import{S as Xd,i as Kd,s as Jd,e as r,k as d,w as f,t as a,M as Qd,c as n,d as s,m as p,a as o,x as h,h as i,b as l,F as e,g as c,y as u,L as Yd,q as g,o as _,B as k}from"../../chunks/vendor-4833417e.js";import{D as T}from"../../chunks/Docstring-4f315ed9.js";import{C as Ce}from"../../chunks/CodeBlock-6a3d1b46.js";import{I as C}from"../../chunks/IconCopyLink-4b81c553.js";import"../../chunks/CopyButton-dacfbfaf.js";function Zd(Ln){let N,Ut,P,A,Ts,Pe,Nn,$s,Dn,$r,U,re,ws,Ae,In,ys,Gn,wr,ne,On,Se,Vn,Un,yr,Wt,Wn,br,Bt,bs,Bn,zr,oe,Hn,Le,Rn,Xn,Mr,Ht,Kn,Er,w,zs,Ms,Ne,Jn,Qn,Es,xs,De,Yn,Zn,qs,Fs,Ie,eo,to,js,Cs,Ge,so,ro,Ps,Rt,Oe,no,oo,xr,D,ao,Ve,io,lo,Ue,po,mo,qr,W,ae,As,We,co,Ss,fo,Fr,S,Be,ho,L,uo,Xt,go,_o,Kt,ko,vo,He,To,$o,wo,B,yo,Jt,bo,zo,Qt,Mo,Eo,jr,H,ie,Ls,Re,xo,Ns,qo,Cr,v,Xe,Fo,Ke,jo,Je,Co,Po,Ao,Qe,So,Yt,Lo,No,Do,I,Ye,Io,Ds,Go,Oo,Ze,Zt,Vo,Is,Uo,Wo,es,Bo,Gs,Ho,Ro,le,et,Xo,Os,Ko,Jo,de,tt,Qo,Vs,Yo,Zo,pe,st,ea,rt,ta,Us,sa,ra,Pr,me,na,ts,oa,aa,Ar,R,ce,Ws,nt,ia,Bs,la,Sr,$,ot,da,X,pa,Hs,ma,ca,at,fa,ha,ua,it,ga,ss,_a,ka,va,G,lt,Ta,Rs,$a,wa,dt,rs,ya,Xs,ba,za,ns,Ma,Ks,Ea,xa,fe,pt,qa,Js,Fa,Lr,he,ja,os,Ca,Pa,Nr,K,ue,Qs,mt,Aa,Ys,Sa,Dr,b,ct,La,ft,Na,as,Da,Ia,Ga,Zs,Oa,Va,ht,Ir,J,ge,er,ut,Ua,tr,Wa,Gr,z,gt,Ba,_t,Ha,is,Ra,Xa,Ka,sr,Ja,Qa,kt,Or,Q,_e,rr,vt,Ya,nr,Za,Vr,M,Tt,ei,$t,ti,ls,si,ri,ni,or,oi,ai,wt,Ur,Y,ke,ar,yt,ii,ir,li,Wr,E,bt,di,zt,pi,ds,mi,ci,fi,lr,hi,ui,Mt,Br,Z,ve,dr,Et,gi,pr,_i,Hr,x,xt,ki,qt,vi,ps,Ti,$i,wi,mr,yi,bi,Ft,Rr,ee,Te,cr,jt,zi,fr,Mi,Xr,q,Ct,Ei,Pt,xi,ms,qi,Fi,ji,hr,Ci,Pi,At,Kr,te,$e,ur,St,Ai,gr,Si,Jr,F,Lt,Li,Nt,Ni,cs,Di,Ii,Gi,_r,Oi,Vi,Dt,Qr,se,we,kr,It,Ui,vr,Wi,Yr,j,Gt,Bi,Ot,Hi,fs,Ri,Xi,Ki,Tr,Ji,Qi,Vt,Zr;return Pe=new C({}),Ae=new C({}),We=new C({}),Be=new T({props:{name:"class transformers.MT5Config",anchor:"transformers.MT5Config",parameters:[{name:"vocab_size",val:" = 250112"},{name:"d_model",val:" = 512"},{name:"d_kv",val:" = 64"},{name:"d_ff",val:" = 1024"},{name:"num_layers",val:" = 8"},{name:"num_decoder_layers",val:" = None"},{name:"num_heads",val:" = 6"},{name:"relative_attention_num_buckets",val:" = 32"},{name:"dropout_rate",val:" = 0.1"},{name:"layer_norm_epsilon",val:" = 1e-06"},{name:"initializer_factor",val:" = 1.0"},{name:"feed_forward_proj",val:" = 'gated-gelu'"},{name:"is_encoder_decoder",val:" = True"},{name:"use_cache",val:" = True"},{name:"tokenizer_class",val:" = 'T5Tokenizer'"},{name:"tie_word_embeddings",val:" = False"},{name:"pad_token_id",val:" = 0"},{name:"eos_token_id",val:" = 1"},{name:"decoder_start_token_id",val:" = 0"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/configuration_mt5.py#L24",parametersDescription:[{anchor:"transformers.MT5Config.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 250112) &#x2014;
Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Model">T5Model</a> or <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a>.`,name:"vocab_size"},{anchor:"transformers.MT5Config.d_model",description:`<strong>d_model</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Size of the encoder layers and the pooler layer.`,name:"d_model"},{anchor:"transformers.MT5Config.d_kv",description:`<strong>d_kv</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Size of the key, query, value projections per attention head. <code>d_kv</code> has to be equal to <code>d_model // num_heads</code>.`,name:"d_kv"},{anchor:"transformers.MT5Config.d_ff",description:`<strong>d_ff</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Size of the intermediate feed forward layer in each <code>T5Block</code>.`,name:"d_ff"},{anchor:"transformers.MT5Config.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_layers"},{anchor:"transformers.MT5Config.num_decoder_layers",description:`<strong>num_decoder_layers</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Number of hidden layers in the Transformer decoder. Will use the same value as <code>num_layers</code> if not set.