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Fix dataset loading error by converting column data to list[str] #1072

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Fix dataset loading error by converting column data to list[str] #1072

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10 changes: 5 additions & 5 deletions chapters/en/chapter3/2.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -129,8 +129,8 @@ from transformers import AutoTokenizer

checkpoint = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
tokenized_sentences_1 = tokenizer(raw_datasets["train"]["sentence1"])
tokenized_sentences_2 = tokenizer(raw_datasets["train"]["sentence2"])
tokenized_sentences_1 = tokenizer(list(raw_datasets["train"]["sentence1"]))
tokenized_sentences_2 = tokenizer(list(raw_datasets["train"]["sentence2"]))
```

<Tip>
Expand Down Expand Up @@ -195,8 +195,8 @@ Now that we have seen how our tokenizer can deal with one pair of sentences, we

```py
tokenized_dataset = tokenizer(
raw_datasets["train"]["sentence1"],
raw_datasets["train"]["sentence2"],
list(raw_datasets["train"]["sentence1"]),
list(raw_datasets["train"]["sentence2"]),
padding=True,
truncation=True,
)
Expand All @@ -208,7 +208,7 @@ To keep the data as a dataset, we will use the [`Dataset.map()`](https://hugging

```py
def tokenize_function(example):
return tokenizer(example["sentence1"], example["sentence2"], truncation=True)
return tokenizer(list(example["sentence1"]), list(example["sentence2"]), truncation=True)
```

This function takes a dictionary (like the items of our dataset) and returns a new dictionary with the keys `input_ids`, `attention_mask`, and `token_type_ids`. Note that it also works if the `example` dictionary contains several samples (each key as a list of sentences) since the `tokenizer` works on lists of pairs of sentences, as seen before. This will allow us to use the option `batched=True` in our call to `map()`, which will greatly speed up the tokenization. The `tokenizer` is backed by a tokenizer written in Rust from the [🤗 Tokenizers](https://github.com/huggingface/tokenizers) library. This tokenizer can be very fast, but only if we give it lots of inputs at once.
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