import{S as vEa,i as FEa,s as TEa,e as a,k as l,w as F,t as o,M as MEa,c as n,d as t,m as i,a as s,x as T,h as r,b as c,G as e,g as b,y as M,q as E,o as C,B as w,v as EEa,L as q}from"../../chunks/vendor-hf-doc-builder.js";import{T as q5t}from"../../chunks/Tip-hf-doc-builder.js";import{D as R}from"../../chunks/Docstring-hf-doc-builder.js";import{C as B}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as oe}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as N}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function CEa($){let g,v,p,m,u,d,h,$o,_d,Xm,Tt,bd,vd,n$,zm,Xe,He,Fd,cs,s$,fs,ms,l$,Td,gs,i$,Md,Qm,on;return{c(){g=a("p"),v=o("If your "),p=a("code"),m=o("NewModelConfig"),u=o(" is a subclass of "),d=a("code"),h=o("~transformer.PretrainedConfig"),$o=o(`, make sure its
`),_d=a("code"),Xm=o("model_type"),Tt=o(" attribute is set to the same key you use when registering the config (here "),bd=a("code"),vd=o('"new-model"'),n$=o(")."),zm=l(),Xe=a("p"),He=o("Likewise, if your "),Fd=a("code"),cs=o("NewModel"),s$=o(" is a subclass of "),fs=a("a"),ms=o("PreTrainedModel"),l$=o(`, make sure its
`),Td=a("code"),gs=o("config_class"),i$=o(` attribute is set to the same class you use when registering the model (here
`),Md=a("code"),Qm=o("NewModelConfig"),on=o(")."),this.h()},l(Je){g=n(Je,"P",{});var Ae=s(g);v=r(Ae,"If your "),p=n(Ae,"CODE",{});var TN=s(p);m=r(TN,"NewModelConfig"),TN.forEach(t),u=r(Ae," is a subclass of "),d=n(Ae,"CODE",{});var Ed=s(d);h=r(Ed,"~transformer.PretrainedConfig"),Ed.forEach(t),$o=r(Ae,`, make sure its
`),_d=n(Ae,"CODE",{});var MN=s(_d);Xm=r(MN,"model_type"),MN.forEach(t),Tt=r(Ae," attribute is set to the same key you use when registering the config (here "),bd=n(Ae,"CODE",{});var EN=s(bd);vd=r(EN,'"new-model"'),EN.forEach(t),n$=r(Ae,")."),Ae.forEach(t),zm=i(Je),Xe=n(Je,"P",{});var ko=s(Xe);He=r(ko,"Likewise, if your "),Fd=n(ko,"CODE",{});var rn=s(Fd);cs=r(rn,"NewModel"),rn.forEach(t),s$=r(ko," is a subclass of "),fs=n(ko,"A",{href:!0});var CN=s(fs);ms=r(CN,"PreTrainedModel"),CN.forEach(t),l$=r(ko,`, make sure its
`),Td=n(ko,"CODE",{});var Wm=s(Td);gs=r(Wm,"config_class"),Wm.forEach(t),i$=r(ko,` attribute is set to the same class you use when registering the model (here
`),Md=n(ko,"CODE",{});var wN=s(Md);Qm=r(wN,"NewModelConfig"),wN.forEach(t),on=r(ko,")."),ko.forEach(t),this.h()},h(){c(fs,"href","/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel")},m(Je,Ae){b(Je,g,Ae),e(g,v),e(g,p),e(p,m),e(g,u),e(g,d),e(d,h),e(g,$o),e(g,_d),e(_d,Xm),e(g,Tt),e(g,bd),e(bd,vd),e(g,n$),b(Je,zm,Ae),b(Je,Xe,Ae),e(Xe,He),e(Xe,Fd),e(Fd,cs),e(Xe,s$),e(Xe,fs),e(fs,ms),e(Xe,l$),e(Xe,Td),e(Td,gs),e(Xe,i$),e(Xe,Md),e(Md,Qm),e(Xe,on)},d(Je){Je&&t(g),Je&&t(zm),Je&&t(Xe)}}}function wEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function AEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function LEa($){let g,v,p,m,u;return{c(){g=a("p"),v=o("Passing "),p=a("code"),m=o("use_auth_token=True"),u=o(" is required when you want to use a private model.")},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Passing "),p=n(h,"CODE",{});var $o=s(p);m=r($o,"use_auth_token=True"),$o.forEach(t),u=r(h," is required when you want to use a private model."),h.forEach(t)},m(d,h){b(d,g,h),e(g,v),e(g,p),e(p,m),e(g,u)},d(d){d&&t(g)}}}function yEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function xEa($){let g,v,p,m,u;return{c(){g=a("p"),v=o("Passing "),p=a("code"),m=o("use_auth_token=True"),u=o(" is required when you want to use a private model.")},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Passing "),p=n(h,"CODE",{});var $o=s(p);m=r($o,"use_auth_token=True"),$o.forEach(t),u=r(h," is required when you want to use a private model."),h.forEach(t)},m(d,h){b(d,g,h),e(g,v),e(g,p),e(p,m),e(g,u)},d(d){d&&t(g)}}}function $Ea($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function kEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function SEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function REa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function PEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function BEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function IEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function NEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function qEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function DEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function jEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function GEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function OEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function VEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function XEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function zEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function QEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function WEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function UEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function HEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function JEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function YEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ZEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function KEa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function e4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function o4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function r4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function t4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function a4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function n4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function s4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function l4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function i4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function d4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function c4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function f4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function m4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function g4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function h4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function p4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function u4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function _4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function b4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function v4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function F4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function T4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function M4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function E4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function C4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function w4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function A4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function L4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function y4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function x4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function $4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function k4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function S4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function R4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function P4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function B4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function I4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function N4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function q4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function D4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function j4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function G4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function O4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function V4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function X4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function z4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function Q4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function W4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function U4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function H4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function J4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function Y4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function Z4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function K4a($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function eCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function oCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function rCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function tCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function aCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function nCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function sCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function lCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function iCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function dCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function cCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function fCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function mCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function gCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function hCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function pCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function uCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function _Ca($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function bCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function vCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function FCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function TCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function MCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ECa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function CCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function wCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ACa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function LCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function yCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function xCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function $Ca($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function kCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function SCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function RCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function PCa($){let g,v,p,m,u;return m=new B({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),u=!0},p:q,i(d){u||(E(m.$$.fragment,d),u=!0)},o(d){C(m.$$.fragment,d),u=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function BCa($){let 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d=new oe({}),on=new B({props:{code:'model = AutoModel.from_pretrained("bert-base-cased")',highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),d$=new oe({}),c$=new B({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)`}}),Hm=new q5t({props:{warning:!0,$$slots:{default:[CEa]},$$scope:{ctx:$}}}),f$=new oe({}),m$=new R({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/configuration_auto.py#L666"}}),p$=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/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/pr_19907/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/vr_19907/src/transformers/models/auto/configuration_auto.py#L689"}}),Cp=new N({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[wEa]},$$scope:{ctx:$}}}),u$=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/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/configuration_auto.py#L812"}}),_$=new oe({}),b$=new R({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/tokenization_auto.py#L430"}}),T$=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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/tokenization_auto.py#L444"}}),du=new N({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[AEa]},$$scope:{ctx:$}}}),M$=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/pr_19907/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/vr_19907/src/transformers/models/auto/tokenization_auto.py#L645"}}),E$=new oe({}),C$=new R({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/feature_extraction_auto.py#L204"}}),L$=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/pr_19907/en/internal/image_processing_utils#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/vr_19907/src/transformers/models/auto/feature_extraction_auto.py#L218"}}),r_=new q5t({props:{$$slots:{default:[LEa]},$$scope:{ctx:$}}}),t_=new N({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[yEa]},$$scope:{ctx:$}}}),y$=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/pr_19907/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/vr_19907/src/transformers/models/auto/feature_extraction_auto.py#L345"}}),x$=new oe({}),$$=new R({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/processing_auto.py#L96"}}),R$=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/vr_19907/src/transformers/models/auto/processing_auto.py#L110"}}),x_=new q5t({props:{$$slots:{default:[xEa]},$$scope:{ctx:$}}}),$_=new N({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[$Ea]},$$scope:{ctx:$}}}),P$=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/pr_19907/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/vr_19907/src/transformers/models/auto/processing_auto.py#L277"}}),B$=new oe({}),I$=new R({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L887"}}),q$=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),R_=new N({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[kEa]},$$scope:{ctx:$}}}),D$=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),o2=new N({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[SEa]},$$scope:{ctx:$}}}),j$=new oe({}),G$=new R({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L894"}}),V$=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),t2=new N({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[REa]},$$scope:{ctx:$}}}),X$=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),K2=new N({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[PEa]},$$scope:{ctx:$}}}),z$=new oe({}),Q$=new R({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L909"}}),U$=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/pr_19907/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/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),ob=new N({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[BEa]},$$scope:{ctx:$}}}),H$=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),zb=new N({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[IEa]},$$scope:{ctx:$}}}),J$=new oe({}),Y$=new R({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1052"}}),K$=new R({props:{name:"from_config",anchor:"transformers.AutoModelForDepthEstimation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),Wb=new N({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[NEa]},$$scope:{ctx:$}}}),ek=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),Yb=new N({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[qEa]},$$scope:{ctx:$}}}),rk=new oe({}),tk=new R({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L916"}}),nk=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),Kb=new N({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[DEa]},$$scope:{ctx:$}}}),sk=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),jv=new N({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[jEa]},$$scope:{ctx:$}}}),lk=new oe({}),ik=new R({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L923"}}),ck=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/pr_19907/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/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),Ov=new N({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[GEa]},$$scope:{ctx:$}}}),fk=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),c0=new N({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[OEa]},$$scope:{ctx:$}}}),mk=new oe({}),gk=new R({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L932"}}),pk=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),m0=new N({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[VEa]},$$scope:{ctx:$}}}),uk=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),uF=new N({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[XEa]},$$scope:{ctx:$}}}),_k=new oe({}),bk=new R({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L988"}}),Fk=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),bF=new N({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[zEa]},$$scope:{ctx:$}}}),Tk=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),ZF=new N({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[QEa]},$$scope:{ctx:$}}}),Mk=new oe({}),Ek=new R({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L995"}}),wk=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/pr_19907/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/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),e7=new N({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[WEa]},$$scope:{ctx:$}}}),Ak=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),d7=new N({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[UEa]},$$scope:{ctx:$}}}),Lk=new oe({}),yk=new R({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L981"}}),$k=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),f7=new N({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[HEa]},$$scope:{ctx:$}}}),kk=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),eT=new N({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[JEa]},$$scope:{ctx:$}}}),Sk=new oe({}),Rk=new R({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L941"}}),Bk=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),rT=new N({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[YEa]},$$scope:{ctx:$}}}),Ik=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),ZT=new N({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[ZEa]},$$scope:{ctx:$}}}),Nk=new oe({}),qk=new R({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L948"}}),jk=new R({props:{name:"from_config",anchor:"transformers.AutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),eM=new N({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[KEa]},$$scope:{ctx:$}}}),Gk=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),tM=new N({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[e4a]},$$scope:{ctx:$}}}),Ok=new oe({}),Vk=new R({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L970"}}),zk=new R({props:{name:"from_config",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),nM=new N({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[o4a]},$$scope:{ctx:$}}}),Qk=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),cM=new N({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[r4a]},$$scope:{ctx:$}}}),Wk=new oe({}),Uk=new R({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1004"}}),Jk=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/pr_19907/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/pr_19907/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/pr_19907/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/pr_19907/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),mM=new N({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[t4a]},$$scope:{ctx:$}}}),Yk=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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),xM=new N({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[a4a]},$$scope:{ctx:$}}}),Zk=new oe({}),Kk=new R({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1059"}}),oS=new R({props:{name:"from_config",anchor:"transformers.AutoModelForVideoClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),kM=new N({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[n4a]},$$scope:{ctx:$}}}),rS=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVideoClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),PM=new N({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[s4a]},$$scope:{ctx:$}}}),tS=new oe({}),aS=new R({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1066"}}),sS=new R({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),IM=new N({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[l4a]},$$scope:{ctx:$}}}),lS=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),DM=new N({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[i4a]},$$scope:{ctx:$}}}),iS=new oe({}),dS=new R({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L959"}}),fS=new R({props:{name:"from_config",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),GM=new N({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[d4a]},$$scope:{ctx:$}}}),mS=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),XM=new N({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[c4a]},$$scope:{ctx:$}}}),gS=new oe({}),hS=new R({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1073"}}),uS=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/pr_19907/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/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),QM=new N({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[f4a]},$$scope:{ctx:$}}}),_S=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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;
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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.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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),tE=new N({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[m4a]},$$scope:{ctx:$}}}),bS=new oe({}),vS=new R({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1096"}}),TS=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/pr_19907/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/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),nE=new N({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[g4a]},$$scope:{ctx:$}}}),MS=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),mE=new N({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[h4a]},$$scope:{ctx:$}}}),ES=new oe({}),CS=new R({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1080"}}),AS=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/pr_19907/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/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),hE=new N({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[p4a]},$$scope:{ctx:$}}}),LS=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),AE=new N({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[u4a]},$$scope:{ctx:$}}}),yS=new oe({}),xS=new R({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1087"}}),kS=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/pr_19907/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/pr_19907/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),yE=new N({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[_4a]},$$scope:{ctx:$}}}),SS=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),RE=new N({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[b4a]},$$scope:{ctx:$}}}),RS=new oe({}),PS=new R({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1105"}}),IS=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/pr_19907/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/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),BE=new N({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[v4a]},$$scope:{ctx:$}}}),NS=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),OE=new N({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[F4a]},$$scope:{ctx:$}}}),qS=new oe({}),DS=new R({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1112"}}),GS=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/pr_19907/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/pr_19907/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),XE=new N({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[T4a]},$$scope:{ctx:$}}}),OS=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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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<li><a href="/docs/transformers/pr_19907/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
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<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_19907/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/pr_19907/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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/pr_19907/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;
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.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
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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.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
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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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),c4=new N({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[A4a]},$$scope:{ctx:$}}}),KS=new oe({}),eR=new R({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1018"}}),rR=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/pr_19907/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/pr_19907/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),m4=new N({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[L4a]},$$scope:{ctx:$}}}),tR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),v4=new N({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[y4a]},$$scope:{ctx:$}}}),aR=new oe({}),nR=new R({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1027"}}),lR=new R({props:{name:"from_config",anchor:"transformers.AutoModelForInstanceSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),T4=new N({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[x4a]},$$scope:{ctx:$}}}),iR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),C4=new N({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[$4a]},$$scope:{ctx:$}}}),dR=new oe({}),cR=new R({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_auto.py#L1043"}}),mR=new R({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),A4=new N({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[k4a]},$$scope:{ctx:$}}}),gR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),x4=new N({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[S4a]},$$scope:{ctx:$}}}),hR=new oe({}),pR=new R({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L444"}}),_R=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),k4=new N({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[R4a]},$$scope:{ctx:$}}}),bR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),BC=new N({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[P4a]},$$scope:{ctx:$}}}),vR=new oe({}),FR=new R({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L451"}}),MR=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),NC=new N({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[B4a]},$$scope:{ctx:$}}}),ER=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),l3=new N({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[I4a]},$$scope:{ctx:$}}}),CR=new oe({}),wR=new R({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L466"}}),LR=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/pr_19907/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/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),d3=new N({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[N4a]},$$scope:{ctx:$}}}),yR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),C3=new N({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[q4a]},$$scope:{ctx:$}}}),xR=new oe({}),$R=new R({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L482"}}),SR=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/pr_19907/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/pr_19907/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/pr_19907/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),A3=new N({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[D4a]},$$scope:{ctx:$}}}),RR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),I3=new N({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[j4a]},$$scope:{ctx:$}}}),PR=new oe({}),BR=new R({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L491"}}),NR=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/pr_19907/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/pr_19907/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),q3=new N({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[G4a]},$$scope:{ctx:$}}}),qR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),O3=new N({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[O4a]},$$scope:{ctx:$}}}),DR=new oe({}),jR=new R({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L507"}}),OR=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),X3=new N({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[V4a]},$$scope:{ctx:$}}}),VR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),m5=new N({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[X4a]},$$scope:{ctx:$}}}),XR=new oe({}),zR=new R({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L514"}}),WR=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/pr_19907/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/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),h5=new N({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[z4a]},$$scope:{ctx:$}}}),UR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),w5=new N({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Q4a]},$$scope:{ctx:$}}}),HR=new oe({}),JR=new R({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L523"}}),ZR=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),L5=new N({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[W4a]},$$scope:{ctx:$}}}),KR=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),rw=new N({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[U4a]},$$scope:{ctx:$}}}),eP=new oe({}),oP=new R({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L570"}}),tP=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),aw=new N({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[H4a]},$$scope:{ctx:$}}}),aP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),Mw=new N({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[J4a]},$$scope:{ctx:$}}}),nP=new oe({}),sP=new R({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L577"}}),iP=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),Cw=new N({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Y4a]},$$scope:{ctx:$}}}),dP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),Lw=new N({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Z4a]},$$scope:{ctx:$}}}),fP=new oe({}),mP=new R({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L550"}}),hP=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),xw=new N({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[K4a]},$$scope:{ctx:$}}}),pP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),kw=new N({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[eCa]},$$scope:{ctx:$}}}),uP=new oe({}),_P=new R({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L539"}}),vP=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),Rw=new N({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[oCa]},$$scope:{ctx:$}}}),FP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),Bw=new N({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[rCa]},$$scope:{ctx:$}}}),TP=new oe({}),MP=new R({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L561"}}),CP=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),Nw=new N({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[tCa]},$$scope:{ctx:$}}}),wP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),sA=new N({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[aCa]},$$scope:{ctx:$}}}),AP=new oe({}),LP=new R({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L532"}}),xP=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),iA=new N({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[nCa]},$$scope:{ctx:$}}}),$P=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),$A=new N({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[sCa]},$$scope:{ctx:$}}}),kP=new oe({}),SP=new R({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L500"}}),PP=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),SA=new N({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[lCa]},$$scope:{ctx:$}}}),BP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),PA=new N({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[iCa]},$$scope:{ctx:$}}}),IP=new oe({}),NP=new R({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_tf_auto.py#L586"}}),DP=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),IA=new N({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[dCa]},$$scope:{ctx:$}}}),jP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),DA=new N({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[cCa]},$$scope:{ctx:$}}}),OP=new oe({}),VP=new R({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L246"}}),zP=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),GA=new N({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[fCa]},$$scope:{ctx:$}}}),QP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),p6=new N({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[mCa]},$$scope:{ctx:$}}}),WP=new oe({}),UP=new R({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L260"}}),JP=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/pr_19907/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/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),_6=new N({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[gCa]},$$scope:{ctx:$}}}),YP=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),y6=new N({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[hCa]},$$scope:{ctx:$}}}),ZP=new oe({}),KP=new R({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L253"}}),oB=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),$6=new N({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[pCa]},$$scope:{ctx:$}}}),rB=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),X6=new N({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[uCa]},$$scope:{ctx:$}}}),tB=new oe({}),aB=new R({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L267"}}),sB=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),Q6=new N({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[_Ca]},$$scope:{ctx:$}}}),lB=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),t8=new N({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[bCa]},$$scope:{ctx:$}}}),iB=new oe({}),dB=new R({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L274"}}),fB=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/pr_19907/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/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),n8=new N({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[vCa]},$$scope:{ctx:$}}}),mB=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),u8=new N({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[FCa]},$$scope:{ctx:$}}}),gB=new oe({}),hB=new R({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L283"}}),uB=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),b8=new N({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[TCa]},$$scope:{ctx:$}}}),_B=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/pr_19907/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/pr_19907/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/pr_19907/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
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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.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
