);P6r=r(A_a,"from_pretrained()"),A_a.forEach(t),I6r=r(bhe," to load the model weights."),bhe.forEach(t),B6r=i(p$),T(zM.$$.fragment,p$),p$.forEach(t),N6r=i(ti),mo=n(ti,"DIV",{class:!0});var ja=s(mo);T(SR.$$.fragment,ja),D6r=i(ja),VAe=n(ja,"P",{});var L_a=s(VAe);q6r=r(L_a,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),L_a.forEach(t),j6r=i(ja),Tn=n(ja,"P",{});var _$=s(Tn);G6r=r(_$,"The model class to instantiate is selected based on the "),XAe=n(_$,"CODE",{});var y_a=s(XAe);O6r=r(y_a,"model_type"),y_a.forEach(t),V6r=r(_$,` property of the config object (either
passed as an argument or loaded from `),zAe=n(_$,"CODE",{});var x_a=s(zAe);X6r=r(x_a,"pretrained_model_name_or_path"),x_a.forEach(t),z6r=r(_$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),QAe=n(_$,"CODE",{});var $_a=s(QAe);Q6r=r($_a,"pretrained_model_name_or_path"),$_a.forEach(t),W6r=r(_$,":"),_$.forEach(t),U6r=i(ja),Ye=n(ja,"UL",{});var bt=s(Ye);QM=n(bt,"LI",{});var doo=s(QM);WAe=n(doo,"STRONG",{});var k_a=s(WAe);H6r=r(k_a,"bert"),k_a.forEach(t),J6r=r(doo," \u2014 "),Poe=n(doo,"A",{href:!0});var S_a=s(Poe);Y6r=r(S_a,"BertForNextSentencePrediction"),S_a.forEach(t),Z6r=r(doo," (BERT model)"),doo.forEach(t),K6r=i(bt),WM=n(bt,"LI",{});var coo=s(WM);UAe=n(coo,"STRONG",{});var R_a=s(UAe);e8r=r(R_a,"ernie"),R_a.forEach(t),o8r=r(coo," \u2014 "),Ioe=n(coo,"A",{href:!0});var P_a=s(Ioe);r8r=r(P_a,"ErnieForNextSentencePrediction"),P_a.forEach(t),t8r=r(coo," (ERNIE model)"),coo.forEach(t),a8r=i(bt),UM=n(bt,"LI",{});var foo=s(UM);HAe=n(foo,"STRONG",{});var I_a=s(HAe);n8r=r(I_a,"fnet"),I_a.forEach(t),s8r=r(foo," \u2014 "),Boe=n(foo,"A",{href:!0});var B_a=s(Boe);l8r=r(B_a,"FNetForNextSentencePrediction"),B_a.forEach(t),i8r=r(foo," (FNet model)"),foo.forEach(t),d8r=i(bt),HM=n(bt,"LI",{});var moo=s(HM);JAe=n(moo,"STRONG",{});var N_a=s(JAe);c8r=r(N_a,"megatron-bert"),N_a.forEach(t),f8r=r(moo," \u2014 "),Noe=n(moo,"A",{href:!0});var D_a=s(Noe);m8r=r(D_a,"MegatronBertForNextSentencePrediction"),D_a.forEach(t),g8r=r(moo," (Megatron-BERT model)"),moo.forEach(t),h8r=i(bt),JM=n(bt,"LI",{});var goo=s(JM);YAe=n(goo,"STRONG",{});var q_a=s(YAe);u8r=r(q_a,"mobilebert"),q_a.forEach(t),p8r=r(goo," \u2014 "),Doe=n(goo,"A",{href:!0});var j_a=s(Doe);_8r=r(j_a,"MobileBertForNextSentencePrediction"),j_a.forEach(t),v8r=r(goo," (MobileBERT model)"),goo.forEach(t),b8r=i(bt),YM=n(bt,"LI",{});var hoo=s(YM);ZAe=n(hoo,"STRONG",{});var G_a=s(ZAe);F8r=r(G_a,"nezha"),G_a.forEach(t),T8r=r(hoo," \u2014 "),qoe=n(hoo,"A",{href:!0});var O_a=s(qoe);M8r=r(O_a,"NezhaForNextSentencePrediction"),O_a.forEach(t),E8r=r(hoo," (Nezha model)"),hoo.forEach(t),C8r=i(bt),ZM=n(bt,"LI",{});var uoo=s(ZM);KAe=n(uoo,"STRONG",{});var V_a=s(KAe);w8r=r(V_a,"qdqbert"),V_a.forEach(t),A8r=r(uoo," \u2014 "),joe=n(uoo,"A",{href:!0});var X_a=s(joe);L8r=r(X_a,"QDQBertForNextSentencePrediction"),X_a.forEach(t),y8r=r(uoo," (QDQBert model)"),uoo.forEach(t),bt.forEach(t),x8r=i(ja),KM=n(ja,"P",{});var poo=s(KM);$8r=r(poo,"The model is set in evaluation mode by default using "),e6e=n(poo,"CODE",{});var z_a=s(e6e);k8r=r(z_a,"model.eval()"),z_a.forEach(t),S8r=r(poo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),o6e=n(poo,"CODE",{});var Q_a=s(o6e);R8r=r(Q_a,"model.train()"),Q_a.forEach(t),poo.forEach(t),P8r=i(ja),T(eE.$$.fragment,ja),ja.forEach(t),ti.forEach(t),qfo=i(f),pc=n(f,"H2",{class:!0});var iho=s(pc);oE=n(iho,"A",{id:!0,class:!0,href:!0});var W_a=s(oE);r6e=n(W_a,"SPAN",{});var U_a=s(r6e);T(RR.$$.fragment,U_a),U_a.forEach(t),W_a.forEach(t),I8r=i(iho),t6e=n(iho,"SPAN",{});var H_a=s(t6e);B8r=r(H_a,"AutoModelForTokenClassification"),H_a.forEach(t),iho.forEach(t),jfo=i(f),Ho=n(f,"DIV",{class:!0});var ai=s(Ho);T(PR.$$.fragment,ai),N8r=i(ai),_c=n(ai,"P",{});var Fhe=s(_c);D8r=r(Fhe,`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 `),Goe=n(Fhe,"A",{href:!0});var J_a=s(Goe);q8r=r(J_a,"from_pretrained()"),J_a.forEach(t),j8r=r(Fhe," class method or the "),Ooe=n(Fhe,"A",{href:!0});var Y_a=s(Ooe);G8r=r(Y_a,"from_config()"),Y_a.forEach(t),O8r=r(Fhe,` class
method.`),Fhe.forEach(t),V8r=i(ai),IR=n(ai,"P",{});var dho=s(IR);X8r=r(dho,"This class cannot be instantiated directly using "),a6e=n(dho,"CODE",{});var Z_a=s(a6e);z8r=r(Z_a,"__init__()"),Z_a.forEach(t),Q8r=r(dho," (throws an error)."),dho.forEach(t),W8r=i(ai),It=n(ai,"DIV",{class:!0});var v$=s(It);T(BR.$$.fragment,v$),U8r=i(v$),n6e=n(v$,"P",{});var K_a=s(n6e);H8r=r(K_a,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),K_a.forEach(t),J8r=i(v$),vc=n(v$,"P",{});var The=s(vc);Y8r=r(The,`Note:
Loading a model from its configuration file does `),s6e=n(The,"STRONG",{});var e1a=s(s6e);Z8r=r(e1a,"not"),e1a.forEach(t),K8r=r(The,` load the model weights. It only affects the
model\u2019s configuration. Use `),Voe=n(The,"A",{href:!0});var o1a=s(Voe);e7r=r(o1a,"from_pretrained()"),o1a.forEach(t),o7r=r(The," to load the model weights."),The.forEach(t),r7r=i(v$),T(rE.$$.fragment,v$),v$.forEach(t),t7r=i(ai),go=n(ai,"DIV",{class:!0});var Ga=s(go);T(NR.$$.fragment,Ga),a7r=i(Ga),l6e=n(Ga,"P",{});var r1a=s(l6e);n7r=r(r1a,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),r1a.forEach(t),s7r=i(Ga),Mn=n(Ga,"P",{});var b$=s(Mn);l7r=r(b$,"The model class to instantiate is selected based on the "),i6e=n(b$,"CODE",{});var t1a=s(i6e);i7r=r(t1a,"model_type"),t1a.forEach(t),d7r=r(b$,` property of the config object (either
passed as an argument or loaded from `),d6e=n(b$,"CODE",{});var a1a=s(d6e);c7r=r(a1a,"pretrained_model_name_or_path"),a1a.forEach(t),f7r=r(b$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),c6e=n(b$,"CODE",{});var n1a=s(c6e);m7r=r(n1a,"pretrained_model_name_or_path"),n1a.forEach(t),g7r=r(b$,":"),b$.forEach(t),h7r=i(Ga),H=n(Ga,"UL",{});var K=s(H);tE=n(K,"LI",{});var _oo=s(tE);f6e=n(_oo,"STRONG",{});var s1a=s(f6e);u7r=r(s1a,"albert"),s1a.forEach(t),p7r=r(_oo," \u2014 "),Xoe=n(_oo,"A",{href:!0});var l1a=s(Xoe);_7r=r(l1a,"AlbertForTokenClassification"),l1a.forEach(t),v7r=r(_oo," (ALBERT model)"),_oo.forEach(t),b7r=i(K),aE=n(K,"LI",{});var voo=s(aE);m6e=n(voo,"STRONG",{});var i1a=s(m6e);F7r=r(i1a,"bert"),i1a.forEach(t),T7r=r(voo," \u2014 "),zoe=n(voo,"A",{href:!0});var d1a=s(zoe);M7r=r(d1a,"BertForTokenClassification"),d1a.forEach(t),E7r=r(voo," (BERT model)"),voo.forEach(t),C7r=i(K),nE=n(K,"LI",{});var boo=s(nE);g6e=n(boo,"STRONG",{});var c1a=s(g6e);w7r=r(c1a,"big_bird"),c1a.forEach(t),A7r=r(boo," \u2014 "),Qoe=n(boo,"A",{href:!0});var f1a=s(Qoe);L7r=r(f1a,"BigBirdForTokenClassification"),f1a.forEach(t),y7r=r(boo," (BigBird model)"),boo.forEach(t),x7r=i(K),sE=n(K,"LI",{});var Foo=s(sE);h6e=n(Foo,"STRONG",{});var m1a=s(h6e);$7r=r(m1a,"bloom"),m1a.forEach(t),k7r=r(Foo," \u2014 "),Woe=n(Foo,"A",{href:!0});var g1a=s(Woe);S7r=r(g1a,"BloomForTokenClassification"),g1a.forEach(t),R7r=r(Foo," (BLOOM model)"),Foo.forEach(t),P7r=i(K),lE=n(K,"LI",{});var Too=s(lE);u6e=n(Too,"STRONG",{});var h1a=s(u6e);I7r=r(h1a,"camembert"),h1a.forEach(t),B7r=r(Too," \u2014 "),Uoe=n(Too,"A",{href:!0});var u1a=s(Uoe);N7r=r(u1a,"CamembertForTokenClassification"),u1a.forEach(t),D7r=r(Too," (CamemBERT model)"),Too.forEach(t),q7r=i(K),iE=n(K,"LI",{});var Moo=s(iE);p6e=n(Moo,"STRONG",{});var p1a=s(p6e);j7r=r(p1a,"canine"),p1a.forEach(t),G7r=r(Moo," \u2014 "),Hoe=n(Moo,"A",{href:!0});var _1a=s(Hoe);O7r=r(_1a,"CanineForTokenClassification"),_1a.forEach(t),V7r=r(Moo," (CANINE model)"),Moo.forEach(t),X7r=i(K),dE=n(K,"LI",{});var Eoo=s(dE);_6e=n(Eoo,"STRONG",{});var v1a=s(_6e);z7r=r(v1a,"convbert"),v1a.forEach(t),Q7r=r(Eoo," \u2014 "),Joe=n(Eoo,"A",{href:!0});var b1a=s(Joe);W7r=r(b1a,"ConvBertForTokenClassification"),b1a.forEach(t),U7r=r(Eoo," (ConvBERT model)"),Eoo.forEach(t),H7r=i(K),cE=n(K,"LI",{});var Coo=s(cE);v6e=n(Coo,"STRONG",{});var F1a=s(v6e);J7r=r(F1a,"data2vec-text"),F1a.forEach(t),Y7r=r(Coo," \u2014 "),Yoe=n(Coo,"A",{href:!0});var T1a=s(Yoe);Z7r=r(T1a,"Data2VecTextForTokenClassification"),T1a.forEach(t),K7r=r(Coo," (Data2VecText model)"),Coo.forEach(t),eLr=i(K),fE=n(K,"LI",{});var woo=s(fE);b6e=n(woo,"STRONG",{});var M1a=s(b6e);oLr=r(M1a,"deberta"),M1a.forEach(t),rLr=r(woo," \u2014 "),Zoe=n(woo,"A",{href:!0});var E1a=s(Zoe);tLr=r(E1a,"DebertaForTokenClassification"),E1a.forEach(t),aLr=r(woo," (DeBERTa model)"),woo.forEach(t),nLr=i(K),mE=n(K,"LI",{});var Aoo=s(mE);F6e=n(Aoo,"STRONG",{});var C1a=s(F6e);sLr=r(C1a,"deberta-v2"),C1a.forEach(t),lLr=r(Aoo," \u2014 "),Koe=n(Aoo,"A",{href:!0});var w1a=s(Koe);iLr=r(w1a,"DebertaV2ForTokenClassification"),w1a.forEach(t),dLr=r(Aoo," (DeBERTa-v2 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"),tre=n($oo,"A",{href:!0});var R1a=s(tre);yLr=r(R1a,"EsmForTokenClassification"),R1a.forEach(t),xLr=r($oo," (ESM model)"),$oo.forEach(t),$Lr=i(K),_E=n(K,"LI",{});var koo=s(_E);w6e=n(koo,"STRONG",{});var P1a=s(w6e);kLr=r(P1a,"flaubert"),P1a.forEach(t),SLr=r(koo," \u2014 "),are=n(koo,"A",{href:!0});var I1a=s(are);RLr=r(I1a,"FlaubertForTokenClassification"),I1a.forEach(t),PLr=r(koo," (FlauBERT model)"),koo.forEach(t),ILr=i(K),vE=n(K,"LI",{});var Soo=s(vE);A6e=n(Soo,"STRONG",{});var B1a=s(A6e);BLr=r(B1a,"fnet"),B1a.forEach(t),NLr=r(Soo," \u2014 "),nre=n(Soo,"A",{href:!0});var N1a=s(nre);DLr=r(N1a,"FNetForTokenClassification"),N1a.forEach(t),qLr=r(Soo," (FNet model)"),Soo.forEach(t),jLr=i(K),bE=n(K,"LI",{});var Roo=s(bE);L6e=n(Roo,"STRONG",{});var D1a=s(L6e);GLr=r(D1a,"funnel"),D1a.forEach(t),OLr=r(Roo," \u2014 "),sre=n(Roo,"A",{href:!0});var q1a=s(sre);VLr=r(q1a,"FunnelForTokenClassification"),q1a.forEach(t),XLr=r(Roo," (Funnel Transformer 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P2a=s(Z6e);axr=r(P2a,"yoso"),P2a.forEach(t),nxr=r(aro," \u2014 "),kre=n(aro,"A",{href:!0});var I2a=s(kre);sxr=r(I2a,"YosoForTokenClassification"),I2a.forEach(t),lxr=r(aro," (YOSO model)"),aro.forEach(t),K.forEach(t),ixr=i(Ga),zE=n(Ga,"P",{});var nro=s(zE);dxr=r(nro,"The model is set in evaluation mode by default using "),K6e=n(nro,"CODE",{});var B2a=s(K6e);cxr=r(B2a,"model.eval()"),B2a.forEach(t),fxr=r(nro,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),e8e=n(nro,"CODE",{});var N2a=s(e8e);mxr=r(N2a,"model.train()"),N2a.forEach(t),nro.forEach(t),gxr=i(Ga),T(QE.$$.fragment,Ga),Ga.forEach(t),ai.forEach(t),Gfo=i(f),bc=n(f,"H2",{class:!0});var cho=s(bc);WE=n(cho,"A",{id:!0,class:!0,href:!0});var D2a=s(WE);o8e=n(D2a,"SPAN",{});var q2a=s(o8e);T(DR.$$.fragment,q2a),q2a.forEach(t),D2a.forEach(t),hxr=i(cho),r8e=n(cho,"SPAN",{});var j2a=s(r8e);uxr=r(j2a,"AutoModelForQuestionAnswering"),j2a.forEach(t),cho.forEach(t),Ofo=i(f),Jo=n(f,"DIV",{class:!0});var ni=s(Jo);T(qR.$$.fragment,ni),pxr=i(ni),Fc=n(ni,"P",{});var Mhe=s(Fc);_xr=r(Mhe,`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 `),Sre=n(Mhe,"A",{href:!0});var G2a=s(Sre);vxr=r(G2a,"from_pretrained()"),G2a.forEach(t),bxr=r(Mhe," class method or the "),Rre=n(Mhe,"A",{href:!0});var O2a=s(Rre);Fxr=r(O2a,"from_config()"),O2a.forEach(t),Txr=r(Mhe,` class
method.`),Mhe.forEach(t),Mxr=i(ni),jR=n(ni,"P",{});var fho=s(jR);Exr=r(fho,"This class cannot be instantiated directly using "),t8e=n(fho,"CODE",{});var V2a=s(t8e);Cxr=r(V2a,"__init__()"),V2a.forEach(t),wxr=r(fho," (throws an error)."),fho.forEach(t),Axr=i(ni),Bt=n(ni,"DIV",{class:!0});var F$=s(Bt);T(GR.$$.fragment,F$),Lxr=i(F$),a8e=n(F$,"P",{});var X2a=s(a8e);yxr=r(X2a,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),X2a.forEach(t),xxr=i(F$),Tc=n(F$,"P",{});var Ehe=s(Tc);$xr=r(Ehe,`Note:
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model\u2019s configuration. Use `),Pre=n(Ehe,"A",{href:!0});var Q2a=s(Pre);Rxr=r(Q2a,"from_pretrained()"),Q2a.forEach(t),Pxr=r(Ehe," to load the model weights."),Ehe.forEach(t),Ixr=i(F$),T(UE.$$.fragment,F$),F$.forEach(t),Bxr=i(ni),ho=n(ni,"DIV",{class:!0});var Oa=s(ho);T(OR.$$.fragment,Oa),Nxr=i(Oa),s8e=n(Oa,"P",{});var W2a=s(s8e);Dxr=r(W2a,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),W2a.forEach(t),qxr=i(Oa),En=n(Oa,"P",{});var T$=s(En);jxr=r(T$,"The model class to instantiate is selected based on the "),l8e=n(T$,"CODE",{});var U2a=s(l8e);Gxr=r(U2a,"model_type"),U2a.forEach(t),Oxr=r(T$,` property of the config object (either
passed as an argument or loaded from `),i8e=n(T$,"CODE",{});var H2a=s(i8e);Vxr=r(H2a,"pretrained_model_name_or_path"),H2a.forEach(t),Xxr=r(T$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),d8e=n(T$,"CODE",{});var J2a=s(d8e);zxr=r(J2a,"pretrained_model_name_or_path"),J2a.forEach(t),Qxr=r(T$,":"),T$.forEach(t),Wxr=i(Oa),X=n(Oa,"UL",{});var Q=s(X);HE=n(Q,"LI",{});var sro=s(HE);c8e=n(sro,"STRONG",{});var Y2a=s(c8e);Uxr=r(Y2a,"albert"),Y2a.forEach(t),Hxr=r(sro," \u2014 "),Ire=n(sro,"A",{href:!0});var Z2a=s(Ire);Jxr=r(Z2a,"AlbertForQuestionAnswering"),Z2a.forEach(t),Yxr=r(sro," (ALBERT model)"),sro.forEach(t),Zxr=i(Q),JE=n(Q,"LI",{});var lro=s(JE);f8e=n(lro,"STRONG",{});var K2a=s(f8e);Kxr=r(K2a,"bart"),K2a.forEach(t),e$r=r(lro," \u2014 "),Bre=n(lro,"A",{href:!0});var eva=s(Bre);o$r=r(eva,"BartForQuestionAnswering"),eva.forEach(t),r$r=r(lro," (BART model)"),lro.forEach(t),t$r=i(Q),YE=n(Q,"LI",{});var iro=s(YE);m8e=n(iro,"STRONG",{});var ova=s(m8e);a$r=r(ova,"bert"),ova.forEach(t),n$r=r(iro," \u2014 "),Nre=n(iro,"A",{href:!0});var rva=s(Nre);s$r=r(rva,"BertForQuestionAnswering"),rva.forEach(t),l$r=r(iro," (BERT 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Ero=s(mC);L8e=n(Ero,"STRONG",{});var kva=s(L8e);wkr=r(kva,"funnel"),kva.forEach(t),Akr=r(Ero," \u2014 "),Zre=n(Ero,"A",{href:!0});var Sva=s(Zre);Lkr=r(Sva,"FunnelForQuestionAnswering"),Sva.forEach(t),ykr=r(Ero," (Funnel Transformer model)"),Ero.forEach(t),xkr=i(Q),gC=n(Q,"LI",{});var Cro=s(gC);y8e=n(Cro,"STRONG",{});var Rva=s(y8e);$kr=r(Rva,"gptj"),Rva.forEach(t),kkr=r(Cro," \u2014 "),Kre=n(Cro,"A",{href:!0});var Pva=s(Kre);Skr=r(Pva,"GPTJForQuestionAnswering"),Pva.forEach(t),Rkr=r(Cro," (GPT-J model)"),Cro.forEach(t),Pkr=i(Q),hC=n(Q,"LI",{});var wro=s(hC);x8e=n(wro,"STRONG",{});var Iva=s(x8e);Ikr=r(Iva,"ibert"),Iva.forEach(t),Bkr=r(wro," \u2014 "),ete=n(wro,"A",{href:!0});var Bva=s(ete);Nkr=r(Bva,"IBertForQuestionAnswering"),Bva.forEach(t),Dkr=r(wro," (I-BERT model)"),wro.forEach(t),qkr=i(Q),uC=n(Q,"LI",{});var Aro=s(uC);$8e=n(Aro,"STRONG",{});var Nva=s($8e);jkr=r(Nva,"layoutlmv2"),Nva.forEach(t),Gkr=r(Aro," \u2014 "),ote=n(Aro,"A",{href:!0});var Dva=s(ote);Okr=r(Dva,"LayoutLMv2ForQuestionAnswering"),Dva.forEach(t),Vkr=r(Aro," (LayoutLMv2 model)"),Aro.forEach(t),Xkr=i(Q),pC=n(Q,"LI",{});var Lro=s(pC);k8e=n(Lro,"STRONG",{});var qva=s(k8e);zkr=r(qva,"layoutlmv3"),qva.forEach(t),Qkr=r(Lro," \u2014 "),rte=n(Lro,"A",{href:!0});var jva=s(rte);Wkr=r(jva,"LayoutLMv3ForQuestionAnswering"),jva.forEach(t),Ukr=r(Lro," (LayoutLMv3 model)"),Lro.forEach(t),Hkr=i(Q),_C=n(Q,"LI",{});var yro=s(_C);S8e=n(yro,"STRONG",{});var Gva=s(S8e);Jkr=r(Gva,"led"),Gva.forEach(t),Ykr=r(yro," \u2014 "),tte=n(yro,"A",{href:!0});var Ova=s(tte);Zkr=r(Ova,"LEDForQuestionAnswering"),Ova.forEach(t),Kkr=r(yro," (LED model)"),yro.forEach(t),eSr=i(Q),vC=n(Q,"LI",{});var xro=s(vC);R8e=n(xro,"STRONG",{});var Vva=s(R8e);oSr=r(Vva,"lilt"),Vva.forEach(t),rSr=r(xro," \u2014 "),ate=n(xro,"A",{href:!0});var Xva=s(ate);tSr=r(Xva,"LiltForQuestionAnswering"),Xva.forEach(t),aSr=r(xro," (LiLT model)"),xro.forEach(t),nSr=i(Q),bC=n(Q,"LI",{});var $ro=s(bC);P8e=n($ro,"STRONG",{});var zva=s(P8e);sSr=r(zva,"longformer"),zva.forEach(t),lSr=r($ro," \u2014 "),nte=n($ro,"A",{href:!0});var Qva=s(nte);iSr=r(Qva,"LongformerForQuestionAnswering"),Qva.forEach(t),dSr=r($ro," (Longformer model)"),$ro.forEach(t),cSr=i(Q),FC=n(Q,"LI",{});var kro=s(FC);I8e=n(kro,"STRONG",{});var Wva=s(I8e);fSr=r(Wva,"luke"),Wva.forEach(t),mSr=r(kro," \u2014 "),ste=n(kro,"A",{href:!0});var Uva=s(ste);gSr=r(Uva,"LukeForQuestionAnswering"),Uva.forEach(t),hSr=r(kro," (LUKE model)"),kro.forEach(t),uSr=i(Q),TC=n(Q,"LI",{});var Sro=s(TC);B8e=n(Sro,"STRONG",{});var Hva=s(B8e);pSr=r(Hva,"lxmert"),Hva.forEach(t),_Sr=r(Sro," \u2014 "),lte=n(Sro,"A",{href:!0});var Jva=s(lte);vSr=r(Jva,"LxmertForQuestionAnswering"),Jva.forEach(t),bSr=r(Sro," (LXMERT model)"),Sro.forEach(t),FSr=i(Q),MC=n(Q,"LI",{});var Rro=s(MC);N8e=n(Rro,"STRONG",{});var Yva=s(N8e);TSr=r(Yva,"markuplm"),Yva.forEach(t),MSr=r(Rro," \u2014 "),ite=n(Rro,"A",{href:!0});var Zva=s(ite);ESr=r(Zva,"MarkupLMForQuestionAnswering"),Zva.forEach(t),CSr=r(Rro," (MarkupLM model)"),Rro.forEach(t),wSr=i(Q),EC=n(Q,"LI",{});var Pro=s(EC);D8e=n(Pro,"STRONG",{});var Kva=s(D8e);ASr=r(Kva,"mbart"),Kva.forEach(t),LSr=r(Pro," \u2014 "),dte=n(Pro,"A",{href:!0});var e4a=s(dte);ySr=r(e4a,"MBartForQuestionAnswering"),e4a.forEach(t),xSr=r(Pro," (mBART model)"),Pro.forEach(t),$Sr=i(Q),CC=n(Q,"LI",{});var Iro=s(CC);q8e=n(Iro,"STRONG",{});var o4a=s(q8e);kSr=r(o4a,"megatron-bert"),o4a.forEach(t),SSr=r(Iro," \u2014 "),cte=n(Iro,"A",{href:!0});var r4a=s(cte);RSr=r(r4a,"MegatronBertForQuestionAnswering"),r4a.forEach(t),PSr=r(Iro," (Megatron-BERT model)"),Iro.forEach(t),ISr=i(Q),wC=n(Q,"LI",{});var Bro=s(wC);j8e=n(Bro,"STRONG",{});var t4a=s(j8e);BSr=r(t4a,"mobilebert"),t4a.forEach(t),NSr=r(Bro," \u2014 "),fte=n(Bro,"A",{href:!0});var a4a=s(fte);DSr=r(a4a,"MobileBertForQuestionAnswering"),a4a.forEach(t),qSr=r(Bro," (MobileBERT model)"),Bro.forEach(t),jSr=i(Q),AC=n(Q,"LI",{});var Nro=s(AC);G8e=n(Nro,"STRONG",{});var n4a=s(G8e);GSr=r(n4a,"mpnet"),n4a.forEach(t),OSr=r(Nro," \u2014 "),mte=n(Nro,"A",{href:!0});var s4a=s(mte);VSr=r(s4a,"MPNetForQuestionAnswering"),s4a.forEach(t),XSr=r(Nro," (MPNet model)"),Nro.forEach(t),zSr=i(Q),LC=n(Q,"LI",{});var Dro=s(LC);O8e=n(Dro,"STRONG",{});var l4a=s(O8e);QSr=r(l4a,"mvp"),l4a.forEach(t),WSr=r(Dro," \u2014 "),gte=n(Dro,"A",{href:!0});var i4a=s(gte);USr=r(i4a,"MvpForQuestionAnswering"),i4a.forEach(t),HSr=r(Dro," (MVP model)"),Dro.forEach(t),JSr=i(Q),yC=n(Q,"LI",{});var qro=s(yC);V8e=n(qro,"STRONG",{});var d4a=s(V8e);YSr=r(d4a,"nezha"),d4a.forEach(t),ZSr=r(qro," \u2014 "),hte=n(qro,"A",{href:!0});var c4a=s(hte);KSr=r(c4a,"NezhaForQuestionAnswering"),c4a.forEach(t),eRr=r(qro," (Nezha model)"),qro.forEach(t),oRr=i(Q),xC=n(Q,"LI",{});var jro=s(xC);X8e=n(jro,"STRONG",{});var f4a=s(X8e);rRr=r(f4a,"nystromformer"),f4a.forEach(t),tRr=r(jro," \u2014 "),ute=n(jro,"A",{href:!0});var m4a=s(ute);aRr=r(m4a,"NystromformerForQuestionAnswering"),m4a.forEach(t),nRr=r(jro," (Nystr\xF6mformer model)"),jro.forEach(t),sRr=i(Q),$C=n(Q,"LI",{});var Gro=s($C);z8e=n(Gro,"STRONG",{});var g4a=s(z8e);lRr=r(g4a,"opt"),g4a.forEach(t),iRr=r(Gro," \u2014 "),pte=n(Gro,"A",{href:!0});var h4a=s(pte);dRr=r(h4a,"OPTForQuestionAnswering"),h4a.forEach(t),cRr=r(Gro," (OPT model)"),Gro.forEach(t),fRr=i(Q),kC=n(Q,"LI",{});var Oro=s(kC);Q8e=n(Oro,"STRONG",{});var u4a=s(Q8e);mRr=r(u4a,"qdqbert"),u4a.forEach(t),gRr=r(Oro," \u2014 "),_te=n(Oro,"A",{href:!0});var p4a=s(_te);hRr=r(p4a,"QDQBertForQuestionAnswering"),p4a.forEach(t),uRr=r(Oro," (QDQBert model)"),Oro.forEach(t),pRr=i(Q),SC=n(Q,"LI",{});var Vro=s(SC);W8e=n(Vro,"STRONG",{});var _4a=s(W8e);_Rr=r(_4a,"reformer"),_4a.forEach(t),vRr=r(Vro," \u2014 "),vte=n(Vro,"A",{href:!0});var v4a=s(vte);bRr=r(v4a,"ReformerForQuestionAnswering"),v4a.forEach(t),FRr=r(Vro," (Reformer model)"),Vro.forEach(t),TRr=i(Q),RC=n(Q,"LI",{});var Xro=s(RC);U8e=n(Xro,"STRONG",{});var b4a=s(U8e);MRr=r(b4a,"rembert"),b4a.forEach(t),ERr=r(Xro," \u2014 "),bte=n(Xro,"A",{href:!0});var F4a=s(bte);CRr=r(F4a,"RemBertForQuestionAnswering"),F4a.forEach(t),wRr=r(Xro," (RemBERT model)"),Xro.forEach(t),ARr=i(Q),PC=n(Q,"LI",{});var zro=s(PC);H8e=n(zro,"STRONG",{});var T4a=s(H8e);LRr=r(T4a,"roberta"),T4a.forEach(t),yRr=r(zro," \u2014 "),Fte=n(zro,"A",{href:!0});var M4a=s(Fte);xRr=r(M4a,"RobertaForQuestionAnswering"),M4a.forEach(t),$Rr=r(zro," (RoBERTa model)"),zro.forEach(t),kRr=i(Q),IC=n(Q,"LI",{});var Qro=s(IC);J8e=n(Qro,"STRONG",{});var E4a=s(J8e);SRr=r(E4a,"roc_bert"),E4a.forEach(t),RRr=r(Qro," \u2014 "),Tte=n(Qro,"A",{href:!0});var C4a=s(Tte);PRr=r(C4a,"RoCBertForQuestionAnswering"),C4a.forEach(t),IRr=r(Qro," (RoCBert model)"),Qro.forEach(t),BRr=i(Q),BC=n(Q,"LI",{});var Wro=s(BC);Y8e=n(Wro,"STRONG",{});var w4a=s(Y8e);NRr=r(w4a,"roformer"),w4a.forEach(t),DRr=r(Wro," \u2014 "),Mte=n(Wro,"A",{href:!0});var A4a=s(Mte);qRr=r(A4a,"RoFormerForQuestionAnswering"),A4a.forEach(t),jRr=r(Wro," (RoFormer model)"),Wro.forEach(t),GRr=i(Q),NC=n(Q,"LI",{});var Uro=s(NC);Z8e=n(Uro,"STRONG",{});var L4a=s(Z8e);ORr=r(L4a,"splinter"),L4a.forEach(t),VRr=r(Uro," \u2014 "),Ete=n(Uro,"A",{href:!0});var y4a=s(Ete);XRr=r(y4a,"SplinterForQuestionAnswering"),y4a.forEach(t),zRr=r(Uro," (Splinter model)"),Uro.forEach(t),QRr=i(Q),DC=n(Q,"LI",{});var Hro=s(DC);K8e=n(Hro,"STRONG",{});var x4a=s(K8e);WRr=r(x4a,"squeezebert"),x4a.forEach(t),URr=r(Hro," \u2014 "),Cte=n(Hro,"A",{href:!0});var $4a=s(Cte);HRr=r($4a,"SqueezeBertForQuestionAnswering"),$4a.forEach(t),JRr=r(Hro," (SqueezeBERT model)"),Hro.forEach(t),YRr=i(Q),qC=n(Q,"LI",{});var Jro=s(qC);e7e=n(Jro,"STRONG",{});var k4a=s(e7e);ZRr=r(k4a,"xlm"),k4a.forEach(t),KRr=r(Jro," \u2014 "),wte=n(Jro,"A",{href:!0});var S4a=s(wte);ePr=r(S4a,"XLMForQuestionAnsweringSimple"),S4a.forEach(t),oPr=r(Jro," (XLM model)"),Jro.forEach(t),rPr=i(Q),jC=n(Q,"LI",{});var Yro=s(jC);o7e=n(Yro,"STRONG",{});var R4a=s(o7e);tPr=r(R4a,"xlm-roberta"),R4a.forEach(t),aPr=r(Yro," \u2014 "),Ate=n(Yro,"A",{href:!0});var P4a=s(Ate);nPr=r(P4a,"XLMRobertaForQuestionAnswering"),P4a.forEach(t),sPr=r(Yro," (XLM-RoBERTa model)"),Yro.forEach(t),lPr=i(Q),GC=n(Q,"LI",{});var Zro=s(GC);r7e=n(Zro,"STRONG",{});var I4a=s(r7e);iPr=r(I4a,"xlm-roberta-xl"),I4a.forEach(t),dPr=r(Zro," \u2014 "),Lte=n(Zro,"A",{href:!0});var B4a=s(Lte);cPr=r(B4a,"XLMRobertaXLForQuestionAnswering"),B4a.forEach(t),fPr=r(Zro," (XLM-RoBERTa-XL model)"),Zro.forEach(t),mPr=i(Q),OC=n(Q,"LI",{});var Kro=s(OC);t7e=n(Kro,"STRONG",{});var N4a=s(t7e);gPr=r(N4a,"xlnet"),N4a.forEach(t),hPr=r(Kro," \u2014 "),yte=n(Kro,"A",{href:!0});var D4a=s(yte);uPr=r(D4a,"XLNetForQuestionAnsweringSimple"),D4a.forEach(t),pPr=r(Kro," (XLNet model)"),Kro.forEach(t),_Pr=i(Q),VC=n(Q,"LI",{});var eto=s(VC);a7e=n(eto,"STRONG",{});var q4a=s(a7e);vPr=r(q4a,"yoso"),q4a.forEach(t),bPr=r(eto," \u2014 "),xte=n(eto,"A",{href:!0});var j4a=s(xte);FPr=r(j4a,"YosoForQuestionAnswering"),j4a.forEach(t),TPr=r(eto," (YOSO model)"),eto.forEach(t),Q.forEach(t),MPr=i(Oa),XC=n(Oa,"P",{});var oto=s(XC);EPr=r(oto,"The model is set in evaluation mode by default using "),n7e=n(oto,"CODE",{});var G4a=s(n7e);CPr=r(G4a,"model.eval()"),G4a.forEach(t),wPr=r(oto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),s7e=n(oto,"CODE",{});var O4a=s(s7e);APr=r(O4a,"model.train()"),O4a.forEach(t),oto.forEach(t),LPr=i(Oa),T(zC.$$.fragment,Oa),Oa.forEach(t),ni.forEach(t),Vfo=i(f),Mc=n(f,"H2",{class:!0});var mho=s(Mc);QC=n(mho,"A",{id:!0,class:!0,href:!0});var V4a=s(QC);l7e=n(V4a,"SPAN",{});var X4a=s(l7e);T(VR.$$.fragment,X4a),X4a.forEach(t),V4a.forEach(t),yPr=i(mho),i7e=n(mho,"SPAN",{});var z4a=s(i7e);xPr=r(z4a,"AutoModelForTableQuestionAnswering"),z4a.forEach(t),mho.forEach(t),Xfo=i(f),Yo=n(f,"DIV",{class:!0});var si=s(Yo);T(XR.$$.fragment,si),$Pr=i(si),Ec=n(si,"P",{});var Che=s(Ec);kPr=r(Che,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),$te=n(Che,"A",{href:!0});var Q4a=s($te);SPr=r(Q4a,"from_pretrained()"),Q4a.forEach(t),RPr=r(Che," class method or the "),kte=n(Che,"A",{href:!0});var W4a=s(kte);PPr=r(W4a,"from_config()"),W4a.forEach(t),IPr=r(Che,` class
method.`),Che.forEach(t),BPr=i(si),zR=n(si,"P",{});var gho=s(zR);NPr=r(gho,"This class cannot be instantiated directly using "),d7e=n(gho,"CODE",{});var U4a=s(d7e);DPr=r(U4a,"__init__()"),U4a.forEach(t),qPr=r(gho," (throws an error)."),gho.forEach(t),jPr=i(si),Nt=n(si,"DIV",{class:!0});var M$=s(Nt);T(QR.$$.fragment,M$),GPr=i(M$),c7e=n(M$,"P",{});var H4a=s(c7e);OPr=r(H4a,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),H4a.forEach(t),VPr=i(M$),Cc=n(M$,"P",{});var whe=s(Cc);XPr=r(whe,`Note:
Loading a model from its configuration file does `),f7e=n(whe,"STRONG",{});var J4a=s(f7e);zPr=r(J4a,"not"),J4a.forEach(t),QPr=r(whe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ste=n(whe,"A",{href:!0});var Y4a=s(Ste);WPr=r(Y4a,"from_pretrained()"),Y4a.forEach(t),UPr=r(whe," to load the model weights."),whe.forEach(t),HPr=i(M$),T(WC.$$.fragment,M$),M$.forEach(t),JPr=i(si),uo=n(si,"DIV",{class:!0});var Va=s(uo);T(WR.$$.fragment,Va),YPr=i(Va),m7e=n(Va,"P",{});var Z4a=s(m7e);ZPr=r(Z4a,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Z4a.forEach(t),KPr=i(Va),Cn=n(Va,"P",{});var E$=s(Cn);eIr=r(E$,"The model class to instantiate is selected based on the "),g7e=n(E$,"CODE",{});var K4a=s(g7e);oIr=r(K4a,"model_type"),K4a.forEach(t),rIr=r(E$,` property of the config object (either
