transformers/main/it/_app/immutable/nodes/13.5b10ce3a.js

import{s as $i,o as gi,n as hl}from"../chunks/scheduler.36a0863c.js";import{S as hi,i as bi,g as s,s as n,r as m,A as vi,h as o,f as l,c as a,j as M,u as f,x as r,k as ui,y as g,a as i,v as c,d,t as u,w as $}from"../chunks/index.9c13489a.js";import{T as gl}from"../chunks/Tip.3b06990e.js";import{C as b}from"../chunks/CodeBlock.05d8ec32.js";import{H as j,E as Ti}from"../chunks/EditOnGithub.e88f2b7b.js";function Mi(w){let p,v="Devi tenere la cartella <code>transformers</code> se vuoi continuare ad utilizzare la libreria.";return{c(){p=s("p"),p.innerHTML=v},l(h){p=o(h,"P",{"data-svelte-h":!0}),r(p)!=="svelte-1uf56if"&&(p.innerHTML=v)},m(h,T){i(h,p,T)},p:hl,d(h){h&&l(p)}}}function wi(w){let p,v="🤗 Transformers utilizzerà le variabili d’ambiente della shell <code>PYTORCH_TRANSFORMERS_CACHE</code> o <code>PYTORCH_PRETRAINED_BERT_CACHE</code> se si proviene da un’iterazione precedente di questa libreria e sono state impostate queste variabili d’ambiente, a meno che non si specifichi la variabile d’ambiente della shell <code>TRANSFORMERS_CACHE</code>.";return{c(){p=s("p"),p.innerHTML=v},l(h){p=o(h,"P",{"data-svelte-h":!0}),r(p)!=="svelte-1f49bj5"&&(p.innerHTML=v)},m(h,T){i(h,p,T)},p:hl,d(h){h&&l(p)}}}function yi(w){let p,v='Aggiungi <a href="https://huggingface.co/docs/datasets/" rel="nofollow">🤗 Datasets</a> al tuo flusso di lavoro offline di training impostando la variabile d’ambiente <code>HF_DATASETS_OFFLINE=1</code>.';return{c(){p=s("p"),p.innerHTML=v},l(h){p=o(h,"P",{"data-svelte-h":!0}),r(p)!=="svelte-ha8mx0"&&(p.innerHTML=v)},m(h,T){i(h,p,T)},p:hl,d(h){h&&l(p)}}}function _i(w){let p,v='Fai riferimento alla sezione <a href="https://huggingface.co/docs/hub/how-to-downstream" rel="nofollow">How to download files from the Hub</a> per avere maggiori dettagli su come scaricare modelli presenti sull Hub.';return{c(){p=s("p"),p.innerHTML=v},l(h){p=o(h,"P",{"data-svelte-h":!0}),r(p)!=="svelte-35lf98"&&(p.innerHTML=v)},m(h,T){i(h,p,T)},p:hl,d(h){h&&l(p)}}}function Ci(w){let p,v,h,T,U,it,W,Hl="Installa 🤗 Transformers per qualsiasi libreria di deep learning con cui stai lavorando, imposta la tua cache, e opzionalmente configura 🤗 Transformers per l’esecuzione offline.",nt,I,jl="🤗 Transformers è testato su Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, e Flax. Segui le istruzioni di installazione seguenti per la libreria di deep learning che stai utilizzando:",at,L,Ul='<li><a href="https://pytorch.org/get-started/locally/" rel="nofollow">PyTorch</a> istruzioni di installazione.</li> <li><a href="https://www.tensorflow.org/install/pip" rel="nofollow">TensorFlow 2.0</a> istruzioni di installazione.</li> <li><a href="https://flax.readthedocs.io/en/latest/" rel="nofollow">Flax</a> istruzioni di installazione.