undo edit #539

chapters/en/chapter6/3.mdx

@@ -105,7 +105,7 @@ encoding.word_ids()
 We can see that the tokenizer's special tokens `[CLS]` and `[SEP]` are mapped to `None`, and then each token is mapped to the word it originates from. This is especially useful to determine if a token is at the start of a word or if two tokens are in the same word. We could rely on the `##` prefix for that, but it only works for BERT-like tokenizers; this method works for any type of tokenizer as long as it's a fast one. In the next chapter, we'll see how we can use this capability to apply the labels we have for each word properly to the tokens in tasks like named entity recognition (NER) and part-of-speech (POS) tagging. We can also use it to mask all the tokens coming from the same word in masked language modeling (a technique called _whole word masking_).
 
 > [!TIP]
-> The notion of what a word is complicated. For instance, does "I'll" (a contraction of "I will") count as one or two words? It actually depends on the tokenizer and the pre-tokenization operation it applies. Some tokenizers just split on spaces, so they will consider this as one word. Others use punctuation on top of spaces, so will consider it two words.
+> The notion of what a word is is complicated. For instance, does "I'll" (a contraction of "I will") count as one or two words? It actually depends on the tokenizer and the pre-tokenization operation it applies. Some tokenizers just split on spaces, so they will consider this as one word. Others use punctuation on top of spaces, so will consider it two words.
 >
 > ✏️ **Try it out!** Create a tokenizer from the `bert-base-cased` and `roberta-base` checkpoints and tokenize "81s" with them. What do you observe? What are the word IDs?