import{s as Xa,o as Ja,n as Ga}from"../chunks/scheduler.9991993c.js";import{S as Qa,i as Ya,g as l,s as o,r as m,A as Za,h as s,f as a,c as n,j as k,u as p,x as d,k as _,y as t,a as i,v as c,d as g,t as f,w as u,m as eo,n as to}from"../chunks/index.ed60ef0f.js";import{T as Ra}from"../chunks/Tip.8eaeb7b5.js";import{D as C}from"../chunks/Docstring.98f0ea00.js";import{H as K,E as ao}from"../chunks/index.3555199c.js";function oo(Me){let h,A=`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/zh/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or a
<a href="/docs/transformers/main/zh/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> with the argument <code>return_special_tokens_mask=True</code>.`,y,w,I="<li><p>Default Behavior:</p> <ul><li><code>mask_replace_prob=0.8</code>, <code>random_replace_prob=0.1</code>.</li> <li>Expect 80% of masked tokens replaced with <code>[MASK]</code>, 10% replaced with random tokens, and 10% left unchanged.</li></ul></li> <li><p>All masked tokens replaced by <code>[MASK]</code>:</p> <ul><li><code>mask_replace_prob=1.0</code>, <code>random_replace_prob=0.0</code>.</li> <li>Expect all masked tokens to be replaced with <code>[MASK]</code>. No tokens are left unchanged or replaced with random tokens.</li></ul></li> <li><p>No <code>[MASK]</code> replacement, only random tokens:</p> <ul><li><code>mask_replace_prob=0.0</code>, <code>random_replace_prob=1.0</code>.</li> <li>Expect all masked tokens to be replaced with random tokens. No <code>[MASK]</code> replacements or unchanged tokens.</li></ul></li> <li><p>Balanced replacement:</p> <ul><li><code>mask_replace_prob=0.5</code>, <code>random_replace_prob=0.4</code>.</li> <li>Expect 50% of masked tokens replaced with <code>[MASK]</code>, 40% replaced with random tokens, and 10% left unchanged.</li></ul></li>",N,T,ot=`Note:
The sum of <code>mask_replace_prob</code> and <code>random_replace_prob</code> must not exceed 1. If their sum is less than 1, the
remaining proportion will consist of masked tokens left unchanged.`;return{c(){h=l("p"),h.innerHTML=A,y=eo(`
<Example Options and Expectations>
`),w=l("ol"),w.innerHTML=I,N=o(),T=l("p"),T.innerHTML=ot},l(v){h=s(v,"P",{"data-svelte-h":!0}),d(h)!=="svelte-1gijru4"&&(h.innerHTML=A),y=to(v,`
<Example Options and Expectations>
`),w=s(v,"OL",{"data-svelte-h":!0}),d(w)!=="svelte-14xwpjc"&&(w.innerHTML=I),N=n(v),T=s(v,"P",{"data-svelte-h":!0}),d(T)!=="svelte-10bg7dp"&&(T.innerHTML=ot)},m(v,M){i(v,h,M),i(v,y,M),i(v,w,M),i(v,N,M),i(v,T,M)},p:Ga,d(v){v&&(a(h),a(y),a(w),a(N),a(T))}}}function no(Me){let h,A=`This collator relies on details of the implementation of subword tokenization by <code>BertTokenizer</code>, 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=l("p"),h.innerHTML=A},l(y){h=s(y,"P",{"data-svelte-h":!0}),d(h)!=="svelte-3v0y1u"&&(h.innerHTML=A)},m(y,w){i(y,h,w)},p:Ga,d(y){y&&a(h)}}}function ro(Me){let h,A,y,w,I,N,T,ot="Data collators是一个对象，通过使用数据集元素列表作为输入来形成一个批次。这些元素与 <code>train_dataset</code> 或 <code>eval_dataset</code> 的元素类型相同。",v,M,ba='为了能够构建批次，Data collators可能会应用一些预处理（比如填充）。其中一些（比如<a href="/docs/transformers/main/zh/main_classes/data_collator#transformers.DataCollatorForLanguageModeling">DataCollatorForLanguageModeling</a>）还会在形成的批次上应用一些随机数据增强（比如随机掩码）。',rt,Z,ka='在<a href="../examples">示例脚本</a>或<a href="../notebooks">示例notebooks</a>中可以找到使用的示例。',lt,ee,st,z,te,qt,ze,va=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,At,Le,$a="<li><code>label</code>: handles a single value (int or float) per object</li> <li><code>label_ids</code>: handles a list of values per object</li>",It,Fe,xa=`Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it’s useful.`,it,ae,dt,D,oe,Et,Se,Ca=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,Ht,qe,ya="<li><code>label</code>: handles a single value (int or float) per object</li> <li><code>label_ids</code>: handles a list of values per object</li>",Wt,Ae,Ta=`Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it’s useful.`,Nt,Ie,Da=`This is an object (like other data collators) rather than a pure function like default_data_collator. This can be
helpful if you need to set a return_tensors value at initialization.`,mt,ne,pt,V,re,Vt,Ee,wa="Data collator that will dynamically pad the inputs received.",ct,le,gt,O,se,Ot,He,Pa="Data collator that will dynamically pad the inputs received, as well as the labels.",ft,ie,ut,j,de,jt,We,Ma="Data collator that will dynamically pad the inputs received, as well as the labels.",ht,me,_t,$,pe,Kt,Ne,za=`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,B,Ut,U,ce,Rt,Ve,La="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",Gt,R,ge,Xt,Oe,Fa="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",Jt,G,fe,Qt,je,Sa="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",bt,ue,kt,b,he,Yt,Ke,qa="Data collator used for language modeling that masks entire words.",Zt,Be,Aa="<li>collates batches of tensors, honoring their tokenizer’s pad_token</li> <li>preprocesses batches for masked language modeling</li>",ea,X,ta,J,_e,aa,Ue,Ia=`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.`,oa,Q,be,na,Re,Ea=`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.`,ra,Y,ke,la,Ge,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.