From f2a10fa6c79ee092b7d36bb70a490d954ca639f8 Mon Sep 17 00:00:00 2001
From: Angel Mendez <amendez1988@gmail.com>
Date: Tue, 25 Oct 2022 23:03:05 -0500
Subject: [PATCH 1/2] feat: Add file content to translate

---
 chapters/es/chapter2/8.mdx | 310 +++++++++++++++++++++++++++++++++++++
 1 file changed, 310 insertions(+)
 create mode 100644 chapters/es/chapter2/8.mdx

diff --git a/chapters/es/chapter2/8.mdx b/chapters/es/chapter2/8.mdx
new file mode 100644
index 000000000..96dfd107e
--- /dev/null
+++ b/chapters/es/chapter2/8.mdx
@@ -0,0 +1,310 @@
+<FrameworkSwitchCourse {fw} />
+
+<!-- DISABLE-FRONTMATTER-SECTIONS -->
+
+# End-of-chapter quiz
+
+<CourseFloatingBanner
+    chapter={2}
+    classNames="absolute z-10 right-0 top-0"
+/>
+
+### 1. What is the order of the language modeling pipeline?
+
+<Question
+	choices={[
+		{
+			text: "First, the model, which handles text and returns raw predictions. The tokenizer then makes sense of these predictions and converts them back to text when needed.",
+			explain: "The model cannot understand text! The tokenizer must first tokenize the text and convert it to IDs so that it is understandable by the model."
+		},
+		{
+			text: "First, the tokenizer, which handles text and returns IDs. The model handles these IDs and outputs a prediction, which can be some text.",
+			explain: "The model's prediction cannot be text straight away. The tokenizer has to be used in order to convert the prediction back to text!"
+		},
+		{
+			text: "The tokenizer handles text and returns IDs. The model handles these IDs and outputs a prediction. The tokenizer can then be used once again to convert these predictions back to some text.",
+			explain: "Correct! The tokenizer can be used for both tokenizing and de-tokenizing.",
+            correct: true
+		}
+	]}
+/>
+
+### 2. How many dimensions does the tensor output by the base Transformer model have, and what are they?
+
+<Question
+	choices={[
+		{
+			text: "2: The sequence length and the batch size",
+			explain: "False! The tensor output by the model has a third dimension: hidden size."
+		},
+		{
+			text: "2: The sequence length and the hidden size",
+			explain: "False! All Transformer models handle batches, even with a single sequence; that would be a batch size of 1!"
+		},
+		{
+			text: "3: The sequence length, the batch size, and the hidden size",
+			explain: "Correct!",
+            correct: true
+		}
+	]}
+/>
+
+### 3. Which of the following is an example of subword tokenization?
+
+<Question
+	choices={[
+		{
+			text: "WordPiece",
+			explain: "Yes, that's one example of subword tokenization!",
+            correct: true
+		},
+		{
+			text: "Character-based tokenization",
+			explain: "Character-based tokenization is not a type of subword tokenization."
+		},
+		{
+			text: "Splitting on whitespace and punctuation",
+			explain: "That's a word-based tokenization scheme!"
+		},
+		{
+			text: "BPE",
+			explain: "Yes, that's one example of subword tokenization!",
+            correct: true
+        },
+		{
+			text: "Unigram",
+			explain: "Yes, that's one example of subword tokenization!",
+            correct: true
+        },
+		{
+			text: "None of the above",
+			explain: "Incorrect!"
+        }
+	]}
+/>
+
+### 4. What is a model head?
+
+<Question
+	choices={[
+		{
+			text: "A component of the base Transformer network that redirects tensors to their correct layers",
+			explain: "Incorrect! There's no such component."
+		},
+		{
+			text: "Also known as the self-attention mechanism, it adapts the representation of a token according to the other tokens of the sequence",
+			explain: "Incorrect! The self-attention layer does contain attention \"heads,\" but these are not adaptation heads."
+		},
+		{
+			text: "An additional component, usually made up of one or a few layers, to convert the transformer predictions to a task-specific output",
