import{s as nc,o as sc,n as v}from"../chunks/scheduler.5f3e6389.js";import{S as oc,i as ac,e as c,s as l,c as h,h as lc,a as p,d as m,b as r,f as J,g as f,j as w,k as j,l as a,m as u,n as M,t as b,o as T,p as y}from"../chunks/index.373ab25c.js";import{T as W}from"../chunks/Tip.3e202241.js";import{C as rc,H as R}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.8a874054.js";import{D as k}from"../chunks/Docstring.697367f8.js";import{C as U}from"../chunks/CodeBlock.ca205683.js";import{E as x}from"../chunks/ExampleCodeBlock.23aeb430.js";function ic($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function mc($){let n,g="Example of text generation:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForCausalLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/gpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;optimum/gpt2&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;My name is Arthur and I live in&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(**inputs,do_sample=<span class="hljs-literal">True</span>,temperature=<span class="hljs-number">0.9</span>, min_length=<span class="hljs-number">20</span>,max_length=<span class="hljs-number">20</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(gen_tokens)`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1bvu0cy"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function cc($){let n,g="Example using <code>transformers.pipelines</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/gpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;optimum/gpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_gen = pipeline(<span class="hljs-string">&quot;text-generation&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My name is Arthur and I live in&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>gen = onnx_gen(text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1da1qsq"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function pc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function dc($){let n,g="Example of feature extraction:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-uncased-for-fill-mask&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-uncased-for-fill-mask&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">8</span>, <span class="hljs-number">28996</span>]`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-12vwt80"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function uc($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-uncased-for-fill-mask&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-uncased-for-fill-mask&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>fill_masker = pipeline(<span class="hljs-string">&quot;fill-mask&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = fill_masker(text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function gc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function hc($){let n,g="Example of text generation:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/t5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;optimum/t5-small&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;My name is Eustache and I like to&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = tokenizer.batch_decode(gen_tokens)`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1bvu0cy"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function fc($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/t5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;optimum/t5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_translation = pipeline(<span class="hljs-string">&quot;translation_en_to_de&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My name is Eustache.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_translation(text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Mc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function bc($){let n,g="Example of single-label classification:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/distilbert-base-uncased-finetuned-sst-2-english&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;optimum/distilbert-base-uncased-finetuned-sst-2-english&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>]`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-ykxpe4"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Tc($){let n,g="Example using <code>transformers.pipelines</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/distilbert-base-uncased-finetuned-sst-2-english&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;optimum/distilbert-base-uncased-finetuned-sst-2-english&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_classifier = pipeline(<span class="hljs-string">&quot;text-classification&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_classifier(text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1da1qsq"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function yc($){let n,g="Example using zero-shot-classification <code>transformers.pipelines</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/distilbert-base-uncased-mnli&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;optimum/distilbert-base-uncased-mnli&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_z0 = pipeline(<span class="hljs-string">&quot;zero-shot-classification&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence_to_classify = <span class="hljs-string">&quot;Who are you voting for in 2020?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>candidate_labels = [<span class="hljs-string">&quot;Europe&quot;</span>, <span class="hljs-string">&quot;public health&quot;</span>, <span class="hljs-string">&quot;politics&quot;</span>, <span class="hljs-string">&quot;elections&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_z0(sequence_to_classify, candidate_labels, multi_label=<span class="hljs-literal">True</span>)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1vtj8d2"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function _c($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function wc($){let n,g="Example of token classification:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-NER&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-NER&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;My name is Philipp and I live in Germany.&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">12</span>, <span class="hljs-number">9</span>]`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-rtgw80"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function $c($){let n,g="Example using <code>transformers.pipelines</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-NER&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;optimum/bert-base-NER&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_ner = pipeline(<span class="hljs-string">&quot;token-classification&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My name is Philipp and I live in Germany.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_ner(text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1da1qsq"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function vc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Jc($){let n,g="Example of multiple choice:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;ehdwns1516/bert-base-uncased_SWAG&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;ehdwns1516/bert-base-uncased_SWAG&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>num_choices = <span class="hljs-number">4</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>first_sentence = [<span class="hljs-string">&quot;Members of the procession walk down the street holding small horn brass instruments.&quot;</span>] * num_choices
<span class="hljs-meta">&gt;&gt;&gt; </span>second_sentence = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A drum line passes by walking down the street playing their instruments.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A drum line has heard approaching them.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A drum line arrives and they&#x27;re outside dancing and asleep.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A drum line turns the lead singer watches the performance.&quot;</span>
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(first_sentence, second_sentence, truncation=<span class="hljs-literal">True</span>, padding=<span class="hljs-literal">True</span>)

<span class="hljs-comment"># Unflatten the inputs values expanding it to the shape [batch_size, num_choices, seq_length]</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> inputs.items():
<span class="hljs-meta">... </span>    inputs[k] = [v[i: i + num_choices] <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(v), num_choices)]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = <span class="hljs-built_in">dict</span>(inputs.convert_to_tensors(tensor_type=<span class="hljs-string">&quot;pt&quot;</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1wk3stq"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function jc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function kc($){let n,g="Example of question answering:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/roberta-base-squad2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;optimum/roberta-base-squad2&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_positions = torch.tensor([<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>end_positions = torch.tensor([<span class="hljs-number">3</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_scores = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_scores = outputs.end_logits`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1ml1brl"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function xc($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/roberta-base-squad2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;optimum/roberta-base-squad2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_qa = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_qa(question, text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Uc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Rc($){let n,g="Example of image classification:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForImageClassification
