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<div class="bg-white leading-none border border-gray-100 rounded-lg flex p-0.5 w-56 text-sm mb-4"><a class="flex justify-center flex-1 py-1.5 px-2.5 focus:outline-none !no-underline rounded-l bg-red-50 dark:bg-transparent text-red-600" href="?fw=pt"><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="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
			Pytorch
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			TensorFlow
		</a></div>
<h1 class="relative group"><a id="affinare-il-modello-con-la-trainer-api" 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="#affinare-il-modello-con-la-trainer-api"><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>Affinare il modello con la Trainer API
	</span></h1>



<div class="flex space-x-1 absolute z-10 right-0 top-0">
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<p>🤗 Transformers fornisce una classe <code>Trainer</code> (addestratore) per aiutare con l’affinamento di uno qualsiasi dei modelli pre-addestrati nel dataset. Dopo tutto il lavoro di preprocessing nella sezione precedente, rimangono giusto gli ultimi passi per definire il <code>Trainer</code>. Probabilmente la parte più complicata sarà preparare l’ambiente per eseguire <code>Trainer.train()</code>, poiché sarà molto lento su una CPU. Se non avete una GPU a disposizione, potete avere accesso gratuitamente a GPU o TPU su <a href="https://colab.research.google.com/" rel="nofollow">Google Colab</a>.</p>
<p>Gli esempi di codice qui sotto partono dal presupposto che gli esempi nella sezione precedente siano già stati eseguiti. Ecco un breve riassunto di cosa serve:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DataCollatorWithPadding

raw_datasets = load_dataset(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
checkpoint = <span class="hljs-string">&quot;bert-base-uncased&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_function</span>(<span class="hljs-params">example</span>):
    <span class="hljs-keyword">return</span> tokenizer(example[<span class="hljs-string">&quot;sentence1&quot;</span>], example[<span class="hljs-string">&quot;sentence2&quot;</span>], truncation=<span class="hljs-literal">True</span>)


tokenized_datasets = raw_datasets.<span class="hljs-built_in">map</span>(tokenize_function, batched=<span class="hljs-literal">True</span>)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="addestramento" 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="#addestramento"><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>Addestramento
	</span></h3>

<p>Il primo passo per definire un <code>Trainer</code> è la definizione di una classe <code>TrainingArguments</code> che contenga tutti gli iperparametri che verranno usati dal <code>Trainer</code> per l’addestramento e la valutazione. L’unico parametro da fornire è la cartella dove verranno salvati il modello addestrato e i vari checkpoint. Per tutto il resto si possono lasciare i parametri di default, che dovrebbero funzionare bene per un affinamento di base.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments

training_args = TrainingArguments(<span class="hljs-string">&quot;test-trainer&quot;</span>)<!-- HTML_TAG_END --></pre></div>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>💡 Se si vuole caricare automaticamente il modello all’Hub durante l’addestramento, basta passare <code>push_to_hub=True</code> come parametro nei <code>TrainingArguments</code>. Maggiori dettagli verranno forniti nel <a href="/course/chapter4/3">Capitolo 4</a>.</p></div>
<p>Il secondo passo è definire il modello. Come nel <a href="/course/chapter2">capitolo precedente</a>, utilizzeremo la classe <code>AutoModelForSequenceClassification</code> con due label:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Diversamente dal <a href="/course/chapter2">Capitolo 2</a>, un avviso di avvertimento verrà visualizzato dopo aver istanziato questo modello pre-addestrato. Ciò avviene perché BERT non è stato pre-addestrato per classificare coppie di frasi, quindi la testa del modello pre-addestrato viene scartata e una nuova testa adeguata per il compito di classificazione di sequenze è stata inserita. Gli avvertimenti indicano che alcuni pesi non verranno usati (quelli corrispondenti alla testa scartata del modello pre-addestrato) e che altri pesi sono stati inizializzati con valori casuali (quelli per la nuova testa). L’avvertimento viene concluso con un’esortazione ad addestrare il modello, che è esattamente ciò che stiamo per fare.  </p>
<p>Una volta ottenuto il modello, si può definire un <code>Trainer</code> passandogli tutti gli oggetti costruiti fino ad adesso — il <code>model</code>, i <code>training_args</code>, i dataset di addestramento e validazione, il <code>data_collator</code>, e il <code>tokenizer</code>:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer

trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>],
    eval_dataset=tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>],
    data_collator=data_collator,
    tokenizer=tokenizer,
)<!-- HTML_TAG_END --></pre></div>
<p>Quando si passa l’argomento <code>tokenizer</code> come appena fatto, il <code>data_collator</code> usato di default dal <code>Trainer</code> sarà del tipo <code>DataCollatorWithPadding</code>, come definito precedentemente, quindi si potrebbe evitare di specificare l’argomento <code>data_collator=data_collator</code> in questa chiamata. Tuttavia era comunque importante mostrare questa parte del processing nella sezione 2!</p>
<p>Per affinare il modello sul nostro dataset, bisogna solo chiamare il metodo <code>train()</code> del <code>Trainer</code>:</p>

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	<pre><!-- HTML_TAG_START -->trainer.train()<!-- HTML_TAG_END --></pre></div>
<p>Questo farà partire l’affinamento (che richiederà un paio di minuti su una GPU) e produrrà un report della funzione obiettivo dell’addestramento ogni 500 passi. Tuttavia, non vi farà sapere quanto sia buona (o cattiva) la performance del modello. Ciò è dovuto al fatto che:</p>
<ol><li>Non è stato detto al <code>Trainer</code> di valutare il modello durante l’addestramento, settando <code>evaluation_strategy</code> o al valore <code>&quot;steps&quot;</code> (valuta il modello ogni <code>eval_steps</code>) oppure al valore <code>&quot;epoch&quot;</code> (valuta il modello alla fine di ogni epoca).</li>
<li>Non è stato fornito al <code>Trainer</code> una funzione <code>compute_metrics()</code> per calcolare le metriche di valutazione (altrimenti la valutazione stamperebbe solo il valore della funzione obiettivo, che non è un valore molto intuitivo).</li></ol>
<h3 class="relative group"><a id="valutazione" 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="#valutazione"><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>Valutazione
	</span></h3>

<p>Vediamo come si può costruire una funzione <code>compute_metrics()</code> utile e usarla per il prossimo addestramento. La funzione deve prendere come parametro un oggetto <code>EvalPrediction</code> (che è una named tuple avente un campo <code>predictions</code> – predizioni – e un campo <code>label_ids</code> – id delle etichette –) e restituirà un dizionario che associa stringhe a numeri floating point (le stringhe saranno i nomi delle metriche, i numeri i loro valori). Per ottenere delle predizioni, si può usare il comando <code>Trainer.predict()</code>:</p>

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	<pre><!-- HTML_TAG_START -->predictions = trainer.predict(tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>])
<span class="hljs-built_in">print</span>(predictions.predictions.shape, predictions.label_ids.shape)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->(<span class="hljs-number">408</span>, <span class="hljs-number">2</span>) (<span class="hljs-number">408</span>,)<!-- HTML_TAG_END --></pre></div>
<p>Il risultato del metodo <code>predict()</code> è un’altra named tuple con tre campi: <code>predictions</code>, <code>label_ids</code>, e <code>metrics</code>. Il campo <code>metrics</code> conterrà solo il valore della funzione obiettivo sul dataset, in aggiunta ad alcune metriche legate al tempo (il tempo necessario per calcolare le predizioni, in totale e in media). Una volta completata la funzione <code>compute_metrics()</code> e passata al <code>Trainer</code>, quel campo conterrà anche le metriche restituite da <code>compute_metrics()</code>.</p>
<p>Come si può vedere, <code>predictions</code> è un array bi-dimensionale con dimensioni 408 x 2 (poiché 408 è il numero di elementi nel dataset). Questi sono i logit per ogni elemento del dataset passato a <code>predict()</code> (come già visto nel <a href="/course/chapter2">capitolo precedente</a>, tutti i modelli Transformer restituiscono logit). Per trasformarli in predizioni associabili alle etichette, bisogna prendere l’indice col valore massimo sul secondo asse:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

