import{s as na,o as ia,n as V}from"../chunks/scheduler.36a0863c.js";import{S as oa,i as ra,g as m,s as p,u as h,A as pa,h as u,f as s,c,j as x,v as g,y as w,k as _,o as z,a as n,w as b,d as M,t as v,x as y}from"../chunks/index.d0f7a4c4.js";import{T as Tt}from"../chunks/Tip.4c7d7eb1.js";import{Y as aa}from"../chunks/Youtube.d15bb502.js";import{I as ce}from"../chunks/IconCopyLink.bc5fad79.js";import{C}from"../chunks/CodeBlock.69433f87.js";import{D as ca}from"../chunks/DocNotebookDropdown.df566a23.js";import{F as jt,M as X}from"../chunks/Markdown.1d1f160a.js";function ma(j){let l,o=`Tutti gli esempi di codice presenti in questa documentazione hanno un pulsante in alto a sinistra che permette di selezionare tra PyTorch e TensorFlow. Se
questo non è presente, ci si aspetta che il codice funzioni per entrambi i backend senza alcun cambiamento.`;return{c(){l=m("p"),l.textContent=o},l(t){l=u(t,"P",{"data-svelte-h":!0}),w(l)!=="svelte-8wrg7y"&&(l.textContent=o)},m(t,i){n(t,l,i)},p:V,d(t){t&&s(l)}}}function ua(j){let l,o='Per maggiori dettagli legati alla <code>pipeline()</code> e ai compiti ad essa associati, fai riferimento alla documentazione <a href="./main_classes/pipelines">qui</a>.';return{c(){l=m("p"),l.innerHTML=o},l(t){l=u(t,"P",{"data-svelte-h":!0}),w(l)!=="svelte-1x9csbh"&&(l.innerHTML=o)},m(t,i){n(t,l,i)},p:V,d(t){t&&s(l)}}}function fa(j){let l,o;return l=new C({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRvcmNo",highlighted:"pip install torch"}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p:V,i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function da(j){let l,o;return l=new X({props:{$$slots:{default:[fa]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function $a(j){let l,o;return l=new C({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRlbnNvcmZsb3c=",highlighted:"pip install tensorflow"}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p:V,i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function ha(j){let l,o;return l=new X({props:{$$slots:{default:[$a]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function ga(j){let l,o="Usa <code>AutoModelForSequenceClassification</code> e <code>AutoTokenizer</code> per caricare il modello pre-allenato e il suo tokenizer associato (maggiori informazioni su una <code>AutoClass</code> in seguito):",t,i,f;return i=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZChtb2RlbF9uYW1lKSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKG1vZGVsX25hbWUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),{c(){l=m("p"),l.innerHTML=o,t=p(),h(i.$$.fragment)},l(d){l=u(d,"P",{"data-svelte-h":!0}),w(l)!=="svelte-64igrm"&&(l.innerHTML=o),t=c(d),g(i.$$.fragment,d)},m(d,U){n(d,l,U),n(d,t,U),b(i,d,U),f=!0},p:V,i(d){f||(M(i.$$.fragment,d),f=!0)},o(d){v(i.$$.fragment,d),f=!1},d(d){d&&(s(l),s(t)),y(i,d)}}}function ba(j){let l,o;return l=new X({props:{$$slots:{default:[ga]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Ma(j){let l,o="Usa <code>TFAutoModelForSequenceClassification</code> e <code>AutoTokenizer</code> per caricare il modello pre-allenato e il suo tokenizer associato (maggiori informazioni su una <code>TFAutoClass</code> in seguito):",t,i,f;return i=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQobW9kZWxfbmFtZSklMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChtb2RlbF9uYW1lKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),{c(){l=m("p"),l.innerHTML=o,t=p(),h(i.$$.fragment)},l(d){l=u(d,"P",{"data-svelte-h":!0}),w(l)!=="svelte-1ihjf5u"&&(l.innerHTML=o),t=c(d),g(i.$$.fragment,d)},m(d,U){n(d,l,U),n(d,t,U),b(i,d,U),f=!0},p:V,i(d){f||(M(i.$$.fragment,d),f=!0)},o(d){v(i.$$.fragment,d),f=!1},d(d){d&&(s(l),s(t)),y(i,d)}}}function va(j){let l,o;return l=new X({props:{$$slots:{default:[Ma]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function ya(j){let l,o;return l=new C({props:{code:"cHRfYmF0Y2glMjAlM0QlMjB0b2tlbml6ZXIoJTBBJTIwJTIwJTIwJTIwJTVCJTIyU2lhbW8lMjBtb2x0byUyMGZlbGljaSUyMGRpJTIwbW9zdHJhcnRpJTIwbGElMjBsaWJyZXJpYSUyMCVGMCU5RiVBNCU5NyUyMFRyYW5zZm9ybWVycy4lMjIlMkMlMjAlMjJTcGVyaWFtbyUyMHRlJTIwbm9uJTIwbGElMjBvZGllcmFpLiUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMHBhZGRpbmclM0RUcnVlJTJDJTBBJTIwJTIwJTIwJTIwdHJ1bmNhdGlvbiUzRFRydWUlMkMlMEElMjAlMjAlMjAlMjBtYXhfbGVuZ3RoJTNENTEyJTJDJTBBJTIwJTIwJTIwJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_batch = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;Siamo molto felici di mostrarti la libreria 🤗 Transformers.&quot;</span>, <span class="hljs-string">&quot;Speriamo te non la odierai.&quot;</span>],
