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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(_,f){u(_,p,f),e(p,$),e(p,g),e(g,b),e(p,k)},d(_){_&&t(p)}}}function l3(C){let p,$,g,b,k,_,f,R,pe,J,E,Z,D,ee,he,S,ue,ie,V,A,te,K,z,q,se,U,le,re,B,fe,de,L,me,W,Y,oe,Q,ae,ge,O,_e,X,be,h,x,G,Me,ce,N,Te,$e,Re,P,H,xe,Fe,I,ke,we,ne,Ee,qd,cm,Oh,mo,cs,pm,ps,hm,Yi,um,fm,Nh,go,ln,Cd,hs,mm,Pd,gm,Ih,je,us,_m,It,bm,el,km,Tm,tl,vm,ym,fs,wm,Mm,Fm,ms,$m,ol,Rm,xm,Em,Qt,gs,zm,jd,Lm,qm,_s,nl,Cm,Ad,Pm,jm,sl,Am,Od,Om,Nm,dn,bs,Im,ks,Xm,Nd,Dm,Sm,Bm,cn,Ts,Wm,Id,Um,Qm,rl,vs,Xh,_o,pn,Xd,ys,Hm,Dd,Vm,Dh,ot,ws,Jm,Lt,Km,Sd,Gm,Zm,al,Ym,eg,il,tg,og,Ms,ng,sg,rg,Fs,ag,ll,ig,lg,dg,Ht,$s,cg,Bd,pg,hg,Rs,dl,ug,Wd,fg,mg,cl,gg,Ud,_g,bg,hn,xs,kg,Qd,Tg,Sh,bo,un,Hd,Es,vg,Vd,yg,Bh,Ue,zs,wg,Jd,Mg,Fg,Ls,$g,pl,Rg,xg,Eg,qs,zg,Cs,Lg,qg,Cg,Ps,Pg,hl,jg,Ag,Og,nt,js,Ng,ko,Ig,ul,Xg,Dg,Kd,Sg,Bg,Wg,fn,Ug,Gd,Qg,Hg,As,Wh,To,mn,Zd,Os,Vg,Yd,Jg,Uh,Qe,Ns,Kg,Is,Gg,ec,Zg,Yg,e_,Xs,t_,fl,o_,n_,s_,Ds,r_,Ss,a_,i_,l_,Bs,d_,ml,c_,p_,h_,st,Ws,u_,vo,f_,gl,m_,g_,tc,__,b_,k_,gn,T_,oc,v_,y_,Us,Qh,yo,_n,nc,Qs,w_,sc,M_,Hh,He,Hs,F_,Vs,$_,rc,R_,x_,E_,Js,z_,_l,L_,q_,C_,Ks,P_,Gs,j_,A_,O_,Zs,N_,bl,I_,X_,D_,rt,Ys,S_,wo,B_,kl,W_,U_,ac,Q_,H_,V_,bn,J_,ic,K_,G_,er,Vh,Mo,kn,lc,tr,Z_,dc,Y_,Jh,Ve,or,eb,cc,tb,ob,nr,nb,Tl,sb,rb,ab,sr,ib,rr,lb,db,cb,ar,pb,vl,hb,ub,fb,We,ir,mb,Fo,gb,yl,_b,bb,pc,kb,Tb,vb,Tn,yb,hc,wb,Mb,lr,Fb,uc,$b,Rb,dr,Kh,$o,vn,fc,cr,xb,mc,Eb,Gh,Je,pr,zb,gc,Lb,qb,hr,Cb,wl,Pb,jb,Ab,ur,Ob,fr,Nb,Ib,Xb,mr,Db,Ml,Sb,Bb,Wb,at,gr,Ub,Ro,Qb,Fl,Hb,Vb,_c,Jb,Kb,Gb,yn,Zb,bc,Yb,ek,_r,Zh,xo,wn,kc,br,tk,Tc,ok,Yh,Ke,kr,nk,vc,sk,rk,Tr,ak,$l,ik,lk,dk,vr,ck,yr,pk,hk,uk,wr,fk,Rl,mk,gk,_k,it,Mr,bk,Eo,kk,xl,Tk,vk,yc,yk,wk,Mk,Mn,Fk,wc,$k,Rk,Fr,eu,zo,Fn,Mc,$r,xk,Fc,Ek,tu,Ge,Rr,zk,Lo,Lk,$c,qk,Ck,Rc,Pk,jk,Ak,xr,Ok,El,Nk,Ik,Xk,Er,Dk,zr,Sk,Bk,Wk,Lr,Uk,zl,Qk,Hk,Vk,lt,qr,Jk,qo,Kk,Ll,Gk,Zk,xc,Yk,eT,tT,$n,oT,Ec,nT,sT,Cr,ou,Co,Rn,zc,Pr,rT,Lc,aT,nu,Ae,jr,iT,qc,lT,dT,Ar,cT,ql,pT,hT,uT,Or,fT,Nr,mT,gT,_T,xn,bT,Ir,kT,Cl,TT,vT,yT,dt,Xr,wT,Po,MT,Pl,FT,$T,Cc,RT,xT,ET,En,zT,Pc,LT,qT,Dr,su,jo,zn,jc,Sr,CT,Ac,PT,ru,Oe,Br,jT,Wr,AT,Oc,OT,NT,IT,Ur,XT,jl,DT,ST,BT,Qr,WT,Hr,UT,QT,HT,Ln,VT,Vr,JT,Al,KT,GT,ZT,ct,Jr,YT,Ao,ev,Ol,tv,ov,Nc,nv,sv,rv,qn,av,Ic,iv,lv,Kr,au,Oo,Cn,Xc,Gr,dv,Dc,cv,iu,Ne,Zr,pv,Sc,hv,uv,Yr,fv,Nl,mv,gv,_v,ea,bv,ta,kv,Tv,vv,Pn,yv,oa,wv,Il,Mv,Fv,$v,pt,na,Rv,No,xv,Xl,Ev,zv,Bc,Lv,qv,Cv,jn,Pv,Wc,jv,Av,sa,lu,Io,An,Uc,ra,Ov,Qc,Nv,du,Ie,aa,Iv,Hc,Xv,Dv,ia,Sv,Dl,Bv,Wv,Uv,la,Qv,da,Hv,Vv,Jv,On,Kv,ca,Gv,Sl,Zv,Yv,e1,ht,pa,t1,Xo,o1,Bl,n1,s1,Vc,r1,a1,i1,Nn,l1,Jc,d1,c1,ha,cu,Do,In,Kc,ua,p1,Gc,h1,pu,Xe,fa,u1,Zc,f1,m1,ma,g1,Wl,_1,b1,k1,ga,T1,_a,v1,y1,w1,Xn,M1,ba,F1,Ul,$1,R1,x1,ut,ka,E1,So,z1,Ql,L1,q1,Yc,C1,P1,j1,Dn,A1,ep,O1,N1,Ta,hu,Bo,Sn,tp,va,I1,op,X1,uu,De,ya,D1,Wo,S1,np,B1,W1,sp,U1,Q1,H1,wa,V1,Hl,J1,K1,G1,Ma,Z1,Fa,Y1,ey,ty,Bn,oy,$a,ny,rp,sy,ry,ay,ft,Ra,iy,Uo,ly,Vl,dy,cy,ap,py,hy,uy,Wn,fy,ip,my,gy,xa,fu,Qo,Un,lp,Ea,_y,dp,by,mu,Se,za,ky,La,Ty,Jl,vy,yy,wy,qa,My,Ca,Fy,$y,Ry,cp,xy,Ey,Xt,pp,Pa,zy,Ly,hp,ja,qy,Cy,up,Aa,Py,jy,fp,Oa,Ay,Oy,Na,Ny,Kl,Iy,Xy,Dy,mt,Ia,Sy,Ho,By,mp,Wy,Uy,gp,Qy,Hy,Vy,Qn,Jy,_p,Ky,Gy,Xa,gu,Vo,Hn,bp,Da,Zy,kp,Yy,_u,Be,Sa,ew,Jo,tw,Tp,ow,nw,Gl,sw,rw,aw,Ba,iw,Wa,lw,dw,cw,vp,pw,hw,Dt,yp,Ua,uw,fw,wp,Qa,mw,gw,Mp,Ha,_w,bw,Fp,Va,kw,Tw,Ja,vw,Zl,yw,ww,Mw,gt,Ka,Fw,Ko,$w,$p,Rw,xw,Rp,Ew,zw,Lw,Vn,qw,xp,Cw,Pw,Ga,bu,Go,Jn,Ep,Za,jw,zp,Aw,ku,ze,Ya,Ow,Lp,Nw,Iw,ei,Xw,Yl,Dw,Sw,Bw,ti,Ww,oi,Uw,Qw,Hw,qp,Vw,Jw,St,Cp,ni,Kw,Gw,Pp,si,Zw,Yw,jp,ri,eM,tM,Ap,ai,oM,nM,ii,sM,ed,rM,aM,iM,_t,li,lM,Zo,dM,Op,cM,pM,Np,hM,uM,fM,Kn,mM,Ip,gM,_M,di,Tu,Yo,Gn,Xp,ci,bM,Dp,kM,vu,Le,pi,TM,Sp,vM,yM,hi,wM,td,MM,FM,$M,ui,RM,fi,xM,EM,zM,Bp,LM,qM,Bt,Wp,mi,CM,PM,Up,gi,jM,AM,Qp,_i,OM,NM,Hp,bi,IM,XM,ki,DM,od,SM,BM,WM,bt,Ti,UM,en,QM,Vp,HM,VM,Jp,JM,KM,GM,Zn,ZM,Kp,YM,eF,vi,yu,tn,Yn,Gp,yi,tF,Zp,oF,wu,qe,wi,nF,Yp,sF,rF,Mi,aF,nd,iF,lF,dF,Fi,cF,$i,pF,hF,uF,eh,fF,mF,Wt,th,Ri,gF,_F,oh,xi,bF,kF,nh,Ei,TF,vF,sh,zi,yF,wF,Li,MF,sd,FF,$F,RF,kt,qi,xF,on,EF,rh,zF,LF,ah,qF,CF,PF,es,jF,ih,AF,OF,Ci,Mu,nn,ts,lh,Pi,NF,dh,IF,Fu,Ce,ji,XF,sn,DF,ch,SF,BF,ph,WF,UF,QF,Ai,HF,rd,VF,JF,KF,Oi,GF,Ni,ZF,YF,e2,hh,t2,o2,Ut,uh,Ii,n2,s2,fh,Xi,r2,a2,mh,Di,i2,l2,gh,Si,d2,c2,Bi,p2,ad,h2,u2,f2,Tt,Wi,m2,rn,g2,_h,_2,b2,bh,k2,T2,v2,os,y2,kh,w2,M2,Ui,$u;return _=new ye({}),ee=new ye({}),ne=new ye({}),cs=new j({props:{name:"class transformers.XLMRobertaConfig",anchor:"transformers.XLMRobertaConfig",parameters:[{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/configuration_xlm_roberta.py#L37"}}),hs=new ye({}),us=new j({props:{name:"class transformers.XLMRobertaTokenizer",anchor:"transformers.XLMRobertaTokenizer",parameters:[{name:"vocab_file",val:""},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"sep_token",val:" = '</s>'"},{name:"cls_token",val:" = '<s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"mask_token",val:" = '<mask>'"},{name:"sp_model_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py#L55",parametersDescription:[{anchor:"transformers.XLMRobertaTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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"}]}}),gs=new j({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.XLMRobertaTokenizer.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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py#L194",parametersDescription:[{anchor:"transformers.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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"}],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>
`}}),bs=new j({props:{name:"get_special_tokens_mask",anchor:"transformers.XLMRobertaTokenizer.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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py#L220",parametersDescription:[{anchor:"transformers.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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"}],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>
`}}),Ts=new j({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.XLMRobertaTokenizer.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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py#L248",parametersDescription:[{anchor:"transformers.XLMRobertaTokenizer.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.XLMRobertaTokenizer.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"}],returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),vs=new j({props:{name:"save_vocabulary",anchor:"transformers.XLMRobertaTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py#L305"}}),ys=new ye({}),ws=new j({props:{name:"class transformers.XLMRobertaTokenizerFast",anchor:"transformers.XLMRobertaTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"sep_token",val:" = '</s>'"},{name:"cls_token",val:" = '<s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"mask_token",val:" = '<mask>'"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py#L67",parametersDescription:[{anchor:"transformers.XLMRobertaTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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"}]}}),$s=new j({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.XLMRobertaTokenizerFast.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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py#L156",parametersDescription:[{anchor:"transformers.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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"}],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>
`}}),xs=new j({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.XLMRobertaTokenizerFast.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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py#L182",parametersDescription:[{anchor:"transformers.XLMRobertaTokenizerFast.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.XLMRobertaTokenizerFast.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"}],returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Es=new ye({}),zs=new j({props:{name:"class transformers.XLMRobertaModel",anchor:"transformers.XLMRobertaModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L66",parametersDescription:[{anchor:"transformers.XLMRobertaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),js=new j({props:{name:"forward",anchor:"transformers.RobertaModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",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:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = 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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_roberta.py#L741",parametersDescription:[{anchor:"transformers.RobertaModel.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.RobertaModel.forward.attention_mask",description:`<strong>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 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.RobertaModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.RobertaModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaModel.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.RobertaModel.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.RobertaModel.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/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaModel.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.RobertaModel.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>:</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:"encoder_attention_mask"},{anchor:"transformers.RobertaModel.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.RobertaModel.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"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</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>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model 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_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>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=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 of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),fn=new ve({props:{$$slots:{default:[OE]},$$scope:{ctx:C}}}),As=new Pe({props:{code:`from transformers import RobertaTokenizer, RobertaModel
import torch

