import{S as Si,i as Fi,s as Ai,e as r,k as l,w as h,t as a,M as Oi,c as s,d as o,m as d,a as n,x as g,h as i,b as f,G as t,g as m,y as _,q as v,o as b,B as y,v as Ii,L as Ni}from"../../chunks/vendor-hf-doc-builder.js";import{T as rr}from"../../chunks/Tip-hf-doc-builder.js";import{D as k}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Hi}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Jt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Mi}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Vi(S){let p,x,u,$,q,w,D,O,P,Q,F,C,B,A,z,G,L;return{c(){p=r("p"),x=a("One of "),u=r("code"),$=a("start_states"),q=a(" or "),w=r("code"),D=a("start_positions"),O=a(" should be not "),P=r("code"),Q=a("None"),F=a(". If both are set, "),C=r("code"),B=a("start_positions"),A=a(` overrides
`),z=r("code"),G=a("start_states"),L=a(".")},l(E){p=s(E,"P",{});var T=n(p);x=i(T,"One of "),u=s(T,"CODE",{});var I=n(u);$=i(I,"start_states"),I.forEach(o),q=i(T," or "),w=s(T,"CODE",{});var ce=n(w);D=i(ce,"start_positions"),ce.forEach(o),O=i(T," should be not "),P=s(T,"CODE",{});var U=n(P);Q=i(U,"None"),U.forEach(o),F=i(T,". If both are set, "),C=s(T,"CODE",{});var pe=n(C);B=i(pe,"start_positions"),pe.forEach(o),A=i(T,` overrides
`),z=s(T,"CODE",{});var ae=n(z);G=i(ae,"start_states"),ae.forEach(o),L=i(T,"."),T.forEach(o)},m(E,T){m(E,p,T),t(p,x),t(p,u),t(u,$),t(p,q),t(p,w),t(w,D),t(p,O),t(p,P),t(P,Q),t(p,F),t(p,C),t(C,B),t(p,A),t(p,z),t(z,G),t(p,L)},d(E){E&&o(p)}}}function ji(S){let p,x,u,$,q,w,D,O,P,Q,F,C,B,A,z,G,L;return{c(){p=r("p"),x=a("One of "),u=r("code"),$=a("start_states"),q=a(" or "),w=r("code"),D=a("start_positions"),O=a(" should be not "),P=r("code"),Q=a("None"),F=a(". If both are set, "),C=r("code"),B=a("start_positions"),A=a(` overrides
`),z=r("code"),G=a("start_states"),L=a(".")},l(E){p=s(E,"P",{});var T=n(p);x=i(T,"One of "),u=s(T,"CODE",{});var I=n(u);$=i(I,"start_states"),I.forEach(o),q=i(T," or "),w=s(T,"CODE",{});var ce=n(w);D=i(ce,"start_positions"),ce.forEach(o),O=i(T," should be not "),P=s(T,"CODE",{});var U=n(P);Q=i(U,"None"),U.forEach(o),F=i(T,". If both are set, "),C=s(T,"CODE",{});var pe=n(C);B=i(pe,"start_positions"),pe.forEach(o),A=i(T,` overrides
`),z=s(T,"CODE",{});var ae=n(z);G=i(ae,"start_states"),ae.forEach(o),L=i(T,"."),T.forEach(o)},m(E,T){m(E,p,T),t(p,x),t(p,u),t(u,$),t(p,q),t(p,w),t(w,D),t(p,O),t(p,P),t(P,Q),t(p,F),t(p,C),t(C,B),t(p,A),t(p,z),t(z,G),t(p,L)},d(E){E&&o(p)}}}function Qi(S){let p,x,u,$,q;return $=new Hi({props:{code:`# rename the usual forward() fn to forward_chunk()
def forward_chunk(self, hidden_states):
    hidden_states = self.decoder(hidden_states)
    return hidden_states


# implement a chunked forward function
def forward(self, hidden_states):
    return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)`,highlighted:`<span class="hljs-comment"># rename the usual forward() fn to forward_chunk()</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward_chunk</span>(<span class="hljs-params">self, hidden_states</span>):
    hidden_states = self.decoder(hidden_states)
    <span class="hljs-keyword">return</span> hidden_states


<span class="hljs-comment"># implement a chunked forward function</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, hidden_states</span>):
    <span class="hljs-keyword">return</span> apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)`}}),{c(){p=r("p"),x=a("Examples:"),u=l(),h($.$$.fragment)},l(w){p=s(w,"P",{});var D=n(p);x=i(D,"Examples:"),D.forEach(o),u=d(w),g($.$$.fragment,w)},m(w,D){m(w,p,D),t(p,x),m(w,u,D),_($,w,D),q=!0},p:Ni,i(w){q||(v($.$$.fragment,w),q=!0)},o(w){b($.$$.fragment,w),q=!1},d(w){w&&o(p),w&&o(u),y($,w)}}}function Bi(S){let p,x;return{c(){p=r("p"),x=a("Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.")},l(u){p=s(u,"P",{});var $=n(p);x=i($,"Any label of -100 will be ignored (along with the corresponding logits) in the loss computation."),$.forEach(o)},m(u,$){m(u,p,$),t(p,x)},d(u){u&&o(p)}}}function Gi(S){let p,x;return{c(){p=r("p"),x=a("Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.")