huggingface_hub/pr_4116/cn/_app/immutable/nodes/7.030d64b6.js

import{s as dt,n as $t,o as gt}from"../chunks/scheduler.de5597d1.js";import{S as bt,i as _t,e as i,s as a,c,h as Mt,a as p,d as s,b as n,f as rt,g as u,j as f,k as ht,l as jt,m as l,n as o,t as r,o as h,p as d}from"../chunks/index.bf1177c2.js";import{C as wt,H as et,E as xt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.a9bcef24.js";import{C as st}from"../chunks/CodeBlock.53f148c0.js";function Tt(lt){let m,S,P,Z,$,z,g,X,b,at="在本教程中，您将学习如何使用 <code>huggingface_hub</code> 在 Hub 上搜索模型、数据集和Spaces。",E,_,W,M,nt="<code>huggingface_hub</code>库包括一个 HTTP 客户端 <code>HfApi</code>，用于与 Hub 交互。 除此之外，它还可以列出存储在 Hub 上的模型、数据集和Spaces：",Q,j,B,w,it="<code>list_models()</code> 返回一个迭代器，包含存储在 Hub 上的模型。",R,x,pt="同样，您可以使用 <code>list_datasets()</code> 列出数据集，使用 <code>list_spaces()</code> 列出 Spaces。",q,T,I,v,mt=`列出仓库是一个好开始，但现在您可能希望对搜索结果进行过滤。
列出时，可以使用多个属性来过滤结果，例如：`,N,H,ft="<li><code>filter</code></li> <li><code>author</code></li> <li><code>search</code></li> <li>…</li>",F,y,ct="让我们看一个示例，获取所有在 Hub 上进行图像分类的模型，这些模型已在 imagenet 数据集上训练，并使用 PyTorch 运行。",Y,C,A,k,ut="在过滤时，您还可以对模型进行排序，并仅获取前几个结果。例如，以下示例获取了 Hub 上下载量最多的前 5 个数据集：",K,J,V,L,ot=`如果您想要在Hub上探索可用的过滤器, 请在浏览器中访问 <a href="https://huggingface.co/models" rel="nofollow">models</a> 和 <a href="https://huggingface.co/datasets" rel="nofollow">datasets</a> 页面
，尝试不同的参数并查看URL中的值。`,D,U,O,G,tt;return $=new wt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),g=new et({props:{title:"搜索 Hub",local:"搜索-hub",headingTag:"h1"}}),_=new et({props:{title:"如何列出仓库？",local:"如何列出仓库",headingTag:"h2"}}),j=new st({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMEhmQXBpJTBBYXBpJTIwJTNEJTIwSGZBcGkoKSUwQW1vZGVscyUyMCUzRCUyMGFwaS5saXN0X21vZGVscygp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> HfApi
<span class="hljs-meta">&gt;&gt;&gt; </span>api = HfApi()
<span class="hljs-meta">&gt;&gt;&gt; </span>models = api.list_models()`,wrap:!1}}),T=new et({props:{title:"如何过滤仓库？",local:"如何过滤仓库",headingTag:"h2"}}),C=new st({props:{code:"bW9kZWxzJTIwJTNEJTIwaGZfYXBpLmxpc3RfbW9kZWxzKCUwQSUwOXRhc2slM0QlMjJpbWFnZS1jbGFzc2lmaWNhdGlvbiUyMiUyQyUwQSUwOWxpYnJhcnklM0QlMjJweXRvcmNoJTIyJTJDJTBBJTA5dHJhaW5lZF9kYXRhc2V0JTNEJTIyaW1hZ2VuZXQlMjIlMkMlMEEp",highlighted:`models = hf_api.list_models(
	task=<span class="hljs-string">&quot;image-classification&quot;</span>,
	library=<span class="hljs-string">&quot;pytorch&quot;</span>,
	trained_dataset=<span class="hljs-string">&quot;imagenet&quot;</span>,
)`,wrap:!1}}),J=new st({props:{code:"bGlzdChsaXN0X2RhdGFzZXRzKHNvcnQlM0QlMjJkb3dubG9hZHMlMjIlMkMlMjBsaW1pdCUzRDUpKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(list_datasets(sort=<span class="hljs-string">&quot;downloads&quot;</span>, limit=<span class="hljs-number">5</span>))
[DatasetInfo(
	<span class="hljs-built_in">id</span>=<span class="hljs-string">&#x27;argilla/databricks-dolly-15k-curated-en&#x27;</span>,
	author=<span class="hljs-string">&#x27;argilla&#x27;</span>,
	sha=<span class="hljs-string">&#x27;4dcd1dedbe148307a833c931b21ca456a1fc4281&#x27;</span>,
	last_modified=datetime.datetime(<span class="hljs-number">2023</span>, <span class="hljs-number">10</span>, <span class="hljs-number">2</span>, <span class="hljs-number">12</span>, <span class="hljs-number">32</span>, <span class="hljs-number">53</span>, tzinfo=datetime.timezone.utc),
