import{S as Zd,i as ec,s as oc,e as r,k as d,w as _,t as n,M as tc,c as a,d as t,m as c,a as i,x as b,h as s,b as l,F as e,g as u,y as L,q as M,o as k,B as v}from"../../chunks/vendor-4833417e.js";import{T as Zo}from"../../chunks/Tip-fffd6df1.js";import{D as F}from"../../chunks/Docstring-4f315ed9.js";import{C as $e}from"../../chunks/CodeBlock-6a3d1b46.js";import{I as re}from"../../chunks/IconCopyLink-4b81c553.js";import"../../chunks/CopyButton-dacfbfaf.js";function nc(x){let h,w,m,X,T;return{c(){h=r("p"),w=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),m=r("code"),X=n("Module"),T=n(`
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(g){h=a(g,"P",{});var f=i(h);w=s(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),m=a(f,"CODE",{});var y=i(m);X=s(y,"Module"),y.forEach(t),T=s(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(g,f){u(g,h,f),e(h,w),e(h,m),e(m,X),e(h,T)},d(g){g&&t(h)}}}function cc(x){let h,w,m,X,T,g,f,y,ks,On,ae,ze,At,to,vs,St,Xs,Dn,Fe,ws,no,Ts,ys,Wn,et,Rs,Bn,ot,Nt,xs,Qn,tt,$s,Hn,nt,so,zs,It,Fs,Cs,Un,Q,qs,ro,Es,js,ao,Ps,As,io,Ss,Ns,Vn,ie,Ce,Ot,lo,Is,Dt,Os,Gn,z,co,Ds,H,Ws,st,Bs,Qs,Wt,Hs,Us,ho,Vs,Gs,Js,le,Ks,rt,Ys,Zs,at,er,or,tr,Bt,nr,sr,po,Jn,de,qe,Qt,uo,rr,Ht,ar,Kn,I,mo,ir,ce,lr,it,dr,cr,fo,hr,pr,ur,K,mr,Ut,fr,$,gr,Vt,_r,br,Gt,Lr,Mr,Jt,kr,vr,Kt,Xr,wr,Yt,Tr,yr,Zt,Rr,xr,en,$r,zr,go,Fr,Cr,C,_o,qr,he,Er,lt,jr,Pr,on,Ar,Sr,Nr,Ee,Ir,tn,Or,Dr,bo,Yn,pe,je,nn,Lo,Wr,sn,Br,Zn,U,Mo,Qr,V,Hr,rn,Ur,Vr,dt,Gr,Jr,ko,Kr,Yr,Zr,q,vo,ea,ue,oa,ct,ta,na,an,sa,ra,aa,Pe,ia,ln,la,da,Xo,es,me,Ae,dn,wo,ca,cn,ha,os,G,To,pa,J,ua,hn,ma,fa,ht,ga,_a,yo,ba,La,Ma,E,Ro,ka,fe,va,pt,Xa,wa,pn,Ta,ya,Ra,Se,xa,un,$a,za,xo,ts,ge,Ne,mn,$o,Fa,fn,Ca,ns,O,zo,qa,gn,Ea,ja,_e,Pa,ut,Aa,Sa,Fo,Na,Ia,Oa,R,Co,Da,be,Wa,mt,Ba,Qa,_n,Ha,Ua,Va,Ie,Ga,bn,Ja,Ka,qo,Ya,Ln,Za,ei,Eo,ss,Le,Oe,Mn,jo,oi,kn,ti,rs,D,Po,ni,vn,si,ri,Me,ai,ft,ii,li,Ao,di,ci,hi,j,So,pi,ke,ui,gt,mi,fi,Xn,gi,_i,bi,De,Li,wn,Mi,ki,No,as,ve,We,Tn,Io,vi,yn,Xi,is,W,Oo,wi,Rn,Ti,yi,Xe,Ri,_t,xi,$i,Do,zi,Fi,Ci,P,Wo,qi,we,Ei,bt,ji,Pi,xn,Ai,Si,Ni,Be,Ii,$n,Oi,Di,Bo,ls,Te,Qe,zn,Qo,Wi,Fn,Bi,ds,B,Ho,Qi,ye,Hi,Cn,Ui,Vi,qn,Gi,Ji,Ki,Re,Yi,Lt,Zi,el,Uo,ol,tl,nl,A,Vo,sl,xe,rl,Mt,al,il,En,ll,dl,cl,He,hl,jn,pl,ul,Go,cs;return g=new re({}),to=new re({}),lo=new re({}),co=new F({props:{name:"class transformers.XLMRobertaXLConfig",anchor:"transformers.XLMRobertaXLConfig",parameters:[{name:"vocab_size",val:" = 250880"},{name:"hidden_size",val:" = 2560"},{name:"num_hidden_layers",val:" = 36"},{name:"num_attention_heads",val:" = 32"},{name:"intermediate_size",val:" = 10240"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 514"},{name:"type_vocab_size",val:" = 1"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"position_embedding_type",val:" = 'absolute'"},{name:"use_cache",val:" = True"},{name:"classifier_dropout",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py#L34",parametersDescription:[{anchor:"transformers.XLMRobertaXLConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 250880) &#x2014;
Vocabulary size of the XLM_ROBERTA_XL model. Defines the number of different tokens that can be represented
by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a>.`,name:"vocab_size"},{anchor:"transformers.XLMRobertaXLConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2560) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.XLMRobertaXLConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 36) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.XLMRobertaXLConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.XLMRobertaXLConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 10240) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.XLMRobertaXLConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>Callable</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.XLMRobertaXLConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.XLMRobertaXLConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.XLMRobertaXLConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 514) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.XLMRobertaXLConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> or
<code>TFXLMRobertaXLModel</code>.`,name:"type_vocab_size"},{anchor:"transformers.XLMRobertaXLConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.XLMRobertaXLConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.XLMRobertaXLConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.XLMRobertaXLConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>.`,name:"use_cache"},{anchor:"transformers.XLMRobertaXLConfig.classifier_dropout",description:`<strong>classifier_dropout</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The dropout ratio for the classification head.`,name:"classifier_dropout"}]}}),po=new $e({props:{code:`from transformers import XLMRobertaXLModel, XLMRobertaXLConfig

