import{s as er,o as tr,n as mn}from"../chunks/scheduler.9bc65507.js";import{S as nr,i as rr,g as r,s,u as _,A as or,h as o,f as n,c as a,j as h,v as g,y as p,k as c,o as t,a as d,w as k,d as v,t as b,x as T}from"../chunks/index.78c82d43.js";import{T as sr}from"../chunks/Tip.87d55b76.js";import{D as P}from"../chunks/Docstring.f3d04bd2.js";import{C as Zn}from"../chunks/CodeBlock.48618ec2.js";import{I as Qe}from"../chunks/IconCopyLink.bedaa44d.js";import{E as Qn}from"../chunks/ExampleCodeBlock.872b014d.js";function ar(N){let m,C="This model is in maintenance mode only, so we won’t accept any new PRs changing its code.",$,f,u=`If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: <code>pip install -U transformers==4.30.0</code>.`;return{c(){m=r("p"),m.textContent=C,$=s(),f=r("p"),f.innerHTML=u},l(i){m=o(i,"P",{"data-svelte-h":!0}),p(m)!=="svelte-lwu440"&&(m.textContent=C),$=a(i),f=o(i,"P",{"data-svelte-h":!0}),p(f)!=="svelte-4042uy"&&(f.innerHTML=u)},m(i,y){d(i,m,y),d(i,$,y),d(i,f,y)},p:mn,d(i){i&&(n(m),n($),n(f))}}}function ir(N){let m,C="pair mask has the following format:",$,f,u;return f=new Zn({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`}}),{c(){m=r("p"),m.textContent=C,$=s(),_(f.$$.fragment)},l(i){m=o(i,"P",{"data-svelte-h":!0}),p(m)!=="svelte-qjgeij"&&(m.textContent=C),$=a(i),g(f.$$.fragment,i)},m(i,y){d(i,m,y),d(i,$,y),k(f,i,y),u=!0},p:mn,i(i){u||(v(f.$$.fragment,i),u=!0)},o(i){b(f.$$.fragment,i),u=!1},d(i){i&&(n(m),n($)),T(f,i)}}}function lr(N){let m,C="pair mask has the following format:",$,f,u;return f=new Zn({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`}}),{c(){m=r("p"),m.textContent=C,$=s(),_(f.$$.fragment)},l(i){m=o(i,"P",{"data-svelte-h":!0}),p(m)!=="svelte-qjgeij"&&(m.textContent=C),$=a(i),g(f.$$.fragment,i)},m(i,y){d(i,m,y),d(i,$,y),k(f,i,y),u=!0},p:mn,i(i){u||(v(f.$$.fragment,i),u=!0)},o(i){b(f.$$.fragment,i),u=!1},d(i){i&&(n(m),n($)),T(f,i)}}}function cr(N){let m,C,$,f,u,i,y,te,Ct,Te,pn="RetriBERT",it,J,lt,A,U,Ze,ne,Rt,we,hn="Overview",ct,re,fn=`The RetriBERT model was proposed in the blog post <a href="https://yjernite.github.io/lfqa.html" rel="nofollow">Explain Anything Like I’m Five: A Model for Open Domain Long Form
Question Answering</a>. RetriBERT is a small model that uses either a single or
pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.`,dt,oe,un=`This model was contributed by <a href="https://huggingface.co/yjernite" rel="nofollow">yjernite</a>. Code to train and use the model can be
found <a href="https://github.com/huggingface/transformers/tree/main/examples/research-projects/distillation" rel="nofollow">here</a>.`,mt,j,V,et,se,qt,$e,_n="RetriBertConfig",pt,L,ae,Mt,ye,gn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.33.3/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a>. It is used to instantiate a
RetriBertModel model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the RetriBERT
<a href="https://huggingface.co/yjernite/retribert-base-uncased" rel="nofollow">yjernite/retribert-base-uncased</a> architecture.`,Lt,xe,kn=`Configuration objects inherit from <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,ht,F,O,tt,ie,Pt,ze,vn="RetriBertTokenizer",ft,w,le,It,Be,bn="Constructs a RetriBERT tokenizer.",Dt,Ce,Tn=`<a href="/docs/transformers/v4.33.3/en/model_doc/retribert#transformers.RetriBertTokenizer">RetriBertTokenizer</a> is identical to <a href="/docs/transformers/v4.33.3/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> and runs end-to-end tokenization: punctuation splitting
and wordpiece.`,Et,Re,wn=`This tokenizer inherits from <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer
