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(air whooshing)

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(logo popping)

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- Let's see how to preprocess
a dataset for translation.

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This is a task of well
translating a sentence

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in another language.

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This video will focus on how
to preprocess your dataset

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once you've managed to put
it in the following format.

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One column for input texts
and one for the target texts.

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Here is how we can achieve
this with the Datasets library

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and the KDE4 dataset for
English to French translation.

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As long as you manage to have
your data look like this,

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you should be able to
follow the same steps.

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For once, our labels are not integers

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corresponding to some
classes, but plain texts.

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We will thus need to tokenize
them, like our inputs.

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There is a trap there though,

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as if you tokenize your
targets like your inputs,

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you will hit a problem.

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Even if you don't speak
French, you might notice

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some weird things in the
tokenization of the targets.

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Most of the words are
tokenized in several subtokens,

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while fish, one of the only English word,

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is tokenized as a single word.

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That's because our inputs have
been tokenized as English.

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Since our model knows two languages,

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you have to warn it when
tokenizing the targets

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so it switches in French mode.

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This is done with the
as_target_tokenizer context manager.

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You can see how it results in
a more compact tokenization.

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Processing the whole dataset

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is then super easy with the map function.

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You can pick different maximum lengths

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for the inputs and targets,

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and choose to pad at this
stage to that maximum length

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by setting padding=max_length.

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Here we'll show you to pad dynamically

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as it requires one more step.

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Your inputs and targets

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are all sentences of various lengths.

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We will pad the inputs
and targets separately,

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as the maximum lengths
of the inputs and targets

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might be different.

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Then we pad the inputs with the pad token

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and the targets with the -100 index

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to make sure they're
not taken into account

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in the loss computation.

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Once this is done,

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batching inputs and
targets become super easy.

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The Transformers library
provides us with data collator

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to do this all automatically.

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You can then pass it to the
Trainer with your datasets

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or use it in the to_tf_dataset method

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before using model.fit()
on your Keras model.

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(air whooshing)