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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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),x8=new N({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[MCa]},$$scope:{ctx:$}}}),bB=new oe({}),vB=new R({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L292"}}),TB=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),k8=new N({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[ECa]},$$scope:{ctx:$}}}),MB=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/pr_19907/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/pr_19907/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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),O8=new N({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[CCa]},$$scope:{ctx:$}}}),EB=new oe({}),CB=new R({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L299"}}),AB=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/pr_19907/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/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),X8=new N({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[wCa]},$$scope:{ctx:$}}}),LB=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/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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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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<ul>
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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
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<li><a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),oL=new N({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[LCa]},$$scope:{ctx:$}}}),SB=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>
<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
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<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
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<li>The model was saved using <a href="/docs/transformers/pr_19907/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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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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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/pr_19907/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/vr_19907/src/transformers/models/auto/auto_factory.py#L417"}}),cL=new N({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[yCa]},$$scope:{ctx:$}}}),RB=new oe({}),PB=new R({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/modeling_flax_auto.py#L315"}}),IB=new R({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),mL=new N({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[xCa]},$$scope:{ctx:$}}}),NB=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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_19907/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/pr_19907/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.FlaxAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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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.FlaxAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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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/pr_19907/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/pr_19907/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_19907/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/auto/auto_factory.py#L389"}}),uL=new N({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[kCa]},$$scope:{ctx:$}}}),OB=new R({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
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method.`),oko=l(),N$=a("p"),rko=o("This class cannot be instantiated directly using "),A1e=a("code"),tko=o("__init__()"),ako=o(" (throws an error)."),nko=l(),Mt=a("div"),F(q$.$$.fragment),sko=l(),L1e=a("p"),lko=o("Instantiates one of the base model classes of the library from a configuration."),iko=l(),Rd=a("p"),dko=o(`Note:
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using "),nve=a("code"),IUo=o("model.eval()"),NUo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),sve=a("code"),qUo=o("model.train()"),DUo=l(),F(K2.$$.fragment),Gto=l(),Nd=a("h2"),eb=a("a"),lve=a("span"),F(z$.$$.fragment),jUo=l(),ive=a("span"),GUo=o("AutoModelForCausalLM"),Oto=l(),qo=a("div"),F(Q$.$$.fragment),OUo=l(),qd=a("p"),VUo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),WW=a("a"),XUo=o("from_pretrained()"),zUo=o(" class method or the "),UW=a("a"),QUo=o("from_config()"),WUo=o(` class
method.`),UUo=l(),W$=a("p"),HUo=o("This class cannot be instantiated directly using "),dve=a("code"),JUo=o("__init__()"),YUo=o(" (throws an error)."),ZUo=l(),Ct=a("div"),F(U$.$$.fragment),KUo=l(),cve=a("p"),eHo=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),oHo=l(),Dd=a("p"),rHo=o(`Note:
Loading a model from its configuration file does `),fve=a("strong"),tHo=o("not"),aHo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),HW=a("a"),nHo=o("from_pretrained()"),sHo=o(" to load the model weights."),lHo=l(),F(ob.$$.fragment),iHo=l(),oo=a("div"),F(H$.$$.fragment),dHo=l(),mve=a("p"),cHo=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),fHo=l(),ln=a("p"),mHo=o("The model class to instantiate is selected based on the "),gve=a("code"),gHo=o("model_type"),hHo=o(` property of the config object (either
passed as an argument or loaded from `),hve=a("code"),pHo=o("pretrained_model_name_or_path"),uHo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pve=a("code"),_Ho=o("pretrained_model_name_or_path"),bHo=o(":"),vHo=l(),W=a("ul"),rb=a("li"),uve=a("strong"),FHo=o("bart"),THo=o(" \u2014 "),JW=a("a"),MHo=o("BartForCausalLM"),EHo=o(" (BART model)"),CHo=l(),tb=a("li"),_ve=a("strong"),wHo=o("bert"),AHo=o(" \u2014 "),YW=a("a"),LHo=o("BertLMHeadModel"),yHo=o(" (BERT model)"),xHo=l(),ab=a("li"),bve=a("strong"),$Ho=o("bert-generation"),kHo=o(" \u2014 "),ZW=a("a"),SHo=o("BertGenerationDecoder"),RHo=o(" (Bert Generation model)"),PHo=l(),nb=a("li"),vve=a("strong"),BHo=o("big_bird"),IHo=o(" \u2014 "),KW=a("a"),NHo=o("BigBirdForCausalLM"),qHo=o(" (BigBird model)"),DHo=l(),sb=a("li"),Fve=a("strong"),jHo=o("bigbird_pegasus"),GHo=o(" \u2014 "),eU=a("a"),OHo=o("BigBirdPegasusForCausalLM"),VHo=o(" (BigBird-Pegasus model)"),XHo=l(),lb=a("li"),Tve=a("strong"),zHo=o("blenderbot"),QHo=o(" \u2014 "),oU=a("a"),WHo=o("BlenderbotForCausalLM"),UHo=o(" (Blenderbot 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model)"),fYo=l(),Mb=a("li"),Ive=a("strong"),mYo=o("mbart"),gYo=o(" \u2014 "),uU=a("a"),hYo=o("MBartForCausalLM"),pYo=o(" (mBART model)"),uYo=l(),Eb=a("li"),Nve=a("strong"),_Yo=o("megatron-bert"),bYo=o(" \u2014 "),_U=a("a"),vYo=o("MegatronBertForCausalLM"),FYo=o(" (Megatron-BERT model)"),TYo=l(),Cb=a("li"),qve=a("strong"),MYo=o("mvp"),EYo=o(" \u2014 "),bU=a("a"),CYo=o("MvpForCausalLM"),wYo=o(" (MVP model)"),AYo=l(),wb=a("li"),Dve=a("strong"),LYo=o("openai-gpt"),yYo=o(" \u2014 "),vU=a("a"),xYo=o("OpenAIGPTLMHeadModel"),$Yo=o(" (OpenAI GPT model)"),kYo=l(),Ab=a("li"),jve=a("strong"),SYo=o("opt"),RYo=o(" \u2014 "),FU=a("a"),PYo=o("OPTForCausalLM"),BYo=o(" (OPT model)"),IYo=l(),Lb=a("li"),Gve=a("strong"),NYo=o("pegasus"),qYo=o(" \u2014 "),TU=a("a"),DYo=o("PegasusForCausalLM"),jYo=o(" (Pegasus model)"),GYo=l(),yb=a("li"),Ove=a("strong"),OYo=o("plbart"),VYo=o(" \u2014 "),MU=a("a"),XYo=o("PLBartForCausalLM"),zYo=o(" (PLBart 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deactivated). To train the model, you should first set it back in training mode with `),n0e=a("code"),_Ko=o("model.train()"),bKo=l(),F(zb.$$.fragment),Vto=l(),jd=a("h2"),Qb=a("a"),s0e=a("span"),F(J$.$$.fragment),vKo=l(),l0e=a("span"),FKo=o("AutoModelForDepthEstimation"),Xto=l(),Do=a("div"),F(Y$.$$.fragment),TKo=l(),Gd=a("p"),MKo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),qU=a("a"),EKo=o("from_pretrained()"),CKo=o(" class method or the "),DU=a("a"),wKo=o("from_config()"),AKo=o(` class
method.`),LKo=l(),Z$=a("p"),yKo=o("This class cannot be instantiated directly using "),i0e=a("code"),xKo=o("__init__()"),$Ko=o(" (throws an error)."),kKo=l(),wt=a("div"),F(K$.$$.fragment),SKo=l(),d0e=a("p"),RKo=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),PKo=l(),Od=a("p"),BKo=o(`Note:
Loading a model from its configuration file does `),c0e=a("strong"),IKo=o("not"),NKo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),jU=a("a"),qKo=o("from_pretrained()"),DKo=o(" to load the model weights."),jKo=l(),F(Wb.$$.fragment),GKo=l(),ro=a("div"),F(ek.$$.fragment),OKo=l(),f0e=a("p"),VKo=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),XKo=l(),dn=a("p"),zKo=o("The model class to instantiate is selected based on the "),m0e=a("code"),QKo=o("model_type"),WKo=o(` property of the config object (either
passed as an argument or loaded from `),g0e=a("code"),UKo=o("pretrained_model_name_or_path"),HKo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),h0e=a("code"),JKo=o("pretrained_model_name_or_path"),YKo=o(":"),ZKo=l(),ok=a("ul"),Ub=a("li"),p0e=a("strong"),KKo=o("dpt"),eer=o(" \u2014 "),GU=a("a"),oer=o("DPTForDepthEstimation"),rer=o(" (DPT model)"),ter=l(),Hb=a("li"),u0e=a("strong"),aer=o("glpn"),ner=o(" \u2014 "),OU=a("a"),ser=o("GLPNForDepthEstimation"),ler=o(" (GLPN model)"),ier=l(),Jb=a("p"),der=o("The model is set in evaluation mode by default using "),_0e=a("code"),cer=o("model.eval()"),fer=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),b0e=a("code"),mer=o("model.train()"),ger=l(),F(Yb.$$.fragment),zto=l(),Vd=a("h2"),Zb=a("a"),v0e=a("span"),F(rk.$$.fragment),her=l(),F0e=a("span"),per=o("AutoModelForMaskedLM"),Qto=l(),jo=a("div"),F(tk.$$.fragment),uer=l(),Xd=a("p"),_er=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),VU=a("a"),ber=o("from_pretrained()"),ver=o(" class method or the "),XU=a("a"),Fer=o("from_config()"),Ter=o(` class
method.`),Mer=l(),ak=a("p"),Eer=o("This class cannot be instantiated directly using "),T0e=a("code"),Cer=o("__init__()"),wer=o(" (throws an error)."),Aer=l(),At=a("div"),F(nk.$$.fragment),Ler=l(),M0e=a("p"),yer=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),xer=l(),zd=a("p"),$er=o(`Note:
Loading a model from its configuration file does `),E0e=a("strong"),ker=o("not"),Ser=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),zU=a("a"),Rer=o("from_pretrained()"),Per=o(" to load the model weights."),Ber=l(),F(Kb.$$.fragment),Ier=l(),to=a("div"),F(sk.$$.fragment),Ner=l(),C0e=a("p"),qer=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Der=l(),cn=a("p"),jer=o("The model class to instantiate is selected based on the "),w0e=a("code"),Ger=o("model_type"),Oer=o(` property of the config object (either
passed as an argument or loaded from `),A0e=a("code"),Ver=o("pretrained_model_name_or_path"),Xer=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),L0e=a("code"),zer=o("pretrained_model_name_or_path"),Qer=o(":"),Wer=l(),Y=a("ul"),ev=a("li"),y0e=a("strong"),Uer=o("albert"),Her=o(" \u2014 "),QU=a("a"),Jer=o("AlbertForMaskedLM"),Yer=o(" (ALBERT model)"),Zer=l(),ov=a("li"),x0e=a("strong"),Ker=o("bart"),eor=o(" \u2014 "),WU=a("a"),oor=o("BartForConditionalGeneration"),ror=o(" (BART model)"),tor=l(),rv=a("li"),$0e=a("strong"),aor=o("bert"),nor=o(" \u2014 "),UU=a("a"),sor=o("BertForMaskedLM"),lor=o(" (BERT model)"),ior=l(),tv=a("li"),k0e=a("strong"),dor=o("big_bird"),cor=o(" \u2014 "),HU=a("a"),mor=o("BigBirdForMaskedLM"),gor=o(" (BigBird model)"),hor=l(),av=a("li"),S0e=a("strong"),por=o("camembert"),uor=o(" \u2014 "),JU=a("a"),_or=o("CamembertForMaskedLM"),bor=o(" (CamemBERT model)"),vor=l(),nv=a("li"),R0e=a("strong"),For=o("convbert"),Tor=o(" \u2014 "),YU=a("a"),Mor=o("ConvBertForMaskedLM"),Eor=o(" (ConvBERT model)"),Cor=l(),sv=a("li"),P0e=a("strong"),wor=o("data2vec-text"),Aor=o(" \u2014 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with the `),$H=a("a"),qar=o("from_pretrained()"),Dar=o(" class method or the "),kH=a("a"),jar=o("from_config()"),Gar=o(` class
method.`),Oar=l(),dk=a("p"),Var=o("This class cannot be instantiated directly using "),bFe=a("code"),Xar=o("__init__()"),zar=o(" (throws an error)."),Qar=l(),Lt=a("div"),F(ck.$$.fragment),War=l(),vFe=a("p"),Uar=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Har=l(),Ud=a("p"),Jar=o(`Note:
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model\u2019s configuration. Use `),SH=a("a"),Kar=o("from_pretrained()"),enr=o(" to load the model weights."),onr=l(),F(Ov.$$.fragment),rnr=l(),ao=a("div"),F(fk.$$.fragment),tnr=l(),TFe=a("p"),anr=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),nnr=l(),fn=a("p"),snr=o("The model class to instantiate is selected based on the "),MFe=a("code"),lnr=o("model_type"),inr=o(` property of the config object (either
passed as an argument or loaded from `),EFe=a("code"),dnr=o("pretrained_model_name_or_path"),cnr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CFe=a("code"),fnr=o("pretrained_model_name_or_path"),mnr=o(":"),gnr=l(),he=a("ul"),Vv=a("li"),wFe=a("strong"),hnr=o("bart"),pnr=o(" \u2014 "),RH=a("a"),unr=o("BartForConditionalGeneration"),_nr=o(" (BART model)"),bnr=l(),Xv=a("li"),AFe=a("strong"),vnr=o("bigbird_pegasus"),Fnr=o(" \u2014 "),PH=a("a"),Tnr=o("BigBirdPegasusForConditionalGeneration"),Mnr=o(" (BigBird-Pegasus model)"),Enr=l(),zv=a("li"),LFe=a("strong"),Cnr=o("blenderbot"),wnr=o(" \u2014 "),BH=a("a"),Anr=o("BlenderbotForConditionalGeneration"),Lnr=o(" (Blenderbot model)"),ynr=l(),Qv=a("li"),yFe=a("strong"),xnr=o("blenderbot-small"),$nr=o(" \u2014 "),IH=a("a"),knr=o("BlenderbotSmallForConditionalGeneration"),Snr=o(" (BlenderbotSmall model)"),Rnr=l(),Wv=a("li"),xFe=a("strong"),Pnr=o("encoder-decoder"),Bnr=o(" \u2014 "),NH=a("a"),Inr=o("EncoderDecoderModel"),Nnr=o(" (Encoder decoder model)"),qnr=l(),Uv=a("li"),$Fe=a("strong"),Dnr=o("fsmt"),jnr=o(" \u2014 "),qH=a("a"),Gnr=o("FSMTForConditionalGeneration"),Onr=o(" (FairSeq Machine-Translation model)"),Vnr=l(),Hv=a("li"),kFe=a("strong"),Xnr=o("led"),znr=o(" \u2014 "),DH=a("a"),Qnr=o("LEDForConditionalGeneration"),Wnr=o(" (LED model)"),Unr=l(),Jv=a("li"),SFe=a("strong"),Hnr=o("longt5"),Jnr=o(" \u2014 "),jH=a("a"),Ynr=o("LongT5ForConditionalGeneration"),Znr=o(" (LongT5 model)"),Knr=l(),Yv=a("li"),RFe=a("strong"),esr=o("m2m_100"),osr=o(" \u2014 "),GH=a("a"),rsr=o("M2M100ForConditionalGeneration"),tsr=o(" (M2M100 model)"),asr=l(),Zv=a("li"),PFe=a("strong"),nsr=o("marian"),ssr=o(" \u2014 "),OH=a("a"),lsr=o("MarianMTModel"),isr=o(" (Marian model)"),dsr=l(),Kv=a("li"),BFe=a("strong"),csr=o("mbart"),fsr=o(" \u2014 "),VH=a("a"),msr=o("MBartForConditionalGeneration"),gsr=o(" (mBART model)"),hsr=l(),e0=a("li"),IFe=a("strong"),psr=o("mt5"),usr=o(" \u2014 "),XH=a("a"),_sr=o("MT5ForConditionalGeneration"),bsr=o(" (MT5 model)"),vsr=l(),o0=a("li"),NFe=a("strong"),Fsr=o("mvp"),Tsr=o(" \u2014 "),zH=a("a"),Msr=o("MvpForConditionalGeneration"),Esr=o(" (MVP model)"),Csr=l(),r0=a("li"),qFe=a("strong"),wsr=o("nllb"),Asr=o(" \u2014 "),QH=a("a"),Lsr=o("M2M100ForConditionalGeneration"),ysr=o(" (NLLB model)"),xsr=l(),t0=a("li"),DFe=a("strong"),$sr=o("pegasus"),ksr=o(" \u2014 "),WH=a("a"),Ssr=o("PegasusForConditionalGeneration"),Rsr=o(" (Pegasus model)"),Psr=l(),a0=a("li"),jFe=a("strong"),Bsr=o("pegasus_x"),Isr=o(" \u2014 "),UH=a("a"),Nsr=o("PegasusXForConditionalGeneration"),qsr=o(" (PEGASUS-X model)"),Dsr=l(),n0=a("li"),GFe=a("strong"),jsr=o("plbart"),Gsr=o(" \u2014 "),HH=a("a"),Osr=o("PLBartForConditionalGeneration"),Vsr=o(" (PLBart model)"),Xsr=l(),s0=a("li"),OFe=a("strong"),zsr=o("prophetnet"),Qsr=o(" \u2014 "),JH=a("a"),Wsr=o("ProphetNetForConditionalGeneration"),Usr=o(" (ProphetNet model)"),Hsr=l(),l0=a("li"),VFe=a("strong"),Jsr=o("t5"),Ysr=o(" \u2014 "),YH=a("a"),Zsr=o("T5ForConditionalGeneration"),Ksr=o(" (T5 model)"),elr=l(),i0=a("li"),XFe=a("strong"),olr=o("xlm-prophetnet"),rlr=o(" \u2014 "),ZH=a("a"),tlr=o("XLMProphetNetForConditionalGeneration"),alr=o(" (XLM-ProphetNet model)"),nlr=l(),d0=a("p"),slr=o("The model is set in evaluation mode by default using "),zFe=a("code"),llr=o("model.eval()"),ilr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),QFe=a("code"),dlr=o("model.train()"),clr=l(),F(c0.$$.fragment),Hto=l(),Hd=a("h2"),f0=a("a"),WFe=a("span"),F(mk.$$.fragment),flr=l(),UFe=a("span"),mlr=o("AutoModelForSequenceClassification"),Jto=l(),Oo=a("div"),F(gk.$$.fragment),glr=l(),Jd=a("p"),hlr=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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falling back to using pattern matching on `),o7e=a("code"),Olr=o("pretrained_model_name_or_path"),Vlr=o(":"),Xlr=l(),D=a("ul"),g0=a("li"),r7e=a("strong"),zlr=o("albert"),Qlr=o(" \u2014 "),rJ=a("a"),Wlr=o("AlbertForSequenceClassification"),Ulr=o(" (ALBERT model)"),Hlr=l(),h0=a("li"),t7e=a("strong"),Jlr=o("bart"),Ylr=o(" \u2014 "),tJ=a("a"),Zlr=o("BartForSequenceClassification"),Klr=o(" (BART model)"),eir=l(),p0=a("li"),a7e=a("strong"),oir=o("bert"),rir=o(" \u2014 "),aJ=a("a"),tir=o("BertForSequenceClassification"),air=o(" (BERT model)"),nir=l(),u0=a("li"),n7e=a("strong"),sir=o("big_bird"),lir=o(" \u2014 "),nJ=a("a"),iir=o("BigBirdForSequenceClassification"),dir=o(" (BigBird model)"),cir=l(),_0=a("li"),s7e=a("strong"),fir=o("bigbird_pegasus"),mir=o(" \u2014 "),sJ=a("a"),gir=o("BigBirdPegasusForSequenceClassification"),hir=o(" (BigBird-Pegasus model)"),pir=l(),b0=a("li"),l7e=a("strong"),uir=o("bloom"),_ir=o(" \u2014 "),lJ=a("a"),bir=o("BloomForSequenceClassification"),vir=o(" (BLOOM 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deactivated). To train the model, you should first set it back in training mode with `),nTe=a("code"),agr=o("model.train()"),ngr=l(),F(uF.$$.fragment),Yto=l(),Zd=a("h2"),_F=a("a"),sTe=a("span"),F(_k.$$.fragment),sgr=l(),lTe=a("span"),lgr=o("AutoModelForMultipleChoice"),Zto=l(),Vo=a("div"),F(bk.$$.fragment),igr=l(),Kd=a("p"),dgr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
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method.`),hgr=l(),vk=a("p"),pgr=o("This class cannot be instantiated directly using "),iTe=a("code"),ugr=o("__init__()"),_gr=o(" (throws an error)."),bgr=l(),xt=a("div"),F(Fk.$$.fragment),vgr=l(),dTe=a("p"),Fgr=o("Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Tgr=l(),ec=a("p"),Mgr=o(`Note:
Loading a model from its configuration file does `),cTe=a("strong"),Egr=o("not"),Cgr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),sY=a("a"),wgr=o("from_pretrained()"),Agr=o(" to load the model weights."),Lgr=l(),F(bF.$$.fragment),ygr=l(),so=a("div"),F(Tk.$$.fragment),xgr=l(),fTe=a("p"),$gr=o("Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),kgr=l(),gn=a("p"),Sgr=o("The model class to instantiate is selected based on the "),mTe=a("code"),Rgr=o("model_type"),Pgr=o(` property of the config object (either
passed as an argument or loaded from `),gTe=a("code"),Bgr=o("pretrained_model_name_or_path"),Igr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hTe=a("code"),Ngr=o("pretrained_model_name_or_path"),qgr=o(":"),Dgr=l(),K=a("ul"),vF=a("li"),pTe=a("strong"),jgr=o("albert"),Ggr=o(" \u2014 "),lY=a("a"),Ogr=o("AlbertForMultipleChoice"),Vgr=o(" (ALBERT model)"),Xgr=l(),FF=a("li"),uTe=a("strong"),zgr=o("bert"),Qgr=o(" \u2014 "),iY=a("a"),Wgr=o("BertForMultipleChoice"),Ugr=o(" (BERT model)"),Hgr=l(),TF=a("li"),_Te=a("strong"),Jgr=o("big_bird"),Ygr=o(" \u2014 "),dY=a("a"),Zgr=o("BigBirdForMultipleChoice"),Kgr=o(" (BigBird model)"),ehr=l(),MF=a("li"),bTe=a("strong"),ohr=o("camembert"),rhr=o(" \u2014 "),cY=a("a"),thr=o("CamembertForMultipleChoice"),ahr=o(" (CamemBERT model)"),nhr=l(),EF=a("li"),vTe=a("strong"),shr=o("canine"),lhr=o(" \u2014 "),fY=a("a"),ihr=o("CanineForMultipleChoice"),dhr=o(" (CANINE model)"),chr=l(),CF=a("li"),FTe=a("strong"),fhr=o("convbert"),mhr=o(" \u2014 "),mY=a("a"),ghr=o("ConvBertForMultipleChoice"),hhr=o(" (ConvBERT model)"),phr=l(),wF=a("li"),TTe=a("strong"),uhr=o("data2vec-text"),_hr=o(" \u2014 "),gY=a("a"),bhr=o("Data2VecTextForMultipleChoice"),vhr=o(" (Data2VecText model)"),Fhr=l(),AF=a("li"),MTe=a("strong"),Thr=o("deberta-v2"),Mhr=o(" \u2014 "),hY=a("a"),Ehr=o("DebertaV2ForMultipleChoice"),Chr=o(" (DeBERTa-v2 model)"),whr=l(),LF=a("li"),ETe=a("strong"),Ahr=o("distilbert"),Lhr=o(" \u2014 "),pY=a("a"),yhr=o("DistilBertForMultipleChoice"),xhr=o(" (DistilBERT model)"),$hr=l(),yF=a("li"),CTe=a("strong"),khr=o("electra"),Shr=o(" \u2014 "),uY=a("a"),Rhr=o("ElectraForMultipleChoice"),Phr=o(" (ELECTRA model)"),Bhr=l(),xF=a("li"),wTe=a("strong"),Ihr=o("ernie"),Nhr=o(" \u2014 "),_Y=a("a"),qhr=o("ErnieForMultipleChoice"),Dhr=o(" (ERNIE model)"),jhr=l(),$F=a("li"),ATe=a("strong"),Ghr=o("flaubert"),Ohr=o(" \u2014 "),bY=a("a"),Vhr=o("FlaubertForMultipleChoice"),Xhr=o(" (FlauBERT model)"),zhr=l(),kF=a("li"),LTe=a("strong"),Qhr=o("fnet"),Whr=o(" \u2014 "),vY=a("a"),Uhr=o("FNetForMultipleChoice"),Hhr=o(" (FNet model)"),Jhr=l(),SF=a("li"),yTe=a("strong"),Yhr=o("funnel"),Zhr=o(" \u2014 "),FY=a("a"),Khr=o("FunnelForMultipleChoice"),epr=o(" (Funnel Transformer model)"),opr=l(),RF=a("li"),xTe=a("strong"),rpr=o("ibert"),tpr=o(" \u2014 "),TY=a("a"),apr=o("IBertForMultipleChoice"),npr=o(" (I-BERT model)"),spr=l(),PF=a("li"),$Te=a("strong"),lpr=o("longformer"),ipr=o(" \u2014 "),MY=a("a"),dpr=o("LongformerForMultipleChoice"),cpr=o(" (Longformer model)"),fpr=l(),BF=a("li"),kTe=a("strong"),mpr=o("luke"),gpr=o(" \u2014 "),EY=a("a"),hpr=o("LukeForMultipleChoice"),ppr=o(" (LUKE model)"),upr=l(),IF=a("li"),STe=a("strong"),_pr=o("megatron-bert"),bpr=o(" \u2014 "),CY=a("a"),vpr=o("MegatronBertForMultipleChoice"),Fpr=o(" (Megatron-BERT model)"),Tpr=l(),NF=a("li"),RTe=a("strong"),Mpr=o("mobilebert"),Epr=o(" \u2014 "),wY=a("a"),Cpr=o("MobileBertForMultipleChoice"),wpr=o(" (MobileBERT model)"),Apr=l(),qF=a("li"),PTe=a("strong"),Lpr=o("mpnet"),ypr=o(" \u2014 "),AY=a("a"),xpr=o("MPNetForMultipleChoice"),$pr=o(" (MPNet model)"),kpr=l(),DF=a("li"),BTe=a("strong"),Spr=o("nezha"),Rpr=o(" \u2014 "),LY=a("a"),Ppr=o("NezhaForMultipleChoice"),Bpr=o(" (Nezha model)"),Ipr=l(),jF=a("li"),ITe=a("strong"),Npr=o("nystromformer"),qpr=o(" \u2014 "),yY=a("a"),Dpr=o("NystromformerForMultipleChoice"),jpr=o(" (Nystr\xF6mformer model)"),Gpr=l(),GF=a("li"),NTe=a("strong"),Opr=o("qdqbert"),Vpr=o(" \u2014 "),xY=a("a"),Xpr=o("QDQBertForMultipleChoice"),zpr=o(" (QDQBert model)"),Qpr=l(),OF=a("li"),qTe=a("strong"),Wpr=o("rembert"),Upr=o(" \u2014 "),$Y=a("a"),Hpr=o("RemBertForMultipleChoice"),Jpr=o(" (RemBERT model)"),Ypr=l(),VF=a("li"),DTe=a("strong"),Zpr=o("roberta"),Kpr=o(" \u2014 "),kY=a("a"),eur=o("RobertaForMultipleChoice"),our=o(" (RoBERTa model)"),rur=l(),XF=a("li"),jTe=a("strong"),tur=o("roformer"),aur=o(" \u2014 "),SY=a("a"),nur=o("RoFormerForMultipleChoice"),sur=o(" (RoFormer model)"),lur=l(),zF=a("li"),GTe=a("strong"),iur=o("squeezebert"),dur=o(" \u2014 "),RY=a("a"),cur=o("SqueezeBertForMultipleChoice"),fur=o(" (SqueezeBERT model)"),mur=l(),QF=a("li"),OTe=a("strong"),gur=o("xlm"),hur=o(" \u2014 "),PY=a("a"),pur=o("XLMForMultipleChoice"),uur=o(" (XLM model)"),_ur=l(),WF=a("li"),VTe=a("strong"),bur=o("xlm-roberta"),vur=o(" \u2014 "),BY=a("a"),Fur=o("XLMRobertaForMultipleChoice"),Tur=o(" (XLM-RoBERTa model)"),Mur=l(),UF=a("li"),XTe=a("strong"),Eur=o("xlm-roberta-xl"),Cur=o(" \u2014 "),IY=a("a"),wur=o("XLMRobertaXLForMultipleChoice"),Aur=o(" (XLM-RoBERTa-XL model)"),Lur=l(),HF=a("li"),zTe=a("strong"),yur=o("xlnet"),xur=o(" \u2014 "),NY=a("a"),$ur=o("XLNetForMultipleChoice"),kur=o(" (XLNet model)"),Sur=l(),JF=a("li"),QTe=a("strong"),Rur=o("yoso"),Pur=o(" \u2014 "),qY=a("a"),Bur=o("YosoForMultipleChoice"),Iur=o(" (YOSO model)"),Nur=l(),YF=a("p"),qur=o("The model is set in evaluation mode by default using "),WTe=a("code"),Dur=o("model.eval()"),jur=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),UTe=a("code"),Gur=o("model.train()"),Our=l(),F(ZF.$$.fragment),Kto=l(),oc=a("h2"),KF=a("a"),HTe=a("span"),F(Mk.$$.fragment),Vur=l(),JTe=a("span"),Xur=o("AutoModelForNextSentencePrediction"),eao=l(),Xo=a("div"),F(Ek.$$.fragment),zur=l(),rc=a("p"),Qur=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),DY=a("a"),Wur=o("from_pretrained()"),Uur=o(" class method or the "),jY=a("a"),Hur=o("from_config()"),Jur=o(` class
method.`),Yur=l(),Ck=a("p"),Zur=o("This class cannot be instantiated directly using "),YTe=a("code"),Kur=o("__init__()"),e_r=o(" (throws an error)."),o_r=l(),$t=a("div"),F(wk.$$.fragment),r_r=l(),ZTe=a("p"),t_r=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),a_r=l(),tc=a("p"),n_r=o(`Note:
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deactivated). To train the model, you should first set it back in training mode with `),mMe=a("code"),l1r=o("model.train()"),i1r=l(),F(d7.$$.fragment),oao=l(),ac=a("h2"),c7=a("a"),gMe=a("span"),F(Lk.$$.fragment),d1r=l(),hMe=a("span"),c1r=o("AutoModelForTokenClassification"),rao=l(),zo=a("div"),F(yk.$$.fragment),f1r=l(),nc=a("p"),m1r=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),HY=a("a"),g1r=o("from_pretrained()"),h1r=o(" class method or the "),JY=a("a"),p1r=o("from_config()"),u1r=o(` class
method.`),_1r=l(),xk=a("p"),b1r=o("This class cannot be instantiated directly using "),pMe=a("code"),v1r=o("__init__()"),F1r=o(" (throws an error)."),T1r=l(),kt=a("div"),F($k.$$.fragment),M1r=l(),uMe=a("p"),E1r=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),C1r=l(),sc=a("p"),w1r=o(`Note:
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model\u2019s configuration. Use `),YY=a("a"),y1r=o("from_pretrained()"),x1r=o(" to load the model weights."),$1r=l(),F(f7.$$.fragment),k1r=l(),io=a("div"),F(kk.$$.fragment),S1r=l(),bMe=a("p"),R1r=o("Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),P1r=l(),pn=a("p"),B1r=o("The model class to instantiate is selected based on the "),vMe=a("code"),I1r=o("model_type"),N1r=o(` property of the config object (either
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model\u2019s configuration. Use `),qK=a("a"),fEr=o("from_pretrained()"),mEr=o(" to load the model weights."),gEr=l(),F(eM.$$.fragment),hEr=l(),fo=a("div"),F(Gk.$$.fragment),pEr=l(),T4e=a("p"),uEr=o("Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),_Er=l(),_n=a("p"),bEr=o("The model class to instantiate is selected based on the "),M4e=a("code"),vEr=o("model_type"),FEr=o(` property of the config object (either
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falling back to using pattern matching on `),C4e=a("code"),EEr=o("pretrained_model_name_or_path"),CEr=o(":"),wEr=l(),w4e=a("ul"),oM=a("li"),A4e=a("strong"),AEr=o("tapas"),LEr=o(" \u2014 "),DK=a("a"),yEr=o("TapasForQuestionAnswering"),xEr=o(" (TAPAS model)"),$Er=l(),rM=a("p"),kEr=o("The model is set in evaluation mode by default using "),L4e=a("code"),SEr=o("model.eval()"),REr=o(` (so for instance, dropout modules are
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method.`),XEr=l(),Xk=a("p"),zEr=o("This class cannot be instantiated directly using "),k4e=a("code"),QEr=o("__init__()"),WEr=o(" (throws an error)."),UEr=l(),Pt=a("div"),F(zk.$$.fragment),HEr=l(),S4e=a("p"),JEr=o("Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),YEr=l(),pc=a("p"),ZEr=o(`Note:
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model\u2019s configuration. Use `),OK=a("a"),o4r=o("from_pretrained()"),r4r=o(" to load the model weights."),t4r=l(),F(nM.$$.fragment),a4r=l(),mo=a("div"),F(Qk.$$.fragment),n4r=l(),P4e=a("p"),s4r=o("Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),l4r=l(),bn=a("p"),i4r=o("The model class to instantiate is selected based on the "),B4e=a("code"),d4r=o("model_type"),c4r=o(` property of the config object (either
passed as an argument or loaded from `),I4e=a("code"),f4r=o("pretrained_model_name_or_path"),m4r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),N4e=a("code"),g4r=o("pretrained_model_name_or_path"),h4r=o(":"),p4r=l(),uc=a("ul"),sM=a("li"),q4e=a("strong"),u4r=o("layoutlm"),_4r=o(" \u2014 "),VK=a("a"),b4r=o("LayoutLMForQuestionAnswering"),v4r=o(" (LayoutLM model)"),F4r=l(),lM=a("li"),D4e=a("strong"),T4r=o("layoutlmv2"),M4r=o(" \u2014 "),XK=a("a"),E4r=o("LayoutLMv2ForQuestionAnswering"),C4r=o(" (LayoutLMv2 model)"),w4r=l(),iM=a("li"),j4e=a("strong"),A4r=o("layoutlmv3"),L4r=o(" \u2014 "),zK=a("a"),y4r=o("LayoutLMv3ForQuestionAnswering"),x4r=o(" (LayoutLMv3 model)"),$4r=l(),dM=a("p"),k4r=o("The model is set in evaluation mode by default using "),G4e=a("code"),S4r=o("model.eval()"),R4r=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),O4e=a("code"),P4r=o("model.train()"),B4r=l(),F(cM.$$.fragment),dao=l(),_c=a("h2"),fM=a("a"),V4e=a("span"),F(Wk.$$.fragment),I4r=l(),X4e=a("span"),N4r=o("AutoModelForImageClassification"),cao=l(),Ho=a("div"),F(Uk.$$.fragment),q4r=l(),bc=a("p"),D4r=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
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method.`),X4r=l(),Hk=a("p"),z4r=o("This class cannot be instantiated directly using "),z4e=a("code"),Q4r=o("__init__()"),W4r=o(" (throws an error)."),U4r=l(),Bt=a("div"),F(Jk.$$.fragment),H4r=l(),Q4e=a("p"),J4r=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Y4r=l(),vc=a("p"),Z4r=o(`Note:
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model\u2019s configuration. Use `),UK=a("a"),oCr=o("from_pretrained()"),rCr=o(" to load the model weights."),tCr=l(),F(mM.$$.fragment),aCr=l(),go=a("div"),F(Yk.$$.fragment),nCr=l(),U4e=a("p"),sCr=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),lCr=l(),vn=a("p"),iCr=o("The model class to instantiate is selected based on the "),H4e=a("code"),dCr=o("model_type"),cCr=o(` property of the config object (either
passed as an argument or loaded from `),J4e=a("code"),fCr=o("pretrained_model_name_or_path"),mCr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Y4e=a("code"),gCr=o("pretrained_model_name_or_path"),hCr=o(":"),pCr=l(),be=a("ul"),gM=a("li"),Z4e=a("strong"),uCr=o("beit"),_Cr=o(" \u2014 "),HK=a("a"),bCr=o("BeitForImageClassification"),vCr=o(" (BEiT model)"),FCr=l(),hM=a("li"),K4e=a("strong"),TCr=o("convnext"),MCr=o(" \u2014 "),JK=a("a"),ECr=o("ConvNextForImageClassification"),CCr=o(" (ConvNeXT model)"),wCr=l(),pM=a("li"),eCe=a("strong"),ACr=o("cvt"),LCr=o(" \u2014 "),YK=a("a"),yCr=o("CvtForImageClassification"),xCr=o(" (CvT model)"),$Cr=l(),uM=a("li"),oCe=a("strong"),kCr=o("data2vec-vision"),SCr=o(" \u2014 "),ZK=a("a"),RCr=o("Data2VecVisionForImageClassification"),PCr=o(" (Data2VecVision model)"),BCr=l(),xl=a("li"),rCe=a("strong"),ICr=o("deit"),NCr=o(" \u2014 "),KK=a("a"),qCr=o("DeiTForImageClassification"),DCr=o(" or "),eee=a("a"),jCr=o("DeiTForImageClassificationWithTeacher"),GCr=o(" (DeiT model)"),OCr=l(),_M=a("li"),tCe=a("strong"),VCr=o("imagegpt"),XCr=o(" \u2014 "),oee=a("a"),zCr=o("ImageGPTForImageClassification"),QCr=o(" (ImageGPT model)"),WCr=l(),$l=a("li"),aCe=a("strong"),UCr=o("levit"),HCr=o(" \u2014 "),ree=a("a"),JCr=o("LevitForImageClassification"),YCr=o(" or "),tee=a("a"),ZCr=o("LevitForImageClassificationWithTeacher"),KCr=o(" (LeViT model)"),e3r=l(),bM=a("li"),nCe=a("strong"),o3r=o("mobilevit"),r3r=o(" \u2014 "),aee=a("a"),t3r=o("MobileViTForImageClassification"),a3r=o(" (MobileViT model)"),n3r=l(),It=a("li"),sCe=a("strong"),s3r=o("perceiver"),l3r=o(" \u2014 "),nee=a("a"),i3r=o("PerceiverForImageClassificationLearned"),d3r=o(" or "),see=a("a"),c3r=o("PerceiverForImageClassificationFourier"),f3r=o(" or "),lee=a("a"),m3r=o("PerceiverForImageClassificationConvProcessing"),g3r=o(" (Perceiver model)"),h3r=l(),vM=a("li"),lCe=a("strong"),p3r=o("poolformer"),u3r=o(" \u2014 "),iee=a("a"),_3r=o("PoolFormerForImageClassification"),b3r=o(" (PoolFormer model)"),v3r=l(),FM=a("li"),iCe=a("strong"),F3r=o("regnet"),T3r=o(" \u2014 "),dee=a("a"),M3r=o("RegNetForImageClassification"),E3r=o(" (RegNet model)"),C3r=l(),TM=a("li"),dCe=a("strong"),w3r=o("resnet"),A3r=o(" \u2014 "),cee=a("a"),L3r=o("ResNetForImageClassification"),y3r=o(" (ResNet model)"),x3r=l(),MM=a("li"),cCe=a("strong"),$3r=o("segformer"),k3r=o(" \u2014 "),fee=a("a"),S3r=o("SegformerForImageClassification"),R3r=o(" (SegFormer model)"),P3r=l(),EM=a("li"),fCe=a("strong"),B3r=o("swin"),I3r=o(" \u2014 "),mee=a("a"),N3r=o("SwinForImageClassification"),q3r=o(" (Swin Transformer model)"),D3r=l(),CM=a("li"),mCe=a("strong"),j3r=o("swinv2"),G3r=o(" \u2014 "),gee=a("a"),O3r=o("Swinv2ForImageClassification"),V3r=o(" (Swin Transformer V2 model)"),X3r=l(),wM=a("li"),gCe=a("strong"),z3r=o("van"),Q3r=o(" \u2014 "),hee=a("a"),W3r=o("VanForImageClassification"),U3r=o(" (VAN model)"),H3r=l(),AM=a("li"),hCe=a("strong"),J3r=o("vit"),Y3r=o(" \u2014 "),pee=a("a"),Z3r=o("ViTForImageClassification"),K3r=o(" (ViT model)"),e5r=l(),LM=a("li"),pCe=a("strong"),o5r=o("vit_msn"),r5r=o(" \u2014 "),uee=a("a"),t5r=o("ViTMSNForImageClassification"),a5r=o(" (ViTMSN model)"),n5r=l(),yM=a("p"),s5r=o("The model is set in evaluation mode by default using "),uCe=a("code"),l5r=o("model.eval()"),i5r=o(` (so for instance, dropout modules are
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method.`),v5r=l(),eS=a("p"),F5r=o("This class cannot be instantiated directly using "),FCe=a("code"),T5r=o("__init__()"),M5r=o(" (throws an error)."),E5r=l(),Nt=a("div"),F(oS.$$.fragment),C5r=l(),TCe=a("p"),w5r=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),A5r=l(),Mc=a("p"),L5r=o(`Note:
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passed as an argument or loaded from `),wCe=a("code"),j5r=o("pretrained_model_name_or_path"),G5r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ACe=a("code"),O5r=o("pretrained_model_name_or_path"),V5r=o(":"),X5r=l(),LCe=a("ul"),SM=a("li"),yCe=a("strong"),z5r=o("videomae"),Q5r=o(" \u2014 "),Fee=a("a"),W5r=o("VideoMAEForVideoClassification"),U5r=o(" (VideoMAE model)"),H5r=l(),RM=a("p"),J5r=o("The model is set in evaluation mode by default using "),xCe=a("code"),Y5r=o("model.eval()"),Z5r=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),$Ce=a("code"),K5r=o("model.train()"),ewr=l(),F(PM.$$.fragment),gao=l(),Ec=a("h2"),BM=a("a"),kCe=a("span"),F(tS.$$.fragment),owr=l(),SCe=a("span"),rwr=o("AutoModelForVision2Seq"),hao=l(),Yo=a("div"),F(aS.$$.fragment),twr=l(),Cc=a("p"),awr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
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model\u2019s configuration. Use `),Eee=a("a"),Fwr=o("from_pretrained()"),Twr=o(" to load the model weights."),Mwr=l(),F(IM.$$.fragment),Ewr=l(),po=a("div"),F(lS.$$.fragment),Cwr=l(),ICe=a("p"),wwr=o("Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Awr=l(),Tn=a("p"),Lwr=o("The model class to instantiate is selected based on the "),NCe=a("code"),ywr=o("model_type"),xwr=o(` property of the config object (either
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falling back to using pattern matching on `),DCe=a("code"),Swr=o("pretrained_model_name_or_path"),Rwr=o(":"),Pwr=l(),jCe=a("ul"),NM=a("li"),GCe=a("strong"),Bwr=o("vision-encoder-decoder"),Iwr=o(" \u2014 "),Cee=a("a"),Nwr=o("VisionEncoderDecoderModel"),qwr=o(" (Vision Encoder decoder model)"),Dwr=l(),qM=a("p"),jwr=o("The model is set in evaluation mode by default using "),OCe=a("code"),Gwr=o("model.eval()"),Owr=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),ZCe=a("code"),MAr=o("pretrained_model_name_or_path"),EAr=o(":"),CAr=l(),KCe=a("ul"),OM=a("li"),e3e=a("strong"),wAr=o("vilt"),AAr=o(" \u2014 "),yee=a("a"),LAr=o("ViltForQuestionAnswering"),yAr=o(" (ViLT model)"),xAr=l(),VM=a("p"),$Ar=o("The model is set in evaluation mode by default using "),o3e=a("code"),kAr=o("model.eval()"),SAr=o(` (so for instance, dropout modules are
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method.`),VAr=l(),pS=a("p"),XAr=o("This class cannot be instantiated directly using "),n3e=a("code"),zAr=o("__init__()"),QAr=o(" (throws an error)."),WAr=l(),jt=a("div"),F(uS.$$.fragment),UAr=l(),s3e=a("p"),HAr=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),JAr=l(),kc=a("p"),YAr=o(`Note:
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falling back to using pattern matching on `),f3e=a("code"),m6r=o("pretrained_model_name_or_path"),g6r=o(":"),h6r=l(),Be=a("ul"),WM=a("li"),m3e=a("strong"),p6r=o("data2vec-audio"),u6r=o(" \u2014 "),See=a("a"),_6r=o("Data2VecAudioForSequenceClassification"),b6r=o(" (Data2VecAudio model)"),v6r=l(),UM=a("li"),g3e=a("strong"),F6r=o("hubert"),T6r=o(" \u2014 "),Ree=a("a"),M6r=o("HubertForSequenceClassification"),E6r=o(" (Hubert model)"),C6r=l(),HM=a("li"),h3e=a("strong"),w6r=o("sew"),A6r=o(" \u2014 "),Pee=a("a"),L6r=o("SEWForSequenceClassification"),y6r=o(" (SEW model)"),x6r=l(),JM=a("li"),p3e=a("strong"),$6r=o("sew-d"),k6r=o(" \u2014 "),Bee=a("a"),S6r=o("SEWDForSequenceClassification"),R6r=o(" (SEW-D model)"),P6r=l(),YM=a("li"),u3e=a("strong"),B6r=o("unispeech"),I6r=o(" \u2014 "),Iee=a("a"),N6r=o("UniSpeechForSequenceClassification"),q6r=o(" (UniSpeech model)"),D6r=l(),ZM=a("li"),_3e=a("strong"),j6r=o("unispeech-sat"),G6r=o(" \u2014 "),Nee=a("a"),O6r=o("UniSpeechSatForSequenceClassification"),V6r=o(" (UniSpeechSat model)"),X6r=l(),KM=a("li"),b3e=a("strong"),z6r=o("wav2vec2"),Q6r=o(" \u2014 "),qee=a("a"),W6r=o("Wav2Vec2ForSequenceClassification"),U6r=o(" (Wav2Vec2 model)"),H6r=l(),eE=a("li"),v3e=a("strong"),J6r=o("wav2vec2-conformer"),Y6r=o(" \u2014 "),Dee=a("a"),Z6r=o("Wav2Vec2ConformerForSequenceClassification"),K6r=o(" (Wav2Vec2-Conformer model)"),e8r=l(),oE=a("li"),F3e=a("strong"),o8r=o("wavlm"),r8r=o(" \u2014 "),jee=a("a"),t8r=o("WavLMForSequenceClassification"),a8r=o(" (WavLM model)"),n8r=l(),rE=a("p"),s8r=o("The model is set in evaluation mode by default using "),T3e=a("code"),l8r=o("model.eval()"),i8r=o(` (so for instance, dropout modules are
