import{s as Xa,o as Ja,n as Ga}from"../chunks/scheduler.25b97de1.js";import{S as Qa,i as Ya,g as s,s as a,r as m,A as Za,h as l,f as n,c as o,j as $,u as c,x as d,k as _,y as t,a as i,v as p,d as g,t as f,w as u}from"../chunks/index.d9030fc9.js";import{T as Ra}from"../chunks/Tip.baa67368.js";import{D as C}from"../chunks/Docstring.e257edda.js";import{H,E as eo}from"../chunks/EditOnGithub.91d95064.js";function to(we){let h,q=`For best performance, this data collator should be used with a dataset having items that are dictionaries or
BatchEncoding, with the <code>&quot;special_tokens_mask&quot;</code> key, as returned by a <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or a
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> with the argument <code>return_special_tokens_mask=True</code>.`;return{c(){h=s("p"),h.innerHTML=q},l(v){h=l(v,"P",{"data-svelte-h":!0}),d(h)!=="svelte-1uxurri"&&(h.innerHTML=q)},m(v,E){i(v,h,E)},p:Ga,d(v){v&&n(h)}}}function ao(we){let h,q=`This collator relies on details of the implementation of subword tokenization by <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>, specifically
that subword tokens are prefixed with <em>##</em>. For tokenizers that do not adhere to this scheme, this collator will
produce an output that is roughly equivalent to <code>.DataCollatorForLanguageModeling</code>.`;return{c(){h=s("p"),h.innerHTML=q},l(v){h=l(v,"P",{"data-svelte-h":!0}),d(h)!=="svelte-kpcut1"&&(h.innerHTML=q)},m(v,E){i(v,h,E)},p:Ga,d(v){v&&n(h)}}}function oo(we){let h,q,v,E,G,at,X,_a=`Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
the same type as the elements of <code>train_dataset</code> or <code>eval_dataset</code>.`,ot,J,ba=`To be able to build batches, data collators may apply some processing (like padding). Some of them (like
<a href="/docs/transformers/main/en/main_classes/data_collator#transformers.DataCollatorForLanguageModeling">DataCollatorForLanguageModeling</a>) also apply some random data augmentation (like random masking)
on the formed batch.`,nt,Q,$a='Examples of use can be found in the <a href="../examples">example scripts</a> or <a href="../notebooks">example notebooks</a>.',rt,Y,st,y,Z,zt,Pe,va=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,St,Fe,ka="<li><code>label</code>: handles a single value (int or float) per object</li> <li><code>label_ids</code>: handles a list of values per object</li>",It,Le,xa=`Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it’s useful.`,lt,ee,it,T,te,Wt,qe,Ca=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,At,Me,Ta="<li><code>label</code>: handles a single value (int or float) per object</li> <li><code>label_ids</code>: handles a list of values per object</li>",Ht,ze,Da=`Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it’s useful.`,Et,Se,ya=`This is an object (like other data collators) rather than a pure function like default_data_collator. This can be
helpful if you need to set a return_tensors value at initialization.`,dt,ae,mt,I,oe,Vt,Ie,wa="Data collator that will dynamically pad the inputs received.",ct,ne,pt,W,re,jt,We,Pa="Data collator that will dynamically pad the inputs received, as well as the labels.",gt,se,ft,A,le,Nt,Ae,Fa="Data collator that will dynamically pad the inputs received, as well as the labels.",ut,ie,ht,k,de,Ot,He,La=`Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.`,Bt,V,Ut,j,me,Kt,Ee,qa="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",Rt,N,ce,Gt,Ve,Ma="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",Xt,O,pe,Jt,je,za="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",_t,ge,bt,b,fe,Qt,Ne,Sa="Data collator used for language modeling that masks entire words.",Yt,Oe,Ia="<li>collates batches of tensors, honoring their tokenizer’s pad_token</li> <li>preprocesses batches for masked language modeling</li>",Zt,B,ea,U,ue,ta,Be,Wa=`Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.`,aa,K,he,oa,Ue,Aa=`Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.`,na,R,_e,ra,Ke,Ha=`Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.`,$t,be,vt,x,$e,sa,Re,Ea="Data collator used for permutation language modeling.",la,Ge,Va="<li>collates batches of tensors, honoring their tokenizer’s pad_token</li> <li>preprocesses batches for permutation language modeling with procedures specific to XLNet</li>",ia,M,ve,da,Xe,ja="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",ma,ke,Na=`<li>Start from the beginning of the sequence by setting <code>cur_len = 0</code> (number of tokens processed so far).</li> <li>Sample a <code>span_length</code> from the interval <code>[1, max_span_length]</code> (length of span of tokens to be masked)</li> <li>Reserve a context of length <code>context_length = span_length / plm_probability</code> to surround span to be