<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>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>preprocessor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/vit-base-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;optimum/vit-base-patch16-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = preprocessor(images=image, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1nh3zco"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Zc($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>preprocessor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/vit-base-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;optimum/vit-base-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_image_classifier = pipeline(<span class="hljs-string">&quot;image-classification&quot;</span>, model=model, feature_extractor=preprocessor)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_image_classifier(url)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Wc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Nc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Fc($){let n,g="Example of semantic segmentation:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSemanticSegmentation
<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>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>preprocessor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/segformer-b0-finetuned-ade-512-512&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;optimum/segformer-b0-finetuned-ade-512-512&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = preprocessor(images=image, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1wzzaup"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Cc($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span>preprocessor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/segformer-b0-finetuned-ade-512-512&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;optimum/segformer-b0-finetuned-ade-512-512&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_image_segmenter = pipeline(<span class="hljs-string">&quot;image-segmentation&quot;</span>, model=model, feature_extractor=preprocessor)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_image_segmenter(url)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Gc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Ic($){let n,g="Example of audio classification:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForAudioClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/hubert-base-superb-ks&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;optimum/hubert-base-superb-ks&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># audio file is decoded on the fly</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_ids = torch.argmax(logits, dim=-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = model.config.id2label[predicted_class_ids]`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1mp8f15"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Vc($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/hubert-base-superb-ks&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;optimum/hubert-base-superb-ks&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_ac = pipeline(<span class="hljs-string">&quot;audio-classification&quot;</span>, model=model, feature_extractor=feature_extractor)

<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_ac(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>])`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Xc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function zc($){let n,g="Example of audio frame classification:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForAudioFrameClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/wav2vec2-base-superb-sd&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model =  ORTModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;optimum/wav2vec2-base-superb-sd&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, sampling_rate=sampling_rate)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>probabilities = torch.sigmoid(logits[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = (probabilities &gt; <span class="hljs-number">0.5</span>).long()
<span class="hljs-meta">&gt;&gt;&gt; </span>labels[<span class="hljs-number">0</span>].tolist()`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-12boqdm"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Bc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Sc($){let n,g="Example of CTC:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, HubertForCTC
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForCTC
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;optimum/hubert-large-ls960-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForCTC.from_pretrained(<span class="hljs-string">&quot;optimum/hubert-large-ls960-ft&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># audio file is decoded on the fly</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_ids = torch.argmax(logits, dim=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>transcription = processor.batch_decode(predicted_ids)`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-mdyxnj"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function qc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Hc($){let n,g="Example of text generation:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSpeechSeq2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;optimum/whisper-tiny.en&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;optimum/whisper-tiny.en&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_dummy&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor.feature_extractor(ds[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(inputs=inputs.input_features)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = processor.tokenizer.batch_decode(gen_tokens)`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1bvu0cy"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Ec($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForSpeechSeq2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;optimum/whisper-tiny.en&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;optimum/whisper-tiny.en&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>speech_recognition = pipeline(<span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>, model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor)

<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_dummy&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = speech_recognition(ds[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>])`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Lc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Qc($){let n,g="Example of Audio XVector:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForAudioXVector
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;optimum/wav2vec2-base-superb-sv&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;optimum/wav2vec2-base-superb-sv&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># audio file is decoded on the fly</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(
<span class="hljs-meta">... </span>    [d[<span class="hljs-string">&quot;array&quot;</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> dataset[:<span class="hljs-number">2</span>][<span class="hljs-string">&quot;audio&quot;</span>]], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    embeddings = model(**inputs).embeddings

<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = torch.nn.functional.normalize(embeddings, dim=-<span class="hljs-number">1</span>).cpu()

<span class="hljs-meta">&gt;&gt;&gt; </span>cosine_sim = torch.nn.CosineSimilarity(dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>similarity = cosine_sim(embeddings[<span class="hljs-number">0</span>], embeddings[<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>threshold = <span class="hljs-number">0.7</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">if</span> similarity &lt; threshold:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Speakers are not the same!&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(similarity.item(), <span class="hljs-number">2</span>)`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1kzdm5c"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Oc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Yc($){let n,g="Example of text generation:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForVision2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests


<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;nlpconnect/vit-gpt2-image-captioning&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;nlpconnect/vit-gpt2-image-captioning&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;nlpconnect/vit-gpt2-image-captioning&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = tokenizer.batch_decode(gen_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1bvu0cy"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Ac($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForVision2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests


<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;nlpconnect/vit-gpt2-image-captioning&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;nlpconnect/vit-gpt2-image-captioning&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;nlpconnect/vit-gpt2-image-captioning&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_to_text = pipeline(<span class="hljs-string">&quot;image-to-text&quot;</span>, model=model, tokenizer=tokenizer, feature_extractor=processor, image_processor=processor)
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = image_to_text(image)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Pc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function Dc($){let n,g="Example of pix2struct:",o,s,d;return s=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMEFmcm9tJTIwb3B0aW11bS5vbm54cnVudGltZSUyMGltcG9ydCUyME9SVE1vZGVsRm9yUGl4MlN0cnVjdCUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQWltcG9ydCUyMHJlcXVlc3RzJTBBJTBBcHJvY2Vzc29yJTIwJTNEJTIwQXV0b1Byb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGcGl4MnN0cnVjdC1haTJkLWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBPUlRNb2RlbEZvclBpeDJTdHJ1Y3QuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRnBpeDJzdHJ1Y3QtYWkyZC1iYXNlJTIyJTJDJTIwZXhwb3J0JTNEVHJ1ZSUyQyUyMHVzZV9pb19iaW5kaW5nJTNEVHJ1ZSklMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZodWdnaW5nZmFjZSUyRmRvY3VtZW50YXRpb24taW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZ0cmFuc2Zvcm1lcnMlMkZ0YXNrcyUyRmFpMmQtZGVtby5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEFxdWVzdGlvbiUyMCUzRCUyMCUyMldoYXQlMjBkb2VzJTIwdGhlJTIwbGFiZWwlMjAxNSUyMHJlcHJlc2VudCUzRiUyMCgxKSUyMGxhdmElMjAoMiklMjBjb3JlJTIwKDMpJTIwdHVubmVsJTIwKDQpJTIwYXNoJTIwY2xvdWQlMjIlMEFpbnB1dHMlMjAlM0QlMjBwcm9jZXNzb3IoaW1hZ2VzJTNEaW1hZ2UlMkMlMjB0ZXh0JTNEcXVlc3Rpb24lMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQSUwQWdlbl90b2tlbnMlMjAlM0QlMjBtb2RlbC5nZW5lcmF0ZSgqKmlucHV0cyklMEFvdXRwdXRzJTIwJTNEJTIwcHJvY2Vzc29yLmJhdGNoX2RlY29kZShnZW5fdG9rZW5zJTJDJTIwc2tpcF9zcGVjaWFsX3Rva2VucyUzRFRydWUp",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-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForPix2Struct
