import{s as bi,f as wi,o as _i,n as tt}from"../chunks/scheduler.36a0863c.js";import{S as yi,i as Mi,g as o,s as r,u as b,A as Ti,h as s,f as t,c as u,j as z,v as w,y as d,k as $,o as C,a as l,w as _,d as y,t as M,x as T}from"../chunks/index.d0f7a4c4.js";import{T as Ci}from"../chunks/Tip.4c7d7eb1.js";import{Y as ki}from"../chunks/Youtube.d15bb502.js";import{I as le}from"../chunks/IconCopyLink.bc5fad79.js";import{C as U}from"../chunks/CodeBlock.4236768d.js";import{F as vi,M as mt}from"../chunks/Markdown.2bf44d4a.js";function zi(H){let a,m='Per condividere un modello con la community, hai bisogno di un account su <a href="https://huggingface.co/join" rel="nofollow">huggingface.co</a>. Puoi anche unirti ad un’organizzazione esistente o crearne una nuova.';return{c(){a=o("p"),a.innerHTML=m},l(n){a=s(n,"P",{"data-svelte-h":!0}),d(a)!=="svelte-1pho539"&&(a.innerHTML=m)},m(n,p){l(n,a,p)},p:tt,d(n){n&&t(a)}}}function Wi(H){let a,m="Specifica <code>from_tf=True</code> per convertire un checkpoint da TensorFlow a PyTorch:",n,p,h;return p=new U({props:{code:"cHRfbW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0Rm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIycGF0aCUyRnZlcnNvJTJGaWwtbm9tZS1tYWduaWZpY28tY2hlLWhhaS1zY2VsdG8lMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUwQSklMEFwdF9tb2RlbC5zYXZlX3ByZXRyYWluZWQoJTIycGF0aCUyRnZlcnNvJTJGaWwtbm9tZS1tYWduaWZpY28tY2hlLWhhaS1zY2VsdG8lMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = DistilBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;path/verso/il-nome-magnifico-che-hai-scelto&quot;</span>, from_tf=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.save_pretrained(<span class="hljs-string">&quot;path/verso/il-nome-magnifico-che-hai-scelto&quot;</span>)`,wrap:!1}}),{c(){a=o("p"),a.innerHTML=m,n=r(),b(p.$$.fragment)},l(c){a=s(c,"P",{"data-svelte-h":!0}),d(a)!=="svelte-1tus5wd"&&(a.innerHTML=m),n=u(c),w(p.$$.fragment,c)},m(c,k){l(c,a,k),l(c,n,k),_(p,c,k),h=!0},p:tt,i(c){h||(y(p.$$.fragment,c),h=!0)},o(c){M(p.$$.fragment,c),h=!1},d(c){c&&(t(a),t(n)),T(p,c)}}}function xi(H){let a,m;return a=new mt({props:{$$slots:{default:[Wi]},$$scope:{ctx:H}}}),{c(){b(a.$$.fragment)},l(n){w(a.$$.fragment,n)},m(n,p){_(a,n,p),m=!0},p(n,p){const h={};p&2&&(h.$$scope={dirty:p,ctx:n}),a.$set(h)},i(n){m||(y(a.$$.fragment,n),m=!0)},o(n){M(a.$$.fragment,n),m=!1},d(n){T(a,n)}}}function Ji(H){let a,m="Specifica <code>from_pt=True</code> per convertire un checkpoint da PyTorch a TensorFlow:",n,p,h,c,k="Poi puoi salvare il tuo nuovo modello in TensorFlow con il suo nuovo checkpoint:",W,J,L;return p=new U({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJwYXRoJTJGdmVyc28lMkZpbC1ub21lLW1hZ25pZmljby1jaGUtaGFpLXNjZWx0byUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;path/verso/il-nome-magnifico-che-hai-scelto&quot;</span>, from_pt=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)`,wrap:!1}}),J=new U({props:{code:"dGZfbW9kZWwuc2F2ZV9wcmV0cmFpbmVkKCUyMnBhdGglMkZ2ZXJzbyUyRmlsLW5vbWUtbWFnbmlmaWNvLWNoZS1oYWktc2NlbHRvJTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model.save_pretrained(<span class="hljs-string">&quot;path/verso/il-nome-magnifico-che-hai-scelto&quot;</span>)',wrap:!1}}),{c(){a=o("p"),a.innerHTML=m,n=r(),b(p.