import{s as Ga,c as Ba,u as Va,g as Ya,a as Oa,o as Ha,d as Qa,n as W}from"../chunks/scheduler.c3dfb84c.js";import{S as Ra,i as Xa,e as l,c as m,s,a as d,f as w,g,d as i,b as a,k as x,m as M,l as n,n as h,w as Ka,t as c,o as p,p as u,h as er,j as T}from"../chunks/index.47c20be5.js";import{T as tr}from"../chunks/Tip.34d6f74f.js";import{au as nr,av as or,C as sr,H as Gt,E as ar}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.423d8c15.js";import{D as j}from"../chunks/Docstring.164b8e5f.js";import{C as z}from"../chunks/CodeBlock.6375aab7.js";const{window:rr}=or;function ir(C){let o,v,f,r,_,t,b,G,J;r=new nr({props:{classNames:"text-smd"}});const H=C[4].default,$=Ba(H,C,C[3],null);return{c(){o=l("div"),v=l("a"),f=l("span"),m(r.$$.fragment),t=s(),$&&$.c(),this.h()},l(S){o=d(S,"DIV",{class:!0});var Z=w(o);v=d(Z,"A",{id:!0,class:!0,href:!0});var re=w(v);f=d(re,"SPAN",{});var Bn=w(f);g(r.$$.fragment,Bn),Bn.forEach(i),re.forEach(i),t=a(Z),$&&$.l(Z),Z.forEach(i),this.h()},h(){x(v,"id",C[0]),x(v,"class","header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),x(v,"href",_=`#${C[0]}`),x(o,"class","relative group rounded-md")},m(S,Z){M(S,o,Z),n(o,v),n(v,f),h(r,f,null),n(o,t),$&&$.m(o,null),C[5](o),b=!0,G||(J=Ka(rr,"hashchange",C[2]),G=!0)},p(S,[Z]){(!b||Z&1)&&x(v,"id",S[0]),(!b||Z&1&&_!==(_=`#${S[0]}`))&&x(v,"href",_),$&&$.p&&(!b||Z&8)&&Va($,H,S,S[3],b?Oa(H,S[3],Z,null):Ya(S[3]),null)},i(S){b||(c(r.$$.fragment,S),c($,S),b=!0)},o(S){p(r.$$.fragment,S),p($,S),b=!1},d(S){S&&i(o),u(r),$&&$.d(S),C[5](null),G=!1,J()}}}const za="bg-yellow-50 dark:bg-[#494a3d]";function lr(C,o,v){let{$$slots:f={},$$scope:r}=o,{anchor:_}=o,t;function b(){const{hash:J}=window.location,H=J.substring(1);t&&t.classList.remove(...za.split(" ")),H===_&&t.classList.add(...za.split(" "))}Ha(()=>{b()});function G(J){Qa[J?"unshift":"push"](()=>{t=J,v(1,t)})}return C.$$set=J=>{"anchor"in J&&v(0,_=J.anchor),"$$scope"in J&&v(3,r=J.$$scope)},[_,t,b,r,f,G]}class E extends Ra{constructor(o){super(),Xa(this,o,lr,ir,Ga,{anchor:0})}}function dr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"ZnJvbSUyMHNldGZpdCUyMGltcG9ydCUyMFNldEZpdE1vZGVsJTBBbW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIydG9tYWFyc2VuJTJGc2V0Zml0LWJnZS1zbWFsbC12MS41LXNzdDItOC1zaG90JTIyKSUwQW1vZGVsLnByZWRpY3QoJTVCJTBBJTIwJTIwJTIwJTIwJTIySXQncyUyMGElMjBjaGFybWluZyUyMGFuZCUyMG9mdGVuJTIwYWZmZWN0aW5nJTIwam91cm5leS4lMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJJdCdzJTIwc2xvdyUyMC0tJTIwdmVyeSUyQyUyMHZlcnklMjBzbG93LiUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMkElMjBzb21ldGltZXMlMjB0ZWRpb3VzJTIwZmlsbS4lMjIlMkMlMEElNUQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> setfit <span class="hljs-keyword">import</span> SetFitModel
<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(<span class="hljs-string">&quot;tomaarsen/setfit-bge-small-v1.5-sst2-8-shot&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict([
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;It&#x27;s a charming and often affecting journey.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;It&#x27;s slow -- very, very slow.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A sometimes tedious film.&quot;</span>,
<span class="hljs-meta">... </span>])
[<span class="hljs-string">&#x27;positive&#x27;</span>, <span class="hljs-string">&#x27;negative&#x27;</span>, <span class="hljs-string">&#x27;negative&#x27;</span>]`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function cr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"ZnJvbSUyMHNldGZpdCUyMGltcG9ydCUyMFNldEZpdE1vZGVsJTBBbW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc2VudGVuY2UtdHJhbnNmb3JtZXJzJTJGcGFyYXBocmFzZS1tcG5ldC1iYXNlLXYyJTIyJTJDJTBBJTIwJTIwJTIwJTIwbGFiZWxzJTNEJTVCJTIycG9zaXRpdmUlMjIlMkMlMjAlMjJuZWdhdGl2ZSUyMiU1RCUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> setfit <span class="hljs-keyword">import</span> SetFitModel
<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;sentence-transformers/paraphrase-mpnet-base-v2&quot;</span>,
<span class="hljs-meta">... </span>    labels=[<span class="hljs-string">&quot;positive&quot;</span>, <span class="hljs-string">&quot;negative&quot;</span>],
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function pr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsKCU1QiUyMldoYXQlMjBhJTIwYm9yaW5nJTIwZGlzcGxheSUyMiUyQyUyMCUyMkV4aGlsYXJhdGluZyUyMHRocm91Z2glMjBhbmQlMjB0aHJvdWdoJTIyJTJDJTIwJTIySSdtJTIwd293ZWQhJTIyJTVEKSUwQW1vZGVsKCUyMlRoYXQlMjB3YXMlMjBjb29sISUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model([<span class="hljs-string">&quot;What a boring display&quot;</span>, <span class="hljs-string">&quot;Exhilarating through and through&quot;</span>, <span class="hljs-string">&quot;I&#x27;m wowed!&quot;</span>])
[<span class="hljs-string">&quot;negative&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>model(<span class="hljs-string">&quot;That was cool!&quot;</span>)
<span class="hljs-string">&quot;positive&quot;</span>`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function mr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsLnByZWRpY3QoJTVCJTIyV2hhdCUyMGElMjBib3JpbmclMjBkaXNwbGF5JTIyJTJDJTIwJTIyRXhoaWxhcmF0aW5nJTIwdGhyb3VnaCUyMGFuZCUyMHRocm91Z2glMjIlMkMlMjAlMjJJJ20lMjB3b3dlZCElMjIlNUQpJTBBbW9kZWwucHJlZGljdCglMjJUaGF0JTIwd2FzJTIwY29vbCElMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict([<span class="hljs-string">&quot;What a boring display&quot;</span>, <span class="hljs-string">&quot;Exhilarating through and through&quot;</span>, <span class="hljs-string">&quot;I&#x27;m wowed!&quot;</span>])
[<span class="hljs-string">&quot;negative&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict(<span class="hljs-string">&quot;That was cool!&quot;</span>)
<span class="hljs-string">&quot;positive&quot;</span>`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function gr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsLnByZWRpY3RfcHJvYmEoJTVCJTIyV2hhdCUyMGElMjBib3JpbmclMjBkaXNwbGF5JTIyJTJDJTIwJTIyRXhoaWxhcmF0aW5nJTIwdGhyb3VnaCUyMGFuZCUyMHRocm91Z2glMjIlMkMlMjAlMjJJJ20lMjB3b3dlZCElMjIlNUQpJTBBbW9kZWwucHJlZGljdF9wcm9iYSglMjJUaGF0JTIwd2FzJTIwY29vbCElMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict_proba([<span class="hljs-string">&quot;What a boring display&quot;</span>, <span class="hljs-string">&quot;Exhilarating through and through&quot;</span>, <span class="hljs-string">&quot;I&#x27;m wowed!&quot;</span>])
tensor([[<span class="hljs-number">0.9367</span>, <span class="hljs-number">0.0633</span>],
[<span class="hljs-number">0.0627</span>, <span class="hljs-number">0.9373</span>],
[<span class="hljs-number">0.0890</span>, <span class="hljs-number">0.9110</span>]], dtype=torch.float64)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict_proba(<span class="hljs-string">&quot;That was cool!&quot;</span>)
tensor([<span class="hljs-number">0.8421</span>, <span class="hljs-number">0.1579</span>], dtype=torch.float64)`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function hr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsLnRvKCUyMmNwdSUyMiklMEFtb2RlbCglNUIlMjJjYXRzJTIwYXJlJTIwY3V0ZSUyMiUyQyUyMCUyMmRvZ3MlMjBhcmUlMjBsb3lhbCUyMiU1RCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(<span class="hljs-string">&quot;cpu&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model([<span class="hljs-string">&quot;cats are cute&quot;</span>, <span class="hljs-string">&quot;dogs are loyal&quot;</span>])`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function ur(C){let o,v=`Install <a href="https://github.com/mlco2/codecarbon" rel="nofollow"><code>codecarbon</code></a> to automatically track carbon emission usage and