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method.`),v8r=l(),FS=a("p"),F8r=o("This class cannot be instantiated directly using "),w3e=a("code"),T8r=o("__init__()"),M8r=o(" (throws an error)."),E8r=l(),Gt=a("div"),F(TS.$$.fragment),C8r=l(),A3e=a("p"),w8r=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),A8r=l(),Pc=a("p"),L8r=o(`Note:
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model\u2019s configuration. Use `),Vee=a("a"),$8r=o("from_pretrained()"),k8r=o(" to load the model weights."),S8r=l(),F(nE.$$.fragment),R8r=l(),bo=a("div"),F(MS.$$.fragment),P8r=l(),y3e=a("p"),B8r=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),I8r=l(),Cn=a("p"),N8r=o("The model class to instantiate is selected based on the "),x3e=a("code"),q8r=o("model_type"),D8r=o(` property of the config object (either
passed as an argument or loaded from `),$3e=a("code"),j8r=o("pretrained_model_name_or_path"),G8r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),k3e=a("code"),O8r=o("pretrained_model_name_or_path"),V8r=o(":"),X8r=l(),pt=a("ul"),sE=a("li"),S3e=a("strong"),z8r=o("data2vec-audio"),Q8r=o(" \u2014 "),Xee=a("a"),W8r=o("Data2VecAudioForAudioFrameClassification"),U8r=o(" (Data2VecAudio model)"),H8r=l(),lE=a("li"),R3e=a("strong"),J8r=o("unispeech-sat"),Y8r=o(" \u2014 "),zee=a("a"),Z8r=o("UniSpeechSatForAudioFrameClassification"),K8r=o(" (UniSpeechSat model)"),eLr=l(),iE=a("li"),P3e=a("strong"),oLr=o("wav2vec2"),rLr=o(" \u2014 "),Qee=a("a"),tLr=o("Wav2Vec2ForAudioFrameClassification"),aLr=o(" (Wav2Vec2 model)"),nLr=l(),dE=a("li"),B3e=a("strong"),sLr=o("wav2vec2-conformer"),lLr=o(" \u2014 "),Wee=a("a"),iLr=o("Wav2Vec2ConformerForAudioFrameClassification"),dLr=o(" (Wav2Vec2-Conformer model)"),cLr=l(),cE=a("li"),I3e=a("strong"),fLr=o("wavlm"),mLr=o(" \u2014 "),Uee=a("a"),gLr=o("WavLMForAudioFrameClassification"),hLr=o(" (WavLM model)"),pLr=l(),fE=a("p"),uLr=o("The model is set in evaluation mode by default using "),N3e=a("code"),_Lr=o("model.eval()"),bLr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),q3e=a("code"),vLr=o("model.train()"),FLr=l(),F(mE.$$.fragment),Tao=l(),Bc=a("h2"),gE=a("a"),D3e=a("span"),F(ES.$$.fragment),TLr=l(),j3e=a("span"),MLr=o("AutoModelForCTC"),Mao=l(),or=a("div"),F(CS.$$.fragment),ELr=l(),Ic=a("p"),CLr=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 `),Hee=a("a"),wLr=o("from_pretrained()"),ALr=o(" class method or the "),Jee=a("a"),LLr=o("from_config()"),yLr=o(` class
method.`),xLr=l(),wS=a("p"),$Lr=o("This class cannot be instantiated directly using "),G3e=a("code"),kLr=o("__init__()"),SLr=o(" (throws an error)."),RLr=l(),Ot=a("div"),F(AS.$$.fragment),PLr=l(),O3e=a("p"),BLr=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),ILr=l(),Nc=a("p"),NLr=o(`Note:
Loading a model from its configuration file does `),V3e=a("strong"),qLr=o("not"),DLr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Yee=a("a"),jLr=o("from_pretrained()"),GLr=o(" to load the model weights."),OLr=l(),F(hE.$$.fragment),VLr=l(),vo=a("div"),F(LS.$$.fragment),XLr=l(),X3e=a("p"),zLr=o("Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),QLr=l(),wn=a("p"),WLr=o("The model class to instantiate is selected based on the "),z3e=a("code"),ULr=o("model_type"),HLr=o(` property of the config object (either
passed as an argument or loaded from `),Q3e=a("code"),JLr=o("pretrained_model_name_or_path"),YLr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),W3e=a("code"),ZLr=o("pretrained_model_name_or_path"),KLr=o(":"),eyr=l(),Le=a("ul"),pE=a("li"),U3e=a("strong"),oyr=o("data2vec-audio"),ryr=o(" \u2014 "),Zee=a("a"),tyr=o("Data2VecAudioForCTC"),ayr=o(" (Data2VecAudio model)"),nyr=l(),uE=a("li"),H3e=a("strong"),syr=o("hubert"),lyr=o(" \u2014 "),Kee=a("a"),iyr=o("HubertForCTC"),dyr=o(" (Hubert model)"),cyr=l(),_E=a("li"),J3e=a("strong"),fyr=o("mctct"),myr=o(" \u2014 "),eoe=a("a"),gyr=o("MCTCTForCTC"),hyr=o(" (M-CTC-T model)"),pyr=l(),bE=a("li"),Y3e=a("strong"),uyr=o("sew"),_yr=o(" \u2014 "),ooe=a("a"),byr=o("SEWForCTC"),vyr=o(" (SEW model)"),Fyr=l(),vE=a("li"),Z3e=a("strong"),Tyr=o("sew-d"),Myr=o(" \u2014 "),roe=a("a"),Eyr=o("SEWDForCTC"),Cyr=o(" (SEW-D model)"),wyr=l(),FE=a("li"),K3e=a("strong"),Ayr=o("unispeech"),Lyr=o(" \u2014 "),toe=a("a"),yyr=o("UniSpeechForCTC"),xyr=o(" (UniSpeech model)"),$yr=l(),TE=a("li"),e5e=a("strong"),kyr=o("unispeech-sat"),Syr=o(" \u2014 "),aoe=a("a"),Ryr=o("UniSpeechSatForCTC"),Pyr=o(" (UniSpeechSat model)"),Byr=l(),ME=a("li"),o5e=a("strong"),Iyr=o("wav2vec2"),Nyr=o(" \u2014 "),noe=a("a"),qyr=o("Wav2Vec2ForCTC"),Dyr=o(" (Wav2Vec2 model)"),jyr=l(),EE=a("li"),r5e=a("strong"),Gyr=o("wav2vec2-conformer"),Oyr=o(" \u2014 "),soe=a("a"),Vyr=o("Wav2Vec2ConformerForCTC"),Xyr=o(" (Wav2Vec2-Conformer model)"),zyr=l(),CE=a("li"),t5e=a("strong"),Qyr=o("wavlm"),Wyr=o(" \u2014 "),loe=a("a"),Uyr=o("WavLMForCTC"),Hyr=o(" (WavLM model)"),Jyr=l(),wE=a("p"),Yyr=o("The model is set in evaluation mode by default using "),a5e=a("code"),Zyr=o("model.eval()"),Kyr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),n5e=a("code"),exr=o("model.train()"),oxr=l(),F(AE.$$.fragment),Eao=l(),qc=a("h2"),LE=a("a"),s5e=a("span"),F(yS.$$.fragment),rxr=l(),l5e=a("span"),txr=o("AutoModelForSpeechSeq2Seq"),Cao=l(),rr=a("div"),F(xS.$$.fragment),axr=l(),Dc=a("p"),nxr=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
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method.`),cxr=l(),$S=a("p"),fxr=o("This class cannot be instantiated directly using "),i5e=a("code"),mxr=o("__init__()"),gxr=o(" (throws an error)."),hxr=l(),Vt=a("div"),F(kS.$$.fragment),pxr=l(),d5e=a("p"),uxr=o("Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),_xr=l(),jc=a("p"),bxr=o(`Note:
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model\u2019s configuration. Use `),coe=a("a"),Txr=o("from_pretrained()"),Mxr=o(" to load the model weights."),Exr=l(),F(yE.$$.fragment),Cxr=l(),Fo=a("div"),F(SS.$$.fragment),wxr=l(),f5e=a("p"),Axr=o("Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Lxr=l(),An=a("p"),yxr=o("The model class to instantiate is selected based on the "),m5e=a("code"),xxr=o("model_type"),$xr=o(` property of the config object (either
passed as an argument or loaded from `),g5e=a("code"),kxr=o("pretrained_model_name_or_path"),Sxr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),h5e=a("code"),Rxr=o("pretrained_model_name_or_path"),Pxr=o(":"),Bxr=l(),Gc=a("ul"),xE=a("li"),p5e=a("strong"),Ixr=o("speech-encoder-decoder"),Nxr=o(" \u2014 "),foe=a("a"),qxr=o("SpeechEncoderDecoderModel"),Dxr=o(" (Speech Encoder decoder model)"),jxr=l(),$E=a("li"),u5e=a("strong"),Gxr=o("speech_to_text"),Oxr=o(" \u2014 "),moe=a("a"),Vxr=o("Speech2TextForConditionalGeneration"),Xxr=o(" (Speech2Text model)"),zxr=l(),kE=a("li"),_5e=a("strong"),Qxr=o("whisper"),Wxr=o(" \u2014 "),goe=a("a"),Uxr=o("WhisperForConditionalGeneration"),Hxr=o(" (Whisper model)"),Jxr=l(),SE=a("p"),Yxr=o("The model is set in evaluation mode by default using "),b5e=a("code"),Zxr=o("model.eval()"),Kxr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),v5e=a("code"),e$r=o("model.train()"),o$r=l(),F(RE.$$.fragment),wao=l(),Oc=a("h2"),PE=a("a"),F5e=a("span"),F(RS.$$.fragment),r$r=l(),T5e=a("span"),t$r=o("AutoModelForAudioXVector"),Aao=l(),tr=a("div"),F(PS.$$.fragment),a$r=l(),Vc=a("p"),n$r=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 `),hoe=a("a"),s$r=o("from_pretrained()"),l$r=o(" class method or the "),poe=a("a"),i$r=o("from_config()"),d$r=o(` class
method.`),c$r=l(),BS=a("p"),f$r=o("This class cannot be instantiated directly using "),M5e=a("code"),m$r=o("__init__()"),g$r=o(" (throws an error)."),h$r=l(),Xt=a("div"),F(IS.$$.fragment),p$r=l(),E5e=a("p"),u$r=o("Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),_$r=l(),Xc=a("p"),b$r=o(`Note:
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model\u2019s configuration. Use `),uoe=a("a"),T$r=o("from_pretrained()"),M$r=o(" to load the model weights."),E$r=l(),F(BE.$$.fragment),C$r=l(),To=a("div"),F(NS.$$.fragment),w$r=l(),w5e=a("p"),A$r=o("Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),L$r=l(),Ln=a("p"),y$r=o("The model class to instantiate is selected based on the "),A5e=a("code"),x$r=o("model_type"),$$r=o(` property of the config object (either
passed as an argument or loaded from `),L5e=a("code"),k$r=o("pretrained_model_name_or_path"),S$r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),y5e=a("code"),R$r=o("pretrained_model_name_or_path"),P$r=o(":"),B$r=l(),ut=a("ul"),IE=a("li"),x5e=a("strong"),I$r=o("data2vec-audio"),N$r=o(" \u2014 "),_oe=a("a"),q$r=o("Data2VecAudioForXVector"),D$r=o(" (Data2VecAudio model)"),j$r=l(),NE=a("li"),$5e=a("strong"),G$r=o("unispeech-sat"),O$r=o(" \u2014 "),boe=a("a"),V$r=o("UniSpeechSatForXVector"),X$r=o(" (UniSpeechSat model)"),z$r=l(),qE=a("li"),k5e=a("strong"),Q$r=o("wav2vec2"),W$r=o(" \u2014 "),voe=a("a"),U$r=o("Wav2Vec2ForXVector"),H$r=o(" (Wav2Vec2 model)"),J$r=l(),DE=a("li"),S5e=a("strong"),Y$r=o("wav2vec2-conformer"),Z$r=o(" \u2014 "),Foe=a("a"),K$r=o("Wav2Vec2ConformerForXVector"),ekr=o(" (Wav2Vec2-Conformer model)"),okr=l(),jE=a("li"),R5e=a("strong"),rkr=o("wavlm"),tkr=o(" \u2014 "),Toe=a("a"),akr=o("WavLMForXVector"),nkr=o(" (WavLM model)"),skr=l(),GE=a("p"),lkr=o("The model is set in evaluation mode by default using "),P5e=a("code"),ikr=o("model.eval()"),dkr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),B5e=a("code"),ckr=o("model.train()"),fkr=l(),F(OE.$$.fragment),Lao=l(),zc=a("h2"),VE=a("a"),I5e=a("span"),F(qS.$$.fragment),mkr=l(),N5e=a("span"),gkr=o("AutoModelForMaskedImageModeling"),yao=l(),ar=a("div"),F(DS.$$.fragment),hkr=l(),Qc=a("p"),pkr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),Moe=a("a"),ukr=o("from_pretrained()"),_kr=o(" class method or the "),Eoe=a("a"),bkr=o("from_config()"),vkr=o(` class
method.`),Fkr=l(),jS=a("p"),Tkr=o("This class cannot be instantiated directly using "),q5e=a("code"),Mkr=o("__init__()"),Ekr=o(" (throws an error)."),Ckr=l(),zt=a("div"),F(GS.$$.fragment),wkr=l(),D5e=a("p"),Akr=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),Lkr=l(),Wc=a("p"),ykr=o(`Note:
Loading a model from its configuration file does `),j5e=a("strong"),xkr=o("not"),$kr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Coe=a("a"),kkr=o("from_pretrained()"),Skr=o(" to load the model weights."),Rkr=l(),F(XE.$$.fragment),Pkr=l(),Mo=a("div"),F(OS.$$.fragment),Bkr=l(),G5e=a("p"),Ikr=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),Nkr=l(),yn=a("p"),qkr=o("The model class to instantiate is selected based on the "),O5e=a("code"),Dkr=o("model_type"),jkr=o(` property of the config object (either
passed as an argument or loaded from `),V5e=a("code"),Gkr=o("pretrained_model_name_or_path"),Okr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),X5e=a("code"),Vkr=o("pretrained_model_name_or_path"),Xkr=o(":"),zkr=l(),xn=a("ul"),zE=a("li"),z5e=a("strong"),Qkr=o("deit"),Wkr=o(" \u2014 "),woe=a("a"),Ukr=o("DeiTForMaskedImageModeling"),Hkr=o(" (DeiT model)"),Jkr=l(),QE=a("li"),Q5e=a("strong"),Ykr=o("swin"),Zkr=o(" \u2014 "),Aoe=a("a"),Kkr=o("SwinForMaskedImageModeling"),eSr=o(" (Swin Transformer model)"),oSr=l(),WE=a("li"),W5e=a("strong"),rSr=o("swinv2"),tSr=o(" \u2014 "),Loe=a("a"),aSr=o("Swinv2ForMaskedImageModeling"),nSr=o(" (Swin Transformer V2 model)"),sSr=l(),UE=a("li"),U5e=a("strong"),lSr=o("vit"),iSr=o(" \u2014 "),yoe=a("a"),dSr=o("ViTForMaskedImageModeling"),cSr=o(" (ViT model)"),fSr=l(),HE=a("p"),mSr=o("The model is set in evaluation mode by default using "),H5e=a("code"),gSr=o("model.eval()"),hSr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),J5e=a("code"),pSr=o("model.train()"),uSr=l(),F(JE.$$.fragment),xao=l(),Uc=a("h2"),YE=a("a"),Y5e=a("span"),F(VS.$$.fragment),_Sr=l(),Z5e=a("span"),bSr=o("AutoModelForObjectDetection"),$ao=l(),nr=a("div"),F(XS.$$.fragment),vSr=l(),Hc=a("p"),FSr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),xoe=a("a"),TSr=o("from_pretrained()"),MSr=o(" class method or the "),$oe=a("a"),ESr=o("from_config()"),CSr=o(` class
method.`),wSr=l(),zS=a("p"),ASr=o("This class cannot be instantiated directly using "),K5e=a("code"),LSr=o("__init__()"),ySr=o(" (throws an error)."),xSr=l(),Qt=a("div"),F(QS.$$.fragment),$Sr=l(),ewe=a("p"),kSr=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),SSr=l(),Jc=a("p"),RSr=o(`Note:
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model\u2019s configuration. Use `),koe=a("a"),ISr=o("from_pretrained()"),NSr=o(" to load the model weights."),qSr=l(),F(ZE.$$.fragment),DSr=l(),Eo=a("div"),F(WS.$$.fragment),jSr=l(),rwe=a("p"),GSr=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),OSr=l(),$n=a("p"),VSr=o("The model class to instantiate is selected based on the "),twe=a("code"),XSr=o("model_type"),zSr=o(` property of the config object (either
passed as an argument or loaded from `),awe=a("code"),QSr=o("pretrained_model_name_or_path"),WSr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nwe=a("code"),USr=o("pretrained_model_name_or_path"),HSr=o(":"),JSr=l(),_t=a("ul"),KE=a("li"),swe=a("strong"),YSr=o("conditional_detr"),ZSr=o(" \u2014 "),Soe=a("a"),KSr=o("ConditionalDetrForObjectDetection"),eRr=o(" (Conditional DETR model)"),oRr=l(),e4=a("li"),lwe=a("strong"),rRr=o("deformable_detr"),tRr=o(" \u2014 "),Roe=a("a"),aRr=o("DeformableDetrForObjectDetection"),nRr=o(" (Deformable DETR model)"),sRr=l(),o4=a("li"),iwe=a("strong"),lRr=o("detr"),iRr=o(" \u2014 "),Poe=a("a"),dRr=o("DetrForObjectDetection"),cRr=o(" (DETR model)"),fRr=l(),r4=a("li"),dwe=a("strong"),mRr=o("table-transformer"),gRr=o(" \u2014 "),Boe=a("a"),hRr=o("TableTransformerForObjectDetection"),pRr=o(" (Table Transformer model)"),uRr=l(),t4=a("li"),cwe=a("strong"),_Rr=o("yolos"),bRr=o(" \u2014 "),Ioe=a("a"),vRr=o("YolosForObjectDetection"),FRr=o(" (YOLOS model)"),TRr=l(),a4=a("p"),MRr=o("The model is set in evaluation mode by default using "),fwe=a("code"),ERr=o("model.eval()"),CRr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),mwe=a("code"),wRr=o("model.train()"),ARr=l(),F(n4.$$.fragment),kao=l(),Yc=a("h2"),s4=a("a"),gwe=a("span"),F(US.$$.fragment),LRr=l(),hwe=a("span"),yRr=o("AutoModelForImageSegmentation"),Sao=l(),sr=a("div"),F(HS.$$.fragment),xRr=l(),Zc=a("p"),$Rr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),Noe=a("a"),kRr=o("from_pretrained()"),SRr=o(" class method or the "),qoe=a("a"),RRr=o("from_config()"),PRr=o(` class
method.`),BRr=l(),JS=a("p"),IRr=o("This class cannot be instantiated directly using "),pwe=a("code"),NRr=o("__init__()"),qRr=o(" (throws an error)."),DRr=l(),Wt=a("div"),F(YS.$$.fragment),jRr=l(),uwe=a("p"),GRr=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),ORr=l(),Kc=a("p"),VRr=o(`Note:
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model\u2019s configuration. Use `),Doe=a("a"),QRr=o("from_pretrained()"),WRr=o(" to load the model weights."),URr=l(),F(l4.$$.fragment),HRr=l(),Co=a("div"),F(ZS.$$.fragment),JRr=l(),bwe=a("p"),YRr=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),ZRr=l(),kn=a("p"),KRr=o("The model class to instantiate is selected based on the "),vwe=a("code"),ePr=o("model_type"),oPr=o(` property of the config object (either
passed as an argument or loaded from `),Fwe=a("code"),rPr=o("pretrained_model_name_or_path"),tPr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Twe=a("code"),aPr=o("pretrained_model_name_or_path"),nPr=o(":"),sPr=l(),Mwe=a("ul"),i4=a("li"),Ewe=a("strong"),lPr=o("detr"),iPr=o(" \u2014 "),joe=a("a"),dPr=o("DetrForSegmentation"),cPr=o(" (DETR model)"),fPr=l(),d4=a("p"),mPr=o("The model is set in evaluation mode by default using "),Cwe=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 `),wwe=a("code"),pPr=o("model.train()"),uPr=l(),F(c4.$$.fragment),Rao=l(),ef=a("h2"),f4=a("a"),Awe=a("span"),F(KS.$$.fragment),_Pr=l(),Lwe=a("span"),bPr=o("AutoModelForSemanticSegmentation"),Pao=l(),lr=a("div"),F(eR.$$.fragment),vPr=l(),of=a("p"),FPr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),Goe=a("a"),TPr=o("from_pretrained()"),MPr=o(" class method or the "),Ooe=a("a"),EPr=o("from_config()"),CPr=o(` class
method.`),wPr=l(),oR=a("p"),APr=o("This class cannot be instantiated directly using "),ywe=a("code"),LPr=o("__init__()"),yPr=o(" (throws an error)."),xPr=l(),Ut=a("div"),F(rR.$$.fragment),$Pr=l(),xwe=a("p"),kPr=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),SPr=l(),rf=a("p"),RPr=o(`Note:
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falling back to using pattern matching on `),Pwe=a("code"),UPr=o("pretrained_model_name_or_path"),HPr=o(":"),JPr=l(),bt=a("ul"),g4=a("li"),Bwe=a("strong"),YPr=o("beit"),ZPr=o(" \u2014 "),Xoe=a("a"),KPr=o("BeitForSemanticSegmentation"),eBr=o(" (BEiT model)"),oBr=l(),h4=a("li"),Iwe=a("strong"),rBr=o("data2vec-vision"),tBr=o(" \u2014 "),zoe=a("a"),aBr=o("Data2VecVisionForSemanticSegmentation"),nBr=o(" (Data2VecVision model)"),sBr=l(),p4=a("li"),Nwe=a("strong"),lBr=o("dpt"),iBr=o(" \u2014 "),Qoe=a("a"),dBr=o("DPTForSemanticSegmentation"),cBr=o(" (DPT model)"),fBr=l(),u4=a("li"),qwe=a("strong"),mBr=o("mobilevit"),gBr=o(" \u2014 "),Woe=a("a"),hBr=o("MobileViTForSemanticSegmentation"),pBr=o(" (MobileViT model)"),uBr=l(),_4=a("li"),Dwe=a("strong"),_Br=o("segformer"),bBr=o(" \u2014 "),Uoe=a("a"),vBr=o("SegformerForSemanticSegmentation"),FBr=o(" (SegFormer model)"),TBr=l(),b4=a("p"),MBr=o("The model is set in evaluation mode by default using "),jwe=a("code"),EBr=o("model.eval()"),CBr=o(` (so for instance, dropout modules are
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model\u2019s configuration. Use `),nre=a("a"),ANr=o("from_pretrained()"),LNr=o(" to load the model weights."),yNr=l(),F(k4.$$.fragment),xNr=l(),jr=a("div"),F(bR.$$.fragment),$Nr=l(),vAe=a("p"),kNr=o("Instantiate one of the base model classes of the library from a pretrained model."),SNr=l(),Bn=a("p"),RNr=o("The model class to instantiate is selected based on the "),FAe=a("code"),PNr=o("model_type"),BNr=o(` property of the config object (either
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falling back to using pattern matching on `),B6e=a("code"),jVr=o("pretrained_model_name_or_path"),GVr=o(":"),OVr=l(),le=a("ul"),qC=a("li"),I6e=a("strong"),VVr=o("albert"),XVr=o(" \u2014 "),hte=a("a"),zVr=o("TFAlbertForPreTraining"),QVr=o(" (ALBERT model)"),WVr=l(),DC=a("li"),N6e=a("strong"),UVr=o("bart"),HVr=o(" \u2014 "),pte=a("a"),JVr=o("TFBartForConditionalGeneration"),YVr=o(" (BART model)"),ZVr=l(),jC=a("li"),q6e=a("strong"),KVr=o("bert"),eXr=o(" \u2014 "),ute=a("a"),oXr=o("TFBertForPreTraining"),rXr=o(" (BERT model)"),tXr=l(),GC=a("li"),D6e=a("strong"),aXr=o("camembert"),nXr=o(" \u2014 "),_te=a("a"),sXr=o("TFCamembertForMaskedLM"),lXr=o(" (CamemBERT model)"),iXr=l(),OC=a("li"),j6e=a("strong"),dXr=o("ctrl"),cXr=o(" \u2014 "),bte=a("a"),fXr=o("TFCTRLLMHeadModel"),mXr=o(" (CTRL model)"),gXr=l(),VC=a("li"),G6e=a("strong"),hXr=o("distilbert"),pXr=o(" \u2014 "),vte=a("a"),uXr=o("TFDistilBertForMaskedLM"),_Xr=o(" (DistilBERT model)"),bXr=l(),XC=a("li"),O6e=a("strong"),vXr=o("electra"),FXr=o(" \u2014 "),Fte=a("a"),TXr=o("TFElectraForPreTraining"),MXr=o(" (ELECTRA model)"),EXr=l(),zC=a("li"),V6e=a("strong"),CXr=o("flaubert"),wXr=o(" \u2014 "),Tte=a("a"),AXr=o("TFFlaubertWithLMHeadModel"),LXr=o(" (FlauBERT model)"),yXr=l(),QC=a("li"),X6e=a("strong"),xXr=o("funnel"),$Xr=o(" \u2014 "),Mte=a("a"),kXr=o("TFFunnelForPreTraining"),SXr=o(" (Funnel Transformer model)"),RXr=l(),WC=a("li"),z6e=a("strong"),PXr=o("gpt2"),BXr=o(" \u2014 "),Ete=a("a"),IXr=o("TFGPT2LMHeadModel"),NXr=o(" (OpenAI GPT-2 model)"),qXr=l(),UC=a("li"),Q6e=a("strong"),DXr=o("layoutlm"),jXr=o(" \u2014 "),Cte=a("a"),GXr=o("TFLayoutLMForMaskedLM"),OXr=o(" (LayoutLM model)"),VXr=l(),HC=a("li"),W6e=a("strong"),XXr=o("lxmert"),zXr=o(" \u2014 "),wte=a("a"),QXr=o("TFLxmertForPreTraining"),WXr=o(" (LXMERT model)"),UXr=l(),JC=a("li"),U6e=a("strong"),HXr=o("mobilebert"),JXr=o(" \u2014 "),Ate=a("a"),YXr=o("TFMobileBertForPreTraining"),ZXr=o(" (MobileBERT model)"),KXr=l(),YC=a("li"),H6e=a("strong"),ezr=o("mpnet"),ozr=o(" \u2014 "),Lte=a("a"),rzr=o("TFMPNetForMaskedLM"),tzr=o(" (MPNet model)"),azr=l(),ZC=a("li"),J6e=a("strong"),nzr=o("openai-gpt"),szr=o(" \u2014 "),yte=a("a"),lzr=o("TFOpenAIGPTLMHeadModel"),izr=o(" (OpenAI GPT model)"),dzr=l(),KC=a("li"),Y6e=a("strong"),czr=o("roberta"),fzr=o(" \u2014 "),xte=a("a"),mzr=o("TFRobertaForMaskedLM"),gzr=o(" (RoBERTa model)"),hzr=l(),e3=a("li"),Z6e=a("strong"),pzr=o("t5"),uzr=o(" \u2014 "),$te=a("a"),_zr=o("TFT5ForConditionalGeneration"),bzr=o(" (T5 model)"),vzr=l(),o3=a("li"),K6e=a("strong"),Fzr=o("tapas"),Tzr=o(" \u2014 "),kte=a("a"),Mzr=o("TFTapasForMaskedLM"),Ezr=o(" (TAPAS model)"),Czr=l(),r3=a("li"),e8e=a("strong"),wzr=o("transfo-xl"),Azr=o(" \u2014 "),Ste=a("a"),Lzr=o("TFTransfoXLLMHeadModel"),yzr=o(" (Transformer-XL model)"),xzr=l(),t3=a("li"),o8e=a("strong"),$zr=o("vit_mae"),kzr=o(" \u2014 "),Rte=a("a"),Szr=o("TFViTMAEForPreTraining"),Rzr=o(" (ViTMAE model)"),Pzr=l(),a3=a("li"),r8e=a("strong"),Bzr=o("xlm"),Izr=o(" \u2014 "),Pte=a("a"),Nzr=o("TFXLMWithLMHeadModel"),qzr=o(" (XLM model)"),Dzr=l(),n3=a("li"),t8e=a("strong"),jzr=o("xlm-roberta"),Gzr=o(" \u2014 "),Bte=a("a"),Ozr=o("TFXLMRobertaForMaskedLM"),Vzr=o(" (XLM-RoBERTa model)"),Xzr=l(),s3=a("li"),a8e=a("strong"),zzr=o("xlnet"),Qzr=o(" \u2014 "),Ite=a("a"),Wzr=o("TFXLNetLMHeadModel"),Uzr=o(" (XLNet model)"),Hzr=l(),F(l3.$$.fragment),Vao=l(),uf=a("h2"),i3=a("a"),n8e=a("span"),F(CR.$$.fragment),Jzr=l(),s8e=a("span"),Yzr=o("TFAutoModelForCausalLM"),Xao=l(),mr=a("div"),F(wR.$$.fragment),Zzr=l(),_f=a("p"),Kzr=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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passed as an argument or loaded from `),B8e=a("code"),TUr=o("pretrained_model_name_or_path"),MUr=o(` if possible), or when it\u2019s missing, by
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passed as an argument or loaded from `),Cke=a("code"),Lpt=o("pretrained_model_name_or_path"),ypt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wke=a("code"),xpt=o("pretrained_model_name_or_path"),$pt=o(":"),kpt=l(),te=a("ul"),OA=a("li"),Ake=a("strong"),Spt=o("albert"),Rpt=o(" \u2014 "),Fle=a("a"),Ppt=o("FlaxAlbertModel"),Bpt=o(" (ALBERT model)"),Ipt=l(),VA=a("li"),Lke=a("strong"),Npt=o("bart"),qpt=o(" \u2014 "),Tle=a("a"),Dpt=o("FlaxBartModel"),jpt=o(" (BART model)"),Gpt=l(),XA=a("li"),yke=a("strong"),Opt=o("beit"),Vpt=o(" \u2014 "),Mle=a("a"),Xpt=o("FlaxBeitModel"),zpt=o(" (BEiT model)"),Qpt=l(),zA=a("li"),xke=a("strong"),Wpt=o("bert"),Upt=o(" \u2014 "),Ele=a("a"),Hpt=o("FlaxBertModel"),Jpt=o(" (BERT model)"),Ypt=l(),QA=a("li"),$ke=a("strong"),Zpt=o("big_bird"),Kpt=o(" \u2014 "),Cle=a("a"),eut=o("FlaxBigBirdModel"),out=o(" (BigBird model)"),rut=l(),WA=a("li"),kke=a("strong"),tut=o("blenderbot"),aut=o(" \u2014 "),wle=a("a"),nut=o("FlaxBlenderbotModel"),sut=o(" (Blenderbot model)"),lut=l(),UA=a("li"),Ske=a("strong"),iut=o("blenderbot-small"),dut=o(" \u2014 "),Ale=a("a"),cut=o("FlaxBlenderbotSmallModel"),fut=o(" (BlenderbotSmall model)"),mut=l(),HA=a("li"),Rke=a("strong"),gut=o("clip"),hut=o(" \u2014 "),Lle=a("a"),put=o("FlaxCLIPModel"),uut=o(" (CLIP model)"),_ut=l(),JA=a("li"),Pke=a("strong"),but=o("distilbert"),vut=o(" \u2014 "),yle=a("a"),Fut=o("FlaxDistilBertModel"),Tut=o(" (DistilBERT model)"),Mut=l(),YA=a("li"),Bke=a("strong"),Eut=o("electra"),Cut=o(" \u2014 "),xle=a("a"),wut=o("FlaxElectraModel"),Aut=o(" (ELECTRA model)"),Lut=l(),ZA=a("li"),Ike=a("strong"),yut=o("gpt2"),xut=o(" \u2014 "),$le=a("a"),$ut=o("FlaxGPT2Model"),kut=o(" (OpenAI GPT-2 model)"),Sut=l(),KA=a("li"),Nke=a("strong"),Rut=o("gpt_neo"),Put=o(" \u2014 "),kle=a("a"),But=o("FlaxGPTNeoModel"),Iut=o(" (GPT Neo model)"),Nut=l(),e6=a("li"),qke=a("strong"),qut=o("gptj"),Dut=o(" \u2014 "),Sle=a("a"),jut=o("FlaxGPTJModel"),Gut=o(" (GPT-J model)"),Out=l(),o6=a("li"),Dke=a("strong"),Vut=o("longt5"),Xut=o(" \u2014 "),Rle=a("a"),zut=o("FlaxLongT5Model"),Qut=o(" (LongT5 model)"),Wut=l(),r6=a("li"),jke=a("strong"),Uut=o("marian"),Hut=o(" \u2014 "),Ple=a("a"),Jut=o("FlaxMarianModel"),Yut=o(" (Marian model)"),Zut=l(),t6=a("li"),Gke=a("strong"),Kut=o("mbart"),e_t=o(" \u2014 "),Ble=a("a"),o_t=o("FlaxMBartModel"),r_t=o(" (mBART model)"),t_t=l(),a6=a("li"),Oke=a("strong"),a_t=o("mt5"),n_t=o(" \u2014 "),Ile=a("a"),s_t=o("FlaxMT5Model"),l_t=o(" (MT5 model)"),i_t=l(),n6=a("li"),Vke=a("strong"),d_t=o("opt"),c_t=o(" \u2014 "),Nle=a("a"),f_t=o("FlaxOPTModel"),m_t=o(" (OPT model)"),g_t=l(),s6=a("li"),Xke=a("strong"),h_t=o("pegasus"),p_t=o(" \u2014 "),qle=a("a"),u_t=o("FlaxPegasusModel"),__t=o(" (Pegasus model)"),b_t=l(),l6=a("li"),zke=a("strong"),v_t=o("roberta"),F_t=o(" \u2014 "),Dle=a("a"),T_t=o("FlaxRobertaModel"),M_t=o(" (RoBERTa model)"),E_t=l(),i6=a("li"),Qke=a("strong"),C_t=o("roformer"),w_t=o(" \u2014 "),jle=a("a"),A_t=o("FlaxRoFormerModel"),L_t=o(" (RoFormer model)"),y_t=l(),d6=a("li"),Wke=a("strong"),x_t=o("t5"),$_t=o(" \u2014 "),Gle=a("a"),k_t=o("FlaxT5Model"),S_t=o(" (T5 model)"),R_t=l(),c6=a("li"),Uke=a("strong"),P_t=o("vision-text-dual-encoder"),B_t=o(" \u2014 "),Ole=a("a"),I_t=o("FlaxVisionTextDualEncoderModel"),N_t=o(" (VisionTextDualEncoder model)"),q_t=l(),f6=a("li"),Hke=a("strong"),D_t=o("vit"),j_t=o(" \u2014 "),Vle=a("a"),G_t=o("FlaxViTModel"),O_t=o(" (ViT model)"),V_t=l(),m6=a("li"),Jke=a("strong"),X_t=o("wav2vec2"),z_t=o(" \u2014 "),Xle=a("a"),Q_t=o("FlaxWav2Vec2Model"),W_t=o(" (Wav2Vec2 model)"),U_t=l(),g6=a("li"),Yke=a("strong"),H_t=o("xglm"),J_t=o(" \u2014 "),zle=a("a"),Y_t=o("FlaxXGLMModel"),Z_t=o(" (XGLM model)"),K_t=l(),h6=a("li"),Zke=a("strong"),e1t=o("xlm-roberta"),o1t=o(" \u2014 "),Qle=a("a"),r1t=o("FlaxXLMRobertaModel"),t1t=o(" (XLM-RoBERTa model)"),a1t=l(),F(p6.$$.fragment),vno=l(),im=a("h2"),u6=a("a"),Kke=a("span"),F(WP.$$.fragment),n1t=l(),eSe=a("span"),s1t=o("FlaxAutoModelForCausalLM"),Fno=l(),Lr=a("div"),F(UP.$$.fragment),l1t=l(),dm=a("p"),i1t=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.`),g1t=l(),HP=a("p"),h1t=o("This class cannot be instantiated directly using "),oSe=a("code"),p1t=o("__init__()"),u1t=o(" (throws an error)."),_1t=l(),ha=a("div"),F(JP.$$.fragment),b1t=l(),rSe=a("p"),v1t=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),F1t=l(),cm=a("p"),T1t=o(`Note:
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passed as an argument or loaded from `),sSe=a("code"),P1t=o("pretrained_model_name_or_path"),B1t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lSe=a("code"),I1t=o("pretrained_model_name_or_path"),N1t=o(":"),q1t=l(),$e=a("ul"),b6=a("li"),iSe=a("strong"),D1t=o("bart"),j1t=o(" \u2014 "),Jle=a("a"),G1t=o("FlaxBartForCausalLM"),O1t=o(" (BART model)"),V1t=l(),v6=a("li"),dSe=a("strong"),X1t=o("bert"),z1t=o(" \u2014 "),Yle=a("a"),Q1t=o("FlaxBertForCausalLM"),W1t=o(" (BERT model)"),U1t=l(),F6=a("li"),cSe=a("strong"),H1t=o("big_bird"),J1t=o(" \u2014 "),Zle=a("a"),Y1t=o("FlaxBigBirdForCausalLM"),Z1t=o(" (BigBird model)"),K1t=l(),T6=a("li"),fSe=a("strong"),e9t=o("electra"),o9t=o(" \u2014 "),Kle=a("a"),r9t=o("FlaxElectraForCausalLM"),t9t=o(" (ELECTRA model)"),a9t=l(),M6=a("li"),mSe=a("strong"),n9t=o("gpt2"),s9t=o(" \u2014 "),eie=a("a"),l9t=o("FlaxGPT2LMHeadModel"),i9t=o(" (OpenAI GPT-2 model)"),d9t=l(),E6=a("li"),gSe=a("strong"),c9t=o("gpt_neo"),f9t=o(" \u2014 "),oie=a("a"),m9t=o("FlaxGPTNeoForCausalLM"),g9t=o(" (GPT Neo model)"),h9t=l(),C6=a("li"),hSe=a("strong"),p9t=o("gptj"),u9t=o(" \u2014 "),rie=a("a"),_9t=o("FlaxGPTJForCausalLM"),b9t=o(" (GPT-J model)"),v9t=l(),w6=a("li"),pSe=a("strong"),F9t=o("opt"),T9t=o(" \u2014 "),tie=a("a"),M9t=o("FlaxOPTForCausalLM"),E9t=o(" (OPT model)"),C9t=l(),A6=a("li"),uSe=a("strong"),w9t=o("roberta"),A9t=o(" \u2014 "),aie=a("a"),L9t=o("FlaxRobertaForCausalLM"),y9t=o(" (RoBERTa model)"),x9t=l(),L6=a("li"),_Se=a("strong"),$9t=o("xglm"),k9t=o(" \u2014 "),nie=a("a"),S9t=o("FlaxXGLMForCausalLM"),R9t=o(" (XGLM model)"),P9t=l(),F(y6.$$.fragment),Tno=l(),fm=a("h2"),x6=a("a"),bSe=a("span"),F(ZP.$$.fragment),B9t=l(),vSe=a("span"),I9t=o("FlaxAutoModelForPreTraining"),Mno=l(),yr=a("div"),F(KP.$$.fragment),N9t=l(),mm=a("p"),q9t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
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method.`),V9t=l(),eB=a("p"),X9t=o("This class cannot be instantiated directly using "),FSe=a("code"),z9t=o("__init__()"),Q9t=o(" (throws an error)."),W9t=l(),pa=a("div"),F(oB.$$.fragment),U9t=l(),TSe=a("p"),H9t=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),J9t=l(),gm=a("p"),Y9t=o(`Note:
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passed as an argument or loaded from `),wSe=a("code"),c2t=o("pretrained_model_name_or_path"),f2t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ASe=a("code"),m2t=o("pretrained_model_name_or_path"),g2t=o(":"),h2t=l(),Ee=a("ul"),k6=a("li"),LSe=a("strong"),p2t=o("albert"),u2t=o(" \u2014 "),die=a("a"),_2t=o("FlaxAlbertForPreTraining"),b2t=o(" (ALBERT model)"),v2t=l(),S6=a("li"),ySe=a("strong"),F2t=o("bart"),T2t=o(" \u2014 "),cie=a("a"),M2t=o("FlaxBartForConditionalGeneration"),E2t=o(" (BART model)"),C2t=l(),R6=a("li"),xSe=a("strong"),w2t=o("bert"),A2t=o(" \u2014 "),fie=a("a"),L2t=o("FlaxBertForPreTraining"),y2t=o(" (BERT model)"),x2t=l(),P6=a("li"),$Se=a("strong"),$2t=o("big_bird"),k2t=o(" \u2014 "),mie=a("a"),S2t=o("FlaxBigBirdForPreTraining"),R2t=o(" (BigBird model)"),P2t=l(),B6=a("li"),kSe=a("strong"),B2t=o("electra"),I2t=o(" \u2014 "),gie=a("a"),N2t=o("FlaxElectraForPreTraining"),q2t=o(" (ELECTRA model)"),D2t=l(),I6=a("li"),SSe=a("strong"),j2t=o("longt5"),G2t=o(" \u2014 "),hie=a("a"),O2t=o("FlaxLongT5ForConditionalGeneration"),V2t=o(" (LongT5 model)"),X2t=l(),N6=a("li"),RSe=a("strong"),z2t=o("mbart"),Q2t=o(" \u2014 "),pie=a("a"),W2t=o("FlaxMBartForConditionalGeneration"),U2t=o(" (mBART model)"),H2t=l(),q6=a("li"),PSe=a("strong"),J2t=o("mt5"),Y2t=o(" \u2014 "),uie=a("a"),Z2t=o("FlaxMT5ForConditionalGeneration"),K2t=o(" (MT5 model)"),ebt=l(),D6=a("li"),BSe=a("strong"),obt=o("roberta"),rbt=o(" \u2014 "),_ie=a("a"),tbt=o("FlaxRobertaForMaskedLM"),abt=o(" (RoBERTa model)"),nbt=l(),j6=a("li"),ISe=a("strong"),sbt=o("roformer"),lbt=o(" \u2014 "),bie=a("a"),ibt=o("FlaxRoFormerForMaskedLM"),dbt=o(" (RoFormer model)"),cbt=l(),G6=a("li"),NSe=a("strong"),fbt=o("t5"),mbt=o(" \u2014 "),vie=a("a"),gbt=o("FlaxT5ForConditionalGeneration"),hbt=o(" (T5 model)"),pbt=l(),O6=a("li"),qSe=a("strong"),ubt=o("wav2vec2"),_bt=o(" \u2014 "),Fie=a("a"),bbt=o("FlaxWav2Vec2ForPreTraining"),vbt=o(" (Wav2Vec2 model)"),Fbt=l(),V6=a("li"),DSe=a("strong"),Tbt=o("xlm-roberta"),Mbt=o(" \u2014 "),Tie=a("a"),Ebt=o("FlaxXLMRobertaForMaskedLM"),Cbt=o(" (XLM-RoBERTa model)"),wbt=l(),F(X6.$$.fragment),Eno=l(),hm=a("h2"),z6=a("a"),jSe=a("span"),F(tB.$$.fragment),Abt=l(),GSe=a("span"),Lbt=o("FlaxAutoModelForMaskedLM"),Cno=l(),xr=a("div"),F(aB.$$.fragment),ybt=l(),pm=a("p"),xbt=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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falling back to using pattern matching on `),gRe=a("code"),R0t=o("pretrained_model_name_or_path"),P0t=o(":"),B0t=l(),Se=a("ul"),s8=a("li"),hRe=a("strong"),I0t=o("bart"),N0t=o(" \u2014 "),qie=a("a"),q0t=o("FlaxBartForConditionalGeneration"),D0t=o(" (BART model)"),j0t=l(),l8=a("li"),pRe=a("strong"),G0t=o("blenderbot"),O0t=o(" \u2014 "),Die=a("a"),V0t=o("FlaxBlenderbotForConditionalGeneration"),X0t=o(" (Blenderbot model)"),z0t=l(),i8=a("li"),uRe=a("strong"),Q0t=o("blenderbot-small"),W0t=o(" \u2014 "),jie=a("a"),U0t=o("FlaxBlenderbotSmallForConditionalGeneration"),H0t=o(" (BlenderbotSmall model)"),J0t=l(),d8=a("li"),_Re=a("strong"),Y0t=o("encoder-decoder"),Z0t=o(" \u2014 "),Gie=a("a"),K0t=o("FlaxEncoderDecoderModel"),eFt=o(" (Encoder decoder model)"),oFt=l(),c8=a("li"),bRe=a("strong"),rFt=o("longt5"),tFt=o(" \u2014 "),Oie=a("a"),aFt=o("FlaxLongT5ForConditionalGeneration"),nFt=o(" (LongT5 model)"),sFt=l(),f8=a("li"),vRe=a("strong"),lFt=o("marian"),iFt=o(" \u2014 "),Vie=a("a"),dFt=o("FlaxMarianMTModel"),cFt=o(" (Marian model)"),fFt=l(),m8=a("li"),FRe=a("strong"),mFt=o("mbart"),gFt=o(" \u2014 "),Xie=a("a"),hFt=o("FlaxMBartForConditionalGeneration"),pFt=o(" (mBART model)"),uFt=l(),g8=a("li"),TRe=a("strong"),_Ft=o("mt5"),bFt=o(" \u2014 "),zie=a("a"),vFt=o("FlaxMT5ForConditionalGeneration"),FFt=o(" (MT5 model)"),TFt=l(),h8=a("li"),MRe=a("strong"),MFt=o("pegasus"),EFt=o(" \u2014 "),Qie=a("a"),CFt=o("FlaxPegasusForConditionalGeneration"),wFt=o(" (Pegasus model)"),AFt=l(),p8=a("li"),ERe=a("strong"),LFt=o("t5"),yFt=o(" \u2014 "),Wie=a("a"),xFt=o("FlaxT5ForConditionalGeneration"),$Ft=o(" (T5 model)"),kFt=l(),F(u8.$$.fragment),Lno=l(),Fm=a("h2"),_8=a("a"),CRe=a("span"),F(gB.$$.fragment),SFt=l(),wRe=a("span"),RFt=o("FlaxAutoModelForSequenceClassification"),yno=l(),kr=a("div"),F(hB.$$.fragment),PFt=l(),Tm=a("p"),BFt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
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zPt=s(C1e);T(B$.$$.fragment,zPt),zPt.forEach(t),XPt.forEach(t),W$o=i(oso),w1e=n(oso,"SPAN",{});var QPt=s(w1e);U$o=r(QPt,"AutoModel"),QPt.forEach(t),oso.forEach(t),qto=i(f),Io=n(f,"DIV",{class:!0});var Nl=s(Io);T(I$.$$.fragment,Nl),H$o=i(Nl),Sd=n(Nl,"P",{});var rce=s(Sd);J$o=r(rce,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),wX=n(rce,"A",{href:!0});var WPt=s(wX);Y$o=r(WPt,"from_pretrained()"),WPt.forEach(t),Z$o=r(rce," class method or the "),AX=n(rce,"A",{href:!0});var UPt=s(AX);K$o=r(UPt,"from_config()"),UPt.forEach(t),eko=r(rce,` class
method.`),rce.forEach(t),oko=i(Nl),N$=n(Nl,"P",{});var rso=s(N$);rko=r(rso,"This class cannot be instantiated directly using "),A1e=n(rso,"CODE",{});var HPt=s(A1e);tko=r(HPt,"__init__()"),HPt.forEach(t),ako=r(rso," (throws an error)."),rso.forEach(t),nko=i(Nl),Mt=n(Nl,"DIV",{class:!0});var ey=s(Mt);T(q$.$$.fragment,ey),sko=i(ey),L1e=n(ey,"P",{});var JPt=s(L1e);lko=r(JPt,"Instantiates one of the base model classes of the library from a configuration."),JPt.forEach(t),iko=i(ey),Rd=n(ey,"P",{});var tce=s(Rd);dko=r(tce,`Note:
Loading a model from its configuration file does `),y1e=n(tce,"STRONG",{});var YPt=s(y1e);cko=r(YPt,"not"),YPt.forEach(t),fko=r(tce,` load the model weights. It only affects the
model\u2019s configuration. Use `),LX=n(tce,"A",{href:!0});var ZPt=s(LX);mko=r(ZPt,"from_pretrained()"),ZPt.forEach(t),gko=r(tce," to load the model weights."),tce.forEach(t),hko=i(ey),T(R_.$$.fragment,ey),ey.forEach(t),pko=i(Nl),Ke=n(Nl,"DIV",{class:!0});var La=s(Ke);T(D$.$$.fragment,La),uko=i(La),x1e=n(La,"P",{});var KPt=s(x1e);_ko=r(KPt,"Instantiate one of the base model classes of the library from a pretrained model."),KPt.forEach(t),bko=i(La),nn=n(La,"P",{});var oy=s(nn);vko=r(oy,"The model class to instantiate is selected based on the "),$1e=n(oy,"CODE",{});var eBt=s($1e);Fko=r(eBt,"model_type"),eBt.forEach(t),Tko=r(oy,` property of the config object (either
