import{s as Xm,o as zm,n as J}from"../chunks/scheduler.6062bdaf.js";import{S as Om,i as Km,g as p,s as r,r as m,A as eh,h as g,f as c,c as l,j as I,u as h,x as T,k as M,y as a,a as j,v as f,d as b,t as _,w as x}from"../chunks/index.4bca734e.js";import{T as re}from"../chunks/Tip.b9ac1f03.js";import{D as w}from"../chunks/Docstring.ed07512f.js";import{C as q}from"../chunks/CodeBlock.fa01571c.js";import{E as Z}from"../chunks/ExampleCodeBlock.823fb173.js";import{H as L}from"../chunks/Heading.723dceba.js";function nh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmF1ZGlvX2NsYXNzaWZpY2F0aW9uKCUyMmF1ZGlvLmZsYWMlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.audio_classification(<span class="hljs-string">&quot;audio.flac&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.4976358711719513</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;hap&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.3677836060523987</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;neu&#x27;</span>},...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function th($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBYXVkaW9fb3V0cHV0JTIwJTNEJTIwY2xpZW50LmF1ZGlvX3RvX2F1ZGlvKCUyMmF1ZGlvLmZsYWMlMjIpJTBBZm9yJTIwaSUyQyUyMGl0ZW0lMjBpbiUyMGVudW1lcmF0ZShhdWRpb19vdXRwdXQpJTNBJTBBJTIwJTIwJTIwJTIwd2l0aCUyMG9wZW4oZiUyMm91dHB1dF8lN0JpJTdELmZsYWMlMjIlMkMlMjAlMjJ3YiUyMiklMjBhcyUyMGYlM0E=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_output = client.audio_to_audio(<span class="hljs-string">&quot;audio.flac&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i, item <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(audio_output):
<span class="hljs-meta">&gt;&gt;&gt; </span>    <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(<span class="hljs-string">f&quot;output_<span class="hljs-subst">{i}</span>.flac&quot;</span>, <span class="hljs-string">&quot;wb&quot;</span>) <span class="hljs-keyword">as</span> f:
            f.write(item[<span class="hljs-string">&quot;blob&quot;</span>])`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function sh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmF1dG9tYXRpY19zcGVlY2hfcmVjb2duaXRpb24oJTIyaGVsbG9fd29ybGQuZmxhYyUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.automatic_speech_recognition(<span class="hljs-string">&quot;hello_world.flac&quot;</span>)
<span class="hljs-string">&quot;hello world&quot;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function ah($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>output = client.conversational(<span class="hljs-string">&quot;Hi, who are you?&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>output
{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;I am the one who knocks.&#x27;</span>, <span class="hljs-string">&#x27;conversation&#x27;</span>: {<span class="hljs-string">&#x27;generated_responses&#x27;</span>: [<span class="hljs-string">&#x27;I am the one who knocks.&#x27;</span>], <span class="hljs-string">&#x27;past_user_inputs&#x27;</span>: [<span class="hljs-string">&#x27;Hi, who are you?&#x27;</span>]}, <span class="hljs-string">&#x27;warnings&#x27;</span>: [<span class="hljs-string">&#x27;Setting \`pad_token_id\` to \`eos_token_id\`:50256 for open-end generation.&#x27;</span>]}
<span class="hljs-meta">&gt;&gt;&gt; </span>client.conversational(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Wow, that&#x27;s scary!&quot;</span>,
<span class="hljs-meta">... </span>    generated_responses=output[<span class="hljs-string">&quot;conversation&quot;</span>][<span class="hljs-string">&quot;generated_responses&quot;</span>],
<span class="hljs-meta">... </span>    past_user_inputs=output[<span class="hljs-string">&quot;conversation&quot;</span>][<span class="hljs-string">&quot;past_user_inputs&quot;</span>],
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function oh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmRvY3VtZW50X3F1ZXN0aW9uX2Fuc3dlcmluZyhpbWFnZSUzRCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZzcGFjZXMlMkZpbXBpcmElMkZkb2NxdWVyeSUyRnJlc29sdmUlMkYyMzU5MjIzYzE4MzdhNzU4NzQwMmJkYTBmMjY0MzM4MmE2ZWVmZWFiJTJGaW52b2ljZS5wbmclMjIlMkMlMjBxdWVzdGlvbiUzRCUyMldoYXQlMjBpcyUyMHRoZSUyMGludm9pY2UlMjBudW1iZXIlM0YlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.document_question_answering(image=<span class="hljs-string">&quot;https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png&quot;</span>, question=<span class="hljs-string">&quot;What is the invoice number?&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.42515629529953003</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;us-001&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">16</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">16</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function rh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmZlYXR1cmVfZXh0cmFjdGlvbiglMjJIaSUyQyUyMHdobyUyMGFyZSUyMHlvdSUzRiUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.feature_extraction(<span class="hljs-string">&quot;Hi, who are you?&quot;</span>)
array([[ <span class="hljs-number">2.424802</span>  ,  <span class="hljs-number">2.93384</span>   ,  <span class="hljs-number">1.1750331</span> , ...,  <span class="hljs-number">1.240499</span>, -<span class="hljs-number">0.13776633</span>, -<span class="hljs-number">0.7889173</span> ],
[-<span class="hljs-number">0.42943227</span>, -<span class="hljs-number">0.6364878</span> , -<span class="hljs-number">1.693462</span>  , ...,  <span class="hljs-number">0.41978157</span>, -<span class="hljs-number">2.4336355</span> ,  <span class="hljs-number">0.6162071</span> ],
...,
[ <span class="hljs-number">0.28552425</span>, -<span class="hljs-number">0.928395</span>  , -<span class="hljs-number">1.2077185</span> , ...,  <span class="hljs-number">0.76810825</span>, -<span class="hljs-number">2.1069427</span> ,  <span class="hljs-number">0.6236161</span> ]], dtype=float32)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function lh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmZpbGxfbWFzayglMjJUaGUlMjBnb2FsJTIwb2YlMjBsaWZlJTIwaXMlMjAlM0NtYXNrJTNFLiUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.fill_mask(<span class="hljs-string">&quot;The goal of life is &lt;mask&gt;.&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.06897063553333282</span>,
<span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">11098</span>,
<span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; happiness&#x27;</span>,
<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;The goal of life is happiness.&#x27;</span>},
{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.06554922461509705</span>,
<span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">45075</span>,
<span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; immortality&#x27;</span>,
<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;The goal of life is immortality.&#x27;</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function ih($){let t,y=`This endpoint is mostly useful when you already know which model you want to use and want to check its
availability. If you want to discover already deployed models, you should rather use <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.list_deployed_models">list_deployed_models()</a>.`;return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-1igz3dx"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function ch($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmdldF9tb2RlbF9zdGF0dXMoJTIyYmlnY29kZSUyRnN0YXJjb2RlciUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.get_model_status(<span class="hljs-string">&quot;bigcode/starcoder&quot;</span>)
ModelStatus(loaded=<span class="hljs-literal">True</span>, state=<span class="hljs-string">&#x27;Loaded&#x27;</span>, compute_type=<span class="hljs-string">&#x27;gpu&#x27;</span>, framework=<span class="hljs-string">&#x27;text-generation-inference&#x27;</span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function ph($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmltYWdlX2NsYXNzaWZpY2F0aW9uKCUyMmh0dHBzJTNBJTJGJTJGdXBsb2FkLndpa2ltZWRpYS5vcmclMkZ3aWtpcGVkaWElMkZjb21tb25zJTJGdGh1bWIlMkY0JTJGNDMlMkZDdXRlX2RvZy5qcGclMkYzMjBweC1DdXRlX2RvZy5qcGclMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.image_classification(<span class="hljs-string">&quot;https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9779096841812134</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;Blenheim spaniel&#x27;</span>}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function gh($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function uh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmltYWdlX3NlZ21lbnRhdGlvbiglMjJjYXQuanBnJTIyKSUzQQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.image_segmentation(<span class="hljs-string">&quot;cat.jpg&quot;</span>):
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.989008</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;LABEL_184&#x27;</span>, <span class="hljs-string">&#x27;mask&#x27;</span>: &lt;PIL.PngImagePlugin.PngImageFile image mode=L size=400x300 at <span class="hljs-number">0x7FDD2B129CC0</span>&gt;}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function dh($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function mh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBaW1hZ2UlMjAlM0QlMjBjbGllbnQuaW1hZ2VfdG9faW1hZ2UoJTIyY2F0LmpwZyUyMiUyQyUyMHByb21wdCUzRCUyMnR1cm4lMjB0aGUlMjBjYXQlMjBpbnRvJTIwYSUyMHRpZ2VyJTIyKSUwQWltYWdlLnNhdmUoJTIydGlnZXIuanBnJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>image = client.image_to_image(<span class="hljs-string">&quot;cat.jpg&quot;</span>, prompt=<span class="hljs-string">&quot;turn the cat into a tiger&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;tiger.jpg&quot;</span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function hh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LmltYWdlX3RvX3RleHQoJTIyY2F0LmpwZyUyMiklMEFjbGllbnQuaW1hZ2VfdG9fdGV4dCglMjJodHRwcyUzQSUyRiUyRnVwbG9hZC53aWtpbWVkaWEub3JnJTJGd2lraXBlZGlhJTJGY29tbW9ucyUyRnRodW1iJTJGNCUyRjQzJTJGQ3V0ZV9kb2cuanBnJTJGMzIwcHgtQ3V0ZV9kb2cuanBnJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.image_to_text(<span class="hljs-string">&quot;cat.jpg&quot;</span>)
<span class="hljs-string">&#x27;a cat standing in a grassy field &#x27;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>client.image_to_text(<span class="hljs-string">&quot;https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg&quot;</span>)
<span class="hljs-string">&#x27;a dog laying on the grass next to a flower pot &#x27;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function fh($){let t,y=`This endpoint is mostly useful for discoverability. If you already know which model you want to use and want to
check its availability, you can directly use <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.get_model_status">get_model_status()</a>.`;return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-wneo7r"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function bh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBJTBBbW9kZWxzJTIwJTNEJTIwY2xpZW50Lmxpc3RfZGVwbG95ZWRfbW9kZWxzKCklMEFtb2RlbHMlNUIlMjJ6ZXJvLXNob3QtY2xhc3NpZmljYXRpb24lMjIlNUQlMEElMEFjbGllbnQubGlzdF9kZXBsb3llZF9tb2RlbHMoJTIydGV4dC1nZW5lcmF0aW9uLWluZmVyZW5jZSUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()

<span class="hljs-comment"># Discover zero-shot-classification models currently deployed</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>models = client.list_deployed_models()
<span class="hljs-meta">&gt;&gt;&gt; </span>models[<span class="hljs-string">&quot;zero-shot-classification&quot;</span>]
[<span class="hljs-string">&#x27;Narsil/deberta-large-mnli-zero-cls&#x27;</span>, <span class="hljs-string">&#x27;facebook/bart-large-mnli&#x27;</span>, ...]

<span class="hljs-comment"># List from only 1 framework</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>client.list_deployed_models(<span class="hljs-string">&quot;text-generation-inference&quot;</span>)
{<span class="hljs-string">&#x27;text-generation&#x27;</span>: [<span class="hljs-string">&#x27;bigcode/starcoder&#x27;</span>, <span class="hljs-string">&#x27;meta-llama/Llama-2-70b-chat-hf&#x27;</span>, ...], ...}`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function _h($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function xh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50Lm9iamVjdF9kZXRlY3Rpb24oJTIycGVvcGxlLmpwZyUyMiklM0E=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.object_detection(<span class="hljs-string">&quot;people.jpg&quot;</span>):
[{<span class="hljs-string">&quot;score&quot;</span>:<span class="hljs-number">0.9486683011054993</span>,<span class="hljs-string">&quot;label&quot;</span>:<span class="hljs-string">&quot;person&quot;</span>,<span class="hljs-string">&quot;box&quot;</span>:{<span class="hljs-string">&quot;xmin&quot;</span>:<span class="hljs-number">59</span>,<span class="hljs-string">&quot;ymin&quot;</span>:<span class="hljs-number">39</span>,<span class="hljs-string">&quot;xmax&quot;</span>:<span class="hljs-number">420</span>,<span class="hljs-string">&quot;ymax&quot;</span>:<span class="hljs-number">510</span>}}, ... ]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function yh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LnF1ZXN0aW9uX2Fuc3dlcmluZyhxdWVzdGlvbiUzRCUyMldoYXQncyUyMG15JTIwbmFtZSUzRiUyMiUyQyUyMGNvbnRleHQlM0QlMjJNeSUyMG5hbWUlMjBpcyUyMENsYXJhJTIwYW5kJTIwSSUyMGxpdmUlMjBpbiUyMEJlcmtlbGV5LiUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.question_answering(question=<span class="hljs-string">&quot;What&#x27;s my name?&quot;</span>, context=<span class="hljs-string">&quot;My name is Clara and I live in Berkeley.&quot;</span>)
{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9326562285423279</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">11</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">16</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;Clara&#x27;</span>}`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function jh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.sentence_similarity(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Machine learning is so easy.&quot;</span>,
<span class="hljs-meta">... </span>    other_sentences=[
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;Deep learning is so straightforward.&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;This is so difficult, like rocket science.&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;I can&#x27;t believe how much I struggled with this.&quot;</span>,
<span class="hljs-meta">... </span>    ],
<span class="hljs-meta">... </span>)
[<span class="hljs-number">0.7785726189613342</span>, <span class="hljs-number">0.45876261591911316</span>, <span class="hljs-number">0.2906220555305481</span>]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Th($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LnN1bW1hcml6YXRpb24oJTIyVGhlJTIwRWlmZmVsJTIwdG93ZXIuLi4lMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.summarization(<span class="hljs-string">&quot;The Eiffel tower...&quot;</span>)
<span class="hljs-string">&#x27;The Eiffel tower is one of the most famous landmarks in the world....&#x27;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function $h($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>query = <span class="hljs-string">&quot;How many stars does the transformers repository have?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>table = {<span class="hljs-string">&quot;Repository&quot;</span>: [<span class="hljs-string">&quot;Transformers&quot;</span>, <span class="hljs-string">&quot;Datasets&quot;</span>, <span class="hljs-string">&quot;Tokenizers&quot;</span>], <span class="hljs-string">&quot;Stars&quot;</span>: [<span class="hljs-string">&quot;36542&quot;</span>, <span class="hljs-string">&quot;4512&quot;</span>, <span class="hljs-string">&quot;3934&quot;</span>]}
<span class="hljs-meta">&gt;&gt;&gt; </span>client.table_question_answering(table, query, model=<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
{<span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;AVERAGE &gt; 36542&#x27;</span>, <span class="hljs-string">&#x27;coordinates&#x27;</span>: [[<span class="hljs-number">0</span>, <span class="hljs-number">1</span>]], <span class="hljs-string">&#x27;cells&#x27;</span>: [<span class="hljs-string">&#x27;36542&#x27;</span>], <span class="hljs-string">&#x27;aggregator&#x27;</span>: <span class="hljs-string">&#x27;AVERAGE&#x27;</span>}`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Ih($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>table = {
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;fixed_acidity&quot;</span>: [<span class="hljs-string">&quot;7.4&quot;</span>, <span class="hljs-string">&quot;7.8&quot;</span>, <span class="hljs-string">&quot;10.3&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;volatile_acidity&quot;</span>: [<span class="hljs-string">&quot;0.7&quot;</span>, <span class="hljs-string">&quot;0.88&quot;</span>, <span class="hljs-string">&quot;0.32&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;citric_acid&quot;</span>: [<span class="hljs-string">&quot;0&quot;</span>, <span class="hljs-string">&quot;0&quot;</span>, <span class="hljs-string">&quot;0.45&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;residual_sugar&quot;</span>: [<span class="hljs-string">&quot;1.9&quot;</span>, <span class="hljs-string">&quot;2.6&quot;</span>, <span class="hljs-string">&quot;6.4&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;chlorides&quot;</span>: [<span class="hljs-string">&quot;0.076&quot;</span>, <span class="hljs-string">&quot;0.098&quot;</span>, <span class="hljs-string">&quot;0.073&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;free_sulfur_dioxide&quot;</span>: [<span class="hljs-string">&quot;11&quot;</span>, <span class="hljs-string">&quot;25&quot;</span>, <span class="hljs-string">&quot;5&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;total_sulfur_dioxide&quot;</span>: [<span class="hljs-string">&quot;34&quot;</span>, <span class="hljs-string">&quot;67&quot;</span>, <span class="hljs-string">&quot;13&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;density&quot;</span>: [<span class="hljs-string">&quot;0.9978&quot;</span>, <span class="hljs-string">&quot;0.9968&quot;</span>, <span class="hljs-string">&quot;0.9976&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;pH&quot;</span>: [<span class="hljs-string">&quot;3.51&quot;</span>, <span class="hljs-string">&quot;3.2&quot;</span>, <span class="hljs-string">&quot;3.23&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;sulphates&quot;</span>: [<span class="hljs-string">&quot;0.56&quot;</span>, <span class="hljs-string">&quot;0.68&quot;</span>, <span class="hljs-string">&quot;0.82&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;alcohol&quot;</span>: [<span class="hljs-string">&quot;9.4&quot;</span>, <span class="hljs-string">&quot;9.8&quot;</span>, <span class="hljs-string">&quot;12.6&quot;</span>],
<span class="hljs-meta">... </span>}
<span class="hljs-meta">&gt;&gt;&gt; </span>client.tabular_classification(table=table, model=<span class="hljs-string">&quot;julien-c/wine-quality&quot;</span>)
[<span class="hljs-string">&quot;5&quot;</span>, <span class="hljs-string">&quot;5&quot;</span>, <span class="hljs-string">&quot;5&quot;</span>]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Mh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>table = {
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Height&quot;</span>: [<span class="hljs-string">&quot;11.52&quot;</span>, <span class="hljs-string">&quot;12.48&quot;</span>, <span class="hljs-string">&quot;12.3778&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Length1&quot;</span>: [<span class="hljs-string">&quot;23.2&quot;</span>, <span class="hljs-string">&quot;24&quot;</span>, <span class="hljs-string">&quot;23.9&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Length2&quot;</span>: [<span class="hljs-string">&quot;25.4&quot;</span>, <span class="hljs-string">&quot;26.3&quot;</span>, <span class="hljs-string">&quot;26.5&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Length3&quot;</span>: [<span class="hljs-string">&quot;30&quot;</span>, <span class="hljs-string">&quot;31.2&quot;</span>, <span class="hljs-string">&quot;31.1&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Species&quot;</span>: [<span class="hljs-string">&quot;Bream&quot;</span>, <span class="hljs-string">&quot;Bream&quot;</span>, <span class="hljs-string">&quot;Bream&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Width&quot;</span>: [<span class="hljs-string">&quot;4.02&quot;</span>, <span class="hljs-string">&quot;4.3056&quot;</span>, <span class="hljs-string">&quot;4.6961&quot;</span>],
<span class="hljs-meta">... </span>}
<span class="hljs-meta">&gt;&gt;&gt; </span>client.tabular_regression(table, model=<span class="hljs-string">&quot;scikit-learn/Fish-Weight&quot;</span>)
[<span class="hljs-number">110</span>, <span class="hljs-number">120</span>, <span class="hljs-number">130</span>]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function wh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LnRleHRfY2xhc3NpZmljYXRpb24oJTIySSUyMGxpa2UlMjB5b3UlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.text_classification(<span class="hljs-string">&quot;I like you&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9998695850372314</span>}, {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;NEGATIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0001304351753788069</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Ch($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()

<span class="hljs-comment"># Case 1: generate text</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>)
<span class="hljs-string">&#x27;100% open source and built to be easy to use.&#x27;</span>

<span class="hljs-comment"># Case 2: iterate over the generated tokens. Useful for large generation.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> token <span class="hljs-keyword">in</span> client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>, stream=<span class="hljs-literal">True</span>):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(token)
<span class="hljs-number">100</span>
%
<span class="hljs-built_in">open</span>
source
<span class="hljs-keyword">and</span>
built
to
be
easy
to
use
.