preds = np.argmax(predictions.predictions, axis=-<span class="hljs-number">1</span>)<!-- HTML_TAG_END --></pre></div>
<p>Ora si possono paragonare i <code>preds</code> con le etichette. Per costruire la funzione <code>compute_metric()</code>, verranno utilizzate le metriche dalla libreria 🤗 Dataset. Si possono caricare le metriche associate con il dataset MRPC in maniera semplice, utilizzando la funzione <code>load_metric()</code>. L’oggetto restituito ha un metodo <code>compute()</code> (calcola) che possiamo usare per calcolare le metriche:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric

metric = load_metric(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
metric.compute(predictions=preds, references=predictions.label_ids)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->{<span class="hljs-string">&#x27;accuracy&#x27;</span>: <span class="hljs-number">0.8578431372549019</span>, <span class="hljs-string">&#x27;f1&#x27;</span>: <span class="hljs-number">0.8996539792387542</span>}<!-- HTML_TAG_END --></pre></div>
<p>L’esatto valore dei risultati potrebbe essere diverso nel vostro caso, a casa dell’inizializzazione casuale della testa del modello. In questo caso il nostro modello ha un’accuratezza del 85.78% sul set di validazione e un valore F1 di 89.97. Queste sono le due metriche utilizzate per valutare i risultati sul dataset MRPC per il benchmark GLUE. La tabella nell’<a href="https://arxiv.org/pdf/1810.04805.pdf" rel="nofollow">articolo su BERT</a> riportava un F1 di 88.9 per il modello base. Quello era il modello <code>uncased</code> (senza distinzione fra minuscole e maiuscole) mentre noi stiamo usando quello <code>cased</code>, il che spiega il risultato migliore. </p>
<p>Mettendo tutto insieme si ottiene la funzione <code>compute_metrics()</code>:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_preds</span>):
    metric = load_metric(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
    logits, labels = eval_preds
    predictions = np.argmax(logits, axis=-<span class="hljs-number">1</span>)
    <span class="hljs-keyword">return</span> metric.compute(predictions=predictions, references=labels)<!-- HTML_TAG_END --></pre></div>
<p>Per vederla in azione e fare il report delle metriche alla fine di ogni epoca, ecco come si definisce un nuovo <code>Trainer</code> che includa questa funzione <code>compute_metrics()</code>:</p>

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	<pre><!-- HTML_TAG_START -->training_args = TrainingArguments(<span class="hljs-string">&quot;test-trainer&quot;</span>, evaluation_strategy=<span class="hljs-string">&quot;epoch&quot;</span>)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)

trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>],
    eval_dataset=tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>],
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)<!-- HTML_TAG_END --></pre></div>
<p>Da notare che bisogna creare un nuovo oggetto <code>TrainingArguments</code> con il valore di <code>evaluation_strategy</code> pari a <code>&quot;epoch&quot;</code> e un nuovo modello — altrimenti si continuerebbe l’addestramento del modello già addestrato. Per lanciare una nuova esecuzione dell’addestramento si usa:</p>

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	<pre><!-- HTML_TAG_START -->trainer.trai<span class="hljs-meta">n</span>()<!-- HTML_TAG_END --></pre></div>
<p>Stavolta vi sarà il report della funzione obiettivo di validazione alla fine di ogni epoca, in aggiunta alla funzione obiettivo dell’addestramento. Di nuovo, i valori esatti di accuratezza/F1 ottenuti da voi potrebbero variare leggermente da quelli mostrati qui a causa dell’inizializzazione casuale della testa del modello, ma dovrebbero essere comparabili. </p>
<p>Il <code>Trainer</code> funzionerà direttamente su svariate GPU e TPU e ha molte opzioni, tra cui addestramento in precisione mista (utilizzare <code>fp16 = True</code> negli argomenti). I dettagli delle opzioni verranno esplorati nel Capitolo 10. </p>
<p>Qui si conclude l’introduzione all’affinamento usando l’API del <code>Trainer</code>. Esempi per i compiti più comuni in NLP verranno forniti nel Capitolo 7, ma per ora vediamo come ottenere la stessa cosa usando puramente Pytorch.</p>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>✏️ <strong>Prova tu!</strong> Affinare un modello sul dataset GLUE SST-2 utilizzando il processing dei dati già fatto nella sezione 2. </p></div>


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