# Initializing a XLM_ROBERTA_XL bert-base-uncased style configuration
configuration = XLMRobertaXLConfig()

# Initializing a model from the bert-base-uncased style configuration
model = XLMRobertaXLModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> XLMRobertaXLModel, XLMRobertaXLConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a XLM_ROBERTA_XL bert-base-uncased style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = XLMRobertaXLConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the bert-base-uncased style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),uo=new re({}),mo=new F({props:{name:"class transformers.XLMRobertaXLModel",anchor:"transformers.XLMRobertaXLModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L672",parametersDescription:[{anchor:"transformers.XLMRobertaXLModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</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"}]}}),_o=new F({props:{name:"forward",anchor:"transformers.XLMRobertaXLModel.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/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L712",parametersDescription:[{anchor:"transformers.XLMRobertaXLModel.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. 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. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMRobertaXLModel.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.XLMRobertaXLModel.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.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.XLMRobertaXLModel.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>. <a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.XLMRobertaXLModel.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.XLMRobertaXLModel.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.XLMRobertaXLModel.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.XLMRobertaXLModel.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.XLMRobertaXLModel.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.XLMRobertaXLModel.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.XLMRobertaXLModel.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.XLMRobertaXLModel.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.XLMRobertaXLModel.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/xlm-roberta-xl#transformers.XLMRobertaXLConfig"
>XLMRobertaXLConfig</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>
`}}),Ee=new Zo({props:{$$slots:{default:[nc]},$$scope:{ctx:x}}}),bo=new $e({props:{code:`from transformers import RobertaTokenizer, XLMRobertaXLModel
import torch

tokenizer = RobertaTokenizer.from_pretrained("xlm-roberta-xlarge")
model = XLMRobertaXLModel.from_pretrained("xlm-roberta-xlarge")