<span class="hljs-comment"># Case 3: get more details about the generation process.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>, details=<span class="hljs-literal">True</span>)
TextGenerationResponse(
    generated_text=<span class="hljs-string">&#x27;100% open source and built to be easy to use.&#x27;</span>,
    details=Details(
        finish_reason=&lt;FinishReason.Length: <span class="hljs-string">&#x27;length&#x27;</span>&gt;,
        generated_tokens=<span class="hljs-number">12</span>,
        seed=<span class="hljs-literal">None</span>,
        prefill=[
            InputToken(<span class="hljs-built_in">id</span>=<span class="hljs-number">487</span>, text=<span class="hljs-string">&#x27;The&#x27;</span>, logprob=<span class="hljs-literal">None</span>),
            InputToken(<span class="hljs-built_in">id</span>=<span class="hljs-number">53789</span>, text=<span class="hljs-string">&#x27; hugging&#x27;</span>, logprob=-<span class="hljs-number">13.171875</span>),
            (...)
            InputToken(<span class="hljs-built_in">id</span>=<span class="hljs-number">204</span>, text=<span class="hljs-string">&#x27; &#x27;</span>, logprob=-<span class="hljs-number">7.0390625</span>)
        ],
        tokens=[
            Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1425</span>, text=<span class="hljs-string">&#x27;100&#x27;</span>, logprob=-<span class="hljs-number">1.0175781</span>, special=<span class="hljs-literal">False</span>),
            Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">16</span>, text=<span class="hljs-string">&#x27;%&#x27;</span>, logprob=-<span class="hljs-number">0.0463562</span>, special=<span class="hljs-literal">False</span>),
            (...)
            Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">25</span>, text=<span class="hljs-string">&#x27;.&#x27;</span>, logprob=-<span class="hljs-number">0.5703125</span>, special=<span class="hljs-literal">False</span>)
        ],
        best_of_sequences=<span class="hljs-literal">None</span>
    )
)

<span class="hljs-comment"># Case 4: iterate over the generated tokens with more details.</span>
<span class="hljs-comment"># Last object is more complete, containing the full generated text and the finish reason.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> details <span class="hljs-keyword">in</span> client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>, details=<span class="hljs-literal">True</span>, stream=<span class="hljs-literal">True</span>):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(details)
...
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1425</span>, text=<span class="hljs-string">&#x27;100&#x27;</span>, logprob=-<span class="hljs-number">1.0175781</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">16</span>, text=<span class="hljs-string">&#x27;%&#x27;</span>, logprob=-<span class="hljs-number">0.0463562</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1314</span>, text=<span class="hljs-string">&#x27; open&#x27;</span>, logprob=-<span class="hljs-number">1.3359375</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">3178</span>, text=<span class="hljs-string">&#x27; source&#x27;</span>, logprob=-<span class="hljs-number">0.28100586</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">273</span>, text=<span class="hljs-string">&#x27; and&#x27;</span>, logprob=-<span class="hljs-number">0.5961914</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">3426</span>, text=<span class="hljs-string">&#x27; built&#x27;</span>, logprob=-<span class="hljs-number">1.9423828</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">271</span>, text=<span class="hljs-string">&#x27; to&#x27;</span>, logprob=-<span class="hljs-number">1.4121094</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">314</span>, text=<span class="hljs-string">&#x27; be&#x27;</span>, logprob=-<span class="hljs-number">1.5224609</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1833</span>, text=<span class="hljs-string">&#x27; easy&#x27;</span>, logprob=-<span class="hljs-number">2.1132812</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">271</span>, text=<span class="hljs-string">&#x27; to&#x27;</span>, logprob=-<span class="hljs-number">0.08520508</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">745</span>, text=<span class="hljs-string">&#x27; use&#x27;</span>, logprob=-<span class="hljs-number">0.39453125</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(
    <span class="hljs-built_in">id</span>=<span class="hljs-number">25</span>,
    text=<span class="hljs-string">&#x27;.&#x27;</span>,
    logprob=-<span class="hljs-number">0.5703125</span>,
    special=<span class="hljs-literal">False</span>),
    generated_text=<span class="hljs-string">&#x27;100% open source and built to be easy to use.&#x27;</span>,
    details=StreamDetails(finish_reason=&lt;FinishReason.Length: <span class="hljs-string">&#x27;length&#x27;</span>&gt;, generated_tokens=<span class="hljs-number">12</span>, seed=<span class="hljs-literal">None</span>)
)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function vh($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function Jh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()

<span class="hljs-meta">&gt;&gt;&gt; </span>image = client.text_to_image(<span class="hljs-string">&quot;An astronaut riding a horse on the moon.&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;astronaut.png&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image = client.text_to_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;An astronaut riding a horse on the moon.&quot;</span>,
<span class="hljs-meta">... </span>    negative_prompt=<span class="hljs-string">&quot;low resolution, blurry&quot;</span>,
<span class="hljs-meta">... </span>    model=<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;better_astronaut.png&quot;</span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Uh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMHBhdGhsaWIlMjBpbXBvcnQlMjBQYXRoJTBBZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBJTBBYXVkaW8lMjAlM0QlMjBjbGllbnQudGV4dF90b19zcGVlY2goJTIySGVsbG8lMjB3b3JsZCUyMiklMEFQYXRoKCUyMmhlbGxvX3dvcmxkLmZsYWMlMjIpLndyaXRlX2J5dGVzKGF1ZGlvKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()

<span class="hljs-meta">&gt;&gt;&gt; </span>audio = client.text_to_speech(<span class="hljs-string">&quot;Hello world&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>Path(<span class="hljs-string">&quot;hello_world.flac&quot;</span>).write_bytes(audio)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function kh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LnRva2VuX2NsYXNzaWZpY2F0aW9uKCUyMk15JTIwbmFtZSUyMGlzJTIwU2FyYWglMjBKZXNzaWNhJTIwUGFya2VyJTIwYnV0JTIweW91JTIwY2FuJTIwY2FsbCUyMG1lJTIwSmVzc2ljYSUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.token_classification(<span class="hljs-string">&quot;My name is Sarah Jessica Parker but you can call me Jessica&quot;</span>)
[{<span class="hljs-string">&#x27;entity_group&#x27;</span>: <span class="hljs-string">&#x27;PER&#x27;</span>,
<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9971321225166321</span>,
<span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Sarah Jessica Parker&#x27;</span>,
<span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">11</span>,
<span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">31</span>},
{<span class="hljs-string">&#x27;entity_group&#x27;</span>: <span class="hljs-string">&#x27;PER&#x27;</span>,
<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9773476123809814</span>,
<span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Jessica&#x27;</span>,
<span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">52</span>,
<span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">59</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function qh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LnRyYW5zbGF0aW9uKCUyMk15JTIwbmFtZSUyMGlzJTIwV29sZmdhbmclMjBhbmQlMjBJJTIwbGl2ZSUyMGluJTIwQmVybGluJTIyKSUwQWNsaWVudC50cmFuc2xhdGlvbiglMjJNeSUyMG5hbWUlMjBpcyUyMFdvbGZnYW5nJTIwYW5kJTIwSSUyMGxpdmUlMjBpbiUyMEJlcmxpbiUyMiUyQyUyMG1vZGVsJTNEJTIySGVsc2lua2ktTkxQJTJGb3B1cy1tdC1lbi1mciUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.translation(<span class="hljs-string">&quot;My name is Wolfgang and I live in Berlin&quot;</span>)
<span class="hljs-string">&#x27;Mein Name ist Wolfgang und ich lebe in Berlin.&#x27;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>client.translation(<span class="hljs-string">&quot;My name is Wolfgang and I live in Berlin&quot;</span>, model=<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-fr&quot;</span>)
<span class="hljs-string">&quot;Je m&#x27;appelle Wolfgang et je vis à Berlin.&quot;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Zh($){let t,y="Specifying languages:",o,s,i;return s=new q({props:{code:"Y2xpZW50LnRyYW5zbGF0aW9uKCUyMk15JTIwbmFtZSUyMGlzJTIwU2FyYWglMjBKZXNzaWNhJTIwUGFya2VyJTIwYnV0JTIweW91JTIwY2FuJTIwY2FsbCUyMG1lJTIwSmVzc2ljYSUyMiUyQyUyMG1vZGVsJTNEJTIyZmFjZWJvb2slMkZtYmFydC1sYXJnZS01MC1tYW55LXRvLW1hbnktbW10JTIyJTJDJTIwc3JjX2xhbmclM0QlMjJlbl9YWCUyMiUyQyUyMHRndF9sYW5nJTNEJTIyZnJfWFglMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>client.translation(<span class="hljs-string">&quot;My name is Sarah Jessica Parker but you can call me Jessica&quot;</span>, model=<span class="hljs-string">&quot;facebook/mbart-large-50-many-to-many-mmt&quot;</span>, src_lang=<span class="hljs-string">&quot;en_XX&quot;</span>, tgt_lang=<span class="hljs-string">&quot;fr_XX&quot;</span>)
<span class="hljs-string">&quot;Mon nom est Sarah Jessica Parker mais vous pouvez m&#x27;appeler Jessica&quot;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-mhcj6c"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Wh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBY2xpZW50LnZpc3VhbF9xdWVzdGlvbl9hbnN3ZXJpbmcoJTBBJTIwJTIwJTIwJTIwaW1hZ2UlM0QlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZtaXNoaWclMkZzYW1wbGVfaW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZ0aWdlci5qcGclMjIlMkMlMEElMjAlMjAlMjAlMjBxdWVzdGlvbiUzRCUyMldoYXQlMjBpcyUyMHRoZSUyMGFuaW1hbCUyMGRvaW5nJTNGJTIyJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>client.visual_question_answering(
<span class="hljs-meta">... </span>    image=<span class="hljs-string">&quot;https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg&quot;</span>,
<span class="hljs-meta">... </span>    question=<span class="hljs-string">&quot;What is the animal doing?&quot;</span>
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.778609573841095</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;laying down&#x27;</span>},{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.6957435607910156</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;sitting&#x27;</span>}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Eh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>text = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A new model offers an explanation for how the Galilean satellites formed around the solar system&#x27;s&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;largest world. Konstantin Batygin did not set out to solve one of the solar system&#x27;s most puzzling&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot; mysteries when he went for a run up a hill in Nice, France.&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = [<span class="hljs-string">&quot;space &amp; cosmos&quot;</span>, <span class="hljs-string">&quot;scientific discovery&quot;</span>, <span class="hljs-string">&quot;microbiology&quot;</span>, <span class="hljs-string">&quot;robots&quot;</span>, <span class="hljs-string">&quot;archeology&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>client.zero_shot_classification(text, labels)
[
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;scientific discovery&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.7961668968200684</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;space &amp; cosmos&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.18570658564567566</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;microbiology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.00730885099619627</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;archeology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.006258360575884581</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;robots&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.004559356719255447</span>},
]
<span class="hljs-meta">&gt;&gt;&gt; </span>client.zero_shot_classification(text, labels, multi_label=<span class="hljs-literal">True</span>)
[
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;scientific discovery&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.9829297661781311</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;space &amp; cosmos&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.755190908908844</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;microbiology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.0005462635890580714</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;archeology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.00047131875180639327</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;robots&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.00030448526376858354</span>},
]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Nh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEluZmVyZW5jZUNsaWVudCUwQWNsaWVudCUyMCUzRCUyMEluZmVyZW5jZUNsaWVudCgpJTBBJTBBY2xpZW50Lnplcm9fc2hvdF9pbWFnZV9jbGFzc2lmaWNhdGlvbiglMEElMjAlMjAlMjAlMjAlMjJodHRwcyUzQSUyRiUyRnVwbG9hZC53aWtpbWVkaWEub3JnJTJGd2lraXBlZGlhJTJGY29tbW9ucyUyRnRodW1iJTJGNCUyRjQzJTJGQ3V0ZV9kb2cuanBnJTJGMzIwcHgtQ3V0ZV9kb2cuanBnJTIyJTJDJTBBJTIwJTIwJTIwJTIwbGFiZWxzJTNEJTVCJTIyZG9nJTIyJTJDJTIwJTIyY2F0JTIyJTJDJTIwJTIyaG9yc2UlMjIlNUQlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = InferenceClient()

<span class="hljs-meta">&gt;&gt;&gt; </span>client.zero_shot_image_classification(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg&quot;</span>,
<span class="hljs-meta">... </span>    labels=[<span class="hljs-string">&quot;dog&quot;</span>, <span class="hljs-string">&quot;cat&quot;</span>, <span class="hljs-string">&quot;horse&quot;</span>],
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;dog&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.956</span>}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Gh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmF1ZGlvX2NsYXNzaWZpY2F0aW9uKCUyMmF1ZGlvLmZsYWMlMjIp",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.audio_classification(<span class="hljs-string">&quot;audio.flac&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.4976358711719513</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;hap&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.3677836060523987</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;neu&#x27;</span>},...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Vh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF1ZGlvX291dHB1dCUyMCUzRCUyMGF3YWl0JTIwY2xpZW50LmF1ZGlvX3RvX2F1ZGlvKCUyMmF1ZGlvLmZsYWMlMjIpJTBBYXN5bmMlMjBmb3IlMjBpJTJDJTIwaXRlbSUyMGluJTIwZW51bWVyYXRlKGF1ZGlvX291dHB1dCklM0ElMEElMjAlMjAlMjAlMjB3aXRoJTIwb3BlbihmJTIyb3V0cHV0XyU3QmklN0QuZmxhYyUyMiUyQyUyMCUyMndiJTIyKSUyMGFzJTIwZiUzQQ==",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_output = <span class="hljs-keyword">await</span> client.audio_to_audio(<span class="hljs-string">&quot;audio.flac&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">async</span> <span class="hljs-keyword">for</span> i, item <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(audio_output):
<span class="hljs-meta">&gt;&gt;&gt; </span>    <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(<span class="hljs-string">f&quot;output_<span class="hljs-subst">{i}</span>.flac&quot;</span>, <span class="hljs-string">&quot;wb&quot;</span>) <span class="hljs-keyword">as</span> f:
            f.write(item[<span class="hljs-string">&quot;blob&quot;</span>])`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Dh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmF1dG9tYXRpY19zcGVlY2hfcmVjb2duaXRpb24oJTIyaGVsbG9fd29ybGQuZmxhYyUyMik=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.automatic_speech_recognition(<span class="hljs-string">&quot;hello_world.flac&quot;</span>)
<span class="hljs-string">&quot;hello world&quot;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Yh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>output = <span class="hljs-keyword">await</span> client.conversational(<span class="hljs-string">&quot;Hi, who are you?&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>output
{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;I am the one who knocks.&#x27;</span>, <span class="hljs-string">&#x27;conversation&#x27;</span>: {<span class="hljs-string">&#x27;generated_responses&#x27;</span>: [<span class="hljs-string">&#x27;I am the one who knocks.&#x27;</span>], <span class="hljs-string">&#x27;past_user_inputs&#x27;</span>: [<span class="hljs-string">&#x27;Hi, who are you?&#x27;</span>]}, <span class="hljs-string">&#x27;warnings&#x27;</span>: [<span class="hljs-string">&#x27;Setting \`pad_token_id\` to \`eos_token_id\`:50256 async for open-end generation.&#x27;</span>]}
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.conversational(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Wow, that&#x27;s scary!&quot;</span>,
<span class="hljs-meta">... </span>    generated_responses=output[<span class="hljs-string">&quot;conversation&quot;</span>][<span class="hljs-string">&quot;generated_responses&quot;</span>],
<span class="hljs-meta">... </span>    past_user_inputs=output[<span class="hljs-string">&quot;conversation&quot;</span>][<span class="hljs-string">&quot;past_user_inputs&quot;</span>],
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Rh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmRvY3VtZW50X3F1ZXN0aW9uX2Fuc3dlcmluZyhpbWFnZSUzRCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZzcGFjZXMlMkZpbXBpcmElMkZkb2NxdWVyeSUyRnJlc29sdmUlMkYyMzU5MjIzYzE4MzdhNzU4NzQwMmJkYTBmMjY0MzM4MmE2ZWVmZWFiJTJGaW52b2ljZS5wbmclMjIlMkMlMjBxdWVzdGlvbiUzRCUyMldoYXQlMjBpcyUyMHRoZSUyMGludm9pY2UlMjBudW1iZXIlM0YlMjIp",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.document_question_answering(image=<span class="hljs-string">&quot;https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png&quot;</span>, question=<span class="hljs-string">&quot;What is the invoice number?&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.42515629529953003</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;us-001&#x27;</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">16</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">16</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Ah($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmZlYXR1cmVfZXh0cmFjdGlvbiglMjJIaSUyQyUyMHdobyUyMGFyZSUyMHlvdSUzRiUyMik=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.feature_extraction(<span class="hljs-string">&quot;Hi, who are you?&quot;</span>)
array([[ <span class="hljs-number">2.424802</span>  ,  <span class="hljs-number">2.93384</span>   ,  <span class="hljs-number">1.1750331</span> , ...,  <span class="hljs-number">1.240499</span>, -<span class="hljs-number">0.13776633</span>, -<span class="hljs-number">0.7889173</span> ],
[-<span class="hljs-number">0.42943227</span>, -<span class="hljs-number">0.6364878</span> , -<span class="hljs-number">1.693462</span>  , ...,  <span class="hljs-number">0.41978157</span>, -<span class="hljs-number">2.4336355</span> ,  <span class="hljs-number">0.6162071</span> ],
...,
[ <span class="hljs-number">0.28552425</span>, -<span class="hljs-number">0.928395</span>  , -<span class="hljs-number">1.2077185</span> , ...,  <span class="hljs-number">0.76810825</span>, -<span class="hljs-number">2.1069427</span> ,  <span class="hljs-number">0.6236161</span> ]], dtype=float32)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Bh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmZpbGxfbWFzayglMjJUaGUlMjBnb2FsJTIwb2YlMjBsaWZlJTIwaXMlMjAlM0NtYXNrJTNFLiUyMik=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.fill_mask(<span class="hljs-string">&quot;The goal of life is &lt;mask&gt;.&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.06897063553333282</span>,
<span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">11098</span>,
<span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; happiness&#x27;</span>,
<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;The goal of life is happiness.&#x27;</span>},
{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.06554922461509705</span>,
<span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">45075</span>,
<span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27; immortality&#x27;</span>,
<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;The goal of life is immortality.&#x27;</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Hh($){let t,y=`This endpoint is mostly useful when you already know which model you want to use and want to check its
availability. If you want to discover already deployed models, you should rather use <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.list_deployed_models">list_deployed_models()</a>.`;return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-1igz3dx"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function Fh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmdldF9tb2RlbF9zdGF0dXMoJTIyYmlnY29kZSUyRnN0YXJjb2RlciUyMik=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.get_model_status(<span class="hljs-string">&quot;bigcode/starcoder&quot;</span>)
ModelStatus(loaded=<span class="hljs-literal">True</span>, state=<span class="hljs-string">&#x27;Loaded&#x27;</span>, compute_type=<span class="hljs-string">&#x27;gpu&#x27;</span>, framework=<span class="hljs-string">&#x27;text-generation-inference&#x27;</span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Ph($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmltYWdlX2NsYXNzaWZpY2F0aW9uKCUyMmh0dHBzJTNBJTJGJTJGdXBsb2FkLndpa2ltZWRpYS5vcmclMkZ3aWtpcGVkaWElMkZjb21tb25zJTJGdGh1bWIlMkY0JTJGNDMlMkZDdXRlX2RvZy5qcGclMkYzMjBweC1DdXRlX2RvZy5qcGclMjIp",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.image_classification(<span class="hljs-string">&quot;https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9779096841812134</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;Blenheim spaniel&#x27;</span>}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Lh($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function Qh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmltYWdlX3NlZ21lbnRhdGlvbiglMjJjYXQuanBnJTIyKSUzQQ==",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.image_segmentation(<span class="hljs-string">&quot;cat.jpg&quot;</span>):
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.989008</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;LABEL_184&#x27;</span>, <span class="hljs-string">&#x27;mask&#x27;</span>: &lt;PIL.PngImagePlugin.PngImageFile image mode=L size=400x300 at <span class="hljs-number">0x7FDD2B129CC0</span>&gt;}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Sh($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function Xh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWltYWdlJTIwJTNEJTIwYXdhaXQlMjBjbGllbnQuaW1hZ2VfdG9faW1hZ2UoJTIyY2F0LmpwZyUyMiUyQyUyMHByb21wdCUzRCUyMnR1cm4lMjB0aGUlMjBjYXQlMjBpbnRvJTIwYSUyMHRpZ2VyJTIyKSUwQWltYWdlLnNhdmUoJTIydGlnZXIuanBnJTIyKQ==",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>image = <span class="hljs-keyword">await</span> client.image_to_image(<span class="hljs-string">&quot;cat.jpg&quot;</span>, prompt=<span class="hljs-string">&quot;turn the cat into a tiger&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;tiger.jpg&quot;</span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function zh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LmltYWdlX3RvX3RleHQoJTIyY2F0LmpwZyUyMiklMEFhd2FpdCUyMGNsaWVudC5pbWFnZV90b190ZXh0KCUyMmh0dHBzJTNBJTJGJTJGdXBsb2FkLndpa2ltZWRpYS5vcmclMkZ3aWtpcGVkaWElMkZjb21tb25zJTJGdGh1bWIlMkY0JTJGNDMlMkZDdXRlX2RvZy5qcGclMkYzMjBweC1DdXRlX2RvZy5qcGclMjIp",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.image_to_text(<span class="hljs-string">&quot;cat.jpg&quot;</span>)
<span class="hljs-string">&#x27;a cat standing in a grassy field &#x27;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.image_to_text(<span class="hljs-string">&quot;https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg&quot;</span>)
<span class="hljs-string">&#x27;a dog laying on the grass next to a flower pot &#x27;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function Oh($){let t,y=`This endpoint is mostly useful for discoverability. If you already know which model you want to use and want to
check its availability, you can directly use <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.get_model_status">get_model_status()</a>.`;return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-wneo7r"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function Kh($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQSUwQW1vZGVscyUyMCUzRCUyMGF3YWl0JTIwY2xpZW50Lmxpc3RfZGVwbG95ZWRfbW9kZWxzKCklMEFtb2RlbHMlNUIlMjJ6ZXJvLXNob3QtY2xhc3NpZmljYXRpb24lMjIlNUQlMEElMEFhd2FpdCUyMGNsaWVudC5saXN0X2RlcGxveWVkX21vZGVscyglMjJ0ZXh0LWdlbmVyYXRpb24taW5mZXJlbmNlJTIyKQ==",highlighted:`<span class="hljs-comment"># Must be run in an async contextthon</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()