passed as an argument or loaded from `),h7e=n(E$,"CODE",{});var eba=s(h7e);tIr=r(eba,"pretrained_model_name_or_path"),eba.forEach(t),aIr=r(E$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),u7e=n(E$,"CODE",{});var oba=s(u7e);nIr=r(oba,"pretrained_model_name_or_path"),oba.forEach(t),sIr=r(E$,":"),E$.forEach(t),lIr=i(Va),p7e=n(Va,"UL",{});var rba=s(p7e);UC=n(rba,"LI",{});var rto=s(UC);_7e=n(rto,"STRONG",{});var tba=s(_7e);iIr=r(tba,"tapas"),tba.forEach(t),dIr=r(rto," \u2014 "),Rte=n(rto,"A",{href:!0});var aba=s(Rte);cIr=r(aba,"TapasForQuestionAnswering"),aba.forEach(t),fIr=r(rto," (TAPAS model)"),rto.forEach(t),rba.forEach(t),mIr=i(Va),HC=n(Va,"P",{});var tto=s(HC);gIr=r(tto,"The model is set in evaluation mode by default using "),v7e=n(tto,"CODE",{});var nba=s(v7e);hIr=r(nba,"model.eval()"),nba.forEach(t),uIr=r(tto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),b7e=n(tto,"CODE",{});var sba=s(b7e);pIr=r(sba,"model.train()"),sba.forEach(t),tto.forEach(t),_Ir=i(Va),T(JC.$$.fragment,Va),Va.forEach(t),si.forEach(t),zfo=i(f),wc=n(f,"H2",{class:!0});var hho=s(wc);YC=n(hho,"A",{id:!0,class:!0,href:!0});var lba=s(YC);F7e=n(lba,"SPAN",{});var iba=s(F7e);T(UR.$$.fragment,iba),iba.forEach(t),lba.forEach(t),vIr=i(hho),T7e=n(hho,"SPAN",{});var dba=s(T7e);bIr=r(dba,"AutoModelForDocumentQuestionAnswering"),dba.forEach(t),hho.forEach(t),Qfo=i(f),Zo=n(f,"DIV",{class:!0});var li=s(Zo);T(HR.$$.fragment,li),FIr=i(li),Ac=n(li,"P",{});var Ahe=s(Ac);TIr=r(Ahe,`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 `),Pte=n(Ahe,"A",{href:!0});var cba=s(Pte);MIr=r(cba,"from_pretrained()"),cba.forEach(t),EIr=r(Ahe," class method or the "),Ite=n(Ahe,"A",{href:!0});var fba=s(Ite);CIr=r(fba,"from_config()"),fba.forEach(t),wIr=r(Ahe,` class
method.`),Ahe.forEach(t),AIr=i(li),JR=n(li,"P",{});var uho=s(JR);LIr=r(uho,"This class cannot be instantiated directly using "),M7e=n(uho,"CODE",{});var mba=s(M7e);yIr=r(mba,"__init__()"),mba.forEach(t),xIr=r(uho," (throws an error)."),uho.forEach(t),$Ir=i(li),Dt=n(li,"DIV",{class:!0});var C$=s(Dt);T(YR.$$.fragment,C$),kIr=i(C$),E7e=n(C$,"P",{});var gba=s(E7e);SIr=r(gba,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),gba.forEach(t),RIr=i(C$),Lc=n(C$,"P",{});var Lhe=s(Lc);PIr=r(Lhe,`Note:
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model\u2019s configuration. Use `),Bte=n(Lhe,"A",{href:!0});var uba=s(Bte);NIr=r(uba,"from_pretrained()"),uba.forEach(t),DIr=r(Lhe," to load the model weights."),Lhe.forEach(t),qIr=i(C$),T(ZC.$$.fragment,C$),C$.forEach(t),jIr=i(li),po=n(li,"DIV",{class:!0});var Xa=s(po);T(ZR.$$.fragment,Xa),GIr=i(Xa),w7e=n(Xa,"P",{});var pba=s(w7e);OIr=r(pba,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),pba.forEach(t),VIr=i(Xa),wn=n(Xa,"P",{});var w$=s(wn);XIr=r(w$,"The model class to instantiate is selected based on the "),A7e=n(w$,"CODE",{});var _ba=s(A7e);zIr=r(_ba,"model_type"),_ba.forEach(t),QIr=r(w$,` property of the config object (either
passed as an argument or loaded from `),L7e=n(w$,"CODE",{});var vba=s(L7e);WIr=r(vba,"pretrained_model_name_or_path"),vba.forEach(t),UIr=r(w$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),y7e=n(w$,"CODE",{});var bba=s(y7e);HIr=r(bba,"pretrained_model_name_or_path"),bba.forEach(t),JIr=r(w$,":"),w$.forEach(t),YIr=i(Xa),yc=n(Xa,"UL",{});var yhe=s(yc);KC=n(yhe,"LI",{});var ato=s(KC);x7e=n(ato,"STRONG",{});var Fba=s(x7e);ZIr=r(Fba,"layoutlm"),Fba.forEach(t),KIr=r(ato," \u2014 "),Nte=n(ato,"A",{href:!0});var Tba=s(Nte);eBr=r(Tba,"LayoutLMForQuestionAnswering"),Tba.forEach(t),oBr=r(ato," (LayoutLM model)"),ato.forEach(t),rBr=i(yhe),e3=n(yhe,"LI",{});var nto=s(e3);$7e=n(nto,"STRONG",{});var Mba=s($7e);tBr=r(Mba,"layoutlmv2"),Mba.forEach(t),aBr=r(nto," \u2014 "),Dte=n(nto,"A",{href:!0});var Eba=s(Dte);nBr=r(Eba,"LayoutLMv2ForQuestionAnswering"),Eba.forEach(t),sBr=r(nto," (LayoutLMv2 model)"),nto.forEach(t),lBr=i(yhe),o3=n(yhe,"LI",{});var sto=s(o3);k7e=n(sto,"STRONG",{});var Cba=s(k7e);iBr=r(Cba,"layoutlmv3"),Cba.forEach(t),dBr=r(sto," \u2014 "),qte=n(sto,"A",{href:!0});var wba=s(qte);cBr=r(wba,"LayoutLMv3ForQuestionAnswering"),wba.forEach(t),fBr=r(sto," (LayoutLMv3 model)"),sto.forEach(t),yhe.forEach(t),mBr=i(Xa),r3=n(Xa,"P",{});var lto=s(r3);gBr=r(lto,"The model is set in evaluation mode by default using "),S7e=n(lto,"CODE",{});var Aba=s(S7e);hBr=r(Aba,"model.eval()"),Aba.forEach(t),uBr=r(lto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),R7e=n(lto,"CODE",{});var Lba=s(R7e);pBr=r(Lba,"model.train()"),Lba.forEach(t),lto.forEach(t),_Br=i(Xa),T(t3.$$.fragment,Xa),Xa.forEach(t),li.forEach(t),Wfo=i(f),xc=n(f,"H2",{class:!0});var pho=s(xc);a3=n(pho,"A",{id:!0,class:!0,href:!0});var yba=s(a3);P7e=n(yba,"SPAN",{});var xba=s(P7e);T(KR.$$.fragment,xba),xba.forEach(t),yba.forEach(t),vBr=i(pho),I7e=n(pho,"SPAN",{});var $ba=s(I7e);bBr=r($ba,"AutoModelForImageClassification"),$ba.forEach(t),pho.forEach(t),Ufo=i(f),Ko=n(f,"DIV",{class:!0});var ii=s(Ko);T(eP.$$.fragment,ii),FBr=i(ii),$c=n(ii,"P",{});var xhe=s($c);TBr=r(xhe,`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 `),jte=n(xhe,"A",{href:!0});var kba=s(jte);MBr=r(kba,"from_pretrained()"),kba.forEach(t),EBr=r(xhe," class method or the "),Gte=n(xhe,"A",{href:!0});var Sba=s(Gte);CBr=r(Sba,"from_config()"),Sba.forEach(t),wBr=r(xhe,` class
method.`),xhe.forEach(t),ABr=i(ii),oP=n(ii,"P",{});var _ho=s(oP);LBr=r(_ho,"This class cannot be instantiated directly using "),B7e=n(_ho,"CODE",{});var Rba=s(B7e);yBr=r(Rba,"__init__()"),Rba.forEach(t),xBr=r(_ho," (throws an error)."),_ho.forEach(t),$Br=i(ii),qt=n(ii,"DIV",{class:!0});var A$=s(qt);T(rP.$$.fragment,A$),kBr=i(A$),N7e=n(A$,"P",{});var Pba=s(N7e);SBr=r(Pba,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Pba.forEach(t),RBr=i(A$),kc=n(A$,"P",{});var $he=s(kc);PBr=r($he,`Note:
Loading a model from its configuration file does `),D7e=n($he,"STRONG",{});var Iba=s(D7e);IBr=r(Iba,"not"),Iba.forEach(t),BBr=r($he,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ote=n($he,"A",{href:!0});var Bba=s(Ote);NBr=r(Bba,"from_pretrained()"),Bba.forEach(t),DBr=r($he," to load the model weights."),$he.forEach(t),qBr=i(A$),T(n3.$$.fragment,A$),A$.forEach(t),jBr=i(ii),_o=n(ii,"DIV",{class:!0});var za=s(_o);T(tP.$$.fragment,za),GBr=i(za),q7e=n(za,"P",{});var Nba=s(q7e);OBr=r(Nba,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Nba.forEach(t),VBr=i(za),An=n(za,"P",{});var L$=s(An);XBr=r(L$,"The model class to instantiate is selected based on the "),j7e=n(L$,"CODE",{});var Dba=s(j7e);zBr=r(Dba,"model_type"),Dba.forEach(t),QBr=r(L$,` property of the config object (either
passed as an argument or loaded from `),G7e=n(L$,"CODE",{});var qba=s(G7e);WBr=r(qba,"pretrained_model_name_or_path"),qba.forEach(t),UBr=r(L$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),O7e=n(L$,"CODE",{});var jba=s(O7e);HBr=r(jba,"pretrained_model_name_or_path"),jba.forEach(t),JBr=r(L$,":"),L$.forEach(t),YBr=i(za),fe=n(za,"UL",{});var _e=s(fe);s3=n(_e,"LI",{});var ito=s(s3);V7e=n(ito,"STRONG",{});var Gba=s(V7e);ZBr=r(Gba,"beit"),Gba.forEach(t),KBr=r(ito," \u2014 "),Vte=n(ito,"A",{href:!0});var Oba=s(Vte);eNr=r(Oba,"BeitForImageClassification"),Oba.forEach(t),oNr=r(ito," (BEiT model)"),ito.forEach(t),rNr=i(_e),l3=n(_e,"LI",{});var dto=s(l3);X7e=n(dto,"STRONG",{});var Vba=s(X7e);tNr=r(Vba,"convnext"),Vba.forEach(t),aNr=r(dto," \u2014 "),Xte=n(dto,"A",{href:!0});var Xba=s(Xte);nNr=r(Xba,"ConvNextForImageClassification"),Xba.forEach(t),sNr=r(dto," (ConvNeXT model)"),dto.forEach(t),lNr=i(_e),i3=n(_e,"LI",{});var cto=s(i3);z7e=n(cto,"STRONG",{});var zba=s(z7e);iNr=r(zba,"cvt"),zba.forEach(t),dNr=r(cto," \u2014 "),zte=n(cto,"A",{href:!0});var Qba=s(zte);cNr=r(Qba,"CvtForImageClassification"),Qba.forEach(t),fNr=r(cto," (CvT model)"),cto.forEach(t),mNr=i(_e),d3=n(_e,"LI",{});var fto=s(d3);Q7e=n(fto,"STRONG",{});var Wba=s(Q7e);gNr=r(Wba,"data2vec-vision"),Wba.forEach(t),hNr=r(fto," \u2014 "),Qte=n(fto,"A",{href:!0});var Uba=s(Qte);uNr=r(Uba,"Data2VecVisionForImageClassification"),Uba.forEach(t),pNr=r(fto," (Data2VecVision model)"),fto.forEach(t),_Nr=i(_e),jl=n(_e,"LI",{});var Lq=s(jl);W7e=n(Lq,"STRONG",{});var Hba=s(W7e);vNr=r(Hba,"deit"),Hba.forEach(t),bNr=r(Lq," \u2014 "),Wte=n(Lq,"A",{href:!0});var Jba=s(Wte);FNr=r(Jba,"DeiTForImageClassification"),Jba.forEach(t),TNr=r(Lq," or "),Ute=n(Lq,"A",{href:!0});var Yba=s(Ute);MNr=r(Yba,"DeiTForImageClassificationWithTeacher"),Yba.forEach(t),ENr=r(Lq," (DeiT model)"),Lq.forEach(t),CNr=i(_e),c3=n(_e,"LI",{});var mto=s(c3);U7e=n(mto,"STRONG",{});var Zba=s(U7e);wNr=r(Zba,"dinat"),Zba.forEach(t),ANr=r(mto," \u2014 "),Hte=n(mto,"A",{href:!0});var Kba=s(Hte);LNr=r(Kba,"DinatForImageClassification"),Kba.forEach(t),yNr=r(mto," (DiNAT 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\u2014 "),tae=n(og,"A",{href:!0});var h5a=s(tae);cDr=r(h5a,"PerceiverForImageClassificationLearned"),h5a.forEach(t),fDr=r(og," or "),aae=n(og,"A",{href:!0});var u5a=s(aae);mDr=r(u5a,"PerceiverForImageClassificationFourier"),u5a.forEach(t),gDr=r(og," or "),nae=n(og,"A",{href:!0});var p5a=s(nae);hDr=r(p5a,"PerceiverForImageClassificationConvProcessing"),p5a.forEach(t),uDr=r(og," (Perceiver model)"),og.forEach(t),pDr=i(_e),p3=n(_e,"LI",{});var vto=s(p3);rLe=n(vto,"STRONG",{});var _5a=s(rLe);_Dr=r(_5a,"poolformer"),_5a.forEach(t),vDr=r(vto," \u2014 "),sae=n(vto,"A",{href:!0});var v5a=s(sae);bDr=r(v5a,"PoolFormerForImageClassification"),v5a.forEach(t),FDr=r(vto," (PoolFormer model)"),vto.forEach(t),TDr=i(_e),_3=n(_e,"LI",{});var bto=s(_3);tLe=n(bto,"STRONG",{});var b5a=s(tLe);MDr=r(b5a,"regnet"),b5a.forEach(t),EDr=r(bto," \u2014 "),lae=n(bto,"A",{href:!0});var F5a=s(lae);CDr=r(F5a,"RegNetForImageClassification"),F5a.forEach(t),wDr=r(bto," (RegNet 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\u2014 "),fae=n(Eto,"A",{href:!0});var y5a=s(fae);XDr=r(y5a,"Swinv2ForImageClassification"),y5a.forEach(t),zDr=r(Eto," (Swin Transformer V2 model)"),Eto.forEach(t),QDr=i(_e),M3=n(_e,"LI",{});var Cto=s(M3);iLe=n(Cto,"STRONG",{});var x5a=s(iLe);WDr=r(x5a,"van"),x5a.forEach(t),UDr=r(Cto," \u2014 "),mae=n(Cto,"A",{href:!0});var $5a=s(mae);HDr=r($5a,"VanForImageClassification"),$5a.forEach(t),JDr=r(Cto," (VAN model)"),Cto.forEach(t),YDr=i(_e),E3=n(_e,"LI",{});var wto=s(E3);dLe=n(wto,"STRONG",{});var k5a=s(dLe);ZDr=r(k5a,"vit"),k5a.forEach(t),KDr=r(wto," \u2014 "),gae=n(wto,"A",{href:!0});var S5a=s(gae);eqr=r(S5a,"ViTForImageClassification"),S5a.forEach(t),oqr=r(wto," (ViT model)"),wto.forEach(t),rqr=i(_e),C3=n(_e,"LI",{});var Ato=s(C3);cLe=n(Ato,"STRONG",{});var R5a=s(cLe);tqr=r(R5a,"vit_msn"),R5a.forEach(t),aqr=r(Ato," \u2014 "),hae=n(Ato,"A",{href:!0});var P5a=s(hae);nqr=r(P5a,"ViTMSNForImageClassification"),P5a.forEach(t),sqr=r(Ato," (ViTMSN 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deactivated). To train the model, you should first set it back in training mode with `),mLe=n(Lto,"CODE",{});var B5a=s(mLe);fqr=r(B5a,"model.train()"),B5a.forEach(t),Lto.forEach(t),mqr=i(za),T(A3.$$.fragment,za),za.forEach(t),ii.forEach(t),Hfo=i(f),Sc=n(f,"H2",{class:!0});var vho=s(Sc);L3=n(vho,"A",{id:!0,class:!0,href:!0});var N5a=s(L3);gLe=n(N5a,"SPAN",{});var D5a=s(gLe);T(aP.$$.fragment,D5a),D5a.forEach(t),N5a.forEach(t),gqr=i(vho),hLe=n(vho,"SPAN",{});var q5a=s(hLe);hqr=r(q5a,"AutoModelForVideoClassification"),q5a.forEach(t),vho.forEach(t),Jfo=i(f),er=n(f,"DIV",{class:!0});var di=s(er);T(nP.$$.fragment,di),uqr=i(di),Rc=n(di,"P",{});var khe=s(Rc);pqr=r(khe,`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 `),uae=n(khe,"A",{href:!0});var j5a=s(uae);_qr=r(j5a,"from_pretrained()"),j5a.forEach(t),vqr=r(khe," class method or the "),pae=n(khe,"A",{href:!0});var G5a=s(pae);bqr=r(G5a,"from_config()"),G5a.forEach(t),Fqr=r(khe,` class
method.`),khe.forEach(t),Tqr=i(di),sP=n(di,"P",{});var bho=s(sP);Mqr=r(bho,"This class cannot be instantiated directly using "),uLe=n(bho,"CODE",{});var O5a=s(uLe);Eqr=r(O5a,"__init__()"),O5a.forEach(t),Cqr=r(bho," (throws an error)."),bho.forEach(t),wqr=i(di),Gt=n(di,"DIV",{class:!0});var y$=s(Gt);T(lP.$$.fragment,y$),Aqr=i(y$),pLe=n(y$,"P",{});var V5a=s(pLe);Lqr=r(V5a,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),V5a.forEach(t),yqr=i(y$),Pc=n(y$,"P",{});var She=s(Pc);xqr=r(She,`Note:
Loading a model from its configuration file does `),_Le=n(She,"STRONG",{});var X5a=s(_Le);$qr=r(X5a,"not"),X5a.forEach(t),kqr=r(She,` load the model weights. It only affects the
model\u2019s configuration. Use `),_ae=n(She,"A",{href:!0});var z5a=s(_ae);Sqr=r(z5a,"from_pretrained()"),z5a.forEach(t),Rqr=r(She," to load the model weights."),She.forEach(t),Pqr=i(y$),T(y3.$$.fragment,y$),y$.forEach(t),Iqr=i(di),vo=n(di,"DIV",{class:!0});var Qa=s(vo);T(iP.$$.fragment,Qa),Bqr=i(Qa),vLe=n(Qa,"P",{});var Q5a=s(vLe);Nqr=r(Q5a,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),Q5a.forEach(t),Dqr=i(Qa),Ln=n(Qa,"P",{});var x$=s(Ln);qqr=r(x$,"The model class to instantiate is selected based on the "),bLe=n(x$,"CODE",{});var W5a=s(bLe);jqr=r(W5a,"model_type"),W5a.forEach(t),Gqr=r(x$,` property of the config object (either
passed as an argument or loaded from `),FLe=n(x$,"CODE",{});var U5a=s(FLe);Oqr=r(U5a,"pretrained_model_name_or_path"),U5a.forEach(t),Vqr=r(x$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),TLe=n(x$,"CODE",{});var H5a=s(TLe);Xqr=r(H5a,"pretrained_model_name_or_path"),H5a.forEach(t),zqr=r(x$,":"),x$.forEach(t),Qqr=i(Qa),MLe=n(Qa,"UL",{});var J5a=s(MLe);x3=n(J5a,"LI",{});var yto=s(x3);ELe=n(yto,"STRONG",{});var Y5a=s(ELe);Wqr=r(Y5a,"videomae"),Y5a.forEach(t),Uqr=r(yto," \u2014 "),vae=n(yto,"A",{href:!0});var Z5a=s(vae);Hqr=r(Z5a,"VideoMAEForVideoClassification"),Z5a.forEach(t),Jqr=r(yto," (VideoMAE model)"),yto.forEach(t),J5a.forEach(t),Yqr=i(Qa),$3=n(Qa,"P",{});var xto=s($3);Zqr=r(xto,"The model is set in evaluation mode by default using "),CLe=n(xto,"CODE",{});var K5a=s(CLe);Kqr=r(K5a,"model.eval()"),K5a.forEach(t),ejr=r(xto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wLe=n(xto,"CODE",{});var eFa=s(wLe);ojr=r(eFa,"model.train()"),eFa.forEach(t),xto.forEach(t),rjr=i(Qa),T(k3.$$.fragment,Qa),Qa.forEach(t),di.forEach(t),Yfo=i(f),Ic=n(f,"H2",{class:!0});var Fho=s(Ic);S3=n(Fho,"A",{id:!0,class:!0,href:!0});var oFa=s(S3);ALe=n(oFa,"SPAN",{});var rFa=s(ALe);T(dP.$$.fragment,rFa),rFa.forEach(t),oFa.forEach(t),tjr=i(Fho),LLe=n(Fho,"SPAN",{});var tFa=s(LLe);ajr=r(tFa,"AutoModelForVision2Seq"),tFa.forEach(t),Fho.forEach(t),Zfo=i(f),or=n(f,"DIV",{class:!0});var ci=s(or);T(cP.$$.fragment,ci),njr=i(ci),Bc=n(ci,"P",{});var Rhe=s(Bc);sjr=r(Rhe,`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 `),bae=n(Rhe,"A",{href:!0});var aFa=s(bae);ljr=r(aFa,"from_pretrained()"),aFa.forEach(t),ijr=r(Rhe," class method or the "),Fae=n(Rhe,"A",{href:!0});var nFa=s(Fae);djr=r(nFa,"from_config()"),nFa.forEach(t),cjr=r(Rhe,` class
method.`),Rhe.forEach(t),fjr=i(ci),fP=n(ci,"P",{});var Tho=s(fP);mjr=r(Tho,"This class cannot be instantiated directly using "),yLe=n(Tho,"CODE",{});var sFa=s(yLe);gjr=r(sFa,"__init__()"),sFa.forEach(t),hjr=r(Tho," (throws an error)."),Tho.forEach(t),ujr=i(ci),Ot=n(ci,"DIV",{class:!0});var $$=s(Ot);T(mP.$$.fragment,$$),pjr=i($$),xLe=n($$,"P",{});var lFa=s(xLe);_jr=r(lFa,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),lFa.forEach(t),vjr=i($$),Nc=n($$,"P",{});var Phe=s(Nc);bjr=r(Phe,`Note:
Loading a model from its configuration file does `),$Le=n(Phe,"STRONG",{});var iFa=s($Le);Fjr=r(iFa,"not"),iFa.forEach(t),Tjr=r(Phe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Tae=n(Phe,"A",{href:!0});var dFa=s(Tae);Mjr=r(dFa,"from_pretrained()"),dFa.forEach(t),Ejr=r(Phe," to load the model weights."),Phe.forEach(t),Cjr=i($$),T(R3.$$.fragment,$$),$$.forEach(t),wjr=i(ci),bo=n(ci,"DIV",{class:!0});var Wa=s(bo);T(gP.$$.fragment,Wa),Ajr=i(Wa),kLe=n(Wa,"P",{});var cFa=s(kLe);Ljr=r(cFa,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),cFa.forEach(t),yjr=i(Wa),yn=n(Wa,"P",{});var k$=s(yn);xjr=r(k$,"The model class to instantiate is selected based on the "),SLe=n(k$,"CODE",{});var fFa=s(SLe);$jr=r(fFa,"model_type"),fFa.forEach(t),kjr=r(k$,` property of the config object (either
passed as an argument or loaded from `),RLe=n(k$,"CODE",{});var mFa=s(RLe);Sjr=r(mFa,"pretrained_model_name_or_path"),mFa.forEach(t),Rjr=r(k$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PLe=n(k$,"CODE",{});var gFa=s(PLe);Pjr=r(gFa,"pretrained_model_name_or_path"),gFa.forEach(t),Ijr=r(k$,":"),k$.forEach(t),Bjr=i(Wa),ILe=n(Wa,"UL",{});var hFa=s(ILe);P3=n(hFa,"LI",{});var $to=s(P3);BLe=n($to,"STRONG",{});var uFa=s(BLe);Njr=r(uFa,"vision-encoder-decoder"),uFa.forEach(t),Djr=r($to," \u2014 "),Mae=n($to,"A",{href:!0});var pFa=s(Mae);qjr=r(pFa,"VisionEncoderDecoderModel"),pFa.forEach(t),jjr=r($to," (Vision Encoder decoder model)"),$to.forEach(t),hFa.forEach(t),Gjr=i(Wa),I3=n(Wa,"P",{});var kto=s(I3);Ojr=r(kto,"The model is set in evaluation mode by default using "),NLe=n(kto,"CODE",{});var _Fa=s(NLe);Vjr=r(_Fa,"model.eval()"),_Fa.forEach(t),Xjr=r(kto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),DLe=n(kto,"CODE",{});var vFa=s(DLe);zjr=r(vFa,"model.train()"),vFa.forEach(t),kto.forEach(t),Qjr=i(Wa),T(B3.$$.fragment,Wa),Wa.forEach(t),ci.forEach(t),Kfo=i(f),Dc=n(f,"H2",{class:!0});var Mho=s(Dc);N3=n(Mho,"A",{id:!0,class:!0,href:!0});var bFa=s(N3);qLe=n(bFa,"SPAN",{});var FFa=s(qLe);T(hP.$$.fragment,FFa),FFa.forEach(t),bFa.forEach(t),Wjr=i(Mho),jLe=n(Mho,"SPAN",{});var TFa=s(jLe);Ujr=r(TFa,"AutoModelForVisualQuestionAnswering"),TFa.forEach(t),Mho.forEach(t),emo=i(f),rr=n(f,"DIV",{class:!0});var fi=s(rr);T(uP.$$.fragment,fi),Hjr=i(fi),qc=n(fi,"P",{});var Ihe=s(qc);Jjr=r(Ihe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),Eae=n(Ihe,"A",{href:!0});var MFa=s(Eae);Yjr=r(MFa,"from_pretrained()"),MFa.forEach(t),Zjr=r(Ihe," class method or the "),Cae=n(Ihe,"A",{href:!0});var EFa=s(Cae);Kjr=r(EFa,"from_config()"),EFa.forEach(t),eGr=r(Ihe,` class
method.`),Ihe.forEach(t),oGr=i(fi),pP=n(fi,"P",{});var Eho=s(pP);rGr=r(Eho,"This class cannot be instantiated directly using "),GLe=n(Eho,"CODE",{});var CFa=s(GLe);tGr=r(CFa,"__init__()"),CFa.forEach(t),aGr=r(Eho," (throws an error)."),Eho.forEach(t),nGr=i(fi),Vt=n(fi,"DIV",{class:!0});var S$=s(Vt);T(_P.$$.fragment,S$),sGr=i(S$),OLe=n(S$,"P",{});var wFa=s(OLe);lGr=r(wFa,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),wFa.forEach(t),iGr=i(S$),jc=n(S$,"P",{});var Bhe=s(jc);dGr=r(Bhe,`Note:
Loading a model from its configuration file does `),VLe=n(Bhe,"STRONG",{});var AFa=s(VLe);cGr=r(AFa,"not"),AFa.forEach(t),fGr=r(Bhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),wae=n(Bhe,"A",{href:!0});var LFa=s(wae);mGr=r(LFa,"from_pretrained()"),LFa.forEach(t),gGr=r(Bhe," to load the model weights."),Bhe.forEach(t),hGr=i(S$),T(D3.$$.fragment,S$),S$.forEach(t),uGr=i(fi),Fo=n(fi,"DIV",{class:!0});var Ua=s(Fo);T(vP.$$.fragment,Ua),pGr=i(Ua),XLe=n(Ua,"P",{});var yFa=s(XLe);_Gr=r(yFa,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),yFa.forEach(t),vGr=i(Ua),xn=n(Ua,"P",{});var R$=s(xn);bGr=r(R$,"The model class to instantiate is selected based on the "),zLe=n(R$,"CODE",{});var xFa=s(zLe);FGr=r(xFa,"model_type"),xFa.forEach(t),TGr=r(R$,` property of the config object (either
passed as an argument or loaded from `),QLe=n(R$,"CODE",{});var $Fa=s(QLe);MGr=r($Fa,"pretrained_model_name_or_path"),$Fa.forEach(t),EGr=r(R$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WLe=n(R$,"CODE",{});var kFa=s(WLe);CGr=r(kFa,"pretrained_model_name_or_path"),kFa.forEach(t),wGr=r(R$,":"),R$.forEach(t),AGr=i(Ua),ULe=n(Ua,"UL",{});var SFa=s(ULe);q3=n(SFa,"LI",{});var Sto=s(q3);HLe=n(Sto,"STRONG",{});var RFa=s(HLe);LGr=r(RFa,"vilt"),RFa.forEach(t),yGr=r(Sto," \u2014 "),Aae=n(Sto,"A",{href:!0});var PFa=s(Aae);xGr=r(PFa,"ViltForQuestionAnswering"),PFa.forEach(t),$Gr=r(Sto," (ViLT model)"),Sto.forEach(t),SFa.forEach(t),kGr=i(Ua),j3=n(Ua,"P",{});var Rto=s(j3);SGr=r(Rto,"The model is set in evaluation mode by default using "),JLe=n(Rto,"CODE",{});var IFa=s(JLe);RGr=r(IFa,"model.eval()"),IFa.forEach(t),PGr=r(Rto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),YLe=n(Rto,"CODE",{});var BFa=s(YLe);IGr=r(BFa,"model.train()"),BFa.forEach(t),Rto.forEach(t),BGr=i(Ua),T(G3.$$.fragment,Ua),Ua.forEach(t),fi.forEach(t),omo=i(f),Gc=n(f,"H2",{class:!0});var Cho=s(Gc);O3=n(Cho,"A",{id:!0,class:!0,href:!0});var NFa=s(O3);ZLe=n(NFa,"SPAN",{});var DFa=s(ZLe);T(bP.$$.fragment,DFa),DFa.forEach(t),NFa.forEach(t),NGr=i(Cho),KLe=n(Cho,"SPAN",{});var qFa=s(KLe);DGr=r(qFa,"AutoModelForAudioClassification"),qFa.forEach(t),Cho.forEach(t),rmo=i(f),tr=n(f,"DIV",{class:!0});var mi=s(tr);T(FP.$$.fragment,mi),qGr=i(mi),Oc=n(mi,"P",{});var Nhe=s(Oc);jGr=r(Nhe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),Lae=n(Nhe,"A",{href:!0});var jFa=s(Lae);GGr=r(jFa,"from_pretrained()"),jFa.forEach(t),OGr=r(Nhe," class method or the "),yae=n(Nhe,"A",{href:!0});var GFa=s(yae);VGr=r(GFa,"from_config()"),GFa.forEach(t),XGr=r(Nhe,` class
method.`),Nhe.forEach(t),zGr=i(mi),TP=n(mi,"P",{});var who=s(TP);QGr=r(who,"This class cannot be instantiated directly using "),eye=n(who,"CODE",{});var OFa=s(eye);WGr=r(OFa,"__init__()"),OFa.forEach(t),UGr=r(who," (throws an error)."),who.forEach(t),HGr=i(mi),Xt=n(mi,"DIV",{class:!0});var P$=s(Xt);T(MP.$$.fragment,P$),JGr=i(P$),oye=n(P$,"P",{});var VFa=s(oye);YGr=r(VFa,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),VFa.forEach(t),ZGr=i(P$),Vc=n(P$,"P",{});var Dhe=s(Vc);KGr=r(Dhe,`Note:
Loading a model from its configuration file does `),rye=n(Dhe,"STRONG",{});var XFa=s(rye);eOr=r(XFa,"not"),XFa.forEach(t),oOr=r(Dhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),xae=n(Dhe,"A",{href:!0});var zFa=s(xae);rOr=r(zFa,"from_pretrained()"),zFa.forEach(t),tOr=r(Dhe," to load the model weights."),Dhe.forEach(t),aOr=i(P$),T(V3.$$.fragment,P$),P$.forEach(t),nOr=i(mi),To=n(mi,"DIV",{class:!0});var Ha=s(To);T(EP.$$.fragment,Ha),sOr=i(Ha),tye=n(Ha,"P",{});var QFa=s(tye);lOr=r(QFa,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),QFa.forEach(t),iOr=i(Ha),$n=n(Ha,"P",{});var I$=s($n);dOr=r(I$,"The model class to instantiate is selected based on the "),aye=n(I$,"CODE",{});var WFa=s(aye);cOr=r(WFa,"model_type"),WFa.forEach(t),fOr=r(I$,` property of the config object (either
passed as an argument or loaded from `),nye=n(I$,"CODE",{});var UFa=s(nye);mOr=r(UFa,"pretrained_model_name_or_path"),UFa.forEach(t),gOr=r(I$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sye=n(I$,"CODE",{});var HFa=s(sye);hOr=r(HFa,"pretrained_model_name_or_path"),HFa.forEach(t),uOr=r(I$,":"),I$.forEach(t),pOr=i(Ha),xe=n(Ha,"UL",{});var De=s(xe);X3=n(De,"LI",{});var Pto=s(X3);lye=n(Pto,"STRONG",{});var JFa=s(lye);_Or=r(JFa,"audio-spectrogram-transformer"),JFa.forEach(t),vOr=r(Pto," \u2014 "),$ae=n(Pto,"A",{href:!0});var YFa=s($ae);bOr=r(YFa,"ASTForAudioClassification"),YFa.forEach(t),FOr=r(Pto," (Audio Spectrogram Transformer model)"),Pto.forEach(t),TOr=i(De),z3=n(De,"LI",{});var Ito=s(z3);iye=n(Ito,"STRONG",{});var ZFa=s(iye);MOr=r(ZFa,"data2vec-audio"),ZFa.forEach(t),EOr=r(Ito," \u2014 "),kae=n(Ito,"A",{href:!0});var KFa=s(kae);COr=r(KFa,"Data2VecAudioForSequenceClassification"),KFa.forEach(t),wOr=r(Ito," (Data2VecAudio model)"),Ito.forEach(t),AOr=i(De),Q3=n(De,"LI",{});var Bto=s(Q3);dye=n(Bto,"STRONG",{});var eTa=s(dye);LOr=r(eTa,"hubert"),eTa.forEach(t),yOr=r(Bto," \u2014 "),Sae=n(Bto,"A",{href:!0});var oTa=s(Sae);xOr=r(oTa,"HubertForSequenceClassification"),oTa.forEach(t),$Or=r(Bto," (Hubert model)"),Bto.forEach(t),kOr=i(De),W3=n(De,"LI",{});var Nto=s(W3);cye=n(Nto,"STRONG",{});var rTa=s(cye);SOr=r(rTa,"sew"),rTa.forEach(t),ROr=r(Nto," \u2014 "),Rae=n(Nto,"A",{href:!0});var tTa=s(Rae);POr=r(tTa,"SEWForSequenceClassification"),tTa.forEach(t),IOr=r(Nto," (SEW model)"),Nto.forEach(t),BOr=i(De),U3=n(De,"LI",{});var Dto=s(U3);fye=n(Dto,"STRONG",{});var aTa=s(fye);NOr=r(aTa,"sew-d"),aTa.forEach(t),DOr=r(Dto," \u2014 "),Pae=n(Dto,"A",{href:!0});var nTa=s(Pae);qOr=r(nTa,"SEWDForSequenceClassification"),nTa.forEach(t),jOr=r(Dto," (SEW-D model)"),Dto.forEach(t),GOr=i(De),H3=n(De,"LI",{});var qto=s(H3);mye=n(qto,"STRONG",{});var sTa=s(mye);OOr=r(sTa,"unispeech"),sTa.forEach(t),VOr=r(qto," \u2014 "),Iae=n(qto,"A",{href:!0});var lTa=s(Iae);XOr=r(lTa,"UniSpeechForSequenceClassification"),lTa.forEach(t),zOr=r(qto," (UniSpeech model)"),qto.forEach(t),QOr=i(De),J3=n(De,"LI",{});var jto=s(J3);gye=n(jto,"STRONG",{});var iTa=s(gye);WOr=r(iTa,"unispeech-sat"),iTa.forEach(t),UOr=r(jto," \u2014 "),Bae=n(jto,"A",{href:!0});var dTa=s(Bae);HOr=r(dTa,"UniSpeechSatForSequenceClassification"),dTa.forEach(t),JOr=r(jto," (UniSpeechSat model)"),jto.forEach(t),YOr=i(De),Y3=n(De,"LI",{});var Gto=s(Y3);hye=n(Gto,"STRONG",{});var cTa=s(hye);ZOr=r(cTa,"wav2vec2"),cTa.forEach(t),KOr=r(Gto," \u2014 "),Nae=n(Gto,"A",{href:!0});var fTa=s(Nae);eVr=r(fTa,"Wav2Vec2ForSequenceClassification"),fTa.forEach(t),oVr=r(Gto," (Wav2Vec2 model)"),Gto.forEach(t),rVr=i(De),Z3=n(De,"LI",{});var Oto=s(Z3);uye=n(Oto,"STRONG",{});var mTa=s(uye);tVr=r(mTa,"wav2vec2-conformer"),mTa.forEach(t),aVr=r(Oto," \u2014 "),Dae=n(Oto,"A",{href:!0});var gTa=s(Dae);nVr=r(gTa,"Wav2Vec2ConformerForSequenceClassification"),gTa.forEach(t),sVr=r(Oto," (Wav2Vec2-Conformer model)"),Oto.forEach(t),lVr=i(De),K3=n(De,"LI",{});var Vto=s(K3);pye=n(Vto,"STRONG",{});var hTa=s(pye);iVr=r(hTa,"wavlm"),hTa.forEach(t),dVr=r(Vto," \u2014 "),qae=n(Vto,"A",{href:!0});var uTa=s(qae);cVr=r(uTa,"WavLMForSequenceClassification"),uTa.forEach(t),fVr=r(Vto," (WavLM model)"),Vto.forEach(t),De.forEach(t),mVr=i(Ha),e0=n(Ha,"P",{});var Xto=s(e0);gVr=r(Xto,"The model is set in evaluation mode by default using "),_ye=n(Xto,"CODE",{});var pTa=s(_ye);hVr=r(pTa,"model.eval()"),pTa.forEach(t),uVr=r(Xto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),vye=n(Xto,"CODE",{});var _Ta=s(vye);pVr=r(_Ta,"model.train()"),_Ta.forEach(t),Xto.forEach(t),_Vr=i(Ha),T(o0.$$.fragment,Ha),Ha.forEach(t),mi.forEach(t),tmo=i(f),Xc=n(f,"H2",{class:!0});var Aho=s(Xc);r0=n(Aho,"A",{id:!0,class:!0,href:!0});var vTa=s(r0);bye=n(vTa,"SPAN",{});var bTa=s(bye);T(CP.$$.fragment,bTa),bTa.forEach(t),vTa.forEach(t),vVr=i(Aho),Fye=n(Aho,"SPAN",{});var FTa=s(Fye);bVr=r(FTa,"AutoModelForAudioFrameClassification"),FTa.forEach(t),Aho.forEach(t),amo=i(f),ar=n(f,"DIV",{class:!0});var gi=s(ar);T(wP.$$.fragment,gi),FVr=i(gi),zc=n(gi,"P",{});var qhe=s(zc);TVr=r(qhe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),jae=n(qhe,"A",{href:!0});var TTa=s(jae);MVr=r(TTa,"from_pretrained()"),TTa.forEach(t),EVr=r(qhe," class method or the "),Gae=n(qhe,"A",{href:!0});var MTa=s(Gae);CVr=r(MTa,"from_config()"),MTa.forEach(t),wVr=r(qhe,` class