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;google/pix2struct-ai2d-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForPix2Struct.from_pretrained(<span class="hljs-string">&quot;google/pix2struct-ai2d-base&quot;</span>, export=<span class="hljs-literal">True</span>, use_io_binding=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>question = <span class="hljs-string">&quot;What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=question, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = processor.batch_decode(gen_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-11ikyn3"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function Kc($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function ep($){let n,g="Example of custom tasks(e.g. a sentence transformers taking <code>pooler_output</code> as output):",o,s,d;return s=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEFmcm9tJTIwb3B0aW11bS5vbm54cnVudGltZSUyMGltcG9ydCUyME9SVE1vZGVsRm9yQ3VzdG9tVGFza3MlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJvcHRpbXVtJTJGc2JlcnQtYWxsLU1pbmlMTS1MNi13aXRoLXBvb2xlciUyMiklMEFtb2RlbCUyMCUzRCUyME9SVE1vZGVsRm9yQ3VzdG9tVGFza3MuZnJvbV9wcmV0cmFpbmVkKCUyMm9wdGltdW0lMkZzYmVydC1hbGwtTWluaUxNLUw2LXdpdGgtcG9vbGVyJTIyKSUwQSUwQWlucHV0cyUyMCUzRCUyMHRva2VuaXplciglMjJJJTIwbG92ZSUyMGJ1cnJpdG9zISUyMiUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIybnAlMjIpJTBBJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQWxhc3RfaGlkZGVuX3N0YXRlJTIwJTNEJTIwb3V0cHV0cy5sYXN0X2hpZGRlbl9zdGF0ZSUwQXBvb2xlcl9vdXRwdXQlMjAlM0QlMjBvdXRwdXRzLnBvb2xlcl9vdXRwdXQ=",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-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForCustomTasks

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/sbert-all-MiniLM-L6-with-pooler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForCustomTasks.from_pretrained(<span class="hljs-string">&quot;optimum/sbert-all-MiniLM-L6-with-pooler&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;I love burritos!&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooler_output = outputs.pooler_output`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-6kynse"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function tp($){let n,g="Example using <code>transformers.pipelines</code>(only if the task is supported):",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForCustomTasks

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/sbert-all-MiniLM-L6-with-pooler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForCustomTasks.from_pretrained(<span class="hljs-string">&quot;optimum/sbert-all-MiniLM-L6-with-pooler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_extractor = pipeline(<span class="hljs-string">&quot;feature-extraction&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;I love burritos!&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_extractor(text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-xxeb0k"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function np($){let n,g=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){n=c("p"),n.innerHTML=g},l(o){n=p(o,"P",{"data-svelte-h":!0}),w(n)!=="svelte-fincs2"&&(n.innerHTML=g)},m(o,s){u(o,n,s)},p:v,d(o){o&&m(n)}}}function sp($){let n,g="Example of feature extraction:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForFeatureExtraction
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/all-MiniLM-L6-v2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForFeatureExtraction.from_pretrained(<span class="hljs-string">&quot;optimum/all-MiniLM-L6-v2&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;My name is Philipp and I live in Germany.&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_state.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">12</span>, <span class="hljs-number">384</span>]`,wrap:!1}}),{c(){n=c("p"),n.textContent=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-12vwt80"&&(n.textContent=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function op($){let n,g="Example using <code>transformers.pipeline</code>:",o,s,d;return s=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModelForFeatureExtraction

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;optimum/all-MiniLM-L6-v2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForFeatureExtraction.from_pretrained(<span class="hljs-string">&quot;optimum/all-MiniLM-L6-v2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>onnx_extractor = pipeline(<span class="hljs-string">&quot;feature-extraction&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;My name is Philipp and I live in Germany.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = onnx_extractor(text)`,wrap:!1}}),{c(){n=c("p"),n.innerHTML=g,o=l(),h(s.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),w(n)!=="svelte-1u1xsxh"&&(n.innerHTML=g),o=r(t),f(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,o,_),M(s,t,_),d=!0},p:v,i(t){d||(b(s.$$.fragment,t),d=!0)},o(t){T(s.$$.fragment,t),d=!1},d(t){t&&(m(n),m(o)),y(s,t)}}}function ap($){let n,g,o,s,d,t,_,So,nn,qo,sn,$i="The following ORT classes are available for instantiating a base model class without a specific head.",Ho,on,Eo,Z,an,Sa,$s,vi="Base class for implementing models using ONNX Runtime.",qa,vs,Ji=`The ORTModel implements generic methods for interacting with the Hugging Face Hub as well as exporting vanilla
transformers models to ONNX using <code>optimum.exporters.onnx</code> toolchain.`,Ha,Js,ji="Class attributes:",Ea,js,ki=`<li>model_type (<code>str</code>, <em>optional</em>, defaults to <code>&quot;onnx_model&quot;</code>) — The name of the model type to use when
registering the ORTModel classes.</li> <li>auto_model_class (<code>Type</code>, <em>optional</em>, defaults to <code>AutoModel</code>) — The “AutoModel” class to represented by the
current ORTModel class.</li>`,La,ot,ln,Qa,ks,xi="Returns whether this model can generate sequences with <code>.generate()</code>.",Oa,F,rn,Ya,xs,Ui="Instantiate a pretrained model from a pre-trained model configuration.",Aa,Us,Ri=`provider (<code>str</code>, defaults to <code>&quot;CPUExecutionProvider&quot;</code>):
ONNX Runtime provider to use for loading the model.
See <a href="https://onnxruntime.ai/docs/execution-providers/" rel="nofollow">https://onnxruntime.ai/docs/execution-providers/</a> for possible providers.
providers (<code>Optional[Sequence[str]]</code>, defaults to <code>None</code>):
List of execution providers to use for loading the model.
This argument takes precedence over the <code>provider</code> argument.
provider_options (<code>Optional[Dict[str, Any]]</code>, defaults to <code>None</code>):
Provider option dictionaries corresponding to the provider used. See available options
for each provider: <a href="https://onnxruntime.ai/docs/api/c/group___global.html" rel="nofollow">https://onnxruntime.ai/docs/api/c/group___global.html</a> .
session_options (<code>Optional[onnxruntime.SessionOptions]</code>, defaults to <code>None</code>),:
ONNX Runtime session options to use for loading the model.
use_io_binding (<code>Optional[bool]</code>, defaults to <code>None</code>):
Whether to use IOBinding during inference to avoid memory copy between the host and device, or between numpy/torch tensors and ONNX Runtime ORTValue. Defaults to
<code>True</code> if the execution provider is CUDAExecutionProvider. For [~onnxruntime.ORTModelForCausalLM], defaults to <code>True</code> on CPUExecutionProvider,
in all other cases defaults to <code>False</code>.
kwargs (<code>Dict[str, Any]</code>):
Will be passed to the underlying model loading methods.`,Pa,Rs,Zi="<p>Parameters for decoder models (ORTModelForCausalLM, ORTModelForSeq2SeqLM, ORTModelForSeq2SeqLM, ORTModelForSpeechSeq2Seq, ORTModelForVision2Seq)</p>",Da,Zs,Wi=`use_cache (<code>Optional[bool]</code>, defaults to <code>True</code>):
Whether or not past key/values cache should be used. Defaults to <code>True</code>.`,Ka,Ws,Ni=`use_merged (<code>Optional[bool]</code>, defaults to <code>None</code>):
whether or not to use a single ONNX that handles both the decoding without and with past key values reuse. This option defaults
to <code>True</code> if loading from a local repository and a merged decoder is found. When exporting with <code>export=True</code>,
defaults to <code>False</code>. This option should be set to <code>True</code> to minimize memory usage.`,Lo,mn,Qo,cn,Fi="The following ORT classes are available for the following natural language processing tasks.",Oo,pn,Yo,ee,dn,el,Ns,Ci=`ONNX model with a causal language modeling head for ONNX Runtime inference. This class officially supports bloom, codegen, falcon, gpt2, gpt-bigcode, gpt_neo, gpt_neox, gptj, llama.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,tl,Fs,Gi='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',nl,S,un,sl,Cs,Ii="The <code>ORTModelForCausalLM</code> forward method, overrides the <code>__call__</code> special method.",ol,at,al,lt,ll,rt,Ao,gn,Po,te,hn,rl,Gs,Vi=`ONNX Model with a MaskedLMOutput for masked language modeling tasks. This class officially supports albert, bert, camembert, convbert, data2vec-text, deberta, deberta_v2, distilbert, electra, flaubert, ibert, mobilebert, roberta, roformer, squeezebert, xlm, xlm_roberta.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,il,Is,Xi='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',ml,q,fn,cl,Vs,zi="The <code>ORTModelForMaskedLM</code> forward method, overrides the <code>__call__</code> special method.",pl,it,dl,mt,ul,ct,Do,Mn,Ko,ne,bn,gl,Xs,Bi=`Sequence-to-sequence model with a language modeling head for ONNX Runtime inference. This class officially supports bart, blenderbot, blenderbot-small, longt5, m2m_100, marian, mbart, mt5, pegasus, t5.