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, XLMRobertaXLModel
<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;xlm-roberta-xlarge&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLModel.from_pretrained(<span class="hljs-string">&quot;xlm-roberta-xlarge&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`}}),Lo=new re({}),Mo=new F({props:{name:"class transformers.XLMRobertaXLForCausalLM",anchor:"transformers.XLMRobertaXLForCausalLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L854",parametersDescription:[{anchor:"transformers.XLMRobertaXLForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</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"}]}}),vo=new F({props:{name:"forward",anchor:"transformers.XLMRobertaXLForCausalLM.forward",parameters:[{name:"input_ids",val:" = None"},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"labels",val:" = None"},{name:"past_key_values",val:" = None"},{name:"use_cache",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L879",parametersDescription:[{anchor:"transformers.XLMRobertaXLForCausalLM.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. 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. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMRobertaXLForCausalLM.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.XLMRobertaXLForCausalLM.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.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.XLMRobertaXLForCausalLM.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>. <a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.XLMRobertaXLForCausalLM.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.
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.XLMRobertaXLForCausalLM.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/xlm-roberta-xl#transformers.XLMRobertaXLConfig"
>XLMRobertaXLConfig</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>
`}}),Pe=new Zo({props:{$$slots:{default:[sc]},$$scope:{ctx:x}}}),Xo=new $e({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`}}),wo=new re({}),To=new F({props:{name:"class transformers.XLMRobertaXLForMaskedLM",anchor:"transformers.XLMRobertaXLForMaskedLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1004",parametersDescription:[{anchor:"transformers.XLMRobertaXLForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</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"}]}}),Ro=new F({props:{name:"forward",anchor:"transformers.XLMRobertaXLForMaskedLM.forward",parameters:[{name:"input_ids",val:" = None"},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"labels",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1032",parametersDescription:[{anchor:"transformers.XLMRobertaXLForMaskedLM.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. 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. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMRobertaXLForMaskedLM.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.XLMRobertaXLForMaskedLM.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.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.XLMRobertaXLForMaskedLM.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>. <a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.XLMRobertaXLForMaskedLM.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.XLMRobertaXLForMaskedLM.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.XLMRobertaXLForMaskedLM.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.XLMRobertaXLForMaskedLM.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.XLMRobertaXLForMaskedLM.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.XLMRobertaXLForMaskedLM.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.XLMRobertaXLForMaskedLM.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/xlm-roberta-xl#transformers.XLMRobertaXLConfig"
>XLMRobertaXLConfig</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>
`}}),Se=new Zo({props:{$$slots:{default:[rc]},$$scope:{ctx:x}}}),xo=new $e({props:{code:`from transformers import RobertaTokenizer, XLMRobertaXLForMaskedLM
import torch

tokenizer = RobertaTokenizer.from_pretrained("xlm-roberta-xlarge")
model = XLMRobertaXLForMaskedLM.from_pretrained("xlm-roberta-xlarge")

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, XLMRobertaXLForMaskedLM
<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;xlm-roberta-xlarge&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLForMaskedLM.from_pretrained(<span class="hljs-string">&quot;xlm-roberta-xlarge&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`}}),$o=new re({}),zo=new F({props:{name:"class transformers.XLMRobertaXLForSequenceClassification",anchor:"transformers.XLMRobertaXLForSequenceClassification",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1132",parametersDescription:[{anchor:"transformers.XLMRobertaXLForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</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"}]}}),Co=new F({props:{name:"forward",anchor:"transformers.XLMRobertaXLForSequenceClassification.forward",parameters:[{name:"input_ids",val:" = None"},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"labels",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1145",parametersDescription:[{anchor:"transformers.XLMRobertaXLForSequenceClassification.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. 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. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMRobertaXLForSequenceClassification.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.XLMRobertaXLForSequenceClassification.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.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.XLMRobertaXLForSequenceClassification.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>. <a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.XLMRobertaXLForSequenceClassification.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.XLMRobertaXLForSequenceClassification.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.XLMRobertaXLForSequenceClassification.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.XLMRobertaXLForSequenceClassification.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.XLMRobertaXLForSequenceClassification.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.XLMRobertaXLForSequenceClassification.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/xlm-roberta-xl#transformers.XLMRobertaXLConfig"
>XLMRobertaXLConfig</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>
`}}),Ie=new Zo({props:{$$slots:{default:[ac]},$$scope:{ctx:x}}}),qo=new $e({props:{code:`import torch
from transformers import RobertaTokenizer, XLMRobertaXLForSequenceClassification

torch.manual_seed(0)
tokenizer = RobertaTokenizer.from_pretrained("xlm-roberta-xlarge")
model = XLMRobertaXLForSequenceClassification.from_pretrained("xlm-roberta-xlarge", 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, XLMRobertaXLForSequenceClassification

