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			Pytorch
		</a><a class="flex justify-center flex-1 py-1.5 px-2.5 focus:outline-none !no-underline rounded-r text-gray-500 filter grayscale" href="?fw=tf"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
			TensorFlow
		</a></div>
<h1 class="relative group"><a id="models" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#models"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Models
	</span></h1>



<div class="flex space-x-1 absolute z-10 right-0 top-0">
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	<a href="https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/chapter2/section3_pt.ipynb" target="_blank"><img alt="Open In Studio Lab" class="!m-0" src="https://studiolab.sagemaker.aws/studiolab.svg"></a></div>
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<p>In questa sezione vedremo da vicino come creare e usare un modello. Utilizzeremo la classe <code>AutoModel</code>, utile quando si vuole istanziare qualsiasi modello da un checkpoint.</p>
<p>La classe <code>AutoModel</code> e tutti i suoi derivati sono in realtà semplici involucri dell’ampia varietà di modelli disponibili nella libreria. Si tratta di un involucro intelligente, in quanto è in grado di indovinare automaticamente l’architettura del modello appropriata per il checkpoint e successivamente di istanziare un modello con questa architettura.</p>
<p>Tuttavia, se si conosce il tipo di modello che si vuole utilizzare, si può usare direttamente la classe che ne definisce l’architettura. Vediamo come funziona con un modello BERT.</p>
<h2 class="relative group"><a id="creare-un-trasformatore" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#creare-un-trasformatore"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Creare un trasformatore
	</span></h2>

<p>La prima cosa da fare per inizializzare un modello BERT è caricare un oggetto di configurazione:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, BertModel

<span class="hljs-comment"># Creazione della configurazione</span>
config = BertConfig()

<span class="hljs-comment"># Creare il modello dalla configurazione</span>
model = BertModel(config)<!-- HTML_TAG_END --></pre></div>
<p>La configurazione contiene molti attributi che vengono utilizzati per costruire il modello:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-built_in">print</span>(config)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->BertConfig {
  [...]
  <span class="hljs-string">&quot;hidden_size&quot;</span>: <span class="hljs-number">768</span>,
  <span class="hljs-string">&quot;intermediate_size&quot;</span>: <span class="hljs-number">3072</span>,
  <span class="hljs-string">&quot;max_position_embeddings&quot;</span>: <span class="hljs-number">512</span>,
  <span class="hljs-string">&quot;num_attention_heads&quot;</span>: <span class="hljs-number">12</span>,
  <span class="hljs-string">&quot;num_hidden_layers&quot;</span>: <span class="hljs-number">12</span>,
  [...]
}<!-- HTML_TAG_END --></pre></div>
<p>Anche se non si è ancora visto cosa fanno tutti questi attributi, se ne dovrebbero riconoscere alcuni: l’attributo <code>hidden_size</code> definisce la dimensione del vettore <code>hidden_states</code>, e l’attributo <code>num_hidden_layers</code> definisce il numero di livelli del modello Transformer.</p>
<h3 class="relative group"><a id="diversi-metodi-di-caricamento" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#diversi-metodi-di-caricamento"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Diversi metodi di caricamento
	</span></h3>

<p>La creazione di un modello dalla configurazione predefinita lo inizializza con valori casuali:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, BertModel

config = BertConfig()
model = BertModel(config)

<span class="hljs-comment"># Il modello è inizializzato in modo casuale!</span><!-- HTML_TAG_END --></pre></div>
<p>Il modello può essere utilizzato in questo stato, ma produrrà risultati incomprensibili; è necessario addestrarlo prima.</p>
<p>Potremmo addestrare il modello da zero sul compito da svolgere, ma come si è visto in <a href="/course/chapter1">Capitolo 1</a>, questo richiederebbe molto tempo e molti dati, oltre ad avere un impatto ambientale non trascurabile. Per evitare sforzi inutili, è indispensabile poter condividere e riutilizzare modelli già addestrati.</p>
<p>Caricare un modello Transformer già addestrato è semplice: lo si può fare usando il metodo <code>from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertModel

model = BertModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>Come abbiamo visto in precedenza, possiamo sostituire <code>BertModel</code> con la classe equivalente <code>AutoModel</code>. Lo faremo d’ora in poi, perché in questo modo si ottiene un codice cosiddetto “checkpoint-agnostic”; se il codice funziona per un checkpoint, dovrebbe funzionare senza problemi anche con un altro. Questo vale anche se l’architettura è diversa, purché il checkpoint sia stato addestrato per un compito simile (per esempio, un compito di sentiment analysis).</p>
<p>Nell’esempio di codice precedente non abbiamo usato <code>BertConfig</code> e abbiamo invece caricato un modello pre-addestrato tramite l’identificatore <code>bert-base-cased</code>. Si tratta di un checkpoint che è stato addestrato dagli stessi autori di BERT; si possono trovare maggiori dettagli su di esso nella sua <a href="https://huggingface.co/bert-base-cased" rel="nofollow">scheda modello</a>.</p>
<p>Questo modello è ora inizializzato con tutti i pesi del checkpoint. Può essere utilizzato direttamente per effettuare inferenza sui compiti su cui è stato addestrato e può anche essere messo adattato ad un nuovo compito, tramite il fine tuning. Allenandosi con i pesi pre-addestrati piuttosto che partendo da zero, si possono ottenere rapidamente buoni risultati.</p>
<p>I pesi sono stati scaricati e messi in cache (in modo che le future chiamate al metodo <code>from_pretrained()</code> non li scarichino di nuovo) nella cartella della cache, che per impostazione predefinita è <em>~/.cache/huggingface/transformers</em>. È possibile personalizzare la cartella della cache impostando la variabile d’ambiente <code>HF_HOME</code>.</p>
<p>L’identificatore usato per caricare il modello può essere l’identificatore di qualsiasi modello presente nel Model Hub, purché sia compatibile con l’architettura del BERT. L’elenco completo dei checkpoint BERT disponibili è disponibile <a href="https://huggingface.co/models?filter=bert" rel="nofollow">qui</a>.</p>
<h3 class="relative group"><a id="metodi-di-salvataggio" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#metodi-di-salvataggio"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Metodi di salvataggio
	</span></h3>

<p>Saving a model is as easy as loading one — we use the <code>save_pretrained()</code> method, which is analogous to the <code>from_pretrained()</code> method:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START -->model.save_pretrained(<span class="hljs-string">&quot;directory_on_my_computer&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>In questo modo si salvano due file sul disco:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
	<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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	<pre><!-- HTML_TAG_START -->ls <span class="hljs-keyword">directory_on_my_computer
</span>
<span class="hljs-built_in">config</span>.<span class="hljs-keyword">json </span>pytorch_model.<span class="hljs-keyword">bin</span><!-- HTML_TAG_END --></pre></div>
<p>Se si dà un’occhiata al file <em>config.json</em>, si riconoscono gli attributi necessari per costruire l’architettura del modello. Questo file contiene anche alcuni metadati, come l’origine del checkpoint e la versione di 🤗 Transformers utilizzata al momento dell’ultimo salvataggio del checkpoint.</p>
<p>Il file <em>pytorch_model.bin</em> è noto come <em>state dictionary</em>; contiene tutti i pesi del modello. I due file vanno di pari passo: la configurazione è necessaria per conoscere l’architettura del modello, mentre i pesi del modello sono i suoi parametri.</p>
<h2 class="relative group"><a id="using-a-transformer-model-for-inference" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#using-a-transformer-model-for-inference"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Using a Transformer model for inference
	</span></h2>

<p>Ora che si sa come caricare e salvare un modello, proviamo a usarlo per fare delle previsioni. I modelli di trasformatori possono elaborare solo numeri - numeri generati dal tokenizer. Ma prima di parlare dei tokenizer, analizziamo quali sono gli input accettati dal modello.</p>
<p>I tokenizer possono occuparsi di effettuare il casting degli input nei tensori del framework appropriato, ma per aiutarti a capire cosa sta succedendo, daremo una rapida occhiata a ciò che deve essere fatto prima di inviare gli input al modello.</p>
<p>Supponiamo di avere un paio di sequenze:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START -->sequences = [<span class="hljs-string">&quot;Hello!&quot;</span>, <span class="hljs-string">&quot;Cool.&quot;</span>, <span class="hljs-string">&quot;Nice!&quot;</span>]<!-- HTML_TAG_END --></pre></div>
<p>The tokenizer converts these to vocabulary indices which are typically called <em>input IDs</em>. Each sequence is now a list of numbers! The resulting output is:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START -->encoded_sequences = [
    [<span class="hljs-number">101</span>, <span class="hljs-number">7592</span>, <span class="hljs-number">999</span>, <span class="hljs-number">102</span>],
    [<span class="hljs-number">101</span>, <span class="hljs-number">4658</span>, <span class="hljs-number">1012</span>, <span class="hljs-number">102</span>],
    [<span class="hljs-number">101</span>, <span class="hljs-number">3835</span>, <span class="hljs-number">999</span>, <span class="hljs-number">102</span>],
]<!-- HTML_TAG_END --></pre></div>
<p>Si tratta di una lista di sequenze codificate: una lista di liste. I tensori accettano solo forme rettangolari (si pensi alle matrici). Questo “array” è già di forma rettangolare, quindi convertirlo in un tensore è facile:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> torch

model_inputs = torch.tensor(encoded_sequences)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="uso-dei-tensori-come-input-del-modello" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#uso-dei-tensori-come-input-del-modello"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Uso dei tensori come input del modello
	</span></h3>

<p>Utilizzare i tensori con il modello è estremamente semplice: basta chiamare il modello con gli input:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START -->output = model(model_inputs)<!-- HTML_TAG_END --></pre></div>
<p>Il modello accetta molti argomenti diversi, ma solo gli ID degli ingressi sono necessari. Spiegheremo in seguito cosa fanno gli altri argomenti e quando sono necessari, ma prima dobbiamo dare un’occhiata più da vicino ai tokenizer che costruiscono gli input che un modello Transformer può comprendere.</p>


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