transformers/pr_43265/en/_app/immutable/nodes/121.9a2bb7e7.js

import{s as Me,o as ye,n as we}from"../chunks/scheduler.31fdf58d.js";import{S as $e,i as ve,e as c,s as i,c as h,h as Be,a as m,d as s,b as l,f as re,j as y,g as u,k as L,l as $,m as a,n as f,t as g,o as k,p as b}from"../chunks/index.2f76fdf0.js";import{C as Je}from"../chunks/CopyLLMTxtMenu.53b607bf.js";import{D as Te}from"../chunks/Docstring.7acc6835.js";import{C as ze}from"../chunks/CodeBlock.e52df5d6.js";import{H as ce,E as qe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.08750ec0.js";import{H as xe,a as _e}from"../chunks/HfOption.fb051768.js";function Ue(v){let n,d;return n=new ze({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBwaXBlbGluZSglMEElMjAlMjAlMjAlMjB0YXNrJTNEJTIyZmlsbC1tYXNrJTIyJTJDJTBBJTIwJTIwJTIwJTIwbW9kZWwlM0QlMjJtb3Vzc2FLYW0lMkZiYXJ0aGV6JTIyJTJDJTBBJTIwJTIwJTIwJTIwZGV2aWNlJTNEMCUwQSklMEFwaXBlbGluZSglMjJMZXMlMjBwbGFudGVzJTIwcHJvZHVpc2VudCUyMCUzQ21hc2slM0UlMjBnciVDMyVBMmNlJTIwJUMzJUEwJTIwdW4lMjBwcm9jZXNzdXMlMjBhcHBlbCVDMyVBOSUyMHBob3Rvc3ludGglQzMlQThzZS4lMjIp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline


pipeline = pipeline(
    task=<span class="hljs-string">&quot;fill-mask&quot;</span>,
    model=<span class="hljs-string">&quot;moussaKam/barthez&quot;</span>,
    device=<span class="hljs-number">0</span>
)
pipeline(<span class="hljs-string">&quot;Les plantes produisent &lt;mask&gt; grâce à un processus appelé photosynthèse.&quot;</span>)`,lang:"python",wrap:!1}}),{c(){h(n.$$.fragment)},l(o){u(n.$$.fragment,o)},m(o,p){f(n,o,p),d=!0},p:we,i(o){d||(g(n.$$.fragment,o),d=!0)},o(o){k(n.$$.fragment,o),d=!1},d(o){b(n,o)}}}function je(v){let n,d;return n=new ze({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch

<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    <span class="hljs-string">&quot;moussaKam/barthez&quot;</span>,
)
model = AutoModelForMaskedLM.from_pretrained(
    <span class="hljs-string">&quot;moussaKam/barthez&quot;</span>,
    device_map=<span class="hljs-string">&quot;auto&quot;</span>,
)
inputs = tokenizer(<span class="hljs-string">&quot;Les plantes produisent &lt;mask&gt; grâce à un processus appelé photosynthèse.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(model.device)

<span class="hljs-keyword">with</span> torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits

masked_index = torch.where(inputs[<span class="hljs-string">&#x27;input_ids&#x27;</span>] == tokenizer.mask_token_id)[<span class="hljs-number">1</span>]
predicted_token_id = predictions[<span class="hljs-number">0</span>, masked_index].argmax(dim=-<span class="hljs-number">1</span>)
predicted_token = tokenizer.decode(predicted_token_id)

