import{S as Vc,i as Yc,s as Jc,e as n,k as c,w as b,t as a,M as Qc,c as r,d as o,m as p,a as s,x as y,h as i,b as l,N as Rc,G as e,g as f,y as T,q as w,o as P,B as M,v as Zc,L as Zn}from"../../chunks/vendor-hf-doc-builder.js";import{T as Jn}from"../../chunks/Tip-hf-doc-builder.js";import{D as q}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Kn}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ee}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Qn}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Kc(N){let h,v,_,g,k;return{c(){h=n("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=a("Module"),k=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(m,"CODE",{});var L=s(_);g=i(L,"Module"),L.forEach(o),k=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(d,m){f(d,h,m),e(h,v),e(h,_),e(_,g),e(h,k)},d(d){d&&o(h)}}}function ep(N){let h,v,_,g,k;return g=new Kn({props:{code:`from transformers import AutoTokenizer, XLMProphetNetModel

tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
model = XLMProphetNetModel.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")

input_ids = tokenizer(
    "Studies have been shown that owning a dog is good for you", return_tensors="pt"
).input_ids  # Batch size 1
decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

last_hidden_states = outputs.last_hidden_state  # main stream hidden states
last_hidden_states_ngram = outputs.last_hidden_state_ngram  # predict hidden states`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, XLMProphetNetModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMProphetNetModel.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Studies have been shown that owning a dog is good for you&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_input_ids = tokenizer(<span class="hljs-string">&quot;Studies show that&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state  <span class="hljs-comment"># main stream hidden states</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states_ngram = outputs.last_hidden_state_ngram  <span class="hljs-comment"># predict hidden states</span>`}}),{c(){h=n("p"),v=a("Example:"),_=c(),b(g.$$.fragment)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Example:"),m.forEach(o),_=p(d),y(g.$$.fragment,d)},m(d,m){f(d,h,m),e(h,v),f(d,_,m),T(g,d,m),k=!0},p:Zn,i(d){k||(w(g.$$.fragment,d),k=!0)},o(d){P(g.$$.fragment,d),k=!1},d(d){d&&o(h),d&&o(_),M(g,d)}}}function tp(N){let h,v,_,g,k;return{c(){h=n("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=a("Module"),k=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(m,"CODE",{});var L=s(_);g=i(L,"Module"),L.forEach(o),k=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(d,m){f(d,h,m),e(h,v),e(h,_),e(_,g),e(h,k)},d(d){d&&o(h)}}}function op(N){let h,v,_,g,k;return g=new Kn({props:{code:`from transformers import AutoTokenizer, XLMProphetNetEncoder
import torch

tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
model = XLMProphetNetEncoder.from_pretrained("patrickvonplaten/prophetnet-large-uncased-standalone")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

last_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> AutoTokenizer, XLMProphetNetEncoder
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMProphetNetEncoder.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/prophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){h=n("p"),v=a("Example:"),_=c(),b(g.$$.fragment)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Example:"),m.forEach(o),_=p(d),y(g.$$.fragment,d)},m(d,m){f(d,h,m),e(h,v),f(d,_,m),T(g,d,m),k=!0},p:Zn,i(d){k||(w(g.$$.fragment,d),k=!0)},o(d){P(g.$$.fragment,d),k=!1},d(d){d&&o(h),d&&o(_),M(g,d)}}}function np(N){let h,v,_,g,k;return{c(){h=n("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=a("Module"),k=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(m,"CODE",{});var L=s(_);g=i(L,"Module"),L.forEach(o),k=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(d,m){f(d,h,m),e(h,v),e(h,_),e(_,g),e(h,k)},d(d){d&&o(h)}}}function rp(N){let h,v,_,g,k;return g=new Kn({props:{code:`from transformers import AutoTokenizer, XLMProphetNetDecoder
import torch

tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
model = XLMProphetNetDecoder.from_pretrained(
    "patrickvonplaten/xprophetnet-large-uncased-standalone", add_cross_attention=False
)
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

last_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> AutoTokenizer, XLMProphetNetDecoder
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMProphetNetDecoder.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>, add_cross_attention=<span class="hljs-literal">False</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){h=n("p"),v=a("Example:"),_=c(),b(g.$$.fragment)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Example:"),m.forEach(o),_=p(d),y(g.$$.fragment,d)},m(d,m){f(d,h,m),e(h,v),f(d,_,m),T(g,d,m),k=!0},p:Zn,i(d){k||(w(g.$$.fragment,d),k=!0)},o(d){P(g.$$.fragment,d),k=!1},d(d){d&&o(h),d&&o(_),M(g,d)}}}function sp(N){let h,v,_,g,k;return{c(){h=n("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=a("Module"),k=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(m,"CODE",{});var L=s(_);g=i(L,"Module"),L.forEach(o),k=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(d,m){f(d,h,m),e(h,v),e(h,_),e(_,g),e(h,k)},d(d){d&&o(h)}}}function ap(N){let h,v,_,g,k;return g=new Kn({props:{code:`from transformers import AutoTokenizer, XLMProphetNetForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
model = XLMProphetNetForConditionalGeneration.from_pretrained(
    "patrickvonplaten/xprophetnet-large-uncased-standalone"
)