`,vt,ve,$t,x,$e,sa,Xe,Wa="Data collator used for permutation language modeling.",ia,Je,Na="<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>",da,E,xe,ma,Qe,Va="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",pa,Ce,Oa=`<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,H,ye,ga,Ye,ja="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",fa,Te,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>`,ua,W,De,ha,Ze,Ba="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",_a,we,Ua=`<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>`,xt,Pe,Ct,nt,yt;return I=new K({props:{title:"Data Collator",local:"data-collator",headingTag:"h1"}}),ee=new K({props:{title:"Default data collator",local:"transformers.default_data_collator",headingTag:"h2"}}),te=new C({props:{name:"transformers.default_data_collator",anchor:"transformers.default_data_collator",parameters:[{name:"features",val:": typing.List[transformers.data.data_collator.InputDataClass]"},{name:"return_tensors",val:" = 'pt'"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py#L75"}}),ae=new K({props:{title:"DefaultDataCollator",local:"transformers.DefaultDataCollator",headingTag:"h2"}}),oe=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#L100"}}),ne=new K({props:{title:"DataCollatorWithPadding",local:"transformers.DataCollatorWithPadding",headingTag:"h2"}}),re=new C({props:{name:"class transformers.DataCollatorWithPadding",anchor:"transformers.DataCollatorWithPadding",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"padding",val:": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_tensors",val:": str = 'pt'"}],parametersDescription:[{anchor:"transformers.DataCollatorWithPadding.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/zh/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/zh/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/zh/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;=
7.0 (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#L237"}}),le=new K({props:{title:"DataCollatorForTokenClassification",local:"transformers.DataCollatorForTokenClassification",headingTag:"h2"}}),se=new C({props:{name:"class transformers.DataCollatorForTokenClassification",anchor:"transformers.DataCollatorForTokenClassification",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"padding",val:": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = 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/zh/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/zh/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/zh/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;=
7.0 (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#L289"}}),ie=new K({props:{title:"DataCollatorForSeq2Seq",local:"transformers.DataCollatorForSeq2Seq",headingTag:"h2"}}),de=new C({props:{name:"class transformers.DataCollatorForSeq2Seq",anchor:"transformers.DataCollatorForSeq2Seq",parameters:[{name:"tokenizer",val:": PreTrainedTokenizerBase"},{name:"model",val:": typing.Optional[typing.Any] = None"},{name:"padding",val:": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = 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/zh/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/zh/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/zh/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/zh/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;=
7.0 (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#L627"}}),me=new K({props:{title:"DataCollatorForLanguageModeling",local:"transformers.DataCollatorForLanguageModeling",headingTag:"h2"}}),pe=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:"mask_replace_prob",val:": float = 0.8"},{name:"random_replace_prob",val:": float = 0.1"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"tf_experimental_compile",val:": bool = False"},{name:"return_tensors",val:": str = 'pt'"},{name:"seed",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"transformers.DataCollatorForLanguageModeling.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/zh/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/main/zh/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.mask_replace_prob",description:`<strong>mask_replace_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.8) &#x2014;
The probability with which masked tokens are replaced by the tokenizer&#x2019;s mask token (e.g., <code>[MASK]</code>).
Defaults to 0.8, meaning 80% of the masked tokens will be replaced with <code>[MASK]</code>.
Only works when <code>mlm</code> is set to <code>True</code>.`,name:"mask_replace_prob"},{anchor:"transformers.DataCollatorForLanguageModeling.random_replace_prob",description:`<strong>random_replace_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The probability with which masked tokens are replaced by random tokens from the tokenizer&#x2019;s vocabulary.
Defaults to 0.1, meaning 10% of the masked tokens will be replaced with random tokens. The remaining
masked tokens (1 - mask_replace_prob - random_replace_prob) are left unchanged.
Only works when <code>mlm</code> is set to <code>True</code>.`,name:"random_replace_prob"},{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"},{anchor:"transformers.DataCollatorForLanguageModeling.seed",description:`<strong>seed</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The seed to use for the random number generator for masking. 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