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

### 1. What is the order of the language modeling pipeline?

### 2. How many dimensions does the tensor output by the base Transformer model have, and what are they?

### 3. Which of the following is an example of subword tokenization?

### 4. What is a model head?

### 5. What is an AutoModel?

AutoTrain product?"
		},
		{
			text: "An object that returns the correct architecture based on the checkpoint",
			explain: "Exactly: the AutoModel only needs to know the checkpoint from which to initialize to return the correct architecture.",
			correct: true
		},
		{
			text: "A model that automatically detects the language used for its inputs to load the correct weights",
			explain: "While some checkpoints and models are capable of handling multiple languages, there are no built-in tools for automatic checkpoint selection according to language. You should head over to the Model Hub to find the best checkpoint for your task!"
		} 
	]}
/>

### 6. What are the techniques to be aware of when batching sequences of different lengths together?

### 7. What is the point of applying a SoftMax function to the logits output by a sequence classification model?

### 8. What method is most of the tokenizer API centered around?

encode, as it can encode text into IDs and IDs into predictions",
			explain: "Wrong! While the encode method does exist on tokenizers, it does not exist on models."
		},
		{
			text: "Calling the tokenizer object directly.",
			explain: "Exactly! The __call__ method of the tokenizer is a very powerful method which can handle pretty much anything. It is also the method used to retrieve predictions from a model.",
			correct: true
		},
		{
			text: "pad",
			explain: "Wrong! Padding is very useful, but it's just one part of the tokenizer API."
		},
		{
			text: "tokenize",
			explain: "The tokenize method is arguably one of the most useful methods, but it isn't the core of the tokenizer API."
		}
	]}
/>

### 9. What does the `result` variable contain in this code sample?

```py
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
result = tokenizer.tokenize("Hello!")
```

__call__ or convert_tokens_to_ids method is for!"
		},
		{
			text: "A string containing all of the tokens",
			explain: "This would be suboptimal, as the goal is to split the string into multiple tokens."
		}
	]}
/>

### 10. Is there something wrong with the following code?

```py
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
model = AutoModel.from_pretrained("gpt2")

encoded = tokenizer("Hey!", return_tensors="pt")
result = model(**encoded)
```