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaModel.from_pretrained("roberta-base")

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaModel.from_pretrained(<span class="hljs-string">&quot;roberta-base&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`}}),Os=new ye({}),Ns=new j({props:{name:"class transformers.XLMRobertaForCausalLM",anchor:"transformers.XLMRobertaForCausalLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L79",parametersDescription:[{anchor:"transformers.XLMRobertaForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Ws=new j({props:{name:"forward",anchor:"transformers.RobertaForCausalLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"past_key_values",val:": typing.Tuple[typing.Tuple[torch.FloatTensor]] = 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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_roberta.py#L908",parametersDescription:[{anchor:"transformers.RobertaForCausalLM.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.RobertaForCausalLM.forward.attention_mask",description:`<strong>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 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.RobertaForCausalLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaForCausalLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaForCausalLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.RobertaForCausalLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaForCausalLM.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.RobertaForCausalLM.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.RobertaForCausalLM.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/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaForCausalLM.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.RobertaForCausalLM.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>:</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:"encoder_attention_mask"},{anchor:"transformers.RobertaForCausalLM.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 in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.RobertaForCausalLM.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.RobertaForCausalLM.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"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</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 (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model 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_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>
<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_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),gn=new ve({props:{$$slots:{default:[NE]},$$scope:{ctx:C}}}),Us=new Pe({props:{code:`from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig
import torch

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
config = RobertaConfig.from_pretrained("roberta-base")
config.is_decoder = True
model = RobertaForCausalLM.from_pretrained("roberta-base", config=config)

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

prediction_logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, RobertaForCausalLM, RobertaConfig
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config = RobertaConfig.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.is_decoder = <span class="hljs-literal">True</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaForCausalLM.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>, config=config)

<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>prediction_logits = outputs.logits`}}),Qs=new ye({}),Hs=new j({props:{name:"class transformers.XLMRobertaForMaskedLM",anchor:"transformers.XLMRobertaForMaskedLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L92",parametersDescription:[{anchor:"transformers.XLMRobertaForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Ys=new j({props:{name:"forward",anchor:"transformers.RobertaForMaskedLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = 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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_roberta.py#L1063",parametersDescription:[{anchor:"transformers.RobertaForMaskedLM.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.RobertaForMaskedLM.forward.attention_mask",description:`<strong>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 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.RobertaForMaskedLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaForMaskedLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaForMaskedLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.RobertaForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaForMaskedLM.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.RobertaForMaskedLM.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.RobertaForMaskedLM.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/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaForMaskedLM.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 masked language modeling loss. 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 in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.RobertaForMaskedLM.forward.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, any]</code>, optional, defaults to <em>{}</em>) &#x2014;
Used to hide legacy arguments that have been deprecated.`,name:"kwargs"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</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 Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model 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_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/pr_15839/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),bn=new ve({props:{$$slots:{default:[IE]},$$scope:{ctx:C}}}),er=new Pe({props:{code:`from transformers import RobertaTokenizer, RobertaForMaskedLM
import torch

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForMaskedLM.from_pretrained("roberta-base")

inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")
labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]

outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, RobertaForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaForMaskedLM.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is &lt;mask&gt;.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),tr=new ye({}),or=new j({props:{name:"class transformers.XLMRobertaForSequenceClassification",anchor:"transformers.XLMRobertaForSequenceClassification",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L108",parametersDescription:[{anchor:"transformers.XLMRobertaForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),ir=new j({props:{name:"forward",anchor:"transformers.RobertaForSequenceClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = 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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_roberta.py#L1177",parametersDescription:[{anchor:"transformers.RobertaForSequenceClassification.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.RobertaForSequenceClassification.forward.attention_mask",description:`<strong>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 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.RobertaForSequenceClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaForSequenceClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaForSequenceClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.RobertaForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaForSequenceClassification.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.RobertaForSequenceClassification.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.RobertaForSequenceClassification.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/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaForSequenceClassification.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>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</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 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model 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_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/pr_15839/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Tn=new ve({props:{$$slots:{default:[XE]},$$scope:{ctx:C}}}),lr=new Pe({props:{code:`import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification

torch.manual_seed(0)
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForSequenceClassification.from_pretrained("roberta-base", num_labels=2)

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits
list(logits.shape)
`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, RobertaForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>, num_labels=<span class="hljs-number">2</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>labels = torch.tensor([<span class="hljs-number">1</span>]).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># Batch size 1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape)
`}}),dr=new Pe({props:{code:`import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification

torch.manual_seed(0)
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForSequenceClassification.from_pretrained("roberta-base", problem_type="multi_label_classification", num_labels=2)

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([[1, 1]], dtype=torch.float)  # need dtype=float for BCEWithLogitsLoss
outputs = model(**inputs, labels=labels)
loss = outputs.loss
list(logits.shape)
`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, RobertaForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>, num_labels=<span class="hljs-number">2</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>labels = torch.tensor([[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>]], dtype=torch.<span class="hljs-built_in">float</span>)  <span class="hljs-comment"># need dtype=float for BCEWithLogitsLoss</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape)
`}}),cr=new ye({}),pr=new j({props:{name:"class transformers.XLMRobertaForMultipleChoice",anchor:"transformers.XLMRobertaForMultipleChoice",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L124",parametersDescription:[{anchor:"transformers.XLMRobertaForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),gr=new j({props:{name:"forward",anchor:"transformers.RobertaForMultipleChoice.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = 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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_roberta.py#L1274",parametersDescription:[{anchor:"transformers.RobertaForMultipleChoice.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.RobertaForMultipleChoice.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, 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.RobertaForMultipleChoice.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaForMultipleChoice.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaForMultipleChoice.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.RobertaForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaForMultipleChoice.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.RobertaForMultipleChoice.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.RobertaForMultipleChoice.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/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaForMultipleChoice.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices-1]</code> where <code>num_choices</code> is the size of the second dimension of the input tensors. (See
<code>input_ids</code> above)`,name:"labels"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <em>(1,)</em>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model 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_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/pr_15839/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),yn=new ve({props:{$$slots:{default:[DE]},$$scope:{ctx:C}}}),_r=new Pe({props:{code:`from transformers import RobertaTokenizer, RobertaForMultipleChoice
import torch

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForMultipleChoice.from_pretrained("roberta-base")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."
labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1

# the linear classifier still needs to be trained
loss = outputs.loss
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, RobertaForMultipleChoice
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># choice0 is correct (according to Wikipedia ;)), batch size 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**{k: v.unsqueeze(<span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()}, labels=labels)  <span class="hljs-comment"># batch size is 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),br=new ye({}),kr=new j({props:{name:"class transformers.XLMRobertaForTokenClassification",anchor:"transformers.XLMRobertaForTokenClassification",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L140",parametersDescription:[{anchor:"transformers.XLMRobertaForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Mr=new j({props:{name:"forward",anchor:"transformers.RobertaForTokenClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = 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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_roberta.py#L1372",parametersDescription:[{anchor:"transformers.RobertaForTokenClassification.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.RobertaForTokenClassification.forward.attention_mask",description:`<strong>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 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.RobertaForTokenClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaForTokenClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaForTokenClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.RobertaForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaForTokenClassification.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.RobertaForTokenClassification.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.RobertaForTokenClassification.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/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaForTokenClassification.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 token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</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 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model 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_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/pr_15839/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Mn=new ve({props:{$$slots:{default:[SE]},$$scope:{ctx:C}}}),Fr=new Pe({props:{code:`from transformers import RobertaTokenizer, RobertaForTokenClassification
import torch

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForTokenClassification.from_pretrained("roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0)  # Batch size 1

outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, RobertaForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaForTokenClassification.from_pretrained(<span class="hljs-string">&quot;roberta-base&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>labels = torch.tensor([<span class="hljs-number">1</span>] * inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].size(<span class="hljs-number">1</span>)).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># Batch size 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),$r=new ye({}),Rr=new j({props:{name:"class transformers.XLMRobertaForQuestionAnswering",anchor:"transformers.XLMRobertaForQuestionAnswering",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L156",parametersDescription:[{anchor:"transformers.XLMRobertaForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),qr=new j({props:{name:"forward",anchor:"transformers.RobertaForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"end_positions",val:": typing.Optional[torch.LongTensor] = 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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_roberta.py#L1475",parametersDescription:[{anchor:"transformers.RobertaForQuestionAnswering.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.RobertaForQuestionAnswering.forward.attention_mask",description:`<strong>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 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.RobertaForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RobertaForQuestionAnswering.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RobertaForQuestionAnswering.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.RobertaForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RobertaForQuestionAnswering.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.RobertaForQuestionAnswering.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.RobertaForQuestionAnswering.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/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RobertaForQuestionAnswering.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.RobertaForQuestionAnswering.forward.end_positions",description:`<strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</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 Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.</p>
</li>
<li>
<p><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model 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_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/pr_15839/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),$n=new ve({props:{$$slots:{default:[BE]},$$scope:{ctx:C}}}),Cr=new Pe({props:{code:`from transformers import RobertaTokenizer, RobertaForQuestionAnswering
import torch

torch.manual_seed(0)
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForQuestionAnswering.from_pretrained("roberta-base")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors="pt")
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])

outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
round(loss.item(), 2)


start_scores = outputs.start_logits
list(start_scores.shape)


end_scores = outputs.end_logits
list(end_scores.shape)
`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, RobertaForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RobertaForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_positions = torch.tensor([<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>end_positions = torch.tensor([<span class="hljs-number">3</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span>start_scores = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(start_scores.shape)