`,name:"num_decoder_layers"},{anchor:"transformers.MT5Config.num_heads",description:`<strong>num_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_heads"},{anchor:"transformers.MT5Config.relative_attention_num_buckets",description:`<strong>relative_attention_num_buckets</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The number of buckets to use for each attention layer.`,name:"relative_attention_num_buckets"},{anchor:"transformers.MT5Config.dropout_rate",description:`<strong>dropout_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The ratio for all dropout layers.`,name:"dropout_rate"},{anchor:"transformers.MT5Config.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-6) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.MT5Config.initializer_factor",description:`<strong>initializer_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"},{anchor:"transformers.MT5Config.feed_forward_proj",description:`<strong>feed_forward_proj</strong> (<code>string</code>, <em>optional</em>, defaults to <code>&quot;gated-gelu&quot;</code>) &#x2014;
Type of feed forward layer to be used. Should be one of <code>&quot;relu&quot;</code> or <code>&quot;gated-gelu&quot;</code>.`,name:"feed_forward_proj"},{anchor:"transformers.MT5Config.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"}]}}),Re=new C({}),Xe=new T({props:{name:"class transformers.T5Tokenizer",anchor:"transformers.T5Tokenizer",parameters:[{name:"vocab_file",val:""},{name:"eos_token",val:" = '</s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"extra_ids",val:" = 100"},{name:"additional_special_tokens",val:" = None"},{name:"sp_model_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5.py#L53",parametersDescription:[{anchor:"transformers.T5Tokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.T5Tokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.T5Tokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.T5Tokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.T5Tokenizer.extra_ids",description:`<strong>extra_ids</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as &#x201C;<extra<em>id{%d}&gt;&#x201D; where &#x201D;{%d}&#x201D; is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginning (&#x201C;<extra_id_0>&#x201D; is the last token in the vocabulary
like in T5 preprocessing see
<a href="https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117" rel="nofollow">here</a>).</extra_id_0></extra<em>`,name:"extra_ids"},{anchor:"transformers.T5Tokenizer.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
Additional special tokens used by the tokenizer.`,name:"additional_special_tokens"},{anchor:"transformers.T5Tokenizer.sp_model_kwargs",description:`<strong>sp_model_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
</ul>`,name:"sp_model_kwargs"},{anchor:"transformers.T5Tokenizer.sp_model",description:`<strong>sp_model</strong> (<code>SentencePieceProcessor</code>) &#x2014;
The <em>SentencePiece</em> processor that is used for every conversion (string, tokens and IDs).`,name:"sp_model"}]}}),Ye=new T({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.T5Tokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5.py#L223",parametersDescription:[{anchor:"transformers.T5Tokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.T5Tokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),et=new T({props:{name:"convert_tokens_to_string",anchor:"transformers.T5Tokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5.py#L284"}}),tt=new