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<h1 class="relative group"><a id="qu-hacer-cuando-se-produce-un-error" 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="#qu-hacer-cuando-se-produce-un-error"><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>¿Qué hacer cuando se produce un error?
	</span></h1>



<div class="flex space-x-1 absolute z-10 right-0 top-0"><a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter8/section2.ipynb" target="_blank"><img alt="Open In Colab" class="!m-0" src="https://colab.research.google.com/assets/colab-badge.svg"></a>
	<a href="https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/chapter8/section2.ipynb" target="_blank"><img alt="Open In Studio Lab" class="!m-0" src="https://studiolab.sagemaker.aws/studiolab.svg"></a></div>
<p>En esta sección veremos algunos errores comunes que pueden ocurrir cuando intentas generar predicciones a partir de tu modelo Transformer recién afinado. Esto te preparará para la <a href="/course/chapter8/section4">sección 4</a>, en la que exploraremos cómo depurar (debug) la fase de entrenamiento.</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/DQ-CpJn6Rc4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Hemos preparado un <a href="https://huggingface.co/lewtun/distilbert-base-uncased-finetuned-squad-d5716d28" rel="nofollow">repositorio de un modelo de ejemplo</a> para esta sección, por lo que si deseas ejecutar el código en este capítulo, primero necesitarás copiar el modelo a tu cuenta en el <a href="https://huggingface.co" rel="nofollow">Hub de Hugging Face</a>. Para ello, primero inicia sesión (log in) ejecutando lo siguiente en una Jupyter notebook:</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>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

notebook_login()<!-- HTML_TAG_END --></pre></div>
<p>o puedes ejecutar lo siguiente en tu terminal favorita:</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>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START -->huggingface-cli login<!-- HTML_TAG_END --></pre></div>
<p>Esto te pedirá que introduzcas tu nombre de usuario y contraseña, y guardará un token en <em>~/.cache/huggingface/</em>. Una vez que hayas iniciado sesión, puedes copiar el repositorio de ejemplo con la siguiente función:</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>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> distutils.dir_util <span class="hljs-keyword">import</span> copy_tree
<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> Repository, snapshot_download, create_repo, get_full_repo_name


<span class="hljs-keyword">def</span> <span class="hljs-title function_">copy_repository_template</span>():
    <span class="hljs-comment"># Clona el repo y extrae la ruta local</span>
    template_repo_id = <span class="hljs-string">&quot;lewtun/distilbert-base-uncased-finetuned-squad-d5716d28&quot;</span>
    commit_hash = <span class="hljs-string">&quot;be3eaffc28669d7932492681cd5f3e8905e358b4&quot;</span>
    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)
    <span class="hljs-comment"># Crea un repo vacío en el Hub</span>
    model_name = template_repo_id.split(<span class="hljs-string">&quot;/&quot;</span>)[<span class="hljs-number">1</span>]
    create_repo(model_name, exist_ok=<span class="hljs-literal">True</span>)
    <span class="hljs-comment"># Clona el repo vacío</span>
    new_repo_id = get_full_repo_name(model_name)
    new_repo_dir = model_name
    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)
    <span class="hljs-comment"># Copia los archivos</span>
    copy_tree(template_repo_dir, new_repo_dir)
    <span class="hljs-comment"># Envia (push) al Hub</span>
    repo.push_to_hub()<!-- HTML_TAG_END --></pre></div>
<p>Ahora cuando llames a la función <code>copy_repository_template()</code>, esta creará una copia del repositorio de ejemplo en tu cuenta.</p>
<h2 class="relative group"><a id="depurando-el-pipeline-de-transformers" 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="#depurando-el-pipeline-de-transformers"><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>Depurando el pipeline de 🤗 Transformers
	</span></h2>