+			explain: "That's right. Adaptation heads, also known simply as heads, come up in different forms: language modeling heads, question answering heads, sequence classification heads... ",
+			correct: true
+		} 
+	]}
+/>
+
+{#if fw === 'pt'}
+### 5. What is an AutoModel?
+
+<Question
+	choices={[
+		{
+			text: "A model that automatically trains on your data",
+			explain: "Incorrect. Are you mistaking this with our <a href='https://huggingface.co/autotrain'>AutoTrain</a> product?"
+		},
+		{
+			text: "An object that returns the correct architecture based on the checkpoint",
+			explain: "Exactly: the <code>AutoModel</code> only needs to know the checkpoint from which to initialize to return the correct architecture.",
+			correct: true
+		},
+		{
+			text: "A model that automatically detects the language used for its inputs to load the correct weights",
+			explain: "Incorrect; while some checkpoints and models are capable of handling multiple languages, there are no built-in tools for automatic checkpoint selection according to language. You should head over to the <a href='https://huggingface.co/models'>Model Hub</a> to find the best checkpoint for your task!"
+		} 
+	]}
+/>
+
+{:else}
+### 5. What is an TFAutoModel?
+
+<Question
+	choices={[
+		{
+			text: "A model that automatically trains on your data",
+			explain: "Incorrect. Are you mistaking this with our <a href='https://huggingface.co/autotrain'>AutoTrain</a> product?"
+		},
+		{
+			text: "An object that returns the correct architecture based on the checkpoint",
+			explain: "Exactly: the <code>TFAutoModel</code> only needs to know the checkpoint from which to initialize to return the correct architecture.",
+			correct: true
+		},
+		{
+			text: "A model that automatically detects the language used for its inputs to load the correct weights",
+			explain: "Incorrect; while some checkpoints and models are capable of handling multiple languages, there are no built-in tools for automatic checkpoint selection according to language. You should head over to the <a href='https://huggingface.co/models'>Model Hub</a> to find the best checkpoint for your task!"
+		} 
+	]}
+/>
+
+{/if}
+
+### 6. What are the techniques to be aware of when batching sequences of different lengths together?
+
+<Question
+	choices={[
+		{
+			text: "Truncating",
+			explain: "Yes, truncation is a correct way of evening out sequences so that they fit in a rectangular shape. Is it the only one, though?",
+			correct: true
+		},
+		{
+			text: "Returning tensors",
+			explain: "While the other techniques allow you to return rectangular tensors, returning tensors isn't helpful when batching sequences together."
+		},
+		{
+			text: "Padding",
+			explain: "Yes, padding is a correct way of evening out sequences so that they fit in a rectangular shape. Is it the only one, though?",
+			correct: true
+		}, 
+		{
+			text: "Attention masking",
+			explain: "Absolutely! Attention masks are of prime importance when handling sequences of different lengths. That's not the only technique to be aware of, however.",
+			correct: true
+		} 
+	]}
+/>
+
+### 7. What is the point of applying a SoftMax function to the logits output by a sequence classification model?
+
+<Question
+	choices={[
+		{
+			text: "It softens the logits so that they're more reliable.",
+			explain: "No, the SoftMax function does not affect the reliability of results."
+		},
+		{
+			text: "It applies a lower and upper bound so that they're understandable.",
+			explain: "Correct! The resulting values are bound between 0 and 1. That's not the only reason we use a SoftMax function, though.",
+            correct: true
+		},
+		{
+			text: "The total sum of the output is then 1, resulting in a possible probabilistic interpretation.",
+			explain: "Correct! That's not the only reason we use a SoftMax function, though.",
+            correct: true