T({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.T5Tokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5.py#L201",parametersDescription:[{anchor:"transformers.T5Tokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.T5Tokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),st=new T({props:{name:"get_special_tokens_mask",anchor:"transformers.T5Tokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5.py#L163",parametersDescription:[{anchor:"transformers.T5Tokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.T5Tokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.T5Tokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),nt=new C({}),ot=new T({props:{name:"class transformers.T5TokenizerFast",anchor:"transformers.T5TokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"eos_token",val:" = '</s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"extra_ids",val:" = 100"},{name:"additional_special_tokens",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5_fast.py#L63",parametersDescription:[{anchor:"transformers.T5TokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.T5TokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.T5TokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.T5TokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.T5TokenizerFast.extra_ids",description:`<strong>extra_ids</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as &#x201C;<extra<em>id{%d}&gt;&#x201D; where &#x201D;{%d}&#x201D; is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginning (&#x201C;<extra_id_0>&#x201D; is the last token in the vocabulary
like in T5 preprocessing see
<a href="https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117" rel="nofollow">here</a>).</extra_id_0></extra<em>`,name:"extra_ids"},{anchor:"transformers.T5TokenizerFast.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
Additional special tokens used by the tokenizer.`,name:"additional_special_tokens"}]}}),lt=new T({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.T5TokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5_fast.py#L166",parametersDescription:[{anchor:"transformers.T5TokenizerFast.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.T5TokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),pt=new T({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.T5TokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/t5/tokenization_t5_fast.py#L192",parametersDescription:[{anchor:"transformers.T5TokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.T5TokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],returnDescription:`
<p>List of zeros.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),mt=new C({}),ct=new T({props:{name:"class transformers.MT5Model",anchor:"transformers.MT5Model",parameters:[{name:"config",val:": T5Config"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_mt5.py#L28"}}),ht=new Ce({props:{code:`from transformers import MT5Model, T5Tokenizer

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], decoder_input_ids=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = outputs.last_hidden_state`}}),ut=new C({}),gt=new T({props:{name:"class transformers.MT5ForConditionalGeneration",anchor:"transformers.MT5ForConditionalGeneration",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_mt5.py#L62"}}),kt=new Ce({props:{code:`from transformers import MT5ForConditionalGeneration, T5Tokenizer

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`}}),vt=new C({}),Tt=new T({props:{name:"class transformers.MT5EncoderModel",anchor:"transformers.MT5EncoderModel",parameters:[{name:"config",val:": T5Config"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_mt5.py#L94"}}),wt=new Ce({props:{code:`from transformers import MT5EncoderModel, T5Tokenizer

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

