subtitles/en/44_fast-tokenizer-superpowers.srt

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- The fast tokenizers

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of the Transformers library are fast,

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but they also implement features
that will be super useful

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for data pre-processing
and post-processing.

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Let's have a look at them!

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First, let's have a look

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at the usual output of a tokenizer.

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We get input IDs that correspond to token,

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but we lose a lot of
information in the process.

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For instance,

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here the tokenization is the
same for the two sentences

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even if one has several
more spaces than the other.

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Just having the input
IDs is thus not enough

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if we want to match some
tokens with a span of text,

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something we'll need to do

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when tackling question
answering, for instance.

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It's also difficult to know

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when two tokens belong
to the same word or not.

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It looks easy when you
just look at the output

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of a BERT tokenizer where
we just need to look

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for the hash hash.

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But other tokenizers have different ways

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to tokenize parts of words.

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For instance, RoBERTa
adds this special G symbol

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to mark the tokens at
the beginning of the word

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and T5 uses this special underscore symbol

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for the same purpose.

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Thankfully, the fast tokenizers
keep track of the word

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each token comes from,

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with a word_ids method you
can use on their outputs.

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The output is not necessarily clear,

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but assembled together in
a nice table like this,

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we can look at the word
position for each token.

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Even better, the fast
tokenizers keep track

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of the span of characters
each token comes from,

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and we can get them when calling it on one

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or several text by adding

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the return_offsets_mapping=True argument.

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In this instance, we can
see how we jump positions

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between the hash hash
token and the super token,

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because of the multiple spaces
in the initial sentence.

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To enable this, the fast tokenizers

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store additional information at each step

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of their internal pipeline.

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That internal pipeline
consists of normalization,

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where we apply some cleaning to the text,

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like lower casing or removing the accents;

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pre-tokenization,

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which is where we split
the texts into words;

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then we apply the model of the tokenizer,

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which is where the words
are split into tokens,

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before finally doing the post processing,

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where special tokens are added.

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From the beginning to
the end of the pipeline,

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the tokenizer keeps track
of each span of text

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that corresponds to each
word, then each token.

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We'll see how useful it is

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when we tackle the following tasks:

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when doing masked language modeling

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one variation that gets
state-of-the-art results

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is to mask all the tokens of a given word

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instead of randomly chosen words.

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This will require us to
use the word IDs we saw.

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When doing token classification,

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we'll need to convert the
labels we have on words,

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to labels on each tokens.

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As for the offset mappings,

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it will be super useful
when we need to convert

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token positions in a
sentence into a span of text,

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which we'll need to
know when we're looking

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at question answering

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or when grouping the tokens corresponding

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to the same entity in
token classification.

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To have a look at these tasks,

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check the videos linked below!