chapter1/material/10.md

End-of-chapter quiz[[end-of-chapter-quiz]]

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?

<Question choices={[ { text: "Summarization", explain: "Look again on the <a href="https://huggingface.co/roberta-large-mnli\">roberta-large-mnli page." }, { text: "Text classification", explain: "More precisely, it classifies if two sentences are logically linked across three labels (contradiction, neutral, entailment) — a task also called natural language inference.", correct: true }, { text: "Text generation", explain: "Look again on the <a href="https://huggingface.co/roberta-large-mnli\">roberta-large-mnli page." } ]} />

2. What will the following code return?

from transformers import pipeline

ner = pipeline("ner", grouped_entities=True)
ner("My name is Sylvain and I work at Hugging Face in Brooklyn.")

<Question choices={[ { text: "It will return classification scores for this sentence, with labels "positive" or "negative".", explain: "This is incorrect — this would be a sentiment-analysis pipeline." }, { text: "It will return a generated text completing this sentence.", explain: "This is incorrect — it would be a text-generation pipeline.", }, { text: "It will return the words representing persons, organizations or locations.", explain: "Furthermore, with grouped_entities=True, it will group together the words belonging to the same entity, like "Hugging Face".", correct: true } ]} />

3. What should replace ... in this code sample?

from transformers import pipeline

filler = pipeline("fill-mask", model="bert-base-cased")
result = filler("...")

<Question choices={[ { text: "This <mask> has been waiting for you.", explain: "This is incorrect. Check out the bert-base-cased 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." } ]} />

4. Why will this code fail?

from transformers import pipeline

classifier = pipeline("zero-shot-classification")
result = classifier("This is a course about the Transformers library")

<Question choices={[ { text: "This pipeline requires that labels be given to classify this text.", explain: "Right — the correct code needs to include candidate_labels=[...].", 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 self-supervised, 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." } ]} />

7. Select the sentence that best describes the terms "model", "architecture", and "weights".

<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 } ]} />