<span class="hljs-meta">... </span>    padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
<span class="hljs-meta">... </span>)`}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p:V,i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function wa(j){let l,o;return l=new X({props:{$$slots:{default:[ya]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function ja(j){let l,o;return l=new C({props:{code:"dGZfYmF0Y2glMjAlM0QlMjB0b2tlbml6ZXIoJTBBJTIwJTIwJTIwJTIwJTVCJTIyU2lhbW8lMjBtb2x0byUyMGZlbGljaSUyMGRpJTIwbW9zdHJhcnRpJTIwbGElMjBsaWJyZXJpYSUyMCVGMCU5RiVBNCU5NyUyMFRyYW5zZm9ybWVycy4lMjIlMkMlMjAlMjJTcGVyaWFtbyUyMHRlJTIwbm9uJTIwbGElMjBvZGllcmFpLiUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMHBhZGRpbmclM0RUcnVlJTJDJTBBJTIwJTIwJTIwJTIwdHJ1bmNhdGlvbiUzRFRydWUlMkMlMEElMjAlMjAlMjAlMjBtYXhfbGVuZ3RoJTNENTEyJTJDJTBBJTIwJTIwJTIwJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJ0ZiUyMiUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tf_batch = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;Siamo molto felici di mostrarti la libreria 🤗 Transformers.&quot;</span>, <span class="hljs-string">&quot;Speriamo te non la odierai.&quot;</span>],
<span class="hljs-meta">... </span>    padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;tf&quot;</span>,
<span class="hljs-meta">... </span>)`}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p:V,i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Ta(j){let l,o;return l=new X({props:{$$slots:{default:[ja]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function _a(j){let l,o='Guarda il <a href="./task_summary">task summary</a> per sapere quale classe di <code>AutoModel</code> utilizzare per quale compito.';return{c(){l=m("p"),l.innerHTML=o},l(t){l=u(t,"P",{"data-svelte-h":!0}),w(l)!=="svelte-11t4fw3"&&(l.innerHTML=o)},m(t,i){n(t,l,i)},p:V,d(t){t&&s(l)}}}function ka(j){let l,o="🤗 Transformers fornisce un metodo semplice e unificato per caricare istanze pre-allenate. Questo significa che puoi caricare un <code>AutoModel</code> come caricheresti un <code>AutoTokenizer</code>. L’unica differenza è selezionare l’<code>AutoModel</code> corretto per il compito di interesse. Dato che stai facendo classificazione di testi, o sequenze, carica <code>AutoModelForSequenceClassification</code>:",t,i,f,d,U,k,R="Ora puoi passare il tuo lotto di input pre-processati direttamente al modello. Devi solo spacchettare il dizionario aggiungendo <code>**</code>:",Z,$,J,G,N="Il modello produrrà le attivazioni finali nell’attributo <code>logits</code>. Applica la funzione softmax a <code>logits</code> per ottenere le probabilità:",H,W,I;return i=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbF9uYW1lJTIwJTNEJTIwJTIybmxwdG93biUyRmJlcnQtYmFzZS1tdWx0aWxpbmd1YWwtdW5jYXNlZC1zZW50aW1lbnQlMjIlMEFwdF9tb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKG1vZGVsX25hbWUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)`}}),d=new Tt({props:{$$slots:{default:[_a]},$$scope:{ctx:j}}}),$=new C({props:{code:"cHRfb3V0cHV0cyUyMCUzRCUyMHB0X21vZGVsKCoqcHRfYmF0Y2gp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_outputs = pt_model(**pt_batch)'}}),W=new C({props:{code:"ZnJvbSUyMHRvcmNoJTIwaW1wb3J0JTIwbm4lMEElMEFwdF9wcmVkaWN0aW9ucyUyMCUzRCUyMG5uLmZ1bmN0aW9uYWwuc29mdG1heChwdF9vdXRwdXRzLmxvZ2l0cyUyQyUyMGRpbSUzRC0xKSUwQXByaW50KHB0X3ByZWRpY3Rpb25zKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn

<span class="hljs-meta">&gt;&gt;&gt; </span>pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(pt_predictions)
tensor([[<span class="hljs-number">0.0041</span>, <span class="hljs-number">0.0037</span>, <span class="hljs-number">0.0203</span>, <span class="hljs-number">0.2005</span>, <span class="hljs-number">0.7713</span>],