<p>Para iniciar nuestro viaje hacia el maravilloso mundo de la depuración de modelos de Transformers, imagina lo siguiente: estás trabajando con un compañero en un proyecto de respuesta a preguntas (question answering) para ayudar a los clientes de un sitio web de comercio electrónico a encontrar respuestas sobre productos de consumo. Tu compañero te envía el siguiente mensaje: </p>
<blockquote><p>¡Buen día! Acabo de lanzar un experimento usando las técnicas del <a href="/course/chapter7/7">Capitulo 7</a> del curso de Hugging Face y ¡obtuvo unos buenos resultados con el conjunto de datos SQuAD! Creo que podemos usar este modelo como punto de partida para nuestro proyecto. El identificador del modelo en el Hub es “lewtun/distillbert-base-uncased-finetuned-squad-d5716d28”. No dudes en probarlo :)</p></blockquote>
<p>y en lo primero que piensas es en cargar el modelo usando el <code>pipeline</code> de la librería 🤗 Transformers:</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>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

model_checkpoint = get_full_repo_name(<span class="hljs-string">&quot;distillbert-base-uncased-finetuned-squad-d5716d28&quot;</span>)
reader = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>, model=model_checkpoint)<!-- HTML_TAG_END --></pre></div>

	<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>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-string">&quot;&quot;&quot;
OSError: Can&#x27;t load config for &#x27;lewtun/distillbert-base-uncased-finetuned-squad-d5716d28&#x27;. Make sure that:

- &#x27;lewtun/distillbert-base-uncased-finetuned-squad-d5716d28&#x27; is a correct model identifier listed on &#x27;https://huggingface.co/models&#x27;

- or &#x27;lewtun/distillbert-base-uncased-finetuned-squad-d5716d28&#x27; is the correct path to a directory containing a config.json file
&quot;&quot;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>¡Oh no, algo parece estar mal! Si eres nuevo en programación, este tipo de errores pueden parecer un poco crípticos al inicio (¿qué es un <code>OSError</code>?). El error mostrado aquí es solo la última parte de un reporte de errores mucho más largo llamado <em>Python traceback</em> (o <em>stack trace</em>). Por ejemplo, si estás ejecutando este código en Google Colab, podrías ver algo parecido como la siguiente captura:</p>
<div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter8/traceback.png" alt="A Python traceback." width="100%"></div>
<p>Hay mucha información contenida en estos reportes, así que vamos a repasar juntos las partes clave. La primera cosa que notamos es que el <em>traceback</em> debería ser leído de <em>abajo hacia arriba</em>. Esto puede sonar extraño si estás acostumbrado a leer en español de arriba hacia abajo, pero refleja el hecho de que el <em>traceback</em> muestra la secuencia de funciones llamadas que el <code>pipeline</code> realiza al descargar el modelo y el tokenizador. (Ve al <a href="/course/chapter2">Capítulo 2</a> para más detalles sobre cómo funciona el <code>pipeline</code> bajo el capó) </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>🚨 ¿Ves el cuadro azul alrededor de “6 frames” en el traceback de Google Colab? Es una característica especial de Colab, que comprime el traceback en “frames”. Si no puedes encontrar el origen de un error, asegúrate de ampliar el traceback completo haciendo clic en esas dos flechitas. </p></div>
<p>Esto significa que la última línea del traceback indica el último mensaje de error y nos da el nombre de la excepción (exception) que se ha generado. En este caso, el tipo de excepción es <code>OSError</code>, lo que indica un error relacionado con el sistema. Si leemos el mensaje de error que lo acompaña, podemos ver que parece haber un problema con el archivo <em>config.json</em> del modelo, y nos da dos sugerencias para solucionarlo:</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>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-string">&quot;&quot;&quot;
Make sure that:

- &#x27;lewtun/distillbert-base-uncased-finetuned-squad-d5716d28&#x27; is a correct model identifier listed on &#x27;https://huggingface.co/models&#x27;