</li>',st,k,ot,G,Wl='Puoi installare 🤗 Transformers in un <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">ambiente virtuale</a>. Se non sei familiare con gli ambienti virtuali in Python, dai un’occhiata a questa <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">guida</a>. Un ambiente virtuale rende più semplice la gestione di progetti differenti, evitando problemi di compatibilità tra dipendenze.',rt,R,Il="Inizia creando un ambiente virtuale nella directory del tuo progetto:",pt,E,mt,P,Ll="Attiva l’ambiente virtuale:",ft,F,ct,V,kl="Ora puoi procedere con l’installazione di 🤗 Transformers eseguendo il comando seguente:",dt,X,ut,N,Gl="Per il solo supporto della CPU, puoi installare facilmente 🤗 Transformers e una libreria di deep learning in solo una riga. Ad esempio, installiamo 🤗 Transformers e PyTorch con:",$t,S,gt,Y,Rl="🤗 Transformers e TensorFlow 2.0:",ht,B,bt,q,El="🤗 Transformers e Flax:",vt,A,Tt,Q,Pl="Infine, verifica se 🤗 Transformers è stato installato in modo appropriato eseguendo il seguente comando. Questo scaricherà un modello pre-allenato:",Mt,O,wt,D,Fl="Dopodiché stampa l’etichetta e il punteggio:",yt,K,_t,ee,Ct,te,Vl="Installa 🤗 Transformers dalla fonte con il seguente comando:",xt,le,Jt,ie,Xl='Questo comando installa la versione <code>main</code> più attuale invece dell’ultima versione stabile. Questo è utile per stare al passo con gli ultimi sviluppi. Ad esempio, se un bug è stato sistemato da quando è uscita l’ultima versione ufficiale ma non è stata ancora rilasciata una nuova versione. Tuttavia, questo significa che questa versione <code>main</code> può non essere sempre stabile. Ci sforziamo per mantenere la versione <code>main</code> operativa, e la maggior parte dei problemi viene risolta in poche ore o in un giorno. Se riscontri un problema, per favore apri una <a href="https://github.com/huggingface/transformers/issues" rel="nofollow">Issue</a> così possiamo sistemarlo ancora più velocemente!',zt,ne,Nl="Controlla se 🤗 Transformers è stata installata in modo appropriato con il seguente comando:",Zt,ae,Ht,se,jt,oe,Sl="Hai bisogno di un’installazione modificabile se vuoi:",Ut,re,Yl="<li>Usare la versione <code>main</code> del codice dalla fonte.</li> <li>Contribuire a 🤗 Transformers e hai bisogno di testare i cambiamenti nel codice.</li>",Wt,pe,Bl="Clona il repository e installa 🤗 Transformers con i seguenti comandi:",It,me,Lt,fe,ql="Questi comandi collegheranno la cartella in cui è stato clonato il repository e i path delle librerie Python. Python guarderà ora all’interno della cartella clonata, oltre ai normali path delle librerie. Per esempio, se i tuoi pacchetti Python sono installati tipicamente in <code>~/anaconda3/envs/main/lib/python3.7/site-packages/</code>, Python cercherà anche nella cartella clonata: <code>~/transformers/</code>.",kt,J,Gt,ce,Al="Ora puoi facilmente aggiornare il tuo clone all’ultima versione di 🤗 Transformers con il seguente comando:",Rt,de,Et,ue,Ql="Il tuo ambiente Python troverà la versione <code>main</code> di 🤗 Transformers alla prossima esecuzione.",Pt,$e,Ft,ge,Ol="Installazione dal canale conda <code>conda-forge</code>:",Vt,he,Xt,be,Nt,ve,Dl="I modelli pre-allenati sono scaricati e memorizzati localmente nella cache in: <code>~/.cache/huggingface/transformers/</code>. Questa è la directory di default data dalla variabile d’ambiente della shell <code>TRANSFORMERS_CACHE</code>. Su Windows, la directory di default è data da <code>C:\\Users\\username\\.cache\\huggingface\\transformers</code>. Puoi cambiare le variabili d’ambiente della shell indicate in seguito, in ordine di priorità, per specificare una directory differente per la cache:",St,Te,Kl="<li>Variabile d’ambiente della shell (default): <code>TRANSFORMERS_CACHE</code>.