Pytorch
TensorFlow

#
End-of-chapter quiz

Let’s test what you learned in this chapter!

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1. What are models on the Hub limited to?

Models from the 🤗 Transformers library. All models with a similar interface to 🤗 Transformers. There are no limits. Models that are in some way related to NLP.

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2. How can you manage models on the Hub?

Through a GCP account. Through peer-to-peer distribution. Through git and git-lfs.

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3. What can you do using the Hugging Face Hub web interface?

Fork an existing repository. Create a new model repository. Manage and edit files. Upload files. See diffs across versions.

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4. What is a model card?

A rough description of the model, therefore less important than the model and tokenizer files. A way to ensure reproducibility, reusability, and fairness. A Python file that can be run to retrieve information about the model.

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5. Which of these objects of the 🤗 Transformers library can be directly shared on the Hub with `push_to_hub()`?

A tokenizer A model configuration A model A Trainer

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6. What is the first step when using the `push_to_hub()` method or the CLI tools?

Log in on the website. Run 'huggingface-cli login' in a terminal. Run 'notebook_login()' in a notebook.

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7. You’re using a model and a tokenizer — how can you upload them to the Hub?

By calling the push_to_hub method directly on the model and the tokenizer. Within the Python runtime, by wrapping them in a `huggingface_hub` utility. By saving them to disk and calling `transformers-cli upload-model`

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8. Which git operations can you do with the `Repository` class?

A commit. A pull A push A merge