<span class="hljs-meta">&gt;&gt;&gt; </span>end_scores = outputs.end_logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(end_scores.shape)
`}}),Pr=new ye({}),jr=new j({props:{name:"class transformers.TFXLMRobertaModel",anchor:"transformers.TFXLMRobertaModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py#L81",parametersDescription:[{anchor:"transformers.TFXLMRobertaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),xn=new ve({props:{$$slots:{default:[WE]},$$scope:{ctx:C}}}),Xr=new j({props:{name:"call",anchor:"transformers.TFRobertaModel.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"encoder_hidden_states",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"encoder_attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"past_key_values",val:": typing.Union[typing.Tuple[typing.Tuple[typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]]], NoneType] = 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"},{name:"training",val:": typing.Optional[bool] = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_tf_roberta.py#L915",parametersDescription:[{anchor:"transformers.TFRobertaModel.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.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.TFRobertaModel.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaModel.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaModel.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.TFRobertaModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaModel.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaModel.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaModel.call.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>tf.Tensor</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.TFRobertaModel.call.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>tf.Tensor</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>:</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:"encoder_attention_mask"},{anchor:"transformers.TFRobertaModel.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
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.TFRobertaModel.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#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>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions</a> or a tuple of <code>tf.Tensor</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</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>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you\u2019re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</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>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) 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(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</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>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(tf.Tensor)</code></p>
`}}),En=new ve({props:{$$slots:{default:[UE]},$$scope:{ctx:C}}}),Dr=new Pe({props:{code:`from transformers import RobertaTokenizer, TFRobertaModel
import tensorflow as tf

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = TFRobertaModel.from_pretrained("roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
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> RobertaTokenizer, TFRobertaModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaModel.from_pretrained(<span class="hljs-string">&quot;roberta-base&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;tf&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`}}),Sr=new ye({}),Br=new j({props:{name:"class transformers.TFXLMRobertaForMaskedLM",anchor:"transformers.TFXLMRobertaForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py#L107",parametersDescription:[{anchor:"transformers.TFXLMRobertaForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Ln=new ve({props:{$$slots:{default:[QE]},$$scope:{ctx:C}}}),Jr=new j({props:{name:"call",anchor:"transformers.TFRobertaForMaskedLM.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_tf_roberta.py#L1072",parametersDescription:[{anchor:"transformers.TFRobertaForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.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.TFRobertaForMaskedLM.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaForMaskedLM.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaForMaskedLM.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.TFRobertaForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaForMaskedLM.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaForMaskedLM.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaForMaskedLM.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaForMaskedLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaForMaskedLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. 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 in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or a tuple of <code>tf.Tensor</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) \u2014 Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</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/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),qn=new ve({props:{$$slots:{default:[HE]},$$scope:{ctx:C}}}),Kr=new Pe({props:{code:`from transformers import RobertaTokenizer, TFRobertaForMaskedLM
import tensorflow as tf

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = TFRobertaForMaskedLM.from_pretrained("roberta-base")

inputs = tokenizer("The capital of France is [MASK].", return_tensors="tf")
inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]

outputs = model(inputs)
loss = outputs.loss
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, TFRobertaForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaForMaskedLM.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs[<span class="hljs-string">&quot;labels&quot;</span>] = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),Gr=new ye({}),Zr=new j({props:{name:"class transformers.TFXLMRobertaForSequenceClassification",anchor:"transformers.TFXLMRobertaForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py#L123",parametersDescription:[{anchor:"transformers.TFXLMRobertaForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Pn=new ve({props:{$$slots:{default:[VE]},$$scope:{ctx:C}}}),na=new j({props:{name:"call",anchor:"transformers.TFRobertaForSequenceClassification.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_tf_roberta.py#L1330",parametersDescription:[{anchor:"transformers.TFRobertaForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.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.TFRobertaForSequenceClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaForSequenceClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaForSequenceClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.TFRobertaForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaForSequenceClassification.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaForSequenceClassification.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaForSequenceClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaForSequenceClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or a tuple of <code>tf.Tensor</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</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/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),jn=new ve({props:{$$slots:{default:[JE]},$$scope:{ctx:C}}}),sa=new Pe({props:{code:`from transformers import RobertaTokenizer, TFRobertaForSequenceClassification
import tensorflow as tf

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = TFRobertaForSequenceClassification.from_pretrained("roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1))  # Batch size 1

outputs = model(inputs)
loss = outputs.loss
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, TFRobertaForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;roberta-base&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;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs[<span class="hljs-string">&quot;labels&quot;</span>] = tf.reshape(tf.constant(<span class="hljs-number">1</span>), (-<span class="hljs-number">1</span>, <span class="hljs-number">1</span>))  <span class="hljs-comment"># Batch size 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),ra=new ye({}),aa=new j({props:{name:"class transformers.TFXLMRobertaForMultipleChoice",anchor:"transformers.TFXLMRobertaForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py#L171",parametersDescription:[{anchor:"transformers.TFXLMRobertaForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),On=new ve({props:{$$slots:{default:[KE]},$$scope:{ctx:C}}}),pa=new j({props:{name:"call",anchor:"transformers.TFRobertaForMultipleChoice.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_tf_roberta.py#L1426",parametersDescription:[{anchor:"transformers.TFRobertaForMultipleChoice.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaForMultipleChoice.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, 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.TFRobertaForMultipleChoice.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaForMultipleChoice.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaForMultipleChoice.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.TFRobertaForMultipleChoice.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaForMultipleChoice.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaForMultipleChoice.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaForMultipleChoice.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaForMultipleChoice.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaForMultipleChoice.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices]</code>
where <code>num_choices</code> is the size of the second dimension of the input tensors. (See <code>input_ids</code> above)`,name:"labels"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or a tuple of <code>tf.Tensor</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <em>(batch_size, )</em>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</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/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Nn=new ve({props:{$$slots:{default:[GE]},$$scope:{ctx:C}}}),ha=new Pe({props:{code:`from transformers import RobertaTokenizer, TFRobertaForMultipleChoice
import tensorflow as tf

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = TFRobertaForMultipleChoice.from_pretrained("roberta-base")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."

encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
outputs = model(inputs)  # batch size is 1

# the linear classifier still needs to be trained
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, TFRobertaForMultipleChoice
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;tf&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = {k: tf.expand_dims(v, <span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()}
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)  <span class="hljs-comment"># batch size is 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),ua=new ye({}),fa=new j({props:{name:"class transformers.TFXLMRobertaForTokenClassification",anchor:"transformers.TFXLMRobertaForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py#L139",parametersDescription:[{anchor:"transformers.TFXLMRobertaForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Xn=new ve({props:{$$slots:{default:[ZE]},$$scope:{ctx:C}}}),ka=new j({props:{name:"call",anchor:"transformers.TFRobertaForTokenClassification.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_tf_roberta.py#L1542",parametersDescription:[{anchor:"transformers.TFRobertaForTokenClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.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.TFRobertaForTokenClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaForTokenClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaForTokenClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.TFRobertaForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaForTokenClassification.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaForTokenClassification.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaForTokenClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaForTokenClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaForTokenClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or a tuple of <code>tf.Tensor</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of unmasked labels, returned when <code>labels</code> is provided)  \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</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/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Dn=new ve({props:{$$slots:{default:[YE]},$$scope:{ctx:C}}}),Ta=new Pe({props:{code:`from transformers import RobertaTokenizer, TFRobertaForTokenClassification
import tensorflow as tf

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = TFRobertaForTokenClassification.from_pretrained("roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
input_ids = inputs["input_ids"]
inputs["labels"] = tf.reshape(
    tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))
)  # Batch size 1

outputs = model(inputs)
loss = outputs.loss
logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, TFRobertaForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaForTokenClassification.from_pretrained(<span class="hljs-string">&quot;roberta-base&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;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs[<span class="hljs-string">&quot;labels&quot;</span>] = tf.reshape(
<span class="hljs-meta">... </span>    tf.constant([<span class="hljs-number">1</span>] * tf.size(input_ids).numpy()), (-<span class="hljs-number">1</span>, tf.size(input_ids))
<span class="hljs-meta">&gt;&gt;&gt; </span>)  <span class="hljs-comment"># Batch size 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),va=new ye({}),ya=new j({props:{name:"class transformers.TFXLMRobertaForQuestionAnswering",anchor:"transformers.TFXLMRobertaForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py#L155",parametersDescription:[{anchor:"transformers.TFXLMRobertaForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Bn=new ve({props:{$$slots:{default:[e3]},$$scope:{ctx:C}}}),Ra=new j({props:{name:"call",anchor:"transformers.TFRobertaForQuestionAnswering.call",parameters:[{name:"input_ids",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"attention_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"token_type_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"position_ids",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"inputs_embeds",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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"},{name:"start_positions",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"end_positions",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_tf_roberta.py#L1627",parametersDescription:[{anchor:"transformers.TFRobertaForQuestionAnswering.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.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.TFRobertaForQuestionAnswering.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.position_ids",description:`<strong>position_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-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:"head_mask"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.start_positions",description:`<strong>start_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.TFRobertaForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or a tuple of <code>tf.Tensor</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>start_positions</code> and <code>end_positions</code> are provided) \u2014 Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.</p>
</li>
<li>
<p><strong>start_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</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/pr_15839/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Wn=new ve({props:{$$slots:{default:[t3]},$$scope:{ctx:C}}}),xa=new Pe({props:{code:`from transformers import RobertaTokenizer, TFRobertaForQuestionAnswering
import tensorflow as tf

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = TFRobertaForQuestionAnswering.from_pretrained("roberta-base")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
input_dict = tokenizer(question, text, return_tensors="tf")
outputs = model(input_dict)
start_logits = outputs.start_logits
end_logits = outputs.end_logits

all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
answer = " ".join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0] + 1])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, TFRobertaForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRobertaForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_dict = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_dict)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_logits = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_logits = outputs.end_logits