},l(u){p=s(u,"P",{});var $=n(p);x=i($,"Any label of -100 will be ignored (along with the corresponding logits) in the loss computation."),$.forEach(o)},m(u,$){m(u,p,$),t(p,x)},d(u){u&&o(p)}}}function Ui(S){let p,x;return{c(){p=r("p"),x=a("Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.")},l(u){p=s(u,"P",{});var $=n(p);x=i($,"Any label of -100 will be ignored (along with the corresponding logits) in the loss computation."),$.forEach(o)},m(u,$){m(u,p,$),t(p,x)},d(u){u&&o(p)}}}function Ki(S){let p,x,u,$,q,w,D,O,P,Q,F,C,B,A,z,G,L,E,T,I,ce,U,pe,ae,K,Be,Yr,Yt,Zr,es,Zt,ts,sr,R,Ge,os,eo,rs,ss,Pt,Ue,nr,W,Ke,ns,to,as,is,De,Re,ls,Le,ar,X,We,ds,oo,cs,ps,Pe,Xe,ms,Ce,ir,me,Je,fs,Ye,us,Ct,hs,gs,lr,J,Ze,_s,ro,vs,bs,zt,et,dr,Y,tt,ys,so,$s,ws,ze,ot,Ts,no,ks,cr,fe,Se,ao,rt,xs,io,Es,pr,N,st,qs,H,Ds,lo,Ls,Ps,co,Cs,zs,po,Ss,Fs,mo,As,Os,Is,M,Ns,fo,Hs,Ms,uo,Vs,js,ho,Qs,Bs,go,Gs,Us,Ks,Fe,mr,ue,nt,Rs,at,Ws,_o,Xs,Js,fr,Z,it,Ys,vo,Zs,en,bo,tn,ur,ee,lt,on,yo,rn,sn,$o,nn,hr,te,dt,an,wo,ln,dn,To,cn,gr,he,Ae,ko,ct,pn,xo,mn,_r,oe,pt,fn,Eo,un,hn,qo,gn,vr,V,mt,_n,Do,vn,bn,Lo,yn,$n,ie,ft,wn,Po,Tn,kn,ut,xn,ht,En,qn,br,ge,gt,Dn,Co,Ln,yr,_e,Oe,zo,_t,Pn,So,Cn,$r,re,vt,zn,Fo,Sn,Fn,Ie,wr,se,bt,An,Ao,On,In,Ne,Tr,ve,yt,Nn,Oo,Hn,kr,be,$t,Mn,Io,Vn,xr,ye,wt,jn,No,Qn,Er,ne,Tt,Bn,Ho,Gn,Un,He,qr,$e,Me,Mo,kt,Kn,Vo,Rn,Dr,we,xt,Wn,Et,Xn,jo,Jn,Yn,Lr,j,qt,Zn,Qo,ea,ta,Bo,oa,ra,Te,ke,sa,Go,na,aa,Uo,ia,la,da,xe,ca,Ko,pa,ma,Ro,fa,ua,ha,Ee,ga,Wo,_a,va,Xo,ba,ya,Pr,qe,Dt,$a,Jo,wa,Cr;return w=new Jt({}),I=new Jt({}),Be=new k({props:{name:"class transformers.Conv1D",anchor:"transformers.Conv1D",parameters:[{name:"nf",val:""},{name:"nx",val:""}],parametersDescription:[{anchor:"transformers.Conv1D.nf",description:"<strong>nf</strong> (<code>int</code>) &#x2014; The number of output features.",name:"nf"},{anchor:"transformers.Conv1D.nx",description:"<strong>nx</strong> (<code>int</code>) &#x2014; The number of input features.",name:"nx"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/pytorch_utils.py#L91"}}),Ge=new k({props:{name:"class transformers.modeling_utils.PoolerStartLogits",anchor:"transformers.modeling_utils.PoolerStartLogits",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.modeling_utils.PoolerStartLogits.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The config used by the model, will be used to grab the <code>hidden_size</code> of the model.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L2818"}}),Ue=new k({props:{name:"forward",anchor:"transformers.modeling_utils.PoolerStartLogits.forward",parameters:[{name:"hidden_states",val:": FloatTensor"},{name:"p_mask",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"transformers.modeling_utils.PoolerStartLogits.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) &#x2014;
The final hidden states of the model.`,name:"hidden_states"},{anchor:"transformers.modeling_utils.PoolerStartLogits.forward.p_mask",description:`<strong>p_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len)</code>, <em>optional</em>) &#x2014;
Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
should be masked.`,name:"p_mask"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L2831",returnDescription:`
<p>The start logits for SQuAD.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),Ke=new k({props:{name:"class transformers.modeling_utils.PoolerEndLogits",anchor:"transformers.modeling_utils.PoolerEndLogits",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.modeling_utils.PoolerEndLogits.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The config used by the model, will be used to grab the <code>hidden_size</code> of the model and the <code>layer_norm_eps</code>
to use.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L2856"}}),Re=new k({props:{name:"forward",anchor:"transformers.modeling_utils.PoolerEndLogits.forward",parameters:[{name:"hidden_states",val:": FloatTensor"},{name:"start_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"p_mask",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"transformers.modeling_utils.PoolerEndLogits.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) &#x2014;