$$.fragment),h=r(),c=o("p"),c.textContent=k,W=r(),b(J.$$.fragment)},l(g){a=s(g,"P",{"data-svelte-h":!0}),d(a)!=="svelte-1v7d0kb"&&(a.innerHTML=m),n=u(g),w(p.$$.fragment,g),h=u(g),c=s(g,"P",{"data-svelte-h":!0}),d(c)!=="svelte-1qx7qo6"&&(c.textContent=k),W=u(g),w(J.$$.fragment,g)},m(g,x){l(g,a,x),l(g,n,x),_(p,g,x),l(g,h,x),l(g,c,x),l(g,W,x),_(J,g,x),L=!0},p:tt,i(g){L||(y(p.$$.fragment,g),y(J.$$.fragment,g),L=!0)},o(g){M(p.$$.fragment,g),M(J.$$.fragment,g),L=!1},d(g){g&&(t(a),t(n),t(h),t(c),t(W)),T(p,g),T(J,g)}}}function Hi(H){let a,m;return a=new mt({props:{$$slots:{default:[Ji]},$$scope:{ctx:H}}}),{c(){b(a.$$.fragment)},l(n){w(a.$$.fragment,n)},m(n,p){_(a,n,p),m=!0},p(n,p){const h={};p&2&&(h.$$scope={dirty:p,ctx:n}),a.$set(h)},i(n){m||(y(a.$$.fragment,n),m=!0)},o(n){M(a.$$.fragment,n),m=!1},d(n){T(a,n)}}}function ji(H){let a,m="Se un modello è disponibile in Flax, puoi anche convertire un checkpoint da PyTorch a Flax:",n,p,h;return p=new U({props:{code:"ZmxheF9tb2RlbCUyMCUzRCUyMEZsYXhEaXN0aWxCZXJ0Rm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIycGF0aCUyRnZlcnNvJTJGaWwtbm9tZS1tYWduaWZpY28tY2hlLWhhaS1zY2VsdG8lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;path/verso/il-nome-magnifico-che-hai-scelto&quot;</span>, from_pt=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){a=o("p"),a.textContent=m,n=r(),b(p.$$.fragment)},l(c){a=s(c,"P",{"data-svelte-h":!0}),d(a)!=="svelte-1scd5nn"&&(a.textContent=m),n=u(c),w(p.$$.fragment,c)},m(c,k){l(c,a,k),l(c,n,k),_(p,c,k),h=!0},p:tt,i(c){h||(y(p.$$.fragment,c),h=!0)},o(c){M(p.$$.fragment,c),h=!1},d(c){c&&(t(a),t(n)),T(p,c)}}}function Zi(H){let a,m;return a=new mt({props:{$$slots:{default:[ji]},$$scope:{ctx:H}}}),{c(){b(a.$$.fragment)},l(n){w(a.$$.fragment,n)},m(n,p){_(a,n,p),m=!0},p(n,p){const h={};p&2&&(h.$$scope={dirty:p,ctx:n}),a.$set(h)},i(n){m||(y(a.$$.fragment,n),m=!0)},o(n){M(a.$$.fragment,n),m=!1},d(n){T(a,n)}}}function Li(H){let a,m,n,p='Condividere un modello nell’Hub è tanto semplice quanto aggiungere un parametro extra o un callback. Ricorda dal <a href="training">tutorial sul fine-tuning</a>, la classe <code>TrainingArguments</code> è dove specifichi gli iperparametri e le opzioni addizionali per l’allenamento. Una di queste opzioni di training include l’abilità di condividere direttamente un modello nell’Hub. Imposta <code>push_to_hub=True</code> in <code>TrainingArguments</code>:',h,c,k,W,J="Passa gli argomenti per il training come di consueto al <code>Trainer</code>:",L,g,x,Z,v="Dopo aver effettuato il fine-tuning del tuo modello, chiama <code>push_to_hub()</code> sul <code>Trainer</code> per condividere il modello allenato nell’Hub. 