subtitles/en/52_wordpiece-tokenization.srt

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(air whooshing)

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- Let's see together what
is the training strategy

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of the WordPiece algorithm,
and how it performs

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the tokenization of a text, once trained.

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WordPiece is a tokenization
algorithm introduced by Google.

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It is used, for example, by BERT.

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To our knowledge,

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the code of WordPiece
has not been open source.

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So we base our explanations

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on our own interpretation
of the published literature.

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So, what is the training
strategy of WordPiece?

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Similarly to the BPE algorithm,

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WordPiece starts by establishing
an initial vocabulary

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composed of elementary units,

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and then increases this
vocabulary to the desired size.

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To build the initial vocabulary,

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we divide each word in the training corpus

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into the sequence of
letters that make it up.

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As you can see, there is a small subtlety.

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We add two hashtags in
front of the letters

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that do not start a word.

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By keeping only one occurrence
per elementary unit,

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we now have our initial vocabulary.

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We will list all the
existing pairs in our corpus.

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Once we have this list,

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we will calculate a score
for each of these pairs.

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As for the BPE algorithm,

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we will select the pair
with the highest score.

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Taking for example,

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the first pair composed
of the letters H and U.

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The score of a pair is
simply equal to the frequency

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of appearance of the pair,
divided by the product

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of the frequency of
appearance of the first token,

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by the frequency of appearance
of the second token.

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Thus, at a fixed frequency
of appearance of the pair,

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if the subparts of the pair are
very frequent in the corpus,

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then this score will be decreased.

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In our example, the pair
HU appears four times,

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the letter H four times,
and the letter U four times.

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This gives us a score of 0.25.

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Now that we know how to
calculate this score,

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we can do it for all pairs.

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We can now add to the vocabulary

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the pair with the highest score,
after merging it of course.

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And now we can apply this same
fusion to our split corpus.

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As you can imagine,

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we just have to repeat the same operations

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until we have the vocabulary
at the desired size.

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Let's look at a few more steps

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to see the evolution of our vocabulary,

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and also the evolution of
the length of the splits.

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And now that we are happy
with our vocabulary,

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you are probably wondering how
to use it to tokenize a text.

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Let's say we want to tokenize
the word "huggingface".

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WordPiece follows these rules.

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We will look for the
longest possible token

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at the beginning of the word.

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Then we start again on the
remaining part of our word,

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and so on until we reach the end.

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And that's it. Huggingface is
divided into four sub-tokens.

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This video is about to end.

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I hope it helped you to understand better

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what is behind the work, WordPiece.

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(air whooshing)