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MT5EncoderModel.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(article, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_state = outputs.last_hidden_state`}}),yt=new C({}),bt=new T({props:{name:"class transformers.TFMT5Model",anchor:"transformers.TFMT5Model",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_tf_mt5.py#L28"}}),Mt=new Ce({props:{code:`from transformers import TFMT5Model, T5Tokenizer

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], decoder_input_ids=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = outputs.last_hidden_state`}}),Et=new C({}),xt=new T({props:{name:"class transformers.TFMT5ForConditionalGeneration",anchor:"transformers.TFMT5ForConditionalGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_tf_mt5.py#L53"}}),Ft=new Ce({props:{code:`from transformers import TFMT5ForConditionalGeneration, T5Tokenizer

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`}}),jt=new C({}),Ct=new T({props:{name:"class transformers.TFMT5EncoderModel",anchor:"transformers.TFMT5EncoderModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_tf_mt5.py#L79"}}),At=new Ce({props:{code:`from transformers import TFMT5EncoderModel, T5Tokenizer

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

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMT5EncoderModel.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = T5Tokenizer.from_pretrained(<span class="hljs-string">&quot;google/mt5-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>article = <span class="hljs-string">&quot;UN Offizier sagt, dass weiter verhandelt werden muss in Syrien.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(article, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_state = outputs.last_hidden_state`}}),St=new C({}),Lt=new T({props:{name:"class transformers.FlaxMT5Model",anchor:"transformers.FlaxMT5Model",parameters:[{name:"config",val:": T5Config"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_flax_mt5.py#L28"}}),Dt=new Ce({props:{code:`from transformers import FlaxMT5Model, T5Tokenizer

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

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

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>], decoder_input_ids=decoder_input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>hidden_states = outputs.last_hidden_state`}}),It=new C({}),Gt=new T({props:{name:"class transformers.FlaxMT5ForConditionalGeneration",anchor:"transformers.FlaxMT5ForConditionalGeneration",parameters:[{name:"config",val:": T5Config"},{name:"input_shape",val:": typing.Tuple[int] = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/mt5/modeling_flax_mt5.py#L55"}}),Vt=new Ce({props:{code:`from transformers import FlaxMT5ForConditionalGeneration, T5Tokenizer

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

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

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, decoder_input_ids=decoder_input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){N=r("meta"),Ut=d(),P=r("h1"),A=r("a"),Ts=r("span"),f(Pe.$$.fragment),Nn=d(),$s=r("span"),Dn=a("mT5"),$r=d(),U=r("h2"),re=r("a"),ws=r("span"),f(Ae.$$.fragment),In=d(),ys=r("span"),Gn=a("Overview"),wr=d(),ne=r("p"),On=a("The mT5 model was presented in "),Se=r("a"),Vn=a("mT5: A massively multilingual pre-trained text-to-text transformer"),Un=a(` by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya
Siddhant, Aditya Barua, Colin Raffel.`),yr=d(),Wt=r("p"),Wn=a("The abstract from the paper is the following:"),br=d(),Bt=r("p"),bs=r("em"),Bn=a(`The recent \u201CText-to-Text Transfer Transformer\u201D (T5) leveraged a unified text-to-text format and scale to attain