+		}
+	]}
+/>
+
+### 8. What method is most of the tokenizer API centered around?
+
+<Question
+	choices={[
+		{
+			text: "<code>encode</code>, as it can encode text into IDs and IDs into predictions",
+			explain: "Wrong! While the <code>encode</code> method does exist on tokenizers, it does not exist on models."
+		},
+		{
+			text: "Calling the tokenizer object directly.",
+			explain: "Exactly! The <code>__call__</code> method of the tokenizer is a very powerful method which can handle pretty much anything. It is also the method used to retrieve predictions from a model.",
+			correct: true
+		},
+		{
+			text: "<code>pad</code>",
+			explain: "Wrong! Padding is very useful, but it's just one part of the tokenizer API."
+		},
+		{
+			text: "<code>tokenize</code>",
+			explain: "The <code>tokenize</code> method is arguably one of the most useful methods, but it isn't the core of the tokenizer API."
+		}
+	]}
+/>
+
+### 9. What does the `result` variable contain in this code sample?
+
+```py
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+result = tokenizer.tokenize("Hello!")
+```
+
+<Question
+	choices={[
+		{
+			text: "A list of strings, each string being a token",
+			explain: "Absolutely! Convert this to IDs, and send them to a model!",
+            correct: true
+		},
+		{
+			text: "A list of IDs",
+			explain: "Incorrect; that's what the <code>__call__</code> or <code>convert_tokens_to_ids</code> method is for!"
+		},
+		{
+			text: "A string containing all of the tokens",
+			explain: "This would be suboptimal, as the goal is to split the string into multiple tokens."
+		}
+	]}
+/>
+
+{#if fw === 'pt'}
+### 10. Is there something wrong with the following code?
+
+```py
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+model = AutoModel.from_pretrained("gpt2")
+
+encoded = tokenizer("Hey!", return_tensors="pt")
+result = model(**encoded)
+```
+
+<Question
+	choices={[
+		{
+			text: "No, it seems correct.",
+			explain: "Unfortunately, coupling a model with a tokenizer that was trained with a different checkpoint is rarely a good idea. The model was not trained to make sense out of this tokenizer's output, so the model output (if it can even run!) will not make any sense."
+		},
+		{
+			text: "The tokenizer and model should always be from the same checkpoint.",
+			explain: "Right!",
+            correct: true
+		},
+		{
+			text: "It's good practice to pad and truncate with the tokenizer as every input is a batch.",
+			explain: "It's true that every model input needs to be a batch. However, truncating or padding this sequence wouldn't necessarily make sense as there is only one of it, and those are techniques to batch together a list of sentences."
+		}
+	]}
+/>
+
+{:else}
+### 10. Is there something wrong with the following code?
+
+```py
+from transformers import AutoTokenizer, TFAutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+model = TFAutoModel.from_pretrained("gpt2")
+
+encoded = tokenizer("Hey!", return_tensors="pt")
+result = model(**encoded)
+```
+
+<Question
+	choices={[
+		{
+			text: "No, it seems correct.",
+			explain: "Unfortunately, coupling a model with a tokenizer that was trained with a different checkpoint is rarely a good idea. The model was not trained to make sense out of this tokenizer's output, so the model output (if it can even run!) will not make any sense."
+		},
+		{
+			text: "The tokenizer and model should always be from the same checkpoint.",
+			explain: "Right!",
+            correct: true
+		},
+		{
+			text: "It's good practice to pad and truncate with the tokenizer as every input is a batch.",
+			explain: "It's true that every model input needs to be a batch. However, truncating or padding this sequence wouldn't necessarily make sense as there is only one of it, and those are techniques to batch together a list of sentences."
+		}
+	]}
+/>
+
+{/if}

