import{S as ua,i as pa,s as ya,e as a,k as h,w as J,t as i,N as ga,c as r,d as s,m as d,a as n,x as Z,h as c,b as l,P as ge,G as t,g as f,y as j,L as va,q as H,o as V,B as W,v as ba}from"../chunks/vendor-hf-doc-builder.js";import{I as He}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as Es}from"../chunks/CodeBlock-hf-doc-builder.js";function wa($s){let g,Ve,v,k,ve,P,_t,be,Mt,We,R,we,Ut,Et,Pe,b,C,_e,Y,$t,Me,kt,Re,m,Ct,Ue,xt,St,L,It,Nt,X,Ee,At,Tt,B,$e,Gt,Jt,z,Zt,jt,Ye,oe,Ht,Le,u,ke,Vt,Wt,Ce,Pt,Rt,xe,Yt,Lt,Se,Xt,Xe,w,ie,ks,Bt,ce,Cs,Be,_,x,Ie,F,zt,Ne,Ft,ze,fe,Qt,Fe,Q,Qe,he,qt,qe,q,De,S,Dt,Ae,Ot,Kt,Oe,M,de,xs,es,me,Ss,Ke,U,ue,Is,ts,pe,Ns,et,E,I,Te,D,ss,Ge,as,tt,N,rs,Je,ns,ls,st,O,at,A,os,K,is,cs,rt,$,T,Ze,ee,fs,je,hs,nt,y,te,ds,se,ms,us,ps,ae,ys,re,gs,vs,bs,ne,ws,le,_s,Ms,lt;return P=new He({}),Y=new He({}),F=new He({}),Q=new Es({props:{code:"ZnJvbSUyMHNlbnRlbmNlX3RyYW5zZm9ybWVycyUyMGltcG9ydCUyMFNlbnRlbmNlVHJhbnNmb3JtZXIlMEFtb2RlbCUyMCUzRCUyMFNlbnRlbmNlVHJhbnNmb3JtZXIoJ21vZGVsX25hbWUnKQ==",highlighted:`<span class="hljs-keyword">from</span> sentence_transformers <span class="hljs-keyword">import</span> SentenceTransformer
model = SentenceTransformer(<span class="hljs-string">&#x27;model_name&#x27;</span>)`}}),q=new Es({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> sentence_transformers <span class="hljs-keyword">import</span> SentenceTransformer, util
model = SentenceTransformer(<span class="hljs-string">&#x27;multi-qa-MiniLM-L6-cos-v1&#x27;</span>)

query_embedding = model.encode(<span class="hljs-string">&#x27;How big is London&#x27;</span>)
passage_embedding = model.encode([<span class="hljs-string">&#x27;London has 9,787,426 inhabitants at the 2011 census&#x27;</span>,
                                  <span class="hljs-string">&#x27;London is known for its finacial district&#x27;</span>])

<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Similarity:&quot;</span>, util.dot_score(query_embedding, passage_embedding))`}}),D=new He({}),O=new Es({props:{code:"ZnJvbSUyMHNlbnRlbmNlX3RyYW5zZm9ybWVycyUyMGltcG9ydCUyMFNlbnRlbmNlVHJhbnNmb3JtZXIlMEElMEElMjMlMjBMb2FkJTIwb3IlMjB0cmFpbiUyMGElMjBtb2RlbCUwQW1vZGVsLnNhdmVfdG9faHViKCUyMm15X25ld19tb2RlbCUyMik=",highlighted:`<span class="hljs-keyword">from</span> sentence_transformers <span class="hljs-keyword">import</span> SentenceTransformer

<span class="hljs-comment"># Load or train a model</span>
model.save_to_hub(<span class="hljs-string">&quot;my_new_model&quot;</span>)`}}),ee=new He({}),{c(){g=a("meta"),Ve=h(),v=a("h1"),k=a("a"),ve=a("span"),J(P.$$.fragment),_t=h(),be=a("span"),Mt=i("Using Sentence Transformers at Hugging Face"),We=h(),R=a("p"),we=a("code"),Ut=i("sentence-transformers"),Et=i(" is a library that provides easy methods to compute embeddings (dense vector representations) for sentences, paragraphs and images. Texts are embedded in a vector space such that similar text is close, which enables applications such as semantic search, clustering, and retrieval."),Pe=h(),b=a("h2"),C=a("a"),_e=a("span"),J(Y.$$.fragment),$t=h(),Me=a("span"),kt=i("Exploring sentence-transformers in the Hub"),Re=h(),m=a("p"),Ct=i("You can find over 500 hundred "),Ue=a("code"),xt=i("sentence-transformer"),St=i(" models by filtering at the left of the "),L=a("a"),It=i("models page"),Nt=i(". 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