state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a
multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail
the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual
benchmarks. We also describe a simple technique to prevent \u201Caccidental translation\u201D in the zero-shot setting, where a
generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model
checkpoints used in this work are publicly available.`),zr=d(),oe=r("p"),Hn=a("Note: mT5 was only pre-trained on "),Le=r("a"),Rn=a("mC4"),Xn=a(` excluding any supervised training.
Therefore, this model has to be fine-tuned before it is useable on a downstream task, unlike the original T5 model.
Since mT5 was pre-trained unsupervisedly, there\u2019s no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.`),Mr=d(),Ht=r("p"),Kn=a("Google has released the following variants:"),Er=d(),w=r("ul"),zs=r("li"),Ms=r("p"),Ne=r("a"),Jn=a("google/mt5-small"),Qn=d(),Es=r("li"),xs=r("p"),De=r("a"),Yn=a("google/mt5-base"),Zn=d(),qs=r("li"),Fs=r("p"),Ie=r("a"),eo=a("google/mt5-large"),to=d(),js=r("li"),Cs=r("p"),Ge=r("a"),so=a("google/mt5-xl"),ro=d(),Ps=r("li"),Rt=r("p"),Oe=r("a"),no=a("google/mt5-xxl"),oo=a("."),xr=d(),D=r("p"),ao=a("This model was contributed by "),Ve=r("a"),io=a("patrickvonplaten"),lo=a(`. The original code can be
found `),Ue=r("a"),po=a("here"),mo=a("."),qr=d(),W=r("h2"),ae=r("a"),As=r("span"),f(We.$$.fragment),co=d(),Ss=r("span"),fo=a("MT5Config"),Fr=d(),S=r("div"),f(Be.$$.fragment),ho=d(),L=r("p"),uo=a("This is the configuration class to store the configuration of a "),Xt=r("a"),go=a("MT5Model"),_o=a(" or a "),Kt=r("a"),ko=a("TFMT5Model"),vo=a(`. It is used to
instantiate a mT5 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the mT5
`),He=r("a"),To=a("google/mt5-small"),$o=a(" architecture."),wo=d(),B=r("p"),yo=a("Configuration objects inherit from "),Jt=r("a"),bo=a("PretrainedConfig"),zo=a(` and can be used to control the model outputs. Read the
documentation from `),Qt=r("a"),Mo=a("PretrainedConfig"),Eo=a(" for more information."),jr=d(),H=r("h2"),ie=r("a"),Ls=r("span"),f(Re.$$.fragment),xo=d(),Ns=r("span"),qo=a("MT5Tokenizer"),Cr=d(),v=r("div"),f(Xe.$$.fragment),Fo=d(),Ke=r("p"),jo=a("Construct a T5 tokenizer. Based on "),Je=r("a"),Co=a("SentencePiece"),Po=a("."),Ao=d(),Qe=r("p"),So=a("This tokenizer inherits from "),Yt=r("a"),Lo=a("PreTrainedTokenizer"),No=a(` which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`),Do=d(),I=r("div"),f(Ye.$$.fragment),Io=d(),Ds=r("p"),Go=a(`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A sequence has the following format:`),Oo=d(),Ze=r("ul"),Zt=r("li"),Vo=a("single sequence: "),Is=r("code"),Uo=a("X </s>"),Wo=d(),es=r("li"),Bo=a("pair of sequences: "),Gs=r("code"),Ho=a("A </s> B </s>"),Ro=d(),le=r("div"),f(et.$$.fragment),Xo=d(),Os=r("p"),Ko=a("Converts a sequence of tokens (string) in a single string."),Jo=d(),de=r("div"),f(tt.$$.fragment),Qo=d(),Vs=r("p"),Yo=a(`Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
use of token type ids, therefore a list of zeros is returned.`),Zo=d(),pe=r("div"),f(st.$$.fragment),ea=d(),rt=r("p"),ta=a(`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `),Us=r("code"),sa=a("prepare_for_model"),ra=a(" method."),Pr=d(),me=r("p"),na=a("See "),ts=r("a"),oa=a("T5Tokenizer"),aa=a(" for all details."),Ar=d(),R=r("h2"),ce=r("a"),Ws=r("span"),f(nt.$$.fragment),ia=d(),Bs=r("span"),la=a("MT5TokenizerFast"),Sr=d(),$=r("div"),f(ot.$$.fragment),da=d(),X=r("p"),pa=a("Construct a \u201Cfast\u201D T5 tokenizer (backed by HuggingFace\u2019s "),Hs=r("em"),ma=a("tokenizers"),ca=a(` library). Based on