<span class="hljs-comment"># Discover zero-shot-classification models currently deployed</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>models = <span class="hljs-keyword">await</span> client.list_deployed_models()
<span class="hljs-meta">&gt;&gt;&gt; </span>models[<span class="hljs-string">&quot;zero-shot-classification&quot;</span>]
[<span class="hljs-string">&#x27;Narsil/deberta-large-mnli-zero-cls&#x27;</span>, <span class="hljs-string">&#x27;facebook/bart-large-mnli&#x27;</span>, ...]

<span class="hljs-comment"># List from only 1 framework</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.list_deployed_models(<span class="hljs-string">&quot;text-generation-inference&quot;</span>)
{<span class="hljs-string">&#x27;text-generation&#x27;</span>: [<span class="hljs-string">&#x27;bigcode/starcoder&#x27;</span>, <span class="hljs-string">&#x27;meta-llama/Llama-2-70b-chat-hf&#x27;</span>, ...], ...}`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function ef($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function nf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50Lm9iamVjdF9kZXRlY3Rpb24oJTIycGVvcGxlLmpwZyUyMiklM0E=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.object_detection(<span class="hljs-string">&quot;people.jpg&quot;</span>):
[{<span class="hljs-string">&quot;score&quot;</span>:<span class="hljs-number">0.9486683011054993</span>,<span class="hljs-string">&quot;label&quot;</span>:<span class="hljs-string">&quot;person&quot;</span>,<span class="hljs-string">&quot;box&quot;</span>:{<span class="hljs-string">&quot;xmin&quot;</span>:<span class="hljs-number">59</span>,<span class="hljs-string">&quot;ymin&quot;</span>:<span class="hljs-number">39</span>,<span class="hljs-string">&quot;xmax&quot;</span>:<span class="hljs-number">420</span>,<span class="hljs-string">&quot;ymax&quot;</span>:<span class="hljs-number">510</span>}}, ... ]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function tf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LnF1ZXN0aW9uX2Fuc3dlcmluZyhxdWVzdGlvbiUzRCUyMldoYXQncyUyMG15JTIwbmFtZSUzRiUyMiUyQyUyMGNvbnRleHQlM0QlMjJNeSUyMG5hbWUlMjBpcyUyMENsYXJhJTIwYW5kJTIwSSUyMGxpdmUlMjBpbiUyMEJlcmtlbGV5LiUyMik=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.question_answering(question=<span class="hljs-string">&quot;What&#x27;s my name?&quot;</span>, context=<span class="hljs-string">&quot;My name is Clara and I live in Berkeley.&quot;</span>)
{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9326562285423279</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">11</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">16</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;Clara&#x27;</span>}`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function sf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.sentence_similarity(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Machine learning is so easy.&quot;</span>,
<span class="hljs-meta">... </span>    other_sentences=[
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;Deep learning is so straightforward.&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;This is so difficult, like rocket science.&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;I can&#x27;t believe how much I struggled with this.&quot;</span>,
<span class="hljs-meta">... </span>    ],
<span class="hljs-meta">... </span>)
[<span class="hljs-number">0.7785726189613342</span>, <span class="hljs-number">0.45876261591911316</span>, <span class="hljs-number">0.2906220555305481</span>]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function af($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LnN1bW1hcml6YXRpb24oJTIyVGhlJTIwRWlmZmVsJTIwdG93ZXIuLi4lMjIp",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.summarization(<span class="hljs-string">&quot;The Eiffel tower...&quot;</span>)
<span class="hljs-string">&#x27;The Eiffel tower is one of the most famous landmarks in the world....&#x27;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function of($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>query = <span class="hljs-string">&quot;How many stars does the transformers repository have?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>table = {<span class="hljs-string">&quot;Repository&quot;</span>: [<span class="hljs-string">&quot;Transformers&quot;</span>, <span class="hljs-string">&quot;Datasets&quot;</span>, <span class="hljs-string">&quot;Tokenizers&quot;</span>], <span class="hljs-string">&quot;Stars&quot;</span>: [<span class="hljs-string">&quot;36542&quot;</span>, <span class="hljs-string">&quot;4512&quot;</span>, <span class="hljs-string">&quot;3934&quot;</span>]}
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.table_question_answering(table, query, model=<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
{<span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;AVERAGE &gt; 36542&#x27;</span>, <span class="hljs-string">&#x27;coordinates&#x27;</span>: [[<span class="hljs-number">0</span>, <span class="hljs-number">1</span>]], <span class="hljs-string">&#x27;cells&#x27;</span>: [<span class="hljs-string">&#x27;36542&#x27;</span>], <span class="hljs-string">&#x27;aggregator&#x27;</span>: <span class="hljs-string">&#x27;AVERAGE&#x27;</span>}`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function rf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQXRhYmxlJTIwJTNEJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIyZml4ZWRfYWNpZGl0eSUyMiUzQSUyMCU1QiUyMjcuNCUyMiUyQyUyMCUyMjcuOCUyMiUyQyUyMCUyMjEwLjMlMjIlNUQlMkMlMEElMjAlMjAlMjAlMjAlMjJ2b2xhdGlsZV9hY2lkaXR5JTIyJTNBJTIwJTVCJTIyMC43JTIyJTJDJTIwJTIyMC44OCUyMiUyQyUyMCUyMjAuMzIlMjIlNUQlMkMlMEElMjAlMjAlMjAlMjAlMjJjaXRyaWNfYWNpZCUyMiUzQSUyMCU1QiUyMjAlMjIlMkMlMjAlMjIwJTIyJTJDJTIwJTIyMC40NSUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMCUyMnJlc2lkdWFsX3N1Z2FyJTIyJTNBJTIwJTVCJTIyMS45JTIyJTJDJTIwJTIyMi42JTIyJTJDJTIwJTIyNi40JTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwJTIyY2hsb3JpZGVzJTIyJTNBJTIwJTVCJTIyMC4wNzYlMjIlMkMlMjAlMjIwLjA5OCUyMiUyQyUyMCUyMjAuMDczJTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwJTIyZnJlZV9zdWxmdXJfZGlveGlkZSUyMiUzQSUyMCU1QiUyMjExJTIyJTJDJTIwJTIyMjUlMjIlMkMlMjAlMjI1JTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwJTIydG90YWxfc3VsZnVyX2Rpb3hpZGUlMjIlM0ElMjAlNUIlMjIzNCUyMiUyQyUyMCUyMjY3JTIyJTJDJTIwJTIyMTMlMjIlNUQlMkMlMEElMjAlMjAlMjAlMjAlMjJkZW5zaXR5JTIyJTNBJTIwJTVCJTIyMC45OTc4JTIyJTJDJTIwJTIyMC45OTY4JTIyJTJDJTIwJTIyMC45OTc2JTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwJTIycEglMjIlM0ElMjAlNUIlMjIzLjUxJTIyJTJDJTIwJTIyMy4yJTIyJTJDJTIwJTIyMy4yMyUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMCUyMnN1bHBoYXRlcyUyMiUzQSUyMCU1QiUyMjAuNTYlMjIlMkMlMjAlMjIwLjY4JTIyJTJDJTIwJTIyMC44MiUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMCUyMmFsY29ob2wlMjIlM0ElMjAlNUIlMjI5LjQlMjIlMkMlMjAlMjI5LjglMjIlMkMlMjAlMjIxMi42JTIyJTVEJTJDJTBBJTdEJTBBYXdhaXQlMjBjbGllbnQudGFidWxhcl9jbGFzc2lmaWNhdGlvbih0YWJsZSUzRHRhYmxlJTJDJTIwbW9kZWwlM0QlMjJqdWxpZW4tYyUyRndpbmUtcXVhbGl0eSUyMik=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>table = {
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;fixed_acidity&quot;</span>: [<span class="hljs-string">&quot;7.4&quot;</span>, <span class="hljs-string">&quot;7.8&quot;</span>, <span class="hljs-string">&quot;10.3&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;volatile_acidity&quot;</span>: [<span class="hljs-string">&quot;0.7&quot;</span>, <span class="hljs-string">&quot;0.88&quot;</span>, <span class="hljs-string">&quot;0.32&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;citric_acid&quot;</span>: [<span class="hljs-string">&quot;0&quot;</span>, <span class="hljs-string">&quot;0&quot;</span>, <span class="hljs-string">&quot;0.45&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;residual_sugar&quot;</span>: [<span class="hljs-string">&quot;1.9&quot;</span>, <span class="hljs-string">&quot;2.6&quot;</span>, <span class="hljs-string">&quot;6.4&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;chlorides&quot;</span>: [<span class="hljs-string">&quot;0.076&quot;</span>, <span class="hljs-string">&quot;0.098&quot;</span>, <span class="hljs-string">&quot;0.073&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;free_sulfur_dioxide&quot;</span>: [<span class="hljs-string">&quot;11&quot;</span>, <span class="hljs-string">&quot;25&quot;</span>, <span class="hljs-string">&quot;5&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;total_sulfur_dioxide&quot;</span>: [<span class="hljs-string">&quot;34&quot;</span>, <span class="hljs-string">&quot;67&quot;</span>, <span class="hljs-string">&quot;13&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;density&quot;</span>: [<span class="hljs-string">&quot;0.9978&quot;</span>, <span class="hljs-string">&quot;0.9968&quot;</span>, <span class="hljs-string">&quot;0.9976&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;pH&quot;</span>: [<span class="hljs-string">&quot;3.51&quot;</span>, <span class="hljs-string">&quot;3.2&quot;</span>, <span class="hljs-string">&quot;3.23&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;sulphates&quot;</span>: [<span class="hljs-string">&quot;0.56&quot;</span>, <span class="hljs-string">&quot;0.68&quot;</span>, <span class="hljs-string">&quot;0.82&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;alcohol&quot;</span>: [<span class="hljs-string">&quot;9.4&quot;</span>, <span class="hljs-string">&quot;9.8&quot;</span>, <span class="hljs-string">&quot;12.6&quot;</span>],
<span class="hljs-meta">... </span>}
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.tabular_classification(table=table, model=<span class="hljs-string">&quot;julien-c/wine-quality&quot;</span>)
[<span class="hljs-string">&quot;5&quot;</span>, <span class="hljs-string">&quot;5&quot;</span>, <span class="hljs-string">&quot;5&quot;</span>]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function lf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQXRhYmxlJTIwJTNEJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIySGVpZ2h0JTIyJTNBJTIwJTVCJTIyMTEuNTIlMjIlMkMlMjAlMjIxMi40OCUyMiUyQyUyMCUyMjEyLjM3NzglMjIlNUQlMkMlMEElMjAlMjAlMjAlMjAlMjJMZW5ndGgxJTIyJTNBJTIwJTVCJTIyMjMuMiUyMiUyQyUyMCUyMjI0JTIyJTJDJTIwJTIyMjMuOSUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMCUyMkxlbmd0aDIlMjIlM0ElMjAlNUIlMjIyNS40JTIyJTJDJTIwJTIyMjYuMyUyMiUyQyUyMCUyMjI2LjUlMjIlNUQlMkMlMEElMjAlMjAlMjAlMjAlMjJMZW5ndGgzJTIyJTNBJTIwJTVCJTIyMzAlMjIlMkMlMjAlMjIzMS4yJTIyJTJDJTIwJTIyMzEuMSUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMCUyMlNwZWNpZXMlMjIlM0ElMjAlNUIlMjJCcmVhbSUyMiUyQyUyMCUyMkJyZWFtJTIyJTJDJTIwJTIyQnJlYW0lMjIlNUQlMkMlMEElMjAlMjAlMjAlMjAlMjJXaWR0aCUyMiUzQSUyMCU1QiUyMjQuMDIlMjIlMkMlMjAlMjI0LjMwNTYlMjIlMkMlMjAlMjI0LjY5NjElMjIlNUQlMkMlMEElN0QlMEFhd2FpdCUyMGNsaWVudC50YWJ1bGFyX3JlZ3Jlc3Npb24odGFibGUlMkMlMjBtb2RlbCUzRCUyMnNjaWtpdC1sZWFybiUyRkZpc2gtV2VpZ2h0JTIyKQ==",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>table = {
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Height&quot;</span>: [<span class="hljs-string">&quot;11.52&quot;</span>, <span class="hljs-string">&quot;12.48&quot;</span>, <span class="hljs-string">&quot;12.3778&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Length1&quot;</span>: [<span class="hljs-string">&quot;23.2&quot;</span>, <span class="hljs-string">&quot;24&quot;</span>, <span class="hljs-string">&quot;23.9&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Length2&quot;</span>: [<span class="hljs-string">&quot;25.4&quot;</span>, <span class="hljs-string">&quot;26.3&quot;</span>, <span class="hljs-string">&quot;26.5&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Length3&quot;</span>: [<span class="hljs-string">&quot;30&quot;</span>, <span class="hljs-string">&quot;31.2&quot;</span>, <span class="hljs-string">&quot;31.1&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Species&quot;</span>: [<span class="hljs-string">&quot;Bream&quot;</span>, <span class="hljs-string">&quot;Bream&quot;</span>, <span class="hljs-string">&quot;Bream&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Width&quot;</span>: [<span class="hljs-string">&quot;4.02&quot;</span>, <span class="hljs-string">&quot;4.3056&quot;</span>, <span class="hljs-string">&quot;4.6961&quot;</span>],
<span class="hljs-meta">... </span>}
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.tabular_regression(table, model=<span class="hljs-string">&quot;scikit-learn/Fish-Weight&quot;</span>)
[<span class="hljs-number">110</span>, <span class="hljs-number">120</span>, <span class="hljs-number">130</span>]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function cf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LnRleHRfY2xhc3NpZmljYXRpb24oJTIySSUyMGxpa2UlMjB5b3UlMjIp",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.text_classification(<span class="hljs-string">&quot;I like you&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9998695850372314</span>}, {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;NEGATIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0001304351753788069</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function pf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()

<span class="hljs-comment"># Case 1: generate text</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>)
<span class="hljs-string">&#x27;100% open source and built to be easy to use.&#x27;</span>

<span class="hljs-comment"># Case 2: iterate over the generated tokens. Useful async for large generation.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">async</span> <span class="hljs-keyword">for</span> token <span class="hljs-keyword">in</span> <span class="hljs-keyword">await</span> client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>, stream=<span class="hljs-literal">True</span>):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(token)
<span class="hljs-number">100</span>
%
<span class="hljs-built_in">open</span>
source
<span class="hljs-keyword">and</span>
built
to
be
easy
to
use
.