This model inherits from <code>~onnxruntime.modeling.ORTModelForConditionalGeneration</code>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,hl,zs,Si="This class should be initialized using the <code>onnxruntime.modeling.ORTModelForConditionalGeneration.from_pretrained</code> method.",fl,H,Tn,Ml,Bs,qi="The <code>ORTModelForSeq2SeqLM</code> forward method, overrides the <code>__call__</code> special method.",bl,pt,Tl,dt,yl,ut,ea,yn,ta,G,_n,_l,Ss,Hi=`ONNX Model with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks. This class officially supports albert, bart, bert, camembert, convbert, data2vec-text, deberta, deberta_v2, distilbert, electra, flaubert, ibert, mbart, mobilebert, nystromformer, roberta, roformer, squeezebert, xlm, xlm_roberta.`,wl,qs,Ei=`This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,$l,Hs,Li='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',vl,C,wn,Jl,Es,Qi="The <code>ORTModelForSequenceClassification</code> forward method, overrides the <code>__call__</code> special method.",jl,gt,kl,ht,xl,ft,Ul,Mt,na,$n,sa,I,vn,Rl,Ls,Oi=`ONNX Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks. This class officially supports albert, bert, bloom, camembert, convbert, data2vec-text, deberta, deberta_v2, distilbert, electra, flaubert, gpt2, ibert, mobilebert, roberta, roformer, squeezebert, xlm, xlm_roberta.`,Zl,Qs,Yi=`This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Wl,Os,Ai='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',Nl,E,Jn,Fl,Ys,Pi="The <code>ORTModelForTokenClassification</code> forward method, overrides the <code>__call__</code> special method.",Cl,bt,Gl,Tt,Il,yt,oa,jn,aa,V,kn,Vl,As,Di=`ONNX Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks. This class officially supports albert, bert, camembert, convbert, data2vec-text, deberta_v2, distilbert, electra, flaubert, ibert, mobilebert, nystromformer, roberta, roformer, squeezebert, xlm, xlm_roberta.`,Xl,Ps,Ki=`This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,zl,Ds,em='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',Bl,Ze,xn,Sl,Ks,tm="The <code>ORTModelForMultipleChoice</code> forward method, overrides the <code>__call__</code> special method.",ql,_t,Hl,wt,la,Un,ra,se,Rn,El,eo,nm=`ONNX Model with a QuestionAnsweringModelOutput for extractive question-answering tasks like SQuAD. This class officially supports albert, bart, bert, camembert, convbert, data2vec-text, deberta, deberta_v2, distilbert, electra, flaubert, gptj, ibert, mbart, mobilebert, nystromformer, roberta, roformer, squeezebert, xlm, xlm_roberta.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Ll,to,sm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',Ql,L,Zn,Ol,no,om="The <code>ORTModelForQuestionAnswering</code> forward method, overrides the <code>__call__</code> special method.",Yl,$t,Al,vt,Pl,Jt,ia,Wn,ma,Nn,am="The following ORT classes are available for the following computer vision tasks.",ca,Fn,pa,oe,Cn,Dl,so,lm=`ONNX Model for image-classification tasks. This class officially supports beit, convnext, convnextv2, data2vec-vision, deit, dinov2, levit, mobilenet_v1, mobilenet_v2, mobilevit, poolformer, resnet, segformer, swin, swinv2, vit.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Kl,oo,rm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',er,Q,Gn,tr,ao,im="The <code>ORTModelForImageClassification</code> forward method, overrides the <code>__call__</code> special method.",nr,jt,sr,kt,or,xt,da,In,ua,ae,Vn,ar,lo,mm=`ONNX Model for zero-shot-image-classification tasks. This class officially supports clip, metaclip-2.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,lr,ro,cm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',rr,nt,Xn,ir,io,pm="The <code>ORTModelForZeroShotImageClassification</code> forward method, overrides the <code>__call__</code> special method.",mr,Ut,ga,zn,ha,le,Bn,cr,mo,dm=`ONNX Model for semantic-segmentation, with an all-MLP decode head on top e.g. for ADE20k, CityScapes. This class officially supports maskformer, segformer.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,pr,co,um='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',dr,O,Sn,ur,po,gm="The <code>ORTModelForSemanticSegmentation</code> forward method, overrides the <code>__call__</code> special method.",gr,Rt,hr,Zt,fr,Wt,fa,qn,Ma,Hn,hm="The following ORT classes are available for the following audio tasks.",ba,En,Ta,X,Ln,Mr,uo,fm=`ONNX Model for audio-classification, with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting. This class officially supports audio_spectrogram_transformer, data2vec-audio, hubert, sew, sew-d, unispeech, unispeech_sat, wavlm, wav2vec2, wav2vec2-conformer.`,br,go,Mm=`This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Tr,ho,bm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',yr,Y,Qn,_r,fo,Tm="The <code>ORTModelForAudioClassification</code> forward method, overrides the <code>__call__</code> special method.",wr,Nt,$r,Ft,vr,Ct,ya,On,_a,re,Yn,Jr,Mo,ym=`ONNX Model with a frame classification head on top for tasks like Speaker Diarization. This class officially supports data2vec-audio, unispeech_sat, wavlm, wav2vec2, wav2vec2-conformer.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,jr,bo,_m='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',kr,We,An,xr,To,wm="The <code>ORTModelForAudioFrameClassification</code> forward method, overrides the <code>__call__</code> special method.",Ur,Gt,Rr,It,wa,Pn,$a,ie,Dn,Zr,yo,$m=`ONNX Model with a language modeling head on top for Connectionist Temporal Classification (CTC). This class officially supports data2vec-audio, hubert, sew, sew-d, unispeech, unispeech_sat, wavlm, wav2vec2, wav2vec2-conformer.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Wr,_o,vm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',Nr,Ne,Kn,Fr,wo,Jm="The <code>ORTModelForCTC</code> forward method, overrides the <code>__call__</code> special method.",Cr,Vt,Gr,Xt,va,es,Ja,me,ts,Ir,$o,jm=`Speech sequence-to-sequence model with a language modeling head for ONNX Runtime inference. This class officially supports whisper, speech_to_text.
This model inherits from <code>~onnxruntime.modeling.ORTModelForConditionalGeneration</code>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Vr,vo,km="This class should be initialized using the <code>onnxruntime.modeling.ORTModelForConditionalGeneration.from_pretrained</code> method.",Xr,A,ns,zr,Jo,xm="The <code>ORTModelForSpeechSeq2Seq</code> forward method, overrides the <code>__call__</code> special method.",Br,zt,Sr,Bt,qr,St,ja,ss,ka,ce,os,Hr,jo,Um=`ONNX Model with an XVector feature extraction head on top for tasks like Speaker Verification. This class officially supports data2vec-audio, unispeech_sat, wavlm, wav2vec2, wav2vec2-conformer.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Er,ko,Rm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',Lr,Fe,as,Qr,xo,Zm="The <code>ORTModelForAudioXVector</code> forward method, overrides the <code>__call__</code> special method.",Or,qt,Yr,Ht,xa,ls,Ua,rs,Wm="The following ORT classes are available for the following multimodal tasks.",Ra,is,Za,pe,ms,Ar,Uo,Nm=`Vision sequence-to-sequence model with a language modeling head for ONNX Runtime inference. This class officially supports vision encoder-decoder and pix2struct.
This model inherits from <code>~onnxruntime.modeling.ORTModelForConditionalGeneration</code>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Pr,Ro,Fm="This class should be initialized using the <code>onnxruntime.modeling.ORTModelForConditionalGeneration.from_pretrained</code> method.",Dr,P,cs,Kr,Zo,Cm="The <code>ORTModelForVision2Seq</code> forward method, overrides the <code>__call__</code> special method.",ei,Et,ti,Lt,ni,Qt,Wa,ps,Na,de,ds,si,Wo,Gm=`Pix2Struct model with a language modeling head for ONNX Runtime inference. This class officially supports pix2struct.