</li> <li>Variabile d’ambiente della shell: <code>HF_HOME</code> + <code>transformers/</code>.</li> <li>Variabile d’ambiente della shell: <code>XDG_CACHE_HOME</code> + <code>/huggingface/transformers</code>.</li>",Yt,z,Bt,Me,qt,we,ei="🤗 Transformers può essere eseguita in un ambiente firewalled o offline utilizzando solo file locali. Imposta la variabile d’ambiente <code>HF_HUB_OFFLINE=1</code> per abilitare questo comportamento.",At,Z,Qt,ye,ti="Ad esempio, in genere si esegue un programma su una rete normale, protetta da firewall per le istanze esterne, con il seguente comando:",Ot,_e,Dt,Ce,li="Esegui lo stesso programma in un’istanza offline con:",Kt,xe,el,Je,ii="Lo script viene ora eseguito senza bloccarsi o attendere il timeout, perché sa di dover cercare solo file locali.",tl,ze,ll,Ze,ni="Un’altra opzione per utilizzare offline 🤗 Transformers è scaricare i file in anticipo, e poi puntare al loro path locale quando hai la necessità di utilizzarli offline. Ci sono tre modi per fare questo:",il,y,qe,ai='<p>Scarica un file tramite l’interfaccia utente sul <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> premendo sull’icona ↓.</p> <p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/download-icon.png" alt="download-icon"/></p>',bl,He,Ae,si="Utilizza il flusso <code>PreTrainedModel.from_pretrained()</code> e <code>PreTrainedModel.save_pretrained()</code>:",vl,_,je,Qe,oi="Scarica i tuoi file in anticipo con <code>PreTrainedModel.from_pretrained()</code>:",Tl,Ue,Ml,We,Oe,ri="Salva i tuoi file in una directory specificata con <code>PreTrainedModel.save_pretrained()</code>:",wl,Ie,yl,Le,De,pi="Ora quando sei offline, carica i tuoi file con <code>PreTrainedModel.from_pretrained()</code> dalla directory specificata:",_l,ke,Cl,Ge,Ke,mi='Scarica in maniera programmatica i file con la libreria <a href="https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub" rel="nofollow">huggingface_hub</a>:',xl,Re,Ee,et,fi="Installa la libreria <code>huggingface_hub</code> nel tuo ambiente virtuale:",Jl,Pe,zl,Fe,tt,ci='Utilizza la funzione <a href="https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub" rel="nofollow"><code>hf_hub_download</code></a> per scaricare un file in un path specifico. Per esempio, il seguente comando scarica il file <code>config.json</code> dal modello <a href="https://huggingface.co/bigscience/T0_3B" rel="nofollow">T0</a> nel path che desideri:',Zl,Ve,nl,Xe,di="Una volta che il tuo file è scaricato e salvato in cache localmente, specifica il suo path locale per caricarlo e utilizzarlo:",al,Ne,sl,H,ol,Se,rl,lt,pl;return U=new j({props:{title:"Installazione",local:"installazione",headingTag:"h1"}}),k=new j({props:{title:"Installazione con pip",local:"installazione-con-pip",headingTag:"h2"}}),E=new b({props:{code:"cHl0aG9uJTIwLW0lMjB2ZW52JTIwLmVudg==",highlighted:'python -m venv .<span class="hljs-built_in">env</span>',wrap:!1}}),F=new b({props:{code:"c291cmNlJTIwLmVudiUyRmJpbiUyRmFjdGl2YXRl",highlighted:'<span class="hljs-built_in">source</span> .