	private=<span class="hljs-literal">False</span>,
	downloads=<span class="hljs-number">8889377</span>,
	(...)`,wrap:!1}}),U=new xt({props:{source:"https://github.com/huggingface/huggingface_hub/blob/main/docs/source/cn/guides/search.md"}}),{c(){m=i("meta"),S=a(),P=i("p"),Z=a(),c($.$$.fragment),z=a(),c(g.$$.fragment),X=a(),b=i("p"),b.innerHTML=at,E=a(),c(_.$$.fragment),W=a(),M=i("p"),M.innerHTML=nt,Q=a(),c(j.$$.fragment),B=a(),w=i("p"),w.innerHTML=it,R=a(),x=i("p"),x.innerHTML=pt,q=a(),c(T.$$.fragment),I=a(),v=i("p"),v.textContent=mt,N=a(),H=i("ul"),H.innerHTML=ft,F=a(),y=i("p"),y.textContent=ct,Y=a(),c(C.$$.fragment),A=a(),k=i("p"),k.textContent=ut,K=a(),c(J.$$.fragment),V=a(),L=i("p"),L.innerHTML=ot,D=a(),c(U.$$.fragment),O=a(),G=i("p"),this.h()},l(t){const e=Mt("svelte-u9bgzb",document.head);m=p(e,"META",{name:!0,content:!0}),e.forEach(s),S=n(t),P=p(t,"P",{}),rt(P).forEach(s),Z=n(t),u($.$$.fragment,t),z=n(t),u(g.$$.fragment,t),X=n(t),b=p(t,"P",{"data-svelte-h":!0}),f(b)!=="svelte-1b1v6pd"&&(b.innerHTML=at),E=n(t),u(_.$$.fragment,t),W=n(t),M=p(t,"P",{"data-svelte-h":!0}),f(M)!=="svelte-18kcau3"&&(M.innerHTML=nt),Q=n(t),u(j.$$.fragment,t),B=n(t),w=p(t,"P",{"data-svelte-h":!0}),f(w)!=="svelte-y1lzfc"&&(w.innerHTML=it),R=n(t),x=p(t,"P",{"data-svelte-h":!0}),f(x)!=="svelte-cvctj4"&&(x.innerHTML=pt),q=n(t),u(T.$$.fragment,t),I=n(t),v=p(t,"P",{"data-svelte-h":!0}),f(v)!=="svelte-ufu8ai"&&(v.textContent=mt),N=n(t),H=p(t,"UL",{"data-svelte-h":!0}),f(H)!=="svelte-11piw8k"&&(H.innerHTML=ft),F=n(t),y=p(t,"P",{"data-svelte-h":!0}),f(y)!=="svelte-sypjiu"&&(y.textContent=ct),Y=n(t),u(C.$$.fragment,t),A=n(t),k=p(t,"P",{"data-svelte-h":!0}),f(k)!=="svelte-cwbh65"&&(k.textContent=ut),K=n(t),u(J.$$.fragment,t),V=n(t),L=p(t,"P",{"data-svelte-h":!0}),f(L)!=="svelte-opj87h"&&(L.innerHTML=ot),D=n(t),u(U.$$.fragment,t),O=n(t),G=p(t,"P",{}),rt(G).forEach(s),this.h()},h(){ht(m,"name","hf:doc:metadata"),ht(m,"content",vt)},m(t,e){jt(document.head,m),l(t,S,e),l(t,P,e),l(t,Z,e),o($,t,e),l(t,z,e),o(g,t,e),l(t,X,e),l(t,b,e),l(t,E,e),o(_,t,e),l(t,W,e),l(t,M,e),l(t,Q,e),o(j,t,e),l(t,B,e),l(t,w,e),l(t,R,e),l(t,x,e),l(t,q,e),o(T,t,e),l(t,I,e),l(t,v,e),l(t,N,e),l(t,H,e),l(t,F,e),l(t,y,e),l(t,Y,e),o(C,t,e),l(t,A,e),l(t,k,e),l(t,K,e),o(J,t,e),l(t,V,e),l(t,L,e),l(t,D,e),o(U,t,e),l(t,O,e),l(t,G,e),tt=!0},p:$t,i(t){tt||(r($.$$.fragment,t),r(g.$$.fragment,t),r(_.$$.fragment,t),r(j.$$.fragment,t),r(T.$$.fragment,t),r(C.$$.fragment,t),r(J.$$.fragment,t),r(U.$$.fragment,t),tt=!0)},o(t){h($.$$.fragment,t),h(g.$$.fragment,t),h(_.$$.fragment,t),h(j.$$.fragment,t),h(T.$$.fragment,t),h(C.$$.fragment,t),h(J.$$.fragment,t),h(U.$$.fragment,t),tt=!1},d(t){t&&(s(S),s(P),s(Z),s(z),s(X),s(b),s(E),s(W),s(M),s(Q),s(B),s(w),s(R),s(x),s(q),s(I),s(v),s(N),s(H),s(F),s(y),s(Y),s(A),s(k),s(K),s(V),s(L),s(D),s(O),s(G)),s(m),d($,t),d(g,t),d(_,t),d(j,t),d(T,t),d(C,t),d(J,t),d(U,t)}}}const vt='{"title":"搜索 Hub","local":"搜索-hub","sections":[{"title":"如何列出仓库？","local":"如何列出仓库","sections":[],"depth":2},{"title":"如何过滤仓库？","local":"如何过滤仓库","sections":[],"depth":2}],"depth":1}';function Ht(lt){return gt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Lt extends bt{constructor(m){super(),_t(this,m,Ht,Tt,dt,{})}}export{Lt as component};