- or &#x27;lewtun/distillbert-base-uncased-finetuned-squad-d5716d28&#x27; is the correct path to a directory containing a config.json file
&quot;&quot;&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>💡 Si te encuentras con un mensaje de error difícil de entender, simplemente copia y pega el mensaje en la barra de búsqueda de Google o de <a href="https://stackoverflow.com/" rel="nofollow">Stack Overflow</a> (¡sí, en serio!). Es muy posible que no seas la primera persona en encontrar el error, y esta es una buena forma de hallar soluciones que otros miembros de la comunidad han publicado. Por ejemplo, al buscar <code>OSError: Can&#39;t load config for</code> en Stack Overflow se obtienen varios resultados que pueden ser utilizados como punto de partida para resolver el problema.</p></div>
<p>La primera sugerencia nos pide que comprobemos si el identificador del modelo es realmente correcto, así que lo primero es copiar el identificador y pegarlo en la barra de búsqueda del Hub:</p>
<div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter8/wrong-model-id.png" alt="The wrong model name." width="100%"></div>
<p>Hmm, efectivamente parece que el modelo de nuestro compañero no está en el Hub… ¡pero hay una errata en el nombre del modelo! DistilBERT solo tiene una “l” en el nombre, así que vamos a corregirlo y a buscar “lewtun/distilbert-base-uncased-finetuned-squad-d5716d28” en su lugar:</p>
<div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter8/true-model-id.png" alt="The right model name." width="100%"></div>
<p>Bien, esto dio resultado. Ahora vamos a intentar descargar el modelo de nuevo con el identificador correcto:</p>

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	<pre><!-- HTML_TAG_START -->model_checkpoint = get_full_repo_name(<span class="hljs-string">&quot;distilbert-base-uncased-finetuned-squad-d5716d28&quot;</span>)
reader = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>, model=model_checkpoint)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START --><span class="hljs-string">&quot;&quot;&quot;
OSError: Can&#x27;t load config for &#x27;lewtun/distilbert-base-uncased-finetuned-squad-d5716d28&#x27;. Make sure that:

- &#x27;lewtun/distilbert-base-uncased-finetuned-squad-d5716d28&#x27; is a correct model identifier listed on &#x27;https://huggingface.co/models&#x27;

- or &#x27;lewtun/distilbert-base-uncased-finetuned-squad-d5716d28&#x27; is the correct path to a directory containing a config.json file
&quot;&quot;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>Argh, falló de nuevo, ¡bienvenido al día a día de un ingeniero de machine learning! Dado que arreglamos el identificador del modelo, el problema debe estar en el repositorio. Una manera rápida de acceder a los contenidos de un repositorio en el 🤗 Hub es por medio de la función <code>list_repo_files()</code> de la librería de <code>huggingface_hub</code>:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> list_repo_files

list_repo_files(repo_id=model_checkpoint)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->[<span class="hljs-string">&#x27;.gitattributes&#x27;</span>, <span class="hljs-string">&#x27;README.md&#x27;</span>, <span class="hljs-string">&#x27;pytorch_model.bin&#x27;</span>, <span class="hljs-string">&#x27;special_tokens_map.json&#x27;</span>, <span class="hljs-string">&#x27;tokenizer_config.json&#x27;</span>, <span class="hljs-string">&#x27;training_args.bin&#x27;</span>, <span class="hljs-string">&#x27;vocab.txt&#x27;</span>]<!-- HTML_TAG_END --></pre></div>
<p>Interesante. ¡No parece haber un archivo <em>config.json</em> en el repositorio! No es de extrañar que nuestro <code>pipeline</code> no pudiera cargar el modelo; nuestro compañero debe haberse olvidado de enviar este archivo al Hub después de ajustarlo (fine-tuned). En este caso, el problema parece bastante simple de resolver: podemos pedirle a nuestro compañero que añada el archivo, o, ya que podemos ver en el identificador del modelo que el modelo preentrenado fue <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow"><code>distilbert-base-uncased</code></a>, podemos descargar la configuración para este modelo y enviarla a nuestro repositorio para ver si eso resuelve el problema. Intentemos esto. Usando las técnicas que aprendimos en el <a href="/course/chapter2">Capítulo 2</a>, podemos descargar la configuración del modelo con la clase <code>AutoConfig</code>:</p>

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

pretrained_checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>
config = AutoConfig.from_pretrained(pretrained_checkpoint)<!-- HTML_TAG_END --></pre></div>


<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>🚨 El enfoque que tomamos aquí no es infalible, ya que nuestro compañero puede haber cambiado la configuración de <code>distilbert-base-uncased</code> antes de ajustar (fine-tuning) el modelo. En la vida real, nos gustaría consultar con él primero, pero para los fines de esta sección asumiremos que usó la configuración predeterminada.</p></div>
<p>Luego podemos enviar esto a nuestro repositorio del modelo con la función de configuración <code>push_to_hub()</code>: </p>

