Sentence Similarity With Transformers and PyTorch (Python)

Easy mode: https://youtu.be/Ey81KfQ3PQU

All we ever seem to talk about nowadays are BERT this, BERT that. I want to talk about something else, but BERT is just too good - so this video will be about BERT for sentence similarity.

A big part of NLP relies on similarity in highly-dimensional spaces. Typically an NLP solution will take some text, process it to create a big vector/array representing said text-then perform several transformations.

It's highly-dimensional magic.

Sentence similarity is one of the clearest examples of how powerful highly-dimensional magic can be.

The logic is this:
- Take a sentence, convert it into a vector.
- Take many other sentences, and convert them into vectors.
- Find sentences that have the smallest distance (Euclidean) or smallest angle (cosine similarity) between them-more on that here.
- We now have a measure of semantic similarity between sentences-easy!

At a high level, there's not much else to it. But of course, we want to understand what is happening in a little more detail and implement this in Python too.

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Medium article:
https://towardsdatascience.com/bert-for-measuring-text-similarity-eec91c6bf9e1

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00:00 Intro
00:16 BERT Base Network
1:11 Sentence Vectors and Similarity
1:47 The Data and Model
3:01 Two Approaches
3:16 Tokenizing Sentences
9:11 Creating last_hidden_state Tensor
11:08 Creating Sentence Vectors
17:53 Cosine Similarity