🤗 Transformers aggiungerà in modo automatico persino gli iperparametri, i risultati del training e le versioni del framework alla scheda del tuo modello (model card, in inglese)!",G,P,ie;return a=new ki({props:{id:"Z1-XMy-GNLQ"}}),c=new U({props:{code:"dHJhaW5pbmdfYXJncyUyMCUzRCUyMFRyYWluaW5nQXJndW1lbnRzKG91dHB1dF9kaXIlM0QlMjJpbC1taW8tYmVsbGlzc2ltby1tb2RlbGxvJTIyJTJDJTIwcHVzaF90b19odWIlM0RUcnVlKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = TrainingArguments(output_dir=<span class="hljs-string">&quot;il-mio-bellissimo-modello&quot;</span>, push_to_hub=<span class="hljs-literal">True</span>)',wrap:!1}}),g=new U({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoJTBBJTIwJTIwJTIwJTIwbW9kZWwlM0Rtb2RlbCUyQyUwQSUyMCUyMCUyMCUyMGFyZ3MlM0R0cmFpbmluZ19hcmdzJTJDJTBBJTIwJTIwJTIwJTIwdHJhaW5fZGF0YXNldCUzRHNtYWxsX3RyYWluX2RhdGFzZXQlMkMlMEElMjAlMjAlMjAlMjBldmFsX2RhdGFzZXQlM0RzbWFsbF9ldmFsX2RhdGFzZXQlMkMlMEElMjAlMjAlMjAlMjBjb21wdXRlX21ldHJpY3MlM0Rjb21wdXRlX21ldHJpY3MlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    train_dataset=small_train_dataset,
<span class="hljs-meta">... </span>    eval_dataset=small_eval_dataset,
<span class="hljs-meta">... </span>    compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>)`,wrap:!1}}),P=new U({props:{code:"dHJhaW5lci5wdXNoX3RvX2h1Yigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.push_to_hub()',wrap:!1}}),{c(){b(a.$$.fragment),m=r(),n=o("p"),n.innerHTML=p,h=r(),b(c.$$.fragment),k=r(),W=o("p"),W.innerHTML=J,L=r(),b(g.$$.fragment),x=r(),Z=o("p"),Z.innerHTML=v,G=r(),b(P.$$.fragment)},l(f){w(a.$$.fragment,f),m=u(f),n=s(f,"P",{"data-svelte-h":!0}),d(n)!=="svelte-54ntez"&&(n.innerHTML=p),h=u(f),w(c.$$.fragment,f),k=u(f),W=s(f,"P",{"data-svelte-h":!0}),d(W)!=="svelte-1bkwfba"&&(W.innerHTML=J),L=u(f),w(g.$$.fragment,f),x=u(f),Z=s(f,"P",{"data-svelte-h":!0}),d(Z)!=="svelte-7ldac1"&&(Z.innerHTML=v),G=u(f),w(P.$$.fragment,f)},m(f,j){_(a,f,j),l(f,m,j),l(f,n,j),l(f,h,j),_(c,f,j),l(f,k,j),l(f,W,j),l(f,L,j),_(g,f,j),l(f,x,j),l(f,Z,j),l(f,G,j),_(P,f,j),ie=!0},p:tt,i(f){ie||(y(a.$$.fragment,f),y(c.$$.fragment,f),y(g.$$.fragment,f),y(P.$$.fragment,f),ie=!0)},o(f){M(a.$$.fragment,f),M(c.$$.fragment,f),M(g.$$.fragment,f),M(P.$$.fragment,f),ie=!1},d(f){f&&(t(m),t(n),t(h),t(k),t(W),t(L),t(x),t(Z),t(G)),T(a,f),T(c,f),T(g,f),T(P,f)}}}function Gi(H){let a,m;return a=new mt({props:{$$slots:{default:[Li]},$$scope:{ctx:H}}}),{c(){b(a.$$.fragment)},l(n){w(a.$$.fragment,n)},m(n,p){_(a,n,p),m=!0},p(n,p){const h={};p&2&&(h.$$scope={dirty:p,ctx:n}),a.$set(h)},i(n){m||(y(a.$$.fragment,n),m=!0)},o(n){M(a.$$.fragment,n),m=!1},d(n){T(a,n)}}}function Ui(H){let a,m="Condividi un modello nell’Hub con <code>PushToHubCallback</code>. Nella funzione <code>PushToHubCallback</code>, aggiungi:",n,p,h="<li>Una directory di output per il tuo modello.</li> <li>Un tokenizer.</li> <li>L’<code>hub_model_id</code>, che è il tuo username sull’Hub e il nome del modello.</li>",c,k,W,J,L='Aggiungi il callback a <a href="https://keras.io/api/models/model_training_apis/" rel="nofollow"><code>fit</code></a>, e 🤗 Transformers caricherà il modello allenato nell’Hub:',g,x,Z;return k=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFB1c2hUb0h1YkNhbGxiYWNrJTBBJTBBcHVzaF90b19odWJfY2FsbGJhY2slMjAlM0QlMjBQdXNoVG9IdWJDYWxsYmFjayglMEElMjAlMjAlMjAlMjBvdXRwdXRfZGlyJTNEJTIyLiUyRmlsX3BhdGhfZG92ZV9zYWx2YXJlX2lsX3R1b19tb2RlbGxvJTIyJTJDJTBBJTIwJTIwJTIwJTIwdG9rZW5pemVyJTNEdG9rZW5pemVyJTJDJTBBJTIwJTIwJTIwJTIwaHViX21vZGVsX2lkJTNEJTIyaWwtdHVvLXVzZXJuYW1lJTJGaWwtbWlvLWJlbGxpc3NpbW8tbW9kZWxsbyUyMiUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PushToHubCallback