passed as an argument or loaded from `),k1e=n(oy,"CODE",{});var oBt=s(k1e);Mko=r(oBt,"pretrained_model_name_or_path"),oBt.forEach(t),Eko=r(oy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),S1e=n(oy,"CODE",{});var rBt=s(S1e);Cko=r(rBt,"pretrained_model_name_or_path"),rBt.forEach(t),wko=r(oy,":"),oy.forEach(t),Ako=i(La),y=n(La,"UL",{});var x=s(y);P_=n(x,"LI",{});var qje=s(P_);R1e=n(qje,"STRONG",{});var tBt=s(R1e);Lko=r(tBt,"albert"),tBt.forEach(t),yko=r(qje," \u2014 "),yX=n(qje,"A",{href:!0});var aBt=s(yX);xko=r(aBt,"AlbertModel"),aBt.forEach(t),$ko=r(qje," (ALBERT model)"),qje.forEach(t),kko=i(x),B_=n(x,"LI",{});var Dje=s(B_);P1e=n(Dje,"STRONG",{});var nBt=s(P1e);Sko=r(nBt,"bart"),nBt.forEach(t),Rko=r(Dje," \u2014 "),xX=n(Dje,"A",{href:!0});var sBt=s(xX);Pko=r(sBt,"BartModel"),sBt.forEach(t),Bko=r(Dje," (BART model)"),Dje.forEach(t),Iko=i(x),I_=n(x,"LI",{});var jje=s(I_);B1e=n(jje,"STRONG",{});var lBt=s(B1e);Nko=r(lBt,"beit"),lBt.forEach(t),qko=r(jje," \u2014 "),$X=n(jje,"A",{href:!0});var iBt=s($X);Dko=r(iBt,"BeitModel"),iBt.forEach(t),jko=r(jje," (BEiT model)"),jje.forEach(t),Gko=i(x),N_=n(x,"LI",{});var Gje=s(N_);I1e=n(Gje,"STRONG",{});var dBt=s(I1e);Oko=r(dBt,"bert"),dBt.forEach(t),Vko=r(Gje," \u2014 "),kX=n(Gje,"A",{href:!0});var cBt=s(kX);Xko=r(cBt,"BertModel"),cBt.forEach(t),zko=r(Gje," (BERT model)"),Gje.forEach(t),Qko=i(x),q_=n(x,"LI",{});var Oje=s(q_);N1e=n(Oje,"STRONG",{});var fBt=s(N1e);Wko=r(fBt,"bert-generation"),fBt.forEach(t),Uko=r(Oje," \u2014 "),SX=n(Oje,"A",{href:!0});var mBt=s(SX);Hko=r(mBt,"BertGenerationEncoder"),mBt.forEach(t),Jko=r(Oje," (Bert Generation model)"),Oje.forEach(t),Yko=i(x),D_=n(x,"LI",{});var Vje=s(D_);q1e=n(Vje,"STRONG",{});var gBt=s(q1e);Zko=r(gBt,"big_bird"),gBt.forEach(t),Kko=r(Vje," \u2014 "),RX=n(Vje,"A",{href:!0});var hBt=s(RX);eSo=r(hBt,"BigBirdModel"),hBt.forEach(t),oSo=r(Vje," (BigBird model)"),Vje.forEach(t),rSo=i(x),j_=n(x,"LI",{});var Xje=s(j_);D1e=n(Xje,"STRONG",{});var pBt=s(D1e);tSo=r(pBt,"bigbird_pegasus"),pBt.forEach(t),aSo=r(Xje," \u2014 "),PX=n(Xje,"A",{href:!0});var uBt=s(PX);nSo=r(uBt,"BigBirdPegasusModel"),uBt.forEach(t),sSo=r(Xje," (BigBird-Pegasus model)"),Xje.forEach(t),lSo=i(x),G_=n(x,"LI",{});var zje=s(G_);j1e=n(zje,"STRONG",{});var _Bt=s(j1e);iSo=r(_Bt,"blenderbot"),_Bt.forEach(t),dSo=r(zje," \u2014 "),BX=n(zje,"A",{href:!0});var bBt=s(BX);cSo=r(bBt,"BlenderbotModel"),bBt.forEach(t),fSo=r(zje," (Blenderbot model)"),zje.forEach(t),mSo=i(x),O_=n(x,"LI",{});var Qje=s(O_);G1e=n(Qje,"STRONG",{});var vBt=s(G1e);gSo=r(vBt,"blenderbot-small"),vBt.forEach(t),hSo=r(Qje," \u2014 "),IX=n(Qje,"A",{href:!0});var FBt=s(IX);pSo=r(FBt,"BlenderbotSmallModel"),FBt.forEach(t),uSo=r(Qje," (BlenderbotSmall model)"),Qje.forEach(t),_So=i(x),V_=n(x,"LI",{});var Wje=s(V_);O1e=n(Wje,"STRONG",{});var TBt=s(O1e);bSo=r(TBt,"bloom"),TBt.forEach(t),vSo=r(Wje," \u2014 "),NX=n(Wje,"A",{href:!0});var MBt=s(NX);FSo=r(MBt,"BloomModel"),MBt.forEach(t),TSo=r(Wje," (BLOOM model)"),Wje.forEach(t),MSo=i(x),X_=n(x,"LI",{});var Uje=s(X_);V1e=n(Uje,"STRONG",{});var EBt=s(V1e);ESo=r(EBt,"camembert"),EBt.forEach(t),CSo=r(Uje," \u2014 "),qX=n(Uje,"A",{href:!0});var CBt=s(qX);wSo=r(CBt,"CamembertModel"),CBt.forEach(t),ASo=r(Uje," (CamemBERT model)"),Uje.forEach(t),LSo=i(x),z_=n(x,"LI",{});var Hje=s(z_);X1e=n(Hje,"STRONG",{});var wBt=s(X1e);ySo=r(wBt,"canine"),wBt.forEach(t),xSo=r(Hje," \u2014 "),DX=n(Hje,"A",{href:!0});var ABt=s(DX);$So=r(ABt,"CanineModel"),ABt.forEach(t),kSo=r(Hje," (CANINE model)"),Hje.forEach(t),SSo=i(x),Q_=n(x,"LI",{});var Jje=s(Q_);z1e=n(Jje,"STRONG",{});var LBt=s(z1e);RSo=r(LBt,"clip"),LBt.forEach(t),PSo=r(Jje," \u2014 "),jX=n(Jje,"A",{href:!0});var yBt=s(jX);BSo=r(yBt,"CLIPModel"),yBt.forEach(t),ISo=r(Jje," (CLIP model)"),Jje.forEach(t),NSo=i(x),W_=n(x,"LI",{});var Yje=s(W_);Q1e=n(Yje,"STRONG",{});var xBt=s(Q1e);qSo=r(xBt,"codegen"),xBt.forEach(t),DSo=r(Yje," \u2014 "),GX=n(Yje,"A",{href:!0});var $Bt=s(GX);jSo=r($Bt,"CodeGenModel"),$Bt.forEach(t),GSo=r(Yje," (CodeGen model)"),Yje.forEach(t),OSo=i(x),U_=n(x,"LI",{});var Zje=s(U_);W1e=n(Zje,"STRONG",{});var kBt=s(W1e);VSo=r(kBt,"conditional_detr"),kBt.forEach(t),XSo=r(Zje," \u2014 "),OX=n(Zje,"A",{href:!0});var SBt=s(OX);zSo=r(SBt,"ConditionalDetrModel"),SBt.forEach(t),QSo=r(Zje," (Conditional DETR model)"),Zje.forEach(t),WSo=i(x),H_=n(x,"LI",{});var Kje=s(H_);U1e=n(Kje,"STRONG",{});var RBt=s(U1e);USo=r(RBt,"convbert"),RBt.forEach(t),HSo=r(Kje," \u2014 "),VX=n(Kje,"A",{href:!0});var PBt=s(VX);JSo=r(PBt,"ConvBertModel"),PBt.forEach(t),YSo=r(Kje," (ConvBERT model)"),Kje.forEach(t),ZSo=i(x),J_=n(x,"LI",{});var eGe=s(J_);H1e=n(eGe,"STRONG",{});var BBt=s(H1e);KSo=r(BBt,"convnext"),BBt.forEach(t),eRo=r(eGe," \u2014 "),XX=n(eGe,"A",{href:!0});var IBt=s(XX);oRo=r(IBt,"ConvNextModel"),IBt.forEach(t),rRo=r(eGe," (ConvNeXT model)"),eGe.forEach(t),tRo=i(x),Y_=n(x,"LI",{});var oGe=s(Y_);J1e=n(oGe,"STRONG",{});var NBt=s(J1e);aRo=r(NBt,"ctrl"),NBt.forEach(t),nRo=r(oGe," \u2014 "),zX=n(oGe,"A",{href:!0});var qBt=s(zX);sRo=r(qBt,"CTRLModel"),qBt.forEach(t),lRo=r(oGe," (CTRL model)"),oGe.forEach(t),iRo=i(x),Z_=n(x,"LI",{});var rGe=s(Z_);Y1e=n(rGe,"STRONG",{});var DBt=s(Y1e);dRo=r(DBt,"cvt"),DBt.forEach(t),cRo=r(rGe," \u2014 "),QX=n(rGe,"A",{href:!0});var jBt=s(QX);fRo=r(jBt,"CvtModel"),jBt.forEach(t),mRo=r(rGe," (CvT model)"),rGe.forEach(t),gRo=i(x),K_=n(x,"LI",{});var tGe=s(K_);Z1e=n(tGe,"STRONG",{});var GBt=s(Z1e);hRo=r(GBt,"data2vec-audio"),GBt.forEach(t),pRo=r(tGe," \u2014 "),WX=n(tGe,"A",{href:!0});var OBt=s(WX);uRo=r(OBt,"Data2VecAudioModel"),OBt.forEach(t),_Ro=r(tGe," (Data2VecAudio model)"),tGe.forEach(t),bRo=i(x),e1=n(x,"LI",{});var aGe=s(e1);K1e=n(aGe,"STRONG",{});var VBt=s(K1e);vRo=r(VBt,"data2vec-text"),VBt.forEach(t),FRo=r(aGe," \u2014 "),UX=n(aGe,"A",{href:!0});var XBt=s(UX);TRo=r(XBt,"Data2VecTextModel"),XBt.forEach(t),MRo=r(aGe," (Data2VecText model)"),aGe.forEach(t),ERo=i(x),o1=n(x,"LI",{});var nGe=s(o1);e9e=n(nGe,"STRONG",{});var zBt=s(e9e);CRo=r(zBt,"data2vec-vision"),zBt.forEach(t),wRo=r(nGe," \u2014 "),HX=n(nGe,"A",{href:!0});var 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with the `),WW=n(sce,"A",{href:!0});var eOt=s(WW);XUo=r(eOt,"from_pretrained()"),eOt.forEach(t),zUo=r(sce," class method or the "),UW=n(sce,"A",{href:!0});var oOt=s(UW);QUo=r(oOt,"from_config()"),oOt.forEach(t),WUo=r(sce,` class
method.`),sce.forEach(t),UUo=i(Dl),W$=n(Dl,"P",{});var sso=s(W$);HUo=r(sso,"This class cannot be instantiated directly using "),dve=n(sso,"CODE",{});var rOt=s(dve);JUo=r(rOt,"__init__()"),rOt.forEach(t),YUo=r(sso," (throws an error)."),sso.forEach(t),ZUo=i(Dl),Ct=n(Dl,"DIV",{class:!0});var ay=s(Ct);T(U$.$$.fragment,ay),KUo=i(ay),cve=n(ay,"P",{});var tOt=s(cve);eHo=r(tOt,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),tOt.forEach(t),oHo=i(ay),Dd=n(ay,"P",{});var lce=s(Dd);rHo=r(lce,`Note:
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model\u2019s configuration. Use `),HW=n(lce,"A",{href:!0});var nOt=s(HW);nHo=r(nOt,"from_pretrained()"),nOt.forEach(t),sHo=r(lce," to load the model weights."),lce.forEach(t),lHo=i(ay),T(ob.$$.fragment,ay),ay.forEach(t),iHo=i(Dl),oo=n(Dl,"DIV",{class:!0});var xa=s(oo);T(H$.$$.fragment,xa),dHo=i(xa),mve=n(xa,"P",{});var sOt=s(mve);cHo=r(sOt,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),sOt.forEach(t),fHo=i(xa),ln=n(xa,"P",{});var ny=s(ln);mHo=r(ny,"The model class to instantiate is selected based on the "),gve=n(ny,"CODE",{});var lOt=s(gve);gHo=r(lOt,"model_type"),lOt.forEach(t),hHo=r(ny,` property of the config object (either
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falling back to using pattern matching on `),pve=n(ny,"CODE",{});var dOt=s(pve);_Ho=r(dOt,"pretrained_model_name_or_path"),dOt.forEach(t),bHo=r(ny,":"),ny.forEach(t),vHo=i(xa),W=n(xa,"UL",{});var H=s(W);rb=n(H,"LI",{});var eXe=s(rb);uve=n(eXe,"STRONG",{});var cOt=s(uve);FHo=r(cOt,"bart"),cOt.forEach(t),THo=r(eXe," \u2014 "),JW=n(eXe,"A",{href:!0});var fOt=s(JW);MHo=r(fOt,"BartForCausalLM"),fOt.forEach(t),EHo=r(eXe," (BART model)"),eXe.forEach(t),CHo=i(H),tb=n(H,"LI",{});var oXe=s(tb);_ve=n(oXe,"STRONG",{});var mOt=s(_ve);wHo=r(mOt,"bert"),mOt.forEach(t),AHo=r(oXe," \u2014 "),YW=n(oXe,"A",{href:!0});var gOt=s(YW);LHo=r(gOt,"BertLMHeadModel"),gOt.forEach(t),yHo=r(oXe," (BERT model)"),oXe.forEach(t),xHo=i(H),ab=n(H,"LI",{});var rXe=s(ab);bve=n(rXe,"STRONG",{});var hOt=s(bve);$Ho=r(hOt,"bert-generation"),hOt.forEach(t),kHo=r(rXe," \u2014 "),ZW=n(rXe,"A",{href:!0});var pOt=s(ZW);SHo=r(pOt,"BertGenerationDecoder"),pOt.forEach(t),RHo=r(rXe," (Bert Generation 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method.`),bce.forEach(t),Yur=i(zl),Ck=n(zl,"P",{});var vso=s(Ck);Zur=r(vso,"This class cannot be instantiated directly using "),YTe=n(vso,"CODE",{});var OJt=s(YTe);Kur=r(OJt,"__init__()"),OJt.forEach(t),e_r=r(vso," (throws an error)."),vso.forEach(t),o_r=i(zl),$t=n(zl,"DIV",{class:!0});var uy=s($t);T(wk.$$.fragment,uy),r_r=i(uy),ZTe=n(uy,"P",{});var VJt=s(ZTe);t_r=r(VJt,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),VJt.forEach(t),a_r=i(uy),tc=n(uy,"P",{});var vce=s(tc);n_r=r(vce,`Note:
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falling back to using pattern matching on `),tMe=n(_y,"CODE",{});var HJt=s(tMe);F_r=r(HJt,"pretrained_model_name_or_path"),HJt.forEach(t),T_r=r(_y,":"),_y.forEach(t),M_r=i(Ba),Ue=n(Ba,"UL",{});var ht=s(Ue);o7=n(ht,"LI",{});var BWe=s(o7);aMe=n(BWe,"STRONG",{});var JJt=s(aMe);E_r=r(JJt,"bert"),JJt.forEach(t),C_r=r(BWe," \u2014 "),OY=n(BWe,"A",{href:!0});var YJt=s(OY);w_r=r(YJt,"BertForNextSentencePrediction"),YJt.forEach(t),A_r=r(BWe," (BERT model)"),BWe.forEach(t),L_r=i(ht),r7=n(ht,"LI",{});var IWe=s(r7);nMe=n(IWe,"STRONG",{});var ZJt=s(nMe);y_r=r(ZJt,"ernie"),ZJt.forEach(t),x_r=r(IWe," \u2014 "),VY=n(IWe,"A",{href:!0});var KJt=s(VY);$_r=r(KJt,"ErnieForNextSentencePrediction"),KJt.forEach(t),k_r=r(IWe," (ERNIE model)"),IWe.forEach(t),S_r=i(ht),t7=n(ht,"LI",{});var NWe=s(t7);sMe=n(NWe,"STRONG",{});var eYt=s(sMe);R_r=r(eYt,"fnet"),eYt.forEach(t),P_r=r(NWe," \u2014 "),XY=n(NWe,"A",{href:!0});var oYt=s(XY);B_r=r(oYt,"FNetForNextSentencePrediction"),oYt.forEach(t),I_r=r(NWe," (FNet model)"),NWe.forEach(t),N_r=i(ht),a7=n(ht,"LI",{});var qWe=s(a7);lMe=n(qWe,"STRONG",{});var rYt=s(lMe);q_r=r(rYt,"megatron-bert"),rYt.forEach(t),D_r=r(qWe," \u2014 "),zY=n(qWe,"A",{href:!0});var tYt=s(zY);j_r=r(tYt,"MegatronBertForNextSentencePrediction"),tYt.forEach(t),G_r=r(qWe," (Megatron-BERT model)"),qWe.forEach(t),O_r=i(ht),n7=n(ht,"LI",{});var DWe=s(n7);iMe=n(DWe,"STRONG",{});var aYt=s(iMe);V_r=r(aYt,"mobilebert"),aYt.forEach(t),X_r=r(DWe," \u2014 "),QY=n(DWe,"A",{href:!0});var nYt=s(QY);z_r=r(nYt,"MobileBertForNextSentencePrediction"),nYt.forEach(t),Q_r=r(DWe," (MobileBERT model)"),DWe.forEach(t),W_r=i(ht),s7=n(ht,"LI",{});var jWe=s(s7);dMe=n(jWe,"STRONG",{});var sYt=s(dMe);U_r=r(sYt,"nezha"),sYt.forEach(t),H_r=r(jWe," \u2014 "),WY=n(jWe,"A",{href:!0});var lYt=s(WY);J_r=r(lYt,"NezhaForNextSentencePrediction"),lYt.forEach(t),Y_r=r(jWe," (Nezha model)"),jWe.forEach(t),Z_r=i(ht),l7=n(ht,"LI",{});var GWe=s(l7);cMe=n(GWe,"STRONG",{});var iYt=s(cMe);K_r=r(iYt,"qdqbert"),iYt.forEach(t),e1r=r(GWe," \u2014 "),UY=n(GWe,"A",{href:!0});var dYt=s(UY);o1r=r(dYt,"QDQBertForNextSentencePrediction"),dYt.forEach(t),r1r=r(GWe," (QDQBert model)"),GWe.forEach(t),ht.forEach(t),t1r=i(Ba),i7=n(Ba,"P",{});var OWe=s(i7);a1r=r(OWe,"The model is set in evaluation mode by default using "),fMe=n(OWe,"CODE",{});var cYt=s(fMe);n1r=r(cYt,"model.eval()"),cYt.forEach(t),s1r=r(OWe,` (so for instance, dropout modules are
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method.`),Fce.forEach(t),_1r=i(Ql),xk=n(Ql,"P",{});var Tso=s(xk);b1r=r(Tso,"This class cannot be instantiated directly using "),pMe=n(Tso,"CODE",{});var _Yt=s(pMe);v1r=r(_Yt,"__init__()"),_Yt.forEach(t),F1r=r(Tso," (throws an error)."),Tso.forEach(t),T1r=i(Ql),kt=n(Ql,"DIV",{class:!0});var by=s(kt);T($k.$$.fragment,by),M1r=i(by),uMe=n(by,"P",{});var bYt=s(uMe);E1r=r(bYt,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),bYt.forEach(t),C1r=i(by),sc=n(by,"P",{});var Tce=s(sc);w1r=r(Tce,`Note:
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model\u2019s configuration. Use `),YY=n(Tce,"A",{href:!0});var FYt=s(YY);y1r=r(FYt,"from_pretrained()"),FYt.forEach(t),x1r=r(Tce," to load the model weights."),Tce.forEach(t),$1r=i(by),T(f7.$$.fragment,by),by.forEach(t),k1r=i(Ql),io=n(Ql,"DIV",{class:!0});var Ia=s(io);T(kk.$$.fragment,Ia),S1r=i(Ia),bMe=n(Ia,"P",{});var TYt=s(bMe);R1r=r(TYt,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),TYt.forEach(t),P1r=i(Ia),pn=n(Ia,"P",{});var vy=s(pn);B1r=r(vy,"The model class to instantiate is selected based on the "),vMe=n(vy,"CODE",{});var MYt=s(vMe);I1r=r(MYt,"model_type"),MYt.forEach(t),N1r=r(vy,` property of the config object (either
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falling back to using pattern matching on `),TMe=n(vy,"CODE",{});var CYt=s(TMe);j1r=r(CYt,"pretrained_model_name_or_path"),CYt.forEach(t),G1r=r(vy,":"),vy.forEach(t),O1r=i(Ia),U=n(Ia,"UL",{});var J=s(U);m7=n(J,"LI",{});var VWe=s(m7);MMe=n(VWe,"STRONG",{});var wYt=s(MMe);V1r=r(wYt,"albert"),wYt.forEach(t),X1r=r(VWe," \u2014 "),ZY=n(VWe,"A",{href:!0});var AYt=s(ZY);z1r=r(AYt,"AlbertForTokenClassification"),AYt.forEach(t),Q1r=r(VWe," (ALBERT model)"),VWe.forEach(t),W1r=i(J),g7=n(J,"LI",{});var XWe=s(g7);EMe=n(XWe,"STRONG",{});var LYt=s(EMe);U1r=r(LYt,"bert"),LYt.forEach(t),H1r=r(XWe," \u2014 "),KY=n(XWe,"A",{href:!0});var yYt=s(KY);J1r=r(yYt,"BertForTokenClassification"),yYt.forEach(t),Y1r=r(XWe," (BERT model)"),XWe.forEach(t),Z1r=i(J),h7=n(J,"LI",{});var zWe=s(h7);CMe=n(zWe,"STRONG",{});var xYt=s(CMe);K1r=r(xYt,"big_bird"),xYt.forEach(t),e9r=r(zWe," \u2014 "),eZ=n(zWe,"A",{href:!0});var $Yt=s(eZ);o9r=r($Yt,"BigBirdForTokenClassification"),$Yt.forEach(t),r9r=r(zWe," (BigBird 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model\u2019s configuration. Use `),GZ=n(Ece,"A",{href:!0});var dKt=s(GZ);r0r=r(dKt,"from_pretrained()"),dKt.forEach(t),t0r=r(Ece," to load the model weights."),Ece.forEach(t),a0r=i(Fy),T(rT.$$.fragment,Fy),Fy.forEach(t),n0r=i(Wl),co=n(Wl,"DIV",{class:!0});var Na=s(co);T(Ik.$$.fragment,Na),s0r=i(Na),uEe=n(Na,"P",{});var cKt=s(uEe);l0r=r(cKt,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),cKt.forEach(t),i0r=i(Na),un=n(Na,"P",{});var Ty=s(un);d0r=r(Ty,"The model class to instantiate is selected based on the "),_Ee=n(Ty,"CODE",{});var fKt=s(_Ee);c0r=r(fKt,"model_type"),fKt.forEach(t),f0r=r(Ty,` property of the config object (either
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Yea=s(RK);yMr=r(Yea,"XLMRobertaXLForQuestionAnswering"),Yea.forEach(t),xMr=r(MHe," (XLM-RoBERTa-XL model)"),MHe.forEach(t),$Mr=i(X),HT=n(X,"LI",{});var EHe=s(HT);m4e=n(EHe,"STRONG",{});var Zea=s(m4e);kMr=r(Zea,"xlnet"),Zea.forEach(t),SMr=r(EHe," \u2014 "),PK=n(EHe,"A",{href:!0});var Kea=s(PK);RMr=r(Kea,"XLNetForQuestionAnsweringSimple"),Kea.forEach(t),PMr=r(EHe," (XLNet model)"),EHe.forEach(t),BMr=i(X),JT=n(X,"LI",{});var CHe=s(JT);g4e=n(CHe,"STRONG",{});var eoa=s(g4e);IMr=r(eoa,"yoso"),eoa.forEach(t),NMr=r(CHe," \u2014 "),BK=n(CHe,"A",{href:!0});var ooa=s(BK);qMr=r(ooa,"YosoForQuestionAnswering"),ooa.forEach(t),DMr=r(CHe," (YOSO model)"),CHe.forEach(t),X.forEach(t),jMr=i(Na),YT=n(Na,"P",{});var wHe=s(YT);GMr=r(wHe,"The model is set in evaluation mode by default using "),h4e=n(wHe,"CODE",{});var roa=s(h4e);OMr=r(roa,"model.eval()"),roa.forEach(t),VMr=r(wHe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),p4e=n(wHe,"CODE",{});var toa=s(p4e);XMr=r(toa,"model.train()"),toa.forEach(t),wHe.forEach(t),zMr=i(Na),T(ZT.$$.fragment,Na),Na.forEach(t),Wl.forEach(t),nao=i(f),cc=n(f,"H2",{class:!0});var Cso=s(cc);KT=n(Cso,"A",{id:!0,class:!0,href:!0});var aoa=s(KT);u4e=n(aoa,"SPAN",{});var noa=s(u4e);T(Nk.$$.fragment,noa),noa.forEach(t),aoa.forEach(t),QMr=i(Cso),_4e=n(Cso,"SPAN",{});var soa=s(_4e);WMr=r(soa,"AutoModelForTableQuestionAnswering"),soa.forEach(t),Cso.forEach(t),sao=i(f),Wo=n(f,"DIV",{class:!0});var Ul=s(Wo);T(qk.$$.fragment,Ul),UMr=i(Ul),fc=n(Ul,"P",{});var Cce=s(fc);HMr=r(Cce,`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 `),IK=n(Cce,"A",{href:!0});var loa=s(IK);JMr=r(loa,"from_pretrained()"),loa.forEach(t),YMr=r(Cce," class method or the "),NK=n(Cce,"A",{href:!0});var ioa=s(NK);ZMr=r(ioa,"from_config()"),ioa.forEach(t),KMr=r(Cce,` class
method.`),Cce.forEach(t),eEr=i(Ul),Dk=n(Ul,"P",{});var wso=s(Dk);oEr=r(wso,"This class cannot be instantiated directly using "),b4e=n(wso,"CODE",{});var doa=s(b4e);rEr=r(doa,"__init__()"),doa.forEach(t),tEr=r(wso," (throws an error)."),wso.forEach(t),aEr=i(Ul),Rt=n(Ul,"DIV",{class:!0});var My=s(Rt);T(jk.$$.fragment,My),nEr=i(My),v4e=n(My,"P",{});var coa=s(v4e);sEr=r(coa,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),coa.forEach(t),lEr=i(My),mc=n(My,"P",{});var wce=s(mc);iEr=r(wce,`Note:
Loading a model from its configuration file does `),F4e=n(wce,"STRONG",{});var foa=s(F4e);dEr=r(foa,"not"),foa.forEach(t),cEr=r(wce,` load the model weights. It only affects the
model\u2019s configuration. Use `),qK=n(wce,"A",{href:!0});var moa=s(qK);fEr=r(moa,"from_pretrained()"),moa.forEach(t),mEr=r(wce," to load the model weights."),wce.forEach(t),gEr=i(My),T(eM.$$.fragment,My),My.forEach(t),hEr=i(Ul),fo=n(Ul,"DIV",{class:!0});var qa=s(fo);T(Gk.$$.fragment,qa),pEr=i(qa),T4e=n(qa,"P",{});var goa=s(T4e);uEr=r(goa,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),goa.forEach(t),_Er=i(qa),_n=n(qa,"P",{});var Ey=s(_n);bEr=r(Ey,"The model class to instantiate is selected based on the "),M4e=n(Ey,"CODE",{});var hoa=s(M4e);vEr=r(hoa,"model_type"),hoa.forEach(t),FEr=r(Ey,` property of the config object (either
passed as an argument or loaded from `),E4e=n(Ey,"CODE",{});var poa=s(E4e);TEr=r(poa,"pretrained_model_name_or_path"),poa.forEach(t),MEr=r(Ey,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),C4e=n(Ey,"CODE",{});var uoa=s(C4e);EEr=r(uoa,"pretrained_model_name_or_path"),uoa.forEach(t),CEr=r(Ey,":"),Ey.forEach(t),wEr=i(qa),w4e=n(qa,"UL",{});var _oa=s(w4e);oM=n(_oa,"LI",{});var AHe=s(oM);A4e=n(AHe,"STRONG",{});var boa=s(A4e);AEr=r(boa,"tapas"),boa.forEach(t),LEr=r(AHe," \u2014 "),DK=n(AHe,"A",{href:!0});var voa=s(DK);yEr=r(voa,"TapasForQuestionAnswering"),voa.forEach(t),xEr=r(AHe," (TAPAS model)"),AHe.forEach(t),_oa.forEach(t),$Er=i(qa),rM=n(qa,"P",{});var LHe=s(rM);kEr=r(LHe,"The model is set in evaluation mode by default using "),L4e=n(LHe,"CODE",{});var Foa=s(L4e);SEr=r(Foa,"model.eval()"),Foa.forEach(t),REr=r(LHe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),y4e=n(LHe,"CODE",{});var Toa=s(y4e);PEr=r(Toa,"model.train()"),Toa.forEach(t),LHe.forEach(t),BEr=i(qa),T(tM.$$.fragment,qa),qa.forEach(t),Ul.forEach(t),lao=i(f),gc=n(f,"H2",{class:!0});var Aso=s(gc);aM=n(Aso,"A",{id:!0,class:!0,href:!0});var Moa=s(aM);x4e=n(Moa,"SPAN",{});var Eoa=s(x4e);T(Ok.$$.fragment,Eoa),Eoa.forEach(t),Moa.forEach(t),IEr=i(Aso),$4e=n(Aso,"SPAN",{});var Coa=s($4e);NEr=r(Coa,"AutoModelForDocumentQuestionAnswering"),Coa.forEach(t),Aso.forEach(t),iao=i(f),Uo=n(f,"DIV",{class:!0});var Hl=s(Uo);T(Vk.$$.fragment,Hl),qEr=i(Hl),hc=n(Hl,"P",{});var Ace=s(hc);DEr=r(Ace,`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 `),jK=n(Ace,"A",{href:!0});var woa=s(jK);jEr=r(woa,"from_pretrained()"),woa.forEach(t),GEr=r(Ace," class method or the "),GK=n(Ace,"A",{href:!0});var Aoa=s(GK);OEr=r(Aoa,"from_config()"),Aoa.forEach(t),VEr=r(Ace,` class
method.`),Ace.forEach(t),XEr=i(Hl),Xk=n(Hl,"P",{});var Lso=s(Xk);zEr=r(Lso,"This class cannot be instantiated directly using "),k4e=n(Lso,"CODE",{});var Loa=s(k4e);QEr=r(Loa,"__init__()"),Loa.forEach(t),WEr=r(Lso," (throws an error)."),Lso.forEach(t),UEr=i(Hl),Pt=n(Hl,"DIV",{class:!0});var Cy=s(Pt);T(zk.$$.fragment,Cy),HEr=i(Cy),S4e=n(Cy,"P",{});var yoa=s(S4e);JEr=r(yoa,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),yoa.forEach(t),YEr=i(Cy),pc=n(Cy,"P",{});var Lce=s(pc);ZEr=r(Lce,`Note:
Loading a model from its configuration file does `),R4e=n(Lce,"STRONG",{});var xoa=s(R4e);KEr=r(xoa,"not"),xoa.forEach(t),e4r=r(Lce,` load the model weights. It only affects the
model\u2019s configuration. Use `),OK=n(Lce,"A",{href:!0});var $oa=s(OK);o4r=r($oa,"from_pretrained()"),$oa.forEach(t),r4r=r(Lce," to load the model weights."),Lce.forEach(t),t4r=i(Cy),T(nM.$$.fragment,Cy),Cy.forEach(t),a4r=i(Hl),mo=n(Hl,"DIV",{class:!0});var Da=s(mo);T(Qk.$$.fragment,Da),n4r=i(Da),P4e=n(Da,"P",{});var koa=s(P4e);s4r=r(koa,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),koa.forEach(t),l4r=i(Da),bn=n(Da,"P",{});var wy=s(bn);i4r=r(wy,"The model class to instantiate is selected based on the "),B4e=n(wy,"CODE",{});var Soa=s(B4e);d4r=r(Soa,"model_type"),Soa.forEach(t),c4r=r(wy,` property of the config object (either
passed as an argument or loaded from `),I4e=n(wy,"CODE",{});var Roa=s(I4e);f4r=r(Roa,"pretrained_model_name_or_path"),Roa.forEach(t),m4r=r(wy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),N4e=n(wy,"CODE",{});var Poa=s(N4e);g4r=r(Poa,"pretrained_model_name_or_path"),Poa.forEach(t),h4r=r(wy,":"),wy.forEach(t),p4r=i(Da),uc=n(Da,"UL",{});var yce=s(uc);sM=n(yce,"LI",{});var yHe=s(sM);q4e=n(yHe,"STRONG",{});var Boa=s(q4e);u4r=r(Boa,"layoutlm"),Boa.forEach(t),_4r=r(yHe," \u2014 "),VK=n(yHe,"A",{href:!0});var Ioa=s(VK);b4r=r(Ioa,"LayoutLMForQuestionAnswering"),Ioa.forEach(t),v4r=r(yHe," (LayoutLM model)"),yHe.forEach(t),F4r=i(yce),lM=n(yce,"LI",{});var xHe=s(lM);D4e=n(xHe,"STRONG",{});var Noa=s(D4e);T4r=r(Noa,"layoutlmv2"),Noa.forEach(t),M4r=r(xHe," \u2014 "),XK=n(xHe,"A",{href:!0});var qoa=s(XK);E4r=r(qoa,"LayoutLMv2ForQuestionAnswering"),qoa.forEach(t),C4r=r(xHe," (LayoutLMv2 model)"),xHe.forEach(t),w4r=i(yce),iM=n(yce,"LI",{});var $He=s(iM);j4e=n($He,"STRONG",{});var Doa=s(j4e);A4r=r(Doa,"layoutlmv3"),Doa.forEach(t),L4r=r($He," \u2014 "),zK=n($He,"A",{href:!0});var joa=s(zK);y4r=r(joa,"LayoutLMv3ForQuestionAnswering"),joa.forEach(t),x4r=r($He," (LayoutLMv3 model)"),$He.forEach(t),yce.forEach(t),$4r=i(Da),dM=n(Da,"P",{});var kHe=s(dM);k4r=r(kHe,"The model is set in evaluation mode by default using "),G4e=n(kHe,"CODE",{});var Goa=s(G4e);S4r=r(Goa,"model.eval()"),Goa.forEach(t),R4r=r(kHe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),O4e=n(kHe,"CODE",{});var Ooa=s(O4e);P4r=r(Ooa,"model.train()"),Ooa.forEach(t),kHe.forEach(t),B4r=i(Da),T(cM.$$.fragment,Da),Da.forEach(t),Hl.forEach(t),dao=i(f),_c=n(f,"H2",{class:!0});var yso=s(_c);fM=n(yso,"A",{id:!0,class:!0,href:!0});var Voa=s(fM);V4e=n(Voa,"SPAN",{});var Xoa=s(V4e);T(Wk.$$.fragment,Xoa),Xoa.forEach(t),Voa.forEach(t),I4r=i(yso),X4e=n(yso,"SPAN",{});var zoa=s(X4e);N4r=r(zoa,"AutoModelForImageClassification"),zoa.forEach(t),yso.forEach(t),cao=i(f),Ho=n(f,"DIV",{class:!0});var Jl=s(Ho);T(Uk.$$.fragment,Jl),q4r=i(Jl),bc=n(Jl,"P",{});var xce=s(bc);D4r=r(xce,`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 `),QK=n(xce,"A",{href:!0});var Qoa=s(QK);j4r=r(Qoa,"from_pretrained()"),Qoa.forEach(t),G4r=r(xce," class method or the "),WK=n(xce,"A",{href:!0});var Woa=s(WK);O4r=r(Woa,"from_config()"),Woa.forEach(t),V4r=r(xce,` class
method.`),xce.forEach(t),X4r=i(Jl),Hk=n(Jl,"P",{});var xso=s(Hk);z4r=r(xso,"This class cannot be instantiated directly using "),z4e=n(xso,"CODE",{});var Uoa=s(z4e);Q4r=r(Uoa,"__init__()"),Uoa.forEach(t),W4r=r(xso," (throws an error)."),xso.forEach(t),U4r=i(Jl),Bt=n(Jl,"DIV",{class:!0});var Ay=s(Bt);T(Jk.$$.fragment,Ay),H4r=i(Ay),Q4e=n(Ay,"P",{});var Hoa=s(Q4e);J4r=r(Hoa,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Hoa.forEach(t),Y4r=i(Ay),vc=n(Ay,"P",{});var $ce=s(vc);Z4r=r($ce,`Note:
Loading a model from its configuration file does `),W4e=n($ce,"STRONG",{});var Joa=s(W4e);K4r=r(Joa,"not"),Joa.forEach(t),eCr=r($ce,` load the model weights. It only affects the
model\u2019s configuration. Use `),UK=n($ce,"A",{href:!0});var Yoa=s(UK);oCr=r(Yoa,"from_pretrained()"),Yoa.forEach(t),rCr=r($ce," to load the model weights."),$ce.forEach(t),tCr=i(Ay),T(mM.$$.fragment,Ay),Ay.forEach(t),aCr=i(Jl),go=n(Jl,"DIV",{class:!0});var ja=s(go);T(Yk.$$.fragment,ja),nCr=i(ja),U4e=n(ja,"P",{});var Zoa=s(U4e);sCr=r(Zoa,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Zoa.forEach(t),lCr=i(ja),vn=n(ja,"P",{});var Ly=s(vn);iCr=r(Ly,"The model class to instantiate is selected based on the "),H4e=n(Ly,"CODE",{});var Koa=s(H4e);dCr=r(Koa,"model_type"),Koa.forEach(t),cCr=r(Ly,` property of the config object (either
passed as an argument or loaded from `),J4e=n(Ly,"CODE",{});var era=s(J4e);fCr=r(era,"pretrained_model_name_or_path"),era.forEach(t),mCr=r(Ly,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Y4e=n(Ly,"CODE",{});var ora=s(Y4e);gCr=r(ora,"pretrained_model_name_or_path"),ora.forEach(t),hCr=r(Ly,":"),Ly.forEach(t),pCr=i(ja),be=n(ja,"UL",{});var Fe=s(be);gM=n(Fe,"LI",{});var SHe=s(gM);Z4e=n(SHe,"STRONG",{});var rra=s(Z4e);uCr=r(rra,"beit"),rra.forEach(t),_Cr=r(SHe," \u2014 "),HK=n(SHe,"A",{href:!0});var tra=s(HK);bCr=r(tra,"BeitForImageClassification"),tra.forEach(t),vCr=r(SHe," (BEiT model)"),SHe.forEach(t),FCr=i(Fe),hM=n(Fe,"LI",{});var RHe=s(hM);K4e=n(RHe,"STRONG",{});var ara=s(K4e);TCr=r(ara,"convnext"),ara.forEach(t),MCr=r(RHe," \u2014 "),JK=n(RHe,"A",{href:!0});var nra=s(JK);ECr=r(nra,"ConvNextForImageClassification"),nra.forEach(t),CCr=r(RHe," (ConvNeXT model)"),RHe.forEach(t),wCr=i(Fe),pM=n(Fe,"LI",{});var PHe=s(pM);eCe=n(PHe,"STRONG",{});var sra=s(eCe);ACr=r(sra,"cvt"),sra.forEach(t),LCr=r(PHe," \u2014 "),YK=n(PHe,"A",{href:!0});var lra=s(YK);yCr=r(lra,"CvtForImageClassification"),lra.forEach(t),xCr=r(PHe," (CvT model)"),PHe.forEach(t),$Cr=i(Fe),uM=n(Fe,"LI",{});var BHe=s(uM);oCe=n(BHe,"STRONG",{});var ira=s(oCe);kCr=r(ira,"data2vec-vision"),ira.forEach(t),SCr=r(BHe," \u2014 "),ZK=n(BHe,"A",{href:!0});var dra=s(ZK);RCr=r(dra,"Data2VecVisionForImageClassification"),dra.forEach(t),PCr=r(BHe," (Data2VecVision model)"),BHe.forEach(t),BCr=i(Fe),xl=n(Fe,"LI",{});var _N=s(xl);rCe=n(_N,"STRONG",{});var cra=s(rCe);ICr=r(cra,"deit"),cra.forEach(t),NCr=r(_N," \u2014 "),KK=n(_N,"A",{href:!0});var fra=s(KK);qCr=r(fra,"DeiTForImageClassification"),fra.forEach(t),DCr=r(_N," or "),eee=n(_N,"A",{href:!0});var mra=s(eee);jCr=r(mra,"DeiTForImageClassificationWithTeacher"),mra.forEach(t),GCr=r(_N," (DeiT model)"),_N.forEach(t),OCr=i(Fe),_M=n(Fe,"LI",{});var IHe=s(_M);tCe=n(IHe,"STRONG",{});var gra=s(tCe);VCr=r(gra,"imagegpt"),gra.forEach(t),XCr=r(IHe," \u2014 "),oee=n(IHe,"A",{href:!0});var hra=s(oee);zCr=r(hra,"ImageGPTForImageClassification"),hra.forEach(t),QCr=r(IHe," (ImageGPT model)"),IHe.forEach(t),WCr=i(Fe),$l=n(Fe,"LI",{});var bN=s($l);aCe=n(bN,"STRONG",{});var pra=s(aCe);UCr=r(pra,"levit"),pra.forEach(t),HCr=r(bN," \u2014 "),ree=n(bN,"A",{href:!0});var ura=s(ree);JCr=r(ura,"LevitForImageClassification"),ura.forEach(t),YCr=r(bN," or "),tee=n(bN,"A",{href:!0});var _ra=s(tee);ZCr=r(_ra,"LevitForImageClassificationWithTeacher"),_ra.forEach(t),KCr=r(bN," (LeViT model)"),bN.forEach(t),e3r=i(Fe),bM=n(Fe,"LI",{});var NHe=s(bM);nCe=n(NHe,"STRONG",{});var bra=s(nCe);o3r=r(bra,"mobilevit"),bra.forEach(t),r3r=r(NHe," \u2014 "),aee=n(NHe,"A",{href:!0});var vra=s(aee);t3r=r(vra,"MobileViTForImageClassification"),vra.forEach(t),a3r=r(NHe," (MobileViT model)"),NHe.forEach(t),n3r=i(Fe),It=n(Fe,"LI",{});var Vm=s(It);sCe=n(Vm,"STRONG",{});var Fra=s(sCe);s3r=r(Fra,"perceiver"),Fra.forEach(t),l3r=r(Vm," \u2014 "),nee=n(Vm,"A",{href:!0});var Tra=s(nee);i3r=r(Tra,"PerceiverForImageClassificationLearned"),Tra.forEach(t),d3r=r(Vm," or "),see=n(Vm,"A",{href:!0});var Mra=s(see);c3r=r(Mra,"PerceiverForImageClassificationFourier"),Mra.forEach(t),f3r=r(Vm," or "),lee=n(Vm,"A",{href:!0});var Era=s(lee);m3r=r(Era,"PerceiverForImageClassificationConvProcessing"),Era.forEach(t),g3r=r(Vm," (Perceiver model)"),Vm.forEach(t),h3r=i(Fe),vM=n(Fe,"LI",{});var qHe=s(vM);lCe=n(qHe,"STRONG",{});var Cra=s(lCe);p3r=r(Cra,"poolformer"),Cra.forEach(t),u3r=r(qHe," \u2014 "),iee=n(qHe,"A",{href:!0});var wra=s(iee);_3r=r(wra,"PoolFormerForImageClassification"),wra.forEach(t),b3r=r(qHe," (PoolFormer model)"),qHe.forEach(t),v3r=i(Fe),FM=n(Fe,"LI",{});var DHe=s(FM);iCe=n(DHe,"STRONG",{});var Ara=s(iCe);F3r=r(Ara,"regnet"),Ara.forEach(t),T3r=r(DHe," \u2014 "),dee=n(DHe,"A",{href:!0});var Lra=s(dee);M3r=r(Lra,"RegNetForImageClassification"),Lra.forEach(t),E3r=r(DHe," (RegNet model)"),DHe.forEach(t),C3r=i(Fe),TM=n(Fe,"LI",{});var jHe=s(TM);dCe=n(jHe,"STRONG",{});var yra=s(dCe);w3r=r(yra,"resnet"),yra.forEach(t),A3r=r(jHe," \u2014 "),cee=n(jHe,"A",{href:!0});var xra=s(cee);L3r=r(xra,"ResNetForImageClassification"),xra.forEach(t),y3r=r(jHe," (ResNet model)"),jHe.forEach(t),x3r=i(Fe),MM=n(Fe,"LI",{});var GHe=s(MM);cCe=n(GHe,"STRONG",{});var $ra=s(cCe);$3r=r($ra,"segformer"),$ra.forEach(t),k3r=r(GHe," \u2014 "),fee=n(GHe,"A",{href:!0});var kra=s(fee);S3r=r(kra,"SegformerForImageClassification"),kra.forEach(t),R3r=r(GHe," (SegFormer model)"),GHe.forEach(t),P3r=i(Fe),EM=n(Fe,"LI",{});var OHe=s(EM);fCe=n(OHe,"STRONG",{});var Sra=s(fCe);B3r=r(Sra,"swin"),Sra.forEach(t),I3r=r(OHe," \u2014 "),mee=n(OHe,"A",{href:!0});var Rra=s(mee);N3r=r(Rra,"SwinForImageClassification"),Rra.forEach(t),q3r=r(OHe," (Swin Transformer model)"),OHe.forEach(t),D3r=i(Fe),CM=n(Fe,"LI",{});var VHe=s(CM);mCe=n(VHe,"STRONG",{});var Pra=s(mCe);j3r=r(Pra,"swinv2"),Pra.forEach(t),G3r=r(VHe," \u2014 "),gee=n(VHe,"A",{href:!0});var Bra=s(gee);O3r=r(Bra,"Swinv2ForImageClassification"),Bra.forEach(t),V3r=r(VHe," (Swin Transformer V2 model)"),VHe.forEach(t),X3r=i(Fe),wM=n(Fe,"LI",{});var XHe=s(wM);gCe=n(XHe,"STRONG",{});var Ira=s(gCe);z3r=r(Ira,"van"),Ira.forEach(t),Q3r=r(XHe," \u2014 "),hee=n(XHe,"A",{href:!0});var Nra=s(hee);W3r=r(Nra,"VanForImageClassification"),Nra.forEach(t),U3r=r(XHe," (VAN model)"),XHe.forEach(t),H3r=i(Fe),AM=n(Fe,"LI",{});var zHe=s(AM);hCe=n(zHe,"STRONG",{});var qra=s(hCe);J3r=r(qra,"vit"),qra.forEach(t),Y3r=r(zHe," \u2014 "),pee=n(zHe,"A",{href:!0});var Dra=s(pee);Z3r=r(Dra,"ViTForImageClassification"),Dra.forEach(t),K3r=r(zHe," (ViT model)"),zHe.forEach(t),e5r=i(Fe),LM=n(Fe,"LI",{});var QHe=s(LM);pCe=n(QHe,"STRONG",{});var jra=s(pCe);o5r=r(jra,"vit_msn"),jra.forEach(t),r5r=r(QHe," \u2014 "),uee=n(QHe,"A",{href:!0});var Gra=s(uee);t5r=r(Gra,"ViTMSNForImageClassification"),Gra.forEach(t),a5r=r(QHe," (ViTMSN model)"),QHe.forEach(t),Fe.forEach(t),n5r=i(ja),yM=n(ja,"P",{});var WHe=s(yM);s5r=r(WHe,"The model is set in evaluation mode by default using "),uCe=n(WHe,"CODE",{});var Ora=s(uCe);l5r=r(Ora,"model.eval()"),Ora.forEach(t),i5r=r(WHe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_Ce=n(WHe,"CODE",{});var Vra=s(_Ce);d5r=r(Vra,"model.train()"),Vra.forEach(t),WHe.forEach(t),c5r=i(ja),T(xM.$$.fragment,ja),ja.forEach(t),Jl.forEach(t),fao=i(f),Fc=n(f,"H2",{class:!0});var $so=s(Fc);$M=n($so,"A",{id:!0,class:!0,href:!0});var Xra=s($M);bCe=n(Xra,"SPAN",{});var zra=s(bCe);T(Zk.$$.fragment,zra),zra.forEach(t),Xra.forEach(t),f5r=i($so),vCe=n($so,"SPAN",{});var Qra=s(vCe);m5r=r(Qra,"AutoModelForVideoClassification"),Qra.forEach(t),$so.forEach(t),mao=i(f),Jo=n(f,"DIV",{class:!0});var Yl=s(Jo);T(Kk.$$.fragment,Yl),g5r=i(Yl),Tc=n(Yl,"P",{});var kce=s(Tc);h5r=r(kce,`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 `),_ee=n(kce,"A",{href:!0});var Wra=s(_ee);p5r=r(Wra,"from_pretrained()"),Wra.forEach(t),u5r=r(kce," class method or the "),bee=n(kce,"A",{href:!0});var Ura=s(bee);_5r=r(Ura,"from_config()"),Ura.forEach(t),b5r=r(kce,` class
method.`),kce.forEach(t),v5r=i(Yl),eS=n(Yl,"P",{});var kso=s(eS);F5r=r(kso,"This class cannot be instantiated directly using "),FCe=n(kso,"CODE",{});var Hra=s(FCe);T5r=r(Hra,"__init__()"),Hra.forEach(t),M5r=r(kso," (throws an error)."),kso.forEach(t),E5r=i(Yl),Nt=n(Yl,"DIV",{class:!0});var yy=s(Nt);T(oS.$$.fragment,yy),C5r=i(yy),TCe=n(yy,"P",{});var Jra=s(TCe);w5r=r(Jra,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),Jra.forEach(t),A5r=i(yy),Mc=n(yy,"P",{});var Sce=s(Mc);L5r=r(Sce,`Note:
Loading a model from its configuration file does `),MCe=n(Sce,"STRONG",{});var Yra=s(MCe);y5r=r(Yra,"not"),Yra.forEach(t),x5r=r(Sce,` load the model weights. It only affects the
model\u2019s configuration. Use `),vee=n(Sce,"A",{href:!0});var Zra=s(vee);$5r=r(Zra,"from_pretrained()"),Zra.forEach(t),k5r=r(Sce," to load the model weights."),Sce.forEach(t),S5r=i(yy),T(kM.$$.fragment,yy),yy.forEach(t),R5r=i(Yl),ho=n(Yl,"DIV",{class:!0});var Ga=s(ho);T(rS.$$.fragment,Ga),P5r=i(Ga),ECe=n(Ga,"P",{});var Kra=s(ECe);B5r=r(Kra,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),Kra.forEach(t),I5r=i(Ga),Fn=n(Ga,"P",{});var xy=s(Fn);N5r=r(xy,"The model class to instantiate is selected based on the "),CCe=n(xy,"CODE",{});var eta=s(CCe);q5r=r(eta,"model_type"),eta.forEach(t),D5r=r(xy,` property of the config object (either
passed as an argument or loaded from `),wCe=n(xy,"CODE",{});var ota=s(wCe);j5r=r(ota,"pretrained_model_name_or_path"),ota.forEach(t),G5r=r(xy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ACe=n(xy,"CODE",{});var rta=s(ACe);O5r=r(rta,"pretrained_model_name_or_path"),rta.forEach(t),V5r=r(xy,":"),xy.forEach(t),X5r=i(Ga),LCe=n(Ga,"UL",{});var tta=s(LCe);SM=n(tta,"LI",{});var UHe=s(SM);yCe=n(UHe,"STRONG",{});var ata=s(yCe);z5r=r(ata,"videomae"),ata.forEach(t),Q5r=r(UHe," \u2014 "),Fee=n(UHe,"A",{href:!0});var nta=s(Fee);W5r=r(nta,"VideoMAEForVideoClassification"),nta.forEach(t),U5r=r(UHe," (VideoMAE model)"),UHe.forEach(t),tta.forEach(t),H5r=i(Ga),RM=n(Ga,"P",{});var HHe=s(RM);J5r=r(HHe,"The model is set in evaluation mode by default using "),xCe=n(HHe,"CODE",{});var sta=s(xCe);Y5r=r(sta,"model.eval()"),sta.forEach(t),Z5r=r(HHe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),$Ce=n(HHe,"CODE",{});var lta=s($Ce);K5r=r(lta,"model.train()"),lta.forEach(t),HHe.forEach(t),ewr=i(Ga),T(PM.$$.fragment,Ga),Ga.forEach(t),Yl.forEach(t),gao=i(f),Ec=n(f,"H2",{class:!0});var Sso=s(Ec);BM=n(Sso,"A",{id:!0,class:!0,href:!0});var ita=s(BM);kCe=n(ita,"SPAN",{});var dta=s(kCe);T(tS.$$.fragment,dta),dta.forEach(t),ita.forEach(t),owr=i(Sso),SCe=n(Sso,"SPAN",{});var cta=s(SCe);rwr=r(cta,"AutoModelForVision2Seq"),cta.forEach(t),Sso.forEach(t),hao=i(f),Yo=n(f,"DIV",{class:!0});var Zl=s(Yo);T(aS.$$.fragment,Zl),twr=i(Zl),Cc=n(Zl,"P",{});var Rce=s(Cc);awr=r(Rce,`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 `),Tee=n(Rce,"A",{href:!0});var fta=s(Tee);nwr=r(fta,"from_pretrained()"),fta.forEach(t),swr=r(Rce," class method or the "),Mee=n(Rce,"A",{href:!0});var mta=s(Mee);lwr=r(mta,"from_config()"),mta.forEach(t),iwr=r(Rce,` class
method.`),Rce.forEach(t),dwr=i(Zl),nS=n(Zl,"P",{});var Rso=s(nS);cwr=r(Rso,"This class cannot be instantiated directly using "),RCe=n(Rso,"CODE",{});var gta=s(RCe);fwr=r(gta,"__init__()"),gta.forEach(t),mwr=r(Rso," (throws an error)."),Rso.forEach(t),gwr=i(Zl),qt=n(Zl,"DIV",{class:!0});var $y=s(qt);T(sS.$$.fragment,$y),hwr=i($y),PCe=n($y,"P",{});var hta=s(PCe);pwr=r(hta,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),hta.forEach(t),uwr=i($y),wc=n($y,"P",{});var Pce=s(wc);_wr=r(Pce,`Note:
Loading a model from its configuration file does `),BCe=n(Pce,"STRONG",{});var pta=s(BCe);bwr=r(pta,"not"),pta.forEach(t),vwr=r(Pce,` load the model weights. It only affects the
model\u2019s configuration. Use `),Eee=n(Pce,"A",{href:!0});var uta=s(Eee);Fwr=r(uta,"from_pretrained()"),uta.forEach(t),Twr=r(Pce," to load the model weights."),Pce.forEach(t),Mwr=i($y),T(IM.$$.fragment,$y),$y.forEach(t),Ewr=i(Zl),po=n(Zl,"DIV",{class:!0});var Oa=s(po);T(lS.$$.fragment,Oa),Cwr=i(Oa),ICe=n(Oa,"P",{});var _ta=s(ICe);wwr=r(_ta,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),_ta.forEach(t),Awr=i(Oa),Tn=n(Oa,"P",{});var ky=s(Tn);Lwr=r(ky,"The model class to instantiate is selected based on the "),NCe=n(ky,"CODE",{});var bta=s(NCe);ywr=r(bta,"model_type"),bta.forEach(t),xwr=r(ky,` property of the config object (either
passed as an argument or loaded from `),qCe=n(ky,"CODE",{});var vta=s(qCe);$wr=r(vta,"pretrained_model_name_or_path"),vta.forEach(t),kwr=r(ky,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DCe=n(ky,"CODE",{});var Fta=s(DCe);Swr=r(Fta,"pretrained_model_name_or_path"),Fta.forEach(t),Rwr=r(ky,":"),ky.forEach(t),Pwr=i(Oa),jCe=n(Oa,"UL",{});var Tta=s(jCe);NM=n(Tta,"LI",{});var JHe=s(NM);GCe=n(JHe,"STRONG",{});var Mta=s(GCe);Bwr=r(Mta,"vision-encoder-decoder"),Mta.forEach(t),Iwr=r(JHe," \u2014 "),Cee=n(JHe,"A",{href:!0});var Eta=s(Cee);Nwr=r(Eta,"VisionEncoderDecoderModel"),Eta.forEach(t),qwr=r(JHe," (Vision Encoder decoder model)"),JHe.forEach(t),Tta.forEach(t),Dwr=i(Oa),qM=n(Oa,"P",{});var YHe=s(qM);jwr=r(YHe,"The model is set in evaluation mode by default using "),OCe=n(YHe,"CODE",{});var Cta=s(OCe);Gwr=r(Cta,"model.eval()"),Cta.forEach(t),Owr=r(YHe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),VCe=n(YHe,"CODE",{});var wta=s(VCe);Vwr=r(wta,"model.train()"),wta.forEach(t),YHe.forEach(t),Xwr=i(Oa),T(DM.$$.fragment,Oa),Oa.forEach(t),Zl.forEach(t),pao=i(f),Ac=n(f,"H2",{class:!0});var Pso=s(Ac);jM=n(Pso,"A",{id:!0,class:!0,href:!0});var Ata=s(jM);XCe=n(Ata,"SPAN",{});var Lta=s(XCe);T(iS.$$.fragment,Lta),Lta.forEach(t),Ata.forEach(t),zwr=i(Pso),zCe=n(Pso,"SPAN",{});var yta=s(zCe);Qwr=r(yta,"AutoModelForVisualQuestionAnswering"),yta.forEach(t),Pso.forEach(t),uao=i(f),Zo=n(f,"DIV",{class:!0});var Kl=s(Zo);T(dS.$$.fragment,Kl),Wwr=i(Kl),Lc=n(Kl,"P",{});var Bce=s(Lc);Uwr=r(Bce,`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 `),wee=n(Bce,"A",{href:!0});var xta=s(wee);Hwr=r(xta,"from_pretrained()"),xta.forEach(t),Jwr=r(Bce," class method or the "),Aee=n(Bce,"A",{href:!0});var $ta=s(Aee);Ywr=r($ta,"from_config()"),$ta.forEach(t),Zwr=r(Bce,` class
method.`),Bce.forEach(t),Kwr=i(Kl),cS=n(Kl,"P",{});var Bso=s(cS);eAr=r(Bso,"This class cannot be instantiated directly using "),QCe=n(Bso,"CODE",{});var kta=s(QCe);oAr=r(kta,"__init__()"),kta.forEach(t),rAr=r(Bso," (throws an error)."),Bso.forEach(t),tAr=i(Kl),Dt=n(Kl,"DIV",{class:!0});var Sy=s(Dt);T(fS.$$.fragment,Sy),aAr=i(Sy),WCe=n(Sy,"P",{});var Sta=s(WCe);nAr=r(Sta,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),Sta.forEach(t),sAr=i(Sy),yc=n(Sy,"P",{});var Ice=s(yc);lAr=r(Ice,`Note:
Loading a model from its configuration file does `),UCe=n(Ice,"STRONG",{});var Rta=s(UCe);iAr=r(Rta,"not"),Rta.forEach(t),dAr=r(Ice,` load the model weights. It only affects the
model\u2019s configuration. Use `),Lee=n(Ice,"A",{href:!0});var Pta=s(Lee);cAr=r(Pta,"from_pretrained()"),Pta.forEach(t),fAr=r(Ice," to load the model weights."),Ice.forEach(t),mAr=i(Sy),T(GM.$$.fragment,Sy),Sy.forEach(t),gAr=i(Kl),uo=n(Kl,"DIV",{class:!0});var Va=s(uo);T(mS.$$.fragment,Va),hAr=i(Va),HCe=n(Va,"P",{});var Bta=s(HCe);pAr=r(Bta,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),Bta.forEach(t),uAr=i(Va),Mn=n(Va,"P",{});var Ry=s(Mn);_Ar=r(Ry,"The model class to instantiate is selected based on the "),JCe=n(Ry,"CODE",{});var Ita=s(JCe);bAr=r(Ita,"model_type"),Ita.forEach(t),vAr=r(Ry,` property of the config object (either
