Merge pull request #894 from huggingface/chapter1-quiz

[ENHANCEMENT] add certified quiz to chapter 1

Author: burtenshaw

chapters/en/_toctree.yml

@@ -18,13 +18,15 @@
   - local: chapter1/6
     title: Transformer Architectures
   - local: chapter1/7
-    title: Inference with LLMs
+    title: Quick quiz
   - local: chapter1/8
-    title: Bias and limitations
+    title: Inference with LLMs
   - local: chapter1/9
-    title: Summary
+    title: Bias and limitations
   - local: chapter1/10
-    title: End-of-chapter quiz
+    title: Summary
+  - local: chapter1/11
+    title: Certification exam
     quiz: 1
 
 - title: 2. Using 🤗 Transformers

chapters/en/chapter1/10.mdx

@@ -1,258 +1,66 @@
-<!-- DISABLE-FRONTMATTER-SECTIONS -->
-
-# End-of-chapter quiz[[end-of-chapter-quiz]]
+# Summary[[summary]]
 
 <CourseFloatingBanner
     chapter={1}
     classNames="absolute z-10 right-0 top-0"
 />
 
-This chapter covered a lot of ground! Don't worry if you didn't grasp all the details; the next chapters will help you understand how things work under the hood.
-
-First, though, let's test what you learned in this chapter!
-
-
-### 1. Explore the Hub and look for the `roberta-large-mnli` checkpoint. What task does it perform?
+In this chapter, you've been introduced to the fundamentals of Transformer models, Large Language Models (LLMs), and how they're revolutionizing AI and beyond.
 
+## Key concepts covered
 
-<Question
-	choices={[
-		{
-			text: "Summarization",
-			explain: "Look again on the <a href=\"https://huggingface.co/roberta-large-mnli\">roberta-large-mnli page</a>."
-		},
-		{
-			text: "Text classification",
-			explain: "More precisely, it classifies if two sentences are logically linked across three labels (contradiction, neutral, entailment) — a task also called <em>natural language inference</em>.",
-			correct: true
-		},
-		{
-			text: "Text generation",
-			explain: "Look again on the <a href=\"https://huggingface.co/roberta-large-mnli\">roberta-large-mnli page</a>."
-		}
-	]}
-/>
-
-### 2. What will the following code return?
+### Natural Language Processing and LLMs
 
-```py
-from transformers import pipeline
+We explored what NLP is and how Large Language Models have transformed the field. You learned that:
+- NLP encompasses a wide range of tasks from classification to generation
+- LLMs are powerful models trained on massive amounts of text data
+- These models can perform multiple tasks within a single architecture
+- Despite their capabilities, LLMs have limitations including hallucinations and bias
 
-ner = pipeline("ner", grouped_entities=True)
-ner("My name is Sylvain and I work at Hugging Face in Brooklyn.")
-```
+### Transformer capabilities
 
-<Question
-	choices={[
-		{
-			text: "It will return classification scores for this sentence, with labels \"positive\" or \"negative\".",
-			explain: "This is incorrect — this would be a <code>sentiment-analysis</code> pipeline."
-		},
-		{
-			text: "It will return a generated text completing this sentence.",
-			explain: "This is incorrect — it would be a <code>text-generation</code> pipeline.",
-		},
-		{
-			text: "It will return the words representing persons, organizations or locations.",
-			explain: "Furthermore, with <code>grouped_entities=True</code>, it will group together the words belonging to the same entity, like \"Hugging Face\".",
-			correct: true
-		}
-	]}
-/>
+You saw how the `pipeline()` function from 🤗 Transformers makes it easy to use pre-trained models for various tasks:
+- Text classification, token classification, and question answering
+- Text generation and summarization
+- Translation and other sequence-to-sequence tasks
+- Speech recognition and image classification
 
-### 3. What should replace ... in this code sample?
+### Transformer architecture
 
-```py
-from transformers import pipeline
+We discussed how Transformer models work at a high level, including:
+- The importance of the attention mechanism
+- How transfer learning enables models to adapt to specific tasks
+- The three main architectural variants: encoder-only, decoder-only, and encoder-decoder
 