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	<pre><!-- HTML_TAG_START -->config.push_to_hub(model_checkpoint, commit_message=<span class="hljs-string">&quot;Add config.json&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>Ahora podemos probar si esto funciona cargando el modelo desde el último commit de la rama <code>main</code>: </p>

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	<pre><!-- HTML_TAG_START -->reader = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>, model=model_checkpoint, revision=<span class="hljs-string">&quot;main&quot;</span>)

context = <span class="hljs-string">r&quot;&quot;&quot;
Extractive Question Answering is the task of extracting an answer from a text
given a question. An example of a question answering dataset is the SQuAD
dataset, which is entirely based on that task. If you would like to fine-tune a
model on a SQuAD task, you may leverage the
examples/pytorch/question-answering/run_squad.py script.

🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX
frameworks, so you can use your favourite tools for a wide variety of tasks!
&quot;&quot;&quot;</span>

context_es = <span class="hljs-string">r&quot;&quot;&quot;
La respuesta a preguntas es la extracción de una respuesta textual a partir de 
una pregunta. Un ejemplo de conjunto de datos de respuesta a preguntas es el 
dataset SQuAD, que se basa por completo en esta tarea. Si deseas afinar un modelo 
en una tarea SQuAD, puedes aprovechar el script
 examples/pytorch/question-answering/run_squad.py

🤗 Transformers es interoperable con los frameworks PyTorch, TensorFlow y JAX, 
así que ¡puedes utilizar tus herramientas favoritas para una gran variedad de tareas!
&quot;&quot;&quot;</span>

question = <span class="hljs-string">&quot;What is extractive question answering?&quot;</span>
<span class="hljs-comment"># ¿Qué es la respuesta extractiva a preguntas?</span>
reader(question=question, context=context)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.38669535517692566</span>,
 <span class="hljs-string">&#x27;start&#x27;</span>: <span class="hljs-number">34</span>,
 <span class="hljs-string">&#x27;end&#x27;</span>: <span class="hljs-number">95</span>,
 <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;the task of extracting an answer from a text given a question&#x27;</span>}
 <span class="hljs-comment"># la tarea de extraer una respuesta de un texto a una pregunta dada</span><!-- HTML_TAG_END --></pre></div>
<p>¡Yuju, funcionó! Recapitulemos lo que acabas de aprender:</p>
<ul><li>Los mensajes de error en Python son conocidos como <em>tracebacks</em> y se leen de abajo hacia arriba. La última línea del mensaje de error generalmente contiene la información que necesitas para ubicar la fuente del problema.</li>
<li>Si la última línea no contiene suficiente información, sigue el traceback y mira si puedes identificar en qué parte del código fuente se produce el error.</li>
<li>Si ninguno de los mensajes de error te ayuda a depurar el problema, trata de buscar en internet una solución a un problema similar.</li>
<li>El 🤗 <code>huggingface_hub</code> de la librería proporciona un conjunto de herramientas que puedes utilizar para interactuar y depurar los repositorios en el Hub. </li></ul>
<p>Ahora que sabes cómo depurar un pipeline, vamos a ver un ejemplo más complicado en la pasada hacia delante (forward pass) del propio modelo.</p>
<h2 class="relative group"><a id="depurando-la-pasada-hacia-delante-forward-pass-de-tu-modelo" 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="#depurando-la-pasada-hacia-delante-forward-pass-de-tu-modelo"><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>Depurando la pasada hacia delante (forward pass) de tu modelo
	</span></h2>

<p>Aunque el <code>pipeline</code> es estupendo para la mayoría de las aplicaciones en las que necesitas generar predicciones rápidamente, a veces necesitarás acceder a los <em>logits</em> del modelo (por ejemplo, si tienes algún postprocesamiento personalizado que te gustaría aplicar). Para ver lo que puede salir mal en este caso, vamos a coger primero el modelo y el tokenizador de nuestro <code>pipeline</code>:</p>

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	<pre><!-- HTML_TAG_START -->tokenizer = reader.tokenizer
model = reader.model<!-- HTML_TAG_END --></pre></div>
<p>A continuación, necesitamos una pregunta, así que veamos si nuestros frameworks son compatibles:</p>