The final hidden states of the model.`,name:"hidden_states"},{anchor:"transformers.modeling_utils.PoolerEndLogits.forward.start_states",description:`<strong>start_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>, <em>optional</em>) &#x2014;
The hidden states of the first tokens for the labeled span.`,name:"start_states"},{anchor:"transformers.modeling_utils.PoolerEndLogits.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
The position of the first token for the labeled span.`,name:"start_positions"},{anchor:"transformers.modeling_utils.PoolerEndLogits.forward.p_mask",description:`<strong>p_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len)</code>, <em>optional</em>) &#x2014;
Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
should be masked.`,name:"p_mask"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L2873",returnDescription:`
<p>The end logits for SQuAD.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),Le=new rr({props:{$$slots:{default:[Vi]},$$scope:{ctx:S}}}),We=new k({props:{name:"class transformers.modeling_utils.PoolerAnswerClass",anchor:"transformers.modeling_utils.PoolerAnswerClass",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.modeling_utils.PoolerAnswerClass.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The config used by the model, will be used to grab the <code>hidden_size</code> of the model.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L2925"}}),Xe=new k({props:{name:"forward",anchor:"transformers.modeling_utils.PoolerAnswerClass.forward",parameters:[{name:"hidden_states",val:": FloatTensor"},{name:"start_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"cls_index",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.modeling_utils.PoolerAnswerClass.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) &#x2014;
The final hidden states of the model.`,name:"hidden_states"},{anchor:"transformers.modeling_utils.PoolerAnswerClass.forward.start_states",description:`<strong>start_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>, <em>optional</em>) &#x2014;
The hidden states of the first tokens for the labeled span.`,name:"start_states"},{anchor:"transformers.modeling_utils.PoolerAnswerClass.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
The position of the first token for the labeled span.`,name:"start_positions"},{anchor:"transformers.modeling_utils.PoolerAnswerClass.forward.cls_index",description:`<strong>cls_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Position of the CLS token for each sentence in the batch. If <code>None</code>, takes the last token.`,name:"cls_index"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L2940",returnDescription:`
<p>The SQuAD 2.0 answer class.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),Ce=new rr({props:{$$slots:{default:[ji]},$$scope:{ctx:S}}}),Je=new k({props:{name:"class transformers.modeling_utils.SquadHeadOutput",anchor:"transformers.modeling_utils.SquadHeadOutput",parameters:[{name:"loss",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_top_log_probs",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_top_index",val:": typing.Optional[torch.LongTensor] = None"},{name:"end_top_log_probs",val:": typing.Optional[torch.FloatTensor] = None"},{name:"end_top_index",val:": typing.Optional[torch.LongTensor] = None"},{name:"cls_logits",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"transformers.modeling_utils.SquadHeadOutput.loss",description:`<strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned if both <code>start_positions</code> and <code>end_positions</code> are provided) &#x2014;
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.`,name:"loss"},{anchor:"transformers.modeling_utils.SquadHeadOutput.start_top_log_probs",description:`<strong>start_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) &#x2014;
Log probabilities for the top config.start_n_top start token possibilities (beam-search).`,name:"start_top_log_probs"},{anchor:"transformers.modeling_utils.SquadHeadOutput.start_top_index",description:`<strong>start_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) &#x2014;