<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;xlm-roberta-xlarge&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;xlm-roberta-xlarge&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)
`}}),Eo=new $e({props:{code:`import torch
from transformers import RobertaTokenizer, XLMRobertaXLForSequenceClassification

torch.manual_seed(0)
tokenizer = RobertaTokenizer.from_pretrained("xlm-roberta-xlarge")
model = XLMRobertaXLForSequenceClassification.from_pretrained("xlm-roberta-xlarge", 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, XLMRobertaXLForSequenceClassification

<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;xlm-roberta-xlarge&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;xlm-roberta-xlarge&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)
`}}),jo=new re({}),Po=new F({props:{name:"class transformers.XLMRobertaXLForMultipleChoice",anchor:"transformers.XLMRobertaXLForMultipleChoice",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1229",parametersDescription:[{anchor:"transformers.XLMRobertaXLForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</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"}]}}),So=new F({props:{name:"forward",anchor:"transformers.XLMRobertaXLForMultipleChoice.forward",parameters:[{name:"input_ids",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"attention_mask",val:" = None"},{name:"labels",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1241",parametersDescription:[{anchor:"transformers.XLMRobertaXLForMultipleChoice.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. 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. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMRobertaXLForMultipleChoice.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.XLMRobertaXLForMultipleChoice.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.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.XLMRobertaXLForMultipleChoice.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>. <a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.XLMRobertaXLForMultipleChoice.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.XLMRobertaXLForMultipleChoice.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.XLMRobertaXLForMultipleChoice.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.XLMRobertaXLForMultipleChoice.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.XLMRobertaXLForMultipleChoice.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.XLMRobertaXLForMultipleChoice.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/xlm-roberta-xl#transformers.XLMRobertaXLConfig"
>XLMRobertaXLConfig</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>
`}}),De=new Zo({props:{$$slots:{default:[ic]},$$scope:{ctx:x}}}),No=new $e({props:{code:`from transformers import RobertaTokenizer, XLMRobertaXLForMultipleChoice
import torch

tokenizer = RobertaTokenizer.from_pretrained("xlm-roberta-xlarge")
model = XLMRobertaXLForMultipleChoice.from_pretrained("xlm-roberta-xlarge")

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, XLMRobertaXLForMultipleChoice
<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;xlm-roberta-xlarge&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;xlm-roberta-xlarge&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`}}),Io=new re({}),Oo=new F({props:{name:"class transformers.XLMRobertaXLForTokenClassification",anchor:"transformers.XLMRobertaXLForTokenClassification",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1323",parametersDescription:[{anchor:"transformers.XLMRobertaXLForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</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"}]}}),Wo=new F({props:{name:"forward",anchor:"transformers.XLMRobertaXLForTokenClassification.forward",parameters:[{name:"input_ids",val:" = None"},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"labels",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1340",parametersDescription:[{anchor:"transformers.XLMRobertaXLForTokenClassification.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. 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. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMRobertaXLForTokenClassification.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.XLMRobertaXLForTokenClassification.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.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.XLMRobertaXLForTokenClassification.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>. <a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.XLMRobertaXLForTokenClassification.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.XLMRobertaXLForTokenClassification.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.XLMRobertaXLForTokenClassification.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.XLMRobertaXLForTokenClassification.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.XLMRobertaXLForTokenClassification.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.XLMRobertaXLForTokenClassification.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/xlm-roberta-xl#transformers.XLMRobertaXLConfig"
>XLMRobertaXLConfig</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>
`}}),Be=new Zo({props:{$$slots:{default:[lc]},$$scope:{ctx:x}}}),Bo=new $e({props:{code:`from transformers import RobertaTokenizer, XLMRobertaXLForTokenClassification
import torch

tokenizer = RobertaTokenizer.from_pretrained("xlm-roberta-xlarge")
model = XLMRobertaXLForTokenClassification.from_pretrained("xlm-roberta-xlarge")