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	<pre><!-- HTML_TAG_START -->question = <span class="hljs-string">&quot;Which frameworks can I use?&quot;</span>  <span class="hljs-comment"># ¿Qué frameworks puedo usar?</span><!-- HTML_TAG_END --></pre></div>
<p>Como vimos en el <a href="/course/chapter7">Capítulo 7</a>, los pasos habituales que debemos seguir son tokenizar los inputs, extraer los <em>logits</em> de los tokens de inicio y fin y luego decodificar el intervalo de la respuesta: </p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> torch

inputs = tokenizer(question, context, add_special_tokens=<span class="hljs-literal">True</span>)
input_ids = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>][<span class="hljs-number">0</span>]
outputs = model(**inputs)
answer_start_scores = outputs.start_logits
answer_end_scores = outputs.end_logits
<span class="hljs-comment"># Obtiene el comienzo más probable de la respuesta con el argmax de la puntuación</span>
answer_start = torch.argmax(answer_start_scores)
<span class="hljs-comment"># Obtiene el final más probable de la respuesta con el argmax de la puntuación</span>
answer_end = torch.argmax(answer_end_scores) + <span class="hljs-number">1</span>
answer = tokenizer.convert_tokens_to_string(
    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])
)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Question: <span class="hljs-subst">{question}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Answer: <span class="hljs-subst">{answer}</span>&quot;</span>)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START --><span class="hljs-string">&quot;&quot;&quot;
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in &lt;module&gt;
      1 inputs = tokenizer(question, text, add_special_tokens=True)
      2 input_ids = inputs[&quot;input_ids&quot;]
----&gt; 3 outputs = model(**inputs)
      4 answer_start_scores = outputs.start_logits
      5 answer_end_scores = outputs.end_logits

~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-&gt; 1051             return forward_call(*input, **kwargs)
   1052         # Do not call functions when jit is used
   1053         full_backward_hooks, non_full_backward_hooks = [], []

~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)
    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
    724
--&gt; 725         distilbert_output = self.distilbert(
    726             input_ids=input_ids,
    727             attention_mask=attention_mask,

~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-&gt; 1051             return forward_call(*input, **kwargs)
   1052         # Do not call functions when jit is used
   1053         full_backward_hooks, non_full_backward_hooks = [], []

~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)
    471             raise ValueError(&quot;You cannot specify both input_ids and inputs_embeds at the same time&quot;)
    472         elif input_ids is not None:
--&gt; 473             input_shape = input_ids.size()
    474         elif inputs_embeds is not None:
    475             input_shape = inputs_embeds.size()[:-1]

AttributeError: &#x27;list&#x27; object has no attribute &#x27;size&#x27;
&quot;&quot;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>Vaya, parece que tenemos un <em>bug</em> en nuestro código. Pero no nos asusta un poco de depuración. Puedes usar el depurador de Python en una notebook:</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/rSPyvPw0p9k" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>o en una terminal: </p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/5PkZ4rbHL6c" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Aquí la lectura del mensaje de error nos dice que el objeto <code>&#39;list&#39;</code> no tiene atributo <code>&#39;size&#39;</code>, y podemos ver una flecha <code>--&gt;</code> apuntando a la línea donde el problema se originó en <code>model(**inputs)</code>. Puedes depurar esto interactivamente usando el <em>debugger</em> de Python, pero por ahora simplemente imprimiremos un fragmento de <code>inputs</code> para ver qué obtenemos:</p>

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	<pre><!-- HTML_TAG_START -->inputs[<span class="hljs-string">&quot;input_ids&quot;</span>][:<span class="hljs-number">5</span>]<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->[<span class="hljs-number">101</span>, <span class="hljs-number">2029</span>, <span class="hljs-number">7705</span>, <span class="hljs-number">2015</span>, <span class="hljs-number">2064</span>]<!-- HTML_TAG_END --></pre></div>
<p>Esto sin duda parece una <code>lista</code> ordinaria de Python, pero vamos a comprobar el tipo:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-built_in">type</span>(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START --><span class="hljs-built_in">list</span><!-- HTML_TAG_END --></pre></div>
<p>Sí, es una lista de Python. Entonces, ¿qué salió mal? Recordemos del <a href="/course/chapter2">Capítulo 2</a> que las clases <code>AutoModelForXxx</code> en 🤗 Transformers operan con <em>tensores</em> (tanto en PyTorch como en TensorFlow), y una operación común es extraer las dimensiones de un tensor usando <code>Tensor.size()</code> en, por ejemplo, PyTorch. Volvamos a echar un vistazo al traceback, para ver qué línea desencadenó la excepción:</p>