masked</li> <li>Sample a starting point <code>start_index</code> from the interval <code>[cur_len, cur_len + context_length - span_length]</code> and mask tokens <code>start_index:start_index + span_length</code></li> <li>Set <code>cur_len = cur_len + context_length</code>. If <code>cur_len &lt; max_len</code> (i.e. there are tokens remaining in the
sequence to be processed), repeat from Step 1.</li>`,ca,z,xe,pa,Je,Oa="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",ga,Ce,Ba=`<li>Start from the beginning of the sequence by setting <code>cur_len = 0</code> (number of tokens processed so far).</li> <li>Sample a <code>span_length</code> from the interval <code>[1, max_span_length]</code> (length of span of tokens to be masked)</li> <li>Reserve a context of length <code>context_length = span_length / plm_probability</code> to surround span to be
masked</li> <li>Sample a starting point <code>start_index</code> from the interval <code>[cur_len, cur_len + context_length - span_length]</code> and mask tokens <code>start_index:start_index + span_length</code></li> <li>Set <code>cur_len = cur_len + context_length</code>. If <code>cur_len &lt; max_len</code> (i.e. there are tokens remaining in the
sequence to be processed), repeat from Step 1.</li>`,fa,S,Te,ua,Qe,Ua="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",ha,De,Ka=`<li>Start from the beginning of the sequence by setting <code>cur_len = 0</code> (number of tokens processed so far).</li> <li>Sample a <code>span_length</code> from the interval <code>[1, max_span_length]</code> (length of span of tokens to be masked)</li> <li>Reserve a context of length <code>context_length = span_length / plm_probability</code> to surround span to be
masked</li> <li>Sample a starting point <code>start_index</code> from the interval <code>[cur_len, cur_len + context_length - span_length]</code> and mask tokens <code>start_index:start_index + span_length</code></li> <li>Set <code>cur_len = cur_len + context_length</code>. If <code>cur_len &lt; max_len</code> (i.e. there are tokens remaining in the
sequence to be processed), repeat from Step 1.</li>`,kt,ye,xt,tt,Ct;return G=new H({props:{title:"Data Collator",local:"data-collator",headingTag:"h1"}}),Y=new H({props:{title:"Default data collator",local:"transformers.default_data_collator",headingTag:"h2"}}),Z=new C({props:{name:"transformers.default_data_collator",anchor:"transformers.default_data_collator",parameters:[{name:"features",val:": List"},{name:"return_tensors",val:" = 'pt'"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L74"}}),ee=new H({props:{title:"DefaultDataCollator",local:"transformers.DefaultDataCollator",headingTag:"h2"}}),te=new C({props:{name:"class transformers.DefaultDataCollator",anchor:"transformers.DefaultDataCollator",parameters:[{name:"return_tensors",val:": str = 'pt'"}],parametersDescription:[{anchor:"transformers.DefaultDataCollator.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pt&quot;</code>) &#x2014;
The type of Tensor to return. Allowable values are &#x201C;np&#x201D;, &#x201C;pt&#x201D; and &#x201C;tf&#x201D;.`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L99"}}),ae=new H({props:{title:"DataCollatorWithPadding",local:"transformers.DataCollatorWithPadding",headingTag:"h2"}}),oe=new C({props:{name:"class transformers.DataCollatorWithPadding",anchor:"transformers.DataCollatorWithPadding",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"padding",val:": Union = True"},{name:"max_length",val:": Optional = None"},{name:"pad_to_multiple_of",val:": Optional = None"},{name:"return_tensors",val:": str = 'pt'"}],parametersDescription:[{anchor:"transformers.DataCollatorWithPadding.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) &#x2014;
The tokenizer used for encoding the data.`,name:"tokenizer"},{anchor:"transformers.DataCollatorWithPadding.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/main/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Select a strategy to pad the returned sequences (according to the model&#x2019;s padding side and padding index)
among:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code> (default): Pad to the longest sequence in the batch (or no padding if only a single