to: this superclass for more information regarding those methods.`,At,I,ce,jt,qe,$n=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,Ft,Me,yn="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",St,W,de,Ht,Le,xn="Converts a sequence of tokens (string) in a single string.",Nt,R,me,Jt,Pe,zn="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",Ut,K,Vt,Ie,Bn="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",Ot,X,pe,Wt,De,Cn=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,ut,S,Y,nt,he,Kt,Ee,Rn="RetriBertTokenizerFast",_t,x,fe,Xt,Ae,qn="Construct a “fast” RetriBERT tokenizer (backed by HuggingFace’s <em>tokenizers</em> library).",Yt,je,Mn=`<a href="/docs/transformers/v4.33.3/en/model_doc/retribert#transformers.RetriBertTokenizerFast">RetriBertTokenizerFast</a> is identical to <a href="/docs/transformers/v4.33.3/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> and runs end-to-end tokenization: punctuation
splitting and wordpiece.`,Gt,Fe,Ln=`This tokenizer inherits from <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`,Qt,D,ue,Zt,Se,Pn=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,en,He,In="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",tn,q,_e,nn,Ne,Dn="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",rn,G,on,Je,En="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",gt,H,Q,rt,ge,sn,Ue,An="RetriBertModel",kt,B,ke,an,Ve,jn="Bert Based model to embed queries or document for document retrieval.",ln,Oe,Fn=`This model inherits from <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,cn,We,Sn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,dn,Ke,ve,vt,ot,bt;return te=new Qe({}),J=new sr({props:{warning:!0,$$slots:{default:[ar]},$$scope:{ctx:N}}}),ne=new Qe({}),se=new Qe({}),ae=new P({props:{name:"class transformers.RetriBertConfig",anchor:"transformers.RetriBertConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 8"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"type_vocab_size",val:" = 2"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"share_encoders",val:" = True"},{name:"projection_dim",val:" = 128"},{name:"pad_token_id",val:" = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RetriBertConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the RetriBERT model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.33.3/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a>`,name:"vocab_size"},{anchor:"transformers.RetriBertConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.RetriBertConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.RetriBertConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.RetriBertConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.RetriBertConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.RetriBertConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.RetriBertConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.RetriBertConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.RetriBertConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <em>token_type_ids</em> passed into <a href="/docs/transformers/v4.33.3/en/model_doc/bert#transformers.BertModel">BertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.RetriBertConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.RetriBertConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.RetriBertConfig.share_encoders",description:`<strong>share_encoders</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to use the same Bert-type encoder for the queries and document`,name:"share_encoders"},{anchor:"transformers.RetriBertConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Final dimension of the query and document representation after projection`,name:"projection_dim"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/configuration_retribert.py#L31"}}),ie=new Qe({}),le=new P({props:{name:"class transformers.RetriBertTokenizer",anchor:"transformers.RetriBertTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RetriBertTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.RetriBertTokenizer.