This model inherits from <code>~onnxruntime.modeling.ORTModelForConditionalGeneration</code>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,oi,No,Im="This class should be initialized using the <code>onnxruntime.modeling.ORTModelForConditionalGeneration.from_pretrained</code> method.",ai,Ce,us,li,Fo,Vm="The <code>ORTModelForPix2Struct</code> forward method, overrides the <code>__call__</code> special method.",ri,Ot,ii,Yt,Fa,gs,Ca,hs,Xm="The following ORT classes are available for the following custom tasks.",Ga,fs,Ia,ue,Ms,mi,Co,zm=`ONNX Model for any custom tasks. It can be used to leverage the inference acceleration for any single-file ONNX model, that may use custom inputs and outputs.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,ci,Go,Bm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',pi,D,bs,di,Io,Sm="The <code>ORTModelForCustomTasks</code> forward method, overrides the <code>__call__</code> special method.",ui,At,gi,Pt,hi,Dt,Va,Ts,Xa,ge,ys,fi,Vo,qm=`ONNX Model for feature-extraction task.
This model inherits from <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel">ORTModel</a>, check its documentation for the generic methods the
library implements for all its model (such as downloading or saving).`,Mi,Xo,Hm='This class should be initialized using the <a href="/docs/optimum/pr_86/en/onnxruntime/package_reference/modeling#optimum.onnxruntime.ORTModel.from_pretrained">onnxruntime.modeling_ort.ORTModel.from_pretrained()</a> method.',bi,K,_s,Ti,zo,Em="The <code>ORTModelForFeatureExtraction</code> forward method, overrides the <code>__call__</code> special method.",yi,Kt,_i,en,wi,tn,za,Bo,Ba;return d=new rc({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),_=new R({props:{title:"ONNX Runtime Models",local:"onnx-runtime-models",headingTag:"h1"}}),nn=new R({props:{title:"Generic model classes",local:"generic-model-classes",headingTag:"h2"}}),on=new R({props:{title:"ORTModel",local:"optimum.onnxruntime.ORTModel",headingTag:"h3"}}),an=new k({props:{name:"class optimum.onnxruntime.ORTModel",anchor:"optimum.onnxruntime.ORTModel",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModel.-",description:"<strong>-</strong> config (<code>PretrainedConfig</code> &#x2014; The configuration of the model. &#x2014;",name:"-"},{anchor:"optimum.onnxruntime.ORTModel.-",description:"<strong>-</strong> session (<code>~onnxruntime.InferenceSession</code>) &#x2014; The ONNX Runtime InferenceSession that is running the model. &#x2014;",name:"-"},{anchor:"optimum.onnxruntime.ORTModel.-",description:"<strong>-</strong> use_io_binding (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014; Whether to use I/O bindings with **ONNX Runtime &#x2014;",name:"-"},{anchor:"optimum.onnxruntime.ORTModel.with",description:"<strong>with</strong> the CUDAExecutionProvider**, this can significantly speedup inference depending on the task. &#x2014;",name:"with"},{anchor:"optimum.onnxruntime.ORTModel.-",description:"<strong>-</strong> model_save_dir (<code>Path</code>) &#x2014; The directory where the model exported to ONNX is saved. &#x2014;",name:"-"},{anchor:"optimum.onnxruntime.ORTModel.By",description:"<strong>By</strong> defaults, if the loaded model is local, the directory where the original model will be used. Otherwise, the &#x2014;",name:"By"},{anchor:"optimum.onnxruntime.ORTModel.cache",description:"<strong>cache</strong> directory is used. &#x2014;",name:"cache"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L156"}}),ln=new k({props:{name:"can_generate",anchor:"optimum.onnxruntime.ORTModel.can_generate",parameters:[],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L594"}}),rn=new k({props:{name:"from_pretrained",anchor:"optimum.onnxruntime.ORTModel.from_pretrained",parameters:[{name:"model_id",val:": str | Path"},{name:"config",val:": PretrainedConfig | None = None"},{name:"export",val:": bool = False"},{name:"subfolder",val:": str = ''"},{name:"revision",val:": str = 'main'"},{name:"force_download",val:": bool = False"},{name:"local_files_only",val:": bool = False"},{name:"trust_remote_code",val:": bool = False"},{name:"cache_dir",val:": str = '/home/runner/.cache/huggingface/hub'"},{name:"token",val:": bool | str | None = None"},{name:"provider",val:": str = 'CPUExecutionProvider'"},{name:"providers",val:": Sequence[str] | None = None"},{name:"provider_options",val:": Sequence[dict[str, Any]] | dict[str, Any] | None = None"},{name:"session_options",val:": SessionOptions | None = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.model_id",description:`<strong>model_id</strong> (<code>Union[str, Path]</code>) &#x2014;
Can be either:
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced 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 model saved using <code>~OptimizedModel.save_pretrained</code>,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"model_id"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.export",description:`<strong>export</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Defines whether the provided <code>model_id</code> needs to be exported to the targeted format.`,name:"export"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, defaults to <code>True</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:"optimum.onnxruntime.ORTModel.from_pretrained.token",description:`<strong>token</strong> (<code>Optional[Union[bool,str]]</code>, defaults to <code>None</code>) &#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_hub.constants.HF_TOKEN_PATH</code>).`,name:"token"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>Optional[str]</code>, defaults to <code>None</code>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, defaults to <code>&quot;&quot;</code>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo either locally or on huggingface.co, you can
specify the folder name here.`,name:"subfolder"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.config",description:`<strong>config</strong> (<code>Optional[transformers.PretrainedConfig]</code>, defaults to <code>None</code>) &#x2014;
The model configuration.`,name:"config"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>Optional[bool]</code>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (i.e., do not try to download the model).`,name:"local_files_only"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom code defined on the Hub in their own modeling. This option should only be set
to <code>True</code> for repositories you trust and in which you have read the code, as it will execute code present on
the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"optimum.onnxruntime.ORTModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>Optional[str]</code>, defaults to <code>None</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L468",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The loaded ORTModel model.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ORTModel</code></p>
`}}),mn=new R({props:{title:"Natural Language Processing",local:"natural-language-processing",headingTag:"h2"}}),pn=new R({props:{title:"ORTModelForCausalLM",local:"optimum.onnxruntime.ORTModelForCausalLM",headingTag:"h3"}}),dn=new k({props:{name:"class optimum.onnxruntime.ORTModelForCausalLM",anchor:"optimum.onnxruntime.ORTModelForCausalLM",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"generation_config",val:": GenerationConfig | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_decoder.py#L123"}}),un=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForCausalLM.forward",parameters:[{name:"input_ids",val:": torch.LongTensor"},{name:"attention_mask",val:": torch.LongTensor | None = None"},{name:"past_key_values",val:": tuple[tuple[torch.Tensor]] | None = None"},{name:"position_ids",val:": torch.LongTensor | None = None"},{name:"use_cache",val:": bool | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of shape <code>(batch_size, sequence_length)</code>.`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.LongTensor</code>) &#x2014;
Mask to avoid performing attention on padding token indices, of shape
<code>(batch_size, sequence_length)</code>. Mask values selected in <code>[0, 1]</code>.`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>.`,name:"past_key_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_decoder.py#L249"}}),at=new W({props:{$$slots:{default:[ic]},$$scope:{ctx:$}}}),lt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForCausalLM.forward.example",$$slots:{default:[mc]},$$scope:{ctx:$}}}),rt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForCausalLM.forward.example-2",$$slots:{default:[cc]},$$scope:{ctx:$}}}),gn=new R({props:{title:"ORTModelForMaskedLM",local:"optimum.onnxruntime.ORTModelForMaskedLM",headingTag:"h3"}}),hn=new k({props:{name:"class optimum.onnxruntime.ORTModelForMaskedLM",anchor:"optimum.onnxruntime.ORTModelForMaskedLM",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L774"}}),fn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForMaskedLM.forward",parameters:[{name:"input_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"attention_mask",val:": torch.Tensor | np.ndarray | None = None"},{name:"token_type_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForMaskedLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
See <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.encode" rel="nofollow"><code>PreTrainedTokenizer.encode</code></a> and
