import{S as kd,i as vd,s as Td,e as r,k as p,w as k,t as i,M as $d,c as n,d as s,m,a as o,x as v,h as l,b as d,G as t,g as f,y as T,q as $,o as b,B as w,v as bd,L as De}from"../../chunks/vendor-hf-doc-builder.js";import{D as E}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ie}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as C}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ne}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function wd(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import MT5Model, T5Tokenizer

model = MT5Model.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
summary = "Weiter Verhandlung in Syrien."
inputs = tokenizer(article, return_tensors="pt")
with tokenizer.as_target_tokenizer():
    labels = tokenizer(summary, return_tensors="pt")

outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MT5Model, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MT5Model.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>summary = <span class="hljs-string">&quot;Weiter Verhandlung in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(article, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span>    labels = tokenizer(summary, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], decoder_input_ids=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = outputs.last_hidden_state`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function yd(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import MT5ForConditionalGeneration, T5Tokenizer

model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
summary = "Weiter Verhandlung in Syrien."
inputs = tokenizer(article, return_tensors="pt")
with tokenizer.as_target_tokenizer():
    labels = tokenizer(summary, return_tensors="pt")

outputs = model(**inputs, labels=labels["input_ids"])
loss = outputs.loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MT5ForConditionalGeneration, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MT5ForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>summary = <span class="hljs-string">&quot;Weiter Verhandlung in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(article, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span>    labels = tokenizer(summary, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function xd(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import MT5EncoderModel, T5Tokenizer

model = MT5EncoderModel.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
input_ids = tokenizer(article, return_tensors="pt").input_ids
outputs = model(input_ids)
hidden_state = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> MT5EncoderModel, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MT5EncoderModel.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(article, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_state = outputs.last_hidden_state`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function Md(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import TFMT5Model, T5Tokenizer

model = TFMT5Model.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
summary = "Weiter Verhandlung in Syrien."
inputs = tokenizer(article, return_tensors="tf")
with tokenizer.as_target_tokenizer():
    labels = tokenizer(summary, return_tensors="tf")

outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFMT5Model, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMT5Model.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>summary = <span class="hljs-string">&quot;Weiter Verhandlung in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(article, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span>    labels = tokenizer(summary, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], decoder_input_ids=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = outputs.last_hidden_state`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function zd(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import TFMT5ForConditionalGeneration, T5Tokenizer

model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
summary = "Weiter Verhandlung in Syrien."
inputs = tokenizer(article, return_tensors="tf")
with tokenizer.as_target_tokenizer():
    labels = tokenizer(summary, return_tensors="tf")

outputs = model(**inputs, labels=labels["input_ids"])
loss = outputs.loss`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFMT5ForConditionalGeneration, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMT5ForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>summary = <span class="hljs-string">&quot;Weiter Verhandlung in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(article, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span>    labels = tokenizer(summary, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function Ed(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import TFMT5EncoderModel, T5Tokenizer

model = TFMT5EncoderModel.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
input_ids = tokenizer(article, return_tensors="tf").input_ids
outputs = model(input_ids)
hidden_state = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFMT5EncoderModel, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMT5EncoderModel.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(article, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_state = outputs.last_hidden_state`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function qd(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import FlaxMT5Model, T5Tokenizer

model = FlaxMT5Model.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")

article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
summary = "Weiter Verhandlung in Syrien."
inputs = tokenizer(article, return_tensors="np")

with tokenizer.as_target_tokenizer():
    decoder_input_ids = tokenizer(summary, return_tensors="np").input_ids

outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=decoder_input_ids)
hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxMT5Model, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxMT5Model.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>summary = <span class="hljs-string">&quot;Weiter Verhandlung in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(article, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span>    decoder_input_ids = tokenizer(summary, return_tensors=<span class="hljs-string">&quot;np&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], decoder_input_ids=decoder_input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = outputs.last_hidden_state`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function Fd(z){let h,x,_,u,y;return u=new Ie({props:{code:`from transformers import FlaxMT5ForConditionalGeneration, T5Tokenizer

model = FlaxMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")

article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
summary = "Weiter Verhandlung in Syrien."
inputs = tokenizer(article, return_tensors="np")