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.RetriBertTokenizer.do_basic_tokenize",description:`<strong>do_basic_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to do basic tokenization before WordPiece.`,name:"do_basic_tokenize"},{anchor:"transformers.RetriBertTokenizer.never_split",description:`<strong>never_split</strong> (<code>Iterable</code>, <em>optional</em>) &#x2014;
Collection of tokens which will never be split during tokenization. Only has an effect when
<code>do_basic_tokenize=True</code>`,name:"never_split"},{anchor:"transformers.RetriBertTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RetriBertTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.RetriBertTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RetriBertTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.RetriBertTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.RetriBertTokenizer.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this
<a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.RetriBertTokenizer.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L70"}}),ce=new P({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.RetriBertTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RetriBertTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.RetriBertTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L213",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),de=new P({props:{name:"convert_tokens_to_string",anchor:"transformers.RetriBertTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L207"}}),me=new P({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.RetriBertTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RetriBertTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.RetriBertTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L268",returnDescription:`
<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),K=new Qn({props:{anchor:"transformers.RetriBertTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[ir]},$$scope:{ctx:N}}}),pe=new P({props:{name:"get_special_tokens_mask",anchor:"transformers.RetriBertTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.RetriBertTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.RetriBertTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.RetriBertTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L239",returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),he=new Qe({}),fe=new P({props:{name:"class transformers.RetriBertTokenizerFast",anchor:"transformers.RetriBertTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RetriBertTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.RetriBertTokenizerFast.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.RetriBertTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RetriBertTokenizerFast.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.RetriBertTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RetriBertTokenizerFast.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.RetriBertTokenizerFast.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.RetriBertTokenizerFast.clean_text",description:`<strong>clean_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.`,name:"clean_text"},{anchor:"transformers.RetriBertTokenizerFast.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see <a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">this
issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.RetriBertTokenizerFast.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"},{anchor:"transformers.RetriBertTokenizerFast.wordpieces_prefix",description:`<strong>wordpieces_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;##&quot;</code>) &#x2014;
The prefix for subwords.`,name:"wordpieces_prefix"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py#L54"}}),ue=new P({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.RetriBertTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:""},{name:"token_ids_1",val:" = None"}],parametersDescription:[{anchor:"transformers.RetriBertTokenizerFast.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.RetriBertTokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py#L148",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),_e=new P({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.RetriBertTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RetriBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.RetriBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py#L173",returnDescription:`
<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),G=new Qn({props:{anchor:"transformers.RetriBertTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[lr]},$$scope:{ctx:N}}}),ge=new Qe({}),ke=new P({props:{name:"class transformers.RetriBertModel",anchor:"transformers.RetriBertModel",parameters:[{name:"config",val:": RetriBertConfig"}],parametersDescription:[{anchor:"transformers.RetriBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.3/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/modeling_retribert.py#L88"}}),ve=new P({props:{name:"forward",anchor:"transformers.RetriBertModel.forward",parameters:[{name:"input_ids_query",val:": LongTensor"},{name:"attention_mask_query",val:": typing.Optional[torch.FloatTensor]"},{name:"input_ids_doc",val:": LongTensor"},{name:"attention_mask_doc",val:": typing.Optional[torch.FloatTensor]"},{name:"checkpoint_batch_size",val:": int = -1"}],parametersDescription:[{anchor:"transformers.RetriBertModel.forward.input_ids_query",description:`<strong>input_ids_query</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary for the queries in a batch.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids_query"},{anchor:"transformers.RetriBertModel.forward.attention_mask_query",description:`<strong>attention_mask_query</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask_query"},{anchor:"transformers.RetriBertModel.forward.input_ids_doc",description:`<strong>input_ids_doc</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary for the documents in a batch.`,name:"input_ids_doc"},{anchor:"transformers.RetriBertModel.forward.attention_mask_doc",description:`<strong>attention_mask_doc</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on documents padding token indices.`,name:"attention_mask_doc"},{anchor:"transformers.RetriBertModel.forward.checkpoint_batch_size",description:`<strong>checkpoint_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>-1</code>) &#x2014;
If greater than 0, uses gradient checkpointing to only compute sequence representation on
<code>checkpoint_batch_size</code> examples at a time on the GPU. All query representations are still compared to
all document representations in the batch.`,name:"checkpoint_batch_size"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/deprecated/retribert/modeling_retribert.py#L176",returnDescription:`
<p>The bidirectional cross-entropy loss obtained while trying to match each query to its
corresponding document and each document to its corresponding query in the batch</p>
`,returnType:`