<span class="hljs-meta">&gt;&gt;&gt; </span>all_tokens = tokenizer.convert_ids_to_tokens(input_dict[<span class="hljs-string">&quot;input_ids&quot;</span>].numpy()[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>answer = <span class="hljs-string">&quot; &quot;</span>.join(all_tokens[tf.math.argmax(start_logits, <span class="hljs-number">1</span>)[<span class="hljs-number">0</span>] : tf.math.argmax(end_logits, <span class="hljs-number">1</span>)[<span class="hljs-number">0</span>] + <span class="hljs-number">1</span>])`}}),Ea=new ye({}),za=new j({props:{name:"class transformers.FlaxXLMRobertaModel",anchor:"transformers.FlaxXLMRobertaModel",parameters:[{name:"config",val:": RobertaConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py#L69",parametersDescription:[{anchor:"transformers.FlaxXLMRobertaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Ia=new j({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreTrainedModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_flax_roberta.py#L609",parametersDescription:[{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.FlaxRobertaPreTrainedModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxRobertaPreTrainedModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxRobertaPreTrainedModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</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>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</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/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qn=new ve({props:{$$slots:{default:[o3]},$$scope:{ctx:C}}}),Xa=new Pe({props:{code:`from transformers import RobertaTokenizer, FlaxRobertaModel

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = FlaxRobertaModel.from_pretrained("roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
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> RobertaTokenizer, FlaxRobertaModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaModel.from_pretrained(<span class="hljs-string">&quot;roberta-base&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;jax&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`}}),Da=new ye({}),Sa=new j({props:{name:"class transformers.FlaxXLMRobertaForMaskedLM",anchor:"transformers.FlaxXLMRobertaForMaskedLM",parameters:[{name:"config",val:": RobertaConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py#L82",parametersDescription:[{anchor:"transformers.FlaxXLMRobertaForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Ka=new j({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreTrainedModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_flax_roberta.py#L609",parametersDescription:[{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.FlaxRobertaPreTrainedModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxRobertaPreTrainedModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxRobertaPreTrainedModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</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>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</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/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Vn=new ve({props:{$$slots:{default:[n3]},$$scope:{ctx:C}}}),Ga=new Pe({props:{code:`from transformers import RobertaTokenizer, FlaxRobertaForMaskedLM

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = FlaxRobertaForMaskedLM.from_pretrained("roberta-base")

inputs = tokenizer("The capital of France is [MASK].", return_tensors="jax")

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaForMaskedLM.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),Za=new ye({}),Ya=new j({props:{name:"class transformers.FlaxXLMRobertaForSequenceClassification",anchor:"transformers.FlaxXLMRobertaForSequenceClassification",parameters:[{name:"config",val:": RobertaConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py#L98",parametersDescription:[{anchor:"transformers.FlaxXLMRobertaForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),li=new j({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreTrainedModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_flax_roberta.py#L609",parametersDescription:[{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.FlaxRobertaPreTrainedModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxRobertaPreTrainedModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxRobertaPreTrainedModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</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/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Kn=new ve({props:{$$slots:{default:[s3]},$$scope:{ctx:C}}}),di=new Pe({props:{code:`from transformers import RobertaTokenizer, FlaxRobertaForSequenceClassification

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = FlaxRobertaForSequenceClassification.from_pretrained("roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;roberta-base&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;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),ci=new ye({}),pi=new j({props:{name:"class transformers.FlaxXLMRobertaForMultipleChoice",anchor:"transformers.FlaxXLMRobertaForMultipleChoice",parameters:[{name:"config",val:": RobertaConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py#L114",parametersDescription:[{anchor:"transformers.FlaxXLMRobertaForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Ti=new j({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreTrainedModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_flax_roberta.py#L609",parametersDescription:[{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.FlaxRobertaPreTrainedModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, 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.FlaxRobertaPreTrainedModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxRobertaPreTrainedModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</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/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Zn=new ve({props:{$$slots:{default:[r3]},$$scope:{ctx:C}}}),vi=new Pe({props:{code:`from transformers import RobertaTokenizer, FlaxRobertaForMultipleChoice

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = FlaxRobertaForMultipleChoice.from_pretrained("roberta-base")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."

encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
outputs = model(**{k: v[None, :] for k, v in encoding.items()})

logits = outputs.logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, FlaxRobertaForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;jax&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**{k: v[<span class="hljs-literal">None</span>, :] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()})

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),yi=new ye({}),wi=new j({props:{name:"class transformers.FlaxXLMRobertaForTokenClassification",anchor:"transformers.FlaxXLMRobertaForTokenClassification",parameters:[{name:"config",val:": RobertaConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py#L130",parametersDescription:[{anchor:"transformers.FlaxXLMRobertaForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),qi=new j({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreTrainedModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_flax_roberta.py#L609",parametersDescription:[{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.FlaxRobertaPreTrainedModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxRobertaPreTrainedModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxRobertaPreTrainedModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</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/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),es=new ve({props:{$$slots:{default:[a3]},$$scope:{ctx:C}}}),Ci=new Pe({props:{code:`from transformers import RobertaTokenizer, FlaxRobertaForTokenClassification

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = FlaxRobertaForTokenClassification.from_pretrained("roberta-base")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")

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

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaForTokenClassification.from_pretrained(<span class="hljs-string">&quot;roberta-base&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;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),Pi=new ye({}),ji=new j({props:{name:"class transformers.FlaxXLMRobertaForQuestionAnswering",anchor:"transformers.FlaxXLMRobertaForQuestionAnswering",parameters:[{name:"config",val:": RobertaConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py#L146",parametersDescription:[{anchor:"transformers.FlaxXLMRobertaForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</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/pr_15839/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}]}}),Wi=new j({props:{name:"__call__",anchor:"transformers.FlaxRobertaPreTrainedModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{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"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/roberta/modeling_flax_roberta.py#L609",parametersDescription:[{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_15839/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__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.FlaxRobertaPreTrainedModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxRobertaPreTrainedModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxRobertaPreTrainedModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxRobertaPreTrainedModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_15839/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],returnDescription:`
<p>A <a
  href="/docs/transformers/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</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/pr_15839/en/model_doc/roberta#transformers.RobertaConfig"
>RobertaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + 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 initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</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/pr_15839/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),os=new ve({props:{$$slots:{default:[i3]},$$scope:{ctx:C}}}),Ui=new Pe({props:{code:`from transformers import RobertaTokenizer, FlaxRobertaForQuestionAnswering

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = FlaxRobertaForQuestionAnswering.from_pretrained("roberta-base")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors="jax")

