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.4e7352e2.js";import{C as Zn}from"../chunks/CodeBlock.73e038be.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 |`,wrap:!1}}),{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 |`,wrap:!1}}),{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,$e,_n="RetriBertConfig",pt,L,ae,Mt,ye,gn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.34.0/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a>. It is used to instantiate a
RetriBertModel model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the RetriBERT
<a href="https://huggingface.co/yjernite/retribert-base-uncased" rel="nofollow">yjernite/retribert-base-uncased</a> architecture.`,Lt,xe,kn=`Configuration objects inherit from <a href="/docs/transformers/v4.34.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.34.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,ht,F,O,tt,ie,Pt,ze,vn="RetriBertTokenizer",ft,w,le,It,Be,bn="Constructs a RetriBERT tokenizer.",Dt,Ce,Tn=`<a href="/docs/transformers/v4.34.0/en/model_doc/retribert#transformers.RetriBertTokenizer">RetriBertTokenizer</a> is identical to <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> and runs end-to-end tokenization: punctuation splitting
and wordpiece.`,Et,Re,wn=`This tokenizer inherits from <a href="/docs/transformers/v4.34.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer
to: this superclass for more information regarding those methods.`,At,I,ce,jt,qe,$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/v4.34.0/en/model_doc/retribert#transformers.RetriBertTokenizerFast">RetriBertTokenizerFast</a> is identical to <a href="/docs/transformers/v4.34.0/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/v4.34.0/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/v4.34.0/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/v4.34.0/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/v4.34.0/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/v4.34.0/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/v4.34.0/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/v4.34.0/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L214",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/v4.34.0/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L208"}}),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/v4.34.0/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L269",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/v4.34.0/src/transformers/models/deprecated/retribert/tokenization_retribert.py#L240",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/v4.34.0/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;
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Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/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;
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Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/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/v4.34.0/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/v4.34.0/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/v4.34.0/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/v4.34.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.34.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.34.0/en/model_doc/vits#transformers.VitsTokenizer.__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;
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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/v4.34.0/src/transformers/models/deprecated/retribert/modeling_retribert.py#L176",returnDescription:`
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corresponding document and each document to its corresponding query in the batch</p>
`,returnType:`
<p>\`torch.FloatTensor“</p>
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