`),at=r("a"),fa=a("Unigram"),ha=a("."),ua=d(),it=r("p"),ga=a("This tokenizer inherits from "),ss=r("a"),_a=a("PreTrainedTokenizerFast"),ka=a(` which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`),va=d(),G=r("div"),f(lt.$$.fragment),Ta=d(),Rs=r("p"),$a=a(`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A sequence has the following format:`),wa=d(),dt=r("ul"),rs=r("li"),ya=a("single sequence: "),Xs=r("code"),ba=a("X </s>"),za=d(),ns=r("li"),Ma=a("pair of sequences: "),Ks=r("code"),Ea=a("A </s> B </s>"),xa=d(),fe=r("div"),f(pt.$$.fragment),qa=d(),Js=r("p"),Fa=a(`Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
use of token type ids, therefore a list of zeros is returned.`),Lr=d(),he=r("p"),ja=a("See "),os=r("a"),Ca=a("T5TokenizerFast"),Pa=a(" for all details."),Nr=d(),K=r("h2"),ue=r("a"),Qs=r("span"),f(mt.$$.fragment),Aa=d(),Ys=r("span"),Sa=a("MT5Model"),Dr=d(),b=r("div"),f(ct.$$.fragment),La=d(),ft=r("p"),Na=a("This class overrides "),as=r("a"),Da=a("T5Model"),Ia=a(`. Please check the superclass for the appropriate documentation alongside usage
examples.`),Ga=d(),Zs=r("p"),Oa=a("Examples:"),Va=d(),f(ht.$$.fragment),Ir=d(),J=r("h2"),ge=r("a"),er=r("span"),f(ut.$$.fragment),Ua=d(),tr=r("span"),Wa=a("MT5ForConditionalGeneration"),Gr=d(),z=r("div"),f(gt.$$.fragment),Ba=d(),_t=r("p"),Ha=a("This class overrides "),is=r("a"),Ra=a("T5ForConditionalGeneration"),Xa=a(`. Please check the superclass for the appropriate documentation
alongside usage examples.`),Ka=d(),sr=r("p"),Ja=a("Examples:"),Qa=d(),f(kt.$$.fragment),Or=d(),Q=r("h2"),_e=r("a"),rr=r("span"),f(vt.$$.fragment),Ya=d(),nr=r("span"),Za=a("MT5EncoderModel"),Vr=d(),M=r("div"),f(Tt.$$.fragment),ei=d(),$t=r("p"),ti=a("This class overrides "),ls=r("a"),si=a("T5EncoderModel"),ri=a(`. Please check the superclass for the appropriate documentation alongside
usage examples.`),ni=d(),or=r("p"),oi=a("Examples:"),ai=d(),f(wt.$$.fragment),Ur=d(),Y=r("h2"),ke=r("a"),ar=r("span"),f(yt.$$.fragment),ii=d(),ir=r("span"),li=a("TFMT5Model"),Wr=d(),E=r("div"),f(bt.$$.fragment),di=d(),zt=r("p"),pi=a("This class overrides "),ds=r("a"),mi=a("TFT5Model"),ci=a(`. Please check the superclass for the appropriate documentation alongside usage
examples.`),fi=d(),lr=r("p"),hi=a("Examples:"),ui=d(),f(Mt.$$.fragment),Br=d(),Z=r("h2"),ve=r("a"),dr=r("span"),f(Et.$$.fragment),gi=d(),pr=r("span"),_i=a("TFMT5ForConditionalGeneration"),Hr=d(),x=r("div"),f(xt.$$.fragment),ki=d(),qt=r("p"),vi=a("This class overrides "),ps=r("a"),Ti=a("TFT5ForConditionalGeneration"),$i=a(`. Please check the superclass for the appropriate
documentation alongside usage examples.`),wi=d(),mr=r("p"),yi=a("Examples:"),bi=d(),f(Ft.$$.fragment),Rr=d(),ee=r("h2"),Te=r("a"),cr=r("span"),f(jt.$$.fragment),zi=d(),fr=r("span"),Mi=a("TFMT5EncoderModel"),Xr=d(),q=r("div"),f(Ct.$$.fragment),Ei=d(),Pt=r("p"),xi=a("This class overrides "),ms=r("a"),qi=a("TFT5EncoderModel"),Fi=a(`. Please check the superclass for the appropriate documentation alongside
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state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a
multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail
the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual
benchmarks. We also describe a simple technique to prevent \u201Caccidental translation\u201D in the zero-shot setting, where a
generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model
checkpoints used in this work are publicly available.`),cl.forEach(s),ml.forEach(s),zr=p(t),oe=n(t,"P",{});var rn=o(oe);Hn=i(rn,"Note: mT5 was only pre-trained on "),Le=n(rn,"A",{href:!0,rel:!0});var fl=o(Le);Rn=i(fl,"mC4"),fl.forEach(s),Xn=i(rn,` excluding any supervised training.
Therefore, this model has to be fine-tuned before it is useable on a downstream task, unlike the original T5 model.