From 4df0c2e33b1c149db35c901413a07c435fd964e6 Mon Sep 17 00:00:00 2001
From: Angel Mendez <amendez1988@gmail.com>
Date: Tue, 25 Oct 2022 23:05:05 -0500
Subject: [PATCH 2/2] i18n: ES - Translate file chapter2/8.mdx

---
 chapters/es/_toctree.yml   |   2 +
 chapters/es/chapter2/8.mdx | 180 ++++++++++++++++++-------------------
 2 files changed, 92 insertions(+), 90 deletions(-)

diff --git a/chapters/es/_toctree.yml b/chapters/es/_toctree.yml
index 5129356c0..a5dffe325 100644
--- a/chapters/es/_toctree.yml
+++ b/chapters/es/_toctree.yml
@@ -33,6 +33,8 @@
     title: Tokenizadores
   - local: chapter2/5
     title: Manejando Secuencias Múltiples
+  - local: chapter2/8
+    title: Quiz de final de capítulo
 
 - title: 3. Ajuste (fine-tuning) de un modelo preentrenado
   sections:
diff --git a/chapters/es/chapter2/8.mdx b/chapters/es/chapter2/8.mdx
index 96dfd107e..b935571c7 100644
--- a/chapters/es/chapter2/8.mdx
+++ b/chapters/es/chapter2/8.mdx
@@ -2,223 +2,223 @@
 
 <!-- DISABLE-FRONTMATTER-SECTIONS -->
 
-# End-of-chapter quiz
+# Quiz de final de capítulo
 
 <CourseFloatingBanner
     chapter={2}
     classNames="absolute z-10 right-0 top-0"
 />
 
-### 1. What is the order of the language modeling pipeline?
+### 1. ¿Cuál es el orden del pipeline de modelado del lenguaje?
 
 <Question
 	choices={[
 		{
-			text: "First, the model, which handles text and returns raw predictions. The tokenizer then makes sense of these predictions and converts them back to text when needed.",
-			explain: "The model cannot understand text! The tokenizer must first tokenize the text and convert it to IDs so that it is understandable by the model."
+			text: "Primero, el modelo que maneja el texto y devuelve las peticiones sin procesar. El tokenizador luego da sentido a estas predicciones y las convierte nuevamente en texto cuando es necesario.",
+			explain: "¡El modelo no puede entender texto! El tokenizador primero debe tokenizar el texto y convertirlo a IDs para que así sea comprensible por el modelo."
 		},
 		{
-			text: "First, the tokenizer, which handles text and returns IDs. The model handles these IDs and outputs a prediction, which can be some text.",
-			explain: "The model's prediction cannot be text straight away. The tokenizer has to be used in order to convert the prediction back to text!"
+			text: "Primero, el tokenizador, que maneja el texto y regresa IDs. El modelo maneja estos IDs y produce una predicción, la cual puede ser algún texto.",
+			explain: "La predicción del modelo no puede ser texto de forma directa. ¡El tokenizador tiene que ser usado de tal forma que convierta la predicción de vuelta a texto!"
 		},
 		{
-			text: "The tokenizer handles text and returns IDs. The model handles these IDs and outputs a prediction. The tokenizer can then be used once again to convert these predictions back to some text.",
-			explain: "Correct! The tokenizer can be used for both tokenizing and de-tokenizing.",
+			text: "El tokenizador maneja texto y regresa IDs. El modelo maneja estos IDs y produce una predicción. El tokenizador puede luego ser usado de nuevo para convertir estas predicciones de vuelta a texto.",
+			explain: "¡Correcto! El tokenizador puede ser usado tanto para tokenizar como des-tokenizar.",
             correct: true
 		}
 	]}
 />
 
-### 2. How many dimensions does the tensor output by the base Transformer model have, and what are they?
+### 2. ¿Cuántas dimensiones tiene el tensor producido por el modelo base de Transformer y cuáles son?
 
 <Question
 	choices={[
 		{
-			text: "2: The sequence length and the batch size",
-			explain: "False! The tensor output by the model has a third dimension: hidden size."
+			text: "1: La longitud de secuencia y el tamaño del lote",
+			explain: "¡Falso! El tensor producido por el modelo tiene una tercer dimensión: tamaño oculto."
 		},
 		{
-			text: "2: The sequence length and the hidden size",
-			explain: "False! All Transformer models handle batches, even with a single sequence; that would be a batch size of 1!"
+			text: "2: La longitud de secuencia y el tamaño oculto",
+			explain: "¡Falso! All Todos los modelos Transformer manejan lotes, aún con una sola secuencia; lo cual sería un lote de tamaño 1!"
 		},
 		{
-			text: "3: The sequence length, the batch size, and the hidden size",
-			explain: "Correct!",
+			text: "3: La longitud de secuencia, el tamaño de lote y el tamaño oculto",
+			explain: "¡Correcto!",
             correct: true
 		}
 	]}
 />
 
-### 3. Which of the following is an example of subword tokenization?
+### 3. ¿Cuál de los siguientes es un ejemplo de tokenización de subpalabras?
 