<span class="hljs-meta">&gt;&gt;&gt; </span>push_to_hub_callback = PushToHubCallback(
<span class="hljs-meta">... </span>    output_dir=<span class="hljs-string">&quot;./il_path_dove_salvare_il_tuo_modello&quot;</span>,
<span class="hljs-meta">... </span>    tokenizer=tokenizer,
<span class="hljs-meta">... </span>    hub_model_id=<span class="hljs-string">&quot;il-tuo-username/il-mio-bellissimo-modello&quot;</span>,
<span class="hljs-meta">... </span>)`,wrap:!1}}),x=new U({props:{code:"bW9kZWwuZml0KHRmX3RyYWluX2RhdGFzZXQlMkMlMjB2YWxpZGF0aW9uX2RhdGElM0R0Zl92YWxpZGF0aW9uX2RhdGFzZXQlMkMlMjBlcG9jaHMlM0QzJTJDJTIwY2FsbGJhY2tzJTNEcHVzaF90b19odWJfY2FsbGJhY2sp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=<span class="hljs-number">3</span>, callbacks=push_to_hub_callback)',wrap:!1}}),{c(){a=o("p"),a.innerHTML=m,n=r(),p=o("ul"),p.innerHTML=h,c=r(),b(k.$$.fragment),W=r(),J=o("p"),J.innerHTML=L,g=r(),b(x.$$.fragment)},l(v){a=s(v,"P",{"data-svelte-h":!0}),d(a)!=="svelte-aa26jg"&&(a.innerHTML=m),n=u(v),p=s(v,"UL",{"data-svelte-h":!0}),d(p)!=="svelte-xulqs"&&(p.innerHTML=h),c=u(v),w(k.$$.fragment,v),W=u(v),J=s(v,"P",{"data-svelte-h":!0}),d(J)!=="svelte-1ed1t6j"&&(J.innerHTML=L),g=u(v),w(x.$$.fragment,v)},m(v,G){l(v,a,G),l(v,n,G),l(v,p,G),l(v,c,G),_(k,v,G),l(v,W,G),l(v,J,G),l(v,g,G),_(x,v,G),Z=!0},p:tt,i(v){Z||(y(k.$$.fragment,v),y(x.$$.fragment,v),Z=!0)},o(v){M(k.$$.fragment,v),M(x.$$.fragment,v),Z=!1},d(v){v&&(t(a),t(n),t(p),t(c),t(W),t(J),t(g)),T(k,v),T(x,v)}}}function Pi(H){let a,m;return a=new mt({props:{$$slots:{default:[Ui]},$$scope:{ctx:H}}}),{c(){b(a.$$.fragment)},l(n){w(a.$$.fragment,n)},m(n,p){_(a,n,p),m=!0},p(n,p){const h={};p&2&&(h.$$scope={dirty:p,ctx:n}),a.$set(h)},i(n){m||(y(a.$$.fragment,n),m=!0)},o(n){M(a.$$.fragment,n),m=!1},d(n){T(a,n)}}}function Ii(H){let a,m,n,p,h,c,k,W,J,L,g="Condividi un modello",x,Z,v="Gli ultimi due tutorial ti hanno mostrato come puoi fare fine-tuning di un modello con PyTorch, Keras e 🤗 Accelerate per configurazioni distribuite. Il prossimo passo è quello di condividere il tuo modello con la community! In Hugging Face, crediamo nella condivisione della conoscenza e delle risorse in modo da democratizzare l’intelligenza artificiale per chiunque. Ti incoraggiamo a considerare di condividere il tuo modello con la community per aiutare altre persone a risparmiare tempo e risorse.",G,P,ie='In questo tutorial, imparerai due metodi per la condivisione di un modello trained o fine-tuned nel <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>:',f,j,yl="<li>Condividi in modo programmatico i tuoi file nell’Hub.</li> <li>Trascina i tuoi file nell’Hub mediante interfaccia grafica.