<span class="hljs-built_in">env</span>/bin/activate',wrap:!1}}),X=new b({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycw==",highlighted:"pip install transformers",wrap:!1}}),S=new b({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyU1QnRvcmNoJTVE",highlighted:"pip install transformers[torch]",wrap:!1}}),B=new b({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyU1QnRmLWNwdSU1RA==",highlighted:"pip install transformers[tf-cpu]",wrap:!1}}),A=new b({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyU1QmZsYXglNUQ=",highlighted:"pip install transformers[flax]",wrap:!1}}),O=new b({props:{code:"cHl0aG9uJTIwLWMlMjAlMjJmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwcGlwZWxpbmUlM0IlMjBwcmludChwaXBlbGluZSgnc2VudGltZW50LWFuYWx5c2lzJykoJ3dlJTIwbG92ZSUyMHlvdScpKSUyMg==",highlighted:'python -c <span class="hljs-string">&quot;from transformers import pipeline; print(pipeline(&#x27;sentiment-analysis&#x27;)(&#x27;we love you&#x27;))&quot;</span>',wrap:!1}}),K=new b({props:{code:"JTVCJTdCJ2xhYmVsJyUzQSUyMCdQT1NJVElWRSclMkMlMjAnc2NvcmUnJTNBJTIwMC45OTk4NzA0NzkxMDY5MDMxJTdEJTVE",highlighted:'[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: 0.9998704791069031}]',wrap:!1}}),ee=new j({props:{title:"Installazione dalla fonte",local:"installazione-dalla-fonte",headingTag:"h2"}}),le=new b({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGdHJhbnNmb3JtZXJz",highlighted:"pip install git+https://github.com/huggingface/transformers",wrap:!1}}),ae=new b({props:{code:"cHl0aG9uJTIwLWMlMjAlMjJmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwcGlwZWxpbmUlM0IlMjBwcmludChwaXBlbGluZSgnc2VudGltZW50LWFuYWx5c2lzJykoJ0klMjBsb3ZlJTIweW91JykpJTIy",highlighted:'python -c <span class="hljs-string">&quot;from transformers import pipeline; print(pipeline(&#x27;sentiment-analysis&#x27;)(&#x27;I love you&#x27;))&quot;</span>',wrap:!1}}),se=new j({props:{title:"Installazione modificabile",local:"installazione-modificabile",headingTag:"h2"}}),me=new b({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmdpdGh1Yi5jb20lMkZodWdnaW5nZmFjZSUyRnRyYW5zZm9ybWVycy5naXQlMEFjZCUyMHRyYW5zZm9ybWVycyUwQXBpcCUyMGluc3RhbGwlMjAtZSUyMC4=",highlighted:`git <span class="hljs-built_in">clone</span> https://github.com/huggingface/transformers.git
<span class="hljs-built_in">cd</span> transformers
pip install -e .`,wrap:!1}}),J=new gl({props:{warning:!0,$$slots:{default:[Mi]},$$scope:{ctx:w}}}),de=new b({props:{code:"Y2QlMjB+JTJGdHJhbnNmb3JtZXJzJTJGJTBBZ2l0JTIwcHVsbA==",highlighted:`<span class="hljs-built_in">cd</span> ~/transformers/
git pull`,wrap:!1}}),$e=new j({props:{title:"Installazione con conda",local:"installazione-con-conda",headingTag:"h2"}}),he=new b({props:{code:"Y29uZGElMjBpbnN0YWxsJTIwY29uZGEtZm9yZ2UlM0ElM0F0cmFuc2Zvcm1lcnM=",highlighted:"conda install conda-forge::transformers",wrap:!1}}),be=new j({props:{title:"Impostazione della cache",local:"impostazione-della-cache",headingTag:"h2"}}),z=new gl({props:{$$slots:{default:[wi]},$$scope:{ctx:w}}}),Me=new j({props:{title:"Modalità Offline",local:"modalità-offline",headingTag:"h2"}}),Z=new gl({props:{$$slots:{default:[yi]},$$scope:{ctx:w}}}),_e=new b({props:{code:"cHl0aG9uJTIwZXhhbXBsZXMlMkZweXRvcmNoJTJGdHJhbnNsYXRpb24lMkZydW5fdHJhbnNsYXRpb24ucHklMjAtLW1vZGVsX25hbWVfb3JfcGF0aCUyMGdvb2dsZS10NSUyRnQ1LXNtYWxsJTIwLS1kYXRhc2V0X25hbWUlMjB3bXQxNiUyMC0tZGF0YXNldF9jb25maWclMjByby1lbiUyMC4uLg==",highlighted:"python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...",wrap:!1}}),xe=new