<span class="hljs-comment"># Case 3: get more details about the generation process.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>, details=<span class="hljs-literal">True</span>)
TextGenerationResponse(
    generated_text=<span class="hljs-string">&#x27;100% open source and built to be easy to use.&#x27;</span>,
    details=Details(
        finish_reason=&lt;FinishReason.Length: <span class="hljs-string">&#x27;length&#x27;</span>&gt;,
        generated_tokens=<span class="hljs-number">12</span>,
        seed=<span class="hljs-literal">None</span>,
        prefill=[
            InputToken(<span class="hljs-built_in">id</span>=<span class="hljs-number">487</span>, text=<span class="hljs-string">&#x27;The&#x27;</span>, logprob=<span class="hljs-literal">None</span>),
            InputToken(<span class="hljs-built_in">id</span>=<span class="hljs-number">53789</span>, text=<span class="hljs-string">&#x27; hugging&#x27;</span>, logprob=-<span class="hljs-number">13.171875</span>),
            (...)
            InputToken(<span class="hljs-built_in">id</span>=<span class="hljs-number">204</span>, text=<span class="hljs-string">&#x27; &#x27;</span>, logprob=-<span class="hljs-number">7.0390625</span>)
        ],
        tokens=[
            Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1425</span>, text=<span class="hljs-string">&#x27;100&#x27;</span>, logprob=-<span class="hljs-number">1.0175781</span>, special=<span class="hljs-literal">False</span>),
            Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">16</span>, text=<span class="hljs-string">&#x27;%&#x27;</span>, logprob=-<span class="hljs-number">0.0463562</span>, special=<span class="hljs-literal">False</span>),
            (...)
            Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">25</span>, text=<span class="hljs-string">&#x27;.&#x27;</span>, logprob=-<span class="hljs-number">0.5703125</span>, special=<span class="hljs-literal">False</span>)
        ],
        best_of_sequences=<span class="hljs-literal">None</span>
    )
)

<span class="hljs-comment"># Case 4: iterate over the generated tokens with more details.</span>
<span class="hljs-comment"># Last object is more complete, containing the full generated text and the finish reason.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">async</span> <span class="hljs-keyword">for</span> details <span class="hljs-keyword">in</span> <span class="hljs-keyword">await</span> client.text_generation(<span class="hljs-string">&quot;The huggingface_hub library is &quot;</span>, max_new_tokens=<span class="hljs-number">12</span>, details=<span class="hljs-literal">True</span>, stream=<span class="hljs-literal">True</span>):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(details)
...
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1425</span>, text=<span class="hljs-string">&#x27;100&#x27;</span>, logprob=-<span class="hljs-number">1.0175781</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">16</span>, text=<span class="hljs-string">&#x27;%&#x27;</span>, logprob=-<span class="hljs-number">0.0463562</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1314</span>, text=<span class="hljs-string">&#x27; open&#x27;</span>, logprob=-<span class="hljs-number">1.3359375</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">3178</span>, text=<span class="hljs-string">&#x27; source&#x27;</span>, logprob=-<span class="hljs-number">0.28100586</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">273</span>, text=<span class="hljs-string">&#x27; and&#x27;</span>, logprob=-<span class="hljs-number">0.5961914</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">3426</span>, text=<span class="hljs-string">&#x27; built&#x27;</span>, logprob=-<span class="hljs-number">1.9423828</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">271</span>, text=<span class="hljs-string">&#x27; to&#x27;</span>, logprob=-<span class="hljs-number">1.4121094</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">314</span>, text=<span class="hljs-string">&#x27; be&#x27;</span>, logprob=-<span class="hljs-number">1.5224609</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">1833</span>, text=<span class="hljs-string">&#x27; easy&#x27;</span>, logprob=-<span class="hljs-number">2.1132812</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">271</span>, text=<span class="hljs-string">&#x27; to&#x27;</span>, logprob=-<span class="hljs-number">0.08520508</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(<span class="hljs-built_in">id</span>=<span class="hljs-number">745</span>, text=<span class="hljs-string">&#x27; use&#x27;</span>, logprob=-<span class="hljs-number">0.39453125</span>, special=<span class="hljs-literal">False</span>), generated_text=<span class="hljs-literal">None</span>, details=<span class="hljs-literal">None</span>)
TextGenerationStreamResponse(token=Token(
    <span class="hljs-built_in">id</span>=<span class="hljs-number">25</span>,
    text=<span class="hljs-string">&#x27;.&#x27;</span>,
    logprob=-<span class="hljs-number">0.5703125</span>,
    special=<span class="hljs-literal">False</span>),
    generated_text=<span class="hljs-string">&#x27;100% open source and built to be easy to use.&#x27;</span>,
    details=StreamDetails(finish_reason=&lt;FinishReason.Length: <span class="hljs-string">&#x27;length&#x27;</span>&gt;, generated_tokens=<span class="hljs-number">12</span>, seed=<span class="hljs-literal">None</span>)
)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function gf($){let t,y="You must have <code>PIL</code> installed if you want to work with images (<code>pip install Pillow</code>).";return{c(){t=p("p"),t.innerHTML=y},l(o){t=g(o,"P",{"data-svelte-h":!0}),T(t)!=="svelte-s2e3n9"&&(t.innerHTML=y)},m(o,s){j(o,t,s)},p:J,d(o){o&&c(t)}}}function uf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()

<span class="hljs-meta">&gt;&gt;&gt; </span>image = <span class="hljs-keyword">await</span> client.text_to_image(<span class="hljs-string">&quot;An astronaut riding a horse on the moon.&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;astronaut.png&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image = <span class="hljs-keyword">await</span> client.text_to_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;An astronaut riding a horse on the moon.&quot;</span>,
<span class="hljs-meta">... </span>    negative_prompt=<span class="hljs-string">&quot;low resolution, blurry&quot;</span>,
<span class="hljs-meta">... </span>    model=<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;better_astronaut.png&quot;</span>)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function df($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMHBhdGhsaWIlMjBpbXBvcnQlMjBQYXRoJTBBZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQSUwQWF1ZGlvJTIwJTNEJTIwYXdhaXQlMjBjbGllbnQudGV4dF90b19zcGVlY2goJTIySGVsbG8lMjB3b3JsZCUyMiklMEFQYXRoKCUyMmhlbGxvX3dvcmxkLmZsYWMlMjIpLndyaXRlX2J5dGVzKGF1ZGlvKQ==",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()

<span class="hljs-meta">&gt;&gt;&gt; </span>audio = <span class="hljs-keyword">await</span> client.text_to_speech(<span class="hljs-string">&quot;Hello world&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>Path(<span class="hljs-string">&quot;hello_world.flac&quot;</span>).write_bytes(audio)`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function mf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LnRva2VuX2NsYXNzaWZpY2F0aW9uKCUyMk15JTIwbmFtZSUyMGlzJTIwU2FyYWglMjBKZXNzaWNhJTIwUGFya2VyJTIwYnV0JTIweW91JTIwY2FuJTIwY2FsbCUyMG1lJTIwSmVzc2ljYSUyMik=",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.token_classification(<span class="hljs-string">&quot;My name is Sarah Jessica Parker but you can call me Jessica&quot;</span>)
[{<span class="hljs-string">&#x27;entity_group&#x27;</span>: <span class="hljs-string">&#x27;PER&#x27;</span>,
<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9971321225166321</span>,
<span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Sarah Jessica Parker&#x27;</span>,
<span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">11</span>,
<span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">31</span>},
{<span class="hljs-string">&#x27;entity_group&#x27;</span>: <span class="hljs-string">&#x27;PER&#x27;</span>,
<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9773476123809814</span>,
<span class="hljs-string">&#x27;word&#x27;</span>: <span class="hljs-string">&#x27;Jessica&#x27;</span>,
<span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">52</span>,
<span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">59</span>}]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function hf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LnRyYW5zbGF0aW9uKCUyMk15JTIwbmFtZSUyMGlzJTIwV29sZmdhbmclMjBhbmQlMjBJJTIwbGl2ZSUyMGluJTIwQmVybGluJTIyKSUwQWF3YWl0JTIwY2xpZW50LnRyYW5zbGF0aW9uKCUyMk15JTIwbmFtZSUyMGlzJTIwV29sZmdhbmclMjBhbmQlMjBJJTIwbGl2ZSUyMGluJTIwQmVybGluJTIyJTJDJTIwbW9kZWwlM0QlMjJIZWxzaW5raS1OTFAlMkZvcHVzLW10LWVuLWZyJTIyKQ==",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.translation(<span class="hljs-string">&quot;My name is Wolfgang and I live in Berlin&quot;</span>)
<span class="hljs-string">&#x27;Mein Name ist Wolfgang und ich lebe in Berlin.&#x27;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.translation(<span class="hljs-string">&quot;My name is Wolfgang and I live in Berlin&quot;</span>, model=<span class="hljs-string">&quot;Helsinki-NLP/opus-mt-en-fr&quot;</span>)
<span class="hljs-string">&quot;Je m&#x27;appelle Wolfgang et je vis à Berlin.&quot;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function ff($){let t,y="Specifying languages:",o,s,i;return s=new q({props:{code:"Y2xpZW50LnRyYW5zbGF0aW9uKCUyMk15JTIwbmFtZSUyMGlzJTIwU2FyYWglMjBKZXNzaWNhJTIwUGFya2VyJTIwYnV0JTIweW91JTIwY2FuJTIwY2FsbCUyMG1lJTIwSmVzc2ljYSUyMiUyQyUyMG1vZGVsJTNEJTIyZmFjZWJvb2slMkZtYmFydC1sYXJnZS01MC1tYW55LXRvLW1hbnktbW10JTIyJTJDJTIwc3JjX2xhbmclM0QlMjJlbl9YWCUyMiUyQyUyMHRndF9sYW5nJTNEJTIyZnJfWFglMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>client.translation(<span class="hljs-string">&quot;My name is Sarah Jessica Parker but you can call me Jessica&quot;</span>, model=<span class="hljs-string">&quot;facebook/mbart-large-50-many-to-many-mmt&quot;</span>, src_lang=<span class="hljs-string">&quot;en_XX&quot;</span>, tgt_lang=<span class="hljs-string">&quot;fr_XX&quot;</span>)
<span class="hljs-string">&quot;Mon nom est Sarah Jessica Parker mais vous pouvez m&#x27;appeler Jessica&quot;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-mhcj6c"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function bf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQWF3YWl0JTIwY2xpZW50LnZpc3VhbF9xdWVzdGlvbl9hbnN3ZXJpbmcoJTBBJTIwJTIwJTIwJTIwaW1hZ2UlM0QlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZtaXNoaWclMkZzYW1wbGVfaW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZ0aWdlci5qcGclMjIlMkMlMEElMjAlMjAlMjAlMjBxdWVzdGlvbiUzRCUyMldoYXQlMjBpcyUyMHRoZSUyMGFuaW1hbCUyMGRvaW5nJTNGJTIyJTBBKQ==",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.visual_question_answering(
<span class="hljs-meta">... </span>    image=<span class="hljs-string">&quot;https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg&quot;</span>,
<span class="hljs-meta">... </span>    question=<span class="hljs-string">&quot;What is the animal doing?&quot;</span>
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.778609573841095</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;laying down&#x27;</span>},{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.6957435607910156</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;sitting&#x27;</span>}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function _f($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()
<span class="hljs-meta">&gt;&gt;&gt; </span>text = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A new model offers an explanation async for how the Galilean satellites formed around the solar system&#x27;s&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;largest world. Konstantin Batygin did not set out to solve one of the solar system&#x27;s most puzzling&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot; mysteries when he went async for a run up a hill in Nice, France.&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = [<span class="hljs-string">&quot;space &amp; cosmos&quot;</span>, <span class="hljs-string">&quot;scientific discovery&quot;</span>, <span class="hljs-string">&quot;microbiology&quot;</span>, <span class="hljs-string">&quot;robots&quot;</span>, <span class="hljs-string">&quot;archeology&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.zero_shot_classification(text, labels)
[
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;scientific discovery&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.7961668968200684</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;space &amp; cosmos&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.18570658564567566</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;microbiology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.00730885099619627</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;archeology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.006258360575884581</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;robots&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.004559356719255447</span>},
]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.zero_shot_classification(text, labels, multi_label=<span class="hljs-literal">True</span>)
[
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;scientific discovery&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.9829297661781311</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;space &amp; cosmos&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.755190908908844</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;microbiology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.0005462635890580714</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;archeology&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.00047131875180639327</span>},
    {<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;robots&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.00030448526376858354</span>},
]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function xf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEFzeW5jSW5mZXJlbmNlQ2xpZW50JTBBY2xpZW50JTIwJTNEJTIwQXN5bmNJbmZlcmVuY2VDbGllbnQoKSUwQSUwQWF3YWl0JTIwY2xpZW50Lnplcm9fc2hvdF9pbWFnZV9jbGFzc2lmaWNhdGlvbiglMEElMjAlMjAlMjAlMjAlMjJodHRwcyUzQSUyRiUyRnVwbG9hZC53aWtpbWVkaWEub3JnJTJGd2lraXBlZGlhJTJGY29tbW9ucyUyRnRodW1iJTJGNCUyRjQzJTJGQ3V0ZV9kb2cuanBnJTJGMzIwcHgtQ3V0ZV9kb2cuanBnJTIyJTJDJTBBJTIwJTIwJTIwJTIwbGFiZWxzJTNEJTVCJTIyZG9nJTIyJTJDJTIwJTIyY2F0JTIyJTJDJTIwJTIyaG9yc2UlMjIlNUQlMkMlMEEp",highlighted:`<span class="hljs-comment"># Must be run in an async context</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> AsyncInferenceClient
<span class="hljs-meta">&gt;&gt;&gt; </span>client = AsyncInferenceClient()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">await</span> client.zero_shot_image_classification(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg&quot;</span>,
<span class="hljs-meta">... </span>    labels=[<span class="hljs-string">&quot;dog&quot;</span>, <span class="hljs-string">&quot;cat&quot;</span>, <span class="hljs-string">&quot;horse&quot;</span>],
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">&quot;label&quot;</span>: <span class="hljs-string">&quot;dog&quot;</span>, <span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-number">0.956</span>}, ...]`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function yf($){let t,y="Example:",o,s,i;return s=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub.inference_api <span class="hljs-keyword">import</span> InferenceApi

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Mask-fill example</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inference = InferenceApi(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inference(inputs=<span class="hljs-string">&quot;The goal of life is [MASK].&quot;</span>)
[{<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;the goal of life is life.&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.10933292657136917</span>, <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">2166</span>, <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27;life&#x27;</span>}]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Question Answering example</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inference = InferenceApi(<span class="hljs-string">&quot;deepset/roberta-base-squad2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = {
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;question&quot;</span>: <span class="hljs-string">&quot;What&#x27;s my name?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;context&quot;</span>: <span class="hljs-string">&quot;My name is Clara and I live in Berkeley.&quot;</span>,
<span class="hljs-meta">... </span>}
<span class="hljs-meta">&gt;&gt;&gt; </span>inference(inputs)
{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9326569437980652</span>, <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">11</span>, <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">16</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;Clara&#x27;</span>}

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Zero-shot example</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inference = InferenceApi(<span class="hljs-string">&quot;typeform/distilbert-base-uncased-mnli&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = <span class="hljs-string">&quot;Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>params = {<span class="hljs-string">&quot;candidate_labels&quot;</span>: [<span class="hljs-string">&quot;refund&quot;</span>, <span class="hljs-string">&quot;legal&quot;</span>, <span class="hljs-string">&quot;faq&quot;</span>]}
<span class="hljs-meta">&gt;&gt;&gt; </span>inference(inputs, params)
{<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!&#x27;</span>, <span class="hljs-string">&#x27;labels&#x27;</span>: [<span class="hljs-string">&#x27;refund&#x27;</span>, <span class="hljs-string">&#x27;faq&#x27;</span>, <span class="hljs-string">&#x27;legal&#x27;</span>], <span class="hljs-string">&#x27;scores&#x27;</span>: [<span class="hljs-number">0.9378499388694763</span>, <span class="hljs-number">0.04914155602455139</span>, <span class="hljs-number">0.013008488342165947</span>]}

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Overriding configured task</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inference = InferenceApi(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>, task=<span class="hljs-string">&quot;feature-extraction&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Text-to-image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inference = InferenceApi(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inference(<span class="hljs-string">&quot;cat&quot;</span>)
&lt;PIL.PngImagePlugin.PngImageFile image (...)&gt;

