import{s as sl,o as tl}from"../chunks/scheduler.37c15a92.js";import{S as nl,i as al,g as d,s as i,r as c,A as il,h as J,f as s,c as o,j as Le,u as m,x as T,k as Ke,y as ol,a as n,v as M,t as r,b as Oe,d as u,w as b,p as el}from"../chunks/index.2bf4358c.js";import{C as w}from"../chunks/CodeBlock.4e987730.js";import{C as ll}from"../chunks/CourseFloatingBanner.9ff4c771.js";import{F as pl}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as Ge,E as rl}from"../chunks/getInferenceSnippets.24b50994.js";function ul(j){let a,p;return a=new ll({props:{chapter:2,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_tf.ipynb"}]}}),{c(){c(a.$$.fragment)},l(t){m(a.$$.fragment,t)},m(t,y){M(a,t,y),p=!0},i(t){p||(u(a.$$.fragment,t),p=!0)},o(t){r(a.$$.fragment,t),p=!1},d(t){b(a,t)}}}function cl(j){let a,p;return a=new ll({props:{chapter:2,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_pt.ipynb"}]}}),{c(){c(a.$$.fragment)},l(t){m(a.$$.fragment,t)},m(t,y){M(a,t,y),p=!0},i(t){p||(u(a.$$.fragment,t),p=!0)},o(t){r(a.$$.fragment,t),p=!1},d(t){b(a,t)}}}function ml(j){let a,p;return a=new w({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

tokens = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
output = model(**tokens)`,wrap:!1}}),{c(){c(a.$$.fragment)},l(t){m(a.$$.fragment,t)},m(t,y){M(a,t,y),p=!0},i(t){p||(u(a.$$.fragment,t),p=!0)},o(t){r(a.$$.fragment,t),p=!1},d(t){b(a,t)}}}function Ml(j){let a,p;return a=new w({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> AutoTokenizer, AutoModelForSequenceClassification

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

tokens = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
output = model(**tokens)`,wrap:!1}}),{c(){c(a.$$.fragment)},l(t){m(a.$$.fragment,t)},m(t,y){M(a,t,y),p=!0},i(t){p||(u(a.$$.fragment,t),p=!0)},o(t){r(a.$$.fragment,t),p=!1},d(t){b(a,t)}}}function bl(j){let a,p,t,y,U,te,Z,ne,h,f,ee,k,ve="Nelle ultime sezioni abbiamo fatto del nostro meglio per fare la maggior parte del lavoro a mano. Abbiamo esplorato il funzionamento dei tokenizer e abbiamo esaminato la tokenizzazione, la conversione in ID di input, il padding, il troncamento e le maschere di attenzione.",ae,z,Se="Tuttavia, come abbiamo visto nella sezione 2, l’API 🤗 Transformers può gestire tutto questo con una funzione di alto livello che approfondiremo qui. Quando si chiama il <code>tokenizer</code> direttamente sulla frase, si ottengono input pronti per passare attraverso il modello:",ie,V,oe,$,qe="Qui, la variabile <code>model_inputs</code> contiene tutto ciò che è necessario per il buon funzionamento del modello. Per DistilBERT, questo include gli ID degli ingressi e la maschera di attenzione. Altri modelli che accettano input aggiuntivi avranno anche questi output dall’oggetto <code>tokenizer</code>.",pe,W,Xe="Come vedremo in alcuni esempi, questo metodo è molto potente. Innanzitutto, può tokenizzare una singola sequenza:",re,B,ue,G,Ee="Gestisce anche più sequenze alla volta, senza alcuna modifica dell’API:",ce,v,me,S,Re="Possiamo implementare il padding in diversi modi",Me,q,be,X,_e="Può anche troncare le sequenze:",de,E,Je,R,xe="L’oggetto <code>tokenizer</code> può gestire la conversione in tensori di framework specifici, che possono successivamente essere inviati direttamente al modello. Per esempio, nel seguente esempio di codice si chiede al tokenizer di restituire i tensori dei diversi framework: <code>&quot;pt&quot;</code> restituisce i tensori di PyTorch, <code>&quot;tf&quot;</code> restituisce i tensori di TensorFlow e <code>&quot;np&quot;</code> restituisce gli array di NumPy:",ye,_,Te,x,we,N,Ne="Se diamo un’occhiata agli ID di input restituiti dal tokenizer, noteremo che sono leggermente diversi da quelli che avevamo prima:",je,Y,he,Q,fe,C,Ye="Un ID token è stato aggiunto all’inizio e uno alla fine. Decodifichiamo le due sequenze di ID qui sopra per capire di cosa si tratta:",ge,F,Ie,H,Ue,A,Qe="Il tokenizer ha aggiunto la parola speciale <code>[CLS]</code> all’inizio e la parola speciale <code>[SEP]</code> alla fine. Questo perché il modello è stato preaddestrato con queste parole, quindi per ottenere gli stessi risultati per l’inferenza dobbiamo aggiungerle anche noi. Si noti che alcuni modelli non aggiungono parole speciali, o ne aggiungono di diverse; i modelli possono anche aggiungere queste parole speciali solo all’inizio o solo alla fine. In ogni caso, il tokenizer sa quali sono previste e se ne occuperà per voi.",Ze,D,ke,P,Ce="Ora che abbiamo visto tutti i singoli passaggi che l’oggetto <code>tokenizer</code> utilizza quando viene applicato ai testi, vediamo un’ultima volta come può gestire sequenze multiple (padding!), sequenze molto lunghe (troncamento!) e diversi tipi di tensori con la sua API principale:",ze,g,I,le,L,Ve,se,$e;U=new pl({props:{fw:j[0]}}),Z=new Ge({props:{title:"Mettiamo insieme i pezzi",local:"mettiamo-insieme-i-pezzi",headingTag:"h1"}});const Fe=[cl,ul],K=[];function He(e,l){return e[0]==="pt"?0:1}h=He(j),f=K[h]=Fe[h](j),V=new w({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEFjaGVja3BvaW50JTIwJTNEJTIwJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQtZmluZXR1bmVkLXNzdC0yLWVuZ2xpc2glMjIlMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KSUwQSUwQXNlcXVlbmNlJTIwJTNEJTIwJTIySSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMEh1Z2dpbmdGYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIyJTBBJTBBbW9kZWxfaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKHNlcXVlbmNlKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