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, XLMRobertaXLForTokenClassification
<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;xlm-roberta-xlarge&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLForTokenClassification.from_pretrained(<span class="hljs-string">&quot;xlm-roberta-xlarge&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`}}),Qo=new re({}),Ho=new F({props:{name:"class transformers.XLMRobertaXLForQuestionAnswering",anchor:"transformers.XLMRobertaXLForQuestionAnswering",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1438",parametersDescription:[{anchor:"transformers.XLMRobertaXLForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</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"}]}}),Vo=new F({props:{name:"forward",anchor:"transformers.XLMRobertaXLForQuestionAnswering.forward",parameters:[{name:"input_ids",val:" = None"},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"start_positions",val:" = None"},{name:"end_positions",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py#L1451",parametersDescription:[{anchor:"transformers.XLMRobertaXLForQuestionAnswering.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. 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. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.XLMRobertaXLForQuestionAnswering.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.XLMRobertaXLForQuestionAnswering.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.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.XLMRobertaXLForQuestionAnswering.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>. <a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.XLMRobertaXLForQuestionAnswering.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.XLMRobertaXLForQuestionAnswering.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.XLMRobertaXLForQuestionAnswering.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.XLMRobertaXLForQuestionAnswering.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.XLMRobertaXLForQuestionAnswering.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.XLMRobertaXLForQuestionAnswering.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.XLMRobertaXLForQuestionAnswering.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/xlm-roberta-xl#transformers.XLMRobertaXLConfig"
>XLMRobertaXLConfig</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>
`}}),He=new Zo({props:{$$slots:{default:[dc]},$$scope:{ctx:x}}}),Go=new $e({props:{code:`from transformers import RobertaTokenizer, XLMRobertaXLForQuestionAnswering
import torch

torch.manual_seed(0)
tokenizer = RobertaTokenizer.from_pretrained("xlm-roberta-xlarge")
model = XLMRobertaXLForQuestionAnswering.from_pretrained("xlm-roberta-xlarge")