input_ids = tokenizer(
    "Studies have been shown that owning a dog is good for you", return_tensors="pt"
).input_ids  # Batch size 1
decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

logits_next_token = outputs.logits  # logits to predict next token as usual
logits_ngram_next_tokens = outputs.logits_ngram  # logits to predict 2nd, 3rd, ... next tokens`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, XLMProphetNetForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMProphetNetForConditionalGeneration.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Studies have been shown that owning a dog is good for you&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder_input_ids = tokenizer(<span class="hljs-string">&quot;Studies show that&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits_next_token = outputs.logits  <span class="hljs-comment"># logits to predict next token as usual</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits_ngram_next_tokens = outputs.logits_ngram  <span class="hljs-comment"># logits to predict 2nd, 3rd, ... next tokens</span>`}}),{c(){h=n("p"),v=a("Example:"),_=c(),b(g.$$.fragment)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Example:"),m.forEach(o),_=p(d),y(g.$$.fragment,d)},m(d,m){f(d,h,m),e(h,v),f(d,_,m),T(g,d,m),k=!0},p:Zn,i(d){k||(w(g.$$.fragment,d),k=!0)},o(d){P(g.$$.fragment,d),k=!1},d(d){d&&o(h),d&&o(_),M(g,d)}}}function ip(N){let h,v,_,g,k;return{c(){h=n("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=a("Module"),k=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(m,"CODE",{});var L=s(_);g=i(L,"Module"),L.forEach(o),k=i(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(d,m){f(d,h,m),e(h,v),e(h,_),e(_,g),e(h,k)},d(d){d&&o(h)}}}function dp(N){let h,v,_,g,k;return g=new Kn({props:{code:`from transformers import AutoTokenizer, XLMProphetNetForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
model = XLMProphetNetForCausalLM.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

logits = outputs.logits

# Model can also be used with EncoderDecoder framework
from transformers import BertTokenizer, EncoderDecoderModel, AutoTokenizer
import torch

tokenizer_enc = BertTokenizer.from_pretrained("bert-large-uncased")
tokenizer_dec = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
model = EncoderDecoderModel.from_encoder_decoder_pretrained(
    "bert-large-uncased", "patrickvonplaten/xprophetnet-large-uncased-standalone"
)

ARTICLE = (
    "the us state department said wednesday it had received no "
    "formal word from bolivia that it was expelling the us ambassador there "
    "but said the charges made against him are \`\` baseless ."
)
input_ids = tokenizer_enc(ARTICLE, return_tensors="pt").input_ids
labels = tokenizer_dec(
    "us rejects charges against its ambassador in bolivia", return_tensors="pt"
).input_ids
outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-1], labels=labels[:, 1:])

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> AutoTokenizer, XLMProphetNetForCausalLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMProphetNetForCausalLM.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">assert</span> model.config.is_decoder, <span class="hljs-string">f&quot;<span class="hljs-subst">{model.__class__}</span> has to be configured as a decoder.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Model can also be used with EncoderDecoder framework</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertTokenizer, EncoderDecoderModel, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer_enc = BertTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-large-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer_dec = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = EncoderDecoderModel.from_encoder_decoder_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;bert-large-uncased&quot;</span>, <span class="hljs-string">&quot;patrickvonplaten/xprophetnet-large-uncased-standalone&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>ARTICLE = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;the us state department said wednesday it had received no &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;formal word from bolivia that it was expelling the us ambassador there &quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;but said the charges made against him are \`\` baseless .&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer_enc(ARTICLE, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer_dec(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;us rejects charges against its ambassador in bolivia&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-<span class="hljs-number">1</span>], labels=labels[:, <span class="hljs-number">1</span>:])