<p>\`torch.FloatTensor“</p>
`}}),{c(){m=r("meta"),C=s(),$=r("p"),f=s(),u=r("h1"),i=r("a"),y=r("span"),_(te.$$.fragment),Ct=s(),Te=r("span"),Te.textContent=pn,it=s(),_(J.$$.fragment),lt=s(),A=r("h2"),U=r("a"),Ze=r("span"),_(ne.$$.fragment),Rt=s(),we=r("span"),we.textContent=hn,ct=s(),re=r("p"),re.innerHTML=fn,dt=s(),oe=r("p"),oe.innerHTML=un,mt=s(),j=r("h2"),V=r("a"),et=r("span"),_(se.$$.fragment),qt=s(),$e=r("span"),$e.textContent=_n,pt=s(),L=r("div"),_(ae.$$.fragment),Mt=s(),ye=r("p"),ye.innerHTML=gn,Lt=s(),xe=r("p"),xe.innerHTML=kn,ht=s(),F=r("h2"),O=r("a"),tt=r("span"),_(ie.$$.fragment),Pt=s(),ze=r("span"),ze.textContent=vn,ft=s(),w=r("div"),_(le.$$.fragment),It=s(),Be=r("p"),Be.textContent=bn,Dt=s(),Ce=r("p"),Ce.innerHTML=Tn,Et=s(),Re=r("p"),Re.innerHTML=wn,At=s(),I=r("div"),_(ce.$$.fragment),jt=s(),qe=r("p"),qe.textContent=$n,Ft=s(),Me=r("ul"),Me.innerHTML=yn,St=s(),W=r("div"),_(de.$$.fragment),Ht=s(),Le=r("p"),Le.textContent=xn,Nt=s(),R=r("div"),_(me.$$.fragment),Jt=s(),Pe=r("p"),Pe.textContent=zn,Ut=s(),_(K.$$.fragment),Vt=s(),Ie=r("p"),Ie.innerHTML=Bn,Ot=s(),X=r("div"),_(pe.$$.fragment),Wt=s(),De=r("p"),De.innerHTML=Cn,ut=s(),S=r("h2"),Y=r("a"),nt=r("span"),_(he.$$.fragment),Kt=s(),Ee=r("span"),Ee.textContent=Rn,_t=s(),x=r("div"),_(fe.$$.fragment),Xt=s(),Ae=r("p"),Ae.innerHTML=qn,Yt=s(),je=r("p"),je.innerHTML=Mn,Gt=s(),Fe=r("p"),Fe.innerHTML=Ln,Qt=s(),D=r("div"),_(ue.$$.fragment),Zt=s(),Se=r("p"),Se.textContent=Pn,en=s(),He=r("ul"),He.innerHTML=In,tn=s(),q=r("div"),_(_e.$$.fragment),nn=s(),Ne=r("p"),Ne.textContent=Dn,rn=s(),_(G.$$.fragment),on=s(),Je=r("p"),Je.innerHTML=En,gt=s(),H=r("h2"),Q=r("a"),rt=r("span"),_(ge.$$.fragment),sn=s(),Ue=r("span"),Ue.textContent=An,kt=s(),B=r("div"),_(ke.$$.fragment),an=s(),Ve=r("p"),Ve.textContent=jn,ln=s(),Oe=r("p"),Oe.innerHTML=Fn,cn=s(),We=r("p"),We.innerHTML=Sn,dn=s(),Ke=r("div"),_(ve.$$.fragment),vt=s(),ot=r("p"),this.h()},l(e){const l=or("svelte-1phssyn",document.head);m=o(l,"META",{name:!0,content:!0}),l.forEach(n),C=a(e),$=o(e,"P",{}),h($).forEach(n),f=a(e),u=o(e,"H1",{class:!0});var be=h(u);i=o(be,"A",{id:!0,class:!0,href:!0});var st=h(i);y=o(st,"SPAN",{});var at=h(y);g(te.$$.fragment,at),at.forEach(n),st.forEach(n),Ct=a(be),Te=o(be,"SPAN",{"data-svelte-h":!0}),p(Te)!=="svelte-61udwe"&&(Te.textContent=pn),be.forEach(n),it=a(e),g(J.$$.fragment,e),lt=a(e),A=o(e,"H2",{class:!0});var Tt=h(A);U=o(Tt,"A",{id:!0,class:!0,href:!0});var Hn=h(U);Ze=o(Hn,"SPAN",{});var Nn=h(Ze);g(ne.$$.fragment,Nn),Nn.forEach(n),Hn.forEach(n),Rt=a(Tt),we=o(Tt,"SPAN",{"data-svelte-h":!0}),p(we)!=="svelte-1jsw1pg"&&(we.textContent=hn),Tt.forEach(n),ct=a(e),re=o(e,"P",{"data-svelte-h":!0}),p(re)!=="svelte-td4eob"&&(re.innerHTML=fn),dt=a(e),oe=o(e,"P",{"data-svelte-h":!0}),p(oe)!=="svelte-1hctczn"&&(oe.innerHTML=un),mt=a(e),j=o(e,"H2",{class:!0});var wt=h(j);V=o(wt,"A",{id:!0,class:!0,href:!0});var Jn=h(V);et=o(Jn,"SPAN",{});var Un=h(et);g(se.$$.fragment,Un),Un.forEach(n),Jn.forEach(n),qt=a(wt),$e=o(wt,"SPAN",{"data-svelte-h":!0}),p($e)!=="svelte-18s88js"&&($e.textContent=_n),wt.forEach(n),pt=a(e),L=o(e,"DIV",{class:!0});var Xe=h(L);g(ae.$$.fragment,Xe),Mt=a(Xe),ye=o(Xe,"P",{"data-svelte-h":!0}),p(ye)!=="svelte-17vgwb1"&&(ye.innerHTML=gn),Lt=a(Xe),xe=o(Xe,"P",{"data-svelte-h":!0}),p(xe)!=="svelte-cfh5wl"&&(xe.innerHTML=kn),Xe.forEach(n),ht=a(e),F=o(e,"H2",{class:!0});var $t=h(F);O=o($t,"A",{id:!0,class:!0,href:!0});var Vn=h(O);tt=o(Vn,"SPAN",{});var On=h(tt);g(ie.$$.fragment,On),On.forEach(n),Vn.forEach(n),Pt=a($t),ze=o($t,"SPAN",{"data-svelte-h":!0}),p(ze)!=="svelte-7osg9r"&&(ze.textContent=vn),$t.forEach(n),ft=a(e),w=o(e,"DIV",{class:!0});var z=h(w);g(le.