subtitles/en/42_training-a-new-tokenizer.srt

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(air whooshing)

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- In this video we will see together

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what is the purpose of
training a tokenizer,

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what are the key steps to follow,

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and what is the easiest way to do it.

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You will ask yourself the question,

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"Should I train a new tokenizer?",

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when you plan to train a
new model from scratch.

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A trained tokenizer would not
be suitable for your corpus

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if your corpus is in a different language,

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uses new characters, such as
accents or upper cased letters,

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has a specific vocabulary,
for example medical or legal,

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or uses a different style,

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a language from another
century for example.

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If I take the tokenizer trained on

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the bert-base-uncased model,

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and ignore its normalization step,

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then we can see that the
tokenization operation

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on the English sentence,

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"Here is a sentence
adapted to our tokenizer",

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produces a rather
satisfactory list of tokens,

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in the sense that this
sentence of eight words

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is tokenized into nine tokens.

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On the other hand, if I
use this same tokenizer

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on a sentence in Bengali, we see that

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either a word is divided
into many sub tokens,

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or that the tokenizer does not know one of

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the unicode characters and
returns only an unknown token.

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The fact that a common word
is split into many tokens

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can be problematic,
because language models

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can only handle a sequence
of tokens of limited length.

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A tokenizer that excessively
splits your initial text

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may even impact the
performance of your model.

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Unknown tokens are also problematic,

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because the model will
not be able to extract

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any information from the
unknown part of the text.

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In this other example, we can see that

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the tokenizer replaces
words containing characters

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with accents and capital
letters with unknown tokens.

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Finally, if we use again this tokenizer

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to tokenize medical
vocabulary, we see again that

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a single word is divided
into many sub tokens,

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four for paracetamol,
and four for pharyngitis.

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Most of the tokenizers used by the current

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state of the art language
models need to be trained

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on a corpus that is
similar to the one used

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to pre-train the language model.

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This training consists in learning rules

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to divide the text into tokens.

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And the way to learn
these rules and use them

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depends on the chosen tokenizer model.

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Thus, to train a new tokenizer,
it is first necessary

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to build a training corpus
composed of raw texts.

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Then, you have to choose an
architecture for your tokenizer.

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Here there are two options.

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The simplest is to reuse the
same architecture as the one

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of a tokenizer used by
another model already trained.

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Otherwise it is also possible

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to completely design your tokenizer.

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But it requires more
experience and attention.

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Once the architecture
is chosen, you can thus

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train this tokenizer on
your constituted corpus.

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Finally, the last thing that
you need to do is to save

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the learned rules to be
able to use this tokenizer.

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Let's take an example.

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Let's say you want to train
a GPT-2 model on Python code.

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Even if the Python code
is usually in English

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this type of text is very specific,

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and deserves a tokenizer trained on it.

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To convince you of this,
we will see at the end

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the difference produced on an example.

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For that we are going to use the method

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"train_new_from_iterator"
that all the fast tokenizers

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of the library have and thus,

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in particular GPT2TokenizerFast.

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This is the simplest method in our case

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to have a tokenizer
adapted to Python code.

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Remember, the first thing is
to gather a training corpus.

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We will use a subpart of
the CodeSearchNet dataset

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containing only Python functions

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from open source libraries on Github.

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It's good timing.

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This dataset is known
by the datasets library

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and we can load it in two lines of code.

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Then, as the "train_new_from_iterator"
method expects

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a iterator of lists of texts,

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we create the
"get_training_corpus" function,

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which will return an iterator.

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Now that we have our iterator

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on our Python functions
corpus, we can load

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the GPT-2 tokenizer architecture.

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Here old_tokenizer is not
adapted to our corpus.

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But we only need

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one more line to train
it on our new corpus.

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An argument that is common
to most of the tokenization

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algorithms used at the moment
is the size of the vocabulary.

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We choose here the value 52,000.

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Finally, once the training is finished,

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we just have to save our
new tokenizer locally,

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or send it to the hub
to be able to reuse it

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very easily afterwards.

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Finally, let's see together
on an example if it was useful

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to re-train a tokenizer
similar to GPT-2 one.

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With the original tokenizer of GPT-2

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we see that all spaces are isolated,

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and the method name randn,

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relatively common in Python
code, is split in two.

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With our new tokenizer,
single and double indentations

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have been learned and the method randn

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is tokenized into one token.

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And with that,

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you now know how to train
your very own tokenizers now.

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(air whooshing)