-filler = pipeline("fill-mask", model="bert-base-cased")
-result = filler("...")
-```
+### Model architectures and their applications
+A key aspect of this chapter was understanding which architecture to use for different tasks:
 
-<Question
-	choices={[
-		{
-			text: "This &#60;mask> has been waiting for you.",
-			explain: "This is incorrect. Check out the <code>bert-base-cased</code> model card and try to spot your mistake."
-		},
-		{
-			text: "This [MASK] has been waiting for you.",
-			explain: "Correct! This model's mask token is [MASK].",
-			correct: true
-		},
-		{
-			text: "This man has been waiting for you.",
-			explain: "This is incorrect. This pipeline fills in masked words, so it needs a mask token somewhere."
-		}
-	]}
-/>
+| Model           | Examples                                   | Tasks                                                                            |
+|-----------------|--------------------------------------------|----------------------------------------------------------------------------------|
+| Encoder-only    | BERT, DistilBERT, ModernBERT               | Sentence classification, named entity recognition, extractive question answering |
+| Decoder-only    | GPT, LLaMA, Gemma, SmolLM                  | Text generation, conversational AI, creative writing                             |
+| Encoder-decoder | BART, T5, Marian, mBART                    | Summarization, translation, generative question answering                        |
 
-### 4. Why will this code fail?
+### Modern LLM developments
+You also learned about recent developments in the field:
+- How LLMs have grown in size and capability over time
+- The concept of scaling laws and how they guide model development
+- Specialized attention mechanisms that help models process longer sequences
+- The two-phase training approach of pretraining and instruction tuning
 
-```py
-from transformers import pipeline
+### Practical applications
+Throughout the chapter, you've seen how these models can be applied to real-world problems:
+- Using the Hugging Face Hub to find and use pre-trained models
+- Leveraging the Inference API to test models directly in your browser
+- Understanding which models are best suited for specific tasks
 
-classifier = pipeline("zero-shot-classification")
-result = classifier("This is a course about the Transformers library")
-```
+## Looking ahead
 
-<Question
-	choices={[
-		{
-			text: "This pipeline requires that labels be given to classify this text.",
-			explain: "Right — the correct code needs to include <code>candidate_labels=[...]</code>.",
-			correct: true
-		},
-		{
-			text: "This pipeline requires several sentences, not just one.",
-			explain: "This is incorrect, though when properly used, this pipeline can take a list of sentences to process (like all other pipelines)."
-		},
-		{
-			text: "The 🤗 Transformers library is broken, as usual.",
-			explain: "We won't dignify this answer with a comment!"
-		},
-		{
-			text: "This pipeline requires longer inputs; this one is too short.",
-			explain: "This is incorrect. Note that a very long text will be truncated when processed by this pipeline."
-		}
-	]}
-/>
-
-### 5. What does "transfer learning" mean?
-
-<Question
-	choices={[
-		{
-			text: "Transferring the knowledge of a pretrained model to a new model by training it on the same dataset.",
-			explain: "No, that would be two versions of the same model."
-		},
-		{
-			text: "Transferring the knowledge of a pretrained model to a new model by initializing the second model with the first model's weights.",
-			explain: "Correct: when the second model is trained on a new task, it *transfers* the knowledge of the first model.",
-			correct: true
-		},
-		{
-			text: "Transferring the knowledge of a pretrained model to a new model by building the second model with the same architecture as the first model.",
-			explain: "The architecture is just the way the model is built; there is no knowledge shared or transferred in this case."
-		}
-	]}
-/>
-
-### 6. True or false? A language model usually does not need labels for its pretraining.
-
-<Question
-	choices={[
-		{
-			text: "True",
-			explain: "The pretraining is usually <em>self-supervised</em>, which means the labels are created automatically from the inputs (like predicting the next word or filling in some masked words).",
-			correct: true
-		},
-		{
-			text: "False",
-			explain: "This is not the correct answer."
-		}
-	]}
-/>
+Now that you have a solid understanding of what Transformer models are and how they work at a high level, you're ready to dive deeper into how to use them effectively. In the next chapters, you'll learn how to:
 
-### 7. Select the sentence that best describes the terms "model", "architecture", and "weights".
+- Use the Transformers library to load and fine-tune models
+- Process different types of data for model input
+- Adapt pre-trained models to your specific tasks
+- Deploy models for practical applications
 