</li>",dt,I,Ml,ft,N,ht,F,S,lt,ae,hl,Be,Tl="Caratteristiche dei repository",gt,ne,Cl="Ogni repository nel Model Hub si comporta come un tipico repository di GitHub. I nostri repository offrono il versionamento, la cronologia dei commit, e la possibilità di visualizzare le differenze.",$t,oe,kl='Il versionamento all’interno del Model Hub è basato su git e <a href="https://git-lfs.github.com/" rel="nofollow">git-lfs</a>. In altre parole, puoi trattare un modello come un unico repository, consentendo un maggiore controllo degli accessi e maggiore scalabilità. Il controllo delle versioni consente <em>revisions</em>, un metodo per appuntare una versione specifica di un modello con un hash di commit, un tag o un branch.',vt,se,zl="Come risultato, puoi caricare una specifica versione di un modello con il parametro <code>revision</code>:",bt,re,wt,ue,Wl="Anche i file possono essere modificati facilmente in un repository ed è possibile visualizzare la cronologia dei commit e le differenze:",_t,pe,xl='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vis_diff.png" alt="vis_diff"/>',yt,R,B,it,ce,gl,Ve,Jl="Configurazione",Mt,me,Hl="Prima di condividere un modello nell’Hub, hai bisogno delle tue credenziali di Hugging Face. Se hai accesso ad un terminale, esegui il seguente comando nell’ambiente virtuale in cui è installata la libreria 🤗 Transformers. Questo memorizzerà il tuo token di accesso nella cartella cache di Hugging Face (di default <code>~/.cache/</code>):",Tt,de,Ct,fe,jl='Se stai usando un notebook come Jupyter o Colaboratory, assicurati di avere la libreria <a href="https://huggingface.co/docs/hub/adding-a-library" rel="nofollow"><code>huggingface_hub</code></a> installata. Questa libreria ti permette di interagire in maniera programmatica con l’Hub.',kt,he,zt,ge,Zl='Utilizza <code>notebook_login</code> per accedere all’Hub, e segui il link <a href="https://huggingface.co/settings/token" rel="nofollow">qui</a> per generare un token con cui effettuare il login:',Wt,$e,xt,X,V,at,ve,$l,Qe,Ll="Converti un modello per tutti i framework",Jt,be,Gl="Per assicurarti che il tuo modello possa essere utilizzato da persone che lavorano con un framework differente, ti raccomandiamo di convertire e caricare il tuo modello sia con i checkpoint di PyTorch che con quelli di TensorFlow. Anche se è possibile caricare il modello da un framework diverso, se si salta questo passaggio, il caricamento sarà più lento perché 🤗 Transformers ha bisogno di convertire i checkpoint al momento.",Ht,we,Ul='Convertire un checkpoint per un altro framework è semplice. Assicurati di avere PyTorch e TensorFlow installati (vedi <a href="installation">qui</a> per le istruzioni d’installazione), e poi trova il modello specifico per il tuo compito nell’altro framework.',jt,Q,Zt,E,D,nt,_e,vl,De,Pl="Condividi un modello durante il training",Lt,K,Gt,Y,O,ot,ye,bl,Ke,Il="Utilizzare la funzione <code>push_to_hub</code>",Ut,Me,Fl="Puoi anche chiamare <code>push_to_hub</code> direttamente sul tuo modello per caricarlo nell’Hub.",Pt,Te,Rl="Specifica il