Indices for the top config.start_n_top start token possibilities (beam-search).`,name:"start_top_index"},{anchor:"transformers.modeling_utils.SquadHeadOutput.end_top_log_probs",description:`<strong>end_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) &#x2014;
Log probabilities for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities
(beam-search).`,name:"end_top_log_probs"},{anchor:"transformers.modeling_utils.SquadHeadOutput.end_top_index",description:`<strong>end_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) &#x2014;
Indices for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities (beam-search).`,name:"end_top_index"},{anchor:"transformers.modeling_utils.SquadHeadOutput.cls_logits",description:`<strong>cls_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) &#x2014;
Log probabilities for the <code>is_impossible</code> label of the answers.`,name:"cls_logits"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L2991"}}),Ze=new k({props:{name:"class transformers.modeling_utils.SQuADHead",anchor:"transformers.modeling_utils.SQuADHead",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.modeling_utils.SQuADHead.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The config used by the model, will be used to grab the <code>hidden_size</code> of the model and the <code>layer_norm_eps</code>
to use.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L3021"}}),et=new k({props:{name:"forward",anchor:"transformers.modeling_utils.SQuADHead.forward",parameters:[{name:"hidden_states",val:": FloatTensor"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"end_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"cls_index",val:": typing.Optional[torch.LongTensor] = None"},{name:"is_impossible",val:": typing.Optional[torch.LongTensor] = None"},{name:"p_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"return_dict",val:": bool = False"}],parametersDescription:[{anchor:"transformers.modeling_utils.SQuADHead.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) &#x2014;
Final hidden states of the model on the sequence tokens.`,name:"hidden_states"},{anchor:"transformers.modeling_utils.SQuADHead.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Positions of the first token for the labeled span.`,name:"start_positions"},{anchor:"transformers.modeling_utils.SQuADHead.forward.end_positions",description:`<strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Positions of the last token for the labeled span.`,name:"end_positions"},{anchor:"transformers.modeling_utils.SQuADHead.forward.cls_index",description:`<strong>cls_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Position of the CLS token for each sentence in the batch. If <code>None</code>, takes the last token.`,name:"cls_index"},{anchor:"transformers.modeling_utils.SQuADHead.forward.is_impossible",description:`<strong>is_impossible</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Whether the question has a possible answer in the paragraph or not.`,name:"is_impossible"},{anchor:"transformers.modeling_utils.SQuADHead.forward.p_mask",description:`<strong>p_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len)</code>, <em>optional</em>) &#x2014;
Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
should be masked.`,name:"p_mask"},{anchor:"transformers.modeling_utils.SQuADHead.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.25.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L3040",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.25.1/en/internal/modeling_utils#transformers.modeling_utils.SquadHeadOutput"
>transformers.modeling_utils.SquadHeadOutput</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 (<code>&lt;class 'transformers.configuration_utils.PretrainedConfig'&gt;</code>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned if both <code>start_positions</code> and <code>end_positions</code> are provided) \u2014 Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.</li>
<li><strong>start_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Log probabilities for the top config.start_n_top start token possibilities (beam-search).</li>