<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`}}),{c(){h=n("p"),v=a("Example:"),_=c(),b(g.$$.fragment)},l(d){h=r(d,"P",{});var m=s(h);v=i(m,"Example:"),m.forEach(o),_=p(d),y(g.$$.fragment,d)},m(d,m){f(d,h,m),e(h,v),f(d,_,m),T(g,d,m),k=!0},p:Zn,i(d){k||(w(g.$$.fragment,d),k=!0)},o(d){P(g.$$.fragment,d),k=!1},d(d){d&&o(h),d&&o(_),M(g,d)}}}function lp(N){let h,v,_,g,k,d,m,L,es,er,te,oo,no,sl,ts,ro,so,al,tr,oe,Jo,os,ns,tt,rs,ss,or,ne,Pe,Qo,ot,as,Zo,is,nr,Me,ds,nt,ls,cs,rr,ao,ps,sr,io,hs,ar,lo,Ko,us,ir,Ne,ms,rt,fs,gs,dr,co,_s,lr,po,en,ks,cr,re,Le,tn,st,vs,on,bs,pr,R,nn,ho,ys,Ts,rn,uo,ws,Ps,sn,mo,Ms,hr,se,$e,an,at,Ns,dn,Ls,ur,H,it,$s,ae,zs,fo,xs,qs,dt,Xs,Es,Cs,ie,Fs,go,As,Os,_o,Ds,js,mr,de,ze,ln,lt,Ss,cn,Is,fr,$,ct,Gs,U,Ws,ko,Bs,Hs,vo,Us,Rs,pt,Vs,Ys,Js,ht,Qs,bo,Zs,Ks,ea,V,ut,ta,pn,oa,na,mt,yo,ra,hn,sa,aa,To,ia,un,da,la,xe,ft,ca,mn,pa,ha,qe,gt,ua,fn,ma,fa,Xe,_t,ga,kt,_a,gn,ka,va,gr,le,Ee,_n,vt,ba,kn,ya,_r,X,bt,Ta,yt,wa,wo,Pa,Ma,Na,ce,La,Tt,$a,za,vn,xa,qa,Xa,wt,Ea,Pt,Ca,Fa,Aa,D,Mt,Oa,pe,Da,Po,ja,Sa,bn,Ia,Ga,Wa,Ce,Ba,Fe,kr,he,Ae,yn,Nt,Ha,Tn,Ua,vr,z,Lt,Ra,$t,Va,Mo,Ya,Ja,Qa,ue,Za,zt,Ka,ei,wn,ti,oi,ni,xt,ri,qt,si,ai,ii,A,di,Pn,li,ci,Mn,pi,hi,Nn,ui,mi,No,fi,gi,_i,j,Xt,ki,me,vi,Lo,bi,yi,Ln,Ti,wi,Pi,Oe,Mi,De,br,fe,je,$n,Et,Ni,zn,Li,yr,x,Ct,$i,Ft,zi,$o,xi,qi,Xi,ge,Ei,At,Ci,Fi,xn,Ai,Oi,Di,Ot,ji,Dt,Si,Ii,Gi,O,Wi,qn,Bi,Hi,Xn,Ui,Ri,En,Vi,Yi,zo,Ji,Qi,Zi,S,jt,Ki,_e,ed,xo,td,od,Cn,nd,rd,sd,Se,ad,Ie,Tr,ke,Ge,Fn,St,id,An,dd,wr,E,It,ld,Gt,cd,qo,pd,hd,ud,ve,md,Wt,fd,gd,On,_d,kd,vd,Bt,bd,Ht,yd,Td,wd,I,Ut,Pd,be,Md,Xo,Nd,Ld,Dn,$d,zd,xd,We,qd,Be,Pr,ye,He,jn,Rt,Xd,Sn,Ed,Mr,C,Vt,Cd,Yt,Fd,Eo,Ad,Od,Dd,Te,jd,Jt,Sd,Id,In,Gd,Wd,Bd,Qt,Hd,Zt,Ud,Rd,Vd,G,Kt,Yd,we,Jd,Co,Qd,Zd,Gn,Kd,el,tl,Ue,ol,Re,Nr;return d=new ee({}),ot=new ee({}),st=new ee({}),at=new ee({}),it=new q({props:{name:"class transformers.XLMProphetNetConfig",anchor:"transformers.XLMProphetNetConfig",parameters:[{name:"activation_dropout",val:": typing.Optional[float] = 0.1"},{name:"activation_function",val:": typing.Union[str, typing.Callable, NoneType] = 'gelu'"},{name:"vocab_size",val:": typing.Optional[int] = 30522"},{name:"hidden_size",val:": typing.Optional[int] = 1024"},{name:"encoder_ffn_dim",val:": typing.Optional[int] = 4096"},{name:"num_encoder_layers",val:": typing.Optional[int] = 12"},{name:"num_encoder_attention_heads",val:": typing.Optional[int] = 16"},{name:"decoder_ffn_dim",val:": typing.Optional[int] = 4096"},{name:"num_decoder_layers",val:": typing.Optional[int] = 12"},{name:"num_decoder_attention_heads",val:": typing.Optional[int] = 16"},{name:"attention_dropout",val:": typing.Optional[float] = 0.1"},{name:"dropout",val:": typing.Optional[float] = 0.1"},{name:"max_position_embeddings",val:": typing.Optional[int] = 512"},{name:"init_std",val:": typing.Optional[float] = 0.02"},{name:"is_encoder_decoder",val:": typing.Optional[bool] = True"},{name:"add_cross_attention",val:": typing.Optional[bool] = True"},{name:"decoder_start_token_id",val:": typing.Optional[int] = 0"},{name:"ngram",val:": typing.Optional[int] = 2"},{name:"num_buckets",val:": typing.Optional[int] = 32"},{name:"relative_max_distance",val:": typing.Optional[int] = 128"},{name:"disable_ngram_loss",val:": typing.Optional[bool] = False"},{name:"eps",val:": typing.Optional[float] = 0.0"},{name:"use_cache",val:": typing.Optional[bool] = True"},{name:"pad_token_id",val:": typing.Optional[int] = 0"},{name:"bos_token_id",val:": typing.Optional[int] = 1"},{name:"eos_token_id",val:": typing.Optional[int] = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.XLMProphetNetConfig.activation_dropout",description:`<strong>activation_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for activations inside the fully connected layer.`,name:"activation_dropout"},{anchor:"transformers.XLMProphetNetConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation_function"},{anchor:"transformers.XLMProphetNetConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a>.`,name:"vocab_size"},{anchor:"transformers.XLMProphetNetConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Dimensionality of the layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.XLMProphetNetConfig.encoder_ffn_dim",description:`<strong>encoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"encoder_ffn_dim"},{anchor:"transformers.XLMProphetNetConfig.num_encoder_layers",description:`<strong>num_encoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of encoder layers.`,name:"num_encoder_layers"},{anchor:"transformers.XLMProphetNetConfig.num_encoder_attention_heads",description:`<strong>num_encoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_encoder_attention_heads"},{anchor:"transformers.XLMProphetNetConfig.decoder_ffn_dim",description:`<strong>decoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the <code>intermediate</code> (often named feed-forward) layer in decoder.