<a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.__call__" rel="nofollow"><code>PreTrainedTokenizer.__call__</code></a> for details.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForMaskedLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForMaskedLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L780"}}),it=new W({props:{$$slots:{default:[pc]},$$scope:{ctx:$}}}),mt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForMaskedLM.forward.example",$$slots:{default:[dc]},$$scope:{ctx:$}}}),ct=new x({props:{anchor:"optimum.onnxruntime.ORTModelForMaskedLM.forward.example-2",$$slots:{default:[uc]},$$scope:{ctx:$}}}),Mn=new R({props:{title:"ORTModelForSeq2SeqLM",local:"optimum.onnxruntime.ORTModelForSeq2SeqLM",headingTag:"h3"}}),bn=new k({props:{name:"class optimum.onnxruntime.ORTModelForSeq2SeqLM",anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"encoder_session",val:": InferenceSession = None"},{name:"decoder_session",val:": InferenceSession = None"},{name:"decoder_with_past_session",val:": InferenceSession | None = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"generation_config",val:": GenerationConfig | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_seq2seq.py#L1218"}}),Tn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward",parameters:[{name:"input_ids",val:": torch.LongTensor = None"},{name:"attention_mask",val:": torch.FloatTensor | None = None"},{name:"decoder_input_ids",val:": torch.LongTensor | None = None"},{name:"decoder_attention_mask",val:": torch.LongTensor | None = None"},{name:"encoder_outputs",val:": BaseModelOutput | list[torch.FloatTensor] | None = None"},{name:"past_key_values",val:": tuple[tuple[torch.Tensor]] | None = None"},{name:"token_type_ids",val:": torch.LongTensor | None = None"},{name:"cache_position",val:": torch.Tensor | None = None"},{name:"use_cache",val:": bool | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code>) &#x2014;
Indices of input sequence tokens in the vocabulary of shape <code>(batch_size, encoder_sequence_length)</code>.`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.LongTensor</code>) &#x2014;
Mask to avoid performing attention on padding token indices, of shape
<code>(batch_size, encoder_sequence_length)</code>. Mask values selected in <code>[0, 1]</code>.`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of shape <code>(batch_size, decoder_sequence_length)</code>.`,name:"decoder_input_ids"},{anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>torch.FloatTensor</code>) &#x2014;
The encoder <code>last_hidden_state</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.`,name:"encoder_outputs"},{anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)</code> and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.`,name:"past_key_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_seq2seq.py#L1226"}}),pt=new W({props:{$$slots:{default:[gc]},$$scope:{ctx:$}}}),dt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward.example",$$slots:{default:[hc]},$$scope:{ctx:$}}}),ut=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSeq2SeqLM.forward.example-2",$$slots:{default:[fc]},$$scope:{ctx:$}}}),yn=new R({props:{title:"ORTModelForSequenceClassification",local:"optimum.onnxruntime.ORTModelForSequenceClassification",headingTag:"h3"}}),_n=new k({props:{name:"class optimum.onnxruntime.ORTModelForSequenceClassification",anchor:"optimum.onnxruntime.ORTModelForSequenceClassification",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L988"}}),wn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForSequenceClassification.forward",parameters:[{name:"input_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"attention_mask",val:": torch.Tensor | np.ndarray | None = None"},{name:"token_type_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForSequenceClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
See <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.encode" rel="nofollow"><code>PreTrainedTokenizer.encode</code></a> and
<a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.__call__" rel="nofollow"><code>PreTrainedTokenizer.__call__</code></a> for details.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForSequenceClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L996"}}),gt=new W({props:{$$slots:{default:[Mc]},$$scope:{ctx:$}}}),ht=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSequenceClassification.forward.example",$$slots:{default:[bc]},$$scope:{ctx:$}}}),ft=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSequenceClassification.forward.example-2",$$slots:{default:[Tc]},$$scope:{ctx:$}}}),Mt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSequenceClassification.forward.example-3",$$slots:{default:[yc]},$$scope:{ctx:$}}}),$n=new R({props:{title:"ORTModelForTokenClassification",local:"optimum.onnxruntime.ORTModelForTokenClassification",headingTag:"h3"}}),vn=new k({props:{name:"class optimum.onnxruntime.ORTModelForTokenClassification",anchor:"optimum.onnxruntime.ORTModelForTokenClassification",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1089"}}),Jn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForTokenClassification.forward",parameters:[{name:"input_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"attention_mask",val:": torch.Tensor | np.ndarray | None = None"},{name:"token_type_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForTokenClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
See <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.encode" rel="nofollow"><code>PreTrainedTokenizer.encode</code></a> and
<a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.__call__" rel="nofollow"><code>PreTrainedTokenizer.__call__</code></a> for details.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForTokenClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForTokenClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1098"}}),bt=new W({props:{$$slots:{default:[_c]},$$scope:{ctx:$}}}),Tt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForTokenClassification.forward.example",$$slots:{default:[wc]},$$scope:{ctx:$}}}),yt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForTokenClassification.forward.example-2",$$slots:{default:[$c]},$$scope:{ctx:$}}}),jn=new R({props:{title:"ORTModelForMultipleChoice",local:"optimum.onnxruntime.ORTModelForMultipleChoice",headingTag:"h3"}}),kn=new k({props:{name:"class optimum.onnxruntime.ORTModelForMultipleChoice",anchor:"optimum.onnxruntime.ORTModelForMultipleChoice",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1185"}}),xn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForMultipleChoice.forward",parameters:[{name:"input_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"attention_mask",val:": torch.Tensor | np.ndarray | None = None"},{name:"token_type_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForMultipleChoice.forward.input_ids",description:`<strong>input_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
See <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.encode" rel="nofollow"><code>PreTrainedTokenizer.encode</code></a> and
<a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.__call__" rel="nofollow"><code>PreTrainedTokenizer.__call__</code></a> for details.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForMultipleChoice.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForMultipleChoice.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1193"}}),_t=new W({props:{$$slots:{default:[vc]},$$scope:{ctx:$}}}),wt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForMultipleChoice.forward.example",$$slots:{default:[Jc]},$$scope:{ctx:$}}}),Un=new R({props:{title:"ORTModelForQuestionAnswering",local:"optimum.onnxruntime.ORTModelForQuestionAnswering",headingTag:"h3"}}),Rn=new k({props:{name:"class optimum.onnxruntime.ORTModelForQuestionAnswering",anchor:"optimum.onnxruntime.ORTModelForQuestionAnswering",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L873"}}),Zn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"attention_mask",val:": torch.Tensor | np.ndarray | None = None"},{name:"token_type_ids",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForQuestionAnswering.forward.input_ids",description:`<strong>input_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
See <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.encode" rel="nofollow"><code>PreTrainedTokenizer.encode</code></a> and
<a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.__call__" rel="nofollow"><code>PreTrainedTokenizer.__call__</code></a> for details.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L879"}}),$t=new W({props:{$$slots:{default:[jc]},$$scope:{ctx:$}}}),vt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForQuestionAnswering.forward.example",$$slots:{default:[kc]},$$scope:{ctx:$}}}),Jt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForQuestionAnswering.forward.example-2",$$slots:{default:[xc]},$$scope:{ctx:$}}}),Wn=new R({props:{title:"Computer vision",local:"computer-vision",headingTag:"h2"}}),Fn=new R({props:{title:"ORTModelForImageClassification",local:"optimum.onnxruntime.ORTModelForImageClassification",headingTag:"h3"}}),Cn=new k({props:{name:"class optimum.onnxruntime.ORTModelForImageClassification",anchor:"optimum.onnxruntime.ORTModelForImageClassification",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1290"}}),Gn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForImageClassification.forward",parameters:[{name:"pixel_values",val:": torch.Tensor | np.ndarray"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, num_channels, height, width)</code>, defaults to <code>None</code>) &#x2014;
Pixel values corresponding to the images in the current batch.
