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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!

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1. Explore the Hub and look for the `roberta-large-mnli` checkpoint. What task does it perform?

Summarization Text classification Text generation

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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.")

It will return classification scores for this sentence, with labels "positive" or "negative". It will return a generated text completing this sentence. It will return the words representing persons, organizations or locations.

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3. What should replace … in this code sample?

    from transformers import pipeline

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

This <mask> has been waiting for you. This [MASK] has been waiting for you. This man has been waiting for you.

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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")

This pipeline requires that labels be given to classify this text. This pipeline requires several sentences, not just one. The 🤗 Transformers library is broken, as usual. This pipeline requires longer inputs; this one is too short.

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5. What does “transfer learning” mean?

Transferring the knowledge of a pretrained model to a new model by training it on the same dataset. Transferring the knowledge of a pretrained model to a new model by initializing the second model with the first model's weights. Transferring the knowledge of a pretrained model to a new model by building the second model with the same architecture as the first model.

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6. True or false? A language model usually does not need labels for its pretraining.

True False

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

If a model is a building, its architecture is the blueprint and the weights are the people living inside. An architecture is a map to build a model and its weights are the cities represented on the map. An architecture is a succession of mathematical functions to build a model and its weights are those functions parameters.

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8. Which of these types of models would you use for completing prompts with generated text?

An encoder model A decoder model A sequence-to-sequence model

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9. Which of those types of models would you use for summarizing texts?

An encoder model A decoder model A sequence-to-sequence model

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10. Which of these types of models would you use for classifying text inputs according to certain labels?

An encoder model A decoder model A sequence-to-sequence model

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11. What possible source can the bias observed in a model have?

The model is a fine-tuned version of a pretrained model and it picked up its bias from it. The data the model was trained on is biased. The metric the model was optimizing for is biased.