subtitles/en/21_preprocessing-sentence-pairs-(pytorch).srt

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(graphics whooshing)

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- How to pre-process pairs of sentences.

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We have seen how to
tokenize single sentences

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and batch them together in the,

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"Batching inputs together video."

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If this code look unfamiliar to you,

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be sure to check that video again.

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Here will focus on tasks that
classify pair of sentences.

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For instance, we may want to
classify whether two texts

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are paraphrased or not.

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Here is an example taken
from the Quora Question Pairs

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dataset, which focuses on
identifying duplicate questions.

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In the first pair, the two
questions are duplicates,

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in the second they are not.

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Another pair classification problem is

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when we want to know if two sentences are

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logically related or not,

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a problem called natural
language inference or NLI.

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In this example, taken
from the MultiNLI data set,

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we have a pair of sentences
for each possible label.

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Contradiction, natural or entailment,

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which is a fancy way of
saying the first sentence

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implies the second.

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So classifying pairs of
sentences is a problem

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worth studying.

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In fact, in the GLUE benchmark,

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which is an academic benchmark
for text classification

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eight of the 10 data sets are focused

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on tasks using pairs of sentences.

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That's why models like BERT

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are often pre-trained
with a dual objective.

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On top of the language modeling objective,

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they often have an objective
related to sentence pairs.

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For instance, during
pretraining BERT is shown

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pairs of sentences and must predict both

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the value of randomly masked
tokens, and whether the second

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sentence follow from the first or not.

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Fortunately, the tokenizer
from the Transformers library

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has a nice API to deal
with pairs of sentences.

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You just have to pass
them as two arguments

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to the tokenizer.

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On top of the input IDs
and the attention mask

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we studied already,

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it returns a new field
called token type IDs,

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which tells the model which tokens belong

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to the first sentence,

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and which ones belong
to the second sentence.

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Zooming in a little bit,
here has an input IDs

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aligned with the tokens
they correspond to,

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their respective token
type ID and attention mask.

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We can see the tokenizer
also added special tokens.

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So we have a CLS token, the
tokens from the first sentence,

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a SEP token, the tokens
from the second sentence,

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and a final SEP token.

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If we have several pairs of sentences,

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we can tokenize them
together by passing the list

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of first sentences, then
the list of second sentences

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and all the keyword
arguments we studied already

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like padding=True.

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Zooming in at the result,

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we can see also tokenize added padding

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to the second pair sentences
to make the two outputs

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the same length, and properly
dealt with token type IDs

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and attention masks for the two sentences.

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This is then all ready to
pass through our model.