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NLP Course documentation

End-of-chapter quiz

0. Setup

1. Transformer models

2. Using 🤗 Transformers

3. Fine-tuning a pretrained model

Introduction Processing the data Fine-tuning a model with the Trainer API or Keras A full training Fine-tuning, Check! End-of-chapter quiz

4. Sharing models and tokenizers

5. The 🤗 Datasets library

6. The 🤗 Tokenizers library

7. Main NLP tasks

8. How to ask for help

9. Building and sharing demos new

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End-of-chapter quiz

Test what you learned in this chapter!

1. The `emotion` dataset contains Twitter messages labeled with emotions. Search for it in the Hub, and read the dataset card. Which of these is not one of its basic emotions?

Joy Love Confusion Surprise

2. Search for the `ar_sarcasm` dataset in the Hub. Which task does it support?

Sentiment classification Machine translation Named entity recognition Question answering

3. How does the BERT model expect a pair of sentences to be processed?

Tokens_of_sentence_1 [SEP] Tokens_of_sentence_2 [CLS] Tokens_of_sentence_1 Tokens_of_sentence_2 [CLS] Tokens_of_sentence_1 [SEP] Tokens_of_sentence_2 [SEP] [CLS] Tokens_of_sentence_1 [SEP] Tokens_of_sentence_2

4. What are the benefits of the `Dataset.map()` method?

The results of the function are cached, so it won't take any time if we re-execute the code. It can apply multiprocessing to go faster than applying the function on each element of the dataset. It does not load the whole dataset into memory, saving the results as soon as one element is processed.

5. What does dynamic padding mean?

It's when you pad the inputs for each batch to the maximum length in the whole dataset. It's when you pad your inputs when the batch is created, to the maximum length of the sentences inside that batch. It's when you pad your inputs so that each sentence has the same number of tokens as the previous one in the dataset.

6. What is the purpose of a collate function?

It ensures all the sequences in the dataset have the same length. It puts together all the samples in a batch. It preprocesses the whole dataset. It truncates the sequences in the dataset.

7. What happens when you instantiate one of the `AutoModelForXxx` classes with a pretrained language model (such as `bert-base-uncased`) that corresponds to a different task than the one for which it was trained?

Nothing, but you get a warning. The head of the pretrained model is discarded and a new head suitable for the task is inserted instead. The head of the pretrained model is discarded. Nothing, since the model can still be fine-tuned for the different task.

8. What’s the purpose of `TrainingArguments`?

It contains all the hyperparameters used for training and evaluation with the Trainer. It specifies the size of the model. It just contains the hyperparameters used for evaluation. It just contains the hyperparameters used for training.

9. Why should you use the 🤗 Accelerate library?

It provides access to faster models. It provides a high-level API so I don't have to implement my own training loop. It makes our training loops work on distributed strategies It provides more optimization functions.

←Fine-tuning, Check!

End-of-chapter quiz