<li><strong>start_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Indices for the top config.start_n_top start token possibilities (beam-search).</li>
<li><strong>end_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Log probabilities for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities
(beam-search).</li>
<li><strong>end_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Indices for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities (beam-search).</li>
<li><strong>cls_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Log probabilities for the <code>is_impossible</code> label of the answers.</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.25.1/en/internal/modeling_utils#transformers.modeling_utils.SquadHeadOutput"
>transformers.modeling_utils.SquadHeadOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),tt=new k({props:{name:"class transformers.modeling_utils.SequenceSummary",anchor:"transformers.modeling_utils.SequenceSummary",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.modeling_utils.SequenceSummary.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for the default values it uses):</p>
<ul>
<li>
<p><strong>summary_type</strong> (<code>str</code>) &#x2014; The method to use to make this summary. Accepted values are:</p>
<ul>
<li><code>&quot;last&quot;</code> &#x2014; Take the last token hidden state (like XLNet)</li>
<li><code>&quot;first&quot;</code> &#x2014; Take the first token hidden state (like Bert)</li>
<li><code>&quot;mean&quot;</code> &#x2014; Take the mean of all tokens hidden states</li>
<li><code>&quot;cls_index&quot;</code> &#x2014; Supply a Tensor of classification token position (GPT/GPT-2)</li>
<li><code>&quot;attn&quot;</code> &#x2014; Not implemented now, use multi-head attention</li>
</ul>
</li>
<li>
<p><strong>summary_use_proj</strong> (<code>bool</code>) &#x2014; Add a projection after the vector extraction.</p>
</li>
<li>
<p><strong>summary_proj_to_labels</strong> (<code>bool</code>) &#x2014; If <code>True</code>, the projection outputs to <code>config.num_labels</code> classes
(otherwise to <code>config.hidden_size</code>).</p>
</li>
<li>
<p><strong>summary_activation</strong> (<code>Optional[str]</code>) &#x2014; Set to <code>&quot;tanh&quot;</code> to add a tanh activation to the output,
another string or <code>None</code> will add no activation.</p>
</li>
<li>
<p><strong>summary_first_dropout</strong> (<code>float</code>) &#x2014; Optional dropout probability before the projection and activation.</p>
</li>
<li>
<p><strong>summary_last_dropout</strong> (<code>float</code>)&#x2014; Optional dropout probability after the projection and activation.</p>
</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L3138"}}),ot=new k({props:{name:"forward",anchor:"transformers.modeling_utils.SequenceSummary.forward",parameters:[{name:"hidden_states",val:": FloatTensor"},{name:"cls_index",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.modeling_utils.SequenceSummary.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>[batch_size, seq_len, hidden_size]</code>) &#x2014;
The hidden states of the last layer.`,name:"hidden_states"},{anchor:"transformers.modeling_utils.SequenceSummary.forward.cls_index",description:`<strong>cls_index</strong> (<code>torch.LongTensor</code> of shape <code>[batch_size]</code> or <code>[batch_size, ...]</code> where &#x2026; are optional leading dimensions of <code>hidden_states</code>, <em>optional</em>) &#x2014;
Used if <code>summary_type == &quot;cls_index&quot;</code> and takes the last token of the sequence as classification token.`,name:"cls_index"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_utils.py#L3193",returnDescription:`
<p>The summary of the sequence hidden states.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),rt=new Jt({}),st=new k({props:{name:"transformers.apply_chunking_to_forward",anchor:"transformers.apply_chunking_to_forward",parameters:[{name:"forward_fn",val:": typing.Callable[..., torch.Tensor]"},{name:"chunk_size",val:": int"},{name:"chunk_dim",val:": int"},{name:"*input_tensors",val:""}],parametersDescription:[{anchor:"transformers.apply_chunking_to_forward.forward_fn",description:`<strong>forward_fn</strong> (<code>Callable[..., torch.Tensor]</code>) &#x2014;