include it in your model cards.`;return{c(){o=l("p"),o.innerHTML=v},l(f){o=d(f,"P",{"data-svelte-h":!0}),T(o)!=="svelte-17nv22f"&&(o.innerHTML=v)},m(f,r){M(f,o,r)},p:W,d(f){f&&i(o)}}}function fr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;sentence-transformers/paraphrase-mpnet-base-v2&quot;</span>,
<span class="hljs-meta">... </span>    labels=[<span class="hljs-string">&quot;negative&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>],
<span class="hljs-meta">... </span>    <span class="hljs-comment"># Model card variables</span>
<span class="hljs-meta">... </span>    model_card_data=SetFitModelCardData(
<span class="hljs-meta">... </span>        model_id=<span class="hljs-string">&quot;tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2&quot;</span>,
<span class="hljs-meta">... </span>        dataset_name=<span class="hljs-string">&quot;SST2&quot;</span>,
<span class="hljs-meta">... </span>        dataset_id=<span class="hljs-string">&quot;sst2&quot;</span>,
<span class="hljs-meta">... </span>        license=<span class="hljs-string">&quot;apache-2.0&quot;</span>,
<span class="hljs-meta">... </span>        language=<span class="hljs-string">&quot;en&quot;</span>,
<span class="hljs-meta">... </span>    ),
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function _r(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> setfit <span class="hljs-keyword">import</span> AbsaModel
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AbsaModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict(<span class="hljs-string">&quot;The food and wine are just exquisite.&quot;</span>)
[{<span class="hljs-string">&#x27;span&#x27;</span>: <span class="hljs-string">&#x27;food&#x27;</span>, <span class="hljs-string">&#x27;polarity&#x27;</span>: <span class="hljs-string">&#x27;positive&#x27;</span>}, {<span class="hljs-string">&#x27;span&#x27;</span>: <span class="hljs-string">&#x27;wine&#x27;</span>, <span class="hljs-string">&#x27;polarity&#x27;</span>: <span class="hljs-string">&#x27;positive&#x27;</span>}]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> setfit <span class="hljs-keyword">import</span> AbsaModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AbsaModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;tomaarsen/setfit-absa-semeval-restaurants&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict(dataset)
Dataset({
features: [<span class="hljs-string">&#x27;text&#x27;</span>, <span class="hljs-string">&#x27;span&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>, <span class="hljs-string">&#x27;ordinal&#x27;</span>, <span class="hljs-string">&#x27;pred_polarity&#x27;</span>],
num_rows: <span class="hljs-number">3693</span>
})`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function br(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsLnByZWRpY3QoJTVCJTIyV2hhdCUyMGElMjBib3JpbmclMjBkaXNwbGF5JTIyJTJDJTIwJTIyRXhoaWxhcmF0aW5nJTIwdGhyb3VnaCUyMGFuZCUyMHRocm91Z2glMjIlMkMlMjAlMjJJJ20lMjB3b3dlZCElMjIlNUQpJTBBbW9kZWwucHJlZGljdCglMjJUaGF0JTIwd2FzJTIwY29vbCElMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict([<span class="hljs-string">&quot;What a boring display&quot;</span>, <span class="hljs-string">&quot;Exhilarating through and through&quot;</span>, <span class="hljs-string">&quot;I&#x27;m wowed!&quot;</span>])
[<span class="hljs-string">&quot;negative&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict(<span class="hljs-string">&quot;That was cool!&quot;</span>)
<span class="hljs-string">&quot;positive&quot;</span>`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function yr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsLnRvKCUyMmNwdSUyMiklMEFtb2RlbCglNUIlMjJjYXRzJTIwYXJlJTIwY3V0ZSUyMiUyQyUyMCUyMmRvZ3MlMjBhcmUlMjBsb3lhbCUyMiU1RCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(<span class="hljs-string">&quot;cpu&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model([<span class="hljs-string">&quot;cats are cute&quot;</span>, <span class="hljs-string">&quot;dogs are loyal&quot;</span>])`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function vr(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsLnByZWRpY3QoJTVCJTIyV2hhdCUyMGElMjBib3JpbmclMjBkaXNwbGF5JTIyJTJDJTIwJTIyRXhoaWxhcmF0aW5nJTIwdGhyb3VnaCUyMGFuZCUyMHRocm91Z2glMjIlMkMlMjAlMjJJJ20lMjB3b3dlZCElMjIlNUQpJTBBbW9kZWwucHJlZGljdCglMjJUaGF0JTIwd2FzJTIwY29vbCElMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict([<span class="hljs-string">&quot;What a boring display&quot;</span>, <span class="hljs-string">&quot;Exhilarating through and through&quot;</span>, <span class="hljs-string">&quot;I&#x27;m wowed!&quot;</span>])
[<span class="hljs-string">&quot;negative&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>, <span class="hljs-string">&quot;positive&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>model.predict(<span class="hljs-string">&quot;That was cool!&quot;</span>)
<span class="hljs-string">&quot;positive&quot;</span>`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function $r(C){let o,v="Example:",f,r,_;return r=new z({props:{code:"bW9kZWwlMjAlM0QlMjBTZXRGaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoLi4uKSUwQW1vZGVsLnRvKCUyMmNwdSUyMiklMEFtb2RlbCglNUIlMjJjYXRzJTIwYXJlJTIwY3V0ZSUyMiUyQyUyMCUyMmRvZ3MlMjBhcmUlMjBsb3lhbCUyMiU1RCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = SetFitModel.from_pretrained(...)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(<span class="hljs-string">&quot;cpu&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model([<span class="hljs-string">&quot;cats are cute&quot;</span>, <span class="hljs-string">&quot;dogs are loyal&quot;</span>])`,wrap:!1}}),{c(){o=l("p"),o.textContent=v,f=s(),m(r.$$.fragment)},l(t){o=d(t,"P",{"data-svelte-h":!0}),T(o)!=="svelte-11lpom8"&&(o.textContent=v),f=a(t),g(r.$$.fragment,t)},m(t,b){M(t,o,b),M(t,f,b),h(r,t,b),_=!0},p:W,i(t){_||(c(r.$$.fragment,t),_=!0)},o(t){p(r.$$.fragment,t),_=!1},d(t){t&&(i(o),i(f)),u(r,t)}}}function xr(C){let o,v,f,r,_,t,b,G,J,H,$,S,Z,re,Bn='A SetFit model with integration to the <a href="https://huggingface.co" rel="nofollow">Hugging Face Hub</a>.',_o,pe,bo,R,We,yo,Ht,Os="Download a model from the Huggingface Hub and instantiate it.",vo,me,$o,ge,Pe,xo,Rt,Qs="Save weights in local directory.",Mo,X,Ee,wo,Xt,Ks="Upload model checkpoint to the Hub.",To,Bt,ea=`Use <code>allow_patterns</code> and <code>ignore_patterns</code> to precisely filter which files should be pushed to the hub. Use
<code>delete_patterns</code> to delete existing remote files in the same commit. See <code>upload_folder</code> reference for more
details.`,jo,B,ze,Co,Vt,ta="Predict the various classes.",Fo,he,So,ue,Ge,Io,Yt,na="Return a mapping from string labels to integer IDs.",Uo,fe,He,ko,Ot,oa="Return a mapping from integer IDs to string labels.",Jo,_e,Re,Do,Qt,sa="Creates and saves a model card for a SetFit model.",No,be,Xe,qo,Kt,aa="Convert input sentences to embeddings using the <code>SentenceTransformer</code> body.",Ao,ye,Be,Zo,en,ra="Train the classifier head, only used if a differentiable PyTorch head is used.",Lo,ve,Ve,Wo,tn,ia="Freeze the model body and/or the head, preventing further training on that component until unfrozen.",Po,$e,Ye,Eo,nn,la="Generate and return a model card string based on the model card data.",zo,V,Oe,Go,on,da="Predict the various classes.",Ho,xe,Ro,Y,Qe,Xo,sn,ca="Predict the probabilities of the various classes.",Bo,Me,Vo,O,Ke,Yo,an,pa="Move this SetFitModel to <em>device</em>, and then return <em>self</em>. This method does not copy.",Oo,we,Qo,Te,et,Ko,rn,ma="Unfreeze the model body and/or the head, allowing further training on that component.",Yn,tt,On,L,nt,es,ln,ga=`A SetFit head that supports multi-class classification for end-to-end training.