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Return as raw response to parse the output yourself</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inference = InferenceApi(<span class="hljs-string">&quot;mio/amadeus&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>response = inference(<span class="hljs-string">&quot;hello world&quot;</span>, raw_response=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>response.headers
{<span class="hljs-string">&quot;Content-Type&quot;</span>: <span class="hljs-string">&quot;audio/flac&quot;</span>, ...}
<span class="hljs-meta">&gt;&gt;&gt; </span>response.content <span class="hljs-comment"># raw bytes from server</span>
<span class="hljs-string">b&#x27;(...)&#x27;</span>`,wrap:!1}}),{c(){t=p("p"),t.textContent=y,o=r(),m(s.$$.fragment)},l(e){t=g(e,"P",{"data-svelte-h":!0}),T(t)!=="svelte-11lpom8"&&(t.textContent=y),o=l(e),h(s.$$.fragment,e)},m(e,u){j(e,t,u),j(e,o,u),f(s,e,u),i=!0},p:J,i(e){i||(b(s.$$.fragment,e),i=!0)},o(e){_(s.$$.fragment,e),i=!1},d(e){e&&(c(t),c(o)),x(s,e)}}}function jf($){let t,y,o,s,i,e,u,zu=`Inference is the process of using a trained model to make predictions on new data. As this process can be compute-intensive,
running on a dedicated server can be an interesting option. The <code>huggingface_hub</code> library provides an easy way to call a
service that runs inference for hosted models. There are several services you can connect to:`,Nl,Es,Ou=`<li><a href="https://huggingface.co/docs/api-inference/index" rel="nofollow">Inference API</a>: a service that allows you to run accelerated inference
on Hugging Face’s infrastructure for free. This service is a fast way to get started, test different models, and
prototype AI products.</li> <li><a href="https://huggingface.co/inference-endpoints" rel="nofollow">Inference Endpoints</a>: a product to easily deploy models to production.
Inference is run by Hugging Face in a dedicated, fully managed infrastructure on a cloud provider of your choice.</li>`,Gl,Ns,Ku=`These services can be called with the <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a> object. Please refer to <a href="../guides/inference">this guide</a>
for more information on how to use it.`,Vl,Gs,Dl,C,Vs,ki,Bo,ed="Initialize a new Inference Client.",qi,Ho,nd=`<a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a> aims to provide a unified experience to perform inference. The client can be used
seamlessly with either the (free) Inference API or self-hosted Inference Endpoints.`,Zi,je,Ds,Wi,Fo,td="Perform audio classification on the provided audio content.",Ei,pt,Ni,Te,Ys,Gi,Po,sd="Performs multiple tasks related to audio-to-audio depending on the model (eg: speech enhancement, source separation).",Vi,gt,Di,$e,Rs,Yi,Lo,ad="Perform automatic speech recognition (ASR or audio-to-text) on the given audio content.",Ri,ut,Ai,Ie,As,Bi,Qo,od="Generate conversational responses based on the given input text (i.e. chat with the API).",Hi,dt,Fi,Me,Bs,Pi,So,rd="Answer questions on document images.",Li,mt,Qi,we,Hs,Si,Xo,ld="Generate embeddings for a given text.",Xi,ht,zi,Ce,Fs,Oi,zo,id="Fill in a hole with a missing word (token to be precise).",Ki,ft,ec,Q,Ps,nc,Oo,cd="Get the status of a model hosted on the Inference API.",tc,bt,sc,_t,ac,xt,Ls,oc,Ko,pd="Get the model Hugging Face recommends for the input task.",rc,ve,Qs,lc,er,gd="Perform image classification on the given image using the specified model.",ic,yt,cc,S,Ss,pc,nr,ud="Perform image segmentation on the given image using the specified model.",gc,jt,uc,Tt,dc,X,Xs,mc,tr,dd="Perform image-to-image translation using a specified model.",hc,$t,fc,It,bc,z,zs,_c,sr,md="Takes an input image and return text.",xc,ar,hd=`Models can have very different outputs depending on your use case (image captioning, optical character recognition
(OCR), Pix2Struct, etc). Please have a look to the model card to learn more about a model’s specificities.`,yc,Mt,jc,D,Os,Tc,or,fd="List models currently deployed on the Inference API service.",$c,rr,bd=`This helper checks deployed models framework by framework. By default, it will check the 4 main frameworks that
are supported and account for 95% of the hosted models. However, if you want a complete list of models you can
specify <code>frameworks=&quot;all&quot;</code> as input. Alternatively, if you know before-hand which framework you are interested
in, you can also restrict to search to this one (e.g. <code>frameworks=&quot;text-generation-inference&quot;</code>). The more
frameworks are checked, the more time it will take.`,Ic,wt,Mc,Ct,wc,O,Ks,Cc,lr,_d="Perform object detection on the given image using the specified model.",vc,vt,Jc,Jt,Uc,Ut,ea,kc,ir,xd="Make a POST request to the inference server.",qc,Je,na,Zc,cr,yd="Retrieve the answer to a question from a given text.",Wc,kt,Ec,Ue,ta,Nc,pr,jd="Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings.",Gc,qt,Vc,ke,sa,Dc,gr,Td="Generate a summary of a given text using a specified model.",Yc,Zt,Rc,qe,aa,Ac,ur,$d="Retrieve the answer to a question from information given in a table.",Bc,Wt,Hc,Ze,oa,Fc,dr,Id="Classifying a target category (a group) based on a set of attributes.",Pc,Et,Lc,We,ra,Qc,mr,Md="Predicting a numerical target value given a set of attributes/features in a table.",Sc,Nt,Xc,Ee,la,zc,hr,wd="Perform text classification (e.g. sentiment-analysis) on the given text.",Oc,Gt,Kc,W,ia,ep,fr,Cd="Given a prompt, generate the following text.",np,br,vd=`It is recommended to have Pydantic installed in order to get inputs validated. This is preferable as it allow
early failures.`,tp,_r,Jd=`API endpoint is supposed to run with the <code>text-generation-inference</code> backend (TGI). This backend is the
go-to solution to run large language models at scale. However, for some smaller models (e.g. “gpt2”) the
default <code>transformers</code> + <code>api-inference</code> solution is still in use. Both approaches have very similar APIs, but
not exactly the same. This method is compatible with both approaches but some parameters are only available for
<code>text-generation-inference</code>. If some parameters are ignored, a warning message is triggered but the process
continues correctly.`,sp,xr,Ud='To learn more about the TGI project, please refer to <a href="https://github.com/huggingface/text-generation-inference" rel="nofollow">https://github.com/huggingface/text-generation-inference</a>.',ap,Vt,op,K,ca,rp,yr,kd="Generate an image based on a given text using a specified model.",lp,Dt,ip,Yt,cp,Ne,pa,pp,jr,qd="Synthesize an audio of a voice pronouncing a given text.",gp,Rt,up,Ge,ga,dp,Tr,Zd=`Perform token classification on the given text.
Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text.`,mp,At,hp,Y,ua,fp,$r,Wd="Convert text from one language to another.",bp,Ir,Ed=`Check out <a href="https://huggingface.co/tasks/translation" rel="nofollow">https://huggingface.co/tasks/translation</a> for more information on how to choose the best model for
your specific use case. Source and target languages usually depend on the model.
However, it is possible to specify source and target languages for certain models. If you are working with one of these models,
you can use <code>src_lang</code> and <code>tgt_lang</code> arguments to pass the relevant information.
You can find this information in the model card.`,_p,Bt,xp,Ht,yp,Ve,da,jp,Mr,Nd="Answering open-ended questions based on an image.",Tp,Ft,$p,De,ma,Ip,wr,Gd="Provide as input a text and a set of candidate labels to classify the input text.",Mp,Pt,wp,Ye,ha,Cp,Cr,Vd="Provide input image and text labels to predict text labels for the image.",vp,Lt,Yl,fa,Rl,ba,Dd=`An async version of the client is also provided, based on <code>asyncio</code> and <code>aiohttp</code>.
To use it, you can either install <code>aiohttp</code> directly or use the <code>[inference]</code> extra:`,Al,_a,Bl,v,xa,Jp,vr,Yd="Initialize a new Inference Client.",Up,Jr,Rd=`<a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a> aims to provide a unified experience to perform inference. The client can be used
seamlessly with either the (free) Inference API or self-hosted Inference Endpoints.`,kp,Re,ya,qp,Ur,Ad="Perform audio classification on the provided audio content.",Zp,Qt,Wp,Ae,ja,Ep,kr,Bd="Performs multiple tasks related to audio-to-audio depending on the model (eg: speech enhancement, source separation).",Np,St,Gp,Be,Ta,Vp,qr,Hd="Perform automatic speech recognition (ASR or audio-to-text) on the given audio content.",Dp,Xt,Yp,He,$a,Rp,Zr,Fd="Generate conversational responses based on the given input text (i.e. chat with the API).",Ap,zt,Bp,Fe,Ia,Hp,Wr,Pd="Answer questions on document images.",Fp,Ot,Pp,Pe,Ma,Lp,Er,Ld="Generate embeddings for a given text.",Qp,Kt,Sp,Le,wa,Xp,Nr,Qd="Fill in a hole with a missing word (token to be precise).",zp,es,Op,ee,Ca,Kp,Gr,Sd="Get the status of a model hosted on the Inference API.",eg,ns,ng,ts,tg,ss,va,sg,Vr,Xd="Get the model Hugging Face recommends for the input task.",ag,Qe,Ja,og,Dr,zd="Perform image classification on the given image using the specified model.",rg,as,lg,ne,Ua,ig,Yr,Od="Perform image segmentation on the given image using the specified model.",cg,os,pg,rs,gg,te,ka,ug,Rr,Kd="Perform image-to-image translation using a specified model.",dg,ls,mg,is,hg,se,qa,fg,Ar,em="Takes an input image and return text.",bg,Br,nm=`Models can have very different outputs depending on your use case (image captioning, optical character recognition
(OCR), Pix2Struct, etc). Please have a look to the model card to learn more about a model’s specificities.`,_g,cs,xg,R,Za,yg,Hr,tm="List models currently deployed on the Inference API service.",jg,Fr,sm=`This helper checks deployed models framework by framework. By default, it will check the 4 main frameworks that
are supported and account for 95% of the hosted models. However, if you want a complete list of models you can
specify <code>frameworks=&quot;all&quot;</code> as input. Alternatively, if you know before-hand which framework you are interested
in, you can also restrict to search to this one (e.g. <code>frameworks=&quot;text-generation-inference&quot;</code>). The more
frameworks are checked, the more time it will take.`,Tg,ps,$g,gs,Ig,ae,Wa,Mg,Pr,am="Perform object detection on the given image using the specified model.",wg,us,Cg,ds,vg,ms,Ea,Jg,Lr,om="Make a POST request to the inference server.",Ug,Se,Na,kg,Qr,rm="Retrieve the answer to a question from a given text.",qg,hs,Zg,Xe,Ga,Wg,Sr,lm="Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings.",Eg,fs,Ng,ze,Va,Gg,Xr,im="Generate a summary of a given text using a specified model.",Vg,bs,Dg,Oe,Da,Yg,zr,cm="Retrieve the answer to a question from information given in a table.",Rg,_s,Ag,Ke,Ya,Bg,Or,pm="Classifying a target category (a group) based on a set of attributes.",Hg,xs,Fg,en,Ra,Pg,Kr,gm="Predicting a numerical target value given a set of attributes/features in a table.",Lg,ys,Qg,nn,Aa,Sg,el,um="Perform text classification (e.g. sentiment-analysis) on the given text.",Xg,js,zg,E,Ba,Og,nl,dm="Given a prompt, generate the following text.",Kg,tl,mm=`It is recommended to have Pydantic installed in order to get inputs validated. This is preferable as it allow
early failures.`,eu,sl,hm=`API endpoint is supposed to run with the <code>text-generation-inference</code> backend (TGI). This backend is the
go-to solution to run large language models at scale. However, for some smaller models (e.g. “gpt2”) the
default <code>transformers</code> + <code>api-inference</code> solution is still in use. Both approaches have very similar APIs, but
not exactly the same. This method is compatible with both approaches but some parameters are only available for
<code>text-generation-inference</code>. If some parameters are ignored, a warning message is triggered but the process
continues correctly.`,nu,al,fm='To learn more about the TGI project, please refer to <a href="https://github.com/huggingface/text-generation-inference" rel="nofollow">https://github.com/huggingface/text-generation-inference</a>.',tu,Ts,su,oe,Ha,au,ol,bm="Generate an image based on a given text using a specified model.",ou,$s,ru,Is,lu,tn,Fa,iu,rl,_m="Synthesize an audio of a voice pronouncing a given text.",cu,Ms,pu,sn,Pa,gu,ll,xm=`Perform token classification on the given text.
Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text.`,uu,ws,du,A,La,mu,il,ym="Convert text from one language to another.",hu,cl,jm=`Check out <a href="https://huggingface.co/tasks/translation" rel="nofollow">https://huggingface.co/tasks/translation</a> for more information on how to choose the best model for
your specific use case. Source and target languages usually depend on the model.
However, it is possible to specify source and target languages for certain models. If you are working with one of these models,
you can use <code>src_lang</code> and <code>tgt_lang</code> arguments to pass the relevant information.
You can find this information in the model card.`,fu,Cs,bu,vs,_u,an,Qa,xu,pl,Tm="Answering open-ended questions based on an image.",yu,Js,ju,on,Sa,Tu,gl,$m="Provide as input a text and a set of candidate labels to classify the input text.",$u,Us,Iu,rn,Xa,Mu,ul,Im="Provide input image and text labels to predict text labels for the image.",wu,ks,Hl,za,Fl,cn,Oa,Cu,dl,Mm="Error raised when a model is unavailable or the request times out.",Pl,Ka,Ll,eo,wm="For most tasks, the return value has a built-in type (string, list, image…). Here is a list for the more complex types.",Ql,no,Sl,pn,to,vu,ml,Cm='Dictionary containing the output of a <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.audio_classification">audio_classification()</a> and  <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.image_classification">image_classification()</a> task.',Xl,so,zl,gn,ao,Ju,hl,vm='Dictionary containing the “conversation” part of a <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.conversational">conversational()</a> task.',Ol,oo,Kl,un,ro,Uu,fl,Jm='Dictionary containing the output of a  <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.conversational">conversational()</a> task.',ei,lo,ni,dn,io,ku,bl,Um=`Dictionary containing information about a <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.image_segmentation">image_segmentation()</a> task. In practice, image segmentation returns a
list of <code>ImageSegmentationOutput</code> with 1 item per mask.`,ti,co,si,mn,po,qu,_l,km="This Dataclass represents the the model status in the Hugging Face Inference API.",ai,go,oi,hn,uo,Zu,xl,qm='Dictionary containing the output of a <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.token_classification">token_classification()</a> task.',ri,mo,li,ho,Zm=`<a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation">text_generation()</a> task has a greater support than other tasks in <code>InferenceClient</code>. In
particular, user inputs and server outputs are validated using <a href="https://docs.pydantic.dev/latest/" rel="nofollow">Pydantic</a>
if this package is installed. Therefore, we recommend installing it (<code>pip install pydantic</code>)
for a better user experience.`,ii,fo,Wm=`You can find below the dataclasses used to validate data and in particular <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.inference._text_generation.TextGenerationParameters">TextGenerationParameters</a> (input),
<a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.inference._text_generation.TextGenerationResponse">TextGenerationResponse</a> (output) and
<a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.inference._text_generation.TextGenerationStreamResponse">TextGenerationStreamResponse</a> (streaming output).`,ci,fn,bo,Wu,yl,Em="Parameters for text generation.",pi,le,_o,Eu,jl,Nm="Represents a response for text generation.",Nu,Tl,Gm="Only returned when <code>details=True</code>, otherwise a string is returned.",gi,ie,xo,Gu,$l,Vm="Represents a response for streaming text generation.",Vu,Il,Dm="Only returned when <code>details=True</code> and <code>stream=True</code>.",ui,bn,yo,Du,Ml,Ym="Represents an input token.",di,_n,jo,Yu,wl,Rm="Represents a token.",mi,xn,To,Ru,Cl,Am="An enumeration.",hi,yn,$o,Au,vl,Bm="Represents a best-of sequence generated during text generation.",fi,jn,Io,Bu,Jl,Hm="Represents details of a text generation.",bi,Tn,Mo,Hu,Ul,Fm="Represents details of a text generation stream.",_i,wo,xi,Co,Pm=`<code>InferenceAPI</code> is the legacy way to call the Inference API. The interface is more simplistic and requires knowing
the input parameters and output format for each task. It also lacks the ability to connect to other services like
Inference Endpoints or AWS SageMaker. <code>InferenceAPI</code> will soon be deprecated so we recommend using <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a>
whenever possible. Check out <a href="../guides/inference#legacy-inferenceapi-client">this guide</a> to learn how to switch from
<code>InferenceAPI</code> to <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a> in your scripts.`,yi,N,vo,Fu,kl,Lm="Client to configure requests and make calls to the HuggingFace Inference API.",Pu,qs,Lu,Zs,Jo,Qu,ql,Qm="Inits headers and API call information.",Su,Ws,Uo,Xu,Zl,Sm="Make a call to the Inference API.",ji,El,Ti;return i=new L({props:{title:"Inference",local:"inference",headingTag:"h1"}}),Gs=new L({props:{title:"Inference Client",local:"huggingface_hub.InferenceClient",headingTag:"h2"}}),Vs=new w({props:{name:"class huggingface_hub.InferenceClient",anchor:"huggingface_hub.InferenceClient",parameters:[{name:"model",val:": Optional = None"},{name:"token",val:": Union = None"},{name:"timeout",val:": Optional = None"},{name:"headers",val:": Optional = None"},{name:"cookies",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.model",description:`<strong>model</strong> (<code>str</code>, <code>optional</code>) &#x2014;
The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. <code>bigcode/starcoder</code>
or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is
automatically selected for the task.`,name:"model"},{anchor:"huggingface_hub.InferenceClient.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Hugging Face token. Will default to the locally saved token. Pass <code>token=False</code> if you don&#x2019;t want to send
your token to the server.`,name:"token"},{anchor:"huggingface_hub.InferenceClient.timeout",description:`<strong>timeout</strong> (<code>float</code>, <code>optional</code>) &#x2014;
The maximum number of seconds to wait for a response from the server. Loading a new model in Inference
API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.`,name:"timeout"},{anchor:"huggingface_hub.InferenceClient.headers",description:`<strong>headers</strong> (<code>Dict[str, str]</code>, <code>optional</code>) &#x2014;
Additional headers to send to the server. By default only the authorization and user-agent headers are sent.
Values in this dictionary will override the default values.`,name:"headers"},{anchor:"huggingface_hub.InferenceClient.cookies",description:`<strong>cookies</strong> (<code>Dict[str, str]</code>, <code>optional</code>) &#x2014;
Additional cookies to send to the server.`,name:"cookies"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L107"}}),Ds=new w({props:{name:"audio_classification",anchor:"huggingface_hub.InferenceClient.audio_classification",parameters:[{name:"audio",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.audio_classification.audio",description:`<strong>audio</strong> (Union[str, Path, bytes, BinaryIO]) &#x2014;
The audio content to classify. It can be raw audio bytes, a local audio file, or a URL pointing to an
audio file.`,name:"audio"},{anchor:"huggingface_hub.InferenceClient.audio_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for audio classification. Can be a model ID hosted on the Hugging Face Hub
or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
audio classification will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L266",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The classification output containing the predicted label and its confidence.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),pt=new Z({props:{anchor:"huggingface_hub.InferenceClient.audio_classification.example",$$slots:{default:[nh]},$$scope:{ctx:$}}}),Ys=new w({props:{name:"audio_to_audio",anchor:"huggingface_hub.InferenceClient.audio_to_audio",parameters:[{name:"audio",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.audio_to_audio.audio",description:`<strong>audio</strong> (Union[str, Path, bytes, BinaryIO]) &#x2014;
The audio content for the model. It can be raw audio bytes, a local audio file, or a URL pointing to an
audio file.`,name:"audio"},{anchor:"huggingface_hub.InferenceClient.audio_to_audio.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model can be any model which takes an audio file and returns another audio file. Can be a model ID hosted on the Hugging Face Hub
or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
audio_to_audio will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L304",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of dictionary where each index contains audios label, content-type, and audio content in blob.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>InferenceTimeoutError</code> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>InferenceTimeoutError</code> or <code>HTTPError</code></p>
`}}),gt=new Z({props:{anchor:"huggingface_hub.InferenceClient.audio_to_audio.example",$$slots:{default:[th]},$$scope:{ctx:$}}}),Rs=new w({props:{name:"automatic_speech_recognition",anchor:"huggingface_hub.InferenceClient.automatic_speech_recognition",parameters:[{name:"audio",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.automatic_speech_recognition.audio",description:`<strong>audio</strong> (Union[str, Path, bytes, BinaryIO]) &#x2014;
The content to transcribe. It can be raw audio bytes, local audio file, or a URL to an audio file.`,name:"audio"},{anchor:"huggingface_hub.InferenceClient.automatic_speech_recognition.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for ASR. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. If not provided, the default recommended model for ASR will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L347",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p>str</p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),ut=new Z({props:{anchor:"huggingface_hub.InferenceClient.automatic_speech_recognition.example",$$slots:{default:[sh]},$$scope:{ctx:$}}}),As=new w({props:{name:"conversational",anchor:"huggingface_hub.InferenceClient.conversational",parameters:[{name:"text",val:": str"},{name:"generated_responses",val:": Optional = None"},{name:"past_user_inputs",val:": Optional = None"},{name:"parameters",val:": Optional = None"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.conversational.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The last input from the user in the conversation.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.conversational.generated_responses",description:`<strong>generated_responses</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
A list of strings corresponding to the earlier replies from the model. Defaults to None.`,name:"generated_responses"},{anchor:"huggingface_hub.InferenceClient.conversational.past_user_inputs",description:`<strong>past_user_inputs</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
A list of strings corresponding to the earlier replies from the user. Should be the same length as
<code>generated_responses</code>. Defaults to None.`,name:"past_user_inputs"},{anchor:"huggingface_hub.InferenceClient.conversational.parameters",description:`<strong>parameters</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional parameters for the conversational task. Defaults to None. For more details about the available
parameters, please refer to <a href="https://huggingface.co/docs/api-inference/detailed_parameters#conversational-task" rel="nofollow">this page</a>`,name:"parameters"},{anchor:"huggingface_hub.InferenceClient.conversational.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L383",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The generated conversational output.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),dt=new Z({props:{anchor:"huggingface_hub.InferenceClient.conversational.example",$$slots:{default:[ah]},$$scope:{ctx:$}}}),Bs=new w({props:{name:"document_question_answering",anchor:"huggingface_hub.InferenceClient.document_question_answering",parameters:[{name:"image",val:": Union"},{name:"question",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.document_question_answering.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image for the context. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.InferenceClient.document_question_answering.question",description:`<strong>question</strong> (<code>str</code>) &#x2014;
Question to be answered.`,name:"question"},{anchor:"huggingface_hub.InferenceClient.document_question_answering.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the document question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended document question answering model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L488",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label, associated probability, word ids, and page number.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),mt=new Z({props:{anchor:"huggingface_hub.InferenceClient.document_question_answering.example",$$slots:{default:[oh]},$$scope:{ctx:$}}}),Hs=new w({props:{name:"feature_extraction",anchor:"huggingface_hub.InferenceClient.feature_extraction",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.feature_extraction.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The text to embed.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.feature_extraction.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L529",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The embedding representing the input text as a float32 numpy array.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>np.ndarray</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),ht=new Z({props:{anchor:"huggingface_hub.InferenceClient.feature_extraction.example",$$slots:{default:[rh]},$$scope:{ctx:$}}}),Fs=new w({props:{name:"fill_mask",anchor:"huggingface_hub.InferenceClient.fill_mask",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.fill_mask.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask).`,name:"text"},{anchor:"huggingface_hub.InferenceClient.fill_mask.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the fill mask task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended fill mask model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L565",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of fill mask output dictionaries containing the predicted label, associated
probability, token reference, and completed text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),ft=new Z({props:{anchor:"huggingface_hub.InferenceClient.fill_mask.example",$$slots:{default:[lh]},$$scope:{ctx:$}}}),Ps=new w({props:{name:"get_model_status",anchor:"huggingface_hub.InferenceClient.get_model_status",parameters:[{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.get_model_status.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Identifier of the model for witch the status gonna be checked. If model is not provided,
the model associated with this instance of <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a> will be used. Only InferenceAPI service can be checked so the