-<Question
-	choices={[
-		{
-			text: "If a model is a building, its architecture is the blueprint and the weights are the people living inside.",
-			explain: "Following this metaphor, the weights would be the bricks and other materials used to construct the building."
-		},
-		{
-			text: "An architecture is a map to build a model and its weights are the cities represented on the map.",
-			explain: "The problem with this metaphor is that a map usually represents one existing reality (there is only one city in France named Paris). For a given architecture, multiple weights are possible."
-		},
-		{
-			text: "An architecture is a succession of mathematical functions to build a model and its weights are those functions parameters.",
-			explain: "The same set of mathematical functions (architecture) can be used to build different models by using different parameters (weights).",
-			correct: true
-		}
-	]}
-/>
-
-
-### 8. Which of these types of models would you use for completing prompts with generated text?
-
-<Question
-	choices={[
-		{
-			text: "An encoder model",
-			explain: "An encoder model generates a representation of the whole sentence that is better suited for tasks like classification."
-		},
-		{
-			text: "A decoder model",
-			explain: "Decoder models are perfectly suited for text generation from a prompt.",
-			correct: true
-		},
-		{
-			text: "A sequence-to-sequence model",
-			explain: "Sequence-to-sequence models are better suited for tasks where you want to generate sentences in relation to the input sentences, not a given prompt."
-		}
-	]}
-/>
-
-### 9. Which of those types of models would you use for summarizing texts?
-
-<Question
-	choices={[
-		{
-			text: "An encoder model",
-			explain: "An encoder model generates a representation of the whole sentence that is better suited for tasks like classification."
-		},
-		{
-			text: "A decoder model",
-			explain: "Decoder models are good for generating output text (like summaries), but they don't have the ability to exploit a context like the whole text to summarize."
-		},
-		{
-			text: "A sequence-to-sequence model",
-			explain: "Sequence-to-sequence models are perfectly suited for a summarization task.",
-			correct: true
-		}
-	]}
-/>
-
-### 10. Which of these types of models would you use for classifying text inputs according to certain labels?
-
-<Question
-	choices={[
-		{
-			text: "An encoder model",
-			explain: "An encoder model generates a representation of the whole sentence which is perfectly suited for a task like classification.",
-			correct: true
-		},
-		{
-			text: "A decoder model",
-			explain: "Decoder models are good for generating output texts, not extracting a label out of a sentence."
-		},
-		{
-			text: "A sequence-to-sequence model",
-			explain: "Sequence-to-sequence models are better suited for tasks where you want to generate text based on an input sentence, not a label.",
-		}
-	]}
-/>
-
-### 11. What possible source can the bias observed in a model have?
-
-<Question
-	choices={[
-		{
-			text: "The model is a fine-tuned version of a pretrained model and it picked up its bias from it.",
-			explain: "When applying Transfer Learning, the bias in the pretrained model used persists in the fine-tuned model.",
-			correct: true
-		},
-		{
-			text: "The data the model was trained on is biased.",
-			explain: "This is the most obvious source of bias, but not the only one.",
-			correct: true
-		},
-		{
-			text: "The metric the model was optimizing for is biased.",
-			explain: "A less obvious source of bias is the way the model is trained. Your model will blindly optimize for whatever metric you chose, without any second thoughts.",
-			correct: true
-		}
-	]}
-/>
+The foundation you've built in this chapter will serve you well as you explore more advanced topics and techniques in the coming sections.

chapters/en/chapter1/11.mdx

@@ -0,0 +1,21 @@
+# Exam Time!
+
+It's time to put your knowledge to the test! We've prepared a short quiz for you to test your understanding of the concepts covered in this chapter.
+
+To take the quiz, you will need to follow these steps:
+
+1. Sign in to your Hugging Face account.
+2. Answer the questions in the quiz.
+3. Submit your answers.
+
+
+## Multiple Choice Quiz
+
+In this quiz, you will be asked to select the correct answer from a list of options. We'll test you on the fundamentals of supervised finetuning.
+
+<iframe
+	src="https://huggingface-course-chapter-1-exam.hf.space"
+	frameborder="0"
+	width="850"
+	height="450"
+></iframe>