`,name:"decoder_ffn_dim"},{anchor:"transformers.XLMProphetNetConfig.num_decoder_layers",description:`<strong>num_decoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of decoder layers.`,name:"num_decoder_layers"},{anchor:"transformers.XLMProphetNetConfig.num_decoder_attention_heads",description:`<strong>num_decoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer decoder.`,name:"num_decoder_attention_heads"},{anchor:"transformers.XLMProphetNetConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.XLMProphetNetConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.XLMProphetNetConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.XLMProphetNetConfig.init_std",description:`<strong>init_std</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"init_std"},{anchor:"transformers.XLMProphetNetConfig.add_cross_attention",description:`<strong>add_cross_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether cross-attention layers should be added to the model.`,name:"add_cross_attention"},{anchor:"transformers.XLMProphetNetConfig.is_encoder_decoder",description:`<strong>is_encoder_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether this is an encoder/decoder model.`,name:"is_encoder_decoder"},{anchor:"transformers.XLMProphetNetConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Padding token id.`,name:"pad_token_id"},{anchor:"transformers.XLMProphetNetConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Beginning of stream token id.`,name:"bos_token_id"},{anchor:"transformers.XLMProphetNetConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
End of stream token id.`,name:"eos_token_id"},{anchor:"transformers.XLMProphetNetConfig.ngram",description:`<strong>ngram</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
token.`,name:"ngram"},{anchor:"transformers.XLMProphetNetConfig.num_buckets",description:`<strong>num_buckets</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The number of buckets to use for each attention layer. This is for relative position calculation. See the
[T5 paper](see <a href="https://arxiv.org/abs/1910.10683" rel="nofollow">https://arxiv.org/abs/1910.10683</a>) for more details.`,name:"num_buckets"},{anchor:"transformers.XLMProphetNetConfig.relative_max_distance",description:`<strong>relative_max_distance</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Relative distances greater than this number will be put into the last same bucket. This is for relative
position calculation. See the [T5 paper](see <a href="https://arxiv.org/abs/1910.10683" rel="nofollow">https://arxiv.org/abs/1910.10683</a>) for more details.`,name:"relative_max_distance"},{anchor:"transformers.XLMProphetNetConfig.disable_ngram_loss",description:`<strong>disable_ngram_loss</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether be trained predicting only the next first token.`,name:"disable_ngram_loss"},{anchor:"transformers.XLMProphetNetConfig.eps",description:`<strong>eps</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Controls the <code>epsilon</code> parameter value for label smoothing in the loss calculation. If set to 0, no label
smoothing is performed.`,name:"eps"},{anchor:"transformers.XLMProphetNetConfig.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/v4.29.0/src/transformers/models/xlm_prophetnet/configuration_xlm_prophetnet.py#L33"}}),lt=new ee({}),ct=new q({props:{name:"class transformers.XLMProphetNetTokenizer",anchor:"transformers.XLMProphetNetTokenizer",parameters:[{name:"vocab_file",val:""},{name:"bos_token",val:" = '[SEP]'"},{name:"eos_token",val:" = '[SEP]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"unk_token",val:" = '[UNK]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"sp_model_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.XLMProphetNetTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.XLMProphetNetTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier 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 beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.XLMProphetNetTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;mask&gt;&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.XLMProphetNetTokenizer.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (<code>List[str]</code>, <em>optional</em>, defaults to <code>[&quot;&lt;s&gt;NOTUSED&quot;, &quot;&lt;/s&gt;NOTUSED&quot;]</code>) &#x2014;
Additional special tokens used by the tokenizer.`,name:"additional_special_tokens"},{anchor:"transformers.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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/v4.29.0/src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py#L59"}}),ut=new q({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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/v4.29.0/src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py#L316",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>