 <Question
 	choices={[
 		{
 			text: "WordPiece",
-			explain: "Yes, that's one example of subword tokenization!",
+			explain: "¡Sí ese es un ejemplo de tokenización de subpalabras!",
             correct: true
 		},
 		{
-			text: "Character-based tokenization",
-			explain: "Character-based tokenization is not a type of subword tokenization."
+			text: "Tokenización basada en caracteres",
+			explain: "La tokenización basada en caracteres no es un tipo de tokenización de subpalabras."
 		},
 		{
-			text: "Splitting on whitespace and punctuation",
-			explain: "That's a word-based tokenization scheme!"
+			text: "División por espacios en blanco y puntuación",
+			explain: "¡Ese es un esquema de tokenización basado en palabras!"
 		},
 		{
 			text: "BPE",
-			explain: "Yes, that's one example of subword tokenization!",
+			explain: "¡Sí ese es un ejemplo de tokenización de subpalabras!",
             correct: true
         },
 		{
-			text: "Unigram",
-			explain: "Yes, that's one example of subword tokenization!",
+			text: "Unigrama",
+			explain: "¡Sí ese es un ejemplo de tokenización de subpalabras!",
             correct: true
         },
 		{
-			text: "None of the above",
-			explain: "Incorrect!"
+			text: "Ninguno de los anteriores",
+			explain: "¡Incorrecto!"
         }
 	]}
 />
 
-### 4. What is a model head?
+### 4. ¿Qué es una cabeza del modelo?
 
 <Question
 	choices={[
 		{
-			text: "A component of the base Transformer network that redirects tensors to their correct layers",
-			explain: "Incorrect! There's no such component."
+			text: "Un componente de la red de Transformer base que redirecciona los tensores a sus capas correctas",
+			explain: "¡Incorrecto! No hay tal componente."
 		},
 		{
-			text: "Also known as the self-attention mechanism, it adapts the representation of a token according to the other tokens of the sequence",
-			explain: "Incorrect! The self-attention layer does contain attention \"heads,\" but these are not adaptation heads."
+			text: "También conocido como el mecanismo de autoatención, adapta la representación de un token de acuerdo a los otros tokens de la secuencia", 
+			explain: "¡Incorrecto! La capa de autoatención contiene \"cabezas\", pero éstas no son cabezas de adaptación."
 		},
 		{
-			text: "An additional component, usually made up of one or a few layers, to convert the transformer predictions to a task-specific output",
-			explain: "That's right. Adaptation heads, also known simply as heads, come up in different forms: language modeling heads, question answering heads, sequence classification heads... ",
+			text: "Un componente adcional, compuesto usualmente de una o unas pocas capas, para convertir las predicciones del transformador a una salida específica de la tarea",
+			explain: "Así es, Las cabezas de adaptación, también conocidas simplemente como habezas, vienen en diferentes formas: cabezas de modelado de lenguaje, cabezas de respuesta a preguntas, cabezas de clasificación de secuencia... ",
 			correct: true
 		} 
 	]}
 />
 
 {#if fw === 'pt'}
-### 5. What is an AutoModel?
+### 5. ¿Qué es un AutoModel?
 
 <Question
 	choices={[
 		{
-			text: "A model that automatically trains on your data",
-			explain: "Incorrect. Are you mistaking this with our <a href='https://huggingface.co/autotrain'>AutoTrain</a> product?"
+			text: "Un modelo que entrena automáticamente en tus datos",
+			explain: "Incorrecto. ¿Estás confundiendo esto con nuestro producto <a href='https://huggingface.co/autotrain'>AutoTrain</a>?"
 		},
 		{
-			text: "An object that returns the correct architecture based on the checkpoint",
-			explain: "Exactly: the <code>AutoModel</code> only needs to know the checkpoint from which to initialize to return the correct architecture.",
+			text: "Un objeto que devuelve la arquitectura correcta basado en el punto de control",
+			explain: "Exacto: el <code>AutoModel</code> sólo necesita conocer el punto de control desde el cual inicializar para devolver la arquitectura correcta.",
 			correct: true
 		},
 		{
-			text: "A model that automatically detects the language used for its inputs to load the correct weights",
-			explain: "Incorrect; while some checkpoints and models are capable of handling multiple languages, there are no built-in tools for automatic checkpoint selection according to language. You should head over to the <a href='https://huggingface.co/models'>Model Hub</a> to find the best checkpoint for your task!"
+			text: "Un modelo que detecta automáticamente el lenguaje usado por sus entradas para cargar los pesos correctos",
+			explain: "Incorrecto; auqneu algunos puntos de control y modelos son capaced de manejar varios lenguajes, no hay herramientas integradas para la selección automática de punto de control de acuerdo al lenguaje. ¡Deberías dirigirte a <a href='https://huggingface.co/models'>Model Hub</a> para encontrar el mejor punto de control para tu tarea!"
 		} 
 	]}
 />
 