sequence is provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code>: No padding (i.e., can output a batch with sequences of different lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DataCollatorWithPadding.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Maximum length of the returned list and optionally padding length (see above).`,name:"max_length"},{anchor:"transformers.DataCollatorWithPadding.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) &#x2014;
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability &gt;=
7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.DataCollatorWithPadding.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pt&quot;</code>) &#x2014;
The type of Tensor to return. Allowable values are &#x201C;np&#x201D;, &#x201C;pt&#x201D; and &#x201C;tf&#x201D;.`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L236"}}),ne=new H({props:{title:"DataCollatorForTokenClassification",local:"transformers.DataCollatorForTokenClassification",headingTag:"h2"}}),re=new C({props:{name:"class transformers.DataCollatorForTokenClassification",anchor:"transformers.DataCollatorForTokenClassification",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"padding",val:": Union = True"},{name:"max_length",val:": Optional = None"},{name:"pad_to_multiple_of",val:": Optional = None"},{name:"label_pad_token_id",val:": int = -100"},{name:"return_tensors",val:": str = 'pt'"}],parametersDescription:[{anchor:"transformers.DataCollatorForTokenClassification.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) &#x2014;
The tokenizer used for encoding the data.`,name:"tokenizer"},{anchor:"transformers.DataCollatorForTokenClassification.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/main/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Select a strategy to pad the returned sequences (according to the model&#x2019;s padding side and padding index)
among:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code> (default): Pad to the longest sequence in the batch (or no padding if only a single
sequence is provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code>: No padding (i.e., can output a batch with sequences of different lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DataCollatorForTokenClassification.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Maximum length of the returned list and optionally padding length (see above).`,name:"max_length"},{anchor:"transformers.DataCollatorForTokenClassification.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) &#x2014;
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability &gt;=
7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.DataCollatorForTokenClassification.label_pad_token_id",description:`<strong>label_pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to -100) &#x2014;
The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).`,name:"label_pad_token_id"},{anchor:"transformers.DataCollatorForTokenClassification.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pt&quot;</code>) &#x2014;
The type of Tensor to return. Allowable values are &#x201C;np&#x201D;, &#x201C;pt&#x201D; and &#x201C;tf&#x201D;.`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L288"}}),se=new H({props:{title:"DataCollatorForSeq2Seq",local:"transformers.DataCollatorForSeq2Seq",headingTag:"h2"}}),le=new C({props:{name:"class transformers.DataCollatorForSeq2Seq",anchor:"transformers.DataCollatorForSeq2Seq",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"model",val:": Optional = None"},{name:"padding",val:": Union = True"},{name:"max_length",val:": Optional = None"},{name:"pad_to_multiple_of",val:": Optional = None"},{name:"label_pad_token_id",val:": int = -100"},{name:"return_tensors",val:": str = 'pt'"}],parametersDescription:[{anchor:"transformers.DataCollatorForSeq2Seq.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) &#x2014;
The tokenizer used for encoding the data.`,name:"tokenizer"},{anchor:"transformers.DataCollatorForSeq2Seq.model",description:`<strong>model</strong> (<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>, <em>optional</em>) &#x2014;
The model that is being trained. If set and has the <em>prepare_decoder_input_ids_from_labels</em>, use it to
prepare the <em>decoder_input_ids</em></p>
<p>This is useful when using <em>label_smoothing</em> to avoid calculating loss twice.`,name:"model"},{anchor:"transformers.DataCollatorForSeq2Seq.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/main/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Select a strategy to pad the returned sequences (according to the model&#x2019;s padding side and padding index)
among:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code> (default): Pad to the longest sequence in the batch (or no padding if only a single