Pixel values can be obtained from encoded images using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"pixel_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1295"}}),jt=new W({props:{$$slots:{default:[Uc]},$$scope:{ctx:$}}}),kt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForImageClassification.forward.example",$$slots:{default:[Rc]},$$scope:{ctx:$}}}),xt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForImageClassification.forward.example-2",$$slots:{default:[Zc]},$$scope:{ctx:$}}}),In=new R({props:{title:"ORTModelForZeroShotImageClassification",local:"optimum.onnxruntime.ORTModelForZeroShotImageClassification",headingTag:"h3"}}),Vn=new k({props:{name:"class optimum.onnxruntime.ORTModelForZeroShotImageClassification",anchor:"optimum.onnxruntime.ORTModelForZeroShotImageClassification",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1346"}}),Xn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForZeroShotImageClassification.forward",parameters:[{name:"input_ids",val:": torch.Tensor | np.ndarray"},{name:"pixel_values",val:": torch.Tensor | np.ndarray"},{name:"attention_mask",val:": torch.Tensor | np.ndarray | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForZeroShotImageClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
See <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.encode" rel="nofollow"><code>PreTrainedTokenizer.encode</code></a> and
<a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.__call__" rel="nofollow"><code>PreTrainedTokenizer.__call__</code></a> for details.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForZeroShotImageClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForZeroShotImageClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"optimum.onnxruntime.ORTModelForZeroShotImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, num_channels, height, width)</code>, defaults to <code>None</code>) &#x2014;
Pixel values corresponding to the images in the current batch.
Pixel values can be obtained from encoded images using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"pixel_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1351"}}),Ut=new W({props:{$$slots:{default:[Wc]},$$scope:{ctx:$}}}),zn=new R({props:{title:"ORTModelForSemanticSegmentation",local:"optimum.onnxruntime.ORTModelForSemanticSegmentation",headingTag:"h3"}}),Bn=new k({props:{name:"class optimum.onnxruntime.ORTModelForSemanticSegmentation",anchor:"optimum.onnxruntime.ORTModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1446"}}),Sn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForSemanticSegmentation.forward",parameters:[{name:"pixel_values",val:": torch.Tensor | np.ndarray"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForSemanticSegmentation.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, num_channels, height, width)</code>, defaults to <code>None</code>) &#x2014;
Pixel values corresponding to the images in the current batch.
Pixel values can be obtained from encoded images using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"pixel_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1460"}}),Rt=new W({props:{$$slots:{default:[Nc]},$$scope:{ctx:$}}}),Zt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSemanticSegmentation.forward.example",$$slots:{default:[Fc]},$$scope:{ctx:$}}}),Wt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSemanticSegmentation.forward.example-2",$$slots:{default:[Cc]},$$scope:{ctx:$}}}),qn=new R({props:{title:"Audio",local:"audio",headingTag:"h2"}}),En=new R({props:{title:"ORTModelForAudioClassification",local:"optimum.onnxruntime.ORTModelForAudioClassification",headingTag:"h3"}}),Ln=new k({props:{name:"class optimum.onnxruntime.ORTModelForAudioClassification",anchor:"optimum.onnxruntime.ORTModelForAudioClassification",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1565"}}),Qn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForAudioClassification.forward",parameters:[{name:"input_values",val:": torch.Tensor | np.ndarray | None = None"},{name:"attention_mask",val:": torch.Tensor | np.ndarray | None = None"},{name:"input_features",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForAudioClassification.forward.input_values",description:`<strong>input_values</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform..
Input values can be obtained from audio file loaded into an array using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"input_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1573"}}),Nt=new W({props:{$$slots:{default:[Gc]},$$scope:{ctx:$}}}),Ft=new x({props:{anchor:"optimum.onnxruntime.ORTModelForAudioClassification.forward.example",$$slots:{default:[Ic]},$$scope:{ctx:$}}}),Ct=new x({props:{anchor:"optimum.onnxruntime.ORTModelForAudioClassification.forward.example-2",$$slots:{default:[Vc]},$$scope:{ctx:$}}}),On=new R({props:{title:"ORTModelForAudioFrameClassification",local:"optimum.onnxruntime.ORTModelForAudioFrameClassification",headingTag:"h3"}}),Yn=new k({props:{name:"class optimum.onnxruntime.ORTModelForAudioFrameClassification",anchor:"optimum.onnxruntime.ORTModelForAudioFrameClassification",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1854"}}),An=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForAudioFrameClassification.forward",parameters:[{name:"input_values",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForAudioFrameClassification.forward.input_values",description:`<strong>input_values</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform..
Input values can be obtained from audio file loaded into an array using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"input_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1860"}}),Gt=new W({props:{$$slots:{default:[Xc]},$$scope:{ctx:$}}}),It=new x({props:{anchor:"optimum.onnxruntime.ORTModelForAudioFrameClassification.forward.example",$$slots:{default:[zc]},$$scope:{ctx:$}}}),Pn=new R({props:{title:"ORTModelForCTC",local:"optimum.onnxruntime.ORTModelForCTC",headingTag:"h3"}}),Dn=new k({props:{name:"class optimum.onnxruntime.ORTModelForCTC",anchor:"optimum.onnxruntime.ORTModelForCTC",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1667"}}),Kn=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForCTC.forward",parameters:[{name:"input_values",val:": torch.Tensor | np.ndarray | None = None"},{name:"input_features",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForCTC.forward.input_values",description:`<strong>input_values</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform..
Input values can be obtained from audio file loaded into an array using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"input_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1673"}}),Vt=new W({props:{$$slots:{default:[Bc]},$$scope:{ctx:$}}}),Xt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForCTC.forward.example",$$slots:{default:[Sc]},$$scope:{ctx:$}}}),es=new R({props:{title:"ORTModelForSpeechSeq2Seq",local:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq",headingTag:"h3"}}),ts=new k({props:{name:"class optimum.onnxruntime.ORTModelForSpeechSeq2Seq",anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_seq2seq.py#L1283"}}),ns=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq.forward",parameters:[{name:"input_features",val:": torch.FloatTensor | None = None"},{name:"attention_mask",val:": torch.LongTensor | None = None"},{name:"decoder_input_ids",val:": torch.LongTensor | None = None"},{name:"decoder_attention_mask",val:": torch.LongTensor | None = None"},{name:"encoder_outputs",val:": tuple[tuple[torch.Tensor]] | None = None"},{name:"past_key_values",val:": tuple[tuple[torch.Tensor]] | None = None"},{name:"cache_position",val:": torch.Tensor | None = None"},{name:"use_cache",val:": bool | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq.forward.input_features",description:`<strong>input_features</strong> (<code>torch.FloatTensor</code>) &#x2014;
Mel features extracted from the raw speech waveform.