method.`),qhe.forEach(t),AVr=i(gi),AP=n(gi,"P",{});var Lho=s(AP);LVr=r(Lho,"This class cannot be instantiated directly using "),Tye=n(Lho,"CODE",{});var ETa=s(Tye);yVr=r(ETa,"__init__()"),ETa.forEach(t),xVr=r(Lho," (throws an error)."),Lho.forEach(t),$Vr=i(gi),zt=n(gi,"DIV",{class:!0});var B$=s(zt);T(LP.$$.fragment,B$),kVr=i(B$),Mye=n(B$,"P",{});var CTa=s(Mye);SVr=r(CTa,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),CTa.forEach(t),RVr=i(B$),Qc=n(B$,"P",{});var jhe=s(Qc);PVr=r(jhe,`Note:
Loading a model from its configuration file does `),Eye=n(jhe,"STRONG",{});var wTa=s(Eye);IVr=r(wTa,"not"),wTa.forEach(t),BVr=r(jhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Oae=n(jhe,"A",{href:!0});var ATa=s(Oae);NVr=r(ATa,"from_pretrained()"),ATa.forEach(t),DVr=r(jhe," to load the model weights."),jhe.forEach(t),qVr=i(B$),T(t0.$$.fragment,B$),B$.forEach(t),jVr=i(gi),Mo=n(gi,"DIV",{class:!0});var Ja=s(Mo);T(yP.$$.fragment,Ja),GVr=i(Ja),Cye=n(Ja,"P",{});var LTa=s(Cye);OVr=r(LTa,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),LTa.forEach(t),VVr=i(Ja),kn=n(Ja,"P",{});var N$=s(kn);XVr=r(N$,"The model class to instantiate is selected based on the "),wye=n(N$,"CODE",{});var yTa=s(wye);zVr=r(yTa,"model_type"),yTa.forEach(t),QVr=r(N$,` property of the config object (either
passed as an argument or loaded from `),Aye=n(N$,"CODE",{});var xTa=s(Aye);WVr=r(xTa,"pretrained_model_name_or_path"),xTa.forEach(t),UVr=r(N$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lye=n(N$,"CODE",{});var $Ta=s(Lye);HVr=r($Ta,"pretrained_model_name_or_path"),$Ta.forEach(t),JVr=r(N$,":"),N$.forEach(t),YVr=i(Ja),Ft=n(Ja,"UL",{});var hi=s(Ft);a0=n(hi,"LI",{});var zto=s(a0);yye=n(zto,"STRONG",{});var kTa=s(yye);ZVr=r(kTa,"data2vec-audio"),kTa.forEach(t),KVr=r(zto," \u2014 "),Vae=n(zto,"A",{href:!0});var STa=s(Vae);eXr=r(STa,"Data2VecAudioForAudioFrameClassification"),STa.forEach(t),oXr=r(zto," (Data2VecAudio model)"),zto.forEach(t),rXr=i(hi),n0=n(hi,"LI",{});var Qto=s(n0);xye=n(Qto,"STRONG",{});var RTa=s(xye);tXr=r(RTa,"unispeech-sat"),RTa.forEach(t),aXr=r(Qto," \u2014 "),Xae=n(Qto,"A",{href:!0});var PTa=s(Xae);nXr=r(PTa,"UniSpeechSatForAudioFrameClassification"),PTa.forEach(t),sXr=r(Qto," (UniSpeechSat model)"),Qto.forEach(t),lXr=i(hi),s0=n(hi,"LI",{});var Wto=s(s0);$ye=n(Wto,"STRONG",{});var ITa=s($ye);iXr=r(ITa,"wav2vec2"),ITa.forEach(t),dXr=r(Wto," \u2014 "),zae=n(Wto,"A",{href:!0});var BTa=s(zae);cXr=r(BTa,"Wav2Vec2ForAudioFrameClassification"),BTa.forEach(t),fXr=r(Wto," (Wav2Vec2 model)"),Wto.forEach(t),mXr=i(hi),l0=n(hi,"LI",{});var Uto=s(l0);kye=n(Uto,"STRONG",{});var NTa=s(kye);gXr=r(NTa,"wav2vec2-conformer"),NTa.forEach(t),hXr=r(Uto," \u2014 "),Qae=n(Uto,"A",{href:!0});var DTa=s(Qae);uXr=r(DTa,"Wav2Vec2ConformerForAudioFrameClassification"),DTa.forEach(t),pXr=r(Uto," (Wav2Vec2-Conformer model)"),Uto.forEach(t),_Xr=i(hi),i0=n(hi,"LI",{});var Hto=s(i0);Sye=n(Hto,"STRONG",{});var qTa=s(Sye);vXr=r(qTa,"wavlm"),qTa.forEach(t),bXr=r(Hto," \u2014 "),Wae=n(Hto,"A",{href:!0});var jTa=s(Wae);FXr=r(jTa,"WavLMForAudioFrameClassification"),jTa.forEach(t),TXr=r(Hto," (WavLM model)"),Hto.forEach(t),hi.forEach(t),MXr=i(Ja),d0=n(Ja,"P",{});var Jto=s(d0);EXr=r(Jto,"The model is set in evaluation mode by default using "),Rye=n(Jto,"CODE",{});var GTa=s(Rye);CXr=r(GTa,"model.eval()"),GTa.forEach(t),wXr=r(Jto,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pye=n(Jto,"CODE",{});var OTa=s(Pye);AXr=r(OTa,"model.train()"),OTa.forEach(t),Jto.forEach(t),LXr=i(Ja),T(c0.$$.fragment,Ja),Ja.forEach(t),gi.forEach(t),nmo=i(f),Wc=n(f,"H2",{class:!0});var yho=s(Wc);f0=n(yho,"A",{id:!0,class:!0,href:!0});var VTa=s(f0);Iye=n(VTa,"SPAN",{});var XTa=s(Iye);T(xP.$$.fragment,XTa),XTa.forEach(t),VTa.forEach(t),yXr=i(yho),Bye=n(yho,"SPAN",{});var zTa=s(Bye);xXr=r(zTa,"AutoModelForCTC"),zTa.forEach(t),yho.forEach(t),smo=i(f),nr=n(f,"DIV",{class:!0});var ui=s(nr);T($P.$$.fragment,ui),$Xr=i(ui),Uc=n(ui,"P",{});var Ghe=s(Uc);kXr=r(Ghe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),Uae=n(Ghe,"A",{href:!0});var QTa=s(Uae);SXr=r(QTa,"from_pretrained()"),QTa.forEach(t),RXr=r(Ghe," class method or the "),Hae=n(Ghe,"A",{href:!0});var WTa=s(Hae);PXr=r(WTa,"from_config()"),WTa.forEach(t),IXr=r(Ghe,` class
method.`),Ghe.forEach(t),BXr=i(ui),kP=n(ui,"P",{});var xho=s(kP);NXr=r(xho,"This class cannot be instantiated directly using "),Nye=n(xho,"CODE",{});var UTa=s(Nye);DXr=r(UTa,"__init__()"),UTa.forEach(t),qXr=r(xho," (throws an error)."),xho.forEach(t),jXr=i(ui),Qt=n(ui,"DIV",{class:!0});var D$=s(Qt);T(SP.$$.fragment,D$),GXr=i(D$),Dye=n(D$,"P",{});var HTa=s(Dye);OXr=r(HTa,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),HTa.forEach(t),VXr=i(D$),Hc=n(D$,"P",{});var Ohe=s(Hc);XXr=r(Ohe,`Note:
Loading a model from its configuration file does `),qye=n(Ohe,"STRONG",{});var JTa=s(qye);zXr=r(JTa,"not"),JTa.forEach(t),QXr=r(Ohe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Jae=n(Ohe,"A",{href:!0});var YTa=s(Jae);WXr=r(YTa,"from_pretrained()"),YTa.forEach(t),UXr=r(Ohe," to load the model weights."),Ohe.forEach(t),HXr=i(D$),T(m0.$$.fragment,D$),D$.forEach(t),JXr=i(ui),Eo=n(ui,"DIV",{class:!0});var Ya=s(Eo);T(RP.$$.fragment,Ya),YXr=i(Ya),jye=n(Ya,"P",{});var ZTa=s(jye);ZXr=r(ZTa,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),ZTa.forEach(t),KXr=i(Ya),Sn=n(Ya,"P",{});var q$=s(Sn);ezr=r(q$,"The model class to instantiate is selected based on the "),Gye=n(q$,"CODE",{});var KTa=s(Gye);ozr=r(KTa,"model_type"),KTa.forEach(t),rzr=r(q$,` property of the config object (either
passed as an argument or loaded from `),Oye=n(q$,"CODE",{});var eMa=s(Oye);tzr=r(eMa,"pretrained_model_name_or_path"),eMa.forEach(t),azr=r(q$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vye=n(q$,"CODE",{});var oMa=s(Vye);nzr=r(oMa,"pretrained_model_name_or_path"),oMa.forEach(t),szr=r(q$,":"),q$.forEach(t),lzr=i(Ya),$e=n(Ya,"UL",{});var qe=s($e);g0=n(qe,"LI",{});var Yto=s(g0);Xye=n(Yto,"STRONG",{});var rMa=s(Xye);izr=r(rMa,"data2vec-audio"),rMa.forEach(t),dzr=r(Yto," \u2014 "),Yae=n(Yto,"A",{href:!0});var tMa=s(Yae);czr=r(tMa,"Data2VecAudioForCTC"),tMa.forEach(t),fzr=r(Yto," (Data2VecAudio model)"),Yto.forEach(t),mzr=i(qe),h0=n(qe,"LI",{});var Zto=s(h0);zye=n(Zto,"STRONG",{});var aMa=s(zye);gzr=r(aMa,"hubert"),aMa.forEach(t),hzr=r(Zto," \u2014 "),Zae=n(Zto,"A",{href:!0});var nMa=s(Zae);uzr=r(nMa,"HubertForCTC"),nMa.forEach(t),pzr=r(Zto," (Hubert model)"),Zto.forEach(t),_zr=i(qe),u0=n(qe,"LI",{});var Kto=s(u0);Qye=n(Kto,"STRONG",{});var sMa=s(Qye);vzr=r(sMa,"mctct"),sMa.forEach(t),bzr=r(Kto," \u2014 "),Kae=n(Kto,"A",{href:!0});var lMa=s(Kae);Fzr=r(lMa,"MCTCTForCTC"),lMa.forEach(t),Tzr=r(Kto," (M-CTC-T model)"),Kto.forEach(t),Mzr=i(qe),p0=n(qe,"LI",{});var eao=s(p0);Wye=n(eao,"STRONG",{});var iMa=s(Wye);Ezr=r(iMa,"sew"),iMa.forEach(t),Czr=r(eao," \u2014 "),ene=n(eao,"A",{href:!0});var dMa=s(ene);wzr=r(dMa,"SEWForCTC"),dMa.forEach(t),Azr=r(eao," (SEW model)"),eao.forEach(t),Lzr=i(qe),_0=n(qe,"LI",{});var oao=s(_0);Uye=n(oao,"STRONG",{});var cMa=s(Uye);yzr=r(cMa,"sew-d"),cMa.forEach(t),xzr=r(oao," \u2014 "),one=n(oao,"A",{href:!0});var fMa=s(one);$zr=r(fMa,"SEWDForCTC"),fMa.forEach(t),kzr=r(oao," (SEW-D model)"),oao.forEach(t),Szr=i(qe),v0=n(qe,"LI",{});var rao=s(v0);Hye=n(rao,"STRONG",{});var mMa=s(Hye);Rzr=r(mMa,"unispeech"),mMa.forEach(t),Pzr=r(rao," \u2014 "),rne=n(rao,"A",{href:!0});var gMa=s(rne);Izr=r(gMa,"UniSpeechForCTC"),gMa.forEach(t),Bzr=r(rao," (UniSpeech model)"),rao.forEach(t),Nzr=i(qe),b0=n(qe,"LI",{});var tao=s(b0);Jye=n(tao,"STRONG",{});var hMa=s(Jye);Dzr=r(hMa,"unispeech-sat"),hMa.forEach(t),qzr=r(tao," \u2014 "),tne=n(tao,"A",{href:!0});var uMa=s(tne);jzr=r(uMa,"UniSpeechSatForCTC"),uMa.forEach(t),Gzr=r(tao," (UniSpeechSat model)"),tao.forEach(t),Ozr=i(qe),F0=n(qe,"LI",{});var aao=s(F0);Yye=n(aao,"STRONG",{});var pMa=s(Yye);Vzr=r(pMa,"wav2vec2"),pMa.forEach(t),Xzr=r(aao," \u2014 "),ane=n(aao,"A",{href:!0});var _Ma=s(ane);zzr=r(_Ma,"Wav2Vec2ForCTC"),_Ma.forEach(t),Qzr=r(aao," (Wav2Vec2 model)"),aao.forEach(t),Wzr=i(qe),T0=n(qe,"LI",{});var nao=s(T0);Zye=n(nao,"STRONG",{});var vMa=s(Zye);Uzr=r(vMa,"wav2vec2-conformer"),vMa.forEach(t),Hzr=r(nao," \u2014 "),nne=n(nao,"A",{href:!0});var bMa=s(nne);Jzr=r(bMa,"Wav2Vec2ConformerForCTC"),bMa.forEach(t),Yzr=r(nao," (Wav2Vec2-Conformer model)"),nao.forEach(t),Zzr=i(qe),M0=n(qe,"LI",{});var sao=s(M0);Kye=n(sao,"STRONG",{});var FMa=s(Kye);Kzr=r(FMa,"wavlm"),FMa.forEach(t),eQr=r(sao," \u2014 "),sne=n(sao,"A",{href:!0});var TMa=s(sne);oQr=r(TMa,"WavLMForCTC"),TMa.forEach(t),rQr=r(sao," (WavLM model)"),sao.forEach(t),qe.forEach(t),tQr=i(Ya),E0=n(Ya,"P",{});var lao=s(E0);aQr=r(lao,"The model is set in evaluation mode by default using "),e9e=n(lao,"CODE",{});var MMa=s(e9e);nQr=r(MMa,"model.eval()"),MMa.forEach(t),sQr=r(lao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),o9e=n(lao,"CODE",{});var EMa=s(o9e);lQr=r(EMa,"model.train()"),EMa.forEach(t),lao.forEach(t),iQr=i(Ya),T(C0.$$.fragment,Ya),Ya.forEach(t),ui.forEach(t),lmo=i(f),Jc=n(f,"H2",{class:!0});var $ho=s(Jc);w0=n($ho,"A",{id:!0,class:!0,href:!0});var CMa=s(w0);r9e=n(CMa,"SPAN",{});var wMa=s(r9e);T(PP.$$.fragment,wMa),wMa.forEach(t),CMa.forEach(t),dQr=i($ho),t9e=n($ho,"SPAN",{});var AMa=s(t9e);cQr=r(AMa,"AutoModelForSpeechSeq2Seq"),AMa.forEach(t),$ho.forEach(t),imo=i(f),sr=n(f,"DIV",{class:!0});var pi=s(sr);T(IP.$$.fragment,pi),fQr=i(pi),Yc=n(pi,"P",{});var Vhe=s(Yc);mQr=r(Vhe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),lne=n(Vhe,"A",{href:!0});var LMa=s(lne);gQr=r(LMa,"from_pretrained()"),LMa.forEach(t),hQr=r(Vhe," class method or the "),ine=n(Vhe,"A",{href:!0});var yMa=s(ine);uQr=r(yMa,"from_config()"),yMa.forEach(t),pQr=r(Vhe,` class
method.`),Vhe.forEach(t),_Qr=i(pi),BP=n(pi,"P",{});var kho=s(BP);vQr=r(kho,"This class cannot be instantiated directly using "),a9e=n(kho,"CODE",{});var xMa=s(a9e);bQr=r(xMa,"__init__()"),xMa.forEach(t),FQr=r(kho," (throws an error)."),kho.forEach(t),TQr=i(pi),Wt=n(pi,"DIV",{class:!0});var j$=s(Wt);T(NP.$$.fragment,j$),MQr=i(j$),n9e=n(j$,"P",{});var $Ma=s(n9e);EQr=r($Ma,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),$Ma.forEach(t),CQr=i(j$),Zc=n(j$,"P",{});var Xhe=s(Zc);wQr=r(Xhe,`Note:
Loading a model from its configuration file does `),s9e=n(Xhe,"STRONG",{});var kMa=s(s9e);AQr=r(kMa,"not"),kMa.forEach(t),LQr=r(Xhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),dne=n(Xhe,"A",{href:!0});var SMa=s(dne);yQr=r(SMa,"from_pretrained()"),SMa.forEach(t),xQr=r(Xhe," to load the model weights."),Xhe.forEach(t),$Qr=i(j$),T(A0.$$.fragment,j$),j$.forEach(t),kQr=i(pi),Co=n(pi,"DIV",{class:!0});var Za=s(Co);T(DP.$$.fragment,Za),SQr=i(Za),l9e=n(Za,"P",{});var RMa=s(l9e);RQr=r(RMa,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),RMa.forEach(t),PQr=i(Za),Rn=n(Za,"P",{});var G$=s(Rn);IQr=r(G$,"The model class to instantiate is selected based on the "),i9e=n(G$,"CODE",{});var PMa=s(i9e);BQr=r(PMa,"model_type"),PMa.forEach(t),NQr=r(G$,` property of the config object (either
passed as an argument or loaded from `),d9e=n(G$,"CODE",{});var IMa=s(d9e);DQr=r(IMa,"pretrained_model_name_or_path"),IMa.forEach(t),qQr=r(G$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),c9e=n(G$,"CODE",{});var BMa=s(c9e);jQr=r(BMa,"pretrained_model_name_or_path"),BMa.forEach(t),GQr=r(G$,":"),G$.forEach(t),OQr=i(Za),Kc=n(Za,"UL",{});var zhe=s(Kc);L0=n(zhe,"LI",{});var iao=s(L0);f9e=n(iao,"STRONG",{});var NMa=s(f9e);VQr=r(NMa,"speech-encoder-decoder"),NMa.forEach(t),XQr=r(iao," \u2014 "),cne=n(iao,"A",{href:!0});var DMa=s(cne);zQr=r(DMa,"SpeechEncoderDecoderModel"),DMa.forEach(t),QQr=r(iao," (Speech Encoder decoder model)"),iao.forEach(t),WQr=i(zhe),y0=n(zhe,"LI",{});var dao=s(y0);m9e=n(dao,"STRONG",{});var qMa=s(m9e);UQr=r(qMa,"speech_to_text"),qMa.forEach(t),HQr=r(dao," \u2014 "),fne=n(dao,"A",{href:!0});var jMa=s(fne);JQr=r(jMa,"Speech2TextForConditionalGeneration"),jMa.forEach(t),YQr=r(dao," (Speech2Text model)"),dao.forEach(t),ZQr=i(zhe),x0=n(zhe,"LI",{});var cao=s(x0);g9e=n(cao,"STRONG",{});var GMa=s(g9e);KQr=r(GMa,"whisper"),GMa.forEach(t),eWr=r(cao," \u2014 "),mne=n(cao,"A",{href:!0});var OMa=s(mne);oWr=r(OMa,"WhisperForConditionalGeneration"),OMa.forEach(t),rWr=r(cao," (Whisper model)"),cao.forEach(t),zhe.forEach(t),tWr=i(Za),$0=n(Za,"P",{});var fao=s($0);aWr=r(fao,"The model is set in evaluation mode by default using "),h9e=n(fao,"CODE",{});var VMa=s(h9e);nWr=r(VMa,"model.eval()"),VMa.forEach(t),sWr=r(fao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),u9e=n(fao,"CODE",{});var XMa=s(u9e);lWr=r(XMa,"model.train()"),XMa.forEach(t),fao.forEach(t),iWr=i(Za),T(k0.$$.fragment,Za),Za.forEach(t),pi.forEach(t),dmo=i(f),ef=n(f,"H2",{class:!0});var Sho=s(ef);S0=n(Sho,"A",{id:!0,class:!0,href:!0});var zMa=s(S0);p9e=n(zMa,"SPAN",{});var QMa=s(p9e);T(qP.$$.fragment,QMa),QMa.forEach(t),zMa.forEach(t),dWr=i(Sho),_9e=n(Sho,"SPAN",{});var WMa=s(_9e);cWr=r(WMa,"AutoModelForAudioXVector"),WMa.forEach(t),Sho.forEach(t),cmo=i(f),lr=n(f,"DIV",{class:!0});var _i=s(lr);T(jP.$$.fragment,_i),fWr=i(_i),of=n(_i,"P",{});var Qhe=s(of);mWr=r(Qhe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),gne=n(Qhe,"A",{href:!0});var UMa=s(gne);gWr=r(UMa,"from_pretrained()"),UMa.forEach(t),hWr=r(Qhe," class method or the "),hne=n(Qhe,"A",{href:!0});var HMa=s(hne);uWr=r(HMa,"from_config()"),HMa.forEach(t),pWr=r(Qhe,` class
method.`),Qhe.forEach(t),_Wr=i(_i),GP=n(_i,"P",{});var Rho=s(GP);vWr=r(Rho,"This class cannot be instantiated directly using "),v9e=n(Rho,"CODE",{});var JMa=s(v9e);bWr=r(JMa,"__init__()"),JMa.forEach(t),FWr=r(Rho," (throws an error)."),Rho.forEach(t),TWr=i(_i),Ut=n(_i,"DIV",{class:!0});var O$=s(Ut);T(OP.$$.fragment,O$),MWr=i(O$),b9e=n(O$,"P",{});var YMa=s(b9e);EWr=r(YMa,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),YMa.forEach(t),CWr=i(O$),rf=n(O$,"P",{});var Whe=s(rf);wWr=r(Whe,`Note:
Loading a model from its configuration file does `),F9e=n(Whe,"STRONG",{});var ZMa=s(F9e);AWr=r(ZMa,"not"),ZMa.forEach(t),LWr=r(Whe,` load the model weights. It only affects the
model\u2019s configuration. Use `),une=n(Whe,"A",{href:!0});var KMa=s(une);yWr=r(KMa,"from_pretrained()"),KMa.forEach(t),xWr=r(Whe," to load the model weights."),Whe.forEach(t),$Wr=i(O$),T(R0.$$.fragment,O$),O$.forEach(t),kWr=i(_i),wo=n(_i,"DIV",{class:!0});var Ka=s(wo);T(VP.$$.fragment,Ka),SWr=i(Ka),T9e=n(Ka,"P",{});var eEa=s(T9e);RWr=r(eEa,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),eEa.forEach(t),PWr=i(Ka),Pn=n(Ka,"P",{});var V$=s(Pn);IWr=r(V$,"The model class to instantiate is selected based on the "),M9e=n(V$,"CODE",{});var oEa=s(M9e);BWr=r(oEa,"model_type"),oEa.forEach(t),NWr=r(V$,` property of the config object (either
passed as an argument or loaded from `),E9e=n(V$,"CODE",{});var rEa=s(E9e);DWr=r(rEa,"pretrained_model_name_or_path"),rEa.forEach(t),qWr=r(V$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),C9e=n(V$,"CODE",{});var tEa=s(C9e);jWr=r(tEa,"pretrained_model_name_or_path"),tEa.forEach(t),GWr=r(V$,":"),V$.forEach(t),OWr=i(Ka),Tt=n(Ka,"UL",{});var vi=s(Tt);P0=n(vi,"LI",{});var mao=s(P0);w9e=n(mao,"STRONG",{});var aEa=s(w9e);VWr=r(aEa,"data2vec-audio"),aEa.forEach(t),XWr=r(mao," \u2014 "),pne=n(mao,"A",{href:!0});var nEa=s(pne);zWr=r(nEa,"Data2VecAudioForXVector"),nEa.forEach(t),QWr=r(mao," (Data2VecAudio model)"),mao.forEach(t),WWr=i(vi),I0=n(vi,"LI",{});var gao=s(I0);A9e=n(gao,"STRONG",{});var sEa=s(A9e);UWr=r(sEa,"unispeech-sat"),sEa.forEach(t),HWr=r(gao," \u2014 "),_ne=n(gao,"A",{href:!0});var lEa=s(_ne);JWr=r(lEa,"UniSpeechSatForXVector"),lEa.forEach(t),YWr=r(gao," (UniSpeechSat model)"),gao.forEach(t),ZWr=i(vi),B0=n(vi,"LI",{});var hao=s(B0);L9e=n(hao,"STRONG",{});var iEa=s(L9e);KWr=r(iEa,"wav2vec2"),iEa.forEach(t),eUr=r(hao," \u2014 "),vne=n(hao,"A",{href:!0});var dEa=s(vne);oUr=r(dEa,"Wav2Vec2ForXVector"),dEa.forEach(t),rUr=r(hao," (Wav2Vec2 model)"),hao.forEach(t),tUr=i(vi),N0=n(vi,"LI",{});var uao=s(N0);y9e=n(uao,"STRONG",{});var cEa=s(y9e);aUr=r(cEa,"wav2vec2-conformer"),cEa.forEach(t),nUr=r(uao," \u2014 "),bne=n(uao,"A",{href:!0});var fEa=s(bne);sUr=r(fEa,"Wav2Vec2ConformerForXVector"),fEa.forEach(t),lUr=r(uao," (Wav2Vec2-Conformer model)"),uao.forEach(t),iUr=i(vi),D0=n(vi,"LI",{});var pao=s(D0);x9e=n(pao,"STRONG",{});var mEa=s(x9e);dUr=r(mEa,"wavlm"),mEa.forEach(t),cUr=r(pao," \u2014 "),Fne=n(pao,"A",{href:!0});var gEa=s(Fne);fUr=r(gEa,"WavLMForXVector"),gEa.forEach(t),mUr=r(pao," (WavLM model)"),pao.forEach(t),vi.forEach(t),gUr=i(Ka),q0=n(Ka,"P",{});var _ao=s(q0);hUr=r(_ao,"The model is set in evaluation mode by default using "),$9e=n(_ao,"CODE",{});var hEa=s($9e);uUr=r(hEa,"model.eval()"),hEa.forEach(t),pUr=r(_ao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),k9e=n(_ao,"CODE",{});var uEa=s(k9e);_Ur=r(uEa,"model.train()"),uEa.forEach(t),_ao.forEach(t),vUr=i(Ka),T(j0.$$.fragment,Ka),Ka.forEach(t),_i.forEach(t),fmo=i(f),tf=n(f,"H2",{class:!0});var Pho=s(tf);G0=n(Pho,"A",{id:!0,class:!0,href:!0});var pEa=s(G0);S9e=n(pEa,"SPAN",{});var _Ea=s(S9e);T(XP.$$.fragment,_Ea),_Ea.forEach(t),pEa.forEach(t),bUr=i(Pho),R9e=n(Pho,"SPAN",{});var vEa=s(R9e);FUr=r(vEa,"AutoModelForMaskedImageModeling"),vEa.forEach(t),Pho.forEach(t),mmo=i(f),ir=n(f,"DIV",{class:!0});var bi=s(ir);T(zP.$$.fragment,bi),TUr=i(bi),af=n(bi,"P",{});var Uhe=s(af);MUr=r(Uhe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),Tne=n(Uhe,"A",{href:!0});var bEa=s(Tne);EUr=r(bEa,"from_pretrained()"),bEa.forEach(t),CUr=r(Uhe," class method or the "),Mne=n(Uhe,"A",{href:!0});var FEa=s(Mne);wUr=r(FEa,"from_config()"),FEa.forEach(t),AUr=r(Uhe,` class
method.`),Uhe.forEach(t),LUr=i(bi),QP=n(bi,"P",{});var Iho=s(QP);yUr=r(Iho,"This class cannot be instantiated directly using "),P9e=n(Iho,"CODE",{});var TEa=s(P9e);xUr=r(TEa,"__init__()"),TEa.forEach(t),$Ur=r(Iho," (throws an error)."),Iho.forEach(t),kUr=i(bi),Ht=n(bi,"DIV",{class:!0});var X$=s(Ht);T(WP.$$.fragment,X$),SUr=i(X$),I9e=n(X$,"P",{});var MEa=s(I9e);RUr=r(MEa,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),MEa.forEach(t),PUr=i(X$),nf=n(X$,"P",{});var Hhe=s(nf);IUr=r(Hhe,`Note:
Loading a model from its configuration file does `),B9e=n(Hhe,"STRONG",{});var EEa=s(B9e);BUr=r(EEa,"not"),EEa.forEach(t),NUr=r(Hhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ene=n(Hhe,"A",{href:!0});var CEa=s(Ene);DUr=r(CEa,"from_pretrained()"),CEa.forEach(t),qUr=r(Hhe," to load the model weights."),Hhe.forEach(t),jUr=i(X$),T(O0.$$.fragment,X$),X$.forEach(t),GUr=i(bi),Ao=n(bi,"DIV",{class:!0});var en=s(Ao);T(UP.$$.fragment,en),OUr=i(en),N9e=n(en,"P",{});var wEa=s(N9e);VUr=r(wEa,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),wEa.forEach(t),XUr=i(en),In=n(en,"P",{});var z$=s(In);zUr=r(z$,"The model class to instantiate is selected based on the "),D9e=n(z$,"CODE",{});var AEa=s(D9e);QUr=r(AEa,"model_type"),AEa.forEach(t),WUr=r(z$,` property of the config object (either
passed as an argument or loaded from `),q9e=n(z$,"CODE",{});var LEa=s(q9e);UUr=r(LEa,"pretrained_model_name_or_path"),LEa.forEach(t),HUr=r(z$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),j9e=n(z$,"CODE",{});var yEa=s(j9e);JUr=r(yEa,"pretrained_model_name_or_path"),yEa.forEach(t),YUr=r(z$,":"),z$.forEach(t),ZUr=i(en),Bn=n(en,"UL",{});var Q$=s(Bn);V0=n(Q$,"LI",{});var vao=s(V0);G9e=n(vao,"STRONG",{});var xEa=s(G9e);KUr=r(xEa,"deit"),xEa.forEach(t),eHr=r(vao," \u2014 "),Cne=n(vao,"A",{href:!0});var $Ea=s(Cne);oHr=r($Ea,"DeiTForMaskedImageModeling"),$Ea.forEach(t),rHr=r(vao," (DeiT model)"),vao.forEach(t),tHr=i(Q$),X0=n(Q$,"LI",{});var bao=s(X0);O9e=n(bao,"STRONG",{});var kEa=s(O9e);aHr=r(kEa,"swin"),kEa.forEach(t),nHr=r(bao," \u2014 "),wne=n(bao,"A",{href:!0});var SEa=s(wne);sHr=r(SEa,"SwinForMaskedImageModeling"),SEa.forEach(t),lHr=r(bao," (Swin Transformer model)"),bao.forEach(t),iHr=i(Q$),z0=n(Q$,"LI",{});var Fao=s(z0);V9e=n(Fao,"STRONG",{});var REa=s(V9e);dHr=r(REa,"swinv2"),REa.forEach(t),cHr=r(Fao," \u2014 "),Ane=n(Fao,"A",{href:!0});var PEa=s(Ane);fHr=r(PEa,"Swinv2ForMaskedImageModeling"),PEa.forEach(t),mHr=r(Fao," (Swin Transformer V2 model)"),Fao.forEach(t),gHr=i(Q$),Q0=n(Q$,"LI",{});var Tao=s(Q0);X9e=n(Tao,"STRONG",{});var IEa=s(X9e);hHr=r(IEa,"vit"),IEa.forEach(t),uHr=r(Tao," \u2014 "),Lne=n(Tao,"A",{href:!0});var BEa=s(Lne);pHr=r(BEa,"ViTForMaskedImageModeling"),BEa.forEach(t),_Hr=r(Tao," (ViT model)"),Tao.forEach(t),Q$.forEach(t),vHr=i(en),W0=n(en,"P",{});var Mao=s(W0);bHr=r(Mao,"The model is set in evaluation mode by default using "),z9e=n(Mao,"CODE",{});var NEa=s(z9e);FHr=r(NEa,"model.eval()"),NEa.forEach(t),THr=r(Mao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Q9e=n(Mao,"CODE",{});var DEa=s(Q9e);MHr=r(DEa,"model.train()"),DEa.forEach(t),Mao.forEach(t),EHr=i(en),T(U0.$$.fragment,en),en.forEach(t),bi.forEach(t),gmo=i(f),sf=n(f,"H2",{class:!0});var Bho=s(sf);H0=n(Bho,"A",{id:!0,class:!0,href:!0});var qEa=s(H0);W9e=n(qEa,"SPAN",{});var jEa=s(W9e);T(HP.$$.fragment,jEa),jEa.forEach(t),qEa.forEach(t),CHr=i(Bho),U9e=n(Bho,"SPAN",{});var GEa=s(U9e);wHr=r(GEa,"AutoModelForObjectDetection"),GEa.forEach(t),Bho.forEach(t),hmo=i(f),dr=n(f,"DIV",{class:!0});var Fi=s(dr);T(JP.$$.fragment,Fi),AHr=i(Fi),lf=n(Fi,"P",{});var Jhe=s(lf);LHr=r(Jhe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),yne=n(Jhe,"A",{href:!0});var OEa=s(yne);yHr=r(OEa,"from_pretrained()"),OEa.forEach(t),xHr=r(Jhe," class method or the "),xne=n(Jhe,"A",{href:!0});var VEa=s(xne);$Hr=r(VEa,"from_config()"),VEa.forEach(t),kHr=r(Jhe,` class
method.`),Jhe.forEach(t),SHr=i(Fi),YP=n(Fi,"P",{});var Nho=s(YP);RHr=r(Nho,"This class cannot be instantiated directly using "),H9e=n(Nho,"CODE",{});var XEa=s(H9e);PHr=r(XEa,"__init__()"),XEa.forEach(t),IHr=r(Nho," (throws an error)."),Nho.forEach(t),BHr=i(Fi),Jt=n(Fi,"DIV",{class:!0});var W$=s(Jt);T(ZP.$$.fragment,W$),NHr=i(W$),J9e=n(W$,"P",{});var zEa=s(J9e);DHr=r(zEa,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),zEa.forEach(t),qHr=i(W$),df=n(W$,"P",{});var Yhe=s(df);jHr=r(Yhe,`Note:
Loading a model from its configuration file does `),Y9e=n(Yhe,"STRONG",{});var QEa=s(Y9e);GHr=r(QEa,"not"),QEa.forEach(t),OHr=r(Yhe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$ne=n(Yhe,"A",{href:!0});var WEa=s($ne);VHr=r(WEa,"from_pretrained()"),WEa.forEach(t),XHr=r(Yhe," to load the model weights."),Yhe.forEach(t),zHr=i(W$),T(J0.$$.fragment,W$),W$.forEach(t),QHr=i(Fi),Lo=n(Fi,"DIV",{class:!0});var on=s(Lo);T(KP.$$.fragment,on),WHr=i(on),Z9e=n(on,"P",{});var UEa=s(Z9e);UHr=r(UEa,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),UEa.forEach(t),HHr=i(on),Nn=n(on,"P",{});var U$=s(Nn);JHr=r(U$,"The model class to instantiate is selected based on the "),K9e=n(U$,"CODE",{});var HEa=s(K9e);YHr=r(HEa,"model_type"),HEa.forEach(t),ZHr=r(U$,` property of the config object (either