nome del tuo modello in <code>push_to_hub</code>:",It,Ce,Ft,ke,Xl="Questo crea un repository sotto il proprio username con il nome del modello <code>il-mio-bellissimo-modello</code>. Ora chiunque può caricare il tuo modello con la funzione <code>from_pretrained</code>:",Rt,ze,Xt,We,El="Se fai parte di un’organizzazione e vuoi invece condividere un modello sotto il nome dell’organizzazione, aggiungi il parametro <code>organization</code>:",Et,xe,Yt,Je,Yl="La funzione <code>push_to_hub</code> può essere anche utilizzata per aggiungere altri file al repository del modello. Per esempio, aggiungi un tokenizer ad un repository di un modello:",qt,He,At,je,ql="O magari potresti voler aggiungere la versione di TensorFlow del tuo modello PyTorch a cui hai fatto fine-tuning:",Nt,Ze,St,Le,Al="Ora quando navighi nel tuo profilo Hugging Face, dovresti vedere il tuo repository del modello appena creato. Premendo sulla scheda <strong>Files</strong> vengono visualizzati tutti i file caricati nel repository.",Bt,Ge,Nl='Per maggiori dettagli su come creare e caricare file ad un repository, fai riferimento alla documentazione <a href="https://huggingface.co/docs/hub/how-to-upstream" rel="nofollow">qui</a>.',Vt,q,ee,st,Ue,wl,Oe,Sl="Carica un modello utilizzando l'interfaccia web",Qt,Pe,Bl='Chi preferisce un approccio senza codice può caricare un modello tramite l’interfaccia web dell’hub. Visita <a href="https://huggingface.co/new" rel="nofollow">huggingface.co/new</a> per creare un nuovo repository:',Dt,Ie,Vl='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_model_repo.png" alt="new_model_repo"/>',Kt,Fe,Ql="Da qui, aggiungi alcune informazioni sul tuo modello:",Ot,Re,Dl="<li>Seleziona il/la <strong>owner</strong> del repository. Puoi essere te o qualunque organizzazione di cui fai parte.</li> <li>Scegli un nome per il tuo modello, il quale sarà anche il nome del repository.</li> <li>Scegli se il tuo modello è pubblico o privato.</li> <li>Specifica la licenza utilizzata per il tuo modello.</li>",el,Xe,Kl="Ora premi sulla scheda <strong>Files</strong> e premi sul pulsante <strong>Add file</strong> per caricare un nuovo file al tuo repository. Trascina poi un file per caricarlo e aggiungere un messaggio di commit.",tl,Ee,Ol='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/upload_file.png" alt="upload_file"/>',ll,A,te,rt,Ye,_l,et,ei="Aggiungi una scheda del modello",il,qe,ti="Per assicurarti che chiunque possa comprendere le abilità, limitazioni, i potenziali bias e le considerazioni etiche del tuo modello, per favore aggiungi una scheda del modello (model card, in inglese) al tuo repository. La scheda del modello è definita nel file <code>README.md</code>. Puoi aggiungere una scheda del modello:",al,Ae,li="<li>Creando manualmente e caricando un file <code>README.md</code>.</li> <li>Premendo sul pulsante <strong>Edit model card</strong> nel repository del tuo modello.