with tokenizer.as_target_tokenizer():
    decoder_input_ids = tokenizer(summary, return_tensors="np").input_ids

outputs = model(**inputs, decoder_input_ids=decoder_input_ids)
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxMT5ForConditionalGeneration, T5Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxMT5ForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>summary = <span class="hljs-string">&quot;Weiter Verhandlung in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(article, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span>    decoder_input_ids = tokenizer(summary, return_tensors=<span class="hljs-string">&quot;np&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, decoder_input_ids=decoder_input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){h=r("p"),x=i("Examples:"),_=p(),k(u.$$.fragment)},l(a){h=n(a,"P",{});var g=o(h);x=l(g,"Examples:"),g.forEach(s),_=m(a),v(u.$$.fragment,a)},m(a,g){f(a,h,g),t(h,x),f(a,_,g),T(u,a,g),y=!0},p:De,i(a){y||($(u.$$.fragment,a),y=!0)},o(a){b(u.$$.fragment,a),y=!1},d(a){a&&s(h),a&&s(_),w(u,a)}}}function jd(z){let h,x,_,u,y,a,g,zs,Dn,xr,X,le,Es,Ge,In,qs,Gn,Mr,de,On,Oe,Vn,Un,zr,Ut,Wn,Er,Wt,Fs,Bn,qr,pe,Hn,Ve,Rn,Xn,Fr,Bt,Kn,jr,F,js,Cs,Ue,Jn,Qn,Ps,As,We,Yn,Zn,Ss,Ls,Be,eo,to,Ns,Ds,He,so,ro,Is,Ht,Re,no,oo,Cr,U,ao,Xe,io,lo,Ke,po,mo,Pr,K,me,Gs,Je,co,Os,fo,Ar,P,Qe,uo,A,ho,Rt,go,_o,Xt,ko,vo,Ye,To,$o,bo,J,wo,Kt,yo,xo,Jt,Mo,zo,Sr,Q,ce,Vs,Ze,Eo,Us,qo,Lr,M,et,Fo,tt,jo,st,Co,Po,Ao,rt,So,Qt,Lo,No,Do,W,nt,Io,Ws,Go,Oo,ot,Yt,Vo,Bs,Uo,Wo,Zt,Bo,Hs,Ho,Ro,fe,at,Xo,Rs,Ko,Jo,ue,it,Qo,Xs,Yo,Zo,he,lt,ea,dt,ta,Ks,sa,ra,Nr,ge,na,es,oa,aa,Dr,Y,_e,Js,pt,ia,Qs,la,Ir,q,mt,da,Z,pa,Ys,ma,ca,ct,fa,ua,ha,ft,ga,ts,_a,ka,va,B,ut,Ta,Zs,$a,ba,ht,ss,wa,er,ya,xa,rs,Ma,tr,za,Ea,ke,gt,qa,sr,Fa,Gr,ve,ja,ns,Ca,Pa,Or,ee,Te,rr,_t,Aa,nr,Sa,Vr,S,kt,La,vt,Na,os,Da,Ia,Ga,$e,Ur,te,be,or,Tt,Oa,ar,Va,Wr,L,$t,Ua,bt,Wa,as,Ba,Ha,Ra,we,Br,se,ye,ir,wt,Xa,lr,Ka,Hr,N,yt,Ja,xt,Qa,is,Ya,Za,ei,xe,Rr,re,Me,dr,Mt,ti,pr,si,Xr,D,zt,ri,Et,ni,ls,oi,ai,ii,ze,Kr,ne,Ee,mr,qt,li,cr,di,Jr,I,Ft,pi,jt,mi,ds,ci,fi,ui,qe,Qr,oe,Fe,fr,Ct,hi,ur,gi,Yr,G,Pt,_i,At,ki,ps,vi,Ti,$i,je,Zr,ae,Ce,hr,St,bi,gr,wi,en,O,Lt,yi,Nt,xi,ms,Mi,zi,Ei,Pe,tn,ie,Ae,_r,Dt,qi,kr,Fi,sn,V,It,ji,Gt,Ci,cs,Pi,Ai,Si,Se,rn;return a=new C({}),Ge=new C({}),Je=new C({}),Qe=new E({props:{name:"class transformers.MT5Config",anchor:"transformers.MT5Config",parameters:[{name:"vocab_size",val:" = 250112"},{name:"d_model",val:" = 512"},{name:"d_kv",val:" = 64"},{name:"d_ff",val:" = 1024"},{name:"num_layers",val:" = 8"},{name:"num_decoder_layers",val:" = None"},{name:"num_heads",val:" = 6"},{name:"relative_attention_num_buckets",val:" = 32"},{name:"relative_attention_max_distance",val:" = 128"},{name:"dropout_rate",val:" = 0.1"},{name:"layer_norm_epsilon",val:" = 1e-06"},{name:"initializer_factor",val:" = 1.0"},{name:"feed_forward_proj",val:" = 'gated-gelu'"},{name:"is_encoder_decoder",val:" = True"},{name:"use_cache",val:" = True"},{name:"tokenizer_class",val:" = 'T5Tokenizer'"},{name:"tie_word_embeddings",val:" = False"},{name:"pad_token_id",val:" = 0"},{name:"eos_token_id",val:" = 1"},{name:"decoder_start_token_id",val:" = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MT5Config.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 250112) &#x2014;
Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Model">T5Model</a> or <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a>.`,name:"vocab_size"},{anchor:"transformers.MT5Config.d_model",description:`<strong>d_model</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Size of the encoder layers and the pooler layer.`,name:"d_model"},{anchor:"transformers.MT5Config.d_kv",description:`<strong>d_kv</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Size of the key, query, value projections per attention head. <code>d_kv</code> has to be equal to <code>d_model // num_heads</code>.`,name:"d_kv"},{anchor:"transformers.MT5Config.d_ff",description:`<strong>d_ff</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Size of the intermediate feed forward layer in each <code>T5Block</code>.`,name:"d_ff"},{anchor:"transformers.MT5Config.