passed as an argument or loaded from `),exe=n(U$,"CODE",{});var JEa=s(exe);KHr=r(JEa,"pretrained_model_name_or_path"),JEa.forEach(t),eJr=r(U$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),oxe=n(U$,"CODE",{});var YEa=s(oxe);oJr=r(YEa,"pretrained_model_name_or_path"),YEa.forEach(t),rJr=r(U$,":"),U$.forEach(t),tJr=i(on),Mt=n(on,"UL",{});var Ti=s(Mt);Y0=n(Ti,"LI",{});var Eao=s(Y0);rxe=n(Eao,"STRONG",{});var ZEa=s(rxe);aJr=r(ZEa,"conditional_detr"),ZEa.forEach(t),nJr=r(Eao," \u2014 "),kne=n(Eao,"A",{href:!0});var KEa=s(kne);sJr=r(KEa,"ConditionalDetrForObjectDetection"),KEa.forEach(t),lJr=r(Eao," (Conditional DETR model)"),Eao.forEach(t),iJr=i(Ti),Z0=n(Ti,"LI",{});var Cao=s(Z0);txe=n(Cao,"STRONG",{});var eCa=s(txe);dJr=r(eCa,"deformable_detr"),eCa.forEach(t),cJr=r(Cao," \u2014 "),Sne=n(Cao,"A",{href:!0});var oCa=s(Sne);fJr=r(oCa,"DeformableDetrForObjectDetection"),oCa.forEach(t),mJr=r(Cao," (Deformable DETR model)"),Cao.forEach(t),gJr=i(Ti),K0=n(Ti,"LI",{});var wao=s(K0);axe=n(wao,"STRONG",{});var rCa=s(axe);hJr=r(rCa,"detr"),rCa.forEach(t),uJr=r(wao," \u2014 "),Rne=n(wao,"A",{href:!0});var tCa=s(Rne);pJr=r(tCa,"DetrForObjectDetection"),tCa.forEach(t),_Jr=r(wao," (DETR model)"),wao.forEach(t),vJr=i(Ti),ew=n(Ti,"LI",{});var Aao=s(ew);nxe=n(Aao,"STRONG",{});var aCa=s(nxe);bJr=r(aCa,"table-transformer"),aCa.forEach(t),FJr=r(Aao," \u2014 "),Pne=n(Aao,"A",{href:!0});var nCa=s(Pne);TJr=r(nCa,"TableTransformerForObjectDetection"),nCa.forEach(t),MJr=r(Aao," (Table Transformer model)"),Aao.forEach(t),EJr=i(Ti),ow=n(Ti,"LI",{});var Lao=s(ow);sxe=n(Lao,"STRONG",{});var sCa=s(sxe);CJr=r(sCa,"yolos"),sCa.forEach(t),wJr=r(Lao," \u2014 "),Ine=n(Lao,"A",{href:!0});var lCa=s(Ine);AJr=r(lCa,"YolosForObjectDetection"),lCa.forEach(t),LJr=r(Lao," (YOLOS model)"),Lao.forEach(t),Ti.forEach(t),yJr=i(on),rw=n(on,"P",{});var yao=s(rw);xJr=r(yao,"The model is set in evaluation mode by default using "),lxe=n(yao,"CODE",{});var iCa=s(lxe);$Jr=r(iCa,"model.eval()"),iCa.forEach(t),kJr=r(yao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ixe=n(yao,"CODE",{});var dCa=s(ixe);SJr=r(dCa,"model.train()"),dCa.forEach(t),yao.forEach(t),RJr=i(on),T(tw.$$.fragment,on),on.forEach(t),Fi.forEach(t),umo=i(f),cf=n(f,"H2",{class:!0});var Dho=s(cf);aw=n(Dho,"A",{id:!0,class:!0,href:!0});var cCa=s(aw);dxe=n(cCa,"SPAN",{});var fCa=s(dxe);T(eI.$$.fragment,fCa),fCa.forEach(t),cCa.forEach(t),PJr=i(Dho),cxe=n(Dho,"SPAN",{});var mCa=s(cxe);IJr=r(mCa,"AutoModelForImageSegmentation"),mCa.forEach(t),Dho.forEach(t),pmo=i(f),cr=n(f,"DIV",{class:!0});var Mi=s(cr);T(oI.$$.fragment,Mi),BJr=i(Mi),ff=n(Mi,"P",{});var Zhe=s(ff);NJr=r(Zhe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),Bne=n(Zhe,"A",{href:!0});var gCa=s(Bne);DJr=r(gCa,"from_pretrained()"),gCa.forEach(t),qJr=r(Zhe," class method or the "),Nne=n(Zhe,"A",{href:!0});var hCa=s(Nne);jJr=r(hCa,"from_config()"),hCa.forEach(t),GJr=r(Zhe,` class
method.`),Zhe.forEach(t),OJr=i(Mi),rI=n(Mi,"P",{});var qho=s(rI);VJr=r(qho,"This class cannot be instantiated directly using "),fxe=n(qho,"CODE",{});var uCa=s(fxe);XJr=r(uCa,"__init__()"),uCa.forEach(t),zJr=r(qho," (throws an error)."),qho.forEach(t),QJr=i(Mi),Yt=n(Mi,"DIV",{class:!0});var H$=s(Yt);T(tI.$$.fragment,H$),WJr=i(H$),mxe=n(H$,"P",{});var pCa=s(mxe);UJr=r(pCa,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),pCa.forEach(t),HJr=i(H$),mf=n(H$,"P",{});var Khe=s(mf);JJr=r(Khe,`Note:
Loading a model from its configuration file does `),gxe=n(Khe,"STRONG",{});var _Ca=s(gxe);YJr=r(_Ca,"not"),_Ca.forEach(t),ZJr=r(Khe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Dne=n(Khe,"A",{href:!0});var vCa=s(Dne);KJr=r(vCa,"from_pretrained()"),vCa.forEach(t),eYr=r(Khe," to load the model weights."),Khe.forEach(t),oYr=i(H$),T(nw.$$.fragment,H$),H$.forEach(t),rYr=i(Mi),yo=n(Mi,"DIV",{class:!0});var rn=s(yo);T(aI.$$.fragment,rn),tYr=i(rn),hxe=n(rn,"P",{});var bCa=s(hxe);aYr=r(bCa,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),bCa.forEach(t),nYr=i(rn),Dn=n(rn,"P",{});var J$=s(Dn);sYr=r(J$,"The model class to instantiate is selected based on the "),uxe=n(J$,"CODE",{});var FCa=s(uxe);lYr=r(FCa,"model_type"),FCa.forEach(t),iYr=r(J$,` property of the config object (either
passed as an argument or loaded from `),pxe=n(J$,"CODE",{});var TCa=s(pxe);dYr=r(TCa,"pretrained_model_name_or_path"),TCa.forEach(t),cYr=r(J$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_xe=n(J$,"CODE",{});var MCa=s(_xe);fYr=r(MCa,"pretrained_model_name_or_path"),MCa.forEach(t),mYr=r(J$,":"),J$.forEach(t),gYr=i(rn),vxe=n(rn,"UL",{});var ECa=s(vxe);sw=n(ECa,"LI",{});var xao=s(sw);bxe=n(xao,"STRONG",{});var CCa=s(bxe);hYr=r(CCa,"detr"),CCa.forEach(t),uYr=r(xao," \u2014 "),qne=n(xao,"A",{href:!0});var wCa=s(qne);pYr=r(wCa,"DetrForSegmentation"),wCa.forEach(t),_Yr=r(xao," (DETR model)"),xao.forEach(t),ECa.forEach(t),vYr=i(rn),lw=n(rn,"P",{});var $ao=s(lw);bYr=r($ao,"The model is set in evaluation mode by default using "),Fxe=n($ao,"CODE",{});var ACa=s(Fxe);FYr=r(ACa,"model.eval()"),ACa.forEach(t),TYr=r($ao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Txe=n($ao,"CODE",{});var LCa=s(Txe);MYr=r(LCa,"model.train()"),LCa.forEach(t),$ao.forEach(t),EYr=i(rn),T(iw.$$.fragment,rn),rn.forEach(t),Mi.forEach(t),_mo=i(f),gf=n(f,"H2",{class:!0});var jho=s(gf);dw=n(jho,"A",{id:!0,class:!0,href:!0});var yCa=s(dw);Mxe=n(yCa,"SPAN",{});var xCa=s(Mxe);T(nI.$$.fragment,xCa),xCa.forEach(t),yCa.forEach(t),CYr=i(jho),Exe=n(jho,"SPAN",{});var $Ca=s(Exe);wYr=r($Ca,"AutoModelForSemanticSegmentation"),$Ca.forEach(t),jho.forEach(t),vmo=i(f),fr=n(f,"DIV",{class:!0});var Ei=s(fr);T(sI.$$.fragment,Ei),AYr=i(Ei),hf=n(Ei,"P",{});var eue=s(hf);LYr=r(eue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),jne=n(eue,"A",{href:!0});var kCa=s(jne);yYr=r(kCa,"from_pretrained()"),kCa.forEach(t),xYr=r(eue," class method or the "),Gne=n(eue,"A",{href:!0});var SCa=s(Gne);$Yr=r(SCa,"from_config()"),SCa.forEach(t),kYr=r(eue,` class
method.`),eue.forEach(t),SYr=i(Ei),lI=n(Ei,"P",{});var Gho=s(lI);RYr=r(Gho,"This class cannot be instantiated directly using "),Cxe=n(Gho,"CODE",{});var RCa=s(Cxe);PYr=r(RCa,"__init__()"),RCa.forEach(t),IYr=r(Gho," (throws an error)."),Gho.forEach(t),BYr=i(Ei),Zt=n(Ei,"DIV",{class:!0});var Y$=s(Zt);T(iI.$$.fragment,Y$),NYr=i(Y$),wxe=n(Y$,"P",{});var PCa=s(wxe);DYr=r(PCa,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),PCa.forEach(t),qYr=i(Y$),uf=n(Y$,"P",{});var oue=s(uf);jYr=r(oue,`Note:
Loading a model from its configuration file does `),Axe=n(oue,"STRONG",{});var ICa=s(Axe);GYr=r(ICa,"not"),ICa.forEach(t),OYr=r(oue,` load the model weights. It only affects the
model\u2019s configuration. Use `),One=n(oue,"A",{href:!0});var BCa=s(One);VYr=r(BCa,"from_pretrained()"),BCa.forEach(t),XYr=r(oue," to load the model weights."),oue.forEach(t),zYr=i(Y$),T(cw.$$.fragment,Y$),Y$.forEach(t),QYr=i(Ei),xo=n(Ei,"DIV",{class:!0});var tn=s(xo);T(dI.$$.fragment,tn),WYr=i(tn),Lxe=n(tn,"P",{});var NCa=s(Lxe);UYr=r(NCa,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),NCa.forEach(t),HYr=i(tn),qn=n(tn,"P",{});var Z$=s(qn);JYr=r(Z$,"The model class to instantiate is selected based on the "),yxe=n(Z$,"CODE",{});var DCa=s(yxe);YYr=r(DCa,"model_type"),DCa.forEach(t),ZYr=r(Z$,` property of the config object (either
passed as an argument or loaded from `),xxe=n(Z$,"CODE",{});var qCa=s(xxe);KYr=r(qCa,"pretrained_model_name_or_path"),qCa.forEach(t),eZr=r(Z$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$xe=n(Z$,"CODE",{});var jCa=s($xe);oZr=r(jCa,"pretrained_model_name_or_path"),jCa.forEach(t),rZr=r(Z$,":"),Z$.forEach(t),tZr=i(tn),mr=n(tn,"UL",{});var an=s(mr);fw=n(an,"LI",{});var kao=s(fw);kxe=n(kao,"STRONG",{});var GCa=s(kxe);aZr=r(GCa,"beit"),GCa.forEach(t),nZr=r(kao," \u2014 "),Vne=n(kao,"A",{href:!0});var OCa=s(Vne);sZr=r(OCa,"BeitForSemanticSegmentation"),OCa.forEach(t),lZr=r(kao," (BEiT model)"),kao.forEach(t),iZr=i(an),mw=n(an,"LI",{});var Sao=s(mw);Sxe=n(Sao,"STRONG",{});var VCa=s(Sxe);dZr=r(VCa,"data2vec-vision"),VCa.forEach(t),cZr=r(Sao," \u2014 "),Xne=n(Sao,"A",{href:!0});var XCa=s(Xne);fZr=r(XCa,"Data2VecVisionForSemanticSegmentation"),XCa.forEach(t),mZr=r(Sao," (Data2VecVision model)"),Sao.forEach(t),gZr=i(an),gw=n(an,"LI",{});var Rao=s(gw);Rxe=n(Rao,"STRONG",{});var zCa=s(Rxe);hZr=r(zCa,"dpt"),zCa.forEach(t),uZr=r(Rao," \u2014 "),zne=n(Rao,"A",{href:!0});var QCa=s(zne);pZr=r(QCa,"DPTForSemanticSegmentation"),QCa.forEach(t),_Zr=r(Rao," (DPT model)"),Rao.forEach(t),vZr=i(an),hw=n(an,"LI",{});var Pao=s(hw);Pxe=n(Pao,"STRONG",{});var WCa=s(Pxe);bZr=r(WCa,"mobilenet_v2"),WCa.forEach(t),FZr=r(Pao," \u2014 "),Qne=n(Pao,"A",{href:!0});var UCa=s(Qne);TZr=r(UCa,"MobileNetV2ForSemanticSegmentation"),UCa.forEach(t),MZr=r(Pao," (MobileNetV2 model)"),Pao.forEach(t),EZr=i(an),uw=n(an,"LI",{});var Iao=s(uw);Ixe=n(Iao,"STRONG",{});var HCa=s(Ixe);CZr=r(HCa,"mobilevit"),HCa.forEach(t),wZr=r(Iao," \u2014 "),Wne=n(Iao,"A",{href:!0});var JCa=s(Wne);AZr=r(JCa,"MobileViTForSemanticSegmentation"),JCa.forEach(t),LZr=r(Iao," (MobileViT model)"),Iao.forEach(t),yZr=i(an),pw=n(an,"LI",{});var Bao=s(pw);Bxe=n(Bao,"STRONG",{});var YCa=s(Bxe);xZr=r(YCa,"segformer"),YCa.forEach(t),$Zr=r(Bao," \u2014 "),Une=n(Bao,"A",{href:!0});var ZCa=s(Une);kZr=r(ZCa,"SegformerForSemanticSegmentation"),ZCa.forEach(t),SZr=r(Bao," (SegFormer model)"),Bao.forEach(t),an.forEach(t),RZr=i(tn),_w=n(tn,"P",{});var Nao=s(_w);PZr=r(Nao,"The model is set in evaluation mode by default using "),Nxe=n(Nao,"CODE",{});var KCa=s(Nxe);IZr=r(KCa,"model.eval()"),KCa.forEach(t),BZr=r(Nao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Dxe=n(Nao,"CODE",{});var e3a=s(Dxe);NZr=r(e3a,"model.train()"),e3a.forEach(t),Nao.forEach(t),DZr=i(tn),T(vw.$$.fragment,tn),tn.forEach(t),Ei.forEach(t),bmo=i(f),pf=n(f,"H2",{class:!0});var Oho=s(pf);bw=n(Oho,"A",{id:!0,class:!0,href:!0});var o3a=s(bw);qxe=n(o3a,"SPAN",{});var r3a=s(qxe);T(cI.$$.fragment,r3a),r3a.forEach(t),o3a.forEach(t),qZr=i(Oho),jxe=n(Oho,"SPAN",{});var t3a=s(jxe);jZr=r(t3a,"AutoModelForInstanceSegmentation"),t3a.forEach(t),Oho.forEach(t),Fmo=i(f),gr=n(f,"DIV",{class:!0});var Ci=s(gr);T(fI.$$.fragment,Ci),GZr=i(Ci),_f=n(Ci,"P",{});var rue=s(_f);OZr=r(rue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),Hne=n(rue,"A",{href:!0});var a3a=s(Hne);VZr=r(a3a,"from_pretrained()"),a3a.forEach(t),XZr=r(rue," class method or the "),Jne=n(rue,"A",{href:!0});var n3a=s(Jne);zZr=r(n3a,"from_config()"),n3a.forEach(t),QZr=r(rue,` class
method.`),rue.forEach(t),WZr=i(Ci),mI=n(Ci,"P",{});var Vho=s(mI);UZr=r(Vho,"This class cannot be instantiated directly using "),Gxe=n(Vho,"CODE",{});var s3a=s(Gxe);HZr=r(s3a,"__init__()"),s3a.forEach(t),JZr=r(Vho," (throws an error)."),Vho.forEach(t),YZr=i(Ci),Kt=n(Ci,"DIV",{class:!0});var K$=s(Kt);T(gI.$$.fragment,K$),ZZr=i(K$),Oxe=n(K$,"P",{});var l3a=s(Oxe);KZr=r(l3a,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),l3a.forEach(t),eKr=i(K$),vf=n(K$,"P",{});var tue=s(vf);oKr=r(tue,`Note:
Loading a model from its configuration file does `),Vxe=n(tue,"STRONG",{});var i3a=s(Vxe);rKr=r(i3a,"not"),i3a.forEach(t),tKr=r(tue,` load the model weights. It only affects the
model\u2019s configuration. Use `),Yne=n(tue,"A",{href:!0});var d3a=s(Yne);aKr=r(d3a,"from_pretrained()"),d3a.forEach(t),nKr=r(tue," to load the model weights."),tue.forEach(t),sKr=i(K$),T(Fw.$$.fragment,K$),K$.forEach(t),lKr=i(Ci),$o=n(Ci,"DIV",{class:!0});var nn=s($o);T(hI.$$.fragment,nn),iKr=i(nn),Xxe=n(nn,"P",{});var c3a=s(Xxe);dKr=r(c3a,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),c3a.forEach(t),cKr=i(nn),jn=n(nn,"P",{});var ek=s(jn);fKr=r(ek,"The model class to instantiate is selected based on the "),zxe=n(ek,"CODE",{});var f3a=s(zxe);mKr=r(f3a,"model_type"),f3a.forEach(t),gKr=r(ek,` property of the config object (either
passed as an argument or loaded from `),Qxe=n(ek,"CODE",{});var m3a=s(Qxe);hKr=r(m3a,"pretrained_model_name_or_path"),m3a.forEach(t),uKr=r(ek,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Wxe=n(ek,"CODE",{});var g3a=s(Wxe);pKr=r(g3a,"pretrained_model_name_or_path"),g3a.forEach(t),_Kr=r(ek,":"),ek.forEach(t),vKr=i(nn),Uxe=n(nn,"UL",{});var h3a=s(Uxe);Tw=n(h3a,"LI",{});var Dao=s(Tw);Hxe=n(Dao,"STRONG",{});var u3a=s(Hxe);bKr=r(u3a,"maskformer"),u3a.forEach(t),FKr=r(Dao," \u2014 "),Zne=n(Dao,"A",{href:!0});var p3a=s(Zne);TKr=r(p3a,"MaskFormerForInstanceSegmentation"),p3a.forEach(t),MKr=r(Dao," (MaskFormer model)"),Dao.forEach(t),h3a.forEach(t),EKr=i(nn),Mw=n(nn,"P",{});var qao=s(Mw);CKr=r(qao,"The model is set in evaluation mode by default using "),Jxe=n(qao,"CODE",{});var _3a=s(Jxe);wKr=r(_3a,"model.eval()"),_3a.forEach(t),AKr=r(qao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Yxe=n(qao,"CODE",{});var v3a=s(Yxe);LKr=r(v3a,"model.train()"),v3a.forEach(t),qao.forEach(t),yKr=i(nn),T(Ew.$$.fragment,nn),nn.forEach(t),Ci.forEach(t),Tmo=i(f),bf=n(f,"H2",{class:!0});var Xho=s(bf);Cw=n(Xho,"A",{id:!0,class:!0,href:!0});var b3a=s(Cw);Zxe=n(b3a,"SPAN",{});var F3a=s(Zxe);T(uI.$$.fragment,F3a),F3a.forEach(t),b3a.forEach(t),xKr=i(Xho),Kxe=n(Xho,"SPAN",{});var T3a=s(Kxe);$Kr=r(T3a,"AutoModelForZeroShotObjectDetection"),T3a.forEach(t),Xho.forEach(t),Mmo=i(f),hr=n(f,"DIV",{class:!0});var wi=s(hr);T(pI.$$.fragment,wi),kKr=i(wi),Ff=n(wi,"P",{});var aue=s(Ff);SKr=r(aue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),Kne=n(aue,"A",{href:!0});var M3a=s(Kne);RKr=r(M3a,"from_pretrained()"),M3a.forEach(t),PKr=r(aue," class method or the "),ese=n(aue,"A",{href:!0});var E3a=s(ese);IKr=r(E3a,"from_config()"),E3a.forEach(t),BKr=r(aue,` class
method.`),aue.forEach(t),NKr=i(wi),_I=n(wi,"P",{});var zho=s(_I);DKr=r(zho,"This class cannot be instantiated directly using "),e$e=n(zho,"CODE",{});var C3a=s(e$e);qKr=r(C3a,"__init__()"),C3a.forEach(t),jKr=r(zho," (throws an error)."),zho.forEach(t),GKr=i(wi),ea=n(wi,"DIV",{class:!0});var ok=s(ea);T(vI.$$.fragment,ok),OKr=i(ok),o$e=n(ok,"P",{});var w3a=s(o$e);VKr=r(w3a,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),w3a.forEach(t),XKr=i(ok),Tf=n(ok,"P",{});var nue=s(Tf);zKr=r(nue,`Note:
Loading a model from its configuration file does `),r$e=n(nue,"STRONG",{});var A3a=s(r$e);QKr=r(A3a,"not"),A3a.forEach(t),WKr=r(nue,` load the model weights. It only affects the
model\u2019s configuration. Use `),ose=n(nue,"A",{href:!0});var L3a=s(ose);UKr=r(L3a,"from_pretrained()"),L3a.forEach(t),HKr=r(nue," to load the model weights."),nue.forEach(t),JKr=i(ok),T(ww.$$.fragment,ok),ok.forEach(t),YKr=i(wi),ko=n(wi,"DIV",{class:!0});var sn=s(ko);T(bI.$$.fragment,sn),ZKr=i(sn),t$e=n(sn,"P",{});var y3a=s(t$e);KKr=r(y3a,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),y3a.forEach(t),eet=i(sn),Gn=n(sn,"P",{});var rk=s(Gn);oet=r(rk,"The model class to instantiate is selected based on the "),a$e=n(rk,"CODE",{});var x3a=s(a$e);ret=r(x3a,"model_type"),x3a.forEach(t),tet=r(rk,` property of the config object (either
passed as an argument or loaded from `),n$e=n(rk,"CODE",{});var $3a=s(n$e);aet=r($3a,"pretrained_model_name_or_path"),$3a.forEach(t),net=r(rk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),s$e=n(rk,"CODE",{});var k3a=s(s$e);set=r(k3a,"pretrained_model_name_or_path"),k3a.forEach(t),iet=r(rk,":"),rk.forEach(t),det=i(sn),l$e=n(sn,"UL",{});var S3a=s(l$e);Aw=n(S3a,"LI",{});var jao=s(Aw);i$e=n(jao,"STRONG",{});var R3a=s(i$e);cet=r(R3a,"owlvit"),R3a.forEach(t),fet=r(jao," \u2014 "),rse=n(jao,"A",{href:!0});var P3a=s(rse);met=r(P3a,"OwlViTForObjectDetection"),P3a.forEach(t),get=r(jao," (OWL-ViT model)"),jao.forEach(t),S3a.forEach(t),het=i(sn),Lw=n(sn,"P",{});var Gao=s(Lw);uet=r(Gao,"The model is set in evaluation mode by default using "),d$e=n(Gao,"CODE",{});var I3a=s(d$e);pet=r(I3a,"model.eval()"),I3a.forEach(t),_et=r(Gao,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),c$e=n(Gao,"CODE",{});var B3a=s(c$e);vet=r(B3a,"model.train()"),B3a.forEach(t),Gao.forEach(t),bet=i(sn),T(yw.$$.fragment,sn),sn.forEach(t),wi.forEach(t),Emo=i(f),Mf=n(f,"H2",{class:!0});var Qho=s(Mf);xw=n(Qho,"A",{id:!0,class:!0,href:!0});var N3a=s(xw);f$e=n(N3a,"SPAN",{});var D3a=s(f$e);T(FI.$$.fragment,D3a),D3a.forEach(t),N3a.forEach(t),Fet=i(Qho),m$e=n(Qho,"SPAN",{});var q3a=s(m$e);Tet=r(q3a,"TFAutoModel"),q3a.forEach(t),Qho.forEach(t),Cmo=i(f),ur=n(f,"DIV",{class:!0});var Ai=s(ur);T(TI.$$.fragment,Ai),Met=i(Ai),Ef=n(Ai,"P",{});var sue=s(Ef);Eet=r(sue,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),tse=n(sue,"A",{href:!0});var j3a=s(tse);Cet=r(j3a,"from_pretrained()"),j3a.forEach(t),wet=r(sue," class method or the "),ase=n(sue,"A",{href:!0});var G3a=s(ase);Aet=r(G3a,"from_config()"),G3a.forEach(t),Let=r(sue,` class
method.`),sue.forEach(t),yet=i(Ai),MI=n(Ai,"P",{});var Who=s(MI);xet=r(Who,"This class cannot be instantiated directly using "),g$e=n(Who,"CODE",{});var O3a=s(g$e);$et=r(O3a,"__init__()"),O3a.forEach(t),ket=r(Who," (throws an error)."),Who.forEach(t),Set=i(Ai),oa=n(Ai,"DIV",{class:!0});var tk=s(oa);T(EI.$$.fragment,tk),Ret=i(tk),h$e=n(tk,"P",{});var V3a=s(h$e);Pet=r(V3a,"Instantiates one of the base model classes of the library from a configuration."),V3a.forEach(t),Iet=i(tk),Cf=n(tk,"P",{});var lue=s(Cf);Bet=r(lue,`Note:
Loading a model from its configuration file does `),u$e=n(lue,"STRONG",{});var X3a=s(u$e);Net=r(X3a,"not"),X3a.forEach(t),Det=r(lue,` load the model weights. It only affects the
model\u2019s configuration. Use `),nse=n(lue,"A",{href:!0});var z3a=s(nse);qet=r(z3a,"from_pretrained()"),z3a.forEach(t),jet=r(lue," to load the model weights."),lue.forEach(t),Get=i(tk),T($w.$$.fragment,tk),tk.forEach(t),Oet=i(Ai),zr=n(Ai,"DIV",{class:!0});var Li=s(zr);T(CI.$$.fragment,Li),Vet=i(Li),p$e=n(Li,"P",{});var Q3a=s(p$e);Xet=r(Q3a,"Instantiate one of the base model classes of the library from a pretrained model."),Q3a.forEach(t),zet=i(Li),On=n(Li,"P",{});var ak=s(On);Qet=r(ak,"The model class to instantiate is selected based on the "),_$e=n(ak,"CODE",{});var W3a=s(_$e);Wet=r(W3a,"model_type"),W3a.forEach(t),Uet=r(ak,` property of the config object (either
passed as an argument or loaded from `),v$e=n(ak,"CODE",{});var U3a=s(v$e);Het=r(U3a,"pretrained_model_name_or_path"),U3a.forEach(t),Jet=r(ak,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),b$e=n(ak,"CODE",{});var H3a=s(b$e);Yet=r(H3a,"pretrained_model_name_or_path"),H3a.forEach(t),Zet=r(ak,":"),ak.forEach(t),Ket=i(Li),P=n(Li,"UL",{});var q=s(P);kw=n(q,"LI",{});var Oao=s(kw);F$e=n(Oao,"STRONG",{});var J3a=s(F$e);eot=r(J3a,"albert"),J3a.forEach(t),oot=r(Oao," \u2014 "),sse=n(Oao,"A",{href:!0});var Y3a=s(sse);rot=r(Y3a,"TFAlbertModel"),Y3a.forEach(t),tot=r(Oao," (ALBERT model)"),Oao.forEach(t),aot=i(q),Sw=n(q,"LI",{});var Vao=s(Sw);T$e=n(Vao,"STRONG",{});var Z3a=s(T$e);not=r(Z3a,"bart"),Z3a.forEach(t),sot=r(Vao," \u2014 "),lse=n(Vao,"A",{href:!0});var K3a=s(lse);lot=r(K3a,"TFBartModel"),K3a.forEach(t),iot=r(Vao," (BART model)"),Vao.forEach(t),dot=i(q),Rw=n(q,"LI",{});var Xao=s(Rw);M$e=n(Xao,"STRONG",{});var e0a=s(M$e);cot=r(e0a,"bert"),e0a.forEach(t),fot=r(Xao," \u2014 "),ise=n(Xao,"A",{href:!0});var o0a=s(ise);mot=r(o0a,"TFBertModel"),o0a.forEach(t),got=r(Xao," (BERT model)"),Xao.forEach(t),hot=i(q),Pw=n(q,"LI",{});var zao=s(Pw);E$e=n(zao,"STRONG",{});var r0a=s(E$e);uot=r(r0a,"blenderbot"),r0a.forEach(t),pot=r(zao," \u2014 "),dse=n(zao,"A",{href:!0});var t0a=s(dse);_ot=r(t0a,"TFBlenderbotModel"),t0a.forEach(t),vot=r(zao," (Blenderbot model)"),zao.forEach(t),bot=i(q),Iw=n(q,"LI",{});var Qao=s(Iw);C$e=n(Qao,"STRONG",{});var a0a=s(C$e);Fot=r(a0a,"blenderbot-small"),a0a.forEach(t),Tot=r(Qao," \u2014 "),cse=n(Qao,"A",{href:!0});var n0a=s(cse);Mot=r(n0a,"TFBlenderbotSmallModel"),n0a.forEach(t),Eot=r(Qao," (BlenderbotSmall model)"),Qao.forEach(t),Cot=i(q),Bw=n(q,"LI",{});var Wao=s(Bw);w$e=n(Wao,"STRONG",{});var s0a=s(w$e);wot=r(s0a,"camembert"),s0a.forEach(t),Aot=r(Wao," \u2014 "),fse=n(Wao,"A",{href:!0});var l0a=s(fse);Lot=r(l0a,"TFCamembertModel"),l0a.forEach(t),yot=r(Wao," (CamemBERT model)"),Wao.forEach(t),xot=i(q),Nw=n(q,"LI",{});var Uao=s(Nw);A$e=n(Uao,"STRONG",{});var i0a=s(A$e);$ot=r(i0a,"clip"),i0a.forEach(t),kot=r(Uao," \u2014 "),mse=n(Uao,"A",{href:!0});var 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model\u2019s configuration. Use `),qle=n(fue,"A",{href:!0});var v6a=s(qle);ydt=r(v6a,"from_pretrained()"),v6a.forEach(t),xdt=r(fue," to load the model weights."),fue.forEach(t),$dt=i(lk),T(i6.$$.fragment,lk),lk.forEach(t),kdt=i($i),Wr=n($i,"DIV",{class:!0});var ki=s(Wr);T(PI.$$.fragment,ki),Sdt=i(ki),lSe=n(ki,"P",{});var b6a=s(lSe);Rdt=r(b6a,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),b6a.forEach(t),Pdt=i(ki),Xn=n(ki,"P",{});var ik=s(Xn);Idt=r(ik,"The model class to instantiate is selected based on the "),iSe=n(ik,"CODE",{});var F6a=s(iSe);Bdt=r(F6a,"model_type"),F6a.forEach(t),Ndt=r(ik,` property of the config object (either
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falling back to using pattern matching on `),cSe=n(ik,"CODE",{});var M6a=s(cSe);jdt=r(M6a,"pretrained_model_name_or_path"),M6a.forEach(t),Gdt=r(ik,":"),ik.forEach(t),Odt=i(ki),Ce=n(ki,"UL",{});var Ae=s(Ce);d6=n(Ae,"LI",{});var hso=s(d6);fSe=n(hso,"STRONG",{});var E6a=s(fSe);Vdt=r(E6a,"bert"),E6a.forEach(t),Xdt=r(hso," \u2014 "),jle=n(hso,"A",{href:!0});var C6a=s(jle);zdt=r(C6a,"TFBertLMHeadModel"),C6a.forEach(t),Qdt=r(hso," (BERT model)"),hso.forEach(t),Wdt=i(Ae),c6=n(Ae,"LI",{});var uso=s(c6);mSe=n(uso,"STRONG",{});var w6a=s(mSe);Udt=r(w6a,"camembert"),w6a.forEach(t),Hdt=r(uso," \u2014 "),Gle=n(uso,"A",{href:!0});var A6a=s(Gle);Jdt=r(A6a,"TFCamembertForCausalLM"),A6a.forEach(t),Ydt=r(uso," (CamemBERT model)"),uso.forEach(t),Zdt=i(Ae),f6=n(Ae,"LI",{});var pso=s(f6);gSe=n(pso,"STRONG",{});var L6a=s(gSe);Kdt=r(L6a,"ctrl"),L6a.forEach(t),ect=r(pso," \u2014 "),Ole=n(pso,"A",{href:!0});var y6a=s(Ole);oct=r(y6a,"TFCTRLLMHeadModel"),y6a.forEach(t),rct=r(pso," (CTRL model)"),pso.forEach(t),tct=i(Ae),m6=n(Ae,"LI",{});var _so=s(m6);hSe=n(_so,"STRONG",{});var x6a=s(hSe);act=r(x6a,"gpt2"),x6a.forEach(t),nct=r(_so," \u2014 "),Vle=n(_so,"A",{href:!0});var $6a=s(Vle);sct=r($6a,"TFGPT2LMHeadModel"),$6a.forEach(t),lct=r(_so," (OpenAI GPT-2 model)"),_so.forEach(t),ict=i(Ae),g6=n(Ae,"LI",{});var vso=s(g6);uSe=n(vso,"STRONG",{});var k6a=s(uSe);dct=r(k6a,"gptj"),k6a.forEach(t),cct=r(vso," \u2014 "),Xle=n(vso,"A",{href:!0});var S6a=s(Xle);fct=r(S6a,"TFGPTJForCausalLM"),S6a.forEach(t),mct=r(vso," (GPT-J model)"),vso.forEach(t),gct=i(Ae),h6=n(Ae,"LI",{});var bso=s(h6);pSe=n(bso,"STRONG",{});var R6a=s(pSe);hct=r(R6a,"openai-gpt"),R6a.forEach(t),uct=r(bso," \u2014 "),zle=n(bso,"A",{href:!0});var P6a=s(zle);pct=r(P6a,"TFOpenAIGPTLMHeadModel"),P6a.forEach(t),_ct=r(bso," (OpenAI GPT model)"),bso.forEach(t),vct=i(Ae),u6=n(Ae,"LI",{});var Fso=s(u6);_Se=n(Fso,"STRONG",{});var I6a=s(_Se);bct=r(I6a,"opt"),I6a.forEach(t),Fct=r(Fso," \u2014 "),Qle=n(Fso,"A",{href:!0});var B6a=s(Qle);Tct=r(B6a,"TFOPTForCausalLM"),B6a.forEach(t),Mct=r(Fso," (OPT model)"),Fso.forEach(t),Ect=i(Ae),p6=n(Ae,"LI",{});var Tso=s(p6);vSe=n(Tso,"STRONG",{});var N6a=s(vSe);Cct=r(N6a,"rembert"),N6a.forEach(t),wct=r(Tso," \u2014 "),Wle=n(Tso,"A",{href:!0});var D6a=s(Wle);Act=r(D6a,"TFRemBertForCausalLM"),D6a.forEach(t),Lct=r(Tso," (RemBERT model)"),Tso.forEach(t),yct=i(Ae),_6=n(Ae,"LI",{});var Mso=s(_6);bSe=n(Mso,"STRONG",{});var q6a=s(bSe);xct=r(q6a,"roberta"),q6a.forEach(t),$ct=r(Mso," \u2014 "),Ule=n(Mso,"A",{href:!0});var j6a=s(Ule);kct=r(j6a,"TFRobertaForCausalLM"),j6a.forEach(t),Sct=r(Mso," (RoBERTa model)"),Mso.forEach(t),Rct=i(Ae),v6=n(Ae,"LI",{});var Eso=s(v6);FSe=n(Eso,"STRONG",{});var G6a=s(FSe);Pct=r(G6a,"roformer"),G6a.forEach(t),Ict=r(Eso," \u2014 "),Hle=n(Eso,"A",{href:!0});var O6a=s(Hle);Bct=r(O6a,"TFRoFormerForCausalLM"),O6a.forEach(t),Nct=r(Eso," (RoFormer model)"),Eso.forEach(t),Dct=i(Ae),b6=n(Ae,"LI",{});var Cso=s(b6);TSe=n(Cso,"STRONG",{});var V6a=s(TSe);qct=r(V6a,"transfo-xl"),V6a.forEach(t),jct=r(Cso," \u2014 "),Jle=n(Cso,"A",{href:!0});var X6a=s(Jle);Gct=r(X6a,"TFTransfoXLLMHeadModel"),X6a.forEach(t),Oct=r(Cso," (Transformer-XL model)"),Cso.forEach(t),Vct=i(Ae),F6=n(Ae,"LI",{});var wso=s(F6);MSe=n(wso,"STRONG",{});var z6a=s(MSe);Xct=r(z6a,"xglm"),z6a.forEach(t),zct=r(wso," \u2014 "),Yle=n(wso,"A",{href:!0});var Q6a=s(Yle);Qct=r(Q6a,"TFXGLMForCausalLM"),Q6a.forEach(t),Wct=r(wso," (XGLM model)"),wso.forEach(t),Uct=i(Ae),T6=n(Ae,"LI",{});var Aso=s(T6);ESe=n(Aso,"STRONG",{});var W6a=s(ESe);Hct=r(W6a,"xlm"),W6a.forEach(t),Jct=r(Aso," \u2014 "),Zle=n(Aso,"A",{href:!0});var U6a=s(Zle);Yct=r(U6a,"TFXLMWithLMHeadModel"),U6a.forEach(t),Zct=r(Aso," (XLM model)"),Aso.forEach(t),Kct=i(Ae),M6=n(Ae,"LI",{});var Lso=s(M6);CSe=n(Lso,"STRONG",{});var H6a=s(CSe);eft=r(H6a,"xlnet"),H6a.forEach(t),oft=r(Lso," \u2014 "),Kle=n(Lso,"A",{href:!0});var J6a=s(Kle);rft=r(J6a,"TFXLNetLMHeadModel"),J6a.forEach(t),tft=r(Lso," (XLNet model)"),Lso.forEach(t),Ae.forEach(t),aft=i(ki),T(E6.$$.fragment,ki),ki.forEach(t),$i.forEach(t),xmo=i(f),kf=n(f,"H2",{class:!0});var Zho=s(kf);C6=n(Zho,"A",{id:!0,class:!0,href:!0});var Y6a=s(C6);wSe=n(Y6a,"SPAN",{});var Z6a=s(wSe);T(II.$$.fragment,Z6a),Z6a.forEach(t),Y6a.forEach(t),nft=i(Zho),ASe=n(Zho,"SPAN",{});var K6a=s(ASe);sft=r(K6a,"TFAutoModelForImageClassification"),K6a.forEach(t),Zho.forEach(t),$mo=i(f),vr=n(f,"DIV",{class:!0});var Si=s(vr);T(BI.$$.fragment,Si),lft=i(Si),Sf=n(Si,"P",{});var mue=s(Sf);ift=r(mue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),eie=n(mue,"A",{href:!0});var e8a=s(eie);dft=r(e8a,"from_pretrained()"),e8a.forEach(t),cft=r(mue," class method or the "),oie=n(mue,"A",{href:!0});var o8a=s(oie);fft=r(o8a,"from_config()"),o8a.forEach(t),mft=r(mue,` class