identifier cannot be a URL.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1988",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>An instance of ModelStatus dataclass, containing information,
about the state of the model: load, state, compute type and framework.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ModelStatus</code></p>
`}}),bt=new re({props:{$$slots:{default:[ih]},$$scope:{ctx:$}}}),_t=new Z({props:{anchor:"huggingface_hub.InferenceClient.get_model_status.example",$$slots:{default:[ch]},$$scope:{ctx:$}}}),Ls=new w({props:{name:"get_recommended_model",anchor:"huggingface_hub.InferenceClient.get_recommended_model",parameters:[{name:"task",val:": str"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.get_recommended_model.task",description:`<strong>task</strong> (<code>str</code>) &#x2014;
The Hugging Face task to get which model Hugging Face recommends.
All available tasks can be found <a href="https://huggingface.co/tasks" rel="nofollow">here</a>.`,name:"task"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1964",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Name of the model recommended for the input task.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValueError</code> — If Hugging Face has no recommendation for the input task.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValueError</code></p>
`}}),Qs=new w({props:{name:"image_classification",anchor:"huggingface_hub.InferenceClient.image_classification",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.image_classification.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The image to classify. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.InferenceClient.image_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for image classification. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for image classification will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L605",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label and associated probability.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),yt=new Z({props:{anchor:"huggingface_hub.InferenceClient.image_classification.example",$$slots:{default:[ph]},$$scope:{ctx:$}}}),Ss=new w({props:{name:"image_segmentation",anchor:"huggingface_hub.InferenceClient.image_segmentation",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.image_segmentation.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The image to segment. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.InferenceClient.image_segmentation.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for image segmentation. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for image segmentation will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L641",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of dictionaries containing the segmented masks and associated attributes.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),jt=new re({props:{warning:!0,$$slots:{default:[gh]},$$scope:{ctx:$}}}),Tt=new Z({props:{anchor:"huggingface_hub.InferenceClient.image_segmentation.example",$$slots:{default:[uh]},$$scope:{ctx:$}}}),Xs=new w({props:{name:"image_to_image",anchor:"huggingface_hub.InferenceClient.image_to_image",parameters:[{name:"image",val:": Union"},{name:"prompt",val:": Optional = None"},{name:"negative_prompt",val:": Optional = None"},{name:"height",val:": Optional = None"},{name:"width",val:": Optional = None"},{name:"num_inference_steps",val:": Optional = None"},{name:"guidance_scale",val:": Optional = None"},{name:"model",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.image_to_image.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image for translation. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.InferenceClient.image_to_image.prompt",description:`<strong>prompt</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The text prompt to guide the image generation.`,name:"prompt"},{anchor:"huggingface_hub.InferenceClient.image_to_image.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code>, <em>optional</em>) &#x2014;
A negative prompt to guide the translation process.`,name:"negative_prompt"},{anchor:"huggingface_hub.InferenceClient.image_to_image.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"huggingface_hub.InferenceClient.image_to_image.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"huggingface_hub.InferenceClient.image_to_image.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"huggingface_hub.InferenceClient.image_to_image.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"huggingface_hub.InferenceClient.image_to_image.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L692",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>Image</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),$t=new re({props:{warning:!0,$$slots:{default:[dh]},$$scope:{ctx:$}}}),It=new Z({props:{anchor:"huggingface_hub.InferenceClient.image_to_image.example",$$slots:{default:[mh]},$$scope:{ctx:$}}}),zs=new w({props:{name:"image_to_text",anchor:"huggingface_hub.InferenceClient.image_to_text",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.image_to_text.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image to caption. It can be raw bytes, an image file, or a URL to an online image..`,name:"image"},{anchor:"huggingface_hub.InferenceClient.image_to_text.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L776",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Mt=new Z({props:{anchor:"huggingface_hub.InferenceClient.image_to_text.example",$$slots:{default:[hh]},$$scope:{ctx:$}}}),Os=new w({props:{name:"list_deployed_models",anchor:"huggingface_hub.InferenceClient.list_deployed_models",parameters:[{name:"frameworks",val:": Union = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.list_deployed_models.frameworks",description:`<strong>frameworks</strong> (<code>Literal[&quot;all&quot;]</code> or <code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
The frameworks to filter on. By default only a subset of the available frameworks are tested. If set to
&#x201C;all&#x201D;, all available frameworks will be tested. It is also possible to provide a single framework or a
custom set of frameworks to check.`,name:"frameworks"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L812",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A dictionary mapping task names to a sorted list of model IDs.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict[str, List[str]]</code></p>
`}}),wt=new re({props:{$$slots:{default:[fh]},$$scope:{ctx:$}}}),Ct=new Z({props:{anchor:"huggingface_hub.InferenceClient.list_deployed_models.example",$$slots:{default:[bh]},$$scope:{ctx:$}}}),Ks=new w({props:{name:"object_detection",anchor:"huggingface_hub.InferenceClient.object_detection",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.object_detection.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The image to detect objects on. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.InferenceClient.object_detection.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for object detection. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for object detection (DETR) will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L887",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of dictionaries containing the bounding boxes and associated attributes.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[ObjectDetectionOutput]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
<li><code>ValueError</code> —
If the request output is not a List.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code> or <code>ValueError</code></p>
`}}),vt=new re({props:{warning:!0,$$slots:{default:[_h]},$$scope:{ctx:$}}}),Jt=new Z({props:{anchor:"huggingface_hub.InferenceClient.object_detection.example",$$slots:{default:[xh]},$$scope:{ctx:$}}}),ea=new w({props:{name:"post",anchor:"huggingface_hub.InferenceClient.post",parameters:[{name:"json",val:": Union = None"},{name:"data",val:": Union = None"},{name:"model",val:": Optional = None"},{name:"task",val:": Optional = None"},{name:"stream",val:": bool = False"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.post.json",description:`<strong>json</strong> (<code>Union[str, Dict, List]</code>, <em>optional</em>) &#x2014;
The JSON data to send in the request body, specific to each task. Defaults to None.`,name:"json"},{anchor:"huggingface_hub.InferenceClient.post.data",description:`<strong>data</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>, <em>optional</em>) &#x2014;
The content to send in the request body, specific to each task.
It can be raw bytes, a pointer to an opened file, a local file path,
or a URL to an online resource (image, audio file,&#x2026;). If both <code>json</code> and <code>data</code> are passed,
<code>data</code> will take precedence. At least <code>json</code> or <code>data</code> must be provided. Defaults to None.`,name:"data"},{anchor:"huggingface_hub.InferenceClient.post.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. Will override the model defined at the instance level. Defaults to None.`,name:"model"},{anchor:"huggingface_hub.InferenceClient.post.task",description:`<strong>task</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The task to perform on the inference. All available tasks can be found
<a href="https://huggingface.co/tasks" rel="nofollow">here</a>. Used only to default to a recommended model if <code>model</code> is not
provided. At least <code>model</code> or <code>task</code> must be provided. Defaults to None.`,name:"task"},{anchor:"huggingface_hub.InferenceClient.post.stream",description:`<strong>stream</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to iterate over streaming APIs.`,name:"stream"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L174",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The raw bytes returned by the server.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>bytes</p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),na=new w({props:{name:"question_answering",anchor:"huggingface_hub.InferenceClient.question_answering",parameters:[{name:"question",val:": str"},{name:"context",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.question_answering.question",description:`<strong>question</strong> (<code>str</code>) &#x2014;
Question to be answered.`,name:"question"},{anchor:"huggingface_hub.InferenceClient.question_answering.context",description:`<strong>context</strong> (<code>str</code>) &#x2014;
The context of the question.`,name:"context"},{anchor:"huggingface_hub.InferenceClient.question_answering.model",description:`<strong>model</strong> (<code>str</code>) &#x2014;
The model to use for the question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L935",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a dictionary of question answering output containing the score, start index, end index, and answer.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),kt=new Z({props:{anchor:"huggingface_hub.InferenceClient.question_answering.example",$$slots:{default:[yh]},$$scope:{ctx:$}}}),ta=new w({props:{name:"sentence_similarity",anchor:"huggingface_hub.InferenceClient.sentence_similarity",parameters:[{name:"sentence",val:": str"},{name:"other_sentences",val:": List"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.sentence_similarity.sentence",description:`<strong>sentence</strong> (<code>str</code>) &#x2014;
The main sentence to compare to others.`,name:"sentence"},{anchor:"huggingface_hub.InferenceClient.sentence_similarity.other_sentences",description:`<strong>other_sentences</strong> (<code>List[str]</code>) &#x2014;
The list of sentences to compare to.`,name:"other_sentences"},{anchor:"huggingface_hub.InferenceClient.sentence_similarity.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L976",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The embedding representing the input text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[float]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),qt=new Z({props:{anchor:"huggingface_hub.InferenceClient.sentence_similarity.example",$$slots:{default:[jh]},$$scope:{ctx:$}}}),sa=new w({props:{name:"summarization",anchor:"huggingface_hub.InferenceClient.summarization",parameters:[{name:"text",val:": str"},{name:"parameters",val:": Optional = None"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.summarization.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The input text to summarize.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.summarization.parameters",description:`<strong>parameters</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional parameters for summarization. Check out this <a href="https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task" rel="nofollow">page</a>
for more details.`,name:"parameters"},{anchor:"huggingface_hub.InferenceClient.summarization.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1023",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The generated summary text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Zt=new Z({props:{anchor:"huggingface_hub.InferenceClient.summarization.example",$$slots:{default:[Th]},$$scope:{ctx:$}}}),aa=new w({props:{name:"table_question_answering",anchor:"huggingface_hub.InferenceClient.table_question_answering",parameters:[{name:"table",val:": Dict"},{name:"query",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.table_question_answering.table",description:`<strong>table</strong> (<code>str</code>) &#x2014;
A table of data represented as a dict of lists where entries are headers and the lists are all the
values, all lists must have the same size.`,name:"table"},{anchor:"huggingface_hub.InferenceClient.table_question_answering.query",description:`<strong>query</strong> (<code>str</code>) &#x2014;
The query in plain text that you want to ask the table.`,name:"query"},{anchor:"huggingface_hub.InferenceClient.table_question_answering.model",description:`<strong>model</strong> (<code>str</code>) &#x2014;
The model to use for the table-question-answering task. Can be a model ID hosted on the Hugging Face
Hub or a URL to a deployed Inference Endpoint.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1066",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a dictionary of table question answering output containing the answer, coordinates, cells and the aggregator used.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Wt=new Z({props:{anchor:"huggingface_hub.InferenceClient.table_question_answering.example",$$slots:{default:[$h]},$$scope:{ctx:$}}}),oa=new w({props:{name:"tabular_classification",anchor:"huggingface_hub.InferenceClient.tabular_classification",parameters:[{name:"table",val:": Dict"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.tabular_classification.table",description:`<strong>table</strong> (<code>Dict[str, Any]</code>) &#x2014;
Set of attributes to classify.`,name:"table"},{anchor:"huggingface_hub.InferenceClient.tabular_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the tabular classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended tabular classification model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1111",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of labels, one per row in the initial table.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Et=new Z({props:{anchor:"huggingface_hub.InferenceClient.tabular_classification.example",$$slots:{default:[Ih]},$$scope:{ctx:$}}}),ra=new w({props:{name:"tabular_regression",anchor:"huggingface_hub.InferenceClient.tabular_regression",parameters:[{name:"table",val:": Dict"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.tabular_regression.table",description:`<strong>table</strong> (<code>Dict[str, Any]</code>) &#x2014;
Set of attributes stored in a table. The attributes used to predict the target can be both numerical and categorical.`,name:"table"},{anchor:"huggingface_hub.InferenceClient.tabular_regression.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the tabular regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended tabular regression model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1156",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of predicted numerical target values.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Nt=new Z({props:{anchor:"huggingface_hub.InferenceClient.tabular_regression.example",$$slots:{default:[Mh]},$$scope:{ctx:$}}}),la=new w({props:{name:"text_classification",anchor:"huggingface_hub.InferenceClient.text_classification",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.text_classification.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
A string to be classified.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.text_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the text classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1196",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label and associated probability.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Gt=new Z({props:{anchor:"huggingface_hub.InferenceClient.text_classification.example",$$slots:{default:[wh]},$$scope:{ctx:$}}}),ia=new w({props:{name:"text_generation",anchor:"huggingface_hub.InferenceClient.text_generation",parameters:[{name:"prompt",val:": str"},{name:"details",val:": bool = False"},{name:"stream",val:": bool = False"},{name:"model",val:": Optional = None"},{name:"do_sample",val:": bool = False"},{name:"max_new_tokens",val:": int = 20"},{name:"best_of",val:": Optional = None"},{name:"repetition_penalty",val:": Optional = None"},{name:"return_full_text",val:": bool = False"},{name:"seed",val:": Optional = None"},{name:"stop_sequences",val:": Optional = None"},{name:"temperature",val:": Optional = None"},{name:"top_k",val:": Optional = None"},{name:"top_p",val:": Optional = None"},{name:"truncate",val:": Optional = None"},{name:"typical_p",val:": Optional = None"},{name:"watermark",val:": bool = False"},{name:"decoder_input_details",val:": bool = False"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.text_generation.prompt",description:`<strong>prompt</strong> (<code>str</code>) &#x2014;
Input text.`,name:"prompt"},{anchor:"huggingface_hub.InferenceClient.text_generation.details",description:`<strong>details</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
By default, text_generation returns a string. Pass <code>details=True</code> if you want a detailed output (tokens,
probabilities, seed, finish reason, etc.). Only available for models running on with the
<code>text-generation-inference</code> backend.`,name:"details"},{anchor:"huggingface_hub.InferenceClient.text_generation.stream",description:`<strong>stream</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
By default, text_generation returns the full generated text. Pass <code>stream=True</code> if you want a stream of
tokens to be returned. Only available for models running on with the <code>text-generation-inference</code>
backend.`,name:"stream"},{anchor:"huggingface_hub.InferenceClient.text_generation.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"},{anchor:"huggingface_hub.InferenceClient.text_generation.do_sample",description:`<strong>do_sample</strong> (<code>bool</code>) &#x2014;
Activate logits sampling`,name:"do_sample"},{anchor:"huggingface_hub.InferenceClient.text_generation.max_new_tokens",description:`<strong>max_new_tokens</strong> (<code>int</code>) &#x2014;
Maximum number of generated tokens`,name:"max_new_tokens"},{anchor:"huggingface_hub.InferenceClient.text_generation.best_of",description:`<strong>best_of</strong> (<code>int</code>) &#x2014;
Generate best_of sequences and return the one if the highest token logprobs`,name:"best_of"},{anchor:"huggingface_hub.InferenceClient.text_generation.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>) &#x2014;
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"},{anchor:"huggingface_hub.InferenceClient.text_generation.return_full_text",description:`<strong>return_full_text</strong> (<code>bool</code>) &#x2014;
Whether to prepend the prompt to the generated text`,name:"return_full_text"},{anchor:"huggingface_hub.InferenceClient.text_generation.seed",description:`<strong>seed</strong> (<code>int</code>) &#x2014;
Random sampling seed`,name:"seed"},{anchor:"huggingface_hub.InferenceClient.text_generation.stop_sequences",description:`<strong>stop_sequences</strong> (<code>List[str]</code>) &#x2014;
Stop generating tokens if a member of <code>stop_sequences</code> is generated`,name:"stop_sequences"},{anchor:"huggingface_hub.InferenceClient.text_generation.temperature",description:`<strong>temperature</strong> (<code>float</code>) &#x2014;
The value used to module the logits distribution.`,name:"temperature"},{anchor:"huggingface_hub.InferenceClient.text_generation.top_k",description:`<strong>top_k</strong> (<code>int</code>) &#x2014;
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"huggingface_hub.InferenceClient.text_generation.top_p",description:`<strong>top_p</strong> (<code>float</code>) &#x2014;
If set to &lt; 1, only the smallest set of most probable tokens with probabilities that add up to <code>top_p</code> or
higher are kept for generation.`,name:"top_p"},{anchor:"huggingface_hub.InferenceClient.text_generation.truncate",description:`<strong>truncate</strong> (<code>int</code>) &#x2014;
Truncate inputs tokens to the given size`,name:"truncate"},{anchor:"huggingface_hub.InferenceClient.text_generation.typical_p",description:`<strong>typical_p</strong> (<code>float</code>) &#x2014;
Typical Decoding mass
See <a href="https://arxiv.org/abs/2202.00666" rel="nofollow">Typical Decoding for Natural Language Generation</a> for more information`,name:"typical_p"},{anchor:"huggingface_hub.InferenceClient.text_generation.watermark",description:`<strong>watermark</strong> (<code>bool</code>) &#x2014;
Watermarking with <a href="https://arxiv.org/abs/2301.10226" rel="nofollow">A Watermark for Large Language Models</a>`,name:"watermark"},{anchor:"huggingface_hub.InferenceClient.text_generation.decoder_input_details",description:`<strong>decoder_input_details</strong> (<code>bool</code>) &#x2014;
Return the decoder input token logprobs and ids. You must set <code>details=True</code> as well for it to be taken
into account. Defaults to <code>False</code>.`,name:"decoder_input_details"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1324",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Generated text returned from the server:</p>
<ul>
<li>if <code>stream=False</code> and <code>details=False</code>, the generated text is returned as a <code>str</code> (default)</li>
<li>if <code>stream=True</code> and <code>details=False</code>, the generated text is returned token by token as a <code>Iterable[str]</code></li>
<li>if <code>stream=False</code> and <code>details=True</code>, the generated text is returned with more details as a <a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.inference._text_generation.TextGenerationResponse"
>TextGenerationResponse</a></li>
<li>if <code>details=True</code> and <code>stream=True</code>, the generated text is returned token by token as a iterable of <a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.inference._text_generation.TextGenerationStreamResponse"
>TextGenerationStreamResponse</a></li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Union[str, TextGenerationResponse, Iterable[str], Iterable[TextGenerationStreamResponse]]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValidationError</code> —
If input values are not valid. No HTTP call is made to the server.</li>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValidationError</code> or <a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Vt=new Z({props:{anchor:"huggingface_hub.InferenceClient.text_generation.example",$$slots:{default:[Ch]},$$scope:{ctx:$}}}),ca=new w({props:{name:"text_to_image",anchor:"huggingface_hub.InferenceClient.text_to_image",parameters:[{name:"prompt",val:": str"},{name:"negative_prompt",val:": Optional = None"},{name:"height",val:": Optional = None"},{name:"width",val:": Optional = None"},{name:"num_inference_steps",val:": Optional = None"},{name:"guidance_scale",val:": Optional = None"},{name:"model",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.text_to_image.prompt",description:`<strong>prompt</strong> (<code>str</code>) &#x2014;
The prompt to generate an image from.`,name:"prompt"},{anchor:"huggingface_hub.InferenceClient.text_to_image.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code>, <em>optional</em>) &#x2014;
An optional negative prompt for the image generation.`,name:"negative_prompt"},{anchor:"huggingface_hub.InferenceClient.text_to_image.height",description:`<strong>height</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The height in pixels of the image to generate.`,name:"height"},{anchor:"huggingface_hub.InferenceClient.text_to_image.width",description:`<strong>width</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The width in pixels of the image to generate.`,name:"width"},{anchor:"huggingface_hub.InferenceClient.text_to_image.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"huggingface_hub.InferenceClient.text_to_image.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"huggingface_hub.InferenceClient.text_to_image.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1591",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>Image</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Dt=new re({props:{warning:!0,$$slots:{default:[vh]},$$scope:{ctx:$}}}),Yt=new Z({props:{anchor:"huggingface_hub.InferenceClient.text_to_image.example",$$slots:{default:[Jh]},$$scope:{ctx:$}}}),pa=new w({props:{name:"text_to_speech",anchor:"huggingface_hub.InferenceClient.text_to_speech",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.text_to_speech.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The text to synthesize.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.text_to_speech.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1671",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>bytes</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Rt=new Z({props:{anchor:"huggingface_hub.InferenceClient.text_to_speech.example",$$slots:{default:[Uh]},$$scope:{ctx:$}}}),ga=new w({props:{name:"token_classification",anchor:"huggingface_hub.InferenceClient.token_classification",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.token_classification.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
A string to be classified.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.token_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the token classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended token classification model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1703",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of token classification outputs containing the entity group, confidence score, word, start and end index.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),At=new Z({props:{anchor:"huggingface_hub.InferenceClient.token_classification.example",$$slots:{default:[kh]},$$scope:{ctx:$}}}),ua=new w({props:{name:"translation",anchor:"huggingface_hub.InferenceClient.translation",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"},{name:"src_lang",val:": Optional = None"},{name:"tgt_lang",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.translation.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