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, XLMRobertaXLForQuestionAnswering
<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;xlm-roberta-xlarge&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMRobertaXLForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;xlm-roberta-xlarge&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)
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an input to the forward pass. .. `),S.forEach(t),en=a(Yo,"EM",{});var Hl=i(en);$r=s(Hl,"Attention is all you need"),Hl.forEach(t),zr=s(Yo,": "),go=a(Yo,"A",{href:!0,rel:!0});var Ul=i(go);Fr=s(Ul,"https://arxiv.org/abs/1706.03762"),Ul.forEach(t),Yo.forEach(t),Cr=c(Ge),C=a(Ge,"DIV",{class:!0});var Z=i(C);b(_o.$$.fragment,Z),qr=c(Z),he=a(Z,"P",{});var Xt=i(he);Er=s(Xt,"The "),lt=a(Xt,"A",{href:!0});var Vl=i(lt);jr=s(Vl,"XLMRobertaXLModel"),Vl.forEach(t),Pr=s(Xt," forward method, overrides the "),on=a(Xt,"CODE",{});var Gl=i(on);Ar=s(Gl,"__call__"),Gl.forEach(t),Sr=s(Xt," special method."),Xt.forEach(t),Nr=c(Z),b(Ee.$$.fragment,Z),Ir=c(Z),tn=a(Z,"P",{});var Jl=i(tn);Or=s(Jl,"Example:"),Jl.forEach(t),Dr=c(Z),b(bo.$$.fragment,Z),Z.forEach(t),Ge.forEach(t),Yn=c(o),pe=a(o,"H2",{class:!0});var fs=i(pe);je=a(fs,"A",{id:!0,class:!0,href:!0});var Kl=i(je);nn=a(Kl,"SPAN",{});var Yl=i(nn);b(Lo.$$.fragment,Yl),Yl.forEach(t),Kl.forEach(t),Wr=c(fs),sn=a(fs,"SPAN",{});var Zl=i(sn);Br=s(Zl,"XLMRobertaXLForCausalLM"),Zl.forEach(t),fs.forEach(t),Zn=c(o),U=a(o,"DIV",{class:!0});var wt=i(U);b(Mo.$$.fragment,wt),Qr=c(wt),V=a(wt,"P",{});var Je=i(V);Hr=s(Je,"XLM-RoBERTa-xlarge Model with a "),rn=a(Je,"CODE",{});var ed=i(rn);Ur=s(ed,"language modeling"),ed.forEach(t),Vr=s(Je,` head on top for CLM fine-tuning.
This model inherits from `),dt=a(Je,"A",{href:!0});var od=i(dt);Gr=s(od,"PreTrainedModel"),od.forEach(t),Jr=s(Je,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch `),ko=a(Je,"A",{href:!0,rel:!0});var td=i(ko);Kr=s(td,"torch.nn.Module"),td.forEach(t),Yr=s(Je,`
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.`),Je.forEach(t),Zr=c(wt),q=a(wt,"DIV",{class:!0});var ee=i(q);b(vo.$$.fragment,ee),ea=c(ee),ue=a(ee,"P",{});var Tt=i(ue);oa=s(Tt,"The "),ct=a(Tt,"A",{href:!0});var nd=i(ct);ta=s(nd,"XLMRobertaXLForCausalLM"),nd.forEach(t),na=s(Tt," forward method, overrides the "),an=a(Tt,"CODE",{});var sd=i(an);sa=s(sd,"__call__"),sd.forEach(t),ra=s(Tt," special method."),Tt.forEach(t),aa=c(ee),b(Pe.$$.fragment,ee),ia=c(ee),ln=a(ee,"P",{});var rd=i(ln);la=s(rd,"Example:"),rd.forEach(t),da=c(ee),b(Xo.$$.fragment,ee),ee.forEach(t),wt.forEach(t),es=c(o),me=a(o,"H2",{class:!0});var gs=i(me);Ae=a(gs,"A",{id:!0,class:!0,href:!0});var ad=i(Ae);dn=a(ad,"SPAN",{});var id=i(dn);b(wo.$$.fragment,id),id.forEach(t),ad.forEach(t),ca=c(gs),cn=a(gs,"SPAN",{});var ld=i(cn);ha=s(ld,"XLMRobertaXLForMaskedLM"),ld.forEach(t),gs.forEach(t),os=c(o),G=a(o,"DIV",{class:!0});var yt=i(G);b(To.$$.fragment,yt),pa=c(yt),J=a(yt,"P",{});var Ke=i(J);ua=s(Ke,"XLM-RoBERTa-xlarge Model with a "),hn=a(Ke,"CODE",{});var dd=i(hn);ma=s(dd,"language modeling"),dd.forEach(t),fa=s(Ke,` head on top.
This model inherits from `),ht=a(Ke,"A",{href:!0});var cd=i(ht);ga=s(cd,"PreTrainedModel"),cd.forEach(t),_a=s(Ke,`. 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.) This model is also a PyTorch `),yo=a(Ke,"A",{href:!0,rel:!0});var hd=i(yo);ba=s(hd,"torch.nn.Module"),hd.forEach(t),La=s(Ke,`