        [<span class="hljs-number">0.3766</span>, <span class="hljs-number">0.3292</span>, <span class="hljs-number">0.1832</span>, <span class="hljs-number">0.0558</span>, <span class="hljs-number">0.0552</span>]], grad_fn=&lt;SoftmaxBackward0&gt;)`}}),{c(){l=m("p"),l.innerHTML=o,t=p(),h(i.$$.fragment),f=p(),h(d.$$.fragment),U=p(),k=m("p"),k.innerHTML=R,Z=p(),h($.$$.fragment),J=p(),G=m("p"),G.innerHTML=N,H=p(),h(W.$$.fragment)},l(r){l=u(r,"P",{"data-svelte-h":!0}),w(l)!=="svelte-1fgrwgv"&&(l.innerHTML=o),t=c(r),g(i.$$.fragment,r),f=c(r),g(d.$$.fragment,r),U=c(r),k=u(r,"P",{"data-svelte-h":!0}),w(k)!=="svelte-17kjm32"&&(k.innerHTML=R),Z=c(r),g($.$$.fragment,r),J=c(r),G=u(r,"P",{"data-svelte-h":!0}),w(G)!=="svelte-1g2h8eb"&&(G.innerHTML=N),H=c(r),g(W.$$.fragment,r)},m(r,T){n(r,l,T),n(r,t,T),b(i,r,T),n(r,f,T),b(d,r,T),n(r,U,T),n(r,k,T),n(r,Z,T),b($,r,T),n(r,J,T),n(r,G,T),n(r,H,T),b(W,r,T),I=!0},p(r,T){const F={};T&2&&(F.$$scope={dirty:T,ctx:r}),d.$set(F)},i(r){I||(M(i.$$.fragment,r),M(d.$$.fragment,r),M($.$$.fragment,r),M(W.$$.fragment,r),I=!0)},o(r){v(i.$$.fragment,r),v(d.$$.fragment,r),v($.$$.fragment,r),v(W.$$.fragment,r),I=!1},d(r){r&&(s(l),s(t),s(f),s(U),s(k),s(Z),s(J),s(G),s(H)),y(i,r),y(d,r),y($,r),y(W,r)}}}function za(j){let l,o;return l=new X({props:{$$slots:{default:[ka]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Ja(j){let l,o='Guarda il <a href="./task_summary">task summary</a> per sapere quale classe di <code>AutoModel</code> utilizzare per quale compito.';return{c(){l=m("p"),l.innerHTML=o},l(t){l=u(t,"P",{"data-svelte-h":!0}),w(l)!=="svelte-11t4fw3"&&(l.innerHTML=o)},m(t,i){n(t,l,i)},p:V,d(t){t&&s(l)}}}function Ca(j){let l,o="🤗 Transformers fornisce un metodo semplice e unificato per caricare istanze pre-allenate. Questo significa che puoi caricare un <code>TFAutoModel</code> come caricheresti un <code>AutoTokenizer</code>. L’unica differenza è selezionare il <code>TFAutoModel</code> corretto per il compito di interesse. Dato che stai facendo classificazione di testi, o sequenze, carica <code>TFAutoModelForSequenceClassification</code>:",t,i,f,d,U,k,R="Ora puoi passare il tuo lotto di input pre-processati direttamente al modello passando le chiavi del dizionario al tensore:",Z,$,J,G,N="Il modello produrrà le attivazioni finali nell’attributo <code>logits</code>. Applica la funzione softmax a <code>logits</code> per ottenere le probabilità:",H,W,I;return i=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQW5vbWVfZGVsX21vZGVsbG8lMjAlM0QlMjAlMjJubHB0b3duJTJGYmVydC1iYXNlLW11bHRpbGluZ3VhbC11bmNhc2VkLXNlbnRpbWVudCUyMiUwQXRmX21vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZChub21lX2RlbF9tb2RlbGxvKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>nome_del_modello = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(nome_del_modello)`}}),d=new Tt({props:{$$slots:{default:[Ja]},$$scope:{ctx:j}}}),$=new C({props:{code:"dGZfb3V0cHV0cyUyMCUzRCUyMHRmX21vZGVsKHRmX2JhdGNoKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_outputs = tf_model(tf_batch)'}}),W=new C({props:{code:"aW1wb3J0JTIwdGVuc29yZmxvdyUyMGFzJTIwdGYlMEElMEF0Zl9wcmVkaWN0aW9ucyUyMCUzRCUyMHRmLm5uLnNvZnRtYXgodGZfb3V0cHV0cy5sb2dpdHMlMkMlMjBheGlzJTNELTEpJTBBdGZfcHJlZGljdGlvbnM=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_predictions`}}),{c(){l=m("p"),l.innerHTML=o,t=p(),h(i.$$.fragment),f=p(),h(d.$$.fragment),U=p(),k=m("p"),k.textContent=R,Z=p(),h($.$$.fragment),J=p(),G=m("p"),G.innerHTML=N,H=p(),h(W.$$.fragment)},l(r){l=u(r,"P",{"data-svelte-h":!0}),w(l)!=="svelte-12ss547"&&(l.innerHTML=o),t=c(r),g(i.$$.fragment,r),f=c(r),g(d.$$.fragment,r),U=c(r),k=u(r,"P",{"data-svelte-h":!0}),w(k)!=="svelte-1pbq5oa"&&(k.textContent=R),Z=c(r),g($.$$.fragment,r),J=c(r),G=u(r,"P",{"data-svelte-h":!0}),w(G)!=="svelte-1g2h8eb"&&(G.innerHTML=N),H=c(r),g(W.$$.fragment,r)},m(r,T){n(r,l,T),n(r,t,T),b(i,r,T),n(r,f,T),b(d,r,T),n(r,U,T),n(r,k,T),n(r,Z,T),b($,r,T),n(r,J,T),n(r,G,T),n(r,H,T),b(W,r,T),I=!0},p(r,T){const F={};T&2&&(F.$$scope={dirty:T,ctx:r}),d.$set(F)},i(r){I||(M(i.$$.fragment,r),M(d.$$.fragment,r),M($.$$.fragment,r),M(W.$$.fragment,r),I=!0)},o(r){v(i.$$.fragment,r),v(d.$$.fragment,r),v($.$$.fragment,r),v(W.$$.fragment,r),I=!1},d(r){r&&(s(l),s(t),s(f),s(U),s(k),s(Z),s(J),s(G),s(H)),y(i,r),y(d,r),y($,r),y(W,r)}}}function Ua(j){let l,o;return l=new X({props:{$$slots:{default:[Ca]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Za(j){let l,o=`Tutti i modelli di 🤗 Transformers (PyTorch e TensorFlow) restituiscono i tensori <em>prima</em> della funzione finale
di attivazione (come la softmax) perché la funzione di attivazione finale viene spesso unita a quella di perdita.`;return{c(){l=m("p"),l.innerHTML=o},l(t){l=u(t,"P",{"data-svelte-h":!0}),w(l)!=="svelte-7wdla3"&&(l.innerHTML=o)},m(t,i){n(t,l,i)},p:V,d(t){t&&s(l)}}}function xa(j){let l,o=`Gli output del modello di 🤗 Transformers sono delle dataclasses speciali in modo che i loro attributi vengano auto-completati all’interno di un IDE.