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<p>A tensor with the same shape as the <code>forward_fn</code> would have given if applied\`.</p>
`,returnType:`
<p><code>torch.Tensor</code></p>
`}}),Fe=new Mi({props:{anchor:"transformers.apply_chunking_to_forward.example",$$slots:{default:[Qi]},$$scope:{ctx:S}}}),nt=new k({props:{name:"transformers.pytorch_utils.find_pruneable_heads_and_indices",anchor:"transformers.pytorch_utils.find_pruneable_heads_and_indices",parameters:[{name:"heads",val:": typing.List[int]"},{name:"n_heads",val:": int"},{name:"head_size",val:": int"},{name:"already_pruned_heads",val:": typing.Set[int]"}],parametersDescription:[{anchor:"transformers.pytorch_utils.find_pruneable_heads_and_indices.heads",description:"<strong>heads</strong> (<code>List[int]</code>) &#x2014; List of the indices of heads to prune.",name:"heads"},{anchor:"transformers.pytorch_utils.find_pruneable_heads_and_indices.n_heads",description:"<strong>n_heads</strong> (<code>int</code>) &#x2014; The number of heads in the model.",name:"n_heads"},{anchor:"transformers.pytorch_utils.find_pruneable_heads_and_indices.head_size",description:"<strong>head_size</strong> (<code>int</code>) &#x2014; The size of each head.",name:"head_size"},{anchor:"transformers.pytorch_utils.find_pruneable_heads_and_indices.already_pruned_heads",description:"<strong>already_pruned_heads</strong> (<code>Set[int]</code>) &#x2014; A set of already pruned heads.",name:"already_pruned_heads"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/pytorch_utils.py#L249",returnDescription:`
<p>A tuple with the remaining heads and their corresponding indices.</p>
`,returnType:`
<p><code>Tuple[Set[int], torch.LongTensor]</code></p>
`}}),it=new k({props:{name:"transformers.prune_layer",anchor:"transformers.prune_layer",parameters:[{name:"layer",val:": typing.Union[torch.nn.modules.linear.Linear, transformers.pytorch_utils.Conv1D]"},{name:"index",val:": LongTensor"},{name:"dim",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"transformers.prune_layer.layer",description:"<strong>layer</strong> (<code>Union[torch.nn.Linear, Conv1D]</code>) &#x2014; The layer to prune.",name:"layer"},{anchor:"transformers.prune_layer.index",description:"<strong>index</strong> (<code>torch.LongTensor</code>) &#x2014; The indices to keep in the layer.",name:"index"},{anchor:"transformers.prune_layer.dim",description:"<strong>dim</strong> (<code>int</code>, <em>optional</em>) &#x2014; The dimension on which to keep the indices.",name:"dim"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/pytorch_utils.py#L150",returnDescription:`
<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
`,returnType:`
<p><code>torch.nn.Linear</code> or <a
  href="/docs/transformers/v4.25.1/en/internal/modeling_utils#transformers.Conv1D"
>Conv1D</a></p>
`}}),lt=new k({props:{name:"transformers.pytorch_utils.prune_conv1d_layer",anchor:"transformers.pytorch_utils.prune_conv1d_layer",parameters:[{name:"layer",val:": Conv1D"},{name:"index",val:": LongTensor"},{name:"dim",val:": int = 1"}],parametersDescription:[{anchor:"transformers.pytorch_utils.prune_conv1d_layer.layer",description:'<strong>layer</strong> (<a href="/docs/transformers/v4.25.1/en/internal/modeling_utils#transformers.Conv1D">Conv1D</a>) &#x2014; The layer to prune.',name:"layer"},{anchor:"transformers.pytorch_utils.prune_conv1d_layer.index",description:"<strong>index</strong> (<code>torch.LongTensor</code>) &#x2014; The indices to keep in the layer.",name:"index"},{anchor:"transformers.pytorch_utils.prune_conv1d_layer.dim",description:"<strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014; The dimension on which to keep the indices.",name:"dim"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/pytorch_utils.py#L117",returnDescription:`
<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
`,returnType:`
<p><a
  href="/docs/transformers/v4.25.1/en/internal/modeling_utils#transformers.Conv1D"
>Conv1D</a></p>