Binary classification is treated as 2-class classification.`,ts,dn,ha=`To be compatible with Sentence Transformers, we inherit <code>Dense</code> from:
<a href="https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/models/Dense.py" rel="nofollow">https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/models/Dense.py</a>`,ns,Q,ot,os,cn,ua="SetFitHead can accept embeddings in:",ss,pn,fa="<li>Output format (<code>dict</code>) from Sentence-Transformers.</li> <li>Pure <code>torch.Tensor</code>.</li>",Qn,st,Kn,D,at,as,mn,_a="A dataclass storing data used in the model card.",rs,je,is,Ce,ls,gn,rt,ds,hn,it,eo,lt,to,I,dt,cs,un,ct,ps,fn,pt,ms,_n,mt,gs,K,gt,hs,bn,ba="Predicts aspects & their polarities of the given inputs.",us,Fe,fs,yn,ht,_s,vn,ut,bs,$n,ft,no,_t,oo,U,bt,ys,xn,yt,vs,Se,vt,$s,Mn,ya="Get the Torch device that this model is on.",xs,Ie,$t,Ms,wn,va="Download a model from the Huggingface Hub and instantiate it.",ws,ee,xt,Ts,Tn,$a="Predict the various classes.",js,Ue,Cs,te,Mt,Fs,jn,xa="Upload model checkpoint to the Hub.",Ss,Cn,Ma=`Use <code>allow_patterns</code> and <code>ignore_patterns</code> to precisely filter which files should be pushed to the hub. Use
<code>delete_patterns</code> to delete existing remote files in the same commit. See <code>upload_folder</code> reference for more
details.`,Is,ke,wt,Us,Fn,wa="Save weights in local directory.",ks,ne,Tt,Js,Sn,Ta="Move this SetFitModel to <em>device</em>, and then return <em>self</em>. This method does not copy.",Ds,Je,so,jt,ao,k,Ct,Ns,In,Ft,qs,De,St,As,Un,ja="Get the Torch device that this model is on.",Zs,Ne,It,Ls,kn,Ca="Download a model from the Huggingface Hub and instantiate it.",Ws,oe,Ut,Ps,Jn,Fa="Predict the various classes.",Es,qe,zs,se,kt,Gs,Dn,Sa="Upload model checkpoint to the Hub.",Hs,Nn,Ia=`Use <code>allow_patterns</code> and <code>ignore_patterns</code> to precisely filter which files should be pushed to the hub. Use
<code>delete_patterns</code> to delete existing remote files in the same commit. See <code>upload_folder</code> reference for more
details.`,Rs,Ae,Jt,Xs,qn,Ua="Save weights in local directory.",Bs,ae,Dt,Vs,An,ka="Move this SetFitModel to <em>device</em>, and then return <em>self</em>. This method does not copy.",Ys,Ze,ro,Nt,io,Vn,lo;return _=new sr({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),b=new Gt({props:{title:"Main Classes",local:"main-classes",headingTag:"h1"}}),J=new Gt({props:{title:"SetFitModel",local:"setfit.SetFitModel",headingTag:"h2"}}),S=new j({props:{name:"class setfit.SetFitModel",anchor:"setfit.SetFitModel",parameters:[{name:"model_body",val:": typing.Optional[sentence_transformers.SentenceTransformer.SentenceTransformer] = None"},{name:"model_head",val:": typing.Union[setfit.modeling.SetFitHead, sklearn.linear_model._logistic.LogisticRegression, NoneType] = None"},{name:"multi_target_strategy",val:": typing.Optional[str] = None"},{name:"normalize_embeddings",val:": bool = False"},{name:"labels",val:": typing.Optional[typing.List[str]] = None"},{name:"model_card_data",val:": typing.Optional[setfit.model_card.SetFitModelCardData] = None"},{name:"sentence_transformers_kwargs",val:": typing.Optional[typing.Dict] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L191"}}),pe=new E({props:{anchor:"setfit.SetFitModel.example",$$slots:{default:[dr]},$$scope:{ctx:C}}}),We=new j({props:{name:"from_pretrained",anchor:"setfit.SetFitModel.from_pretrained",parameters:[{name:"force_download",val:": bool = False"},{name:"resume_download",val:": typing.Optional[bool] = None"},{name:"proxies",val:": typing.Optional[typing.Dict] = None"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"},{name:"cache_dir",val:": typing.Union[str, pathlib.Path, NoneType] = None"},{name:"local_files_only",val:": bool = False"},{name:"revision",val:": typing.Optional[str] = None"},{name:"**model_kwargs",val:""}],parametersDescription:[{anchor:"setfit.SetFitModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<em>str</em>, <em>Path</em>) &#x2014;
<ul>
<li>Either the <em>model_id</em> (string) of a model hosted on the Hub, e.g. <em>bigscience/bloom</em>.</li>
<li>Or a path to a <em>directory</em> containing model weights saved using
[<em>~transformers.PreTrainedModel.save_pretrained</em>], e.g., <em>../path/to/my_model_directory/</em>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"setfit.SetFitModel.from_pretrained.revision",description:`<strong>revision</strong> (<em>str</em>, <em>optional</em>) &#x2014;
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
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Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.`,name:"force_download"},{anchor:"setfit.SetFitModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<em>Dict[str, str]</em>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <em>{&#x2018;http&#x2019;: &#x2018;foo.bar:3128&#x2019;,
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The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <em>hf auth login</em>.`,name:"token"},{anchor:"setfit.SetFitModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<em>str</em>, <em>Path</em>, <em>optional</em>) &#x2014;
Path to the folder where cached files are stored.`,name:"cache_dir"},{anchor:"setfit.SetFitModel.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<em>bool</em>, <em>optional</em>, defaults to <em>False</em>) &#x2014;
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If the labels are integers ranging from <em>0</em> to <em>num_classes-1</em>, then these labels indicate
the corresponding labels.`,name:"labels"},{anchor:"setfit.SetFitModel.from_pretrained.model_card_data",description:`<strong>model_card_data</strong> (<em>SetFitModelCardData</em>, <em>optional</em>) &#x2014;
A <em>SetFitModelCardData</em> instance storing data such as model language, license, dataset name,
etc. to be used in the automatically generated model cards.`,name:"model_card_data"},{anchor:"setfit.SetFitModel.from_pretrained.multi_target_strategy",description:`<strong>multi_target_strategy</strong> (<em>str</em>, <em>optional</em>) &#x2014;
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Whether to load SetFit using a differentiable (i.e., Torch) head instead of Logistic Regression.`,name:"use_differentiable_head"},{anchor:"setfit.SetFitModel.from_pretrained.normalize_embeddings",description:`<strong>normalize_embeddings</strong> (<em>bool</em>, <em>optional</em>) &#x2014;
Whether to apply normalization on the embeddings produced by the Sentence Transformer body.`,name:"normalize_embeddings"},{anchor:"setfit.SetFitModel.from_pretrained.device",description:`<strong>device</strong> (<em>Union[torch.device, str]</em>, <em>optional</em>) &#x2014;
The device on which to load the SetFit model, e.g. <em>&#x201C;cuda:0&#x201D;</em>, <em>&#x201C;mps&#x201D;</em> or <em>torch.device(&#x201C;cuda&#x201D;)</em>.`,name:"device"},{anchor:"setfit.SetFitModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<em>bool</em>, defaults to <em>False</em>) &#x2014; Whether or not to allow for custom Sentence Transformers
models defined on the Hub in their own modeling files. This option should only be set to True for
repositories you trust and in which you have read the code, as it will execute code present on
the Hub on your local machine. Defaults to False.`,name:"trust_remote_code"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L462"}}),me=new E({props:{anchor:"setfit.SetFitModel.from_pretrained.example",$$slots:{default:[cr]},$$scope:{ctx:C}}}),Pe=new j({props:{name:"save_pretrained",anchor:"setfit.SetFitModel.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"config",val:": typing.Union[dict, huggingface_hub.hub_mixin.DataclassInstance, NoneType] = None"},{name:"repo_id",val:": typing.Optional[str] = None"},{name:"push_to_hub",val:": bool = False"},{name:"model_card_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"**push_to_hub_kwargs",val:""}],parametersDescription:[{anchor:"setfit.SetFitModel.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>Path</code>) &#x2014;
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Whether or not to push your model to the Huggingface Hub after saving it.`,name:"push_to_hub"},{anchor:"setfit.SetFitModel.save_pretrained.repo_id",description:`<strong>repo_id</strong> (<code>str</code>, <em>optional</em>) &#x2014;
ID of your repository on the Hub. Used only if <code>push_to_hub=True</code>. Will default to the folder name if
not provided.`,name:"repo_id"},{anchor:"setfit.SetFitModel.save_pretrained.model_card_kwargs",description:`<strong>model_card_kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional arguments passed to the model card template to customize the model card.`,name:"model_card_kwargs"},{anchor:"setfit.SetFitModel.save_pretrained.push_to_hub_kwargs",description:`<strong>push_to_hub_kwargs</strong> &#x2014;
Additional key word arguments passed along to the <code>~ModelHubMixin.push_to_hub</code> method.`,name:"push_to_hub_kwargs"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L383",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>url of the commit on the Hub if <code>push_to_hub=True</code>, <code>None</code> otherwise.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code> or <code>None</code></p>
`}}),Ee=new j({props:{name:"push_to_hub",anchor:"setfit.SetFitModel.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"config",val:": typing.Union[dict, huggingface_hub.hub_mixin.DataclassInstance, NoneType] = None"},{name:"commit_message",val:": str = 'Push model using huggingface_hub.'"},{name:"private",val:": typing.Optional[bool] = None"},{name:"token",val:": typing.Optional[str] = None"},{name:"branch",val:": typing.Optional[str] = None"},{name:"create_pr",val:": typing.Optional[bool] = None"},{name:"allow_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"ignore_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"delete_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"model_card_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"}],parametersDescription:[{anchor:"setfit.SetFitModel.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
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Model configuration specified as a key/value dictionary or a dataclass instance.`,name:"config"},{anchor:"setfit.SetFitModel.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Message to commit while pushing.`,name:"commit_message"},{anchor:"setfit.SetFitModel.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether the repository created should be private.
