Chapter 3 questions

In terms of code, just list()

will fix it.

Why the notebook code fails now

Root cause

The tokenizer expects text inputs like:

str

list[str]

• batched text-pair inputs built from those

That is how the tokenizer API is documented today. (Hugging Face)

But dataset column access is no longer best thought of as “always a plain Python list.” Current Datasets docs explicitly say that **indexing by column name first returns a **Column

object. (Hugging Face)

So this line:

tokenizer(raw_datasets["train"]["sentence1"])

can fail because the tokenizer sees a dataset column object, not a plain Python list[str]

.

Background: why this is confusing

Older Datasets docs described dataset["sentence1"]

as returning a Python list of values. (Hugging Face)

Current docs describe column-first indexing as returning a Column

object. (Hugging Face)

That difference explains why:

• older examples could work as written
• current environments can reject the same code
• the course notebook looks reasonable, but still breaks in practice

So the real background is API/documentation drift across library versions and implementations, not that your code idea was conceptually wrong. (Hugging Face)

Why this notebook uses two sentences at all

This dataset is MRPC, a sentence-pair classification task. Each example has:

sentence1

sentence2

• a label telling whether they are paraphrases

The course explains that for BERT, the correct pair format is:

[CLS] sentence1 [SEP] sentence2 [SEP]

and that token_type_ids

distinguish the first sentence from the second. (GitHub)

So even when the notebook first tokenizes sentence1

and sentence2

separately, that is only an introductory step. The actual task is to tokenize the pair together. (GitHub)

Minimal fix for the notebook

The smallest code change is to convert each dataset column to a plain list.

Replace this

tokenized_sentences_1 = tokenizer(raw_datasets["train"]["sentence1"])
tokenized_sentences_2 = tokenizer(raw_datasets["train"]["sentence2"])

With this

tokenized_sentences_1 = tokenizer(list(raw_datasets["train"]["sentence1"]))
tokenized_sentences_2 = tokenizer(list(raw_datasets["train"]["sentence2"]))

And replace this:

tokenized_dataset = tokenizer(
    raw_datasets["train"]["sentence1"],
    raw_datasets["train"]["sentence2"],
    padding=True,
    truncation=True,
)

with this:

tokenized_dataset = tokenizer(
    list(raw_datasets["train"]["sentence1"]),
    list(raw_datasets["train"]["sentence2"]),
    padding=True,
    truncation=True,
)

Why this works

list(...)

converts the dataset column into the kind of container the tokenizer is documented to accept: a plain list[str]

. (Hugging Face)