method.`),mue.forEach(t),gft=i(Si),NI=n(Si,"P",{});var Kho=s(NI);hft=r(Kho,"This class cannot be instantiated directly using "),LSe=n(Kho,"CODE",{});var r8a=s(LSe);uft=r(r8a,"__init__()"),r8a.forEach(t),pft=r(Kho," (throws an error)."),Kho.forEach(t),_ft=i(Si),aa=n(Si,"DIV",{class:!0});var dk=s(aa);T(DI.$$.fragment,dk),vft=i(dk),ySe=n(dk,"P",{});var t8a=s(ySe);bft=r(t8a,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),t8a.forEach(t),Fft=i(dk),Rf=n(dk,"P",{});var gue=s(Rf);Tft=r(gue,`Note:
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model\u2019s configuration. Use `),rie=n(gue,"A",{href:!0});var n8a=s(rie);Cft=r(n8a,"from_pretrained()"),n8a.forEach(t),wft=r(gue," to load the model weights."),gue.forEach(t),Aft=i(dk),T(w6.$$.fragment,dk),dk.forEach(t),Lft=i(Si),Ur=n(Si,"DIV",{class:!0});var Ri=s(Ur);T(qI.$$.fragment,Ri),yft=i(Ri),$Se=n(Ri,"P",{});var s8a=s($Se);xft=r(s8a,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),s8a.forEach(t),$ft=i(Ri),zn=n(Ri,"P",{});var ck=s(zn);kft=r(ck,"The model class to instantiate is selected based on the "),kSe=n(ck,"CODE",{});var l8a=s(kSe);Sft=r(l8a,"model_type"),l8a.forEach(t),Rft=r(ck,` property of the config object (either
passed as an argument or loaded from `),SSe=n(ck,"CODE",{});var i8a=s(SSe);Pft=r(i8a,"pretrained_model_name_or_path"),i8a.forEach(t),Ift=r(ck,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),RSe=n(ck,"CODE",{});var d8a=s(RSe);Bft=r(d8a,"pretrained_model_name_or_path"),d8a.forEach(t),Nft=r(ck,":"),ck.forEach(t),Dft=i(Ri),ke=n(Ri,"UL",{});var je=s(ke);A6=n(je,"LI",{});var yso=s(A6);PSe=n(yso,"STRONG",{});var c8a=s(PSe);qft=r(c8a,"convnext"),c8a.forEach(t),jft=r(yso," \u2014 "),tie=n(yso,"A",{href:!0});var f8a=s(tie);Gft=r(f8a,"TFConvNextForImageClassification"),f8a.forEach(t),Oft=r(yso," (ConvNeXT model)"),yso.forEach(t),Vft=i(je),L6=n(je,"LI",{});var xso=s(L6);ISe=n(xso,"STRONG",{});var m8a=s(ISe);Xft=r(m8a,"cvt"),m8a.forEach(t),zft=r(xso," \u2014 "),aie=n(xso,"A",{href:!0});var g8a=s(aie);Qft=r(g8a,"TFCvtForImageClassification"),g8a.forEach(t),Wft=r(xso," (CvT model)"),xso.forEach(t),Uft=i(je),y6=n(je,"LI",{});var $so=s(y6);BSe=n($so,"STRONG",{});var h8a=s(BSe);Hft=r(h8a,"data2vec-vision"),h8a.forEach(t),Jft=r($so," \u2014 "),nie=n($so,"A",{href:!0});var u8a=s(nie);Yft=r(u8a,"TFData2VecVisionForImageClassification"),u8a.forEach(t),Zft=r($so," (Data2VecVision model)"),$so.forEach(t),Kft=i(je),Vl=n(je,"LI",{});var $q=s(Vl);NSe=n($q,"STRONG",{});var p8a=s(NSe);emt=r(p8a,"deit"),p8a.forEach(t),omt=r($q," \u2014 "),sie=n($q,"A",{href:!0});var _8a=s(sie);rmt=r(_8a,"TFDeiTForImageClassification"),_8a.forEach(t),tmt=r($q," or "),lie=n($q,"A",{href:!0});var v8a=s(lie);amt=r(v8a,"TFDeiTForImageClassificationWithTeacher"),v8a.forEach(t),nmt=r($q," (DeiT model)"),$q.forEach(t),smt=i(je),x6=n(je,"LI",{});var kso=s(x6);DSe=n(kso,"STRONG",{});var b8a=s(DSe);lmt=r(b8a,"mobilevit"),b8a.forEach(t),imt=r(kso," \u2014 "),iie=n(kso,"A",{href:!0});var F8a=s(iie);dmt=r(F8a,"TFMobileViTForImageClassification"),F8a.forEach(t),cmt=r(kso," (MobileViT model)"),kso.forEach(t),fmt=i(je),$6=n(je,"LI",{});var Sso=s($6);qSe=n(Sso,"STRONG",{});var T8a=s(qSe);mmt=r(T8a,"regnet"),T8a.forEach(t),gmt=r(Sso," \u2014 "),die=n(Sso,"A",{href:!0});var M8a=s(die);hmt=r(M8a,"TFRegNetForImageClassification"),M8a.forEach(t),umt=r(Sso," (RegNet model)"),Sso.forEach(t),pmt=i(je),k6=n(je,"LI",{});var Rso=s(k6);jSe=n(Rso,"STRONG",{});var E8a=s(jSe);_mt=r(E8a,"resnet"),E8a.forEach(t),vmt=r(Rso," \u2014 "),cie=n(Rso,"A",{href:!0});var C8a=s(cie);bmt=r(C8a,"TFResNetForImageClassification"),C8a.forEach(t),Fmt=r(Rso," (ResNet model)"),Rso.forEach(t),Tmt=i(je),S6=n(je,"LI",{});var Pso=s(S6);GSe=n(Pso,"STRONG",{});var w8a=s(GSe);Mmt=r(w8a,"segformer"),w8a.forEach(t),Emt=r(Pso," \u2014 "),fie=n(Pso,"A",{href:!0});var A8a=s(fie);Cmt=r(A8a,"TFSegformerForImageClassification"),A8a.forEach(t),wmt=r(Pso," (SegFormer model)"),Pso.forEach(t),Amt=i(je),R6=n(je,"LI",{});var Iso=s(R6);OSe=n(Iso,"STRONG",{});var L8a=s(OSe);Lmt=r(L8a,"swin"),L8a.forEach(t),ymt=r(Iso," \u2014 "),mie=n(Iso,"A",{href:!0});var y8a=s(mie);xmt=r(y8a,"TFSwinForImageClassification"),y8a.forEach(t),$mt=r(Iso," (Swin Transformer model)"),Iso.forEach(t),kmt=i(je),P6=n(je,"LI",{});var Bso=s(P6);VSe=n(Bso,"STRONG",{});var x8a=s(VSe);Smt=r(x8a,"vit"),x8a.forEach(t),Rmt=r(Bso," \u2014 "),gie=n(Bso,"A",{href:!0});var $8a=s(gie);Pmt=r($8a,"TFViTForImageClassification"),$8a.forEach(t),Imt=r(Bso," (ViT model)"),Bso.forEach(t),je.forEach(t),Bmt=i(Ri),T(I6.$$.fragment,Ri),Ri.forEach(t),Si.forEach(t),kmo=i(f),Pf=n(f,"H2",{class:!0});var euo=s(Pf);B6=n(euo,"A",{id:!0,class:!0,href:!0});var k8a=s(B6);XSe=n(k8a,"SPAN",{});var S8a=s(XSe);T(jI.$$.fragment,S8a),S8a.forEach(t),k8a.forEach(t),Nmt=i(euo),zSe=n(euo,"SPAN",{});var R8a=s(zSe);Dmt=r(R8a,"TFAutoModelForSemanticSegmentation"),R8a.forEach(t),euo.forEach(t),Smo=i(f),br=n(f,"DIV",{class:!0});var Pi=s(br);T(GI.$$.fragment,Pi),qmt=i(Pi),If=n(Pi,"P",{});var hue=s(If);jmt=r(hue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),hie=n(hue,"A",{href:!0});var P8a=s(hie);Gmt=r(P8a,"from_pretrained()"),P8a.forEach(t),Omt=r(hue," class method or the "),uie=n(hue,"A",{href:!0});var I8a=s(uie);Vmt=r(I8a,"from_config()"),I8a.forEach(t),Xmt=r(hue,` class
method.`),hue.forEach(t),zmt=i(Pi),OI=n(Pi,"P",{});var ouo=s(OI);Qmt=r(ouo,"This class cannot be instantiated directly using "),QSe=n(ouo,"CODE",{});var B8a=s(QSe);Wmt=r(B8a,"__init__()"),B8a.forEach(t),Umt=r(ouo," (throws an error)."),ouo.forEach(t),Hmt=i(Pi),na=n(Pi,"DIV",{class:!0});var fk=s(na);T(VI.$$.fragment,fk),Jmt=i(fk),WSe=n(fk,"P",{});var N8a=s(WSe);Ymt=r(N8a,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),N8a.forEach(t),Zmt=i(fk),Bf=n(fk,"P",{});var uue=s(Bf);Kmt=r(uue,`Note:
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model\u2019s configuration. Use `),pie=n(uue,"A",{href:!0});var q8a=s(pie);rgt=r(q8a,"from_pretrained()"),q8a.forEach(t),tgt=r(uue," to load the model weights."),uue.forEach(t),agt=i(fk),T(N6.$$.fragment,fk),fk.forEach(t),ngt=i(Pi),Hr=n(Pi,"DIV",{class:!0});var Ii=s(Hr);T(XI.$$.fragment,Ii),sgt=i(Ii),HSe=n(Ii,"P",{});var j8a=s(HSe);lgt=r(j8a,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),j8a.forEach(t),igt=i(Ii),Qn=n(Ii,"P",{});var mk=s(Qn);dgt=r(mk,"The model class to instantiate is selected based on the "),JSe=n(mk,"CODE",{});var G8a=s(JSe);cgt=r(G8a,"model_type"),G8a.forEach(t),fgt=r(mk,` property of the config object (either
passed as an argument or loaded from `),YSe=n(mk,"CODE",{});var O8a=s(YSe);mgt=r(O8a,"pretrained_model_name_or_path"),O8a.forEach(t),ggt=r(mk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZSe=n(mk,"CODE",{});var V8a=s(ZSe);hgt=r(V8a,"pretrained_model_name_or_path"),V8a.forEach(t),ugt=r(mk,":"),mk.forEach(t),pgt=i(Ii),Nf=n(Ii,"UL",{});var pue=s(Nf);D6=n(pue,"LI",{});var Nso=s(D6);KSe=n(Nso,"STRONG",{});var X8a=s(KSe);_gt=r(X8a,"data2vec-vision"),X8a.forEach(t),vgt=r(Nso," \u2014 "),_ie=n(Nso,"A",{href:!0});var z8a=s(_ie);bgt=r(z8a,"TFData2VecVisionForSemanticSegmentation"),z8a.forEach(t),Fgt=r(Nso," (Data2VecVision model)"),Nso.forEach(t),Tgt=i(pue),q6=n(pue,"LI",{});var Dso=s(q6);eRe=n(Dso,"STRONG",{});var Q8a=s(eRe);Mgt=r(Q8a,"mobilevit"),Q8a.forEach(t),Egt=r(Dso," \u2014 "),vie=n(Dso,"A",{href:!0});var W8a=s(vie);Cgt=r(W8a,"TFMobileViTForSemanticSegmentation"),W8a.forEach(t),wgt=r(Dso," (MobileViT model)"),Dso.forEach(t),Agt=i(pue),j6=n(pue,"LI",{});var qso=s(j6);oRe=n(qso,"STRONG",{});var U8a=s(oRe);Lgt=r(U8a,"segformer"),U8a.forEach(t),ygt=r(qso," \u2014 "),bie=n(qso,"A",{href:!0});var H8a=s(bie);xgt=r(H8a,"TFSegformerForSemanticSegmentation"),H8a.forEach(t),$gt=r(qso," (SegFormer model)"),qso.forEach(t),pue.forEach(t),kgt=i(Ii),T(G6.$$.fragment,Ii),Ii.forEach(t),Pi.forEach(t),Rmo=i(f),Df=n(f,"H2",{class:!0});var ruo=s(Df);O6=n(ruo,"A",{id:!0,class:!0,href:!0});var J8a=s(O6);rRe=n(J8a,"SPAN",{});var Y8a=s(rRe);T(zI.$$.fragment,Y8a),Y8a.forEach(t),J8a.forEach(t),Sgt=i(ruo),tRe=n(ruo,"SPAN",{});var Z8a=s(tRe);Rgt=r(Z8a,"TFAutoModelForMaskedLM"),Z8a.forEach(t),ruo.forEach(t),Pmo=i(f),Fr=n(f,"DIV",{class:!0});var Bi=s(Fr);T(QI.$$.fragment,Bi),Pgt=i(Bi),qf=n(Bi,"P",{});var _ue=s(qf);Igt=r(_ue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),Fie=n(_ue,"A",{href:!0});var K8a=s(Fie);Bgt=r(K8a,"from_pretrained()"),K8a.forEach(t),Ngt=r(_ue," class method or the "),Tie=n(_ue,"A",{href:!0});var e7a=s(Tie);Dgt=r(e7a,"from_config()"),e7a.forEach(t),qgt=r(_ue,` class
method.`),_ue.forEach(t),jgt=i(Bi),WI=n(Bi,"P",{});var tuo=s(WI);Ggt=r(tuo,"This class cannot be instantiated directly using "),aRe=n(tuo,"CODE",{});var o7a=s(aRe);Ogt=r(o7a,"__init__()"),o7a.forEach(t),Vgt=r(tuo," (throws an error)."),tuo.forEach(t),Xgt=i(Bi),sa=n(Bi,"DIV",{class:!0});var gk=s(sa);T(UI.$$.fragment,gk),zgt=i(gk),nRe=n(gk,"P",{});var r7a=s(nRe);Qgt=r(r7a,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),r7a.forEach(t),Wgt=i(gk),jf=n(gk,"P",{});var vue=s(jf);Ugt=r(vue,`Note:
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model\u2019s configuration. Use `),Mie=n(vue,"A",{href:!0});var a7a=s(Mie);Ygt=r(a7a,"from_pretrained()"),a7a.forEach(t),Zgt=r(vue," to load the model weights."),vue.forEach(t),Kgt=i(gk),T(V6.$$.fragment,gk),gk.forEach(t),eht=i(Bi),Jr=n(Bi,"DIV",{class:!0});var Ni=s(Jr);T(HI.$$.fragment,Ni),oht=i(Ni),lRe=n(Ni,"P",{});var n7a=s(lRe);rht=r(n7a,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),n7a.forEach(t),tht=i(Ni),Wn=n(Ni,"P",{});var hk=s(Wn);aht=r(hk,"The model class to instantiate is selected based on the "),iRe=n(hk,"CODE",{});var s7a=s(iRe);nht=r(s7a,"model_type"),s7a.forEach(t),sht=r(hk,` property of the config object (either
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falling back to using pattern matching on `),cRe=n(hk,"CODE",{});var i7a=s(cRe);dht=r(i7a,"pretrained_model_name_or_path"),i7a.forEach(t),cht=r(hk,":"),hk.forEach(t),fht=i(Ni),ue=n(Ni,"UL",{});var Fe=s(ue);X6=n(Fe,"LI",{});var jso=s(X6);fRe=n(jso,"STRONG",{});var d7a=s(fRe);mht=r(d7a,"albert"),d7a.forEach(t),ght=r(jso," \u2014 "),Eie=n(jso,"A",{href:!0});var c7a=s(Eie);hht=r(c7a,"TFAlbertForMaskedLM"),c7a.forEach(t),uht=r(jso," (ALBERT model)"),jso.forEach(t),pht=i(Fe),z6=n(Fe,"LI",{});var Gso=s(z6);mRe=n(Gso,"STRONG",{});var f7a=s(mRe);_ht=r(f7a,"bert"),f7a.forEach(t),vht=r(Gso," \u2014 "),Cie=n(Gso,"A",{href:!0});var m7a=s(Cie);bht=r(m7a,"TFBertForMaskedLM"),m7a.forEach(t),Fht=r(Gso," (BERT model)"),Gso.forEach(t),Tht=i(Fe),Q6=n(Fe,"LI",{});var Oso=s(Q6);gRe=n(Oso,"STRONG",{});var g7a=s(gRe);Mht=r(g7a,"camembert"),g7a.forEach(t),Eht=r(Oso," \u2014 "),wie=n(Oso,"A",{href:!0});var h7a=s(wie);Cht=r(h7a,"TFCamembertForMaskedLM"),h7a.forEach(t),wht=r(Oso," (CamemBERT 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method.`),bue.forEach(t),_pt=i(Di),ZI=n(Di,"P",{});var nuo=s(ZI);vpt=r(nuo,"This class cannot be instantiated directly using "),PRe=n(nuo,"CODE",{});var rLa=s(PRe);bpt=r(rLa,"__init__()"),rLa.forEach(t),Fpt=r(nuo," (throws an error)."),nuo.forEach(t),Tpt=i(Di),la=n(Di,"DIV",{class:!0});var uk=s(la);T(KI.$$.fragment,uk),Mpt=i(uk),IRe=n(uk,"P",{});var tLa=s(IRe);Ept=r(tLa,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),tLa.forEach(t),Cpt=i(uk),Vf=n(uk,"P",{});var Fue=s(Vf);wpt=r(Fue,`Note:
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falling back to using pattern matching on `),jRe=n(pk,"CODE",{});var dLa=s(jRe);jpt=r(dLa,"pretrained_model_name_or_path"),dLa.forEach(t),Gpt=r(pk,":"),pk.forEach(t),Opt=i(qi),Se=n(qi,"UL",{});var Ge=s(Se);h8=n(Ge,"LI",{});var llo=s(h8);GRe=n(llo,"STRONG",{});var cLa=s(GRe);Vpt=r(cLa,"bart"),cLa.forEach(t),Xpt=r(llo," \u2014 "),Wie=n(llo,"A",{href:!0});var fLa=s(Wie);zpt=r(fLa,"TFBartForConditionalGeneration"),fLa.forEach(t),Qpt=r(llo," (BART model)"),llo.forEach(t),Wpt=i(Ge),u8=n(Ge,"LI",{});var ilo=s(u8);ORe=n(ilo,"STRONG",{});var mLa=s(ORe);Upt=r(mLa,"blenderbot"),mLa.forEach(t),Hpt=r(ilo," \u2014 "),Uie=n(ilo,"A",{href:!0});var gLa=s(Uie);Jpt=r(gLa,"TFBlenderbotForConditionalGeneration"),gLa.forEach(t),Ypt=r(ilo," (Blenderbot model)"),ilo.forEach(t),Zpt=i(Ge),p8=n(Ge,"LI",{});var dlo=s(p8);VRe=n(dlo,"STRONG",{});var hLa=s(VRe);Kpt=r(hLa,"blenderbot-small"),hLa.forEach(t),e_t=r(dlo," \u2014 "),Hie=n(dlo,"A",{href:!0});var uLa=s(Hie);o_t=r(uLa,"TFBlenderbotSmallForConditionalGeneration"),uLa.forEach(t),r_t=r(dlo," (BlenderbotSmall model)"),dlo.forEach(t),t_t=i(Ge),_8=n(Ge,"LI",{});var clo=s(_8);XRe=n(clo,"STRONG",{});var pLa=s(XRe);a_t=r(pLa,"encoder-decoder"),pLa.forEach(t),n_t=r(clo," \u2014 "),Jie=n(clo,"A",{href:!0});var _La=s(Jie);s_t=r(_La,"TFEncoderDecoderModel"),_La.forEach(t),l_t=r(clo," (Encoder decoder model)"),clo.forEach(t),i_t=i(Ge),v8=n(Ge,"LI",{});var flo=s(v8);zRe=n(flo,"STRONG",{});var vLa=s(zRe);d_t=r(vLa,"led"),vLa.forEach(t),c_t=r(flo," \u2014 "),Yie=n(flo,"A",{href:!0});var bLa=s(Yie);f_t=r(bLa,"TFLEDForConditionalGeneration"),bLa.forEach(t),m_t=r(flo," (LED model)"),flo.forEach(t),g_t=i(Ge),b8=n(Ge,"LI",{});var mlo=s(b8);QRe=n(mlo,"STRONG",{});var FLa=s(QRe);h_t=r(FLa,"marian"),FLa.forEach(t),u_t=r(mlo," \u2014 "),Zie=n(mlo,"A",{href:!0});var TLa=s(Zie);p_t=r(TLa,"TFMarianMTModel"),TLa.forEach(t),__t=r(mlo," (Marian model)"),mlo.forEach(t),v_t=i(Ge),F8=n(Ge,"LI",{});var glo=s(F8);WRe=n(glo,"STRONG",{});var MLa=s(WRe);b_t=r(MLa,"mbart"),MLa.forEach(t),F_t=r(glo," \u2014 "),Kie=n(glo,"A",{href:!0});var ELa=s(Kie);T_t=r(ELa,"TFMBartForConditionalGeneration"),ELa.forEach(t),M_t=r(glo," (mBART model)"),glo.forEach(t),E_t=i(Ge),T8=n(Ge,"LI",{});var hlo=s(T8);URe=n(hlo,"STRONG",{});var CLa=s(URe);C_t=r(CLa,"mt5"),CLa.forEach(t),w_t=r(hlo," \u2014 "),ede=n(hlo,"A",{href:!0});var wLa=s(ede);A_t=r(wLa,"TFMT5ForConditionalGeneration"),wLa.forEach(t),L_t=r(hlo," (MT5 model)"),hlo.forEach(t),y_t=i(Ge),M8=n(Ge,"LI",{});var ulo=s(M8);HRe=n(ulo,"STRONG",{});var ALa=s(HRe);x_t=r(ALa,"pegasus"),ALa.forEach(t),$_t=r(ulo," \u2014 "),ode=n(ulo,"A",{href:!0});var LLa=s(ode);k_t=r(LLa,"TFPegasusForConditionalGeneration"),LLa.forEach(t),S_t=r(ulo," (Pegasus model)"),ulo.forEach(t),R_t=i(Ge),E8=n(Ge,"LI",{});var plo=s(E8);JRe=n(plo,"STRONG",{});var yLa=s(JRe);P_t=r(yLa,"t5"),yLa.forEach(t),I_t=r(plo," \u2014 "),rde=n(plo,"A",{href:!0});var xLa=s(rde);B_t=r(xLa,"TFT5ForConditionalGeneration"),xLa.forEach(t),N_t=r(plo," (T5 model)"),plo.forEach(t),Ge.forEach(t),D_t=i(qi),T(C8.$$.fragment,qi),qi.forEach(t),Di.forEach(t),Nmo=i(f),Xf=n(f,"H2",{class:!0});var suo=s(Xf);w8=n(suo,"A",{id:!0,class:!0,href:!0});var $La=s(w8);YRe=n($La,"SPAN",{});var kLa=s(YRe);T(oB.$$.fragment,kLa),kLa.forEach(t),$La.forEach(t),q_t=i(suo),ZRe=n(suo,"SPAN",{});var SLa=s(ZRe);j_t=r(SLa,"TFAutoModelForSequenceClassification"),SLa.forEach(t),suo.forEach(t),Dmo=i(f),Mr=n(f,"DIV",{class:!0});var ji=s(Mr);T(rB.$$.fragment,ji),G_t=i(ji),zf=n(ji,"P",{});var Tue=s(zf);O_t=r(Tue,`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
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model\u2019s configuration. Use `),nde=n(Mue,"A",{href:!0});var DLa=s(nde);a1t=r(DLa,"from_pretrained()"),DLa.forEach(t),n1t=r(Mue," to load the model weights."),Mue.forEach(t),s1t=i(_k),T(A8.$$.fragment,_k),_k.forEach(t),l1t=i(ji),Zr=n(ji,"DIV",{class:!0});var Gi=s(Zr);T(nB.$$.fragment,Gi),i1t=i(Gi),rPe=n(Gi,"P",{});var qLa=s(rPe);d1t=r(qLa,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),qLa.forEach(t),c1t=i(Gi),Hn=n(Gi,"P",{});var vk=s(Hn);f1t=r(vk,"The model class to instantiate is selected based on the "),tPe=n(vk,"CODE",{});var jLa=s(tPe);m1t=r(jLa,"model_type"),jLa.forEach(t),g1t=r(vk,` property of the config object (either
passed as an argument or loaded from `),aPe=n(vk,"CODE",{});var GLa=s(aPe);h1t=r(GLa,"pretrained_model_name_or_path"),GLa.forEach(t),u1t=r(vk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nPe=n(vk,"CODE",{});var OLa=s(nPe);p1t=r(OLa,"pretrained_model_name_or_path"),OLa.forEach(t),_1t=r(vk,":"),vk.forEach(t),v1t=i(Gi),ae=n(Gi,"UL",{});var se=s(ae);L8=n(se,"LI",{});var _lo=s(L8);sPe=n(_lo,"STRONG",{});var VLa=s(sPe);b1t=r(VLa,"albert"),VLa.forEach(t),F1t=r(_lo," \u2014 "),sde=n(_lo,"A",{href:!0});var XLa=s(sde);T1t=r(XLa,"TFAlbertForSequenceClassification"),XLa.forEach(t),M1t=r(_lo," (ALBERT model)"),_lo.forEach(t),E1t=i(se),y8=n(se,"LI",{});var vlo=s(y8);lPe=n(vlo,"STRONG",{});var zLa=s(lPe);C1t=r(zLa,"bert"),zLa.forEach(t),w1t=r(vlo," \u2014 "),lde=n(vlo,"A",{href:!0});var QLa=s(lde);A1t=r(QLa,"TFBertForSequenceClassification"),QLa.forEach(t),L1t=r(vlo," (BERT model)"),vlo.forEach(t),y1t=i(se),x8=n(se,"LI",{});var blo=s(x8);iPe=n(blo,"STRONG",{});var WLa=s(iPe);x1t=r(WLa,"camembert"),WLa.forEach(t),$1t=r(blo," \u2014 "),ide=n(blo,"A",{href:!0});var ULa=s(ide);k1t=r(ULa,"TFCamembertForSequenceClassification"),ULa.forEach(t),S1t=r(blo," (CamemBERT model)"),blo.forEach(t),R1t=i(se),$8=n(se,"LI",{});var Flo=s($8);dPe=n(Flo,"STRONG",{});var HLa=s(dPe);P1t=r(HLa,"convbert"),HLa.forEach(t),I1t=r(Flo," \u2014 "),dde=n(Flo,"A",{href:!0});var JLa=s(dde);B1t=r(JLa,"TFConvBertForSequenceClassification"),JLa.forEach(t),N1t=r(Flo," (ConvBERT model)"),Flo.forEach(t),D1t=i(se),k8=n(se,"LI",{});var Tlo=s(k8);cPe=n(Tlo,"STRONG",{});var YLa=s(cPe);q1t=r(YLa,"ctrl"),YLa.forEach(t),j1t=r(Tlo," \u2014 "),cde=n(Tlo,"A",{href:!0});var ZLa=s(cde);G1t=r(ZLa,"TFCTRLForSequenceClassification"),ZLa.forEach(t),O1t=r(Tlo," (CTRL model)"),Tlo.forEach(t),V1t=i(se),S8=n(se,"LI",{});var Mlo=s(S8);fPe=n(Mlo,"STRONG",{});var KLa=s(fPe);X1t=r(KLa,"deberta"),KLa.forEach(t),z1t=r(Mlo," \u2014 "),fde=n(Mlo,"A",{href:!0});var eya=s(fde);Q1t=r(eya,"TFDebertaForSequenceClassification"),eya.forEach(t),W1t=r(Mlo," (DeBERTa 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lya=s(uPe);c2t=r(lya,"esm"),lya.forEach(t),f2t=r(Alo," \u2014 "),ude=n(Alo,"A",{href:!0});var iya=s(ude);m2t=r(iya,"TFEsmForSequenceClassification"),iya.forEach(t),g2t=r(Alo," (ESM model)"),Alo.forEach(t),h2t=i(se),N8=n(se,"LI",{});var Llo=s(N8);pPe=n(Llo,"STRONG",{});var dya=s(pPe);u2t=r(dya,"flaubert"),dya.forEach(t),p2t=r(Llo," \u2014 "),pde=n(Llo,"A",{href:!0});var cya=s(pde);_2t=r(cya,"TFFlaubertForSequenceClassification"),cya.forEach(t),v2t=r(Llo," (FlauBERT model)"),Llo.forEach(t),b2t=i(se),D8=n(se,"LI",{});var ylo=s(D8);_Pe=n(ylo,"STRONG",{});var fya=s(_Pe);F2t=r(fya,"funnel"),fya.forEach(t),T2t=r(ylo," \u2014 "),_de=n(ylo,"A",{href:!0});var mya=s(_de);M2t=r(mya,"TFFunnelForSequenceClassification"),mya.forEach(t),E2t=r(ylo," (Funnel Transformer model)"),ylo.forEach(t),C2t=i(se),q8=n(se,"LI",{});var xlo=s(q8);vPe=n(xlo,"STRONG",{});var gya=s(vPe);w2t=r(gya,"gpt2"),gya.forEach(t),A2t=r(xlo," \u2014 "),vde=n(xlo,"A",{href:!0});var hya=s(vde);L2t=r(hya,"TFGPT2ForSequenceClassification"),hya.forEach(t),y2t=r(xlo," (OpenAI GPT-2 model)"),xlo.forEach(t),x2t=i(se),j8=n(se,"LI",{});var $lo=s(j8);bPe=n($lo,"STRONG",{});var uya=s(bPe);$2t=r(uya,"gptj"),uya.forEach(t),k2t=r($lo," \u2014 "),bde=n($lo,"A",{href:!0});var pya=s(bde);S2t=r(pya,"TFGPTJForSequenceClassification"),pya.forEach(t),R2t=r($lo," (GPT-J model)"),$lo.forEach(t),P2t=i(se),G8=n(se,"LI",{});var klo=s(G8);FPe=n(klo,"STRONG",{});var _ya=s(FPe);I2t=r(_ya,"layoutlm"),_ya.forEach(t),B2t=r(klo," \u2014 "),Fde=n(klo,"A",{href:!0});var vya=s(Fde);N2t=r(vya,"TFLayoutLMForSequenceClassification"),vya.forEach(t),D2t=r(klo," (LayoutLM model)"),klo.forEach(t),q2t=i(se),O8=n(se,"LI",{});var Slo=s(O8);TPe=n(Slo,"STRONG",{});var bya=s(TPe);j2t=r(bya,"layoutlmv3"),bya.forEach(t),G2t=r(Slo," \u2014 "),Tde=n(Slo,"A",{href:!0});var Fya=s(Tde);O2t=r(Fya,"TFLayoutLMv3ForSequenceClassification"),Fya.forEach(t),V2t=r(Slo," (LayoutLMv3 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Lya=s(wPe);svt=r(Lya,"openai-gpt"),Lya.forEach(t),lvt=r(Blo," \u2014 "),wde=n(Blo,"A",{href:!0});var yya=s(wde);ivt=r(yya,"TFOpenAIGPTForSequenceClassification"),yya.forEach(t),dvt=r(Blo," (OpenAI GPT model)"),Blo.forEach(t),cvt=i(se),W8=n(se,"LI",{});var Nlo=s(W8);APe=n(Nlo,"STRONG",{});var xya=s(APe);fvt=r(xya,"rembert"),xya.forEach(t),mvt=r(Nlo," \u2014 "),Ade=n(Nlo,"A",{href:!0});var $ya=s(Ade);gvt=r($ya,"TFRemBertForSequenceClassification"),$ya.forEach(t),hvt=r(Nlo," (RemBERT model)"),Nlo.forEach(t),uvt=i(se),U8=n(se,"LI",{});var Dlo=s(U8);LPe=n(Dlo,"STRONG",{});var kya=s(LPe);pvt=r(kya,"roberta"),kya.forEach(t),_vt=r(Dlo," \u2014 "),Lde=n(Dlo,"A",{href:!0});var Sya=s(Lde);vvt=r(Sya,"TFRobertaForSequenceClassification"),Sya.forEach(t),bvt=r(Dlo," (RoBERTa model)"),Dlo.forEach(t),Fvt=i(se),H8=n(se,"LI",{});var qlo=s(H8);yPe=n(qlo,"STRONG",{});var Rya=s(yPe);Tvt=r(Rya,"roformer"),Rya.forEach(t),Mvt=r(qlo," \u2014 "),yde=n(qlo,"A",{href:!0});var Pya=s(yde);Evt=r(Pya,"TFRoFormerForSequenceClassification"),Pya.forEach(t),Cvt=r(qlo," (RoFormer model)"),qlo.forEach(t),wvt=i(se),J8=n(se,"LI",{});var jlo=s(J8);xPe=n(jlo,"STRONG",{});var Iya=s(xPe);Avt=r(Iya,"tapas"),Iya.forEach(t),Lvt=r(jlo," \u2014 "),xde=n(jlo,"A",{href:!0});var Bya=s(xde);yvt=r(Bya,"TFTapasForSequenceClassification"),Bya.forEach(t),xvt=r(jlo," (TAPAS model)"),jlo.forEach(t),$vt=i(se),Y8=n(se,"LI",{});var Glo=s(Y8);$Pe=n(Glo,"STRONG",{});var Nya=s($Pe);kvt=r(Nya,"transfo-xl"),Nya.forEach(t),Svt=r(Glo," \u2014 "),$de=n(Glo,"A",{href:!0});var Dya=s($de);Rvt=r(Dya,"TFTransfoXLForSequenceClassification"),Dya.forEach(t),Pvt=r(Glo," (Transformer-XL model)"),Glo.forEach(t),Ivt=i(se),Z8=n(se,"LI",{});var Olo=s(Z8);kPe=n(Olo,"STRONG",{});var qya=s(kPe);Bvt=r(qya,"xlm"),qya.forEach(t),Nvt=r(Olo," \u2014 "),kde=n(Olo,"A",{href:!0});var jya=s(kde);Dvt=r(jya,"TFXLMForSequenceClassification"),jya.forEach(t),qvt=r(Olo," (XLM model)"),Olo.forEach(t),jvt=i(se),K8=n(se,"LI",{});var Vlo=s(K8);SPe=n(Vlo,"STRONG",{});var Gya=s(SPe);Gvt=r(Gya,"xlm-roberta"),Gya.forEach(t),Ovt=r(Vlo," \u2014 "),Sde=n(Vlo,"A",{href:!0});var Oya=s(Sde);Vvt=r(Oya,"TFXLMRobertaForSequenceClassification"),Oya.forEach(t),Xvt=r(Vlo," (XLM-RoBERTa model)"),Vlo.forEach(t),zvt=i(se),e7=n(se,"LI",{});var Xlo=s(e7);RPe=n(Xlo,"STRONG",{});var Vya=s(RPe);Qvt=r(Vya,"xlnet"),Vya.forEach(t),Wvt=r(Xlo," \u2014 "),Rde=n(Xlo,"A",{href:!0});var Xya=s(Rde);Uvt=r(Xya,"TFXLNetForSequenceClassification"),Xya.forEach(t),Hvt=r(Xlo," (XLNet model)"),Xlo.forEach(t),se.forEach(t),Jvt=i(Gi),T(o7.$$.fragment,Gi),Gi.forEach(t),ji.forEach(t),qmo=i(f),Wf=n(f,"H2",{class:!0});var iuo=s(Wf);r7=n(iuo,"A",{id:!0,class:!0,href:!0});var zya=s(r7);PPe=n(zya,"SPAN",{});var Qya=s(PPe);T(sB.$$.fragment,Qya),Qya.forEach(t),zya.forEach(t),Yvt=i(iuo),IPe=n(iuo,"SPAN",{});var Wya=s(IPe);Zvt=r(Wya,"TFAutoModelForMultipleChoice"),Wya.forEach(t),iuo.forEach(t),jmo=i(f),Er=n(f,"DIV",{class:!0});var Oi=s(Er);T(lB.$$.fragment,Oi),Kvt=i(Oi),Uf=n(Oi,"P",{});var Eue=s(Uf);e4t=r(Eue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),Pde=n(Eue,"A",{href:!0});var Uya=s(Pde);o4t=r(Uya,"from_pretrained()"),Uya.forEach(t),r4t=r(Eue," class method or the "),Ide=n(Eue,"A",{href:!0});var Hya=s(Ide);t4t=r(Hya,"from_config()"),Hya.forEach(t),a4t=r(Eue,` class
method.`),Eue.forEach(t),n4t=i(Oi),iB=n(Oi,"P",{});var duo=s(iB);s4t=r(duo,"This class cannot be instantiated directly using "),BPe=n(duo,"CODE",{});var Jya=s(BPe);l4t=r(Jya,"__init__()"),Jya.forEach(t),i4t=r(duo," (throws an error)."),duo.forEach(t),d4t=i(Oi),da=n(Oi,"DIV",{class:!0});var bk=s(da);T(dB.$$.fragment,bk),c4t=i(bk),NPe=n(bk,"P",{});var Yya=s(NPe);f4t=r(Yya,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Yya.forEach(t),m4t=i(bk),Hf=n(bk,"P",{});var Cue=s(Hf);g4t=r(Cue,`Note:
Loading a model from its configuration file does `),DPe=n(Cue,"STRONG",{});var Zya=s(DPe);h4t=r(Zya,"not"),Zya.forEach(t),u4t=r(Cue,` load the model weights. It only affects the
model\u2019s configuration. Use `),Bde=n(Cue,"A",{href:!0});var Kya=s(Bde);p4t=r(Kya,"from_pretrained()"),Kya.forEach(t),_4t=r(Cue," to load the model weights."),Cue.forEach(t),v4t=i(bk),T(t7.$$.fragment,bk),bk.forEach(t),b4t=i(Oi),Kr=n(Oi,"DIV",{class:!0});var Vi=s(Kr);T(cB.$$.fragment,Vi),F4t=i(Vi),qPe=n(Vi,"P",{});var e9a=s(qPe);T4t=r(e9a,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),e9a.forEach(t),M4t=i(Vi),Jn=n(Vi,"P",{});var Fk=s(Jn);E4t=r(Fk,"The model class to instantiate is selected based on the "),jPe=n(Fk,"CODE",{});var o9a=s(jPe);C4t=r(o9a,"model_type"),o9a.forEach(t),w4t=r(Fk,` property of the config object (either