Since mT5 was pre-trained unsupervisedly, there\u2019s no real advantage to using a task prefix during single-task
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found `),Ue=n(hs,"A",{href:!0,rel:!0});var Fl=o(Ue);po=i(Fl,"here"),Fl.forEach(s),mo=i(hs,"."),hs.forEach(s),qr=p(t),W=n(t,"H2",{class:!0});var nn=o(W);ae=n(nn,"A",{id:!0,class:!0,href:!0});var jl=o(ae);As=n(jl,"SPAN",{});var Cl=o(As);h(We.$$.fragment,Cl),Cl.forEach(s),jl.forEach(s),co=p(nn),Ss=n(nn,"SPAN",{});var Pl=o(Ss);fo=i(Pl,"MT5Config"),Pl.forEach(s),nn.forEach(s),Fr=p(t),S=n(t,"DIV",{class:!0});var us=o(S);h(Be.$$.fragment,us),ho=p(us),L=n(us,"P",{});var ye=o(L);uo=i(ye,"This is the configuration class to store the configuration of a "),Xt=n(ye,"A",{href:!0});var Al=o(Xt);go=i(Al,"MT5Model"),Al.forEach(s),_o=i(ye," or a "),Kt=n(ye,"A",{href:!0});var Sl=o(Kt);ko=i(Sl,"TFMT5Model"),Sl.forEach(s),vo=i(ye,`. It is used to
instantiate a mT5 model according to the specified arguments, defining the model architecture. Instantiating a
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`),He=n(ye,"A",{href:!0,rel:!0});var Ll=o(He);To=i(Ll,"google/mt5-small"),Ll.forEach(s),$o=i(ye," architecture."),ye.forEach(s),wo=p(us),B=n(us,"P",{});var gs=o(B);yo=i(gs,"Configuration objects inherit from "),Jt=n(gs,"A",{href:!0});var Nl=o(Jt);bo=i(Nl,"PretrainedConfig"),Nl.forEach(s),zo=i(gs,` and can be used to control the model outputs. Read the
documentation from `),Qt=n(gs,"A",{href:!0});var Dl=o(Qt);Mo=i(Dl,"PretrainedConfig"),Dl.forEach(s),Eo=i(gs," for more information."),gs.forEach(s),us.forEach(s),jr=p(t),H=n(t,"H2",{class:!0});var on=o(H);ie=n(on,"A",{id:!0,class:!0,href:!0});var Il=o(ie);Ls=n(Il,"SPAN",{});var Gl=o(Ls);h(Re.$$.fragment,Gl),Gl.forEach(s),Il.forEach(s),xo=p(on),Ns=n(on,"SPAN",{});var Ol=o(Ns);qo=i(Ol,"MT5Tokenizer"),Ol.forEach(s),on.forEach(s),Cr=p(t),v=n(t,"DIV",{class:!0});var y=o(v);h(Xe.$$.fragment,y),Fo=p(y),Ke=n(y,"P",{});var an=o(Ke);jo=i(an,"Construct a T5 tokenizer. Based on "),Je=n(an,"A",{href:!0,rel:!0});var Vl=o(Je);Co=i(Vl,"SentencePiece"),Vl.forEach(s),Po=i(an,"."),an.forEach(s),Ao=p(y),Qe=n(y,"P",{});var ln=o(Qe);So=i(ln,"This tokenizer inherits from "),Yt=n(ln,"A",{href:!0});var Ul=o(Yt);Lo=i(Ul,"PreTrainedTokenizer"),Ul.forEach(s),No=i(ln,` which contains most of the main methods. Users should refer to
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special tokens using the tokenizer `),Us=n(fn,"CODE",{});var Kl=o(Us);sa=i(Kl,"prepare_for_model"),Kl.forEach(s),ra=i(fn," method."),fn.forEach(s),cn.forEach(s),y.forEach(s),Pr=p(t),me=n(t,"P",{});var hn=o(me);na=i(hn,"See "),ts=n(hn,"A",{href:!0});var Jl=o(ts);oa=i(Jl,"T5Tokenizer"),Jl.forEach(s),aa=i(hn," for all details."),hn.forEach(s),Ar=p(t),R=n(t,"H2",{class:!0});var un=o(R);ce=n(un,"A",{id:!0,class:!0,href:!0});var Ql=o(ce);Ws=n(Ql,"SPAN",{});var Yl=o(Ws);h(nt.$$.fragment,Yl),Yl.forEach(s),Ql.forEach(s),ia=p(un),Bs=n(un,"SPAN",{});var Zl=o(Bs);la=i(Zl,"MT5TokenizerFast"),Zl.forEach(s),un.forEach(s),Sr=p(t),$=n(t,"DIV",{class:!0});var V=o($);h(ot.$$.fragment,V),da=p(V),X=n(V,"P",{});var ks=o(X);pa=i(ks,"Construct a \u201Cfast\u201D T5 tokenizer (backed by HuggingFace\u2019s "),Hs=n(ks,"EM",{});var ed=o(Hs);ma=i(ed,"tokenizers"),ed.forEach(s),ca=i(ks,` library). Based on