`}}),ft=new q({props:{name:"convert_tokens_to_string",anchor:"transformers.XLMProphetNetTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py#L294"}}),gt=new q({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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/v4.29.0/src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py#L243",returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),_t=new q({props:{name:"get_special_tokens_mask",anchor:"transformers.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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.XLMProphetNetTokenizer.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/v4.29.0/src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py#L215",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>
`}}),vt=new ee({}),bt=new q({props:{name:"class transformers.XLMProphetNetModel",anchor:"transformers.XLMProphetNetModel",parameters:[{name:"config",val:": XLMProphetNetConfig"}],parametersDescription:[{anchor:"transformers.XLMProphetNetModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1770"}}),Mt=new q({props:{name:"forward",anchor:"transformers.XLMProphetNetModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.BoolTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_outputs",val:": typing.Optional[typing.Tuple] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.XLMProphetNetModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMProphetNetModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.XLMProphetNetModel.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>XLMProphetNet uses the <code>eos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.XLMProphetNetModel.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.XLMProphetNetModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.XLMProphetNetModel.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.XLMProphetNetModel.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.XLMProphetNetModel.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.XLMProphetNetModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.XLMProphetNetModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.XLMProphetNetModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.XLMProphetNetModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.XLMProphetNetModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1804",returnDescription:`
<p>A <code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqModelOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig"
>XLMProphetNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>) \u2014 Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>last_hidden_state_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,ngram * decoder_sequence_length, config.vocab_size)</code>, <em>optional</em>) \u2014 Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>decoder_ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ce=new Jn({props:{$$slots:{default:[Kc]},$$scope:{ctx:N}}}),Fe=new Qn({props:{anchor:"transformers.XLMProphetNetModel.forward.example",$$slots:{default:[ep]},$$scope:{ctx:N}}}),Nt=new ee({}),Lt=new q({props:{name:"class transformers.XLMProphetNetEncoder",anchor:"transformers.XLMProphetNetEncoder",parameters:[{name:"config",val:": XLMProphetNetConfig"},{name:"word_embeddings",val:": Embedding = None"}],parametersDescription:[{anchor:"transformers.XLMProphetNetEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1252"}}),Xt=new q({props:{name:"forward",anchor:"transformers.XLMProphetNetEncoder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.XLMProphetNetEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMProphetNetEncoder.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.XLMProphetNetEncoder.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.XLMProphetNetEncoder.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.XLMProphetNetEncoder.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.XLMProphetNetEncoder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1282",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig"
>XLMProphetNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Oe=new Jn({props:{$$slots:{default:[tp]},$$scope:{ctx:N}}}),De=new Qn({props:{anchor:"transformers.XLMProphetNetEncoder.forward.example",$$slots:{default:[op]},$$scope:{ctx:N}}}),Et=new ee({}),Ct=new q({props:{name:"class transformers.XLMProphetNetDecoder",anchor:"transformers.XLMProphetNetDecoder",parameters:[{name:"config",val:": XLMProphetNetConfig"},{name:"word_embeddings",val:": typing.Optional[torch.nn.modules.sparse.Embedding] = None"}],parametersDescription:[{anchor:"transformers.XLMProphetNetDecoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1393"}}),jt=new q({props:{name:"forward",anchor:"transformers.XLMProphetNetDecoder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.XLMProphetNetDecoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMProphetNetDecoder.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.XLMProphetNetDecoder.