outputs = model(**inputs)
start_scores = outputs.start_logits
end_scores = outputs.end_logits`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RobertaTokenizer, FlaxRobertaForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RobertaTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRobertaForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_scores = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_scores = outputs.end_logits`}}),{c(){p=o("meta"),$=l(),g=o("h1"),b=o("a"),k=o("span"),T(_.$$.fragment),f=l(),R=o("span"),pe=r("XLM-RoBERTa"),J=l(),E=o("h2"),Z=o("a"),D=o("span"),T(ee.$$.fragment),he=l(),S=o("span"),ue=r("Overview"),ie=l(),V=o("p"),A=r("The XLM-RoBERTa model was proposed in "),te=o("a"),K=r("Unsupervised Cross-lingual Representation Learning at Scale"),z=r(` by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
Wenzek, Francisco Guzm\xE1n, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook\u2019s
RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
data.`),q=l(),se=o("p"),U=r("The abstract from the paper is the following:"),le=l(),re=o("p"),B=o("em"),fe=r(`This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a
wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
will make XLM-R code, data, and models publicly available.`),de=l(),L=o("p"),me=r("Tips:"),W=l(),Y=o("ul"),oe=o("li"),Q=r(`XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
not require `),ae=o("code"),ge=r("lang"),O=r(` tensors to understand which language is used, and should be able to determine the correct
language from the input ids.`),_e=l(),X=o("li"),be=r("This implementation is the same as RoBERTa. Refer to the "),h=o("a"),x=r("documentation of RoBERTa"),G=r(` for usage examples
as well as the information relative to the inputs and outputs.`),Me=l(),ce=o("p"),N=r("This model was contributed by "),Te=o("a"),$e=r("stefan-it"),Re=r(". The original code can be found "),P=o("a"),H=r("here"),xe=r("."),Fe=l(),I=o("h2"),ke=o("a"),we=o("span"),T(ne.$$.fragment),Ee=l(),qd=o("span"),cm=r("XLMRobertaConfig"),Oh=l(),mo=o("div"),T(cs.$$.fragment),pm=l(),ps=o("p"),hm=r("This class overrides "),Yi=o("a"),um=r("RobertaConfig"),fm=r(`. Please check the superclass for the appropriate documentation alongside
usage examples.`),Nh=l(),go=o("h2"),ln=o("a"),Cd=o("span"),T(hs.$$.fragment),mm=l(),Pd=o("span"),gm=r("XLMRobertaTokenizer"),Ih=l(),je=o("div"),T(us.$$.fragment),_m=l(),It=o("p"),bm=r("Adapted from "),el=o("a"),km=r("RobertaTokenizer"),Tm=r(" and "),tl=o("a"),vm=r("XLNetTokenizer"),ym=r(`. Based on
`),fs=o("a"),wm=r("SentencePiece"),Mm=r("."),Fm=l(),ms=o("p"),$m=r("This tokenizer inherits from "),ol=o("a"),Rm=r("PreTrainedTokenizer"),xm=r(` which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`),Em=l(),Qt=o("div"),T(gs.$$.fragment),zm=l(),jd=o("p"),Lm=r(`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLM-RoBERTa sequence has the following format:`),qm=l(),_s=o("ul"),nl=o("li"),Cm=r("single sequence: "),Ad=o("code"),Pm=r("<s> X </s>"),jm=l(),sl=o("li"),Am=r("pair of sequences: "),Od=o("code"),Om=r("<s> A </s></s> B </s>"),Nm=l(),dn=o("div"),T(bs.$$.fragment),Im=l(),ks=o("p"),Xm=r(`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `),Nd=o("code"),Dm=r("prepare_for_model"),Sm=r(" method."),Bm=l(),cn=o("div"),T(Ts.$$.fragment),Wm=l(),Id=o("p"),Um=r(`Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
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etc.)`),_v=l(),ea=o("p"),bv=r("This model is also a "),ta=o("a"),kv=r("tf.keras.Model"),Tv=r(` subclass. Use it
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Wenzek, Francisco Guzm\xE1n, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook\u2019s
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languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Xu.forEach(t),Eg=d(qt),qs=n(qt,"P",{});var Du=s(qs);zg=a(Du,"This model is also a PyTorch "),Cs=n(Du,"A",{href:!0,rel:!0});var i$=s(Cs);Lg=a(i$,"torch.nn.Module"),i$.forEach(t),qg=a(Du,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Du.forEach(t),Cg=d(qt),Ps=n(qt,"P",{});var Su=s(Ps);Pg=a(Su,"This class overrides "),hl=n(Su,"A",{href:!0});var l$=s(hl);jg=a(l$,"RobertaModel"),l$.forEach(t),Ag=a(Su,`. Please check the superclass for the appropriate documentation alongside
usage examples.`),Su.forEach(t),Og=d(qt),nt=n(qt,"DIV",{class:!0});var Kt=s(nt);v(js.$$.fragment,Kt),Ng=d(Kt),ko=n(Kt,"P",{});var dd=s(ko);Ig=a(dd,"The "),ul=n(dd,"A",{href:!0});var d$=s(ul);Xg=a(d$,"RobertaModel"),d$.forEach(t),Dg=a(dd," forward method, overrides the "),Kd=n(dd,"CODE",{});var c$=s(Kd);Sg=a(c$,"__call__"),c$.forEach(t),Bg=a(dd," special method."),dd.forEach(t),Wg=d(Kt),v(fn.$$.fragment,Kt),Ug=d(Kt),Gd=n(Kt,"P",{});var p$=s(Gd);Qg=a(p$,"Example:"),p$.forEach(t),Hg=d(Kt),v(As.$$.fragment,Kt),Kt.forEach(t),qt.forEach(t),Wh=d(i),To=n(i,"H2",{class:!0});var Bu=s(To);mn=n(Bu,"A",{id:!0,class:!0,href:!0});var h$=s(mn);Zd=n(h$,"SPAN",{});var u$=s(Zd);v(Os.$$.fragment,u$),u$.forEach(t),h$.forEach(t),Vg=d(Bu),Yd=n(Bu,"SPAN",{});var f$=s(Yd);Jg=a(f$,"XLMRobertaForCausalLM"),f$.forEach(t),Bu.forEach(t),Uh=d(i),Qe=n(i,"DIV",{class:!0});var Ct=s(Qe);v(Ns.$$.fragment,Ct),Kg=d(Ct),Is=n(Ct,"P",{});var Wu=s(Is);Gg=a(Wu,"XLM-RoBERTa Model with a "),ec=n(Wu,"CODE",{});var m$=s(ec);Zg=a(m$,"language modeling"),m$.forEach(t),Yg=a(Wu," head on top for CLM fine-tuning."),Wu.forEach(t),e_=d(Ct),Xs=n(Ct,"P",{});var Uu=s(Xs);t_=a(Uu,"This model inherits from "),fl=n(Uu,"A",{href:!0});var g$=s(fl);o_=a(g$,"PreTrainedModel"),g$.forEach(t),n_=a(Uu,`. 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.)`),Uu.forEach(t),s_=d(Ct),Ds=n(Ct,"P",{});var Qu=s(Ds);r_=a(Qu,"This model is also a PyTorch "),Ss=n(Qu,"A",{href:!0,rel:!0});var _$=s(Ss);a_=a(_$,"torch.nn.Module"),_$.forEach(t),i_=a(Qu,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Qu.forEach(t),l_=d(Ct),Bs=n(Ct,"P",{});var Hu=s(Bs);d_=a(Hu,"This class overrides "),ml=n(Hu,"A",{href:!0});var b$=s(ml);c_=a(b$,"RobertaForCausalLM"),b$.forEach(t),p_=a(Hu,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),Hu.forEach(t),h_=d(Ct),st=n(Ct,"DIV",{class:!0});var Gt=s(st);v(Ws.$$.fragment,Gt),u_=d(Gt),vo=n(Gt,"P",{});var cd=s(vo);f_=a(cd,"The "),gl=n(cd,"A",{href:!0});var k$=s(gl);m_=a(k$,"RobertaForCausalLM"),k$.forEach(t),g_=a(cd," forward method, overrides the "),tc=n(cd,"CODE",{});var T$=s(tc);__=a(T$,"__call__"),T$.forEach(t),b_=a(cd," special method."),cd.forEach(t),k_=d(Gt),v(gn.$$.fragment,Gt),T_=d(Gt),oc=n(Gt,"P",{});var v$=s(oc);v_=a(v$,"Example:"),v$.forEach(t),y_=d(Gt),v(Us.$$.fragment,Gt),Gt.forEach(t),Ct.forEach(t),Qh=d(i),yo=n(i,"H2",{class:!0});var Vu=s(yo);_n=n(Vu,"A",{id:!0,class:!0,href:!0});var y$=s(_n);nc=n(y$,"SPAN",{});var w$=s(nc);v(Qs.$$.fragment,w$),w$.forEach(t),y$.forEach(t),w_=d(Vu),sc=n(Vu,"SPAN",{});var M$=s(sc);M_=a(M$,"XLMRobertaForMaskedLM"),M$.forEach(t),Vu.forEach(t),Hh=d(i),He=n(i,"DIV",{class:!0});var Pt=s(He);v(Hs.$$.fragment,Pt),F_=d(Pt),Vs=n(Pt,"P",{});var Ju=s(Vs);$_=a(Ju,"XLM-RoBERTa Model with a "),rc=n(Ju,"CODE",{});var F$=s(rc);R_=a(F$,"language modeling"),F$.forEach(t),x_=a(Ju," head on top."),Ju.forEach(t),E_=d(Pt),Js=n(Pt,"P",{});var Ku=s(Js);z_=a(Ku,"This model inherits from "),_l=n(Ku,"A",{href:!0});var $$=s(_l);L_=a($$,"PreTrainedModel"),$$.forEach(t),q_=a(Ku,`. 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.)`),Ku.forEach(t),C_=d(Pt),Ks=n(Pt,"P",{});var Gu=s(Ks);P_=a(Gu,"This model is also a PyTorch "),Gs=n(Gu,"A",{href:!0,rel:!0});var R$=s(Gs);j_=a(R$,"torch.nn.Module"),R$.forEach(t),A_=a(Gu,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Gu.forEach(t),O_=d(Pt),Zs=n(Pt,"P",{});var Zu=s(Zs);N_=a(Zu,"This class overrides "),bl=n(Zu,"A",{href:!0});var x$=s(bl);I_=a(x$,"RobertaForMaskedLM"),x$.forEach(t),X_=a(Zu,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),Zu.forEach(t),D_=d(Pt),rt=n(Pt,"DIV",{class:!0});var Zt=s(rt);v(Ys.$$.fragment,Zt),S_=d(Zt),wo=n(Zt,"P",{});var pd=s(wo);B_=a(pd,"The "),kl=n(pd,"A",{href:!0});var E$=s(kl);W_=a(E$,"RobertaForMaskedLM"),E$.forEach(t),U_=a(pd," forward method, overrides the "),ac=n(pd,"CODE",{});var z$=s(ac);Q_=a(z$,"__call__"),z$.forEach(t),H_=a(pd," special method."),pd.forEach(t),V_=d(Zt),v(bn.$$.fragment,Zt),J_=d(Zt),ic=n(Zt,"P",{});var L$=s(ic);K_=a(L$,"Example:"),L$.forEach(t),G_=d(Zt),v(er.$$.fragment,Zt),Zt.forEach(t),Pt.forEach(t),Vh=d(i),Mo=n(i,"H2",{class:!0});var Yu=s(Mo);kn=n(Yu,"A",{id:!0,class:!0,href:!0});var q$=s(kn);lc=n(q$,"SPAN",{});var C$=s(lc);v(tr.$$.fragment,C$),C$.forEach(t),q$.forEach(t),Z_=d(Yu),dc=n(Yu,"SPAN",{});var P$=s(dc);Y_=a(P$,"XLMRobertaForSequenceClassification"),P$.forEach(t),Yu.forEach(t),Jh=d(i),Ve=n(i,"DIV",{class:!0});var jt=s(Ve);v(or.$$.fragment,jt),eb=d(jt),cc=n(jt,"P",{});var j$=s(cc);tb=a(j$,`XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),j$.forEach(t),ob=d(jt),nr=n(jt,"P",{});var ef=s(nr);nb=a(ef,"This model inherits from "),Tl=n(ef,"A",{href:!0});var A$=s(Tl);sb=a(A$,"PreTrainedModel"),A$.forEach(t),rb=a(ef,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),ef.forEach(t),ab=d(jt),sr=n(jt,"P",{});var tf=s(sr);ib=a(tf,"This model is also a PyTorch "),rr=n(tf,"A",{href:!0,rel:!0});var O$=s(rr);lb=a(O$,"torch.nn.Module"),O$.forEach(t),db=a(tf,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),tf.forEach(t),cb=d(jt),ar=n(jt,"P",{});var of=s(ar);pb=a(of,"This class overrides "),vl=n(of,"A",{href:!0});var N$=s(vl);hb=a(N$,"RobertaForSequenceClassification"),N$.forEach(t),ub=a(of,`. Please check the superclass for the appropriate
