import{s as xo,o as $o,n as ko}from"../chunks/scheduler.9bc65507.js";import{S as Co,i as yo,g as r,s as n,u as c,A as wo,h as o,f as a,c as l,j as d,v as h,y as p,k as s,o as t,a as m,w as g,d as f,t as u,x as _}from"../chunks/index.78c82d43.js";import{T as bo}from"../chunks/Tip.87d55b76.js";import{D as C}from"../chunks/Docstring.f3d04bd2.js";import{I as K}from"../chunks/IconCopyLink.bedaa44d.js";function Do(Ue){let v,S=`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/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or a
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> with the argument <code>return_special_tokens_mask=True</code>.`;return{c(){v=r("p"),v.innerHTML=S},l(k){v=o(k,"P",{"data-svelte-h":!0}),p(v)!=="svelte-as97g6"&&(v.innerHTML=S)},m(k,R){m(k,v,R)},p:ko,d(k){k&&a(v)}}}function To(Ue){let v,S=`This collator relies on details of the implementation of subword tokenization by <a href="/docs/transformers/v4.33.3/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(){v=r("p"),v.innerHTML=S},l(k){v=o(k,"P",{"data-svelte-h":!0}),p(v)!=="svelte-srzikp"&&(v.innerHTML=S)},m(k,R){m(k,v,R)},p:ko,d(k){k&&a(v)}}}function Po(Ue){let v,S,k,R,z,J,Pt,ce,Ca,Ke,ur="Data Collator",Ht,he,_r=`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>.`,Wt,ge,vr=`To be able to build batches, data collators may apply some processing (like padding). Some of them (like
<a href="/docs/transformers/v4.33.3/en/main_classes/data_collator#transformers.DataCollatorForLanguageModeling">DataCollatorForLanguageModeling</a>) also apply some random data augmentation (like random masking)
on the formed batch.`,Vt,fe,br='Examples of use can be found in the <a href="../examples">example scripts</a> or <a href="../notebooks">example notebooks</a>.',jt,A,X,St,ue,ya,Re,kr="Default data collator",Ot,D,_e,wa,Je,xr=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,Da,Xe,$r="<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>",Ta,Ge,Cr=`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.`,Bt,E,G,Ft,ve,Pa,Qe,yr="DefaultDataCollator",Ut,y,be,Sa,Ye,wr=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,Fa,Ze,Dr="<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>",La,et,Tr=`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.`,Ma,tt,Pr=`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.`,Kt,N,Q,Lt,ke,qa,at,Sr="DataCollatorWithPadding",Rt,I,xe,za,rt,Fr="Data collator that will dynamically pad the inputs received.",Jt,H,Y,Mt,$e,Aa,ot,Lr="DataCollatorForTokenClassification",Xt,W,Ce,Ea,nt,Mr="Data collator that will dynamically pad the inputs received, as well as the labels.",Gt,V,Z,qt,ye,Na,lt,qr="DataCollatorForSeq2Seq",Qt,j,we,Ia,st,zr="Data collator that will dynamically pad the inputs received, as well as the labels.",Yt,O,ee,zt,De,Ha,it,Ar="DataCollatorForLanguageModeling",Zt,x,Te,Wa,dt,Er=`Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.`,Va,te,ja,ae,Pe,Oa,mt,Nr="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",Ba,re,Se,Ua,pt,Ir="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",Ka,oe,Fe,Ra,ct,Hr="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",ea,B,ne,At,Le,Ja,ht,Wr="DataCollatorForWholeWordMask",ta,b,Me,Xa,gt,Vr="Data collator used for language modeling that masks entire words.",Ga,ft,jr="<li>collates batches of tensors, honoring their tokenizer’s pad_token</li> <li>preprocesses batches for masked language modeling</li>",Qa,le,Ya,se,qe,Za,ut,Or=`Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.`,er,ie,ze,tr,_t,Br=`Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.`,ar,de,Ae,rr,vt,Ur=`Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.`,aa,U,me,Et,Ee,or,bt,Kr="DataCollatorForPermutationLanguageModeling",ra,$,Ne,nr,kt,Rr="Data collator used for permutation language modeling.",lr,xt,Jr="<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>",sr,F,Ie,ir,$t,Xr="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",dr,He,Gr=`<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>`,mr,L,We,pr,Ct,Qr="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",cr,Ve,Yr=`<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>`,hr,M,je,gr,yt,Zr="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",fr,Oe,eo=`<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>`,oa,Nt,na;return ce=new K({}),ue=new K({}),_e=new C({props:{name:"transformers.default_data_collator",anchor:"transformers.default_data_collator",parameters:[{name:"features",val:": typing.List[InputDataClass]"},{name:"return_tensors",val:" = 'pt'"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/data/data_collator.py#L52"}}),ve=new K({}),be=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>) &#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/v4.33.3/src/transformers/data/data_collator.py#L78"}}),ke=new K({}),xe=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/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/v4.33.3/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/v4.33.3/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>) &#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/v4.33.3/src/transformers/data/data_collator.py#L215"}}),$e=new K({}),Ce=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/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/v4.33.3/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/v4.33.3/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>) &#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/v4.33.3/src/transformers/data/data_collator.py#L266"}}),ye=new K({}),we=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/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>) &#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/v4.33.3/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>) &#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/v4.33.3/src/transformers/data/data_collator.py#L517"}}),De=new K({}),Te=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:": typing.Optional[int] = 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/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/v4.33.3/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/v4.33.3/src/transformers/data/data_collator.py#L607"}}),te=new bo({props:{$$slots:{default:[Do]},$$scope:{ctx:Ue}}}),Pe=new C({props:{name:"numpy_mask_tokens",anchor:"transformers.DataCollatorForLanguageModeling.numpy_mask_tokens",parameters:[{name:"inputs",val:": typing.Any"},{name:"special_tokens_mask",val:": typing.Optional[typing.Any] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/data/data_collator.py#L806"}}),Se=new C({props:{name:"tf_mask_tokens",anchor:"transformers.DataCollatorForLanguageModeling.tf_mask_tokens",parameters:[{name:"inputs",val:": typing.Any"},{name:"vocab_size",val:""},{name:"mask_token_id",val:""},{name:"special_tokens_mask",val:": typing.Optional[typing.Any] = 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