passed as an argument or loaded from `),GPe=n(Fk,"CODE",{});var r9a=s(GPe);A4t=r(r9a,"pretrained_model_name_or_path"),r9a.forEach(t),L4t=r(Fk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OPe=n(Fk,"CODE",{});var t9a=s(OPe);y4t=r(t9a,"pretrained_model_name_or_path"),t9a.forEach(t),x4t=r(Fk,":"),Fk.forEach(t),$4t=i(Vi),Me=n(Vi,"UL",{});var Ee=s(Me);a7=n(Ee,"LI",{});var zlo=s(a7);VPe=n(zlo,"STRONG",{});var a9a=s(VPe);k4t=r(a9a,"albert"),a9a.forEach(t),S4t=r(zlo," \u2014 "),Nde=n(zlo,"A",{href:!0});var n9a=s(Nde);R4t=r(n9a,"TFAlbertForMultipleChoice"),n9a.forEach(t),P4t=r(zlo," (ALBERT model)"),zlo.forEach(t),I4t=i(Ee),n7=n(Ee,"LI",{});var Qlo=s(n7);XPe=n(Qlo,"STRONG",{});var s9a=s(XPe);B4t=r(s9a,"bert"),s9a.forEach(t),N4t=r(Qlo," \u2014 "),Dde=n(Qlo,"A",{href:!0});var l9a=s(Dde);D4t=r(l9a,"TFBertForMultipleChoice"),l9a.forEach(t),q4t=r(Qlo," (BERT model)"),Qlo.forEach(t),j4t=i(Ee),s7=n(Ee,"LI",{});var Wlo=s(s7);zPe=n(Wlo,"STRONG",{});var i9a=s(zPe);G4t=r(i9a,"camembert"),i9a.forEach(t),O4t=r(Wlo," \u2014 "),qde=n(Wlo,"A",{href:!0});var d9a=s(qde);V4t=r(d9a,"TFCamembertForMultipleChoice"),d9a.forEach(t),X4t=r(Wlo," (CamemBERT model)"),Wlo.forEach(t),z4t=i(Ee),l7=n(Ee,"LI",{});var Ulo=s(l7);QPe=n(Ulo,"STRONG",{});var c9a=s(QPe);Q4t=r(c9a,"convbert"),c9a.forEach(t),W4t=r(Ulo," \u2014 "),jde=n(Ulo,"A",{href:!0});var f9a=s(jde);U4t=r(f9a,"TFConvBertForMultipleChoice"),f9a.forEach(t),H4t=r(Ulo," (ConvBERT model)"),Ulo.forEach(t),J4t=i(Ee),i7=n(Ee,"LI",{});var Hlo=s(i7);WPe=n(Hlo,"STRONG",{});var m9a=s(WPe);Y4t=r(m9a,"distilbert"),m9a.forEach(t),Z4t=r(Hlo," \u2014 "),Gde=n(Hlo,"A",{href:!0});var g9a=s(Gde);K4t=r(g9a,"TFDistilBertForMultipleChoice"),g9a.forEach(t),ebt=r(Hlo," (DistilBERT model)"),Hlo.forEach(t),obt=i(Ee),d7=n(Ee,"LI",{});var Jlo=s(d7);UPe=n(Jlo,"STRONG",{});var h9a=s(UPe);rbt=r(h9a,"electra"),h9a.forEach(t),tbt=r(Jlo," \u2014 "),Ode=n(Jlo,"A",{href:!0});var u9a=s(Ode);abt=r(u9a,"TFElectraForMultipleChoice"),u9a.forEach(t),nbt=r(Jlo," (ELECTRA model)"),Jlo.forEach(t),sbt=i(Ee),c7=n(Ee,"LI",{});var Ylo=s(c7);HPe=n(Ylo,"STRONG",{});var p9a=s(HPe);lbt=r(p9a,"flaubert"),p9a.forEach(t),ibt=r(Ylo," \u2014 "),Vde=n(Ylo,"A",{href:!0});var _9a=s(Vde);dbt=r(_9a,"TFFlaubertForMultipleChoice"),_9a.forEach(t),cbt=r(Ylo," (FlauBERT model)"),Ylo.forEach(t),fbt=i(Ee),f7=n(Ee,"LI",{});var Zlo=s(f7);JPe=n(Zlo,"STRONG",{});var v9a=s(JPe);mbt=r(v9a,"funnel"),v9a.forEach(t),gbt=r(Zlo," \u2014 "),Xde=n(Zlo,"A",{href:!0});var b9a=s(Xde);hbt=r(b9a,"TFFunnelForMultipleChoice"),b9a.forEach(t),ubt=r(Zlo," (Funnel Transformer model)"),Zlo.forEach(t),pbt=i(Ee),m7=n(Ee,"LI",{});var Klo=s(m7);YPe=n(Klo,"STRONG",{});var F9a=s(YPe);_bt=r(F9a,"longformer"),F9a.forEach(t),vbt=r(Klo," \u2014 "),zde=n(Klo,"A",{href:!0});var T9a=s(zde);bbt=r(T9a,"TFLongformerForMultipleChoice"),T9a.forEach(t),Fbt=r(Klo," (Longformer model)"),Klo.forEach(t),Tbt=i(Ee),g7=n(Ee,"LI",{});var eio=s(g7);ZPe=n(eio,"STRONG",{});var M9a=s(ZPe);Mbt=r(M9a,"mobilebert"),M9a.forEach(t),Ebt=r(eio," \u2014 "),Qde=n(eio,"A",{href:!0});var E9a=s(Qde);Cbt=r(E9a,"TFMobileBertForMultipleChoice"),E9a.forEach(t),wbt=r(eio," (MobileBERT model)"),eio.forEach(t),Abt=i(Ee),h7=n(Ee,"LI",{});var oio=s(h7);KPe=n(oio,"STRONG",{});var C9a=s(KPe);Lbt=r(C9a,"mpnet"),C9a.forEach(t),ybt=r(oio," \u2014 "),Wde=n(oio,"A",{href:!0});var w9a=s(Wde);xbt=r(w9a,"TFMPNetForMultipleChoice"),w9a.forEach(t),$bt=r(oio," (MPNet model)"),oio.forEach(t),kbt=i(Ee),u7=n(Ee,"LI",{});var rio=s(u7);eIe=n(rio,"STRONG",{});var A9a=s(eIe);Sbt=r(A9a,"rembert"),A9a.forEach(t),Rbt=r(rio," \u2014 "),Ude=n(rio,"A",{href:!0});var L9a=s(Ude);Pbt=r(L9a,"TFRemBertForMultipleChoice"),L9a.forEach(t),Ibt=r(rio," (RemBERT model)"),rio.forEach(t),Bbt=i(Ee),p7=n(Ee,"LI",{});var tio=s(p7);oIe=n(tio,"STRONG",{});var y9a=s(oIe);Nbt=r(y9a,"roberta"),y9a.forEach(t),Dbt=r(tio," \u2014 "),Hde=n(tio,"A",{href:!0});var x9a=s(Hde);qbt=r(x9a,"TFRobertaForMultipleChoice"),x9a.forEach(t),jbt=r(tio," (RoBERTa model)"),tio.forEach(t),Gbt=i(Ee),_7=n(Ee,"LI",{});var aio=s(_7);rIe=n(aio,"STRONG",{});var $9a=s(rIe);Obt=r($9a,"roformer"),$9a.forEach(t),Vbt=r(aio," \u2014 "),Jde=n(aio,"A",{href:!0});var k9a=s(Jde);Xbt=r(k9a,"TFRoFormerForMultipleChoice"),k9a.forEach(t),zbt=r(aio," (RoFormer model)"),aio.forEach(t),Qbt=i(Ee),v7=n(Ee,"LI",{});var nio=s(v7);tIe=n(nio,"STRONG",{});var S9a=s(tIe);Wbt=r(S9a,"xlm"),S9a.forEach(t),Ubt=r(nio," \u2014 "),Yde=n(nio,"A",{href:!0});var R9a=s(Yde);Hbt=r(R9a,"TFXLMForMultipleChoice"),R9a.forEach(t),Jbt=r(nio," (XLM model)"),nio.forEach(t),Ybt=i(Ee),b7=n(Ee,"LI",{});var sio=s(b7);aIe=n(sio,"STRONG",{});var P9a=s(aIe);Zbt=r(P9a,"xlm-roberta"),P9a.forEach(t),Kbt=r(sio," \u2014 "),Zde=n(sio,"A",{href:!0});var I9a=s(Zde);e5t=r(I9a,"TFXLMRobertaForMultipleChoice"),I9a.forEach(t),o5t=r(sio," (XLM-RoBERTa model)"),sio.forEach(t),r5t=i(Ee),F7=n(Ee,"LI",{});var lio=s(F7);nIe=n(lio,"STRONG",{});var B9a=s(nIe);t5t=r(B9a,"xlnet"),B9a.forEach(t),a5t=r(lio," \u2014 "),Kde=n(lio,"A",{href:!0});var N9a=s(Kde);n5t=r(N9a,"TFXLNetForMultipleChoice"),N9a.forEach(t),s5t=r(lio," (XLNet model)"),lio.forEach(t),Ee.forEach(t),l5t=i(Vi),T(T7.$$.fragment,Vi),Vi.forEach(t),Oi.forEach(t),Gmo=i(f),Jf=n(f,"H2",{class:!0});var cuo=s(Jf);M7=n(cuo,"A",{id:!0,class:!0,href:!0});var D9a=s(M7);sIe=n(D9a,"SPAN",{});var q9a=s(sIe);T(fB.$$.fragment,q9a),q9a.forEach(t),D9a.forEach(t),i5t=i(cuo),lIe=n(cuo,"SPAN",{});var j9a=s(lIe);d5t=r(j9a,"TFAutoModelForNextSentencePrediction"),j9a.forEach(t),cuo.forEach(t),Omo=i(f),Cr=n(f,"DIV",{class:!0});var Xi=s(Cr);T(mB.$$.fragment,Xi),c5t=i(Xi),Yf=n(Xi,"P",{});var wue=s(Yf);f5t=r(wue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),ece=n(wue,"A",{href:!0});var G9a=s(ece);m5t=r(G9a,"from_pretrained()"),G9a.forEach(t),g5t=r(wue," class method or the "),oce=n(wue,"A",{href:!0});var O9a=s(oce);h5t=r(O9a,"from_config()"),O9a.forEach(t),u5t=r(wue,` class
method.`),wue.forEach(t),p5t=i(Xi),gB=n(Xi,"P",{});var fuo=s(gB);_5t=r(fuo,"This class cannot be instantiated directly using "),iIe=n(fuo,"CODE",{});var V9a=s(iIe);v5t=r(V9a,"__init__()"),V9a.forEach(t),b5t=r(fuo," (throws an error)."),fuo.forEach(t),F5t=i(Xi),ca=n(Xi,"DIV",{class:!0});var Tk=s(ca);T(hB.$$.fragment,Tk),T5t=i(Tk),dIe=n(Tk,"P",{});var X9a=s(dIe);M5t=r(X9a,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),X9a.forEach(t),E5t=i(Tk),Zf=n(Tk,"P",{});var Aue=s(Zf);C5t=r(Aue,`Note:
Loading a model from its configuration file does `),cIe=n(Aue,"STRONG",{});var z9a=s(cIe);w5t=r(z9a,"not"),z9a.forEach(t),A5t=r(Aue,` load the model weights. It only affects the
model\u2019s configuration. Use `),rce=n(Aue,"A",{href:!0});var Q9a=s(rce);L5t=r(Q9a,"from_pretrained()"),Q9a.forEach(t),y5t=r(Aue," to load the model weights."),Aue.forEach(t),x5t=i(Tk),T(E7.$$.fragment,Tk),Tk.forEach(t),$5t=i(Xi),et=n(Xi,"DIV",{class:!0});var zi=s(et);T(uB.$$.fragment,zi),k5t=i(zi),fIe=n(zi,"P",{});var W9a=s(fIe);S5t=r(W9a,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),W9a.forEach(t),R5t=i(zi),Yn=n(zi,"P",{});var Mk=s(Yn);P5t=r(Mk,"The model class to instantiate is selected based on the "),mIe=n(Mk,"CODE",{});var U9a=s(mIe);I5t=r(U9a,"model_type"),U9a.forEach(t),B5t=r(Mk,` property of the config object (either
passed as an argument or loaded from `),gIe=n(Mk,"CODE",{});var H9a=s(gIe);N5t=r(H9a,"pretrained_model_name_or_path"),H9a.forEach(t),D5t=r(Mk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hIe=n(Mk,"CODE",{});var J9a=s(hIe);q5t=r(J9a,"pretrained_model_name_or_path"),J9a.forEach(t),j5t=r(Mk,":"),Mk.forEach(t),G5t=i(zi),pB=n(zi,"UL",{});var muo=s(pB);C7=n(muo,"LI",{});var iio=s(C7);uIe=n(iio,"STRONG",{});var Y9a=s(uIe);O5t=r(Y9a,"bert"),Y9a.forEach(t),V5t=r(iio," \u2014 "),tce=n(iio,"A",{href:!0});var Z9a=s(tce);X5t=r(Z9a,"TFBertForNextSentencePrediction"),Z9a.forEach(t),z5t=r(iio," (BERT model)"),iio.forEach(t),Q5t=i(muo),w7=n(muo,"LI",{});var dio=s(w7);pIe=n(dio,"STRONG",{});var K9a=s(pIe);W5t=r(K9a,"mobilebert"),K9a.forEach(t),U5t=r(dio," \u2014 "),ace=n(dio,"A",{href:!0});var exa=s(ace);H5t=r(exa,"TFMobileBertForNextSentencePrediction"),exa.forEach(t),J5t=r(dio," (MobileBERT model)"),dio.forEach(t),muo.forEach(t),Y5t=i(zi),T(A7.$$.fragment,zi),zi.forEach(t),Xi.forEach(t),Vmo=i(f),Kf=n(f,"H2",{class:!0});var guo=s(Kf);L7=n(guo,"A",{id:!0,class:!0,href:!0});var oxa=s(L7);_Ie=n(oxa,"SPAN",{});var rxa=s(_Ie);T(_B.$$.fragment,rxa),rxa.forEach(t),oxa.forEach(t),Z5t=i(guo),vIe=n(guo,"SPAN",{});var txa=s(vIe);K5t=r(txa,"TFAutoModelForTableQuestionAnswering"),txa.forEach(t),guo.forEach(t),Xmo=i(f),wr=n(f,"DIV",{class:!0});var Qi=s(wr);T(vB.$$.fragment,Qi),eFt=i(Qi),em=n(Qi,"P",{});var Lue=s(em);oFt=r(Lue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),nce=n(Lue,"A",{href:!0});var axa=s(nce);rFt=r(axa,"from_pretrained()"),axa.forEach(t),tFt=r(Lue," class method or the "),sce=n(Lue,"A",{href:!0});var nxa=s(sce);aFt=r(nxa,"from_config()"),nxa.forEach(t),nFt=r(Lue,` class
method.`),Lue.forEach(t),sFt=i(Qi),bB=n(Qi,"P",{});var huo=s(bB);lFt=r(huo,"This class cannot be instantiated directly using "),bIe=n(huo,"CODE",{});var sxa=s(bIe);iFt=r(sxa,"__init__()"),sxa.forEach(t),dFt=r(huo," (throws an error)."),huo.forEach(t),cFt=i(Qi),fa=n(Qi,"DIV",{class:!0});var Ek=s(fa);T(FB.$$.fragment,Ek),fFt=i(Ek),FIe=n(Ek,"P",{});var lxa=s(FIe);mFt=r(lxa,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),lxa.forEach(t),gFt=i(Ek),om=n(Ek,"P",{});var yue=s(om);hFt=r(yue,`Note:
Loading a model from its configuration file does `),TIe=n(yue,"STRONG",{});var ixa=s(TIe);uFt=r(ixa,"not"),ixa.forEach(t),pFt=r(yue,` load the model weights. It only affects the
model\u2019s configuration. Use `),lce=n(yue,"A",{href:!0});var dxa=s(lce);_Ft=r(dxa,"from_pretrained()"),dxa.forEach(t),vFt=r(yue," to load the model weights."),yue.forEach(t),bFt=i(Ek),T(y7.$$.fragment,Ek),Ek.forEach(t),FFt=i(Qi),ot=n(Qi,"DIV",{class:!0});var Wi=s(ot);T(TB.$$.fragment,Wi),TFt=i(Wi),MIe=n(Wi,"P",{});var cxa=s(MIe);MFt=r(cxa,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),cxa.forEach(t),EFt=i(Wi),Zn=n(Wi,"P",{});var Ck=s(Zn);CFt=r(Ck,"The model class to instantiate is selected based on the "),EIe=n(Ck,"CODE",{});var fxa=s(EIe);wFt=r(fxa,"model_type"),fxa.forEach(t),AFt=r(Ck,` property of the config object (either
passed as an argument or loaded from `),CIe=n(Ck,"CODE",{});var mxa=s(CIe);LFt=r(mxa,"pretrained_model_name_or_path"),mxa.forEach(t),yFt=r(Ck,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wIe=n(Ck,"CODE",{});var gxa=s(wIe);xFt=r(gxa,"pretrained_model_name_or_path"),gxa.forEach(t),$Ft=r(Ck,":"),Ck.forEach(t),kFt=i(Wi),AIe=n(Wi,"UL",{});var hxa=s(AIe);x7=n(hxa,"LI",{});var cio=s(x7);LIe=n(cio,"STRONG",{});var uxa=s(LIe);SFt=r(uxa,"tapas"),uxa.forEach(t),RFt=r(cio," \u2014 "),ice=n(cio,"A",{href:!0});var pxa=s(ice);PFt=r(pxa,"TFTapasForQuestionAnswering"),pxa.forEach(t),IFt=r(cio," (TAPAS model)"),cio.forEach(t),hxa.forEach(t),BFt=i(Wi),T($7.$$.fragment,Wi),Wi.forEach(t),Qi.forEach(t),zmo=i(f),rm=n(f,"H2",{class:!0});var uuo=s(rm);k7=n(uuo,"A",{id:!0,class:!0,href:!0});var _xa=s(k7);yIe=n(_xa,"SPAN",{});var vxa=s(yIe);T(MB.$$.fragment,vxa),vxa.forEach(t),_xa.forEach(t),NFt=i(uuo),xIe=n(uuo,"SPAN",{});var bxa=s(xIe);DFt=r(bxa,"TFAutoModelForDocumentQuestionAnswering"),bxa.forEach(t),uuo.forEach(t),Qmo=i(f),Ar=n(f,"DIV",{class:!0});var Ui=s(Ar);T(EB.$$.fragment,Ui),qFt=i(Ui),tm=n(Ui,"P",{});var xue=s(tm);jFt=r(xue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),dce=n(xue,"A",{href:!0});var Fxa=s(dce);GFt=r(Fxa,"from_pretrained()"),Fxa.forEach(t),OFt=r(xue," class method or the "),cce=n(xue,"A",{href:!0});var Txa=s(cce);VFt=r(Txa,"from_config()"),Txa.forEach(t),XFt=r(xue,` class
method.`),xue.forEach(t),zFt=i(Ui),CB=n(Ui,"P",{});var puo=s(CB);QFt=r(puo,"This class cannot be instantiated directly using "),$Ie=n(puo,"CODE",{});var Mxa=s($Ie);WFt=r(Mxa,"__init__()"),Mxa.forEach(t),UFt=r(puo," (throws an error)."),puo.forEach(t),HFt=i(Ui),ma=n(Ui,"DIV",{class:!0});var wk=s(ma);T(wB.$$.fragment,wk),JFt=i(wk),kIe=n(wk,"P",{});var Exa=s(kIe);YFt=r(Exa,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),Exa.forEach(t),ZFt=i(wk),am=n(wk,"P",{});var $ue=s(am);KFt=r($ue,`Note:
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model\u2019s configuration. Use `),fce=n($ue,"A",{href:!0});var wxa=s(fce);rTt=r(wxa,"from_pretrained()"),wxa.forEach(t),tTt=r($ue," to load the model weights."),$ue.forEach(t),aTt=i(wk),T(S7.$$.fragment,wk),wk.forEach(t),nTt=i(Ui),rt=n(Ui,"DIV",{class:!0});var Hi=s(rt);T(AB.$$.fragment,Hi),sTt=i(Hi),RIe=n(Hi,"P",{});var Axa=s(RIe);lTt=r(Axa,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),Axa.forEach(t),iTt=i(Hi),Kn=n(Hi,"P",{});var Ak=s(Kn);dTt=r(Ak,"The model class to instantiate is selected based on the "),PIe=n(Ak,"CODE",{});var Lxa=s(PIe);cTt=r(Lxa,"model_type"),Lxa.forEach(t),fTt=r(Ak,` property of the config object (either
passed as an argument or loaded from `),IIe=n(Ak,"CODE",{});var yxa=s(IIe);mTt=r(yxa,"pretrained_model_name_or_path"),yxa.forEach(t),gTt=r(Ak,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BIe=n(Ak,"CODE",{});var xxa=s(BIe);hTt=r(xxa,"pretrained_model_name_or_path"),xxa.forEach(t),uTt=r(Ak,":"),Ak.forEach(t),pTt=i(Hi),NIe=n(Hi,"UL",{});var $xa=s(NIe);R7=n($xa,"LI",{});var fio=s(R7);DIe=n(fio,"STRONG",{});var kxa=s(DIe);_Tt=r(kxa,"layoutlm"),kxa.forEach(t),vTt=r(fio," \u2014 "),mce=n(fio,"A",{href:!0});var Sxa=s(mce);bTt=r(Sxa,"TFLayoutLMForQuestionAnswering"),Sxa.forEach(t),FTt=r(fio," (LayoutLM model)"),fio.forEach(t),$xa.forEach(t),TTt=i(Hi),T(P7.$$.fragment,Hi),Hi.forEach(t),Ui.forEach(t),Wmo=i(f),nm=n(f,"H2",{class:!0});var _uo=s(nm);I7=n(_uo,"A",{id:!0,class:!0,href:!0});var Rxa=s(I7);qIe=n(Rxa,"SPAN",{});var Pxa=s(qIe);T(LB.$$.fragment,Pxa),Pxa.forEach(t),Rxa.forEach(t),MTt=i(_uo),jIe=n(_uo,"SPAN",{});var Ixa=s(jIe);ETt=r(Ixa,"TFAutoModelForTokenClassification"),Ixa.forEach(t),_uo.forEach(t),Umo=i(f),Lr=n(f,"DIV",{class:!0});var Ji=s(Lr);T(yB.$$.fragment,Ji),CTt=i(Ji),sm=n(Ji,"P",{});var kue=s(sm);wTt=r(kue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),gce=n(kue,"A",{href:!0});var Bxa=s(gce);ATt=r(Bxa,"from_pretrained()"),Bxa.forEach(t),LTt=r(kue," class method or the "),hce=n(kue,"A",{href:!0});var Nxa=s(hce);yTt=r(Nxa,"from_config()"),Nxa.forEach(t),xTt=r(kue,` class
method.`),kue.forEach(t),$Tt=i(Ji),xB=n(Ji,"P",{});var vuo=s(xB);kTt=r(vuo,"This class cannot be instantiated directly using "),GIe=n(vuo,"CODE",{});var Dxa=s(GIe);STt=r(Dxa,"__init__()"),Dxa.forEach(t),RTt=r(vuo," (throws an error)."),vuo.forEach(t),PTt=i(Ji),ga=n(Ji,"DIV",{class:!0});var Lk=s(ga);T($B.$$.fragment,Lk),ITt=i(Lk),OIe=n(Lk,"P",{});var qxa=s(OIe);BTt=r(qxa,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),qxa.forEach(t),NTt=i(Lk),lm=n(Lk,"P",{});var Sue=s(lm);DTt=r(Sue,`Note:
Loading a model from its configuration file does `),VIe=n(Sue,"STRONG",{});var jxa=s(VIe);qTt=r(jxa,"not"),jxa.forEach(t),jTt=r(Sue,` load the model weights. It only affects the
model\u2019s configuration. Use `),uce=n(Sue,"A",{href:!0});var Gxa=s(uce);GTt=r(Gxa,"from_pretrained()"),Gxa.forEach(t),OTt=r(Sue," to load the model weights."),Sue.forEach(t),VTt=i(Lk),T(B7.$$.fragment,Lk),Lk.forEach(t),XTt=i(Ji),tt=n(Ji,"DIV",{class:!0});var Yi=s(tt);T(kB.$$.fragment,Yi),zTt=i(Yi),XIe=n(Yi,"P",{});var Oxa=s(XIe);QTt=r(Oxa,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),Oxa.forEach(t),WTt=i(Yi),es=n(Yi,"P",{});var yk=s(es);UTt=r(yk,"The model class to instantiate is selected based on the "),zIe=n(yk,"CODE",{});var Vxa=s(zIe);HTt=r(Vxa,"model_type"),Vxa.forEach(t),JTt=r(yk,` property of the config object (either
passed as an argument or loaded from `),QIe=n(yk,"CODE",{});var Xxa=s(QIe);YTt=r(Xxa,"pretrained_model_name_or_path"),Xxa.forEach(t),ZTt=r(yk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WIe=n(yk,"CODE",{});var zxa=s(WIe);KTt=r(zxa,"pretrained_model_name_or_path"),zxa.forEach(t),eMt=r(yk,":"),yk.forEach(t),oMt=i(Yi),me=n(Yi,"UL",{});var ve=s(me);N7=n(ve,"LI",{});var mio=s(N7);UIe=n(mio,"STRONG",{});var Qxa=s(UIe);rMt=r(Qxa,"albert"),Qxa.forEach(t),tMt=r(mio," \u2014 "),pce=n(mio,"A",{href:!0});var Wxa=s(pce);aMt=r(Wxa,"TFAlbertForTokenClassification"),Wxa.forEach(t),nMt=r(mio," (ALBERT model)"),mio.forEach(t),sMt=i(ve),D7=n(ve,"LI",{});var gio=s(D7);HIe=n(gio,"STRONG",{});var Uxa=s(HIe);lMt=r(Uxa,"bert"),Uxa.forEach(t),iMt=r(gio," \u2014 "),_ce=n(gio,"A",{href:!0});var Hxa=s(_ce);dMt=r(Hxa,"TFBertForTokenClassification"),Hxa.forEach(t),cMt=r(gio," (BERT model)"),gio.forEach(t),fMt=i(ve),q7=n(ve,"LI",{});var hio=s(q7);JIe=n(hio,"STRONG",{});var Jxa=s(JIe);mMt=r(Jxa,"camembert"),Jxa.forEach(t),gMt=r(hio," \u2014 "),vce=n(hio,"A",{href:!0});var Yxa=s(vce);hMt=r(Yxa,"TFCamembertForTokenClassification"),Yxa.forEach(t),uMt=r(hio," 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D$a=s(_Be);nCt=r(D$a,"TFAutoModelForQuestionAnswering"),D$a.forEach(t),buo.forEach(t),Jmo=i(f),yr=n(f,"DIV",{class:!0});var Zi=s(yr);T(RB.$$.fragment,Zi),sCt=i(Zi),dm=n(Zi,"P",{});var Rue=s(dm);lCt=r(Rue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
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method.`),Rue.forEach(t),mCt=i(Zi),PB=n(Zi,"P",{});var Fuo=s(PB);gCt=r(Fuo,"This class cannot be instantiated directly using "),vBe=n(Fuo,"CODE",{});var G$a=s(vBe);hCt=r(G$a,"__init__()"),G$a.forEach(t),uCt=r(Fuo," (throws an error)."),Fuo.forEach(t),pCt=i(Zi),ha=n(Zi,"DIV",{class:!0});var xk=s(ha);T(IB.$$.fragment,xk),_Ct=i(xk),bBe=n(xk,"P",{});var O$a=s(bBe);vCt=r(O$a,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),O$a.forEach(t),bCt=i(xk),cm=n(xk,"P",{});var Pue=s(cm);FCt=r(Pue,`Note:
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hka=s(Jce);k3t=r(hka,"TFFunnelForQuestionAnswering"),hka.forEach(t),S3t=r(Vio," (Funnel Transformer model)"),Vio.forEach(t),R3t=i(Te),vL=n(Te,"LI",{});var Xio=s(vL);IBe=n(Xio,"STRONG",{});var uka=s(IBe);P3t=r(uka,"gptj"),uka.forEach(t),I3t=r(Xio," \u2014 "),Yce=n(Xio,"A",{href:!0});var pka=s(Yce);B3t=r(pka,"TFGPTJForQuestionAnswering"),pka.forEach(t),N3t=r(Xio," (GPT-J model)"),Xio.forEach(t),D3t=i(Te),bL=n(Te,"LI",{});var zio=s(bL);BBe=n(zio,"STRONG",{});var _ka=s(BBe);q3t=r(_ka,"layoutlmv3"),_ka.forEach(t),j3t=r(zio," \u2014 "),Zce=n(zio,"A",{href:!0});var vka=s(Zce);G3t=r(vka,"TFLayoutLMv3ForQuestionAnswering"),vka.forEach(t),O3t=r(zio," (LayoutLMv3 model)"),zio.forEach(t),V3t=i(Te),FL=n(Te,"LI",{});var Qio=s(FL);NBe=n(Qio,"STRONG",{});var bka=s(NBe);X3t=r(bka,"longformer"),bka.forEach(t),z3t=r(Qio," \u2014 "),Kce=n(Qio,"A",{href:!0});var Fka=s(Kce);Q3t=r(Fka,"TFLongformerForQuestionAnswering"),Fka.forEach(t),W3t=r(Qio," (Longformer 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"),tfe=n(Jio,"A",{href:!0});var yka=s(tfe);m0t=r(yka,"TFRobertaForQuestionAnswering"),yka.forEach(t),g0t=r(Jio," (RoBERTa model)"),Jio.forEach(t),h0t=i(Te),wL=n(Te,"LI",{});var Yio=s(wL);OBe=n(Yio,"STRONG",{});var xka=s(OBe);u0t=r(xka,"roformer"),xka.forEach(t),p0t=r(Yio," \u2014 "),afe=n(Yio,"A",{href:!0});var $ka=s(afe);_0t=r($ka,"TFRoFormerForQuestionAnswering"),$ka.forEach(t),v0t=r(Yio," (RoFormer model)"),Yio.forEach(t),b0t=i(Te),AL=n(Te,"LI",{});var Zio=s(AL);VBe=n(Zio,"STRONG",{});var kka=s(VBe);F0t=r(kka,"xlm"),kka.forEach(t),T0t=r(Zio," \u2014 "),nfe=n(Zio,"A",{href:!0});var Ska=s(nfe);M0t=r(Ska,"TFXLMForQuestionAnsweringSimple"),Ska.forEach(t),E0t=r(Zio," (XLM model)"),Zio.forEach(t),C0t=i(Te),LL=n(Te,"LI",{});var Kio=s(LL);XBe=n(Kio,"STRONG",{});var Rka=s(XBe);w0t=r(Rka,"xlm-roberta"),Rka.forEach(t),A0t=r(Kio," \u2014 "),sfe=n(Kio,"A",{href:!0});var Pka=s(sfe);L0t=r(Pka,"TFXLMRobertaForQuestionAnswering"),Pka.forEach(t),y0t=r(Kio," (XLM-RoBERTa model)"),Kio.forEach(t),x0t=i(Te),yL=n(Te,"LI",{});var edo=s(yL);zBe=n(edo,"STRONG",{});var Ika=s(zBe);$0t=r(Ika,"xlnet"),Ika.forEach(t),k0t=r(edo," \u2014 "),lfe=n(edo,"A",{href:!0});var Bka=s(lfe);S0t=r(Bka,"TFXLNetForQuestionAnsweringSimple"),Bka.forEach(t),R0t=r(edo," (XLNet model)"),edo.forEach(t),Te.forEach(t),P0t=i(Ki),T(xL.$$.fragment,Ki),Ki.forEach(t),Zi.forEach(t),Ymo=i(f),fm=n(f,"H2",{class:!0});var Tuo=s(fm);$L=n(Tuo,"A",{id:!0,class:!0,href:!0});var Nka=s($L);QBe=n(Nka,"SPAN",{});var Dka=s(QBe);T(NB.$$.fragment,Dka),Dka.forEach(t),Nka.forEach(t),I0t=i(Tuo),WBe=n(Tuo,"SPAN",{});var qka=s(WBe);B0t=r(qka,"TFAutoModelForVision2Seq"),qka.forEach(t),Tuo.forEach(t),Zmo=i(f),xr=n(f,"DIV",{class:!0});var ed=s(xr);T(DB.$$.fragment,ed),N0t=i(ed),mm=n(ed,"P",{});var Iue=s(mm);D0t=r(Iue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),ife=n(Iue,"A",{href:!0});var jka=s(ife);q0t=r(jka,"from_pretrained()"),jka.forEach(t),j0t=r(Iue," class method or the "),dfe=n(Iue,"A",{href:!0});var Gka=s(dfe);G0t=r(Gka,"from_config()"),Gka.forEach(t),O0t=r(Iue,` class
method.`),Iue.forEach(t),V0t=i(ed),qB=n(ed,"P",{});var Muo=s(qB);X0t=r(Muo,"This class cannot be instantiated directly using "),UBe=n(Muo,"CODE",{});var Oka=s(UBe);z0t=r(Oka,"__init__()"),Oka.forEach(t),Q0t=r(Muo," (throws an error)."),Muo.forEach(t),W0t=i(ed),ua=n(ed,"DIV",{class:!0});var kk=s(ua);T(jB.$$.fragment,kk),U0t=i(kk),HBe=n(kk,"P",{});var Vka=s(HBe);H0t=r(Vka,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Vka.forEach(t),J0t=i(kk),gm=n(kk,"P",{});var Bue=s(gm);Y0t=r(Bue,`Note:
Loading a model from its configuration file does `),JBe=n(Bue,"STRONG",{});var Xka=s(JBe);Z0t=r(Xka,"not"),Xka.forEach(t),K0t=r(Bue,` load the model weights. It only affects the
model\u2019s configuration. Use `),cfe=n(Bue,"A",{href:!0});var zka=s(cfe);ewt=r(zka,"from_pretrained()"),zka.forEach(t),owt=r(Bue," to load the model weights."),Bue.forEach(t),rwt=i(kk),T(kL.$$.fragment,kk),kk.forEach(t),twt=i(ed),nt=n(ed,"DIV",{class:!0});var od=s(nt);T(GB.$$.fragment,od),awt=i(od),YBe=n(od,"P",{});var Qka=s(YBe);nwt=r(Qka,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Qka.forEach(t),swt=i(od),rs=n(od,"P",{});var Sk=s(rs);lwt=r(Sk,"The model class to instantiate is selected based on the "),ZBe=n(Sk,"CODE",{});var Wka=s(ZBe);iwt=r(Wka,"model_type"),Wka.forEach(t),dwt=r(Sk,` property of the config object (either
passed as an argument or loaded from `),KBe=n(Sk,"CODE",{});var Uka=s(KBe);cwt=r(Uka,"pretrained_model_name_or_path"),Uka.forEach(t),fwt=r(Sk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eNe=n(Sk,"CODE",{});var Hka=s(eNe);mwt=r(Hka,"pretrained_model_name_or_path"),Hka.forEach(t),gwt=r(Sk,":"),Sk.forEach(t),hwt=i(od),oNe=n(od,"UL",{});var Jka=s(oNe);SL=n(Jka,"LI",{});var odo=s(SL);rNe=n(odo,"STRONG",{});var Yka=s(rNe);uwt=r(Yka,"vision-encoder-decoder"),Yka.forEach(t),pwt=r(odo," \u2014 "),ffe=n(odo,"A",{href:!0});var Zka=s(ffe);_wt=r(Zka,"TFVisionEncoderDecoderModel"),Zka.forEach(t),vwt=r(odo," (Vision Encoder decoder model)"),odo.forEach(t),Jka.forEach(t),bwt=i(od),T(RL.$$.fragment,od),od.forEach(t),ed.forEach(t),Kmo=i(f),hm=n(f,"H2",{class:!0});var Euo=s(hm);PL=n(Euo,"A",{id:!0,class:!0,href:!0});var Kka=s(PL);tNe=n(Kka,"SPAN",{});var eSa=s(tNe);T(OB.$$.fragment,eSa),eSa.forEach(t),Kka.forEach(t),Fwt=i(Euo),aNe=n(Euo,"SPAN",{});var oSa=s(aNe);Twt=r(oSa,"TFAutoModelForSpeechSeq2Seq"),oSa.forEach(t),Euo.forEach(t),ego=i(f),$r=n(f,"DIV",{class:!0});var rd=s($r);T(VB.$$.fragment,rd),Mwt=i(rd),um=n(rd,"P",{});var Nue=s(um);Ewt=r(Nue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),mfe=n(Nue,"A",{href:!0});var rSa=s(mfe);Cwt=r(rSa,"from_pretrained()"),rSa.forEach(t),wwt=r(Nue," class method or the "),gfe=n(Nue,"A",{href:!0});var tSa=s(gfe);Awt=r(tSa,"from_config()"),tSa.forEach(t),Lwt=r(Nue,` class
method.`),Nue.forEach(t),ywt=i(rd),XB=n(rd,"P",{});var Cuo=s(XB);xwt=r(Cuo,"This class cannot be instantiated directly using "),nNe=n(Cuo,"CODE",{});var aSa=s(nNe);$wt=r(aSa,"__init__()"),aSa.forEach(t),kwt=r(Cuo," (throws an error)."),Cuo.forEach(t),Swt=i(rd),pa=n(rd,"DIV",{class:!0});var Rk=s(pa);T(zB.$$.fragment,Rk),Rwt=i(Rk),sNe=n(Rk,"P",{});var nSa=s(sNe);Pwt=r(nSa,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),nSa.forEach(t),Iwt=i(Rk),pm=n(Rk,"P",{});var Due=s(pm);Bwt=r(Due,`Note:
Loading a model from its configuration file does `),lNe=n(Due,"STRONG",{});var sSa=s(lNe);Nwt=r(sSa,"not"),sSa.forEach(t),Dwt=r(Due,` load the model weights. It only affects the
model\u2019s configuration. Use `),hfe=n(Due,"A",{href:!0});var lSa=s(hfe);qwt=r(lSa,"from_pretrained()"),lSa.forEach(t),jwt=r(Due," to load the model weights."),Due.forEach(t),Gwt=i(Rk),T(IL.$$.fragment,Rk),Rk.forEach(t),Owt=i(rd),st=n(rd,"DIV",{class:!0});var td=s(st);T(QB.$$.fragment,td),Vwt=i(td),iNe=n(td,"P",{});var iSa=s(iNe);Xwt=r(iSa,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),iSa.forEach(t),zwt=i(td),ts=n(td,"P",{});var Pk=s(ts);Qwt=r(Pk,"The model class to instantiate is selected based on the "),dNe=n(Pk,"CODE",{});var dSa=s(dNe);Wwt=r(dSa,"model_type"),dSa.forEach(t),Uwt=r(Pk,` property of the config object (either
passed as an argument or loaded from `),cNe=n(Pk,"CODE",{});var cSa=s(cNe);Hwt=r(cSa,"pretrained_model_name_or_path"),cSa.forEach(t),Jwt=r(Pk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fNe=n(Pk,"CODE",{});var fSa=s(fNe);Ywt=r(fSa,"pretrained_model_name_or_path"),fSa.forEach(t),Zwt=r(Pk,":"),Pk.forEach(t),Kwt=i(td),WB=n(td,"UL",{});var wuo=s(WB);BL=n(wuo,"LI",{});var rdo=s(BL);mNe=n(rdo,"STRONG",{});var mSa=s(mNe);eAt=r(mSa,"speech_to_text"),mSa.forEach(t),oAt=r(rdo," \u2014 "),ufe=n(rdo,"A",{href:!0});var gSa=s(ufe);rAt=r(gSa,"TFSpeech2TextForConditionalGeneration"),gSa.forEach(t),tAt=r(rdo," (Speech2Text model)"),rdo.forEach(t),aAt=i(wuo),NL=n(wuo,"LI",{});var tdo=s(NL);gNe=n(tdo,"STRONG",{});var hSa=s(gNe);nAt=r(hSa,"whisper"),hSa.forEach(t),sAt=r(tdo," \u2014 "),pfe=n(tdo,"A",{href:!0});var uSa=s(pfe);lAt=r(uSa,"TFWhisperForConditionalGeneration"),uSa.forEach(t),iAt=r(tdo," (Whisper model)"),tdo.forEach(t),wuo.forEach(t),dAt=i(td),T(DL.$$.fragment,td),td.forEach(t),rd.forEach(t),ogo=i(f),_m=n(f,"H2",{class:!0});var Auo=s(_m);qL=n(Auo,"A",{id:!0,class:!0,href:!0});var pSa=s(qL);hNe=n(pSa,"SPAN",{});var _Sa=s(hNe);T(UB.$$.fragment,_Sa),_Sa.forEach(t),pSa.forEach(t),cAt=i(Auo),uNe=n(Auo,"SPAN",{});var vSa=s(uNe);fAt=r(vSa,"FlaxAutoModel"),vSa.forEach(t),Auo.forEach(t),rgo=i(f),kr=n(f,"DIV",{class:!0});var ad=s(kr);T(HB.$$.fragment,ad),mAt=i(ad),vm=n(ad,"P",{});var que=s(vm);gAt=r(que,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),_fe=n(que,"A",{href:!0});var bSa=s(_fe);hAt=r(bSa,"from_pretrained()"),bSa.forEach(t),uAt=r(que," class method or the "),vfe=n(que,"A",{href:!0});var FSa=s(vfe);pAt=r(FSa,"from_config()"),FSa.forEach(t),_At=r(que,` class