A string to be translated.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.translation.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the translation task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended translation model will be used.
Defaults to None.`,name:"model"},{anchor:"huggingface_hub.InferenceClient.translation.src_lang",description:`<strong>src_lang</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Source language of the translation task, i.e. input language. Cannot be passed without <code>tgt_lang</code>.`,name:"src_lang"},{anchor:"huggingface_hub.InferenceClient.translation.tgt_lang",description:`<strong>tgt_lang</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Target language of the translation task, i.e. output language. Cannot be passed without <code>src_lang</code>.`,name:"tgt_lang"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1750",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The generated translated text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
<li><code>ValueError</code> —
If only one of the <code>src_lang</code> and <code>tgt_lang</code> arguments are provided.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code> or <code>ValueError</code></p>
`}}),Bt=new Z({props:{anchor:"huggingface_hub.InferenceClient.translation.example",$$slots:{default:[qh]},$$scope:{ctx:$}}}),Ht=new Z({props:{anchor:"huggingface_hub.InferenceClient.translation.example-2",$$slots:{default:[Zh]},$$scope:{ctx:$}}}),da=new w({props:{name:"visual_question_answering",anchor:"huggingface_hub.InferenceClient.visual_question_answering",parameters:[{name:"image",val:": Union"},{name:"question",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.visual_question_answering.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image for the context. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.InferenceClient.visual_question_answering.question",description:`<strong>question</strong> (<code>str</code>) &#x2014;
Question to be answered.`,name:"question"},{anchor:"huggingface_hub.InferenceClient.visual_question_answering.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the visual question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended visual question answering model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L444",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label and associated probability.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>InferenceTimeoutError</code> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>InferenceTimeoutError</code> or <code>HTTPError</code></p>
`}}),Ft=new Z({props:{anchor:"huggingface_hub.InferenceClient.visual_question_answering.example",$$slots:{default:[Wh]},$$scope:{ctx:$}}}),ma=new w({props:{name:"zero_shot_classification",anchor:"huggingface_hub.InferenceClient.zero_shot_classification",parameters:[{name:"text",val:": str"},{name:"labels",val:": List"},{name:"multi_label",val:": bool = False"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.zero_shot_classification.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The input text to classify.`,name:"text"},{anchor:"huggingface_hub.InferenceClient.zero_shot_classification.labels",description:`<strong>labels</strong> (<code>List[str]</code>) &#x2014;
List of string possible labels. There must be at least 2 labels.`,name:"labels"},{anchor:"huggingface_hub.InferenceClient.zero_shot_classification.multi_label",description:`<strong>multi_label</strong> (<code>bool</code>) &#x2014;
Boolean that is set to True if classes can overlap.`,name:"multi_label"},{anchor:"huggingface_hub.InferenceClient.zero_shot_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1815",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of classification outputs containing the predicted labels and their confidence.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Pt=new Z({props:{anchor:"huggingface_hub.InferenceClient.zero_shot_classification.example",$$slots:{default:[Eh]},$$scope:{ctx:$}}}),ha=new w({props:{name:"zero_shot_image_classification",anchor:"huggingface_hub.InferenceClient.zero_shot_image_classification",parameters:[{name:"image",val:": Union"},{name:"labels",val:": List"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceClient.zero_shot_image_classification.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image to caption. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.InferenceClient.zero_shot_image_classification.labels",description:`<strong>labels</strong> (<code>List[str]</code>) &#x2014;
List of string possible labels. There must be at least 2 labels.`,name:"labels"},{anchor:"huggingface_hub.InferenceClient.zero_shot_image_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_client.py#L1887",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of classification outputs containing the predicted labels and their confidence.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>HTTPError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>HTTPError</code></p>
`}}),Lt=new Z({props:{anchor:"huggingface_hub.InferenceClient.zero_shot_image_classification.example",$$slots:{default:[Nh]},$$scope:{ctx:$}}}),fa=new L({props:{title:"Async Inference Client",local:"huggingface_hub.AsyncInferenceClient",headingTag:"h2"}}),_a=new q({props:{code:"cGlwJTIwaW5zdGFsbCUyMC0tdXBncmFkZSUyMGh1Z2dpbmdmYWNlX2h1YiU1QmluZmVyZW5jZSU1RCUwQSUyMyUyMG9yJTBBJTIzJTIwcGlwJTIwaW5zdGFsbCUyMGFpb2h0dHA=",highlighted:`pip install --upgrade huggingface_hub[inference]
<span class="hljs-comment"># or</span>
<span class="hljs-comment"># pip install aiohttp</span>`,wrap:!1}}),xa=new w({props:{name:"class huggingface_hub.AsyncInferenceClient",anchor:"huggingface_hub.AsyncInferenceClient",parameters:[{name:"model",val:": Optional = None"},{name:"token",val:": Union = None"},{name:"timeout",val:": Optional = None"},{name:"headers",val:": Optional = None"},{name:"cookies",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.model",description:`<strong>model</strong> (<code>str</code>, <code>optional</code>) &#x2014;
The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. <code>bigcode/starcoder</code>
or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is
automatically selected for the task.`,name:"model"},{anchor:"huggingface_hub.AsyncInferenceClient.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Hugging Face token. Will default to the locally saved token. Pass <code>token=False</code> if you don&#x2019;t want to send
your token to the server.`,name:"token"},{anchor:"huggingface_hub.AsyncInferenceClient.timeout",description:`<strong>timeout</strong> (<code>float</code>, <code>optional</code>) &#x2014;
The maximum number of seconds to wait for a response from the server. Loading a new model in Inference
API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.`,name:"timeout"},{anchor:"huggingface_hub.AsyncInferenceClient.headers",description:`<strong>headers</strong> (<code>Dict[str, str]</code>, <code>optional</code>) &#x2014;
Additional headers to send to the server. By default only the authorization and user-agent headers are sent.
Values in this dictionary will override the default values.`,name:"headers"},{anchor:"huggingface_hub.AsyncInferenceClient.cookies",description:`<strong>cookies</strong> (<code>Dict[str, str]</code>, <code>optional</code>) &#x2014;
Additional cookies to send to the server.`,name:"cookies"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L91"}}),ya=new w({props:{name:"audio_classification",anchor:"huggingface_hub.AsyncInferenceClient.audio_classification",parameters:[{name:"audio",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.audio_classification.audio",description:`<strong>audio</strong> (Union[str, Path, bytes, BinaryIO]) &#x2014;
The audio content to classify. It can be raw audio bytes, a local audio file, or a URL pointing to an
audio file.`,name:"audio"},{anchor:"huggingface_hub.AsyncInferenceClient.audio_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for audio classification. Can be a model ID hosted on the Hugging Face Hub
or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
audio classification will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L261",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The classification output containing the predicted label and its confidence.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Qt=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.audio_classification.example",$$slots:{default:[Gh]},$$scope:{ctx:$}}}),ja=new w({props:{name:"audio_to_audio",anchor:"huggingface_hub.AsyncInferenceClient.audio_to_audio",parameters:[{name:"audio",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.audio_to_audio.audio",description:`<strong>audio</strong> (Union[str, Path, bytes, BinaryIO]) &#x2014;
The audio content for the model. It can be raw audio bytes, a local audio file, or a URL pointing to an
audio file.`,name:"audio"},{anchor:"huggingface_hub.AsyncInferenceClient.audio_to_audio.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model can be any model which takes an audio file and returns another audio file. Can be a model ID hosted on the Hugging Face Hub
or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for
audio_to_audio will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L300",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of dictionary where each index contains audios label, content-type, and audio content in blob.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>InferenceTimeoutError</code> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>InferenceTimeoutError</code> or <code>aiohttp.ClientResponseError</code></p>
`}}),St=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.audio_to_audio.example",$$slots:{default:[Vh]},$$scope:{ctx:$}}}),Ta=new w({props:{name:"automatic_speech_recognition",anchor:"huggingface_hub.AsyncInferenceClient.automatic_speech_recognition",parameters:[{name:"audio",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.automatic_speech_recognition.audio",description:`<strong>audio</strong> (Union[str, Path, bytes, BinaryIO]) &#x2014;
The content to transcribe. It can be raw audio bytes, local audio file, or a URL to an audio file.`,name:"audio"},{anchor:"huggingface_hub.AsyncInferenceClient.automatic_speech_recognition.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for ASR. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. If not provided, the default recommended model for ASR will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L344",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p>str</p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Xt=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.automatic_speech_recognition.example",$$slots:{default:[Dh]},$$scope:{ctx:$}}}),$a=new w({props:{name:"conversational",anchor:"huggingface_hub.AsyncInferenceClient.conversational",parameters:[{name:"text",val:": str"},{name:"generated_responses",val:": Optional = None"},{name:"past_user_inputs",val:": Optional = None"},{name:"parameters",val:": Optional = None"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.conversational.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The last input from the user in the conversation.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.conversational.generated_responses",description:`<strong>generated_responses</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
A list of strings corresponding to the earlier replies from the model. Defaults to None.`,name:"generated_responses"},{anchor:"huggingface_hub.AsyncInferenceClient.conversational.past_user_inputs",description:`<strong>past_user_inputs</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
A list of strings corresponding to the earlier replies from the user. Should be the same length as
<code>generated_responses</code>. Defaults to None.`,name:"past_user_inputs"},{anchor:"huggingface_hub.AsyncInferenceClient.conversational.parameters",description:`<strong>parameters</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional parameters for the conversational task. Defaults to None. For more details about the available
parameters, please refer to <a href="https://huggingface.co/docs/api-inference/detailed_parameters#conversational-task" rel="nofollow">this page</a>`,name:"parameters"},{anchor:"huggingface_hub.AsyncInferenceClient.conversational.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L381",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The generated conversational output.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),zt=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.conversational.example",$$slots:{default:[Yh]},$$scope:{ctx:$}}}),Ia=new w({props:{name:"document_question_answering",anchor:"huggingface_hub.AsyncInferenceClient.document_question_answering",parameters:[{name:"image",val:": Union"},{name:"question",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.document_question_answering.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image for the context. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.document_question_answering.question",description:`<strong>question</strong> (<code>str</code>) &#x2014;
Question to be answered.`,name:"question"},{anchor:"huggingface_hub.AsyncInferenceClient.document_question_answering.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the document question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended document question answering model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L488",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label, associated probability, word ids, and page number.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Ot=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.document_question_answering.example",$$slots:{default:[Rh]},$$scope:{ctx:$}}}),Ma=new w({props:{name:"feature_extraction",anchor:"huggingface_hub.AsyncInferenceClient.feature_extraction",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.feature_extraction.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The text to embed.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.feature_extraction.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L530",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The embedding representing the input text as a float32 numpy array.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>np.ndarray</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Kt=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.feature_extraction.example",$$slots:{default:[Ah]},$$scope:{ctx:$}}}),wa=new w({props:{name:"fill_mask",anchor:"huggingface_hub.AsyncInferenceClient.fill_mask",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.fill_mask.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask).`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.fill_mask.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the fill mask task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended fill mask model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L567",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of fill mask output dictionaries containing the predicted label, associated
probability, token reference, and completed text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),es=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.fill_mask.example",$$slots:{default:[Bh]},$$scope:{ctx:$}}}),Ca=new w({props:{name:"get_model_status",anchor:"huggingface_hub.AsyncInferenceClient.get_model_status",parameters:[{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.get_model_status.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Identifier of the model for witch the status gonna be checked. If model is not provided,
the model associated with this instance of <a href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a> will be used. Only InferenceAPI service can be checked so the
identifier cannot be a URL.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L2017",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>An instance of ModelStatus dataclass, containing information,
about the state of the model: load, state, compute type and framework.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ModelStatus</code></p>
`}}),ns=new re({props:{$$slots:{default:[Hh]},$$scope:{ctx:$}}}),ts=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.get_model_status.example",$$slots:{default:[Fh]},$$scope:{ctx:$}}}),va=new w({props:{name:"get_recommended_model",anchor:"huggingface_hub.AsyncInferenceClient.get_recommended_model",parameters:[{name:"task",val:": str"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.get_recommended_model.task",description:`<strong>task</strong> (<code>str</code>) &#x2014;
The Hugging Face task to get which model Hugging Face recommends.
All available tasks can be found <a href="https://huggingface.co/tasks" rel="nofollow">here</a>.`,name:"task"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1993",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Name of the model recommended for the input task.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValueError</code> — If Hugging Face has no recommendation for the input task.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValueError</code></p>
`}}),Ja=new w({props:{name:"image_classification",anchor:"huggingface_hub.AsyncInferenceClient.image_classification",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.image_classification.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The image to classify. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.image_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for image classification. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for image classification will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L608",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label and associated probability.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),as=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.image_classification.example",$$slots:{default:[Ph]},$$scope:{ctx:$}}}),Ua=new w({props:{name:"image_segmentation",anchor:"huggingface_hub.AsyncInferenceClient.image_segmentation",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.image_segmentation.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The image to segment. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.image_segmentation.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for image segmentation. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for image segmentation will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L645",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of dictionaries containing the segmented masks and associated attributes.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),os=new re({props:{warning:!0,$$slots:{default:[Lh]},$$scope:{ctx:$}}}),rs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.image_segmentation.example",$$slots:{default:[Qh]},$$scope:{ctx:$}}}),ka=new w({props:{name:"image_to_image",anchor:"huggingface_hub.AsyncInferenceClient.image_to_image",parameters:[{name:"image",val:": Union"},{name:"prompt",val:": Optional = None"},{name:"negative_prompt",val:": Optional = None"},{name:"height",val:": Optional = None"},{name:"width",val:": Optional = None"},{name:"num_inference_steps",val:": Optional = None"},{name:"guidance_scale",val:": Optional = None"},{name:"model",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image for translation. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.prompt",description:`<strong>prompt</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The text prompt to guide the image generation.`,name:"prompt"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code>, <em>optional</em>) &#x2014;
A negative prompt to guide the translation process.`,name:"negative_prompt"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L697",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>Image</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),ls=new re({props:{warning:!0,$$slots:{default:[Sh]},$$scope:{ctx:$}}}),is=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.image_to_image.example",$$slots:{default:[Xh]},$$scope:{ctx:$}}}),qa=new w({props:{name:"image_to_text",anchor:"huggingface_hub.AsyncInferenceClient.image_to_text",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.image_to_text.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image to caption. It can be raw bytes, an image file, or a URL to an online image..`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.image_to_text.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L782",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),cs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.image_to_text.example",$$slots:{default:[zh]},$$scope:{ctx:$}}}),Za=new w({props:{name:"list_deployed_models",anchor:"huggingface_hub.AsyncInferenceClient.list_deployed_models",parameters:[{name:"frameworks",val:": Union = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.list_deployed_models.frameworks",description:`<strong>frameworks</strong> (<code>Literal[&quot;all&quot;]</code> or <code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
The frameworks to filter on. By default only a subset of the available frameworks are tested. If set to
&#x201C;all&#x201D;, all available frameworks will be tested. It is also possible to provide a single framework or a
custom set of frameworks to check.`,name:"frameworks"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L819",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A dictionary mapping task names to a sorted list of model IDs.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict[str, List[str]]</code></p>
`}}),ps=new re({props:{$$slots:{default:[Oh]},$$scope:{ctx:$}}}),gs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.list_deployed_models.example",$$slots:{default:[Kh]},$$scope:{ctx:$}}}),Wa=new w({props:{name:"object_detection",anchor:"huggingface_hub.AsyncInferenceClient.object_detection",parameters:[{name:"image",val:": Union"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.object_detection.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The image to detect objects on. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.object_detection.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for object detection. Can be a model ID hosted on the Hugging Face Hub or a URL to a
deployed Inference Endpoint. If not provided, the default recommended model for object detection (DETR) will be used.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L900",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of dictionaries containing the bounding boxes and associated attributes.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[ObjectDetectionOutput]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
<li><code>ValueError</code> —
If the request output is not a List.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code> or <code>ValueError</code></p>
`}}),us=new re({props:{warning:!0,$$slots:{default:[ef]},$$scope:{ctx:$}}}),ds=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.object_detection.example",$$slots:{default:[nf]},$$scope:{ctx:$}}}),Ea=new w({props:{name:"post",anchor:"huggingface_hub.AsyncInferenceClient.post",parameters:[{name:"json",val:": Union = None"},{name:"data",val:": Union = None"},{name:"model",val:": Optional = None"},{name:"task",val:": Optional = None"},{name:"stream",val:": bool = False"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.post.json",description:`<strong>json</strong> (<code>Union[str, Dict, List]</code>, <em>optional</em>) &#x2014;
The JSON data to send in the request body, specific to each task. Defaults to None.`,name:"json"},{anchor:"huggingface_hub.AsyncInferenceClient.post.data",description:`<strong>data</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>, <em>optional</em>) &#x2014;
The content to send in the request body, specific to each task.
It can be raw bytes, a pointer to an opened file, a local file path,
or a URL to an online resource (image, audio file,&#x2026;). If both <code>json</code> and <code>data</code> are passed,
<code>data</code> will take precedence. At least <code>json</code> or <code>data</code> must be provided. Defaults to None.`,name:"data"},{anchor:"huggingface_hub.AsyncInferenceClient.post.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. Will override the model defined at the instance level. Defaults to None.`,name:"model"},{anchor:"huggingface_hub.AsyncInferenceClient.post.task",description:`<strong>task</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The task to perform on the inference. All available tasks can be found
<a href="https://huggingface.co/tasks" rel="nofollow">here</a>. Used only to default to a recommended model if <code>model</code> is not
provided. At least <code>model</code> or <code>task</code> must be provided. Defaults to None.`,name:"task"},{anchor:"huggingface_hub.AsyncInferenceClient.post.stream",description:`<strong>stream</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to iterate over streaming APIs.`,name:"stream"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L158",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The raw bytes returned by the server.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>bytes</p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Na=new w({props:{name:"question_answering",anchor:"huggingface_hub.AsyncInferenceClient.question_answering",parameters:[{name:"question",val:": str"},{name:"context",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.question_answering.question",description:`<strong>question</strong> (<code>str</code>) &#x2014;
Question to be answered.`,name:"question"},{anchor:"huggingface_hub.AsyncInferenceClient.question_answering.context",description:`<strong>context</strong> (<code>str</code>) &#x2014;
The context of the question.`,name:"context"},{anchor:"huggingface_hub.AsyncInferenceClient.question_answering.model",description:`<strong>model</strong> (<code>str</code>) &#x2014;
The model to use for the question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L949",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a dictionary of question answering output containing the score, start index, end index, and answer.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),hs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.question_answering.example",$$slots:{default:[tf]},$$scope:{ctx:$}}}),Ga=new w({props:{name:"sentence_similarity",anchor:"huggingface_hub.AsyncInferenceClient.sentence_similarity",parameters:[{name:"sentence",val:": str"},{name:"other_sentences",val:": List"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.sentence_similarity.sentence",description:`<strong>sentence</strong> (<code>str</code>) &#x2014;
The main sentence to compare to others.`,name:"sentence"},{anchor:"huggingface_hub.AsyncInferenceClient.sentence_similarity.other_sentences",description:`<strong>other_sentences</strong> (<code>List[str]</code>) &#x2014;