passed as an argument or loaded from `),YCe=n(Ry,"CODE",{});var Nta=s(YCe);FAr=r(Nta,"pretrained_model_name_or_path"),Nta.forEach(t),TAr=r(Ry,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZCe=n(Ry,"CODE",{});var qta=s(ZCe);MAr=r(qta,"pretrained_model_name_or_path"),qta.forEach(t),EAr=r(Ry,":"),Ry.forEach(t),CAr=i(Va),KCe=n(Va,"UL",{});var Dta=s(KCe);OM=n(Dta,"LI",{});var ZHe=s(OM);e3e=n(ZHe,"STRONG",{});var jta=s(e3e);wAr=r(jta,"vilt"),jta.forEach(t),AAr=r(ZHe," \u2014 "),yee=n(ZHe,"A",{href:!0});var Gta=s(yee);LAr=r(Gta,"ViltForQuestionAnswering"),Gta.forEach(t),yAr=r(ZHe," (ViLT model)"),ZHe.forEach(t),Dta.forEach(t),xAr=i(Va),VM=n(Va,"P",{});var KHe=s(VM);$Ar=r(KHe,"The model is set in evaluation mode by default using "),o3e=n(KHe,"CODE",{});var Ota=s(o3e);kAr=r(Ota,"model.eval()"),Ota.forEach(t),SAr=r(KHe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),r3e=n(KHe,"CODE",{});var Vta=s(r3e);RAr=r(Vta,"model.train()"),Vta.forEach(t),KHe.forEach(t),PAr=i(Va),T(XM.$$.fragment,Va),Va.forEach(t),Kl.forEach(t),_ao=i(f),xc=n(f,"H2",{class:!0});var Iso=s(xc);zM=n(Iso,"A",{id:!0,class:!0,href:!0});var Xta=s(zM);t3e=n(Xta,"SPAN",{});var zta=s(t3e);T(gS.$$.fragment,zta),zta.forEach(t),Xta.forEach(t),BAr=i(Iso),a3e=n(Iso,"SPAN",{});var Qta=s(a3e);IAr=r(Qta,"AutoModelForAudioClassification"),Qta.forEach(t),Iso.forEach(t),bao=i(f),Ko=n(f,"DIV",{class:!0});var ei=s(Ko);T(hS.$$.fragment,ei),NAr=i(ei),$c=n(ei,"P",{});var Nce=s($c);qAr=r(Nce,`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 `),xee=n(Nce,"A",{href:!0});var Wta=s(xee);DAr=r(Wta,"from_pretrained()"),Wta.forEach(t),jAr=r(Nce," class method or the "),$ee=n(Nce,"A",{href:!0});var Uta=s($ee);GAr=r(Uta,"from_config()"),Uta.forEach(t),OAr=r(Nce,` class
method.`),Nce.forEach(t),VAr=i(ei),pS=n(ei,"P",{});var Nso=s(pS);XAr=r(Nso,"This class cannot be instantiated directly using "),n3e=n(Nso,"CODE",{});var Hta=s(n3e);zAr=r(Hta,"__init__()"),Hta.forEach(t),QAr=r(Nso," (throws an error)."),Nso.forEach(t),WAr=i(ei),jt=n(ei,"DIV",{class:!0});var Py=s(jt);T(uS.$$.fragment,Py),UAr=i(Py),s3e=n(Py,"P",{});var Jta=s(s3e);HAr=r(Jta,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Jta.forEach(t),JAr=i(Py),kc=n(Py,"P",{});var qce=s(kc);YAr=r(qce,`Note:
Loading a model from its configuration file does `),l3e=n(qce,"STRONG",{});var Yta=s(l3e);ZAr=r(Yta,"not"),Yta.forEach(t),KAr=r(qce,` load the model weights. It only affects the
model\u2019s configuration. Use `),kee=n(qce,"A",{href:!0});var Zta=s(kee);e6r=r(Zta,"from_pretrained()"),Zta.forEach(t),o6r=r(qce," to load the model weights."),qce.forEach(t),r6r=i(Py),T(QM.$$.fragment,Py),Py.forEach(t),t6r=i(ei),_o=n(ei,"DIV",{class:!0});var Xa=s(_o);T(_S.$$.fragment,Xa),a6r=i(Xa),i3e=n(Xa,"P",{});var Kta=s(i3e);n6r=r(Kta,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Kta.forEach(t),s6r=i(Xa),En=n(Xa,"P",{});var By=s(En);l6r=r(By,"The model class to instantiate is selected based on the "),d3e=n(By,"CODE",{});var eaa=s(d3e);i6r=r(eaa,"model_type"),eaa.forEach(t),d6r=r(By,` property of the config object (either
passed as an argument or loaded from `),c3e=n(By,"CODE",{});var oaa=s(c3e);c6r=r(oaa,"pretrained_model_name_or_path"),oaa.forEach(t),f6r=r(By,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),f3e=n(By,"CODE",{});var raa=s(f3e);m6r=r(raa,"pretrained_model_name_or_path"),raa.forEach(t),g6r=r(By,":"),By.forEach(t),h6r=i(Xa),Be=n(Xa,"UL",{});var We=s(Be);WM=n(We,"LI",{});var eJe=s(WM);m3e=n(eJe,"STRONG",{});var taa=s(m3e);p6r=r(taa,"data2vec-audio"),taa.forEach(t),u6r=r(eJe," \u2014 "),See=n(eJe,"A",{href:!0});var aaa=s(See);_6r=r(aaa,"Data2VecAudioForSequenceClassification"),aaa.forEach(t),b6r=r(eJe," (Data2VecAudio model)"),eJe.forEach(t),v6r=i(We),UM=n(We,"LI",{});var oJe=s(UM);g3e=n(oJe,"STRONG",{});var naa=s(g3e);F6r=r(naa,"hubert"),naa.forEach(t),T6r=r(oJe," \u2014 "),Ree=n(oJe,"A",{href:!0});var saa=s(Ree);M6r=r(saa,"HubertForSequenceClassification"),saa.forEach(t),E6r=r(oJe," (Hubert model)"),oJe.forEach(t),C6r=i(We),HM=n(We,"LI",{});var rJe=s(HM);h3e=n(rJe,"STRONG",{});var laa=s(h3e);w6r=r(laa,"sew"),laa.forEach(t),A6r=r(rJe," \u2014 "),Pee=n(rJe,"A",{href:!0});var iaa=s(Pee);L6r=r(iaa,"SEWForSequenceClassification"),iaa.forEach(t),y6r=r(rJe," (SEW model)"),rJe.forEach(t),x6r=i(We),JM=n(We,"LI",{});var tJe=s(JM);p3e=n(tJe,"STRONG",{});var daa=s(p3e);$6r=r(daa,"sew-d"),daa.forEach(t),k6r=r(tJe," \u2014 "),Bee=n(tJe,"A",{href:!0});var caa=s(Bee);S6r=r(caa,"SEWDForSequenceClassification"),caa.forEach(t),R6r=r(tJe," (SEW-D model)"),tJe.forEach(t),P6r=i(We),YM=n(We,"LI",{});var aJe=s(YM);u3e=n(aJe,"STRONG",{});var faa=s(u3e);B6r=r(faa,"unispeech"),faa.forEach(t),I6r=r(aJe," \u2014 "),Iee=n(aJe,"A",{href:!0});var maa=s(Iee);N6r=r(maa,"UniSpeechForSequenceClassification"),maa.forEach(t),q6r=r(aJe," (UniSpeech model)"),aJe.forEach(t),D6r=i(We),ZM=n(We,"LI",{});var nJe=s(ZM);_3e=n(nJe,"STRONG",{});var gaa=s(_3e);j6r=r(gaa,"unispeech-sat"),gaa.forEach(t),G6r=r(nJe," \u2014 "),Nee=n(nJe,"A",{href:!0});var haa=s(Nee);O6r=r(haa,"UniSpeechSatForSequenceClassification"),haa.forEach(t),V6r=r(nJe," (UniSpeechSat model)"),nJe.forEach(t),X6r=i(We),KM=n(We,"LI",{});var sJe=s(KM);b3e=n(sJe,"STRONG",{});var paa=s(b3e);z6r=r(paa,"wav2vec2"),paa.forEach(t),Q6r=r(sJe," \u2014 "),qee=n(sJe,"A",{href:!0});var uaa=s(qee);W6r=r(uaa,"Wav2Vec2ForSequenceClassification"),uaa.forEach(t),U6r=r(sJe," (Wav2Vec2 model)"),sJe.forEach(t),H6r=i(We),eE=n(We,"LI",{});var lJe=s(eE);v3e=n(lJe,"STRONG",{});var _aa=s(v3e);J6r=r(_aa,"wav2vec2-conformer"),_aa.forEach(t),Y6r=r(lJe," \u2014 "),Dee=n(lJe,"A",{href:!0});var baa=s(Dee);Z6r=r(baa,"Wav2Vec2ConformerForSequenceClassification"),baa.forEach(t),K6r=r(lJe," (Wav2Vec2-Conformer model)"),lJe.forEach(t),e8r=i(We),oE=n(We,"LI",{});var iJe=s(oE);F3e=n(iJe,"STRONG",{});var vaa=s(F3e);o8r=r(vaa,"wavlm"),vaa.forEach(t),r8r=r(iJe," \u2014 "),jee=n(iJe,"A",{href:!0});var Faa=s(jee);t8r=r(Faa,"WavLMForSequenceClassification"),Faa.forEach(t),a8r=r(iJe," (WavLM model)"),iJe.forEach(t),We.forEach(t),n8r=i(Xa),rE=n(Xa,"P",{});var dJe=s(rE);s8r=r(dJe,"The model is set in evaluation mode by default using "),T3e=n(dJe,"CODE",{});var Taa=s(T3e);l8r=r(Taa,"model.eval()"),Taa.forEach(t),i8r=r(dJe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),M3e=n(dJe,"CODE",{});var Maa=s(M3e);d8r=r(Maa,"model.train()"),Maa.forEach(t),dJe.forEach(t),c8r=i(Xa),T(tE.$$.fragment,Xa),Xa.forEach(t),ei.forEach(t),vao=i(f),Sc=n(f,"H2",{class:!0});var qso=s(Sc);aE=n(qso,"A",{id:!0,class:!0,href:!0});var Eaa=s(aE);E3e=n(Eaa,"SPAN",{});var Caa=s(E3e);T(bS.$$.fragment,Caa),Caa.forEach(t),Eaa.forEach(t),f8r=i(qso),C3e=n(qso,"SPAN",{});var waa=s(C3e);m8r=r(waa,"AutoModelForAudioFrameClassification"),waa.forEach(t),qso.forEach(t),Fao=i(f),er=n(f,"DIV",{class:!0});var oi=s(er);T(vS.$$.fragment,oi),g8r=i(oi),Rc=n(oi,"P",{});var Dce=s(Rc);h8r=r(Dce,`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 `),Gee=n(Dce,"A",{href:!0});var Aaa=s(Gee);p8r=r(Aaa,"from_pretrained()"),Aaa.forEach(t),u8r=r(Dce," class method or the "),Oee=n(Dce,"A",{href:!0});var Laa=s(Oee);_8r=r(Laa,"from_config()"),Laa.forEach(t),b8r=r(Dce,` class
method.`),Dce.forEach(t),v8r=i(oi),FS=n(oi,"P",{});var Dso=s(FS);F8r=r(Dso,"This class cannot be instantiated directly using "),w3e=n(Dso,"CODE",{});var yaa=s(w3e);T8r=r(yaa,"__init__()"),yaa.forEach(t),M8r=r(Dso," (throws an error)."),Dso.forEach(t),E8r=i(oi),Gt=n(oi,"DIV",{class:!0});var Iy=s(Gt);T(TS.$$.fragment,Iy),C8r=i(Iy),A3e=n(Iy,"P",{});var xaa=s(A3e);w8r=r(xaa,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),xaa.forEach(t),A8r=i(Iy),Pc=n(Iy,"P",{});var jce=s(Pc);L8r=r(jce,`Note:
Loading a model from its configuration file does `),L3e=n(jce,"STRONG",{});var $aa=s(L3e);y8r=r($aa,"not"),$aa.forEach(t),x8r=r(jce,` load the model weights. It only affects the
model\u2019s configuration. Use `),Vee=n(jce,"A",{href:!0});var kaa=s(Vee);$8r=r(kaa,"from_pretrained()"),kaa.forEach(t),k8r=r(jce," to load the model weights."),jce.forEach(t),S8r=i(Iy),T(nE.$$.fragment,Iy),Iy.forEach(t),R8r=i(oi),bo=n(oi,"DIV",{class:!0});var za=s(bo);T(MS.$$.fragment,za),P8r=i(za),y3e=n(za,"P",{});var Saa=s(y3e);B8r=r(Saa,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),Saa.forEach(t),I8r=i(za),Cn=n(za,"P",{});var Ny=s(Cn);N8r=r(Ny,"The model class to instantiate is selected based on the "),x3e=n(Ny,"CODE",{});var Raa=s(x3e);q8r=r(Raa,"model_type"),Raa.forEach(t),D8r=r(Ny,` property of the config object (either
passed as an argument or loaded from `),$3e=n(Ny,"CODE",{});var Paa=s($3e);j8r=r(Paa,"pretrained_model_name_or_path"),Paa.forEach(t),G8r=r(Ny,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),k3e=n(Ny,"CODE",{});var Baa=s(k3e);O8r=r(Baa,"pretrained_model_name_or_path"),Baa.forEach(t),V8r=r(Ny,":"),Ny.forEach(t),X8r=i(za),pt=n(za,"UL",{});var ri=s(pt);sE=n(ri,"LI",{});var cJe=s(sE);S3e=n(cJe,"STRONG",{});var Iaa=s(S3e);z8r=r(Iaa,"data2vec-audio"),Iaa.forEach(t),Q8r=r(cJe," \u2014 "),Xee=n(cJe,"A",{href:!0});var Naa=s(Xee);W8r=r(Naa,"Data2VecAudioForAudioFrameClassification"),Naa.forEach(t),U8r=r(cJe," (Data2VecAudio model)"),cJe.forEach(t),H8r=i(ri),lE=n(ri,"LI",{});var fJe=s(lE);R3e=n(fJe,"STRONG",{});var qaa=s(R3e);J8r=r(qaa,"unispeech-sat"),qaa.forEach(t),Y8r=r(fJe," \u2014 "),zee=n(fJe,"A",{href:!0});var Daa=s(zee);Z8r=r(Daa,"UniSpeechSatForAudioFrameClassification"),Daa.forEach(t),K8r=r(fJe," (UniSpeechSat model)"),fJe.forEach(t),eLr=i(ri),iE=n(ri,"LI",{});var mJe=s(iE);P3e=n(mJe,"STRONG",{});var jaa=s(P3e);oLr=r(jaa,"wav2vec2"),jaa.forEach(t),rLr=r(mJe," \u2014 "),Qee=n(mJe,"A",{href:!0});var Gaa=s(Qee);tLr=r(Gaa,"Wav2Vec2ForAudioFrameClassification"),Gaa.forEach(t),aLr=r(mJe," (Wav2Vec2 model)"),mJe.forEach(t),nLr=i(ri),dE=n(ri,"LI",{});var gJe=s(dE);B3e=n(gJe,"STRONG",{});var Oaa=s(B3e);sLr=r(Oaa,"wav2vec2-conformer"),Oaa.forEach(t),lLr=r(gJe," \u2014 "),Wee=n(gJe,"A",{href:!0});var Vaa=s(Wee);iLr=r(Vaa,"Wav2Vec2ConformerForAudioFrameClassification"),Vaa.forEach(t),dLr=r(gJe," (Wav2Vec2-Conformer model)"),gJe.forEach(t),cLr=i(ri),cE=n(ri,"LI",{});var hJe=s(cE);I3e=n(hJe,"STRONG",{});var Xaa=s(I3e);fLr=r(Xaa,"wavlm"),Xaa.forEach(t),mLr=r(hJe," \u2014 "),Uee=n(hJe,"A",{href:!0});var zaa=s(Uee);gLr=r(zaa,"WavLMForAudioFrameClassification"),zaa.forEach(t),hLr=r(hJe," (WavLM model)"),hJe.forEach(t),ri.forEach(t),pLr=i(za),fE=n(za,"P",{});var pJe=s(fE);uLr=r(pJe,"The model is set in evaluation mode by default using "),N3e=n(pJe,"CODE",{});var Qaa=s(N3e);_Lr=r(Qaa,"model.eval()"),Qaa.forEach(t),bLr=r(pJe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),q3e=n(pJe,"CODE",{});var Waa=s(q3e);vLr=r(Waa,"model.train()"),Waa.forEach(t),pJe.forEach(t),FLr=i(za),T(mE.$$.fragment,za),za.forEach(t),oi.forEach(t),Tao=i(f),Bc=n(f,"H2",{class:!0});var jso=s(Bc);gE=n(jso,"A",{id:!0,class:!0,href:!0});var Uaa=s(gE);D3e=n(Uaa,"SPAN",{});var Haa=s(D3e);T(ES.$$.fragment,Haa),Haa.forEach(t),Uaa.forEach(t),TLr=i(jso),j3e=n(jso,"SPAN",{});var Jaa=s(j3e);MLr=r(Jaa,"AutoModelForCTC"),Jaa.forEach(t),jso.forEach(t),Mao=i(f),or=n(f,"DIV",{class:!0});var ti=s(or);T(CS.$$.fragment,ti),ELr=i(ti),Ic=n(ti,"P",{});var Gce=s(Ic);CLr=r(Gce,`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 `),Hee=n(Gce,"A",{href:!0});var Yaa=s(Hee);wLr=r(Yaa,"from_pretrained()"),Yaa.forEach(t),ALr=r(Gce," class method or the "),Jee=n(Gce,"A",{href:!0});var Zaa=s(Jee);LLr=r(Zaa,"from_config()"),Zaa.forEach(t),yLr=r(Gce,` class
method.`),Gce.forEach(t),xLr=i(ti),wS=n(ti,"P",{});var Gso=s(wS);$Lr=r(Gso,"This class cannot be instantiated directly using "),G3e=n(Gso,"CODE",{});var Kaa=s(G3e);kLr=r(Kaa,"__init__()"),Kaa.forEach(t),SLr=r(Gso," (throws an error)."),Gso.forEach(t),RLr=i(ti),Ot=n(ti,"DIV",{class:!0});var qy=s(Ot);T(AS.$$.fragment,qy),PLr=i(qy),O3e=n(qy,"P",{});var ena=s(O3e);BLr=r(ena,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),ena.forEach(t),ILr=i(qy),Nc=n(qy,"P",{});var Oce=s(Nc);NLr=r(Oce,`Note:
Loading a model from its configuration file does `),V3e=n(Oce,"STRONG",{});var ona=s(V3e);qLr=r(ona,"not"),ona.forEach(t),DLr=r(Oce,` load the model weights. It only affects the
model\u2019s configuration. Use `),Yee=n(Oce,"A",{href:!0});var rna=s(Yee);jLr=r(rna,"from_pretrained()"),rna.forEach(t),GLr=r(Oce," to load the model weights."),Oce.forEach(t),OLr=i(qy),T(hE.$$.fragment,qy),qy.forEach(t),VLr=i(ti),vo=n(ti,"DIV",{class:!0});var Qa=s(vo);T(LS.$$.fragment,Qa),XLr=i(Qa),X3e=n(Qa,"P",{});var tna=s(X3e);zLr=r(tna,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),tna.forEach(t),QLr=i(Qa),wn=n(Qa,"P",{});var Dy=s(wn);WLr=r(Dy,"The model class to instantiate is selected based on the "),z3e=n(Dy,"CODE",{});var ana=s(z3e);ULr=r(ana,"model_type"),ana.forEach(t),HLr=r(Dy,` property of the config object (either
passed as an argument or loaded from `),Q3e=n(Dy,"CODE",{});var nna=s(Q3e);JLr=r(nna,"pretrained_model_name_or_path"),nna.forEach(t),YLr=r(Dy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),W3e=n(Dy,"CODE",{});var sna=s(W3e);ZLr=r(sna,"pretrained_model_name_or_path"),sna.forEach(t),KLr=r(Dy,":"),Dy.forEach(t),eyr=i(Qa),Le=n(Qa,"UL",{});var Ie=s(Le);pE=n(Ie,"LI",{});var uJe=s(pE);U3e=n(uJe,"STRONG",{});var lna=s(U3e);oyr=r(lna,"data2vec-audio"),lna.forEach(t),ryr=r(uJe," \u2014 "),Zee=n(uJe,"A",{href:!0});var ina=s(Zee);tyr=r(ina,"Data2VecAudioForCTC"),ina.forEach(t),ayr=r(uJe," (Data2VecAudio model)"),uJe.forEach(t),nyr=i(Ie),uE=n(Ie,"LI",{});var _Je=s(uE);H3e=n(_Je,"STRONG",{});var dna=s(H3e);syr=r(dna,"hubert"),dna.forEach(t),lyr=r(_Je," \u2014 "),Kee=n(_Je,"A",{href:!0});var cna=s(Kee);iyr=r(cna,"HubertForCTC"),cna.forEach(t),dyr=r(_Je," (Hubert model)"),_Je.forEach(t),cyr=i(Ie),_E=n(Ie,"LI",{});var bJe=s(_E);J3e=n(bJe,"STRONG",{});var fna=s(J3e);fyr=r(fna,"mctct"),fna.forEach(t),myr=r(bJe," \u2014 "),eoe=n(bJe,"A",{href:!0});var mna=s(eoe);gyr=r(mna,"MCTCTForCTC"),mna.forEach(t),hyr=r(bJe," (M-CTC-T model)"),bJe.forEach(t),pyr=i(Ie),bE=n(Ie,"LI",{});var vJe=s(bE);Y3e=n(vJe,"STRONG",{});var gna=s(Y3e);uyr=r(gna,"sew"),gna.forEach(t),_yr=r(vJe," \u2014 "),ooe=n(vJe,"A",{href:!0});var hna=s(ooe);byr=r(hna,"SEWForCTC"),hna.forEach(t),vyr=r(vJe," (SEW model)"),vJe.forEach(t),Fyr=i(Ie),vE=n(Ie,"LI",{});var FJe=s(vE);Z3e=n(FJe,"STRONG",{});var pna=s(Z3e);Tyr=r(pna,"sew-d"),pna.forEach(t),Myr=r(FJe," \u2014 "),roe=n(FJe,"A",{href:!0});var una=s(roe);Eyr=r(una,"SEWDForCTC"),una.forEach(t),Cyr=r(FJe," (SEW-D model)"),FJe.forEach(t),wyr=i(Ie),FE=n(Ie,"LI",{});var TJe=s(FE);K3e=n(TJe,"STRONG",{});var _na=s(K3e);Ayr=r(_na,"unispeech"),_na.forEach(t),Lyr=r(TJe," \u2014 "),toe=n(TJe,"A",{href:!0});var bna=s(toe);yyr=r(bna,"UniSpeechForCTC"),bna.forEach(t),xyr=r(TJe," (UniSpeech model)"),TJe.forEach(t),$yr=i(Ie),TE=n(Ie,"LI",{});var MJe=s(TE);e5e=n(MJe,"STRONG",{});var vna=s(e5e);kyr=r(vna,"unispeech-sat"),vna.forEach(t),Syr=r(MJe," \u2014 "),aoe=n(MJe,"A",{href:!0});var Fna=s(aoe);Ryr=r(Fna,"UniSpeechSatForCTC"),Fna.forEach(t),Pyr=r(MJe," (UniSpeechSat model)"),MJe.forEach(t),Byr=i(Ie),ME=n(Ie,"LI",{});var EJe=s(ME);o5e=n(EJe,"STRONG",{});var Tna=s(o5e);Iyr=r(Tna,"wav2vec2"),Tna.forEach(t),Nyr=r(EJe," \u2014 "),noe=n(EJe,"A",{href:!0});var Mna=s(noe);qyr=r(Mna,"Wav2Vec2ForCTC"),Mna.forEach(t),Dyr=r(EJe," (Wav2Vec2 model)"),EJe.forEach(t),jyr=i(Ie),EE=n(Ie,"LI",{});var CJe=s(EE);r5e=n(CJe,"STRONG",{});var Ena=s(r5e);Gyr=r(Ena,"wav2vec2-conformer"),Ena.forEach(t),Oyr=r(CJe," \u2014 "),soe=n(CJe,"A",{href:!0});var Cna=s(soe);Vyr=r(Cna,"Wav2Vec2ConformerForCTC"),Cna.forEach(t),Xyr=r(CJe," (Wav2Vec2-Conformer model)"),CJe.forEach(t),zyr=i(Ie),CE=n(Ie,"LI",{});var wJe=s(CE);t5e=n(wJe,"STRONG",{});var wna=s(t5e);Qyr=r(wna,"wavlm"),wna.forEach(t),Wyr=r(wJe," \u2014 "),loe=n(wJe,"A",{href:!0});var Ana=s(loe);Uyr=r(Ana,"WavLMForCTC"),Ana.forEach(t),Hyr=r(wJe," (WavLM model)"),wJe.forEach(t),Ie.forEach(t),Jyr=i(Qa),wE=n(Qa,"P",{});var AJe=s(wE);Yyr=r(AJe,"The model is set in evaluation mode by default using "),a5e=n(AJe,"CODE",{});var Lna=s(a5e);Zyr=r(Lna,"model.eval()"),Lna.forEach(t),Kyr=r(AJe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),n5e=n(AJe,"CODE",{});var yna=s(n5e);exr=r(yna,"model.train()"),yna.forEach(t),AJe.forEach(t),oxr=i(Qa),T(AE.$$.fragment,Qa),Qa.forEach(t),ti.forEach(t),Eao=i(f),qc=n(f,"H2",{class:!0});var Oso=s(qc);LE=n(Oso,"A",{id:!0,class:!0,href:!0});var xna=s(LE);s5e=n(xna,"SPAN",{});var $na=s(s5e);T(yS.$$.fragment,$na),$na.forEach(t),xna.forEach(t),rxr=i(Oso),l5e=n(Oso,"SPAN",{});var kna=s(l5e);txr=r(kna,"AutoModelForSpeechSeq2Seq"),kna.forEach(t),Oso.forEach(t),Cao=i(f),rr=n(f,"DIV",{class:!0});var ai=s(rr);T(xS.$$.fragment,ai),axr=i(ai),Dc=n(ai,"P",{});var Vce=s(Dc);nxr=r(Vce,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),ioe=n(Vce,"A",{href:!0});var Sna=s(ioe);sxr=r(Sna,"from_pretrained()"),Sna.forEach(t),lxr=r(Vce," class method or the "),doe=n(Vce,"A",{href:!0});var Rna=s(doe);ixr=r(Rna,"from_config()"),Rna.forEach(t),dxr=r(Vce,` class
method.`),Vce.forEach(t),cxr=i(ai),$S=n(ai,"P",{});var Vso=s($S);fxr=r(Vso,"This class cannot be instantiated directly using "),i5e=n(Vso,"CODE",{});var Pna=s(i5e);mxr=r(Pna,"__init__()"),Pna.forEach(t),gxr=r(Vso," (throws an error)."),Vso.forEach(t),hxr=i(ai),Vt=n(ai,"DIV",{class:!0});var jy=s(Vt);T(kS.$$.fragment,jy),pxr=i(jy),d5e=n(jy,"P",{});var Bna=s(d5e);uxr=r(Bna,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Bna.forEach(t),_xr=i(jy),jc=n(jy,"P",{});var Xce=s(jc);bxr=r(Xce,`Note:
Loading a model from its configuration file does `),c5e=n(Xce,"STRONG",{});var Ina=s(c5e);vxr=r(Ina,"not"),Ina.forEach(t),Fxr=r(Xce,` load the model weights. It only affects the
model\u2019s configuration. Use `),coe=n(Xce,"A",{href:!0});var Nna=s(coe);Txr=r(Nna,"from_pretrained()"),Nna.forEach(t),Mxr=r(Xce," to load the model weights."),Xce.forEach(t),Exr=i(jy),T(yE.$$.fragment,jy),jy.forEach(t),Cxr=i(ai),Fo=n(ai,"DIV",{class:!0});var Wa=s(Fo);T(SS.$$.fragment,Wa),wxr=i(Wa),f5e=n(Wa,"P",{});var qna=s(f5e);Axr=r(qna,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),qna.forEach(t),Lxr=i(Wa),An=n(Wa,"P",{});var Gy=s(An);yxr=r(Gy,"The model class to instantiate is selected based on the "),m5e=n(Gy,"CODE",{});var Dna=s(m5e);xxr=r(Dna,"model_type"),Dna.forEach(t),$xr=r(Gy,` property of the config object (either
passed as an argument or loaded from `),g5e=n(Gy,"CODE",{});var jna=s(g5e);kxr=r(jna,"pretrained_model_name_or_path"),jna.forEach(t),Sxr=r(Gy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),h5e=n(Gy,"CODE",{});var Gna=s(h5e);Rxr=r(Gna,"pretrained_model_name_or_path"),Gna.forEach(t),Pxr=r(Gy,":"),Gy.forEach(t),Bxr=i(Wa),Gc=n(Wa,"UL",{});var zce=s(Gc);xE=n(zce,"LI",{});var LJe=s(xE);p5e=n(LJe,"STRONG",{});var Ona=s(p5e);Ixr=r(Ona,"speech-encoder-decoder"),Ona.forEach(t),Nxr=r(LJe," \u2014 "),foe=n(LJe,"A",{href:!0});var Vna=s(foe);qxr=r(Vna,"SpeechEncoderDecoderModel"),Vna.forEach(t),Dxr=r(LJe," (Speech Encoder decoder model)"),LJe.forEach(t),jxr=i(zce),$E=n(zce,"LI",{});var yJe=s($E);u5e=n(yJe,"STRONG",{});var Xna=s(u5e);Gxr=r(Xna,"speech_to_text"),Xna.forEach(t),Oxr=r(yJe," \u2014 "),moe=n(yJe,"A",{href:!0});var zna=s(moe);Vxr=r(zna,"Speech2TextForConditionalGeneration"),zna.forEach(t),Xxr=r(yJe," (Speech2Text model)"),yJe.forEach(t),zxr=i(zce),kE=n(zce,"LI",{});var xJe=s(kE);_5e=n(xJe,"STRONG",{});var Qna=s(_5e);Qxr=r(Qna,"whisper"),Qna.forEach(t),Wxr=r(xJe," \u2014 "),goe=n(xJe,"A",{href:!0});var Wna=s(goe);Uxr=r(Wna,"WhisperForConditionalGeneration"),Wna.forEach(t),Hxr=r(xJe," (Whisper model)"),xJe.forEach(t),zce.forEach(t),Jxr=i(Wa),SE=n(Wa,"P",{});var $Je=s(SE);Yxr=r($Je,"The model is set in evaluation mode by default using "),b5e=n($Je,"CODE",{});var Una=s(b5e);Zxr=r(Una,"model.eval()"),Una.forEach(t),Kxr=r($Je,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),v5e=n($Je,"CODE",{});var Hna=s(v5e);e$r=r(Hna,"model.train()"),Hna.forEach(t),$Je.forEach(t),o$r=i(Wa),T(RE.$$.fragment,Wa),Wa.forEach(t),ai.forEach(t),wao=i(f),Oc=n(f,"H2",{class:!0});var Xso=s(Oc);PE=n(Xso,"A",{id:!0,class:!0,href:!0});var Jna=s(PE);F5e=n(Jna,"SPAN",{});var Yna=s(F5e);T(RS.$$.fragment,Yna),Yna.forEach(t),Jna.forEach(t),r$r=i(Xso),T5e=n(Xso,"SPAN",{});var Zna=s(T5e);t$r=r(Zna,"AutoModelForAudioXVector"),Zna.forEach(t),Xso.forEach(t),Aao=i(f),tr=n(f,"DIV",{class:!0});var ni=s(tr);T(PS.$$.fragment,ni),a$r=i(ni),Vc=n(ni,"P",{});var Qce=s(Vc);n$r=r(Qce,`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 `),hoe=n(Qce,"A",{href:!0});var Kna=s(hoe);s$r=r(Kna,"from_pretrained()"),Kna.forEach(t),l$r=r(Qce," class method or the "),poe=n(Qce,"A",{href:!0});var esa=s(poe);i$r=r(esa,"from_config()"),esa.forEach(t),d$r=r(Qce,` class
method.`),Qce.forEach(t),c$r=i(ni),BS=n(ni,"P",{});var zso=s(BS);f$r=r(zso,"This class cannot be instantiated directly using "),M5e=n(zso,"CODE",{});var osa=s(M5e);m$r=r(osa,"__init__()"),osa.forEach(t),g$r=r(zso," (throws an error)."),zso.forEach(t),h$r=i(ni),Xt=n(ni,"DIV",{class:!0});var Oy=s(Xt);T(IS.$$.fragment,Oy),p$r=i(Oy),E5e=n(Oy,"P",{});var rsa=s(E5e);u$r=r(rsa,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),rsa.forEach(t),_$r=i(Oy),Xc=n(Oy,"P",{});var Wce=s(Xc);b$r=r(Wce,`Note:
Loading a model from its configuration file does `),C5e=n(Wce,"STRONG",{});var tsa=s(C5e);v$r=r(tsa,"not"),tsa.forEach(t),F$r=r(Wce,` load the model weights. It only affects the
model\u2019s configuration. Use `),uoe=n(Wce,"A",{href:!0});var asa=s(uoe);T$r=r(asa,"from_pretrained()"),asa.forEach(t),M$r=r(Wce," to load the model weights."),Wce.forEach(t),E$r=i(Oy),T(BE.$$.fragment,Oy),Oy.forEach(t),C$r=i(ni),To=n(ni,"DIV",{class:!0});var Ua=s(To);T(NS.$$.fragment,Ua),w$r=i(Ua),w5e=n(Ua,"P",{});var nsa=s(w5e);A$r=r(nsa,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),nsa.forEach(t),L$r=i(Ua),Ln=n(Ua,"P",{});var Vy=s(Ln);y$r=r(Vy,"The model class to instantiate is selected based on the "),A5e=n(Vy,"CODE",{});var ssa=s(A5e);x$r=r(ssa,"model_type"),ssa.forEach(t),$$r=r(Vy,` property of the config object (either
passed as an argument or loaded from `),L5e=n(Vy,"CODE",{});var lsa=s(L5e);k$r=r(lsa,"pretrained_model_name_or_path"),lsa.forEach(t),S$r=r(Vy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),y5e=n(Vy,"CODE",{});var isa=s(y5e);R$r=r(isa,"pretrained_model_name_or_path"),isa.forEach(t),P$r=r(Vy,":"),Vy.forEach(t),B$r=i(Ua),ut=n(Ua,"UL",{});var si=s(ut);IE=n(si,"LI",{});var kJe=s(IE);x5e=n(kJe,"STRONG",{});var dsa=s(x5e);I$r=r(dsa,"data2vec-audio"),dsa.forEach(t),N$r=r(kJe," \u2014 "),_oe=n(kJe,"A",{href:!0});var csa=s(_oe);q$r=r(csa,"Data2VecAudioForXVector"),csa.forEach(t),D$r=r(kJe," (Data2VecAudio model)"),kJe.forEach(t),j$r=i(si),NE=n(si,"LI",{});var SJe=s(NE);$5e=n(SJe,"STRONG",{});var fsa=s($5e);G$r=r(fsa,"unispeech-sat"),fsa.forEach(t),O$r=r(SJe," \u2014 "),boe=n(SJe,"A",{href:!0});var msa=s(boe);V$r=r(msa,"UniSpeechSatForXVector"),msa.forEach(t),X$r=r(SJe," (UniSpeechSat model)"),SJe.forEach(t),z$r=i(si),qE=n(si,"LI",{});var RJe=s(qE);k5e=n(RJe,"STRONG",{});var gsa=s(k5e);Q$r=r(gsa,"wav2vec2"),gsa.forEach(t),W$r=r(RJe," \u2014 "),voe=n(RJe,"A",{href:!0});var hsa=s(voe);U$r=r(hsa,"Wav2Vec2ForXVector"),hsa.forEach(t),H$r=r(RJe," (Wav2Vec2 model)"),RJe.forEach(t),J$r=i(si),DE=n(si,"LI",{});var PJe=s(DE);S5e=n(PJe,"STRONG",{});var psa=s(S5e);Y$r=r(psa,"wav2vec2-conformer"),psa.forEach(t),Z$r=r(PJe," \u2014 "),Foe=n(PJe,"A",{href:!0});var usa=s(Foe);K$r=r(usa,"Wav2Vec2ConformerForXVector"),usa.forEach(t),ekr=r(PJe," (Wav2Vec2-Conformer model)"),PJe.forEach(t),okr=i(si),jE=n(si,"LI",{});var BJe=s(jE);R5e=n(BJe,"STRONG",{});var _sa=s(R5e);rkr=r(_sa,"wavlm"),_sa.forEach(t),tkr=r(BJe," \u2014 "),Toe=n(BJe,"A",{href:!0});var bsa=s(Toe);akr=r(bsa,"WavLMForXVector"),bsa.forEach(t),nkr=r(BJe," (WavLM model)"),BJe.forEach(t),si.forEach(t),skr=i(Ua),GE=n(Ua,"P",{});var IJe=s(GE);lkr=r(IJe,"The model is set in evaluation mode by default using "),P5e=n(IJe,"CODE",{});var vsa=s(P5e);ikr=r(vsa,"model.eval()"),vsa.forEach(t),dkr=r(IJe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),B5e=n(IJe,"CODE",{});var Fsa=s(B5e);ckr=r(Fsa,"model.train()"),Fsa.forEach(t),IJe.forEach(t),fkr=i(Ua),T(OE.$$.fragment,Ua),Ua.forEach(t),ni.forEach(t),Lao=i(f),zc=n(f,"H2",{class:!0});var Qso=s(zc);VE=n(Qso,"A",{id:!0,class:!0,href:!0});var Tsa=s(VE);I5e=n(Tsa,"SPAN",{});var Msa=s(I5e);T(qS.$$.fragment,Msa),Msa.forEach(t),Tsa.forEach(t),mkr=i(Qso),N5e=n(Qso,"SPAN",{});var Esa=s(N5e);gkr=r(Esa,"AutoModelForMaskedImageModeling"),Esa.forEach(t),Qso.forEach(t),yao=i(f),ar=n(f,"DIV",{class:!0});var li=s(ar);T(DS.$$.fragment,li),hkr=i(li),Qc=n(li,"P",{});var Uce=s(Qc);pkr=r(Uce,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),Moe=n(Uce,"A",{href:!0});var Csa=s(Moe);ukr=r(Csa,"from_pretrained()"),Csa.forEach(t),_kr=r(Uce," class method or the "),Eoe=n(Uce,"A",{href:!0});var wsa=s(Eoe);bkr=r(wsa,"from_config()"),wsa.forEach(t),vkr=r(Uce,` class
method.`),Uce.forEach(t),Fkr=i(li),jS=n(li,"P",{});var Wso=s(jS);Tkr=r(Wso,"This class cannot be instantiated directly using "),q5e=n(Wso,"CODE",{});var Asa=s(q5e);Mkr=r(Asa,"__init__()"),Asa.forEach(t),Ekr=r(Wso," (throws an error)."),Wso.forEach(t),Ckr=i(li),zt=n(li,"DIV",{class:!0});var Xy=s(zt);T(GS.$$.fragment,Xy),wkr=i(Xy),D5e=n(Xy,"P",{});var Lsa=s(D5e);Akr=r(Lsa,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),Lsa.forEach(t),Lkr=i(Xy),Wc=n(Xy,"P",{});var Hce=s(Wc);ykr=r(Hce,`Note:
Loading a model from its configuration file does `),j5e=n(Hce,"STRONG",{});var ysa=s(j5e);xkr=r(ysa,"not"),ysa.forEach(t),$kr=r(Hce,` load the model weights. It only affects the
model\u2019s configuration. Use `),Coe=n(Hce,"A",{href:!0});var xsa=s(Coe);kkr=r(xsa,"from_pretrained()"),xsa.forEach(t),Skr=r(Hce," to load the model weights."),Hce.forEach(t),Rkr=i(Xy),T(XE.$$.fragment,Xy),Xy.forEach(t),Pkr=i(li),Mo=n(li,"DIV",{class:!0});var Ha=s(Mo);T(OS.$$.fragment,Ha),Bkr=i(Ha),G5e=n(Ha,"P",{});var $sa=s(G5e);Ikr=r($sa,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),$sa.forEach(t),Nkr=i(Ha),yn=n(Ha,"P",{});var zy=s(yn);qkr=r(zy,"The model class to instantiate is selected based on the "),O5e=n(zy,"CODE",{});var ksa=s(O5e);Dkr=r(ksa,"model_type"),ksa.forEach(t),jkr=r(zy,` property of the config object (either
passed as an argument or loaded from `),V5e=n(zy,"CODE",{});var Ssa=s(V5e);Gkr=r(Ssa,"pretrained_model_name_or_path"),Ssa.forEach(t),Okr=r(zy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),X5e=n(zy,"CODE",{});var Rsa=s(X5e);Vkr=r(Rsa,"pretrained_model_name_or_path"),Rsa.forEach(t),Xkr=r(zy,":"),zy.forEach(t),zkr=i(Ha),xn=n(Ha,"UL",{});var Qy=s(xn);zE=n(Qy,"LI",{});var NJe=s(zE);z5e=n(NJe,"STRONG",{});var Psa=s(z5e);Qkr=r(Psa,"deit"),Psa.forEach(t),Wkr=r(NJe," \u2014 "),woe=n(NJe,"A",{href:!0});var Bsa=s(woe);Ukr=r(Bsa,"DeiTForMaskedImageModeling"),Bsa.forEach(t),Hkr=r(NJe," (DeiT model)"),NJe.forEach(t),Jkr=i(Qy),QE=n(Qy,"LI",{});var qJe=s(QE);Q5e=n(qJe,"STRONG",{});var Isa=s(Q5e);Ykr=r(Isa,"swin"),Isa.forEach(t),Zkr=r(qJe," \u2014 "),Aoe=n(qJe,"A",{href:!0});var Nsa=s(Aoe);Kkr=r(Nsa,"SwinForMaskedImageModeling"),Nsa.forEach(t),eSr=r(qJe," (Swin Transformer model)"),qJe.forEach(t),oSr=i(Qy),WE=n(Qy,"LI",{});var DJe=s(WE);W5e=n(DJe,"STRONG",{});var qsa=s(W5e);rSr=r(qsa,"swinv2"),qsa.forEach(t),tSr=r(DJe," \u2014 "),Loe=n(DJe,"A",{href:!0});var Dsa=s(Loe);aSr=r(Dsa,"Swinv2ForMaskedImageModeling"),Dsa.forEach(t),nSr=r(DJe," (Swin Transformer V2 model)"),DJe.forEach(t),sSr=i(Qy),UE=n(Qy,"LI",{});var jJe=s(UE);U5e=n(jJe,"STRONG",{});var jsa=s(U5e);lSr=r(jsa,"vit"),jsa.forEach(t),iSr=r(jJe," \u2014 "),yoe=n(jJe,"A",{href:!0});var Gsa=s(yoe);dSr=r(Gsa,"ViTForMaskedImageModeling"),Gsa.forEach(t),cSr=r(jJe," (ViT model)"),jJe.forEach(t),Qy.forEach(t),fSr=i(Ha),HE=n(Ha,"P",{});var GJe=s(HE);mSr=r(GJe,"The model is set in evaluation mode by default using "),H5e=n(GJe,"CODE",{});var Osa=s(H5e);gSr=r(Osa,"model.eval()"),Osa.forEach(t),hSr=r(GJe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),J5e=n(GJe,"CODE",{});var Vsa=s(J5e);pSr=r(Vsa,"model.train()"),Vsa.forEach(t),GJe.forEach(t),uSr=i(Ha),T(JE.$$.fragment,Ha),Ha.forEach(t),li.forEach(t),xao=i(f),Uc=n(f,"H2",{class:!0});var Uso=s(Uc);YE=n(Uso,"A",{id:!0,class:!0,href:!0});var Xsa=s(YE);Y5e=n(Xsa,"SPAN",{});var zsa=s(Y5e);T(VS.$$.fragment,zsa),zsa.forEach(t),Xsa.forEach(t),_Sr=i(Uso),Z5e=n(Uso,"SPAN",{});var Qsa=s(Z5e);bSr=r(Qsa,"AutoModelForObjectDetection"),Qsa.forEach(t),Uso.forEach(t),$ao=i(f),nr=n(f,"DIV",{class:!0});var ii=s(nr);T(XS.$$.fragment,ii),vSr=i(ii),Hc=n(ii,"P",{});var Jce=s(Hc);FSr=r(Jce,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),xoe=n(Jce,"A",{href:!0});var Wsa=s(xoe);TSr=r(Wsa,"from_pretrained()"),Wsa.forEach(t),MSr=r(Jce," class method or the "),$oe=n(Jce,"A",{href:!0});var Usa=s($oe);ESr=r(Usa,"from_config()"),Usa.forEach(t),CSr=r(Jce,` class
method.`),Jce.forEach(t),wSr=i(ii),zS=n(ii,"P",{});var Hso=s(zS);ASr=r(Hso,"This class cannot be instantiated directly using "),K5e=n(Hso,"CODE",{});var Hsa=s(K5e);LSr=r(Hsa,"__init__()"),Hsa.forEach(t),ySr=r(Hso," (throws an error)."),Hso.forEach(t),xSr=i(ii),Qt=n(ii,"DIV",{class:!0});var Wy=s(Qt);T(QS.$$.fragment,Wy),$Sr=i(Wy),ewe=n(Wy,"P",{});var Jsa=s(ewe);kSr=r(Jsa,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),Jsa.forEach(t),SSr=i(Wy),Jc=n(Wy,"P",{});var Yce=s(Jc);RSr=r(Yce,`Note:
Loading a model from its configuration file does `),owe=n(Yce,"STRONG",{});var Ysa=s(owe);PSr=r(Ysa,"not"),Ysa.forEach(t),BSr=r(Yce,` load the model weights. It only affects the
model\u2019s configuration. Use `),koe=n(Yce,"A",{href:!0});var Zsa=s(koe);ISr=r(Zsa,"from_pretrained()"),Zsa.forEach(t),NSr=r(Yce," to load the model weights."),Yce.forEach(t),qSr=i(Wy),T(ZE.$$.fragment,Wy),Wy.forEach(t),DSr=i(ii),Eo=n(ii,"DIV",{class:!0});var Ja=s(Eo);T(WS.$$.fragment,Ja),jSr=i(Ja),rwe=n(Ja,"P",{});var Ksa=s(rwe);GSr=r(Ksa,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),Ksa.forEach(t),OSr=i(Ja),$n=n(Ja,"P",{});var Uy=s($n);VSr=r(Uy,"The model class to instantiate is selected based on the "),twe=n(Uy,"CODE",{});var ela=s(twe);XSr=r(ela,"model_type"),ela.forEach(t),zSr=r(Uy,` property of the config object (either
passed as an argument or loaded from `),awe=n(Uy,"CODE",{});var ola=s(awe);QSr=r(ola,"pretrained_model_name_or_path"),ola.forEach(t),WSr=r(Uy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nwe=n(Uy,"CODE",{});var rla=s(nwe);USr=r(rla,"pretrained_model_name_or_path"),rla.forEach(t),HSr=r(Uy,":"),Uy.forEach(t),JSr=i(Ja),_t=n(Ja,"UL",{});var di=s(_t);KE=n(di,"LI",{});var OJe=s(KE);swe=n(OJe,"STRONG",{});var tla=s(swe);YSr=r(tla,"conditional_detr"),tla.forEach(t),ZSr=r(OJe," \u2014 "),Soe=n(OJe,"A",{href:!0});var ala=s(Soe);KSr=r(ala,"ConditionalDetrForObjectDetection"),ala.forEach(t),eRr=r(OJe," (Conditional DETR model)"),OJe.forEach(t),oRr=i(di),e4=n(di,"LI",{});var VJe=s(e4);lwe=n(VJe,"STRONG",{});var nla=s(lwe);rRr=r(nla,"deformable_detr"),nla.forEach(t),tRr=r(VJe," \u2014 "),Roe=n(VJe,"A",{href:!0});var sla=s(Roe);aRr=r(sla,"DeformableDetrForObjectDetection"),sla.forEach(t),nRr=r(VJe," (Deformable DETR model)"),VJe.forEach(t),sRr=i(di),o4=n(di,"LI",{});var XJe=s(o4);iwe=n(XJe,"STRONG",{});var lla=s(iwe);lRr=r(lla,"detr"),lla.forEach(t),iRr=r(XJe," \u2014 "),Poe=n(XJe,"A",{href:!0});var ila=s(Poe);dRr=r(ila,"DetrForObjectDetection"),ila.forEach(t),cRr=r(XJe," (DETR model)"),XJe.forEach(t),fRr=i(di),r4=n(di,"LI",{});var zJe=s(r4);dwe=n(zJe,"STRONG",{});var dla=s(dwe);mRr=r(dla,"table-transformer"),dla.forEach(t),gRr=r(zJe," \u2014 "),Boe=n(zJe,"A",{href:!0});var cla=s(Boe);hRr=r(cla,"TableTransformerForObjectDetection"),cla.forEach(t),pRr=r(zJe," (Table Transformer model)"),zJe.forEach(t),uRr=i(di),t4=n(di,"LI",{});var QJe=s(t4);cwe=n(QJe,"STRONG",{});var fla=s(cwe);_Rr=r(fla,"yolos"),fla.forEach(t),bRr=r(QJe," \u2014 "),Ioe=n(QJe,"A",{href:!0});var mla=s(Ioe);vRr=r(mla,"YolosForObjectDetection"),mla.forEach(t),FRr=r(QJe," (YOLOS model)"),QJe.forEach(t),di.forEach(t),TRr=i(Ja),a4=n(Ja,"P",{});var WJe=s(a4);MRr=r(WJe,"The model is set in evaluation mode by default using "),fwe=n(WJe,"CODE",{});var gla=s(fwe);ERr=r(gla,"model.eval()"),gla.forEach(t),CRr=r(WJe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),mwe=n(WJe,"CODE",{});var hla=s(mwe);wRr=r(hla,"model.train()"),hla.forEach(t),WJe.forEach(t),ARr=i(Ja),T(n4.$$.fragment,Ja),Ja.forEach(t),ii.forEach(t),kao=i(f),Yc=n(f,"H2",{class:!0});var Jso=s(Yc);s4=n(Jso,"A",{id:!0,class:!0,href:!0});var pla=s(s4);gwe=n(pla,"SPAN",{});var ula=s(gwe);T(US.$$.fragment,ula),ula.forEach(t),pla.forEach(t),LRr=i(Jso),hwe=n(Jso,"SPAN",{});var _la=s(hwe);yRr=r(_la,"AutoModelForImageSegmentation"),_la.forEach(t),Jso.forEach(t),Sao=i(f),sr=n(f,"DIV",{class:!0});var ci=s(sr);T(HS.$$.fragment,ci),xRr=i(ci),Zc=n(ci,"P",{});var Zce=s(Zc);$Rr=r(Zce,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),Noe=n(Zce,"A",{href:!0});var bla=s(Noe);kRr=r(bla,"from_pretrained()"),bla.forEach(t),SRr=r(Zce," class method or the "),qoe=n(Zce,"A",{href:!0});var vla=s(qoe);RRr=r(vla,"from_config()"),vla.forEach(t),PRr=r(Zce,` class
method.`),Zce.forEach(t),BRr=i(ci),JS=n(ci,"P",{});var Yso=s(JS);IRr=r(Yso,"This class cannot be instantiated directly using "),pwe=n(Yso,"CODE",{});var Fla=s(pwe);NRr=r(Fla,"__init__()"),Fla.forEach(t),qRr=r(Yso," (throws an error)."),Yso.forEach(t),DRr=i(ci),Wt=n(ci,"DIV",{class:!0});var Hy=s(Wt);T(YS.$$.fragment,Hy),jRr=i(Hy),uwe=n(Hy,"P",{});var Tla=s(uwe);GRr=r(Tla,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Tla.forEach(t),ORr=i(Hy),Kc=n(Hy,"P",{});var Kce=s(Kc);VRr=r(Kce,`Note:
Loading a model from its configuration file does `),_we=n(Kce,"STRONG",{});var Mla=s(_we);XRr=r(Mla,"not"),Mla.forEach(t),zRr=r(Kce,` load the model weights. It only affects the
model\u2019s configuration. Use `),Doe=n(Kce,"A",{href:!0});var Ela=s(Doe);QRr=r(Ela,"from_pretrained()"),Ela.forEach(t),WRr=r(Kce," to load the model weights."),Kce.forEach(t),URr=i(Hy),T(l4.$$.fragment,Hy),Hy.forEach(t),HRr=i(ci),Co=n(ci,"DIV",{class:!0});var Ya=s(Co);T(ZS.$$.fragment,Ya),JRr=i(Ya),bwe=n(Ya,"P",{});var Cla=s(bwe);YRr=r(Cla,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),Cla.forEach(t),ZRr=i(Ya),kn=n(Ya,"P",{});var Jy=s(kn);KRr=r(Jy,"The model class to instantiate is selected based on the "),vwe=n(Jy,"CODE",{});var wla=s(vwe);ePr=r(wla,"model_type"),wla.forEach(t),oPr=r(Jy,` property of the config object (either
passed as an argument or loaded from `),Fwe=n(Jy,"CODE",{});var Ala=s(Fwe);rPr=r(Ala,"pretrained_model_name_or_path"),Ala.forEach(t),tPr=r(Jy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Twe=n(Jy,"CODE",{});var Lla=s(Twe);aPr=r(Lla,"pretrained_model_name_or_path"),Lla.forEach(t),nPr=r(Jy,":"),Jy.forEach(t),sPr=i(Ya),Mwe=n(Ya,"UL",{});var yla=s(Mwe);i4=n(yla,"LI",{});var UJe=s(i4);Ewe=n(UJe,"STRONG",{});var xla=s(Ewe);lPr=r(xla,"detr"),xla.forEach(t),iPr=r(UJe," \u2014 "),joe=n(UJe,"A",{href:!0});var $la=s(joe);dPr=r($la,"DetrForSegmentation"),$la.forEach(t),cPr=r(UJe," (DETR model)"),UJe.forEach(t),yla.forEach(t),fPr=i(Ya),d4=n(Ya,"P",{});var HJe=s(d4);mPr=r(HJe,"The model is set in evaluation mode by default using "),Cwe=n(HJe,"CODE",{});var kla=s(Cwe);gPr=r(kla,"model.eval()"),kla.forEach(t),hPr=r(HJe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wwe=n(HJe,"CODE",{});var Sla=s(wwe);pPr=r(Sla,"model.train()"),Sla.forEach(t),HJe.forEach(t),uPr=i(Ya),T(c4.$$.fragment,Ya),Ya.forEach(t),ci.forEach(t),Rao=i(f),ef=n(f,"H2",{class:!0});var Zso=s(ef);f4=n(Zso,"A",{id:!0,class:!0,href:!0});var Rla=s(f4);Awe=n(Rla,"SPAN",{});var Pla=s(Awe);T(KS.$$.fragment,Pla),Pla.forEach(t),Rla.forEach(t),_Pr=i(Zso),Lwe=n(Zso,"SPAN",{});var Bla=s(Lwe);bPr=r(Bla,"AutoModelForSemanticSegmentation"),Bla.forEach(t),Zso.forEach(t),Pao=i(f),lr=n(f,"DIV",{class:!0});var fi=s(lr);T(eR.$$.fragment,fi),vPr=i(fi),of=n(fi,"P",{});var efe=s(of);FPr=r(efe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),Goe=n(efe,"A",{href:!0});var Ila=s(Goe);TPr=r(Ila,"from_pretrained()"),Ila.forEach(t),MPr=r(efe," class method or the "),Ooe=n(efe,"A",{href:!0});var Nla=s(Ooe);EPr=r(Nla,"from_config()"),Nla.forEach(t),CPr=r(efe,` class
method.`),efe.forEach(t),wPr=i(fi),oR=n(fi,"P",{});var Kso=s(oR);APr=r(Kso,"This class cannot be instantiated directly using "),ywe=n(Kso,"CODE",{});var qla=s(ywe);LPr=r(qla,"__init__()"),qla.forEach(t),yPr=r(Kso," (throws an error)."),Kso.forEach(t),xPr=i(fi),Ut=n(fi,"DIV",{class:!0});var Yy=s(Ut);T(rR.$$.fragment,Yy),$Pr=i(Yy),xwe=n(Yy,"P",{});var Dla=s(xwe);kPr=r(Dla,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Dla.forEach(t),SPr=i(Yy),rf=n(Yy,"P",{});var ofe=s(rf);RPr=r(ofe,`Note:
Loading a model from its configuration file does `),$we=n(ofe,"STRONG",{});var jla=s($we);PPr=r(jla,"not"),jla.forEach(t),BPr=r(ofe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Voe=n(ofe,"A",{href:!0});var Gla=s(Voe);IPr=r(Gla,"from_pretrained()"),Gla.forEach(t),NPr=r(ofe," to load the model weights."),ofe.forEach(t),qPr=i(Yy),T(m4.$$.fragment,Yy),Yy.forEach(t),DPr=i(fi),wo=n(fi,"DIV",{class:!0});var Za=s(wo);T(tR.$$.fragment,Za),jPr=i(Za),kwe=n(Za,"P",{});var Ola=s(kwe);GPr=r(Ola,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Ola.forEach(t),OPr=i(Za),Sn=n(Za,"P",{});var Zy=s(Sn);VPr=r(Zy,"The model class to instantiate is selected based on the "),Swe=n(Zy,"CODE",{});var Vla=s(Swe);XPr=r(Vla,"model_type"),Vla.forEach(t),zPr=r(Zy,` property of the config object (either