documentation alongside usage examples.`),of.forEach(t),fb=d(jt),We=n(jt,"DIV",{class:!0});var yt=s(We);v(ir.$$.fragment,yt),mb=d(yt),Fo=n(yt,"P",{});var hd=s(Fo);gb=a(hd,"The "),yl=n(hd,"A",{href:!0});var I$=s(yl);_b=a(I$,"RobertaForSequenceClassification"),I$.forEach(t),bb=a(hd," forward method, overrides the "),pc=n(hd,"CODE",{});var X$=s(pc);kb=a(X$,"__call__"),X$.forEach(t),Tb=a(hd," special method."),hd.forEach(t),vb=d(yt),v(Tn.$$.fragment,yt),yb=d(yt),hc=n(yt,"P",{});var D$=s(hc);wb=a(D$,"Example of single-label classification:"),D$.forEach(t),Mb=d(yt),v(lr.$$.fragment,yt),Fb=d(yt),uc=n(yt,"P",{});var S$=s(uc);$b=a(S$,"Example of multi-label classification:"),S$.forEach(t),Rb=d(yt),v(dr.$$.fragment,yt),yt.forEach(t),jt.forEach(t),Kh=d(i),$o=n(i,"H2",{class:!0});var nf=s($o);vn=n(nf,"A",{id:!0,class:!0,href:!0});var B$=s(vn);fc=n(B$,"SPAN",{});var W$=s(fc);v(cr.$$.fragment,W$),W$.forEach(t),B$.forEach(t),xb=d(nf),mc=n(nf,"SPAN",{});var U$=s(mc);Eb=a(U$,"XLMRobertaForMultipleChoice"),U$.forEach(t),nf.forEach(t),Gh=d(i),Je=n(i,"DIV",{class:!0});var At=s(Je);v(pr.$$.fragment,At),zb=d(At),gc=n(At,"P",{});var Q$=s(gc);Lb=a(Q$,`XLM-RoBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.`),Q$.forEach(t),qb=d(At),hr=n(At,"P",{});var sf=s(hr);Cb=a(sf,"This model inherits from "),wl=n(sf,"A",{href:!0});var H$=s(wl);Pb=a(H$,"PreTrainedModel"),H$.forEach(t),jb=a(sf,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),sf.forEach(t),Ab=d(At),ur=n(At,"P",{});var rf=s(ur);Ob=a(rf,"This model is also a PyTorch "),fr=n(rf,"A",{href:!0,rel:!0});var V$=s(fr);Nb=a(V$,"torch.nn.Module"),V$.forEach(t),Ib=a(rf,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),rf.forEach(t),Xb=d(At),mr=n(At,"P",{});var af=s(mr);Db=a(af,"This class overrides "),Ml=n(af,"A",{href:!0});var J$=s(Ml);Sb=a(J$,"RobertaForMultipleChoice"),J$.forEach(t),Bb=a(af,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),af.forEach(t),Wb=d(At),at=n(At,"DIV",{class:!0});var Yt=s(at);v(gr.$$.fragment,Yt),Ub=d(Yt),Ro=n(Yt,"P",{});var ud=s(Ro);Qb=a(ud,"The "),Fl=n(ud,"A",{href:!0});var K$=s(Fl);Hb=a(K$,"RobertaForMultipleChoice"),K$.forEach(t),Vb=a(ud," forward method, overrides the "),_c=n(ud,"CODE",{});var G$=s(_c);Jb=a(G$,"__call__"),G$.forEach(t),Kb=a(ud," special method."),ud.forEach(t),Gb=d(Yt),v(yn.$$.fragment,Yt),Zb=d(Yt),bc=n(Yt,"P",{});var Z$=s(bc);Yb=a(Z$,"Example:"),Z$.forEach(t),ek=d(Yt),v(_r.$$.fragment,Yt),Yt.forEach(t),At.forEach(t),Zh=d(i),xo=n(i,"H2",{class:!0});var lf=s(xo);wn=n(lf,"A",{id:!0,class:!0,href:!0});var Y$=s(wn);kc=n(Y$,"SPAN",{});var e0=s(kc);v(br.$$.fragment,e0),e0.forEach(t),Y$.forEach(t),tk=d(lf),Tc=n(lf,"SPAN",{});var t0=s(Tc);ok=a(t0,"XLMRobertaForTokenClassification"),t0.forEach(t),lf.forEach(t),Yh=d(i),Ke=n(i,"DIV",{class:!0});var Ot=s(Ke);v(kr.$$.fragment,Ot),nk=d(Ot),vc=n(Ot,"P",{});var o0=s(vc);sk=a(o0,`XLM-RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`),o0.forEach(t),rk=d(Ot),Tr=n(Ot,"P",{});var df=s(Tr);ak=a(df,"This model inherits from "),$l=n(df,"A",{href:!0});var n0=s($l);ik=a(n0,"PreTrainedModel"),n0.forEach(t),lk=a(df,`. 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.)`),df.forEach(t),dk=d(Ot),vr=n(Ot,"P",{});var cf=s(vr);ck=a(cf,"This model is also a PyTorch "),yr=n(cf,"A",{href:!0,rel:!0});var s0=s(yr);pk=a(s0,"torch.nn.Module"),s0.forEach(t),hk=a(cf,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),cf.forEach(t),uk=d(Ot),wr=n(Ot,"P",{});var pf=s(wr);fk=a(pf,"This class overrides "),Rl=n(pf,"A",{href:!0});var r0=s(Rl);mk=a(r0,"RobertaForTokenClassification"),r0.forEach(t),gk=a(pf,`. Please check the superclass for the appropriate
documentation alongside usage examples.`),pf.forEach(t),_k=d(Ot),it=n(Ot,"DIV",{class:!0});var eo=s(it);v(Mr.$$.fragment,eo),bk=d(eo),Eo=n(eo,"P",{});var fd=s(Eo);kk=a(fd,"The "),xl=n(fd,"A",{href:!0});var a0=s(xl);Tk=a(a0,"RobertaForTokenClassification"),a0.forEach(t),vk=a(fd," forward method, overrides the "),yc=n(fd,"CODE",{});var i0=s(yc);yk=a(i0,"__call__"),i0.forEach(t),wk=a(fd," special method."),fd.forEach(t),Mk=d(eo),v(Mn.$$.fragment,eo),Fk=d(eo),wc=n(eo,"P",{});var l0=s(wc);$k=a(l0,"Example:"),l0.forEach(t),Rk=d(eo),v(Fr.$$.fragment,eo),eo.forEach(t),Ot.forEach(t),eu=d(i),zo=n(i,"H2",{class:!0});var hf=s(zo);Fn=n(hf,"A",{id:!0,class:!0,href:!0});var d0=s(Fn);Mc=n(d0,"SPAN",{});var c0=s(Mc);v($r.$$.fragment,c0),c0.forEach(t),d0.forEach(t),xk=d(hf),Fc=n(hf,"SPAN",{});var p0=s(Fc);Ek=a(p0,"XLMRobertaForQuestionAnswering"),p0.forEach(t),hf.forEach(t),tu=d(i),Ge=n(i,"DIV",{class:!0});var Nt=s(Ge);v(Rr.$$.fragment,Nt),zk=d(Nt),Lo=n(Nt,"P",{});var md=s(Lo);Lk=a(md,`XLM-RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute `),$c=n(md,"CODE",{});var h0=s($c);qk=a(h0,"span start logits"),h0.forEach(t),Ck=a(md," and "),Rc=n(md,"CODE",{});var u0=s(Rc);Pk=a(u0,"span end logits"),u0.forEach(t),jk=a(md,")."),md.forEach(t),Ak=d(Nt),xr=n(Nt,"P",{});var uf=s(xr);Ok=a(uf,"This model inherits from "),El=n(uf,"A",{href:!0});var f0=s(El);Nk=a(f0,"PreTrainedModel"),f0.forEach(t),Ik=a(uf,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),uf.forEach(t),Xk=d(Nt),Er=n(Nt,"P",{});var ff=s(Er);Dk=a(ff,"This model is also a PyTorch "),zr=n(ff,"A",{href:!0,rel:!0});var m0=s(zr);Sk=a(m0,"torch.nn.Module"),m0.forEach(t),Bk=a(ff,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),ff.forEach(t),Wk=d(Nt),Lr=n(Nt,"P",{});var mf=s(Lr);Uk=a(mf,"This class overrides "),zl=n(mf,"A",{href:!0});var g0=s(zl);Qk=a(g0,"RobertaForQuestionAnswering"),g0.forEach(t),Hk=a(mf,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),mf.forEach(t),Vk=d(Nt),lt=n(Nt,"DIV",{class:!0});var to=s(lt);v(qr.$$.fragment,to),Jk=d(to),qo=n(to,"P",{});var gd=s(qo);Kk=a(gd,"The "),Ll=n(gd,"A",{href:!0});var _0=s(Ll);Gk=a(_0,"RobertaForQuestionAnswering"),_0.forEach(t),Zk=a(gd," forward method, overrides the "),xc=n(gd,"CODE",{});var b0=s(xc);Yk=a(b0,"__call__"),b0.forEach(t),eT=a(gd," special method."),gd.forEach(t),tT=d(to),v($n.$$.fragment,to),oT=d(to),Ec=n(to,"P",{});var k0=s(Ec);nT=a(k0,"Example:"),k0.forEach(t),sT=d(to),v(Cr.$$.fragment,to),to.forEach(t),Nt.forEach(t),ou=d(i),Co=n(i,"H2",{class:!0});var gf=s(Co);Rn=n(gf,"A",{id:!0,class:!0,href:!0});var T0=s(Rn);zc=n(T0,"SPAN",{});var v0=s(zc);v(Pr.$$.fragment,v0),v0.forEach(t),T0.forEach(t),rT=d(gf),Lc=n(gf,"SPAN",{});var y0=s(Lc);aT=a(y0,"TFXLMRobertaModel"),y0.forEach(t),gf.forEach(t),nu=d(i),Ae=n(i,"DIV",{class:!0});var wt=s(Ae);v(jr.$$.fragment,wt),iT=d(wt),qc=n(wt,"P",{});var w0=s(qc);lT=a(w0,"The bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top."),w0.forEach(t),dT=d(wt),Ar=n(wt,"P",{});var _f=s(Ar);cT=a(_f,"This model inherits from "),ql=n(_f,"A",{href:!0});var M0=s(ql);pT=a(M0,"TFPreTrainedModel"),M0.forEach(t),hT=a(_f,`. 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.)`),_f.forEach(t),uT=d(wt),Or=n(wt,"P",{});var bf=s(Or);fT=a(bf,"This model is also a "),Nr=n(bf,"A",{href:!0,rel:!0});var F0=s(Nr);mT=a(F0,"tf.keras.Model"),F0.forEach(t),gT=a(bf,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),bf.forEach(t),_T=d(wt),v(xn.$$.fragment,wt),bT=d(wt),Ir=n(wt,"P",{});var kf=s(Ir);kT=a(kf,"This class overrides "),Cl=n(kf,"A",{href:!0});var $0=s(Cl);TT=a($0,"TFRobertaModel"),$0.forEach(t),vT=a(kf,`. Please check the superclass for the appropriate documentation alongside