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	<pre><!-- HTML_TAG_START -->~<span class="hljs-regexp">/miniconda3/</span>envs<span class="hljs-regexp">/huggingface/</span>lib<span class="hljs-regexp">/python3.8/</span>site-packages<span class="hljs-regexp">/transformers/m</span>odels<span class="hljs-regexp">/distilbert/m</span>odeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)
    <span class="hljs-number">471</span>             raise ValueError(<span class="hljs-string">&quot;You cannot specify both input_ids and inputs_embeds at the same time&quot;</span>)
    <span class="hljs-number">472</span>         elif input_ids is not None:
--&gt; <span class="hljs-number">473</span>             input_shape = input_ids.<span class="hljs-keyword">size</span>()
    <span class="hljs-number">474</span>         elif inputs_embeds is not None:
    <span class="hljs-number">475</span>             input_shape = inputs_embeds.<span class="hljs-keyword">size</span>()[:-<span class="hljs-number">1</span>]

AttributeError: <span class="hljs-string">&#x27;list&#x27;</span> object has no attribute <span class="hljs-string">&#x27;size&#x27;</span><!-- HTML_TAG_END --></pre></div>
<p>Parece que nuestro código trata de llamar a la función <code>input_ids.size()</code>, pero esta claramente no funcionará con una lista de Python, la cual solo es un contenedor. ¿Cómo podemos resolver este problema? La búsqueda del mensaje de error en Stack Overflow da bastantes <a href="https://stackoverflow.com/search?q=AttributeError%3A+%27list%27+object+has+no+attribute+%27size%27&s=c15ec54c-63cb-481d-a749-408920073e8f" rel="nofollow">resultados</a> relevantes. Al hacer clic en el primero, aparece una pregunta similar a la nuestra, con la respuesta que se muestra en la siguiente captura de pantalla:</p>
<div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter8/stack-overflow.png" alt="An answer from Stack Overflow." width="100%"></div>
<p>La respuesta recomienda que adicionemos <code>return_tensors=&#39;pt&#39;</code> al tokenizador, así que veamos si esto nos funciona:</p>

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	<pre><!-- HTML_TAG_START -->inputs = tokenizer(question, context, add_special_tokens=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
input_ids = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>][<span class="hljs-number">0</span>]
outputs = model(**inputs)
answer_start_scores = outputs.start_logits
answer_end_scores = outputs.end_logits
<span class="hljs-comment"># Obtiene el comienzo más probable de la respuesta con el argmax de la puntuación</span>
answer_start = torch.argmax(answer_start_scores)
<span class="hljs-comment"># Obtiene el final más probable de la respuesta con el argmax de la puntuación</span>
answer_end = torch.argmax(answer_end_scores) + <span class="hljs-number">1</span>
answer = tokenizer.convert_tokens_to_string(
    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])
)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Question: <span class="hljs-subst">{question}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Answer: <span class="hljs-subst">{answer}</span>&quot;</span>)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START --><span class="hljs-string">&quot;&quot;&quot;
Question: Which frameworks can I use? # ¿Qué frameworks puedo usar?
Answer: pytorch, tensorflow, and jax
&quot;&quot;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>¡Excelente, funcionó! Este en un gran ejemplo de lo útil que puede ser Stack Overflow: al identificar un problema similar, fuimos capaces de beneficiarnos de la experiencia de otros en la comunidad. Sin embargo, una búsqueda como esta no siempre dará una respuesta relevante, así que ¿qué podemos hacer en esos casos? Afortunadamente hay una comunidad acogedora de desarrolladores en los <a href="https://discuss.huggingface.co/" rel="nofollow">foros de Hugging Face</a> que pueden ayudarte. En la siguiente sección, veremos cómo elaborar buenas preguntas en el foro que tengan posibilidades de ser respondidas.</p>


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