model_inputs = tokenizer(sequence)`,wrap:!1}}),B=new w({props:{code:"c2VxdWVuY2UlMjAlM0QlMjAlMjJJJ3ZlJTIwYmVlbiUyMHdhaXRpbmclMjBmb3IlMjBhJTIwSHVnZ2luZ0ZhY2UlMjBjb3Vyc2UlMjBteSUyMHdob2xlJTIwbGlmZS4lMjIlMEElMEFtb2RlbF9pbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoc2VxdWVuY2Up",highlighted:`sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

model_inputs = tokenizer(sequence)`,wrap:!1}}),v=new w({props:{code:"c2VxdWVuY2VzJTIwJTNEJTIwJTVCJTIySSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMEh1Z2dpbmdGYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIyJTJDJTIwJTIyU28lMjBoYXZlJTIwSSElMjIlNUQlMEElMEFtb2RlbF9pbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoc2VxdWVuY2VzKQ==",highlighted:`sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

model_inputs = tokenizer(sequences)`,wrap:!1}}),q=new w({props:{code:"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",highlighted:`<span class="hljs-comment"># Effettua il padding della sequenza fino allla massima lunghezza della sequenza</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-string">&quot;longest&quot;</span>)

<span class="hljs-comment"># Effettua il padding fino alla lunghezza massima del modello</span>
<span class="hljs-comment"># (512 per BERT o DistilBERT)</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-string">&quot;max_length&quot;</span>)

<span class="hljs-comment"># Effettua il padding fino alla lunghezza massima specificata</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-string">&quot;max_length&quot;</span>, max_length=<span class="hljs-number">8</span>)`,wrap:!1}}),E=new w({props:{code:"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",highlighted:`sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

<span class="hljs-comment"># Tronca le sequenze più lunghe della lunghezza massima del modello.</span>
<span class="hljs-comment"># (512 per BERT o DistilBERT)</span>
model_inputs = tokenizer(sequences, truncation=<span class="hljs-literal">True</span>)

<span class="hljs-comment"># Tronca le sequenze più lunghe della lunghezza massima specificata.</span>
model_inputs = tokenizer(sequences, max_length=<span class="hljs-number">8</span>, truncation=<span class="hljs-literal">True</span>)`,wrap:!1}}),_=new w({props:{code:"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",highlighted:`sequences = [<span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>, <span class="hljs-string">&quot;So have I!&quot;</span>]

<span class="hljs-comment"># Ritorna tensori PyTorch</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-comment"># Ritorna tensori TensorFlow</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-comment"># Ritorna NumPy arrays</span>
model_inputs = tokenizer(sequences, padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)`,wrap:!1}}),x=new Ge({props:{title:"Token speciali",local:"token-speciali",headingTag:"h2"}}),Y=new w({props:{code:"c2VxdWVuY2UlMjAlM0QlMjAlMjJJJ3ZlJTIwYmVlbiUyMHdhaXRpbmclMjBmb3IlMjBhJTIwSHVnZ2luZ0ZhY2UlMjBjb3Vyc2UlMjBteSUyMHdob2xlJTIwbGlmZS4lMjIlMEElMEFtb2RlbF9pbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoc2VxdWVuY2UpJTBBcHJpbnQobW9kZWxfaW5wdXRzJTVCJTIyaW5wdXRfaWRzJTIyJTVEKSUwQSUwQXRva2VucyUyMCUzRCUyMHRva2VuaXplci50b2tlbml6ZShzZXF1ZW5jZSklMEFpZHMlMjAlM0QlMjB0b2tlbml6ZXIuY29udmVydF90b2tlbnNfdG9faWRzKHRva2VucyklMEFwcmludChpZHMp",highlighted:`sequence = <span class="hljs-string">&quot;I&#x27;ve been waiting for a HuggingFace course my whole life.&quot;</span>