Gli output del modello si comportano anche come una tupla o un dizionario (ad esempio, puoi indicizzare con un intero, una slice o una stringa) nel qual caso gli attributi che sono <code>None</code> vengono ignorati.`;return{c(){l=m("p"),l.innerHTML=o},l(t){l=u(t,"P",{"data-svelte-h":!0}),w(l)!=="svelte-fglpz9"&&(l.innerHTML=o)},m(t,i){n(t,l,i)},p:V,d(t){t&&s(l)}}}function Ra(j){let l,o="Una volta completato il fine-tuning del tuo modello, puoi salvarlo con il suo tokenizer utilizzando <code>PreTrainedModel.save_pretrained()</code>:",t,i,f,d,U="Quando desideri utilizzare il tuo modello nuovamente, puoi ri-caricarlo con <code>PreTrainedModel.from_pretrained()</code>:",k,R,Z;return i=new C({props:{code:"cHRfc2F2ZV9kaXJlY3RvcnklMjAlM0QlMjAlMjIuJTJGcHRfc2F2ZV9wcmV0cmFpbmVkJTIyJTBBdG9rZW5pemVyLnNhdmVfcHJldHJhaW5lZChwdF9zYXZlX2RpcmVjdG9yeSklMEFwdF9tb2RlbC5zYXZlX3ByZXRyYWluZWQocHRfc2F2ZV9kaXJlY3Rvcnkp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_save_directory = <span class="hljs-string">&quot;./pt_save_pretrained&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(pt_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.save_pretrained(pt_save_directory)`}}),R=new C({props:{code:"cHRfbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfc2F2ZV9wcmV0cmFpbmVkJTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;./pt_save_pretrained&quot;</span>)'}}),{c(){l=m("p"),l.innerHTML=o,t=p(),h(i.$$.fragment),f=p(),d=m("p"),d.innerHTML=U,k=p(),h(R.$$.fragment)},l($){l=u($,"P",{"data-svelte-h":!0}),w(l)!=="svelte-112corq"&&(l.innerHTML=o),t=c($),g(i.$$.fragment,$),f=c($),d=u($,"P",{"data-svelte-h":!0}),w(d)!=="svelte-fshbgg"&&(d.innerHTML=U),k=c($),g(R.$$.fragment,$)},m($,J){n($,l,J),n($,t,J),b(i,$,J),n($,f,J),n($,d,J),n($,k,J),b(R,$,J),Z=!0},p:V,i($){Z||(M(i.$$.fragment,$),M(R.$$.fragment,$),Z=!0)},o($){v(i.$$.fragment,$),v(R.$$.fragment,$),Z=!1},d($){$&&(s(l),s(t),s(f),s(d),s(k)),y(i,$),y(R,$)}}}function Ga(j){let l,o;return l=new X({props:{$$slots:{default:[Ra]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Wa(j){let l,o="Una volta completato il fine-tuning del tuo modello, puoi salvarlo con il suo tokenizer utilizzando <code>TFPreTrainedModel.save_pretrained()</code>:",t,i,f,d,U="Quando desideri utilizzare il tuo modello nuovamente, puoi ri-caricarlo con <code>TFPreTrainedModel.from_pretrained()</code>:",k,R,Z;return i=new C({props:{code:"dGZfc2F2ZV9kaXJlY3RvcnklMjAlM0QlMjAlMjIuJTJGdGZfc2F2ZV9wcmV0cmFpbmVkJTIyJTBBdG9rZW5pemVyLnNhdmVfcHJldHJhaW5lZCh0Zl9zYXZlX2RpcmVjdG9yeSklMEF0Zl9tb2RlbC5zYXZlX3ByZXRyYWluZWQodGZfc2F2ZV9kaXJlY3Rvcnkp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tf_save_directory = <span class="hljs-string">&quot;./tf_save_pretrained&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(tf_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model.save_pretrained(tf_save_directory)`}}),R=new C({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZ0Zl9zYXZlX3ByZXRyYWluZWQlMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;./tf_save_pretrained&quot;</span>)'}}),{c(){l=m("p"),l.innerHTML=o,t=p(),h(i.$$.fragment),f=p(),d=m("p"),d.innerHTML=U,k=p(),h(R.$$.fragment)},l($){l=u($,"P",{"data-svelte-h":!0}),w(l)!=="svelte-5qobr8"&&(l.innerHTML=o),t=c($),g(i.$$.fragment,$),f=c($),d=u($,"P",{"data-svelte-h":!0}),w(d)!=="svelte-15tj3au"&&(d.innerHTML=U),k=c($),g(R.$$.fragment,$)},m($,J){n($,l,J),n($,t,J),b(i,$,J),n($,f,J),n($,d,J),n($,k,J),b(R,$,J),Z=!0},p:V,i($){Z||(M(i.$$.fragment,$),M(R.$$.fragment,$),Z=!0)},o($){v(i.$$.fragment,$),v(R.$$.fragment,$),Z=!1},d($){$&&(s(l),s(t),s(f),s(d),s(k)),y(i,$),y(R,$)}}}function Ha(j){let l,o;return l=new X({props:{$$slots:{default:[Wa]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Va(j){let l,o;return l=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKHRmX3NhdmVfZGlyZWN0b3J5KSUwQXB0X21vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQodGZfc2F2ZV9kaXJlY3RvcnklMkMlMjBmcm9tX3RmJTNEVHJ1ZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=<span class="hljs-literal">True</span>)`}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p:V,i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Fa(j){let l,o;return l=new X({props:{$$slots:{default:[Va]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Ia(j){let l,o;return l=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQocHRfc2F2ZV9kaXJlY3RvcnkpJTBBdGZfbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKHB0X3NhdmVfZGlyZWN0b3J5JTJDJTIwZnJvbV9wdCUzRFRydWUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=<span class="hljs-literal">True</span>)`}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p:V,i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Xa(j){let l,o;return l=new X({props:{$$slots:{default:[Ia]},$$scope:{ctx:j}}}),{c(){h(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,i){b(l,t,i),o=!0},p(t,i){const f={};i&2&&(f.