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_layers"},{anchor:"transformers.MT5Config.num_decoder_layers",description:`<strong>num_decoder_layers</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Number of hidden layers in the Transformer decoder. Will use the same value as <code>num_layers</code> if not set.`,name:"num_decoder_layers"},{anchor:"transformers.MT5Config.num_heads",description:`<strong>num_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_heads"},{anchor:"transformers.MT5Config.relative_attention_num_buckets",description:`<strong>relative_attention_num_buckets</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The number of buckets to use for each attention layer.`,name:"relative_attention_num_buckets"},{anchor:"transformers.MT5Config.relative_attention_max_distance",description:`<strong>relative_attention_max_distance</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
The maximum distance of the longer sequences for the bucket separation.`,name:"relative_attention_max_distance"},{anchor:"transformers.MT5Config.dropout_rate",description:`<strong>dropout_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The ratio for all dropout layers.`,name:"dropout_rate"},{anchor:"transformers.MT5Config.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-6) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.MT5Config.initializer_factor",description:`<strong>initializer_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"},{anchor:"transformers.MT5Config.feed_forward_proj",description:`<strong>feed_forward_proj</strong> (<code>string</code>, <em>optional</em>, defaults to <code>&quot;gated-gelu&quot;</code>) &#x2014;
Type of feed forward layer to be used. Should be one of <code>&quot;relu&quot;</code> or <code>&quot;gated-gelu&quot;</code>.`,name:"feed_forward_proj"},{anchor:"transformers.MT5Config.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/configuration_mt5.py#L24"}}),Ze=new C({}),et=new E({props:{name:"class transformers.T5Tokenizer",anchor:"transformers.T5Tokenizer",parameters:[{name:"vocab_file",val:""},{name:"eos_token",val:" = '</s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"extra_ids",val:" = 100"},{name:"additional_special_tokens",val:" = None"},{name:"sp_model_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.T5Tokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.T5Tokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.T5Tokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.T5Tokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.T5Tokenizer.extra_ids",description:`<strong>extra_ids</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as &#x201C;<extra<em>id{%d}&gt;&#x201D; where &#x201D;{%d}&#x201D; is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginning (&#x201C;<extra_id_0>&#x201D; is the last token in the vocabulary
like in T5 preprocessing see
<a href="https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117" rel="nofollow">here</a>).</extra_id_0></extra<em>`,name:"extra_ids"},{anchor:"transformers.T5Tokenizer.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
Additional special tokens used by the tokenizer.`,name:"additional_special_tokens"},{anchor:"transformers.T5Tokenizer.sp_model_kwargs",description:`<strong>sp_model_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
</ul>`,name:"sp_model_kwargs"},{anchor:"transformers.T5Tokenizer.sp_model",description:`<strong>sp_model</strong> (<code>SentencePieceProcessor</code>) &#x2014;