passed as an argument or loaded from `),Rwe=n(Zy,"CODE",{});var Xla=s(Rwe);QPr=r(Xla,"pretrained_model_name_or_path"),Xla.forEach(t),WPr=r(Zy,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pwe=n(Zy,"CODE",{});var zla=s(Pwe);UPr=r(zla,"pretrained_model_name_or_path"),zla.forEach(t),HPr=r(Zy,":"),Zy.forEach(t),JPr=i(Za),bt=n(Za,"UL",{});var mi=s(bt);g4=n(mi,"LI",{});var JJe=s(g4);Bwe=n(JJe,"STRONG",{});var Qla=s(Bwe);YPr=r(Qla,"beit"),Qla.forEach(t),ZPr=r(JJe," \u2014 "),Xoe=n(JJe,"A",{href:!0});var Wla=s(Xoe);KPr=r(Wla,"BeitForSemanticSegmentation"),Wla.forEach(t),eBr=r(JJe," (BEiT model)"),JJe.forEach(t),oBr=i(mi),h4=n(mi,"LI",{});var YJe=s(h4);Iwe=n(YJe,"STRONG",{});var Ula=s(Iwe);rBr=r(Ula,"data2vec-vision"),Ula.forEach(t),tBr=r(YJe," \u2014 "),zoe=n(YJe,"A",{href:!0});var Hla=s(zoe);aBr=r(Hla,"Data2VecVisionForSemanticSegmentation"),Hla.forEach(t),nBr=r(YJe," (Data2VecVision model)"),YJe.forEach(t),sBr=i(mi),p4=n(mi,"LI",{});var ZJe=s(p4);Nwe=n(ZJe,"STRONG",{});var Jla=s(Nwe);lBr=r(Jla,"dpt"),Jla.forEach(t),iBr=r(ZJe," \u2014 "),Qoe=n(ZJe,"A",{href:!0});var Yla=s(Qoe);dBr=r(Yla,"DPTForSemanticSegmentation"),Yla.forEach(t),cBr=r(ZJe," (DPT model)"),ZJe.forEach(t),fBr=i(mi),u4=n(mi,"LI",{});var KJe=s(u4);qwe=n(KJe,"STRONG",{});var Zla=s(qwe);mBr=r(Zla,"mobilevit"),Zla.forEach(t),gBr=r(KJe," \u2014 "),Woe=n(KJe,"A",{href:!0});var Kla=s(Woe);hBr=r(Kla,"MobileViTForSemanticSegmentation"),Kla.forEach(t),pBr=r(KJe," (MobileViT model)"),KJe.forEach(t),uBr=i(mi),_4=n(mi,"LI",{});var eYe=s(_4);Dwe=n(eYe,"STRONG",{});var eia=s(Dwe);_Br=r(eia,"segformer"),eia.forEach(t),bBr=r(eYe," \u2014 "),Uoe=n(eYe,"A",{href:!0});var oia=s(Uoe);vBr=r(oia,"SegformerForSemanticSegmentation"),oia.forEach(t),FBr=r(eYe," (SegFormer model)"),eYe.forEach(t),mi.forEach(t),TBr=i(Za),b4=n(Za,"P",{});var oYe=s(b4);MBr=r(oYe,"The model is set in evaluation mode by default using "),jwe=n(oYe,"CODE",{});var ria=s(jwe);EBr=r(ria,"model.eval()"),ria.forEach(t),CBr=r(oYe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Gwe=n(oYe,"CODE",{});var tia=s(Gwe);wBr=r(tia,"model.train()"),tia.forEach(t),oYe.forEach(t),ABr=i(Za),T(v4.$$.fragment,Za),Za.forEach(t),fi.forEach(t),Bao=i(f),tf=n(f,"H2",{class:!0});var elo=s(tf);F4=n(elo,"A",{id:!0,class:!0,href:!0});var aia=s(F4);Owe=n(aia,"SPAN",{});var nia=s(Owe);T(aR.$$.fragment,nia),nia.forEach(t),aia.forEach(t),LBr=i(elo),Vwe=n(elo,"SPAN",{});var sia=s(Vwe);yBr=r(sia,"AutoModelForInstanceSegmentation"),sia.forEach(t),elo.forEach(t),Iao=i(f),ir=n(f,"DIV",{class:!0});var gi=s(ir);T(nR.$$.fragment,gi),xBr=i(gi),af=n(gi,"P",{});var rfe=s(af);$Br=r(rfe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),Hoe=n(rfe,"A",{href:!0});var lia=s(Hoe);kBr=r(lia,"from_pretrained()"),lia.forEach(t),SBr=r(rfe," class method or the "),Joe=n(rfe,"A",{href:!0});var iia=s(Joe);RBr=r(iia,"from_config()"),iia.forEach(t),PBr=r(rfe,` class
method.`),rfe.forEach(t),BBr=i(gi),sR=n(gi,"P",{});var olo=s(sR);IBr=r(olo,"This class cannot be instantiated directly using "),Xwe=n(olo,"CODE",{});var dia=s(Xwe);NBr=r(dia,"__init__()"),dia.forEach(t),qBr=r(olo," (throws an error)."),olo.forEach(t),DBr=i(gi),Ht=n(gi,"DIV",{class:!0});var Ky=s(Ht);T(lR.$$.fragment,Ky),jBr=i(Ky),zwe=n(Ky,"P",{});var cia=s(zwe);GBr=r(cia,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),cia.forEach(t),OBr=i(Ky),nf=n(Ky,"P",{});var tfe=s(nf);VBr=r(tfe,`Note:
Loading a model from its configuration file does `),Qwe=n(tfe,"STRONG",{});var fia=s(Qwe);XBr=r(fia,"not"),fia.forEach(t),zBr=r(tfe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Yoe=n(tfe,"A",{href:!0});var mia=s(Yoe);QBr=r(mia,"from_pretrained()"),mia.forEach(t),WBr=r(tfe," to load the model weights."),tfe.forEach(t),UBr=i(Ky),T(T4.$$.fragment,Ky),Ky.forEach(t),HBr=i(gi),Ao=n(gi,"DIV",{class:!0});var Ka=s(Ao);T(iR.$$.fragment,Ka),JBr=i(Ka),Wwe=n(Ka,"P",{});var gia=s(Wwe);YBr=r(gia,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),gia.forEach(t),ZBr=i(Ka),Rn=n(Ka,"P",{});var ex=s(Rn);KBr=r(ex,"The model class to instantiate is selected based on the "),Uwe=n(ex,"CODE",{});var hia=s(Uwe);eIr=r(hia,"model_type"),hia.forEach(t),oIr=r(ex,` property of the config object (either
passed as an argument or loaded from `),Hwe=n(ex,"CODE",{});var pia=s(Hwe);rIr=r(pia,"pretrained_model_name_or_path"),pia.forEach(t),tIr=r(ex,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jwe=n(ex,"CODE",{});var uia=s(Jwe);aIr=r(uia,"pretrained_model_name_or_path"),uia.forEach(t),nIr=r(ex,":"),ex.forEach(t),sIr=i(Ka),Ywe=n(Ka,"UL",{});var _ia=s(Ywe);M4=n(_ia,"LI",{});var rYe=s(M4);Zwe=n(rYe,"STRONG",{});var bia=s(Zwe);lIr=r(bia,"maskformer"),bia.forEach(t),iIr=r(rYe," \u2014 "),Zoe=n(rYe,"A",{href:!0});var via=s(Zoe);dIr=r(via,"MaskFormerForInstanceSegmentation"),via.forEach(t),cIr=r(rYe," (MaskFormer model)"),rYe.forEach(t),_ia.forEach(t),fIr=i(Ka),E4=n(Ka,"P",{});var tYe=s(E4);mIr=r(tYe,"The model is set in evaluation mode by default using "),Kwe=n(tYe,"CODE",{});var Fia=s(Kwe);gIr=r(Fia,"model.eval()"),Fia.forEach(t),hIr=r(tYe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),eAe=n(tYe,"CODE",{});var Tia=s(eAe);pIr=r(Tia,"model.train()"),Tia.forEach(t),tYe.forEach(t),uIr=i(Ka),T(C4.$$.fragment,Ka),Ka.forEach(t),gi.forEach(t),Nao=i(f),sf=n(f,"H2",{class:!0});var rlo=s(sf);w4=n(rlo,"A",{id:!0,class:!0,href:!0});var Mia=s(w4);oAe=n(Mia,"SPAN",{});var Eia=s(oAe);T(dR.$$.fragment,Eia),Eia.forEach(t),Mia.forEach(t),_Ir=i(rlo),rAe=n(rlo,"SPAN",{});var Cia=s(rAe);bIr=r(Cia,"AutoModelForZeroShotObjectDetection"),Cia.forEach(t),rlo.forEach(t),qao=i(f),dr=n(f,"DIV",{class:!0});var hi=s(dr);T(cR.$$.fragment,hi),vIr=i(hi),lf=n(hi,"P",{});var afe=s(lf);FIr=r(afe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),Koe=n(afe,"A",{href:!0});var wia=s(Koe);TIr=r(wia,"from_pretrained()"),wia.forEach(t),MIr=r(afe," class method or the "),ere=n(afe,"A",{href:!0});var Aia=s(ere);EIr=r(Aia,"from_config()"),Aia.forEach(t),CIr=r(afe,` class
method.`),afe.forEach(t),wIr=i(hi),fR=n(hi,"P",{});var tlo=s(fR);AIr=r(tlo,"This class cannot be instantiated directly using "),tAe=n(tlo,"CODE",{});var Lia=s(tAe);LIr=r(Lia,"__init__()"),Lia.forEach(t),yIr=r(tlo," (throws an error)."),tlo.forEach(t),xIr=i(hi),Jt=n(hi,"DIV",{class:!0});var ox=s(Jt);T(mR.$$.fragment,ox),$Ir=i(ox),aAe=n(ox,"P",{});var yia=s(aAe);kIr=r(yia,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),yia.forEach(t),SIr=i(ox),df=n(ox,"P",{});var nfe=s(df);RIr=r(nfe,`Note:
Loading a model from its configuration file does `),nAe=n(nfe,"STRONG",{});var xia=s(nAe);PIr=r(xia,"not"),xia.forEach(t),BIr=r(nfe,` load the model weights. It only affects the
model\u2019s configuration. Use `),ore=n(nfe,"A",{href:!0});var $ia=s(ore);IIr=r($ia,"from_pretrained()"),$ia.forEach(t),NIr=r(nfe," to load the model weights."),nfe.forEach(t),qIr=i(ox),T(A4.$$.fragment,ox),ox.forEach(t),DIr=i(hi),Lo=n(hi,"DIV",{class:!0});var en=s(Lo);T(gR.$$.fragment,en),jIr=i(en),sAe=n(en,"P",{});var kia=s(sAe);GIr=r(kia,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),kia.forEach(t),OIr=i(en),Pn=n(en,"P",{});var rx=s(Pn);VIr=r(rx,"The model class to instantiate is selected based on the "),lAe=n(rx,"CODE",{});var Sia=s(lAe);XIr=r(Sia,"model_type"),Sia.forEach(t),zIr=r(rx,` property of the config object (either
passed as an argument or loaded from `),iAe=n(rx,"CODE",{});var Ria=s(iAe);QIr=r(Ria,"pretrained_model_name_or_path"),Ria.forEach(t),WIr=r(rx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dAe=n(rx,"CODE",{});var Pia=s(dAe);UIr=r(Pia,"pretrained_model_name_or_path"),Pia.forEach(t),HIr=r(rx,":"),rx.forEach(t),JIr=i(en),cAe=n(en,"UL",{});var Bia=s(cAe);L4=n(Bia,"LI",{});var aYe=s(L4);fAe=n(aYe,"STRONG",{});var Iia=s(fAe);YIr=r(Iia,"owlvit"),Iia.forEach(t),ZIr=r(aYe," \u2014 "),rre=n(aYe,"A",{href:!0});var Nia=s(rre);KIr=r(Nia,"OwlViTForObjectDetection"),Nia.forEach(t),eNr=r(aYe," (OWL-ViT model)"),aYe.forEach(t),Bia.forEach(t),oNr=i(en),y4=n(en,"P",{});var nYe=s(y4);rNr=r(nYe,"The model is set in evaluation mode by default using "),mAe=n(nYe,"CODE",{});var qia=s(mAe);tNr=r(qia,"model.eval()"),qia.forEach(t),aNr=r(nYe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),gAe=n(nYe,"CODE",{});var Dia=s(gAe);nNr=r(Dia,"model.train()"),Dia.forEach(t),nYe.forEach(t),sNr=i(en),T(x4.$$.fragment,en),en.forEach(t),hi.forEach(t),Dao=i(f),cf=n(f,"H2",{class:!0});var alo=s(cf);$4=n(alo,"A",{id:!0,class:!0,href:!0});var jia=s($4);hAe=n(jia,"SPAN",{});var Gia=s(hAe);T(hR.$$.fragment,Gia),Gia.forEach(t),jia.forEach(t),lNr=i(alo),pAe=n(alo,"SPAN",{});var Oia=s(pAe);iNr=r(Oia,"TFAutoModel"),Oia.forEach(t),alo.forEach(t),jao=i(f),cr=n(f,"DIV",{class:!0});var pi=s(cr);T(pR.$$.fragment,pi),dNr=i(pi),ff=n(pi,"P",{});var sfe=s(ff);cNr=r(sfe,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),tre=n(sfe,"A",{href:!0});var Via=s(tre);fNr=r(Via,"from_pretrained()"),Via.forEach(t),mNr=r(sfe," class method or the "),are=n(sfe,"A",{href:!0});var Xia=s(are);gNr=r(Xia,"from_config()"),Xia.forEach(t),hNr=r(sfe,` class
method.`),sfe.forEach(t),pNr=i(pi),uR=n(pi,"P",{});var nlo=s(uR);uNr=r(nlo,"This class cannot be instantiated directly using "),uAe=n(nlo,"CODE",{});var zia=s(uAe);_Nr=r(zia,"__init__()"),zia.forEach(t),bNr=r(nlo," (throws an error)."),nlo.forEach(t),vNr=i(pi),Yt=n(pi,"DIV",{class:!0});var tx=s(Yt);T(_R.$$.fragment,tx),FNr=i(tx),_Ae=n(tx,"P",{});var Qia=s(_Ae);TNr=r(Qia,"Instantiates one of the base model classes of the library from a configuration."),Qia.forEach(t),MNr=i(tx),mf=n(tx,"P",{});var lfe=s(mf);ENr=r(lfe,`Note:
Loading a model from its configuration file does `),bAe=n(lfe,"STRONG",{});var Wia=s(bAe);CNr=r(Wia,"not"),Wia.forEach(t),wNr=r(lfe,` load the model weights. It only affects the
model\u2019s configuration. Use `),nre=n(lfe,"A",{href:!0});var Uia=s(nre);ANr=r(Uia,"from_pretrained()"),Uia.forEach(t),LNr=r(lfe," to load the model weights."),lfe.forEach(t),yNr=i(tx),T(k4.$$.fragment,tx),tx.forEach(t),xNr=i(pi),jr=n(pi,"DIV",{class:!0});var ui=s(jr);T(bR.$$.fragment,ui),$Nr=i(ui),vAe=n(ui,"P",{});var Hia=s(vAe);kNr=r(Hia,"Instantiate one of the base model classes of the library from a pretrained model."),Hia.forEach(t),SNr=i(ui),Bn=n(ui,"P",{});var ax=s(Bn);RNr=r(ax,"The model class to instantiate is selected based on the "),FAe=n(ax,"CODE",{});var Jia=s(FAe);PNr=r(Jia,"model_type"),Jia.forEach(t),BNr=r(ax,` property of the config object (either
passed as an argument or loaded from `),TAe=n(ax,"CODE",{});var Yia=s(TAe);INr=r(Yia,"pretrained_model_name_or_path"),Yia.forEach(t),NNr=r(ax,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MAe=n(ax,"CODE",{});var Zia=s(MAe);qNr=r(Zia,"pretrained_model_name_or_path"),Zia.forEach(t),DNr=r(ax,":"),ax.forEach(t),jNr=i(ui),P=n(ui,"UL",{});var I=s(P);S4=n(I,"LI",{});var sYe=s(S4);EAe=n(sYe,"STRONG",{});var Kia=s(EAe);GNr=r(Kia,"albert"),Kia.forEach(t),ONr=r(sYe," \u2014 "),sre=n(sYe,"A",{href:!0});var eda=s(sre);VNr=r(eda,"TFAlbertModel"),eda.forEach(t),XNr=r(sYe," (ALBERT model)"),sYe.forEach(t),zNr=i(I),R4=n(I,"LI",{});var lYe=s(R4);CAe=n(lYe,"STRONG",{});var oda=s(CAe);QNr=r(oda,"bart"),oda.forEach(t),WNr=r(lYe," \u2014 "),lre=n(lYe,"A",{href:!0});var rda=s(lre);UNr=r(rda,"TFBartModel"),rda.forEach(t),HNr=r(lYe," (BART model)"),lYe.forEach(t),JNr=i(I),P4=n(I,"LI",{});var iYe=s(P4);wAe=n(iYe,"STRONG",{});var tda=s(wAe);YNr=r(tda,"bert"),tda.forEach(t),ZNr=r(iYe," \u2014 "),ire=n(iYe,"A",{href:!0});var ada=s(ire);KNr=r(ada,"TFBertModel"),ada.forEach(t),eqr=r(iYe," (BERT model)"),iYe.forEach(t),oqr=i(I),B4=n(I,"LI",{});var dYe=s(B4);AAe=n(dYe,"STRONG",{});var nda=s(AAe);rqr=r(nda,"blenderbot"),nda.forEach(t),tqr=r(dYe," \u2014 "),dre=n(dYe,"A",{href:!0});var sda=s(dre);aqr=r(sda,"TFBlenderbotModel"),sda.forEach(t),nqr=r(dYe," (Blenderbot model)"),dYe.forEach(t),sqr=i(I),I4=n(I,"LI",{});var cYe=s(I4);LAe=n(cYe,"STRONG",{});var lda=s(LAe);lqr=r(lda,"blenderbot-small"),lda.forEach(t),iqr=r(cYe," \u2014 "),cre=n(cYe,"A",{href:!0});var ida=s(cre);dqr=r(ida,"TFBlenderbotSmallModel"),ida.forEach(t),cqr=r(cYe," (BlenderbotSmall model)"),cYe.forEach(t),fqr=i(I),N4=n(I,"LI",{});var fYe=s(N4);yAe=n(fYe,"STRONG",{});var dda=s(yAe);mqr=r(dda,"camembert"),dda.forEach(t),gqr=r(fYe," \u2014 "),fre=n(fYe,"A",{href:!0});var cda=s(fre);hqr=r(cda,"TFCamembertModel"),cda.forEach(t),pqr=r(fYe," (CamemBERT model)"),fYe.forEach(t),uqr=i(I),q4=n(I,"LI",{});var mYe=s(q4);xAe=n(mYe,"STRONG",{});var fda=s(xAe);_qr=r(fda,"clip"),fda.forEach(t),bqr=r(mYe," \u2014 "),mre=n(mYe,"A",{href:!0});var mda=s(mre);vqr=r(mda,"TFCLIPModel"),mda.forEach(t),Fqr=r(mYe," (CLIP model)"),mYe.forEach(t),Tqr=i(I),D4=n(I,"LI",{});var gYe=s(D4);$Ae=n(gYe,"STRONG",{});var gda=s($Ae);Mqr=r(gda,"convbert"),gda.forEach(t),Eqr=r(gYe," \u2014 "),gre=n(gYe,"A",{href:!0});var hda=s(gre);Cqr=r(hda,"TFConvBertModel"),hda.forEach(t),wqr=r(gYe," (ConvBERT model)"),gYe.forEach(t),Aqr=i(I),j4=n(I,"LI",{});var hYe=s(j4);kAe=n(hYe,"STRONG",{});var pda=s(kAe);Lqr=r(pda,"convnext"),pda.forEach(t),yqr=r(hYe," \u2014 "),hre=n(hYe,"A",{href:!0});var uda=s(hre);xqr=r(uda,"TFConvNextModel"),uda.forEach(t),$qr=r(hYe," (ConvNeXT model)"),hYe.forEach(t),kqr=i(I),G4=n(I,"LI",{});var pYe=s(G4);SAe=n(pYe,"STRONG",{});var _da=s(SAe);Sqr=r(_da,"ctrl"),_da.forEach(t),Rqr=r(pYe," \u2014 "),pre=n(pYe,"A",{href:!0});var bda=s(pre);Pqr=r(bda,"TFCTRLModel"),bda.forEach(t),Bqr=r(pYe," (CTRL model)"),pYe.forEach(t),Iqr=i(I),O4=n(I,"LI",{});var uYe=s(O4);RAe=n(uYe,"STRONG",{});var vda=s(RAe);Nqr=r(vda,"cvt"),vda.forEach(t),qqr=r(uYe," 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FYe=s(Q4);NAe=n(FYe,"STRONG",{});var Lda=s(NAe);tDr=r(Lda,"deit"),Lda.forEach(t),aDr=r(FYe," \u2014 "),Fre=n(FYe,"A",{href:!0});var yda=s(Fre);nDr=r(yda,"TFDeiTModel"),yda.forEach(t),sDr=r(FYe," (DeiT model)"),FYe.forEach(t),lDr=i(I),W4=n(I,"LI",{});var TYe=s(W4);qAe=n(TYe,"STRONG",{});var xda=s(qAe);iDr=r(xda,"distilbert"),xda.forEach(t),dDr=r(TYe," \u2014 "),Tre=n(TYe,"A",{href:!0});var $da=s(Tre);cDr=r($da,"TFDistilBertModel"),$da.forEach(t),fDr=r(TYe," (DistilBERT model)"),TYe.forEach(t),mDr=i(I),U4=n(I,"LI",{});var MYe=s(U4);DAe=n(MYe,"STRONG",{});var kda=s(DAe);gDr=r(kda,"dpr"),kda.forEach(t),hDr=r(MYe," \u2014 "),Mre=n(MYe,"A",{href:!0});var Sda=s(Mre);pDr=r(Sda,"TFDPRQuestionEncoder"),Sda.forEach(t),uDr=r(MYe," (DPR model)"),MYe.forEach(t),_Dr=i(I),H4=n(I,"LI",{});var EYe=s(H4);jAe=n(EYe,"STRONG",{});var Rda=s(jAe);bDr=r(Rda,"electra"),Rda.forEach(t),vDr=r(EYe," \u2014 "),Ere=n(EYe,"A",{href:!0});var Pda=s(Ere);FDr=r(Pda,"TFElectraModel"),Pda.forEach(t),TDr=r(EYe," (ELECTRA 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model\u2019s configuration. Use `),rae=n(gfe,"A",{href:!0});var iga=s(rae);fUr=r(iga,"from_pretrained()"),iga.forEach(t),mUr=r(gfe," to load the model weights."),gfe.forEach(t),gUr=i(dx),T(A3.$$.fragment,dx),dx.forEach(t),hUr=i(Ti),Vr=n(Ti,"DIV",{class:!0});var Mi=s(Vr);T(RR.$$.fragment,Mi),pUr=i(Mi),R8e=n(Mi,"P",{});var dga=s(R8e);uUr=r(dga,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),dga.forEach(t),_Ur=i(Mi),qn=n(Mi,"P",{});var cx=s(qn);bUr=r(cx,"The model class to instantiate is selected based on the "),P8e=n(cx,"CODE",{});var cga=s(P8e);vUr=r(cga,"model_type"),cga.forEach(t),FUr=r(cx,` property of the config object (either
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falling back to using pattern matching on `),I8e=n(cx,"CODE",{});var mga=s(I8e);EUr=r(mga,"pretrained_model_name_or_path"),mga.forEach(t),CUr=r(cx,":"),cx.forEach(t),wUr=i(Mi),ye=n(Mi,"UL",{});var Ne=s(ye);L3=n(Ne,"LI",{});var QZe=s(L3);N8e=n(QZe,"STRONG",{});var gga=s(N8e);AUr=r(gga,"convnext"),gga.forEach(t),LUr=r(QZe," \u2014 "),tae=n(QZe,"A",{href:!0});var hga=s(tae);yUr=r(hga,"TFConvNextForImageClassification"),hga.forEach(t),xUr=r(QZe," (ConvNeXT model)"),QZe.forEach(t),$Ur=i(Ne),y3=n(Ne,"LI",{});var WZe=s(y3);q8e=n(WZe,"STRONG",{});var pga=s(q8e);kUr=r(pga,"cvt"),pga.forEach(t),SUr=r(WZe," \u2014 "),aae=n(WZe,"A",{href:!0});var uga=s(aae);RUr=r(uga,"TFCvtForImageClassification"),uga.forEach(t),PUr=r(WZe," (CvT model)"),WZe.forEach(t),BUr=i(Ne),x3=n(Ne,"LI",{});var UZe=s(x3);D8e=n(UZe,"STRONG",{});var _ga=s(D8e);IUr=r(_ga,"data2vec-vision"),_ga.forEach(t),NUr=r(UZe," \u2014 "),nae=n(UZe,"A",{href:!0});var bga=s(nae);qUr=r(bga,"TFData2VecVisionForImageClassification"),bga.forEach(t),DUr=r(UZe," (Data2VecVision model)"),UZe.forEach(t),jUr=i(Ne),Sl=n(Ne,"LI",{});var FN=s(Sl);j8e=n(FN,"STRONG",{});var vga=s(j8e);GUr=r(vga,"deit"),vga.forEach(t),OUr=r(FN," \u2014 "),sae=n(FN,"A",{href:!0});var Fga=s(sae);VUr=r(Fga,"TFDeiTForImageClassification"),Fga.forEach(t),XUr=r(FN," or "),lae=n(FN,"A",{href:!0});var Tga=s(lae);zUr=r(Tga,"TFDeiTForImageClassificationWithTeacher"),Tga.forEach(t),QUr=r(FN," (DeiT model)"),FN.forEach(t),WUr=i(Ne),$3=n(Ne,"LI",{});var HZe=s($3);G8e=n(HZe,"STRONG",{});var Mga=s(G8e);UUr=r(Mga,"mobilevit"),Mga.forEach(t),HUr=r(HZe," \u2014 "),iae=n(HZe,"A",{href:!0});var Ega=s(iae);JUr=r(Ega,"TFMobileViTForImageClassification"),Ega.forEach(t),YUr=r(HZe," (MobileViT model)"),HZe.forEach(t),ZUr=i(Ne),k3=n(Ne,"LI",{});var JZe=s(k3);O8e=n(JZe,"STRONG",{});var Cga=s(O8e);KUr=r(Cga,"regnet"),Cga.forEach(t),eHr=r(JZe," \u2014 "),dae=n(JZe,"A",{href:!0});var wga=s(dae);oHr=r(wga,"TFRegNetForImageClassification"),wga.forEach(t),rHr=r(JZe," (RegNet model)"),JZe.forEach(t),tHr=i(Ne),S3=n(Ne,"LI",{});var YZe=s(S3);V8e=n(YZe,"STRONG",{});var Aga=s(V8e);aHr=r(Aga,"resnet"),Aga.forEach(t),nHr=r(YZe," \u2014 "),cae=n(YZe,"A",{href:!0});var Lga=s(cae);sHr=r(Lga,"TFResNetForImageClassification"),Lga.forEach(t),lHr=r(YZe," (ResNet model)"),YZe.forEach(t),iHr=i(Ne),R3=n(Ne,"LI",{});var ZZe=s(R3);X8e=n(ZZe,"STRONG",{});var yga=s(X8e);dHr=r(yga,"segformer"),yga.forEach(t),cHr=r(ZZe," \u2014 "),fae=n(ZZe,"A",{href:!0});var xga=s(fae);fHr=r(xga,"TFSegformerForImageClassification"),xga.forEach(t),mHr=r(ZZe," (SegFormer model)"),ZZe.forEach(t),gHr=i(Ne),P3=n(Ne,"LI",{});var KZe=s(P3);z8e=n(KZe,"STRONG",{});var $ga=s(z8e);hHr=r($ga,"swin"),$ga.forEach(t),pHr=r(KZe," \u2014 "),mae=n(KZe,"A",{href:!0});var kga=s(mae);uHr=r(kga,"TFSwinForImageClassification"),kga.forEach(t),_Hr=r(KZe," (Swin Transformer model)"),KZe.forEach(t),bHr=i(Ne),B3=n(Ne,"LI",{});var eKe=s(B3);Q8e=n(eKe,"STRONG",{});var Sga=s(Q8e);vHr=r(Sga,"vit"),Sga.forEach(t),FHr=r(eKe," \u2014 "),gae=n(eKe,"A",{href:!0});var Rga=s(gae);THr=r(Rga,"TFViTForImageClassification"),Rga.forEach(t),MHr=r(eKe," (ViT model)"),eKe.forEach(t),Ne.forEach(t),EHr=i(Mi),T(I3.$$.fragment,Mi),Mi.forEach(t),Ti.forEach(t),Wao=i(f),Mf=n(f,"H2",{class:!0});var mlo=s(Mf);N3=n(mlo,"A",{id:!0,class:!0,href:!0});var Pga=s(N3);W8e=n(Pga,"SPAN",{});var Bga=s(W8e);T(PR.$$.fragment,Bga),Bga.forEach(t),Pga.forEach(t),CHr=i(mlo),U8e=n(mlo,"SPAN",{});var Iga=s(U8e);wHr=r(Iga,"TFAutoModelForSemanticSegmentation"),Iga.forEach(t),mlo.forEach(t),Uao=i(f),hr=n(f,"DIV",{class:!0});var Ei=s(hr);T(BR.$$.fragment,Ei),AHr=i(Ei),Ef=n(Ei,"P",{});var hfe=s(Ef);LHr=r(hfe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),hae=n(hfe,"A",{href:!0});var Nga=s(hae);yHr=r(Nga,"from_pretrained()"),Nga.forEach(t),xHr=r(hfe," class method or the "),pae=n(hfe,"A",{href:!0});var qga=s(pae);$Hr=r(qga,"from_config()"),qga.forEach(t),kHr=r(hfe,` class
method.`),hfe.forEach(t),SHr=i(Ei),IR=n(Ei,"P",{});var glo=s(IR);RHr=r(glo,"This class cannot be instantiated directly using "),H8e=n(glo,"CODE",{});var Dga=s(H8e);PHr=r(Dga,"__init__()"),Dga.forEach(t),BHr=r(glo," (throws an error)."),glo.forEach(t),IHr=i(Ei),oa=n(Ei,"DIV",{class:!0});var fx=s(oa);T(NR.$$.fragment,fx),NHr=i(fx),J8e=n(fx,"P",{});var jga=s(J8e);qHr=r(jga,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),jga.forEach(t),DHr=i(fx),Cf=n(fx,"P",{});var pfe=s(Cf);jHr=r(pfe,`Note:
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model\u2019s configuration. Use `),uae=n(pfe,"A",{href:!0});var Oga=s(uae);VHr=r(Oga,"from_pretrained()"),Oga.forEach(t),XHr=r(pfe," to load the model weights."),pfe.forEach(t),zHr=i(fx),T(q3.$$.fragment,fx),fx.forEach(t),QHr=i(Ei),Xr=n(Ei,"DIV",{class:!0});var Ci=s(Xr);T(qR.$$.fragment,Ci),WHr=i(Ci),Z8e=n(Ci,"P",{});var Vga=s(Z8e);UHr=r(Vga,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Vga.forEach(t),HHr=i(Ci),Dn=n(Ci,"P",{});var mx=s(Dn);JHr=r(mx,"The model class to instantiate is selected based on the "),K8e=n(mx,"CODE",{});var Xga=s(K8e);YHr=r(Xga,"model_type"),Xga.forEach(t),ZHr=r(mx,` property of the config object (either
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falling back to using pattern matching on `),oLe=n(mx,"CODE",{});var Qga=s(oLe);oJr=r(Qga,"pretrained_model_name_or_path"),Qga.forEach(t),rJr=r(mx,":"),mx.forEach(t),tJr=i(Ci),wf=n(Ci,"UL",{});var ufe=s(wf);D3=n(ufe,"LI",{});var oKe=s(D3);rLe=n(oKe,"STRONG",{});var Wga=s(rLe);aJr=r(Wga,"data2vec-vision"),Wga.forEach(t),nJr=r(oKe," \u2014 "),_ae=n(oKe,"A",{href:!0});var Uga=s(_ae);sJr=r(Uga,"TFData2VecVisionForSemanticSegmentation"),Uga.forEach(t),lJr=r(oKe," (Data2VecVision model)"),oKe.forEach(t),iJr=i(ufe),j3=n(ufe,"LI",{});var rKe=s(j3);tLe=n(rKe,"STRONG",{});var Hga=s(tLe);dJr=r(Hga,"mobilevit"),Hga.forEach(t),cJr=r(rKe," \u2014 "),bae=n(rKe,"A",{href:!0});var Jga=s(bae);fJr=r(Jga,"TFMobileViTForSemanticSegmentation"),Jga.forEach(t),mJr=r(rKe," (MobileViT model)"),rKe.forEach(t),gJr=i(ufe),G3=n(ufe,"LI",{});var tKe=s(G3);aLe=n(tKe,"STRONG",{});var Yga=s(aLe);hJr=r(Yga,"segformer"),Yga.forEach(t),pJr=r(tKe," \u2014 "),vae=n(tKe,"A",{href:!0});var Zga=s(vae);uJr=r(Zga,"TFSegformerForSemanticSegmentation"),Zga.forEach(t),_Jr=r(tKe," (SegFormer model)"),tKe.forEach(t),ufe.forEach(t),bJr=i(Ci),T(O3.$$.fragment,Ci),Ci.forEach(t),Ei.forEach(t),Hao=i(f),Af=n(f,"H2",{class:!0});var hlo=s(Af);V3=n(hlo,"A",{id:!0,class:!0,href:!0});var Kga=s(V3);nLe=n(Kga,"SPAN",{});var eha=s(nLe);T(DR.$$.fragment,eha),eha.forEach(t),Kga.forEach(t),vJr=i(hlo),sLe=n(hlo,"SPAN",{});var oha=s(sLe);FJr=r(oha,"TFAutoModelForMaskedLM"),oha.forEach(t),hlo.forEach(t),Jao=i(f),pr=n(f,"DIV",{class:!0});var wi=s(pr);T(jR.$$.fragment,wi),TJr=i(wi),Lf=n(wi,"P",{});var _fe=s(Lf);MJr=r(_fe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),Fae=n(_fe,"A",{href:!0});var rha=s(Fae);EJr=r(rha,"from_pretrained()"),rha.forEach(t),CJr=r(_fe," class method or the "),Tae=n(_fe,"A",{href:!0});var tha=s(Tae);wJr=r(tha,"from_config()"),tha.forEach(t),AJr=r(_fe,` class
method.`),_fe.forEach(t),LJr=i(wi),GR=n(wi,"P",{});var plo=s(GR);yJr=r(plo,"This class cannot be instantiated directly using "),lLe=n(plo,"CODE",{});var aha=s(lLe);xJr=r(aha,"__init__()"),aha.forEach(t),$Jr=r(plo," (throws an error)."),plo.forEach(t),kJr=i(wi),ra=n(wi,"DIV",{class:!0});var gx=s(ra);T(OR.$$.fragment,gx),SJr=i(gx),iLe=n(gx,"P",{});var nha=s(iLe);RJr=r(nha,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),nha.forEach(t),PJr=i(gx),yf=n(gx,"P",{});var bfe=s(yf);BJr=r(bfe,`Note:
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model\u2019s configuration. Use `),Mae=n(bfe,"A",{href:!0});var lha=s(Mae);qJr=r(lha,"from_pretrained()"),lha.forEach(t),DJr=r(bfe," to load the model weights."),bfe.forEach(t),jJr=i(gx),T(X3.$$.fragment,gx),gx.forEach(t),GJr=i(wi),zr=n(wi,"DIV",{class:!0});var Ai=s(zr);T(VR.$$.fragment,Ai),OJr=i(Ai),cLe=n(Ai,"P",{});var iha=s(cLe);VJr=r(iha,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),iha.forEach(t),XJr=i(Ai),jn=n(Ai,"P",{});var hx=s(jn);zJr=r(hx,"The model class to instantiate is selected based on the "),fLe=n(hx,"CODE",{});var dha=s(fLe);QJr=r(dha,"model_type"),dha.forEach(t),WJr=r(hx,` property of the config object (either
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falling back to using pattern matching on `),gLe=n(hx,"CODE",{});var fha=s(gLe);JJr=r(fha,"pretrained_model_name_or_path"),fha.forEach(t),YJr=r(hx,":"),hx.forEach(t),ZJr=i(Ai),fe=n(Ai,"UL",{});var pe=s(fe);z3=n(pe,"LI",{});var aKe=s(z3);hLe=n(aKe,"STRONG",{});var mha=s(hLe);KJr=r(mha,"albert"),mha.forEach(t),eYr=r(aKe," \u2014 "),Eae=n(aKe,"A",{href:!0});var gha=s(Eae);oYr=r(gha,"TFAlbertForMaskedLM"),gha.forEach(t),rYr=r(aKe," (ALBERT model)"),aKe.forEach(t),tYr=i(pe),Q3=n(pe,"LI",{});var nKe=s(Q3);pLe=n(nKe,"STRONG",{});var hha=s(pLe);aYr=r(hha,"bert"),hha.forEach(t),nYr=r(nKe," \u2014 "),Cae=n(nKe,"A",{href:!0});var pha=s(Cae);sYr=r(pha,"TFBertForMaskedLM"),pha.forEach(t),lYr=r(nKe," (BERT model)"),nKe.forEach(t),iYr=i(pe),W3=n(pe,"LI",{});var sKe=s(W3);uLe=n(sKe,"STRONG",{});var uha=s(uLe);dYr=r(uha,"camembert"),uha.forEach(t),cYr=r(sKe," \u2014 "),wae=n(sKe,"A",{href:!0});var _ha=s(wae);fYr=r(_ha,"TFCamembertForMaskedLM"),_ha.forEach(t),mYr=r(sKe," (CamemBERT 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Qha=s(kLe);$Zr=r(Qha,"roformer"),Qha.forEach(t),kZr=r(TKe," \u2014 "),jae=n(TKe,"A",{href:!0});var Wha=s(jae);SZr=r(Wha,"TFRoFormerForMaskedLM"),Wha.forEach(t),RZr=r(TKe," (RoFormer model)"),TKe.forEach(t),PZr=i(pe),d5=n(pe,"LI",{});var MKe=s(d5);SLe=n(MKe,"STRONG",{});var Uha=s(SLe);BZr=r(Uha,"tapas"),Uha.forEach(t),IZr=r(MKe," \u2014 "),Gae=n(MKe,"A",{href:!0});var Hha=s(Gae);NZr=r(Hha,"TFTapasForMaskedLM"),Hha.forEach(t),qZr=r(MKe," (TAPAS model)"),MKe.forEach(t),DZr=i(pe),c5=n(pe,"LI",{});var EKe=s(c5);RLe=n(EKe,"STRONG",{});var Jha=s(RLe);jZr=r(Jha,"xlm"),Jha.forEach(t),GZr=r(EKe," \u2014 "),Oae=n(EKe,"A",{href:!0});var Yha=s(Oae);OZr=r(Yha,"TFXLMWithLMHeadModel"),Yha.forEach(t),VZr=r(EKe," (XLM model)"),EKe.forEach(t),XZr=i(pe),f5=n(pe,"LI",{});var CKe=s(f5);PLe=n(CKe,"STRONG",{});var Zha=s(PLe);zZr=r(Zha,"xlm-roberta"),Zha.forEach(t),QZr=r(CKe," \u2014 "),Vae=n(CKe,"A",{href:!0});var Kha=s(Vae);WZr=r(Kha,"TFXLMRobertaForMaskedLM"),Kha.forEach(t),UZr=r(CKe," (XLM-RoBERTa 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with the `),Xae=n(vfe,"A",{href:!0});var tpa=s(Xae);eKr=r(tpa,"from_pretrained()"),tpa.forEach(t),oKr=r(vfe," class method or the "),zae=n(vfe,"A",{href:!0});var apa=s(zae);rKr=r(apa,"from_config()"),apa.forEach(t),tKr=r(vfe,` class
method.`),vfe.forEach(t),aKr=i(Li),QR=n(Li,"P",{});var _lo=s(QR);nKr=r(_lo,"This class cannot be instantiated directly using "),NLe=n(_lo,"CODE",{});var npa=s(NLe);sKr=r(npa,"__init__()"),npa.forEach(t),lKr=r(_lo," (throws an error)."),_lo.forEach(t),iKr=i(Li),ta=n(Li,"DIV",{class:!0});var px=s(ta);T(WR.$$.fragment,px),dKr=i(px),qLe=n(px,"P",{});var spa=s(qLe);cKr=r(spa,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),spa.forEach(t),fKr=i(px),kf=n(px,"P",{});var Ffe=s(kf);mKr=r(Ffe,`Note:
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model\u2019s configuration. Use `),Qae=n(Ffe,"A",{href:!0});var ipa=s(Qae);pKr=r(ipa,"from_pretrained()"),ipa.forEach(t),uKr=r(Ffe," to load the model weights."),Ffe.forEach(t),_Kr=i(px),T(h5.$$.fragment,px),px.forEach(t),bKr=i(Li),Qr=n(Li,"DIV",{class:!0});var yi=s(Qr);T(UR.$$.fragment,yi),vKr=i(yi),jLe=n(yi,"P",{});var dpa=s(jLe);FKr=r(dpa,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),dpa.forEach(t),TKr=i(yi),Gn=n(yi,"P",{});var ux=s(Gn);MKr=r(ux,"The model class to instantiate is selected based on the "),GLe=n(ux,"CODE",{});var cpa=s(GLe);EKr=r(cpa,"model_type"),cpa.forEach(t),CKr=r(ux,` property of the config object (either
passed as an argument or loaded from `),OLe=n(ux,"CODE",{});var fpa=s(OLe);wKr=r(fpa,"pretrained_model_name_or_path"),fpa.forEach(t),AKr=r(ux,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),VLe=n(ux,"CODE",{});var mpa=s(VLe);LKr=r(mpa,"pretrained_model_name_or_path"),mpa.forEach(t),yKr=r(ux,":"),ux.forEach(t),xKr=i(yi),xe=n(yi,"UL",{});var qe=s(xe);p5=n(qe,"LI",{});var wKe=s(p5);XLe=n(wKe,"STRONG",{});var gpa=s(XLe);$Kr=r(gpa,"bart"),gpa.forEach(t),kKr=r(wKe," \u2014 "),Wae=n(wKe,"A",{href:!0});var hpa=s(Wae);SKr=r(hpa,"TFBartForConditionalGeneration"),hpa.forEach(t),RKr=r(wKe," (BART model)"),wKe.forEach(t),PKr=i(qe),u5=n(qe,"LI",{});var AKe=s(u5);zLe=n(AKe,"STRONG",{});var ppa=s(zLe);BKr=r(ppa,"blenderbot"),ppa.forEach(t),IKr=r(AKe," \u2014 "),Uae=n(AKe,"A",{href:!0});var upa=s(Uae);NKr=r(upa,"TFBlenderbotForConditionalGeneration"),upa.forEach(t),qKr=r(AKe," (Blenderbot model)"),AKe.forEach(t),DKr=i(qe),_5=n(qe,"LI",{});var LKe=s(_5);QLe=n(LKe,"STRONG",{});var _pa=s(QLe);jKr=r(_pa,"blenderbot-small"),_pa.forEach(t),GKr=r(LKe," \u2014 "),Hae=n(LKe,"A",{href:!0});var bpa=s(Hae);OKr=r(bpa,"TFBlenderbotSmallForConditionalGeneration"),bpa.forEach(t),VKr=r(LKe," (BlenderbotSmall model)"),LKe.forEach(t),XKr=i(qe),b5=n(qe,"LI",{});var yKe=s(b5);WLe=n(yKe,"STRONG",{});var vpa=s(WLe);zKr=r(vpa,"encoder-decoder"),vpa.forEach(t),QKr=r(yKe," \u2014 "),Jae=n(yKe,"A",{href:!0});var Fpa=s(Jae);WKr=r(Fpa,"TFEncoderDecoderModel"),Fpa.forEach(t),UKr=r(yKe," (Encoder decoder model)"),yKe.forEach(t),HKr=i(qe),v5=n(qe,"LI",{});var xKe=s(v5);ULe=n(xKe,"STRONG",{});var Tpa=s(ULe);JKr=r(Tpa,"led"),Tpa.forEach(t),YKr=r(xKe," \u2014 "),Yae=n(xKe,"A",{href:!0});var Mpa=s(Yae);ZKr=r(Mpa,"TFLEDForConditionalGeneration"),Mpa.forEach(t),KKr=r(xKe," (LED model)"),xKe.forEach(t),eet=i(qe),F5=n(qe,"LI",{});var $Ke=s(F5);HLe=n($Ke,"STRONG",{});var Epa=s(HLe);oet=r(Epa,"marian"),Epa.forEach(t),ret=r($Ke," \u2014 "),Zae=n($Ke,"A",{href:!0});var Cpa=s(Zae);tet=r(Cpa,"TFMarianMTModel"),Cpa.forEach(t),aet=r($Ke," (Marian model)"),$Ke.forEach(t),net=i(qe),T5=n(qe,"LI",{});var kKe=s(T5);JLe=n(kKe,"STRONG",{});var wpa=s(JLe);set=r(wpa,"mbart"),wpa.forEach(t),iet=r(kKe," \u2014 "),Kae=n(kKe,"A",{href:!0});var Apa=s(Kae);det=r(Apa,"TFMBartForConditionalGeneration"),Apa.forEach(t),cet=r(kKe," (mBART model)"),kKe.forEach(t),fet=i(qe),M5=n(qe,"LI",{});var SKe=s(M5);YLe=n(SKe,"STRONG",{});var Lpa=s(YLe);met=r(Lpa,"mt5"),Lpa.forEach(t),get=r(SKe," \u2014 "),ene=n(SKe,"A",{href:!0});var ypa=s(ene);het=r(ypa,"TFMT5ForConditionalGeneration"),ypa.forEach(t),pet=r(SKe," (MT5 model)"),SKe.forEach(t),uet=i(qe),E5=n(qe,"LI",{});var RKe=s(E5);ZLe=n(RKe,"STRONG",{});var xpa=s(ZLe);_et=r(xpa,"pegasus"),xpa.forEach(t),bet=r(RKe," \u2014 "),one=n(RKe,"A",{href:!0});var $pa=s(one);vet=r($pa,"TFPegasusForConditionalGeneration"),$pa.forEach(t),Fet=r(RKe," (Pegasus model)"),RKe.forEach(t),Tet=i(qe),C5=n(qe,"LI",{});var PKe=s(C5);KLe=n(PKe,"STRONG",{});var kpa=s(KLe);Met=r(kpa,"t5"),kpa.forEach(t),Eet=r(PKe," \u2014 "),rne=n(PKe,"A",{href:!0});var Spa=s(rne);Cet=r(Spa,"TFT5ForConditionalGeneration"),Spa.forEach(t),wet=r(PKe," (T5 model)"),PKe.forEach(t),qe.forEach(t),Aet=i(yi),T(w5.$$.fragment,yi),yi.forEach(t),Li.forEach(t),Kao=i(f),Sf=n(f,"H2",{class:!0});var blo=s(Sf);A5=n(blo,"A",{id:!0,class:!0,href:!0});var Rpa=s(A5);eye=n(Rpa,"SPAN",{});var Ppa=s(eye);T(HR.$$.fragment,Ppa),Ppa.forEach(t),Rpa.forEach(t),Let=i(blo),oye=n(blo,"SPAN",{});var Bpa=s(oye);yet=r(Bpa,"TFAutoModelForSequenceClassification"),Bpa.forEach(t),blo.forEach(t),eno=i(f),_r=n(f,"DIV",{class:!0});var xi=s(_r);T(JR.$$.fragment,xi),xet=i(xi),Rf=n(xi,"P",{});var Tfe=s(Rf);$et=r(Tfe,`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.`),Tfe.forEach(t),Bet=i(xi),YR=n(xi,"P",{});var vlo=s(YR);Iet=r(vlo,"This class cannot be instantiated directly using "),rye=n(vlo,"CODE",{});var qpa=s(rye);Net=r(qpa,"__init__()"),qpa.forEach(t),qet=r(vlo," (throws an error)."),vlo.forEach(t),Det=i(xi),aa=n(xi,"DIV",{class:!0});var _x=s(aa);T(ZR.$$.fragment,_x),jet=i(_x),tye=n(_x,"P",{});var Dpa=s(tye);Get=r(Dpa,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Dpa.forEach(t),Oet=i(_x),Pf=n(_x,"P",{});var Mfe=s(Pf);Vet=r(Mfe,`Note:
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model\u2019s configuration. Use `),nne=n(Mfe,"A",{href:!0});var Gpa=s(nne);Qet=r(Gpa,"from_pretrained()"),Gpa.forEach(t),Wet=r(Mfe," to load the model weights."),Mfe.forEach(t),Uet=i(_x),T(L5.$$.fragment,_x),_x.forEach(t),Het=i(xi),Wr=n(xi,"DIV",{class:!0});var $i=s(Wr);T(KR.$$.fragment,$i),Jet=i($i),nye=n($i,"P",{});var Opa=s(nye);Yet=r(Opa,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),Opa.forEach(t),Zet=i($i),On=n($i,"P",{});var bx=s(On);Ket=r(bx,"The model class to instantiate is selected based on the "),sye=n(bx,"CODE",{});var Vpa=s(sye);eot=r(Vpa,"model_type"),Vpa.forEach(t),oot=r(bx,` property of the config object (either
passed as an argument or loaded from `),lye=n(bx,"CODE",{});var Xpa=s(lye);rot=r(Xpa,"pretrained_model_name_or_path"),Xpa.forEach(t),tot=r(bx,` if possible), or when it\u2019s missing, by
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Ypa=s(ine);vot=r(Ypa,"TFCamembertForSequenceClassification"),Ypa.forEach(t),Fot=r(NKe," (CamemBERT model)"),NKe.forEach(t),Tot=i(ae),k5=n(ae,"LI",{});var qKe=s(k5);mye=n(qKe,"STRONG",{});var Zpa=s(mye);Mot=r(Zpa,"convbert"),Zpa.forEach(t),Eot=r(qKe," \u2014 "),dne=n(qKe,"A",{href:!0});var Kpa=s(dne);Cot=r(Kpa,"TFConvBertForSequenceClassification"),Kpa.forEach(t),wot=r(qKe," (ConvBERT model)"),qKe.forEach(t),Aot=i(ae),S5=n(ae,"LI",{});var DKe=s(S5);gye=n(DKe,"STRONG",{});var eua=s(gye);Lot=r(eua,"ctrl"),eua.forEach(t),yot=r(DKe," \u2014 "),cne=n(DKe,"A",{href:!0});var oua=s(cne);xot=r(oua,"TFCTRLForSequenceClassification"),oua.forEach(t),$ot=r(DKe," (CTRL model)"),DKe.forEach(t),kot=i(ae),R5=n(ae,"LI",{});var jKe=s(R5);hye=n(jKe,"STRONG",{});var rua=s(hye);Sot=r(rua,"deberta"),rua.forEach(t),Rot=r(jKe," \u2014 "),fne=n(jKe,"A",{href:!0});var tua=s(fne);Pot=r(tua,"TFDebertaForSequenceClassification"),tua.forEach(t),Bot=r(jKe," (DeBERTa 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ki=s(br);T(oP.$$.fragment,ki),Gtt=i(ki),If=n(ki,"P",{});var Efe=s(If);Ott=r(Efe,`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 `),Pne=n(Efe,"A",{href:!0});var Yua=s(Pne);Vtt=r(Yua,"from_pretrained()"),Yua.forEach(t),Xtt=r(Efe," class method or the "),Bne=n(Efe,"A",{href:!0});var Zua=s(Bne);ztt=r(Zua,"from_config()"),Zua.forEach(t),Qtt=r(Efe,` class
method.`),Efe.forEach(t),Wtt=i(ki),rP=n(ki,"P",{});var Tlo=s(rP);Utt=r(Tlo,"This class cannot be instantiated directly using "),Dye=n(Tlo,"CODE",{});var Kua=s(Dye);Htt=r(Kua,"__init__()"),Kua.forEach(t),Jtt=r(Tlo," (throws an error)."),Tlo.forEach(t),Ytt=i(ki),na=n(ki,"DIV",{class:!0});var vx=s(na);T(tP.$$.fragment,vx),Ztt=i(vx),jye=n(vx,"P",{});var e_a=s(jye);Ktt=r(e_a,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),e_a.forEach(t),eat=i(vx),Nf=n(vx,"P",{});var Cfe=s(Nf);oat=r(Cfe,`Note:
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model\u2019s configuration. Use `),Ine=n(Cfe,"A",{href:!0});var r_a=s(Ine);aat=r(r_a,"from_pretrained()"),r_a.forEach(t),nat=r(Cfe," to load the model weights."),Cfe.forEach(t),sat=i(vx),T(aw.$$.fragment,vx),vx.forEach(t),lat=i(ki),Ur=n(ki,"DIV",{class:!0});var Si=s(Ur);T(aP.$$.fragment,Si),iat=i(Si),Oye=n(Si,"P",{});var t_a=s(Oye);dat=r(t_a,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),t_a.forEach(t),cat=i(Si),Vn=n(Si,"P",{});var Fx=s(Vn);fat=r(Fx,"The model class to instantiate is selected based on the "),Vye=n(Fx,"CODE",{});var a_a=s(Vye);mat=r(a_a,"model_type"),a_a.forEach(t),gat=r(Fx,` property of the config object (either
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model\u2019s configuration. Use `),rse=n(Afe,"A",{href:!0});var H_a=s(rse);pst=r(H_a,"from_pretrained()"),H_a.forEach(t),ust=r(Afe," to load the model weights."),Afe.forEach(t),_st=i(Tx),T(Cw.$$.fragment,Tx),Tx.forEach(t),bst=i(Ri),Hr=n(Ri,"DIV",{class:!0});var Pi=s(Hr);T(dP.$$.fragment,Pi),vst=i(Pi),hxe=n(Pi,"P",{});var J_a=s(hxe);Fst=r(J_a,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),J_a.forEach(t),Tst=i(Pi),Xn=n(Pi,"P",{});var Mx=s(Xn);Mst=r(Mx,"The model class to instantiate is selected based on the "),pxe=n(Mx,"CODE",{});var Y_a=s(pxe);Est=r(Y_a,"model_type"),Y_a.forEach(t),Cst=r(Mx,` property of the config object (either
passed as an argument or loaded from `),uxe=n(Mx,"CODE",{});var Z_a=s(uxe);wst=r(Z_a,"pretrained_model_name_or_path"),Z_a.forEach(t),Ast=r(Mx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_xe=n(Mx,"CODE",{});var K_a=s(_xe);Lst=r(K_a,"pretrained_model_name_or_path"),K_a.forEach(t),yst=r(Mx,":"),Mx.forEach(t),xst=i(Pi),cP=n(Pi,"UL",{});var Clo=s(cP);ww=n(Clo,"LI",{});var Aeo=s(ww);bxe=n(Aeo,"STRONG",{});var e1a=s(bxe);$st=r(e1a,"bert"),e1a.forEach(t),kst=r(Aeo," \u2014 "),tse=n(Aeo,"A",{href:!0});var o1a=s(tse);Sst=r(o1a,"TFBertForNextSentencePrediction"),o1a.forEach(t),Rst=r(Aeo," (BERT model)"),Aeo.forEach(t),Pst=i(Clo),Aw=n(Clo,"LI",{});var Leo=s(Aw);vxe=n(Leo,"STRONG",{});var r1a=s(vxe);Bst=r(r1a,"mobilebert"),r1a.forEach(t),Ist=r(Leo," \u2014 "),ase=n(Leo,"A",{href:!0});var t1a=s(ase);Nst=r(t1a,"TFMobileBertForNextSentencePrediction"),t1a.forEach(t),qst=r(Leo," (MobileBERT model)"),Leo.forEach(t),Clo.forEach(t),Dst=i(Pi),T(Lw.$$.fragment,Pi),Pi.forEach(t),Ri.forEach(t),nno=i(f),Gf=n(f,"H2",{class:!0});var wlo=s(Gf);yw=n(wlo,"A",{id:!0,class:!0,href:!0});var a1a=s(yw);Fxe=n(a1a,"SPAN",{});var n1a=s(Fxe);T(fP.$$.fragment,n1a),n1a.forEach(t),a1a.forEach(t),jst=i(wlo),Txe=n(wlo,"SPAN",{});var s1a=s(Txe);Gst=r(s1a,"TFAutoModelForTableQuestionAnswering"),s1a.forEach(t),wlo.forEach(t),sno=i(f),Fr=n(f,"DIV",{class:!0});var Bi=s(Fr);T(mP.$$.fragment,Bi),Ost=i(Bi),Of=n(Bi,"P",{});var Lfe=s(Of);Vst=r(Lfe,`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 `),nse=n(Lfe,"A",{href:!0});var l1a=s(nse);Xst=r(l1a,"from_pretrained()"),l1a.forEach(t),zst=r(Lfe," class method or the "),sse=n(Lfe,"A",{href:!0});var i1a=s(sse);Qst=r(i1a,"from_config()"),i1a.forEach(t),Wst=r(Lfe,` class