$$scope={dirty:i,ctx:t}),l.$set(f)},i(t){o||(M(l.$$.fragment,t),o=!0)},o(t){v(l.$$.fragment,t),o=!1},d(t){y(l,t)}}}function Na(j){let l,o,t,i,f,d,U,k,R,Z,$="Quick tour",J,G,N,H,W='Entra in azione con 🤗 Transformers! Inizia utilizzando <code>pipeline()</code> per un’inferenza veloce, carica un modello pre-allenato e un tokenizer con una <a href="./model_doc/auto">AutoClass</a> per risolvere i tuoi compiti legati a testo, immagini o audio.',I,r,T,F,S,_t,me,es,ht,os="Pipeline",It,ue,rs="<code>pipeline()</code> è il modo più semplice per utilizzare un modello pre-allenato per un dato compito.",Xt,fe,Nt,de,ps="La <code>pipeline()</code> supporta molti compiti comuni:",Lt,$e,cs="<strong>Testo</strong>:",qt,he,ms="<li>Analisi del Sentimento (Sentiment Analysis, in inglese): classifica la polarità di un testo dato.</li> <li>Generazione del Testo (Text Generation, in inglese): genera del testo a partire da un dato input.</li> <li>Riconoscimento di Entità (Name Entity Recognition o NER, in inglese): etichetta ogni parola con l’entità che questa rappresenta (persona, data, luogo, ecc.).</li> <li>Rispondere a Domande (Question answering, in inglese): estrae la risposta da un contesto, dato del contesto e una domanda.</li> <li>Riempimento di Maschere (Fill-mask, in inglese): riempie gli spazi mancanti in un testo che ha parole mascherate.</li> <li>Riassumere (Summarization, in inglese): genera una sintesi di una lunga sequenza di testo o di un documento.</li> <li>Traduzione (Translation, in inglese): traduce un testo in un’altra lingua.</li> <li>Estrazione di Caratteristiche (Feature Extraction, in inglese): crea un tensore che rappresenta un testo.</li>",At,ge,us="<strong>Immagini</strong>:",Bt,be,fs="<li>Classificazione di Immagini (Image Classification, in inglese): classifica un’immagine.</li> <li>Segmentazione di Immagini (Image Segmentation, in inglese): classifica ogni pixel di un’immagine.</li> <li>Rilevazione di Oggetti (Object Detection, in inglese): rileva oggetti all’interno di un’immagine.</li>",Et,Me,ds="<strong>Audio</strong>:",Yt,ve,$s="<li>Classificazione di Audio (Audio Classification, in inglese): assegna un’etichetta ad un segmento di audio dato.</li> <li>Riconoscimento Vocale Automatico (Automatic Speech Recognition o ASR, in inglese): trascrive il contenuto di un audio dato in un testo.</li>",St,P,Pt,L,Q,kt,ye,ts,gt,hs="Utilizzo della Pipeline",Qt,we,gs="Nel seguente esempio, utilizzerai la <code>pipeline()</code> per l’analisi del sentimento.",Dt,je,bs="Installa le seguenti dipendenze se non lo hai già fatto:",Kt,D,Ot,Te,Ms="Importa <code>pipeline()</code> e specifica il compito che vuoi completare:",el,_e,tl,ke,vs='La pipeline scarica e salva il <a href="https://huggingface.co/MilaNLProc/feel-it-italian-sentiment" rel="nofollow">modello pre-allenato</a> e il tokenizer per l’analisi del sentimento. Se non avessimo scelto un modello, la pipeline ne avrebbe scelto uno di default. Ora puoi utilizzare il <code>classifier</code> sul tuo testo obiettivo:',ll,ze,sl,Je,ys="Per più di una frase, passa una lista di frasi alla <code>pipeline()</code> la quale restituirà una lista di dizionari:",al,Ce,nl,Ue,ws='La <code>pipeline()</code> può anche iterare su un dataset intero. Inizia installando la libreria <a href="https://huggingface.co/docs/datasets/" rel="nofollow">🤗 Datasets</a>:',il,Ze,ol,xe,js="Crea una <code>pipeline()</code> con il compito che vuoi risolvere e con il modello che vuoi utilizzare.",rl,Re,pl,Ge,Ts='Poi, carica un dataset (vedi 🤗 Datasets <a href="https://huggingface.co/docs/datasets/quickstart.html" rel="nofollow">Quick Start</a> per maggiori dettagli) sul quale vuoi iterare. Per esempio, carichiamo il dataset <a href="https://huggingface.co/datasets/PolyAI/minds14" rel="nofollow">MInDS-14</a>:',cl,We,ml,He,_s="Dobbiamo assicurarci che la frequenza di campionamento del set di dati corrisponda alla frequenza di campionamento con cui è stato addestrato <code>radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram</code>.",ul,Ve,fl,Fe,ks=`I file audio vengono caricati automaticamente e ri-campionati quando chiamiamo la colonna “audio”.
