Please correct me if I’m wrong.
A DataSet object can be thought of as some tabular data, whose rows and columns are examples and features respectively.  The length of a DataSet object is the number of examples, which equals to the number of its rows. Each column corresponds to one feature.
Given this understanding of DataSet, I found the following descriptions in chapter 5 are confusing (in other words incorrect?).

… here those 1,000 examples gave 1,463 new features, resulting in a shape error.

1,463 is the number of rows (i.e. examples) of the newly added columns (i.e. features) such as  attention_mask, input_ids, etc.

We can check that our new dataset has many more features than the original dataset by comparing the lengths:
len(tokenized_dataset[“train”]), len(drug_dataset[“train”])
(206772, 138514)

Obviously the above two numbers are the numbers of rows, i.e. the numbers of examples, not the number of columns (features). The number of features in this case is 4. Specifically, these 4 features are
attention_mask
input_ids
overflow_to_sample_mapping
and
token_type_ids