If <code>None</code> (default), the repo will be public unless the organization&#x2019;s default is private.`,name:"private"},{anchor:"setfit.SetFitModel.push_to_hub.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <code>hf auth login</code>.`,name:"token"},{anchor:"setfit.SetFitModel.push_to_hub.branch",description:`<strong>branch</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The git branch on which to push the model. This defaults to <code>&quot;main&quot;</code>.`,name:"branch"},{anchor:"setfit.SetFitModel.push_to_hub.create_pr",description:`<strong>create_pr</strong> (<code>boolean</code>, <em>optional</em>) &#x2014;
Whether or not to create a Pull Request from <code>branch</code> with that commit. Defaults to <code>False</code>.`,name:"create_pr"},{anchor:"setfit.SetFitModel.push_to_hub.allow_patterns",description:`<strong>allow_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, only files matching at least one pattern are pushed.`,name:"allow_patterns"},{anchor:"setfit.SetFitModel.push_to_hub.ignore_patterns",description:`<strong>ignore_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, files matching any of the patterns are not pushed.`,name:"ignore_patterns"},{anchor:"setfit.SetFitModel.push_to_hub.delete_patterns",description:`<strong>delete_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, remote files matching any of the patterns will be deleted from the repo.`,name:"delete_patterns"},{anchor:"setfit.SetFitModel.push_to_hub.model_card_kwargs",description:`<strong>model_card_kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional arguments passed to the model card template to customize the model card.`,name:"model_card_kwargs"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L634",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The url of the commit of your model in the given repository.</p>
`}}),ze=new j({props:{name:"__call__",anchor:"setfit.SetFitModel.__call__",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str]]"},{name:"batch_size",val:": int = 32"},{name:"as_numpy",val:": bool = False"},{name:"use_labels",val:": bool = True"},{name:"show_progress_bar",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"setfit.SetFitModel.__call__.inputs",description:"<strong>inputs</strong> (<em>Union[str, List[str]]</em>) &#x2014; The input sentence or sentences to predict classes for.",name:"inputs"},{anchor:"setfit.SetFitModel.__call__.batch_size",description:`<strong>batch_size</strong> (<em>int</em>, defaults to <em>32</em>) &#x2014; The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.`,name:"batch_size"},{anchor:"setfit.SetFitModel.__call__.as_numpy",description:"<strong>as_numpy</strong> (<em>bool</em>, defaults to <em>False</em>) &#x2014; Whether to output as numpy array instead.",name:"as_numpy"},{anchor:"setfit.SetFitModel.__call__.use_labels",description:"<strong>use_labels</strong> (<em>bool</em>, defaults to <em>True</em>) &#x2014; Whether to try and return elements of <em>SetFitModel.labels</em>.",name:"use_labels"},{anchor:"setfit.SetFitModel.__call__.show_progress_bar",description:"<strong>show_progress_bar</strong> (<em>Optional[bool]</em>, defaults to <em>None</em>) &#x2014; Whether to show a progress bar while encoding.",name:"show_progress_bar"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L575",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of string labels with equal length to the
inputs if <em>use_labels</em> is <em>True</em> and <em>SetFitModel.labels</em> has been defined. Otherwise a vector with
equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
is a single string, then the output is a single label as well.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>Union[torch.Tensor, np.ndarray, List[str], int, str]</em></p>
`}}),he=new E({props:{anchor:"setfit.SetFitModel.__call__.example",$$slots:{default:[pr]},$$scope:{ctx:C}}}),Ge=new j({props:{name:"label2id",anchor:"setfit.SetFitModel.label2id",parameters:[],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L241"}}),He=new j({props:{name:"id2label",anchor:"setfit.SetFitModel.id2label",parameters:[],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L234"}}),Re=new j({props:{name:"create_model_card",anchor:"setfit.SetFitModel.create_model_card",parameters:[{name:"path",val:": str"},{name:"model_name",val:": typing.Optional[str] = 'SetFit Model'"}],parametersDescription:[{anchor:"setfit.SetFitModel.create_model_card.path",description:"<strong>path</strong> (str) &#x2014; The path to save the model card to.",name:"path"},{anchor:"setfit.SetFitModel.create_model_card.model_name",description:"<strong>model_name</strong> (str, <em>optional</em>) &#x2014; The name of the model. Defaults to <code>SetFit Model</code>.",name:"model_name"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L653"}}),Xe=new j({props:{name:"encode",anchor:"setfit.SetFitModel.encode",parameters:[{name:"inputs",val:": typing.List[str]"},{name:"batch_size",val:": int = 32"},{name:"show_progress_bar",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"setfit.SetFitModel.encode.inputs",description:"<strong>inputs</strong> (<code>List[str]</code>) &#x2014; The input sentences to embed.",name:"inputs"},{anchor:"setfit.SetFitModel.encode.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, defaults to <code>32</code>) &#x2014; The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.`,name:"batch_size"},{anchor:"setfit.SetFitModel.encode.show_progress_bar",description:"<strong>show_progress_bar</strong> (<code>Optional[bool]</code>, defaults to <code>None</code>) &#x2014; Whether to show a progress bar while encoding.",name:"show_progress_bar"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L436",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A matrix with shape [INPUT_LENGTH, EMBEDDING_SIZE], as a
torch Tensor if this model has a differentiable Torch head, or otherwise as a numpy array.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Union[torch.Tensor, np.ndarray]</p>
`}}),Be=new j({props:{name:"fit",anchor:"setfit.SetFitModel.fit",parameters:[{name:"x_train",val:": typing.List[str]"},{name:"y_train",val:": typing.Union[typing.List[int], typing.List[typing.List[int]]]"},{name:"num_epochs",val:": int"},{name:"batch_size",val:": typing.Optional[int] = None"},{name:"body_learning_rate",val:": typing.Optional[float] = None"},{name:"head_learning_rate",val:": typing.Optional[float] = None"},{name:"end_to_end",val:": bool = False"},{name:"l2_weight",val:": typing.Optional[float] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"show_progress_bar",val:": bool = True"}],parametersDescription:[{anchor:"setfit.SetFitModel.fit.x_train",description:"<strong>x_train</strong> (<code>List[str]</code>) &#x2014; A list of training sentences.",name:"x_train"},{anchor:"setfit.SetFitModel.fit.y_train",description:"<strong>y_train</strong> (<code>Union[List[int], List[List[int]]]</code>) &#x2014; A list of labels corresponding to the training sentences.",name:"y_train"},{anchor:"setfit.SetFitModel.fit.num_epochs",description:"<strong>num_epochs</strong> (<code>int</code>) &#x2014; The number of epochs to train for.",name:"num_epochs"},{anchor:"setfit.SetFitModel.fit.batch_size",description:"<strong>batch_size</strong> (<code>int</code>, <em>optional</em>) &#x2014; The batch size to use.",name:"batch_size"},{anchor:"setfit.SetFitModel.fit.body_learning_rate",description:`<strong>body_learning_rate</strong> (<code>float</code>, <em>optional</em>) &#x2014; The learning rate for the <code>SentenceTransformer</code> body
in the <code>AdamW</code> optimizer. Disregarded if <code>end_to_end=False</code>.`,name:"body_learning_rate"},{anchor:"setfit.SetFitModel.fit.head_learning_rate",description:`<strong>head_learning_rate</strong> (<code>float</code>, <em>optional</em>) &#x2014; The learning rate for the differentiable torch head
in the <code>AdamW</code> optimizer.`,name:"head_learning_rate"},{anchor:"setfit.SetFitModel.fit.end_to_end",description:`<strong>end_to_end</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014; If True, train the entire model end-to-end.
Otherwise, freeze the <code>SentenceTransformer</code> body and only train the head.`,name:"end_to_end"},{anchor:"setfit.SetFitModel.fit.l2_weight",description:`<strong>l2_weight</strong> (<code>float</code>, <em>optional</em>) &#x2014; The l2 weight for both the model body and head
in the <code>AdamW</code> optimizer.`,name:"l2_weight"},{anchor:"setfit.SetFitModel.fit.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014; The maximum token length a tokenizer can generate. If not provided,
the maximum length for the <code>SentenceTransformer</code> body is used.`,name:"max_length"},{anchor:"setfit.SetFitModel.fit.show_progress_bar",description:`<strong>show_progress_bar</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to display a progress bar for the training
epochs and iterations.`,name:"show_progress_bar"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L248"}}),Ve=new j({props:{name:"freeze",anchor:"setfit.SetFitModel.freeze",parameters:[{name:"component",val:": typing.Optional[typing.Literal['body', 'head']] = None"}],parametersDescription:[{anchor:"setfit.SetFitModel.freeze.component",description:`<strong>component</strong> (<code>Literal[&quot;body&quot;, &quot;head&quot;]</code>, <em>optional</em>) &#x2014; Either &#x201C;body&#x201D; or &#x201C;head&#x201D; to freeze that component.