The list of sentences to compare to.`,name:"other_sentences"},{anchor:"huggingface_hub.AsyncInferenceClient.sentence_similarity.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L991",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The embedding representing the input text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[float]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),fs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.sentence_similarity.example",$$slots:{default:[sf]},$$scope:{ctx:$}}}),Va=new w({props:{name:"summarization",anchor:"huggingface_hub.AsyncInferenceClient.summarization",parameters:[{name:"text",val:": str"},{name:"parameters",val:": Optional = None"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.summarization.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The input text to summarize.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.summarization.parameters",description:`<strong>parameters</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional parameters for summarization. Check out this <a href="https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task" rel="nofollow">page</a>
for more details.`,name:"parameters"},{anchor:"huggingface_hub.AsyncInferenceClient.summarization.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1039",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The generated summary text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),bs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.summarization.example",$$slots:{default:[af]},$$scope:{ctx:$}}}),Da=new w({props:{name:"table_question_answering",anchor:"huggingface_hub.AsyncInferenceClient.table_question_answering",parameters:[{name:"table",val:": Dict"},{name:"query",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.table_question_answering.table",description:`<strong>table</strong> (<code>str</code>) &#x2014;
A table of data represented as a dict of lists where entries are headers and the lists are all the
values, all lists must have the same size.`,name:"table"},{anchor:"huggingface_hub.AsyncInferenceClient.table_question_answering.query",description:`<strong>query</strong> (<code>str</code>) &#x2014;
The query in plain text that you want to ask the table.`,name:"query"},{anchor:"huggingface_hub.AsyncInferenceClient.table_question_answering.model",description:`<strong>model</strong> (<code>str</code>) &#x2014;
The model to use for the table-question-answering task. Can be a model ID hosted on the Hugging Face
Hub or a URL to a deployed Inference Endpoint.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1083",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a dictionary of table question answering output containing the answer, coordinates, cells and the aggregator used.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),_s=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.table_question_answering.example",$$slots:{default:[of]},$$scope:{ctx:$}}}),Ya=new w({props:{name:"tabular_classification",anchor:"huggingface_hub.AsyncInferenceClient.tabular_classification",parameters:[{name:"table",val:": Dict"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.tabular_classification.table",description:`<strong>table</strong> (<code>Dict[str, Any]</code>) &#x2014;
Set of attributes to classify.`,name:"table"},{anchor:"huggingface_hub.AsyncInferenceClient.tabular_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the tabular classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended tabular classification model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1129",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of labels, one per row in the initial table.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),xs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.tabular_classification.example",$$slots:{default:[rf]},$$scope:{ctx:$}}}),Ra=new w({props:{name:"tabular_regression",anchor:"huggingface_hub.AsyncInferenceClient.tabular_regression",parameters:[{name:"table",val:": Dict"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.tabular_regression.table",description:`<strong>table</strong> (<code>Dict[str, Any]</code>) &#x2014;
Set of attributes stored in a table. The attributes used to predict the target can be both numerical and categorical.`,name:"table"},{anchor:"huggingface_hub.AsyncInferenceClient.tabular_regression.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the tabular regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended tabular regression model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1175",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of predicted numerical target values.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),ys=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.tabular_regression.example",$$slots:{default:[lf]},$$scope:{ctx:$}}}),Aa=new w({props:{name:"text_classification",anchor:"huggingface_hub.AsyncInferenceClient.text_classification",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.text_classification.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
A string to be classified.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.text_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the text classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1216",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label and associated probability.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),js=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.text_classification.example",$$slots:{default:[cf]},$$scope:{ctx:$}}}),Ba=new w({props:{name:"text_generation",anchor:"huggingface_hub.AsyncInferenceClient.text_generation",parameters:[{name:"prompt",val:": str"},{name:"details",val:": bool = False"},{name:"stream",val:": bool = False"},{name:"model",val:": Optional = None"},{name:"do_sample",val:": bool = False"},{name:"max_new_tokens",val:": int = 20"},{name:"best_of",val:": Optional = None"},{name:"repetition_penalty",val:": Optional = None"},{name:"return_full_text",val:": bool = False"},{name:"seed",val:": Optional = None"},{name:"stop_sequences",val:": Optional = None"},{name:"temperature",val:": Optional = None"},{name:"top_k",val:": Optional = None"},{name:"top_p",val:": Optional = None"},{name:"truncate",val:": Optional = None"},{name:"typical_p",val:": Optional = None"},{name:"watermark",val:": bool = False"},{name:"decoder_input_details",val:": bool = False"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.prompt",description:`<strong>prompt</strong> (<code>str</code>) &#x2014;
Input text.`,name:"prompt"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.details",description:`<strong>details</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
By default, text_generation returns a string. Pass <code>details=True</code> if you want a detailed output (tokens,
probabilities, seed, finish reason, etc.). Only available for models running on with the
<code>text-generation-inference</code> backend.`,name:"details"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.stream",description:`<strong>stream</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
By default, text_generation returns the full generated text. Pass <code>stream=True</code> if you want a stream of
tokens to be returned. Only available for models running on with the <code>text-generation-inference</code>
backend.`,name:"stream"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.do_sample",description:`<strong>do_sample</strong> (<code>bool</code>) &#x2014;
Activate logits sampling`,name:"do_sample"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.max_new_tokens",description:`<strong>max_new_tokens</strong> (<code>int</code>) &#x2014;
Maximum number of generated tokens`,name:"max_new_tokens"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.best_of",description:`<strong>best_of</strong> (<code>int</code>) &#x2014;
Generate best_of sequences and return the one if the highest token logprobs`,name:"best_of"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>) &#x2014;
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.return_full_text",description:`<strong>return_full_text</strong> (<code>bool</code>) &#x2014;
Whether to prepend the prompt to the generated text`,name:"return_full_text"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.seed",description:`<strong>seed</strong> (<code>int</code>) &#x2014;
Random sampling seed`,name:"seed"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.stop_sequences",description:`<strong>stop_sequences</strong> (<code>List[str]</code>) &#x2014;
Stop generating tokens if a member of <code>stop_sequences</code> is generated`,name:"stop_sequences"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.temperature",description:`<strong>temperature</strong> (<code>float</code>) &#x2014;
The value used to module the logits distribution.`,name:"temperature"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.top_k",description:`<strong>top_k</strong> (<code>int</code>) &#x2014;
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.top_p",description:`<strong>top_p</strong> (<code>float</code>) &#x2014;
If set to &lt; 1, only the smallest set of most probable tokens with probabilities that add up to <code>top_p</code> or
higher are kept for generation.`,name:"top_p"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.truncate",description:`<strong>truncate</strong> (<code>int</code>) &#x2014;
Truncate inputs tokens to the given size`,name:"truncate"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.typical_p",description:`<strong>typical_p</strong> (<code>float</code>) &#x2014;
Typical Decoding mass
See <a href="https://arxiv.org/abs/2202.00666" rel="nofollow">Typical Decoding for Natural Language Generation</a> for more information`,name:"typical_p"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.watermark",description:`<strong>watermark</strong> (<code>bool</code>) &#x2014;
Watermarking with <a href="https://arxiv.org/abs/2301.10226" rel="nofollow">A Watermark for Large Language Models</a>`,name:"watermark"},{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.decoder_input_details",description:`<strong>decoder_input_details</strong> (<code>bool</code>) &#x2014;
Return the decoder input token logprobs and ids. You must set <code>details=True</code> as well for it to be taken
into account. Defaults to <code>False</code>.`,name:"decoder_input_details"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1345",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Generated text returned from the server:</p>
<ul>
<li>if <code>stream=False</code> and <code>details=False</code>, the generated text is returned as a <code>str</code> (default)</li>
<li>if <code>stream=True</code> and <code>details=False</code>, the generated text is returned token by token as a <code>Iterable[str]</code></li>
<li>if <code>stream=False</code> and <code>details=True</code>, the generated text is returned with more details as a <a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.inference._text_generation.TextGenerationResponse"
>TextGenerationResponse</a></li>
<li>if <code>details=True</code> and <code>stream=True</code>, the generated text is returned token by token as a iterable of <a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.inference._text_generation.TextGenerationStreamResponse"
>TextGenerationStreamResponse</a></li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Union[str, TextGenerationResponse, Iterable[str], Iterable[TextGenerationStreamResponse]]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValidationError</code> —
If input values are not valid. No HTTP call is made to the server.</li>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValidationError</code> or <a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Ts=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.text_generation.example",$$slots:{default:[pf]},$$scope:{ctx:$}}}),Ha=new w({props:{name:"text_to_image",anchor:"huggingface_hub.AsyncInferenceClient.text_to_image",parameters:[{name:"prompt",val:": str"},{name:"negative_prompt",val:": Optional = None"},{name:"height",val:": Optional = None"},{name:"width",val:": Optional = None"},{name:"num_inference_steps",val:": Optional = None"},{name:"guidance_scale",val:": Optional = None"},{name:"model",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.prompt",description:`<strong>prompt</strong> (<code>str</code>) &#x2014;
The prompt to generate an image from.`,name:"prompt"},{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code>, <em>optional</em>) &#x2014;
An optional negative prompt for the image generation.`,name:"negative_prompt"},{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.height",description:`<strong>height</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The height in pixels of the image to generate.`,name:"height"},{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.width",description:`<strong>width</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The width in pixels of the image to generate.`,name:"width"},{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1614",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>Image</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),$s=new re({props:{warning:!0,$$slots:{default:[gf]},$$scope:{ctx:$}}}),Is=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.text_to_image.example",$$slots:{default:[uf]},$$scope:{ctx:$}}}),Fa=new w({props:{name:"text_to_speech",anchor:"huggingface_hub.AsyncInferenceClient.text_to_speech",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.text_to_speech.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The text to synthesize.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.text_to_speech.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1695",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>bytes</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Ms=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.text_to_speech.example",$$slots:{default:[df]},$$scope:{ctx:$}}}),Pa=new w({props:{name:"token_classification",anchor:"huggingface_hub.AsyncInferenceClient.token_classification",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.token_classification.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
A string to be classified.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.token_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the token classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended token classification model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1728",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of token classification outputs containing the entity group, confidence score, word, start and end index.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),ws=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.token_classification.example",$$slots:{default:[mf]},$$scope:{ctx:$}}}),La=new w({props:{name:"translation",anchor:"huggingface_hub.AsyncInferenceClient.translation",parameters:[{name:"text",val:": str"},{name:"model",val:": Optional = None"},{name:"src_lang",val:": Optional = None"},{name:"tgt_lang",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.translation.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
A string to be translated.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.translation.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the translation task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended translation model will be used.
Defaults to None.`,name:"model"},{anchor:"huggingface_hub.AsyncInferenceClient.translation.src_lang",description:`<strong>src_lang</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Source language of the translation task, i.e. input language. Cannot be passed without <code>tgt_lang</code>.`,name:"src_lang"},{anchor:"huggingface_hub.AsyncInferenceClient.translation.tgt_lang",description:`<strong>tgt_lang</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Target language of the translation task, i.e. output language. Cannot be passed without <code>src_lang</code>.`,name:"tgt_lang"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1776",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The generated translated text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
<li><code>ValueError</code> —
If only one of the <code>src_lang</code> and <code>tgt_lang</code> arguments are provided.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code> or <code>ValueError</code></p>
`}}),Cs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.translation.example",$$slots:{default:[hf]},$$scope:{ctx:$}}}),vs=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.translation.example-2",$$slots:{default:[ff]},$$scope:{ctx:$}}}),Qa=new w({props:{name:"visual_question_answering",anchor:"huggingface_hub.AsyncInferenceClient.visual_question_answering",parameters:[{name:"image",val:": Union"},{name:"question",val:": str"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.visual_question_answering.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image for the context. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.visual_question_answering.question",description:`<strong>question</strong> (<code>str</code>) &#x2014;
Question to be answered.`,name:"question"},{anchor:"huggingface_hub.AsyncInferenceClient.visual_question_answering.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for the visual question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
a deployed Inference Endpoint. If not provided, the default recommended visual question answering model will be used.
Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L443",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>a list of dictionaries containing the predicted label and associated probability.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>InferenceTimeoutError</code> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>InferenceTimeoutError</code> or <code>aiohttp.ClientResponseError</code></p>
`}}),Js=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.visual_question_answering.example",$$slots:{default:[bf]},$$scope:{ctx:$}}}),Sa=new w({props:{name:"zero_shot_classification",anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_classification",parameters:[{name:"text",val:": str"},{name:"labels",val:": List"},{name:"multi_label",val:": bool = False"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_classification.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The input text to classify.`,name:"text"},{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_classification.labels",description:`<strong>labels</strong> (<code>List[str]</code>) &#x2014;
List of string possible labels. There must be at least 2 labels.`,name:"labels"},{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_classification.multi_label",description:`<strong>multi_label</strong> (<code>bool</code>) &#x2014;
Boolean that is set to True if classes can overlap.`,name:"multi_label"},{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1842",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of classification outputs containing the predicted labels and their confidence.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),Us=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_classification.example",$$slots:{default:[_f]},$$scope:{ctx:$}}}),Xa=new w({props:{name:"zero_shot_image_classification",anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_image_classification",parameters:[{name:"image",val:": Union"},{name:"labels",val:": List"},{name:"model",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_image_classification.image",description:`<strong>image</strong> (<code>Union[str, Path, bytes, BinaryIO]</code>) &#x2014;
The input image to caption. It can be raw bytes, an image file, or a URL to an online image.`,name:"image"},{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_image_classification.labels",description:`<strong>labels</strong> (<code>List[str]</code>) &#x2014;
List of string possible labels. There must be at least 2 labels.`,name:"labels"},{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_image_classification.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed
Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None.`,name:"model"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_generated/_async_client.py#L1915",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of classification outputs containing the predicted labels and their confidence.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[Dict]</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> —
If the model is unavailable or the request times out.</li>
<li><code>aiohttp.ClientResponseError</code> —
If the request fails with an HTTP error status code other than HTTP 503.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceTimeoutError"
>InferenceTimeoutError</a> or <code>aiohttp.ClientResponseError</code></p>
`}}),ks=new Z({props:{anchor:"huggingface_hub.AsyncInferenceClient.zero_shot_image_classification.example",$$slots:{default:[xf]},$$scope:{ctx:$}}}),za=new L({props:{title:"InferenceTimeoutError",local:"huggingface_hub.InferenceTimeoutError",headingTag:"h2"}}),Oa=new w({props:{name:"class huggingface_hub.InferenceTimeoutError",anchor:"huggingface_hub.InferenceTimeoutError",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_common.py#L101"}}),Ka=new L({props:{title:"Return types",local:"return-types",headingTag:"h2"}}),no=new L({props:{title:"ClassificationOutput",local:"huggingface_hub.inference._types.ClassificationOutput",headingTag:"h3"}}),to=new w({props:{name:"class huggingface_hub.inference._types.ClassificationOutput",anchor:"huggingface_hub.inference._types.ClassificationOutput",parameters:"",parametersDescription:[{anchor:"huggingface_hub.inference._types.ClassificationOutput.label",description:`<strong>label</strong> (<code>str</code>) &#x2014;
The label predicted by the model.`,name:"label"},{anchor:"huggingface_hub.inference._types.ClassificationOutput.score",description:`<strong>score</strong> (<code>float</code>) &#x2014;
The score of the label predicted by the model.`,name:"score"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_types.py#L39"}}),so=new L({props:{title:"ConversationalOutputConversation",local:"huggingface_hub.inference._types.ConversationalOutputConversation",headingTag:"h3"}}),ao=new w({props:{name:"class huggingface_hub.inference._types.ConversationalOutputConversation",anchor:"huggingface_hub.inference._types.ConversationalOutputConversation",parameters:"",parametersDescription:[{anchor:"huggingface_hub.inference._types.ConversationalOutputConversation.generated_responses",description:`<strong>generated_responses</strong> (<code>List[str]</code>) &#x2014;
A list of the responses from the model.`,name:"generated_responses"},{anchor:"huggingface_hub.inference._types.ConversationalOutputConversation.past_user_inputs",description:`<strong>past_user_inputs</strong> (<code>List[str]</code>) &#x2014;
A list of the inputs from the user. Must be the same length as <code>generated_responses</code>.`,name:"past_user_inputs"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_types.py#L53"}}),oo=new L({props:{title:"ConversationalOutput",local:"huggingface_hub.inference._types.ConversationalOutput",headingTag:"h3"}}),ro=new w({props:{name:"class huggingface_hub.inference._types.ConversationalOutput",anchor:"huggingface_hub.inference._types.ConversationalOutput",parameters:"",parametersDescription:[{anchor:"huggingface_hub.inference._types.ConversationalOutput.generated_text",description:`<strong>generated_text</strong> (<code>str</code>) &#x2014;
The last response from the model.`,name:"generated_text"},{anchor:"huggingface_hub.inference._types.ConversationalOutput.conversation",description:`<strong>conversation</strong> (<code>ConversationalOutputConversation</code>) &#x2014;
The past conversation.`,name:"conversation"},{anchor:"huggingface_hub.inference._types.ConversationalOutput.warnings",description:`<strong>warnings</strong> (<code>List[str]</code>) &#x2014;
A list of warnings associated with the process.`,name:"warnings"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_types.py#L67"}}),lo=new L({props:{title:"ImageSegmentationOutput",local:"huggingface_hub.inference._types.ImageSegmentationOutput",headingTag:"h3"}}),io=new w({props:{name:"class huggingface_hub.inference._types.ImageSegmentationOutput",anchor:"huggingface_hub.inference._types.ImageSegmentationOutput",parameters:"",parametersDescription:[{anchor:"huggingface_hub.inference._types.ImageSegmentationOutput.label",description:`<strong>label</strong> (<code>str</code>) &#x2014;
The label corresponding to the mask.`,name:"label"},{anchor:"huggingface_hub.inference._types.ImageSegmentationOutput.mask",description:`<strong>mask</strong> (<code>Image</code>) &#x2014;
An Image object representing the mask predicted by the model.`,name:"mask"},{anchor:"huggingface_hub.inference._types.ImageSegmentationOutput.score",description:`<strong>score</strong> (<code>float</code>) &#x2014;
The score associated with the label for this mask.`,name:"score"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_types.py#L104"}}),co=new L({props:{title:"ModelStatus",local:"huggingface_hub.inference._common.ModelStatus",headingTag:"h3"}}),po=new w({props:{name:"class huggingface_hub.inference._common.ModelStatus",anchor:"huggingface_hub.inference._common.ModelStatus",parameters:[{name:"loaded",val:": bool"},{name:"state",val:": str"},{name:"compute_type",val:": Dict"},{name:"framework",val:": str"}],parametersDescription:[{anchor:"huggingface_hub.inference._common.ModelStatus.loaded",description:`<strong>loaded</strong> (<code>bool</code>) &#x2014;
If the model is currently loaded into Hugging Face&#x2019;s InferenceAPI. Models
are loaded on-demand, leading to the user&#x2019;s first request taking longer.
If a model is loaded, you can be assured that it is in a healthy state.`,name:"loaded"},{anchor:"huggingface_hub.inference._common.ModelStatus.state",description:`<strong>state</strong> (<code>str</code>) &#x2014;
The current state of the model. This can be &#x2018;Loaded&#x2019;, &#x2018;Loadable&#x2019;, &#x2018;TooBig&#x2019;.
If a model&#x2019;s state is &#x2018;Loadable&#x2019;, it&#x2019;s not too big and has a supported
backend. Loadable models are automatically loaded when the user first
requests inference on the endpoint. This means it is transparent for the
user to load a model, except that the first call takes longer to complete.`,name:"state"},{anchor:"huggingface_hub.inference._common.ModelStatus.compute_type",description:`<strong>compute_type</strong> (<code>Dict</code>) &#x2014;
Information about the compute resource the model is using or will use, such as &#x2018;gpu&#x2019; type and number of
replicas.`,name:"compute_type"},{anchor:"huggingface_hub.inference._common.ModelStatus.framework",description:`<strong>framework</strong> (<code>str</code>) &#x2014;
The name of the framework that the model was built with, such as &#x2018;transformers&#x2019;
or &#x2018;text-generation-inference&#x2019;.`,name:"framework"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/inference/_common.py#L71"}}),go=new L({props:{title:"TokenClassificationOutput",local:"huggingface_hub.inference._types.TokenClassificationOutput",headingTag:"h3"}}),uo=new w({props:{name:"class huggingface_hub.inference._types.TokenClassificationOutput",anchor:"huggingface_hub.inference._types.TokenClassificationOutput",parameters:"",parametersDescription:[{anchor:"huggingface_hub.inference._types.TokenClassificationOutput.entity_group",description:`<strong>entity_group</strong> (<code>str</code>) &#x2014;
The type for the entity being recognized (model specific).`,name:"entity_group"},{anchor:"huggingface_hub.inference._types.TokenClassificationOutput.score",description:`<strong>score</strong> (<code>float</code>) &#x2014;
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