import{s as so,o as io,n as lo}from"../chunks/scheduler.25b97de1.js";import{S as mo,i as co,g as l,s as a,r as m,A as po,h as s,f as n,c as o,j as b,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 ro}from"../chunks/Tip.baa67368.js";import{D as v}from"../chunks/Docstring.ffac8efa.js";import{H as I,E as go}from"../chunks/EditOnGithub.91d95064.js";function fo(Le){let h,M=`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=l("p"),h.innerHTML=M},l(k){h=s(k,"P",{"data-svelte-h":!0}),d(h)!=="svelte-1uxurri"&&(h.innerHTML=M)},m(k,V){i(k,h,V)},p:lo,d(k){k&&n(h)}}}function uo(Le){let h,M=`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=l("p"),h.innerHTML=M},l(k){h=s(k,"P",{"data-svelte-h":!0}),d(h)!=="svelte-kpcut1"&&(h.innerHTML=M)},m(k,V){i(k,h,V)},p:lo,d(k){k&&n(h)}}}function ho(Le){let h,M,k,V,X,it,J,wa=`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>.`,dt,Q,Pa=`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.`,mt,Y,Fa='Examples of use can be found in the <a href="../examples">example scripts</a> or <a href="../notebooks">example notebooks</a>.',ct,Z,pt,y,ee,jt,qe,La=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,Nt,Me,qa="<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>",Ot,ze,Ma=`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.`,gt,te,ft,T,ae,Bt,Se,za=`Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:`,Ut,We,Sa="<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>",Kt,Ie,Wa=`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.`,Rt,Ae,Ia=`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.`,ut,oe,ht,A,ne,Gt,He,Aa="Data collator that will dynamically pad the inputs received.",_t,re,bt,H,le,Xt,Ee,Ha="Data collator that will dynamically pad the inputs received, as well as the labels.",$t,se,vt,E,ie,Jt,Ve,Ea="Data collator that will dynamically pad the inputs received, as well as the labels.",kt,de,xt,x,me,Qt,je,Va=`Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.`,Yt,j,Zt,N,ce,ea,Ne,ja="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",ta,O,pe,aa,Oe,Na="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",oa,B,ge,na,Be,Oa="Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.",Ct,fe,Tt,$,ue,ra,Ue,Ba="Data collator used for language modeling that masks entire words.",la,Ke,Ua="<li>collates batches of tensors, honoring their tokenizer’s pad_token</li> <li>preprocesses batches for masked language modeling</li>",sa,U,ia,K,he,da,Re,Ka=`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.`,ma,R,_e,ca,Ge,Ra=`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.`,pa,G,be,ga,Xe,Ga=`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.`,Dt,$e,yt,C,ve,fa,Je,Xa="Data collator used for permutation language modeling.",ua,Qe,Ja="<li>collates batches of tensors, honoring their tokenizer’s pad_token</li> <li>preprocesses batches for permutation language modeling with procedures specific to XLNet</li>",ha,z,ke,_a,Ye,Qa="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",ba,xe,Ya=`<li>Start from the beginning of the sequence by setting <code>cur_len = 0</code> (number of tokens processed so far).</li> <li>Sample a <code>span_length</code> from the interval <code>[1, max_span_length]</code> (length of span of tokens to be masked)</li> <li>Reserve a context of length <code>context_length = span_length / plm_probability</code> to surround span to be
masked</li> <li>Sample a starting point <code>start_index</code> from the interval <code>[cur_len, cur_len + context_length - span_length]</code> and mask tokens <code>start_index:start_index + span_length</code></li> <li>Set <code>cur_len = cur_len + context_length</code>. If <code>cur_len &lt; max_len</code> (i.e. there are tokens remaining in the
sequence to be processed), repeat from Step 1.</li>`,$a,S,Ce,va,Ze,Za="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",ka,Te,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>`,xa,W,De,Ca,et,to="The masked tokens to be predicted for a particular sequence are determined by the following algorithm:",Ta,ye,ao=`<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>`,wt,we,Pt,L,Pe,Da,tt,oo="Data collator used for padding free approach. Does the following:",ya,at,no="<li>concatate the entire mini batch into single long sequence [1, total_tokens]</li> <li>uses <code>separator_id</code> to separate sequences within the concatenated <code>labels</code>, default value is -100</li> <li>no padding will be added, returns <code>input_ids</code>, <code>labels</code> and <code>position_ids</code></li>",Ft,Fe,Lt,st,qt;return X=new I({props:{title:"Data Collator",local:"data-collator",headingTag:"h1"}}),Z=new I({props:{title:"Default data collator",local:"transformers.default_data_collator",headingTag:"h2"}}),ee=new v({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"}}),te=new I({props:{title:"DefaultDataCollator",local:"transformers.DefaultDataCollator",headingTag:"h2"}}),ae=new v({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"}}),oe=new I({props:{title:"DataCollatorWithPadding",local:"transformers.DataCollatorWithPadding",headingTag:"h2"}}),ne=new v({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"}}),re=new I({props:{title:"DataCollatorForTokenClassification",local:"transformers.DataCollatorForTokenClassification",headingTag:"h2"}}),le=new v({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 I({props:{title:"DataCollatorForSeq2Seq",local:"transformers.DataCollatorForSeq2Seq",headingTag:"h2"}}),ie=new v({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"}}),de=new I({props:{title:"DataCollatorForLanguageModeling",local:"transformers.DataCollatorForLanguageModeling",headingTag:"h2"}}),me=new v({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. 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bo(Le){return io(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class To extends mo{constructor(h){super(),co(this,h,bo,ho,so,{})}}export{To as component};