If no component is provided, freeze both. Defaults to None.`,name:"component"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L390"}}),Ye=new j({props:{name:"generate_model_card",anchor:"setfit.SetFitModel.generate_model_card",parameters:[],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L677",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p>str</p>
`}}),Oe=new j({props:{name:"predict",anchor:"setfit.SetFitModel.predict",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str]]"},{name:"batch_size",val:": int = 32"},{name:"as_numpy",val:": bool = False"},{name:"use_labels",val:": bool = True"},{name:"show_progress_bar",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"setfit.SetFitModel.predict.inputs",description:"<strong>inputs</strong> (<em>Union[str, List[str]]</em>) &#x2014; The input sentence or sentences to predict classes for.",name:"inputs"},{anchor:"setfit.SetFitModel.predict.batch_size",description:`<strong>batch_size</strong> (<em>int</em>, defaults to <em>32</em>) &#x2014; The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.`,name:"batch_size"},{anchor:"setfit.SetFitModel.predict.as_numpy",description:"<strong>as_numpy</strong> (<em>bool</em>, defaults to <em>False</em>) &#x2014; Whether to output as numpy array instead.",name:"as_numpy"},{anchor:"setfit.SetFitModel.predict.use_labels",description:"<strong>use_labels</strong> (<em>bool</em>, defaults to <em>True</em>) &#x2014; Whether to try and return elements of <em>SetFitModel.labels</em>.",name:"use_labels"},{anchor:"setfit.SetFitModel.predict.show_progress_bar",description:"<strong>show_progress_bar</strong> (<em>Optional[bool]</em>, defaults to <em>None</em>) &#x2014; Whether to show a progress bar while encoding.",name:"show_progress_bar"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L525",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of string labels with equal length to the
inputs if <em>use_labels</em> is <em>True</em> and <em>SetFitModel.labels</em> has been defined. Otherwise a vector with
equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
is a single string, then the output is a single label as well.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>Union[torch.Tensor, np.ndarray, List[str], int, str]</em></p>
`}}),xe=new E({props:{anchor:"setfit.SetFitModel.predict.example",$$slots:{default:[mr]},$$scope:{ctx:C}}}),Qe=new j({props:{name:"predict_proba",anchor:"setfit.SetFitModel.predict_proba",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str]]"},{name:"batch_size",val:": int = 32"},{name:"as_numpy",val:": bool = False"},{name:"show_progress_bar",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"setfit.SetFitModel.predict_proba.inputs",description:"<strong>inputs</strong> (<em>Union[str, List[str]]</em>) &#x2014; The input sentences to predict class probabilities for.",name:"inputs"},{anchor:"setfit.SetFitModel.predict_proba.batch_size",description:`<strong>batch_size</strong> (<em>int</em>, defaults to <em>32</em>) &#x2014; The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.`,name:"batch_size"},{anchor:"setfit.SetFitModel.predict_proba.as_numpy",description:"<strong>as_numpy</strong> (<em>bool</em>, defaults to <em>False</em>) &#x2014; Whether to output as numpy array instead.",name:"as_numpy"},{anchor:"setfit.SetFitModel.predict_proba.show_progress_bar",description:"<strong>show_progress_bar</strong> (<em>Optional[bool]</em>, defaults to <em>None</em>) &#x2014; Whether to show a progress bar while encoding.",name:"show_progress_bar"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L480",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A matrix with shape [INPUT_LENGTH, NUM_CLASSES] denoting
probabilities of predicting an input as a class. If the input is a string, then the output
is a vector with shape [NUM_CLASSES,].</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>Union[torch.Tensor, np.ndarray]</em></p>
`}}),Me=new E({props:{anchor:"setfit.SetFitModel.predict_proba.example",$$slots:{default:[gr]},$$scope:{ctx:C}}}),Ke=new j({props:{name:"to",anchor:"setfit.SetFitModel.to",parameters:[{name:"device",val:": typing.Union[str, torch.device]"}],parametersDescription:[{anchor:"setfit.SetFitModel.to.device",description:"<strong>device</strong> (Union[str, torch.device]) &#x2014; The identifier of the device to move the model to.",name:"device"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L627",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Returns the original model, but now on the desired device.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>SetFitModel</p>
`}}),we=new E({props:{anchor:"setfit.SetFitModel.to.example",$$slots:{default:[hr]},$$scope:{ctx:C}}}),et=new j({props:{name:"unfreeze",anchor:"setfit.SetFitModel.unfreeze",parameters:[{name:"component",val:": typing.Optional[typing.Literal['body', 'head']] = None"},{name:"keep_body_frozen",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"setfit.SetFitModel.unfreeze.component",description:`<strong>component</strong> (<code>Literal[&quot;body&quot;, &quot;head&quot;]</code>, <em>optional</em>) &#x2014; Either &#x201C;body&#x201D; or &#x201C;head&#x201D; to unfreeze that component.
If no component is provided, unfreeze both. Defaults to None.`,name:"component"},{anchor:"setfit.SetFitModel.unfreeze.keep_body_frozen",description:"<strong>keep_body_frozen</strong> (<code>bool</code>, <em>optional</em>) &#x2014; Deprecated argument, use <code>component</code> instead.",name:"keep_body_frozen"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L403"}}),tt=new Gt({props:{title:"SetFitHead",local:"setfit.SetFitHead",headingTag:"h2"}}),nt=new j({props:{name:"class setfit.SetFitHead",anchor:"setfit.SetFitHead",parameters:[{name:"in_features",val:": typing.Optional[int] = None"},{name:"out_features",val:": int = 2"},{name:"temperature",val:": float = 1.0"},{name:"eps",val:": float = 1e-05"},{name:"bias",val:": bool = True"},{name:"device",val:": typing.Union[torch.device, str, NoneType] = None"},{name:"multitarget",val:": bool = False"}],parametersDescription:[{anchor:"setfit.SetFitHead.in_features",description:`<strong>in_features</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The embedding dimension from the output of the SetFit body. If <code>None</code>, defaults to <code>LazyLinear</code>.`,name:"in_features"},{anchor:"setfit.SetFitHead.out_features",description:`<strong>out_features</strong> (<code>int</code>, defaults to <code>2</code>) &#x2014;
The number of targets. If set <code>out_features</code> to 1 for binary classification, it will be changed to 2 as 2-class classification.`,name:"out_features"},{anchor:"setfit.SetFitHead.temperature",description:`<strong>temperature</strong> (<code>float</code>, defaults to <code>1.0</code>) &#x2014;
A logits&#x2019; scaling factor. Higher values make the model less confident and lower values make
it more confident.`,name:"temperature"},{anchor:"setfit.SetFitHead.eps",description:`<strong>eps</strong> (<code>float</code>, defaults to <code>1e-5</code>) &#x2014;
A value for numerical stability when scaling logits.`,name:"eps"},{anchor:"setfit.SetFitHead.bias",description:`<strong>bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add bias to the head.`,name:"bias"},{anchor:"setfit.SetFitHead.device",description:`<strong>device</strong> (<code>torch.device</code>, str, <em>optional</em>) &#x2014;
The device the model will be sent to. If <code>None</code>, will check whether GPU is available.`,name:"device"},{anchor:"setfit.SetFitHead.multitarget",description:`<strong>multitarget</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Enable multi-target classification by making <code>out_features</code> binary predictions instead
of a single multinomial prediction.`,name:"multitarget"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L39"}}),ot=new j({props:{name:"forward",anchor:"setfit.SetFitHead.forward",parameters:[{name:"features",val:": typing.Union[typing.Dict[str, torch.Tensor], torch.Tensor]"},{name:"temperature",val:": typing.Optional[float] = None"}],parametersDescription:[{anchor:"setfit.SetFitHead.forward.features",description:`<strong>features</strong> (<code>Dict[str, torch.Tensor]</code> or <code>torch.Tensor) -- The embeddings from the encoder. If using </code>dict\` format,
make sure to store embeddings under the key: &#x2018;sentence_embedding&#x2019;
and the outputs will be under the key: &#x2018;prediction&#x2019;.`,name:"features"},{anchor:"setfit.SetFitHead.forward.temperature",description:`<strong>temperature</strong> (<code>float</code>, <em>optional</em>) &#x2014;
A logits&#x2019; scaling factor. Higher values make the model less
confident and lower values make it more confident.
Will override the temperature given during initialization.`,name:"temperature"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L100",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>[<code>Dict[str, torch.Tensor]</code> or <code>Tuple[torch.Tensor]</code>]</p>
`}}),st=new Gt({props:{title:"SetFitModelCardData",local:"setfit.SetFitModelCardData",headingTag:"h2"}}),at=new j({props:{name:"class setfit.SetFitModelCardData",anchor:"setfit.SetFitModelCardData",parameters:[{name:"language",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"license",val:": typing.Optional[str] = None"},{name:"tags",val:": typing.Optional[typing.List[str]] = <factory>"},{name:"model_name",val:": typing.Optional[str] = None"},{name:"model_id",val:": typing.Optional[str] = None"},{name:"dataset_name",val:": typing.Optional[str] = None"},{name:"dataset_id",val:": typing.Optional[str] = None"},{name:"dataset_revision",val:": typing.Optional[str] = None"},{name:"task_name",val:": typing.Optional[str] = None"},{name:"st_id",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"setfit.SetFitModelCardData.language",description:`<strong>language</strong> (<em>Optional[Union[str, List[str]]]</em>) &#x2014; The model language, either a string or a list,
e.g. &#x201C;en&#x201D; or [&#x201C;en&#x201D;, &#x201C;de&#x201D;, &#x201C;nl&#x201D;]`,name:"language"},{anchor:"setfit.SetFitModelCardData.license",description:`<strong>license</strong> (<em>Optional[str]</em>) &#x2014; The license of the model, e.g. &#x201C;apache-2.0&#x201D;, &#x201C;mit&#x201D;,
or &#x201C;cc-by-nc-sa-4.0&#x201D;`,name:"license"},{anchor:"setfit.SetFitModelCardData.model_name",description:`<strong>model_name</strong> (<em>Optional[str]</em>) &#x2014; The pretty name of the model, e.g. &#x201C;SetFit with mBERT-base on SST2&#x201D;.