usage examples.`),kf.forEach(t),yT=d(wt),dt=n(wt,"DIV",{class:!0});var oo=s(dt);v(Xr.$$.fragment,oo),wT=d(oo),Po=n(oo,"P",{});var _d=s(Po);MT=a(_d,"The "),Pl=n(_d,"A",{href:!0});var R0=s(Pl);FT=a(R0,"TFRobertaModel"),R0.forEach(t),$T=a(_d," forward method, overrides the "),Cc=n(_d,"CODE",{});var x0=s(Cc);RT=a(x0,"__call__"),x0.forEach(t),xT=a(_d," special method."),_d.forEach(t),ET=d(oo),v(En.$$.fragment,oo),zT=d(oo),Pc=n(oo,"P",{});var E0=s(Pc);LT=a(E0,"Example:"),E0.forEach(t),qT=d(oo),v(Dr.$$.fragment,oo),oo.forEach(t),wt.forEach(t),su=d(i),jo=n(i,"H2",{class:!0});var Tf=s(jo);zn=n(Tf,"A",{id:!0,class:!0,href:!0});var z0=s(zn);jc=n(z0,"SPAN",{});var L0=s(jc);v(Sr.$$.fragment,L0),L0.forEach(t),z0.forEach(t),CT=d(Tf),Ac=n(Tf,"SPAN",{});var q0=s(Ac);PT=a(q0,"TFXLMRobertaForMaskedLM"),q0.forEach(t),Tf.forEach(t),ru=d(i),Oe=n(i,"DIV",{class:!0});var Mt=s(Oe);v(Br.$$.fragment,Mt),jT=d(Mt),Wr=n(Mt,"P",{});var vf=s(Wr);AT=a(vf,"XLM-RoBERTa Model with a "),Oc=n(vf,"CODE",{});var C0=s(Oc);OT=a(C0,"language modeling"),C0.forEach(t),NT=a(vf," head on top."),vf.forEach(t),IT=d(Mt),Ur=n(Mt,"P",{});var yf=s(Ur);XT=a(yf,"This model inherits from "),jl=n(yf,"A",{href:!0});var P0=s(jl);DT=a(P0,"TFPreTrainedModel"),P0.forEach(t),ST=a(yf,`. 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.)`),yf.forEach(t),BT=d(Mt),Qr=n(Mt,"P",{});var wf=s(Qr);WT=a(wf,"This model is also a "),Hr=n(wf,"A",{href:!0,rel:!0});var j0=s(Hr);UT=a(j0,"tf.keras.Model"),j0.forEach(t),QT=a(wf,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),wf.forEach(t),HT=d(Mt),v(Ln.$$.fragment,Mt),VT=d(Mt),Vr=n(Mt,"P",{});var Mf=s(Vr);JT=a(Mf,"This class overrides "),Al=n(Mf,"A",{href:!0});var A0=s(Al);KT=a(A0,"TFRobertaForMaskedLM"),A0.forEach(t),GT=a(Mf,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),Mf.forEach(t),ZT=d(Mt),ct=n(Mt,"DIV",{class:!0});var no=s(ct);v(Jr.$$.fragment,no),YT=d(no),Ao=n(no,"P",{});var bd=s(Ao);ev=a(bd,"The "),Ol=n(bd,"A",{href:!0});var O0=s(Ol);tv=a(O0,"TFRobertaForMaskedLM"),O0.forEach(t),ov=a(bd," forward method, overrides the "),Nc=n(bd,"CODE",{});var N0=s(Nc);nv=a(N0,"__call__"),N0.forEach(t),sv=a(bd," special method."),bd.forEach(t),rv=d(no),v(qn.$$.fragment,no),av=d(no),Ic=n(no,"P",{});var I0=s(Ic);iv=a(I0,"Example:"),I0.forEach(t),lv=d(no),v(Kr.$$.fragment,no),no.forEach(t),Mt.forEach(t),au=d(i),Oo=n(i,"H2",{class:!0});var Ff=s(Oo);Cn=n(Ff,"A",{id:!0,class:!0,href:!0});var X0=s(Cn);Xc=n(X0,"SPAN",{});var D0=s(Xc);v(Gr.$$.fragment,D0),D0.forEach(t),X0.forEach(t),dv=d(Ff),Dc=n(Ff,"SPAN",{});var S0=s(Dc);cv=a(S0,"TFXLMRobertaForSequenceClassification"),S0.forEach(t),Ff.forEach(t),iu=d(i),Ne=n(i,"DIV",{class:!0});var Ft=s(Ne);v(Zr.$$.fragment,Ft),pv=d(Ft),Sc=n(Ft,"P",{});var B0=s(Sc);hv=a(B0,`XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),B0.forEach(t),uv=d(Ft),Yr=n(Ft,"P",{});var $f=s(Yr);fv=a($f,"This model inherits from "),Nl=n($f,"A",{href:!0});var W0=s(Nl);mv=a(W0,"TFPreTrainedModel"),W0.forEach(t),gv=a($f,`. 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.)`),$f.forEach(t),_v=d(Ft),ea=n(Ft,"P",{});var Rf=s(ea);bv=a(Rf,"This model is also a "),ta=n(Rf,"A",{href:!0,rel:!0});var U0=s(ta);kv=a(U0,"tf.keras.Model"),U0.forEach(t),Tv=a(Rf,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Rf.forEach(t),vv=d(Ft),v(Pn.$$.fragment,Ft),yv=d(Ft),oa=n(Ft,"P",{});var xf=s(oa);wv=a(xf,"This class overrides "),Il=n(xf,"A",{href:!0});var Q0=s(Il);Mv=a(Q0,"TFRobertaForSequenceClassification"),Q0.forEach(t),Fv=a(xf,`. Please check the superclass for the appropriate
documentation alongside usage examples.`),xf.forEach(t),$v=d(Ft),pt=n(Ft,"DIV",{class:!0});var so=s(pt);v(na.$$.fragment,so),Rv=d(so),No=n(so,"P",{});var kd=s(No);xv=a(kd,"The "),Xl=n(kd,"A",{href:!0});var H0=s(Xl);Ev=a(H0,"TFRobertaForSequenceClassification"),H0.forEach(t),zv=a(kd," forward method, overrides the "),Bc=n(kd,"CODE",{});var V0=s(Bc);Lv=a(V0,"__call__"),V0.forEach(t),qv=a(kd," special method."),kd.forEach(t),Cv=d(so),v(jn.$$.fragment,so),Pv=d(so),Wc=n(so,"P",{});var J0=s(Wc);jv=a(J0,"Example:"),J0.forEach(t),Av=d(so),v(sa.$$.fragment,so),so.forEach(t),Ft.forEach(t),lu=d(i),Io=n(i,"H2",{class:!0});var Ef=s(Io);An=n(Ef,"A",{id:!0,class:!0,href:!0});var K0=s(An);Uc=n(K0,"SPAN",{});var G0=s(Uc);v(ra.$$.fragment,G0),G0.forEach(t),K0.forEach(t),Ov=d(Ef),Qc=n(Ef,"SPAN",{});var Z0=s(Qc);Nv=a(Z0,"TFXLMRobertaForMultipleChoice"),Z0.forEach(t),Ef.forEach(t),du=d(i),Ie=n(i,"DIV",{class:!0});var $t=s(Ie);v(aa.$$.fragment,$t),Iv=d($t),Hc=n($t,"P",{});var Y0=s(Hc);Xv=a(Y0,`Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.`),Y0.forEach(t),Dv=d($t),ia=n($t,"P",{});var zf=s(ia);Sv=a(zf,"This model inherits from "),Dl=n(zf,"A",{href:!0});var eR=s(Dl);Bv=a(eR,"TFPreTrainedModel"),eR.forEach(t),Wv=a(zf,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),zf.forEach(t),Uv=d($t),la=n($t,"P",{});var Lf=s(la);Qv=a(Lf,"This model is also a "),da=n(Lf,"A",{href:!0,rel:!0});var tR=s(da);Hv=a(tR,"tf.keras.Model"),tR.forEach(t),Vv=a(Lf,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),Lf.forEach(t),Jv=d($t),v(On.$$.fragment,$t),Kv=d($t),ca=n($t,"P",{});var qf=s(ca);Gv=a(qf,"This class overrides "),Sl=n(qf,"A",{href:!0});var oR=s(Sl);Zv=a(oR,"TFRobertaForMultipleChoice"),oR.forEach(t),Yv=a(qf,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),qf.forEach(t),e1=d($t),ht=n($t,"DIV",{class:!0});var ro=s(ht);v(pa.$$.fragment,ro),t1=d(ro),Xo=n(ro,"P",{});var Td=s(Xo);o1=a(Td,"The "),Bl=n(Td,"A",{href:!0});var nR=s(Bl);n1=a(nR,"TFRobertaForMultipleChoice"),nR.forEach(t),s1=a(Td," forward method, overrides the "),Vc=n(Td,"CODE",{});var sR=s(Vc);r1=a(sR,"__call__"),sR.forEach(t),a1=a(Td," special method."),Td.forEach(t),i1=d(ro),v(Nn.$$.fragment,ro),l1=d(ro),Jc=n(ro,"P",{});var rR=s(Jc);d1=a(rR,"Example:"),rR.forEach(t),c1=d(ro),v(ha.$$.fragment,ro),ro.forEach(t),$t.forEach(t),cu=d(i),Do=n(i,"H2",{class:!0});var Cf=s(Do);In=n(Cf,"A",{id:!0,class:!0,href:!0});var aR=s(In);Kc=n(aR,"SPAN",{});var iR=s(Kc);v(ua.$$.fragment,iR),iR.forEach(t),aR.forEach(t),p1=d(Cf),Gc=n(Cf,"SPAN",{});var lR=s(Gc);h1=a(lR,"TFXLMRobertaForTokenClassification"),lR.forEach(t),Cf.forEach(t),pu=d(i),Xe=n(i,"DIV",{class:!0});var Rt=s(Xe);v(fa.$$.fragment,Rt),u1=d(Rt),Zc=n(Rt,"P",{});var dR=s(Zc);f1=a(dR,`XLM-RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`),dR.forEach(t),m1=d(Rt),ma=n(Rt,"P",{});var Pf=s(ma);g1=a(Pf,"This model inherits from "),Wl=n(Pf,"A",{href:!0});var cR=s(Wl);_1=a(cR,"TFPreTrainedModel"),cR.forEach(t),b1=a(Pf,`. 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.)`),Pf.forEach(t),k1=d(Rt),ga=n(Rt,"P",{});var jf=s(ga);T1=a(jf,"This model is also a "),_a=n(jf,"A",{href:!0,rel:!0});var pR=s(_a);v1=a(pR,"tf.keras.Model"),pR.forEach(t),y1=a(jf,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),jf.forEach(t),w1=d(Rt),v(Xn.$$.fragment,Rt),M1=d(Rt),ba=n(Rt,"P",{});var Af=s(ba);F1=a(Af,"This class overrides "),Ul=n(Af,"A",{href:!0});var hR=s(Ul);$1=a(hR,"TFRobertaForTokenClassification"),hR.forEach(t),R1=a(Af,`. Please check the superclass for the appropriate
documentation alongside usage examples.`),Af.forEach(t),x1=d(Rt),ut=n(Rt,"DIV",{class:!0});var ao=s(ut);v(ka.$$.fragment,ao),E1=d(ao),So=n(ao,"P",{});var vd=s(So);z1=a(vd,"The "),Ql=n(vd,"A",{href:!0});var uR=s(Ql);L1=a(uR,"TFRobertaForTokenClassification"),uR.forEach(t),q1=a(vd," forward method, overrides the "),Yc=n(vd,"CODE",{});var fR=s(Yc);C1=a(fR,"__call__"),fR.forEach(t),P1=a(vd," special method."),vd.forEach(t),j1=d(ao),v(Dn.$$.fragment,ao),A1=d(ao),ep=n(ao,"P",{});var mR=s(ep);O1=a(mR,"Example:"),mR.forEach(t),N1=d(ao),v(Ta.$$.fragment,ao),ao.forEach(t),Rt.forEach(t),hu=d(i),Bo=n(i,"H2",{class:!0});var Of=s(Bo);Sn=n(Of,"A",{id:!0,class:!0,href:!0});var gR=s(Sn);tp=n(gR,"SPAN",{});var _R=s(tp);v(va.$$.fragment,_R),_R.forEach(t),gR.forEach(t),I1=d(Of),op=n(Of,"SPAN",{});var bR=s(op);X1=a(bR,"TFXLMRobertaForQuestionAnswering"),bR.forEach(t),Of.forEach(t),uu=d(i),De=n(i,"DIV",{class:!0});var xt=s(De);v(ya.$$.fragment,xt),D1=d(xt),Wo=n(xt,"P",{});var yd=s(Wo);S1=a(yd,`XLM-RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `),np=n(yd,"CODE",{});var kR=s(np);B1=a(kR,"span start logits"),kR.forEach(t),W1=a(yd," and "),sp=n(yd,"CODE",{});var TR=s(sp);U1=a(TR,"span end logits"),TR.forEach(t),Q1=a(yd,")."),yd.forEach(t),H1=d(xt),wa=n(xt,"P",{});var Nf=s(wa);V1=a(Nf,"This model inherits from "),Hl=n(Nf,"A",{href:!0});var vR=s(Hl);J1=a(vR,"TFPreTrainedModel"),vR.forEach(t),K1=a(Nf,`. 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.)`),Nf.forEach(t),G1=d(xt),Ma=n(xt,"P",{});var If=s(Ma);Z1=a(If,"This model is also a "),Fa=n(If,"A",{href:!0,rel:!0});var yR=s(Fa);Y1=a(yR,"tf.keras.Model"),yR.forEach(t),ey=a(If,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),If.forEach(t),ty=d(xt),v(Bn.$$.fragment,xt),oy=d(xt),$a=n(xt,"P",{});var Xf=s($a);ny=a(Xf,"This class overrides "),rp=n(Xf,"CODE",{});var wR=s(rp);sy=a(wR,"TFRobertaForQuestionAnsweringSimple"),wR.forEach(t),ry=a(Xf,`. Please check the superclass for the appropriate