</li>",nl,Ne,ii='Dai un’occhiata alla <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">scheda del modello</a> di DistilBert per avere un buon esempio del tipo di informazioni che una scheda di un modello deve includere. Per maggiori dettagli legati ad altre opzioni che puoi controllare nel file <code>README.md</code>, come l’impatto ambientale o widget di esempio, fai riferimento alla documentazione <a href="https://huggingface.co/docs/hub/models-cards" rel="nofollow">qui</a>.',ol,ut,sl;return W=new le({}),N=new Ci({props:{$$slots:{default:[zi]},$$scope:{ctx:H}}}),ae=new le({}),re=new U({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMmp1bGllbi1jJTJGRXNwZXJCRVJUby1zbWFsbCUyMiUyQyUyMHJldmlzaW9uJTNEJTIydjIuMC4xJTIyJTIwJTIwJTIzJTIwbm9tZSUyMGRpJTIwdW4lMjB0YWclMkMlMjBkaSUyMHVuJTIwYnJhbmNoJTJDJTIwbyUyMGNvbW1pdCUyMGhhc2glMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;julien-c/EsperBERTo-small&quot;</span>, revision=<span class="hljs-string">&quot;v2.0.1&quot;</span>  <span class="hljs-comment"># nome di un tag, di un branch, o commit hash</span>
<span class="hljs-meta">... </span>)`,wrap:!1}}),ce=new le({}),de=new U({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login",wrap:!1}}),he=new U({props:{code:"cGlwJTIwaW5zdGFsbCUyMGh1Z2dpbmdmYWNlX2h1Yg==",highlighted:"pip install huggingface_hub",wrap:!1}}),$e=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`,wrap:!1}}),ve=new le({}),Q=new vi({props:{pytorch:!0,tensorflow:!0,jax:!0,$$slots:{jax:[Zi],tensorflow:[Hi],pytorch:[xi]},$$scope:{ctx:H}}}),_e=new le({}),K=new vi({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Pi],pytorch:[Gi]},$$scope:{ctx:H}}}),ye=new le({}),Ce=new U({props:{code:"cHRfbW9kZWwucHVzaF90b19odWIoJTIyaWwtbWlvLWJlbGxpc3NpbW8tbW9kZWxsbyUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.push_to_hub(<span class="hljs-string">&quot;il-mio-bellissimo-modello&quot;</span>)',wrap:!1}}),ze=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fcHJldHJhaW5lZCglMjJpbC10dW8tdXNlcm5hbWUlMkZpbC1taW8tYmVsbGlzc2ltby1tb2RlbGxvJTIyKQ==",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>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;il-tuo-username/il-mio-bellissimo-modello&quot;</span>)`,wrap:!1}}),xe=new U({props:{code:"cHRfbW9kZWwucHVzaF90b19odWIoJTIyaWwtbWlvLWJlbGxpc3NpbW8tbW9kZWxsbyUyMiUyQyUyMG9yZ2FuaXphdGlvbiUzRCUyMmxhLW1pYS1mYW50YXN0aWNhLW9yZyUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.push_to_hub(<span class="hljs-string">&quot;il-mio-bellissimo-modello&quot;</span>, organization=<span class="hljs-string">&quot;la-mia-fantastica-org&quot;</span>)',wrap:!1}}),He=new U({props:{code:"dG9rZW5pemVyLnB1c2hfdG9faHViKCUyMmlsLW1pby1iZWxsaXNzaW1vLW1vZGVsbG8lMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.push_to_hub(<span class="hljs-string">&quot;il-mio-bellissimo-modello&quot;</span>)',wrap:!1}}),Ze=new U({props:{code:"dGZfbW9kZWwucHVzaF90b19odWIoJTIyaWwtbWlvLWJlbGxpc3NpbW8tbW9kZWxsbyUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model.push_to_hub(<span class="hljs-string">&quot;il-mio-bellissimo-modello&quot;</span>)',wrap:!1}}),Ue=new le({}),Ye=new 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