If not defined, uses encoder_name/encoder_id and dataset_name/dataset_id to generate a model name.`,name:"model_name"},{anchor:"setfit.SetFitModelCardData.model_id",description:`<strong>model_id</strong> (<em>Optional[str]</em>) &#x2014; The model ID when pushing the model to the Hub,
e.g. &#x201C;tomaarsen/span-marker-mbert-base-multinerd&#x201D;.`,name:"model_id"},{anchor:"setfit.SetFitModelCardData.dataset_name",description:"<strong>dataset_name</strong> (<em>Optional[str]</em>) &#x2014; The pretty name of the dataset, e.g. &#x201C;SST2&#x201D;.",name:"dataset_name"},{anchor:"setfit.SetFitModelCardData.dataset_id",description:"<strong>dataset_id</strong> (<em>Optional[str]</em>) &#x2014; The dataset ID of the dataset, e.g. &#x201C;dair-ai/emotion&#x201D;.",name:"dataset_id"},{anchor:"setfit.SetFitModelCardData.dataset_revision",description:"<strong>dataset_revision</strong> (<em>Optional[str]</em>) &#x2014; The dataset revision/commit that was for training/evaluation.",name:"dataset_revision"},{anchor:"setfit.SetFitModelCardData.st_id",description:"<strong>st_id</strong> (<em>Optional[str]</em>) &#x2014; The Sentence Transformers model ID.",name:"st_id"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/model_card.py#L168"}}),je=new tr({props:{$$slots:{default:[ur]},$$scope:{ctx:C}}}),Ce=new E({props:{anchor:"setfit.SetFitModelCardData.example",$$slots:{default:[fr]},$$scope:{ctx:C}}}),rt=new j({props:{name:"to_dict",anchor:"setfit.SetFitModelCardData.to_dict",parameters:[],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/model_card.py#L479"}}),it=new j({props:{name:"to_yaml",anchor:"setfit.SetFitModelCardData.to_yaml",parameters:[{name:"line_break",val:" = None"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/model_card.py#L548"}}),lt=new Gt({props:{title:"AbsaModel",local:"setfit.AbsaModel",headingTag:"h2"}}),dt=new j({props:{name:"class setfit.AbsaModel",anchor:"setfit.AbsaModel",parameters:[{name:"aspect_extractor",val:": AspectExtractor"},{name:"aspect_model",val:": AspectModel"},{name:"polarity_model",val:": PolarityModel"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L151"}}),ct=new j({props:{name:"__call__",anchor:"setfit.AbsaModel.__call__",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str]]"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L278"}}),pt=new j({props:{name:"device",anchor:"setfit.AbsaModel.device",parameters:[],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L270"}}),mt=new j({props:{name:"from_pretrained",anchor:"setfit.AbsaModel.from_pretrained",parameters:[{name:"model_id",val:": str"},{name:"polarity_model_id",val:": typing.Optional[str] = None"},{name:"spacy_model",val:": typing.Optional[str] = None"},{name:"span_contexts",val:": typing.Tuple[typing.Optional[int], typing.Optional[int]] = (None, None)"},{name:"force_download",val:": bool = None"},{name:"resume_download",val:": bool = None"},{name:"proxies",val:": typing.Optional[typing.Dict] = None"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"},{name:"cache_dir",val:": typing.Optional[str] = None"},{name:"local_files_only",val:": bool = None"},{name:"use_differentiable_head",val:": bool = None"},{name:"normalize_embeddings",val:": bool = None"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L295"}}),gt=new j({props:{name:"predict",anchor:"setfit.AbsaModel.predict",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str], datasets.arrow_dataset.Dataset]"}],parametersDescription:[{anchor:"setfit.AbsaModel.predict.inputs",description:`<strong>inputs</strong> (Union[str, List[str], Dataset]) &#x2014; Either a sentence, a list of sentences,
or a dataset with columns <em>text</em> and <em>span</em> and optionally <em>ordinal</em>. This dataset
contains gold aspects, and we only predict the polarities for them.`,name:"inputs"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L210",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Either a list of dictionaries with keys <em>span</em>
and <em>polarity</em> if the input was a sentence or a list of sentences, or a dataset with
columns <em>text</em>, <em>span</em>, <em>ordinal</em>, and <em>pred_polarity</em>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Union[List[Dict[str, Any]], Dataset]</p>
`}}),Fe=new E({props:{anchor:"setfit.AbsaModel.predict.example",$$slots:{default:[_r]},$$scope:{ctx:C}}}),ht=new j({props:{name:"push_to_hub",anchor:"setfit.AbsaModel.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"polarity_repo_id",val:": typing.Optional[str] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L368"}}),ut=new j({props:{name:"to",anchor:"setfit.AbsaModel.to",parameters:[{name:"device",val:": typing.Union[str, torch.device]"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L274"}}),ft=new j({props:{name:"save_pretrained",anchor:"setfit.AbsaModel.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"polarity_save_directory",val:": typing.Union[str, pathlib.Path, NoneType] = None"},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L281"}}),_t=new Gt({props:{title:"AspectModel",local:"setfit.AspectModel",headingTag:"h3"}}),bt=new j({props:{name:"class setfit.AspectModel",anchor:"setfit.AspectModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L127"}}),yt=new j({props:{name:"__call__",anchor:"setfit.AspectModel.__call__",parameters:[{name:"docs",val:": typing.List[ForwardRef('Doc')]"},{name:"aspects_list",val:": typing.List[typing.List[slice]]"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L128"}}),vt=new j({props:{name:"device",anchor:"setfit.AspectModel.device",parameters:[],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L615",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The device that the model is on.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>torch.device</p>
`}}),$t=new j({props:{name:"from_pretrained",anchor:"setfit.AspectModel.from_pretrained",parameters:"",parametersDescription:[{anchor:"setfit.AspectModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code>, <code>Path</code>) &#x2014;
<ul>
<li>Either the <code>model_id</code> (string) of a model hosted on the Hub, e.g. <code>bigscience/bloom</code>.</li>
<li>Or a path to a <code>directory</code> containing model weights saved using
<a href="https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained" rel="nofollow">save_pretrained</a>, e.g., <code>../path/to/my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"setfit.AspectModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
Defaults to the latest commit on <code>main</code> branch.`,name:"revision"},{anchor:"setfit.AspectModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.`,name:"force_download"},{anchor:"setfit.AspectModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on every request.`,name:"proxies"},{anchor:"setfit.AspectModel.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <code>hf auth login</code>.`,name:"token"},{anchor:"setfit.AspectModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code>, <code>Path</code>, <em>optional</em>) &#x2014;
Path to the folder where cached files are stored.`,name:"cache_dir"},{anchor:"setfit.AspectModel.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>True</code>, avoid downloading the file and return the path to the local cached file if it exists.`,name:"local_files_only"},{anchor:"setfit.AspectModel.from_pretrained.labels",description:`<strong>labels</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
If the labels are integers ranging from <code>0</code> to <code>num_classes-1</code>, then these labels indicate
the corresponding labels.`,name:"labels"},{anchor:"setfit.AspectModel.from_pretrained.model_card_data",description:`<strong>model_card_data</strong> (<code>SetFitModelCardData</code>, <em>optional</em>) &#x2014;
A <code>SetFitModelCardData</code> instance storing data such as model language, license, dataset name,
etc. to be used in the automatically generated model cards.`,name:"model_card_data"},{anchor:"setfit.AspectModel.from_pretrained.model_card_data",description:`<strong>model_card_data</strong> (<code>SetFitModelCardData</code>, <em>optional</em>) &#x2014;
A <code>SetFitModelCardData</code> instance storing data such as model language, license, dataset name,
etc. to be used in the automatically generated model cards.`,name:"model_card_data"},{anchor:"setfit.AspectModel.from_pretrained.use_differentiable_head",description:`<strong>use_differentiable_head</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to load SetFit using a differentiable (i.e., Torch) head instead of Logistic Regression.`,name:"use_differentiable_head"},{anchor:"setfit.AspectModel.from_pretrained.normalize_embeddings",description:`<strong>normalize_embeddings</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to apply normalization on the embeddings produced by the Sentence Transformer body.`,name:"normalize_embeddings"},{anchor:"setfit.AspectModel.from_pretrained.span_context",description:`<strong>span_context</strong> (<code>int</code>, defaults to <code>0</code>) &#x2014;
The number of words before and after the span candidate that should be prepended to the full sentence.