<code>(batch_size, feature_size, encoder_sequence_length)</code>.`,name:"input_features"},{anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of shape <code>(batch_size, decoder_sequence_length)</code>.`,name:"decoder_input_ids"},{anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>torch.FloatTensor</code>) &#x2014;
The encoder <code>last_hidden_state</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.`,name:"encoder_outputs"},{anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)</code> and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.`,name:"past_key_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_seq2seq.py#L1308"}}),zt=new W({props:{$$slots:{default:[qc]},$$scope:{ctx:$}}}),Bt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq.forward.example",$$slots:{default:[Hc]},$$scope:{ctx:$}}}),St=new x({props:{anchor:"optimum.onnxruntime.ORTModelForSpeechSeq2Seq.forward.example-2",$$slots:{default:[Ec]},$$scope:{ctx:$}}}),ss=new R({props:{title:"ORTModelForAudioXVector",local:"optimum.onnxruntime.ORTModelForAudioXVector",headingTag:"h3"}}),os=new k({props:{name:"class optimum.onnxruntime.ORTModelForAudioXVector",anchor:"optimum.onnxruntime.ORTModelForAudioXVector",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"session",val:": InferenceSession = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1768"}}),as=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForAudioXVector.forward",parameters:[{name:"input_values",val:": torch.Tensor | np.ndarray | None = None"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForAudioXVector.forward.input_values",description:`<strong>input_values</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform..
Input values can be obtained from audio file loaded into an array using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"input_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling.py#L1774"}}),qt=new W({props:{$$slots:{default:[Lc]},$$scope:{ctx:$}}}),Ht=new x({props:{anchor:"optimum.onnxruntime.ORTModelForAudioXVector.forward.example",$$slots:{default:[Qc]},$$scope:{ctx:$}}}),ls=new R({props:{title:"Multimodal",local:"multimodal",headingTag:"h2"}}),is=new R({props:{title:"ORTModelForVision2Seq",local:"optimum.onnxruntime.ORTModelForVision2Seq",headingTag:"h3"}}),ms=new k({props:{name:"class optimum.onnxruntime.ORTModelForVision2Seq",anchor:"optimum.onnxruntime.ORTModelForVision2Seq",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"encoder_session",val:": InferenceSession = None"},{name:"decoder_session",val:": InferenceSession = None"},{name:"decoder_with_past_session",val:": InferenceSession | None = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"generation_config",val:": GenerationConfig | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_seq2seq.py#L1455"}}),cs=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForVision2Seq.forward",parameters:[{name:"pixel_values",val:": torch.FloatTensor | None = None"},{name:"attention_mask",val:": torch.LongTensor | None = None"},{name:"decoder_input_ids",val:": torch.LongTensor | None = None"},{name:"decoder_attention_mask",val:": torch.BoolTensor | None = None"},{name:"encoder_outputs",val:": BaseModelOutput | list[torch.FloatTensor] | None = None"},{name:"past_key_values",val:": tuple[tuple[torch.Tensor]] | None = None"},{name:"cache_position",val:": torch.Tensor | None = None"},{name:"use_cache",val:": bool | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForVision2Seq.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code>) &#x2014;
Features extracted from an Image. This tensor should be of shape
<code>(batch_size, num_channels, height, width)</code>.`,name:"pixel_values"},{anchor:"optimum.onnxruntime.ORTModelForVision2Seq.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of shape <code>(batch_size, decoder_sequence_length)</code>.`,name:"decoder_input_ids"},{anchor:"optimum.onnxruntime.ORTModelForVision2Seq.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>torch.FloatTensor</code>) &#x2014;
The encoder <code>last_hidden_state</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.`,name:"encoder_outputs"},{anchor:"optimum.onnxruntime.ORTModelForVision2Seq.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)</code> and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.`,name:"past_key_values"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_seq2seq.py#L1470"}}),Et=new W({props:{$$slots:{default:[Oc]},$$scope:{ctx:$}}}),Lt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForVision2Seq.forward.example",$$slots:{default:[Yc]},$$scope:{ctx:$}}}),Qt=new x({props:{anchor:"optimum.onnxruntime.ORTModelForVision2Seq.forward.example-2",$$slots:{default:[Ac]},$$scope:{ctx:$}}}),ps=new R({props:{title:"ORTModelForPix2Struct",local:"optimum.onnxruntime.ORTModelForPix2Struct",headingTag:"h3"}}),ds=new k({props:{name:"class optimum.onnxruntime.ORTModelForPix2Struct",anchor:"optimum.onnxruntime.ORTModelForPix2Struct",parameters:[{name:"config",val:": PretrainedConfig = None"},{name:"encoder_session",val:": InferenceSession = None"},{name:"decoder_session",val:": InferenceSession = None"},{name:"decoder_with_past_session",val:": InferenceSession | None = None"},{name:"use_io_binding",val:": bool | None = None"},{name:"generation_config",val:": GenerationConfig | None = None"},{name:"model_save_dir",val:": str | Path | TemporaryDirectory | None = None"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_86/optimum/onnxruntime/modeling_seq2seq.py#L1527"}}),us=new k({props:{name:"forward",anchor:"optimum.onnxruntime.ORTModelForPix2Struct.forward",parameters:[{name:"flattened_patches",val:": torch.FloatTensor | None = None"},{name:"attention_mask",val:": torch.LongTensor | None = None"},{name:"decoder_input_ids",val:": torch.LongTensor | None = None"},{name:"decoder_attention_mask",val:": torch.BoolTensor | None = None"},{name:"encoder_outputs",val:": BaseModelOutput | list[torch.FloatTensor] | None = None"},{name:"past_key_values",val:": tuple[tuple[torch.Tensor]] | None = None"},{name:"use_cache",val:": bool | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.onnxruntime.ORTModelForPix2Struct.forward.flattened_patches",description:`<strong>flattened_patches</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_length, hidden_size)</code>) &#x2014;
Flattened pixel patches. the <code>hidden_size</code> is obtained by the following formula: <code>hidden_size</code> =
<code>num_channels</code> <em> <code>patch_size</code> </em> <code>patch_size</code>
The process of flattening the pixel patches is done by <code>Pix2StructProcessor</code>.`,name:"flattened_patches"},{anchor:"optimum.onnxruntime.ORTModelForPix2Struct.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices.`,name:"attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForPix2Struct.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.
Pix2StructText uses the <code>pad_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"optimum.onnxruntime.ORTModelForPix2Struct.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"optimum.onnxruntime.ORTModelForPix2Struct.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <code>optional</code>: <em>hidden_states</em>, <code>optional</code>: <em>attentions</em>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code> is a sequence of hidden states at
the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"optimum.onnxruntime.ORTModelForPix2Struct.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)</code> and 2 additional tensors of shape
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See <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.encode" rel="nofollow"><code>PreTrainedTokenizer.encode</code></a> and
<a href="https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizerBase.__call__" rel="nofollow"><code>PreTrainedTokenizer.__call__</code></a> for details.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.onnxruntime.ORTModelForFeatureExtraction.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>Union[torch.Tensor, np.ndarray, None]</code> of shape <code>(batch_size, sequence_length)</code>, defaults to <code>None</code>) &#x2014;
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<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
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Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
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