Estraiamo i vettori delle forme d’onda grezze delle prime 4 osservazioni e passiamoli come lista alla pipeline:`,dl,Ie,$l,Xe,zs='Per un dataset più grande dove gli input sono di dimensione maggiore (come nel parlato/audio o nella visione), dovrai passare un generatore al posto di una lista che carica tutti gli input in memoria. Guarda la <a href="./main_classes/pipelines">documentazione della pipeline</a> per maggiori informazioni.',hl,q,K,zt,Ne,ls,bt,Js="Utilizzare un altro modello e tokenizer nella pipeline",gl,Le,Cs='La <code>pipeline()</code> può ospitare qualsiasi modello del <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>, rendendo semplice l’adattamento della <code>pipeline()</code> per altri casi d’uso. Per esempio, se si vuole un modello capace di trattare testo in francese, usa i tag presenti nel Model Hub in modo da filtrare per ottenere un modello appropriato. Il miglior risultato filtrato restituisce un modello multi-lingua <a href="https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment" rel="nofollow">BERT model</a> fine-tuned per l’analisi del sentimento. Ottimo, utilizziamo questo modello!',bl,qe,Ml,O,vl,Ae,Us="Poi puoi specificare il modello e il tokenizer nella <code>pipeline()</code>, e applicare il <code>classifier</code> sul tuo testo obiettivo:",yl,Be,wl,Ee,Zs='Se non riesci a trovare un modello per il tuo caso d’uso, dovrai fare fine-tuning di un modello pre-allenato sui tuoi dati. Dai un’occhiata al nostro tutorial <a href="./training">fine-tuning tutorial</a> per imparare come. Infine, dopo che hai completato il fine-tuning del tuo modello pre-allenato, considera per favore di condividerlo (vedi il tutorial <a href="./model_sharing">qui</a>) con la comunità sul Model Hub per democratizzare l’NLP! 🤗',jl,A,ee,Jt,Ye,ss,Mt,xs="AutoClass",Tl,Se,_l,Pe,Rs='Al suo interno, le classi <code>AutoModelForSequenceClassification</code> e <code>AutoTokenizer</code> lavorano assieme per dare potere alla <code>pipeline()</code>. Una <a href="./model_doc/auto">AutoClass</a> è una scorciatoia che automaticamente recupera l’architettura di un modello pre-allenato a partire dal suo nome o path. Hai solo bisogno di selezionare la <code>AutoClass</code> appropriata per il tuo compito e il suo tokenizer associato con <code>AutoTokenizer</code>.',kl,Qe,Gs="Ritorniamo al nostro esempio e vediamo come puoi utilizzare la <code>AutoClass</code> per replicare i risultati della <code>pipeline()</code>.",zl,B,te,Ct,De,as,vt,Ws="AutoTokenizer",Jl,Ke,Hs='Un tokenizer è responsabile dell’elaborazione del testo in modo da trasformarlo in un formato comprensibile dal modello. Per prima cosa, il tokenizer dividerà il testo in parole chiamate <em>token</em>. Ci sono diverse regole che governano il processo di tokenizzazione, tra cui come dividere una parola e a quale livello (impara di più sulla tokenizzazione <a href="./tokenizer_summary">qui</a>). La cosa più importante da ricordare comunque è che hai bisogno di inizializzare il tokenizer con lo stesso nome del modello in modo da assicurarti che stai utilizzando le stesse regole di tokenizzazione con cui il modello è stato pre-allenato.',Cl,Oe,Vs="Carica un tokenizer con <code>AutoTokenizer</code>:",Ul,et,Zl,tt,Fs="Dopodiché, il tokenizer converte i token in numeri in modo da costruire un tensore come input del modello. Questo è conosciuto come il <em>vocabolario</em> del modello.",xl,lt,Is="Passa il tuo testo al tokenizer:",Rl,st,Gl,at,Xs="Il tokenizer restituirà un dizionario contenente:",Wl,nt,Ns='<li><a href="./glossary#input-ids">input_ids</a>: rappresentazioni numeriche dei tuoi token.</li> <li><a href=".glossary#attention-mask">attention_mask</a>: indica quali token devono essere presi in considerazione.</li>',Hl,it,Ls="Come con la <code>pipeline()</code>, il tokenizer accetterà una lista di input. In più, il tokenizer può anche completare (pad, in inglese) e troncare il testo in modo da restituire un lotto (batch, in inglese) di lunghezza uniforme:",Vl,le,Fl,ot,qs='Leggi il tutorial sul <a href="./preprocessing">preprocessing</a> per maggiori dettagli sulla tokenizzazione.',Il,E,se,Ut,rt,ns,yt,As="AutoModel",Xl,ae,Nl,ne,Ll,pt,Bs='I modelli sono <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow"><code>torch.nn.Module</code></a> o <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow"><code>tf.keras.Model</code></a> standard così puoi utilizzarli all’interno del tuo training loop usuale. Tuttavia, per