method.`),Lfe.forEach(t),Ust=i(Bi),gP=n(Bi,"P",{});var Alo=s(gP);Hst=r(Alo,"This class cannot be instantiated directly using "),Mxe=n(Alo,"CODE",{});var d1a=s(Mxe);Jst=r(d1a,"__init__()"),d1a.forEach(t),Yst=r(Alo," (throws an error)."),Alo.forEach(t),Zst=i(Bi),la=n(Bi,"DIV",{class:!0});var Ex=s(la);T(hP.$$.fragment,Ex),Kst=i(Ex),Exe=n(Ex,"P",{});var c1a=s(Exe);elt=r(c1a,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),c1a.forEach(t),olt=i(Ex),Vf=n(Ex,"P",{});var yfe=s(Vf);rlt=r(yfe,`Note:
Loading a model from its configuration file does `),Cxe=n(yfe,"STRONG",{});var f1a=s(Cxe);tlt=r(f1a,"not"),f1a.forEach(t),alt=r(yfe,` load the model weights. It only affects the
model\u2019s configuration. Use `),lse=n(yfe,"A",{href:!0});var m1a=s(lse);nlt=r(m1a,"from_pretrained()"),m1a.forEach(t),slt=r(yfe," to load the model weights."),yfe.forEach(t),llt=i(Ex),T(xw.$$.fragment,Ex),Ex.forEach(t),ilt=i(Bi),Jr=n(Bi,"DIV",{class:!0});var Ii=s(Jr);T(pP.$$.fragment,Ii),dlt=i(Ii),wxe=n(Ii,"P",{});var g1a=s(wxe);clt=r(g1a,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),g1a.forEach(t),flt=i(Ii),zn=n(Ii,"P",{});var Cx=s(zn);mlt=r(Cx,"The model class to instantiate is selected based on the "),Axe=n(Cx,"CODE",{});var h1a=s(Axe);glt=r(h1a,"model_type"),h1a.forEach(t),hlt=r(Cx,` property of the config object (either
passed as an argument or loaded from `),Lxe=n(Cx,"CODE",{});var p1a=s(Lxe);plt=r(p1a,"pretrained_model_name_or_path"),p1a.forEach(t),ult=r(Cx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yxe=n(Cx,"CODE",{});var u1a=s(yxe);_lt=r(u1a,"pretrained_model_name_or_path"),u1a.forEach(t),blt=r(Cx,":"),Cx.forEach(t),vlt=i(Ii),xxe=n(Ii,"UL",{});var _1a=s(xxe);$w=n(_1a,"LI",{});var yeo=s($w);$xe=n(yeo,"STRONG",{});var b1a=s($xe);Flt=r(b1a,"tapas"),b1a.forEach(t),Tlt=r(yeo," \u2014 "),ise=n(yeo,"A",{href:!0});var v1a=s(ise);Mlt=r(v1a,"TFTapasForQuestionAnswering"),v1a.forEach(t),Elt=r(yeo," (TAPAS model)"),yeo.forEach(t),_1a.forEach(t),Clt=i(Ii),T(kw.$$.fragment,Ii),Ii.forEach(t),Bi.forEach(t),lno=i(f),Xf=n(f,"H2",{class:!0});var Llo=s(Xf);Sw=n(Llo,"A",{id:!0,class:!0,href:!0});var F1a=s(Sw);kxe=n(F1a,"SPAN",{});var T1a=s(kxe);T(uP.$$.fragment,T1a),T1a.forEach(t),F1a.forEach(t),wlt=i(Llo),Sxe=n(Llo,"SPAN",{});var M1a=s(Sxe);Alt=r(M1a,"TFAutoModelForDocumentQuestionAnswering"),M1a.forEach(t),Llo.forEach(t),ino=i(f),Tr=n(f,"DIV",{class:!0});var Ni=s(Tr);T(_P.$$.fragment,Ni),Llt=i(Ni),zf=n(Ni,"P",{});var xfe=s(zf);ylt=r(xfe,`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 `),dse=n(xfe,"A",{href:!0});var E1a=s(dse);xlt=r(E1a,"from_pretrained()"),E1a.forEach(t),$lt=r(xfe," class method or the "),cse=n(xfe,"A",{href:!0});var C1a=s(cse);klt=r(C1a,"from_config()"),C1a.forEach(t),Slt=r(xfe,` class
method.`),xfe.forEach(t),Rlt=i(Ni),bP=n(Ni,"P",{});var ylo=s(bP);Plt=r(ylo,"This class cannot be instantiated directly using "),Rxe=n(ylo,"CODE",{});var w1a=s(Rxe);Blt=r(w1a,"__init__()"),w1a.forEach(t),Ilt=r(ylo," (throws an error)."),ylo.forEach(t),Nlt=i(Ni),ia=n(Ni,"DIV",{class:!0});var wx=s(ia);T(vP.$$.fragment,wx),qlt=i(wx),Pxe=n(wx,"P",{});var A1a=s(Pxe);Dlt=r(A1a,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),A1a.forEach(t),jlt=i(wx),Qf=n(wx,"P",{});var $fe=s(Qf);Glt=r($fe,`Note:
Loading a model from its configuration file does `),Bxe=n($fe,"STRONG",{});var L1a=s(Bxe);Olt=r(L1a,"not"),L1a.forEach(t),Vlt=r($fe,` load the model weights. It only affects the
model\u2019s configuration. Use `),fse=n($fe,"A",{href:!0});var y1a=s(fse);Xlt=r(y1a,"from_pretrained()"),y1a.forEach(t),zlt=r($fe," to load the model weights."),$fe.forEach(t),Qlt=i(wx),T(Rw.$$.fragment,wx),wx.forEach(t),Wlt=i(Ni),Yr=n(Ni,"DIV",{class:!0});var qi=s(Yr);T(FP.$$.fragment,qi),Ult=i(qi),Ixe=n(qi,"P",{});var x1a=s(Ixe);Hlt=r(x1a,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),x1a.forEach(t),Jlt=i(qi),Qn=n(qi,"P",{});var Ax=s(Qn);Ylt=r(Ax,"The model class to instantiate is selected based on the "),Nxe=n(Ax,"CODE",{});var $1a=s(Nxe);Zlt=r($1a,"model_type"),$1a.forEach(t),Klt=r(Ax,` property of the config object (either
passed as an argument or loaded from `),qxe=n(Ax,"CODE",{});var k1a=s(qxe);eit=r(k1a,"pretrained_model_name_or_path"),k1a.forEach(t),oit=r(Ax,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dxe=n(Ax,"CODE",{});var S1a=s(Dxe);rit=r(S1a,"pretrained_model_name_or_path"),S1a.forEach(t),tit=r(Ax,":"),Ax.forEach(t),ait=i(qi),jxe=n(qi,"UL",{});var R1a=s(jxe);Pw=n(R1a,"LI",{});var xeo=s(Pw);Gxe=n(xeo,"STRONG",{});var P1a=s(Gxe);nit=r(P1a,"layoutlm"),P1a.forEach(t),sit=r(xeo," \u2014 "),mse=n(xeo,"A",{href:!0});var B1a=s(mse);lit=r(B1a,"TFLayoutLMForQuestionAnswering"),B1a.forEach(t),iit=r(xeo," (LayoutLM model)"),xeo.forEach(t),R1a.forEach(t),dit=i(qi),T(Bw.$$.fragment,qi),qi.forEach(t),Ni.forEach(t),dno=i(f),Wf=n(f,"H2",{class:!0});var xlo=s(Wf);Iw=n(xlo,"A",{id:!0,class:!0,href:!0});var I1a=s(Iw);Oxe=n(I1a,"SPAN",{});var N1a=s(Oxe);T(TP.$$.fragment,N1a),N1a.forEach(t),I1a.forEach(t),cit=i(xlo),Vxe=n(xlo,"SPAN",{});var q1a=s(Vxe);fit=r(q1a,"TFAutoModelForTokenClassification"),q1a.forEach(t),xlo.forEach(t),cno=i(f),Mr=n(f,"DIV",{class:!0});var Di=s(Mr);T(MP.$$.fragment,Di),mit=i(Di),Uf=n(Di,"P",{});var kfe=s(Uf);git=r(kfe,`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 `),gse=n(kfe,"A",{href:!0});var D1a=s(gse);hit=r(D1a,"from_pretrained()"),D1a.forEach(t),pit=r(kfe," class method or the "),hse=n(kfe,"A",{href:!0});var j1a=s(hse);uit=r(j1a,"from_config()"),j1a.forEach(t),_it=r(kfe,` class
method.`),kfe.forEach(t),bit=i(Di),EP=n(Di,"P",{});var $lo=s(EP);vit=r($lo,"This class cannot be instantiated directly using "),Xxe=n($lo,"CODE",{});var G1a=s(Xxe);Fit=r(G1a,"__init__()"),G1a.forEach(t),Tit=r($lo," (throws an error)."),$lo.forEach(t),Mit=i(Di),da=n(Di,"DIV",{class:!0});var Lx=s(da);T(CP.$$.fragment,Lx),Eit=i(Lx),zxe=n(Lx,"P",{});var O1a=s(zxe);Cit=r(O1a,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),O1a.forEach(t),wit=i(Lx),Hf=n(Lx,"P",{});var Sfe=s(Hf);Ait=r(Sfe,`Note:
Loading a model from its configuration file does `),Qxe=n(Sfe,"STRONG",{});var V1a=s(Qxe);Lit=r(V1a,"not"),V1a.forEach(t),yit=r(Sfe,` load the model weights. It only affects the
model\u2019s configuration. Use `),pse=n(Sfe,"A",{href:!0});var X1a=s(pse);xit=r(X1a,"from_pretrained()"),X1a.forEach(t),$it=r(Sfe," to load the model weights."),Sfe.forEach(t),kit=i(Lx),T(Nw.$$.fragment,Lx),Lx.forEach(t),Sit=i(Di),Zr=n(Di,"DIV",{class:!0});var ji=s(Zr);T(wP.$$.fragment,ji),Rit=i(ji),Wxe=n(ji,"P",{});var z1a=s(Wxe);Pit=r(z1a,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),z1a.forEach(t),Bit=i(ji),Wn=n(ji,"P",{});var yx=s(Wn);Iit=r(yx,"The model class to instantiate is selected based on the "),Uxe=n(yx,"CODE",{});var Q1a=s(Uxe);Nit=r(Q1a,"model_type"),Q1a.forEach(t),qit=r(yx,` property of the config object (either
passed as an argument or loaded from `),Hxe=n(yx,"CODE",{});var W1a=s(Hxe);Dit=r(W1a,"pretrained_model_name_or_path"),W1a.forEach(t),jit=r(yx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jxe=n(yx,"CODE",{});var U1a=s(Jxe);Git=r(U1a,"pretrained_model_name_or_path"),U1a.forEach(t),Oit=r(yx,":"),yx.forEach(t),Vit=i(ji),ie=n(ji,"UL",{});var ge=s(ie);qw=n(ge,"LI",{});var $eo=s(qw);Yxe=n($eo,"STRONG",{});var H1a=s(Yxe);Xit=r(H1a,"albert"),H1a.forEach(t),zit=r($eo," \u2014 "),use=n($eo,"A",{href:!0});var J1a=s(use);Qit=r(J1a,"TFAlbertForTokenClassification"),J1a.forEach(t),Wit=r($eo," (ALBERT model)"),$eo.forEach(t),Uit=i(ge),Dw=n(ge,"LI",{});var keo=s(Dw);Zxe=n(keo,"STRONG",{});var Y1a=s(Zxe);Hit=r(Y1a,"bert"),Y1a.forEach(t),Jit=r(keo," \u2014 "),_se=n(keo,"A",{href:!0});var Z1a=s(_se);Yit=r(Z1a,"TFBertForTokenClassification"),Z1a.forEach(t),Zit=r(keo," (BERT model)"),keo.forEach(t),Kit=i(ge),jw=n(ge,"LI",{});var Seo=s(jw);Kxe=n(Seo,"STRONG",{});var K1a=s(Kxe);edt=r(K1a,"camembert"),K1a.forEach(t),odt=r(Seo," \u2014 "),bse=n(Seo,"A",{href:!0});var e9a=s(bse);rdt=r(e9a,"TFCamembertForTokenClassification"),e9a.forEach(t),tdt=r(Seo," (CamemBERT model)"),Seo.forEach(t),adt=i(ge),Gw=n(ge,"LI",{});var Reo=s(Gw);e$e=n(Reo,"STRONG",{});var o9a=s(e$e);ndt=r(o9a,"convbert"),o9a.forEach(t),sdt=r(Reo," \u2014 "),vse=n(Reo,"A",{href:!0});var r9a=s(vse);ldt=r(r9a,"TFConvBertForTokenClassification"),r9a.forEach(t),idt=r(Reo," (ConvBERT model)"),Reo.forEach(t),ddt=i(ge),Ow=n(ge,"LI",{});var Peo=s(Ow);o$e=n(Peo,"STRONG",{});var t9a=s(o$e);cdt=r(t9a,"deberta"),t9a.forEach(t),fdt=r(Peo," \u2014 "),Fse=n(Peo,"A",{href:!0});var a9a=s(Fse);mdt=r(a9a,"TFDebertaForTokenClassification"),a9a.forEach(t),gdt=r(Peo," (DeBERTa model)"),Peo.forEach(t),hdt=i(ge),Vw=n(ge,"LI",{});var Beo=s(Vw);r$e=n(Beo,"STRONG",{});var n9a=s(r$e);pdt=r(n9a,"deberta-v2"),n9a.forEach(t),udt=r(Beo," \u2014 "),Tse=n(Beo,"A",{href:!0});var s9a=s(Tse);_dt=r(s9a,"TFDebertaV2ForTokenClassification"),s9a.forEach(t),bdt=r(Beo," (DeBERTa-v2 model)"),Beo.forEach(t),vdt=i(ge),Xw=n(ge,"LI",{});var Ieo=s(Xw);t$e=n(Ieo,"STRONG",{});var l9a=s(t$e);Fdt=r(l9a,"distilbert"),l9a.forEach(t),Tdt=r(Ieo," \u2014 "),Mse=n(Ieo,"A",{href:!0});var i9a=s(Mse);Mdt=r(i9a,"TFDistilBertForTokenClassification"),i9a.forEach(t),Edt=r(Ieo," (DistilBERT model)"),Ieo.forEach(t),Cdt=i(ge),zw=n(ge,"LI",{});var Neo=s(zw);a$e=n(Neo,"STRONG",{});var d9a=s(a$e);wdt=r(d9a,"electra"),d9a.forEach(t),Adt=r(Neo," \u2014 "),Ese=n(Neo,"A",{href:!0});var c9a=s(Ese);Ldt=r(c9a,"TFElectraForTokenClassification"),c9a.forEach(t),ydt=r(Neo," (ELECTRA model)"),Neo.forEach(t),xdt=i(ge),Qw=n(ge,"LI",{});var qeo=s(Qw);n$e=n(qeo,"STRONG",{});var f9a=s(n$e);$dt=r(f9a,"esm"),f9a.forEach(t),kdt=r(qeo," \u2014 "),Cse=n(qeo,"A",{href:!0});var m9a=s(Cse);Sdt=r(m9a,"TFEsmForTokenClassification"),m9a.forEach(t),Rdt=r(qeo," (ESM model)"),qeo.forEach(t),Pdt=i(ge),Ww=n(ge,"LI",{});var Deo=s(Ww);s$e=n(Deo,"STRONG",{});var g9a=s(s$e);Bdt=r(g9a,"flaubert"),g9a.forEach(t),Idt=r(Deo," \u2014 "),wse=n(Deo,"A",{href:!0});var h9a=s(wse);Ndt=r(h9a,"TFFlaubertForTokenClassification"),h9a.forEach(t),qdt=r(Deo," (FlauBERT model)"),Deo.forEach(t),Ddt=i(ge),Uw=n(ge,"LI",{});var jeo=s(Uw);l$e=n(jeo,"STRONG",{});var p9a=s(l$e);jdt=r(p9a,"funnel"),p9a.forEach(t),Gdt=r(jeo," \u2014 "),Ase=n(jeo,"A",{href:!0});var u9a=s(Ase);Odt=r(u9a,"TFFunnelForTokenClassification"),u9a.forEach(t),Vdt=r(jeo," (Funnel Transformer model)"),jeo.forEach(t),Xdt=i(ge),Hw=n(ge,"LI",{});var Geo=s(Hw);i$e=n(Geo,"STRONG",{});var _9a=s(i$e);zdt=r(_9a,"layoutlm"),_9a.forEach(t),Qdt=r(Geo," \u2014 "),Lse=n(Geo,"A",{href:!0});var b9a=s(Lse);Wdt=r(b9a,"TFLayoutLMForTokenClassification"),b9a.forEach(t),Udt=r(Geo," (LayoutLM model)"),Geo.forEach(t),Hdt=i(ge),Jw=n(ge,"LI",{});var Oeo=s(Jw);d$e=n(Oeo,"STRONG",{});var v9a=s(d$e);Jdt=r(v9a,"layoutlmv3"),v9a.forEach(t),Ydt=r(Oeo," \u2014 "),yse=n(Oeo,"A",{href:!0});var F9a=s(yse);Zdt=r(F9a,"TFLayoutLMv3ForTokenClassification"),F9a.forEach(t),Kdt=r(Oeo," (LayoutLMv3 model)"),Oeo.forEach(t),ect=i(ge),Yw=n(ge,"LI",{});var Veo=s(Yw);c$e=n(Veo,"STRONG",{});var T9a=s(c$e);oct=r(T9a,"longformer"),T9a.forEach(t),rct=r(Veo," \u2014 "),xse=n(Veo,"A",{href:!0});var M9a=s(xse);tct=r(M9a,"TFLongformerForTokenClassification"),M9a.forEach(t),act=r(Veo," (Longformer model)"),Veo.forEach(t),nct=i(ge),Zw=n(ge,"LI",{});var Xeo=s(Zw);f$e=n(Xeo,"STRONG",{});var E9a=s(f$e);sct=r(E9a,"mobilebert"),E9a.forEach(t),lct=r(Xeo," \u2014 "),$se=n(Xeo,"A",{href:!0});var C9a=s($se);ict=r(C9a,"TFMobileBertForTokenClassification"),C9a.forEach(t),dct=r(Xeo," (MobileBERT model)"),Xeo.forEach(t),cct=i(ge),Kw=n(ge,"LI",{});var zeo=s(Kw);m$e=n(zeo,"STRONG",{});var w9a=s(m$e);fct=r(w9a,"mpnet"),w9a.forEach(t),mct=r(zeo," \u2014 "),kse=n(zeo,"A",{href:!0});var A9a=s(kse);gct=r(A9a,"TFMPNetForTokenClassification"),A9a.forEach(t),hct=r(zeo," (MPNet model)"),zeo.forEach(t),pct=i(ge),eA=n(ge,"LI",{});var Qeo=s(eA);g$e=n(Qeo,"STRONG",{});var L9a=s(g$e);uct=r(L9a,"rembert"),L9a.forEach(t),_ct=r(Qeo," \u2014 "),Sse=n(Qeo,"A",{href:!0});var y9a=s(Sse);bct=r(y9a,"TFRemBertForTokenClassification"),y9a.forEach(t),vct=r(Qeo," (RemBERT model)"),Qeo.forEach(t),Fct=i(ge),oA=n(ge,"LI",{});var Weo=s(oA);h$e=n(Weo,"STRONG",{});var x9a=s(h$e);Tct=r(x9a,"roberta"),x9a.forEach(t),Mct=r(Weo," \u2014 "),Rse=n(Weo,"A",{href:!0});var $9a=s(Rse);Ect=r($9a,"TFRobertaForTokenClassification"),$9a.forEach(t),Cct=r(Weo," (RoBERTa model)"),Weo.forEach(t),wct=i(ge),rA=n(ge,"LI",{});var Ueo=s(rA);p$e=n(Ueo,"STRONG",{});var k9a=s(p$e);Act=r(k9a,"roformer"),k9a.forEach(t),Lct=r(Ueo," \u2014 "),Pse=n(Ueo,"A",{href:!0});var S9a=s(Pse);yct=r(S9a,"TFRoFormerForTokenClassification"),S9a.forEach(t),xct=r(Ueo," (RoFormer model)"),Ueo.forEach(t),$ct=i(ge),tA=n(ge,"LI",{});var Heo=s(tA);u$e=n(Heo,"STRONG",{});var R9a=s(u$e);kct=r(R9a,"xlm"),R9a.forEach(t),Sct=r(Heo," \u2014 "),Bse=n(Heo,"A",{href:!0});var P9a=s(Bse);Rct=r(P9a,"TFXLMForTokenClassification"),P9a.forEach(t),Pct=r(Heo," (XLM model)"),Heo.forEach(t),Bct=i(ge),aA=n(ge,"LI",{});var Jeo=s(aA);_$e=n(Jeo,"STRONG",{});var B9a=s(_$e);Ict=r(B9a,"xlm-roberta"),B9a.forEach(t),Nct=r(Jeo," \u2014 "),Ise=n(Jeo,"A",{href:!0});var I9a=s(Ise);qct=r(I9a,"TFXLMRobertaForTokenClassification"),I9a.forEach(t),Dct=r(Jeo," (XLM-RoBERTa model)"),Jeo.forEach(t),jct=i(ge),nA=n(ge,"LI",{});var Yeo=s(nA);b$e=n(Yeo,"STRONG",{});var N9a=s(b$e);Gct=r(N9a,"xlnet"),N9a.forEach(t),Oct=r(Yeo," \u2014 "),Nse=n(Yeo,"A",{href:!0});var q9a=s(Nse);Vct=r(q9a,"TFXLNetForTokenClassification"),q9a.forEach(t),Xct=r(Yeo," (XLNet model)"),Yeo.forEach(t),ge.forEach(t),zct=i(ji),T(sA.$$.fragment,ji),ji.forEach(t),Di.forEach(t),fno=i(f),Jf=n(f,"H2",{class:!0});var klo=s(Jf);lA=n(klo,"A",{id:!0,class:!0,href:!0});var D9a=s(lA);v$e=n(D9a,"SPAN",{});var j9a=s(v$e);T(AP.$$.fragment,j9a),j9a.forEach(t),D9a.forEach(t),Qct=i(klo),F$e=n(klo,"SPAN",{});var G9a=s(F$e);Wct=r(G9a,"TFAutoModelForQuestionAnswering"),G9a.forEach(t),klo.forEach(t),mno=i(f),Er=n(f,"DIV",{class:!0});var Gi=s(Er);T(LP.$$.fragment,Gi),Uct=i(Gi),Yf=n(Gi,"P",{});var Rfe=s(Yf);Hct=r(Rfe,`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 `),qse=n(Rfe,"A",{href:!0});var O9a=s(qse);Jct=r(O9a,"from_pretrained()"),O9a.forEach(t),Yct=r(Rfe," class method or the "),Dse=n(Rfe,"A",{href:!0});var V9a=s(Dse);Zct=r(V9a,"from_config()"),V9a.forEach(t),Kct=r(Rfe,` class
method.`),Rfe.forEach(t),eft=i(Gi),yP=n(Gi,"P",{});var Slo=s(yP);oft=r(Slo,"This class cannot be instantiated directly using "),T$e=n(Slo,"CODE",{});var X9a=s(T$e);rft=r(X9a,"__init__()"),X9a.forEach(t),tft=r(Slo," (throws an error)."),Slo.forEach(t),aft=i(Gi),ca=n(Gi,"DIV",{class:!0});var xx=s(ca);T(xP.$$.fragment,xx),nft=i(xx),M$e=n(xx,"P",{});var z9a=s(M$e);sft=r(z9a,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),z9a.forEach(t),lft=i(xx),Zf=n(xx,"P",{});var Pfe=s(Zf);ift=r(Pfe,`Note:
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model\u2019s configuration. Use `),jse=n(Pfe,"A",{href:!0});var W9a=s(jse);fft=r(W9a,"from_pretrained()"),W9a.forEach(t),mft=r(Pfe," to load the model weights."),Pfe.forEach(t),gft=i(xx),T(iA.$$.fragment,xx),xx.forEach(t),hft=i(Gi),Kr=n(Gi,"DIV",{class:!0});var Oi=s(Kr);T($P.$$.fragment,Oi),pft=i(Oi),C$e=n(Oi,"P",{});var U9a=s(C$e);uft=r(U9a,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),U9a.forEach(t),_ft=i(Oi),Un=n(Oi,"P",{});var $x=s(Un);bft=r($x,"The model class to instantiate is selected based on the "),w$e=n($x,"CODE",{});var H9a=s(w$e);vft=r(H9a,"model_type"),H9a.forEach(t),Fft=r($x,` property of the config object (either
passed as an argument or loaded from `),A$e=n($x,"CODE",{});var J9a=s(A$e);Tft=r(J9a,"pretrained_model_name_or_path"),J9a.forEach(t),Mft=r($x,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),L$e=n($x,"CODE",{});var Y9a=s(L$e);Eft=r(Y9a,"pretrained_model_name_or_path"),Y9a.forEach(t),Cft=r($x,":"),$x.forEach(t),wft=i(Oi),me=n(Oi,"UL",{});var ue=s(me);dA=n(ue,"LI",{});var Zeo=s(dA);y$e=n(Zeo,"STRONG",{});var Z9a=s(y$e);Aft=r(Z9a,"albert"),Z9a.forEach(t),Lft=r(Zeo," \u2014 "),Gse=n(Zeo,"A",{href:!0});var K9a=s(Gse);yft=r(K9a,"TFAlbertForQuestionAnswering"),K9a.forEach(t),xft=r(Zeo," (ALBERT model)"),Zeo.forEach(t),$ft=i(ue),cA=n(ue,"LI",{});var Keo=s(cA);x$e=n(Keo,"STRONG",{});var e2a=s(x$e);kft=r(e2a,"bert"),e2a.forEach(t),Sft=r(Keo," \u2014 "),Ose=n(Keo,"A",{href:!0});var o2a=s(Ose);Rft=r(o2a,"TFBertForQuestionAnswering"),o2a.forEach(t),Pft=r(Keo," (BERT model)"),Keo.forEach(t),Bft=i(ue),fA=n(ue,"LI",{});var eoo=s(fA);$$e=n(eoo,"STRONG",{});var r2a=s($$e);Ift=r(r2a,"camembert"),r2a.forEach(t),Nft=r(eoo," \u2014 "),Vse=n(eoo,"A",{href:!0});var t2a=s(Vse);qft=r(t2a,"TFCamembertForQuestionAnswering"),t2a.forEach(t),Dft=r(eoo," 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with the `),ile=n(Bfe,"A",{href:!0});var V2a=s(ile);Lgt=r(V2a,"from_pretrained()"),V2a.forEach(t),ygt=r(Bfe," class method or the "),dle=n(Bfe,"A",{href:!0});var X2a=s(dle);xgt=r(X2a,"from_config()"),X2a.forEach(t),$gt=r(Bfe,` class
method.`),Bfe.forEach(t),kgt=i(Vi),RP=n(Vi,"P",{});var Plo=s(RP);Sgt=r(Plo,"This class cannot be instantiated directly using "),Y$e=n(Plo,"CODE",{});var z2a=s(Y$e);Rgt=r(z2a,"__init__()"),z2a.forEach(t),Pgt=r(Plo," (throws an error)."),Plo.forEach(t),Bgt=i(Vi),fa=n(Vi,"DIV",{class:!0});var kx=s(fa);T(PP.$$.fragment,kx),Igt=i(kx),Z$e=n(kx,"P",{});var Q2a=s(Z$e);Ngt=r(Q2a,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Q2a.forEach(t),qgt=i(kx),om=n(kx,"P",{});var Ife=s(om);Dgt=r(Ife,`Note:
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model\u2019s configuration. Use `),cle=n(Ife,"A",{href:!0});var U2a=s(cle);Ogt=r(U2a,"from_pretrained()"),U2a.forEach(t),Vgt=r(Ife," to load the model weights."),Ife.forEach(t),Xgt=i(kx),T(SA.$$.fragment,kx),kx.forEach(t),zgt=i(Vi),et=n(Vi,"DIV",{class:!0});var Xi=s(et);T(BP.$$.fragment,Xi),Qgt=i(Xi),eke=n(Xi,"P",{});var H2a=s(eke);Wgt=r(H2a,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),H2a.forEach(t),Ugt=i(Xi),Hn=n(Xi,"P",{});var Sx=s(Hn);Hgt=r(Sx,"The model class to instantiate is selected based on the "),oke=n(Sx,"CODE",{});var J2a=s(oke);Jgt=r(J2a,"model_type"),J2a.forEach(t),Ygt=r(Sx,` property of the config object (either
passed as an argument or loaded from `),rke=n(Sx,"CODE",{});var Y2a=s(rke);Zgt=r(Y2a,"pretrained_model_name_or_path"),Y2a.forEach(t),Kgt=r(Sx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tke=n(Sx,"CODE",{});var Z2a=s(tke);eht=r(Z2a,"pretrained_model_name_or_path"),Z2a.forEach(t),oht=r(Sx,":"),Sx.forEach(t),rht=i(Xi),ake=n(Xi,"UL",{});var K2a=s(ake);RA=n(K2a,"LI",{});var voo=s(RA);nke=n(voo,"STRONG",{});var eba=s(nke);tht=r(eba,"vision-encoder-decoder"),eba.forEach(t),aht=r(voo," \u2014 "),fle=n(voo,"A",{href:!0});var oba=s(fle);nht=r(oba,"TFVisionEncoderDecoderModel"),oba.forEach(t),sht=r(voo," (Vision Encoder decoder model)"),voo.forEach(t),K2a.forEach(t),lht=i(Xi),T(PA.$$.fragment,Xi),Xi.forEach(t),Vi.forEach(t),pno=i(f),rm=n(f,"H2",{class:!0});var Blo=s(rm);BA=n(Blo,"A",{id:!0,class:!0,href:!0});var rba=s(BA);ske=n(rba,"SPAN",{});var tba=s(ske);T(IP.$$.fragment,tba),tba.forEach(t),rba.forEach(t),iht=i(Blo),lke=n(Blo,"SPAN",{});var aba=s(lke);dht=r(aba,"TFAutoModelForSpeechSeq2Seq"),aba.forEach(t),Blo.forEach(t),uno=i(f),wr=n(f,"DIV",{class:!0});var zi=s(wr);T(NP.$$.fragment,zi),cht=i(zi),tm=n(zi,"P",{});var Nfe=s(tm);fht=r(Nfe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),mle=n(Nfe,"A",{href:!0});var nba=s(mle);mht=r(nba,"from_pretrained()"),nba.forEach(t),ght=r(Nfe," class method or the "),gle=n(Nfe,"A",{href:!0});var sba=s(gle);hht=r(sba,"from_config()"),sba.forEach(t),pht=r(Nfe,` class
method.`),Nfe.forEach(t),uht=i(zi),qP=n(zi,"P",{});var Ilo=s(qP);_ht=r(Ilo,"This class cannot be instantiated directly using "),ike=n(Ilo,"CODE",{});var lba=s(ike);bht=r(lba,"__init__()"),lba.forEach(t),vht=r(Ilo," (throws an error)."),Ilo.forEach(t),Fht=i(zi),ma=n(zi,"DIV",{class:!0});var Rx=s(ma);T(DP.$$.fragment,Rx),Tht=i(Rx),dke=n(Rx,"P",{});var iba=s(dke);Mht=r(iba,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),iba.forEach(t),Eht=i(Rx),am=n(Rx,"P",{});var qfe=s(am);Cht=r(qfe,`Note:
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model\u2019s configuration. Use `),hle=n(qfe,"A",{href:!0});var cba=s(hle);Lht=r(cba,"from_pretrained()"),cba.forEach(t),yht=r(qfe," to load the model weights."),qfe.forEach(t),xht=i(Rx),T(IA.$$.fragment,Rx),Rx.forEach(t),$ht=i(zi),ot=n(zi,"DIV",{class:!0});var Qi=s(ot);T(jP.$$.fragment,Qi),kht=i(Qi),fke=n(Qi,"P",{});var fba=s(fke);Sht=r(fba,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),fba.forEach(t),Rht=i(Qi),Jn=n(Qi,"P",{});var Px=s(Jn);Pht=r(Px,"The model class to instantiate is selected based on the "),mke=n(Px,"CODE",{});var mba=s(mke);Bht=r(mba,"model_type"),mba.forEach(t),Iht=r(Px,` property of the config object (either
passed as an argument or loaded from `),gke=n(Px,"CODE",{});var gba=s(gke);Nht=r(gba,"pretrained_model_name_or_path"),gba.forEach(t),qht=r(Px,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hke=n(Px,"CODE",{});var hba=s(hke);Dht=r(hba,"pretrained_model_name_or_path"),hba.forEach(t),jht=r(Px,":"),Px.forEach(t),Ght=i(Qi),GP=n(Qi,"UL",{});var Nlo=s(GP);NA=n(Nlo,"LI",{});var Foo=s(NA);pke=n(Foo,"STRONG",{});var pba=s(pke);Oht=r(pba,"speech_to_text"),pba.forEach(t),Vht=r(Foo," \u2014 "),ple=n(Foo,"A",{href:!0});var uba=s(ple);Xht=r(uba,"TFSpeech2TextForConditionalGeneration"),uba.forEach(t),zht=r(Foo," (Speech2Text model)"),Foo.forEach(t),Qht=i(Nlo),qA=n(Nlo,"LI",{});var Too=s(qA);uke=n(Too,"STRONG",{});var _ba=s(uke);Wht=r(_ba,"whisper"),_ba.forEach(t),Uht=r(Too," \u2014 "),ule=n(Too,"A",{href:!0});var bba=s(ule);Hht=r(bba,"TFWhisperForConditionalGeneration"),bba.forEach(t),Jht=r(Too," (Whisper model)"),Too.forEach(t),Nlo.forEach(t),Yht=i(Qi),T(DA.$$.fragment,Qi),Qi.forEach(t),zi.forEach(t),_no=i(f),nm=n(f,"H2",{class:!0});var qlo=s(nm);jA=n(qlo,"A",{id:!0,class:!0,href:!0});var vba=s(jA);_ke=n(vba,"SPAN",{});var Fba=s(_ke);T(OP.$$.fragment,Fba),Fba.forEach(t),vba.forEach(t),Zht=i(qlo),bke=n(qlo,"SPAN",{});var Tba=s(bke);Kht=r(Tba,"FlaxAutoModel"),Tba.forEach(t),qlo.forEach(t),bno=i(f),Ar=n(f,"DIV",{class:!0});var Wi=s(Ar);T(VP.$$.fragment,Wi),ept=i(Wi),sm=n(Wi,"P",{});var Dfe=s(sm);opt=r(Dfe,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),_le=n(Dfe,"A",{href:!0});var Mba=s(_le);rpt=r(Mba,"from_pretrained()"),Mba.forEach(t),tpt=r(Dfe," class method or the "),ble=n(Dfe,"A",{href:!0});var Eba=s(ble);apt=r(Eba,"from_config()"),Eba.forEach(t),npt=r(Dfe,` class
method.`),Dfe.forEach(t),spt=i(Wi),XP=n(Wi,"P",{});var Dlo=s(XP);lpt=r(Dlo,"This class cannot be instantiated directly using "),vke=n(Dlo,"CODE",{});var Cba=s(vke);ipt=r(Cba,"__init__()"),Cba.forEach(t),dpt=r(Dlo," (throws an error)."),Dlo.forEach(t),cpt=i(Wi),ga=n(Wi,"DIV",{class:!0});var Bx=s(ga);T(zP.$$.fragment,Bx),fpt=i(Bx),Fke=n(Bx,"P",{});var wba=s(Fke);mpt=r(wba,"Instantiates one of the base model classes of the library from a configuration."),wba.forEach(t),gpt=i(Bx),lm=n(Bx,"P",{});var jfe=s(lm);hpt=r(jfe,`Note:
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model\u2019s configuration. Use `),vle=n(jfe,"A",{href:!0});var Lba=s(vle);_pt=r(Lba,"from_pretrained()"),Lba.forEach(t),bpt=r(jfe," to load the model weights."),jfe.forEach(t),vpt=i(Bx),T(GA.$$.fragment,Bx),Bx.forEach(t),Fpt=i(Wi),rt=n(Wi,"DIV",{class:!0});var Ui=s(rt);T(QP.$$.fragment,Ui),Tpt=i(Ui),Mke=n(Ui,"P",{});var yba=s(Mke);Mpt=r(yba,"Instantiate one of the base model classes of the library from a pretrained model."),yba.forEach(t),Ept=i(Ui),Yn=n(Ui,"P",{});var Ix=s(Yn);Cpt=r(Ix,"The model class to instantiate is selected based on the "),Eke=n(Ix,"CODE",{});var xba=s(Eke);wpt=r(xba,"model_type"),xba.forEach(t),Apt=r(Ix,` property of the config object (either
passed as an argument or loaded from `),Cke=n(Ix,"CODE",{});var $ba=s(Cke);Lpt=r($ba,"pretrained_model_name_or_path"),$ba.forEach(t),ypt=r(Ix,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wke=n(Ix,"CODE",{});var kba=s(wke);xpt=r(kba,"pretrained_model_name_or_path"),kba.forEach(t),$pt=r(Ix,":"),Ix.forEach(t),kpt=i(Ui),te=n(Ui,"UL",{});var ne=s(te);OA=n(ne,"LI",{});var Moo=s(OA);Ake=n(Moo,"STRONG",{});var Sba=s(Ake);Spt=r(Sba,"albert"),Sba.forEach(t),Rpt=r(Moo," \u2014 "),Fle=n(Moo,"A",{href:!0});var Rba=s(Fle);Ppt=r(Rba,"FlaxAlbertModel"),Rba.forEach(t),Bpt=r(Moo," (ALBERT model)"),Moo.forEach(t),Ipt=i(ne),VA=n(ne,"LI",{});var Eoo=s(VA);Lke=n(Eoo,"STRONG",{});var Pba=s(Lke);Npt=r(Pba,"bart"),Pba.forEach(t),qpt=r(Eoo," \u2014 "),Tle=n(Eoo,"A",{href:!0});var Bba=s(Tle);Dpt=r(Bba,"FlaxBartModel"),Bba.forEach(t),jpt=r(Eoo," (BART model)"),Eoo.forEach(t),Gpt=i(ne),XA=n(ne,"LI",{});var Coo=s(XA);yke=n(Coo,"STRONG",{});var Iba=s(yke);Opt=r(Iba,"beit"),Iba.forEach(t),Vpt=r(Coo," \u2014 "),Mle=n(Coo,"A",{href:!0});var Nba=s(Mle);Xpt=r(Nba,"FlaxBeitModel"),Nba.forEach(t),zpt=r(Coo," (BEiT 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zba=s(Ale);cut=r(zba,"FlaxBlenderbotSmallModel"),zba.forEach(t),fut=r(yoo," (BlenderbotSmall model)"),yoo.forEach(t),mut=i(ne),HA=n(ne,"LI",{});var xoo=s(HA);Rke=n(xoo,"STRONG",{});var Qba=s(Rke);gut=r(Qba,"clip"),Qba.forEach(t),hut=r(xoo," \u2014 "),Lle=n(xoo,"A",{href:!0});var Wba=s(Lle);put=r(Wba,"FlaxCLIPModel"),Wba.forEach(t),uut=r(xoo," (CLIP model)"),xoo.forEach(t),_ut=i(ne),JA=n(ne,"LI",{});var $oo=s(JA);Pke=n($oo,"STRONG",{});var Uba=s(Pke);but=r(Uba,"distilbert"),Uba.forEach(t),vut=r($oo," \u2014 "),yle=n($oo,"A",{href:!0});var Hba=s(yle);Fut=r(Hba,"FlaxDistilBertModel"),Hba.forEach(t),Tut=r($oo," (DistilBERT model)"),$oo.forEach(t),Mut=i(ne),YA=n(ne,"LI",{});var koo=s(YA);Bke=n(koo,"STRONG",{});var Jba=s(Bke);Eut=r(Jba,"electra"),Jba.forEach(t),Cut=r(koo," \u2014 "),xle=n(koo,"A",{href:!0});var Yba=s(xle);wut=r(Yba,"FlaxElectraModel"),Yba.forEach(t),Aut=r(koo," (ELECTRA model)"),koo.forEach(t),Lut=i(ne),ZA=n(ne,"LI",{});var Soo=s(ZA);Ike=n(Soo,"STRONG",{});var 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gva=s(Nle);f_t=r(gva,"FlaxOPTModel"),gva.forEach(t),m_t=r(Doo," (OPT model)"),Doo.forEach(t),g_t=i(ne),s6=n(ne,"LI",{});var joo=s(s6);Xke=n(joo,"STRONG",{});var hva=s(Xke);h_t=r(hva,"pegasus"),hva.forEach(t),p_t=r(joo," \u2014 "),qle=n(joo,"A",{href:!0});var pva=s(qle);u_t=r(pva,"FlaxPegasusModel"),pva.forEach(t),__t=r(joo," (Pegasus model)"),joo.forEach(t),b_t=i(ne),l6=n(ne,"LI",{});var Goo=s(l6);zke=n(Goo,"STRONG",{});var uva=s(zke);v_t=r(uva,"roberta"),uva.forEach(t),F_t=r(Goo," \u2014 "),Dle=n(Goo,"A",{href:!0});var _va=s(Dle);T_t=r(_va,"FlaxRobertaModel"),_va.forEach(t),M_t=r(Goo," (RoBERTa model)"),Goo.forEach(t),E_t=i(ne),i6=n(ne,"LI",{});var Ooo=s(i6);Qke=n(Ooo,"STRONG",{});var bva=s(Qke);C_t=r(bva,"roformer"),bva.forEach(t),w_t=r(Ooo," \u2014 "),jle=n(Ooo,"A",{href:!0});var vva=s(jle);A_t=r(vva,"FlaxRoFormerModel"),vva.forEach(t),L_t=r(Ooo," (RoFormer model)"),Ooo.forEach(t),y_t=i(ne),d6=n(ne,"LI",{});var Voo=s(d6);Wke=n(Voo,"STRONG",{});var 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with the `),Wle=n(Gfe,"A",{href:!0});var Bva=s(Wle);d1t=r(Bva,"from_pretrained()"),Bva.forEach(t),c1t=r(Gfe," class method or the "),Ule=n(Gfe,"A",{href:!0});var Iva=s(Ule);f1t=r(Iva,"from_config()"),Iva.forEach(t),m1t=r(Gfe,` class
method.`),Gfe.forEach(t),g1t=i(Hi),HP=n(Hi,"P",{});var Glo=s(HP);h1t=r(Glo,"This class cannot be instantiated directly using "),oSe=n(Glo,"CODE",{});var Nva=s(oSe);p1t=r(Nva,"__init__()"),Nva.forEach(t),u1t=r(Glo," (throws an error)."),Glo.forEach(t),_1t=i(Hi),ha=n(Hi,"DIV",{class:!0});var Nx=s(ha);T(JP.$$.fragment,Nx),b1t=i(Nx),rSe=n(Nx,"P",{});var qva=s(rSe);v1t=r(qva,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),qva.forEach(t),F1t=i(Nx),cm=n(Nx,"P",{});var Ofe=s(cm);T1t=r(Ofe,`Note:
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model\u2019s configuration. Use `),Hle=n(Ofe,"A",{href:!0});var jva=s(Hle);C1t=r(jva,"from_pretrained()"),jva.forEach(t),w1t=r(Ofe," to load the model weights."),Ofe.forEach(t),A1t=i(Nx),T(_6.$$.fragment,Nx),Nx.forEach(t),L1t=i(Hi),tt=n(Hi,"DIV",{class:!0});var Ji=s(tt);T(YP.$$.fragment,Ji),y1t=i(Ji),aSe=n(Ji,"P",{});var Gva=s(aSe);x1t=r(Gva,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Gva.forEach(t),$1t=i(Ji),Zn=n(Ji,"P",{});var qx=s(Zn);k1t=r(qx,"The model class to instantiate is selected based on the "),nSe=n(qx,"CODE",{});var Ova=s(nSe);S1t=r(Ova,"model_type"),Ova.forEach(t),R1t=r(qx,` property of the config object (either
passed as an argument or loaded from `),sSe=n(qx,"CODE",{});var Vva=s(sSe);P1t=r(Vva,"pretrained_model_name_or_path"),Vva.forEach(t),B1t=r(qx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lSe=n(qx,"CODE",{});var Xva=s(lSe);I1t=r(Xva,"pretrained_model_name_or_path"),Xva.forEach(t),N1t=r(qx,":"),qx.forEach(t),q1t=i(Ji),$e=n(Ji,"UL",{});var De=s($e);b6=n(De,"LI",{});var Hoo=s(b6);iSe=n(Hoo,"STRONG",{});var zva=s(iSe);D1t=r(zva,"bart"),zva.forEach(t),j1t=r(Hoo," \u2014 "),Jle=n(Hoo,"A",{href:!0});var Qva=s(Jle);G1t=r(Qva,"FlaxBartForCausalLM"),Qva.forEach(t),O1t=r(Hoo," (BART model)"),Hoo.forEach(t),V1t=i(De),v6=n(De,"LI",{});var Joo=s(v6);dSe=n(Joo,"STRONG",{});var Wva=s(dSe);X1t=r(Wva,"bert"),Wva.forEach(t),z1t=r(Joo," \u2014 "),Yle=n(Joo,"A",{href:!0});var Uva=s(Yle);Q1t=r(Uva,"FlaxBertForCausalLM"),Uva.forEach(t),W1t=r(Joo," (BERT model)"),Joo.forEach(t),U1t=i(De),F6=n(De,"LI",{});var Yoo=s(F6);cSe=n(Yoo,"STRONG",{});var Hva=s(cSe);H1t=r(Hva,"big_bird"),Hva.forEach(t),J1t=r(Yoo," \u2014 "),Zle=n(Yoo,"A",{href:!0});var Jva=s(Zle);Y1t=r(Jva,"FlaxBigBirdForCausalLM"),Jva.forEach(t),Z1t=r(Yoo," (BigBird model)"),Yoo.forEach(t),K1t=i(De),T6=n(De,"LI",{});var Zoo=s(T6);fSe=n(Zoo,"STRONG",{});var Yva=s(fSe);e9t=r(Yva,"electra"),Yva.forEach(t),o9t=r(Zoo," \u2014 "),Kle=n(Zoo,"A",{href:!0});var Zva=s(Kle);r9t=r(Zva,"FlaxElectraForCausalLM"),Zva.forEach(t),t9t=r(Zoo," (ELECTRA model)"),Zoo.forEach(t),a9t=i(De),M6=n(De,"LI",{});var Koo=s(M6);mSe=n(Koo,"STRONG",{});var Kva=s(mSe);n9t=r(Kva,"gpt2"),Kva.forEach(t),s9t=r(Koo," \u2014 "),eie=n(Koo,"A",{href:!0});var e0a=s(eie);l9t=r(e0a,"FlaxGPT2LMHeadModel"),e0a.forEach(t),i9t=r(Koo," (OpenAI GPT-2 model)"),Koo.forEach(t),d9t=i(De),E6=n(De,"LI",{});var ero=s(E6);gSe=n(ero,"STRONG",{});var o0a=s(gSe);c9t=r(o0a,"gpt_neo"),o0a.forEach(t),f9t=r(ero," \u2014 "),oie=n(ero,"A",{href:!0});var r0a=s(oie);m9t=r(r0a,"FlaxGPTNeoForCausalLM"),r0a.forEach(t),g9t=r(ero," (GPT Neo model)"),ero.forEach(t),h9t=i(De),C6=n(De,"LI",{});var oro=s(C6);hSe=n(oro,"STRONG",{});var t0a=s(hSe);p9t=r(t0a,"gptj"),t0a.forEach(t),u9t=r(oro," \u2014 "),rie=n(oro,"A",{href:!0});var a0a=s(rie);_9t=r(a0a,"FlaxGPTJForCausalLM"),a0a.forEach(t),b9t=r(oro," (GPT-J model)"),oro.forEach(t),v9t=i(De),w6=n(De,"LI",{});var rro=s(w6);pSe=n(rro,"STRONG",{});var n0a=s(pSe);F9t=r(n0a,"opt"),n0a.forEach(t),T9t=r(rro," \u2014 "),tie=n(rro,"A",{href:!0});var s0a=s(tie);M9t=r(s0a,"FlaxOPTForCausalLM"),s0a.forEach(t),E9t=r(rro," (OPT model)"),rro.forEach(t),C9t=i(De),A6=n(De,"LI",{});var tro=s(A6);uSe=n(tro,"STRONG",{});var l0a=s(uSe);w9t=r(l0a,"roberta"),l0a.forEach(t),A9t=r(tro," \u2014 "),aie=n(tro,"A",{href:!0});var i0a=s(aie);L9t=r(i0a,"FlaxRobertaForCausalLM"),i0a.forEach(t),y9t=r(tro," (RoBERTa model)"),tro.forEach(t),x9t=i(De),L6=n(De,"LI",{});var aro=s(L6);_Se=n(aro,"STRONG",{});var d0a=s(_Se);$9t=r(d0a,"xglm"),d0a.forEach(t),k9t=r(aro," \u2014 "),nie=n(aro,"A",{href:!0});var c0a=s(nie);S9t=r(c0a,"FlaxXGLMForCausalLM"),c0a.forEach(t),R9t=r(aro," (XGLM model)"),aro.forEach(t),De.forEach(t),P9t=i(Ji),T(y6.$$.fragment,Ji),Ji.forEach(t),Hi.forEach(t),Tno=i(f),fm=n(f,"H2",{class:!0});var Olo=s(fm);x6=n(Olo,"A",{id:!0,class:!0,href:!0});var f0a=s(x6);bSe=n(f0a,"SPAN",{});var m0a=s(bSe);T(ZP.$$.fragment,m0a),m0a.forEach(t),f0a.forEach(t),B9t=i(Olo),vSe=n(Olo,"SPAN",{});var g0a=s(vSe);I9t=r(g0a,"FlaxAutoModelForPreTraining"),g0a.forEach(t),Olo.forEach(t),Mno=i(f),yr=n(f,"DIV",{class:!0});var Yi=s(yr);T(KP.$$.fragment,Yi),N9t=i(Yi),mm=n(Yi,"P",{});var Vfe=s(mm);q9t=r(Vfe,`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 `),sie=n(Vfe,"A",{href:!0});var h0a=s(sie);D9t=r(h0a,"from_pretrained()"),h0a.forEach(t),j9t=r(Vfe," class method or the "),lie=n(Vfe,"A",{href:!0});var p0a=s(lie);G9t=r(p0a,"from_config()"),p0a.forEach(t),O9t=r(Vfe,` class
method.`),Vfe.forEach(t),V9t=i(Yi),eB=n(Yi,"P",{});var Vlo=s(eB);X9t=r(Vlo,"This class cannot be instantiated directly using "),FSe=n(Vlo,"CODE",{});var u0a=s(FSe);z9t=r(u0a,"__init__()"),u0a.forEach(t),Q9t=r(Vlo," (throws an error)."),Vlo.forEach(t),W9t=i(Yi),pa=n(Yi,"DIV",{class:!0});var Dx=s(pa);T(oB.$$.fragment,Dx),U9t=i(Dx),TSe=n(Dx,"P",{});var _0a=s(TSe);H9t=r(_0a,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),_0a.forEach(t),J9t=i(Dx),gm=n(Dx,"P",{});var Xfe=s(gm);Y9t=r(Xfe,`Note:
Loading a model from its configuration file does `),MSe=n(Xfe,"STRONG",{});var b0a=s(MSe);Z9t=r(b0a,"not"),b0a.forEach(t),K9t=r(Xfe,` load the model weights. It only affects the
model\u2019s configuration. Use `),iie=n(Xfe,"A",{href:!0});var v0a=s(iie);e2t=r(v0a,"from_pretrained()"),v0a.forEach(t),o2t=r(Xfe," to load the model weights."),Xfe.forEach(t),r2t=i(Dx),T($6.$$.fragment,Dx),Dx.forEach(t),t2t=i(Yi),at=n(Yi,"DIV",{class:!0});var Zi=s(at);T(rB.$$.fragment,Zi),a2t=i(Zi),ESe=n(Zi,"P",{});var F0a=s(ESe);n2t=r(F0a,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),F0a.forEach(t),s2t=i(Zi),Kn=n(Zi,"P",{});var jx=s(Kn);l2t=r(jx,"The model class to instantiate is selected based on the "),CSe=n(jx,"CODE",{});var T0a=s(CSe);i2t=r(T0a,"model_type"),T0a.forEach(t),d2t=r(jx,` property of the config object (either
passed as an argument or loaded from `),wSe=n(jx,"CODE",{});var M0a=s(wSe);c2t=r(M0a,"pretrained_model_name_or_path"),M0a.forEach(t),f2t=r(jx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ASe=n(jx,"CODE",{});var E0a=s(ASe);m2t=r(E0a,"pretrained_model_name_or_path"),E0a.forEach(t),g2t=r(jx,":"),jx.forEach(t),h2t=i(Zi),Ee=n(Zi,"UL",{});var we=s(Ee);k6=n(we,"LI",{});var nro=s(k6);LSe=n(nro,"STRONG",{});var C0a=s(LSe);p2t=r(C0a,"albert"),C0a.forEach(t),u2t=r(nro," \u2014 "),die=n(nro,"A",{href:!0});var w0a=s(die);_2t=r(w0a,"FlaxAlbertForPreTraining"),w0a.forEach(t),b2t=r(nro," (ALBERT model)"),nro.forEach(t),v2t=i(we),S6=n(we,"LI",{});var sro=s(S6);ySe=n(sro,"STRONG",{});var A0a=s(ySe);F2t=r(A0a,"bart"),A0a.forEach(t),T2t=r(sro," \u2014 "),cie=n(sro,"A",{href:!0});var L0a=s(cie);M2t=r(L0a,"FlaxBartForConditionalGeneration"),L0a.forEach(t),E2t=r(sro," (BART model)"),sro.forEach(t),C2t=i(we),R6=n(we,"LI",{});var lro=s(R6);xSe=n(lro,"STRONG",{});var y0a=s(xSe);w2t=r(y0a,"bert"),y0a.forEach(t),A2t=r(lro," \u2014 "),fie=n(lro,"A",{href:!0});var x0a=s(fie);L2t=r(x0a,"FlaxBertForPreTraining"),x0a.forEach(t),y2t=r(lro," (BERT model)"),lro.forEach(t),x2t=i(we),P6=n(we,"LI",{});var iro=s(P6);$Se=n(iro,"STRONG",{});var $0a=s($Se);$2t=r($0a,"big_bird"),$0a.forEach(t),k2t=r(iro," \u2014 "),mie=n(iro,"A",{href:!0});var k0a=s(mie);S2t=r(k0a,"FlaxBigBirdForPreTraining"),k0a.forEach(t),R2t=r(iro," (BigBird model)"),iro.forEach(t),P2t=i(we),B6=n(we,"LI",{});var dro=s(B6);kSe=n(dro,"STRONG",{});var S0a=s(kSe);B2t=r(S0a,"electra"),S0a.forEach(t),I2t=r(dro," \u2014 "),gie=n(dro,"A",{href:!0});var R0a=s(gie);N2t=r(R0a,"FlaxElectraForPreTraining"),R0a.forEach(t),q2t=r(dro," (ELECTRA model)"),dro.forEach(t),D2t=i(we),I6=n(we,"LI",{});var cro=s(I6);SSe=n(cro,"STRONG",{});var P0a=s(SSe);j2t=r(P0a,"longt5"),P0a.forEach(t),G2t=r(cro," \u2014 "),hie=n(cro,"A",{href:!0});var B0a=s(hie);O2t=r(B0a,"FlaxLongT5ForConditionalGeneration"),B0a.forEach(t),V2t=r(cro," (LongT5 model)"),cro.forEach(t),X2t=i(we),N6=n(we,"LI",{});var fro=s(N6);RSe=n(fro,"STRONG",{});var I0a=s(RSe);z2t=r(I0a,"mbart"),I0a.forEach(t),Q2t=r(fro," \u2014 "),pie=n(fro,"A",{href:!0});var N0a=s(pie);W2t=r(N0a,"FlaxMBartForConditionalGeneration"),N0a.forEach(t),U2t=r(fro," (mBART model)"),fro.forEach(t),H2t=i(we),q6=n(we,"LI",{});var mro=s(q6);PSe=n(mro,"STRONG",{});var q0a=s(PSe);J2t=r(q0a,"mt5"),q0a.forEach(t),Y2t=r(mro," \u2014 "),uie=n(mro,"A",{href:!0});var D0a=s(uie);Z2t=r(D0a,"FlaxMT5ForConditionalGeneration"),D0a.forEach(t),K2t=r(mro," (MT5 model)"),mro.forEach(t),ebt=i(we),D6=n(we,"LI",{});var gro=s(D6);BSe=n(gro,"STRONG",{});var j0a=s(BSe);obt=r(j0a,"roberta"),j0a.forEach(t),rbt=r(gro," \u2014 "),_ie=n(gro,"A",{href:!0});var G0a=s(_ie);tbt=r(G0a,"FlaxRobertaForMaskedLM"),G0a.forEach(t),abt=r(gro," (RoBERTa model)"),gro.forEach(t),nbt=i(we),j6=n(we,"LI",{});var hro=s(j6);ISe=n(hro,"STRONG",{});var O0a=s(ISe);sbt=r(O0a,"roformer"),O0a.forEach(t),lbt=r(hro," \u2014 "),bie=n(hro,"A",{href:!0});var V0a=s(bie);ibt=r(V0a,"FlaxRoFormerForMaskedLM"),V0a.forEach(t),dbt=r(hro," (RoFormer model)"),hro.forEach(t),cbt=i(we),G6=n(we,"LI",{});var pro=s(G6);NSe=n(pro,"STRONG",{});var X0a=s(NSe);fbt=r(X0a,"t5"),X0a.forEach(t),mbt=r(pro," \u2014 "),vie=n(pro,"A",{href:!0});var z0a=s(vie);gbt=r(z0a,"FlaxT5ForConditionalGeneration"),z0a.forEach(t),hbt=r(pro," (T5 model)"),pro.forEach(t),pbt=i(we),O6=n(we,"LI",{});var uro=s(O6);qSe=n(uro,"STRONG",{});var Q0a=s(qSe);ubt=r(Q0a,"wav2vec2"),Q0a.forEach(t),_bt=r(uro," \u2014 "),Fie=n(uro,"A",{href:!0});var W0a=s(Fie);bbt=r(W0a,"FlaxWav2Vec2ForPreTraining"),W0a.forEach(t),vbt=r(uro," (Wav2Vec2 model)"),uro.forEach(t),Fbt=i(we),V6=n(we,"LI",{});var _ro=s(V6);DSe=n(_ro,"STRONG",{});var U0a=s(DSe);Tbt=r(U0a,"xlm-roberta"),U0a.forEach(t),Mbt=r(_ro," \u2014 "),Tie=n(_ro,"A",{href:!0});var H0a=s(Tie);Ebt=r(H0a,"FlaxXLMRobertaForMaskedLM"),H0a.forEach(t),Cbt=r(_ro," (XLM-RoBERTa model)"),_ro.forEach(t),we.forEach(t),wbt=i(Zi),T(X6.$$.fragment,Zi),Zi.forEach(t),Yi.forEach(t),Eno=i(f),hm=n(f,"H2",{class:!0});var Xlo=s(hm);z6=n(Xlo,"A",{id:!0,class:!0,href:!0});var J0a=s(z6);jSe=n(J0a,"SPAN",{});var Y0a=s(jSe);T(tB.$$.fragment,Y0a),Y0a.forEach(t),J0a.forEach(t),Abt=i(Xlo),GSe=n(Xlo,"SPAN",{});var Z0a=s(GSe);Lbt=r(Z0a,"FlaxAutoModelForMaskedLM"),Z0a.forEach(t),Xlo.forEach(t),Cno=i(f),xr=n(f,"DIV",{class:!0});var Ki=s(xr);T(aB.$$.fragment,Ki),ybt=i(Ki),pm=n(Ki,"P",{});var zfe=s(pm);xbt=r(zfe,`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 `),Mie=n(zfe,"A",{href:!0});var K0a=s(Mie);$bt=r(K0a,"from_pretrained()"),K0a.forEach(t),kbt=r(zfe," class method or the "),Eie=n(zfe,"A",{href:!0});var eFa=s(Eie);Sbt=r(eFa,"from_config()"),eFa.forEach(t),Rbt=r(zfe,` class