`),at=n(ks,"A",{href:!0,rel:!0});var td=o(at);fa=i(td,"Unigram"),td.forEach(s),ha=i(ks,"."),ks.forEach(s),ua=p(V),it=n(V,"P",{});var gn=o(it);ga=i(gn,"This tokenizer inherits from "),ss=n(gn,"A",{href:!0});var sd=o(ss);_a=i(sd,"PreTrainedTokenizerFast"),sd.forEach(s),ka=i(gn,` which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`),gn.forEach(s),va=p(V),G=n(V,"DIV",{class:!0});var vs=o(G);h(lt.$$.fragment,vs),Ta=p(vs),Rs=n(vs,"P",{});var rd=o(Rs);$a=i(rd,`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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examples.`),$n.forEach(s),Ga=p(be),Zs=n(be,"P",{});var cd=o(Zs);Oa=i(cd,"Examples:"),cd.forEach(s),Va=p(be),h(ht.$$.fragment,be),be.forEach(s),Ir=p(t),J=n(t,"H2",{class:!0});var wn=o(J);ge=n(wn,"A",{id:!0,class:!0,href:!0});var fd=o(ge);er=n(fd,"SPAN",{});var hd=o(er);h(ut.$$.fragment,hd),hd.forEach(s),fd.forEach(s),Ua=p(wn),tr=n(wn,"SPAN",{});var ud=o(tr);Wa=i(ud,"MT5ForConditionalGeneration"),ud.forEach(s),wn.forEach(s),Gr=p(t),z=n(t,"DIV",{class:!0});var ze=o(z);h(gt.$$.fragment,ze),Ba=p(ze),_t=n(ze,"P",{});var yn=o(_t);Ha=i(yn,"This class overrides "),is=n(yn,"A",{href:!0});var gd=o(is);Ra=i(gd,"T5ForConditionalGeneration"),gd.forEach(s),Xa=i(yn,`. Please check the superclass for the appropriate documentation
alongside usage examples.`),yn.forEach(s),Ka=p(ze),sr=n(ze,"P",{});var _d=o(sr);Ja=i(_d,"Examples:"),_d.forEach(s),Qa=p(ze),h(kt.$$.fragment,ze),ze.forEach(s),Or=p(t),Q=n(t,"H2",{class:!0});var bn=o(Q);_e=n(bn,"A",{id:!0,class:!0,href:!0});var kd=o(_e);rr=n(kd,"SPAN",{});var vd=o(rr);h(vt.$$.fragment,vd),vd.forEach(s),kd.forEach(s),Ya=p(bn),nr=n(bn,"SPAN",{});var Td=o(nr);Za=i(Td,"MT5EncoderModel"),Td.forEach(s),bn.forEach(s),Vr=p(t),M=n(t,"DIV",{class:!0});var Me=o(M);h(Tt.$$.fragment,Me),ei=p(Me),$t=n(Me,"P",{});var zn=o($t);ti=i(zn,"This class overrides "),ls=n(zn,"A",{href:!0});var $d=o(ls);si=i($d,"T5EncoderModel"),$d.forEach(s),ri=i(zn,`. Please check the superclass for the appropriate documentation alongside
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examples.`),En.forEach(s),fi=p(Ee),lr=n(Ee,"P",{});var Ed=o(lr);hi=i(Ed,"Examples:"),Ed.forEach(s),ui=p(Ee),h(Mt.$$.fragment,Ee),Ee.forEach(s),Br=p(t),Z=n(t,"H2",{class:!0});var xn=o(Z);ve=n(xn,"A",{id:!0,class:!0,href:!0});var xd=o(ve);dr=n(xd,"SPAN",{});var qd=o(dr);h(Et.$$.fragment,qd),qd.forEach(s),xd.forEach(s),gi=p(xn),pr=n(xn,"SPAN",{});var Fd=o(pr);_i=i(Fd,"TFMT5ForConditionalGeneration"),Fd.forEach(s),xn.forEach(s),Hr=p(t),x=n(t,"DIV",{class:!0});var xe=o(x);h(xt.$$.fragment,xe),ki=p(xe),qt=n(xe,"P",{});var qn=o(qt);vi=i(qn,"This class overrides "),ps=n(qn,"A",{href:!0});var jd=o(ps);Ti=i(jd,"TFT5ForConditionalGeneration"),jd.forEach(s),$i=i(qn,`. Please check the superclass for the appropriate
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