By default, 0 for Aspect models and 3 for Polarity models.`,name:"span_context"},{anchor:"setfit.AspectModel.from_pretrained.device",description:`<strong>device</strong> (<code>Union[torch.device, str]</code>, <em>optional</em>) &#x2014;
The device on which to load the SetFit model, e.g. <code>&quot;cuda:0&quot;</code>, <code>&quot;mps&quot;</code> or <code>torch.device(&quot;cuda&quot;)</code>.`,name:"device"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L462"}}),xt=new j({props:{name:"predict",anchor:"setfit.AspectModel.predict",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str]]"},{name:"batch_size",val:": int = 32"},{name:"as_numpy",val:": bool = False"},{name:"use_labels",val:": bool = True"},{name:"show_progress_bar",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"setfit.AspectModel.predict.inputs",description:"<strong>inputs</strong> (<em>Union[str, List[str]]</em>) &#x2014; The input sentence or sentences to predict classes for.",name:"inputs"},{anchor:"setfit.AspectModel.predict.batch_size",description:`<strong>batch_size</strong> (<em>int</em>, defaults to <em>32</em>) &#x2014; The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.`,name:"batch_size"},{anchor:"setfit.AspectModel.predict.as_numpy",description:"<strong>as_numpy</strong> (<em>bool</em>, defaults to <em>False</em>) &#x2014; Whether to output as numpy array instead.",name:"as_numpy"},{anchor:"setfit.AspectModel.predict.use_labels",description:"<strong>use_labels</strong> (<em>bool</em>, defaults to <em>True</em>) &#x2014; Whether to try and return elements of <em>SetFitModel.labels</em>.",name:"use_labels"},{anchor:"setfit.AspectModel.predict.show_progress_bar",description:"<strong>show_progress_bar</strong> (<em>Optional[bool]</em>, defaults to <em>None</em>) &#x2014; Whether to show a progress bar while encoding.",name:"show_progress_bar"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L525",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of string labels with equal length to the
inputs if <em>use_labels</em> is <em>True</em> and <em>SetFitModel.labels</em> has been defined. Otherwise a vector with
equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
is a single string, then the output is a single label as well.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>Union[torch.Tensor, np.ndarray, List[str], int, str]</em></p>
`}}),Ue=new E({props:{anchor:"setfit.AspectModel.predict.example",$$slots:{default:[br]},$$scope:{ctx:C}}}),Mt=new j({props:{name:"push_to_hub",anchor:"setfit.AspectModel.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"config",val:": typing.Union[dict, huggingface_hub.hub_mixin.DataclassInstance, NoneType] = None"},{name:"commit_message",val:": str = 'Push model using huggingface_hub.'"},{name:"private",val:": typing.Optional[bool] = None"},{name:"token",val:": typing.Optional[str] = None"},{name:"branch",val:": typing.Optional[str] = None"},{name:"create_pr",val:": typing.Optional[bool] = None"},{name:"allow_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"ignore_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"delete_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"model_card_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"}],parametersDescription:[{anchor:"setfit.AspectModel.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
ID of the repository to push to (example: <code>&quot;username/my-model&quot;</code>).`,name:"repo_id"},{anchor:"setfit.AspectModel.push_to_hub.config",description:`<strong>config</strong> (<code>dict</code> or <code>DataclassInstance</code>, <em>optional</em>) &#x2014;
Model configuration specified as a key/value dictionary or a dataclass instance.`,name:"config"},{anchor:"setfit.AspectModel.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Message to commit while pushing.`,name:"commit_message"},{anchor:"setfit.AspectModel.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether the repository created should be private.
If <code>None</code> (default), the repo will be public unless the organization&#x2019;s default is private.`,name:"private"},{anchor:"setfit.AspectModel.push_to_hub.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <code>hf auth login</code>.`,name:"token"},{anchor:"setfit.AspectModel.push_to_hub.branch",description:`<strong>branch</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The git branch on which to push the model. This defaults to <code>&quot;main&quot;</code>.`,name:"branch"},{anchor:"setfit.AspectModel.push_to_hub.create_pr",description:`<strong>create_pr</strong> (<code>boolean</code>, <em>optional</em>) &#x2014;
Whether or not to create a Pull Request from <code>branch</code> with that commit. Defaults to <code>False</code>.`,name:"create_pr"},{anchor:"setfit.AspectModel.push_to_hub.allow_patterns",description:`<strong>allow_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, only files matching at least one pattern are pushed.`,name:"allow_patterns"},{anchor:"setfit.AspectModel.push_to_hub.ignore_patterns",description:`<strong>ignore_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, files matching any of the patterns are not pushed.`,name:"ignore_patterns"},{anchor:"setfit.AspectModel.push_to_hub.delete_patterns",description:`<strong>delete_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, remote files matching any of the patterns will be deleted from the repo.`,name:"delete_patterns"},{anchor:"setfit.AspectModel.push_to_hub.model_card_kwargs",description:`<strong>model_card_kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional arguments passed to the model card template to customize the model card.`,name:"model_card_kwargs"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L634",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The url of the commit of your model in the given repository.</p>
`}}),wt=new j({props:{name:"save_pretrained",anchor:"setfit.AspectModel.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"config",val:": typing.Union[dict, huggingface_hub.hub_mixin.DataclassInstance, NoneType] = None"},{name:"repo_id",val:": typing.Optional[str] = None"},{name:"push_to_hub",val:": bool = False"},{name:"model_card_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"**push_to_hub_kwargs",val:""}],parametersDescription:[{anchor:"setfit.AspectModel.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>Path</code>) &#x2014;
Path to directory in which the model weights and configuration will be saved.`,name:"save_directory"},{anchor:"setfit.AspectModel.save_pretrained.config",description:`<strong>config</strong> (<code>dict</code> or <code>DataclassInstance</code>, <em>optional</em>) &#x2014;
Model configuration specified as a key/value dictionary or a dataclass instance.`,name:"config"},{anchor:"setfit.AspectModel.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to push your model to the Huggingface Hub after saving it.`,name:"push_to_hub"},{anchor:"setfit.AspectModel.save_pretrained.repo_id",description:`<strong>repo_id</strong> (<code>str</code>, <em>optional</em>) &#x2014;
ID of your repository on the Hub. Used only if <code>push_to_hub=True</code>. Will default to the folder name if
not provided.`,name:"repo_id"},{anchor:"setfit.AspectModel.save_pretrained.model_card_kwargs",description:`<strong>model_card_kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional arguments passed to the model card template to customize the model card.`,name:"model_card_kwargs"},{anchor:"setfit.AspectModel.save_pretrained.push_to_hub_kwargs",description:`<strong>push_to_hub_kwargs</strong> &#x2014;
Additional key word arguments passed along to the <code>~ModelHubMixin.push_to_hub</code> method.`,name:"push_to_hub_kwargs"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L383",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>url of the commit on the Hub if <code>push_to_hub=True</code>, <code>None</code> otherwise.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code> or <code>None</code></p>
`}}),Tt=new j({props:{name:"to",anchor:"setfit.AspectModel.to",parameters:[{name:"device",val:": typing.Union[str, torch.device]"}],parametersDescription:[{anchor:"setfit.AspectModel.to.device",description:"<strong>device</strong> (Union[str, torch.device]) &#x2014; The identifier of the device to move the model to.",name:"device"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L627",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Returns the original model, but now on the desired device.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>SetFitModel</p>
`}}),Je=new E({props:{anchor:"setfit.AspectModel.to.example",$$slots:{default:[yr]},$$scope:{ctx:C}}}),jt=new Gt({props:{title:"PolarityModel",local:"setfit.PolarityModel",headingTag:"h3"}}),Ct=new j({props:{name:"class setfit.PolarityModel",anchor:"setfit.PolarityModel",parameters:[{name:"span_context",val:": int = 3"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L141"}}),Ft=new j({props:{name:"__call__",anchor:"setfit.PolarityModel.__call__",parameters:[{name:"docs",val:": typing.List[ForwardRef('Doc')]"},{name:"aspects_list",val:": typing.List[typing.List[slice]]"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/span/modeling.py#L47"}}),St=new j({props:{name:"device",anchor:"setfit.PolarityModel.device",parameters:[],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L615",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The device that the model is on.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>torch.device</p>
`}}),It=new j({props:{name:"from_pretrained",anchor:"setfit.PolarityModel.from_pretrained",parameters:"",parametersDescription:[{anchor:"setfit.PolarityModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code>, <code>Path</code>) &#x2014;
<ul>
<li>Either the <code>model_id</code> (string) of a model hosted on the Hub, e.g. <code>bigscience/bloom</code>.</li>
<li>Or a path to a <code>directory</code> containing model weights saved using
<a href="https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained" rel="nofollow">save_pretrained</a>, e.g., <code>../path/to/my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"setfit.PolarityModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
Defaults to the latest commit on <code>main</code> branch.`,name:"revision"},{anchor:"setfit.PolarityModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.`,name:"force_download"},{anchor:"setfit.PolarityModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on every request.`,name:"proxies"},{anchor:"setfit.PolarityModel.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <code>hf auth login</code>.`,name:"token"},{anchor:"setfit.PolarityModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code>, <code>Path</code>, <em>optional</em>) &#x2014;
Path to the folder where cached files are stored.`,name:"cache_dir"},{anchor:"setfit.PolarityModel.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>True</code>, avoid downloading the file and return the path to the local cached file if it exists.`,name:"local_files_only"},{anchor:"setfit.PolarityModel.from_pretrained.labels",description:`<strong>labels</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
If the labels are integers ranging from <code>0</code> to <code>num_classes-1</code>, then these labels indicate
the corresponding labels.`,name:"labels"},{anchor:"setfit.PolarityModel.from_pretrained.model_card_data",description:`<strong>model_card_data</strong> (<code>SetFitModelCardData</code>, <em>optional</em>) &#x2014;
A <code>SetFitModelCardData</code> instance storing data such as model language, license, dataset name,
etc. to be used in the automatically generated model cards.`,name:"model_card_data"},{anchor:"setfit.PolarityModel.from_pretrained.model_card_data",description:`<strong>model_card_data</strong> (<code>SetFitModelCardData</code>, <em>optional</em>) &#x2014;
A <code>SetFitModelCardData</code> instance storing data such as model language, license, dataset name,
etc. to be used in the automatically generated model cards.`,name:"model_card_data"},{anchor:"setfit.PolarityModel.from_pretrained.use_differentiable_head",description:`<strong>use_differentiable_head</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to load SetFit using a differentiable (i.e., Torch) head instead of Logistic Regression.`,name:"use_differentiable_head"},{anchor:"setfit.PolarityModel.from_pretrained.normalize_embeddings",description:`<strong>normalize_embeddings</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to apply normalization on the embeddings produced by the Sentence Transformer body.`,name:"normalize_embeddings"},{anchor:"setfit.PolarityModel.from_pretrained.span_context",description:`<strong>span_context</strong> (<code>int</code>, defaults to <code>0</code>) &#x2014;
The number of words before and after the span candidate that should be prepended to the full sentence.
