So let's take a look at this in a slightly more sophisticated example. So I'm going to take the tokenizer as we had before, but I'm also going to introduce this Pad Sequences tool. The idea behind the pad sequences tool is that it allows you to use sentences of different lengths, and use padding or truncation to make all of the sentences the same length. So in this case, I have the same sentences as before; 'I love my dog,' 'I love my cat,' You love my dog, but I've added this new sentence; 'Do you think my dog is amazing?' Which is a different length from these other sentences. These all had four words, this one has more. So my tokenizer, I'm going to create as before. But I'm also going to use this parameter called an OOV token. The idea here is that I'm going to create a new token, a special token that I'm going to use for words that aren't recognized, that aren't in the word index itself. So I am going to just create this. I'm going to create something unique here that I wouldn't expect to see in the corpus. Something like bracket OOV, and I'm going to specify my OOV token is that. So then I'm going to call tokenizer fit and text sentences, and I'm going to take a look at the word index for that. Let's actually run this. We'll see now, that in my word index, OOV is now value one, my is value two, love is three, etc. We have a total of 11 words, 11 unique words in this corpus. It's actually 10 words plus the OOV token. So on the tokenizer, I can then convert the words in those sentences to sequences of tokens by calling the text to sequences method. That's going to produce sequences. That's what I'm printing out here. So my sequences are five, three, two, four, for the first sentence, which is, 'I love my dog,' five, three, two, four, etc. So these are the sequence is 5324, 5327, 6324, 8692, 41011. Now, we can see these are all different length, but we want to make them the same length. So that's where Pad Sequences comes into it. So I'm going to say here my pad is set is pad sequences with the sequences. Let's make it a maximum length of five words. So this maximum length of five words means that these four-letter, or these four-words sentences end up being pre-padded with a zero. This six-word sentence ends up having the first word cutoff because we did say maximum length equals five. I said maximum length equals eight, for example, and then ran this. We can see now that they're all pre-padded with zeros, including this long sentence, it's being prepared with a single zero. There are methods on pad sequences that we saw, and the lessons that will allow us to do a post if we want to do so, and then the zeros would appear afterwards. So now, if I want to take a look at words that the tokenizer wasn't fit to. So for example, my test data is I really love my dog and my dog loves my manatee, if I now tokenized them and create sequences out of that, we'll see 51324 for the first sentence. Five is I, one is out of vocabulary, because really wasn't actually there, and three to four, 'I still love my dog.' So this is how the outer vocabulary token comes into it. When it sees a word that wasn't in the word index, it will replace it, it will just use the out of vocabulary token one for that. Similarly, for 'my dog loves my manatee,' I get 24121, the word 'loves' is not in it, even though the word love is, and of course, manatee isn't in it either. So I end up with just 242, other words that really have meaning in this, and that's 'my dog,' 'my,' which is, 'my dog my loves manatee,' out of vocabulary tokens. Of course here, you can see I'm also padding them. So my 51324 gets padded, and my 24121 also gets padded. Because I'd said a max length of 10, if I set that for example to two, we'll see they end up getting truncated. I am getting the last two words here, and getting the last two words here. So that's a basic introduction to how tokenizer works, and how padding actually works, to give you padding, to be able to get your sentences all the same length. I hope this was useful for you.