documentation alongside usage examples.`),Xf.forEach(t),ay=d(xt),ft=n(xt,"DIV",{class:!0});var io=s(ft);v(Ra.$$.fragment,io),iy=d(io),Uo=n(io,"P",{});var wd=s(Uo);ly=a(wd,"The "),Vl=n(wd,"A",{href:!0});var MR=s(Vl);dy=a(MR,"TFRobertaForQuestionAnswering"),MR.forEach(t),cy=a(wd," forward method, overrides the "),ap=n(wd,"CODE",{});var FR=s(ap);py=a(FR,"__call__"),FR.forEach(t),hy=a(wd," special method."),wd.forEach(t),uy=d(io),v(Wn.$$.fragment,io),fy=d(io),ip=n(io,"P",{});var $R=s(ip);my=a($R,"Example:"),$R.forEach(t),gy=d(io),v(xa.$$.fragment,io),io.forEach(t),xt.forEach(t),fu=d(i),Qo=n(i,"H2",{class:!0});var Df=s(Qo);Un=n(Df,"A",{id:!0,class:!0,href:!0});var RR=s(Un);lp=n(RR,"SPAN",{});var xR=s(lp);v(Ea.$$.fragment,xR),xR.forEach(t),RR.forEach(t),_y=d(Df),dp=n(Df,"SPAN",{});var ER=s(dp);by=a(ER,"FlaxXLMRobertaModel"),ER.forEach(t),Df.forEach(t),mu=d(i),Se=n(i,"DIV",{class:!0});var Et=s(Se);v(za.$$.fragment,Et),ky=d(Et),La=n(Et,"P",{});var Sf=s(La);Ty=a(Sf,`The bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from `),Jl=n(Sf,"A",{href:!0});var zR=s(Jl);vy=a(zR,"FlaxPreTrainedModel"),zR.forEach(t),yy=a(Sf,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),Sf.forEach(t),wy=d(Et),qa=n(Et,"P",{});var Bf=s(qa);My=a(Bf,"This model is also a Flax Linen "),Ca=n(Bf,"A",{href:!0,rel:!0});var LR=s(Ca);Fy=a(LR,"flax.linen.Module"),LR.forEach(t),$y=a(Bf,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),Bf.forEach(t),Ry=d(Et),cp=n(Et,"P",{});var qR=s(cp);xy=a(qR,"Finally, this model supports inherent JAX features such as:"),qR.forEach(t),Ey=d(Et),Xt=n(Et,"UL",{});var ss=s(Xt);pp=n(ss,"LI",{});var CR=s(pp);Pa=n(CR,"A",{href:!0,rel:!0});var PR=s(Pa);zy=a(PR,"Just-In-Time (JIT) compilation"),PR.forEach(t),CR.forEach(t),Ly=d(ss),hp=n(ss,"LI",{});var jR=s(hp);ja=n(jR,"A",{href:!0,rel:!0});var AR=s(ja);qy=a(AR,"Automatic Differentiation"),AR.forEach(t),jR.forEach(t),Cy=d(ss),up=n(ss,"LI",{});var OR=s(up);Aa=n(OR,"A",{href:!0,rel:!0});var NR=s(Aa);Py=a(NR,"Vectorization"),NR.forEach(t),OR.forEach(t),jy=d(ss),fp=n(ss,"LI",{});var IR=s(fp);Oa=n(IR,"A",{href:!0,rel:!0});var XR=s(Oa);Ay=a(XR,"Parallelization"),XR.forEach(t),IR.forEach(t),ss.forEach(t),Oy=d(Et),Na=n(Et,"P",{});var Wf=s(Na);Ny=a(Wf,"This class overrides "),Kl=n(Wf,"A",{href:!0});var DR=s(Kl);Iy=a(DR,"FlaxRobertaModel"),DR.forEach(t),Xy=a(Wf,`. Please check the superclass for the appropriate documentation alongside
usage examples.`),Wf.forEach(t),Dy=d(Et),mt=n(Et,"DIV",{class:!0});var lo=s(mt);v(Ia.$$.fragment,lo),Sy=d(lo),Ho=n(lo,"P",{});var Md=s(Ho);By=a(Md,"The "),mp=n(Md,"CODE",{});var SR=s(mp);Wy=a(SR,"FlaxRobertaPreTrainedModel"),SR.forEach(t),Uy=a(Md,"forward method, overrides the "),gp=n(Md,"CODE",{});var BR=s(gp);Qy=a(BR,"__call__"),BR.forEach(t),Hy=a(Md," special method."),Md.forEach(t),Vy=d(lo),v(Qn.$$.fragment,lo),Jy=d(lo),_p=n(lo,"P",{});var WR=s(_p);Ky=a(WR,"Example:"),WR.forEach(t),Gy=d(lo),v(Xa.$$.fragment,lo),lo.forEach(t),Et.forEach(t),gu=d(i),Vo=n(i,"H2",{class:!0});var Uf=s(Vo);Hn=n(Uf,"A",{id:!0,class:!0,href:!0});var UR=s(Hn);bp=n(UR,"SPAN",{});var QR=s(bp);v(Da.$$.fragment,QR),QR.forEach(t),UR.forEach(t),Zy=d(Uf),kp=n(Uf,"SPAN",{});var HR=s(kp);Yy=a(HR,"FlaxXLMRobertaForMaskedLM"),HR.forEach(t),Uf.forEach(t),_u=d(i),Be=n(i,"DIV",{class:!0});var zt=s(Be);v(Sa.$$.fragment,zt),ew=d(zt),Jo=n(zt,"P",{});var Fd=s(Jo);tw=a(Fd,"XLM-RoBERTa Model with a "),Tp=n(Fd,"CODE",{});var VR=s(Tp);ow=a(VR,"language modeling"),VR.forEach(t),nw=a(Fd,` head on top.
This model inherits from `),Gl=n(Fd,"A",{href:!0});var JR=s(Gl);sw=a(JR,"FlaxPreTrainedModel"),JR.forEach(t),rw=a(Fd,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),Fd.forEach(t),aw=d(zt),Ba=n(zt,"P",{});var Qf=s(Ba);iw=a(Qf,"This model is also a Flax Linen "),Wa=n(Qf,"A",{href:!0,rel:!0});var KR=s(Wa);lw=a(KR,"flax.linen.Module"),KR.forEach(t),dw=a(Qf,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),Qf.forEach(t),cw=d(zt),vp=n(zt,"P",{});var GR=s(vp);pw=a(GR,"Finally, this model supports inherent JAX features such as:"),GR.forEach(t),hw=d(zt),Dt=n(zt,"UL",{});var rs=s(Dt);yp=n(rs,"LI",{});var ZR=s(yp);Ua=n(ZR,"A",{href:!0,rel:!0});var YR=s(Ua);uw=a(YR,"Just-In-Time (JIT) compilation"),YR.forEach(t),ZR.forEach(t),fw=d(rs),wp=n(rs,"LI",{});var ex=s(wp);Qa=n(ex,"A",{href:!0,rel:!0});var tx=s(Qa);mw=a(tx,"Automatic Differentiation"),tx.forEach(t),ex.forEach(t),gw=d(rs),Mp=n(rs,"LI",{});var ox=s(Mp);Ha=n(ox,"A",{href:!0,rel:!0});var nx=s(Ha);_w=a(nx,"Vectorization"),nx.forEach(t),ox.forEach(t),bw=d(rs),Fp=n(rs,"LI",{});var sx=s(Fp);Va=n(sx,"A",{href:!0,rel:!0});var rx=s(Va);kw=a(rx,"Parallelization"),rx.forEach(t),sx.forEach(t),rs.forEach(t),Tw=d(zt),Ja=n(zt,"P",{});var Hf=s(Ja);vw=a(Hf,"This class overrides "),Zl=n(Hf,"A",{href:!0});var ax=s(Zl);yw=a(ax,"FlaxRobertaForMaskedLM"),ax.forEach(t),ww=a(Hf,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),Hf.forEach(t),Mw=d(zt),gt=n(zt,"DIV",{class:!0});var co=s(gt);v(Ka.$$.fragment,co),Fw=d(co),Ko=n(co,"P",{});var $d=s(Ko);$w=a($d,"The "),$p=n($d,"CODE",{});var ix=s($p);Rw=a(ix,"FlaxRobertaPreTrainedModel"),ix.forEach(t),xw=a($d,"forward method, overrides the "),Rp=n($d,"CODE",{});var lx=s(Rp);Ew=a(lx,"__call__"),lx.forEach(t),zw=a($d," special method."),$d.forEach(t),Lw=d(co),v(Vn.$$.fragment,co),qw=d(co),xp=n(co,"P",{});var dx=s(xp);Cw=a(dx,"Example:"),dx.forEach(t),Pw=d(co),v(Ga.$$.fragment,co),co.forEach(t),zt.forEach(t),bu=d(i),Go=n(i,"H2",{class:!0});var Vf=s(Go);Jn=n(Vf,"A",{id:!0,class:!0,href:!0});var cx=s(Jn);Ep=n(cx,"SPAN",{});var px=s(Ep);v(Za.$$.fragment,px),px.forEach(t),cx.forEach(t),jw=d(Vf),zp=n(Vf,"SPAN",{});var hx=s(zp);Aw=a(hx,"FlaxXLMRobertaForSequenceClassification"),hx.forEach(t),Vf.forEach(t),ku=d(i),ze=n(i,"DIV",{class:!0});var Ze=s(ze);v(Ya.$$.fragment,Ze),Ow=d(Ze),Lp=n(Ze,"P",{});var ux=s(Lp);Nw=a(ux,`XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),ux.forEach(t),Iw=d(Ze),ei=n(Ze,"P",{});var Jf=s(ei);Xw=a(Jf,"This model inherits from "),Yl=n(Jf,"A",{href:!0});var fx=s(Yl);Dw=a(fx,"FlaxPreTrainedModel"),fx.forEach(t),Sw=a(Jf,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`),Jf.forEach(t),Bw=d(Ze),ti=n(Ze,"P",{});var Kf=s(ti);Ww=a(Kf,"This model is also a Flax Linen "),oi=n(Kf,"A",{href:!0,rel:!0});var mx=s(oi);Uw=a(mx,"flax.linen.Module"),mx.forEach(t),Qw=a(Kf,`
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`),Kf.forEach(t),Hw=d(Ze),qp=n(Ze,"P",{});var gx=s(qp);Vw=a(gx,"Finally, this model supports inherent JAX features such as:"),gx.forEach(t),Jw=d(Ze),St=n(Ze,"UL",{});var as=s(St);Cp=n(as,"LI",{});var _x=s(Cp);ni=n(_x,"A",{href:!0,rel:!0});var bx=s(ni);Kw=a(bx,"Just-In-Time (JIT) compilation"),bx.forEach(t),_x.forEach(t),Gw=d(as),Pp=n(as,"LI",{});var kx=s(Pp);si=n(kx,"A",{href:!0,rel:!0});var Tx=s(si);Zw=a(Tx,"Automatic Differentiation"),Tx.forEach(t),kx.forEach(t),Yw=d(as),jp=n(as,"LI",{});var vx=s(jp);ri=n(vx,"A",{href:!0,rel:!0});var yx=s(ri);eM=a(yx,"Vectorization"),yx.forEach(t),vx.forEach(t),tM=d(as),Ap=n(as,"LI",{});var wx=s(Ap);ai=n(wx,"A",{href:!0,rel:!0});var Mx=s(ai);oM=a(Mx,"Parallelization"),Mx.forEach(t),wx.forEach(t),as.forEach(t),nM=d(Ze),ii=n(Ze,"P",{});var Gf=s(ii);sM=a(Gf,"This class overrides "),ed=n(Gf,"A",{href:!0});var Fx=s(ed);rM=a(Fx,"FlaxRobertaForSequenceClassification"),Fx.forEach(t),aM=a(Gf,`. Please check the superclass for the appropriate
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