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.`),Ke.forEach(t),Ma=c(yt),E=a(yt,"DIV",{class:!0});var oe=i(E);b(Ro.$$.fragment,oe),ka=c(oe),fe=a(oe,"P",{});var Rt=i(fe);va=s(Rt,"The "),pt=a(Rt,"A",{href:!0});var pd=i(pt);Xa=s(pd,"XLMRobertaXLForMaskedLM"),pd.forEach(t),wa=s(Rt," forward method, overrides the "),pn=a(Rt,"CODE",{});var ud=i(pn);Ta=s(ud,"__call__"),ud.forEach(t),ya=s(Rt," special method."),Rt.forEach(t),Ra=c(oe),b(Se.$$.fragment,oe),xa=c(oe),un=a(oe,"P",{});var md=i(un);$a=s(md,"Example:"),md.forEach(t),za=c(oe),b(xo.$$.fragment,oe),oe.forEach(t),yt.forEach(t),ts=c(o),ge=a(o,"H2",{class:!0});var _s=i(ge);Ne=a(_s,"A",{id:!0,class:!0,href:!0});var fd=i(Ne);mn=a(fd,"SPAN",{});var gd=i(mn);b($o.$$.fragment,gd),gd.forEach(t),fd.forEach(t),Fa=c(_s),fn=a(_s,"SPAN",{});var _d=i(fn);Ca=s(_d,"XLMRobertaXLForSequenceClassification"),_d.forEach(t),_s.forEach(t),ns=c(o),O=a(o,"DIV",{class:!0});var Ye=i(O);b(zo.$$.fragment,Ye),qa=c(Ye),gn=a(Ye,"P",{});var bd=i(gn);Ea=s(bd,`XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top
of the pooled output) e.g. for GLUE tasks.`),bd.forEach(t),ja=c(Ye),_e=a(Ye,"P",{});var xt=i(_e);Pa=s(xt,"This model inherits from "),ut=a(xt,"A",{href:!0});var Ld=i(ut);Aa=s(Ld,"PreTrainedModel"),Ld.forEach(t),Sa=s(xt,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch `),Fo=a(xt,"A",{href:!0,rel:!0});var Md=i(Fo);Na=s(Md,"torch.nn.Module"),Md.forEach(t),Ia=s(xt,`
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.`),xt.forEach(t),Oa=c(Ye),R=a(Ye,"DIV",{class:!0});var N=i(R);b(Co.$$.fragment,N),Da=c(N),be=a(N,"P",{});var $t=i(be);Wa=s($t,"The "),mt=a($t,"A",{href:!0});var kd=i(mt);Ba=s(kd,"XLMRobertaXLForSequenceClassification"),kd.forEach(t),Qa=s($t," forward method, overrides the "),_n=a($t,"CODE",{});var vd=i(_n);Ha=s(vd,"__call__"),vd.forEach(t),Ua=s($t," special method."),$t.forEach(t),Va=c(N),b(Ie.$$.fragment,N),Ga=c(N),bn=a(N,"P",{});var Xd=i(bn);Ja=s(Xd,"Example of single-label classification:"),Xd.forEach(t),Ka=c(N),b(qo.$$.fragment,N),Ya=c(N),Ln=a(N,"P",{});var wd=i(Ln);Za=s(wd,"Example of multi-label classification:"),wd.forEach(t),ei=c(N),b(Eo.$$.fragment,N),N.forEach(t),Ye.forEach(t),ss=c(o),Le=a(o,"H2",{class:!0});var bs=i(Le);Oe=a(bs,"A",{id:!0,class:!0,href:!0});var Td=i(Oe);Mn=a(Td,"SPAN",{});var yd=i(Mn);b(jo.$$.fragment,yd),yd.forEach(t),Td.forEach(t),oi=c(bs),kn=a(bs,"SPAN",{});var Rd=i(kn);ti=s(Rd,"XLMRobertaXLForMultipleChoice"),Rd.forEach(t),bs.forEach(t),rs=c(o),D=a(o,"DIV",{class:!0});var Ze=i(D);b(Po.$$.fragment,Ze),ni=c(Ze),vn=a(Ze,"P",{});var xd=i(vn);si=s(xd,`XLM-Roberta-xlarge Model with a multiple choice classification head on top (a linear layer on top of the pooled
output and a softmax) e.g. for RocStories/SWAG tasks.`),xd.forEach(t),ri=c(Ze),Me=a(Ze,"P",{});var zt=i(Me);ai=s(zt,"This model inherits from "),ft=a(zt,"A",{href:!0});var $d=i(ft);ii=s($d,"PreTrainedModel"),$d.forEach(t),li=s(zt,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch `),Ao=a(zt,"A",{href:!0,rel:!0});var zd=i(Ao);di=s(zd,"torch.nn.Module"),zd.forEach(t),ci=s(zt,`