$$.fragment,z),It=a(z),Be=o(z,"P",{"data-svelte-h":!0}),p(Be)!=="svelte-otns3l"&&(Be.textContent=bn),Dt=a(z),Ce=o(z,"P",{"data-svelte-h":!0}),p(Ce)!=="svelte-9pgjfc"&&(Ce.innerHTML=Tn),Et=a(z),Re=o(z,"P",{"data-svelte-h":!0}),p(Re)!=="svelte-1nrgg26"&&(Re.innerHTML=wn),At=a(z),I=o(z,"DIV",{class:!0});var Ye=h(I);g(ce.$$.fragment,Ye),jt=a(Ye),qe=o(Ye,"P",{"data-svelte-h":!0}),p(qe)!=="svelte-t7qurq"&&(qe.textContent=$n),Ft=a(Ye),Me=o(Ye,"UL",{"data-svelte-h":!0}),p(Me)!=="svelte-xi6653"&&(Me.innerHTML=yn),Ye.forEach(n),St=a(z),W=o(z,"DIV",{class:!0});var yt=h(W);g(de.$$.fragment,yt),Ht=a(yt),Le=o(yt,"P",{"data-svelte-h":!0}),p(Le)!=="svelte-b3k2yi"&&(Le.textContent=xn),yt.forEach(n),Nt=a(z),R=o(z,"DIV",{class:!0});var Z=h(R);g(me.$$.fragment,Z),Jt=a(Z),Pe=o(Z,"P",{"data-svelte-h":!0}),p(Pe)!=="svelte-gn6wi7"&&(Pe.textContent=zn),Ut=a(Z),g(K.$$.fragment,Z),Vt=a(Z),Ie=o(Z,"P",{"data-svelte-h":!0}),p(Ie)!=="svelte-owoxgn"&&(Ie.innerHTML=Bn),Z.forEach(n),Ot=a(z),X=o(z,"DIV",{class:!0});var xt=h(X);g(pe.$$.fragment,xt),Wt=a(xt),De=o(xt,"P",{"data-svelte-h":!0}),p(De)!=="svelte-1f4f5kp"&&(De.innerHTML=Cn),xt.forEach(n),z.forEach(n),ut=a(e),S=o(e,"H2",{class:!0});var zt=h(S);Y=o(zt,"A",{id:!0,class:!0,href:!0});var Wn=h(Y);nt=o(Wn,"SPAN",{});var Kn=h(nt);g(he.$$.fragment,Kn),Kn.forEach(n),Wn.forEach(n),Kt=a(zt),Ee=o(zt,"SPAN",{"data-svelte-h":!0}),p(Ee)!=="svelte-n7amuh"&&(Ee.textContent=Rn),zt.forEach(n),_t=a(e),x=o(e,"DIV",{class:!0});var M=h(x);g(fe.$$.fragment,M),Xt=a(M),Ae=o(M,"P",{"data-svelte-h":!0}),p(Ae)!=="svelte-1g6s8hh"&&(Ae.innerHTML=qn),Yt=a(M),je=o(M,"P",{"data-svelte-h":!0}),p(je)!=="svelte-w8kr7c"&&(je.innerHTML=Mn),Gt=a(M),Fe=o(M,"P",{"data-svelte-h":!0}),p(Fe)!=="svelte-10uwgx2"&&(Fe.innerHTML=Ln),Qt=a(M),D=o(M,"DIV",{class:!0});var Ge=h(D);g(ue.$$.fragment,Ge),Zt=a(Ge),Se=o(Ge,"P",{"data-svelte-h":!0}),p(Se)!=="svelte-t7qurq"&&(Se.textContent=Pn),en=a(Ge),He=o(Ge,"UL",{"data-svelte-h":!0}),p(He)!=="svelte-xi6653"&&(He.innerHTML=In),Ge.forEach(n),tn=a(M),q=o(M,"DIV",{class:!0});var ee=h(q);g(_e.$$.fragment,ee),nn=a(ee),Ne=o(ee,"P",{"data-svelte-h":!0}),p(Ne)!=="svelte-gn6wi7"&&(Ne.textContent=Dn),rn=a(ee),g(G.$$.fragment,ee),on=a(ee),Je=o(ee,"P",{"data-svelte-h":!0}),p(Je)!=="svelte-owoxgn"&&(Je.innerHTML=En),ee.forEach(n),M.forEach(n),gt=a(e),H=o(e,"H2",{class:!0});var Bt=h(H);Q=o(Bt,"A",{id:!0,class:!0,href:!0});var Xn=h(Q);rt=o(Xn,"SPAN",{});var Yn=h(rt);g(ge.$$.fragment,Yn),Yn.forEach(n),Xn.forEach(n),sn=a(Bt),Ue=o(Bt,"SPAN",{"data-svelte-h":!0}),p(Ue)!=="svelte-160emrj"&&(Ue.textContent=An),Bt.forEach(n),kt=a(e),B=o(e,"DIV",{class:!0});var E=h(B);g(ke.$$.fragment,E),an=a(E),Ve=o(E,"P",{"data-svelte-h":!0}),p(Ve)!=="svelte-1c8tt6f"&&(Ve.textContent=jn),ln=a(E),Oe=o(E,"P",{"data-svelte-h":!0}),p(Oe)!=="svelte-ev63c"&&(Oe.innerHTML=Fn),cn=a(E),We=o(E,"P",{"data-svelte-h":!0}),p(We)!=="svelte-hswkmf"&&(We.innerHTML=Sn),dn=a(E),Ke=o(E,"DIV",{class:!0});var Gn=h(Ke);g(ve.$$.fragment,Gn),Gn.forEach(n),E.forEach(n),vt=a(e),ot=o(e,"P",{}),h(ot).forEach(n),this.h()},h(){c(m,"name","hf:doc:metadata"),c(m,"content",JSON.stringify(dr)),c(i,"id","retribert"),c(i,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(i,"href","#retribert"),c(u,"class","relative group"),c(U,"id","overview"),c(U,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(U,"href","#overview"),c(A,"class","relative group"),c(V,"id","transformers.RetriBertConfig"),c(V,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(V,"href","#transformers.RetriBertConfig"),c(j,"class","relative group"),c(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(O,"id","transformers.RetriBertTokenizer"),c(O,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(O,"href","#transformers.RetriBertTokenizer"),c(F,"class","relative