Week 4 : 10 words are vectorized to only 9 numbers?

Course Q&A Deep Learning Specialization Sequence Models
1

In Week 4 Assignment (Transformer Network), section 2.1 of instructions says:

2.1 - Padding Mask
Oftentimes your input sequence will exceed the maximum length of a sequence your network can process. Let's say the maximum length of your model is five, it is fed the following sequences:

[["Do", "you", "know", "when", "Jane", "is", "going", "to", "visit", "Africa"], 
 ["Jane", "visits", "Africa", "in", "September" ],
 ["Exciting", "!"]
]
which might get vectorized as:

[[ 71, 121, 4, 56, 99, 2344, 345, 1284, 15],
 [ 56, 1285, 15, 181, 545],
 [ 87, 600]

How come the sequence “Do you know when Jane is going to visit Africa” has 10 words, but it is vectorized to only 9 numbers [ 71, 121, 4, 56, 99, 2344, 345, 1284, 15] ?

2

I’ll check into it. It’s either a typo, or maybe some common words are excluded from the embedding.