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.`),zt.forEach(t),hi=c(Ze),j=a(Ze,"DIV",{class:!0});var te=i(j);b(So.$$.fragment,te),pi=c(te),ke=a(te,"P",{});var Ft=i(ke);ui=s(Ft,"The "),gt=a(Ft,"A",{href:!0});var Fd=i(gt);mi=s(Fd,"XLMRobertaXLForMultipleChoice"),Fd.forEach(t),fi=s(Ft," forward method, overrides the "),Xn=a(Ft,"CODE",{});var Cd=i(Xn);gi=s(Cd,"__call__"),Cd.forEach(t),_i=s(Ft," special method."),Ft.forEach(t),bi=c(te),b(De.$$.fragment,te),Li=c(te),wn=a(te,"P",{});var qd=i(wn);Mi=s(qd,"Example:"),qd.forEach(t),ki=c(te),b(No.$$.fragment,te),te.forEach(t),Ze.forEach(t),as=c(o),ve=a(o,"H2",{class:!0});var Ls=i(ve);We=a(Ls,"A",{id:!0,class:!0,href:!0});var Ed=i(We);Tn=a(Ed,"SPAN",{});var jd=i(Tn);b(Io.$$.fragment,jd),jd.forEach(t),Ed.forEach(t),vi=c(Ls),yn=a(Ls,"SPAN",{});var Pd=i(yn);Xi=s(Pd,"XLMRobertaXLForTokenClassification"),Pd.forEach(t),Ls.forEach(t),is=c(o),W=a(o,"DIV",{class:!0});var eo=i(W);b(Oo.$$.fragment,eo),wi=c(eo),Rn=a(eo,"P",{});var Ad=i(Rn);Ti=s(Ad,`XLM-Roberta-xlarge 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.`),Ad.forEach(t),yi=c(eo),Xe=a(eo,"P",{});var Ct=i(Xe);Ri=s(Ct,"This model inherits from "),_t=a(Ct,"A",{href:!0});var Sd=i(_t);xi=s(Sd,"PreTrainedModel"),Sd.forEach(t),$i=s(Ct,`. 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.) This model is also a PyTorch `),Do=a(Ct,"A",{href:!0,rel:!0});var Nd=i(Do);zi=s(Nd,"torch.nn.Module"),Nd.forEach(t),Fi=s(Ct,`
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.`),Ct.forEach(t),Ci=c(eo),P=a(eo,"DIV",{class:!0});var ne=i(P);b(Wo.$$.fragment,ne),qi=c(ne),we=a(ne,"P",{});var qt=i(we);Ei=s(qt,"The "),bt=a(qt,"A",{href:!0});var Id=i(bt);ji=s(Id,"XLMRobertaXLForTokenClassification"),Id.forEach(t),Pi=s(qt," forward method, overrides the "),xn=a(qt,"CODE",{});var Od=i(xn);Ai=s(Od,"__call__"),Od.forEach(t),Si=s(qt," special method."),qt.forEach(t),Ni=c(ne),b(Be.$$.fragment,ne),Ii=c(ne),$n=a(ne,"P",{});var Dd=i($n);Oi=s(Dd,"Example:"),Dd.forEach(t),Di=c(ne),b(Bo.$$.fragment,ne),ne.forEach(t),eo.forEach(t),ls=c(o),Te=a(o,"H2",{class:!0});var Ms=i(Te);Qe=a(Ms,"A",{id:!0,class:!0,href:!0});var Wd=i(Qe);zn=a(Wd,"SPAN",{});var Bd=i(zn);b(Qo.$$.fragment,Bd),Bd.forEach(t),Wd.forEach(t),Wi=c(Ms),Fn=a(Ms,"SPAN",{});var Qd=i(Fn);Bi=s(Qd,"XLMRobertaXLForQuestionAnswering"),Qd.forEach(t),Ms.forEach(t),ds=c(o),B=a(o,"DIV",{class:!0});var oo=i(B);b(Ho.$$.fragment,oo),Qi=c(oo),ye=a(oo,"P",{});var Et=i(ye);Hi=s(Et,`XLM-Roberta-xlarge Model with a span classification head on top for extractive question-answering tasks like SQuAD
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch `),Uo=a(jt,"A",{href:!0,rel:!0});var Gd=i(Uo);ol=s(Gd,"torch.nn.Module"),Gd.forEach(t),tl=s(jt,`
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.`),jt.forEach(t),nl=c(oo),A=a(oo,"DIV",{class:!0});var se=i(A);b(Vo.$$.fragment,se),sl=c(se),xe=a(se,"P",{});var Pt=i(xe);rl=s(Pt,"The "),Mt=a(Pt,"A",{href:!0});var Jd=i(Mt);al=s(Jd,"XLMRobertaXLForQuestionAnswering"),Jd.forEach(t),il=s(Pt," forward method, overrides the "),En=a(Pt,"CODE",{});var Kd=i(En);ll=s(Kd,"__call__"),Kd.forEach(t),dl=s(Pt," special method."),Pt.forEach(t),cl=c(se),b(He.$$.fragment,se),hl=c(se),jn=a(se,"P",{});var Yd=i(jn);pl=s(Yd,"Example:"),Yd.forEach(t),ul=c(se),b(Go.$$.fragment,se),se.forEach(t),oo.forEach(t),this.h()},h(){l(h,"name","hf:doc:metadata"),l(h,"content",JSON.stringify(hc)),l(X,"id","xlmrobertaxl"),l(X,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),l(X,"href","#xlmrobertaxl"),l(m,"class","relative group"),l(ze,"id","overview"),l(ze,"class","header-link block pr-1.5 text-lg no-hover:hidden 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