 {:else}
-### 5. What is an TFAutoModel?
+### 5. ¿Qué es un TFAutoModel?
 
 <Question
 	choices={[
 		{
-			text: "A model that automatically trains on your data",
-			explain: "Incorrect. Are you mistaking this with our <a href='https://huggingface.co/autotrain'>AutoTrain</a> product?"
+			text: "Un modelo que entrena automáticamente en tus datos",
+			explain: "Incorrecto. ¿Estás confundiendo esto con nuestro producto <a href='https://huggingface.co/autotrain'>AutoTrain</a>?"
 		},
 		{
-			text: "An object that returns the correct architecture based on the checkpoint",
-			explain: "Exactly: the <code>TFAutoModel</code> only needs to know the checkpoint from which to initialize to return the correct architecture.",
+			text: "Un objeto que devuelve la arquitectura correcta basado en el punto de control",
+			explain: "Exacto: el <code>TFAutoModel</code> sólo necesita conocer el punto de control desde el cual inicializar para devolver la arquitectura correcta.",
 			correct: true
 		},
 		{
-			text: "A model that automatically detects the language used for its inputs to load the correct weights",
-			explain: "Incorrect; while some checkpoints and models are capable of handling multiple languages, there are no built-in tools for automatic checkpoint selection according to language. You should head over to the <a href='https://huggingface.co/models'>Model Hub</a> to find the best checkpoint for your task!"
+			text: "Un modelo que detecta automáticamente el lenguaje usado por sus entradas para cargar los pesos correctos",
+			explain: "Incorrecto; auqneu algunos puntos de control y modelos son capaced de manejar varios lenguajes, no hay herramientas integradas para la selección automática de punto de control de acuerdo al lenguaje. ¡Deberías dirigirte a <a href='https://huggingface.co/models'>Model Hub</a> para encontrar el mejor punto de control para tu tarea!"
 		} 
 	]}
 />
 
 {/if}
 
-### 6. What are the techniques to be aware of when batching sequences of different lengths together?
+### 6. ¿Cuáles son las técnicas a tener en cuenta al realizar batching de secuencias de diferentes longitudes juntas?
 
 <Question
 	choices={[
 		{
-			text: "Truncating",
-			explain: "Yes, truncation is a correct way of evening out sequences so that they fit in a rectangular shape. Is it the only one, though?",
+			text: "Truncado",
+			explain: "Sí, el truncamiento es una forma correcta de emparejar secuencias de modo que se ajusten a una forma rectangular. ¿Aunque, es la única?",
 			correct: true
 		},
 		{
 			text: "Returning tensors",
-			explain: "While the other techniques allow you to return rectangular tensors, returning tensors isn't helpful when batching sequences together."
+			explain: "Mientras las otras técnicas te permiten devolver tensores rectangulares, returning tensors no es útil cuando se hace batching en secuencias juntas."
 		},
 		{
-			text: "Padding",
-			explain: "Yes, padding is a correct way of evening out sequences so that they fit in a rectangular shape. Is it the only one, though?",
+			text: "Relleno",
+			explain: "Sí, el relleno es una forma correcta de emparejar secuencias de modo que se ajusten a una forma rectangular. ¿Aunque, es la única?",
 			correct: true
 		}, 
 		{
-			text: "Attention masking",
-			explain: "Absolutely! Attention masks are of prime importance when handling sequences of different lengths. That's not the only technique to be aware of, however.",
+			text: "Enmascarado de atención",
+			explain: "¡Absolutamente! Las máscaras de atención son de primera importancia cuando se manejan secuencias de diferentes longitudes. Sin embargo, no es la única técnica a tener en cuenta.",
 			correct: true
 		} 
 	]}
 />
 
-### 7. What is the point of applying a SoftMax function to the logits output by a sequence classification model?
+### 7. ¿Cuál es el punto de aplicar una funcion SoftMax a las salidas logits por un modelo de clasificación de secuencias?
 