model_inputs = tokenizer(sequence)
<span class="hljs-built_in">print</span>(model_inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
<span class="hljs-built_in">print</span>(ids)`,wrap:!1}}),Q=new w({props:{code:"JTVCMTAxJTJDJTIwMTA0NSUyQyUyMDEwMDUlMkMlMjAyMzEwJTJDJTIwMjA0MiUyQyUyMDM0MDMlMkMlMjAyMDA1JTJDJTIwMTAzNyUyQyUyMDE3NjYyJTJDJTIwMTIxNzIlMkMlMjAyNjA3JTJDJTIwMjAyNiUyQyUyMDI4NzglMkMlMjAyMTY2JTJDJTIwMTAxMiUyQyUyMDEwMiU1RCUwQSU1QjEwNDUlMkMlMjAxMDA1JTJDJTIwMjMxMCUyQyUyMDIwNDIlMkMlMjAzNDAzJTJDJTIwMjAwNSUyQyUyMDEwMzclMkMlMjAxNzY2MiUyQyUyMDEyMTcyJTJDJTIwMjYwNyUyQyUyMDIwMjYlMkMlMjAyODc4JTJDJTIwMjE2NiUyQyUyMDEwMTIlNUQ=",highlighted:`[<span class="hljs-number">101</span>, <span class="hljs-number">1045</span>, <span class="hljs-number">1005</span>, <span class="hljs-number">2310</span>, <span class="hljs-number">2042</span>, <span class="hljs-number">3403</span>, <span class="hljs-number">2005</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">17662</span>, <span class="hljs-number">12172</span>, <span class="hljs-number">2607</span>, <span class="hljs-number">2026</span>, <span class="hljs-number">2878</span>, <span class="hljs-number">2166</span>, <span class="hljs-number">1012</span>, <span class="hljs-number">102</span>]
[<span class="hljs-number">1045</span>, <span class="hljs-number">1005</span>, <span class="hljs-number">2310</span>, <span class="hljs-number">2042</span>, <span class="hljs-number">3403</span>, <span class="hljs-number">2005</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">17662</span>, <span class="hljs-number">12172</span>, <span class="hljs-number">2607</span>, <span class="hljs-number">2026</span>, <span class="hljs-number">2878</span>, <span class="hljs-number">2166</span>, <span class="hljs-number">1012</span>]`,wrap:!1}}),F=new w({props:{code:"cHJpbnQodG9rZW5pemVyLmRlY29kZShtb2RlbF9pbnB1dHMlNUIlMjJpbnB1dF9pZHMlMjIlNUQpKSUwQXByaW50KHRva2VuaXplci5kZWNvZGUoaWRzKSk=",highlighted:`<span class="hljs-built_in">print</span>(tokenizer.decode(model_inputs[<span class="hljs-string">&quot;input_ids&quot;</span>]))
<span class="hljs-built_in">print</span>(tokenizer.decode(ids))`,wrap:!1}}),H=new w({props:{code:"JTIyJTVCQ0xTJTVEJTIwaSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMGh1Z2dpbmdmYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIwJTVCU0VQJTVEJTIyJTBBJTIyaSd2ZSUyMGJlZW4lMjB3YWl0aW5nJTIwZm9yJTIwYSUyMGh1Z2dpbmdmYWNlJTIwY291cnNlJTIwbXklMjB3aG9sZSUyMGxpZmUuJTIy",highlighted:`<span class="hljs-string">&quot;[CLS] i&#x27;ve been waiting for a huggingface course my whole life. [SEP]&quot;</span>
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dl='{"title":"Mettiamo insieme i pezzi","local":"mettiamo-insieme-i-pezzi","sections":[{"title":"Token speciali","local":"token-speciali","sections":[],"depth":2},{"title":"Conclusione: Dal tokenizer al modello","local":"conclusione-dal-tokenizer-al-modello","sections":[],"depth":2}],"depth":1}';function Jl(j,a,p){let t="pt";return tl(()=>{const y=new URLSearchParams(window.location.search);p(0,t=y.get("fw")||"pt")}),[t]}class gl extends nl{constructor(a){super(),al(this,a,Jl,bl,sl,{})}}export{gl as component};