By default, 0 for Aspect models and 3 for Polarity models.`,name:"span_context"},{anchor:"setfit.PolarityModel.from_pretrained.device",description:`<strong>device</strong> (<code>Union[torch.device, str]</code>, <em>optional</em>) &#x2014;
The device on which to load the SetFit model, e.g. <code>&quot;cuda:0&quot;</code>, <code>&quot;mps&quot;</code> or <code>torch.device(&quot;cuda&quot;)</code>.`,name:"device"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L462"}}),Ut=new j({props:{name:"predict",anchor:"setfit.PolarityModel.predict",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str]]"},{name:"batch_size",val:": int = 32"},{name:"as_numpy",val:": bool = False"},{name:"use_labels",val:": bool = True"},{name:"show_progress_bar",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"setfit.PolarityModel.predict.inputs",description:"<strong>inputs</strong> (<em>Union[str, List[str]]</em>) &#x2014; The input sentence or sentences to predict classes for.",name:"inputs"},{anchor:"setfit.PolarityModel.predict.batch_size",description:`<strong>batch_size</strong> (<em>int</em>, defaults to <em>32</em>) &#x2014; The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.`,name:"batch_size"},{anchor:"setfit.PolarityModel.predict.as_numpy",description:"<strong>as_numpy</strong> (<em>bool</em>, defaults to <em>False</em>) &#x2014; Whether to output as numpy array instead.",name:"as_numpy"},{anchor:"setfit.PolarityModel.predict.use_labels",description:"<strong>use_labels</strong> (<em>bool</em>, defaults to <em>True</em>) &#x2014; Whether to try and return elements of <em>SetFitModel.labels</em>.",name:"use_labels"},{anchor:"setfit.PolarityModel.predict.show_progress_bar",description:"<strong>show_progress_bar</strong> (<em>Optional[bool]</em>, defaults to <em>None</em>) &#x2014; Whether to show a progress bar while encoding.",name:"show_progress_bar"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L525",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of string labels with equal length to the
inputs if <em>use_labels</em> is <em>True</em> and <em>SetFitModel.labels</em> has been defined. Otherwise a vector with
equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
is a single string, then the output is a single label as well.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>Union[torch.Tensor, np.ndarray, List[str], int, str]</em></p>
`}}),qe=new E({props:{anchor:"setfit.PolarityModel.predict.example",$$slots:{default:[vr]},$$scope:{ctx:C}}}),kt=new j({props:{name:"push_to_hub",anchor:"setfit.PolarityModel.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"config",val:": typing.Union[dict, huggingface_hub.hub_mixin.DataclassInstance, NoneType] = None"},{name:"commit_message",val:": str = 'Push model using huggingface_hub.'"},{name:"private",val:": typing.Optional[bool] = None"},{name:"token",val:": typing.Optional[str] = None"},{name:"branch",val:": typing.Optional[str] = None"},{name:"create_pr",val:": typing.Optional[bool] = None"},{name:"allow_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"ignore_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"delete_patterns",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"model_card_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"}],parametersDescription:[{anchor:"setfit.PolarityModel.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
ID of the repository to push to (example: <code>&quot;username/my-model&quot;</code>).`,name:"repo_id"},{anchor:"setfit.PolarityModel.push_to_hub.config",description:`<strong>config</strong> (<code>dict</code> or <code>DataclassInstance</code>, <em>optional</em>) &#x2014;
Model configuration specified as a key/value dictionary or a dataclass instance.`,name:"config"},{anchor:"setfit.PolarityModel.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Message to commit while pushing.`,name:"commit_message"},{anchor:"setfit.PolarityModel.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether the repository created should be private.
If <code>None</code> (default), the repo will be public unless the organization&#x2019;s default is private.`,name:"private"},{anchor:"setfit.PolarityModel.push_to_hub.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <code>hf auth login</code>.`,name:"token"},{anchor:"setfit.PolarityModel.push_to_hub.branch",description:`<strong>branch</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The git branch on which to push the model. This defaults to <code>&quot;main&quot;</code>.`,name:"branch"},{anchor:"setfit.PolarityModel.push_to_hub.create_pr",description:`<strong>create_pr</strong> (<code>boolean</code>, <em>optional</em>) &#x2014;
Whether or not to create a Pull Request from <code>branch</code> with that commit. Defaults to <code>False</code>.`,name:"create_pr"},{anchor:"setfit.PolarityModel.push_to_hub.allow_patterns",description:`<strong>allow_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, only files matching at least one pattern are pushed.`,name:"allow_patterns"},{anchor:"setfit.PolarityModel.push_to_hub.ignore_patterns",description:`<strong>ignore_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, files matching any of the patterns are not pushed.`,name:"ignore_patterns"},{anchor:"setfit.PolarityModel.push_to_hub.delete_patterns",description:`<strong>delete_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, remote files matching any of the patterns will be deleted from the repo.`,name:"delete_patterns"},{anchor:"setfit.PolarityModel.push_to_hub.model_card_kwargs",description:`<strong>model_card_kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional arguments passed to the model card template to customize the model card.`,name:"model_card_kwargs"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L634",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The url of the commit of your model in the given repository.</p>
`}}),Jt=new j({props:{name:"save_pretrained",anchor:"setfit.PolarityModel.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"config",val:": typing.Union[dict, huggingface_hub.hub_mixin.DataclassInstance, NoneType] = None"},{name:"repo_id",val:": typing.Optional[str] = None"},{name:"push_to_hub",val:": bool = False"},{name:"model_card_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"**push_to_hub_kwargs",val:""}],parametersDescription:[{anchor:"setfit.PolarityModel.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>Path</code>) &#x2014;
Path to directory in which the model weights and configuration will be saved.`,name:"save_directory"},{anchor:"setfit.PolarityModel.save_pretrained.config",description:`<strong>config</strong> (<code>dict</code> or <code>DataclassInstance</code>, <em>optional</em>) &#x2014;
Model configuration specified as a key/value dictionary or a dataclass instance.`,name:"config"},{anchor:"setfit.PolarityModel.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to push your model to the Huggingface Hub after saving it.`,name:"push_to_hub"},{anchor:"setfit.PolarityModel.save_pretrained.repo_id",description:`<strong>repo_id</strong> (<code>str</code>, <em>optional</em>) &#x2014;
ID of your repository on the Hub. Used only if <code>push_to_hub=True</code>. Will default to the folder name if
not provided.`,name:"repo_id"},{anchor:"setfit.PolarityModel.save_pretrained.model_card_kwargs",description:`<strong>model_card_kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional arguments passed to the model card template to customize the model card.`,name:"model_card_kwargs"},{anchor:"setfit.PolarityModel.save_pretrained.push_to_hub_kwargs",description:`<strong>push_to_hub_kwargs</strong> &#x2014;
Additional key word arguments passed along to the <code>~ModelHubMixin.push_to_hub</code> method.`,name:"push_to_hub_kwargs"}],source:"https://github.com/huggingface/setfit/blob/main/src/huggingface_hub/hub_mixin.py#L383",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>url of the commit on the Hub if <code>push_to_hub=True</code>, <code>None</code> otherwise.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code> or <code>None</code></p>
`}}),Dt=new j({props:{name:"to",anchor:"setfit.PolarityModel.to",parameters:[{name:"device",val:": typing.Union[str, torch.device]"}],parametersDescription:[{anchor:"setfit.PolarityModel.to.device",description:"<strong>device</strong> (Union[str, torch.device]) &#x2014; The identifier of the device to move the model to.",name:"device"}],source:"https://github.com/huggingface/setfit/blob/main/src/setfit/modeling.py#L627",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Returns the original model, but now on the desired device.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>SetFitModel</p>
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