 <Question
 	choices={[
 		{
-			text: "It softens the logits so that they're more reliable.",
-			explain: "No, the SoftMax function does not affect the reliability of results."
+			text: "Suaviza los logits para que sean más fiables.",
+			explain: "No, la función SoftMax no afecta en la fiabilidad de los resultados."
 		},
 		{
-			text: "It applies a lower and upper bound so that they're understandable.",
-			explain: "Correct! The resulting values are bound between 0 and 1. That's not the only reason we use a SoftMax function, though.",
+			text: "Aplica un límiete inferior y superior de modo que sean comprensibles.",
+			explain: "¡Correcto! Los valores resultantes están limitados entre 0 y 1. Aunque, no es la única razón por la cual usamos una función SoftMax.",
             correct: true
 		},
 		{
-			text: "The total sum of the output is then 1, resulting in a possible probabilistic interpretation.",
-			explain: "Correct! That's not the only reason we use a SoftMax function, though.",
+			text: "La suma total de la salida es entonces 1, dando como resultado una posible interpretación probabilística.",
+			explain: "¡Correcto! Aunque, esa no es la única razón por la que usamos una función SoftMax.",
             correct: true
 		}
 	]}
 />
 
-### 8. What method is most of the tokenizer API centered around?
+### 8. ¿En qué método se centra la mayor parte de la API del tokenizador?
 
 <Question
 	choices={[
 		{
-			text: "<code>encode</code>, as it can encode text into IDs and IDs into predictions",
-			explain: "Wrong! While the <code>encode</code> method does exist on tokenizers, it does not exist on models."
+			text: "<code>encode</code>, ya que puede codificar texto en IDs e IDs en predicciones",
+			explain: "¡Incorrecto! Aunque el método <code>encode</code> existe en los tokenizadores, no existe en los modelos." 
 		},
 		{
-			text: "Calling the tokenizer object directly.",
-			explain: "Exactly! The <code>__call__</code> method of the tokenizer is a very powerful method which can handle pretty much anything. It is also the method used to retrieve predictions from a model.",
+			text: "Llamar al objeto tokenizador directamente.",
+			explain: "¡Exactamente! El método <code>__call__</code> del tokenizador es un método muy poderoso el cual puede manejar casi cualquier cosa.También es el método usado para recuperar las predicciones de un modelo.",
 			correct: true
 		},
 		{
 			text: "<code>pad</code>",
-			explain: "Wrong! Padding is very useful, but it's just one part of the tokenizer API."
+			explain: "¡Incorrecto! El relleno es muy útil, pero es solo una parte de la API tokenizador."
 		},
 		{
 			text: "<code>tokenize</code>",
-			explain: "The <code>tokenize</code> method is arguably one of the most useful methods, but it isn't the core of the tokenizer API."
+			explain: "El método <code>tokenize</code> es posiblemente uno de los métodos más útiles, pero no es el núcleo de la API tokenizador."
 		}
 	]}
 />
 
-### 9. What does the `result` variable contain in this code sample?
+### 9. ¿Qué contiene la variable `result` en este código de ejemplo?
 
 ```py
 from transformers import AutoTokenizer
@@ -230,23 +230,23 @@ result = tokenizer.tokenize("Hello!")
 <Question
 	choices={[
 		{
-			text: "A list of strings, each string being a token",
-			explain: "Absolutely! Convert this to IDs, and send them to a model!",
+			text: "Una lista de strings, cada string es un token",
+			explain: "¡Por supuesto! ¡Convierte esto a IDs, y los envía a los modelos!",
             correct: true
 		},
 		{
-			text: "A list of IDs",
-			explain: "Incorrect; that's what the <code>__call__</code> or <code>convert_tokens_to_ids</code> method is for!"
+			text: "Una lista de IDs",
+			explain: "Incorrecto; ¡para eso están los métodos <code>__call__</code> o <code>convert_tokens_to_ids</code>!"
 		},
 		{
-			text: "A string containing all of the tokens",
-			explain: "This would be suboptimal, as the goal is to split the string into multiple tokens."
+			text: "Una cadena que contiene todos los tokens",
+			explain: "Esto sería subóptimo, ya que el objetivo es dividir la cadena en varios tokens."
 		}
 	]}
 />
 
 {#if fw === 'pt'}
-### 10. Is there something wrong with the following code?
+### 10. ¿Hay algo mal con el siguiente código?
 