group"),c(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(Y,"id","transformers.RetriBertTokenizerFast"),c(Y,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(Y,"href","#transformers.RetriBertTokenizerFast"),c(S,"class","relative group"),c(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(Q,"id","transformers.RetriBertModel"),c(Q,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(Q,"href","#transformers.RetriBertModel"),c(H,"class","relative group"),c(Ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),c(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,l){t(document.head,m),d(e,C,l),d(e,$,l),d(e,f,l),d(e,u,l),t(u,i),t(i,y),k(te,y,null),t(u,Ct),t(u,Te),d(e,it,l),k(J,e,l),d(e,lt,l),d(e,A,l),t(A,U),t(U,Ze),k(ne,Ze,null),t(A,Rt),t(A,we),d(e,ct,l),d(e,re,l),d(e,dt,l),d(e,oe,l),d(e,mt,l),d(e,j,l),t(j,V),t(V,et),k(se,et,null),t(j,qt),t(j,$e),d(e,pt,l),d(e,L,l),k(ae,L,null),t(L,Mt),t(L,ye),t(L,Lt),t(L,xe),d(e,ht,l),d(e,F,l),t(F,O),t(O,tt),k(ie,tt,null),t(F,Pt),t(F,ze),d(e,ft,l),d(e,w,l),k(le,w,null),t(w,It),t(w,Be),t(w,Dt),t(w,Ce),t(w,Et),t(w,Re),t(w,At),t(w,I),k(ce,I,null),t(I,jt),t(I,qe),t(I,Ft),t(I,Me),t(w,St),t(w,W),k(de,W,null),t(W,Ht),t(W,Le),t(w,Nt),t(w,R),k(me,R,null),t(R,Jt),t(R,Pe),t(R,Ut),k(K,R,null),t(R,Vt),t(R,Ie),t(w,Ot),t(w,X),k(pe,X,null),t(X,Wt),t(X,De),d(e,ut,l),d(e,S,l),t(S,Y),t(Y,nt),k(he,nt,null),t(S,Kt),t(S,Ee),d(e,_t,l),d(e,x,l),k(fe,x,null),t(x,Xt),t(x,Ae),t(x,Yt),t(x,je),t(x,Gt),t(x,Fe),t(x,Qt),t(x,D),k(ue,D,null),t(D,Zt),t(D,Se),t(D,en),t(D,He),t(x,tn),t(x,q),k(_e,q,null),t(q,nn),t(q,Ne),t(q,rn),k(G,q,null),t(q,on),t(q,Je),d(e,gt,l),d(e,H,l),t(H,Q),t(Q,rt),k(ge,rt,null),t(H,sn),t(H,Ue),d(e,kt,l),d(e,B,l),k(ke,B,null),t(B,an),t(B,Ve),t(B,ln),t(B,Oe),t(B,cn),t(B,We),t(B,dn),t(B,Ke),k(ve,Ke,null),d(e,vt,l),d(e,ot,l),bt=!0},p(e,[l]){const be={};l&2&&(be.$$scope={dirty:l,ctx:e}),J.$set(be);const st={};l&2&&(st.$$scope={dirty:l,ctx:e}),K.$set(st);const at={};l&2&&(at.$$scope={dirty:l,ctx:e}),G.$set(at)},i(e){bt||(v(te.$$.fragment,e),v(J.$$.fragment,e),v(ne.$$.fragment,e),v(se.$$.fragment,e),v(ae.$$.fragment,e),v(ie.$$.fragment,e),v(le.$$.fragment,e),v(ce.$$.fragment,e),v(de.$$.fragment,e),v(me.$$.fragment,e),v(K.$$.fragment,e),v(pe.$$.fragment,e),v(he.$$.fragment,e),v(fe.$$.fragment,e),v(ue.$$.fragment,e),v(_e.$$.fragment,e),v(G.$$.fragment,e),v(ge.$$.fragment,e),v(ke.$$.fragment,e),v(ve.$$.fragment,e),bt=!0)},o(e){b(te.$$.fragment,e),b(J.$$.fragment,e),b(ne.$$.fragment,e),b(se.$$.fragment,e),b(ae.$$.fragment,e),b(ie.$$.fragment,e),b(le.$$.fragment,e),b(ce.$$.fragment,e),b(de.$$.fragment,e),b(me.$$.fragment,e),b(K.$$.fragment,e),b(pe.$$.fragment,e),b(he.$$.fragment,e),b(fe.$$.fragment,e),b(ue.$$.fragment,e),b(_e.$$.fragment,e),b(G.$$.fragment,e),b(ge.$$.fragment,e),b(ke.$$.fragment,e),b(ve.$$.fragment,e),bt=!1},d(e){e&&(n(C),n($),n(f),n(u),n(it),n(lt),n(A),n(ct),n(re),n(dt),n(oe),n(mt),n(j),n(pt),n(L),n(ht),n(F),n(ft),n(w),n(ut),n(S),n(_t),n(x),n(gt),n(H),n(kt),n(B),n(vt),n(ot)),n(m),T(te),T(J,e),T(ne),T(se),T(ae),T(ie),T(le),T(ce),T(de),T(me),T(K),T(pe),T(he),T(fe),T(ue),T(_e),T(G),T(ge),T(ke),T(ve)}}}const dr={local:"retribert",sections:[{local:"overview",title:"Overview"},{local:"transformers.RetriBertConfig",title:"RetriBertConfig"},{local:"transformers.RetriBertTokenizer",title:"RetriBertTokenizer"},{local:"transformers.RetriBertTokenizerFast",title:"RetriBertTokenizerFast"},{local:"transformers.RetriBertModel",title:"RetriBertModel"}],title:"RetriBERT"};function mr(N){return tr(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class vr extends nr{constructor(m){super(),rr(this,m,mr,cr,er,{})}}export{vr as component};