method.`),zfe.forEach(t),Pbt=i(Ki),nB=n(Ki,"P",{});var zlo=s(nB);Bbt=r(zlo,"This class cannot be instantiated directly using "),OSe=n(zlo,"CODE",{});var oFa=s(OSe);Ibt=r(oFa,"__init__()"),oFa.forEach(t),Nbt=r(zlo," (throws an error)."),zlo.forEach(t),qbt=i(Ki),ua=n(Ki,"DIV",{class:!0});var Gx=s(ua);T(sB.$$.fragment,Gx),Dbt=i(Gx),VSe=n(Gx,"P",{});var rFa=s(VSe);jbt=r(rFa,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),rFa.forEach(t),Gbt=i(Gx),um=n(Gx,"P",{});var Qfe=s(um);Obt=r(Qfe,`Note:
Loading a model from its configuration file does `),XSe=n(Qfe,"STRONG",{});var tFa=s(XSe);Vbt=r(tFa,"not"),tFa.forEach(t),Xbt=r(Qfe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Cie=n(Qfe,"A",{href:!0});var aFa=s(Cie);zbt=r(aFa,"from_pretrained()"),aFa.forEach(t),Qbt=r(Qfe," to load the model weights."),Qfe.forEach(t),Wbt=i(Gx),T(Q6.$$.fragment,Gx),Gx.forEach(t),Ubt=i(Ki),nt=n(Ki,"DIV",{class:!0});var ed=s(nt);T(lB.$$.fragment,ed),Hbt=i(ed),zSe=n(ed,"P",{});var nFa=s(zSe);Jbt=r(nFa,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),nFa.forEach(t),Ybt=i(ed),es=n(ed,"P",{});var Ox=s(es);Zbt=r(Ox,"The model class to instantiate is selected based on the "),QSe=n(Ox,"CODE",{});var sFa=s(QSe);Kbt=r(sFa,"model_type"),sFa.forEach(t),evt=r(Ox,` property of the config object (either
passed as an argument or loaded from `),WSe=n(Ox,"CODE",{});var lFa=s(WSe);ovt=r(lFa,"pretrained_model_name_or_path"),lFa.forEach(t),rvt=r(Ox,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),USe=n(Ox,"CODE",{});var iFa=s(USe);tvt=r(iFa,"pretrained_model_name_or_path"),iFa.forEach(t),avt=r(Ox,":"),Ox.forEach(t),nvt=i(ed),ke=n(ed,"UL",{});var je=s(ke);W6=n(je,"LI",{});var bro=s(W6);HSe=n(bro,"STRONG",{});var dFa=s(HSe);svt=r(dFa,"albert"),dFa.forEach(t),lvt=r(bro," \u2014 "),wie=n(bro,"A",{href:!0});var cFa=s(wie);ivt=r(cFa,"FlaxAlbertForMaskedLM"),cFa.forEach(t),dvt=r(bro," (ALBERT model)"),bro.forEach(t),cvt=i(je),U6=n(je,"LI",{});var vro=s(U6);JSe=n(vro,"STRONG",{});var fFa=s(JSe);fvt=r(fFa,"bart"),fFa.forEach(t),mvt=r(vro," \u2014 "),Aie=n(vro,"A",{href:!0});var mFa=s(Aie);gvt=r(mFa,"FlaxBartForConditionalGeneration"),mFa.forEach(t),hvt=r(vro," (BART model)"),vro.forEach(t),pvt=i(je),H6=n(je,"LI",{});var Fro=s(H6);YSe=n(Fro,"STRONG",{});var gFa=s(YSe);uvt=r(gFa,"bert"),gFa.forEach(t),_vt=r(Fro," \u2014 "),Lie=n(Fro,"A",{href:!0});var hFa=s(Lie);bvt=r(hFa,"FlaxBertForMaskedLM"),hFa.forEach(t),vvt=r(Fro," (BERT model)"),Fro.forEach(t),Fvt=i(je),J6=n(je,"LI",{});var Tro=s(J6);ZSe=n(Tro,"STRONG",{});var pFa=s(ZSe);Tvt=r(pFa,"big_bird"),pFa.forEach(t),Mvt=r(Tro," \u2014 "),yie=n(Tro,"A",{href:!0});var uFa=s(yie);Evt=r(uFa,"FlaxBigBirdForMaskedLM"),uFa.forEach(t),Cvt=r(Tro," (BigBird model)"),Tro.forEach(t),wvt=i(je),Y6=n(je,"LI",{});var Mro=s(Y6);KSe=n(Mro,"STRONG",{});var _Fa=s(KSe);Avt=r(_Fa,"distilbert"),_Fa.forEach(t),Lvt=r(Mro," \u2014 "),xie=n(Mro,"A",{href:!0});var bFa=s(xie);yvt=r(bFa,"FlaxDistilBertForMaskedLM"),bFa.forEach(t),xvt=r(Mro," (DistilBERT model)"),Mro.forEach(t),$vt=i(je),Z6=n(je,"LI",{});var Ero=s(Z6);eRe=n(Ero,"STRONG",{});var vFa=s(eRe);kvt=r(vFa,"electra"),vFa.forEach(t),Svt=r(Ero," \u2014 "),$ie=n(Ero,"A",{href:!0});var FFa=s($ie);Rvt=r(FFa,"FlaxElectraForMaskedLM"),FFa.forEach(t),Pvt=r(Ero," (ELECTRA model)"),Ero.forEach(t),Bvt=i(je),K6=n(je,"LI",{});var Cro=s(K6);oRe=n(Cro,"STRONG",{});var TFa=s(oRe);Ivt=r(TFa,"mbart"),TFa.forEach(t),Nvt=r(Cro," \u2014 "),kie=n(Cro,"A",{href:!0});var MFa=s(kie);qvt=r(MFa,"FlaxMBartForConditionalGeneration"),MFa.forEach(t),Dvt=r(Cro," (mBART model)"),Cro.forEach(t),jvt=i(je),e8=n(je,"LI",{});var wro=s(e8);rRe=n(wro,"STRONG",{});var EFa=s(rRe);Gvt=r(EFa,"roberta"),EFa.forEach(t),Ovt=r(wro," \u2014 "),Sie=n(wro,"A",{href:!0});var CFa=s(Sie);Vvt=r(CFa,"FlaxRobertaForMaskedLM"),CFa.forEach(t),Xvt=r(wro," (RoBERTa model)"),wro.forEach(t),zvt=i(je),o8=n(je,"LI",{});var Aro=s(o8);tRe=n(Aro,"STRONG",{});var wFa=s(tRe);Qvt=r(wFa,"roformer"),wFa.forEach(t),Wvt=r(Aro," \u2014 "),Rie=n(Aro,"A",{href:!0});var AFa=s(Rie);Uvt=r(AFa,"FlaxRoFormerForMaskedLM"),AFa.forEach(t),Hvt=r(Aro," (RoFormer model)"),Aro.forEach(t),Jvt=i(je),r8=n(je,"LI",{});var Lro=s(r8);aRe=n(Lro,"STRONG",{});var LFa=s(aRe);Yvt=r(LFa,"xlm-roberta"),LFa.forEach(t),Zvt=r(Lro," \u2014 "),Pie=n(Lro,"A",{href:!0});var yFa=s(Pie);Kvt=r(yFa,"FlaxXLMRobertaForMaskedLM"),yFa.forEach(t),e0t=r(Lro," (XLM-RoBERTa model)"),Lro.forEach(t),je.forEach(t),o0t=i(ed),T(t8.$$.fragment,ed),ed.forEach(t),Ki.forEach(t),wno=i(f),_m=n(f,"H2",{class:!0});var Qlo=s(_m);a8=n(Qlo,"A",{id:!0,class:!0,href:!0});var xFa=s(a8);nRe=n(xFa,"SPAN",{});var $Fa=s(nRe);T(iB.$$.fragment,$Fa),$Fa.forEach(t),xFa.forEach(t),r0t=i(Qlo),sRe=n(Qlo,"SPAN",{});var kFa=s(sRe);t0t=r(kFa,"FlaxAutoModelForSeq2SeqLM"),kFa.forEach(t),Qlo.forEach(t),Ano=i(f),$r=n(f,"DIV",{class:!0});var od=s($r);T(dB.$$.fragment,od),a0t=i(od),bm=n(od,"P",{});var Wfe=s(bm);n0t=r(Wfe,`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 `),Bie=n(Wfe,"A",{href:!0});var SFa=s(Bie);s0t=r(SFa,"from_pretrained()"),SFa.forEach(t),l0t=r(Wfe," class method or the "),Iie=n(Wfe,"A",{href:!0});var RFa=s(Iie);i0t=r(RFa,"from_config()"),RFa.forEach(t),d0t=r(Wfe,` class
method.`),Wfe.forEach(t),c0t=i(od),cB=n(od,"P",{});var Wlo=s(cB);f0t=r(Wlo,"This class cannot be instantiated directly using "),lRe=n(Wlo,"CODE",{});var PFa=s(lRe);m0t=r(PFa,"__init__()"),PFa.forEach(t),g0t=r(Wlo," (throws an error)."),Wlo.forEach(t),h0t=i(od),_a=n(od,"DIV",{class:!0});var Vx=s(_a);T(fB.$$.fragment,Vx),p0t=i(Vx),iRe=n(Vx,"P",{});var BFa=s(iRe);u0t=r(BFa,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),BFa.forEach(t),_0t=i(Vx),vm=n(Vx,"P",{});var Ufe=s(vm);b0t=r(Ufe,`Note:
Loading a model from its configuration file does `),dRe=n(Ufe,"STRONG",{});var IFa=s(dRe);v0t=r(IFa,"not"),IFa.forEach(t),F0t=r(Ufe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Nie=n(Ufe,"A",{href:!0});var NFa=s(Nie);T0t=r(NFa,"from_pretrained()"),NFa.forEach(t),M0t=r(Ufe," to load the model weights."),Ufe.forEach(t),E0t=i(Vx),T(n8.$$.fragment,Vx),Vx.forEach(t),C0t=i(od),st=n(od,"DIV",{class:!0});var rd=s(st);T(mB.$$.fragment,rd),w0t=i(rd),cRe=n(rd,"P",{});var qFa=s(cRe);A0t=r(qFa,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),qFa.forEach(t),L0t=i(rd),os=n(rd,"P",{});var Xx=s(os);y0t=r(Xx,"The model class to instantiate is selected based on the "),fRe=n(Xx,"CODE",{});var DFa=s(fRe);x0t=r(DFa,"model_type"),DFa.forEach(t),$0t=r(Xx,` property of the config object (either
passed as an argument or loaded from `),mRe=n(Xx,"CODE",{});var jFa=s(mRe);k0t=r(jFa,"pretrained_model_name_or_path"),jFa.forEach(t),S0t=r(Xx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gRe=n(Xx,"CODE",{});var GFa=s(gRe);R0t=r(GFa,"pretrained_model_name_or_path"),GFa.forEach(t),P0t=r(Xx,":"),Xx.forEach(t),B0t=i(rd),Se=n(rd,"UL",{});var Ge=s(Se);s8=n(Ge,"LI",{});var yro=s(s8);hRe=n(yro,"STRONG",{});var OFa=s(hRe);I0t=r(OFa,"bart"),OFa.forEach(t),N0t=r(yro," \u2014 "),qie=n(yro,"A",{href:!0});var VFa=s(qie);q0t=r(VFa,"FlaxBartForConditionalGeneration"),VFa.forEach(t),D0t=r(yro," (BART model)"),yro.forEach(t),j0t=i(Ge),l8=n(Ge,"LI",{});var xro=s(l8);pRe=n(xro,"STRONG",{});var XFa=s(pRe);G0t=r(XFa,"blenderbot"),XFa.forEach(t),O0t=r(xro," \u2014 "),Die=n(xro,"A",{href:!0});var zFa=s(Die);V0t=r(zFa,"FlaxBlenderbotForConditionalGeneration"),zFa.forEach(t),X0t=r(xro," (Blenderbot model)"),xro.forEach(t),z0t=i(Ge),i8=n(Ge,"LI",{});var $ro=s(i8);uRe=n($ro,"STRONG",{});var QFa=s(uRe);Q0t=r(QFa,"blenderbot-small"),QFa.forEach(t),W0t=r($ro," \u2014 "),jie=n($ro,"A",{href:!0});var WFa=s(jie);U0t=r(WFa,"FlaxBlenderbotSmallForConditionalGeneration"),WFa.forEach(t),H0t=r($ro," (BlenderbotSmall model)"),$ro.forEach(t),J0t=i(Ge),d8=n(Ge,"LI",{});var kro=s(d8);_Re=n(kro,"STRONG",{});var UFa=s(_Re);Y0t=r(UFa,"encoder-decoder"),UFa.forEach(t),Z0t=r(kro," \u2014 "),Gie=n(kro,"A",{href:!0});var HFa=s(Gie);K0t=r(HFa,"FlaxEncoderDecoderModel"),HFa.forEach(t),eFt=r(kro," (Encoder decoder model)"),kro.forEach(t),oFt=i(Ge),c8=n(Ge,"LI",{});var Sro=s(c8);bRe=n(Sro,"STRONG",{});var JFa=s(bRe);rFt=r(JFa,"longt5"),JFa.forEach(t),tFt=r(Sro," \u2014 "),Oie=n(Sro,"A",{href:!0});var YFa=s(Oie);aFt=r(YFa,"FlaxLongT5ForConditionalGeneration"),YFa.forEach(t),nFt=r(Sro," (LongT5 model)"),Sro.forEach(t),sFt=i(Ge),f8=n(Ge,"LI",{});var Rro=s(f8);vRe=n(Rro,"STRONG",{});var ZFa=s(vRe);lFt=r(ZFa,"marian"),ZFa.forEach(t),iFt=r(Rro," \u2014 "),Vie=n(Rro,"A",{href:!0});var KFa=s(Vie);dFt=r(KFa,"FlaxMarianMTModel"),KFa.forEach(t),cFt=r(Rro," (Marian model)"),Rro.forEach(t),fFt=i(Ge),m8=n(Ge,"LI",{});var Pro=s(m8);FRe=n(Pro,"STRONG",{});var e7a=s(FRe);mFt=r(e7a,"mbart"),e7a.forEach(t),gFt=r(Pro," \u2014 "),Xie=n(Pro,"A",{href:!0});var o7a=s(Xie);hFt=r(o7a,"FlaxMBartForConditionalGeneration"),o7a.forEach(t),pFt=r(Pro," (mBART model)"),Pro.forEach(t),uFt=i(Ge),g8=n(Ge,"LI",{});var Bro=s(g8);TRe=n(Bro,"STRONG",{});var r7a=s(TRe);_Ft=r(r7a,"mt5"),r7a.forEach(t),bFt=r(Bro," \u2014 "),zie=n(Bro,"A",{href:!0});var t7a=s(zie);vFt=r(t7a,"FlaxMT5ForConditionalGeneration"),t7a.forEach(t),FFt=r(Bro," (MT5 model)"),Bro.forEach(t),TFt=i(Ge),h8=n(Ge,"LI",{});var Iro=s(h8);MRe=n(Iro,"STRONG",{});var a7a=s(MRe);MFt=r(a7a,"pegasus"),a7a.forEach(t),EFt=r(Iro," \u2014 "),Qie=n(Iro,"A",{href:!0});var n7a=s(Qie);CFt=r(n7a,"FlaxPegasusForConditionalGeneration"),n7a.forEach(t),wFt=r(Iro," (Pegasus model)"),Iro.forEach(t),AFt=i(Ge),p8=n(Ge,"LI",{});var Nro=s(p8);ERe=n(Nro,"STRONG",{});var s7a=s(ERe);LFt=r(s7a,"t5"),s7a.forEach(t),yFt=r(Nro," \u2014 "),Wie=n(Nro,"A",{href:!0});var l7a=s(Wie);xFt=r(l7a,"FlaxT5ForConditionalGeneration"),l7a.forEach(t),$Ft=r(Nro," (T5 model)"),Nro.forEach(t),Ge.forEach(t),kFt=i(rd),T(u8.$$.fragment,rd),rd.forEach(t),od.forEach(t),Lno=i(f),Fm=n(f,"H2",{class:!0});var Ulo=s(Fm);_8=n(Ulo,"A",{id:!0,class:!0,href:!0});var i7a=s(_8);CRe=n(i7a,"SPAN",{});var d7a=s(CRe);T(gB.$$.fragment,d7a),d7a.forEach(t),i7a.forEach(t),SFt=i(Ulo),wRe=n(Ulo,"SPAN",{});var c7a=s(wRe);RFt=r(c7a,"FlaxAutoModelForSequenceClassification"),c7a.forEach(t),Ulo.forEach(t),yno=i(f),kr=n(f,"DIV",{class:!0});var td=s(kr);T(hB.$$.fragment,td),PFt=i(td),Tm=n(td,"P",{});var Hfe=s(Tm);BFt=r(Hfe,`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 `),Uie=n(Hfe,"A",{href:!0});var f7a=s(Uie);IFt=r(f7a,"from_pretrained()"),f7a.forEach(t),NFt=r(Hfe," class method or the "),Hie=n(Hfe,"A",{href:!0});var m7a=s(Hie);qFt=r(m7a,"from_config()"),m7a.forEach(t),DFt=r(Hfe,` class
method.`),Hfe.forEach(t),jFt=i(td),pB=n(td,"P",{});var Hlo=s(pB);GFt=r(Hlo,"This class cannot be instantiated directly using "),ARe=n(Hlo,"CODE",{});var g7a=s(ARe);OFt=r(g7a,"__init__()"),g7a.forEach(t),VFt=r(Hlo," (throws an error)."),Hlo.forEach(t),XFt=i(td),ba=n(td,"DIV",{class:!0});var zx=s(ba);T(uB.$$.fragment,zx),zFt=i(zx),LRe=n(zx,"P",{});var h7a=s(LRe);QFt=r(h7a,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),h7a.forEach(t),WFt=i(zx),Mm=n(zx,"P",{});var Jfe=s(Mm);UFt=r(Jfe,`Note:
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model\u2019s configuration. Use `),Jie=n(Jfe,"A",{href:!0});var u7a=s(Jie);YFt=r(u7a,"from_pretrained()"),u7a.forEach(t),ZFt=r(Jfe," to load the model weights."),Jfe.forEach(t),KFt=i(zx),T(b8.$$.fragment,zx),zx.forEach(t),e7t=i(td),lt=n(td,"DIV",{class:!0});var ad=s(lt);T(_B.$$.fragment,ad),o7t=i(ad),xRe=n(ad,"P",{});var _7a=s(xRe);r7t=r(_7a,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),_7a.forEach(t),t7t=i(ad),rs=n(ad,"P",{});var Qx=s(rs);a7t=r(Qx,"The model class to instantiate is selected based on the "),$Re=n(Qx,"CODE",{});var b7a=s($Re);n7t=r(b7a,"model_type"),b7a.forEach(t),s7t=r(Qx,` property of the config object (either
passed as an argument or loaded from `),kRe=n(Qx,"CODE",{});var v7a=s(kRe);l7t=r(v7a,"pretrained_model_name_or_path"),v7a.forEach(t),i7t=r(Qx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),SRe=n(Qx,"CODE",{});var F7a=s(SRe);d7t=r(F7a,"pretrained_model_name_or_path"),F7a.forEach(t),c7t=r(Qx,":"),Qx.forEach(t),f7t=i(ad),Re=n(ad,"UL",{});var Oe=s(Re);v8=n(Oe,"LI",{});var qro=s(v8);RRe=n(qro,"STRONG",{});var T7a=s(RRe);m7t=r(T7a,"albert"),T7a.forEach(t),g7t=r(qro," \u2014 "),Yie=n(qro,"A",{href:!0});var M7a=s(Yie);h7t=r(M7a,"FlaxAlbertForSequenceClassification"),M7a.forEach(t),p7t=r(qro," (ALBERT model)"),qro.forEach(t),u7t=i(Oe),F8=n(Oe,"LI",{});var Dro=s(F8);PRe=n(Dro,"STRONG",{});var E7a=s(PRe);_7t=r(E7a,"bart"),E7a.forEach(t),b7t=r(Dro," \u2014 "),Zie=n(Dro,"A",{href:!0});var C7a=s(Zie);v7t=r(C7a,"FlaxBartForSequenceClassification"),C7a.forEach(t),F7t=r(Dro," (BART model)"),Dro.forEach(t),T7t=i(Oe),T8=n(Oe,"LI",{});var jro=s(T8);BRe=n(jro,"STRONG",{});var w7a=s(BRe);M7t=r(w7a,"bert"),w7a.forEach(t),E7t=r(jro," \u2014 "),Kie=n(jro,"A",{href:!0});var A7a=s(Kie);C7t=r(A7a,"FlaxBertForSequenceClassification"),A7a.forEach(t),w7t=r(jro," (BERT model)"),jro.forEach(t),A7t=i(Oe),M8=n(Oe,"LI",{});var Gro=s(M8);IRe=n(Gro,"STRONG",{});var L7a=s(IRe);L7t=r(L7a,"big_bird"),L7a.forEach(t),y7t=r(Gro," \u2014 "),ede=n(Gro,"A",{href:!0});var y7a=s(ede);x7t=r(y7a,"FlaxBigBirdForSequenceClassification"),y7a.forEach(t),$7t=r(Gro," (BigBird model)"),Gro.forEach(t),k7t=i(Oe),E8=n(Oe,"LI",{});var Oro=s(E8);NRe=n(Oro,"STRONG",{});var x7a=s(NRe);S7t=r(x7a,"distilbert"),x7a.forEach(t),R7t=r(Oro," \u2014 "),ode=n(Oro,"A",{href:!0});var $7a=s(ode);P7t=r($7a,"FlaxDistilBertForSequenceClassification"),$7a.forEach(t),B7t=r(Oro," (DistilBERT model)"),Oro.forEach(t),I7t=i(Oe),C8=n(Oe,"LI",{});var Vro=s(C8);qRe=n(Vro,"STRONG",{});var k7a=s(qRe);N7t=r(k7a,"electra"),k7a.forEach(t),q7t=r(Vro," \u2014 "),rde=n(Vro,"A",{href:!0});var S7a=s(rde);D7t=r(S7a,"FlaxElectraForSequenceClassification"),S7a.forEach(t),j7t=r(Vro," (ELECTRA model)"),Vro.forEach(t),G7t=i(Oe),w8=n(Oe,"LI",{});var Xro=s(w8);DRe=n(Xro,"STRONG",{});var R7a=s(DRe);O7t=r(R7a,"mbart"),R7a.forEach(t),V7t=r(Xro," \u2014 "),tde=n(Xro,"A",{href:!0});var P7a=s(tde);X7t=r(P7a,"FlaxMBartForSequenceClassification"),P7a.forEach(t),z7t=r(Xro," (mBART model)"),Xro.forEach(t),Q7t=i(Oe),A8=n(Oe,"LI",{});var zro=s(A8);jRe=n(zro,"STRONG",{});var B7a=s(jRe);W7t=r(B7a,"roberta"),B7a.forEach(t),U7t=r(zro," \u2014 "),ade=n(zro,"A",{href:!0});var I7a=s(ade);H7t=r(I7a,"FlaxRobertaForSequenceClassification"),I7a.forEach(t),J7t=r(zro," (RoBERTa model)"),zro.forEach(t),Y7t=i(Oe),L8=n(Oe,"LI",{});var Qro=s(L8);GRe=n(Qro,"STRONG",{});var N7a=s(GRe);Z7t=r(N7a,"roformer"),N7a.forEach(t),K7t=r(Qro," \u2014 "),nde=n(Qro,"A",{href:!0});var q7a=s(nde);eTt=r(q7a,"FlaxRoFormerForSequenceClassification"),q7a.forEach(t),oTt=r(Qro," (RoFormer model)"),Qro.forEach(t),rTt=i(Oe),y8=n(Oe,"LI",{});var Wro=s(y8);ORe=n(Wro,"STRONG",{});var D7a=s(ORe);tTt=r(D7a,"xlm-roberta"),D7a.forEach(t),aTt=r(Wro," \u2014 "),sde=n(Wro,"A",{href:!0});var j7a=s(sde);nTt=r(j7a,"FlaxXLMRobertaForSequenceClassification"),j7a.forEach(t),sTt=r(Wro," (XLM-RoBERTa model)"),Wro.forEach(t),Oe.forEach(t),lTt=i(ad),T(x8.$$.fragment,ad),ad.forEach(t),td.forEach(t),xno=i(f),Em=n(f,"H2",{class:!0});var Jlo=s(Em);$8=n(Jlo,"A",{id:!0,class:!0,href:!0});var G7a=s($8);VRe=n(G7a,"SPAN",{});var O7a=s(VRe);T(bB.$$.fragment,O7a),O7a.forEach(t),G7a.forEach(t),iTt=i(Jlo),XRe=n(Jlo,"SPAN",{});var V7a=s(XRe);dTt=r(V7a,"FlaxAutoModelForQuestionAnswering"),V7a.forEach(t),Jlo.forEach(t),$no=i(f),Sr=n(f,"DIV",{class:!0});var nd=s(Sr);T(vB.$$.fragment,nd),cTt=i(nd),Cm=n(nd,"P",{});var Yfe=s(Cm);fTt=r(Yfe,`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 `),lde=n(Yfe,"A",{href:!0});var X7a=s(lde);mTt=r(X7a,"from_pretrained()"),X7a.forEach(t),gTt=r(Yfe," class method or the "),ide=n(Yfe,"A",{href:!0});var z7a=s(ide);hTt=r(z7a,"from_config()"),z7a.forEach(t),pTt=r(Yfe,` class
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falling back to using pattern matching on `),YRe=n(Ux,"CODE",{});var K7a=s(YRe);DTt=r(K7a,"pretrained_model_name_or_path"),K7a.forEach(t),jTt=r(Ux,":"),Ux.forEach(t),GTt=i(sd),Pe=n(sd,"UL",{});var Ve=s(Pe);S8=n(Ve,"LI",{});var Uro=s(S8);ZRe=n(Uro,"STRONG",{});var eTa=s(ZRe);OTt=r(eTa,"albert"),eTa.forEach(t),VTt=r(Uro," \u2014 "),cde=n(Uro,"A",{href:!0});var oTa=s(cde);XTt=r(oTa,"FlaxAlbertForQuestionAnswering"),oTa.forEach(t),zTt=r(Uro," (ALBERT model)"),Uro.forEach(t),QTt=i(Ve),R8=n(Ve,"LI",{});var Hro=s(R8);KRe=n(Hro,"STRONG",{});var rTa=s(KRe);WTt=r(rTa,"bart"),rTa.forEach(t),UTt=r(Hro," \u2014 "),fde=n(Hro,"A",{href:!0});var tTa=s(fde);HTt=r(tTa,"FlaxBartForQuestionAnswering"),tTa.forEach(t),JTt=r(Hro," (BART model)"),Hro.forEach(t),YTt=i(Ve),P8=n(Ve,"LI",{});var Jro=s(P8);ePe=n(Jro,"STRONG",{});var aTa=s(ePe);ZTt=r(aTa,"bert"),aTa.forEach(t),KTt=r(Jro," \u2014 "),mde=n(Jro,"A",{href:!0});var nTa=s(mde);eMt=r(nTa,"FlaxBertForQuestionAnswering"),nTa.forEach(t),oMt=r(Jro," (BERT model)"),Jro.forEach(t),rMt=i(Ve),B8=n(Ve,"LI",{});var Yro=s(B8);oPe=n(Yro,"STRONG",{});var sTa=s(oPe);tMt=r(sTa,"big_bird"),sTa.forEach(t),aMt=r(Yro," \u2014 "),gde=n(Yro,"A",{href:!0});var lTa=s(gde);nMt=r(lTa,"FlaxBigBirdForQuestionAnswering"),lTa.forEach(t),sMt=r(Yro," (BigBird model)"),Yro.forEach(t),lMt=i(Ve),I8=n(Ve,"LI",{});var Zro=s(I8);rPe=n(Zro,"STRONG",{});var iTa=s(rPe);iMt=r(iTa,"distilbert"),iTa.forEach(t),dMt=r(Zro," \u2014 "),hde=n(Zro,"A",{href:!0});var dTa=s(hde);cMt=r(dTa,"FlaxDistilBertForQuestionAnswering"),dTa.forEach(t),fMt=r(Zro," (DistilBERT model)"),Zro.forEach(t),mMt=i(Ve),N8=n(Ve,"LI",{});var Kro=s(N8);tPe=n(Kro,"STRONG",{});var cTa=s(tPe);gMt=r(cTa,"electra"),cTa.forEach(t),hMt=r(Kro," \u2014 "),pde=n(Kro,"A",{href:!0});var fTa=s(pde);pMt=r(fTa,"FlaxElectraForQuestionAnswering"),fTa.forEach(t),uMt=r(Kro," (ELECTRA model)"),Kro.forEach(t),_Mt=i(Ve),q8=n(Ve,"LI",{});var eto=s(q8);aPe=n(eto,"STRONG",{});var mTa=s(aPe);bMt=r(mTa,"mbart"),mTa.forEach(t),vMt=r(eto," \u2014 "),ude=n(eto,"A",{href:!0});var gTa=s(ude);FMt=r(gTa,"FlaxMBartForQuestionAnswering"),gTa.forEach(t),TMt=r(eto," (mBART model)"),eto.forEach(t),MMt=i(Ve),D8=n(Ve,"LI",{});var oto=s(D8);nPe=n(oto,"STRONG",{});var hTa=s(nPe);EMt=r(hTa,"roberta"),hTa.forEach(t),CMt=r(oto," \u2014 "),_de=n(oto,"A",{href:!0});var pTa=s(_de);wMt=r(pTa,"FlaxRobertaForQuestionAnswering"),pTa.forEach(t),AMt=r(oto," (RoBERTa model)"),oto.forEach(t),LMt=i(Ve),j8=n(Ve,"LI",{});var rto=s(j8);sPe=n(rto,"STRONG",{});var uTa=s(sPe);yMt=r(uTa,"roformer"),uTa.forEach(t),xMt=r(rto," \u2014 "),bde=n(rto,"A",{href:!0});var _Ta=s(bde);$Mt=r(_Ta,"FlaxRoFormerForQuestionAnswering"),_Ta.forEach(t),kMt=r(rto," (RoFormer model)"),rto.forEach(t),SMt=i(Ve),G8=n(Ve,"LI",{});var tto=s(G8);lPe=n(tto,"STRONG",{});var bTa=s(lPe);RMt=r(bTa,"xlm-roberta"),bTa.forEach(t),PMt=r(tto," \u2014 "),vde=n(tto,"A",{href:!0});var vTa=s(vde);BMt=r(vTa,"FlaxXLMRobertaForQuestionAnswering"),vTa.forEach(t),IMt=r(tto," (XLM-RoBERTa model)"),tto.forEach(t),Ve.forEach(t),NMt=i(sd),T(O8.$$.fragment,sd),sd.forEach(t),nd.forEach(t),kno=i(f),Am=n(f,"H2",{class:!0});var Zlo=s(Am);V8=n(Zlo,"A",{id:!0,class:!0,href:!0});var FTa=s(V8);iPe=n(FTa,"SPAN",{});var TTa=s(iPe);T(EB.$$.fragment,TTa),TTa.forEach(t),FTa.forEach(t),qMt=i(Zlo),dPe=n(Zlo,"SPAN",{});var MTa=s(dPe);DMt=r(MTa,"FlaxAutoModelForTokenClassification"),MTa.forEach(t),Zlo.forEach(t),Sno=i(f),Rr=n(f,"DIV",{class:!0});var ld=s(Rr);T(CB.$$.fragment,ld),jMt=i(ld),Lm=n(ld,"P",{});var Kfe=s(Lm);GMt=r(Kfe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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model\u2019s configuration. Use `),Mde=n(eme,"A",{href:!0});var yTa=s(Mde);tEt=r(yTa,"from_pretrained()"),yTa.forEach(t),aEt=r(eme," to load the model weights."),eme.forEach(t),nEt=i(Hx),T(X8.$$.fragment,Hx),Hx.forEach(t),sEt=i(ld),dt=n(ld,"DIV",{class:!0});var id=s(dt);T(LB.$$.fragment,id),lEt=i(id),gPe=n(id,"P",{});var xTa=s(gPe);iEt=r(xTa,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),xTa.forEach(t),dEt=i(id),as=n(id,"P",{});var Jx=s(as);cEt=r(Jx,"The model class to instantiate is selected based on the "),hPe=n(Jx,"CODE",{});var $Ta=s(hPe);fEt=r($Ta,"model_type"),$Ta.forEach(t),mEt=r(Jx,` property of the config object (either
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falling back to using pattern matching on `),uPe=n(Jx,"CODE",{});var STa=s(uPe);pEt=r(STa,"pretrained_model_name_or_path"),STa.forEach(t),uEt=r(Jx,":"),Jx.forEach(t),_Et=i(id),ze=n(id,"UL",{});var yo=s(ze);z8=n(yo,"LI",{});var ato=s(z8);_Pe=n(ato,"STRONG",{});var RTa=s(_Pe);bEt=r(RTa,"albert"),RTa.forEach(t),vEt=r(ato," \u2014 "),Ede=n(ato,"A",{href:!0});var PTa=s(Ede);FEt=r(PTa,"FlaxAlbertForTokenClassification"),PTa.forEach(t),TEt=r(ato," (ALBERT model)"),ato.forEach(t),MEt=i(yo),Q8=n(yo,"LI",{});var nto=s(Q8);bPe=n(nto,"STRONG",{});var BTa=s(bPe);EEt=r(BTa,"bert"),BTa.forEach(t),CEt=r(nto," \u2014 "),Cde=n(nto,"A",{href:!0});var ITa=s(Cde);wEt=r(ITa,"FlaxBertForTokenClassification"),ITa.forEach(t),AEt=r(nto," (BERT model)"),nto.forEach(t),LEt=i(yo),W8=n(yo,"LI",{});var sto=s(W8);vPe=n(sto,"STRONG",{});var NTa=s(vPe);yEt=r(NTa,"big_bird"),NTa.forEach(t),xEt=r(sto," \u2014 "),wde=n(sto,"A",{href:!0});var qTa=s(wde);$Et=r(qTa,"FlaxBigBirdForTokenClassification"),qTa.forEach(t),kEt=r(sto," (BigBird model)"),sto.forEach(t),SEt=i(yo),U8=n(yo,"LI",{});var lto=s(U8);FPe=n(lto,"STRONG",{});var DTa=s(FPe);REt=r(DTa,"distilbert"),DTa.forEach(t),PEt=r(lto," \u2014 "),Ade=n(lto,"A",{href:!0});var jTa=s(Ade);BEt=r(jTa,"FlaxDistilBertForTokenClassification"),jTa.forEach(t),IEt=r(lto," (DistilBERT model)"),lto.forEach(t),NEt=i(yo),H8=n(yo,"LI",{});var ito=s(H8);TPe=n(ito,"STRONG",{});var GTa=s(TPe);qEt=r(GTa,"electra"),GTa.forEach(t),DEt=r(ito," \u2014 "),Lde=n(ito,"A",{href:!0});var OTa=s(Lde);jEt=r(OTa,"FlaxElectraForTokenClassification"),OTa.forEach(t),GEt=r(ito," (ELECTRA model)"),ito.forEach(t),OEt=i(yo),J8=n(yo,"LI",{});var dto=s(J8);MPe=n(dto,"STRONG",{});var VTa=s(MPe);VEt=r(VTa,"roberta"),VTa.forEach(t),XEt=r(dto," \u2014 "),yde=n(dto,"A",{href:!0});var XTa=s(yde);zEt=r(XTa,"FlaxRobertaForTokenClassification"),XTa.forEach(t),QEt=r(dto," (RoBERTa model)"),dto.forEach(t),WEt=i(yo),Y8=n(yo,"LI",{});var cto=s(Y8);EPe=n(cto,"STRONG",{});var zTa=s(EPe);UEt=r(zTa,"roformer"),zTa.forEach(t),HEt=r(cto," \u2014 "),xde=n(cto,"A",{href:!0});var QTa=s(xde);JEt=r(QTa,"FlaxRoFormerForTokenClassification"),QTa.forEach(t),YEt=r(cto," (RoFormer model)"),cto.forEach(t),ZEt=i(yo),Z8=n(yo,"LI",{});var fto=s(Z8);CPe=n(fto,"STRONG",{});var WTa=s(CPe);KEt=r(WTa,"xlm-roberta"),WTa.forEach(t),e4t=r(fto," \u2014 "),$de=n(fto,"A",{href:!0});var UTa=s($de);o4t=r(UTa,"FlaxXLMRobertaForTokenClassification"),UTa.forEach(t),r4t=r(fto," (XLM-RoBERTa model)"),fto.forEach(t),yo.forEach(t),t4t=i(id),T(K8.$$.fragment,id),id.forEach(t),ld.forEach(t),Rno=i(f),xm=n(f,"H2",{class:!0});var eio=s(xm);eL=n(eio,"A",{id:!0,class:!0,href:!0});var HTa=s(eL);wPe=n(HTa,"SPAN",{});var JTa=s(wPe);T(yB.$$.fragment,JTa),JTa.forEach(t),HTa.forEach(t),a4t=i(eio),APe=n(eio,"SPAN",{});var YTa=s(APe);n4t=r(YTa,"FlaxAutoModelForMultipleChoice"),YTa.forEach(t),eio.forEach(t),Pno=i(f),Pr=n(f,"DIV",{class:!0});var dd=s(Pr);T(xB.$$.fragment,dd),s4t=i(dd),$m=n(dd,"P",{});var ome=s($m);l4t=r(ome,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
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method.`),ome.forEach(t),m4t=i(dd),$B=n(dd,"P",{});var oio=s($B);g4t=r(oio,"This class cannot be instantiated directly using "),LPe=n(oio,"CODE",{});var eMa=s(LPe);h4t=r(eMa,"__init__()"),eMa.forEach(t),p4t=r(oio," (throws an error)."),oio.forEach(t),u4t=i(dd),Ta=n(dd,"DIV",{class:!0});var Yx=s(Ta);T(kB.$$.fragment,Yx),_4t=i(Yx),yPe=n(Yx,"P",{});var oMa=s(yPe);b4t=r(oMa,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),oMa.forEach(t),v4t=i(Yx),km=n(Yx,"P",{});var rme=s(km);F4t=r(rme,`Note:
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model\u2019s configuration. Use `),Rde=n(rme,"A",{href:!0});var tMa=s(Rde);E4t=r(tMa,"from_pretrained()"),tMa.forEach(t),C4t=r(rme," to load the model weights."),rme.forEach(t),w4t=i(Yx),T(oL.$$.fragment,Yx),Yx.forEach(t),A4t=i(dd),ct=n(dd,"DIV",{class:!0});var cd=s(ct);T(SB.$$.fragment,cd),L4t=i(cd),$Pe=n(cd,"P",{});var aMa=s($Pe);y4t=r(aMa,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),aMa.forEach(t),x4t=i(cd),ns=n(cd,"P",{});var Zx=s(ns);$4t=r(Zx,"The model class to instantiate is selected based on the "),kPe=n(Zx,"CODE",{});var nMa=s(kPe);k4t=r(nMa,"model_type"),nMa.forEach(t),S4t=r(Zx,` property of the config object (either
passed as an argument or loaded from `),SPe=n(Zx,"CODE",{});var sMa=s(SPe);R4t=r(sMa,"pretrained_model_name_or_path"),sMa.forEach(t),P4t=r(Zx,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),RPe=n(Zx,"CODE",{});var lMa=s(RPe);B4t=r(lMa,"pretrained_model_name_or_path"),lMa.forEach(t),I4t=r(Zx,":"),Zx.forEach(t),N4t=i(cd),Qe=n(cd,"UL",{});var xo=s(Qe);rL=n(xo,"LI",{});var mto=s(rL);PPe=n(mto,"STRONG",{});var iMa=s(PPe);q4t=r(iMa,"albert"),iMa.forEach(t),D4t=r(mto," \u2014 "),Pde=n(mto,"A",{href:!0});var dMa=s(Pde);j4t=r(dMa,"FlaxAlbertForMultipleChoice"),dMa.forEach(t),G4t=r(mto," (ALBERT model)"),mto.forEach(t),O4t=i(xo),tL=n(xo,"LI",{});var gto=s(tL);BPe=n(gto,"STRONG",{});var cMa=s(BPe);V4t=r(cMa,"bert"),cMa.forEach(t),X4t=r(gto," \u2014 "),Bde=n(gto,"A",{href:!0});var fMa=s(Bde);z4t=r(fMa,"FlaxBertForMultipleChoice"),fMa.forEach(t),Q4t=r(gto," (BERT model)"),gto.forEach(t),W4t=i(xo),aL=n(xo,"LI",{});var hto=s(aL);IPe=n(hto,"STRONG",{});var mMa=s(IPe);U4t=r(mMa,"big_bird"),mMa.forEach(t),H4t=r(hto," \u2014 "),Ide=n(hto,"A",{href:!0});var gMa=s(Ide);J4t=r(gMa,"FlaxBigBirdForMultipleChoice"),gMa.forEach(t),Y4t=r(hto," (BigBird model)"),hto.forEach(t),Z4t=i(xo),nL=n(xo,"LI",{});var pto=s(nL);NPe=n(pto,"STRONG",{});var hMa=s(NPe);K4t=r(hMa,"distilbert"),hMa.forEach(t),eCt=r(pto," \u2014 "),Nde=n(pto,"A",{href:!0});var pMa=s(Nde);oCt=r(pMa,"FlaxDistilBertForMultipleChoice"),pMa.forEach(t),rCt=r(pto," (DistilBERT model)"),pto.forEach(t),tCt=i(xo),sL=n(xo,"LI",{});var uto=s(sL);qPe=n(uto,"STRONG",{});var uMa=s(qPe);aCt=r(uMa,"electra"),uMa.forEach(t),nCt=r(uto," \u2014 "),qde=n(uto,"A",{href:!0});var _Ma=s(qde);sCt=r(_Ma,"FlaxElectraForMultipleChoice"),_Ma.forEach(t),lCt=r(uto," (ELECTRA model)"),uto.forEach(t),iCt=i(xo),lL=n(xo,"LI",{});var _to=s(lL);DPe=n(_to,"STRONG",{});var bMa=s(DPe);dCt=r(bMa,"roberta"),bMa.forEach(t),cCt=r(_to," \u2014 "),Dde=n(_to,"A",{href:!0});var vMa=s(Dde);fCt=r(vMa,"FlaxRobertaForMultipleChoice"),vMa.forEach(t),mCt=r(_to," (RoBERTa model)"),_to.forEach(t),gCt=i(xo),iL=n(xo,"LI",{});var bto=s(iL);jPe=n(bto,"STRONG",{});var FMa=s(jPe);hCt=r(FMa,"roformer"),FMa.forEach(t),pCt=r(bto," \u2014 "),jde=n(bto,"A",{href:!0});var TMa=s(jde);uCt=r(TMa,"FlaxRoFormerForMultipleChoice"),TMa.forEach(t),_Ct=r(bto," (RoFormer model)"),bto.forEach(t),bCt=i(xo),dL=n(xo,"LI",{});var vto=s(dL);GPe=n(vto,"STRONG",{});var MMa=s(GPe);vCt=r(MMa,"xlm-roberta"),MMa.forEach(t),FCt=r(vto," \u2014 "),Gde=n(vto,"A",{href:!0});var EMa=s(Gde);TCt=r(EMa,"FlaxXLMRobertaForMultipleChoice"),EMa.forEach(t),MCt=r(vto," (XLM-RoBERTa model)"),vto.forEach(t),xo.forEach(t),ECt=i(cd),T(cL.$$.fragment,cd),cd.forEach(t),dd.forEach(t),Bno=i(f),Sm=n(f,"H2",{class:!0});var rio=s(Sm);fL=n(rio,"A",{id:!0,class:!0,href:!0});var CMa=s(fL);OPe=n(CMa,"SPAN",{});var wMa=s(OPe);T(RB.$$.fragment,wMa),wMa.forEach(t),CMa.forEach(t),CCt=i(rio),VPe=n(rio,"SPAN",{});var AMa=s(VPe);wCt=r(AMa,"FlaxAutoModelForNextSentencePrediction"),AMa.forEach(t),rio.forEach(t),Ino=i(f),Br=n(f,"DIV",{class:!0});var fd=s(Br);T(PB.$$.fragment,fd),ACt=i(fd),Rm=n(fd,"P",{});var tme=s(Rm);LCt=r(tme,`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 `),Ode=n(tme,"A",{href:!0});var LMa=s(Ode);yCt=r(LMa,"from_pretrained()"),LMa.forEach(t),xCt=r(tme," class method or the "),Vde=n(tme,"A",{href:!0});var yMa=s(Vde);$Ct=r(yMa,"from_config()"),yMa.forEach(t),kCt=r(tme,` class
method.`),tme.forEach(t),SCt=i(fd),BB=n(fd,"P",{});var tio=s(BB);RCt=r(tio,"This class cannot be instantiated directly using "),XPe=n(tio,"CODE",{});var xMa=s(XPe);PCt=r(xMa,"__init__()"),xMa.forEach(t),BCt=r(tio," (throws an error)."),tio.forEach(t),ICt=i(fd),Ma=n(fd,"DIV",{class:!0});var Kx=s(Ma);T(IB.$$.fragment,Kx),NCt=i(Kx),zPe=n(Kx,"P",{});var $Ma=s(zPe);qCt=r($Ma,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),$Ma.forEach(t),DCt=i(Kx),Pm=n(Kx,"P",{});var ame=s(Pm);jCt=r(ame,`Note:
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model\u2019s configuration. Use `),Xde=n(ame,"A",{href:!0});var SMa=s(Xde);VCt=r(SMa,"from_pretrained()"),SMa.forEach(t),XCt=r(ame," to load the model weights."),ame.forEach(t),zCt=i(Kx),T(mL.$$.fragment,Kx),Kx.forEach(t),QCt=i(fd),ft=n(fd,"DIV",{class:!0});var md=s(ft);T(NB.$$.fragment,md),WCt=i(md),WPe=n(md,"P",{});var RMa=s(WPe);UCt=r(RMa,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),RMa.forEach(t),HCt=i(md),ss=n(md,"P",{});var e$=s(ss);JCt=r(e$,"The model class to instantiate is selected based on the "),UPe=n(e$,"CODE",{});var PMa=s(UPe);YCt=r(PMa,"model_type"),PMa.forEach(t),ZCt=r(e$,` property of the config object (either
passed as an argument or loaded from `),HPe=n(e$,"CODE",{});var BMa=s(HPe);KCt=r(BMa,"pretrained_model_name_or_path"),BMa.forEach(t),e3t=r(e$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JPe=n(e$,"CODE",{});var IMa=s(JPe);o3t=r(IMa,"pretrained_model_name_or_path"),IMa.forEach(t),r3t=r(e$,":"),e$.forEach(t),t3t=i(md),YPe=n(md,"UL",{});var NMa=s(YPe);gL=n(NMa,"LI",{});var Fto=s(gL);ZPe=n(Fto,"STRONG",{});var qMa=s(ZPe);a3t=r(qMa,"bert"),qMa.forEach(t),n3t=r(Fto," \u2014 "),zde=n(Fto,"A",{href:!0});var DMa=s(zde);s3t=r(DMa,"FlaxBertForNextSentencePrediction"),DMa.forEach(t),l3t=r(Fto," (BERT model)"),Fto.forEach(t),NMa.forEach(t),i3t=i(md),T(hL.$$.fragment,md),md.forEach(t),fd.forEach(t),Nno=i(f),Bm=n(f,"H2",{class:!0});var aio=s(Bm);pL=n(aio,"A",{id:!0,class:!0,href:!0});var jMa=s(pL);KPe=n(jMa,"SPAN",{});var GMa=s(KPe);T(qB.$$.fragment,GMa),GMa.forEach(t),jMa.forEach(t),d3t=i(aio),eBe=n(aio,"SPAN",{});var OMa=s(eBe);c3t=r(OMa,"FlaxAutoModelForImageClassification"),OMa.forEach(t),aio.forEach(t),qno=i(f),Ir=n(f,"DIV",{class:!0});var gd=s(Ir);T(DB.$$.fragment,gd),f3t=i(gd),Im=n(gd,"P",{});var nme=s(Im);m3t=r(nme,`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 `),Qde=n(nme,"A",{href:!0});var VMa=s(Qde);g3t=r(VMa,"from_pretrained()"),VMa.forEach(t),h3t=r(nme," class method or the "),Wde=n(nme,"A",{href:!0});var XMa=s(Wde);p3t=r(XMa,"from_config()"),XMa.forEach(t),u3t=r(nme,` class
method.`),nme.forEach(t),_3t=i(gd),jB=n(gd,"P",{});var nio=s(jB);b3t=r(nio,"This class cannot be instantiated directly using "),oBe=n(nio,"CODE",{});var zMa=s(oBe);v3t=r(zMa,"__init__()"),zMa.forEach(t),F3t=r(nio," (throws an error)."),nio.forEach(t),T3t=i(gd),Ea=n(gd,"DIV",{class:!0});var o$=s(Ea);T(GB.$$.fragment,o$),M3t=i(o$),rBe=n(o$,"P",{});var QMa=s(rBe);E3t=r(QMa,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),QMa.forEach(t),C3t=i(o$),Nm=n(o$,"P",{});var sme=s(Nm);w3t=r(sme,`Note:
Loading a model from its configuration file does `),tBe=n(sme,"STRONG",{});var WMa=s(tBe);A3t=r(WMa,"not"),WMa.forEach(t),L3t=r(sme,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ude=n(sme,"A",{href:!0});var UMa=s(Ude);y3t=r(UMa,"from_pretrained()"),UMa.forEach(t),x3t=r(sme," to load the model weights."),sme.forEach(t),$3t=i(o$),T(uL.$$.fragment,o$),o$.forEach(t),k3t=i(gd),mt=n(gd,"DIV",{class:!0});var hd=s(mt);T(OB.$$.fragment,hd),S3t=i(hd),aBe=n(hd,"P",{});var HMa=s(aBe);R3t=r(HMa,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),HMa.forEach(t),P3t=i(hd),ls=n(hd,"P",{});var r$=s(ls);B3t=r(r$,"The model class to instantiate is selected based on the "),nBe=n(r$,"CODE",{});var JMa=s(nBe);I3t=r(JMa,"model_type"),JMa.forEach(t),N3t=r(r$,` property of the config object (either
passed as an argument or loaded from `),sBe=n(r$,"CODE",{});var YMa=s(sBe);q3t=r(YMa,"pretrained_model_name_or_path"),YMa.forEach(t),D3t=r(r$,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lBe=n(r$,"CODE",{});var ZMa=s(lBe);j3t=r(ZMa,"pretrained_model_name_or_path"),ZMa.forEach(t),G3t=r(r$,":"),r$.forEach(t),O3t=i(hd),VB=n(hd,"UL",{});var sio=s(VB);_L=n(sio,"LI",{});var Tto=s(_L);iBe=n(Tto,"STRONG",{});var KMa=s(iBe);V3t=r(KMa,"beit"),KMa.forEach(t),X3t=r(Tto," \u2014 "),Hde=n(Tto,"A",{href:!0});var eEa=s(Hde);z3t=r(eEa,"FlaxBeitForImageClassification"),eEa.forEach(t),Q3t=r(Tto," (BEiT model)"),Tto.forEach(t),W3t=i(sio),bL=n(sio,"LI",{});var Mto=s(bL);dBe=n(Mto,"STRONG",{});var oEa=s(dBe);U3t=r(oEa,"vit"),oEa.forEach(t),H3t=r(Mto," \u2014 "),Jde=n(Mto,"A",{href:!0});var rEa=s(Jde);J3t=r(rEa,"FlaxViTForImageClassification"),rEa.forEach(t),Y3t=r(Mto," (ViT model)"),Mto.forEach(t),sio.forEach(t),Z3t=i(hd),T(vL.$$.fragment,hd),hd.forEach(t),gd.forEach(t),Dno=i(f),qm=n(f,"H2",{class:!0});var lio=s(qm);FL=n(lio,"A",{id:!0,class:!0,href:!0});var tEa=s(FL);cBe=n(tEa,"SPAN",{});var aEa=s(cBe);T(XB.$$.fragment,aEa),aEa.forEach(t),tEa.forEach(t),K3t=i(lio),fBe=n(lio,"SPAN",{});var nEa=s(fBe);e5t=r(nEa,"FlaxAutoModelForVision2Seq"),nEa.forEach(t),lio.forEach(t),jno=i(f),Nr=n(f,"DIV",{class:!0});var pd=s(Nr);T(zB.$$.fragment,pd),o5t=i(pd),Dm=n(pd,"P",{});var lme=s(Dm);r5t=r(lme,`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 `),Yde=n(lme,"A",{href:!0});var sEa=s(Yde);t5t=r(sEa,"from_pretrained()"),sEa.forEach(t),a5t=r(lme," class method or the "),Zde=n(lme,"A",{href:!0});var lEa=s(Zde);n5t=r(lEa,"from_config()"),lEa.forEach(t),s5t=r(lme,` class
method.`),lme.forEach(t),l5t=i(pd),QB=n(pd,"P",{});var iio=s(QB);i5t=r(iio,"This class cannot be instantiated directly using "),mBe=n(iio,"CODE",{});var iEa=s(mBe);d5t=r(iEa,"__init__()"),iEa.forEach(t),c5t=r(iio," (throws an error)."),iio.forEach(t),f5t=i(pd),Ca=n(pd,"DIV",{class:!0});var t$=s(Ca);T(WB.$$.fragment,t$),m5t=i(t$),gBe=n(t$,"P",{});var dEa=s(gBe);g5t=r(dEa,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),dEa.forEach(t),h5t=i(t$),jm=n(t$,"P",{});var ime=s(jm);p5t=r(ime,`Note:
Loading a model from its configuration file does `),hBe=n(ime,"STRONG",{});var cEa=s(hBe);u5t=r(cEa,"not"),cEa.forEach(t),_5t=r(ime,` load the model weights. It only affects the
model\u2019s configuration. Use `),Kde=n(ime,"A",{href:!0});var fEa=s(Kde);b5t=r(fEa,"from_pretrained()"),fEa.forEach(t),v5t=r(ime," to load the model weights."),ime.forEach(t),F5t=i(t$),T(TL.$$.fragment,t$),t$.forEach(t),T5t=i(pd),gt=n(pd,"DIV",{class:!0});var ud=s(gt);T(UB.$$.fragment,ud),M5t=i(ud),pBe=n(ud,"P",{});var mEa=s(pBe);E5t=r(mEa,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),mEa.forEach(t),C5t=i(ud),is=n(ud,"P",{});var a$=s(is);w5t=r(a$,"The model class to instantiate is selected based on the "),uBe=n(a$,"CODE",{});var gEa=s(uBe);A5t=r(gEa,"model_type"),gEa.forEach(t),L5t=r(a$,` property of the config object (either
passed as an argument or loaded from `),_Be=n(a$,"CODE",{});var hEa=s(_Be);y5t=r(hEa,"pretrained_model_name_or_path"),hEa.forEach(t),x5t=r(a$,` if possible), or when it\u2019s missing, by
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Classes"};function NCa($){return EEa(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class XCa extends vEa{constructor(g){super();FEa(this,g,NCa,BCa,TEa,{})}}export{XCa as default,ICa as metadata};