`}}),dt=new k({props:{name:"transformers.pytorch_utils.prune_linear_layer",anchor:"transformers.pytorch_utils.prune_linear_layer",parameters:[{name:"layer",val:": Linear"},{name:"index",val:": LongTensor"},{name:"dim",val:": int = 0"}],parametersDescription:[{anchor:"transformers.pytorch_utils.prune_linear_layer.layer",description:"<strong>layer</strong> (<code>torch.nn.Linear</code>) &#x2014; The layer to prune.",name:"layer"},{anchor:"transformers.pytorch_utils.prune_linear_layer.index",description:"<strong>index</strong> (<code>torch.LongTensor</code>) &#x2014; The indices to keep in the layer.",name:"index"},{anchor:"transformers.pytorch_utils.prune_linear_layer.dim",description:"<strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014; The dimension on which to keep the indices.",name:"dim"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/pytorch_utils.py#L57",returnDescription:`
<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
`,returnType:`
<p><code>torch.nn.Linear</code></p>
`}}),ct=new Jt({}),pt=new k({props:{name:"class transformers.modeling_tf_utils.TFConv1D",anchor:"transformers.modeling_tf_utils.TFConv1D",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.modeling_tf_utils.TFConv1D.nf",description:`<strong>nf</strong> (<code>int</code>) &#x2014;
The number of output features.`,name:"nf"},{anchor:"transformers.modeling_tf_utils.TFConv1D.nx",description:`<strong>nx</strong> (<code>int</code>) &#x2014;
The number of input features.`,name:"nx"},{anchor:"transformers.modeling_tf_utils.TFConv1D.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation to use to initialize the weights.
kwargs &#x2014;
Additional keyword arguments passed along to the <code>__init__</code> of <code>tf.keras.layers.Layer</code>.`,name:"initializer_range"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L2950"}}),mt=new k({props:{name:"class transformers.TFSharedEmbeddings",anchor:"transformers.TFSharedEmbeddings",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFSharedEmbeddings.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>) &#x2014;
The size of the vocabulary, e.g., the number of unique tokens.`,name:"vocab_size"},{anchor:"transformers.TFSharedEmbeddings.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>) &#x2014;
The size of the embedding vectors.`,name:"hidden_size"},{anchor:"transformers.TFSharedEmbeddings.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The standard deviation to use when initializing the weights. If no value is provided, it will default to
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Additional keyword arguments passed along to the <code>__init__</code> of <code>tf.keras.layers.Layer</code>.`,name:"initializer_range"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L2990"}}),ft=new k({props:{name:"call",anchor:"transformers.TFSharedEmbeddings.call",parameters:[{name:"inputs",val:": Tensor"},{name:"mode",val:": str = 'embedding'"}],parametersDescription:[{anchor:"transformers.TFSharedEmbeddings.call.inputs",description:`<strong>inputs</strong> (<code>tf.Tensor</code>) &#x2014;
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<p>In embedding mode, the output is a float32 embedding tensor, with shape <code>[batch_size, length, embedding_size]</code>.</p>
<p>In linear mode, the output is a float32 with shape <code>[batch_size, length, vocab_size]</code>.</p>
`,returnType:`
<p><code>tf.Tensor</code></p>
`,raiseDescription:`
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<li><code>ValueError</code> \u2014 if <code>mode</code> is not valid.</li>
</ul>
`,raiseType:`
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`}}),gt=new k({props:{name:"class transformers.TFSequenceSummary",anchor:"transformers.TFSequenceSummary",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFSequenceSummary.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.25.1/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for the default values it uses):</p>
<ul>
<li>
<p><strong>summary_type</strong> (<code>str</code>) &#x2014; The method to use to make this summary. Accepted values are:</p>
<ul>