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.XLMProphetNetDecoder.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.XLMProphetNetDecoder.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.XLMProphetNetDecoder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.XLMProphetNetDecoder.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.XLMProphetNetDecoder.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:`,name:"encoder_attention_mask"},{anchor:"transformers.XLMProphetNetDecoder.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.XLMProphetNetDecoder.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.XLMProphetNetDecoder.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1430",returnDescription:`
<p>A <code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderModelOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig"
>XLMProphetNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>) \u2014 Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>last_hidden_state_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) \u2014 Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Se=new Jn({props:{$$slots:{default:[np]},$$scope:{ctx:N}}}),Ie=new Qn({props:{anchor:"transformers.XLMProphetNetDecoder.forward.example",$$slots:{default:[rp]},$$scope:{ctx:N}}}),St=new ee({}),It=new q({props:{name:"class transformers.XLMProphetNetForConditionalGeneration",anchor:"transformers.XLMProphetNetForConditionalGeneration",parameters:[{name:"config",val:": XLMProphetNetConfig"}],parametersDescription:[{anchor:"transformers.XLMProphetNetForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1900"}}),Ut=new q({props:{name:"forward",anchor:"transformers.XLMProphetNetForConditionalGeneration.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.BoolTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_outputs",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>XLMProphetNet uses the <code>eos_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.decoder_head_mask",description:`<strong>decoder_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"decoder_head_mask"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[-100, 0, ..., config.vocab_size - 1]</code>. All labels set to <code>-100</code> are ignored (masked), the loss is only computed for
labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L1927",returnDescription:`
<p>A <code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqLMOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig"
>XLMProphetNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>logits_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>decoder_ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>decoder_ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
<li>
<p><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</p>
</li>
<li>
<p><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length)</code>. Attentions weights of the encoder, after the attention
softmax, used to compute the weighted average in the self-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqLMOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),We=new Jn({props:{$$slots:{default:[sp]},$$scope:{ctx:N}}}),Be=new Qn({props:{anchor:"transformers.XLMProphetNetForConditionalGeneration.forward.example",$$slots:{default:[ap]},$$scope:{ctx:N}}}),Rt=new ee({}),Vt=new q({props:{name:"class transformers.XLMProphetNetForCausalLM",anchor:"transformers.XLMProphetNetForCausalLM",parameters:[{name:"config",val:": XLMProphetNetConfig"}],parametersDescription:[{anchor:"transformers.XLMProphetNetForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L2119"}}),Kt=new q({props:{name:"forward",anchor:"transformers.XLMProphetNetForCausalLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attn_head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.XLMProphetNetForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> of shape <code>(encoder_layers, encoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:`,name:"encoder_attention_mask"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.cross_attn_head_mask",description:`<strong>cross_attn_head_mask</strong> (<code>torch.Tensor</code> of shape <code>(decoder_layers, decoder_attention_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the cross-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"cross_attn_head_mask"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"use_cache"},{anchor:"transformers.XLMProphetNetForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
<code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are
ignored (masked), the loss is only computed for the tokens with labels n <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py#L2156",returnDescription:`
<p>A <code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderLMOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig"
>XLMProphetNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, decoder_sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>logits_ngram</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, ngram * decoder_sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>torch.FloatTensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>ngram_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, ngram * decoder_sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding
outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</p>
</li>
<li>
<p><strong>ngram_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
weighted average in the</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length)</code>.</p>
<p>Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderLMOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual
\u201Cwiki100\u201D Wikipedia dump.`),sr=c(),io=n("p"),hs=a("The abstract from the paper is the following:"),ar=c(),lo=n("p"),Ko=n("em"),us=a(`In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.`),ir=c(),Ne=n("p"),ms=a("The Authors\u2019 code can be found "),rt=n("a"),fs=a("here"),gs=a("."),dr=c(),co=n("p"),_s=a("Tips:"),lr=c(),po=n("ul"),en=n("li"),ks=a("XLM-ProphetNet\u2019s model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained on the cross-lingual dataset XGLUE."),cr=c(),re=n("h2"),Le=n("a"),tn=n("span"),b(st.$$.fragment),vs=c(),on=n("span"),bs=a("Documentation resources"),pr=c(),R=n("ul"),nn=n("li"),ho=n("a"),ys=a("Causal language modeling task guide"),Ts=c(),rn=n("li"),uo=n("a"),ws=a("Translation task guide"),Ps=c(),sn=n("li"),mo=n("a"),Ms=a("Summarization task guide"),hr=c(),se=n("h2"),$e=n("a"),an=n("span"),b(at.$$.fragment),Ns=c(),dn=n("span"),Ls=a("XLMProphetNetConfig"),ur=c(),H=n("div"),b(it.$$.fragment),$s=c(),ae=n("p"),zs=a("This is the configuration class to store the configuration of a "),fo=n("a"),xs=a("XLMProphetNetModel"),qs=a(`. It is used to instantiate a
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configuration with the defaults will yield a similar configuration to that of the XLMProphetNet
`),dt=n("a"),Xs=a("microsoft/xprophetnet-large-wiki100-cased"),Es=a(`
architecture.`),Cs=c(),ie=n("p"),Fs=a("Configuration objects inherit from "),go=n("a"),As=a("PretrainedConfig"),Os=a(` and can be used to control the model outputs. Read the
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This model inherits from `),wo=n("a"),Pa=a("PreTrainedModel"),Ma=a(`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Na=c(),ce=n("p"),La=a("Original ProphetNet code can be found "),Tt=n("a"),$a=a("here"),za=a(`. Checkpoints were converted
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file `),vn=n("code"),xa=a("convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py"),qa=a("."),Xa=c(),wt=n("p"),Ea=a("This model is a PyTorch "),Pt=n("a"),Ca=a("torch.nn.Module"),Fa=a(` sub-class. Use
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Qa=c(),ue=n("p"),Za=a("Original ProphetNet code can be found "),zt=n("a"),Ka=a("here"),ei=a(`. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file `),wn=n("code"),ti=a("convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py"),oi=a("."),ni=c(),xt=n("p"),ri=a("This model is a PyTorch "),qt=n("a"),si=a("torch.nn.Module"),ai=a(` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and
behavior.`),ii=c(),A=n("p"),di=a("word_embeddings  ("),Pn=n("code"),li=a("torch.nn.Embeddings"),ci=a(" of shape "),Mn=n("code"),pi=a("(config.vocab_size, config.hidden_size)"),hi=a(", "),Nn=n("em"),ui=a("optional"),mi=a(`):
The word embedding parameters. This can be used to initialize `),No=n("a"),fi=a("XLMProphetNetEncoder"),gi=a(` with pre-defined word
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Xi=c(),ge=n("p"),Ei=a("Original ProphetNet code can be found "),At=n("a"),Ci=a("here"),Fi=a(`. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file `),xn=n("code"),Ai=a("convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py"),Oi=a("."),Di=c(),Ot=n("p"),ji=a("This model is a PyTorch "),Dt=n("a"),Si=a("torch.nn.Module"),Ii=a(` sub-class. Use
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behavior.`),Gi=c(),O=n("p"),Wi=a("word_embeddings  ("),qn=n("code"),Bi=a("torch.nn.Embeddings"),Hi=a(" of shape "),Xn=n("code"),Ui=a("(config.vocab_size, config.hidden_size)"),Ri=a(", "),En=n("em"),Vi=a("optional"),Yi=a(`):
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This model inherits from `),qo=n("a"),pd=a("PreTrainedModel"),hd=a(`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),ud=c(),ve=n("p"),md=a("Original ProphetNet code can be found "),Wt=n("a"),fd=a("here"),gd=a(`. Checkpoints were converted
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file `),On=n("code"),_d=a("convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py"),kd=a("."),vd=c(),Bt=n("p"),bd=a("This model is a PyTorch "),Ht=n("a"),yd=a("torch.nn.Module"),Td=a(` sub-class. Use