rendere le cose più semplici, 🤗 Transformers fornisce una classe <code>Trainer</code> per PyTorch che aggiunge delle funzionalità per l’allenamento distribuito, precisione mista, e altro ancora. Per TensorFlow, puoi utilizzare il metodo <code>fit</code> di <a href="https://keras.io/" rel="nofollow">Keras</a>. Fai riferimento al <a href="./training">tutorial per il training</a> per maggiori dettagli.',ql,ie,Al,Y,oe,Zt,ct,is,wt,Es="Salva un modello",Bl,re,El,mt,Ys="Una caratteristica particolarmente interessante di 🤗 Transformers è la sua abilità di salvare un modello e ri-caricarlo sia come modello di PyTorch che di TensorFlow. I parametri <code>from_pt</code> o <code>from_tf</code> possono convertire un modello da un framework all’altro:",Yl,pe,Sl,xt,Pl;return k=new ce({}),G=new ca({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/it/quicktour.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/it/pytorch/quicktour.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/it/tensorflow/quicktour.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/it/quicktour.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/it/pytorch/quicktour.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/it/tensorflow/quicktour.ipynb"}]}}),r=new Tt({props:{$$slots:{default:[ma]},$$scope:{ctx:j}}}),me=new ce({}),fe=new aa({props:{id:"tiZFewofSLM"}}),P=new Tt({props:{$$slots:{default:[ua]},$$scope:{ctx:j}}}),ye=new ce({}),D=new jt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[ha],pytorch:[da]},$$scope:{ctx:j}}}),_e=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBY2xhc3NpZmljYXRvcmUlMjAlM0QlMjBwaXBlbGluZSglMjJzZW50aW1lbnQtYW5hbHlzaXMlMjIlMkMlMjBtb2RlbCUzRCUyMk1pbGFOTFByb2MlMkZmZWVsLWl0LWl0YWxpYW4tc2VudGltZW50JTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>classificatore = pipeline(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>, model=<span class="hljs-string">&quot;MilaNLProc/feel-it-italian-sentiment&quot;</span>)`}}),ze=new C({props:{code:"Y2xhc3NpZmljYXRvcmUoJTIyU2lhbW8lMjBtb2x0byUyMGZlbGljaSUyMGRpJTIwbW9zdHJhcnRpJTIwbGElMjBsaWJyZXJpYSUyMCVGMCU5RiVBNCU5NyUyMFRyYW5zZm9ybWVycy4lMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>classificatore(<span class="hljs-string">&quot;Siamo molto felici di mostrarti la libreria 🤗 Transformers.&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;positive&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9997</span>}]`}}),Ce=new C({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>risultati = classificatore(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;Siamo molto felici di mostrarti la libreria 🤗 Transformers.&quot;</span>, <span class="hljs-string">&quot;Speriamo te non la odierai.&quot;</span>]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> risultato <span class="hljs-keyword">in</span> risultati:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;etichetta: <span class="hljs-subst">{risultato[<span class="hljs-string">&#x27;label&#x27;</span>]}</span>, con punteggio: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(risultato[<span class="hljs-string">&#x27;score&#x27;</span>], <span class="hljs-number">4</span>)}</span>&quot;</span>)
etichetta: positive, con punteggio: <span class="hljs-number">0.9998</span>
etichetta: negative, con punteggio: <span class="hljs-number">0.9998</span>`}}),Ze=new C({props:{code:"cGlwJTIwaW5zdGFsbCUyMGRhdGFzZXRzJTIw",highlighted:"pip install datasets "}}),Re=new C({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwcGlwZWxpbmUlMEElMEFyaWNvbm9zY2l0b3JlX3ZvY2FsZSUyMCUzRCUyMHBpcGVsaW5lKCUwQSUyMCUyMCUyMCUyMCUyMmF1dG9tYXRpYy1zcGVlY2gtcmVjb2duaXRpb24lMjIlMkMlMjBtb2RlbCUzRCUyMnJhZGlvZ3JvdXAtY3JpdHMlMkZ3YXYydmVjMi14bHMtci0xYi1pdGFsaWFuLWRvYzRsbS01Z3JhbSUyMiUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>riconoscitore_vocale = pipeline(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>, model=<span class="hljs-string">&quot;radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram&quot;</span>