method.`),que.forEach(t),vAt=i(ad),JB=n(ad,"P",{});var Luo=s(JB);bAt=r(Luo,"This class cannot be instantiated directly using "),pNe=n(Luo,"CODE",{});var TSa=s(pNe);FAt=r(TSa,"__init__()"),TSa.forEach(t),TAt=r(Luo," (throws an error)."),Luo.forEach(t),MAt=i(ad),_a=n(ad,"DIV",{class:!0});var Ik=s(_a);T(YB.$$.fragment,Ik),EAt=i(Ik),_Ne=n(Ik,"P",{});var MSa=s(_Ne);CAt=r(MSa,"Instantiates one of the base model classes of the library from a configuration."),MSa.forEach(t),wAt=i(Ik),bm=n(Ik,"P",{});var jue=s(bm);AAt=r(jue,`Note:
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model\u2019s configuration. Use `),bfe=n(jue,"A",{href:!0});var CSa=s(bfe);xAt=r(CSa,"from_pretrained()"),CSa.forEach(t),$At=r(jue," to load the model weights."),jue.forEach(t),kAt=i(Ik),T(jL.$$.fragment,Ik),Ik.forEach(t),SAt=i(ad),lt=n(ad,"DIV",{class:!0});var nd=s(lt);T(ZB.$$.fragment,nd),RAt=i(nd),bNe=n(nd,"P",{});var wSa=s(bNe);PAt=r(wSa,"Instantiate one of the base model classes of the library from a pretrained model."),wSa.forEach(t),IAt=i(nd),as=n(nd,"P",{});var Bk=s(as);BAt=r(Bk,"The model class to instantiate is selected based on the "),FNe=n(Bk,"CODE",{});var ASa=s(FNe);NAt=r(ASa,"model_type"),ASa.forEach(t),DAt=r(Bk,` property of the config object (either
passed as an argument or loaded from `),TNe=n(Bk,"CODE",{});var LSa=s(TNe);qAt=r(LSa,"pretrained_model_name_or_path"),LSa.forEach(t),jAt=r(Bk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MNe=n(Bk,"CODE",{});var ySa=s(MNe);GAt=r(ySa,"pretrained_model_name_or_path"),ySa.forEach(t),OAt=r(Bk,":"),Bk.forEach(t),VAt=i(nd),ne=n(nd,"UL",{});var le=s(ne);GL=n(le,"LI",{});var ado=s(GL);ENe=n(ado,"STRONG",{});var xSa=s(ENe);XAt=r(xSa,"albert"),xSa.forEach(t),zAt=r(ado," \u2014 "),Ffe=n(ado,"A",{href:!0});var $Sa=s(Ffe);QAt=r($Sa,"FlaxAlbertModel"),$Sa.forEach(t),WAt=r(ado," (ALBERT model)"),ado.forEach(t),UAt=i(le),OL=n(le,"LI",{});var ndo=s(OL);CNe=n(ndo,"STRONG",{});var kSa=s(CNe);HAt=r(kSa,"bart"),kSa.forEach(t),JAt=r(ndo," \u2014 "),Tfe=n(ndo,"A",{href:!0});var SSa=s(Tfe);YAt=r(SSa,"FlaxBartModel"),SSa.forEach(t),ZAt=r(ndo," (BART model)"),ndo.forEach(t),KAt=i(le),VL=n(le,"LI",{});var sdo=s(VL);wNe=n(sdo,"STRONG",{});var RSa=s(wNe);e6t=r(RSa,"beit"),RSa.forEach(t),o6t=r(sdo," \u2014 "),Mfe=n(sdo,"A",{href:!0});var PSa=s(Mfe);r6t=r(PSa,"FlaxBeitModel"),PSa.forEach(t),t6t=r(sdo," (BEiT model)"),sdo.forEach(t),a6t=i(le),XL=n(le,"LI",{});var ldo=s(XL);ANe=n(ldo,"STRONG",{});var ISa=s(ANe);n6t=r(ISa,"bert"),ISa.forEach(t),s6t=r(ldo," \u2014 "),Efe=n(ldo,"A",{href:!0});var BSa=s(Efe);l6t=r(BSa,"FlaxBertModel"),BSa.forEach(t),i6t=r(ldo," (BERT model)"),ldo.forEach(t),d6t=i(le),zL=n(le,"LI",{});var ido=s(zL);LNe=n(ido,"STRONG",{});var NSa=s(LNe);c6t=r(NSa,"big_bird"),NSa.forEach(t),f6t=r(ido," \u2014 "),Cfe=n(ido,"A",{href:!0});var DSa=s(Cfe);m6t=r(DSa,"FlaxBigBirdModel"),DSa.forEach(t),g6t=r(ido," (BigBird model)"),ido.forEach(t),h6t=i(le),QL=n(le,"LI",{});var ddo=s(QL);yNe=n(ddo,"STRONG",{});var qSa=s(yNe);u6t=r(qSa,"blenderbot"),qSa.forEach(t),p6t=r(ddo," \u2014 "),wfe=n(ddo,"A",{href:!0});var jSa=s(wfe);_6t=r(jSa,"FlaxBlenderbotModel"),jSa.forEach(t),v6t=r(ddo," (Blenderbot model)"),ddo.forEach(t),b6t=i(le),WL=n(le,"LI",{});var cdo=s(WL);xNe=n(cdo,"STRONG",{});var GSa=s(xNe);F6t=r(GSa,"blenderbot-small"),GSa.forEach(t),T6t=r(cdo," \u2014 "),Afe=n(cdo,"A",{href:!0});var OSa=s(Afe);M6t=r(OSa,"FlaxBlenderbotSmallModel"),OSa.forEach(t),E6t=r(cdo," (BlenderbotSmall model)"),cdo.forEach(t),C6t=i(le),UL=n(le,"LI",{});var fdo=s(UL);$Ne=n(fdo,"STRONG",{});var VSa=s($Ne);w6t=r(VSa,"clip"),VSa.forEach(t),A6t=r(fdo," \u2014 "),Lfe=n(fdo,"A",{href:!0});var XSa=s(Lfe);L6t=r(XSa,"FlaxCLIPModel"),XSa.forEach(t),y6t=r(fdo," (CLIP model)"),fdo.forEach(t),x6t=i(le),HL=n(le,"LI",{});var mdo=s(HL);kNe=n(mdo,"STRONG",{});var zSa=s(kNe);$6t=r(zSa,"distilbert"),zSa.forEach(t),k6t=r(mdo," \u2014 "),yfe=n(mdo,"A",{href:!0});var QSa=s(yfe);S6t=r(QSa,"FlaxDistilBertModel"),QSa.forEach(t),R6t=r(mdo," (DistilBERT model)"),mdo.forEach(t),P6t=i(le),JL=n(le,"LI",{});var gdo=s(JL);SNe=n(gdo,"STRONG",{});var WSa=s(SNe);I6t=r(WSa,"electra"),WSa.forEach(t),B6t=r(gdo," \u2014 "),xfe=n(gdo,"A",{href:!0});var USa=s(xfe);N6t=r(USa,"FlaxElectraModel"),USa.forEach(t),D6t=r(gdo," (ELECTRA model)"),gdo.forEach(t),q6t=i(le),YL=n(le,"LI",{});var hdo=s(YL);RNe=n(hdo,"STRONG",{});var HSa=s(RNe);j6t=r(HSa,"gpt2"),HSa.forEach(t),G6t=r(hdo," \u2014 "),$fe=n(hdo,"A",{href:!0});var JSa=s($fe);O6t=r(JSa,"FlaxGPT2Model"),JSa.forEach(t),V6t=r(hdo," (OpenAI GPT-2 model)"),hdo.forEach(t),X6t=i(le),ZL=n(le,"LI",{});var udo=s(ZL);PNe=n(udo,"STRONG",{});var YSa=s(PNe);z6t=r(YSa,"gpt_neo"),YSa.forEach(t),Q6t=r(udo," \u2014 "),kfe=n(udo,"A",{href:!0});var ZSa=s(kfe);W6t=r(ZSa,"FlaxGPTNeoModel"),ZSa.forEach(t),U6t=r(udo," (GPT Neo model)"),udo.forEach(t),H6t=i(le),KL=n(le,"LI",{});var pdo=s(KL);INe=n(pdo,"STRONG",{});var KSa=s(INe);J6t=r(KSa,"gptj"),KSa.forEach(t),Y6t=r(pdo," \u2014 "),Sfe=n(pdo,"A",{href:!0});var eRa=s(Sfe);Z6t=r(eRa,"FlaxGPTJModel"),eRa.forEach(t),K6t=r(pdo," (GPT-J model)"),pdo.forEach(t),e8t=i(le),ey=n(le,"LI",{});var _do=s(ey);BNe=n(_do,"STRONG",{});var oRa=s(BNe);o8t=r(oRa,"longt5"),oRa.forEach(t),r8t=r(_do," \u2014 "),Rfe=n(_do,"A",{href:!0});var rRa=s(Rfe);t8t=r(rRa,"FlaxLongT5Model"),rRa.forEach(t),a8t=r(_do," (LongT5 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cRa=s(Nfe);E8t=r(cRa,"FlaxOPTModel"),cRa.forEach(t),C8t=r(Tdo," (OPT model)"),Tdo.forEach(t),w8t=i(le),ny=n(le,"LI",{});var Mdo=s(ny);GNe=n(Mdo,"STRONG",{});var fRa=s(GNe);A8t=r(fRa,"pegasus"),fRa.forEach(t),L8t=r(Mdo," \u2014 "),Dfe=n(Mdo,"A",{href:!0});var mRa=s(Dfe);y8t=r(mRa,"FlaxPegasusModel"),mRa.forEach(t),x8t=r(Mdo," (Pegasus model)"),Mdo.forEach(t),$8t=i(le),sy=n(le,"LI",{});var Edo=s(sy);ONe=n(Edo,"STRONG",{});var gRa=s(ONe);k8t=r(gRa,"roberta"),gRa.forEach(t),S8t=r(Edo," \u2014 "),qfe=n(Edo,"A",{href:!0});var hRa=s(qfe);R8t=r(hRa,"FlaxRobertaModel"),hRa.forEach(t),P8t=r(Edo," (RoBERTa model)"),Edo.forEach(t),I8t=i(le),ly=n(le,"LI",{});var Cdo=s(ly);VNe=n(Cdo,"STRONG",{});var uRa=s(VNe);B8t=r(uRa,"roformer"),uRa.forEach(t),N8t=r(Cdo," \u2014 "),jfe=n(Cdo,"A",{href:!0});var pRa=s(jfe);D8t=r(pRa,"FlaxRoFormerModel"),pRa.forEach(t),q8t=r(Cdo," (RoFormer model)"),Cdo.forEach(t),j8t=i(le),iy=n(le,"LI",{});var wdo=s(iy);XNe=n(wdo,"STRONG",{});var 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with the `),Wfe=n(Gue,"A",{href:!0});var SRa=s(Wfe);T7t=r(SRa,"from_pretrained()"),SRa.forEach(t),M7t=r(Gue," class method or the "),Ufe=n(Gue,"A",{href:!0});var RRa=s(Ufe);E7t=r(RRa,"from_config()"),RRa.forEach(t),C7t=r(Gue,` class
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falling back to using pattern matching on `),aDe=n(Dk,"CODE",{});var GRa=s(aDe);U7t=r(GRa,"pretrained_model_name_or_path"),GRa.forEach(t),H7t=r(Dk,":"),Dk.forEach(t),J7t=i(ld),Re=n(ld,"UL",{});var Oe=s(Re);_y=n(Oe,"LI",{});var kdo=s(_y);nDe=n(kdo,"STRONG",{});var ORa=s(nDe);Y7t=r(ORa,"bart"),ORa.forEach(t),Z7t=r(kdo," \u2014 "),Jfe=n(kdo,"A",{href:!0});var VRa=s(Jfe);K7t=r(VRa,"FlaxBartForCausalLM"),VRa.forEach(t),eLt=r(kdo," (BART model)"),kdo.forEach(t),oLt=i(Oe),vy=n(Oe,"LI",{});var Sdo=s(vy);sDe=n(Sdo,"STRONG",{});var XRa=s(sDe);rLt=r(XRa,"bert"),XRa.forEach(t),tLt=r(Sdo," \u2014 "),Yfe=n(Sdo,"A",{href:!0});var zRa=s(Yfe);aLt=r(zRa,"FlaxBertForCausalLM"),zRa.forEach(t),nLt=r(Sdo," (BERT model)"),Sdo.forEach(t),sLt=i(Oe),by=n(Oe,"LI",{});var Rdo=s(by);lDe=n(Rdo,"STRONG",{});var QRa=s(lDe);lLt=r(QRa,"big_bird"),QRa.forEach(t),iLt=r(Rdo," \u2014 "),Zfe=n(Rdo,"A",{href:!0});var WRa=s(Zfe);dLt=r(WRa,"FlaxBigBirdForCausalLM"),WRa.forEach(t),cLt=r(Rdo," (BigBird model)"),Rdo.forEach(t),fLt=i(Oe),Fy=n(Oe,"LI",{});var Pdo=s(Fy);iDe=n(Pdo,"STRONG",{});var URa=s(iDe);mLt=r(URa,"electra"),URa.forEach(t),gLt=r(Pdo," \u2014 "),Kfe=n(Pdo,"A",{href:!0});var HRa=s(Kfe);hLt=r(HRa,"FlaxElectraForCausalLM"),HRa.forEach(t),uLt=r(Pdo," (ELECTRA model)"),Pdo.forEach(t),pLt=i(Oe),Ty=n(Oe,"LI",{});var Ido=s(Ty);dDe=n(Ido,"STRONG",{});var JRa=s(dDe);_Lt=r(JRa,"gpt2"),JRa.forEach(t),vLt=r(Ido," \u2014 "),eme=n(Ido,"A",{href:!0});var YRa=s(eme);bLt=r(YRa,"FlaxGPT2LMHeadModel"),YRa.forEach(t),FLt=r(Ido," (OpenAI GPT-2 model)"),Ido.forEach(t),TLt=i(Oe),My=n(Oe,"LI",{});var Bdo=s(My);cDe=n(Bdo,"STRONG",{});var ZRa=s(cDe);MLt=r(ZRa,"gpt_neo"),ZRa.forEach(t),ELt=r(Bdo," \u2014 "),ome=n(Bdo,"A",{href:!0});var KRa=s(ome);CLt=r(KRa,"FlaxGPTNeoForCausalLM"),KRa.forEach(t),wLt=r(Bdo," (GPT Neo model)"),Bdo.forEach(t),ALt=i(Oe),Ey=n(Oe,"LI",{});var Ndo=s(Ey);fDe=n(Ndo,"STRONG",{});var ePa=s(fDe);LLt=r(ePa,"gptj"),ePa.forEach(t),yLt=r(Ndo," \u2014 "),rme=n(Ndo,"A",{href:!0});var oPa=s(rme);xLt=r(oPa,"FlaxGPTJForCausalLM"),oPa.forEach(t),$Lt=r(Ndo," (GPT-J model)"),Ndo.forEach(t),kLt=i(Oe),Cy=n(Oe,"LI",{});var Ddo=s(Cy);mDe=n(Ddo,"STRONG",{});var rPa=s(mDe);SLt=r(rPa,"opt"),rPa.forEach(t),RLt=r(Ddo," \u2014 "),tme=n(Ddo,"A",{href:!0});var tPa=s(tme);PLt=r(tPa,"FlaxOPTForCausalLM"),tPa.forEach(t),ILt=r(Ddo," (OPT model)"),Ddo.forEach(t),BLt=i(Oe),wy=n(Oe,"LI",{});var qdo=s(wy);gDe=n(qdo,"STRONG",{});var aPa=s(gDe);NLt=r(aPa,"roberta"),aPa.forEach(t),DLt=r(qdo," \u2014 "),ame=n(qdo,"A",{href:!0});var nPa=s(ame);qLt=r(nPa,"FlaxRobertaForCausalLM"),nPa.forEach(t),jLt=r(qdo," (RoBERTa model)"),qdo.forEach(t),GLt=i(Oe),Ay=n(Oe,"LI",{});var jdo=s(Ay);hDe=n(jdo,"STRONG",{});var sPa=s(hDe);OLt=r(sPa,"xglm"),sPa.forEach(t),VLt=r(jdo," \u2014 "),nme=n(jdo,"A",{href:!0});var lPa=s(nme);XLt=r(lPa,"FlaxXGLMForCausalLM"),lPa.forEach(t),zLt=r(jdo," (XGLM model)"),jdo.forEach(t),Oe.forEach(t),QLt=i(ld),T(Ly.$$.fragment,ld),ld.forEach(t),sd.forEach(t),ngo=i(f),Em=n(f,"H2",{class:!0});var $uo=s(Em);yy=n($uo,"A",{id:!0,class:!0,href:!0});var iPa=s(yy);uDe=n(iPa,"SPAN",{});var dPa=s(uDe);T(aN.$$.fragment,dPa),dPa.forEach(t),iPa.forEach(t),WLt=i($uo),pDe=n($uo,"SPAN",{});var cPa=s(pDe);ULt=r(cPa,"FlaxAutoModelForPreTraining"),cPa.forEach(t),$uo.forEach(t),sgo=i(f),Rr=n(f,"DIV",{class:!0});var id=s(Rr);T(nN.$$.fragment,id),HLt=i(id),Cm=n(id,"P",{});var Vue=s(Cm);JLt=r(Vue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
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method.`),Vue.forEach(t),oyt=i(id),sN=n(id,"P",{});var kuo=s(sN);ryt=r(kuo,"This class cannot be instantiated directly using "),_De=n(kuo,"CODE",{});var gPa=s(_De);tyt=r(gPa,"__init__()"),gPa.forEach(t),ayt=r(kuo," (throws an error)."),kuo.forEach(t),nyt=i(id),ba=n(id,"DIV",{class:!0});var qk=s(ba);T(lN.$$.fragment,qk),syt=i(qk),vDe=n(qk,"P",{});var hPa=s(vDe);lyt=r(hPa,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),hPa.forEach(t),iyt=i(qk),wm=n(qk,"P",{});var Xue=s(wm);dyt=r(Xue,`Note:
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model\u2019s configuration. Use `),ime=n(Xue,"A",{href:!0});var pPa=s(ime);myt=r(pPa,"from_pretrained()"),pPa.forEach(t),gyt=r(Xue," to load the model weights."),Xue.forEach(t),hyt=i(qk),T(xy.$$.fragment,qk),qk.forEach(t),uyt=i(id),dt=n(id,"DIV",{class:!0});var dd=s(dt);T(iN.$$.fragment,dd),pyt=i(dd),FDe=n(dd,"P",{});var _Pa=s(FDe);_yt=r(_Pa,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),_Pa.forEach(t),vyt=i(dd),ss=n(dd,"P",{});var jk=s(ss);byt=r(jk,"The model class to instantiate is selected based on the "),TDe=n(jk,"CODE",{});var vPa=s(TDe);Fyt=r(vPa,"model_type"),vPa.forEach(t),Tyt=r(jk,` property of the config object (either
passed as an argument or loaded from `),MDe=n(jk,"CODE",{});var bPa=s(MDe);Myt=r(bPa,"pretrained_model_name_or_path"),bPa.forEach(t),Eyt=r(jk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EDe=n(jk,"CODE",{});var FPa=s(EDe);Cyt=r(FPa,"pretrained_model_name_or_path"),FPa.forEach(t),wyt=r(jk,":"),jk.forEach(t),Ayt=i(dd),we=n(dd,"UL",{});var Le=s(we);$y=n(Le,"LI",{});var Gdo=s($y);CDe=n(Gdo,"STRONG",{});var TPa=s(CDe);Lyt=r(TPa,"albert"),TPa.forEach(t),yyt=r(Gdo," \u2014 "),dme=n(Gdo,"A",{href:!0});var MPa=s(dme);xyt=r(MPa,"FlaxAlbertForPreTraining"),MPa.forEach(t),$yt=r(Gdo," (ALBERT model)"),Gdo.forEach(t),kyt=i(Le),ky=n(Le,"LI",{});var Odo=s(ky);wDe=n(Odo,"STRONG",{});var EPa=s(wDe);Syt=r(EPa,"bart"),EPa.forEach(t),Ryt=r(Odo," \u2014 "),cme=n(Odo,"A",{href:!0});var CPa=s(cme);Pyt=r(CPa,"FlaxBartForConditionalGeneration"),CPa.forEach(t),Iyt=r(Odo," (BART model)"),Odo.forEach(t),Byt=i(Le),Sy=n(Le,"LI",{});var Vdo=s(Sy);ADe=n(Vdo,"STRONG",{});var wPa=s(ADe);Nyt=r(wPa,"bert"),wPa.forEach(t),Dyt=r(Vdo," \u2014 "),fme=n(Vdo,"A",{href:!0});var APa=s(fme);qyt=r(APa,"FlaxBertForPreTraining"),APa.forEach(t),jyt=r(Vdo," (BERT model)"),Vdo.forEach(t),Gyt=i(Le),Ry=n(Le,"LI",{});var Xdo=s(Ry);LDe=n(Xdo,"STRONG",{});var LPa=s(LDe);Oyt=r(LPa,"big_bird"),LPa.forEach(t),Vyt=r(Xdo," \u2014 "),mme=n(Xdo,"A",{href:!0});var yPa=s(mme);Xyt=r(yPa,"FlaxBigBirdForPreTraining"),yPa.forEach(t),zyt=r(Xdo," (BigBird model)"),Xdo.forEach(t),Qyt=i(Le),Py=n(Le,"LI",{});var zdo=s(Py);yDe=n(zdo,"STRONG",{});var xPa=s(yDe);Wyt=r(xPa,"electra"),xPa.forEach(t),Uyt=r(zdo," \u2014 "),gme=n(zdo,"A",{href:!0});var $Pa=s(gme);Hyt=r($Pa,"FlaxElectraForPreTraining"),$Pa.forEach(t),Jyt=r(zdo," (ELECTRA model)"),zdo.forEach(t),Yyt=i(Le),Iy=n(Le,"LI",{});var Qdo=s(Iy);xDe=n(Qdo,"STRONG",{});var kPa=s(xDe);Zyt=r(kPa,"longt5"),kPa.forEach(t),Kyt=r(Qdo," \u2014 "),hme=n(Qdo,"A",{href:!0});var SPa=s(hme);e9t=r(SPa,"FlaxLongT5ForConditionalGeneration"),SPa.forEach(t),o9t=r(Qdo," (LongT5 model)"),Qdo.forEach(t),r9t=i(Le),By=n(Le,"LI",{});var Wdo=s(By);$De=n(Wdo,"STRONG",{});var RPa=s($De);t9t=r(RPa,"mbart"),RPa.forEach(t),a9t=r(Wdo," \u2014 "),ume=n(Wdo,"A",{href:!0});var PPa=s(ume);n9t=r(PPa,"FlaxMBartForConditionalGeneration"),PPa.forEach(t),s9t=r(Wdo," (mBART model)"),Wdo.forEach(t),l9t=i(Le),Ny=n(Le,"LI",{});var Udo=s(Ny);kDe=n(Udo,"STRONG",{});var IPa=s(kDe);i9t=r(IPa,"mt5"),IPa.forEach(t),d9t=r(Udo," \u2014 "),pme=n(Udo,"A",{href:!0});var BPa=s(pme);c9t=r(BPa,"FlaxMT5ForConditionalGeneration"),BPa.forEach(t),f9t=r(Udo," (MT5 model)"),Udo.forEach(t),m9t=i(Le),Dy=n(Le,"LI",{});var Hdo=s(Dy);SDe=n(Hdo,"STRONG",{});var NPa=s(SDe);g9t=r(NPa,"roberta"),NPa.forEach(t),h9t=r(Hdo," \u2014 "),_me=n(Hdo,"A",{href:!0});var DPa=s(_me);u9t=r(DPa,"FlaxRobertaForMaskedLM"),DPa.forEach(t),p9t=r(Hdo," (RoBERTa model)"),Hdo.forEach(t),_9t=i(Le),qy=n(Le,"LI",{});var Jdo=s(qy);RDe=n(Jdo,"STRONG",{});var qPa=s(RDe);v9t=r(qPa,"roformer"),qPa.forEach(t),b9t=r(Jdo," \u2014 "),vme=n(Jdo,"A",{href:!0});var jPa=s(vme);F9t=r(jPa,"FlaxRoFormerForMaskedLM"),jPa.forEach(t),T9t=r(Jdo," (RoFormer model)"),Jdo.forEach(t),M9t=i(Le),jy=n(Le,"LI",{});var Ydo=s(jy);PDe=n(Ydo,"STRONG",{});var GPa=s(PDe);E9t=r(GPa,"t5"),GPa.forEach(t),C9t=r(Ydo," \u2014 "),bme=n(Ydo,"A",{href:!0});var OPa=s(bme);w9t=r(OPa,"FlaxT5ForConditionalGeneration"),OPa.forEach(t),A9t=r(Ydo," (T5 model)"),Ydo.forEach(t),L9t=i(Le),Gy=n(Le,"LI",{});var Zdo=s(Gy);IDe=n(Zdo,"STRONG",{});var VPa=s(IDe);y9t=r(VPa,"wav2vec2"),VPa.forEach(t),x9t=r(Zdo," \u2014 "),Fme=n(Zdo,"A",{href:!0});var XPa=s(Fme);$9t=r(XPa,"FlaxWav2Vec2ForPreTraining"),XPa.forEach(t),k9t=r(Zdo," (Wav2Vec2 model)"),Zdo.forEach(t),S9t=i(Le),Oy=n(Le,"LI",{});var Kdo=s(Oy);BDe=n(Kdo,"STRONG",{});var zPa=s(BDe);R9t=r(zPa,"xlm-roberta"),zPa.forEach(t),P9t=r(Kdo," \u2014 "),Tme=n(Kdo,"A",{href:!0});var QPa=s(Tme);I9t=r(QPa,"FlaxXLMRobertaForMaskedLM"),QPa.forEach(t),B9t=r(Kdo," (XLM-RoBERTa model)"),Kdo.forEach(t),Le.forEach(t),N9t=i(dd),T(Vy.$$.fragment,dd),dd.forEach(t),id.forEach(t),lgo=i(f),Am=n(f,"H2",{class:!0});var Suo=s(Am);Xy=n(Suo,"A",{id:!0,class:!0,href:!0});var WPa=s(Xy);NDe=n(WPa,"SPAN",{});var UPa=s(NDe);T(dN.$$.fragment,UPa),UPa.forEach(t),WPa.forEach(t),D9t=i(Suo),DDe=n(Suo,"SPAN",{});var HPa=s(DDe);q9t=r(HPa,"FlaxAutoModelForMaskedLM"),HPa.forEach(t),Suo.forEach(t),igo=i(f),Pr=n(f,"DIV",{class:!0});var cd=s(Pr);T(cN.$$.fragment,cd),j9t=i(cd),Lm=n(cd,"P",{});var zue=s(Lm);G9t=r(zue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),Mme=n(zue,"A",{href:!0});var JPa=s(Mme);O9t=r(JPa,"from_pretrained()"),JPa.forEach(t),V9t=r(zue," class method or the "),Eme=n(zue,"A",{href:!0});var YPa=s(Eme);X9t=r(YPa,"from_config()"),YPa.forEach(t),z9t=r(zue,` class
method.`),zue.forEach(t),Q9t=i(cd),fN=n(cd,"P",{});var Ruo=s(fN);W9t=r(Ruo,"This class cannot be instantiated directly using "),qDe=n(Ruo,"CODE",{});var ZPa=s(qDe);U9t=r(ZPa,"__init__()"),ZPa.forEach(t),H9t=r(Ruo," (throws an error)."),Ruo.forEach(t),J9t=i(cd),Fa=n(cd,"DIV",{class:!0});var Gk=s(Fa);T(mN.$$.fragment,Gk),Y9t=i(Gk),jDe=n(Gk,"P",{});var KPa=s(jDe);Z9t=r(KPa,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),KPa.forEach(t),K9t=i(Gk),ym=n(Gk,"P",{});var Que=s(ym);ext=r(Que,`Note:
Loading a model from its configuration file does `),GDe=n(Que,"STRONG",{});var eIa=s(GDe);oxt=r(eIa,"not"),eIa.forEach(t),rxt=r(Que,` load the model weights. It only affects the
model\u2019s configuration. Use `),Cme=n(Que,"A",{href:!0});var oIa=s(Cme);txt=r(oIa,"from_pretrained()"),oIa.forEach(t),axt=r(Que," to load the model weights."),Que.forEach(t),nxt=i(Gk),T(zy.$$.fragment,Gk),Gk.forEach(t),sxt=i(cd),ct=n(cd,"DIV",{class:!0});var fd=s(ct);T(gN.$$.fragment,fd),lxt=i(fd),ODe=n(fd,"P",{});var rIa=s(ODe);ixt=r(rIa,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),rIa.forEach(t),dxt=i(fd),ls=n(fd,"P",{});var Ok=s(ls);cxt=r(Ok,"The model class to instantiate is selected based on the "),VDe=n(Ok,"CODE",{});var tIa=s(VDe);fxt=r(tIa,"model_type"),tIa.forEach(t),mxt=r(Ok,` property of the config object (either
passed as an argument or loaded from `),XDe=n(Ok,"CODE",{});var aIa=s(XDe);gxt=r(aIa,"pretrained_model_name_or_path"),aIa.forEach(t),hxt=r(Ok,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zDe=n(Ok,"CODE",{});var nIa=s(zDe);uxt=r(nIa,"pretrained_model_name_or_path"),nIa.forEach(t),pxt=r(Ok,":"),Ok.forEach(t),_xt=i(fd),Pe=n(fd,"UL",{});var Ve=s(Pe);Qy=n(Ve,"LI",{});var eco=s(Qy);QDe=n(eco,"STRONG",{});var sIa=s(QDe);vxt=r(sIa,"albert"),sIa.forEach(t),bxt=r(eco," \u2014 "),wme=n(eco,"A",{href:!0});var lIa=s(wme);Fxt=r(lIa,"FlaxAlbertForMaskedLM"),lIa.forEach(t),Txt=r(eco," (ALBERT model)"),eco.forEach(t),Mxt=i(Ve),Wy=n(Ve,"LI",{});var oco=s(Wy);WDe=n(oco,"STRONG",{});var iIa=s(WDe);Ext=r(iIa,"bart"),iIa.forEach(t),Cxt=r(oco," \u2014 "),Ame=n(oco,"A",{href:!0});var dIa=s(Ame);wxt=r(dIa,"FlaxBartForConditionalGeneration"),dIa.forEach(t),Axt=r(oco," (BART model)"),oco.forEach(t),Lxt=i(Ve),Uy=n(Ve,"LI",{});var rco=s(Uy);UDe=n(rco,"STRONG",{});var cIa=s(UDe);yxt=r(cIa,"bert"),cIa.forEach(t),xxt=r(rco," \u2014 "),Lme=n(rco,"A",{href:!0});var fIa=s(Lme);$xt=r(fIa,"FlaxBertForMaskedLM"),fIa.forEach(t),kxt=r(rco," (BERT model)"),rco.forEach(t),Sxt=i(Ve),Hy=n(Ve,"LI",{});var tco=s(Hy);HDe=n(tco,"STRONG",{});var mIa=s(HDe);Rxt=r(mIa,"big_bird"),mIa.forEach(t),Pxt=r(tco," \u2014 "),yme=n(tco,"A",{href:!0});var gIa=s(yme);Ixt=r(gIa,"FlaxBigBirdForMaskedLM"),gIa.forEach(t),Bxt=r(tco," (BigBird model)"),tco.forEach(t),Nxt=i(Ve),Jy=n(Ve,"LI",{});var aco=s(Jy);JDe=n(aco,"STRONG",{});var hIa=s(JDe);Dxt=r(hIa,"distilbert"),hIa.forEach(t),qxt=r(aco," \u2014 "),xme=n(aco,"A",{href:!0});var uIa=s(xme);jxt=r(uIa,"FlaxDistilBertForMaskedLM"),uIa.forEach(t),Gxt=r(aco," (DistilBERT model)"),aco.forEach(t),Oxt=i(Ve),Yy=n(Ve,"LI",{});var nco=s(Yy);YDe=n(nco,"STRONG",{});var pIa=s(YDe);Vxt=r(pIa,"electra"),pIa.forEach(t),Xxt=r(nco," \u2014 "),$me=n(nco,"A",{href:!0});var _Ia=s($me);zxt=r(_Ia,"FlaxElectraForMaskedLM"),_Ia.forEach(t),Qxt=r(nco," (ELECTRA model)"),nco.forEach(t),Wxt=i(Ve),Zy=n(Ve,"LI",{});var sco=s(Zy);ZDe=n(sco,"STRONG",{});var vIa=s(ZDe);Uxt=r(vIa,"mbart"),vIa.forEach(t),Hxt=r(sco," \u2014 "),kme=n(sco,"A",{href:!0});var bIa=s(kme);Jxt=r(bIa,"FlaxMBartForConditionalGeneration"),bIa.forEach(t),Yxt=r(sco," (mBART model)"),sco.forEach(t),Zxt=i(Ve),Ky=n(Ve,"LI",{});var lco=s(Ky);KDe=n(lco,"STRONG",{});var FIa=s(KDe);Kxt=r(FIa,"roberta"),FIa.forEach(t),e$t=r(lco," \u2014 "),Sme=n(lco,"A",{href:!0});var TIa=s(Sme);o$t=r(TIa,"FlaxRobertaForMaskedLM"),TIa.forEach(t),r$t=r(lco," (RoBERTa model)"),lco.forEach(t),t$t=i(Ve),e9=n(Ve,"LI",{});var ico=s(e9);eqe=n(ico,"STRONG",{});var MIa=s(eqe);a$t=r(MIa,"roformer"),MIa.forEach(t),n$t=r(ico," \u2014 "),Rme=n(ico,"A",{href:!0});var EIa=s(Rme);s$t=r(EIa,"FlaxRoFormerForMaskedLM"),EIa.forEach(t),l$t=r(ico," (RoFormer model)"),ico.forEach(t),i$t=i(Ve),o9=n(Ve,"LI",{});var dco=s(o9);oqe=n(dco,"STRONG",{});var CIa=s(oqe);d$t=r(CIa,"xlm-roberta"),CIa.forEach(t),c$t=r(dco," \u2014 "),Pme=n(dco,"A",{href:!0});var wIa=s(Pme);f$t=r(wIa,"FlaxXLMRobertaForMaskedLM"),wIa.forEach(t),m$t=r(dco," (XLM-RoBERTa model)"),dco.forEach(t),Ve.forEach(t),g$t=i(fd),T(r9.$$.fragment,fd),fd.forEach(t),cd.forEach(t),dgo=i(f),xm=n(f,"H2",{class:!0});var Puo=s(xm);t9=n(Puo,"A",{id:!0,class:!0,href:!0});var AIa=s(t9);rqe=n(AIa,"SPAN",{});var LIa=s(rqe);T(hN.$$.fragment,LIa),LIa.forEach(t),AIa.forEach(t),h$t=i(Puo),tqe=n(Puo,"SPAN",{});var yIa=s(tqe);u$t=r(yIa,"FlaxAutoModelForSeq2SeqLM"),yIa.forEach(t),Puo.forEach(t),cgo=i(f),Ir=n(f,"DIV",{class:!0});var md=s(Ir);T(uN.$$.fragment,md),p$t=i(md),$m=n(md,"P",{});var Wue=s($m);_$t=r(Wue,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Ime=n(Wue,"A",{href:!0});var xIa=s(Ime);v$t=r(xIa,"from_pretrained()"),xIa.forEach(t),b$t=r(Wue," class method or the "),Bme=n(Wue,"A",{href:!0});var $Ia=s(Bme);F$t=r($Ia,"from_config()"),$Ia.forEach(t),T$t=r(Wue,` class
method.`),Wue.forEach(t),M$t=i(md),pN=n(md,"P",{});var Iuo=s(pN);E$t=r(Iuo,"This class cannot be instantiated directly using "),aqe=n(Iuo,"CODE",{});var kIa=s(aqe);C$t=r(kIa,"__init__()"),kIa.forEach(t),w$t=r(Iuo," (throws an error)."),Iuo.forEach(t),A$t=i(md),Ta=n(md,"DIV",{class:!0});var Vk=s(Ta);T(_N.$$.fragment,Vk),L$t=i(Vk),nqe=n(Vk,"P",{});var SIa=s(nqe);y$t=r(SIa,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),SIa.forEach(t),x$t=i(Vk),km=n(Vk,"P",{});var Uue=s(km);$$t=r(Uue,`Note:
Loading a model from its configuration file does `),sqe=n(Uue,"STRONG",{});var RIa=s(sqe);k$t=r(RIa,"not"),RIa.forEach(t),S$t=r(Uue,` load the model weights. It only affects the
model\u2019s configuration. Use `),Nme=n(Uue,"A",{href:!0});var PIa=s(Nme);R$t=r(PIa,"from_pretrained()"),PIa.forEach(t),P$t=r(Uue," to load the model weights."),Uue.forEach(t),I$t=i(Vk),T(a9.$$.fragment,Vk),Vk.forEach(t),B$t=i(md),ft=n(md,"DIV",{class:!0});var gd=s(ft);T(vN.$$.fragment,gd),N$t=i(gd),lqe=n(gd,"P",{});var IIa=s(lqe);D$t=r(IIa,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),IIa.forEach(t),q$t=i(gd),is=n(gd,"P",{});var Xk=s(is);j$t=r(Xk,"The model class to instantiate is selected based on the "),iqe=n(Xk,"CODE",{});var BIa=s(iqe);G$t=r(BIa,"model_type"),BIa.forEach(t),O$t=r(Xk,` property of the config object (either