<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;The predicted token is: <span class="hljs-subst">{predicted_token}</span>&quot;</span>)`,lang:"python",wrap:!1}}),{c(){h(n.$$.fragment)},l(o){u(n.$$.fragment,o)},m(o,p){f(n,o,p),d=!0},p:we,i(o){d||(g(n.$$.fragment,o),d=!0)},o(o){k(n.$$.fragment,o),d=!1},d(o){b(n,o)}}}function Ie(v){let n,d,o,p;return n=new _e({props:{id:"usage",option:"Pipeline",$$slots:{default:[Ue]},$$scope:{ctx:v}}}),o=new _e({props:{id:"usage",option:"AutoModel",$$slots:{default:[je]},$$scope:{ctx:v}}}),{c(){h(n.$$.fragment),d=i(),h(o.$$.fragment)},l(r){u(n.$$.fragment,r),d=l(r),u(o.$$.fragment,r)},m(r,T){f(n,r,T),a(r,d,T),f(o,r,T),p=!0},p(r,T){const q={};T&2&&(q.$$scope={dirty:T,ctx:r}),n.$set(q);const z={};T&2&&(z.$$scope={dirty:T,ctx:r}),o.$set(z)},i(r){p||(g(n.$$.fragment,r),g(o.$$.fragment,r),p=!0)},o(r){k(n.$$.fragment,r),k(o.$$.fragment,r),p=!1},d(r){r&&s(d),b(n,r),b(o,r)}}}function Ce(v){let n,d,o,p,r,T="<em>This model was published in HF papers on 2020-10-23 and contributed to Hugging Face Transformers on 2020-11-27.</em>",q,z,F,x,Q,U,me='<a href="https://huggingface.co/papers/2010.12321" rel="nofollow">BARThez</a> is a <a href="./bart">BART</a> model designed for French language tasks. Unlike existing French BERT models, BARThez includes a pretrained encoder-decoder, allowing it to generate text as well. This model is also available as a multilingual variant, mBARThez, by continuing pretraining multilingual BART on a French corpus.',S,j,pe='You can find all of the original BARThez checkpoints under the <a href="https://huggingface.co/collections/dascim/barthez-670920b569a07aa53e3b6887" rel="nofollow">BARThez</a> collection.',P,B,he=`<p>This model was contributed by <a href="https://huggingface.co/moussakam" rel="nofollow">moussakam</a>.
Refer to the <a href="./bart">BART</a> docs for more usage examples.</p>`,Y,I,ue='The example below demonstrates how to predict the <code>&lt;mask&gt;</code> token with <a href="/docs/transformers/pr_43265/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a>, <a href="/docs/transformers/pr_43265/en/model_doc/auto#transformers.AutoModel">AutoModel</a>, and from the command line.',D,J,K,C,O,_,R,ae,V,fe=`Adapted from <a href="/docs/transformers/pr_43265/en/model_doc/camembert#transformers.CamembertTokenizer">CamembertTokenizer</a> and <a href="/docs/transformers/pr_43265/en/model_doc/led#transformers.RobertaTokenizer">BartTokenizer</a>. Construct a “fast” BARThez tokenizer. Based on
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a>.`,ie,G,ge=`This tokenizer inherits from <a href="/docs/transformers/pr_43265/en/main_classes/tokenizer#transformers.TokenizersBackend">PreTrainedTokenizerFast</a> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`,ee,W,te,w,H,le,E,ke=`Adapted from <a href="/docs/transformers/pr_43265/en/model_doc/camembert#transformers.CamembertTokenizer">CamembertTokenizer</a> and <a href="/docs/transformers/pr_43265/en/model_doc/led#transformers.RobertaTokenizer">BartTokenizer</a>. Construct a “fast” BARThez tokenizer. Based on
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a>.`,de,A,be=`This tokenizer inherits from <a href="/docs/transformers/pr_43265/en/main_classes/tokenizer#transformers.TokenizersBackend">PreTrainedTokenizerFast</a> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`,ne,Z,oe,N,se;return z=new Je({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),x=new ce({props:{title:"BARThez",local:"barthez",headingTag:"h1"}}),J=new xe({props:{id:"usage",options:["Pipeline","AutoModel"],$$slots:{default:[Ie]},$$scope:{ctx:v}}}),C=new ce({props:{title:"BarthezTokenizer",local:"transformers.BarthezTokenizer",headingTag:"h2"}}),R=new Te({props:{name:"class transformers.BarthezTokenizer",anchor:"transformers.BarthezTokenizer",parameters:[{name:"vocab",val:": str | dict | list | None = None"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"sep_token",val:" = '</s>'"},{name:"cls_token",val:" = '<s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"mask_token",val:" = '<mask>'"},{name:"add_prefix_space",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarthezTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.BarthezTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.BarthezTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&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.BarthezTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&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.BarthezTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&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.BarthezTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.BarthezTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;mask&gt;&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.BarthezTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.BarthezTokenizer.vocab",description:`<strong>vocab</strong> (<code>str</code>, <code>dict</code> or <code>list</code>, <em>optional</em>) &#x2014;
Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.`,name:"vocab"},{anchor:"transformers.BarthezTokenizer.add_prefix_space",description:`<strong>add_prefix_space</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word.`,name:"add_prefix_space"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/barthez/tokenization_barthez.py#L32"}}),W=new ce({props:{title:"BarthezTokenizerFast",local:"transformers.BarthezTokenizer",headingTag:"h2"}}),H=new Te({props:{name:"class transformers.BarthezTokenizer",anchor:"transformers.BarthezTokenizer",parameters:[{name:"vocab",val:": str | dict | list | None = None"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"sep_token",val:" = '</s>'"},{name:"cls_token",val:" = '<s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"mask_token",val:" = '<mask>'"},{name:"add_prefix_space",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BarthezTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.BarthezTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.BarthezTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&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.BarthezTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&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.BarthezTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&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.BarthezTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.BarthezTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;mask&gt;&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.BarthezTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.BarthezTokenizer.vocab",description:`<strong>vocab</strong> (<code>str</code>, <code>dict</code> or <code>list</code>, <em>optional</em>) &#x2014;
Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.`,name:"vocab"},{anchor:"transformers.BarthezTokenizer.add_prefix_space",description:`<strong>add_prefix_space</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
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