<span class="hljs-meta">... </span>)`}}),We=new C({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTJDJTIwQXVkaW8lMEElMEFkYXRhc2V0JTIwJTNEJTIwbG9hZF9kYXRhc2V0KCUyMlBvbHlBSSUyRm1pbmRzMTQlMjIlMkMlMjBuYW1lJTNEJTIyaXQtSVQlMjIlMkMlMjBzcGxpdCUzRCUyMnRyYWluJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>, name=<span class="hljs-string">&quot;it-IT&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)`}}),Ve=new C({props:{code:"ZGF0YXNldCUyMCUzRCUyMGRhdGFzZXQuY2FzdF9jb2x1bW4oJTIyYXVkaW8lMjIlMkMlMjBBdWRpbyhzYW1wbGluZ19yYXRlJTNEcmljb25vc2NpdG9yZV92b2NhbGUuZmVhdHVyZV9leHRyYWN0b3Iuc2FtcGxpbmdfcmF0ZSkp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio(sampling_rate=riconoscitore_vocale.feature_extractor.sampling_rate))'}}),Ie=new C({props:{code:"cmlzdWx0YXRvJTIwJTNEJTIwcmljb25vc2NpdG9yZV92b2NhbGUoZGF0YXNldCU1QiUzQTQlNUQlNUIlMjJhdWRpbyUyMiU1RCklMEFwcmludCglNUJkJTVCJTIydGV4dCUyMiU1RCUyMGZvciUyMGQlMjBpbiUyMHJpc3VsdGF0byU1RCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>risultato = riconoscitore_vocale(dataset[:<span class="hljs-number">4</span>][<span class="hljs-string">&quot;audio&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>([d[<span class="hljs-string">&quot;text&quot;</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> risultato])
[<span class="hljs-string">&#x27;dovrei caricare dei soldi sul mio conto corrente&#x27;</span>, <span class="hljs-string">&#x27;buongiorno e senza vorrei depositare denaro sul mio conto corrente come devo fare per cortesia&#x27;</span>, <span class="hljs-string">&#x27;sì salve vorrei depositare del denaro sul mio conto&#x27;</span>, <span class="hljs-string">&#x27;e buon pomeriggio vorrei depositare dei soldi sul mio conto bancario volleo sapere come posso fare se e posso farlo online ed un altro conto o andandoo tramite bancomut&#x27;</span>]`}}),Ne=new ce({}),qe=new C({props:{code:"bW9kZWxfbmFtZSUyMCUzRCUyMCUyMm5scHRvd24lMkZiZXJ0LWJhc2UtbXVsdGlsaW5ndWFsLXVuY2FzZWQtc2VudGltZW50JTIy",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>'}}),O=new jt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[va],pytorch:[ba]},$$scope:{ctx:j}}}),Be=new C({props:{code:"Y2xhc3NpZmllciUyMCUzRCUyMHBpcGVsaW5lKCUyMnNlbnRpbWVudC1hbmFseXNpcyUyMiUyQyUyMG1vZGVsJTNEbW9kZWwlMkMlMjB0b2tlbml6ZXIlM0R0b2tlbml6ZXIpJTBBY2xhc3NpZmllciglMjJOb3VzJTIwc29tbWVzJTIwdHIlQzMlQThzJTIwaGV1cmV1eCUyMGRlJTIwdm91cyUyMHByJUMzJUE5c2VudGVyJTIwbGElMjBiaWJsaW90aCVDMyVBOHF1ZSUyMCVGMCU5RiVBNCU5NyUyMFRyYW5zZm9ybWVycy4lMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>classifier = pipeline(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>classifier(<span class="hljs-string">&quot;Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;5 stars&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.7273</span>}]`}}),Ye=new ce({}),Se=new aa({props:{id:"AhChOFRegn4"}}),De=new ce({}),et=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEFub21lX2RlbF9tb2RlbGxvJTIwJTNEJTIwJTIybmxwdG93biUyRmJlcnQtYmFzZS1tdWx0aWxpbmd1YWwtdW5jYXNlZC1zZW50aW1lbnQlMjIlMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChub21lX2RlbF9tb2RlbGxvKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>nome_del_modello = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(nome_del_modello)`}}),st=new C({props:{code:"ZW5jb2RpbmclMjAlM0QlMjB0b2tlbml6ZXIoJTIyU2lhbW8lMjBtb2x0byUyMGZlbGljaSUyMGRpJTIwbW9zdHJhcnRpJTIwbGElMjBsaWJyZXJpYSUyMCVGMCU5RiVBNCU5NyUyMFRyYW5zZm9ybWVycy4lMjIpJTBBcHJpbnQoZW5jb2Rpbmcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer(<span class="hljs-string">&quot;Siamo molto felici di mostrarti la libreria 🤗 Transformers.&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoding)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">56821</span>, <span class="hljs-number">10132</span>, <span class="hljs-number">14407</span>, <span class="hljs-number">13019</span>, <span class="hljs-number">13007</span>, <span class="hljs-number">10120</span>, <span class="hljs-number">47201</span>, <span class="hljs-number">10330</span>, <span class="hljs-number">10106</span>, <span class="hljs-number">91686</span>, <span class="hljs-number">100</span>, <span class="hljs-number">58263</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
<span class="hljs-string">&#x27;token_type_ids&#x27;</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">&#x27;attention_mask&#x27;</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]}`}}),le=new jt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Ta],pytorch:[wa]},$$scope:{ctx:j}}}),rt=new ce({}),ae=new jt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Ua],pytorch:[za]},$$scope:{ctx:j}}}),ne=new Tt({props:{$$slots:{default:[Za]},$$scope:{ctx:j}}}),ie=new Tt({props:{$$slots:{default:[xa]},$$scope:{ctx:j}}}),ct=new ce({}),re=new jt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Ha],pytorch:[Ga]},$$scope:{ctx:j}}}),pe=new 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