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Dd=c(),Te=n("p"),jd=a("Original ProphetNet code can be found "),Jt=n("a"),Sd=a("here"),Id=a(`. Checkpoints were converted
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file `),In=n("code"),Gd=a("convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py"),Wd=a("."),Bd=c(),Qt=n("p"),Hd=a("This model is a PyTorch "),Zt=n("a"),Ud=a("torch.nn.Module"),Rd=a(` sub-class. Use
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behavior.`),Vd=c(),G=n("div"),b(Kt.$$.fragment),Yd=c(),we=n("p"),Jd=a("The "),Co=n("a"),Qd=a("XLMProphetNetForCausalLM"),Zd=a(" forward method, overrides the "),Gn=n("code"),Kd=a("__call__"),el=a(" special method."),tl=c(),b(Ue.$$.fragment),ol=c(),b(Re.$$.fragment),this.h()},l(t){const u=Qc('[data-svelte="svelte-1phssyn"]',document.head);h=r(u,"META",{name:!0,content:!0}),u.forEach(o),v=p(t),_=r(t,"H1",{class:!0});var eo=s(_);g=r(eo,"A",{id:!0,class:!0,href:!0});var Wn=s(g);k=r(Wn,"SPAN",{});var Bn=s(k);y(d.$$.fragment,Bn),Bn.forEach(o),Wn.forEach(o),m=p(eo),L=r(eo,"SPAN",{});var Hn=s(L);es=i(Hn,"XLM-ProphetNet"),Hn.forEach(o),eo.forEach(o),er=p(t),te=r(t,"DIV",{class:!0});var to=s(te);oo=r(to,"A",{href:!0});var Un=s(oo);no=r(Un,"IMG",{alt:!0,src:!0}),Un.forEach(o),ts=p(to),ro=r(to,"A",{href:!0});var Rn=s(ro);so=r(Rn,"IMG",{alt:!0,src:!0}),Rn.forEach(o),to.forEach(o),tr=p(t),oe=r(t,"P",{});var Ve=s(oe);Jo=r(Ve,"STRONG",{});var Vn=s(Jo);os=i(Vn,"DISCLAIMER:"),Vn.forEach(o),ns=i(Ve," If you see something strange, file a "),tt=r(Ve,"A",{href:!0,rel:!0});var Yn=s(tt);rs=i(Yn,"Github Issue"),Yn.forEach(o),ss=i(Ve,` and assign
@patrickvonplaten`),Ve.forEach(o),or=p(t),ne=r(t,"H2",{class:!0});var Lr=s(ne);Pe=r(Lr,"A",{id:!0,class:!0,href:!0});var il=s(Pe);Qo=r(il,"SPAN",{});var dl=s(Qo);y(ot.$$.fragment,dl),dl.forEach(o),il.forEach(o),as=p(Lr),Zo=r(Lr,"SPAN",{});var ll=s(Zo);is=i(ll,"Overview"),ll.forEach(o),Lr.forEach(o),nr=p(t),Me=r(t,"P",{});var $r=s(Me);ds=i($r,"The XLM-ProphetNet model was proposed in "),nt=r($r,"A",{href:!0,rel:!0});var cl=s(nt);ls=i(cl,"ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,"),cl.forEach(o),cs=i($r,` by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
Zhang, Ming Zhou on 13 Jan, 2020.`),$r.forEach(o),rr=p(t),ao=r(t,"P",{});var pl=s(ao);ps=i(pl,`XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for \u201Cngram\u201D language modeling instead of
just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual
\u201Cwiki100\u201D Wikipedia dump.`),pl.forEach(o),sr=p(t),io=r(t,"P",{});var hl=s(io);hs=i(hl,"The abstract from the paper is the following:"),hl.forEach(o),ar=p(t),lo=r(t,"P",{});var ul=s(lo);Ko=r(ul,"EM",{});var ml=s(Ko);us=i(ml,`In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Sr.forEach(o),Na=p(Y),ce=r(Y,"P",{});var So=s(ce);La=i(So,"Original ProphetNet code can be found "),Tt=r(So,"A",{href:!0,rel:!0});var Zl=s(Tt);$a=i(Zl,"here"),Zl.forEach(o),za=i(So,`. Checkpoints were converted
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file `),vn=r(So,"CODE",{});var Kl=s(vn);xa=i(Kl,"convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py"),Kl.forEach(o),qa=i(So,"."),So.forEach(o),Xa=p(Y),wt=r(Y,"P",{});var Ir=s(wt);Ea=i(Ir,"This model is a PyTorch "),Pt=r(Ir,"A",{href:!0,rel:!0});var ec=s(Pt);Ca=i(ec,"torch.nn.Module"),ec.forEach(o),Fa=i(Ir,` sub-class. Use
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behavior.`),Ir.forEach(o),Aa=p(Y),D=r(Y,"DIV",{class:!0});var Je=s(D);y(Mt.$$.fragment,Je),Oa=p(Je),pe=r(Je,"P",{});var Io=s(pe);Da=i(Io,"The "),Po=r(Io,"A",{href:!0});var tc=s(Po);ja=i(tc,"XLMProphetNetModel"),tc.forEach(o),Sa=i(Io," forward method, overrides the "),bn=r(Io,"CODE",{});var oc=s(bn);Ia=i(oc,"__call__"),oc.forEach(o),Ga=i(Io," special method."),Io.forEach(o),Wa=p(Je),y(Ce.$$.fragment,Je),Ba=p(Je),y(Fe.$$.fragment,Je),Je.forEach(o),Y.forEach(o),kr=p(t),he=r(t,"H2",{class:!0});var Gr=s(he);Ae=r(Gr,"A",{id:!0,class:!0,href:!0});var nc=s(Ae);yn=r(nc,"SPAN",{});var rc=s(yn);y(Nt.$$.fragment,rc),rc.forEach(o),nc.forEach(o),Ha=p(Gr),Tn=r(Gr,"SPAN",{});var sc=s(Tn);Ua=i(sc,"XLMProphetNetEncoder"),sc.forEach(o),Gr.forEach(o),vr=p(t),z=r(t,"DIV",{class:!0});var W=s(z);y(Lt.$$.fragment,W),Ra=p(W),$t=r(W,"P",{});var Wr=s($t);Va=i(Wr,`The standalone encoder part of the XLMProphetNetModel.
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Wr.forEach(o),Qa=p(W),ue=r(W,"P",{});var Go=s(ue);Za=i(Go,"Original ProphetNet code can be found "),zt=r(Go,"A",{href:!0,rel:!0});var ic=s(zt);Ka=i(ic,"here"),ic.forEach(o),ei=i(Go,`. Checkpoints were converted
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etc.)`),Ur.forEach(o),Xi=p(B),ge=r(B,"P",{});var Bo=s(ge);Ei=i(Bo,"Original ProphetNet code can be found "),At=r(Bo,"A",{href:!0,rel:!0});var bc=s(At);Ci=i(bc,"here"),bc.forEach(o),Fi=i(Bo,`. Checkpoints were converted
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