passed as an argument or loaded from `),dqe=n(Xk,"CODE",{});var NIa=s(dqe);V$t=r(NIa,"pretrained_model_name_or_path"),NIa.forEach(t),X$t=r(Xk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cqe=n(Xk,"CODE",{});var DIa=s(cqe);z$t=r(DIa,"pretrained_model_name_or_path"),DIa.forEach(t),Q$t=r(Xk,":"),Xk.forEach(t),W$t=i(gd),Ie=n(gd,"UL",{});var Xe=s(Ie);n9=n(Xe,"LI",{});var cco=s(n9);fqe=n(cco,"STRONG",{});var qIa=s(fqe);U$t=r(qIa,"bart"),qIa.forEach(t),H$t=r(cco," \u2014 "),Dme=n(cco,"A",{href:!0});var jIa=s(Dme);J$t=r(jIa,"FlaxBartForConditionalGeneration"),jIa.forEach(t),Y$t=r(cco," (BART model)"),cco.forEach(t),Z$t=i(Xe),s9=n(Xe,"LI",{});var fco=s(s9);mqe=n(fco,"STRONG",{});var GIa=s(mqe);K$t=r(GIa,"blenderbot"),GIa.forEach(t),ekt=r(fco," \u2014 "),qme=n(fco,"A",{href:!0});var OIa=s(qme);okt=r(OIa,"FlaxBlenderbotForConditionalGeneration"),OIa.forEach(t),rkt=r(fco," (Blenderbot model)"),fco.forEach(t),tkt=i(Xe),l9=n(Xe,"LI",{});var mco=s(l9);gqe=n(mco,"STRONG",{});var VIa=s(gqe);akt=r(VIa,"blenderbot-small"),VIa.forEach(t),nkt=r(mco," \u2014 "),jme=n(mco,"A",{href:!0});var XIa=s(jme);skt=r(XIa,"FlaxBlenderbotSmallForConditionalGeneration"),XIa.forEach(t),lkt=r(mco," (BlenderbotSmall model)"),mco.forEach(t),ikt=i(Xe),i9=n(Xe,"LI",{});var gco=s(i9);hqe=n(gco,"STRONG",{});var zIa=s(hqe);dkt=r(zIa,"encoder-decoder"),zIa.forEach(t),ckt=r(gco," \u2014 "),Gme=n(gco,"A",{href:!0});var QIa=s(Gme);fkt=r(QIa,"FlaxEncoderDecoderModel"),QIa.forEach(t),mkt=r(gco," (Encoder decoder model)"),gco.forEach(t),gkt=i(Xe),d9=n(Xe,"LI",{});var hco=s(d9);uqe=n(hco,"STRONG",{});var WIa=s(uqe);hkt=r(WIa,"longt5"),WIa.forEach(t),ukt=r(hco," \u2014 "),Ome=n(hco,"A",{href:!0});var UIa=s(Ome);pkt=r(UIa,"FlaxLongT5ForConditionalGeneration"),UIa.forEach(t),_kt=r(hco," (LongT5 model)"),hco.forEach(t),vkt=i(Xe),c9=n(Xe,"LI",{});var uco=s(c9);pqe=n(uco,"STRONG",{});var HIa=s(pqe);bkt=r(HIa,"marian"),HIa.forEach(t),Fkt=r(uco," \u2014 "),Vme=n(uco,"A",{href:!0});var JIa=s(Vme);Tkt=r(JIa,"FlaxMarianMTModel"),JIa.forEach(t),Mkt=r(uco," (Marian model)"),uco.forEach(t),Ekt=i(Xe),f9=n(Xe,"LI",{});var pco=s(f9);_qe=n(pco,"STRONG",{});var YIa=s(_qe);Ckt=r(YIa,"mbart"),YIa.forEach(t),wkt=r(pco," \u2014 "),Xme=n(pco,"A",{href:!0});var ZIa=s(Xme);Akt=r(ZIa,"FlaxMBartForConditionalGeneration"),ZIa.forEach(t),Lkt=r(pco," (mBART model)"),pco.forEach(t),ykt=i(Xe),m9=n(Xe,"LI",{});var _co=s(m9);vqe=n(_co,"STRONG",{});var KIa=s(vqe);xkt=r(KIa,"mt5"),KIa.forEach(t),$kt=r(_co," \u2014 "),zme=n(_co,"A",{href:!0});var eBa=s(zme);kkt=r(eBa,"FlaxMT5ForConditionalGeneration"),eBa.forEach(t),Skt=r(_co," (MT5 model)"),_co.forEach(t),Rkt=i(Xe),g9=n(Xe,"LI",{});var vco=s(g9);bqe=n(vco,"STRONG",{});var oBa=s(bqe);Pkt=r(oBa,"pegasus"),oBa.forEach(t),Ikt=r(vco," \u2014 "),Qme=n(vco,"A",{href:!0});var rBa=s(Qme);Bkt=r(rBa,"FlaxPegasusForConditionalGeneration"),rBa.forEach(t),Nkt=r(vco," (Pegasus model)"),vco.forEach(t),Dkt=i(Xe),h9=n(Xe,"LI",{});var bco=s(h9);Fqe=n(bco,"STRONG",{});var tBa=s(Fqe);qkt=r(tBa,"t5"),tBa.forEach(t),jkt=r(bco," \u2014 "),Wme=n(bco,"A",{href:!0});var aBa=s(Wme);Gkt=r(aBa,"FlaxT5ForConditionalGeneration"),aBa.forEach(t),Okt=r(bco," (T5 model)"),bco.forEach(t),Xe.forEach(t),Vkt=i(gd),T(u9.$$.fragment,gd),gd.forEach(t),md.forEach(t),fgo=i(f),Sm=n(f,"H2",{class:!0});var Buo=s(Sm);p9=n(Buo,"A",{id:!0,class:!0,href:!0});var nBa=s(p9);Tqe=n(nBa,"SPAN",{});var sBa=s(Tqe);T(bN.$$.fragment,sBa),sBa.forEach(t),nBa.forEach(t),Xkt=i(Buo),Mqe=n(Buo,"SPAN",{});var lBa=s(Mqe);zkt=r(lBa,"FlaxAutoModelForSequenceClassification"),lBa.forEach(t),Buo.forEach(t),mgo=i(f),Br=n(f,"DIV",{class:!0});var hd=s(Br);T(FN.$$.fragment,hd),Qkt=i(hd),Rm=n(hd,"P",{});var Hue=s(Rm);Wkt=r(Hue,`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 `),Ume=n(Hue,"A",{href:!0});var iBa=s(Ume);Ukt=r(iBa,"from_pretrained()"),iBa.forEach(t),Hkt=r(Hue," class method or the "),Hme=n(Hue,"A",{href:!0});var dBa=s(Hme);Jkt=r(dBa,"from_config()"),dBa.forEach(t),Ykt=r(Hue,` class
method.`),Hue.forEach(t),Zkt=i(hd),TN=n(hd,"P",{});var Nuo=s(TN);Kkt=r(Nuo,"This class cannot be instantiated directly using "),Eqe=n(Nuo,"CODE",{});var cBa=s(Eqe);eSt=r(cBa,"__init__()"),cBa.forEach(t),oSt=r(Nuo," (throws an error)."),Nuo.forEach(t),rSt=i(hd),Ma=n(hd,"DIV",{class:!0});var zk=s(Ma);T(MN.$$.fragment,zk),tSt=i(zk),Cqe=n(zk,"P",{});var fBa=s(Cqe);aSt=r(fBa,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),fBa.forEach(t),nSt=i(zk),Pm=n(zk,"P",{});var Jue=s(Pm);sSt=r(Jue,`Note:
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model\u2019s configuration. Use `),Jme=n(Jue,"A",{href:!0});var gBa=s(Jme);dSt=r(gBa,"from_pretrained()"),gBa.forEach(t),cSt=r(Jue," to load the model weights."),Jue.forEach(t),fSt=i(zk),T(_9.$$.fragment,zk),zk.forEach(t),mSt=i(hd),mt=n(hd,"DIV",{class:!0});var ud=s(mt);T(EN.$$.fragment,ud),gSt=i(ud),Aqe=n(ud,"P",{});var hBa=s(Aqe);hSt=r(hBa,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),hBa.forEach(t),uSt=i(ud),ds=n(ud,"P",{});var Qk=s(ds);pSt=r(Qk,"The model class to instantiate is selected based on the "),Lqe=n(Qk,"CODE",{});var uBa=s(Lqe);_St=r(uBa,"model_type"),uBa.forEach(t),vSt=r(Qk,` property of the config object (either
passed as an argument or loaded from `),yqe=n(Qk,"CODE",{});var pBa=s(yqe);bSt=r(pBa,"pretrained_model_name_or_path"),pBa.forEach(t),FSt=r(Qk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xqe=n(Qk,"CODE",{});var _Ba=s(xqe);TSt=r(_Ba,"pretrained_model_name_or_path"),_Ba.forEach(t),MSt=r(Qk,":"),Qk.forEach(t),ESt=i(ud),Be=n(ud,"UL",{});var ze=s(Be);v9=n(ze,"LI",{});var Fco=s(v9);$qe=n(Fco,"STRONG",{});var vBa=s($qe);CSt=r(vBa,"albert"),vBa.forEach(t),wSt=r(Fco," \u2014 "),Yme=n(Fco,"A",{href:!0});var bBa=s(Yme);ASt=r(bBa,"FlaxAlbertForSequenceClassification"),bBa.forEach(t),LSt=r(Fco," (ALBERT model)"),Fco.forEach(t),ySt=i(ze),b9=n(ze,"LI",{});var Tco=s(b9);kqe=n(Tco,"STRONG",{});var FBa=s(kqe);xSt=r(FBa,"bart"),FBa.forEach(t),$St=r(Tco," \u2014 "),Zme=n(Tco,"A",{href:!0});var TBa=s(Zme);kSt=r(TBa,"FlaxBartForSequenceClassification"),TBa.forEach(t),SSt=r(Tco," (BART model)"),Tco.forEach(t),RSt=i(ze),F9=n(ze,"LI",{});var Mco=s(F9);Sqe=n(Mco,"STRONG",{});var MBa=s(Sqe);PSt=r(MBa,"bert"),MBa.forEach(t),ISt=r(Mco," \u2014 "),Kme=n(Mco,"A",{href:!0});var EBa=s(Kme);BSt=r(EBa,"FlaxBertForSequenceClassification"),EBa.forEach(t),NSt=r(Mco," (BERT model)"),Mco.forEach(t),DSt=i(ze),T9=n(ze,"LI",{});var Eco=s(T9);Rqe=n(Eco,"STRONG",{});var CBa=s(Rqe);qSt=r(CBa,"big_bird"),CBa.forEach(t),jSt=r(Eco," \u2014 "),ege=n(Eco,"A",{href:!0});var wBa=s(ege);GSt=r(wBa,"FlaxBigBirdForSequenceClassification"),wBa.forEach(t),OSt=r(Eco," (BigBird model)"),Eco.forEach(t),VSt=i(ze),M9=n(ze,"LI",{});var Cco=s(M9);Pqe=n(Cco,"STRONG",{});var ABa=s(Pqe);XSt=r(ABa,"distilbert"),ABa.forEach(t),zSt=r(Cco," \u2014 "),oge=n(Cco,"A",{href:!0});var LBa=s(oge);QSt=r(LBa,"FlaxDistilBertForSequenceClassification"),LBa.forEach(t),WSt=r(Cco," (DistilBERT model)"),Cco.forEach(t),USt=i(ze),E9=n(ze,"LI",{});var wco=s(E9);Iqe=n(wco,"STRONG",{});var yBa=s(Iqe);HSt=r(yBa,"electra"),yBa.forEach(t),JSt=r(wco," \u2014 "),rge=n(wco,"A",{href:!0});var xBa=s(rge);YSt=r(xBa,"FlaxElectraForSequenceClassification"),xBa.forEach(t),ZSt=r(wco," (ELECTRA model)"),wco.forEach(t),KSt=i(ze),C9=n(ze,"LI",{});var Aco=s(C9);Bqe=n(Aco,"STRONG",{});var $Ba=s(Bqe);eRt=r($Ba,"mbart"),$Ba.forEach(t),oRt=r(Aco," \u2014 "),tge=n(Aco,"A",{href:!0});var kBa=s(tge);rRt=r(kBa,"FlaxMBartForSequenceClassification"),kBa.forEach(t),tRt=r(Aco," (mBART model)"),Aco.forEach(t),aRt=i(ze),w9=n(ze,"LI",{});var Lco=s(w9);Nqe=n(Lco,"STRONG",{});var SBa=s(Nqe);nRt=r(SBa,"roberta"),SBa.forEach(t),sRt=r(Lco," \u2014 "),age=n(Lco,"A",{href:!0});var RBa=s(age);lRt=r(RBa,"FlaxRobertaForSequenceClassification"),RBa.forEach(t),iRt=r(Lco," (RoBERTa model)"),Lco.forEach(t),dRt=i(ze),A9=n(ze,"LI",{});var yco=s(A9);Dqe=n(yco,"STRONG",{});var PBa=s(Dqe);cRt=r(PBa,"roformer"),PBa.forEach(t),fRt=r(yco," \u2014 "),nge=n(yco,"A",{href:!0});var IBa=s(nge);mRt=r(IBa,"FlaxRoFormerForSequenceClassification"),IBa.forEach(t),gRt=r(yco," (RoFormer model)"),yco.forEach(t),hRt=i(ze),L9=n(ze,"LI",{});var xco=s(L9);qqe=n(xco,"STRONG",{});var BBa=s(qqe);uRt=r(BBa,"xlm-roberta"),BBa.forEach(t),pRt=r(xco," \u2014 "),sge=n(xco,"A",{href:!0});var NBa=s(sge);_Rt=r(NBa,"FlaxXLMRobertaForSequenceClassification"),NBa.forEach(t),vRt=r(xco," (XLM-RoBERTa model)"),xco.forEach(t),ze.forEach(t),bRt=i(ud),T(y9.$$.fragment,ud),ud.forEach(t),hd.forEach(t),ggo=i(f),Im=n(f,"H2",{class:!0});var Duo=s(Im);x9=n(Duo,"A",{id:!0,class:!0,href:!0});var DBa=s(x9);jqe=n(DBa,"SPAN",{});var qBa=s(jqe);T(CN.$$.fragment,qBa),qBa.forEach(t),DBa.forEach(t),FRt=i(Duo),Gqe=n(Duo,"SPAN",{});var jBa=s(Gqe);TRt=r(jBa,"FlaxAutoModelForQuestionAnswering"),jBa.forEach(t),Duo.forEach(t),hgo=i(f),Nr=n(f,"DIV",{class:!0});var pd=s(Nr);T(wN.$$.fragment,pd),MRt=i(pd),Bm=n(pd,"P",{});var Yue=s(Bm);ERt=r(Yue,`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 `),lge=n(Yue,"A",{href:!0});var GBa=s(lge);CRt=r(GBa,"from_pretrained()"),GBa.forEach(t),wRt=r(Yue," class method or the "),ige=n(Yue,"A",{href:!0});var OBa=s(ige);ARt=r(OBa,"from_config()"),OBa.forEach(t),LRt=r(Yue,` class
method.`),Yue.forEach(t),yRt=i(pd),AN=n(pd,"P",{});var quo=s(AN);xRt=r(quo,"This class cannot be instantiated directly using "),Oqe=n(quo,"CODE",{});var VBa=s(Oqe);$Rt=r(VBa,"__init__()"),VBa.forEach(t),kRt=r(quo," (throws an error)."),quo.forEach(t),SRt=i(pd),Ea=n(pd,"DIV",{class:!0});var Wk=s(Ea);T(LN.$$.fragment,Wk),RRt=i(Wk),Vqe=n(Wk,"P",{});var XBa=s(Vqe);PRt=r(XBa,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),XBa.forEach(t),IRt=i(Wk),Nm=n(Wk,"P",{});var Zue=s(Nm);BRt=r(Zue,`Note:
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model\u2019s configuration. Use `),dge=n(Zue,"A",{href:!0});var QBa=s(dge);qRt=r(QBa,"from_pretrained()"),QBa.forEach(t),jRt=r(Zue," to load the model weights."),Zue.forEach(t),GRt=i(Wk),T($9.$$.fragment,Wk),Wk.forEach(t),ORt=i(pd),gt=n(pd,"DIV",{class:!0});var _d=s(gt);T(yN.$$.fragment,_d),VRt=i(_d),zqe=n(_d,"P",{});var WBa=s(zqe);XRt=r(WBa,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),WBa.forEach(t),zRt=i(_d),cs=n(_d,"P",{});var Uk=s(cs);QRt=r(Uk,"The model class to instantiate is selected based on the "),Qqe=n(Uk,"CODE",{});var UBa=s(Qqe);WRt=r(UBa,"model_type"),UBa.forEach(t),URt=r(Uk,` property of the config object (either
passed as an argument or loaded from `),Wqe=n(Uk,"CODE",{});var HBa=s(Wqe);HRt=r(HBa,"pretrained_model_name_or_path"),HBa.forEach(t),JRt=r(Uk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Uqe=n(Uk,"CODE",{});var JBa=s(Uqe);YRt=r(JBa,"pretrained_model_name_or_path"),JBa.forEach(t),ZRt=r(Uk,":"),Uk.forEach(t),KRt=i(_d),Ne=n(_d,"UL",{});var Qe=s(Ne);k9=n(Qe,"LI",{});var $co=s(k9);Hqe=n($co,"STRONG",{});var YBa=s(Hqe);ePt=r(YBa,"albert"),YBa.forEach(t),oPt=r($co," \u2014 "),cge=n($co,"A",{href:!0});var ZBa=s(cge);rPt=r(ZBa,"FlaxAlbertForQuestionAnswering"),ZBa.forEach(t),tPt=r($co," (ALBERT model)"),$co.forEach(t),aPt=i(Qe),S9=n(Qe,"LI",{});var kco=s(S9);Jqe=n(kco,"STRONG",{});var KBa=s(Jqe);nPt=r(KBa,"bart"),KBa.forEach(t),sPt=r(kco," \u2014 "),fge=n(kco,"A",{href:!0});var eNa=s(fge);lPt=r(eNa,"FlaxBartForQuestionAnswering"),eNa.forEach(t),iPt=r(kco," (BART model)"),kco.forEach(t),dPt=i(Qe),R9=n(Qe,"LI",{});var Sco=s(R9);Yqe=n(Sco,"STRONG",{});var oNa=s(Yqe);cPt=r(oNa,"bert"),oNa.forEach(t),fPt=r(Sco," \u2014 "),mge=n(Sco,"A",{href:!0});var rNa=s(mge);mPt=r(rNa,"FlaxBertForQuestionAnswering"),rNa.forEach(t),gPt=r(Sco," (BERT model)"),Sco.forEach(t),hPt=i(Qe),P9=n(Qe,"LI",{});var Rco=s(P9);Zqe=n(Rco,"STRONG",{});var tNa=s(Zqe);uPt=r(tNa,"big_bird"),tNa.forEach(t),pPt=r(Rco," \u2014 "),gge=n(Rco,"A",{href:!0});var aNa=s(gge);_Pt=r(aNa,"FlaxBigBirdForQuestionAnswering"),aNa.forEach(t),vPt=r(Rco," (BigBird model)"),Rco.forEach(t),bPt=i(Qe),I9=n(Qe,"LI",{});var Pco=s(I9);Kqe=n(Pco,"STRONG",{});var nNa=s(Kqe);FPt=r(nNa,"distilbert"),nNa.forEach(t),TPt=r(Pco," \u2014 "),hge=n(Pco,"A",{href:!0});var sNa=s(hge);MPt=r(sNa,"FlaxDistilBertForQuestionAnswering"),sNa.forEach(t),EPt=r(Pco," (DistilBERT model)"),Pco.forEach(t),CPt=i(Qe),B9=n(Qe,"LI",{});var Ico=s(B9);eje=n(Ico,"STRONG",{});var lNa=s(eje);wPt=r(lNa,"electra"),lNa.forEach(t),APt=r(Ico," \u2014 "),uge=n(Ico,"A",{href:!0});var iNa=s(uge);LPt=r(iNa,"FlaxElectraForQuestionAnswering"),iNa.forEach(t),yPt=r(Ico," (ELECTRA model)"),Ico.forEach(t),xPt=i(Qe),N9=n(Qe,"LI",{});var Bco=s(N9);oje=n(Bco,"STRONG",{});var dNa=s(oje);$Pt=r(dNa,"mbart"),dNa.forEach(t),kPt=r(Bco," \u2014 "),pge=n(Bco,"A",{href:!0});var cNa=s(pge);SPt=r(cNa,"FlaxMBartForQuestionAnswering"),cNa.forEach(t),RPt=r(Bco," (mBART model)"),Bco.forEach(t),PPt=i(Qe),D9=n(Qe,"LI",{});var Nco=s(D9);rje=n(Nco,"STRONG",{});var fNa=s(rje);IPt=r(fNa,"roberta"),fNa.forEach(t),BPt=r(Nco," \u2014 "),_ge=n(Nco,"A",{href:!0});var mNa=s(_ge);NPt=r(mNa,"FlaxRobertaForQuestionAnswering"),mNa.forEach(t),DPt=r(Nco," (RoBERTa model)"),Nco.forEach(t),qPt=i(Qe),q9=n(Qe,"LI",{});var Dco=s(q9);tje=n(Dco,"STRONG",{});var gNa=s(tje);jPt=r(gNa,"roformer"),gNa.forEach(t),GPt=r(Dco," \u2014 "),vge=n(Dco,"A",{href:!0});var hNa=s(vge);OPt=r(hNa,"FlaxRoFormerForQuestionAnswering"),hNa.forEach(t),VPt=r(Dco," (RoFormer model)"),Dco.forEach(t),XPt=i(Qe),j9=n(Qe,"LI",{});var qco=s(j9);aje=n(qco,"STRONG",{});var uNa=s(aje);zPt=r(uNa,"xlm-roberta"),uNa.forEach(t),QPt=r(qco," \u2014 "),bge=n(qco,"A",{href:!0});var pNa=s(bge);WPt=r(pNa,"FlaxXLMRobertaForQuestionAnswering"),pNa.forEach(t),UPt=r(qco," (XLM-RoBERTa model)"),qco.forEach(t),Qe.forEach(t),HPt=i(_d),T(G9.$$.fragment,_d),_d.forEach(t),pd.forEach(t),ugo=i(f),Dm=n(f,"H2",{class:!0});var juo=s(Dm);O9=n(juo,"A",{id:!0,class:!0,href:!0});var _Na=s(O9);nje=n(_Na,"SPAN",{});var vNa=s(nje);T(xN.$$.fragment,vNa),vNa.forEach(t),_Na.forEach(t),JPt=i(juo),sje=n(juo,"SPAN",{});var bNa=s(sje);YPt=r(bNa,"FlaxAutoModelForTokenClassification"),bNa.forEach(t),juo.forEach(t),pgo=i(f),Dr=n(f,"DIV",{class:!0});var vd=s(Dr);T($N.$$.fragment,vd),ZPt=i(vd),qm=n(vd,"P",{});var Kue=s(qm);KPt=r(Kue,`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 `),Fge=n(Kue,"A",{href:!0});var FNa=s(Fge);eIt=r(FNa,"from_pretrained()"),FNa.forEach(t),oIt=r(Kue," class method or the "),Tge=n(Kue,"A",{href:!0});var TNa=s(Tge);rIt=r(TNa,"from_config()"),TNa.forEach(t),tIt=r(Kue,` class
method.`),Kue.forEach(t),aIt=i(vd),kN=n(vd,"P",{});var Guo=s(kN);nIt=r(Guo,"This class cannot be instantiated directly using "),lje=n(Guo,"CODE",{});var MNa=s(lje);sIt=r(MNa,"__init__()"),MNa.forEach(t),lIt=r(Guo," (throws an error)."),Guo.forEach(t),iIt=i(vd),Ca=n(vd,"DIV",{class:!0});var Hk=s(Ca);T(SN.$$.fragment,Hk),dIt=i(Hk),ije=n(Hk,"P",{});var ENa=s(ije);cIt=r(ENa,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),ENa.forEach(t),fIt=i(Hk),jm=n(Hk,"P",{});var epe=s(jm);mIt=r(epe,`Note:
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model\u2019s configuration. Use `),Mge=n(epe,"A",{href:!0});var wNa=s(Mge);uIt=r(wNa,"from_pretrained()"),wNa.forEach(t),pIt=r(epe," to load the model weights."),epe.forEach(t),_It=i(Hk),T(V9.$$.fragment,Hk),Hk.forEach(t),vIt=i(vd),ht=n(vd,"DIV",{class:!0});var bd=s(ht);T(RN.$$.fragment,bd),bIt=i(bd),cje=n(bd,"P",{});var ANa=s(cje);FIt=r(ANa,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),ANa.forEach(t),TIt=i(bd),fs=n(bd,"P",{});var Jk=s(fs);MIt=r(Jk,"The model class to instantiate is selected based on the "),fje=n(Jk,"CODE",{});var LNa=s(fje);EIt=r(LNa,"model_type"),LNa.forEach(t),CIt=r(Jk,` property of the config object (either
passed as an argument or loaded from `),mje=n(Jk,"CODE",{});var yNa=s(mje);wIt=r(yNa,"pretrained_model_name_or_path"),yNa.forEach(t),AIt=r(Jk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gje=n(Jk,"CODE",{});var xNa=s(gje);LIt=r(xNa,"pretrained_model_name_or_path"),xNa.forEach(t),yIt=r(Jk,":"),Jk.forEach(t),xIt=i(bd),Ue=n(bd,"UL",{});var So=s(Ue);X9=n(So,"LI",{});var jco=s(X9);hje=n(jco,"STRONG",{});var $Na=s(hje);$It=r($Na,"albert"),$Na.forEach(t),kIt=r(jco," \u2014 "),Ege=n(jco,"A",{href:!0});var kNa=s(Ege);SIt=r(kNa,"FlaxAlbertForTokenClassification"),kNa.forEach(t),RIt=r(jco," (ALBERT model)"),jco.forEach(t),PIt=i(So),z9=n(So,"LI",{});var Gco=s(z9);uje=n(Gco,"STRONG",{});var SNa=s(uje);IIt=r(SNa,"bert"),SNa.forEach(t),BIt=r(Gco," \u2014 "),Cge=n(Gco,"A",{href:!0});var RNa=s(Cge);NIt=r(RNa,"FlaxBertForTokenClassification"),RNa.forEach(t),DIt=r(Gco," (BERT model)"),Gco.forEach(t),qIt=i(So),Q9=n(So,"LI",{});var Oco=s(Q9);pje=n(Oco,"STRONG",{});var PNa=s(pje);jIt=r(PNa,"big_bird"),PNa.forEach(t),GIt=r(Oco," \u2014 "),wge=n(Oco,"A",{href:!0});var INa=s(wge);OIt=r(INa,"FlaxBigBirdForTokenClassification"),INa.forEach(t),VIt=r(Oco," (BigBird model)"),Oco.forEach(t),XIt=i(So),W9=n(So,"LI",{});var Vco=s(W9);_je=n(Vco,"STRONG",{});var BNa=s(_je);zIt=r(BNa,"distilbert"),BNa.forEach(t),QIt=r(Vco," \u2014 "),Age=n(Vco,"A",{href:!0});var NNa=s(Age);WIt=r(NNa,"FlaxDistilBertForTokenClassification"),NNa.forEach(t),UIt=r(Vco," (DistilBERT model)"),Vco.forEach(t),HIt=i(So),U9=n(So,"LI",{});var Xco=s(U9);vje=n(Xco,"STRONG",{});var DNa=s(vje);JIt=r(DNa,"electra"),DNa.forEach(t),YIt=r(Xco," \u2014 "),Lge=n(Xco,"A",{href:!0});var qNa=s(Lge);ZIt=r(qNa,"FlaxElectraForTokenClassification"),qNa.forEach(t),KIt=r(Xco," (ELECTRA model)"),Xco.forEach(t),eBt=i(So),H9=n(So,"LI",{});var zco=s(H9);bje=n(zco,"STRONG",{});var jNa=s(bje);oBt=r(jNa,"roberta"),jNa.forEach(t),rBt=r(zco," \u2014 "),yge=n(zco,"A",{href:!0});var GNa=s(yge);tBt=r(GNa,"FlaxRobertaForTokenClassification"),GNa.forEach(t),aBt=r(zco," (RoBERTa model)"),zco.forEach(t),nBt=i(So),J9=n(So,"LI",{});var Qco=s(J9);Fje=n(Qco,"STRONG",{});var ONa=s(Fje);sBt=r(ONa,"roformer"),ONa.forEach(t),lBt=r(Qco," \u2014 "),xge=n(Qco,"A",{href:!0});var VNa=s(xge);iBt=r(VNa,"FlaxRoFormerForTokenClassification"),VNa.forEach(t),dBt=r(Qco," (RoFormer model)"),Qco.forEach(t),cBt=i(So),Y9=n(So,"LI",{});var Wco=s(Y9);Tje=n(Wco,"STRONG",{});var XNa=s(Tje);fBt=r(XNa,"xlm-roberta"),XNa.forEach(t),mBt=r(Wco," \u2014 "),$ge=n(Wco,"A",{href:!0});var zNa=s($ge);gBt=r(zNa,"FlaxXLMRobertaForTokenClassification"),zNa.forEach(t),hBt=r(Wco," (XLM-RoBERTa model)"),Wco.forEach(t),So.forEach(t),uBt=i(bd),T(Z9.$$.fragment,bd),bd.forEach(t),vd.forEach(t),_go=i(f),Gm=n(f,"H2",{class:!0});var Ouo=s(Gm);K9=n(Ouo,"A",{id:!0,class:!0,href:!0});var QNa=s(K9);Mje=n(QNa,"SPAN",{});var WNa=s(Mje);T(PN.$$.fragment,WNa),WNa.forEach(t),QNa.forEach(t),pBt=i(Ouo),Eje=n(Ouo,"SPAN",{});var UNa=s(Eje);_Bt=r(UNa,"FlaxAutoModelForMultipleChoice"),UNa.forEach(t),Ouo.forEach(t),vgo=i(f),qr=n(f,"DIV",{class:!0});var Fd=s(qr);T(IN.$$.fragment,Fd),vBt=i(Fd),Om=n(Fd,"P",{});var ope=s(Om);bBt=r(ope,`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 `),kge=n(ope,"A",{href:!0});var HNa=s(kge);FBt=r(HNa,"from_pretrained()"),HNa.forEach(t),TBt=r(ope," class method or the "),Sge=n(ope,"A",{href:!0});var JNa=s(Sge);MBt=r(JNa,"from_config()"),JNa.forEach(t),EBt=r(ope,` class
method.`),ope.forEach(t),CBt=i(Fd),BN=n(Fd,"P",{});var Vuo=s(BN);wBt=r(Vuo,"This class cannot be instantiated directly using "),Cje=n(Vuo,"CODE",{});var YNa=s(Cje);ABt=r(YNa,"__init__()"),YNa.forEach(t),LBt=r(Vuo," (throws an error)."),Vuo.forEach(t),yBt=i(Fd),wa=n(Fd,"DIV",{class:!0});var Yk=s(wa);T(NN.$$.fragment,Yk),xBt=i(Yk),wje=n(Yk,"P",{});var ZNa=s(wje);$Bt=r(ZNa,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),ZNa.forEach(t),kBt=i(Yk),Vm=n(Yk,"P",{});var rpe=s(Vm);SBt=r(rpe,`Note:
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model\u2019s configuration. Use `),Rge=n(rpe,"A",{href:!0});var eDa=s(Rge);IBt=r(eDa,"from_pretrained()"),eDa.forEach(t),BBt=r(rpe," to load the model weights."),rpe.forEach(t),NBt=i(Yk),T(ex.$$.fragment,Yk),Yk.forEach(t),DBt=i(Fd),ut=n(Fd,"DIV",{class:!0});var Td=s(ut);T(DN.$$.fragment,Td),qBt=i(Td),Lje=n(Td,"P",{});var oDa=s(Lje);jBt=r(oDa,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),oDa.forEach(t),GBt=i(Td),ms=n(Td,"P",{});var Zk=s(ms);OBt=r(Zk,"The model class to instantiate is selected based on the "),yje=n(Zk,"CODE",{});var rDa=s(yje);VBt=r(rDa,"model_type"),rDa.forEach(t),XBt=r(Zk,` property of the config object (either
passed as an argument or loaded from `),xje=n(Zk,"CODE",{});var tDa=s(xje);zBt=r(tDa,"pretrained_model_name_or_path"),tDa.forEach(t),QBt=r(Zk,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$je=n(Zk,"CODE",{});var aDa=s($je);WBt=r(aDa,"pretrained_model_name_or_path"),aDa.forEach(t),UBt=r(Zk,":"),Zk.forEach(t),HBt=i(Td),He=n(Td,"UL",{});var Ro=s(He);ox=n(Ro,"LI",{});var Uco=s(ox);kje=n(Uco,"STRONG",{});var nDa=s(kje);JBt=r(nDa,"albert"),nDa.forEach(t),YBt=r(Uco," \u2014 "),Pge=n(Uco,"A",{href:!0});var sDa=s(Pge);ZBt=r(sDa,"FlaxAlbertForMultipleChoice"),sDa.forEach(t),KBt=r(Uco," (ALBERT model)"),Uco.forEach(t),eNt=i(Ro),rx=n(Ro,"LI",{});var Hco=s(rx);Sje=n(Hco,"STRONG",{});var lDa=s(Sje);oNt=r(lDa,"bert"),lDa.forEach(t),rNt=r(Hco," \u2014 "),Ige=n(Hco,"A",{href:!0});var iDa=s(Ige);tNt=r(iDa,"FlaxBertForMultipleChoice"),iDa.forEach(t),aNt=r(Hco," (BERT model)"),Hco.forEach(t),nNt=i(Ro),tx=n(Ro,"LI",{});var Jco=s(tx);Rje=n(Jco,"STRONG",{});var dDa=s(Rje);sNt=r(dDa,"big_bird"),dDa.forEach(t),lNt=r(Jco," \u2014 "),Bge=n(Jco,"A",{href:!0});var cDa=s(Bge);iNt=r(cDa,"FlaxBigBirdForMultipleChoice"),cDa.forEach(t),dNt=r(Jco," (BigBird model)"),Jco.forEach(t),cNt=i(Ro),ax=n(Ro,"LI",{});var Yco=s(ax);Pje=n(Yco,"STRONG",{});var fDa=s(Pje);fNt=r(fDa,"distilbert"),fDa.forEach(t),mNt=r(Yco," \u2014 "),Nge=n(Yco,"A",{href:!0});var mDa=s(Nge);gNt=r(mDa,"FlaxDistilBertForMultipleChoice"),mDa.forEach(t),hNt=r(Yco," (DistilBERT model)"),Yco.forEach(t),uNt=i(Ro),nx=n(Ro,"LI",{});var Zco=s(nx);Ije=n(Zco,"STRONG",{});var gDa=s(Ije);pNt=r(gDa,"electra"),gDa.forEach(t),_Nt=r(Zco," \u2014 "),Dge=n(Zco,"A",{href:!0});var hDa=s(Dge);vNt=r(hDa,"FlaxElectraForMultipleChoice"),hDa.forEach(t),bNt=r(Zco," (ELECTRA model)"),Zco.forEach(t),FNt=i(Ro),sx=n(Ro,"LI",{});var Kco=s(sx);Bje=n(Kco,"STRONG",{});var uDa=s(Bje);TNt=r(uDa,"roberta"),uDa.forEach(t),MNt=r(Kco," \u2014 "),qge=n(Kco,"A",{href:!0});var pDa=s(qge);ENt=r(pDa,"FlaxRobertaForMultipleChoice"),pDa.forEach(t),CNt=r(Kco," (RoBERTa model)"),Kco.forEach(t),wNt=i(Ro),lx=n(Ro,"LI",{});var efo=s(lx);Nje=n(efo,"STRONG",{});var _Da=s(Nje);ANt=r(_Da,"roformer"),_Da.forEach(t),LNt=r(efo," \u2014 "),jge=n(efo,"A",{href:!0});var vDa=s(jge);yNt=r(vDa,"FlaxRoFormerForMultipleChoice"),vDa.forEach(t),xNt=r(efo," (RoFormer model)"),efo.forEach(t),$Nt=i(Ro),ix=n(Ro,"LI",{});var ofo=s(ix);Dje=n(ofo,"STRONG",{});var bDa=s(Dje);kNt=r(bDa,"xlm-roberta"),bDa.forEach(t),SNt=r(ofo," \u2014 "),Gge=n(ofo,"A",{href:!0});var FDa=s(Gge);RNt=r(FDa,"FlaxXLMRobertaForMultipleChoice"),FDa.forEach(t),PNt=r(ofo," (XLM-RoBERTa model)"),ofo.forEach(t),Ro.forEach(t),INt=i(Td),T(dx.$$.fragment,Td),Td.forEach(t),Fd.forEach(t),bgo=i(f),Xm=n(f,"H2",{class:!0});var Xuo=s(Xm);cx=n(Xuo,"A",{id:!0,class:!0,href:!0});var TDa=s(cx);qje=n(TDa,"SPAN",{});var MDa=s(qje);T(qN.$$.fragment,MDa),MDa.forEach(t),TDa.forEach(t),BNt=i(Xuo),jje=n(Xuo,"SPAN",{});var EDa=s(jje);NNt=r(EDa,"FlaxAutoModelForNextSentencePrediction"),EDa.forEach(t),Xuo.forEach(t),Fgo=i(f),jr=n(f,"DIV",{class:!0});var Md=s(jr);T(jN.$$.fragment,Md),DNt=i(Md),zm=n(Md,"P",{});var tpe=s(zm);qNt=r(tpe,`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 `),Oge=n(tpe,"A",{href:!0});var CDa=s(Oge);jNt=r(CDa,"from_pretrained()"),CDa.forEach(t),GNt=r(tpe," class method or the "),Vge=n(tpe,"A",{href:!0});var wDa=s(Vge);ONt=r(wDa,"from_config()"),wDa.forEach(t),VNt=r(tpe,` class
method.`),tpe.forEach(t),XNt=i(Md),GN=n(Md,"P",{});var zuo=s(GN);zNt=r(zuo,"This class cannot be instantiated directly using "),Gje=n(zuo,"CODE",{});var ADa=s(Gje);QNt=r(ADa,"__init__()"),ADa.forEach(t),WNt=r(zuo," (throws an error)."),zuo.forEach(t),UNt=i(Md),Aa=n(Md,"DIV",{class:!0});var Kk=s(Aa);T(ON.$$.fragment,Kk),HNt=i(Kk),Oje=n(Kk,"P",{});var LDa=s(Oje);JNt=r(LDa,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),LDa.forEach(t),YNt=i(Kk),Qm=n(Kk,"P",{});var ape=s(Qm);ZNt=r(ape,`Note:
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model\u2019s configuration. Use `),Xge=n(ape,"A",{href:!0});var xDa=s(Xge);oDt=r(xDa,"from_pretrained()"),xDa.forEach(t),rDt=r(ape," to load the model weights."),ape.forEach(t),tDt=i(Kk),T(fx.$$.fragment,Kk),Kk.forEach(t),aDt=i(Md),pt=n(Md,"DIV",{class:!0});var Ed=s(pt);T(VN.$$.fragment,Ed),nDt=i(Ed),Xje=n(Ed,"P",{});var $Da=s(Xje);sDt=r($Da,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),$Da.forEach(t),lDt=i(Ed),gs=n(Ed,"P",{});var eS=s(gs);iDt=r(eS,"The model class to instantiate is selected based on the "),zje=n(eS,"CODE",{});var kDa=s(zje);dDt=r(kDa,"model_type"),kDa.forEach(t),cDt=r(eS,` property of the config object (either
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falling back to using pattern matching on `),Wje=n(eS,"CODE",{});var RDa=s(Wje);gDt=r(RDa,"pretrained_model_name_or_path"),RDa.forEach(t),hDt=r(eS,":"),eS.forEach(t),uDt=i(Ed),Uje=n(Ed,"UL",{});var PDa=s(Uje);mx=n(PDa,"LI",{});var rfo=s(mx);Hje=n(rfo,"STRONG",{});var IDa=s(Hje);pDt=r(IDa,"bert"),IDa.forEach(t),_Dt=r(rfo," \u2014 "),zge=n(rfo,"A",{href:!0});var BDa=s(zge);vDt=r(BDa,"FlaxBertForNextSentencePrediction"),BDa.forEach(t),bDt=r(rfo," (BERT model)"),rfo.forEach(t),PDa.forEach(t),FDt=i(Ed),T(gx.$$.fragment,Ed),Ed.forEach(t),Md.forEach(t),Tgo=i(f),Wm=n(f,"H2",{class:!0});var Quo=s(Wm);hx=n(Quo,"A",{id:!0,class:!0,href:!0});var NDa=s(hx);Jje=n(NDa,"SPAN",{});var DDa=s(Jje);T(XN.$$.fragment,DDa),DDa.forEach(t),NDa.forEach(t),TDt=i(Quo),Yje=n(Quo,"SPAN",{});var qDa=s(Yje);MDt=r(qDa,"FlaxAutoModelForImageClassification"),qDa.forEach(t),Quo.forEach(t),Mgo=i(f),Gr=n(f,"DIV",{class:!0});var Cd=s(Gr);T(zN.$$.fragment,Cd),EDt=i(Cd),Um=n(Cd,"P",{});var npe=s(Um);CDt=r(npe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
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method.`),npe.forEach(t),xDt=i(Cd),QN=n(Cd,"P",{});var Wuo=s(QN);$Dt=r(Wuo,"This class cannot be instantiated directly using "),Zje=n(Wuo,"CODE",{});var ODa=s(Zje);kDt=r(ODa,"__init__()"),ODa.forEach(t),SDt=r(Wuo," (throws an error)."),Wuo.forEach(t),RDt=i(Cd),La=n(Cd,"DIV",{class:!0});var oS=s(La);T(WN.$$.fragment,oS),PDt=i(oS),Kje=n(oS,"P",{});var VDa=s(Kje);IDt=r(VDa,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),VDa.forEach(t),BDt=i(oS),Hm=n(oS,"P",{});var spe=s(Hm);NDt=r(spe,`Note:
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