b({props:{code:"SEZfREFUQVNFVFNfT0ZGTElORSUzRDElMjBIRl9IVUJfT0ZGTElORSUzRDElMjAlNUMlMEFweXRob24lMjBleGFtcGxlcyUyRnB5dG9yY2glMkZ0cmFuc2xhdGlvbiUyRnJ1bl90cmFuc2xhdGlvbi5weSUyMC0tbW9kZWxfbmFtZV9vcl9wYXRoJTIwZ29vZ2xlLXQ1JTJGdDUtc21hbGwlMjAtLWRhdGFzZXRfbmFtZSUyMHdtdDE2JTIwLS1kYXRhc2V0X2NvbmZpZyUyMHJvLWVuJTIwLi4u",highlighted:`HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \\
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...`,wrap:!1}}),ze=new j({props:{title:"Ottenere modelli e tokenizer per l’uso offline",local:"ottenere-modelli-e-tokenizer-per-luso-offline",headingTag:"h3"}}),Ue=new b({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0lMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJiaWdzY2llbmNlJTJGVDBfM0IlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmJpZ3NjaWVuY2UlMkZUMF8zQiUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bigscience/T0_3B&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;bigscience/T0_3B&quot;</span>)`,wrap:!1}}),Ie=new b({props:{code:"dG9rZW5pemVyLnNhdmVfcHJldHJhaW5lZCglMjIuJTJGaWwlMkZ0dW8lMkZwYXRoJTJGYmlnc2NpZW5jZV90MCUyMiklMEFtb2RlbC5zYXZlX3ByZXRyYWluZWQoJTIyLiUyRmlsJTJGdHVvJTJGcGF0aCUyRmJpZ3NjaWVuY2VfdDAlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(<span class="hljs-string">&quot;./il/tuo/path/bigscience_t0&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_pretrained(<span class="hljs-string">&quot;./il/tuo/path/bigscience_t0&quot;</span>)`,wrap:!1}}),ke=new b({props:{code:"dG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRmlsJTJGdHVvJTJGcGF0aCUyRmJpZ3NjaWVuY2VfdDAlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZpbCUyRnR1byUyRnBhdGglMkZiaWdzY2llbmNlX3QwJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;./il/tuo/path/bigscience_t0&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;./il/tuo/path/bigscience_t0&quot;</span>)`,wrap:!1}}),Pe=new b({props:{code:"cHl0aG9uJTIwLW0lMjBwaXAlMjBpbnN0YWxsJTIwaHVnZ2luZ2ZhY2VfaHVi",highlighted:"python -m pip install huggingface_hub",wrap:!1}}),Ve=new b({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGhmX2h1Yl9kb3dubG9hZCUwQSUwQWhmX2h1Yl9kb3dubG9hZChyZXBvX2lkJTNEJTIyYmlnc2NpZW5jZSUyRlQwXzNCJTIyJTJDJTIwZmlsZW5hbWUlM0QlMjJjb25maWcuanNvbiUyMiUyQyUyMGNhY2hlX2RpciUzRCUyMi4lMkZpbCUyRnR1byUyRnBhdGglMkZiaWdzY2llbmNlX3QwJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

<span class="hljs-meta">&gt;&gt;&gt; </span>hf_hub_download(repo_id=<span class="hljs-string">&quot;bigscience/T0_3B&quot;</span>, filename=<span class="hljs-string">&quot;config.json&quot;</span>, cache_dir=<span class="hljs-string">&quot;./il/tuo/path/bigscience_t0&quot;</span>)`,wrap:!1}}),Ne=new b({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMEElMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGaWwlMkZ0dW8lMkZwYXRoJTJGYmlnc2NpZW5jZV90MCUyRmNvbmZpZy5qc29uJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig

<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./il/tuo/path/bigscience_t0/config.json&quot;</span>)`,wrap:!1}}),H=new gl({props:{$$slots:{default:[_i]},$$scope:{ctx:w}}}),Se=new 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