subtitles/en/41_text-embeddings-&-semantic-search.srt

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(upbeat music)

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- Text embeddings and semantic search.

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In this video we'll explore
how Transformer models

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represent text as embedding vectors

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and how these vectors can be
used to find similar documents

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in a corpus.

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Text embeddings are just
a fancy way of saying

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that we can represent text
as an array of numbers

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called a vector.

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To create these embeddings we usually use

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an encoder-based model like BERT.

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In this example, you can see
how we feed three sentences

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to the encoder and get
three vectors as the output.

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Reading the text, we can
see that walking the dog

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seems to be most similar
to walking the cat,

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but let's see if we can quantify this.

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The trick to do the comparison

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is to compute a similarity metric

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between each pair of embedding vectors.

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These vectors usually live in
a very high-dimensional space,

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so a similarity metric can
be anything that measures

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some sort of distance between vectors.

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One very popular metric
is cosine similarity,

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which uses the angle between two vectors

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to measure how close they are.

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In this example, our
embedding vectors live in 3D

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and we can see that the
orange and Grey vectors

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are close to each other
and have a smaller angle.

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Now one problem we have to deal with

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is that Transformer models
like BERT will actually return

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one embedding vector per token.

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For example in the sentence,
"I took my dog for a walk,"

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we can expect several embedding
vectors, one for each word.

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For example, here we can
see the output of our model

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has produced 9 embedding
vectors per sentence,

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and each vector has 384 dimensions.

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But what we really want is
a single embedding vector

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for each sentence.

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To deal with this, we can use
a technique called pooling.

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The simplest pooling method

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is to just take the token embedding

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of the special CLS token.

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Alternatively, we can
average the token embeddings

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which is called mean_pooling
and this is what we do here.

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With mean_pooling the only
thing we need to make sure

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is that we don't include the
padding tokens in the average,

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which is why you can see the
attention_mask being used here.

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This gives us a 384 dimensional
vector for each sentence

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which is exactly what we want.

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And once we have our sentence embeddings,

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we can compute the cosine similarity

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for each pair of vectors.

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In this example we use the
function from scikit-learn

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and you can see that the sentence
"I took my dog for a walk"

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has indeed a strong overlap
with "I took my cat for a walk".

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Hooray! We've done it.

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We can actually take this
idea one step further

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by comparing the similarity
between a question

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and a corpus of documents.

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For example, suppose we embed every post

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in the Hugging Face forums.

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We can then ask a question, embed it,

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and check which forum
posts are most similar.

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This process is often
called semantic search,

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because it allows us to
compare queries with context.

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To create a semantic search engine

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is actually quite simple
in the datasets library.

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First we need to embed all the documents.

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And in this example,
we take a small sample

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from the squad dataset and apply

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the same embedding logic as before.

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This gives us a new
column called embeddings,

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which stores the embeddings
of every passage.

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Once we have our embeddings,

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we need a way to find nearest
neighbors for a query.

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The datasets library provides
a special object called FAISS

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which allows you to quickly
compare embedding vectors.

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So we add the FAISS index,
embed a question and voila,

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we've now found the 3
most similar articles

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which might store the answer.

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(upbeat music)