The <em>SentencePiece</em> processor that is used for every conversion (string, tokens and IDs).`,name:"sp_model"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5.py#L55"}}),nt=new E({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.T5Tokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.T5Tokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.T5Tokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5.py#L249",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),at=new E({props:{name:"convert_tokens_to_string",anchor:"transformers.T5Tokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5.py#L310"}}),it=new E({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.T5Tokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.T5Tokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.T5Tokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5.py#L227",returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),lt=new E({props:{name:"get_special_tokens_mask",anchor:"transformers.T5Tokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.T5Tokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.T5Tokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.T5Tokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5.py#L188",returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),pt=new C({}),mt=new E({props:{name:"class transformers.T5TokenizerFast",anchor:"transformers.T5TokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"eos_token",val:" = '</s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"extra_ids",val:" = 100"},{name:"additional_special_tokens",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.T5TokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.T5TokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.T5TokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.T5TokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.T5TokenizerFast.extra_ids",description:`<strong>extra_ids</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as &#x201C;<extra<em>id{%d}&gt;&#x201D; where &#x201D;{%d}&#x201D; is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginning (&#x201C;<extra_id_0>&#x201D; is the last token in the vocabulary
like in T5 preprocessing see
<a href="https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117" rel="nofollow">here</a>).</extra_id_0></extra<em>`,name:"extra_ids"},{anchor:"transformers.T5TokenizerFast.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
Additional special tokens used by the tokenizer.`,name:"additional_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5_fast.py#L65"}}),ut=new E({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.T5TokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.T5TokenizerFast.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.T5TokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5_fast.py#L191",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),gt=new E({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.T5TokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.T5TokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.T5TokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/t5/tokenization_t5_fast.py#L217",returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),_t=new C({}),kt=new E({props:{name:"class transformers.MT5Model",anchor:"transformers.MT5Model",parameters:[{name:"config",val:": T5Config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_mt5.py#L28"}}),$e=new Ne({props:{anchor:"transformers.MT5Model.example",$$slots:{default:[wd]},$$scope:{ctx:z}}}),Tt=new C({}),$t=new E({props:{name:"class transformers.MT5ForConditionalGeneration",anchor:"transformers.MT5ForConditionalGeneration",parameters:[{name:"config",val:": T5Config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_mt5.py#L62"}}),we=new Ne({props:{anchor:"transformers.MT5ForConditionalGeneration.example",$$slots:{default:[yd]},$$scope:{ctx:z}}}),wt=new C({}),yt=new E({props:{name:"class transformers.MT5EncoderModel",anchor:"transformers.MT5EncoderModel",parameters:[{name:"config",val:": T5Config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_mt5.py#L94"}}),xe=new Ne({props:{anchor:"transformers.MT5EncoderModel.example",$$slots:{default:[xd]},$$scope:{ctx:z}}}),Mt=new C({}),zt=new E({props:{name:"class transformers.TFMT5Model",anchor:"transformers.TFMT5Model",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_tf_mt5.py#L28"}}),ze=new Ne({props:{anchor:"transformers.TFMT5Model.example",$$slots:{default:[Md]},$$scope:{ctx:z}}}),qt=new C({}),Ft=new E({props:{name:"class transformers.TFMT5ForConditionalGeneration",anchor:"transformers.TFMT5ForConditionalGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_tf_mt5.py#L53"}}),qe=new