subtitles/en/51_byte-pair-encoding-tokenization.srt

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(air whooshing)

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- You are at the right place

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if you want to understand
what the Byte Pair Encoding

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subword tokenization algorithm is,

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how to train it

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and how the tokenization of a text is done

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with this algorithm.

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The BPE algorithm

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was initially proposed as a
text compression algorithm

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but it is also very well suited

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as a tokenizer for your language models.

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The idea of BPE is to divide words

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into a sequence of 'subword units'

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which are units that appear
frequently in a reference corpus

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which is, the corpus we used to train it.

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How is a BPE tokenizer trained?

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First of all, we have to
get a corpus of texts.

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We will not train our
tokenizer on this raw text

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but we will first normalize it

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then pre-tokenize it.

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As the pre-tokenization

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divides the text into a list of words,

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we can represent our corpus in another way

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by gathering together the same words

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and by maintaining a counter,

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here represented in blue.

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To understand how the training works,

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we consider this toy corpus
composed of the following words:

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huggingface, hugging, hug, hugger, etc.

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BPE is an algorithm that starts
with an initial vocabulary

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and then increases it to the desired size.

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To build the initial vocabulary,

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we start by separating
each word of the corpus

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into a list of elementary
units that compose them,

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here, the characters.

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We list in our vocabulary all
the characters that appear

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and that will constitute
our initial vocabulary.

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Let's now see how to increase it.

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We return to our split corpus,

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we will go through the words one by one

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and count all the
occurrences of token pairs.

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The first pair is composed
of the token 'h' and 'u',

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the second 'u' and 'g',

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and we continue like that until
we have the complete list.

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Once we know all the pairs

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and their frequency of appearance,

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we will choose the one that
appears the most frequently.

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Here it is the pair composed
of the letters 'l' and 'e'.

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We note our first merging rule

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and we add the new
token to our vocabulary.

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We can then apply this
merging rule to our splits.

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You can see that we have
merged all the pairs of tokens

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composed of the tokens 'l' and 'e'.

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And now, we just have to
reproduce the same steps

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with our new splits.

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We calculate the frequency of occurrence

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of each pair of tokens,

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we select the pair with
the highest frequency,

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we note it in our merge rules,

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we add the new one token the vocabulary

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and then we merge all the pairs of tokens

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composed of the token 'le'
and 'a' into our splits.

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And we can repeat this operation

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until we reach the
desired vocabulary size.

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Here, we stopped when our
vocabulary reached 21 tokens.

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We can see now that
the words of our corpus

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are now divided into far fewer tokens

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than at the beginning of the training.

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And that our algorithm

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has learned the radicals 'hug' and 'learn'

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and also the verbal ending 'ing'.

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Now that we have learned our vocabulary

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and merging rules, we
can tokenize new texts.

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For example,

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if we want to tokenize the word 'hugs',

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first we'll divide it
into elementary units

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so it became a sequence of characters.

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Then, we'll go through our merge rules

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until we have one we can apply.

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Here, we can merge the
letters 'h' and 'u'.

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And here, we can merge 2 tokens
to get the new token 'hug'.

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When we get to the end of our merge rules,

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the tokenization is finished.

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And that's it.

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I hope that now the BPE algorithm

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has no more secret for you!

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(air whooshing)