<li><code>&quot;last&quot;</code> &#x2014; Take the last token hidden state (like XLNet)</li>
<li><code>&quot;first&quot;</code> &#x2014; Take the first token hidden state (like Bert)</li>
<li><code>&quot;mean&quot;</code> &#x2014; Take the mean of all tokens hidden states</li>
<li><code>&quot;cls_index&quot;</code> &#x2014; Supply a Tensor of classification token position (GPT/GPT-2)</li>
<li><code>&quot;attn&quot;</code> &#x2014; Not implemented now, use multi-head attention</li>
</ul>
</li>
<li>
<p><strong>summary_use_proj</strong> (<code>bool</code>) &#x2014; Add a projection after the vector extraction.</p>
</li>
<li>
<p><strong>summary_proj_to_labels</strong> (<code>bool</code>) &#x2014; If <code>True</code>, the projection outputs to <code>config.num_labels</code> classes
(otherwise to <code>config.hidden_size</code>).</p>
</li>
<li>
<p><strong>summary_activation</strong> (<code>Optional[str]</code>) &#x2014; Set to <code>&quot;tanh&quot;</code> to add a tanh activation to the output,
another string or <code>None</code> will add no activation.</p>
</li>
<li>
<p><strong>summary_first_dropout</strong> (<code>float</code>) &#x2014; Optional dropout probability before the projection and activation.</p>
</li>
<li>
<p><strong>summary_last_dropout</strong> (<code>float</code>)&#x2014; Optional dropout probability after the projection and activation.</p>
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Additional keyword arguments passed along to the <code>__init__</code> of <code>tf.keras.layers.Layer</code>.`,name:"initializer_range"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L3089"}}),_t=new Jt({}),vt=new k({props:{name:"class transformers.modeling_tf_utils.TFCausalLanguageModelingLoss",anchor:"transformers.modeling_tf_utils.TFCausalLanguageModelingLoss",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L199"}}),Ie=new rr({props:{$$slots:{default:[Bi]},$$scope:{ctx:S}}}),bt=new k({props:{name:"class transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss",anchor:"transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L318"}}),Ne=new rr({props:{$$slots:{default:[Gi]},$$scope:{ctx:S}}}),yt=new k({props:{name:"class transformers.modeling_tf_utils.TFMultipleChoiceLoss",anchor:"transformers.modeling_tf_utils.TFMultipleChoiceLoss",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L308"}}),$t=new k({props:{name:"class transformers.modeling_tf_utils.TFQuestionAnsweringLoss",anchor:"transformers.modeling_tf_utils.TFQuestionAnsweringLoss",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L230"}}),wt=new k({props:{name:"class transformers.modeling_tf_utils.TFSequenceClassificationLoss",anchor:"transformers.modeling_tf_utils.TFSequenceClassificationLoss",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L289"}}),Tt=new k({props:{name:"class transformers.modeling_tf_utils.TFTokenClassificationLoss",anchor:"transformers.modeling_tf_utils.TFTokenClassificationLoss",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L245"}}),He=new rr({props:{$$slots:{default:[Ui]},$$scope:{ctx:S}}}),kt=new Jt({}),xt=new k({props:{name:"transformers.modeling_tf_utils.get_initializer",anchor:"transformers.modeling_tf_utils.get_initializer",parameters:[{name:"initializer_range",val:": float = 0.02"}],parametersDescription:[{anchor:"transformers.modeling_tf_utils.get_initializer.initializer_range",description:"<strong>initializer_range</strong> (<em>float</em>, defaults to 0.02) &#x2014; Standard deviation of the initializer range.",name:"initializer_range"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/modeling_tf_utils.py#L3205",returnDescription:`
<p>The truncated normal initializer.</p>
`,returnType:`
<p><code>tf.initializers.TruncatedNormal</code></p>
`}}),qt=new k({props:{name:"transformers.modeling_tf_utils.keras_serializable",anchor:"transformers.modeling_tf_utils.keras_serializable",parameters:[],parametersDescription:[{anchor:"transformers.modeling_tf_utils.keras_serializable.cls",description:`<strong>cls</strong> (a <code>tf.keras.layers.Layers subclass</code>) &#x2014;
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