Ne({props:{anchor:"transformers.TFMT5ForConditionalGeneration.example",$$slots:{default:[zd]},$$scope:{ctx:z}}}),Ct=new C({}),Pt=new E({props:{name:"class transformers.TFMT5EncoderModel",anchor:"transformers.TFMT5EncoderModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_tf_mt5.py#L79"}}),je=new Ne({props:{anchor:"transformers.TFMT5EncoderModel.example",$$slots:{default:[Ed]},$$scope:{ctx:z}}}),St=new C({}),Lt=new E({props:{name:"class transformers.FlaxMT5Model",anchor:"transformers.FlaxMT5Model",parameters:[{name:"config",val:": T5Config"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_flax_mt5.py#L43"}}),Pe=new Ne({props:{anchor:"transformers.FlaxMT5Model.example",$$slots:{default:[qd]},$$scope:{ctx:z}}}),Dt=new C({}),It=new E({props:{name:"class transformers.FlaxMT5ForConditionalGeneration",anchor:"transformers.FlaxMT5ForConditionalGeneration",parameters:[{name:"config",val:": T5Config"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/mt5/modeling_flax_mt5.py#L70"}}),Se=new Ne({props:{anchor:"transformers.FlaxMT5ForConditionalGeneration.example",$$slots:{default:[Fd]},$$scope:{ctx:z}}}),{c(){h=r("meta"),x=p(),_=r("h1"),u=r("a"),y=r("span"),k(a.$$.fragment),g=p(),zs=r("span"),Dn=i("mT5"),xr=p(),X=r("h2"),le=r("a"),Es=r("span"),k(Ge.$$.fragment),In=p(),qs=r("span"),Gn=i("Overview"),Mr=p(),de=r("p"),On=i("The mT5 model was presented in "),Oe=r("a"),Vn=i("mT5: A massively multilingual pre-trained text-to-text transformer"),Un=i(` by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya
Siddhant, Aditya Barua, Colin Raffel.`),zr=p(),Ut=r("p"),Wn=i("The abstract from the paper is the following:"),Er=p(),Wt=r("p"),Fs=r("em"),Bn=i(`The recent \u201CText-to-Text Transfer Transformer\u201D (T5) leveraged a unified text-to-text format and scale to attain
state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a
multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail
the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual
benchmarks. We also describe a simple technique to prevent \u201Caccidental translation\u201D in the zero-shot setting, where a
generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model
checkpoints used in this work are publicly available.`),qr=p(),pe=r("p"),Hn=i("Note: mT5 was only pre-trained on "),Ve=r("a"),Rn=i("mC4"),Xn=i(` excluding any supervised training.
Therefore, this model has to be fine-tuned before it is useable on a downstream task, unlike the original T5 model.
Since mT5 was pre-trained unsupervisedly, there\u2019s no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.`),Fr=p(),Bt=r("p"),Kn=i("Google has released the following variants:"),jr=p(),F=r("ul"),js=r("li"),Cs=r("p"),Ue=r("a"),Jn=i("google/mt5-small"),Qn=p(),Ps=r("li"),As=r("p"),We=r("a"),Yn=i("google/mt5-base"),Zn=p(),Ss=r("li"),Ls=r("p"),Be=r("a"),eo=i("google/mt5-large"),to=p(),Ns=r("li"),Ds=r("p"),He=r("a"),so=i("google/mt5-xl"),ro=p(),Is=r("li"),Ht=r("p"),Re=r("a"),no=i("google/mt5-xxl"),oo=i("."),Cr=p(),U=r("p"),ao=i("This model was contributed by "),Xe=r("a"),io=i("patrickvonplaten"),lo=i(`. The original code can be
found `),Ke=r("a"),po=i("here"),mo=i("."),Pr=p(),K=r("h2"),me=r("a"),Gs=r("span"),k(Je.$$.fragment),co=p(),Os=r("span"),fo=i("MT5Config"),Ar=p(),P=r("div"),k(Qe.$$.fragment),uo=p(),A=r("p"),ho=i("This is the configuration class to store the configuration of a "),Rt=r("a"),go=i("MT5Model"),_o=i(" or a "),Xt=r("a"),ko=i("TFMT5Model"),vo=i(`. It is used to
instantiate a mT5 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the mT5
`),Ye=r("a"),To=i("google/mt5-small"),$o=i(" architecture."),bo=p(),J=r("p"),wo=i("Configuration objects inherit from "),Kt=r("a"),yo=i("PretrainedConfig"),xo=i(` and can be used to control the model outputs. Read the
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benchmarks. We also describe a simple technique to prevent \u201Caccidental translation\u201D in the zero-shot setting, where a
generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model
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