 ```py
 from transformers import AutoTokenizer, AutoModel
@@ -261,23 +261,23 @@ result = model(**encoded)
 <Question
 	choices={[
 		{
-			text: "No, it seems correct.",
-			explain: "Unfortunately, coupling a model with a tokenizer that was trained with a different checkpoint is rarely a good idea. The model was not trained to make sense out of this tokenizer's output, so the model output (if it can even run!) will not make any sense."
+			text: "No, parece correcto.",
+			explain: "Desafortunadamente, acoplar un modelo con un tokenizador que fue entrenado con un punto de control distinto raramente es una buena idea. El modelo no fue entrenado para dar sentido a la salida de este tokenizador, así la salida del modelo (¡si es que puede correr!) no tendrá ningún sentido."
 		},
 		{
-			text: "The tokenizer and model should always be from the same checkpoint.",
-			explain: "Right!",
+			text: "El tokenizador y el modelo siempre deben ser del mismo punto de control.",
+			explain: "¡Correcto!",
             correct: true
 		},
 		{
-			text: "It's good practice to pad and truncate with the tokenizer as every input is a batch.",
-			explain: "It's true that every model input needs to be a batch. However, truncating or padding this sequence wouldn't necessarily make sense as there is only one of it, and those are techniques to batch together a list of sentences."
+			text: "Es una buena práctica rellenar y truncar con el tokenizador ya que cada entrada es un lote.",
+			explain: "Es cierto que cada entrada de modelo necesita ser un lote. Sin embargo, truncar o rellenar esta secuencia no necesariamente hace sentido ya que sólo hay una, y esas son técnicas para juntar una lista de oraciones."
 		}
 	]}
 />
 
 {:else}
-### 10. Is there something wrong with the following code?
+### 10. ¿Hay algo mal con el siguiente código?
 
 ```py
 from transformers import AutoTokenizer, TFAutoModel
@@ -292,17 +292,17 @@ result = model(**encoded)
 <Question
 	choices={[
 		{
-			text: "No, it seems correct.",
-			explain: "Unfortunately, coupling a model with a tokenizer that was trained with a different checkpoint is rarely a good idea. The model was not trained to make sense out of this tokenizer's output, so the model output (if it can even run!) will not make any sense."
+			text: "No, parece correcto.",
+			explain: "Desafortunadamente, acoplar un modelo con un tokenizador que fue entrenado con un punto de control distinto raramente es una buena idea. El modelo no fue entrenado para dar sentido a la salida de este tokenizador, así la salida del modelo (¡si es que puede correr!) no tendrá ningún sentido."
 		},
 		{
-			text: "The tokenizer and model should always be from the same checkpoint.",
-			explain: "Right!",
+			text: "El tokenizador y el modelo siempre deben ser del mismo punto de control.",
+			explain: "¡Correcto!",
             correct: true
 		},
 		{
-			text: "It's good practice to pad and truncate with the tokenizer as every input is a batch.",
-			explain: "It's true that every model input needs to be a batch. However, truncating or padding this sequence wouldn't necessarily make sense as there is only one of it, and those are techniques to batch together a list of sentences."
+			text: "Es una buena práctica rellenar y truncar con el tokenizador ya que cada entrada es un lote.",
+			explain: "Es cierto que cada entrada de modelo necesita ser un lote. Sin embargo, truncar o rellenar esta secuencia no necesariamente hace sentido ya que sólo hay una, y esas son técnicas para juntar una lista de oraciones."
 		}
 	]}
 />