sequence is provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code>: No padding (i.e., can output a batch with sequences of different lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DataCollatorForSeq2Seq.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Maximum length of the returned list and optionally padding length (see above).`,name:"max_length"},{anchor:"transformers.DataCollatorForSeq2Seq.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) &#x2014;
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability &gt;=
7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.DataCollatorForSeq2Seq.label_pad_token_id",description:`<strong>label_pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to -100) &#x2014;
The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).`,name:"label_pad_token_id"},{anchor:"transformers.DataCollatorForSeq2Seq.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pt&quot;</code>) &#x2014;
The type of Tensor to return. Allowable values are &#x201C;np&#x201D;, &#x201C;pt&#x201D; and &#x201C;tf&#x201D;.`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L542"}}),ie=new H({props:{title:"DataCollatorForLanguageModeling",local:"transformers.DataCollatorForLanguageModeling",headingTag:"h2"}}),de=new C({props:{name:"class transformers.DataCollatorForLanguageModeling",anchor:"transformers.DataCollatorForLanguageModeling",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"mlm",val:": bool = True"},{name:"mlm_probability",val:": float = 0.15"},{name:"pad_to_multiple_of",val:": Optional = None"},{name:"tf_experimental_compile",val:": bool = False"},{name:"return_tensors",val:": str = 'pt'"}],parametersDescription:[{anchor:"transformers.DataCollatorForLanguageModeling.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) &#x2014;
The tokenizer used for encoding the data.`,name:"tokenizer"},{anchor:"transformers.DataCollatorForLanguageModeling.mlm",description:`<strong>mlm</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to use masked language modeling. If set to <code>False</code>, the labels are the same as the inputs
with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for non-masked
tokens and the value to predict for the masked token.`,name:"mlm"},{anchor:"transformers.DataCollatorForLanguageModeling.mlm_probability",description:`<strong>mlm_probability</strong> (<code>float</code>, <em>optional</em>, defaults to 0.15) &#x2014;
The probability with which to (randomly) mask tokens in the input, when <code>mlm</code> is set to <code>True</code>.`,name:"mlm_probability"},{anchor:"transformers.DataCollatorForLanguageModeling.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) &#x2014;
If set will pad the sequence to a multiple of the provided value.`,name:"pad_to_multiple_of"},{anchor:"transformers.DataCollatorForLanguageModeling.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code>) &#x2014;
The type of Tensor to return. Allowable values are &#x201C;np&#x201D;, &#x201C;pt&#x201D; and &#x201C;tf&#x201D;.`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L678"}}),V=new Ra({props:{$$slots:{default:[to]},$$scope:{ctx:we}}}),me=new C({props:{name:"numpy_mask_tokens",anchor:"transformers.DataCollatorForLanguageModeling.numpy_mask_tokens",parameters:[{name:"inputs",val:": Any"},{name:"special_tokens_mask",val:": Optional = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L884"}}),ce=new C({props:{name:"tf_mask_tokens",anchor:"transformers.DataCollatorForLanguageModeling.tf_mask_tokens",parameters:[{name:"inputs",val:": Any"},{name:"vocab_size",val:""},{name:"mask_token_id",val:""},{name:"special_tokens_mask",val:": Optional = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L731"}}),pe=new C({props:{name:"torch_mask_tokens",anchor:"transformers.DataCollatorForLanguageModeling.torch_mask_tokens",parameters:[{name:"inputs",val:": Any"},{name:"special_tokens_mask",val:": Optional = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L827"}}),ge=new H({props:{title:"DataCollatorForWholeWordMask",local:"transformers.DataCollatorForWholeWordMask",headingTag:"h2"}}),fe=new C({props:{name:"class transformers.DataCollatorForWholeWordMask",anchor:"transformers.DataCollatorForWholeWordMask",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"mlm",val:": bool = True"},{name:"mlm_probability",val:": float = 0.15"},{name:"pad_to_multiple_of",val:": Optional = None"},{name:"tf_experimental_compile",val:": bool = False"},{name:"return_tensors",val:": str = 'pt'"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L922"}}),B=new 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