Learn how to implement sentence similarity using BERT and PyTorch in this 21-minute Python tutorial. Explore the power of highly-dimensional NLP techniques as you convert sentences into vectors and measure their semantic similarity. Follow along step-by-step to tokenize sentences, create hidden state tensors, generate sentence vectors, and calculate cosine similarity. Gain practical insights into BERT's architecture and its application in natural language processing tasks.
Overview
Syllabus
Intro
BERT Base Network
Sentence Vectors and Similarity
The Data and Model
Two Approaches
Tokenizing Sentences
Creating last_hidden_state Tensor
Creating Sentence Vectors
Cosine Similarity
Taught by
James Briggs
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